Relationship between Mutant Genes, SNPs and Mutation Types in Children with Hyperthyroidism Based on Canonical correlation Analysis

Relationship between Mutant Genes, SNPs and Mutation Types in Children with Hyperthyroidism Based on Canonical correlation Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Relationship between Mutant Genes, SNPs and Mutation Types in Children with Hyperthyroidism Based on Canonical correlation Analysis Xiaojian Mao, Liangliang Tang, Heyong Wang, Hongyi Li, Yongxian Shao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3983196/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Objective Generally, different mutation types affect patients to various degrees. Some mutation types, such as synonymous mutations, do not cause changes in amino acid sequence and have little impact on patients. In contrast, some types of mutations will lead to wrong encoding or stop encoding of amino acid sequences, which will have a more significant impact on patients. Therefore, this paper intends to find the genetic characteristics of hyperthyroidism in children from the perspective of mutation types: missense mutations, synonymous mutations, nonsense mutations, and splice site mutations, and explore the relationship between mutant Genes, SNPs, and mutation types in patients. Finally, find the mutation type in which the specific mutant gene and the mutation site were biased. Methods Use the canonical correlation analysis method to find the relationship between the mutant gene, the mutation site, and the mutation type. Results Based on the complete linear correlation, single value, and Pearson correlation coefficient, screen the mutant genes and SNPs, then get 23 representative mutant genes from 144 mutant genes in 39 children and 8 representative SNPs from 1221 SNPs in 39 children. Canonical variables were constructed using data from representative mutant genes, SNPs, and their corresponding mutation types for canonical correlation analysis. A significant positive correlation between the mutant gene and the mutation type and a significant positive correlation between SNPs and the mutation type were found through canonical variable correlation and significance tests.The mutant genes for children with hyperthyroidism consist of 23 representative mutant genes, among which the genes ACADM, ATM, BARD1, CALCA, CAPN3, CRP, DNMT1, DNMT3A, FANCC, GHRL, KANK1, LRSAM1, TPO, TSHR, VWF are all prone to missense mutations, nonsense mutation, and synonymous mutation, the gene CGA is prone to splice site mutation. Among the 23 genes, the gene KANK1 has the greatest impact on the ensemble of mutant genes. SNPs for children with hyperthyroidism consist of 8 representative SNPs, among which g.1529224C > T, g.23019637C > G and g.44651599T > C are prone to missense mutations, g.1533961C > T, g.12718004G > A, g.43082453A > G, g.44652143G > A and g.71809992C > T are prone to synonymous mutations. The site g.44651599T > C has the greatest impact on the ensemble of SNPs. Conclusion There is a strong correlation between gene mutation types and mutant genes and between mutation types and SNPs in children with hyperthyroidism. Each mutant gene and SNP has a more preferred mutation type, among which gene CGA and gene KANK1 may be the mutant gene that has the most significant impact on children, and SNP g.44651599T > C may be the SNP that has the greatest impact on children. Hyperthyroidism Mutant Genes SNPs Mutation Types Canonical correlation analysis Figures Figure 1 Figure 2 1. Introduction Mutation is an important form of genetic variation and plays an essential role in the occurrence and progression of many genetic diseases. Among mutations, different mutation types have different effects on patients. This difference is mainly caused by the extent to which mutations affect protein structure, function, and regulation, thereby affecting the normal function of cells and organisms. Among the mutation types, nonsense mutations usually lead to truncated non-functional proteins or complete loss of protein synthesis, which may lead to impairment of important physiological functions. Nonstop mutations occur in the stop codon of the gene coding region, resulting in the inability to properly terminate protein synthesis, which has an important impact on the function and stability of the protein. Splice site mutation occurs in the gene sequence and affects the RNA splicing process, which will affect the splicing process to varying degrees, and then have an important impact on protein synthesis. Missense mutations can lead to changes in amino acids in the protein sequence, and the specific impact depends on the nature and position of the amino acid after replacement: some missense mutations may have serious effects on protein structure and function, leading to serious genetic diseases or diseases There is an increased risk, and there are also some missense mutations that have minor effects on protein function and may not lead to overt clinical manifestations. Synonymous mutation does not change the amino acid sequence of the protein-coding region, and will not affect the amino acid composition and structure of the protein. In summary, among the five types of mutations, in most cases, nonsense mutations have the greatest impact on patients, followed by non-stop mutations, splice site mutations, missense mutations, and synonymous mutations. Hyperthyroidism is a polygenic disease, and the pathogenesis at the genetic level are relatively complex, involving the interaction between multiple mutated genes and external environmental factors, so the disease of hyperthyroidism involves multiple independent or interacting genes joint action, while the individual contribution of each gene may be small or even insignificant [ 1] . In existing studies, some genes related to hyperthyroidism have been found, such as: TSHR gene[ 2-3] , CTLA4 gene[ 4-5] and GNAS gene[ 6-7] , but incomplete gene set cannot fully explain the genetic characteristics and genetic mechanism of hyperthyroidism, so this paper considers highlight ting the holistic perspective of mutation in the hyperthyroidism and using the overall data of mutant genes, SNPs and mutation types in children with hyperthyroidism to find the relationship between mutant genes, SNPs and mutation types and the mutant genes and SNPs which have larger impact. Currently, there are many studies on disease gene mutation data analysis. Many researchers use GWAS to analyze large quantities of SNP data and phenotype data. For example, use GWAS analysis method to obtain depression susceptibility from genetic data of depression patients Genes[ 8] , detect SNPs with high associations between brain white matter fiber tracts and imaging data[ 9] , study the relationship between genetic variants and cardiovascular disease and related phenotypic traits[ 10] , and obtaining genetic variants associated with COVID-19 patients[ 11] . In addition, some researchers have used machine learning algorithms to study gene mutation data, such as LASSO regression and machine learning-support vector machine recursive feature elimination algorithms to screen the characteristic genes related to intracranial aneurysm rupture ferroptosis[ 12] , using LASSO algorithm and The SVM-RFE algorithm screens the diagnostic genes related to ferroptosis in endometriosis[ 13] , uses the random double clustering algorithm to obtain the central gene from the gene expression data[ 14] , and uses the co-inertia analysis method to analyze the relationship between multiple gene sets[ 15] . There are also studies that use canonical correlation algorithms to analyze the interaction between genes [16-17] . To sum up, most of the current studies on disease mutation gene identification and mutation gene relationship analysis focus on the analysis of the relationship between SNP and phenotypes, genes and phenotypes, and genes, and the methods used are mostly regression, classification and clustering Algorithms, there are few overall analyzes of mutant genes, SNPs and mutation types, so it could be considered to use correlation algorithms to study the relationship between SNPs, mutant genes and mutation types. Canonical Component Analysis (CCA), proposed by Hotelling in 1936[ 18] , is used to explore the potential linear relationship between two groups of random variables. It is different from the traditional correlation analysis that only considers the correlation between two variables. It is a tool for multivariate statistical analysis. CCA is often applied to the study of the relationship between two wholes. For example, CCA combined with gene expression datasets can study gene-gene interactions [ 19]-[ a20a]. At the same time, CCA is widely used in pattern analysis[ 21] , and also in imaging genetics [ 22]-[ a23a], signal denoising [ 24] , and the prediction of functional sites of biological sequences [ 25] . Therefore, this paper uses canonical correlation analysis to study the relationship between mutant genes and mutation types, SNPs, and mutation types, which will help to understand the distribution pattern of mutations in the genome and the genetic characteristics of hyperthyroidism. The principle of CCA is similar to that of principal component analysis. Several groups of comprehensive canonical-related variables are constructed. The canonical variables between the groups are not correlated with each other. Each canonical variable is linearly weighted by the original variable through the canonical coefficient. The canonical coefficient makes the correlation between the canonical variables in the group sex maximization. The calculation steps of the canonical correlation analysis in this paper are as follows: Let the sample characteristic SNP site be \(X=({x}_{1},{x}_{2}\dots {x}_{n}\) ), the mutation type be \(Y=({y}_{1},{y}_{2}\dots {y}_{m}\) ), \(X\) and \(Y\) be two groups of zero-mean random variables. First, it is necessary to find a pair of projection directions. \({a}_{1},{b}_{1}\) to maximize the correlation between the linear combination of \({u}_{1}={a}_{1}^{T}X\) and \({v}_{1}={b}_{1}^{T}Y\) . Similarly, find a second pair of projections \({a}_{2},{b}_{2}\) to maximize the correlation between \({u}_{2}={a}_{2}^{T}X\) and \({v}_{2}={b}_{2}^{T}Y\) , and \({u}_{2}\) , \({v}_{2}\) are not correlated with \({u}_{1}\) , \({v}_{1}\) , so there are: $$\text{v}\text{a}\text{r}\left({u}_{1}\right)=\text{v}\text{a}\text{r}\left({v}_{1}\right)=1$$ 1 $$\text{c}\text{o}\text{v}\left({u}_{1},{u}_{2}\right)=\text{c}\text{o}\text{v}\left({v}_{1}{,v}_{2}\right)=0$$ 2 The calculation of the second and third equivalent typical variables is analogized according to the above steps. The correlation was measured using the Pearson correlation coefficient, and the formula is as follows: $$\rho \left({{a}^{T}}_{1}x,{{b}^{T}}_{1}y\right)=\frac{cov({{a}^{T}}_{1}x,{{b}^{T}}_{1}y)}{\sqrt{Var\left({{a}^{T}}_{1}x\right)*Var\left({{b}^{T}}_{1}y\right)}}$$ 3 The typical variable group \(\text{U}=({\text{u}}_{1},{\text{u}}_{2}\dots {\text{u}}_{\text{n}}\) ) of \(\text{X}\) and the typical variable group \(\text{V}=({\text{v}}_{1},{\text{v}}_{2}\dots {\text{v}}_{\text{m}}\) ) of \(\text{Y}\) can be obtained in the end, and the correlation coefficient between each group of typical variables can be calculated to get \({{\rho }}_{1},{{\rho }}_{2}\dots {{\rho }}_{\text{m}\text{i}\text{n}(\text{n},\text{m})}\) , and the index \({\text{p}}_{1},{\text{p}}_{2}\dots {\text{p}}_{\text{m}\text{i}\text{n}(\text{n},\text{m})}\) describing the significance of the correlation coefficient is calculated, in which the most significant correlation coefficient is selected as the correlation index corresponding to \(\text{X}\) and \(\text{Y}\) . In addition, to ensure the solution of \(\text{U}\) and \(\text{V}\) , canonical correlation analysis requires that the covariance matrices of \(\text{X}\) and \(\text{Y}\) are non-singular, so the data need to be processed before canonical correlation analysis. 2. Material and methods 2.1 Data acquisition The cases in this paper come from the Guangzhou Women and Children's Medical Center. After approval by the ethics committee of the center and the informed consent of the sick children's guardians, the peripheral blood of 39 children with hyperthyroidism was collected, and DNA was extracted. The DNA samples were used for whole-genome sequencing to obtain whole-exome sequencing data. Search public disease databases such as Malacards[ 26] , MutationView [ 27] , and Clinvar to obtain genes related to hyperthyroidism, and compare with 39 patient mutation genes, screen out 144 genes related to hyperthyroid disease. The sequencing data of 144 mutant genes in 39 children with hyperthyroidism were used as the data for this study. 2.2 Data processing Classify the data according to the patient ID, count the mutant genes, SNPs, and mutation types, and obtain the variation frequency of each hyperthyroid child in different mutated genes, SNPs, and mutation types as the overall characteristics of the mutant genes, SNPs and mutation types. For example, Table 1. shows the variation frequency of each mutation gene in hyperthyroidism children, with each column representing a mutation gene and each row representing a patient; Table 2. shows the variation frequency of SNP in children with hyperthyroidism, with each column representing an snp and each row representing a patient. Table 3. shows the variation frequency of each mutation type in children with hyperthyroidism, with each column representing a mutation type and each row representing a patient. Table 1. The Frequency of Each Mutant Gene in Children with Hyperthyroidism mutant gene Sample ACADM ACADS ACP5 … VWF WDR37 WRN GD1 1 4 0 … 5 1 5 GD10 1 3 0 … 13 1 3 GD11 3 3 0 … 6 1 4 … … … … … … … … GD7 1 2 0 … 14 1 3 GD8 1 2 0 … 6 1 3 GD9 1 2 0 … 11 1 3 Table 2. the Frequency of Each SNP in Children with Hyperthyroidism SNP Sample g.100748174T>C g.101206854C>T g.101206863G>A … g.9822347C>G g.9849809C>T g.9849941A>G GD1 1 1 0 … 0 0 0 GD10 1 0 0 … 0 0 0 GD11 1 0 0 … 0 1 0 … … … … … … … … GD7 0 1 0 … 1 1 0 GD8 1 0 0 … 1 1 0 GD9 1 0 0 … 0 0 0 Table 3. Frequency of Each Mutation Type in Children with Hyperthyroidism Mutation type Sample Missense_Mutation Nonsense_Mutation Nonstop_Mutation Silent Splice_Site GD1 183 1 0 200 2 GD10 173 0 0 185 3 GD11 179 1 0 176 3 … … … … … … GD7 157 0 0 180 3 GD8 171 0 0 172 3 GD9 161 1 0 180 2 However, the data of children with hyperthyroidism include 144 mutant genes and 1221 SNPs, so the number of features is too large. Canonical correlation analysis has certain requirements for the distribution of data. Therefore, the filtered data are processed by deleting the completely linear correlation features, deleting the single-valued features, and selecting the features with the highest correlation. 2.2.1 Remove completely linear correlation features The calculation of canonical correlation analysis will be affected when there is a complete linear correlation between two SNPs or two mutant genes,. Therefore, it is necessary to find out the pairs of mutant genes and SNPs with a correlation of 1 and delete one of them. After calculating the correlation between genes, a pair of mutated genes with a correlation of 1 is obtained, as shown in Table 4, which are the gene ELP3 and gene OPA3. It is known that ELP3 mutations are associated with allergic airway inflammation[ 28] , and neurodevelopmental disorders[ 29] , and gene OPA3 mutation is related to optical optic neuropathy[ 30] , and optic nerve dysfunction[ 31] , but no research has shown that there is a link between the gene ELP3 and OPA3 and hyperthyroidism; after calculating the correlation between the sites, 510 pairs of correlations are obtained. 30 pairs of them are shown in Table 5. Table 4. 1 Pair of Mutant Genes with Complete Linear Correlation NO Gene 1 Gene 2 corr 0 ELP3 OPA3 1 Table 5. 30 Pairs of SNPs with Complete Linear Correlation NO SNP 1 SNP 2 corr 1 g.101206863G>A g.101207627G>A 1 2 g.101206863G>A g.101210433G>A 1 3 g.101206863G>A g.101212542G>A 1 4 g.101206863G>A g.101212596G>A 1 5 g.101207609G>A g.10148979G>A 1 6 g.101207609G>A g.129369961G>A 1 7 g.101207609G>A g.132911525A>G 1 8 g.101207609G>A g.133113558C>T 1 9 g.101207609G>A g.133116676G>A 1 10 g.101207609G>A g.1529224C>T 1 11 g.101207609G>A g.170404400G>T 1 12 g.101207609G>A g.51942464T>C 1 13 g.101207609G>A g.71810550C>T 1 14 g.101213149G>A g.108289005C>T 1 15 g.101213149G>A g.115982363A>T 1 16 g.101213149G>A g.129454283A>T 1 17 g.101213149G>A g.134690982C>A 1 18 g.101213149G>A g.134784997C>T 1 19 g.101213149G>A g.2080242C>T 1 20 g.101213149G>A g.23418374C>T 1 21 g.101213149G>A g.32362833G>A 1 22 g.101213149G>A g.42410432A>G 1 23 g.101213149G>A g.48515401C>T 1 24 g.101213149G>A g.51970512C>G 1 25 g.101213149G>A g.61978132A>C 1 26 g.101213149G>A g.63638759C>T 1 27 g.101213149G>A g.71645972G>A 1 28 g.101213149G>A g.76708914C>T 1 29 g.101216719C>G g.117642590A>G 1 30 g.101216719C>G g.122261908G>C 1 2.2.2 Delete single-valued features Since the mutation frequency of a single-value SNP or a single-value mutant gene is constant in each patient sample, a characteristic variance of 0 will affect the calculation of canonical correlation analysis, so this paper find out these characteristics and delete them. A single-value gene was obtained through screening, as shown in Table 6, which is the gene WDR37. It is known that the mutation of the gene WDR37 is related to eye development[ 32] , developmental delay, intellectual disability, cerebellar hypoplasia[ 33] , and the risk of thyroid cancer[ 34] . However, no research has yet shown that the gene WDR37 is associated with hyperthyroidism disease correlation. At the same time, 60 single-valued SNPs were calculated, 6 of which are shown in Table 7. Table 6. 1 Mutant Gene with the Single Value Sample WDR37 GD1 1 GD10 1 GD11 1 GD12 1 GD13 1 GD14 1 GD15 1 GD16 1 GD17 1 GD18 1 GD19 1 GD2 1 GD20 1 GD21 1 GD22 1 GD23 1 GD24 1 GD25 1 GD26 1 GD27 1 GD28 1 GD29 1 GD3 1 GD30 1 GD31 1 GD32 1 GD33 1 GD34 1 GD35 1 GD36 1 GD37 1 GD38 1 GD39 1 GD4 1 GD5 1 GD6 1 GD7 1 GD8 1 GD9 1 Table 7. 6 SNPs with the Single Value SNP Sample g.10154636T>C g.1096268T>C g.112841059T>A g.121162269G>A g.122284198G>C g.122284985G>C GD1 1 1 1 1 1 1 GD10 1 1 1 1 1 1 GD11 1 1 1 1 1 1 GD12 1 1 1 1 1 1 GD13 1 1 1 1 1 1 GD14 1 1 1 1 1 1 GD15 1 1 1 1 1 1 GD16 1 1 1 1 1 1 GD17 1 1 1 1 1 1 GD18 1 1 1 1 1 1 GD19 1 1 1 1 1 1 GD2 1 1 1 1 1 1 GD20 1 1 1 1 1 1 GD21 1 1 1 1 1 1 GD22 1 1 1 1 1 1 GD23 1 1 1 1 1 1 GD24 1 1 1 1 1 1 GD25 1 1 1 1 1 1 GD26 1 1 1 1 1 1 GD27 1 1 1 1 1 1 GD28 1 1 1 1 1 1 GD29 1 1 1 1 1 1 GD3 1 1 1 1 1 1 GD30 1 1 1 1 1 1 GD31 1 1 1 1 1 1 GD32 1 1 1 1 1 1 GD33 1 1 1 1 1 1 GD34 1 1 1 1 1 1 GD35 1 1 1 1 1 1 GD36 1 1 1 1 1 1 GD37 1 1 1 1 1 1 GD38 1 1 1 1 1 1 GD39 1 1 1 1 1 1 GD4 1 1 1 1 1 1 GD5 1 1 1 1 1 1 GD6 1 1 1 1 1 1 GD7 1 1 1 1 1 1 GD8 1 1 1 1 1 1 GD9 1 1 1 1 1 1 2.2.3 Select the most relevant features There are still 142 mutant genes and 649 SNPs after screening, so there is still a risk of over-fitting. Therefore, the relationship between each mutant gene and each The correlation between each mutation type and the correlation between each SNP and each mutation type, select the gene with a high correlation with the mutation type as the representative gene and select the site with a high correlation with the mutation type as the representative site. As shown in Figure 1. and Figure 2, the correlation between each mutant gene and each mutation type, and the correlation between each SNP and each mutation type are calculated. The gene with high correlation with mutation type is selected as the representative gene, and the SNP with high correlation with mutation type is selected as the representative SNP. Above all, as shown in Table 8, 23 mutant genes were selected as representative features for canonical correlation analysis, and these genes all had a high correlation with a certain mutation type. As shown in Table 9, 8 SNPs were selected as representative features for canonical correlation analysis, and these SNPs all had a high correlation with a certain mutation type. Table 8. 23 Representative Mutant Genes Mutation Type Mutant Gene Missense Mutation Nonsense Mutation Nonstop Mutation Silent Splice Site ACADM 0 0.481879 -0.02819 -0.17702 0.127042 ATM -0.01547 0.084471 0.380443 -0.01842 0.084075 BARD1 0.325491 -0.04904 0.070867 -0.04699 -0.4034 CALCA -0.01545 -0.10712 1 -0.10773 -0.03732 CAPN3 0.102514 -0.04016 0.429833 -0.05117 -0.11419 CGA 0.026811 0.079417 0.075872 0.097248 0.707011 CRP -0.09555 0.304997 -0.04683 0.004596 0.42689 DNMT1 -0.07916 -0.10719 0.324332 -0.22261 -0.40267 DNMT3A -0.36748 -0.19413 0.065033 -0.1249 0.092219 FANCC 0.053652 -0.03754 0.380443 -0.02473 -0.28025 GHRL 0.197126 0.254727 -0.1353 0.375448 0.142183 KANK1 0.277087 -0.13368 -0.19616 0.562936 0.01786 LRSAM1 0.170789 0.349303 0.098634 0.387583 0.022978 MSH2 0.270829 0.40641 -0.02995 0.237663 0.115284 PMS2 0.073578 0.42558 -0.06534 0.14081 -0.01891 PTH -0.10436 -0.15353 0.697741 -0.01005 0.095503 SCO1 0.136939 -0.21824 -0.10864 0.404585 0.035441 SYT12 0.092892 -0.05719 0.00878 0.363988 0.012451 TG -0.28742 0.010129 -0.06221 0.078664 0.403265 THRB 0.390706 0.141808 -0.06221 0.418787 -0.18651 TPO 0.099514 -0.01251 0.376389 0.054058 0.048238 TSHR 0.707251 -0.08631 -0.0212 0.56769 -0.25341 VWF 0.02506 0.286751 -0.09606 0.379986 0.201081 Table 9. 8 representative SNPs Mutation Type SNP Missense Mutation Nonsense Mutation Nonstop Mutation Silent Splice Site g.1529224C>T 0.10864 0.635411 -0.04683 -0.03808 -0.18973 g.1533961C>T -0.01545 -0.10712 1 -0.10773 -0.03732 g.12718004G>A 0.05269 0.645745 -0.08241 0.130114 0.045908 g.23019637C>G 0.060088 0.245641 0.697741 -0.01005 -0.05348 g.43082453A>G 0.026811 0.079417 0.075872 0.097248 0.707011 g.44651599T>C -0.1537 0.206484 -0.06917 -0.10667 0.630559 g.44652143G>A -0.04269 -0.15353 0.697741 -0.1647 -0.20247 g.71809992C>T 0.620452 -0.02258 -0.10168 0.518723 -0.14418 According to these mutant genes and SNPs, the frequency of the corresponding mutation types was calculated, and the data finally used for canonical correlation analysis are obtained：23 mutant genes and 4 mutation types were obtained and some of them were shown in Table 10. 8 SNPs and 2 mutation types were obtained in Table 11. Table 10. the Calculation Data of Mutant Gene and Mutation Type Mutant Gene Sample ACADM ATM BARD1 CALCA CAPN3 CGA CRP DNMT1 DNMT3A GD1 1 0 1 0 0 1 0 1 1 GD10 1 1 3 0 0 1 0 1 1 GD11 3 0 1 0 1 1 0 2 1 GD12 3 0 3 0 0 1 1 2 0 GD13 1 0 3 0 0 1 0 2 2 GD14 1 0 3 0 0 1 0 3 0 GD15 1 0 2 0 0 1 0 3 1 GD16 1 0 1 0 1 1 0 2 1 GD17 1 0 3 0 0 1 0 3 0 GD18 1 0 3 0 1 1 0 4 0 GD19 0 0 3 0 0 1 0 4 1 GD2 1 0 3 0 0 0 0 4 0 GD20 1 0 1 0 0 1 0 4 2 GD21 1 0 5 0 0 1 0 3 1 GD22 1 0 3 0 0 1 1 4 0 GD23 1 1 3 1 2 1 0 6 1 GD24 1 0 5 0 1 0 0 3 1 GD25 1 0 3 0 1 0 0 5 1 GD26 1 0 4 0 1 1 0 4 1 GD27 1 0 2 0 0 1 1 4 2 GD28 3 1 3 0 1 1 0 3 0 GD29 1 0 2 0 0 1 0 5 1 GD3 1 0 1 0 0 1 0 3 1 GD30 1 0 4 0 1 1 0 4 1 GD31 1 0 2 0 1 0 0 6 1 GD32 1 0 4 0 0 1 0 4 1 GD33 1 0 4 0 1 0 0 5 1 GD34 1 0 3 0 0 1 0 2 0 GD35 0 0 1 0 0 1 0 4 1 GD36 1 0 4 0 1 0 0 5 0 GD37 1 1 2 0 1 1 0 5 0 GD38 1 0 3 0 0 0 0 3 1 GD39 1 1 1 0 2 1 0 2 0 GD4 1 0 1 0 0 1 0 3 1 GD5 1 0 2 0 0 1 0 4 1 GD6 1 0 2 0 1 1 0 3 0 GD7 1 0 1 0 0 1 0 1 1 GD8 1 0 1 0 0 1 0 2 1 GD9 1 1 1 0 0 1 0 5 1 Table 11. the Calculation Data of SNP and mutation type SNP Sample g.1529224C>T g.1533961C>T g.12718004G>A g.23019637C>G g.43082453A>G g.44651599T>C g.44652143G>A g.71809992C>T Missense Mutation Silent GD1 0 1 1 0 1 0 0 0 0 3 GD10 0 0 0 0 1 0 0 0 0 1 GD11 0 0 1 0 1 1 0 0 1 2 GD12 0 0 0 0 1 0 0 0 0 1 GD13 0 0 0 0 0 0 0 0 GD14 0 0 1 0 1 1 0 0 1 2 GD15 0 0 0 0 1 0 1 0 0 2 GD16 0 0 0 0 1 1 0 0 1 1 GD17 0 0 0 0 1 1 0 0 1 1 GD18 0 0 0 0 1 1 0 0 1 1 GD19 0 1 0 0 0 0 1 0 0 2 GD2 0 0 0 0 1 1 0 1 1 2 GD20 0 0 0 0 1 0 1 0 0 2 GD21 0 0 0 0 1 0 0 0 0 1 GD22 0 0 0 0 1 1 0 0 1 1 GD23 0 0 0 0 1 0 0 0 0 1 GD24 0 0 0 0 1 0 0 0 0 1 GD25 1 0 0 0 0 0 1 0 1 1 GD26 0 0 0 0 0 0 0 0 GD27 0 0 0 0 0 0 0 0 GD28 0 0 0 0 1 0 0 0 0 1 GD29 0 0 0 0 1 0 0 0 0 1 GD3 0 0 0 0 1 0 0 0 0 1 GD30 0 0 0 0 0 0 0 0 GD31 0 0 0 0 0 1 0 0 1 0 GD32 0 0 1 0 1 0 0 0 0 2 GD33 0 0 1 0 1 1 0 0 1 2 GD34 0 0 0 0 1 1 0 0 1 1 GD35 0 0 0 0 1 0 1 0 0 2 GD36 0 0 0 0 0 0 1 0 0 1 GD37 0 0 0 0 1 0 1 0 0 2 GD38 0 0 0 0 0 0 0 0 GD39 0 0 0 0 1 0 0 0 0 1 GD4 0 0 0 0 0 1 0 0 1 0 GD5 0 0 0 0 1 1 0 0 1 1 GD6 0 0 0 0 1 0 1 0 0 2 GD7 0 0 0 1 0 1 0 0 2 0 GD8 0 0 0 0 1 0 0 0 0 1 GD9 0 0 1 0 0 1 0 0 1 1 3.Result 3.1Canonical correlation between mutant genes and mutation types 3.1.1Canonical correlation and significance Using the processed mutant gene and mutation type data to conduct canonical correlation analysis, 4 groups of canonical-related variables and their corresponding eigenvalues were obtained. The size of the eigenvalues reflected the strength of the correlation between canonical variables. Then test whether the correlation of typical variables is significant by Wilks, and finally obtain the correlation and significance test results of these 4 groups of variables, as shown in Table 12. The p values of the canonical correlation variables in the first three groups are less than 0.05, all of which are significant Correlations. The correlation is 1, indicating that there is a strong positive correlation between the mutation gene and the mutation type in children with hyperthyroidism. Table 12. Canonical Correlation Analysis Results of Mutant Genes and Mutation Types No correlation eigenvalues Wilks F df d df n significance 1 1.000 4503599627370495.000 .000 291223378001.505 92.000 49.995 .000 2 1.000 1501199875790164.200 .000 12109744450.912 66.000 39.671 .000 3 1.000 1501199875790164.200 .000 41826062.664 42.000 28.000 .000 4 .787 1.622 .381 1.217 20.000 15.000 .354 Table 13. shows the proportion of variance explained by the 3 groups of significantly correlated typical variables, indicating the degree of variance explained in each typical variable set. It is found that the overall explanation ratio of the Group 2 is relatively high, so the Group 2 of typical variables is selected to represent the original variable. Table 13. the Proportion of Variance Explained by the Canonical Variable of Mutant Gene and Mutation Type Group Gene Set Gene Set * Mutation Type Set Mutation Type Set Mutation Type Set * Gene Set 1 .097 .097 .229 .229 2 .070 .070 .410 .410 3 .068 .068 .267 .267 3.1.2 Structure Analysis of Canonical Correlation Analysis Results The structure analysis of the canonical correlation analysis results is carried out through the correlation coefficient, as shown in Table 14. The plus-minus sign represents the positive or negative degree of correlation between the mutant gene and the mutation type, and the magnitude of the value represents the degree of influence of the original variable on its overall characteristics. Table 14. Correlation coefficient between original variable and typical variable of mutant gene and mutation type Gene Gene_coefficient ACADM -0.091 ATM -0.055 BARD1 -0.181 CALCA -0.052 CAPN3 -0.091 CGA 0.143 CRP -0.041 DNMT1 -0.199 DNMT3A -0.089 FANCC -0.055 GHRL -0.076 KANK1 -0.581 LRSAM1 -0.13 MSH2 -0.064 PMS2 -0.17 PTH -0.062 SCO1 -0.034 SYT12 -0.051 TG -0.35 THRB -0.051 TPO -0.071 TSHR -0.134 VWF -0.447 Mutation Type Mutation Type_coefficient Missense_Mutation -0.518 Nonstop_Mutation -0.052 Silent -0.566 Splice_Site 0.143 In the Table 14, from the plus-minus sign and absolute value of the coefficient, the genes ACADM, ATM, BARD1, CALCA, CAPN3, CRP, DNMT1, DNMT3A, FANCC, GHRL, KANK1, LRSAM1, TPO, TSHR, VWF are all prone to missense mutations, nonsense mutation(nonstop mutation) and synonymous mutation. Gene CGA tends to occur through splice site mutation. Among all genes, the coefficient of gene KANK1 is -0.581, the largest absolute value, indicating that this gene has a greater influence on the whole gene set. In the mutation type set, the coefficient of missense mutation is -0.518, and the coefficient of synonymous mutation is -0.566, indicating that the change of missense mutation and synonymous mutation has a greater impact on the mutation type set. 3.2Canonical correlation between SNPs and mutation types 3.2.1Canonical correlation and significance Using the processed SNP and mutation type data to conduct canonical correlation analysis, 2 groups of canonical-related variables and their corresponding eigenvalues were obtained. The size of the eigenvalues reflected the strength of the correlation between canonical variables. Then test whether the correlation of typical variables is significant by Wilks, and finally obtain the correlation and significance test results of these 2 groups of variables, as shown in Table 15. The p values of the canonical correlation variables in the two groups are less than 0.05, all of which are significant Correlations. The correlation is 1, indicating that there is a strong positive correlation between SNP and the mutation type in children with hyperthyroidism. Table 15. Canonical Correlation Analysis Results of SNPs and Mutation Types No correlation eigenvalues Wilks F df d df n significance 1 1.000 1501199875790164.200 .000 2642968595161067.500 16.000 46.000 .000 2 1.000 562949953421311.000 .000 1930114126015923.500 7.000 24.000 .000 Table 16. shows the proportion of variance explained by the 2 groups of significantly correlated typical variables, indicating the degree of variance explained in each typical variable set. It is found that the overall explanation ratio of Group 2 is higher than Group 1, so the Group 2 of typical variables is selected to represent the original variable. Table 16. The Proportion of Variance Explained by the Canonical Variable of SNP and Mutation Type Group SNP Set SNP Set * Mutation Type Set Mutation Type Set Mutation Type Set * SNP Set 1 .083 .083 .411 .411 2 .182 .182 .589 .589 3.2.2 Structure Analysis of Canonical Correlation Analysis Results The structure analysis of the canonical correlation analysis results is carried out through the correlation coefficient of Group 2, as shown in Table 17. The plus-minus sign represents the positive or negative degree of correlation between the mutant gene and the mutation type, and the magnitude of the value represents the degree of influence of the original variable on its overall characteristics. Table 17. Correlation coefficient between original variable and typical variable of SNP and mutation type SNP SNP_coefficient g.1529224C>T -0.296 g.1533961C>T 0.027 g.12718004G>A 0.057 g.23019637C>G -0.296 g.43082453A>G 0.065 g.44651599T>C -0.854 g.44652143G>A 0.068 g.71809992C>T 0.027 Mutation Type Mutation Type_coefficient Missense_Mutation -0.962 Silent 0.096 According to Table 17, the coefficient of SNP g.1529224C>T is -0.296, positively correlated with missense mutation, indicating that this SNP tends to have missense mutation. SNP g.1533961C>T coefficient is 0.027, positively correlated with synonymous mutation, indicating that this SNP tends to have synonymous mutation. SNP g.12718004G>A coefficient is 0.057, which is positively correlated with synonymous mutation, indicating that this SNP tends to have synonymous mutation. The coefficient of SNP g.23019637C>G is -0.296, which is positively correlated with a missense mutation, indicating that this SNP tends to have a missense mutation. The coefficient of SNP g.43082453A>G is 0.065, which is positively correlated with synonymous mutation, indicating that this SNP tends to have synonymous mutation. The coefficient of SNP g.44651599T>C is -0.854, which is positively correlated with a missense mutation, indicating that this SNP tends to have a missense mutation. SNP g.44652143G>A coefficient is 0.068, which is positively correlated with synonymous mutation, indicating that this SNP tends to have synonymous mutation. The coefficient of SNP g.71809992C>T is 0.027, which is positively correlated with synonymous mutation, indicating that this SNP tends to have synonymous mutation. Among all SNPs, SNP g.44651599T>C has the largest absolute value of the correlation coefficient, which shows that this SNP has a great influence on the whole SNP characteristics. In the set of mutation types, the coefficient of missense mutation is -0.962, and the coefficient of synonymous mutation is 0.096, which shows that the change of missense mutation has a more significant influence on mutation types. According to the results of data processing in the second section, it is found that SNP g.1529224C>T, g.23019637C>G, g.44652143G>A, and g.71809992C>T all have SNPs that are completely linearly related to them. As shown in Table 18, these pairs of SNPs should have the same mutation tendency. Table 18. Snp with complete linear correlation in canonical correlation analysis results SNP in represents SNP set completely linearly related SNP g.1529224C>T g.101207609G>A g.23019637C>G g.112838968T>C g.44652143G>A g.44657131T>C g.71809992C>T g.144506501G>A 4.Discussion In the process of data processing, this paper found SNPs and genes with special mutation frequencies in the sample: 1 pair of mutant genes with complete linear correlation: ELP3 and OPA3, and 1 mutant gene WDR37 with a single value; 510 pairs of complete linear correlation SNPs and 60 single-value SNPs. In this paper, SPSS was used to analyze the canonical correlation between mutant genes and mutation types, and between SNPs and mutation types. Through canonical variable correlation and significance tests, it was found that there was a high positive correlation between mutant genes and mutation types, and the relationship between SNPs and mutation types has a high positive correlation. The mutant genes are composed of 23 representative mutant genes, among which the genes ACADM, ATM, BARD1, CALCA, CAPN3, CRP, DNMT1, DNMT3A, FANCC, GHRL, KANK1, LRSAM1, TPO, TSHR, VWF are all prone to missense mutation, nonsense mutation, and synonymous mutation, the gene CGA tends to have splice site mutation, and the gene KANK1 has the greatest influence on the whole mutant gene set. Therefore, gene CGA and gene KANK1 may be the mutant genes with the most significant influence on children. The snp consists of 8 representative SNPs, among which the SNPs g.1529224C>T, g.23019637C>G, and g.44651599T>C are prone to missense mutation, and the SNPs g.1533961C>T, g.12718004G>A, g.43082453A>G, g.44652143G>A and g.71809992C>T tend to have synonymous mutations, while SNP g.44651599T>C has the most significant influence on the SNP set. Therefore, the SNP g.44651599T>C may be the most influential SNP in children. The discovery of these associations means that some mutation types are more common in certain SNP and mutation genes in children with hyperthyroidism, which can help to distinguish the SNPs and genes that have a more significant impact on children with hyperthyroidism to some extent and increase the understanding of the distribution pattern of mutations in the genome and the characteristics of mutations in children with hyperthyroidism. Declarations Data availability statement The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author. Ethics Statement The studies involving human participants were reviewed and approved by the Center's Ethics Committee. The patients provided their written informed consent to participate in this study. Author contributions Conceptualization ideas: XM, LT, HW. Data curation: XM, LT, HW, HL,YH. Investigation: XM, WZ. Statistical analysis and interpretation: XM, LT, HL, YS,LL,YH. Methodology: XM, LT, HL, HW, YS, AH. Resources: XM, HL, WZ, LL. Supervision: XM. Project administration: XM,YH. Visualization: LT, HL, AH. Writing – original draft: XM, LT, HL, YH. Writing—review and editing: XM, LT, WZ. All authors read, contributed to the research design, and approved the final manuscript. Funding This research was supported by Project of Guangzhou Science and technology plan project , Grant No. 202201020609. Acknowledgements This study was performed with the support of Department of Genetics and Endocrinology, Guangzhou Women and Children's Medical Center and Center of Big Data and Business Intelligent, South China University of Technology. The authors are indebted to the patients, physicians, and nurses who made this work possible. 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An OPA3 gene mutation is responsible for the disease associating optic atrophy and cataract with extrapyramidal signs. Revue neurologique, 2005, 161(4): 451-454. doi:10.1016/s0035-3787(05)85075-1 Gaier E D, Sahai I, Wiggs J L, McGeeney B, Hoffman J, Peeler CE. Novel homozygous OPA3 mutation in an Afghani family with 3-methylglutaconic aciduria type III and optic atrophy. Ophthalmic genetics, 2019, 40(6): 570-573. doi:10.1080/13816810.2019.1711428 Corona-Rivera J R, Zenteno J C, López-Pérez L G,Yokoyama-Rebollar E, Villarroel CE, Barragán-Arévalo T, et al. First Report of Mexican Patients with PACS1-Related Neurodevelopmental Disorder and Review of the PACS1-, PACS2-, and WDR37-Related Ophthalmological Manifestations. Molecular Syndromology, 2023, 14(2): 143-151. doi:10.1159/000526975 Kanca O, Andrews J C, Lee P T, Patel C, Braddock SR, Slavotinek AM, et al. De novo variants in WDR37 are associated with epilepsy, colobomas, dysmorphism, developmental delay, intellectual disability, and cerebellar hypoplasia. The American Journal of Human Genetics, 2019, 105(2): 413-424. doi:10.1016/j.ajhg.2019.06.014 Akdi A, Giménez E M, García-Quispes W, Pastor S, Castell J, Biarnés J, et al. WDR3 gene haplotype is associated with thyroid cancer risk in a Spanish population. Thyroid, 2010, 20(7): 803-809. doi:10.1089/thy.2010.0072 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3983196","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276657686,"identity":"e9792707-f1c6-447d-93c3-7e1e20dda7a8","order_by":0,"name":"Xiaojian Mao","email":"","orcid":"","institution":"Guangzhou Women and Children's Medical Center, Guangzhou Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xiaojian","middleName":"","lastName":"Mao","suffix":""},{"id":276657687,"identity":"1a4224b2-50db-4607-96e5-7cd9d0f68cf2","order_by":1,"name":"Liangliang 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2","display":"","copyAsset":false,"role":"figure","size":84325,"visible":true,"origin":"","legend":"\u003cp\u003eCorrelation Heatmap of SNPs and Mutation Types\u003c/p\u003e","description":"","filename":"figure12.png","url":"https://assets-eu.researchsquare.com/files/rs-3983196/v1/51dd36175c938bc15530593f.png"},{"id":59842403,"identity":"6abc8fcb-0969-4bf9-8886-6884b11c27a3","added_by":"auto","created_at":"2024-07-08 09:49:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2023293,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3983196/v1/1de78161-8a1c-4ad6-8758-6731f7dcaffe.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Relationship between Mutant Genes, SNPs and Mutation Types in Children with Hyperthyroidism Based on Canonical correlation Analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMutation is an important form of genetic variation and plays an essential role in the occurrence and progression of many genetic diseases. Among mutations, different mutation types have different effects on patients. This difference is mainly caused by the extent to which mutations affect protein structure, function, and regulation, thereby affecting the normal function of cells and organisms.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAmong the mutation types, nonsense mutations usually lead to truncated non-functional proteins or complete loss of protein synthesis, which may lead to impairment of important physiological functions. Nonstop mutations occur in the stop codon of the gene coding region, resulting in the inability to properly terminate protein synthesis, which has an important impact on the function and stability of the protein. Splice site mutation occurs in the gene sequence and affects the RNA splicing process, which will affect the splicing process to varying degrees, and then have an important impact on protein synthesis. Missense mutations can lead to changes in amino acids in the protein sequence, and the specific impact depends on the nature and position of the amino acid after replacement: some missense mutations may have serious effects on protein structure and function, leading to serious genetic diseases or diseases There is an increased risk, and there are also some missense mutations that have minor effects on protein function and may not lead to overt clinical manifestations. Synonymous mutation does not change the amino acid sequence of the protein-coding region, and will not affect the amino acid composition and structure of the protein. In summary, among the five types of mutations, in most cases, nonsense mutations have the greatest impact on patients, followed by non-stop mutations, splice site mutations, missense mutations, and synonymous mutations.\u003c/p\u003e\n\u003cp\u003eHyperthyroidism is a polygenic disease, and the pathogenesis at the genetic level are relatively complex, involving the interaction between multiple mutated genes and external environmental factors, so the disease of hyperthyroidism involves multiple independent or interacting genes joint action, while the individual contribution of each gene may be small or even insignificant\u0026nbsp;[\u003csup\u003e1]\u0026nbsp;\u003c/sup\u003e. In existing studies, some genes related to hyperthyroidism have been found, such as: TSHR gene[\u003csup\u003e2-3]\u003c/sup\u003e, CTLA4 gene[\u003csup\u003e4-5]\u003c/sup\u003e and GNAS gene[\u003csup\u003e6-7]\u003c/sup\u003e, but incomplete gene set cannot fully explain the genetic characteristics and genetic mechanism of hyperthyroidism, so this paper considers highlight ting the holistic perspective of mutation in the hyperthyroidism and using the overall data of mutant genes, SNPs and mutation types in children with hyperthyroidism to find the relationship between mutant genes, SNPs and mutation types and the mutant genes and SNPs which have larger impact.\u003c/p\u003e\n\u003cp\u003eCurrently, there are many studies on disease gene mutation data analysis. Many researchers use GWAS to analyze large quantities of SNP data and phenotype data. For example, use GWAS analysis method to obtain depression susceptibility from genetic data of depression patients Genes[\u003csup\u003e8]\u003c/sup\u003e, detect SNPs with high associations between brain white matter fiber tracts and imaging data[\u003csup\u003e9]\u003c/sup\u003e, study the relationship between genetic variants and cardiovascular disease and related phenotypic traits[\u003csup\u003e10]\u003c/sup\u003e, and obtaining genetic variants associated with COVID-19 patients[\u003csup\u003e11]\u003c/sup\u003e. In addition, some researchers have used machine learning algorithms to study gene mutation data, such as LASSO regression and machine learning-support vector machine recursive feature elimination algorithms to screen the characteristic genes related to intracranial aneurysm rupture ferroptosis[\u003csup\u003e12]\u003c/sup\u003e, using LASSO algorithm and The SVM-RFE algorithm screens the diagnostic genes related to ferroptosis in endometriosis[\u003csup\u003e13]\u003c/sup\u003e, uses the random double clustering algorithm to obtain the central gene from the gene expression data[\u003csup\u003e14]\u003c/sup\u003e, and uses the co-inertia analysis method to analyze the relationship between multiple gene sets[\u003csup\u003e15]\u003c/sup\u003e. There are also studies that use canonical correlation algorithms to analyze the interaction between genes\u003csup\u003e[16-17]\u003c/sup\u003e. To sum up, most of the current studies on disease mutation gene identification and mutation gene relationship analysis focus on the analysis of the relationship between SNP and phenotypes, genes and phenotypes, and genes, and the methods used are mostly regression, classification and clustering Algorithms, there are few overall analyzes of mutant genes, SNPs and mutation types, so it could be considered to use correlation algorithms to study the relationship between SNPs, mutant genes and mutation types.\u003c/p\u003e\n\u003cp\u003eCanonical Component Analysis (CCA), proposed by Hotelling in 1936[\u003csup\u003e18]\u003c/sup\u003e, is used to explore the potential linear relationship between two groups of random variables. It is different from the traditional correlation analysis that only considers the correlation between two variables. It is a tool for multivariate statistical analysis. CCA is often applied to the study of the relationship between two wholes. For example, CCA combined with gene expression datasets can study gene-gene interactions\u0026nbsp;[\u003csup\u003e19]-[\u003c/sup\u003ea20a]. At the same time, CCA is widely used in pattern analysis[\u003csup\u003e21]\u003c/sup\u003e, and also in imaging genetics\u0026nbsp;[\u003csup\u003e22]-[\u003c/sup\u003ea23a], signal denoising\u0026nbsp;[\u003csup\u003e24]\u003c/sup\u003e,\u0026nbsp;and\u0026nbsp;the prediction of functional sites of biological sequences\u0026nbsp;[\u003csup\u003e25]\u003c/sup\u003e. Therefore, this paper uses canonical correlation analysis to study the relationship between mutant genes and mutation types, SNPs, and mutation types, which will help to understand the distribution pattern of mutations in the genome and the genetic characteristics of hyperthyroidism.\u003c/p\u003e\n\u003cp\u003eThe principle of CCA is similar to that of principal component analysis. Several groups of comprehensive canonical-related variables are constructed. The canonical variables between the groups are not correlated with each other. Each canonical variable is linearly weighted by the original variable through the canonical coefficient. The canonical coefficient makes the correlation between the canonical variables in the group sex maximization.\u003c/p\u003e\n\u003cp\u003eThe calculation steps of the canonical correlation analysis in this paper are as follows: Let the sample characteristic SNP site be \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(X=({x}_{1},{x}_{2}\\dots {x}_{n}\\)\u003c/span\u003e\u003c/span\u003e), the mutation type be \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Y=({y}_{1},{y}_{2}\\dots {y}_{m}\\)\u003c/span\u003e\u003c/span\u003e), \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(X\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(Y\\)\u003c/span\u003e\u003c/span\u003e be two groups of zero-mean random variables.\u003c/p\u003e\n\u003cp\u003eFirst, it is necessary to find a pair of projection directions. \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{1},{b}_{1}\\)\u003c/span\u003e\u003c/span\u003e to maximize the correlation between the linear combination of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({u}_{1}={a}_{1}^{T}X\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({v}_{1}={b}_{1}^{T}Y\\)\u003c/span\u003e\u003c/span\u003e. Similarly, find a second pair of projections \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({a}_{2},{b}_{2}\\)\u003c/span\u003e\u003c/span\u003e to maximize the correlation between \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({u}_{2}={a}_{2}^{T}X\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({v}_{2}={b}_{2}^{T}Y\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({u}_{2}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({v}_{2}\\)\u003c/span\u003e\u003c/span\u003e are not correlated with \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({u}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({v}_{1}\\)\u003c/span\u003e\u003c/span\u003e, so there are:\u003c/p\u003e\n\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\text{v}\\text{a}\\text{r}\\left({u}_{1}\\right)=\\text{v}\\text{a}\\text{r}\\left({v}_{1}\\right)=1$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\text{c}\\text{o}\\text{v}\\left({u}_{1},{u}_{2}\\right)=\\text{c}\\text{o}\\text{v}\\left({v}_{1}{,v}_{2}\\right)=0$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe calculation of the second and third equivalent typical variables is analogized according to the above steps.\u003c/p\u003e\n\u003cp\u003eThe correlation was measured using the Pearson correlation coefficient, and the formula is as follows:\u003c/p\u003e\n\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$\\rho \\left({{a}^{T}}_{1}x,{{b}^{T}}_{1}y\\right)=\\frac{cov({{a}^{T}}_{1}x,{{b}^{T}}_{1}y)}{\\sqrt{Var\\left({{a}^{T}}_{1}x\\right)*Var\\left({{b}^{T}}_{1}y\\right)}}$$\u003c/div\u003e\n \u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe typical variable group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{U}=({\\text{u}}_{1},{\\text{u}}_{2}\\dots {\\text{u}}_{\\text{n}}\\)\u003c/span\u003e\u003c/span\u003e) of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{X}\\)\u003c/span\u003e\u003c/span\u003e and the typical variable group \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{V}=({\\text{v}}_{1},{\\text{v}}_{2}\\dots {\\text{v}}_{\\text{m}}\\)\u003c/span\u003e\u003c/span\u003e) of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{Y}\\)\u003c/span\u003e\u003c/span\u003e can be obtained in the end, and the correlation coefficient between each group of typical variables can be calculated to get \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({{\\rho }}_{1},{{\\rho }}_{2}\\dots {{\\rho }}_{\\text{m}\\text{i}\\text{n}(\\text{n},\\text{m})}\\)\u003c/span\u003e\u003c/span\u003e, and the index \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\({\\text{p}}_{1},{\\text{p}}_{2}\\dots {\\text{p}}_{\\text{m}\\text{i}\\text{n}(\\text{n},\\text{m})}\\)\u003c/span\u003e\u003c/span\u003e describing the significance of the correlation coefficient is calculated, in which the most significant correlation coefficient is selected as the correlation index corresponding to \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{X}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{Y}\\)\u003c/span\u003e\u003c/span\u003e. In addition, to ensure the solution of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{U}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{V}\\)\u003c/span\u003e\u003c/span\u003e, canonical correlation analysis requires that the covariance matrices of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{X}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{Y}\\)\u003c/span\u003e\u003c/span\u003e are non-singular, so the data need to be processed before canonical correlation analysis.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003ch3\u003e2.1\u0026nbsp;Data acquisition\u003c/h3\u003e\n\u003cp\u003eThe cases in this paper come from the Guangzhou Women and Children\u0026apos;s Medical Center. \u0026nbsp;After approval by the ethics committee of the center and the informed consent of the sick children\u0026apos;s guardians, the peripheral blood of 39 children with hyperthyroidism was collected, and DNA was extracted. The DNA samples were used for whole-genome sequencing to obtain whole-exome sequencing data. Search public disease databases such as Malacards[\u003csup\u003e26]\u003c/sup\u003e, MutationView\u0026nbsp;[\u003csup\u003e27]\u003c/sup\u003e, and Clinvar to obtain genes related to hyperthyroidism, and compare with 39 patient mutation genes, screen out 144 genes related to hyperthyroid disease. The sequencing data of 144 mutant genes in 39 children with hyperthyroidism were used as the data for this study.\u003c/p\u003e\n\u003ch3\u003e2.2 Data processing\u003c/h3\u003e\n\u003cp\u003eClassify the data according to the patient ID, count the mutant genes, SNPs, and mutation types, and obtain the variation frequency of each hyperthyroid child in different mutated genes, SNPs, and mutation types as the overall characteristics of the mutant genes, SNPs and mutation types. For example, Table 1. shows the variation frequency of each mutation gene in hyperthyroidism children, with each column representing a mutation gene and each row representing a patient; Table 2. shows the variation frequency of SNP in children with hyperthyroidism, with each column representing an snp and each row representing a patient. Table 3. shows the variation frequency of each mutation type in children with hyperthyroidism, with each column representing a mutation type and each row representing a patient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eThe Frequency of Each Mutant Gene in Children with Hyperthyroidism\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"94%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; mutant gene\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e\u003cstrong\u003eACADM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u003cstrong\u003eACADS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u003cstrong\u003eACP5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026hellip;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e\u003cstrong\u003eVWF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWDR37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWRN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\"\u003e\n \u003cp\u003eGD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\"\u003e\n \u003cp\u003eGD10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\"\u003e\n \u003cp\u003eGD11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\"\u003e\n \u003cp\u003eGD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\"\u003e\n \u003cp\u003eGD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"17.894736842105264%\"\u003e\n \u003cp\u003eGD9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.526315789473685%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.631578947368421%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u003c/strong\u003ethe Frequency of Each SNP in Children with Hyperthyroidism\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"616\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.372168284789645%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.048543689320388%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.100748174T\u0026gt;C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.915857605177994%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.101206854C\u0026gt;T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.101206863G\u0026gt;A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0453074433656955%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026hellip;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.459546925566343%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.9822347C\u0026gt;G\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.9849809C\u0026gt;T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.9849941A\u0026gt;G\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.372168284789645%\"\u003e\n \u003cp\u003eGD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.048543689320388%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.915857605177994%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0453074433656955%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.459546925566343%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.372168284789645%\"\u003e\n \u003cp\u003eGD10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.048543689320388%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.915857605177994%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0453074433656955%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.459546925566343%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.372168284789645%\"\u003e\n \u003cp\u003eGD11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.048543689320388%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.915857605177994%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0453074433656955%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.459546925566343%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.372168284789645%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.048543689320388%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.915857605177994%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0453074433656955%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.459546925566343%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.372168284789645%\"\u003e\n \u003cp\u003eGD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.048543689320388%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.915857605177994%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0453074433656955%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.459546925566343%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.372168284789645%\"\u003e\n \u003cp\u003eGD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.048543689320388%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.915857605177994%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0453074433656955%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.459546925566343%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.372168284789645%\"\u003e\n \u003cp\u003eGD9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.048543689320388%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.915857605177994%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.239482200647249%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"4.0453074433656955%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.459546925566343%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.297734627831716%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.62135922330097%\" valign=\"top\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003eFrequency of Each Mutation Type in Children with Hyperthyroidism\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; Mutation type\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMissense_Mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonsense_Mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonstop_Mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSilent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSplice_Site\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eGD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eGD10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eGD11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e176\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e\u0026hellip;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eGD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e157\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eGD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eGD9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e161\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.387755102040817%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.244897959183673%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eHowever, the data of children with hyperthyroidism include 144 mutant genes and 1221 SNPs, so the number of features is too large. Canonical correlation analysis has certain requirements for the distribution of data. Therefore, the filtered data are processed by deleting the completely linear correlation features, deleting the single-valued features, and selecting the features with the highest correlation.\u003c/p\u003e\n\u003ch4\u003e2.2.1\u0026nbsp;Remove completely linear correlation features\u003c/h4\u003e\n\u003cp\u003eThe calculation of canonical correlation analysis will be affected when there is a complete linear correlation between two SNPs or two mutant genes,. Therefore, it is necessary to find out the pairs of mutant genes and SNPs with a correlation of 1 and delete one of them. After calculating the correlation between genes, a pair of mutated genes with a correlation of 1 is obtained, as shown in Table 4, which are the gene ELP3 and gene OPA3. It is known that ELP3 mutations are associated with allergic airway inflammation[\u003csup\u003e28]\u003c/sup\u003e, and neurodevelopmental disorders[\u003csup\u003e29]\u003c/sup\u003e, and gene OPA3 mutation is related to optical optic neuropathy[\u003csup\u003e30]\u003c/sup\u003e, and optic nerve dysfunction[\u003csup\u003e31]\u003c/sup\u003e, but no research has shown that there is a link between the gene ELP3 and OPA3 and hyperthyroidism; after calculating the correlation between the sites, 510 pairs of correlations are obtained. 30 pairs of them are shown in Table 5.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;4.\u003c/strong\u003e 1 Pair of Mutant Genes with Complete Linear Correlation\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"288\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e\u003cstrong\u003ecorr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eELP3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003eOPA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;5.\u0026nbsp;\u003c/strong\u003e30 Pairs of SNPs with Complete Linear Correlation\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"347\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e\u003cstrong\u003ecorr\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101206863G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101207627G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101206863G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101210433G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101206863G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101212542G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101206863G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101212596G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101207609G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.10148979G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101207609G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.129369961G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101207609G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.132911525A\u0026gt;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101207609G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.133113558C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101207609G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.133116676G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101207609G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.1529224C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101207609G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.170404400G\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101207609G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.51942464T\u0026gt;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101207609G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.71810550C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101213149G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.108289005C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101213149G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.115982363A\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101213149G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.129454283A\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101213149G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.134690982C\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101213149G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.134784997C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101213149G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.2080242C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101213149G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.23418374C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101213149G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.32362833G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101213149G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.42410432A\u0026gt;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101213149G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.48515401C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101213149G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.51970512C\u0026gt;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101213149G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.61978132A\u0026gt;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101213149G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.63638759C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101213149G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.71645972G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101213149G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.76708914C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101216719C\u0026gt;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.117642590A\u0026gt;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.101216719C\u0026gt;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.310344827586206%\"\u003e\n \u003cp\u003eg.122261908G\u0026gt;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.689655172413794%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch4\u003e2.2.2 Delete single-valued features\u003c/h4\u003e\n\u003cp\u003eSince the mutation frequency of a single-value SNP or a single-value mutant gene is constant in each patient sample, a characteristic variance of 0 will affect the calculation of canonical correlation analysis, so this paper find out these characteristics and delete them. A single-value gene was obtained through screening, as shown in Table 6, which is the gene WDR37.\u003c/p\u003e\n\u003cp\u003eIt is known that the mutation of the gene WDR37 is related to eye development[\u003csup\u003e32]\u003c/sup\u003e, developmental delay, intellectual disability, cerebellar hypoplasia[\u003csup\u003e33]\u003c/sup\u003e, and the risk of thyroid cancer[\u003csup\u003e34]\u003c/sup\u003e. However, no research has yet shown that the gene WDR37 is associated with hyperthyroidism disease correlation. At the same time, 60 single-valued SNPs were calculated, 6 of which are shown in Table 7.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;6.\u003c/strong\u003e1 Mutant Gene with the Single Value\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"288\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWDR37\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003eGD9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;7.\u003c/strong\u003e 6 SNPs with the Single Value\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"603\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.10154636T\u0026gt;C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.1096268T\u0026gt;C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.112841059T\u0026gt;A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.121162269G\u0026gt;A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.122284198G\u0026gt;C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.122284985G\u0026gt;C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n 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\u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n 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\u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n 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\u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.754560530679933%\"\u003e\n \u003cp\u003eGD9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.945273631840797%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.603648424543946%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.759535655058043%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.091210613598673%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.257048092868988%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.588723051409618%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch4\u003e2.2.3\u0026nbsp;Select the most relevant features\u003c/h4\u003e\n\u003cp\u003eThere are still 142 mutant genes and 649 SNPs after screening, so there is still a risk of over-fitting. Therefore, the relationship between each mutant gene and each The correlation between each mutation type and the correlation between each SNP and each mutation type, select the gene with a high correlation with the mutation type as the representative gene and select the site with a high correlation with the mutation type as the representative site.\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 1. and Figure 2, the correlation between each mutant gene and each mutation type, and the correlation between each SNP and each mutation type are calculated. The gene with high correlation with mutation type is selected as the representative gene, and the SNP with high correlation with mutation type is selected as the representative SNP.\u003c/p\u003e\n\u003cp\u003eAbove all, as shown in Table 8, 23 mutant genes were selected as representative features for canonical correlation analysis, and these genes all had a high correlation with a certain mutation type. As shown in Table 9, 8 SNPs were selected as representative features for canonical correlation analysis, and these SNPs all had a high correlation with a certain mutation type.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;8.\u003c/strong\u003e23 Representative Mutant Genes\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"109%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMutation\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eType\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMutant Gene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMissense Mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonsense Mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonstop Mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSilent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSplice Site\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eACADM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e0.481879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.02819\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.17702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.127042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eATM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.01547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e0.084471\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.380443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.01842\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.084075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eBARD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.325491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e-0.04904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.070867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.04699\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e-0.4034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eCALCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.01545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e-0.10712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.10773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e-0.03732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eCAPN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.102514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e-0.04016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.429833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.05117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e-0.11419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eCGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.026811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e0.079417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.075872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.097248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.707011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.09555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e0.304997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.04683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.004596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.42689\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eDNMT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.07916\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e-0.10719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.324332\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.22261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e-0.40267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eDNMT3A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.36748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e-0.19413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.065033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.1249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.092219\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eFANCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.053652\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e-0.03754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.380443\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.02473\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e-0.28025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eGHRL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.197126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e0.254727\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.1353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.375448\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.142183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eKANK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.277087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e-0.13368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.19616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.562936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.01786\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eLRSAM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.170789\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e0.349303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.098634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.387583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.022978\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eMSH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.270829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e0.40641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.02995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.237663\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.115284\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003ePMS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.073578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e0.42558\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.06534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.14081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e-0.01891\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003ePTH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.10436\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e-0.15353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.697741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.01005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.095503\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eSCO1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.136939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e-0.21824\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.10864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.404585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.035441\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eSYT12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.092892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e-0.05719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.00878\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.363988\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.012451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.28742\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e0.010129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.06221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.078664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.403265\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eTHRB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.390706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e0.141808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.06221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.418787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e-0.18651\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eTPO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.099514\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e-0.01251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.376389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.054058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.048238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eTSHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.707251\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e-0.08631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.0212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.56769\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e-0.25341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003eVWF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.02506\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e0.286751\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e-0.09606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.49484536082474%\"\u003e\n \u003cp\u003e0.379986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.402061855670103%\"\u003e\n \u003cp\u003e0.201081\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;9.\u003c/strong\u003e8 representative SNPs\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMutation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eType\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMissense Mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonsense Mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNonstop Mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSilent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSplice Site\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eg.1529224C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.10864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.635411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e-0.04683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e-0.03808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e-0.18973\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eg.1533961C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e-0.01545\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e-0.10712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e-0.10773\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e-0.03732\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eg.12718004G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.05269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.645745\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e-0.08241\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e0.130114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.045908\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eg.23019637C\u0026gt;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.060088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.245641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e0.697741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e-0.01005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e-0.05348\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eg.43082453A\u0026gt;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.026811\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.079417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e0.075872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e0.097248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.707011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eg.44651599T\u0026gt;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e-0.1537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.206484\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e-0.06917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e-0.10667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e0.630559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eg.44652143G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e-0.04269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e-0.15353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e0.697741\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e-0.1647\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e-0.20247\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.463917525773196%\"\u003e\n \u003cp\u003eg.71809992C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e0.620452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.61855670103093%\"\u003e\n \u003cp\u003e-0.02258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.587628865979383%\"\u003e\n \u003cp\u003e-0.10168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e0.518723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.371134020618557%\"\u003e\n \u003cp\u003e-0.14418\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAccording to these mutant genes and SNPs, the frequency of the corresponding mutation types was calculated, and the data finally used for canonical correlation analysis are obtained：23 mutant genes and 4 mutation types were obtained and some of them were shown in Table 10. 8 SNPs and 2 mutation types were obtained in Table 11.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;10.\u003c/strong\u003ethe Calculation Data of Mutant Gene and Mutation Type\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMutant\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e\u003cstrong\u003eACADM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e\u003cstrong\u003eATM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e\u003cstrong\u003eBARD1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCALCA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCAPN3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCGA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCRP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDNMT1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDNMT3A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n 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\u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n 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\u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.58609794628752%\"\u003e\n \u003cp\u003eGD9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.740916271721959%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.109004739336493%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.42654028436019%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.424960505529226%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.164296998420221%\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.270142180094787%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;11.\u003c/strong\u003ethe Calculation Data of SNP and mutation type\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"644\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSample\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.1529224C\u0026gt;T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.1533961C\u0026gt;T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.12718004G\u0026gt;A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.23019637C\u0026gt;G\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.43082453A\u0026gt;G\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.44651599T\u0026gt;C\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.44652143G\u0026gt;A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e\u003cstrong\u003eg.71809992C\u0026gt;T\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMissense Mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSilent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n 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width=\"9.782608695652174%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD28\u003c/p\u003e\n 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width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n 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\u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD37\u003c/p\u003e\n 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\u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"9.627329192546584%\"\u003e\n \u003cp\u003eGD9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.919254658385094%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.142857142857143%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.453416149068323%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.608695652173913%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.869565217391305%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.782608695652174%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.248447204968944%\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.801242236024844%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.298136645962733%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"3.Result","content":"\u003ch3\u003e3.1Canonical correlation between mutant genes and mutation types\u003c/h3\u003e\n\u003ch4\u003e3.1.1Canonical correlation and significance\u003c/h4\u003e\n\u003cp\u003eUsing the processed mutant gene and mutation type data to conduct canonical correlation analysis, 4 groups of canonical-related variables and their corresponding eigenvalues were obtained. The size of the eigenvalues reflected the strength of the correlation between canonical variables. Then test whether the correlation of typical variables is significant by Wilks, and finally obtain the correlation and significance test results of these 4 groups of variables, as shown in Table 12. The p values of the canonical correlation variables in the first three groups are less than 0.05, all of which are significant Correlations. The correlation is 1, indicating that there is a strong positive correlation between the mutation gene and the mutation type in children with hyperthyroidism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;12.\u003c/strong\u003eCanonical Correlation Analysis Results of Mutant Genes and Mutation Types\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.185567010309279%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003ecorrelation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\"\u003e\n \u003cp\u003e\u003cstrong\u003eeigenvalues\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWilks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003csub\u003ed\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003csub\u003en\u003c/sub\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e\u003cstrong\u003esignificance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.185567010309279%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\"\u003e\n \u003cp\u003e4503599627370495.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e291223378001.505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e92.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e49.995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.185567010309279%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\"\u003e\n \u003cp\u003e1501199875790164.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e12109744450.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e66.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e39.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.185567010309279%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\"\u003e\n \u003cp\u003e1501199875790164.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e41826062.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e42.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e28.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"6.185567010309279%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e.787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.68041237113402%\"\u003e\n \u003cp\u003e1.622\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e.381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.556701030927837%\"\u003e\n \u003cp\u003e1.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e20.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.34020618556701%\"\u003e\n \u003cp\u003e15.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.278350515463918%\"\u003e\n \u003cp\u003e.354\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 13. shows the proportion of variance explained by the 3 groups of significantly correlated typical variables, indicating the degree of variance explained in each typical variable set. It is found that the overall explanation ratio of the Group 2 is relatively high, so the Group 2 of typical variables is selected to represent the original variable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;13.\u003c/strong\u003ethe Proportion of Variance Explained by the Canonical Variable of Mutant Gene and Mutation Type\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"603\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.266998341625207%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.442786069651742%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Set\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.6849087893864%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene Set * Mutation Type Set\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.5787728026534%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMutation Type Set\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.026533996683252%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Mutation Type Set * Gene Set\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.266998341625207%\" valign=\"top\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.442786069651742%\" valign=\"top\"\u003e\n \u003cp\u003e.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.6849087893864%\" valign=\"top\"\u003e\n \u003cp\u003e.097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.5787728026534%\" valign=\"top\"\u003e\n \u003cp\u003e.229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.026533996683252%\" valign=\"top\"\u003e\n \u003cp\u003e.229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.266998341625207%\" valign=\"top\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.442786069651742%\" valign=\"top\"\u003e\n \u003cp\u003e.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.6849087893864%\" valign=\"top\"\u003e\n \u003cp\u003e.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.5787728026534%\" valign=\"top\"\u003e\n \u003cp\u003e.410\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.026533996683252%\" valign=\"top\"\u003e\n \u003cp\u003e.410\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.266998341625207%\" valign=\"top\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.442786069651742%\" valign=\"top\"\u003e\n \u003cp\u003e.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"29.6849087893864%\" valign=\"top\"\u003e\n \u003cp\u003e.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.5787728026534%\" valign=\"top\"\u003e\n \u003cp\u003e.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.026533996683252%\" valign=\"top\"\u003e\n \u003cp\u003e.267\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch4\u003e3.1.2 Structure Analysis of Canonical Correlation Analysis Results\u003c/h4\u003e\n\u003cp\u003eThe structure analysis of the canonical correlation analysis results is carried out through the correlation coefficient, as shown in Table 14. The plus-minus sign represents the positive or negative degree of correlation between the mutant gene and the mutation type, and the magnitude of the value represents the degree of influence of the original variable on its overall characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;14.\u003c/strong\u003eCorrelation coefficient between original variable and typical variable of mutant gene and mutation type\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"278\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene_coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eACADM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eATM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eBARD1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eCALCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eCAPN3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.091\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eCGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eCRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eDNMT1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eDNMT3A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eFANCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eGHRL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eKANK1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.581\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eLRSAM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eMSH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.064\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003ePMS2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003ePTH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eSCO1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.034\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eSYT12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eTHRB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eTPO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.071\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eTSHR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eVWF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.447\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMutation Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMutation Type_coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eMissense_Mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.518\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eNonstop_Mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eSilent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e-0.566\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"39.20863309352518%\"\u003e\n \u003cp\u003eSplice_Site\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"60.79136690647482%\"\u003e\n \u003cp\u003e0.143\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eIn the Table 14, from the plus-minus sign and absolute value of the coefficient, the genes ACADM, ATM, BARD1, CALCA, CAPN3, CRP, DNMT1, DNMT3A, FANCC, GHRL, KANK1, LRSAM1, TPO, TSHR, VWF are all prone to missense mutations, nonsense mutation(nonstop mutation) and synonymous mutation. Gene CGA tends to occur through splice site mutation. Among all genes, the coefficient of gene KANK1 is -0.581, the largest absolute value, indicating that this gene has a greater influence on the whole gene set. In the mutation type set, the coefficient of missense mutation is -0.518, and the coefficient of synonymous mutation is -0.566, indicating that the change of missense mutation and synonymous mutation has a greater impact on the mutation type set.\u003c/p\u003e\n\u003ch3\u003e3.2Canonical correlation between SNPs and mutation types\u003c/h3\u003e\n\u003ch4\u003e3.2.1Canonical correlation and significance\u003c/h4\u003e\n\u003cp\u003eUsing the processed SNP and mutation type data to conduct canonical correlation analysis, 2 groups of canonical-related variables and their corresponding eigenvalues were obtained. The size of the eigenvalues reflected the strength of the correlation between canonical variables. Then test whether the correlation of typical variables is significant by Wilks, and finally obtain the correlation and significance test results of these 2 groups of variables, as shown in Table 15. The p values of the canonical correlation variables in the two groups are less than 0.05, all of which are significant Correlations. The correlation is 1, indicating that there is a strong positive correlation between SNP and the mutation type in children with hyperthyroidism.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 15.\u003c/strong\u003e Canonical Correlation Analysis Results of SNPs and Mutation Types\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.707581227436823%\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.288808664259928%\"\u003e\n \u003cp\u003e\u003cstrong\u003ecorrelation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.895306859205775%\"\u003e\n \u003cp\u003e\u003cstrong\u003eeigenvalues\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.371841155234657%\"\u003e\n \u003cp\u003e\u003cstrong\u003eWilks\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.646209386281587%\"\u003e\n \u003cp\u003e\u003cstrong\u003eF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.205776173285198%\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003csub\u003ed\u003c/sub\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.205776173285198%\"\u003e\n \u003cp\u003e\u003cstrong\u003edf\u003csub\u003en\u003c/sub\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.67870036101083%\"\u003e\n \u003cp\u003e\u003cstrong\u003esignificance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.0833333333333335%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\"\u003e\n \u003cp\u003e1501199875790164.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\"\u003e\n \u003cp\u003e2642968595161067.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e16.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e46.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"2.0833333333333335%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.416666666666666%\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.083333333333332%\"\u003e\n \u003cp\u003e562949953421311.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.458333333333334%\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\"\u003e\n \u003cp\u003e1930114126015923.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e7.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\"\u003e\n \u003cp\u003e24.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.25%\"\u003e\n \u003cp\u003e.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eTable 16. shows the proportion of variance explained by the 2 groups of significantly correlated typical variables, indicating the degree of variance explained in each typical variable set. It is found that the overall explanation ratio of Group 2 is higher than Group 1, so the Group 2 of typical variables is selected to represent the original variable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;16.\u0026nbsp;\u003c/strong\u003eThe Proportion of Variance Explained by the Canonical Variable of SNP and Mutation Type\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"563\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.170212765957446%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eGroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.070921985815604%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP Set\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77304964539007%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP Set * Mutation Type Set\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26241134751773%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003eMutation Type Set\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.72340425531915%\" valign=\"bottom\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Mutation Type Set * SNP Set\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.170212765957446%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.070921985815604%\"\u003e\n \u003cp\u003e.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77304964539007%\"\u003e\n \u003cp\u003e.083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26241134751773%\"\u003e\n \u003cp\u003e.411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.72340425531915%\"\u003e\n \u003cp\u003e.411\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"11.170212765957446%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.070921985815604%\"\u003e\n \u003cp\u003e.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.77304964539007%\"\u003e\n \u003cp\u003e.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.26241134751773%\"\u003e\n \u003cp\u003e.589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.72340425531915%\"\u003e\n \u003cp\u003e.589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003ch4\u003e3.2.2 Structure Analysis of Canonical Correlation Analysis Results\u0026nbsp;\u003c/h4\u003e\n\u003cp\u003eThe structure analysis of the canonical correlation analysis results is carried out through the correlation coefficient of Group 2, as shown in Table 17. The plus-minus sign represents the positive or negative degree of correlation between the mutant gene and the mutation type, and the magnitude of the value represents the degree of influence of the original variable on its overall characteristics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;17.\u003c/strong\u003eCorrelation coefficient between original variable and typical variable of SNP and mutation type\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"288\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.19444444444444%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.80555555555556%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP_coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.19444444444444%\"\u003e\n \u003cp\u003eg.1529224C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.80555555555556%\"\u003e\n \u003cp\u003e-0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.19444444444444%\"\u003e\n \u003cp\u003eg.1533961C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.80555555555556%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.19444444444444%\"\u003e\n \u003cp\u003eg.12718004G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.80555555555556%\"\u003e\n \u003cp\u003e0.057\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.19444444444444%\"\u003e\n \u003cp\u003eg.23019637C\u0026gt;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.80555555555556%\"\u003e\n \u003cp\u003e-0.296\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.19444444444444%\"\u003e\n \u003cp\u003eg.43082453A\u0026gt;G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.80555555555556%\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.19444444444444%\"\u003e\n \u003cp\u003eg.44651599T\u0026gt;C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.80555555555556%\"\u003e\n \u003cp\u003e-0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.19444444444444%\"\u003e\n \u003cp\u003eg.44652143G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.80555555555556%\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.19444444444444%\"\u003e\n \u003cp\u003eg.71809992C\u0026gt;T\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.80555555555556%\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.19444444444444%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMutation Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.80555555555556%\"\u003e\n \u003cp\u003e\u003cstrong\u003eMutation Type_coefficient\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.19444444444444%\"\u003e\n \u003cp\u003eMissense_Mutation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.80555555555556%\"\u003e\n \u003cp\u003e-0.962\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"38.19444444444444%\"\u003e\n \u003cp\u003eSilent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"61.80555555555556%\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAccording to Table 17, the coefficient of SNP g.1529224C\u0026gt;T is -0.296, positively correlated with missense mutation, indicating that this SNP tends to have missense mutation. SNP g.1533961C\u0026gt;T coefficient is 0.027, positively correlated with synonymous mutation, indicating that this SNP tends to have synonymous mutation. SNP g.12718004G\u0026gt;A coefficient is 0.057, which is positively correlated with synonymous mutation, indicating that this SNP tends to have synonymous mutation. The coefficient of SNP g.23019637C\u0026gt;G is -0.296, which is positively correlated with a missense mutation, indicating that this SNP tends to have a missense mutation. The coefficient of SNP g.43082453A\u0026gt;G is 0.065, which is positively correlated with synonymous mutation, indicating that this SNP tends to have synonymous mutation. The coefficient of SNP g.44651599T\u0026gt;C is -0.854, which is positively correlated with a missense mutation, indicating that this SNP tends to have a missense mutation. SNP g.44652143G\u0026gt;A coefficient is 0.068, which is positively correlated with synonymous mutation, indicating that this SNP tends to have synonymous mutation. The coefficient of SNP g.71809992C\u0026gt;T is 0.027, which is positively correlated with synonymous mutation, indicating that this SNP tends to have synonymous mutation. Among all SNPs, SNP g.44651599T\u0026gt;C has the largest absolute value of the correlation coefficient, which shows that this SNP has a great influence on the whole SNP characteristics. In the set of mutation types, the coefficient of missense mutation is -0.962, and the coefficient of synonymous mutation is 0.096, which shows that the change of missense mutation has a more significant influence on mutation types.\u003c/p\u003e\n\u003cp\u003eAccording to the results of data processing in the second section, it is found that SNP g.1529224C\u0026gt;T, g.23019637C\u0026gt;G, g.44652143G\u0026gt;A, and g.71809992C\u0026gt;T all have SNPs that are completely linearly related to them. As shown in Table 18, these pairs of SNPs should have the same mutation tendency.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;18.\u003c/strong\u003eSnp with complete linear correlation in canonical correlation analysis results\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"53%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.494949494949495%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP in represents SNP set\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.505050505050505%\"\u003e\n \u003cp\u003e\u003cstrong\u003ecompletely linearly related\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eSNP\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.494949494949495%\"\u003e\n \u003cp\u003e\u003cem\u003eg.1529224C\u0026gt;T\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.505050505050505%\"\u003e\n \u003cp\u003eg.101207609G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.494949494949495%\"\u003e\n \u003cp\u003e\u003cem\u003eg.23019637C\u0026gt;G\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.505050505050505%\"\u003e\n \u003cp\u003eg.112838968T\u0026gt;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.494949494949495%\"\u003e\n \u003cp\u003e\u003cem\u003eg.44652143G\u0026gt;A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.505050505050505%\"\u003e\n \u003cp\u003eg.44657131T\u0026gt;C\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"49.494949494949495%\"\u003e\n \u003cp\u003e\u003cem\u003eg.71809992C\u0026gt;T\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"50.505050505050505%\"\u003e\n \u003cp\u003eg.144506501G\u0026gt;A\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"},{"header":"4.Discussion","content":"\u003cp\u003eIn the process of data processing, this paper found SNPs and genes with special mutation frequencies in the sample: 1 pair of mutant genes with complete linear correlation: ELP3 and OPA3, and 1 mutant gene WDR37 with a single value; 510 pairs of complete linear correlation SNPs and 60 single-value SNPs.\u003c/p\u003e\n\u003cp\u003eIn this paper, SPSS was used to analyze the canonical correlation between mutant genes and mutation types, and between SNPs and mutation types. Through canonical variable correlation and significance tests, it was found that there was a high positive correlation between mutant genes and mutation types, and the relationship between SNPs and mutation types has a high positive correlation.\u003c/p\u003e\n\u003cp\u003eThe mutant genes are composed of 23 representative mutant genes, among which the genes ACADM, ATM, BARD1, CALCA, CAPN3, CRP, DNMT1, DNMT3A, FANCC, GHRL, KANK1, LRSAM1, TPO, TSHR, VWF are all prone to missense mutation, nonsense mutation, and synonymous mutation, the gene CGA tends to have splice site mutation, and the gene KANK1 has the greatest influence on the whole mutant gene set. Therefore, gene CGA and gene KANK1 may be the mutant genes with the most significant influence on children. The snp consists of 8 representative SNPs, among which the SNPs g.1529224C\u0026gt;T, g.23019637C\u0026gt;G, and g.44651599T\u0026gt;C are prone to missense mutation, and the SNPs g.1533961C\u0026gt;T, g.12718004G\u0026gt;A, g.43082453A\u0026gt;G, g.44652143G\u0026gt;A and g.71809992C\u0026gt;T tend to have synonymous mutations, while SNP g.44651599T\u0026gt;C has the most significant influence on the SNP set. Therefore, the SNP g.44651599T\u0026gt;C may be the most influential SNP in children.\u003c/p\u003e\n\u003cp\u003eThe discovery of these associations means that some mutation types are more common in certain SNP and mutation genes in children with hyperthyroidism, which can help to distinguish the SNPs and genes that have a more significant impact on children with hyperthyroidism to some extent and increase the understanding of the distribution pattern of mutations in the genome and the characteristics of mutations in children with hyperthyroidism.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eData availability statement\u003c/h2\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003ch2\u003eEthics Statement\u003c/h2\u003e\n\u003cp\u003eThe studies involving human participants were reviewed and approved by the Center\u0026apos;s Ethics Committee. The patients provided their written informed consent to participate in this study.\u003c/p\u003e\n\u003ch2\u003eAuthor contributions\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eConceptualization ideas: XM, LT, HW. Data curation: XM, LT, HW, HL,YH. Investigation: XM, WZ. Statistical analysis and interpretation: XM, LT, HL, YS,LL,YH. Methodology: XM, LT, HL, HW, YS, AH. Resources: XM, HL, WZ, LL. Supervision: XM. Project administration: XM,YH. Visualization: LT, HL, AH. Writing \u0026ndash; original draft: XM, LT, HL, YH. Writing\u0026mdash;review and editing: XM, LT, WZ. All authors read, contributed to the research design, and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research was supported by Project of Guangzhou Science and technology plan project , Grant No. 202201020609.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eThis study was performed with the support of Department of Genetics and Endocrinology, Guangzhou Women and Children\u0026apos;s Medical Center and Center of Big Data and Business Intelligent, South China University of Technology. The authors are indebted to the patients, physicians, and nurses who made this work possible.\u003c/p\u003e\n\u003ch2\u003eConflict of interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e Lvovs D, Favorova O O, Favorov A V. A polygenic approach to the study of polygenic diseases. Acta Naturae (англоязычная версия),2012, 4(3 (14)): 59-71.\u003c/li\u003e\n\u003cli\u003e Patel KA, Knight B, Aziz A, Babiker T, Tamar A, Findlay J, et al. Utility of systematic TSHR gene testing in adults with hyperthyroidism lacking overt autoimmunity and diffuse uptake on thyroid scintigraphy. Clin Endocrinol (Oxf),2019;90(2):328-333. doi:10.1111/cen.13892\u003c/li\u003e\n\u003cli\u003e Jaeschke H, Eszlinger M, Lueblinghoff J, Coslovsky R, Paschke R. 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Ophthalmic genetics, 2019, 40(6): 570-573. doi:10.1080/13816810.2019.1711428\u003c/li\u003e\n\u003cli\u003e Corona-Rivera J R, Zenteno J C, L\u0026oacute;pez-P\u0026eacute;rez L G,Yokoyama-Rebollar E, Villarroel CE, Barrag\u0026aacute;n-Ar\u0026eacute;valo T, et al. First Report of Mexican Patients with PACS1-Related Neurodevelopmental Disorder and Review of the PACS1-, PACS2-, and WDR37-Related Ophthalmological Manifestations. Molecular Syndromology, 2023, 14(2): 143-151. doi:10.1159/000526975\u003c/li\u003e\n\u003cli\u003e Kanca O, Andrews J C, Lee P T, Patel C, Braddock SR, Slavotinek AM, et al. De novo variants in WDR37 are associated with epilepsy, colobomas, dysmorphism, developmental delay, intellectual disability, and cerebellar hypoplasia. The American Journal of Human Genetics, 2019, 105(2): 413-424. doi:10.1016/j.ajhg.2019.06.014\u003c/li\u003e\n\u003cli\u003e Akdi A, Gim\u0026eacute;nez E M, Garc\u0026iacute;a-Quispes W, Pastor S, Castell J, Biarn\u0026eacute;s J, et al. WDR3 gene haplotype is associated with thyroid cancer risk in a Spanish population. Thyroid, 2010, 20(7): 803-809. doi:10.1089/thy.2010.0072\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Hyperthyroidism, Mutant Genes, SNPs, Mutation Types, Canonical correlation analysis","lastPublishedDoi":"10.21203/rs.3.rs-3983196/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3983196/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eGenerally, different mutation types affect patients to various degrees. Some mutation types, such as synonymous mutations, do not cause changes in amino acid sequence and have little impact on patients. In contrast, some types of mutations will lead to wrong encoding or stop encoding of amino acid sequences, which will have a more significant impact on patients. Therefore, this paper intends to find the genetic characteristics of hyperthyroidism in children from the perspective of mutation types: missense mutations, synonymous mutations, nonsense mutations, and splice site mutations, and explore the relationship between mutant Genes, SNPs, and mutation types in patients. Finally, find the mutation type in which the specific mutant gene and the mutation site were biased.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUse the canonical correlation analysis method to find the relationship between the mutant gene, the mutation site, and the mutation type.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eBased on the complete linear correlation, single value, and Pearson correlation coefficient, screen the mutant genes and SNPs, then get 23 representative mutant genes from 144 mutant genes in 39 children and 8 representative SNPs from 1221 SNPs in 39 children. Canonical variables were constructed using data from representative mutant genes, SNPs, and their corresponding mutation types for canonical correlation analysis. A significant positive correlation between the mutant gene and the mutation type and a significant positive correlation between SNPs and the mutation type were found through canonical variable correlation and significance tests.The mutant genes for children with hyperthyroidism consist of 23 representative mutant genes, among which the genes ACADM, ATM, BARD1, CALCA, CAPN3, CRP, DNMT1, DNMT3A, FANCC, GHRL, KANK1, LRSAM1, TPO, TSHR, VWF are all prone to missense mutations, nonsense mutation, and synonymous mutation, the gene CGA is prone to splice site mutation. Among the 23 genes, the gene KANK1 has the greatest impact on the ensemble of mutant genes. SNPs for children with hyperthyroidism consist of 8 representative SNPs, among which g.1529224C\u0026thinsp;\u0026gt;\u0026thinsp;T, g.23019637C\u0026thinsp;\u0026gt;\u0026thinsp;G and g.44651599T\u0026thinsp;\u0026gt;\u0026thinsp;C are prone to missense mutations, g.1533961C\u0026thinsp;\u0026gt;\u0026thinsp;T, g.12718004G\u0026thinsp;\u0026gt;\u0026thinsp;A, g.43082453A\u0026thinsp;\u0026gt;\u0026thinsp;G, g.44652143G\u0026thinsp;\u0026gt;\u0026thinsp;A and g.71809992C\u0026thinsp;\u0026gt;\u0026thinsp;T are prone to synonymous mutations. The site g.44651599T\u0026thinsp;\u0026gt;\u0026thinsp;C has the greatest impact on the ensemble of SNPs.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThere is a strong correlation between gene mutation types and mutant genes and between mutation types and SNPs in children with hyperthyroidism. Each mutant gene and SNP has a more preferred mutation type, among which gene CGA and gene KANK1 may be the mutant gene that has the most significant impact on children, and SNP g.44651599T\u0026thinsp;\u0026gt;\u0026thinsp;C may be the SNP that has the greatest impact on children.\u003c/p\u003e","manuscriptTitle":"Relationship between Mutant Genes, SNPs and Mutation Types in Children with Hyperthyroidism Based on Canonical correlation Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-08 18:39:57","doi":"10.21203/rs.3.rs-3983196/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6c357ff1-ba99-4585-8e60-2838f17973df","owner":[],"postedDate":"March 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-07-08T09:41:02+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-08 18:39:57","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3983196","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3983196","identity":"rs-3983196","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}