Molecular Diagnosis of Kallmann Syndrome by Whole Exon Sequencing and Bioinformatic Approaches 

Molecular Diagnosis of Kallmann Syndrome by Whole Exon Sequencing and Bioinformatic Approaches  | 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 Help Center Sign In Submit a Preprint Cite Share Download PDF Research Molecular Diagnosis of Kallmann Syndrome by Whole Exon Sequencing and Bioinformatic Approaches shuangshuang sun, ruixue wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-285888/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 Background: Kallmann syndrome is a hypogonadotropic hypogonadism accompanied by anosmia or hypoxia. More than 10 Kallmann syndrome pathogenic genes have been found. However, only 30% of the incidence of Kallmann syndrome is related to the above genes, suggesting that there are other disease-related genes of KS that have not been found. Methods： We studied Kallmann syndrome pathogenesis through high-throughput exome sequencing on four patients with Kallmann syndrome for screening the potential pathogenic sites and exploring the genotype-phenotype correlation. Clinical data and peripheral blood samples were collected from the patients. White blood cells were separated and genomic DNA was extracted. High-throughput sequencing of all exons in the candidate pathogenic genes of the probands was performed, and the results obtained were analyzed. Results: Sequencing revealed mutations in the KLB, p.T313M, ANOS1, p.C172F, and IGSF10 (p.Lys1819Arg and p.Arg1035Thr) at different sites, which may have been associated with disease onset. Discovery of these sites can improve our understanding of KS pathogenesis and serve as a novel target in further studies on KS. Conclusion: our analysis found new mutation sites to facilitate follow-up research. Diagnosis of Kallmann syndrome is a challenging, timely diagnosis and treatment can induce puberty, thereby improving sexual, bone, metabolic, and mental health. Biomedical Engineering Kallmann syndrome whole-exome sequencing bioinformatics analysis IGSF10 KLB ANOS1 Figures Figure 1 Figure 2 Figure 3 1. Introduction Idiopathic hypogonadotropic hypogonadism (IHH) is caused by congenital hypothalamic gonadotropin-releasing hormone (GnRH) neuron deficiency or dysfunction in GnRH synthesis or secretion, which leads to a reduction in gonadotropin secretion by the pituitary[1]. Low levels of gonadotropin secretion may consequently lead to insufficient gonadal function. This can manifest in the form of underdeveloped secondary sex characteristics, gamete synthesis disorders, and delayed bone closure, among other conditions. According to the clinical symptoms, there are two types: those with impaired sense of smell are called Ka11mann syndrome（KS）; those with normal sense of smell are called idiopathic hypogonadotropic hypogonadism (nIHH) with normal sense of smell. Measurement of the levels of gonadotropins (luteinizing hormone, LH; follicle-stimulating hormone, FSH) and imaging examinations can help confirm the diagnosis of IHH. and hormone replacement therapy can be used as a suitable therapeutic strategy. Kallmann syndrome (Kallmann Syndrome, KS) is a hypogonadotropic hypogonadism accompanied by anosmia (cannot recognize any odor) or hypoxia (recognizable part of a strong pungent odor). It is a disease with clinical and genetic heterogeneity. KS can be familial or sporadic. There are three ways of inheritance: X-linked recessive inheritance, autosomal dominant inheritance, and autosomal recessive inheritance. The pathogenesis of KS is not fully understood. It is currently believed that GnRH neurons originating from the olfactory substrate cannot migrate normally and locate in the hypothalamus due to various reasons, resulting in complete or partial loss of the ability to synthesize and secrete GnRH, causing hypothalamic-pituitary gonadal axis dysfunction and failure to activate Puberty. The diagnosis of KS is challenging, especially in early puberty, and the clinical manifestations reflect physical delays in development and puberty. Timely diagnosis and treatment can induce puberty, thereby improving sexual, bone, metabolic, and mental health. In this study, we reported four cases of clinically confirmed KS along with the investigation of the mutation sites in both patients. The aim is to identify the existing mutation sites and reveal the relationship between genotype and phenotype 2. Patients And Methods 2.1. Ethical compliance The study, consistent with the Declaration of Helsinki, was approved by the Ethics Committee of the Shanxi Provincial People's Hospital (Shanxi, china). All persons gave their informed consent prior to their inclusion in the study. 2.2. Patients Patient 1: A 35-year-old woman suffering from amenorrhea for 17 years was hospitalized in March 2018. The patient had menstrual cramps, underdeveloped breasts, sparse armpit and pubic hair, female secondary sexual characteristics, hyposmia , normal intelligence, pubic hair – Tanner II, and breast Tanner – V. Hospitalization was recommended for further diagnosis and treatment. Patient 2: A 24-year-old man diagnosed with sexual underdevelopment 11 years prior was hospitalized in May 2018. The patient first observed at 13 years of age that his penis and testicles were underdeveloped. The patient suffered from spermatorrhea. Secondary sexual characteristics, such as pubic hair, axillary hair, laryngeal knots, and voice changes, among others, were absent. No secondary sexual characteristics were observed. Bilateral breast enlargement was a feminine characteristic observed. Other characteristics included pubic hair – Tanner II, testicular development – Tanner II, and loss of sense of smell. Patient 3: A 22-year-old man diagnosed with diabetes，who found the penis and testicles were underdeveloped, secondary sexual characteristics, such as pubic hair, axillary hair, laryngeal knot, among others, were absent. and also with hyposmia. Patient 4: A 16-year-old man diagnosed with diabetes，also found the penis and testicles were underdeveloped, secondary sexual characteristics, such as pubic hair, axillary hair, laryngeal knot, among others, were absent. and with loss of sense of smell. 2.3. General clinical data collection Data on the medical history of the patients and their family members were collected in detail and analyzed. Physical examination, routine electrocardiogram, and echocardiography were performed and the results were analyzed. laboratory tests were performed with a chemiluminescence instrument(MAGLUMI 4000 PLUS. China) We also need to do the GnRH stimulation test, first, take blood to check the basic values of LH and FSH, then inject GnRH intravenously, the LH and FSH values were checked 15 minutes, 30 minutes, 1 hour, and 120 minutes later. Observe the dynamic changes of these two hormones. 2.4. Exome sequencing and bioinformatics analysis 2.4.1. DNA extraction For DNA extraction, 2 mL of venous blood sample was collected from each patient and their parents, which were treated with heparin to prevent coagulation. Genomic DNA was extracted according to the instructions provided by the Se Blood DNA kit (Omega Bio-Tek, inc.), and the patients’ DNA sample was dispatched to Aiji Taikang for whole-exome sequencing (WES), and their parents’ DNA samples were used for sanger sequecing. 2.4.2. Bioinformatics analysis of WES data The raw sequence data obtained was subjected to quality control using FastQC[2]and the clean reads were aligned to the human reference genome (hg19) using Bwa[3]. The duplicate reads were labeled using SAMBLASTER[4], and the results were compared for quality control. We used the GATK HaplotypeCaller[5] for mutation analysis, and data from dbSNP, 1000 Genomes, dbNSFP, and clinVar, among other databases were used to annotate the mutation sites. 2.4.3. Analysis of mutations and candidate genes The following were filtered from the data: population with a mutation frequency greater than 1%, sites in the dbSNP database, and nonsense mutation sites (intron regions, synonymous mutations, and other mutations that do not affect protein function). The FGFR1, FGF8[6,7], GNRHR[8], IGSF10[9], PROK2, PROKR2[10,11], TAC3, TACR3[12], DAX1[13], NSMF[14,15], CHD7[16], SOX2[17], FEZF1[18], HS6ST1[19], SOX10[20], SEMA3A[21], KISS1[22], KISS1R[23], IL17RD[24], and WDR11[25] genes were analyzed for identification of the relevant pathogenic sites. Published literature was search based on the sites selected by PUBMED. 3. Results 3.1. Laboratory data In patient 1, the level of 25-hydroxyvitamin D was found to be 11.78 ng/mL. Liver function, renal function, erythrocyte sedimentation rate, and the levels of electrolytes, calcium, phosphate, parathyroid hormone, and C-reactive protein were normal. Blood osmotic pressure was 297 mOsm/kg·H 2 0 and urine osmotic pressure was 620 mOsm/kg·H 2 0. The growth hormone level was found to be 0.895 ng/mL. No abnormalities were observed in the thyroid function test. The cortisol circadian rhythm pattern was as follows: 08:00 120 ng/mL; 16:00 106.6 ng/mL; 00:00 13.93 ng/mL. The 24-h urine-free cortisol level was 188.40 µg/24 h. Color Doppler ultrasound examination revealed that the uterus size was small. The levels of the following sex hormones were measured: blood prolactin, 94.67 µIU/mL; estradiol, 13.7 pg/mL; progesterone, 0.303 ng/mL; testosterone 0.13 ng/mL; LH <0.1 mIU/mL; FSH 0.894 mIU/mL. The GnRH stimulation test revealed that the peak values of LH and FSH exceeded 1 mIU/mL, which indicated stimulation, see Table 1 . MRI scan of the pituitary and CT scan of the adrenal glands revealed no abnormalities. No abnormal lesions were observed in the uterus and breasts. The following observations were made in patient 2: ACTH 08:00 91.32 pg/mL; alanine aminotransferase 12.58 IU/L; aspartate aminotransferase 12.81 IU/L; blood creatinine 43.75 μmol/L; urine osmotic pressure 898 mOsm/kg·H 2 0; blood osmotic pressure 291 mOsm/kg·H 2 0. The GnRH stimulation test revealed that the basal values of LH and FSH secretion were low. Both peaked at 120 min, but the values did not exceed 1 mIU/ml, see Table 1. The levels of the following sex hormones were tested: blood prolactin, 201.1 µIU/mL; estradiol, 14.12 pg/mL; progesterone, 0.479 ng/mL; testosterone 0.361 ng/mL; LH <0.1 mIU/mL; FSH 0.257 mIU/mL. The cortisol rhythm was found to be normal, and MRI scan of the pituitary and CT scan of the bilateral adrenal glands revealed no obvious abnormalities. In patient 3, Urine protein 2+; urine glucose 3+, ketone body 3+, blood ketone 2.8mmol/L, the growth hormone level was found to be 0.301 ng/mL. No abnormalities were observed in the thyroid function test. The cortisol circadian rhythm pattern was as follows: 08:00 103.1 ng/mL; 16:00 29.67 ng/mL; 00:00 37.39 ng/mL. The levels of the following sex hormones were measured: blood prolactin, 350.6 µIU/mL; estradiol, 21.17 pg/mL; progesterone, 1.07 ng/mL; testosterone 0.46 ng/mL; LH <0.1 mIU/mL; FSH 0.61 mIU/mL. THE islet function test, see Table 2 The GnRH stimulation test revealed that the peak values of LH and FSH exceeded 1 mIU/mL, which indicated stimulation, see Table 1 In patient 4, the growth hormone level was found to be 0.175 ng/mL. No abnormalities were observed in the thyroid function test. The cortisol circadian rhythm pattern was normal. The levels of the following sex hormones were measured: blood prolactin157.2 µIU/mL; estradiol, 5 pg/mL; progesterone, 0.13 ng/mL; testosterone 0.46 ng/mL; LH <0.1 mIU/mL; FSH 0.91 mIU/mL. The GnRH stimulation test revealed that the peak values of LH and FSH exceeded 1 mIU/mL, which indicated stimulation, see Table 1 3.2. Genetic testing Quality control of raw WES data Quality control analysis of the raw WES data (using FastQC) of the two samples is illustrated in Figure 1. The average quality of the bases was greater than 30 (accuracy greater than 99.9%), and the sequence quality was satisfactory. 3.2.1. Sequence alignment and sequencing depth Exome sequencing of the two samples yielded 39M paired-end reads, of which 99% (mapped reads) sequences could be matched to the human reference genome, and the proportion of duplicate reads was approximately 15%. The average sequencing depth (mean depth) exceeded 130X. see Table 3. 3.2.2. Extent of variation The bioinformatics analysis revealed that the sample from patient 1 had 82,986 SNPs and 13,495 INDELs. Through dbSNP annotation, 99.06% of the SNPs and 91.15% of the INDELs could be annotated. Patient 2 had 84,748 SNPs and 13,931 INDELs. Through dbSNP annotation, 98.98% of SNPs and 90.98% of the INDELs could be annotated. Patient 3 had 84，579 SNPs and 13，760 INDELs. Through dbSNP annotation, 99.02% of SNPs and 90.99% of the INDELs could be annotated. Patient 4 had 84,731 SNPs and 13,794 INDELs. Through dbSNP annotation, 98.94% of SNPs and 90.99% of the INDELs could be annotated. see Table 4 and Table 5 3.2.3. Analysis of candidate gene mutations The gene mutations were filtered according to the following criteria: (1) the mutation should be located in the exon; (2) the mutation should not be synonymous; (3) population frequency should be greater than 0.001. After filtering, only two mutation sites were detected (one each in IGSF10, and CHD72 genes) for patient 1, whereas four mutation sites were detected (one each in SOX6, DMXL2, IGSF10, and ANOS1 genes) for patient 2. One mutation sites were detected in the KLB for patient 3，one mutation sites were detected in the ANOS1 for patient 4. Mutations in the IGSF10 gene were common to both patients. Furthermore, the literature search revealed that the genes CHD72, ANOS1, IGSF10, and DMXL2 were also related to IHH. 3.2.4. Verification of candidate sites To verify the pathogenic sites in the four patients, we compared the parental genotypes and found that IGSF10 (p.Lys1819Arg), KLB p.T313M and ANOS1 p.C172F may harbor the pathogenic site, Population data did not reveal the presence of a mutation at this site, and the mutation frequency of p.Arg1035Thr in the gnomAD database was found to be 0.0004. and the mutation frequency of p.T313M in the gnomAD database was found to be 0.0001. and the mutation frequency of p.C172F in the gnomAD database was found to be 0. which is illustrated in Table 6 and Figure 2. List of primer pairs used for PCR, see Table 7. MutationTaster predicted Lys1819Arg to be a harmful mutation, whereas SIFT_pred and Polyphen2_HDIV predicted Arg1035Thr to be a harmful mutation. whereas SIFT_pred predicted p.C172F to be a harmful mutation.The two mutations of IGSF10 are located in the region encoding the immunoglobulin I-set domain and in the nondomain region. The mutations of ANOS1 are located in the region encoding the WAP domain，the mutations of KLBare are located in the region encoding the Glyco_hydro_1 domain. （see Figure 3） 4. Discussion With further research on KS genetics, some genes related to KS pathogenesis have been found, such as KAL1, FGFRI, FGF8, PROKR2, PROK2, the function of these genes may be related to the normal migration of GnRH neurons and development of the olfactory bulb. However, only 30% of the incidence of Kallmann syndrome is related to the above genes, suggesting that there are other disease-related genes of KS that have not been found In this study, WES was performed to analyze the mutant genes and loci in four patients with KS, and two potential pathogenic loci of the IGSF10 gene (p.Lys1819Arg and p.Arg1035Thr) were identified. According to the analysis of the IGSF10 gene mutations in the two patients, the variations included alteration of the amino acid at the 1819th position from lysine to arginine and at the 1035th position from arginine to threonine. The Lys1819Arg site is located in the I-set domain of the protein, which is primarily associated with immune function and angiogenesis. The discovery of this site can improve our understanding of KS pathogenesis and serve as a novel target for further studies on KS. IGSF10 is a member of the immunoglobulin superfamily[26]. While its exact function is yet to be clarified, studies have shown that IGSF10 expression is associated with combined pituitary hormone deficiency. It is also considered a novel prognostic biomarker for breast cancer and lung cancer and has been associated with various diseases, such as primary ovarian insufficiency and endometrial cancer. Mutations in the IGSF10 gene are reportedly associated with abnormal regulation of the migration of GnRH neurons, which may delay puberty and other developmental processes. Among KS patients, about 90% of the patients, pubic hair and armpit hair were absent. Bone age lagged behind chronological age in some patients. some have anosmia or hyposmia. Some males has breast hyperplasia. small penis, cryptorchidism and vas deferens lack. Some patients can also be accompanied by other body or organ abnormalities, such as facial cranial midline deformity. Nervous system abnormalities. Musculoskeletal system abnormalities and Other systemic abnormalities KLB and KL genes have homology. It is highly expressed in metabolic tissues, especially fat tissues. FGF21 is an endocrine FGF mainly secreted by the liver, which regulates the main metabolic processes such as glucose and lipid metabolism. Endogenous FGF21 regulates the physiological response to starvation and various other metabolic stresses. FGF21 signals through the KLB/FGFR1c receptor complex in a tissue-specific manner. KLB enhances the binding of FGF21-FGFR1c, thereby promoting FGF21 signal transduction by binding FGF21 and FGFR1c to itself through two different sites at the same time. In addition, the competitive binding of FGF8 and b-Klotho to the same site of FGFR1 will facilitate the binding to endocrine FGF21 and inhibit the binding and signal transduction of paracrine FGF8. Most patients with KLB mutations exhibit KS and metabolic defects, such as overweight, diabetes, and dyslipidemia, which are consistent with the metabolic effects of this pathway[27]. ANOS1 gene is a pathogenic gene found to cause x-linked KS. It is located on the X chromosome (Xp22.3), which contains 14 exons, adjacent to the pseudo-autosomal 1 region (PAR1), which is a highly variable and unstable region on the chromosome. ANOS1 encodes anosmin-1, an extracellular matrix protein. Anosmin-1 consists of a cysteine-rich region (CR domain), a whey acidic protein (WAP)-like domain similar, four consecutive fibronectin type III domains and a C-terminal region rich in basic histidine and proline. Anosmin-1 promotes neuronal cell adhesion, neurite outgrowth, axon guidance, and CNS projection neuron branching. In addition, it is also involved in the migration of many types of neural precursors, including GnRH-producing neurons and oligodendrocyte precursors[28,29]. The ANOS1 mutation is found in patients with familial and sporadic KS. The ANOS1 gene mutation has a low incidence in patients with sporadic KS, but a high incidence in patients with familial KS. In KS patients with ANOS1 mutations, the loss or dysplasia of cryptorchidism and olfactory bulb is high. In our study, another two potential pathogenic loci of the KLB p.T313M and ANOS1 p.C172F were identified，it suggests that KS disease with KLB mutation should be alert to the risk of diabetes, and KS disease with ANOS1 mutation is related to X-linked recessive inheritance. Although our analysis is limited to 4 patients with KS, it supports the previous view. and found new mutation sites to facilitate follow-up research Because KS hyposmia can be manifested in different degrees, therefore, sometimes it is not easy to distinguish KS and nIHH, especially in patients with hypogonadism, often without careful evaluation of olfactory function. There is genetic evidence that the genes encoding GnRH and kisspeptin receptors are related to nIHH, but not related to the migration of GnRH neuroendocrine cells (KS patients may have abnormal migration of GnRH neuroendocrine cells), suggesting that KS and nIHH may have different inheritance background and pathogenesis Exon sequencing can be used for studying various diseases. It is useful as a diagnostic tool owing to its low cost and high throughput. The method can be used to detect all mutations in human exons simultaneously. With technological advancements, exon capture has emerged as a useful method. Currently, the chip used has been up to 60M, which can include multiple introns and untranslated regions, and provides valuable information for the study of disease-causing sites. 5. Conclusion We found mutations in the same disease-causing gene in four patients through WES. This is relatively rare, particularly in diseases in which multiple genes are implicated. This finding also illustrates the value of second-generation sequencing for precision therapy. Correct olfactory judgment combined with genetic testing can diagnose Kalman syndrome in time. timely diagnosis and treatment can induce puberty, thereby improving sexual, bone, metabolic, and mental health. Declarations Authors' contributions: Sun Shuangshuang performed the experiments and wrote the manuscript; Wang Ruixue was involved in processing data. All authors have read and approved the final version of this manuscript. Consent for publication: Not applicable. Acknowledgements: We would like to thank the families who participated in this study. Competing interests: The authors have no conflict of interest to declare Funding: This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Ethics Approval and Consent to participate: The study was approved by the Research Ethics Committee of Shanxi Provincial People's Hospital and complied with the ethical standards of the Declaration of Helsinki as well as the relevant national and international guidelines. Each patient signed an informed consent for the use of their venous blood in the research. Patient consent for publication: Written informed consent was obtained from each patient. Availability of data and materials : The data to support the findings of the study are available from the corresponding author upon reasonable request Authors' information: Department of Clinical Lab, Shanxi Provincial People's Hospital Affiliated to Shanxi Medical University, Taiyuan, 030012, Shanxi Province, China。 References Topaloğlu A. K. (2017). Update on the Genetics of Idiopathic Hypogonadotropic Hypogonadism. 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Tables Table 1 GnRH stimulation test result 0 15min 30min 60min 120min Patient 1 FSH（mIU/ml） 0.79 1.33 1.72 2.47 3.07 LH（mIU/ml） <0.1 1.38 2.32 2.714 2.67 Patient 2 FSH（mIU/ml） 0.32 1.19 1.26 1.61 3.07 LH（mIU/ml） <0.1 0.32 0.38 0.64 1.61 Patient 3 FSH（mIU/ml） 0.45 1.29 1.64 1.88 2.42 LH（mIU/ml） <0.1 0.84 1.02 1.36 1.53 Patient 4 FSH（mIU/ml） 0.78 1.30 1.64 1.84 2.02 LH（mIU/ml） <0.1 0.94 1.29 1.33 1.63 Table 2 Islet function test of patient 3 0分 30min 60min 120min 180min blood glucose（mmol/l） 7.5 9.0 15.6 18.4 13.3 insulin release（uIU/ml） 1.50 4.55 5.26 5.79 3.88 C-Peptide（ng/ml） 0.27 0.47 0.59 0.84 0.62 Table 3 Sequence alignment and sequencing depth Sample Patient 1 Patient 2 Patient 3 Patient 4 Raw reads(PE M) 36.83373 39.8752 39.856 39.975 reads mapping rate(%) 99.59 99.58 99.64 99.65 Target duplication rate(%) 14.15 14.32 14.59 14.75 Target mean depth 130.55 139.46 151.43 145.43 T 10X coverage rate(%) 99.57 99.6 99.45 99.27 T 20X coverage rate(%) 99.04 99.13 99.26 99.04 T 30X coverage rate(%) 97.98 98.23 98.57 98.35 Table 4 Distribution of single-nucleotide polymorphisms Sample Patient 1 Patient 2 Patient 3 Patient 4 total 82986 84748 84579 84731 dbsnp 82205(99.06%) 83887(98.98%) 83752(99.02%) 83835(98.94%) 1000g_EAS 77404(93.27%) 78717(92.88%) 78737(93.09%) 78824(93.03%) ExAC_EAS 45558(54.90%) 45630(53.84%) 45773(54.12%) 45936(54.21%) gnomAD_exome_EAS 45617(54.97%) 45698(53.92%) 45866(54.23%) 46025(54.32%) gnomAD_genome_EAS 81924(98.72%) 83549(98.59%) 83441(98.65%) 83536(98.59%) exonic 22847(27.53%) 23016(27.16%) 23100(27.31%) 22921(27.05%) splicing 239(0.29%) 246(0.29%) 251(0.30%) 259(0.31%) UTR3 3103(3.74%) 3161(3.73%) 3152(3.73%) 3084(3.64%) UTR5 2234(2.69%) 2310(2.73%) 2305(2.73%) 2333(2.75%) intronic 48754(58.75%) 50084(59.10%) 49683(58.74%) 50121(59.15%) intergenic 1902(2.29%) 2108(2.49%) 2184(2.58%) 2145(2.53%) upstream 816(0.98%) 881(1.04%) 882(1.04%) 827(0.98%) downstream 371(0.45%) 379(0.45%) 379(0.45%) 366(0.43%) ncRNA_exonic 764(0.92%) 726(0.86%) 730(0.86%) 790(0.93%) ncRNA_splicing 6(0.01%) 8(0.01%) 4(0.00%) 3(0.00%) ncRNA_intronic 1888(2.28%) 1764(2.08%) 1848(2.18%) 1823(2.15%) synonymous SNV 11529(13.89%) 11613(13.70%) 11559(13.67%) 11518(13.59%) nonsynonymous SNV 10755(12.96%) 10727(12.66%) 10815(12.79%) 10749(12.69%) stopgain 85(0.10%) 92(0.11%) 94(0.11%) 84(0.10%) stoploss 10(0.01%) 10(0.01%) 11(0.01%) 8(0.01%) unknown 483(0.58%) 589(0.70%) 635(0.75%) 576(0.68%) Table 5 Indel distribution Sample Patient 1 Patient 2 Patient 3 Patient 4 total 13495 13931 13760 13794 dbsnp 12301(91.15%) 12675(90.98%) 12538(91.12%) 12551(90.99%) 1000g_EAS 8269(61.27%) 8454(60.68%) 8378(60.89%) 8410(60.97%) ExAC_EAS 5560(41.20%) 5547(39.82%) 5562(40.42%) 5637(40.87%) gnomAD_exome_EAS 5306(39.32%) 5284(37.93%) 5286(38.42%) 5355(38.82%) gnomAD_genome_EAS 12567(93.12%) 12991(93.25%) 12760(92.73%) 12812(92.88%) exonic 708(5.25%) 733(5.26%) 707(5.14%) 694(5.03%) splicing 199(1.47%) 182(1.31%) 201(1.46%) 192(1.39%) UTR3 669(4.96%) 676(4.85%) 667(4.85%) 661(4.79%) UTR5 375(2.78%) 384(2.76%) 364(2.65%) 373(2.70%) intronic 10469(77.58%) 10845(77.85%) 10712(77.85%) 10767(78.06%) intergenic 297(2.20%) 312(2.24%) 309(2.25%) 307(2.23%) upstream 160(1.19%) 187(1.34%) 170(1.24%) 184(1.33%) downstream 51(0.38%) 65(0.47%) 66(0.48%) 68(0.49%) ncRNA_exonic 103(0.76%) 93(0.67%) 102(0.74%) 99(0.72%) ncRNA_splicing 0(0.00%) 2(0.01%) 4(0.03%) 1(0.01%) ncRNA_intronic 407(3.02%) 398(2.86%) 400(2.91%) 391(2.83%) frameshift insertion 94(0.70%) 103(0.74%) 96(0.70%) 100(0.72%) frameshift deletion 135(1.00%) 124(0.89%) 132(0.96%) 137(0.99%) nonframeshift insertion 198(1.47%) 216(1.55%) 200(1.45%) 181(1.31%) nonframeshift deletion 217(1.61%) 215(1.54%) 202(1.47%) 203(1.47%) stopgain 7(0.05%) 8(0.06%) 9(0.07%) 10(0.07%) stoploss 1(0.01%) 1(0.01%) 0(0.00%) 1(0.01%) unknown 99(0.73%) 106(0.76%) 111(0.81%) 103(0.75%) Table 6 Specific information about IGSF10、KLB、ANOS1 mutation Type Patient 1 Patient 2 Patient 3 Patient 4 Chr.Start.End chr3.151161279.151161279 chr3.151164665.151164665 chr4.39435942.39435942 chrX.8565101.8565101 Vcf_mut T/C C/G C/T C/A GT 0/1 0/1 0/1 1/1 AD 51/53 30/31 34/47 0/86 AAChange.HGVS IGSF10:NM_178822.4:5/6:c.5456A>G:p.(Lys1819Arg) IGSF10:NM_178822.4:4/6:c.3104G>C:p.(Arg1035Thr) KLB:NM_175737:exon2:c.C938T:p.T313M ANOS1:NM_000216:exon4:c.G515T:p.C172F cytoBand 3q25.1 3q25.1 4p14 Xp22.31 InterVar_automated Uncertain significance Uncertain significance Uncertain significance Uncertain significance ACMG(missense only) PM1,PM2,BP4 . PM1,BP4 PM1,PM2,PP3 gnomAD_exome_ALL . 0.0004 0.0001 0 SIFT_pred T D T D Polyphen2_HDIV_score 0.011 0.981 0.036 1 Polyphen2_HDIV_pred B D B D Polyphen2_HVAR_score 0.056 0.69 0.016 1 Polyphen2_HVAR_pred B P B D LRT_score 0.039 0 0.59 0 LRT_pred N D N D MutationTaster_score 0.808 1 1 1 MutationTaster_pred D N N D MutationAssessor_score 0.15 2.47 2.535 4.455 MutationAssessor_pred N M M H FATHMM_score -0.27 -0.65 1.43 -5.61 FATHMM_pred T T T D CADD_raw 0.585 1.94 -0.287 6.358 CADD_phred 8.054 15.84 0.711 29.4 fathmm-MKL_coding_score 0.039 0.257 0.068 0.967 fathmm-MKL_coding_pred N N N D GERP++_RS 0.193 5.46 -7.48 4.51 Table 7 List of primer pairs used for PCR CC Gene TemplateID ForwardPrimer(Fp) ReversePrimer(Rp) AmpSize (bp) Patient 1 IGSF10 chr4:39435930-39435950 ACATTTCCGCCCACATCAGAAG TCAGCTGTGCCTCTCATCTCAT 246 Patient 2 IGSF10 chr3:151161270-151161285 TAACAGGTGGTGCTGCAATGAC AAGCACTGTGGAACTGAAGTGC 251 Patient 3 KLB chr3:151164660-151164670 AGCAATGTCAGCTTTGGGGAAG GCTTTGGGAGGCAGAGGAAAAT 260 Patient 4 ANOS1 chrX:8565100-8565108 TGTGACACTGCATGTGTCTTCAC TGACCAGCTGTGAGTTCCTCAA 236 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. 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Also discoverable on Platform About In Review Editorial Policies 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-285888","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":14123815,"identity":"6f9a2028-7ec9-4da7-b082-7c5cc282369f","order_by":0,"name":"shuangshuang sun","email":"","orcid":"","institution":"Shanxi Provincial Peoples Hospital","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"shuangshuang","middleName":"","lastName":"sun","suffix":""},{"id":14123816,"identity":"f7746a5c-d9d7-481e-b680-b85010358e25","order_by":1,"name":"ruixue wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0ElEQVRIiWNgGAWjYHACxsc/DGzk7I83Nj78QKQWZmOGgjRjhjOHm40liNTCJszw4XBiw430NgEeYtTL9x9/xlxgkJbYOPNhG4MEg52cbgMBLQY3cswezzCwMW6WTmx7UMCQbGx2gJAWCR52Ax6DNNk26cR2AwmGA4nbCGkBOUyCx+AwY4/kwTYJHmK0MBxIMJMGalGcIcFIpBagX4wNZxikGRvwJAID2YAIvwAd9vDBhz82cgbsxx8+/FBhJ0dQC7qlpCkfBaNgFIyCUYADAACvoUPLsZlOGAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-0579-4458","institution":"Shanxi Provincial Peoples Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"ruixue","middleName":"","lastName":"wang","suffix":""}],"badges":[],"createdAt":"2021-02-28 14:26:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-285888/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-285888/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":6676846,"identity":"0760ce44-a12c-4fa3-a76c-41721241d30f","added_by":"auto","created_at":"2021-03-06 15:42:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54906,"visible":true,"origin":"","legend":"Original results of exome sequencing\n A-B is the sequence quality distribution map of reads 1 and 2 of patient 1. \nC-D is the sequence quality distribution map of reads 1 and 2 of patient 2. \nE-F is the sequence quality distribution map of reads 1 and 2 of patient 3.\nG-H is the sequence quality distribution map of reads 1 and 2 of patient 4.\n","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-285888/v1/7935a663c19c0cb1aa3ee6f3.jpg"},{"id":6676785,"identity":"ef65d8bf-1ad7-49a1-87f2-a40927540976","added_by":"auto","created_at":"2021-03-06 15:39:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":66386,"visible":true,"origin":"","legend":"Sanger sequencing of 4 KS family results.","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-285888/v1/b14cc2e94f5367420aeea1b6.jpg"},{"id":6676786,"identity":"2d16c63c-d8b3-4d74-9e7a-140d05c0b837","added_by":"auto","created_at":"2021-03-06 15:39:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":46345,"visible":true,"origin":"","legend":"The location of four mutation sites","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-285888/v1/66ac946c0d334b5145fbf73d.jpg"},{"id":13675617,"identity":"e2597961-a4c5-40bb-ad4a-55545e990f3b","added_by":"auto","created_at":"2021-09-17 11:27:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":418719,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-285888/v1/b0d30842-b5b4-4235-9caf-8e39bcc732e7.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eMolecular Diagnosis of Kallmann Syndrome by Whole Exon Sequencing and Bioinformatic Approaches\u0026nbsp;\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u0026nbsp;Idiopathic hypogonadotropic hypogonadism (IHH) is caused by congenital hypothalamic gonadotropin-releasing hormone (GnRH) neuron deficiency or dysfunction in GnRH synthesis or secretion, which leads to a reduction in gonadotropin secretion by the pituitary[1]. Low levels of gonadotropin secretion may consequently lead to insufficient gonadal function. This can manifest in the form of underdeveloped secondary sex characteristics, gamete synthesis disorders, and delayed bone closure, among other conditions. According to the clinical symptoms, there are two types: those with impaired sense of smell are called Ka11mann syndrome（KS）; those with normal sense of smell are called idiopathic hypogonadotropic hypogonadism (nIHH) with normal sense of smell. Measurement of the levels of gonadotropins (luteinizing hormone, LH; follicle-stimulating hormone, FSH) and imaging examinations can help confirm the diagnosis of IHH. and hormone replacement therapy can be used as a suitable therapeutic strategy.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Kallmann syndrome (Kallmann Syndrome, KS) is a hypogonadotropic hypogonadism accompanied by anosmia (cannot recognize any odor) or hypoxia (recognizable part of a strong pungent odor). It is a disease with clinical and genetic heterogeneity. KS can be familial or sporadic. There are three ways of inheritance: X-linked recessive inheritance, autosomal dominant inheritance, and autosomal recessive inheritance. The pathogenesis of KS is not fully understood. It is currently believed that GnRH neurons originating from the olfactory substrate cannot migrate normally and locate in the hypothalamus due to various reasons, resulting in complete or partial loss of the ability to synthesize and secrete GnRH, causing hypothalamic-pituitary gonadal axis dysfunction and failure to activate Puberty.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The diagnosis of KS is challenging, especially in early puberty, and the clinical manifestations reflect physical delays in development and puberty. Timely diagnosis and treatment can induce puberty, thereby improving sexual, bone, metabolic, and mental health.\u003c/p\u003e\n\u003cp\u003eIn this study, we reported four cases of clinically confirmed KS along with the investigation of the mutation sites in both patients. The aim is to identify the existing mutation sites and reveal the relationship between genotype and phenotype\u003cstrong\u003e\u003cbr /\u003e\u003cbr /\u003e\u003c/strong\u003e\u003c/p\u003e"},{"header":"2. Patients And Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1. Ethical compliance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study, consistent with the Declaration of Helsinki, was approved by the Ethics Committee of the Shanxi Provincial People's Hospital (Shanxi, china). All persons gave their informed consent prior to their inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient 1: A 35-year-old woman suffering from amenorrhea for 17 years was hospitalized in March 2018. The patient had menstrual cramps, underdeveloped breasts, sparse armpit and pubic hair, female secondary sexual characteristics, hyposmia , normal intelligence, pubic hair \u0026ndash; Tanner II, and breast Tanner \u0026ndash; V. Hospitalization was recommended for further diagnosis and treatment. Patient 2: A 24-year-old man diagnosed with sexual underdevelopment 11 years prior was hospitalized in May 2018. The patient first observed at 13 years of age that his penis and testicles were underdeveloped. The patient suffered from spermatorrhea. Secondary sexual characteristics, such as pubic hair, axillary hair, laryngeal knots, and voice changes, among others, were absent. No secondary sexual characteristics were observed. Bilateral breast enlargement was a feminine characteristic observed. Other characteristics included pubic hair \u0026ndash; Tanner II, testicular development \u0026ndash; Tanner II, and loss of sense of smell. Patient 3: A 22-year-old man diagnosed with diabetes，who found the penis and testicles were underdeveloped, secondary sexual characteristics, such as pubic hair, axillary hair, laryngeal knot, among others, were absent. and also with hyposmia. Patient 4: A 16-year-old man diagnosed with diabetes，also found the penis and testicles were underdeveloped, secondary sexual characteristics, such as pubic hair, axillary hair, laryngeal knot, among others, were absent. and with loss of sense of smell.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3. General clinical data collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Data on the medical history of the patients and their family members were collected in detail and analyzed. Physical examination, routine electrocardiogram, and echocardiography were performed and the results were analyzed. laboratory tests were performed with a chemiluminescence instrument(MAGLUMI 4000 PLUS. China)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We also need to do the GnRH stimulation test, first, take blood to check the basic values of LH and FSH, then inject GnRH intravenously, the LH and FSH values were checked 15 minutes, 30 minutes, 1 hour, and 120 minutes later. Observe the dynamic changes of these two hormones.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4. Exome sequencing and bioinformatics analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4.1. DNA extraction\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFor DNA extraction, 2 mL of venous blood sample was collected from each patient and their parents, which were treated with heparin to prevent coagulation. Genomic DNA was extracted according to the instructions provided by the Se Blood DNA kit (Omega Bio-Tek, inc.), and the patients\u0026rsquo; DNA sample was dispatched to Aiji Taikang for whole-exome sequencing (WES), and their parents\u0026rsquo; DNA samples were used for sanger sequecing.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4.2. Bioinformatics analysis of WES data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe raw sequence data obtained was subjected to quality control using FastQC[2]and the clean reads were aligned to the human reference genome (hg19) using Bwa[3]. The duplicate reads were labeled using SAMBLASTER[4], and the results were compared for quality control. We used the GATK HaplotypeCaller[5] for mutation analysis, and data from dbSNP, 1000 Genomes, dbNSFP, and clinVar, among other databases were used to annotate the mutation sites.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.4.3. Analysis of mutations and candidate genes\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe following were filtered from the data: population with a mutation frequency greater than 1%, sites in the dbSNP database, and nonsense mutation sites (intron regions, synonymous mutations, and other mutations that do not affect protein function). The FGFR1, FGF8[6,7], GNRHR[8], IGSF10[9], PROK2, PROKR2[10,11], TAC3, TACR3[12], DAX1[13], NSMF[14,15], CHD7[16], SOX2[17], FEZF1[18], HS6ST1[19], SOX10[20], SEMA3A[21], KISS1[22], KISS1R[23], IL17RD[24], and WDR11[25] genes were analyzed for identification of the relevant pathogenic sites. Published literature was search based on the sites selected by PUBMED.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1.\u0026nbsp; Laboratory data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn patient 1, the level of 25-hydroxyvitamin D was found to be 11.78 ng/mL. Liver function, renal function, erythrocyte sedimentation rate, and the levels of electrolytes, calcium, phosphate, parathyroid hormone, and C-reactive protein were normal. Blood osmotic pressure was 297 mOsm/kg\u0026middot;H\u003csub\u003e2\u003c/sub\u003e0 and urine osmotic pressure was 620 mOsm/kg\u0026middot;H\u003csub\u003e2\u003c/sub\u003e0. The growth hormone level was found to be 0.895 ng/mL. No abnormalities were observed in the thyroid function test. The cortisol circadian rhythm pattern was as follows: 08:00 120 ng/mL; 16:00 106.6 ng/mL; 00:00 13.93 ng/mL. The 24-h urine-free cortisol level was 188.40 \u0026micro;g/24 h. Color Doppler ultrasound examination revealed that the uterus size was small. The levels of the following sex hormones were measured: blood prolactin, 94.67 \u0026micro;IU/mL; estradiol, 13.7 pg/mL; progesterone, 0.303 ng/mL; testosterone 0.13 ng/mL; LH \u0026lt;0.1 mIU/mL; FSH 0.894 mIU/mL.\u003c/p\u003e\n\u003cp\u003eThe GnRH stimulation test revealed that the peak values of LH and FSH exceeded 1 mIU/mL, which indicated stimulation, see Table 1 . MRI scan of the pituitary and CT scan of the adrenal glands revealed no abnormalities. No abnormal lesions were observed in the uterus and breasts.\u003c/p\u003e\n\u003cp\u003eThe following observations were made in patient 2: ACTH 08:00 91.32 pg/mL; alanine aminotransferase 12.58 IU/L; aspartate aminotransferase 12.81 IU/L; blood creatinine 43.75 \u0026mu;mol/L; urine osmotic pressure 898 mOsm/kg\u0026middot;H\u003csub\u003e2\u003c/sub\u003e0; blood osmotic pressure 291 mOsm/kg\u0026middot;H\u003csub\u003e2\u003c/sub\u003e0.\u003c/p\u003e\n\u003cp\u003eThe GnRH stimulation test revealed that the basal values of LH and FSH secretion were low. Both peaked at 120 min, but the values did not exceed 1 mIU/ml, see Table 1. The levels of the following sex hormones were tested: blood prolactin, 201.1 \u0026micro;IU/mL; estradiol, 14.12 pg/mL; progesterone, 0.479 ng/mL; testosterone 0.361 ng/mL; LH \u0026lt;0.1 mIU/mL; FSH 0.257 mIU/mL. The cortisol rhythm was found to be normal, and MRI scan of the pituitary and CT scan of the bilateral adrenal glands revealed no obvious abnormalities.\u003c/p\u003e\n\u003cp\u003eIn patient 3, Urine protein 2+; urine glucose 3+, ketone body 3+, blood ketone 2.8mmol/L, the growth hormone level was found to be 0.301 ng/mL. No abnormalities were observed in the thyroid function test. The cortisol circadian rhythm pattern was as follows: 08:00 103.1 ng/mL; 16:00 29.67 ng/mL; 00:00 37.39 ng/mL. The levels of the following sex hormones were measured: blood prolactin, 350.6 \u0026micro;IU/mL; estradiol, 21.17 pg/mL; progesterone, 1.07 ng/mL; testosterone 0.46 ng/mL; LH \u0026lt;0.1 mIU/mL; FSH 0.61 mIU/mL. THE islet function test, see Table 2\u003c/p\u003e\n\u003cp\u003eThe GnRH stimulation test revealed that the peak values of LH and FSH exceeded 1 mIU/mL, which indicated stimulation, see Table 1\u003c/p\u003e\n\u003cp\u003eIn patient 4, the growth hormone level was found to be 0.175 ng/mL. No abnormalities were observed in the thyroid function test. The cortisol circadian rhythm pattern was normal. The levels of the following sex hormones were measured: blood prolactin157.2 \u0026micro;IU/mL; estradiol, 5 pg/mL; progesterone, 0.13 ng/mL; testosterone 0.46 ng/mL; LH \u0026lt;0.1 mIU/mL; FSH 0.91 mIU/mL.\u003c/p\u003e\n\u003cp\u003eThe GnRH stimulation test revealed that the peak values of LH and FSH exceeded 1 mIU/mL, which indicated stimulation, see Table 1\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2. Genetic testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eQuality control of raw WES data\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eQuality control analysis of the raw WES data (using FastQC) of the two samples is illustrated in Figure 1. The average quality of the bases was greater than 30 (accuracy greater than 99.9%), and the sequence quality was satisfactory.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2.1. Sequence alignment and sequencing depth\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eExome sequencing of the two samples yielded 39M paired-end reads, of which 99% (mapped reads) sequences could be matched to the human reference genome, and the proportion of duplicate reads was approximately 15%. The average sequencing depth (mean depth) exceeded 130X. see Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2.2. Extent of variation\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe bioinformatics analysis revealed that the sample from patient 1 had 82,986 SNPs and 13,495 INDELs. Through dbSNP annotation, 99.06% of the SNPs and 91.15% of the INDELs could be annotated.\u003c/p\u003e\n\u003cp\u003ePatient 2 had 84,748 SNPs and 13,931 INDELs. Through dbSNP annotation, 98.98% of SNPs and 90.98% of the INDELs could be annotated.\u003c/p\u003e\n\u003cp\u003ePatient 3 had 84，579 SNPs and 13，760 INDELs. Through dbSNP annotation, 99.02% of SNPs and 90.99% of the INDELs could be annotated.\u003c/p\u003e\n\u003cp\u003ePatient 4 had 84,731 SNPs and 13,794 INDELs. Through dbSNP annotation, 98.94% of SNPs and 90.99% of the INDELs could be annotated. see Table 4 and Table 5\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2.3. Analysis of candidate gene mutations\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe gene mutations were filtered according to the following criteria: (1) the mutation should be located in the exon; (2) the mutation should not be synonymous; (3) population frequency should be greater than 0.001.\u003c/p\u003e\n\u003cp\u003eAfter filtering, only two mutation sites were detected (one each in IGSF10, and CHD72 genes) for patient 1, whereas four mutation sites were detected (one each in SOX6, DMXL2, IGSF10, and ANOS1 genes) for patient 2. One mutation sites were detected in the KLB for patient 3，one mutation sites were detected in the ANOS1 for patient 4. \u0026nbsp;Mutations in the IGSF10 gene were common to both patients. Furthermore, the literature search revealed that the genes CHD72, ANOS1, IGSF10, and DMXL2 were also related to IHH.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e3.2.4. Verification of candidate sites\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTo verify the pathogenic sites in the four patients, we compared the parental genotypes and found that IGSF10 (p.Lys1819Arg), KLB p.T313M and ANOS1 p.C172F may harbor the pathogenic site, \u0026nbsp;Population data did not reveal the presence of a mutation at this site, and the mutation frequency of p.Arg1035Thr in the gnomAD database was found to be 0.0004. and the mutation frequency of p.T313M in the gnomAD database was found to be 0.0001. and the mutation frequency of p.C172F in the gnomAD database was found to be 0. which is illustrated in Table 6 and Figure 2. List of primer pairs used for PCR, see Table 7.\u003c/p\u003e\n\u003cp\u003eMutationTaster predicted Lys1819Arg to be a harmful mutation, whereas SIFT_pred and Polyphen2_HDIV predicted Arg1035Thr to be a harmful mutation. whereas SIFT_pred predicted p.C172F to be a harmful mutation.The two mutations of IGSF10 are located in the region encoding the immunoglobulin I-set domain and in the nondomain region. The mutations of ANOS1 are located in the region encoding the WAP domain，the mutations of KLBare are located in the region encoding the Glyco_hydro_1 domain. （see Figure 3）\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eWith further research on KS genetics, some genes related to KS pathogenesis have been found, such as KAL1, FGFRI, FGF8, PROKR2, PROK2, the function of these genes may be related to the normal migration of GnRH neurons and development of the olfactory bulb. However, only 30% of the incidence of Kallmann syndrome is related to the above genes, suggesting that there are other disease-related genes of KS that have not been found\u003c/p\u003e\n\u003cp\u003eIn this study, WES was performed to analyze the mutant genes and loci in four patients with KS, and two potential pathogenic loci of the IGSF10 gene (p.Lys1819Arg and p.Arg1035Thr) were identified. According to the analysis of the IGSF10 gene mutations in the two patients, the variations included alteration of the amino acid at the 1819th position from lysine to arginine and at the 1035th position from arginine to threonine. The Lys1819Arg site is located in the I-set domain of the protein, which is primarily associated with immune function and angiogenesis. The discovery of this site can improve our understanding of KS pathogenesis and serve as a novel target for further studies on KS.\u003c/p\u003e\n\u003cp\u003eIGSF10 is a member of the immunoglobulin superfamily[26]. While its exact function is yet to be clarified, studies have shown that IGSF10 expression is associated with combined pituitary hormone deficiency. It is also considered a novel prognostic biomarker for breast cancer and lung cancer and has been associated with various diseases, such as primary ovarian insufficiency and endometrial cancer. Mutations in the IGSF10 gene are reportedly associated with abnormal regulation of the migration of GnRH neurons, which may delay puberty and other developmental processes.\u003c/p\u003e\n\u003cp\u003eAmong KS patients, about 90% of the patients, pubic hair and armpit hair were absent. Bone age lagged behind chronological age in some patients. some have anosmia or hyposmia. Some males has breast hyperplasia. small penis, cryptorchidism and vas deferens lack. Some patients can also be accompanied by other body or organ abnormalities, such as facial cranial midline deformity. Nervous system abnormalities. Musculoskeletal system abnormalities and Other systemic abnormalities\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; KLB and KL genes have homology. It is highly expressed in metabolic tissues, especially fat tissues. FGF21 is an endocrine FGF mainly secreted by the liver, which regulates the main metabolic processes such as glucose and lipid metabolism. Endogenous FGF21 regulates the physiological response to starvation and various other metabolic stresses. FGF21 signals through the KLB/FGFR1c receptor complex in a tissue-specific manner. KLB enhances the binding of FGF21-FGFR1c, thereby promoting FGF21 signal transduction by binding FGF21 and FGFR1c to itself through two different sites at the same time. In addition, the competitive binding of FGF8 and b-Klotho to the same site of FGFR1 will facilitate the binding to endocrine FGF21 and inhibit the binding and signal transduction of paracrine FGF8. Most patients with KLB mutations exhibit KS and metabolic defects, such as overweight, diabetes, and dyslipidemia, which are consistent with the metabolic effects of this pathway[27].\u003c/p\u003e\n\u003cp\u003eANOS1 gene is a pathogenic gene found to cause x-linked KS. It is located on the X chromosome (Xp22.3), which contains 14 exons, adjacent to the pseudo-autosomal 1 region (PAR1), which is a highly variable and unstable region on the chromosome. ANOS1 encodes anosmin-1, an extracellular matrix protein. Anosmin-1 consists of a cysteine-rich region (CR domain), a whey acidic protein (WAP)-like domain similar, four consecutive fibronectin type III domains and a C-terminal region rich in basic histidine and proline. Anosmin-1 promotes neuronal cell adhesion, neurite outgrowth, axon guidance, and CNS projection neuron branching. In addition, it is also involved in the migration of many types of neural precursors, including GnRH-producing neurons and oligodendrocyte precursors[28,29]. The ANOS1 mutation is found in patients with familial and sporadic KS. The ANOS1 gene mutation has a low incidence in patients with sporadic KS, but a high incidence in patients with familial KS. In KS patients with ANOS1 mutations, the loss or dysplasia of cryptorchidism and olfactory bulb is high.\u003c/p\u003e\n\u003cp\u003eIn our study, another two potential pathogenic loci of the KLB p.T313M and ANOS1 p.C172F were identified，it suggests that KS disease with KLB mutation should be alert to the risk of diabetes, and KS disease with ANOS1 mutation is related to X-linked recessive inheritance. Although our analysis is limited to 4 patients with KS, it supports the previous view. and found new mutation sites to facilitate follow-up research\u003c/p\u003e\n\u003cp\u003eBecause KS hyposmia can be manifested in different degrees, therefore, sometimes it is not easy to distinguish KS and nIHH, especially in patients with hypogonadism, often without careful evaluation of olfactory function. There is genetic evidence that the genes encoding GnRH and kisspeptin receptors are related to nIHH, but not related to the migration of GnRH neuroendocrine cells (KS patients may have abnormal migration of GnRH neuroendocrine cells), suggesting that KS and nIHH may have different inheritance background and pathogenesis\u003c/p\u003e\n\u003cp\u003eExon sequencing can be used for studying various diseases. It is useful as a diagnostic tool owing to its low cost and high throughput. The method can be used to detect all mutations in human exons simultaneously. With technological advancements, exon capture has emerged as a useful method. Currently, the chip used has been up to 60M, which can include multiple introns and untranslated regions, and provides valuable information for the study of disease-causing sites.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eWe found mutations in the same disease-causing gene in four patients through WES. This is relatively rare, particularly in diseases in which multiple genes are implicated. This finding also illustrates the value of second-generation sequencing for precision therapy. Correct olfactory judgment combined with genetic testing can diagnose Kalman syndrome in time. timely diagnosis and treatment can induce puberty, thereby improving sexual, bone, metabolic, and mental health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthors' contributions:\u003c/strong\u003e Sun Shuangshuang performed the experiments and wrote the manuscript; Wang Ruixue was involved in processing data. All authors have read and approved the final version of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e We would like to thank the families who participated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e The authors have no conflict of interest to declare\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding: \u003c/strong\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to participate: \u003c/strong\u003eThe study was approved by the Research Ethics Committee of Shanxi Provincial People's Hospital and complied with the ethical standards of the Declaration of Helsinki as well as the relevant national and international guidelines. Each patient signed an informed consent for the use of their venous blood in the research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient consent for publication: \u003c/strong\u003eWritten informed consent was obtained from each patient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e: The data to support the findings of the study are available from the corresponding author upon reasonable request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information: \u003c/strong\u003eDepartment of Clinical Lab, Shanxi Provincial People's Hospital Affiliated to Shanxi Medical University, Taiyuan, 030012, Shanxi Province, China。\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTopaloğlu A. K. (2017). 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Three Siblings with Idiopathic Hypogonadotropic Hypogonadism in a Nonconsanguineous Family: A Novel\u0026nbsp;\u003cem\u003eKISS1R/GPR54\u003c/em\u003eLoss-of-Function Mutation.\u0026nbsp;\u003cem\u003eJournal of clinical research in pediatric endocrinology\u003c/em\u003e,\u0026nbsp;\u003cem\u003e11\u003c/em\u003e(4), 444\u0026ndash;448. https://doi.org/10.4274/jcrpe.galenos.2019.2018.0230\u003c/li\u003e\n\u003cli\u003eKurio, N., Saunders, C., Bechtold, T. E., et al (2018). Roles of Ihh signaling in chondroprogenitor function in postnatal condylar cartilage.\u0026nbsp;\u003cem\u003eMatrix biology : journal of the International Society for Matrix Biology\u003c/em\u003e,\u0026nbsp;\u003cem\u003e67\u003c/em\u003e, 15\u0026ndash;31. \u003ca href=\"https://doi.org/10.1016/j.matbio.2018.02.011\"\u003ehttps://doi.org/10.1016/j.matbio.2018.02.011\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eMen, M., Wang, X., Wu, J., et al (2020). Prevalence and associated phenotypes of DUSP6, IL17RD and SPRY4 variants in a large Chinese cohort with isolated hypogonadotropic hypogonadism.\u0026nbsp;\u003cem\u003eJournal of medical genetics\u003c/em\u003e, jmedgenet-2019-106786. Advance online publication. \u003ca href=\"https://doi.org/10.1136/jmedgenet-2019-106786\"\u003ehttps://doi.org/10.1136/jmedgenet-2019-106786\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eKim, H. G., \u0026amp; Layman, L. C. (2011). The role of CHD7 and the newly identified WDR11 gene in patients with idiopathic hypogonadotropic hypogonadism and Kallmann syndrome.\u0026nbsp;\u003cem\u003eMolecular and cellular endocrinology\u003c/em\u003e,\u0026nbsp;\u003cem\u003e346\u003c/em\u003e(1-2), 74\u0026ndash;83. \u003ca href=\"https://doi.org/10.1016/j.mce.2011.07.013\"\u003ehttps://doi.org/10.1016/j.mce.2011.07.013\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eHoward, S. R., Guasti, L., Ruiz-Babot, G., et al (2016). IGSF10 mutations dysregulate gonadotropin-releasing hormone neuronal migration resulting in delayed puberty.\u0026nbsp;\u003cem\u003eEMBO molecular medicine\u003c/em\u003e,\u0026nbsp;\u003cem\u003e8\u003c/em\u003e(6), 626\u0026ndash;642. \u003ca href=\"https://doi.org/10.15252/emmm.201606250\"\u003ehttps://doi.org/10.15252/emmm.201606250\u003c/a\u003e\u003c/li\u003e\n\u003cli\u003eMisrahi M. \u0026beta;-Klotho sustains postnatal GnRH biology and spins the thread of puberty. EMBO Mol Med. 2017 Oct;9(10):1334-1337. doi: 10.15252/emmm.201708180. PMID: 28778954; PMCID: PMC5623834.\u003c/li\u003e\n\u003cli\u003eNie M, Xu H, Chen R, Mao J, Wang X, Xiong S, Zheng J, Yu B, Cui M, Ma W, Huang Q, Zhang H, Wu X. Analysis of genetic and clinical characteristics of a Chinese Kallmann syndrome cohort with\u0026nbsp;ANOS1\u0026nbsp;mutations. Eur J Endocrinol. 2017 Oct;177(4):389-398. doi: 10.1530/EJE-17-0335. Epub 2017 Aug 5. PMID: 28780519.\u003c/li\u003e\n\u003cli\u003eJiang X, Li D, Gao Y, Zhang X, Wang X, Yang Y, Shen Y. A novel splice site variant in ANOS1 gene leads to Kallmann syndrome in three siblings. Gene. 2020 Feb 5;726:144177. doi: 10.1016/j.gene.2019.144177. Epub 2019 Oct 26. PMID: 31669640.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 GnRH stimulation test result\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"3\" width=\"180\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e15min\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e30min\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e60min\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e120min\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"66\"\u003e\n\u003cp\u003ePatient 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eFSH（mIU/ml）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"68\"\u003e\n\u003cp\u003e0.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e2.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e3.07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eLH（mIU/ml）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"68\"\u003e\n\u003cp\u003e\u0026lt;0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e2.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e2.714\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e2.67\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"66\"\u003e\n\u003cp\u003ePatient 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eFSH（mIU/ml）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"68\"\u003e\n\u003cp\u003e0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.19\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e3.07\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eLH（mIU/ml）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"68\"\u003e\n\u003cp\u003e\u0026lt;0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.61\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"66\"\u003e\n\u003cp\u003ePatient 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eFSH（mIU/ml）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"68\"\u003e\n\u003cp\u003e0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.88\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e2.42\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eLH（mIU/ml）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"68\"\u003e\n\u003cp\u003e\u0026lt;0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.02\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.53\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd rowspan=\"2\" width=\"66\"\u003e\n\u003cp\u003ePatient 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eFSH（mIU/ml）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"68\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e2.02\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eLH（mIU/ml）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd colspan=\"2\" width=\"68\"\u003e\n\u003cp\u003e\u0026lt;0.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e1.63\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"9\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"59\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 2 Islet function test of patient 3\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"217\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0分\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e30min\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e60min\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e120min\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e180min\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"217\"\u003e\n\u003cp\u003eblood glucose（mmol/l）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e7.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e9.0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e15.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e18.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e13.3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"217\"\u003e\n\u003cp\u003einsulin release（uIU/ml）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e1.50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e4.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e5.79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e3.88\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"217\"\u003e\n\u003cp\u003eC-Peptide（ng/ml）\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003e0.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"71\"\u003e\n\u003cp\u003e0.62\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Table 3 Sequence alignment and sequencing depth\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"218\"\u003e\n\u003cp\u003eSample\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003ePatient 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003ePatient 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003ePatient 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003ePatient 4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"218\"\u003e\n\u003cp\u003eRaw reads(PE M)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e36.83373\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e39.8752\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e39.856\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e39.975\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"218\"\u003e\n\u003cp\u003ereads mapping rate(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e99.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e99.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e99.64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e99.65\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"218\"\u003e\n\u003cp\u003eTarget duplication rate(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e14.15\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e14.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e14.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e14.75\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"218\"\u003e\n\u003cp\u003eTarget mean depth\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e130.55\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e139.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e151.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e145.43\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"218\"\u003e\n\u003cp\u003eT 10X coverage rate(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e99.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e99.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e99.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e99.27\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"218\"\u003e\n\u003cp\u003eT 20X coverage rate(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e99.04\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e99.13\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e99.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e99.04\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"218\"\u003e\n\u003cp\u003eT 30X coverage rate(%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"101\"\u003e\n\u003cp\u003e97.98\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"83\"\u003e\n\u003cp\u003e98.23\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e98.57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"79\"\u003e\n\u003cp\u003e98.35\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; Table 4 Distribution of single-nucleotide polymorphisms\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eSample\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003ePatient 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003ePatient 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003ePatient 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003ePatient 4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003etotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e82986\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e84748\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e84579\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e84731\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003edbsnp\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e82205(99.06%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e83887(98.98%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e83752(99.02%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e83835(98.94%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003e1000g_EAS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e77404(93.27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e78717(92.88%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e78737(93.09%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e78824(93.03%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eExAC_EAS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e45558(54.90%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e45630(53.84%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e45773(54.12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e45936(54.21%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003egnomAD_exome_EAS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e45617(54.97%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e45698(53.92%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e45866(54.23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e46025(54.32%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003egnomAD_genome_EAS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e81924(98.72%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e83549(98.59%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e83441(98.65%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e83536(98.59%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eexonic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e22847(27.53%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e23016(27.16%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e23100(27.31%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e22921(27.05%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003esplicing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e239(0.29%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e246(0.29%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e251(0.30%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e259(0.31%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eUTR3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e3103(3.74%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e3161(3.73%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e3152(3.73%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e3084(3.64%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eUTR5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e2234(2.69%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e2310(2.73%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e2305(2.73%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e2333(2.75%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eintronic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e48754(58.75%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e50084(59.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e49683(58.74%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e50121(59.15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eintergenic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e1902(2.29%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e2108(2.49%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e2184(2.58%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e2145(2.53%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eupstream\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e816(0.98%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e881(1.04%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e882(1.04%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e827(0.98%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003edownstream\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e371(0.45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e379(0.45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e379(0.45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e366(0.43%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003encRNA_exonic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e764(0.92%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e726(0.86%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e730(0.86%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e790(0.93%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003encRNA_splicing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e6(0.01%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e8(0.01%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e4(0.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e3(0.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003encRNA_intronic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e1888(2.28%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e1764(2.08%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e1848(2.18%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e1823(2.15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003esynonymous SNV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e11529(13.89%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e11613(13.70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e11559(13.67%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e11518(13.59%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003enonsynonymous SNV\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e10755(12.96%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e10727(12.66%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e10815(12.79%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e10749(12.69%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003estopgain\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e85(0.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e92(0.11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e94(0.11%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e84(0.10%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003estoploss\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e10(0.01%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e10(0.01%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e11(0.01%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e8(0.01%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"147\"\u003e\n\u003cp\u003eunknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e483(0.58%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e589(0.70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e635(0.75%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"117\"\u003e\n\u003cp\u003e576(0.68%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5 Indel distribution\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eSample\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003ePatient 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003ePatient 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003ePatient 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003ePatient 4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003etotal\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e13495\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e13931\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e13760\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e13794\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003edbsnp\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e12301(91.15%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e12675(90.98%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e12538(91.12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e12551(90.99%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003e1000g_EAS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e8269(61.27%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e8454(60.68%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e8378(60.89%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e8410(60.97%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eExAC_EAS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e5560(41.20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e5547(39.82%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e5562(40.42%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e5637(40.87%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003egnomAD_exome_EAS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e5306(39.32%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e5284(37.93%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e5286(38.42%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e5355(38.82%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003egnomAD_genome_EAS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e12567(93.12%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e12991(93.25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e12760(92.73%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e12812(92.88%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eexonic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e708(5.25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e733(5.26%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e707(5.14%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e694(5.03%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003esplicing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e199(1.47%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e182(1.31%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e201(1.46%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e192(1.39%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eUTR3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e669(4.96%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e676(4.85%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e667(4.85%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e661(4.79%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eUTR5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e375(2.78%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e384(2.76%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e364(2.65%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e373(2.70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eintronic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e10469(77.58%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e10845(77.85%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e10712(77.85%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e10767(78.06%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eintergenic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e297(2.20%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e312(2.24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e309(2.25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e307(2.23%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eupstream\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e160(1.19%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e187(1.34%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e170(1.24%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e184(1.33%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003edownstream\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e51(0.38%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e65(0.47%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e66(0.48%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e68(0.49%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003encRNA_exonic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e103(0.76%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e93(0.67%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e102(0.74%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e99(0.72%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003encRNA_splicing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e0(0.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e2(0.01%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e4(0.03%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e1(0.01%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003encRNA_intronic\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e407(3.02%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e398(2.86%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e400(2.91%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e391(2.83%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eframeshift insertion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e94(0.70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e103(0.74%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e96(0.70%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e100(0.72%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eframeshift deletion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e135(1.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e124(0.89%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e132(0.96%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e137(0.99%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003enonframeshift insertion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e198(1.47%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e216(1.55%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e200(1.45%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e181(1.31%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003enonframeshift deletion\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e217(1.61%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e215(1.54%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e202(1.47%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e203(1.47%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003estopgain\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e7(0.05%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e8(0.06%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e9(0.07%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e10(0.07%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003estoploss\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e1(0.01%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e1(0.01%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e0(0.00%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e1(0.01%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"138\"\u003e\n\u003cp\u003eunknown\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e99(0.73%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e106(0.76%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e111(0.81%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"110\"\u003e\n\u003cp\u003e103(0.75%)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 6 Specific information about IGSF10、KLB、ANOS1 mutation\u003c/p\u003e\n\u003ctable border=\"1\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003eType\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003ePatient 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003ePatient 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003ePatient 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003ePatient 4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003eChr.Start.End\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003echr3.151161279.151161279\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003echr3.151164665.151164665\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003echr4.39435942.39435942\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003echrX.8565101.8565101\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003eVcf_mut\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eT/C\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eC/G\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eC/T\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003eC/A\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003eGT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e0/1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e0/1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e0/1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003e1/1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003eAD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e51/53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e30/31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e34/47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003e0/86\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003eAAChange.HGVS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eIGSF10:NM_178822.4:5/6:c.5456A\u0026gt;G:p.(Lys1819Arg)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eIGSF10:NM_178822.4:4/6:c.3104G\u0026gt;C:p.(Arg1035Thr)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eKLB:NM_175737:exon2:c.C938T:p.T313M\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003eANOS1:NM_000216:exon4:c.G515T:p.C172F\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003ecytoBand\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e3q25.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e3q25.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e4p14\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003eXp22.31\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003eInterVar_automated\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eUncertain significance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eUncertain significance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eUncertain significance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003eUncertain significance\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003eACMG(missense only)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003ePM1,PM2,BP4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003ePM1,BP4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003ePM1,PM2,PP3\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003egnomAD_exome_ALL\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e0.0004\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e0.0001\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003eSIFT_pred\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003ePolyphen2_HDIV_score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e0.011\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e0.981\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e0.036\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003ePolyphen2_HDIV_pred\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003ePolyphen2_HVAR_score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e0.056\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e0.69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e0.016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003ePolyphen2_HVAR_pred\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eP\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003eLRT_score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e0.039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e0.59\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003e0\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003eLRT_pred\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003eMutationTaster_score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e0.808\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003e1\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003eMutationTaster_pred\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003eMutationAssessor_score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd 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width=\"224\"\u003e\n\u003cp\u003e-0.287\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003e6.358\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003eCADD_phred\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e8.054\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e15.84\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e0.711\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003e29.4\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003efathmm-MKL_coding_score\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e0.039\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e0.257\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e0.068\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003e0.967\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003efathmm-MKL_coding_pred\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003eN\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003eD\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003eGERP++_RS\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e0.193\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e5.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e-7.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003e4.51\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"146\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"296\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"224\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"236\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 7\u0026nbsp; List of primer pairs used for PCR\u003c/p\u003e\n\u003ctable border=\"1\" width=\"0\"\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003eCC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003eGene\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003eTemplateID\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eForwardPrimer(Fp)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eReversePrimer(Rp)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"56\"\u003e\n\u003cp\u003eAmpSize\u003c/p\u003e\n\u003cp\u003e(bp)\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003ePatient 1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003eIGSF10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003echr4:39435930-39435950\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eACATTTCCGCCCACATCAGAAG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eTCAGCTGTGCCTCTCATCTCAT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"56\"\u003e\n\u003cp\u003e246\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003ePatient 2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003eIGSF10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003echr3:151161270-151161285\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eTAACAGGTGGTGCTGCAATGAC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eAAGCACTGTGGAACTGAAGTGC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"56\"\u003e\n\u003cp\u003e251\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003ePatient 3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003eKLB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003echr3:151164660-151164670\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eAGCAATGTCAGCTTTGGGGAAG\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eGCTTTGGGAGGCAGAGGAAAAT\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"56\"\u003e\n\u003cp\u003e260\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd width=\"66\"\u003e\n\u003cp\u003ePatient 4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"50\"\u003e\n\u003cp\u003eANOS1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"104\"\u003e\n\u003cp\u003echrX:8565100-8565108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"152\"\u003e\n\u003cp\u003eTGTGACACTGCATGTGTCTTCAC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"150\"\u003e\n\u003cp\u003eTGACCAGCTGTGAGTTCCTCAA\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd width=\"56\"\u003e\n\u003cp\u003e236\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\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":"Kallmann syndrome, whole-exome sequencing, bioinformatics analysis, IGSF10, KLB, ANOS1","lastPublishedDoi":"10.21203/rs.3.rs-285888/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-285888/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: Kallmann syndrome is a hypogonadotropic hypogonadism accompanied by anosmia or hypoxia. More than 10 Kallmann syndrome pathogenic genes have been found. However, only 30% of the incidence of Kallmann syndrome is related to the above genes, suggesting that there are other disease-related genes of KS that have not been found. \u003c/p\u003e\u003cp\u003eMethods： We studied Kallmann syndrome pathogenesis through high-throughput exome sequencing on four patients with Kallmann syndrome for screening the potential pathogenic sites and exploring the genotype-phenotype correlation. Clinical data and peripheral blood samples were collected from the patients. White blood cells were separated and genomic DNA was extracted. High-throughput sequencing of all exons in the candidate pathogenic genes of the probands was performed, and the results obtained were analyzed. \u003c/p\u003e\u003cp\u003eResults: Sequencing revealed mutations in the KLB, p.T313M, ANOS1, p.C172F, and IGSF10 (p.Lys1819Arg and p.Arg1035Thr) at different sites, which may have been associated with disease onset. Discovery of these sites can improve our understanding of KS pathogenesis and serve as a novel target in further studies on KS.\u003c/p\u003e\u003cp\u003eConclusion: our analysis found new mutation sites to facilitate follow-up research. Diagnosis of Kallmann syndrome is a challenging, timely diagnosis and treatment can induce puberty, thereby improving sexual, bone, metabolic, and mental health.\u003c/p\u003e","manuscriptTitle":"Molecular Diagnosis of Kallmann Syndrome by Whole Exon Sequencing and Bioinformatic Approaches\u0026nbsp;","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-03-06 15:36:32","doi":"10.21203/rs.3.rs-285888/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":"33a7c0ca-8599-4d05-b3c1-5bea0016230e","owner":[],"postedDate":"March 6th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":2792600,"name":"Biomedical Engineering"}],"tags":[],"updatedAt":"2021-03-07T12:14:33+00:00","versionOfRecord":[],"versionCreatedAt":"2021-03-06 15:36:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-285888","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-285888","identity":"rs-285888","version":["v1"]},"buildId":"i_I_jhV-QviBjPLXJga9V","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}