Identification of breeders’ smQTL and candidate genes associated with growth and fillet quality in Rainbow Trout (Oncorhynchus mykiss) with meta-analysis of QTLs

Identification of breeders’ smQTL and candidate genes associated with growth and fillet quality in Rainbow Trout (Oncorhynchus mykiss) with meta-analysis of QTLs | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Identification of breeders’ smQTL and candidate genes associated with growth and fillet quality in Rainbow Trout (Oncorhynchus mykiss) with meta-analysis of QTLs Azadeh Shakeri, Hossein Askari, Masood Soltani Najafabadi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6178047/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 Rainbow trout ( Oncorhynchus mykiss ) is one of the most frequently farmed cold water fish species. The effective identification of quantitative trait loci (QTL) associated with the growth and fillet quality of rainbow trout can play an effective role in genetic improvement in global aquaculture. In this study, we used the strategy of meta-QTL analysis and identified the position of consensus genetic map markers on the reference genome of rainbow trout, to identify breeders’ sequence based meta-QTLs (sm-QTL). After reviewing QTL studies over the past 22 years, we collected 308 QTLs distributed on 14 chromosomes. Based on the distribution of QTLs related to different studies on each chromosome, six genetic map meta-QTLs (gm-QTL) and four sm-QTL were detected on three chromosomes. Confidence interval (CI) of gm-QTLs decreased on average 6.39 times compared to initial QTLs. The results showed the most promising areas for controlling two traits (breeders’ smQTL) are located on Chromosome 1 and 13. So probably the flanking and interval markers with these genomic regions, have effective applications for marker-assisted selection (MAS). Our study will be effective in investigating diversity and MAS with the aim of molecular breeding of rainbow trout due to the identification of markers close to the consensus of QTLs of growth and fillet quality. Biological sciences/Biotechnology Biological sciences/Genetics Health sciences/Biomarkers meta-QTL analysis rainbow trout growth fillet quality Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Growth in companion with feed conservation ratio (FCR) is the most important economic trait in aquaculture species and quality is effective on consumer satisfaction and makes the aquaculture industry profitable. Considering that aquaculture has a special place in global nutrition, the selection of economically important aquaculture species with high genetic potential for growth and quality constitutes one of the crucial factors in the food supply worldwide[ 1 – 11 ]. Fish is a rich source of protein and fat with polyunsaturated fatty acids. Therefore, it plays an important role in a healthy life system and is considered a suitable option for a healthy diet [ 12 ]. Although fish is known as the best source of healthy fats. However, too much fat reduces the firmness of the fillet and affects its taste. reduces the value of the fillet and is very influential in the choice of consumers[ 13 ]. The firmness of the fillet is determined by measuring the maximum force required to divide the meat (shear force)[ 14 , 15 ]. Accordingly, in improving the quality of fillets, it is very critical to identify the QTLs that control the protein content, fat content, and shear force [ 16 , 17 ]. In addition, since high humidity leads to rapid spoilage of fish fillets, identifying areas controlling fillet moisture content has been considered in research [ 18 ]. Concerning growth, in addition to body weight and body length, condition factor (weight to length ratio) and centroid size (analysis of the body shape by estimating through the square root of the sum of the squared distances of each specific point to the center) are of interest and their related QTLs have been reported in different studies[ 19 – 25 ]. Rainbow trout ( Oncorhynchus mykiss ) is one of the most frequently farmed cold water fish species in the world [ 26 – 29 ]. So, improving these traits in rainbow trout is very important, and in recent years, extensive studies have been conducted to identify the loci of these quantitative traits as a prerequisite for marker-assisted selection for breeding programs [ 19 , 20 , 30 – 37 ] The effective use of the marker-assisted selection (MAS) method is based on the principle of achieving stable quantitative trait loci (QTLs) on chromosomes; Therefore, despite the value of QTL mapping performed about the growth and fillet quality of rainbow trout in different studies, the QTLs identified in individual studies will not be effective in marker selection due to different genetic backgrounds and different environmental conditions[ 38 , 39 ]. Meta-QTL analysis is a powerful technique that identifies useful concordant meta-QTLs for breeding purposes by consensus of QTLs from different studies[ 40 , 41 ]. Accordingly used by many researchers to identify molecular markers associated with increased yield, diseased resistance, and biological and non-biological stresses in crops such as wheat[ 41 , 42 ], rice[ 43 , 44 ], sorghum [ 45 ], and maize[ 46 ]. To the best of our knowledge, there is no report on the analysis and interpretation of the results of the meta-QTL analysis for the growth and fillet quality of rainbow trout. Since it has been found that growth and fillet quality are genetically correlated [ 47 , 48 ]. Therefore, in this study, attempts were carried out to identify the genomic regions responsible for growth and fillet quality in rainbow trout. To this end, meta-QTL analysis was performed on the data gathered from individual QTL to allocate the growth and fillet quality traits to genomic regions harboring large effects and meanwhile small confidence intervals (CI). Results literature review and classification of the selected QTLs related to the growth and fillet quality After performing a comprehensive review from 2000 to 2022 on Google Scholar and PubMed (Figure 2), and taking into consideration explained phenotypic variance (R 2 ), the log odds ratio (LOD score) and the availability of linkage map information as criteria, a total of six articles (original study) were found to cover growth and fillet quality of Rainbow Trout and thus were selected for further analysis. These articles collectively contained 308 QTL related to growth and fillet quality traits which were categorized into 10 subclasses (Table 1). The phenotypic variation explained (PVE) by each QTL, position of flanking markers, LOD, and types of trait are presented in supplementary file 1. The QTLs were unequally allocated to the subclasses and chromosomes. The marker types used in each original study and the traits subclasses are summarized in Table 2. The highest number of QTLs was for subclasses of body weight and protein content accounting for 34% and 31% of the 308 identified QTLs, respectively (Figure 2-a). QTLs related to body weight were mainly located on chromosomes 2, 4, 8, 9 and 13, while protein content QTLs were distributed on chromosomes 1 and 7 (Figure 2-b). Distribution of marker types associated with the QTLs across the chromosomes Three types of markers, that is microsatellites, SNPs, and RADs , were found to be employed in the original studies to identify QTLs (Figure 3). SNP markers were the most abundant markers although most SNPs were located on chromosomes 1, 8 and 13. The highest SNP density was found at 1 Mb intervals on chromosomes 8, 1, and 4. Microsatellite and RAD markers were accumulated in chromosomes 2, 4, 13,14, and 21 respectively(Figures 3-b and c). Features of the selected QTLs Some features of the reported QTLs in the literature and those selected in the present investigation are shown in Figure 4-a,b. The comparison of total QTL found in the literature and those submitted to meta-QTL analysis is summarized in Figure 4-c. As mentioned before, the availability of the genetic map, marker information, and the LOD threshold were important factors in the decision to include or not to include QTLs in the meta-analysis. Although the reported QTLs related to growth and fillet quality were distributed on 14 chromosomes, only QTLs located on 7 chromosomes (1, 2, 7, 8, 12, 13, and 21) could be analyzed. For most of the selected/reported QTLs, the average phenotypic variation explained (PVE) centered around 9.8%. QTLs located on chromosomes 4, 1, and 13 had the biggest PVE value, respectively. The status of the LOD corresponding to the reported QTLs is shown in Figure 4-b. One of the determining factors in our study was . As is shown in Figure 4-b, most of the LODs reported for QTLs were in the range of 4 to 10 with a standard deviation of 2.5. QTLs projection on the consensus genetic map and Meta-QTL analysis A consensus genetic map containing 1948 markers with a density of 0.2 markers per cM, was created by merging three genetic maps containing 1119, 1421, and 587 markers[20, 52, 53]. Description related to map length and the number of markers on the linkage groups in which meta-QTLs were identified are summarized in Table 3. The initial projection of QTLs on the consensus genetic map was based on the peak position of QTL flanking markers (microsatellite and RAD) and CIs. In order to estimate on the linkage map the position of the SNPs associated with growth and fillet quality traits, the following steps were undertaken: First the physical position of the markers (microsatellite and RAD) was found on the reference genome of rainbow trout. Through this step, the physical position for 46 out of 88 markers located on chromosome 1 of the consensus genetic map was determined on the Rainbow trout reference genome. Moreover, the position for 42 out of 86 markers and 15 out of 56 markers on chromosomes 7, and 13, respectively, were determined on the reference genome (Figure 5, supplementary file 2). Then, using one of the two methods A and B mentioned in the material and method chapter, SNPs associated with the growth and fillet quality were projected on the consensus genetic map. Finally, based on the distribution of QTLs reported in different original studies the chromosomes were analyzed using Biomercator v.4.2.3 software (Figure 4. c). The QTLs that could be analyzed based on the studied indices were distributed on chromosomes 1, 2, 7, 8, 12, 13 and 21. After meta-QTL analysis, after examining the formed clusters, only 3, 1, and 2 meta-QTL were identified on chromosomes 1, 7, and 13, respectively. Figure 6 shows the number and position of identified meta-QTLs on the linkage map. Calculating the rate of change in the CI value of gm-QTLs compared to that of primary QTLs (CI reduction rate, CIrr) showed that gm-QTL1 (GM-1-1) on chromosome one and gm-QTL1 (GM-13-1) on chromosome 13 had the lowest CIrr, of 11.78 and 2.2, respectively. Information about the meta-QTLs related to each chromosome is listed in Table 4. Identification of smQTLs, markers and SNPs associated with growth and fillet quality in smQTL intervals In addition to identifying the position of meta-QTLs on the consensus genetic map, using flanking markers of identified meta-QTLs, meta-QTLs' position on the reference genome of rainbow trout was determined. The mean physical CI of smQTLs was 12.96 Mb, ranging from 0.5 Mb, (smQTL7) to 35.9 Mb (smQTL1_1). Although identified gmQTLs on chromosome one did not overlap on the genetic map, smQTL1_1 and smQTL1_2 at position 48579512-66054801 with a confidence interval of 17.4 Mb and smQTL1_1 and smQTL1_3 at position 42287756-45344689 with a confidence interval of 3Mb were overlapped. Based on the created physical map (Figure 5), 11 markers (OMM5237, OMM1205, OMM1780, R43999, OMM1302, OMM1454, R42795, R13377, OMM1046, R01951, R39919) and two markers (OMM1646 and R36287) found in intervals of two flanking markers of meta QTL1 on chromosome 1 and meta QTL2 on chromosome 13, respectively (Table 5).The possibility of the presence of SNPs associated with growth and fillet quality in smQTL intervals was investigated using the reported position of the SNPs in the original studies (Table 5). Three SNPs (AX-171607624, AX-171620793, AX-89924960) was detected in the overlapped position of smQTL1 and smQTL2 chromosome one. 23 SNPs were identified in smQTL1of chromosome 13. information related to these coding/functional SNPs is summarized in Table 6. Detection of candidate genes for growth and fillet quality Based on the position of smQTLs (position of two flanking markers related to gmQTLs on reference genome), 264 gene symbols were identified in the ensemble site and shiny GO. The identified SNPs in the smQTLs region were located in 14 genes (Table 6). The highest number of Candida genes was detected on chromosome 1. Separately, 140, 153, and one candidate genes were located in m_1_1, m_1_2, and m_1_3, respectively. 103 candidate genes were identified in overlap m_1_1 and m_1_2. One and 73 Candida genes were located On chromosomes 7 and 13 (m_13_1), respectively. Gene ontology (GO) enrichment analysis of identified candidate genes The identification of common characteristics of 264 candidate gene symbols regarding the biological process was performed through gene ontology (GO) analysis. The results showed that candidate genes were assigned to 284 biological process pathways which are summarized in Figure 7 and supplementary File 3. The most significantly enriched GO terms were related to, small molecule binding (47 genes), organic substance biosynthetic process (42 genes), anion binding and cellular response to stimulus (41 genes), cellular biosynthetic process, membrane-bounded organelle, nucleoside phosphate binding, hydrolase activity, nucleotide binding, intracellular membrane-bounded organelle (40 genes). the results showed that genes in pathways of developmental process, multicellular organism development, anatomical structure development, multicellular organism development, multicellular organismal process, and system development were interconnected (Figure 7-b). The summary of the results obtained in the present investigation is illustrated in Figure 8. The results are shown in different layers: Chromosomal distribution of 308 QTL related to growth and fillet quality traits and the phenotypic variance explained by each QTL, number of identified gm-QTLs, the corresponding CI of each gm-QTL and CI reduction rate compared to the CI of the initial QTLs. Discussion Characteristics of QTLs associated with growth and fillet quality and meta-QTL analysis Increasing the growth rate makes the fish reach the desired weight at a younger age and reduces the costs per kilogram of fish produced, it is very beneficial from an economic point of view and is very important in breeding programs [48]. In addition to the importance of growth on profitability, high fillet quality increases the value of the produced product and increases consumer satisfaction. Therefore, many studies have considered identifying regions controlling these quantitative traits (Figure 1). Quantitative traits are complex and the identification of their precise control locus is affected by genetic background, population size, and environmental conditions. So hence the reliability of QTLs on individual studies for use in MAS is not guaranteed. This issue is the main factor limiting the use of identified QTLs in marker-assisted selection and molecular breeding programs[58] . Based on the study of Leeds et al.[59], the increase in growth rate is genetically related to the increase in fillet quality [47]. Because identifying common loci that control both traits will play an effective role in genetic improvement, in this study, we used meta-analysis to pool QTLs for identify stable QTLs associated with both traits. The literature review results showed an obvious difference in the type, number of markers, and population size in the QTL mapping studies. QTLs detected by microsatellite markers had a small population size and a wide range of CI in the respective studies. In contrast, QTL mapping studies conducted with SNP markers had a larger sample size and high phenotypic variance. Therefore, the integration of QTLs from fillet quality studies that had a large sample size and were identified by coding/functional SNPs with growth-related QTLs that mostly had very large CIs and were identified by microsatellite and RAD markers was important and valuable in two ways: First, it led to the identification of stable QTLs effective for fillet growth and quality, which are two important economic traits in aquaculture. Second, due to the smaller CI of SNP-detected QTLs compared to other studies and the high phenotypic variance, it helped us to reach a small CI. We used the term subclass for convenience in distinguishing between the QTLs belonging to two traits. Each of the QTLs related to body weight, body length, condition factor, fork length, and, centroid size are included in a set under the title of subclasses related to growth. Due to the lack of sufficient information on the reported QTLs, the presence of LOD less than 3, or the lack of consistency between markers linked with QTLs and those located on the consensus genetic map, only QTLs subclasses of body weight and length were used in the analysis. Regarding QTLs related to fillet quality, among the subclasses related to fat content, moisture content, muscle yield, shear force, and protein content, only protein content and shear force could be analyzed. The results showed that chromosomes 2, 4, 8, 9, and 13 were enriched for body weight, chromosomes 1, 3, 7, and 1 for protein content, and chromosomes 8 and 13 for shear force. This distribution on specific chromosomes may show the effect of inter-chromosomal rearrangement, which consists of the formation of some chromosomes as mosaics of different chromosomes, after the tetraploidization process (Salmonid-specific fourth round of whole genome duplication, Ss4R). This is probably due to the presence of a genetic protection system of the regions controlling the desired traits during evolution, which has caused the preservation and stabilization of these regions after chromosomal rearrangement in different ecotypes and species[60-65]. Considering that meta-QTL analysis requires the integration of QTLs from more than one study [65, 66], after meta-QTL analysis, clusters composed of QTLs from one study were not reported in the results. Finally, according to the QTLs that can be analyzed, six meta-QTLs related to the respective traits were identified in three chromosomes. In our study, the identified meta-QTLs had an average of 6.39-fold (range 2.2 cM to 11.78 cM) narrower CIs than the original QTLs, as expected, meta-QTLs analysis showed a strong effect on reduction CI had all meta-QTLs[67, 68]. Identification of new genomic regions controlling the growth and fillet quality and breeders’ MQTLs In the present study, we identified six gm-QTLs in three chromosomes using meta-QTL analysis based on reported QTLs and the consensus of three genetic maps with high density of microsatellite and RAD markers. Separately, three loci controlling with an average phenotypic variance of 12.39% on chromosome 1, one locus controlling with 8.8% phenotypic variance on chromosome 7, and two loci with an average phenotypic variance of 8.7% on chromosome 13 were identified. Three proposed criteria to determine the most reliable and robust gmQTLs in the different meta-QTL analyses for use in breeding programs (breeders’ gmQTLs) include: 1) Small CI: less than 2 cM [68-70] or 2.5 cM [71], 2) initial QTL numbers (overlapping QTLs): 2−7 original QTLs [72] or at least three original QTLs [71] or five original QTLs [73] and or six original QTLs [69, 74], 3) PVE more than 10% [68, 70-72, 75]. Based on these criteria, we identified two “breeders’ gmQTL” (gmQTL1-1 AND gmQTL1-2 with10 and 25 original QTLs, CI 0.89 and 1.54 cM and PVE 12.32% and 15.5%, respectively) for growth and fillet quality (Table 4). These genomic regions were located on chromosome one, so this chromosome probably has a stronger role in the growth and fillet quality traits. Therefore, it was possible that the associated flanking markers (R29784, SSAF43NUIG, OMM1005 and R39679) with these genomic regions would be more effective for use in MAS. Based on the reference sequence-based map (physical map) created in this study, it was found that the order of the markers in the consensus genetic map and the physical map do not match. Therefore, it was essential to identify the location of meta-QTLs identified in the consensus genetic map (gmQTLs), on the reference genome (smQTLs), as well as to identify markers located in smQTL intervals. As expected, there was a significant difference in the position, markers, and confidence interval of identified gmQTLs and smQTLs. Although the lowest CI was observed in gmQTLs identified on chromosome 1 and chromosome 13 (gmQTL13-2), they were the most distant in the physical map and the lowest CI was observed in smQTL7. Large confidence interval in smQTLs may be due to the unavailability of the number of original QTLs with correct and sufficient information in the respective region and as a result of poor overlap between the analyzed QTLs, gmQTLs being located in a region with depressed recombination, or a mismatch between the position of markers of consensus genetic map and physical map. Results showed the three identified gmQTLs on chromosome 1 overlapped at position 48579512-66054801(smQTL1_1 and smQTL1_2) with a confidence interval of 17.4 Mb and position 42287756-45344689 with a confidence interval of 3Mb (smQTL1_1 and smQTL1_3). Considering that 85.41% of the key candidate genes (table 5) identified in chromosome one and SNPs related to fillet quality (AX-171607624, AX-171620793, AX-89924960) were located in the overlapping region of smQTL1_1 and smQTL1_2, as a result the consensus of smQTL1_1 and smQTL1_2 is reasonable. Because the gene and SNP were not identified in the interval of smQTL1_3, though on chromosome one three gmQTLs were identified, one smQTL can be introduced as breeders’smQTL. In addition to the two flanking markers in gmQTLs described earlier, locus-specific markers in the interval of smQTLs can be introduced as high-quality markers for use in molecular breeders (table 5). Considering that microsatellite markers are more user-friendly, six microsatellite markers include OMM5237, OMM1205, OMM1780, OMM1302, OMM1454, and OMM1046 in the interval of smQTL chromosome one, it seems that they are a good option for MAS. Although the lowest confidence interval was observed in smQTL chromosome 7, effective key genes were not identified in this locus, so this region does not have a special role in controlling growth and fillet quality. The results showed that 23 SNPs related to shear force were present in smQTL1 of chromosome 13, confirming this locus's effective role in fillet quality. Due to the lack of identification of growth-controlling genes in this locus and the identification of candidate genes related to growth and fillet quality in smQTL2. Therefore, among the two smQTL identified in chromosome 13, smQTL2 is an important genomic locus for controlling both traits and can be introduced as a breeders’smQTL. Role of candidate genes identified in smQTLs interval We identified a total of 265 candidate genes in Meta-QTL regions (supplementary file 4). The results of go enrichment and review of previous studies showed that these genes were enriched in different pathways that were directly or indirectly related to growth and fillet quality (supplementary file 5). Among the most important identified candidate genes effective in growth and development, we can mention, PTK7, PAX6, Asz1, dusp6, CAPZA2, and CKAP5. Protein tyrosine kinase 7 (PTK7) plays a central role in morphogenesis [66, 67] . Hayes et al [68] found that ptk7 mutants have vertebral abnormalities. CKAP5 is important in oocyte maturation and early embryo development [69]. PAX6 encodes a transcription factor that is effective in the development of the eye, brain, olfactory system, and pancreas [70]. The researchers found that CAPZA2 is an essential gene in the general and skeletal development of the head [71] and dusp6 is requirement in embryogenesis [72]. Ahmad et al [73] showed that Asz1 is essential for spermatogenesis and oogenesis germ cell and gonad development. Yu et al found the pleiotropic effect of the rrp12 gene on both growth and swimming performance in a hypoxic environment. Previous studies have shown that HCRT stimulates appetite, which can affect feeding frequency and food intake, and thus growth [74, 75]. Important genes affecting fat metabolism and oxidation were also identified in the identified smQTLs of chromosomes 1 and 13. Since the fillet quality is affected by the amount of protein and fat, as a result, these genes control fillet quality. In the study of the relationship between fillet firmness and the expression of genes of different functional groups in salmon skeletal muscles by Larson et al[76], they was found that Dnajb9a is in the group of genes with positive regression with fillet firmness. Jia et al [77], showed that in cyp17a1-/- zebrafish strains, the lipid synthesis pathway was significantly activated and caused an increase in visceral adipose tissue and fat content. CYB5R2 is involved in various biological processes such as electron transport, oxidation-reduction, lipid metabolism, fatty acid desaturation and/or elongation, and cholesterol biosynthesis [78]. CAV1 has roles in cellular lipid processing and systemic lipid metabolism [79]. ECHS1 is the key enzymes that regulate lipid metabolism [80] and Slc7a10 prevents excess fat storage and fat hypertrophy [81]. The results showed that the identified genes, in addition to growth and fillet quality, were also effective in response to bacteria, viruses, and stress. Since the genetic correlation between different traits is due to genes that are close together or exist pleiotropic genes[76, 77]. Therefore, the identified meta-QTL regions control the important economic traits of growth, fillet quality, disease and stress resistance. The most important disease and stress resistance genes are: DUSP, which is a pleiotropic gene and is effective in growth, development, temperature stress, and response to bacteria[82] [83]. Liang et al [84] show that EcBAG3 can affect mediating cell adaptability to stress. HSPA12A has been suggested to possess cytoprotective functions in response to various stressors, especially viruses or bacterial pathogens, toxic metals, and heat shock[85]. Aeromonas salmonicida subsp salmonicida (Ass) and nervous necrosis virus are one of the most important bacterial and viral diseases in salmon farms, and their prevention and control are very important economically. In the present study, Nkiras and Dhx58 were identified in the smQTL genomic region of chromosome 13, which were introduced in previous studies as the primary activator genes of salmonid fish immunity against Aeromonas salmonicida infections and one of the DEGs responding to the immune system following NNV infection, respectively [86] [87]. Considering that the results indicated the possibility of the effective role of these candidate genes not only in the growth and fillet quality, but also in the response to bacteria and viruses and adaptation to stress conditions. Therefore, according to the meta-QTL analysis performed on based on the available data, two chromosomes 1 and 13 play a key role in the economic traits of rainbow trout. Although the function of Candida genes was determined to some extent by using previous studies, the exact role of these genes will be determined by experimental investigations in rainbow trout. Conclusion The results of this study showed that meta-QTL analysis can validate the QTLs of previous studies and using consensus genetic map and sequence map, it provides the possibility to identify the most stable QTLs. We identified two breeders’ sm-QTLs (smQTL1 and smQTL13-2) with high accuracy associated with growth and fillet quality. Candida genes identified in this research, in addition to growth and fillet quality, were effective in responding to bacteria, viruses and stress conditions. Given that in the aquaculture industry, disease and stress conditions management are one of the biggest challenges. Therefore, it can be concluded that the meta-QTLs identified are effective in the important economic traits of rainbow trout. It seems that the flanking and interval markers associated with these genomic regions, are likely to play an effective role in advancing the objectives of the breeding program for economically important traits with MAS. Materials and methods Literature review Exhaustive literature review using PubMed syntax "(Rainbow trout [tiab] AND QTL [tiab] OR QTLs [tiab] OR "Quantitative Adjective Locations " [tiab] AND 2000/1/1: 2022 [db] )" in NCBI ( https://pubmed.ncbi.nlm.nih.gov/ ) was performed (Fig. 1 ). The review was completed in Google Scholar ( https://scholar.google.com/ ) using the keywords “QTL”, “Rainbow trout”, “Salmonide” and “Meta-QTL Analysis” for the period 2000 to 2022. The number of articles screened and evaluated based on the reviewed criteria such as LOD value and the commonality of linked markers with desired traits reported in QTL mapping studies with consensus genetic map markers (the possibility of identifying the position of flanking markers) or the possibility of identifying their position on the physical map, can be seen in the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) flow diagram (Fig. 1 )[ 49 ]. Characteristics of studies and data collection Because meta-QTL analysis allows for the integration of data gathered from various genetic backgrounds, after selecting the studies related to the investigation of quantitative traits of rainbow trout, the analysis was executed considering the following criteria: 1) QTLs related to growth and fillet quality 2) proportion of phenotypic variance explained by each QTL (PVE or R 2 ( 3) information on the position of the flanking markers or the QTL-linked markers on the linkage map and their physical position on the genetic map 4) Logarithm of odds score (LOD) or p-value for each QTL 5) population type and size LOD and PVE were required for each QTL to perform the meta-QTL analysis. When this information was not available, P-values were converted to LOD adopting Nyholt [ 50 ]. When genotypic variance explained by each QTL (GVE) was available, LOD and the PVE were calculated using the following formulae: $$\:LOD=-\frac{N}{2}{\text{log}}_{10}(1-GVE)$$ where GVE and N represent genetic variance explained by each QTL and population size, respectively. and $$\:PEV={H}^{2}\:GVE$$ where H 2 stands for broad-sense heritability. Confidence interval (CI) of QTL locations Based on the formula proposed and explained for F 2 and backcross populations by Darvasi and Soller[ 51 ], the confidence interval (CI) was calculated to estimate 95% of the CI of each QTL. $$\:CI=\begin{array}{c}\frac{530}{{NR}^{2}}\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:\:for\:\:\:\:{F}_{2}\:and\:backcross\:populations\\\:\:\:\end{array}$$ where N and R 2 stand for population size and percentage of phenotypic variation explained by the identified QTLs, respectively. Consensus genetic map construction and QTL projection A consensus genetic map employing microsatellite and restricted locus-associated DNA (RAD) markers was constructed by integrating three published genetic maps [ 20 , 52 , 53 ] using Biomercator v.4.2.3 software[ 54 ]. Because the consensus genetic map consisted of microsatellite and RAD markers, the QTLs identified with these two markers were easily predicted by adding and subtracting half of the CI to the peak position of the flanking markers (middle position of the flanking markers) of QTLs on the consensus genetic map. Position estimation of growth and fillet quality-related SNPs on the consensus genetic map The position of each growth- and/or fillet quality-related SNP on the consensus genetic map (in cM) was estimated, based on position of the other mapped markers, during two steps: in the first step, the sequences of all the mapped markers (RAD or Microsatellite) on the consensus map were surfed and found on the physical map of rainbow trout by blasting (BLAST N and primer-BLAST) them against the rainbow trout reference genome. In the second step, the position of the traits-related SNPs on the genetic map was figured out. In this step and upon surrounding the SNP(s) by either one or two adjacent markers, one of the following methods was undertaken: Method a) If SNP was surrounded by two markers spaced 1–4 Mbp, the arithmetic mean of the flanking marker position on the genetic map (peak position) was considered as the corresponding position of the SNP[ 55 ]. Method b) When there was only a single marker in the vicinity of SNPs, the position of the SNPs on the linkage map was estimated based on their positions on the physical map, using the tump of the rule of Jacobs et al [ 56 ]: one Mbp on the physical map is corresponding to one cM on the linkage map. Meta-QTL analysis Meta-QTL analysis was performed for growth and fillet quality traits by Biomercator v.4.2.3. Based on the Biomercator user guide, two input files (consensus genetic map information and QTL information) were prepared and after uploading, QTLs were projected on the consensus genetic map. Among the models presented, based on the Akaike Information Criterion (AIC), the model with the lowest AIC value was selected as the best model to provide the number and position of meta-QTL(real QTL) [ 57 ]. Projection of identified meta-QTL onto the reference genome After identifying the position of meta-QTLs on the genetic map (gmQTL), using flanking markers linked with the gmQTL, their position (sequences of flanking markers were blasted against the reference genome) was determined on the rainbow trout reference genome (sequence-based meta-QTL). Identification of markers in smQTL intervals According to the physical position of the sequence-based meta-QTL (smQTL), the markers located in smQTL intervals were identified using the sequence map. The presence of SNPs associated with growth and fillet quality in smQTL intervals was investigated based on the position of the SNPs reported in the original QTL mapping studies. Identification of candidate genes Candidate genes harboring in the identified smQTL were figured out using the BioMart section of the Ensemble site ( https://asia.ensembl.org/index.html ). In addition, according to the SNP ID located in the smQTL region, the gene ID and annotation associated with each SNP were extracted from the relevant GWAS study. Previous studies were reviewed in order to identify genes with specific functions related to the respective traits. GO enrichment analysis of the candidate genes The GO enrichment analysis was done by ShinyGO 0.80 ( http://bioinformatics.sdstate.edu/go/ ). Declarations Data availability All data generated or analysed during this study are included in this published article [and its supplementary information files Acknowledgements We are grateful to the Hellfork Food Company for providing us financial (Grant number "1212HF") and technical supports. Authors’ contributions “Conceptualization, H.A, M.S.N; Methodology, H.A, A.S, M.S.N; Software, A.S; Validation, H.A, M.S.N, A.S; Formal Analysis, H.A, M.S.N, A.S; Investigation, H.A, A.S; Resources, H.A, M.S.N, A.S; Data Curation, A.S; Writing – Original Draft Preparation, A.S; Writing – Review & Editing, H.A, M.S.N; Visualization, A.S; Supervision, H.A; Project Administration, H.A; Funding Acquisition, H.A. The author(s) read and approved the final manuscript”. Fuding This research received no specific external funding. Availability of data and materials All data generated or analysed during this study are included in this published article [and its additional fles]. Declarations Ethics approval and consent to participate Not Applicable. Consent for publication Not Applicable. Competing interests The authors declare no competing interests. References Bendriem, N., R. Roman, and U.R. Sumaila, Enhancing land-based culture of coho salmon through genomic technologies: An economic analysis. Aquaculture Economics & Management, 2021. 25 (1): p. 27-52. Kjetså, M.H., J. Ødegård, and T. Meuwissen, Accuracy of genomic prediction of host resistance to salmon lice in Atlantic salmon (Salmo salar) using imputed high-density genotypes. Aquaculture, 2020. 526 : p. 735415. Gallardo-Hidalgo, J., et al., Genetics of growth and survival under chronic heat stress and trade-offs with growth-and robustness-related traits in rainbow trout. Aquaculture, 2021. 531 : p. 735685. Joshi, R., et al., Genomic selection for resistance to Francisellosis in commercial Nile tilapia population: genetic and genomic parameters, correlation with growth rate and predictive ability. Aquaculture, 2021. 537 : p. 736515. Ferosekhan, S., et al., Influence of Genetic Selection for Growth and Broodstock Diet n-3 LC-PUFA Levels on Reproductive Performance of Gilthead Seabream, Sparus aurata. Animals (Basel), 2021. 11 (2). Yılmaz, S., et al., Probiotics, prebiotics, and synbiotics used to control vibriosis in fish: A review. Aquaculture, 2022. Magwanga, R.O., et al., Map-Based Functional Analysis of the GhNLP Genes Reveals Their Roles in Enhancing Tolerance to N-Deficiency in Cotton. Int J Mol Sci, 2019. 20 (19). Van Doan, H., et al., Modulation of growth, innate immunity, and disease resistance of Nile tilapia (Oreochromis niloticus) culture under biofloc system by supplementing pineapple peel powder and Lactobacillus plantarum. Fish Shellfish Immunol, 2021. 115 : p. 212-220. Nasr-Eldahan, S., et al., A review article on nanotechnology in aquaculture sustainability as a novel tool in fish disease control. Aquac Int, 2021. 29 (4): p. 1459-1480. Islam, M.J., A. Kunzmann, and M. Slater, Responses of aquaculture fish to climate change‐induced extreme temperatures: A review. Journal of the World Aquaculture Society, 2021. 53 . Freitas, M.V., et al., Genotype by environment interaction and genetic parameters for growth traits in the Neotropical fish pacu (Piaractus mesopotamicus). Aquaculture, 2021. 530 : p. 735933. Komprda, T., et al., Effect of high dietary level (8%) of fish oil on long-chain polyunsaturated fatty acid n-3 content in pig tissues and plasma biochemical parameters. Animals, 2020. 10 (9): p. 1657. Lefevre, F., et al., Colonization of a new environment by a migratory fish is conditioned by introduction history and landscape parameters: Dispersion of the brown trout (Salmo trutta L.) in the Kerguelen Islands. Abstracts/Aquaculture 272S1, 2007. 238 : p. S321. Komolka, K., et al., Determination and comparison of physical meat quality parameters of percidae and salmonidae in aquaculture. Foods, 2020. 9 (4): p. 388. Novaković, S. and I. Tomašević. A comparison between Warner-Bratzler shear force measurement and texture profile analysis of meat and meat products: A review . in IOP Conference Series: Earth and Environmental Science . 2017. IOP Publishing. Blay, C., et al., Genetic Parameters and Genome-Wide Association Studies of Quality Traits Characterised Using Imaging Technologies in Rainbow Trout, Oncorhynchus mykiss. Front Genet, 2021. 12 : p. 639223. Gonzalez-Pena, D., et al., Genome-wide association study for identifying loci that affect fillet yield, carcass, and body weight traits in rainbow trout (Oncorhynchus mykiss). Frontiers in genetics, 2016. 7 : p. 203. Ali, A., et al., Genome-wide identification of loci associated with growth in rainbow trout. BMC genomics, 2020. 21 (1): p. 1-16. Wringe, B.F., et al., Growth-related quantitative trait loci in domestic and wild rainbow trout (Oncorhynchus mykiss). BMC Genet, 2010. 11 : p. 63. Hecht, B.C., et al., Genetic Architecture of Migration-Related Traits in Rainbow and Steelhead Trout, Oncorhynchus mykis s. G3: Genes| Genomes| Genetics, 2012. 2 (9): p. 1113-1127. Liotta, M.N., et al., Antagonistic selection on body size and sword length in a wild population of the swordtail fish, Xiphophorus multilineatus: Potential for intralocus tactical conflict. Ecology and Evolution, 2021. 11 (9): p. 3941-3955. Sánchez-González, J.R., et al., Fish morphology and passage through velocity barriers. Experience with northern straight-mouth nase (Pseudochondrostoma duriense Coelho, 1985) in an open channel flume. Hydrobiologia, 2022. 849 (6): p. 1351-1366. Sánchez-González, J.R. and A.G. Nicieza, Individual differences in dominance-related traits drive dispersal and settlement in hatchery-reared juvenile brown trout. Scientific Reports, 2021. 11 (1): p. 7277. Küttner, E., et al., Genetic architecture of body weight, condition factor and age of sexual maturation in Icelandic Arctic charr (Salvelinus alpinus). Molecular Genetics and Genomics, 2011. 286 : p. 67-79. Laghari, M., et al., Mapping quantitative trait loci (QTL) for body weight, length and condition factor traits in backcross (BC1) family of Common carp (Cyprinus carpio L.). Molecular biology reports, 2014. 41 : p. 721-731. Nikoo, M., et al., Autolysis of rainbow trout (Oncorhynchus mykiss) by-products: Enzymatic activities, lipid and protein oxidation, and antioxidant activity of protein hydrolysates. Lwt, 2021. 140 : p. 110702. Yoğurtçuoğlu, B., et al., Mapping the establishment and invasiveness potential of rainbow trout (Oncorhynchus mykiss) in Turkey: With special emphasis on the conservation of native salmonids. Frontiers in Ecology and Evolution, 2021. 8 : p. 599881. Nichols, K., et al., A consolidated linkage map for rainbow trout (Oncorhynchus mykiss). Animal genetics, 2003. 34 (2): p. 102-115. Galezan, F.H., et al., Evaluating the rearing condition of rainbow trout (Oncorhynchus Mykiss) using fuzzy inference system. Aquacultural engineering, 2020. 89 : p. 102051. Ozaki, A., et al., Progress of DNA marker-assisted breeding in maricultured finfish. Bull Fisheries Res Agency, 2012. 35 : p. 31-7. Barroso, R., et al., QTL for IHNV resistance and growth identified in a rainbow× cutthroat trout cross. Aquaculture, 2007(272): p. S243-S244. Sakamoto, T., et al., Linkage analysis of quantitative trait loci associated with spawning time in rainbow trout (Oncorhynchus mykiss). Aquaculture, 1999. 173 (1-4): p. 33-43. Perry, G., et al., Characterization of rainbow trout cell lines using microsatellite DNA profiling. Cytotechnology, 2001. 37 : p. 143-151. O'malley, K., et al., Quantitative trait loci for spawning date and body weight in rainbow trout: testing for conserved effects across ancestrally duplicated chromosomes. Journal of Heredity, 2003. 94 (4): p. 273-284. Easton, A., et al., The genetic architecture of embryonic developmental rate and genetic covariation with age at maturation in rainbow trout Oncorhynchus mykiss. Journal of Fish Biology, 2011. 78 (2): p. 602-623. Rexroad, C.E., 3rd, et al., QTL affecting stress response to crowding in a rainbow trout broodstock population. BMC Genet, 2012. 13 : p. 97. Baerwald, M., et al., A major effect quantitative trait locus for whirling disease resistance identified in rainbow trout (Oncorhynchus mykiss). Heredity, 2011. 106 (6): p. 920-926. Broman, K.W., et al., R/qtl: QTL mapping in experimental crosses. Bioinformatics, 2003. 19 (7): p. 889-90. Lu, Q., et al., Consensus map integration and QTL meta-analysis narrowed a locus for yield traits to 0.7 cM and refined a region for late leaf spot resistance traits to 0.38 cM on linkage group A05 in peanut (Arachis hypogaea L.). BMC genomics, 2018. 19 (1): p. 1-10. Cao, X.-K., et al., Integrating CNVs into meta-QTL identified GBP4 as positional candidate for adult cattle stature. Functional & integrative genomics, 2018. 18 : p. 559-567. Yang, Y., et al., Large-scale integration of meta-QTL and genome-wide association study discovers the genomic regions and candidate genes for yield and yield-related traits in bread wheat. Theoretical and Applied Genetics, 2021. 134 (9): p. 3083-3109. Liu, H., et al., Major genomic regions responsible for wheat yield and its components as revealed by meta-QTL and genotype–phenotype association analyses. Planta, 2020. 252 : p. 1-22. Selamat, N. and K.K. Nadarajah, Meta-Analysis of Quantitative Traits Loci (QTL) Identified in Drought Response in Rice (Oryza sativa L.). Plants (Basel), 2021. 10 (4). Li, L., et al., Mapping QTL for leaf pigment content at dynamic development stage and analyzing Meta-QTL in rice. Euphytica, 2021. 217 : p. 1-23. Aquib, A. and S. NafisB, A Meta-Analysis of Quantitative Trait Loci Associated with Stay-Green in Sorghum. Journal of Tropical Crop Science Vol, 2022. 9 (1). Wang, Y., et al., Integrated meta-QTL and genome-wide association study analyses reveal candidate genes for maize yield. Journal of Plant Growth Regulation, 2020. 39 : p. 229-238. Leeds, T., P. Kenney, and M. Manor. Genetic parameter estimates for feed intake, body composition, and fillet quality traits in a rainbow trout population selected for improved growth . in International Symposium on Genetics in Aquaculture . 2012. Kristjánsson, Ó.H., et al., Quantitative genetics of growth rate and filet quality traits in Atlantic salmon inferred from a longitudinal bayesian model for the left-censored Gaussian trait growth rate. Frontiers in Genetics, 2020. 11 : p. 573265. Shuping, L.S., et al., The Prevalence of Viruses Related to the Production of Mussels and Oysters in Saldanha Bay: A Systematic Review. Aquaculture Journal, 2023. 3 (2): p. 90-106. Nyholt, D.R., All LODs are not created equal. The American Journal of Human Genetics, 2000. 67 (2): p. 282-288. Darvasi, A. and M. Soller, A simple method to calculate resolving power and confidence interval of QTL map location. Behavior genetics, 1997. 27 : p. 125-132. Rexroad, C.E., et al., A second generation genetic map for rainbow trout (Oncorhynchus mykiss). BMC genetics, 2008. 9 : p. 1-14. Palti, Y., R. DANZMAN, and C. Rexroad, CHARACTERIZATION AND MAPPING OF NINETEEN POLYMORPHIC MICROSATELLITE MARKERS FOR RAINBOW TROUT (ONCORHYNCHUS MYKISS). Animal genetics, 2003. 34 (2): p. 153-156. Arcade, A., et al., BioMercator: integrating genetic maps and QTL towards discovery of candidate genes. Bioinformatics, 2004. 20 (14): p. 2324-2326. Khahani, B., et al., Genome wide screening and comparative genome analysis for Meta-QTLs, ortho-MQTLs and candidate genes controlling yield and yield-related traits in rice. BMC Genomics, 2020. 21 (1): p. 294. Jacobs, A., et al., Significant synteny and colocalization of ecologically relevant quantitative trait loci within and across species of salmonid fishes. Genetics, 2017. 207 (2): p. 741-754. Veyrieras, J.-B., B. Goffinet, and A. Charcosset, MetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments. BMC bioinformatics, 2007. 8 : p. 1-16. Cabas-Lühmann, P., et al., Meta-QTL analysis and candidate genes for quality traits, mineral content, and abiotic-related traits in wild emmer. Frontiers in Plant Science, 2024. 15 : p. 1305196. Leeds, T.D. and G.M. Weber, Effects of triploidy on genetic gains in a rainbow trout (Oncorhynchus mykiss) population selectively bred for diploid growth performance. Aquaculture, 2019. 505 : p. 481-487. Bo, K., et al., QTL mapping and genome-wide association study reveal two novel loci associated with green flesh color in cucumber. BMC plant biology, 2019. 19 : p. 1-13. Varadharajan, S., Comparative genomics in teleost fish: Insights into forces driving genome evolution. 2019. Nugent, C., The Arctic charr (Salvelinus alpinus) genome: An analysis of evolutionary change after whole genome duplication . 2019, University of Guelph. Griffin, D.K., et al., Whole genome comparative studies between chicken and turkey and their implications for avian genome evolution. BMC genomics, 2008. 9 : p. 1-16. Zhou, S., et al., Dynamics of synthetic yeast chromosome evolution shaped by hierarchical chromatin organization. National Science Review, 2023. 10 (5): p. nwad073. Bobe, J., et al., The rainbow trout genome, an important landmark for aquaculture and genome evolution , in Genomics in aquaculture . 2016, Elsevier. p. 21-43. Berger, H., A. Wodarz, and A. Borchers, PTK7 faces the Wnt in development and disease. Frontiers in Cell and Developmental Biology, 2017. 5 : p. 31. Ji, J., et al., FOXP4-mediated induction of PTK7 activates the Wnt/β-catenin pathway and promotes ovarian cancer development. Cell Death & Disease, 2024. 15 (5): p. 332. Hayes, M., et al., ptk7 mutant zebrafish models of congenital and idiopathic scoliosis implicate dysregulated Wnt signalling in disease. Nature communications, 2014. 5 (1): p. 4777. Lu, L.X., et al., Construction of a genetic linkage map and QTL mapping of agronomic traits in Auricularia auricula-judae. J Microbiol, 2017. 55 (10): p. 792-799. Kleinjan, D.A., et al., Subfunctionalization of duplicated zebrafish pax6 genes by cis-regulatory divergence. PLoS genetics, 2008. 4 (2): p. e29. Tang, H., et al., Genome-wide association study reveals the genetic determinism of serum biochemical indicators in ducks. BMC genomics, 2022. 23 (1): p. 856. Maurer, A., et al., Estimating parent-specific QTL effects through cumulating linked identity-by-state SNP effects in multiparental populations. Heredity (Edinb), 2017. 118 (5): p. 477-485. Ahmad, A., et al., The piRNA protein Asz1 is essential for germ cell and gonad development in in zebrafish and exhibits differential necessities in distinct types of RNP granules. bioRxiv, 2023: p. 2023.07. 18.549483. Ganjavi, H. and C.M. Shapiro, Hypocretin/Orexin: a molecular link between sleep, energy regulation, and pleasure. The Journal of neuropsychiatry and clinical neurosciences, 2007. 19 (4): p. 413-419. Tuziak, S.M., M.L. Rise, and H. Volkoff, An investigation of appetite-related peptide transcript expression in Atlantic cod (Gadus morhua) brain following a Camelina sativa meal-supplemented feeding trial. Gene, 2014. 550 (2): p. 253-263. Larson, S.R., et al., Leymus EST linkage maps identify 4NsL-5NsL reciprocal translocation, wheat-Leymus chromosome introgressions, and functionally important gene loci. Theor Appl Genet, 2012. 124 (1): p. 189-206. Jia, J.-Y., et al., Androgen signaling inhibits de novo lipogenesis to alleviate lipid deposition in zebrafish. Zoological Research, 2024. 45 (2): p. 355. Xiao, N., et al., Fine mapping of qRC10-2, a quantitative trait locus for cold tolerance of rice roots at seedling and mature stages. PLoS One, 2014. 9 (5): p. e96046. Otis, J.P., et al., Intestinal epithelial cell caveolin 1 regulates fatty acid and lipoprotein cholesterol plasma levels. Disease models & mechanisms, 2017. 10 (3): p. 283-295. Guo, R., et al., Effects of Fat and Carnitine on the Expression of Carnitine Acetyltransferase and Enoyl-CoA Hydratase Short-Chain 1 in the Liver of Juvenile GIFT (Oreochromis niloticus). Genes, 2024. 15 (4): p. 480. Jersin, R.Å., L.R. Jonassen, and S.N. Dankel, The neutral amino acid transporter SLC7A10 in adipose tissue, obesity and insulin resistance. Frontiers in cell and developmental biology, 2022. 10 : p. 974338. Li, Y.-H. and H.-P. Wang, Advances of genotyping-by-sequencing in fisheries and aquaculture. Reviews in Fish Biology and Fisheries, 2017. 27 (3): p. 535-559. Han, P., et al., Identification and functional analysis of dual-specificity phosphatases (DUSP) genes in Japanese flounder (Paralichthys olivaceus) against temperature and Edwardsiella tarda stress. Fish & Shellfish Immunology, 2022. 130 : p. 453-461. Liang, Z., et al., Functional characterization of BAG3 in orange-spotted grouper (Epinephelus coioides) during viral infection. Fish & Shellfish Immunology, 2022. 122 : p. 465-475. You, X., et al., Construction of high-density genetic linkage maps for orange-spotted grouper Epinephelus coioides using multiplexed shotgun genotyping. BMC genetics, 2013. 14 : p. 1-8. Toubanaki, D.K., A. Efstathiou, and E. Karagouni, Transcriptomic analysis of fish hosts responses to nervous necrosis virus. Pathogens, 2022. 11 (2): p. 201. Sarais, F., et al., Characterisation of the teleostean κB-Ras family: The two members NKIRAS1 and NKIRAS2 from rainbow trout influence the activity of NF-κB in opposite ways. Fish & Shellfish Immunology, 2020. 106 : p. 1004-1013. Tables Table 1. Classification of the growth and fillet quality attributes extracted from the selected studies Trait Description of subclasses Abbreviation of subclasses Growth Body weight BW Fork length FL length L Condition factor CF Centroid size CS Fillet quality Protein content PC Fat content FC Moisture content MC Muscle yield MY Shear force SF Table 2. Summary of characterization of the selected QTL studies No. Markers Subclasses of two traits References 1 RAD, Microsatellite L 1 , CF 2 ,FL 3 ,CS 4 Hecht et al 2012 2 SNP BW 5 , MC 6 ,FC 7 AliAli et al 2020 3 SNP PC 8 ,SF 9 AliAli et al 2019 4 SNP MY 10 Salem et al 2018 5 SNP BW Reis Neto et al 2019 6 Microsatellite CF, BW Wringe et al 2010 1 : length; 2 : Condition factor; 3 : Fork length; 4 : Centroid size; 5 : Body weight; 6 : Moisture content; 7 : Fat content; 8 : Protein content; 9 : Shear force; 10 : Muscle yield Table3. Information related to the linkage groups in which meta-QTLs were identified. Maps linkage groups References 1 7 13 Consensus map Number of markers 88 86 57 present study Map Length 433.22 1219.48 144.22 Rexroad et al Number of markers 32 57 31 [52] Map Length 96.8 128.3 94.7 Hecht et al Number of markers 33 13 8 [20] Map Length 189.44 107.3 47.88 Palti et al Number of markers 51 70 42 [53] Map Length 140.6 134.3 119.8 Table 4. Characteristics of gm-QTLs obtained in this study Chr 1 gm-QTL CI 2 (cM) Peak position (cM ) L.Marker 3 R-Marker 4 QTLS 5 (AVGLOD 6 -AVGR2 7 ) CI QTLs/CIgmQTL 8 1 GM-1-1* 1.54 208.01 R29784 SSAF43NUIG 10(7.2-12.32) 11.78 1 GM-1-2 0.89 216.27 OMM1005 R39679 25(7.34-15.5) 4.87 1 GM-1-3 2.8 237.54 OMM5147 SSA16 5(6.49-9.36) 9.005 7 GM-7-1 4.23 708.08 OMM1574 OMM3048 13(3.81-8.8) 6.26 13 GM-13-1 5.25 51.44 R30335 R43229 7(4.47-7.8) 2.2 13 GM-13-2 1.7 65.63 OMM1671 R42910 33(5.6-9.6) 4.29 1 : Chromosome, 2 : Confidence interval, 3 : Left Marker, 4 : Right Marker, 5 : The number of QTLs participating in each meta-QTL, 6 : Average of LOD, 7 : Average of phenotypic variance explained by QTLs, 8 : The ratio of confidence interval QTLs to meta-QTLs. *: In the second column:G is genetic map based, M is the meta-QTL and the first and second numbers are the number of chromosomes and meta-QTLs, Table5. Summary of sequence-based meta-QTL (smQTL) intervals Chr 1 SmQTL 2 L.Marker 3 Markers in sm-QTLs interval (bp ) R-Marker 4 smQTL intervals (bp) CI (bp) 5 1 SM-1-1* SSAF43NUIG(30055974-30055865) AX-171607624 (49520794) AX-171620793(54958459) AX-89924960(54948051) OMM5237(39398251-39398189) OMM1205(30442542 - 30443013) OMM1780(48034627-48034470) R43999(46508750 - 46508817) R29784(66054734-66054801) 30055974-66054801 35998827 OMM1302(34983177-34982915) OMM1454(52583557 - 52583809) R42795(52797894 - 52797961) R13377(62949046 - 62949113) OMM1046(62887269 - 62887847) R01951(63207125 - 63207192) R39919(63350009 - 63350076) 1 SM-1-2 R39679(48579512-48579579) AX-171607624 (49520794) AX-171620793(54958459) AX-89924960(54948051) OMM1005(79240016-79240371) 48579512-79240371 30660859 OMM1454(52583557 - 52583809) R42795(52797894 - 52797961) R13377(62949046 - 62949113) OMM1046(62887269 - 62887847) R01951(63207125 - 63207192) R39919(63350009 - 63350076) 1 SM-1-3 SSA16(42287756-42287823) OMM5147(45344622-45344689) 42287756-45344689 3056933 7 SM-7-1 OMM3048(48121823-48122077) OMM1574(48634713-48635051) 48121823-48635051 513228 13 SM-13-1 R43229(45650041-45650108) AX-171603464(45731245) R30335(49582869-49582936) 45650108-49582936 3932895 AX-171603124(45744877) AX-171606422(45826267) AX-171606423(45826307) AX-171604417(45826604) AX-172555500(45900926) AX-171603120(45785431) AX-172553249(45795312) AX-174104388(45825907) AX-171606420(45826199) AX-171604416(45826876) AX-171602066(45857332) AX-172555502(45864000) AX-172555501(45876868) AX-171604665(45913986) AX-172550441(45982532) AX-172557872(46017811) AX-172548069(46017924) AX-171609134(46017963) AX-171609135(46018191) AX-171609138(46019477) AX-171609139(46046944) AX-171634202(46060142) SM-13-2 OMM1671(34913635-34914072) R36287(35.676.065 - 35676132) R42910(38542963-38543030) 34913635-38543030 3629395 OMM1646(36275479 - 36276000) Table6. Characteristics of SNPs identified in smeta-QTL intervals Chr 1 sm-QTL 2 SNP ID Subclass Gene ID Gene annotation 1 SM-1-1* AX-171607624 Fat content LOC110524937 rab GDP dissociation inhibitor beta-like AX-171620793 Moisture content LOC110525667 serum response factor-like AX-89924960 Moisture content fam160b1 family with sequence similarity 160 member B1 1 SM-1-2 AX-171607624 Fat content LOC110524937 rab GDP dissociation inhibitor beta-like AX-171620793 Moisture content LOC110525667 serum response factor-like AX-89924960 Moisture content fam160b1 family with sequence similarity 160 member B1 13 SM-13-1 AX-171603464 shear force LOC110486671 cytochrome c oxidase subunit 6A, mitochondrial AX-171606422 shear force LOC110486680 myosin regulatory light chain 2, skeletal muscle isoform-like AX-171606423 shear force LOC110486679 TBC1 domain family member 10B-like AX-171604417 shear force LOC110486679 TBC1 domain family member 10B-like AX-171606423 shear force LOC110486679 TBC1 domain family member 10B-like AX-171604417 shear force LOC110486679 TBC1 domain family member 10B-like AX-171604416 shear force LOC110486679 TBC1 domain family member 10B-like AX-171604417 shear force LOC110486679 TBC1 domain family member 10B-like AX-172555500 shear force LOC110486684 DDB1- and CUL4-associated factor 7-like AX-171604665 shear force DDB1- and CUL4-associated factor 7-like AX-171604665A shear force DDB1- and CUL4-associated factor 7-like AX-171602066 shear force 3 beta-hydroxysteroid dehydrogenase type 7-like AX-172555502 shear force 3 beta-hydroxysteroid dehydrogenase type 7-like AX-171609139 shear force LOC110486688 ATP-dependent RNA helicase DDX42-like AX-171609139B shear force LOC110486688 ATP-dependent RNA helicase DDX42-like AX-171609139A shear force LOC110486688 ATP-dependent RNA helicase DDX42-like AX-171603120 shear force chromosome 13 C16orf58 homolog AX-172550441 shear force LOC100136168 glial fibrillary acidic protein AX-172555501 shear force LOC110485207 histone-lysine N-methyltransferase SETD1A-like AX-172557872 shear force LOC110486687 LIM domain-containing protein 2-like AX-172548069 shear force LOC110486687 LIM domain-containing protein 2-like AX-171609134 shear force LOC110486687 LIM domain-containing protein 2-like AX-171609135 shear force LOC110486687 LIM domain-containing protein 2-like AX-171609138 AX-172557872 shear force shear force LOC110486687 LOC110486687 LIM domain-containing protein 2-like LIM domain-containing protein 2-like AX-172548069 AX-171609134 AX-171609135 AX-172548069 shear force shear force shear force shear force LOC110486687 LOC110486687 LOC110486687 LOC110486687 LIM domain-containing protein 2-like LIM domain-containing protein 2-like LIM domain-containing protein 2-like LIM domain-containing protein 2-like AX-171609138A AX-171609138B shear force shear force LOC110486687 LOC110486687 LOC110486687 LIM domain-containing protein 2-like LIM domain-containing protein 2-like LIM domain-containing protein 2-like AX-174104388 AX-171606420 shear force shear force LOC110486680 LOC110486680 myosin regulatory light chain 2, skeletal muscle isoform-like myosin regulatory light chain 2, skeletal muscle isoform-like AX-171606422 shear force LOC110486680 myosin regulatory light chain 2, skeletal muscle isoform-like AX-171634202 AX-171606423 AX-171604417 shear force shear force shear force LOC110486690 LOC110486679 LOC110486679 STE20-related kinase adapter protein alpha-like TBC1 domain family member 10B-like TBC1 domain family member 10B-like AX-171604416 AX-171604417A shear force shear force LOC110486679 LOC110486679 TBC1 domain family member 10B-like TBC1 domain family member 10B-like Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6178047","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":443429484,"identity":"393277cb-e6f2-4b85-8e20-fbe46876482b","order_by":0,"name":"Azadeh Shakeri","email":"","orcid":"","institution":"Shahid Beheshti University","correspondingAuthor":false,"prefix":"","firstName":"Azadeh","middleName":"","lastName":"Shakeri","suffix":""},{"id":443429485,"identity":"71d9ef5c-bc6a-49e4-9ed7-fe9773797041","order_by":1,"name":"Hossein Askari","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzElEQVRIiWNgGAWjYBACAyBmbGBg4OFn4IGIsBGtRbKBVC0MBgd4iHSYOQPz448zau7JGJ8/e0yCocaOgU/6AH4tlg1sZpIbjhXzmN3IS5NgOJbMwMaXQMBhBxjMGB+wJQC18JhJMLAdYGAj5ECDA+yfPz74l8Bj3H8GqOUfUVp4DCQ3tiXwGDDkmEkwthGnpUxyZl8Cj8SNvGSLxL5kHmIctvljz7cEe/7+swdvfPhmJyffQ0ALg/wDJE4CAwOxsTMKRsEoGAWjAB8AAMA7OE7zNFxfAAAAAElFTkSuQmCC","orcid":"","institution":"Shahid Beheshti University","correspondingAuthor":true,"prefix":"","firstName":"Hossein","middleName":"","lastName":"Askari","suffix":""},{"id":443429486,"identity":"4553d7d1-e9de-4add-8444-09d5cc2ca936","order_by":2,"name":"Masood Soltani Najafabadi","email":"","orcid":"","institution":"National Plant Genebank, Agricultural Research, Education and Extension Organization (AREEO)","correspondingAuthor":false,"prefix":"","firstName":"Masood","middleName":"Soltani","lastName":"Najafabadi","suffix":""}],"badges":[],"createdAt":"2025-03-07 11:53:25","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6178047/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6178047/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80811201,"identity":"673ba4dd-8821-4e7b-a33e-eb27c9ed1d7d","added_by":"auto","created_at":"2025-04-17 10:25:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":182806,"visible":true,"origin":"","legend":"\u003cp\u003eThe systematic review method using the PRISMA flow diagram (http://www.prisma-statement.org/ ). This diagram indicates the number of removed and selected articles in each stage. 139 articles were removed because they were duplicated. The reason for removing the articles in the first and second stages of screening (n=7968 and n=46 respectively), the articles were not related to growth and fillet quality traits or target fish species (rainbow trout) or lacked sufficient information for further analysis. The reason for exclusion in the third stage of the screening was the impossibility of identifying the position on the genetic map (n=7) or reported QTLs did not meet the defined criteria, ie cases that had LOD less than 3 after converting P-values to LOD using the formula (n=5).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6178047/v1/5ebf9b5529d5b8528e65d442.png"},{"id":80812048,"identity":"387d92b5-dd46-4079-bf3d-ffcc77a93671","added_by":"auto","created_at":"2025-04-17 10:33:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95511,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe percentage and distribution of classified QTLs into different subclasses on the chromosomes. a. \u003c/strong\u003eThe percentage of QTLs classified into each subgroup was collected by reviewing studies from 2000-2022.\u003cstrong\u003eb\u003c/strong\u003e. Distribution of QTLs attributed to each subclass on the rainbow trout chromosomes. The abbreviations for the traits subclasses are: BW: Body weight, FL: Fork length, CF: Condition factor, CS: Centroid size and L: length, PC: Protein content, MC: Moisture content, MY: Muscle Yield, SF: Shear force and FC: Fat content.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6178047/v1/9e9b6f1a42203e186089e75d.png"},{"id":80812049,"identity":"ba9b2cab-3254-495a-b537-1bb909cc5f46","added_by":"auto","created_at":"2025-04-17 10:33:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61800,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDescription of the type of markers associated with QTLs and their distribution over the rainbow trout chromosomes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ea.\u003c/strong\u003eThe number of markers associated with growth and fillet quality employed in this investigation\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eb.\u003c/strong\u003eThe distribution of three marker types on the chromosomes. Red, yellow, and purple colors represent microsatellite, RAD, and SNP, respectively\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ec.\u003c/strong\u003eThe distribution of SNPs associated with growth and fillet quality in the 1Mb window size of the rainbow trout chromosome was used in this investigation (SNPs selected to perform meta-qtl analysis). The color scale, from green to red, shows increased marker density.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6178047/v1/5e8a5c9a25d9ff074db71ac0.png"},{"id":80811209,"identity":"95a3ea4c-eaee-4b8f-87bf-96e386d81c2b","added_by":"auto","created_at":"2025-04-17 10:25:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":101949,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFeatures of the identified QTLs. a\u003c/strong\u003e.Violin plot showing phenotypic variation explained by QTLs associated with growth and fillet quality on each chromosome. \u003cstrong\u003eb.\u003c/strong\u003e The distribution of the LOD values of the selected QTLs. \u003cstrong\u003ec\u003c/strong\u003e. The difference between the number of QTLs obtained from the selected studies (blue color) and those that passed the criteria and were subjected to the meta-analysis (pink color).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6178047/v1/f64da2a1d2655efc03cd5225.png"},{"id":80813462,"identity":"9e9b8718-8615-45c2-8e58-e7c0690b7c2f","added_by":"auto","created_at":"2025-04-17 10:41:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":226248,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe physical position of consensus genetic map markers (sequence map). \u003c/strong\u003ethe physical position of the markers on the chromosomes that had meta-QTLs after analysis was shown.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6178047/v1/14d4a837ef604fb3dd7059a9.png"},{"id":80812046,"identity":"7c483331-9c7c-444a-bb77-1c5dfa1d4fe2","added_by":"auto","created_at":"2025-04-17 10:33:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":57918,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe chromosomal locations of gm-QTLs.\u003c/strong\u003e red, green, and blue vertical lines indicate meta-QTL1, meta-QTL2, and meta-QTL3 on each chromosome (Chr), respectively. The length of these lines indicates the genetic distance of flanking markers with the detected meta-QTLs.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6178047/v1/6acca5fc47ef97a831ba78bc.png"},{"id":80811207,"identity":"f997e242-c73d-4dee-b9ef-1509a5117079","added_by":"auto","created_at":"2025-04-17 10:25:00","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":216020,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of GO enrichment analysis of candidate genes\u003c/strong\u003e \u003cstrong\u003ea.\u003c/strong\u003ebiological processes of all gene symbols\u003cstrong\u003e b.\u003c/strong\u003enetwork of GO pathways.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6178047/v1/e4b86c3524d5c053a5e9e80d.png"},{"id":80811215,"identity":"9c30f888-c0ff-4355-898e-a2917a9f3879","added_by":"auto","created_at":"2025-04-17 10:25:01","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":262886,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCircos plot showing the summary of the information of selected QTLs related to growth and fillet quality and results of Meta-QTL analysis.\u003c/strong\u003e Concentric circles from outside to inside indicate the length of 13 rainbow trout chromosomes (cM) that have QTLs related to fillet growth and quality, the green part indicates chromosomes with meta-QTLs. The second circle shows the position of meta-QTLs (red vertical lines) and the names of markers linked to them. The bandwidth represents the confidence interval (ci) associated with each meta-nucleotide. In the third circle, blue horizontal lines indicate the position of early QTLs. The green dots in the fourth circle show the phenotypic variance explained by each QTL (PVE value), this section is divided into 4 layers, and the placement of green dots in the outer layers indicates an increase in the phenotypic variance. The central circle shows the reduction of the CI of the MQTL relative to the CI of the primary QTLs with red circles in different layers.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6178047/v1/ebb947c21d17f6a221fa3223.png"},{"id":81797483,"identity":"efbebb58-7c4c-4793-95bc-530ee63772bb","added_by":"auto","created_at":"2025-05-02 04:16:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3303195,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6178047/v1/e486ef77-12d7-4855-a035-e0820d4b6fbf.pdf"},{"id":80811203,"identity":"21868657-920b-4b7b-8cbb-4888613d6e43","added_by":"auto","created_at":"2025-04-17 10:25:00","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":37845,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6178047/v1/6df27b461b0234e37c1fde47.xlsx"},{"id":80811199,"identity":"0e65be98-4f12-4664-abca-e9ac6455bd8a","added_by":"auto","created_at":"2025-04-17 10:25:00","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":14968,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6178047/v1/456e3f9f33ddbdedfde3ba51.xlsx"},{"id":80812045,"identity":"364d4d73-5232-424c-8ef9-bc737d4e9e1e","added_by":"auto","created_at":"2025-04-17 10:33:00","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":39441,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6178047/v1/8d03c40256efa457d403b772.xlsx"},{"id":80811211,"identity":"0a6f90a0-2d41-417c-bfbd-a2e6ee831bb5","added_by":"auto","created_at":"2025-04-17 10:25:01","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":123904,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6178047/v1/085c56cd5ded056369c44956.xlsx"},{"id":80812051,"identity":"b0ecfd33-4a64-44ba-985f-7843d2dd3788","added_by":"auto","created_at":"2025-04-17 10:33:01","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":15683,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfile5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-6178047/v1/527e925be6e6a46f2cba1a68.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of breeders’ smQTL and candidate genes associated with growth and fillet quality in Rainbow Trout (Oncorhynchus mykiss) with meta-analysis of QTLs","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGrowth in companion with feed conservation ratio (FCR) is the most important economic trait in aquaculture species and quality is effective on consumer satisfaction and makes the aquaculture industry profitable. Considering that aquaculture has a special place in global nutrition, the selection of economically important aquaculture species with high genetic potential for growth and quality constitutes one of the crucial factors in the food supply worldwide[\u003cspan additionalcitationids=\"CR2 CR3 CR4 CR5 CR6 CR7 CR8 CR9 CR10\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFish is a rich source of protein and fat with polyunsaturated fatty acids. Therefore, it plays an important role in a healthy life system and is considered a suitable option for a healthy diet [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Although fish is known as the best source of healthy fats. However, too much fat reduces the firmness of the fillet and affects its taste. reduces the value of the fillet and is very influential in the choice of consumers[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The firmness of the fillet is determined by measuring the maximum force required to divide the meat (shear force)[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Accordingly, in improving the quality of fillets, it is very critical to identify the QTLs that control the protein content, fat content, and shear force [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In addition, since high humidity leads to rapid spoilage of fish fillets, identifying areas controlling fillet moisture content has been considered in research [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Concerning growth, in addition to body weight and body length, condition factor (weight to length ratio) and centroid size (analysis of the body shape by estimating through the square root of the sum of the squared distances of each specific point to the center) are of interest and their related QTLs have been reported in different studies[\u003cspan additionalcitationids=\"CR20 CR21 CR22 CR23 CR24\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e) is one of the most frequently farmed cold water fish species in the world [\u003cspan additionalcitationids=\"CR27 CR28\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. So, improving these traits in rainbow trout is very important, and in recent years, extensive studies have been conducted to identify the loci of these quantitative traits as a prerequisite for marker-assisted selection for breeding programs [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan additionalcitationids=\"CR31 CR32 CR33 CR34 CR35 CR36\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] The effective use of the marker-assisted selection (MAS) method is based on the principle of achieving stable quantitative trait loci (QTLs) on chromosomes; Therefore, despite the value of QTL mapping performed about the growth and fillet quality of rainbow trout in different studies, the QTLs identified in individual studies will not be effective in marker selection due to different genetic backgrounds and different environmental conditions[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Meta-QTL analysis is a powerful technique that identifies useful concordant meta-QTLs for breeding purposes by consensus of QTLs from different studies[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Accordingly used by many researchers to identify molecular markers associated with increased yield, diseased resistance, and biological and non-biological stresses in crops such as wheat[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], rice[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], sorghum [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and maize[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. To the best of our knowledge, there is no report on the analysis and interpretation of the results of the meta-QTL analysis for the growth and fillet quality of rainbow trout. Since it has been found that growth and fillet quality are genetically correlated [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Therefore, in this study, attempts were carried out to identify the genomic regions responsible for growth and fillet quality in rainbow trout. To this end, meta-QTL analysis was performed on the data gathered from individual QTL to allocate the growth and fillet quality traits to genomic regions harboring large effects and meanwhile small confidence intervals (CI).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eliterature review and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eclassification of the selected QTLs related to the growth and fillet quality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter performing a comprehensive review from 2000 to 2022 on Google Scholar and PubMed (Figure 2), and taking into consideration explained phenotypic variance (R\u003csup\u003e2\u003c/sup\u003e), the log odds ratio (LOD score) and the availability of linkage map information as criteria, a total of six articles (original study) were found to cover growth and fillet quality of Rainbow Trout and thus were selected for further analysis. These articles collectively contained 308 QTL related to growth and fillet quality traits which were categorized into 10 subclasses (Table 1). The phenotypic variation explained (PVE) by each QTL, position of flanking markers, LOD, and types of trait are presented in supplementary file 1.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eThe QTLs were unequally allocated to the subclasses and chromosomes. The marker types used in each original study and the traits subclasses are summarized in Table 2.\u003c/p\u003e\n\u003cp\u003eThe highest number of QTLs was for subclasses of body weight and protein content accounting for 34% and 31% of the 308 identified QTLs, respectively (Figure 2-a). QTLs related to body weight were mainly located on chromosomes 2, 4, 8, 9 and 13, while protein content QTLs were distributed on chromosomes 1 and 7 (Figure 2-b).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDistribution of marker types associated with the QTLs across the chromosomes\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThree types of markers, that is microsatellites, SNPs, and RADs , were found to be employed in the original studies to identify QTLs (Figure 3). SNP markers were the most abundant markers although most SNPs were located on chromosomes 1, 8 and 13. The highest SNP density was found at 1 Mb intervals on chromosomes 8, 1, and 4. Microsatellite and RAD markers were accumulated in chromosomes 2, 4, 13,14, and 21 respectively(Figures 3-b and c).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFeatures of the selected QTLs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSome features of the reported QTLs in the literature and those selected in the present investigation are shown in Figure 4-a,b. The comparison of total QTL found in the literature and those submitted to meta-QTL analysis is summarized in Figure 4-c. As mentioned before, the availability of the genetic map, marker information, and the LOD threshold were important factors in the decision to include or not to include QTLs in the meta-analysis. Although the reported QTLs related to growth and fillet quality were distributed on 14 chromosomes, only QTLs located on 7 chromosomes (1, 2, 7, 8, 12, 13, and 21) could be analyzed. For most of the selected/reported QTLs, the average phenotypic variation explained (PVE) centered around 9.8%. \u0026nbsp;QTLs located on chromosomes 4, 1, and 13 had the biggest PVE value, respectively. The status of the LOD corresponding to the reported QTLs is shown in Figure 4-b. One of the determining factors in our study was \u0026nbsp;. As is shown in Figure 4-b, most of the LODs reported for QTLs were in the range of 4 to 10 with a standard deviation of 2.5.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQTLs projection on the\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econsensus genetic map\u0026nbsp;and Meta-QTL analysis\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA consensus genetic map containing 1948 markers with a density of 0.2 markers per cM, was created by merging three genetic maps containing 1119, 1421, and 587 markers[20, 52, 53]. Description related to map length and the number of markers on the linkage groups in which meta-QTLs were identified are summarized in Table 3.\u003c/p\u003e\n\u003cp\u003eThe initial projection of QTLs on the consensus genetic map was based on the peak position of QTL flanking markers (microsatellite and RAD) and CIs. In order to estimate on the linkage map the position of the SNPs associated with growth and fillet quality traits, the following steps were undertaken: First the physical position of the markers (microsatellite and RAD) was found on the reference genome of rainbow trout. Through this step, the physical position for 46 out of 88 markers located on chromosome 1 of the consensus genetic map was determined on the Rainbow trout reference genome. Moreover, the position for 42 out of 86 markers and 15 out of 56 markers on chromosomes 7, and 13, respectively, were determined on the reference genome (Figure 5, supplementary file 2). \u0026nbsp;Then, using one of the two methods A and B mentioned in the material and method chapter, SNPs associated with the growth and fillet quality were projected on the consensus genetic map. Finally, based on the distribution of QTLs reported in different original studies the chromosomes were analyzed using\u0026nbsp;Biomercator v.4.2.3 software (Figure 4. c). The QTLs that could be analyzed based on the studied indices were distributed on chromosomes 1, 2, 7, 8, 12, 13 and 21. After meta-QTL analysis, after examining the formed clusters, only 3, 1, and 2 meta-QTL were identified on chromosomes 1, 7, and 13, respectively. Figure 6 shows the number and position of identified meta-QTLs on the linkage map.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCalculating the rate of change in the CI value of gm-QTLs compared to that of primary QTLs (CI reduction rate, CIrr) showed that gm-QTL1 (GM-1-1) on chromosome one and gm-QTL1 (GM-13-1) on chromosome 13 had the lowest CIrr, of 11.78 and 2.2, respectively. Information about the meta-QTLs related to each chromosome is listed in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of smQTLs, markers and SNPs associated with growth and fillet quality in smQTL intervals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn addition to identifying the position of meta-QTLs on the consensus genetic map, using flanking markers of identified meta-QTLs, meta-QTLs\u0026apos; position on the reference genome of rainbow trout was determined. The mean physical CI of smQTLs was 12.96 Mb, ranging from 0.5 Mb, (smQTL7) to 35.9 Mb (smQTL1_1). Although identified gmQTLs on chromosome one did not overlap on the genetic map, smQTL1_1 and smQTL1_2 at position 48579512-66054801 with a confidence interval of 17.4 Mb and smQTL1_1 and smQTL1_3 at position 42287756-45344689 with a confidence interval of 3Mb were overlapped. Based on the created physical map (Figure 5), 11 markers (OMM5237, OMM1205, OMM1780, R43999, OMM1302, OMM1454, R42795, R13377, OMM1046, \u0026nbsp;R01951, \u0026nbsp; R39919) and two markers (OMM1646 and R36287) found in intervals of two flanking markers of meta QTL1 on chromosome 1 and meta QTL2 on chromosome 13, respectively (Table 5).The possibility of the presence of SNPs associated with growth and fillet quality in smQTL intervals was investigated using the reported position of the SNPs in the original studies (Table 5). Three SNPs (AX-171607624, AX-171620793, AX-89924960) was detected in the overlapped position of smQTL1 and smQTL2 chromosome one. 23 SNPs were identified in smQTL1of chromosome 13. information related to these coding/functional SNPs is summarized in Table 6.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetection of candidate genes for growth and fillet quality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the position of smQTLs (position of two flanking markers related to gmQTLs on reference genome), 264 gene symbols were identified in the ensemble site and shiny GO. The identified SNPs in the smQTLs region were located in 14 genes (Table 6).\u003c/p\u003e\n\u003cp\u003eThe highest number of Candida genes was detected on chromosome 1. Separately, 140, 153, and one candidate genes were located in m_1_1, m_1_2, and m_1_3, respectively. 103 candidate genes were identified in overlap m_1_1 and m_1_2. One and 73 Candida genes were located On chromosomes 7 and 13 (m_13_1), respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene ontology (GO)\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eenrichment analysis of identified candidate genes\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe identification of common characteristics of 264 candidate gene symbols regarding the biological process was performed through gene ontology (GO) analysis. The results showed that candidate genes were assigned to 284 biological process pathways which are summarized in Figure 7 and supplementary File\u0026nbsp;3.\u0026nbsp;The most significantly enriched GO terms were related to, small molecule binding (47 genes), organic substance biosynthetic process (42 genes), anion binding and cellular response to stimulus (41 genes), cellular biosynthetic process, membrane-bounded organelle, nucleoside phosphate binding, hydrolase activity, nucleotide binding, intracellular membrane-bounded organelle (40 genes).\u003c/p\u003e\n\u003cp\u003ethe results showed that genes in pathways of developmental process, multicellular organism development, anatomical structure development, multicellular organism development, multicellular organismal process, and system development were interconnected (Figure 7-b).\u003c/p\u003e\n\u003cp\u003eThe summary of the results obtained in the present investigation is illustrated in Figure 8. The results are shown in different layers: Chromosomal distribution of 308 QTL related to growth and fillet quality traits and the phenotypic variance explained by each QTL, number of identified gm-QTLs, the corresponding CI of each gm-QTL and CI reduction rate compared to the CI of the initial QTLs.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cstrong\u003eCharacteristics of QTLs associated with growth and fillet quality and meta-QTL analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIncreasing the growth rate makes the fish reach the desired weight at a younger age and reduces the costs per kilogram of fish produced, it is very beneficial from an economic point of view and is very important in breeding programs [48]. In addition to the importance of growth on profitability, high fillet quality increases the value of the produced product and increases consumer satisfaction. Therefore, many studies have considered identifying regions controlling these quantitative traits (Figure 1). Quantitative traits are complex and the identification of their precise control locus is affected by genetic background, population size, and environmental conditions. So hence the reliability of QTLs on individual studies for use in MAS is not guaranteed. This issue is the main factor limiting the use of identified QTLs in marker-assisted selection and molecular breeding programs[58] . Based on the study of Leeds et al.[59], the increase in growth rate is genetically related to the increase in fillet quality [47]. Because identifying common loci that control both traits will play an effective role in genetic improvement, in this study, we used meta-analysis to pool QTLs for identify stable QTLs associated with both traits. The literature review results showed an obvious difference in the type, number of markers, and population size in the QTL mapping studies. QTLs detected by microsatellite markers had a small population size and a wide range of CI in the respective studies. In contrast, QTL mapping studies conducted with SNP markers had a larger sample size and high phenotypic variance. Therefore, the integration of QTLs from fillet quality studies that had a large sample size and were identified by coding/functional SNPs with growth-related QTLs that mostly had very large CIs and were identified by microsatellite and RAD markers was important and valuable in two ways: First, it led to the identification of stable QTLs effective for fillet growth and quality, which are two important economic traits in aquaculture. Second, due to the smaller CI of SNP-detected QTLs compared to other studies and the high phenotypic variance, it helped us to reach a small CI. We used the term subclass for convenience in distinguishing between the QTLs belonging to two traits. Each of the QTLs related to body weight, body length, condition factor, fork length, and, centroid size are included in a set under the title of subclasses related to growth. Due to the lack of sufficient information on the reported QTLs, the presence of LOD less than 3, or the lack of consistency between markers linked with QTLs and those located on the consensus genetic map, only QTLs subclasses of body weight and length were used in the analysis. Regarding QTLs related to fillet quality, among the subclasses related to fat content, moisture content, muscle yield, shear force, and protein content, only protein content and shear force could be analyzed. The results showed that chromosomes 2, 4, 8, 9, and 13 were enriched for body weight, chromosomes 1, 3, 7, and 1 for protein content, and chromosomes 8 and 13 for shear force. This distribution on specific chromosomes may show the effect of inter-chromosomal rearrangement, which consists of the formation of some chromosomes as mosaics of different chromosomes, after the tetraploidization process (Salmonid-specific fourth round of whole genome duplication, Ss4R). This is probably due to the presence of a genetic protection system of the regions controlling the desired traits during evolution, which has caused the preservation and stabilization of these regions after chromosomal rearrangement in different ecotypes and species[60-65].\u003c/p\u003e\n\u003cp\u003eConsidering that meta-QTL analysis requires the integration of QTLs from more than one study [65, 66], after meta-QTL analysis, clusters composed of QTLs from one study were not reported in the results. Finally, according to the QTLs that can be analyzed, six meta-QTLs related to the respective traits were identified in three chromosomes. In our study, the identified meta-QTLs had an average of 6.39-fold (range 2.2 cM to 11.78 cM) narrower CIs than the original QTLs, as expected, meta-QTLs analysis showed a strong effect on reduction CI had all meta-QTLs[67, 68].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of new\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003egenomic regions\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003econtrolling the growth and fillet quality and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ebreeders\u0026rsquo; MQTLs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the present study, we identified six gm-QTLs in three chromosomes using meta-QTL analysis based on reported QTLs and the consensus of three genetic maps with high density of microsatellite and RAD markers. Separately, three loci controlling with an average phenotypic variance of 12.39% on chromosome 1, one locus controlling with 8.8% phenotypic variance on chromosome 7, and two loci with an average phenotypic variance of 8.7% on chromosome 13 were identified. \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThree proposed criteria to determine the most reliable and robust gmQTLs in the different meta-QTL analyses for use in breeding programs (breeders\u0026rsquo; gmQTLs) include: 1) Small CI: less than 2 cM [68-70] or 2.5 cM [71], 2) initial QTL numbers (overlapping QTLs): 2\u0026minus;7 original QTLs [72] or at least three original QTLs [71] or five original QTLs [73] and or six original QTLs [69, 74], 3) PVE more than 10% [68, 70-72, 75]. Based on these criteria, we identified two \u0026ldquo;breeders\u0026rsquo; gmQTL\u0026rdquo; (gmQTL1-1 AND gmQTL1-2 with10 and 25 original QTLs, CI 0.89 and 1.54 cM and PVE 12.32% and 15.5%, respectively) for growth and fillet quality (Table 4). These genomic regions were located on chromosome one, so this chromosome probably has a stronger role in the growth and fillet quality traits. Therefore, it was possible that the associated flanking markers (R29784, SSAF43NUIG, OMM1005 and R39679) with these genomic regions would be more effective for use in MAS. Based on the reference sequence-based map (physical map) created in this study, it was found that the order of the markers in the consensus genetic map and the physical map do not match. Therefore, it was essential to identify the location of meta-QTLs identified in the consensus genetic map (gmQTLs), on the reference genome (smQTLs), as well as to identify markers located in smQTL intervals. As expected, there was a significant difference in the position, markers, and confidence interval of identified gmQTLs and smQTLs. Although the lowest CI was observed in gmQTLs identified on chromosome 1 and chromosome 13 (gmQTL13-2), they were the most distant in the physical map and the lowest CI was observed in smQTL7. Large confidence interval in smQTLs may be due to the unavailability of the number of original QTLs with correct and sufficient information in the respective region and as a result of poor overlap between the analyzed QTLs, gmQTLs being located in a region with depressed recombination, or a mismatch between the position of markers of consensus genetic map and physical map.\u003c/p\u003e\n\u003cp\u003eResults showed the three identified gmQTLs on chromosome 1 overlapped at position 48579512-66054801(smQTL1_1 and smQTL1_2) with a confidence interval of 17.4 Mb and position 42287756-45344689 with a confidence interval of 3Mb (smQTL1_1 and smQTL1_3). Considering that 85.41% of the key candidate genes (table 5) identified in chromosome one and SNPs related to fillet quality (AX-171607624, AX-171620793, AX-89924960) were located in the overlapping region of smQTL1_1 and smQTL1_2, as a result the consensus of smQTL1_1 and smQTL1_2 is reasonable. Because the gene and SNP were not identified in the interval of smQTL1_3, though on chromosome one three gmQTLs were identified, one smQTL can be introduced as breeders\u0026rsquo;smQTL. \u0026nbsp;In addition to the two flanking markers in gmQTLs described earlier, locus-specific markers in the interval of smQTLs can be introduced as high-quality markers for use in molecular breeders (table 5). Considering that microsatellite markers are more user-friendly, six microsatellite markers include OMM5237, OMM1205, OMM1780, OMM1302, OMM1454, and OMM1046 in the interval of smQTL chromosome one, it seems that they are a good option for MAS.\u003c/p\u003e\n\u003cp\u003eAlthough the lowest confidence interval was observed in smQTL chromosome 7, effective key genes were not identified in this locus, so this region does not have a special role in controlling growth and fillet quality. The results showed that 23 SNPs related to shear force were present in smQTL1 of chromosome 13, confirming this locus\u0026apos;s effective role in fillet quality. Due to the lack of identification of growth-controlling genes in this locus and the identification of candidate genes related to growth and fillet quality in smQTL2. Therefore, among the two smQTL identified in chromosome 13, smQTL2 is an important genomic locus for controlling both traits and can be introduced as a\u0026nbsp;breeders\u0026rsquo;smQTL.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of candidate genes identified in smQTLs interval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe identified a total of 265 candidate genes in Meta-QTL regions (supplementary file 4). The results of go enrichment and review of previous studies showed that these genes were enriched in different pathways that were directly or indirectly related to growth and fillet quality (supplementary file 5). Among the most important identified candidate genes effective in growth and development, we can mention, PTK7, PAX6, Asz1, dusp6, CAPZA2, and CKAP5. Protein tyrosine kinase 7 (PTK7) plays a central role in morphogenesis [66, 67] . Hayes et al [68] found that ptk7 mutants have vertebral abnormalities. CKAP5 is important in oocyte maturation and early embryo development [69]. PAX6 encodes a transcription factor that is effective in the development of the eye, brain, olfactory system, and pancreas [70]. The researchers found that CAPZA2 is an essential gene in the general and skeletal development of the head [71]\u0026nbsp; and dusp6 is requirement in embryogenesis [72]. Ahmad et al [73]\u0026nbsp; showed that Asz1 is essential for spermatogenesis and oogenesis germ cell and gonad development. Yu et al found the pleiotropic effect of the rrp12 gene on both growth and swimming performance in a hypoxic environment. Previous studies have shown that HCRT stimulates appetite, which can affect feeding frequency and food intake, and thus growth [74, 75]. Important genes affecting fat metabolism and oxidation were also identified in the identified smQTLs of chromosomes 1 and 13. Since the fillet quality is affected by the amount of protein and fat, as a result, these genes control fillet quality. In the study of the relationship between fillet firmness and the expression of genes of different functional groups in salmon skeletal muscles by Larson et al[76], they was found that Dnajb9a is in the group of genes with positive regression with fillet firmness.\u0026nbsp;Jia et al\u0026nbsp;[77], showed that in cyp17a1-/- zebrafish strains, the lipid synthesis pathway was significantly activated and caused an increase in visceral adipose tissue and fat content. CYB5R2 is involved in various biological processes such as electron transport, oxidation-reduction, lipid metabolism, fatty acid desaturation and/or elongation, and cholesterol biosynthesis\u0026nbsp;[78]. CAV1 has roles in cellular lipid processing and systemic lipid metabolism\u0026nbsp;[79]. ECHS1 is the key enzymes that regulate lipid metabolism \u0026nbsp;[80]\u0026nbsp;and Slc7a10 prevents excess fat storage and fat hypertrophy\u0026nbsp;[81]. The results showed that the identified genes, in addition to growth and fillet quality, were also effective in response to bacteria, viruses, and stress. Since the genetic correlation between different traits is due to genes that are close together or exist pleiotropic genes[76, 77]. Therefore, the identified meta-QTL regions control the important economic traits of growth, fillet quality, disease and stress resistance. The most important disease and stress resistance genes are: DUSP, which is a pleiotropic gene and is effective in growth, development, temperature stress, and response to bacteria[82]\u0026nbsp;[83]. Liang et al [84]\u0026nbsp;show that EcBAG3 can affect mediating cell adaptability to stress. HSPA12A has been suggested to possess cytoprotective functions in response to various stressors, especially viruses or bacterial pathogens, toxic metals, and heat shock[85]. Aeromonas salmonicida subsp salmonicida (Ass) and nervous necrosis virus are one of the most important bacterial and viral diseases in salmon farms, and their prevention and control are very important economically. In the present study, Nkiras and Dhx58 were identified in the smQTL genomic region of chromosome 13, which were introduced in previous studies as the primary activator genes of salmonid fish immunity against Aeromonas salmonicida infections and one of the DEGs responding to the immune system following NNV infection, respectively\u0026nbsp;[86]\u0026nbsp;[87].\u003c/p\u003e\n\u003cp\u003eConsidering that the results indicated the possibility of the effective role of these candidate genes not only in the growth and fillet quality, but also in the response to bacteria and viruses and adaptation to stress conditions. Therefore, according to the meta-QTL analysis performed on based on the available data, two chromosomes 1 and 13 play a key role in the economic traits of rainbow trout. Although the function of Candida genes was determined to some extent by using previous studies, the exact role of these genes will be determined by experimental investigations in rainbow trout.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe results of this study showed that meta-QTL analysis can validate the QTLs of previous studies and using consensus genetic map and sequence map, it provides the possibility to identify the most stable QTLs. We identified two breeders\u0026rsquo; sm-QTLs (smQTL1 and smQTL13-2) with high accuracy associated with growth and fillet quality. Candida genes identified in this research, in addition to growth and fillet quality, were effective in responding to bacteria, viruses and stress conditions. Given that in the aquaculture industry, disease and stress conditions management are one of the biggest challenges. Therefore, it can be concluded that the meta-QTLs identified are effective in the important economic traits of rainbow trout. It seems that the flanking and interval markers associated with these genomic regions, are likely to play an effective role in advancing the objectives of the breeding program for economically important traits with MAS.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eLiterature review\u003c/h2\u003e \u003cp\u003eExhaustive literature review using PubMed syntax \"(Rainbow trout [tiab] AND QTL [tiab] OR QTLs [tiab] OR \"Quantitative Adjective Locations \" [tiab] AND 2000/1/1: 2022 [db] )\" in NCBI ( \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubmed.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubmed.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ) was performed (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The review was completed in Google Scholar (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://scholar.google.com/\u003c/span\u003e\u003cspan address=\"https://scholar.google.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using the keywords \u0026ldquo;QTL\u0026rdquo;, \u0026ldquo;Rainbow trout\u0026rdquo;, \u0026ldquo;Salmonide\u0026rdquo; and \u0026ldquo;Meta-QTL Analysis\u0026rdquo; for the period 2000 to 2022.\u003c/p\u003e \u003cp\u003eThe number of articles screened and evaluated based on the reviewed criteria such as LOD value and the commonality of linked markers with desired traits reported in QTL mapping studies with consensus genetic map markers (the possibility of identifying the position of flanking markers) or the possibility of identifying their position on the physical map, can be seen in the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) flow diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003e)[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCharacteristics of studies and data collection\u003c/h2\u003e \u003cp\u003eBecause meta-QTL analysis allows for the integration of data gathered from various genetic backgrounds, after selecting the studies related to the investigation of quantitative traits of rainbow trout, the analysis was executed considering the following criteria:\u003c/p\u003e \u003cp\u003e1) QTLs related to growth and fillet quality\u003c/p\u003e \u003cp\u003e2) proportion of phenotypic variance explained by each QTL (PVE or R\u003csup\u003e2\u003c/sup\u003e (\u003c/p\u003e \u003cp\u003e3) information on the position of the flanking markers or the QTL-linked markers on the linkage map and their physical position on the genetic map\u003c/p\u003e \u003cp\u003e4) Logarithm of odds score (LOD) or p-value for each QTL\u003c/p\u003e \u003cp\u003e5) population type and size\u003c/p\u003e \u003cp\u003eLOD and PVE were required for each QTL to perform the meta-QTL analysis. When this information was not available, P-values were converted to LOD adopting Nyholt [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. When genotypic variance explained by each QTL (GVE) was available, LOD and the PVE were calculated using the following formulae:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:LOD=-\\frac{N}{2}{\\text{log}}_{10}(1-GVE)$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere GVE and N represent genetic variance explained by each QTL and population size, respectively.\u003c/p\u003e \u003cp\u003eand\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:PEV={H}^{2}\\:GVE$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere H\u003csup\u003e2\u003c/sup\u003e stands for broad-sense heritability.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eConfidence interval (CI) of QTL locations\u003c/h2\u003e \u003cp\u003eBased on the formula proposed and explained for F\u003csub\u003e2\u003c/sub\u003e and backcross populations by Darvasi and Soller[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e], the confidence interval (CI) was calculated to estimate 95% of the CI of each QTL.\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\:CI=\\begin{array}{c}\\frac{530}{{NR}^{2}}\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:\\:for\\:\\:\\:\\:{F}_{2}\\:and\\:backcross\\:populations\\\\\\:\\:\\:\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere N and R\u003csup\u003e2\u003c/sup\u003e stand for population size and percentage of phenotypic variation explained by the identified QTLs, respectively.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eConsensus genetic map construction and QTL projection\u003c/h2\u003e \u003cp\u003eA consensus genetic map employing microsatellite and restricted locus-associated DNA (RAD) markers was constructed by integrating three published genetic maps [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e] using Biomercator v.4.2.3 software[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Because the consensus genetic map consisted of microsatellite and RAD markers, the QTLs identified with these two markers were easily predicted by adding and subtracting half of the CI to the peak position of the flanking markers (middle position of the flanking markers) of QTLs on the consensus genetic map.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003ePosition estimation of growth and fillet quality-related SNPs on the consensus genetic map\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe position of each growth- and/or fillet quality-related SNP on the consensus genetic map (in cM) was estimated, based on position of the other mapped markers, during two steps: in the first step, the sequences of all the mapped markers (RAD or Microsatellite) on the consensus map were surfed and found on the physical map of rainbow trout by blasting (BLAST N and primer-BLAST) them against the rainbow trout reference genome. In the second step, the position of the traits-related SNPs on the genetic map was figured out. In this step and upon surrounding the SNP(s) by either one or two adjacent markers, one of the following methods was undertaken: Method a) If SNP was surrounded by two markers spaced 1\u0026ndash;4 Mbp, the arithmetic mean of the flanking marker position on the genetic map (peak position) was considered as the corresponding position of the SNP[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Method b) When there was only a single marker in the vicinity of SNPs, the position of the SNPs on the linkage map was estimated based on their positions on the physical map, using the tump of the rule of Jacobs et al [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]: one Mbp on the physical map is corresponding to one cM on the linkage map.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eMeta-QTL analysis\u003c/h2\u003e \u003cp\u003eMeta-QTL analysis was performed for growth and fillet quality traits by Biomercator v.4.2.3. Based on the Biomercator user guide, two input files (consensus genetic map information and QTL information) were prepared and after uploading, QTLs were projected on the consensus genetic map. Among the models presented, based on the Akaike Information Criterion (AIC), the model with the lowest AIC value was selected as the best model to provide the number and position of meta-QTL(real QTL) [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eProjection of identified meta-QTL onto the reference genome\u003c/h2\u003e \u003cp\u003eAfter identifying the position of meta-QTLs on the genetic map (gmQTL), using flanking markers linked with the gmQTL, their position (sequences of flanking markers were blasted against the reference genome) was determined on the rainbow trout reference genome (sequence-based meta-QTL).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of markers in smQTL intervals\u003c/h2\u003e \u003cp\u003eAccording to the physical position of the sequence-based meta-QTL (smQTL), the markers located in smQTL intervals were identified using the sequence map. The presence of SNPs associated with growth and fillet quality in smQTL intervals was investigated based on the position of the SNPs reported in the original QTL mapping studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of candidate genes\u003c/h2\u003e \u003cp\u003eCandidate genes harboring in the identified smQTL were figured out using the BioMart section of the Ensemble site (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://asia.ensembl.org/index.html\u003c/span\u003e\u003cspan address=\"https://asia.ensembl.org/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In addition, according to the SNP ID located in the smQTL region, the gene ID and annotation associated with each SNP were extracted from the relevant GWAS study. Previous studies were reviewed in order to identify genes with specific functions related to the respective traits.\u003c/p\u003e \u003cdiv id=\"Sec23\" class=\"Section3\"\u003e \u003ch2\u003eGO enrichment analysis of the candidate genes\u003c/h2\u003e \u003cp\u003eThe GO enrichment analysis was done by ShinyGO 0.80 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioinformatics.sdstate.edu/go/\u003c/span\u003e\u003cspan address=\"http://bioinformatics.sdstate.edu/go/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article [and its supplementary information files\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the Hellfork Food Company for providing us financial (Grant number \u0026quot;1212HF\u0026quot;) and technical supports.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026ldquo;Conceptualization, H.A, M.S.N; Methodology, H.A, A.S, M.S.N; Software, A.S; Validation, H.A, M.S.N, A.S; Formal Analysis, H.A, M.S.N, A.S; Investigation, H.A, A.S; Resources, H.A, M.S.N, A.S; Data Curation, A.S; Writing \u0026ndash; Original Draft Preparation, A.S; Writing \u0026ndash; Review \u0026amp; Editing, H.A, M.S.N; Visualization, A.S; Supervision, H.A; Project Administration, H.A; Funding Acquisition, H.A. The author(s) read and approved the final manuscript\u0026rdquo;. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFuding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article [and its additional fles].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e Declarations Ethics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests. \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBendriem, N., R. Roman, and U.R. Sumaila, \u003cem\u003eEnhancing land-based culture of coho salmon through genomic technologies: An economic analysis.\u003c/em\u003e Aquaculture Economics \u0026amp; Management, 2021. \u003cstrong\u003e25\u003c/strong\u003e(1): p. 27-52.\u003c/li\u003e\n\u003cli\u003eKjets\u0026aring;, M.H., J. \u0026Oslash;deg\u0026aring;rd, and T. Meuwissen, \u003cem\u003eAccuracy of genomic prediction of host resistance to salmon lice in Atlantic salmon (Salmo salar) using imputed high-density genotypes.\u003c/em\u003e Aquaculture, 2020. \u003cstrong\u003e526\u003c/strong\u003e: p. 735415.\u003c/li\u003e\n\u003cli\u003eGallardo-Hidalgo, J., et al., \u003cem\u003eGenetics of growth and survival under chronic heat stress and trade-offs with growth-and robustness-related traits in rainbow trout.\u003c/em\u003e Aquaculture, 2021. \u003cstrong\u003e531\u003c/strong\u003e: p. 735685.\u003c/li\u003e\n\u003cli\u003eJoshi, R., et al., \u003cem\u003eGenomic selection for resistance to Francisellosis in commercial Nile tilapia population: genetic and genomic parameters, correlation with growth rate and predictive ability.\u003c/em\u003e Aquaculture, 2021. \u003cstrong\u003e537\u003c/strong\u003e: p. 736515.\u003c/li\u003e\n\u003cli\u003eFerosekhan, S., et al., \u003cem\u003eInfluence of Genetic Selection for Growth and Broodstock Diet n-3 LC-PUFA Levels on Reproductive Performance of Gilthead Seabream, Sparus aurata.\u003c/em\u003e Animals (Basel), 2021. \u003cstrong\u003e11\u003c/strong\u003e(2).\u003c/li\u003e\n\u003cli\u003eYılmaz, S., et al., \u003cem\u003eProbiotics, prebiotics, and synbiotics used to control vibriosis in fish: A review.\u003c/em\u003e Aquaculture, 2022.\u003c/li\u003e\n\u003cli\u003eMagwanga, R.O., et al., \u003cem\u003eMap-Based Functional Analysis of the GhNLP Genes Reveals Their Roles in Enhancing Tolerance to N-Deficiency in Cotton.\u003c/em\u003e Int J Mol Sci, 2019. \u003cstrong\u003e20\u003c/strong\u003e(19).\u003c/li\u003e\n\u003cli\u003eVan Doan, H., et al., \u003cem\u003eModulation of growth, innate immunity, and disease resistance of Nile tilapia (Oreochromis niloticus) culture under biofloc system by supplementing pineapple peel powder and Lactobacillus plantarum.\u003c/em\u003e Fish Shellfish Immunol, 2021. \u003cstrong\u003e115\u003c/strong\u003e: p. 212-220.\u003c/li\u003e\n\u003cli\u003eNasr-Eldahan, S., et al., \u003cem\u003eA review article on nanotechnology in aquaculture sustainability as a novel tool in fish disease control.\u003c/em\u003e Aquac Int, 2021. \u003cstrong\u003e29\u003c/strong\u003e(4): p. 1459-1480.\u003c/li\u003e\n\u003cli\u003eIslam, M.J., A. Kunzmann, and M. Slater, \u003cem\u003eResponses of aquaculture fish to climate change‐induced extreme temperatures: A review.\u003c/em\u003e Journal of the World Aquaculture Society, 2021. \u003cstrong\u003e53\u003c/strong\u003e.\u003c/li\u003e\n\u003cli\u003eFreitas, M.V., et al., \u003cem\u003eGenotype by environment interaction and genetic parameters for growth traits in the Neotropical fish pacu (Piaractus mesopotamicus).\u003c/em\u003e Aquaculture, 2021. \u003cstrong\u003e530\u003c/strong\u003e: p. 735933.\u003c/li\u003e\n\u003cli\u003eKomprda, T., et al., \u003cem\u003eEffect of high dietary level (8%) of fish oil on long-chain polyunsaturated fatty acid n-3 content in pig tissues and plasma biochemical parameters.\u003c/em\u003e Animals, 2020. \u003cstrong\u003e10\u003c/strong\u003e(9): p. 1657.\u003c/li\u003e\n\u003cli\u003eLefevre, F., et al., \u003cem\u003eColonization of a new environment by a migratory fish is conditioned by introduction history and landscape parameters: Dispersion of the brown trout (Salmo trutta L.) in the Kerguelen Islands.\u003c/em\u003e Abstracts/Aquaculture 272S1, 2007. \u003cstrong\u003e238\u003c/strong\u003e: p. S321.\u003c/li\u003e\n\u003cli\u003eKomolka, K., et al., \u003cem\u003eDetermination and comparison of physical meat quality parameters of percidae and salmonidae in aquaculture.\u003c/em\u003e Foods, 2020. \u003cstrong\u003e9\u003c/strong\u003e(4): p. 388.\u003c/li\u003e\n\u003cli\u003eNovaković, S. and I. Toma\u0026scaron;ević. \u003cem\u003eA comparison between Warner-Bratzler shear force measurement and texture profile analysis of meat and meat products: A review\u003c/em\u003e. in \u003cem\u003eIOP Conference Series: Earth and Environmental Science\u003c/em\u003e. 2017. IOP Publishing.\u003c/li\u003e\n\u003cli\u003eBlay, C., et al., \u003cem\u003eGenetic Parameters and Genome-Wide Association Studies of Quality Traits Characterised Using Imaging Technologies in Rainbow Trout, Oncorhynchus mykiss.\u003c/em\u003e Front Genet, 2021. \u003cstrong\u003e12\u003c/strong\u003e: p. 639223.\u003c/li\u003e\n\u003cli\u003eGonzalez-Pena, D., et al., \u003cem\u003eGenome-wide association study for identifying loci that affect fillet yield, carcass, and body weight traits in rainbow trout (Oncorhynchus mykiss).\u003c/em\u003e Frontiers in genetics, 2016. \u003cstrong\u003e7\u003c/strong\u003e: p. 203.\u003c/li\u003e\n\u003cli\u003eAli, A., et al., \u003cem\u003eGenome-wide identification of loci associated with growth in rainbow trout.\u003c/em\u003e BMC genomics, 2020. \u003cstrong\u003e21\u003c/strong\u003e(1): p. 1-16.\u003c/li\u003e\n\u003cli\u003eWringe, B.F., et al., \u003cem\u003eGrowth-related quantitative trait loci in domestic and wild rainbow trout (Oncorhynchus mykiss).\u003c/em\u003e BMC Genet, 2010. \u003cstrong\u003e11\u003c/strong\u003e: p. 63.\u003c/li\u003e\n\u003cli\u003eHecht, B.C., et al., \u003cem\u003eGenetic Architecture of Migration-Related Traits in Rainbow and Steelhead Trout, Oncorhynchus mykis s.\u003c/em\u003e G3: Genes| Genomes| Genetics, 2012. \u003cstrong\u003e2\u003c/strong\u003e(9): p. 1113-1127.\u003c/li\u003e\n\u003cli\u003eLiotta, M.N., et al., \u003cem\u003eAntagonistic selection on body size and sword length in a wild population of the swordtail fish, Xiphophorus multilineatus: Potential for intralocus tactical conflict.\u003c/em\u003e Ecology and Evolution, 2021. \u003cstrong\u003e11\u003c/strong\u003e(9): p. 3941-3955.\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-Gonz\u0026aacute;lez, J.R., et al., \u003cem\u003eFish morphology and passage through velocity barriers. Experience with northern straight-mouth nase (Pseudochondrostoma duriense Coelho, 1985) in an open channel flume.\u003c/em\u003e Hydrobiologia, 2022. \u003cstrong\u003e849\u003c/strong\u003e(6): p. 1351-1366.\u003c/li\u003e\n\u003cli\u003eS\u0026aacute;nchez-Gonz\u0026aacute;lez, J.R. and A.G. Nicieza, \u003cem\u003eIndividual differences in dominance-related traits drive dispersal and settlement in hatchery-reared juvenile brown trout.\u003c/em\u003e Scientific Reports, 2021. \u003cstrong\u003e11\u003c/strong\u003e(1): p. 7277.\u003c/li\u003e\n\u003cli\u003eK\u0026uuml;ttner, E., et al., \u003cem\u003eGenetic architecture of body weight, condition factor and age of sexual maturation in Icelandic Arctic charr (Salvelinus alpinus).\u003c/em\u003e Molecular Genetics and Genomics, 2011. \u003cstrong\u003e286\u003c/strong\u003e: p. 67-79.\u003c/li\u003e\n\u003cli\u003eLaghari, M., et al., \u003cem\u003eMapping quantitative trait loci (QTL) for body weight, length and condition factor traits in backcross (BC1) family of Common carp (Cyprinus carpio L.).\u003c/em\u003e Molecular biology reports, 2014. \u003cstrong\u003e41\u003c/strong\u003e: p. 721-731.\u003c/li\u003e\n\u003cli\u003eNikoo, M., et al., \u003cem\u003eAutolysis of rainbow trout (Oncorhynchus mykiss) by-products: Enzymatic activities, lipid and protein oxidation, and antioxidant activity of protein hydrolysates.\u003c/em\u003e Lwt, 2021. \u003cstrong\u003e140\u003c/strong\u003e: p. 110702.\u003c/li\u003e\n\u003cli\u003eYoğurt\u0026ccedil;uoğlu, B., et al., \u003cem\u003eMapping the establishment and invasiveness potential of rainbow trout (Oncorhynchus mykiss) in Turkey: With special emphasis on the conservation of native salmonids.\u003c/em\u003e Frontiers in Ecology and Evolution, 2021. \u003cstrong\u003e8\u003c/strong\u003e: p. 599881.\u003c/li\u003e\n\u003cli\u003eNichols, K., et al., \u003cem\u003eA consolidated linkage map for rainbow trout (Oncorhynchus mykiss).\u003c/em\u003e Animal genetics, 2003. \u003cstrong\u003e34\u003c/strong\u003e(2): p. 102-115.\u003c/li\u003e\n\u003cli\u003eGalezan, F.H., et al., \u003cem\u003eEvaluating the rearing condition of rainbow trout (Oncorhynchus Mykiss) using fuzzy inference system.\u003c/em\u003e Aquacultural engineering, 2020. \u003cstrong\u003e89\u003c/strong\u003e: p. 102051.\u003c/li\u003e\n\u003cli\u003eOzaki, A., et al., \u003cem\u003eProgress of DNA marker-assisted breeding in maricultured finfish.\u003c/em\u003e Bull Fisheries Res Agency, 2012. \u003cstrong\u003e35\u003c/strong\u003e: p. 31-7.\u003c/li\u003e\n\u003cli\u003eBarroso, R., et al., \u003cem\u003eQTL for IHNV resistance and growth identified in a rainbow\u0026times; cutthroat trout cross.\u003c/em\u003e Aquaculture, 2007(272): p. S243-S244.\u003c/li\u003e\n\u003cli\u003eSakamoto, T., et al., \u003cem\u003eLinkage analysis of quantitative trait loci associated with spawning time in rainbow trout (Oncorhynchus mykiss).\u003c/em\u003e Aquaculture, 1999. \u003cstrong\u003e173\u003c/strong\u003e(1-4): p. 33-43.\u003c/li\u003e\n\u003cli\u003ePerry, G., et al., \u003cem\u003eCharacterization of rainbow trout cell lines using microsatellite DNA profiling.\u003c/em\u003e Cytotechnology, 2001. \u003cstrong\u003e37\u003c/strong\u003e: p. 143-151.\u003c/li\u003e\n\u003cli\u003eO\u0026apos;malley, K., et al., \u003cem\u003eQuantitative trait loci for spawning date and body weight in rainbow trout: testing for conserved effects across ancestrally duplicated chromosomes.\u003c/em\u003e Journal of Heredity, 2003. \u003cstrong\u003e94\u003c/strong\u003e(4): p. 273-284.\u003c/li\u003e\n\u003cli\u003eEaston, A., et al., \u003cem\u003eThe genetic architecture of embryonic developmental rate and genetic covariation with age at maturation in rainbow trout Oncorhynchus mykiss.\u003c/em\u003e Journal of Fish Biology, 2011. \u003cstrong\u003e78\u003c/strong\u003e(2): p. 602-623.\u003c/li\u003e\n\u003cli\u003eRexroad, C.E., 3rd, et al., \u003cem\u003eQTL affecting stress response to crowding in a rainbow trout broodstock population.\u003c/em\u003e BMC Genet, 2012. \u003cstrong\u003e13\u003c/strong\u003e: p. 97.\u003c/li\u003e\n\u003cli\u003eBaerwald, M., et al., \u003cem\u003eA major effect quantitative trait locus for whirling disease resistance identified in rainbow trout (Oncorhynchus mykiss).\u003c/em\u003e Heredity, 2011. \u003cstrong\u003e106\u003c/strong\u003e(6): p. 920-926.\u003c/li\u003e\n\u003cli\u003eBroman, K.W., et al., \u003cem\u003eR/qtl: QTL mapping in experimental crosses.\u003c/em\u003e Bioinformatics, 2003. \u003cstrong\u003e19\u003c/strong\u003e(7): p. 889-90.\u003c/li\u003e\n\u003cli\u003eLu, Q., et al., \u003cem\u003eConsensus map integration and QTL meta-analysis narrowed a locus for yield traits to 0.7 cM and refined a region for late leaf spot resistance traits to 0.38 cM on linkage group A05 in peanut (Arachis hypogaea L.).\u003c/em\u003e BMC genomics, 2018. \u003cstrong\u003e19\u003c/strong\u003e(1): p. 1-10.\u003c/li\u003e\n\u003cli\u003eCao, X.-K., et al., \u003cem\u003eIntegrating CNVs into meta-QTL identified GBP4 as positional candidate for adult cattle stature.\u003c/em\u003e Functional \u0026amp; integrative genomics, 2018. \u003cstrong\u003e18\u003c/strong\u003e: p. 559-567.\u003c/li\u003e\n\u003cli\u003eYang, Y., et al., \u003cem\u003eLarge-scale integration of meta-QTL and genome-wide association study discovers the genomic regions and candidate genes for yield and yield-related traits in bread wheat.\u003c/em\u003e Theoretical and Applied Genetics, 2021. \u003cstrong\u003e134\u003c/strong\u003e(9): p. 3083-3109.\u003c/li\u003e\n\u003cli\u003eLiu, H., et al., \u003cem\u003eMajor genomic regions responsible for wheat yield and its components as revealed by meta-QTL and genotype\u0026ndash;phenotype association analyses.\u003c/em\u003e Planta, 2020. \u003cstrong\u003e252\u003c/strong\u003e: p. 1-22.\u003c/li\u003e\n\u003cli\u003eSelamat, N. and K.K. Nadarajah, \u003cem\u003eMeta-Analysis of Quantitative Traits Loci (QTL) Identified in Drought Response in Rice (Oryza sativa L.).\u003c/em\u003e Plants (Basel), 2021. \u003cstrong\u003e10\u003c/strong\u003e(4).\u003c/li\u003e\n\u003cli\u003eLi, L., et al., \u003cem\u003eMapping QTL for leaf pigment content at dynamic development stage and analyzing Meta-QTL in rice.\u003c/em\u003e Euphytica, 2021. \u003cstrong\u003e217\u003c/strong\u003e: p. 1-23.\u003c/li\u003e\n\u003cli\u003eAquib, A. and S. NafisB, \u003cem\u003eA Meta-Analysis of Quantitative Trait Loci Associated with Stay-Green in Sorghum.\u003c/em\u003e Journal of Tropical Crop Science Vol, 2022. \u003cstrong\u003e9\u003c/strong\u003e(1).\u003c/li\u003e\n\u003cli\u003eWang, Y., et al., \u003cem\u003eIntegrated meta-QTL and genome-wide association study analyses reveal candidate genes for maize yield.\u003c/em\u003e Journal of Plant Growth Regulation, 2020. \u003cstrong\u003e39\u003c/strong\u003e: p. 229-238.\u003c/li\u003e\n\u003cli\u003eLeeds, T., P. Kenney, and M. Manor. \u003cem\u003eGenetic parameter estimates for feed intake, body composition, and fillet quality traits in a rainbow trout population selected for improved growth\u003c/em\u003e. in \u003cem\u003eInternational Symposium on Genetics in Aquaculture\u003c/em\u003e. 2012.\u003c/li\u003e\n\u003cli\u003eKristj\u0026aacute;nsson, \u0026Oacute;.H., et al., \u003cem\u003eQuantitative genetics of growth rate and filet quality traits in Atlantic salmon inferred from a longitudinal bayesian model for the left-censored Gaussian trait growth rate.\u003c/em\u003e Frontiers in Genetics, 2020. \u003cstrong\u003e11\u003c/strong\u003e: p. 573265.\u003c/li\u003e\n\u003cli\u003eShuping, L.S., et al., \u003cem\u003eThe Prevalence of Viruses Related to the Production of Mussels and Oysters in Saldanha Bay: A Systematic Review.\u003c/em\u003e Aquaculture Journal, 2023. \u003cstrong\u003e3\u003c/strong\u003e(2): p. 90-106.\u003c/li\u003e\n\u003cli\u003eNyholt, D.R., \u003cem\u003eAll LODs are not created equal.\u003c/em\u003e The American Journal of Human Genetics, 2000. \u003cstrong\u003e67\u003c/strong\u003e(2): p. 282-288.\u003c/li\u003e\n\u003cli\u003eDarvasi, A. and M. Soller, \u003cem\u003eA simple method to calculate resolving power and confidence interval of QTL map location.\u003c/em\u003e Behavior genetics, 1997. \u003cstrong\u003e27\u003c/strong\u003e: p. 125-132.\u003c/li\u003e\n\u003cli\u003eRexroad, C.E., et al., \u003cem\u003eA second generation genetic map for rainbow trout (Oncorhynchus mykiss).\u003c/em\u003e BMC genetics, 2008. \u003cstrong\u003e9\u003c/strong\u003e: p. 1-14.\u003c/li\u003e\n\u003cli\u003ePalti, Y., R. DANZMAN, and C. Rexroad, \u003cem\u003eCHARACTERIZATION AND MAPPING OF NINETEEN POLYMORPHIC MICROSATELLITE MARKERS FOR RAINBOW TROUT (ONCORHYNCHUS MYKISS).\u003c/em\u003e Animal genetics, 2003. \u003cstrong\u003e34\u003c/strong\u003e(2): p. 153-156.\u003c/li\u003e\n\u003cli\u003eArcade, A., et al., \u003cem\u003eBioMercator: integrating genetic maps and QTL towards discovery of candidate genes.\u003c/em\u003e Bioinformatics, 2004. \u003cstrong\u003e20\u003c/strong\u003e(14): p. 2324-2326.\u003c/li\u003e\n\u003cli\u003eKhahani, B., et al., \u003cem\u003eGenome wide screening and comparative genome analysis for Meta-QTLs, ortho-MQTLs and candidate genes controlling yield and yield-related traits in rice.\u003c/em\u003e BMC Genomics, 2020. \u003cstrong\u003e21\u003c/strong\u003e(1): p. 294.\u003c/li\u003e\n\u003cli\u003eJacobs, A., et al., \u003cem\u003eSignificant synteny and colocalization of ecologically relevant quantitative trait loci within and across species of salmonid fishes.\u003c/em\u003e Genetics, 2017. \u003cstrong\u003e207\u003c/strong\u003e(2): p. 741-754.\u003c/li\u003e\n\u003cli\u003eVeyrieras, J.-B., B. Goffinet, and A. Charcosset, \u003cem\u003eMetaQTL: a package of new computational methods for the meta-analysis of QTL mapping experiments.\u003c/em\u003e BMC bioinformatics, 2007. \u003cstrong\u003e8\u003c/strong\u003e: p. 1-16.\u003c/li\u003e\n\u003cli\u003eCabas-L\u0026uuml;hmann, P., et al., \u003cem\u003eMeta-QTL analysis and candidate genes for quality traits, mineral content, and abiotic-related traits in wild emmer.\u003c/em\u003e Frontiers in Plant Science, 2024. \u003cstrong\u003e15\u003c/strong\u003e: p. 1305196.\u003c/li\u003e\n\u003cli\u003eLeeds, T.D. and G.M. Weber, \u003cem\u003eEffects of triploidy on genetic gains in a rainbow trout (Oncorhynchus mykiss) population selectively bred for diploid growth performance.\u003c/em\u003e Aquaculture, 2019. \u003cstrong\u003e505\u003c/strong\u003e: p. 481-487.\u003c/li\u003e\n\u003cli\u003eBo, K., et al., \u003cem\u003eQTL mapping and genome-wide association study reveal two novel loci associated with green flesh color in cucumber.\u003c/em\u003e BMC plant biology, 2019. \u003cstrong\u003e19\u003c/strong\u003e: p. 1-13.\u003c/li\u003e\n\u003cli\u003eVaradharajan, S., \u003cem\u003eComparative genomics in teleost fish: Insights into forces driving genome evolution.\u003c/em\u003e 2019.\u003c/li\u003e\n\u003cli\u003eNugent, C., \u003cem\u003eThe Arctic charr (Salvelinus alpinus) genome: An analysis of evolutionary change after whole genome duplication\u003c/em\u003e. 2019, University of Guelph.\u003c/li\u003e\n\u003cli\u003eGriffin, D.K., et al., \u003cem\u003eWhole genome comparative studies between chicken and turkey and their implications for avian genome evolution.\u003c/em\u003e BMC genomics, 2008. \u003cstrong\u003e9\u003c/strong\u003e: p. 1-16.\u003c/li\u003e\n\u003cli\u003eZhou, S., et al., \u003cem\u003eDynamics of synthetic yeast chromosome evolution shaped by hierarchical chromatin organization.\u003c/em\u003e National Science Review, 2023. \u003cstrong\u003e10\u003c/strong\u003e(5): p. nwad073.\u003c/li\u003e\n\u003cli\u003eBobe, J., et al., \u003cem\u003eThe rainbow trout genome, an important landmark for aquaculture and genome evolution\u003c/em\u003e, in \u003cem\u003eGenomics in aquaculture\u003c/em\u003e. 2016, Elsevier. p. 21-43.\u003c/li\u003e\n\u003cli\u003eBerger, H., A. Wodarz, and A. Borchers, \u003cem\u003ePTK7 faces the Wnt in development and disease.\u003c/em\u003e Frontiers in Cell and Developmental Biology, 2017. \u003cstrong\u003e5\u003c/strong\u003e: p. 31.\u003c/li\u003e\n\u003cli\u003eJi, J., et al., \u003cem\u003eFOXP4-mediated induction of PTK7 activates the Wnt/\u0026beta;-catenin pathway and promotes ovarian cancer development.\u003c/em\u003e Cell Death \u0026amp; Disease, 2024. \u003cstrong\u003e15\u003c/strong\u003e(5): p. 332.\u003c/li\u003e\n\u003cli\u003eHayes, M., et al., \u003cem\u003eptk7 mutant zebrafish models of congenital and idiopathic scoliosis implicate dysregulated Wnt signalling in disease.\u003c/em\u003e Nature communications, 2014. \u003cstrong\u003e5\u003c/strong\u003e(1): p. 4777.\u003c/li\u003e\n\u003cli\u003eLu, L.X., et al., \u003cem\u003eConstruction of a genetic linkage map and QTL mapping of agronomic traits in Auricularia auricula-judae.\u003c/em\u003e J Microbiol, 2017. \u003cstrong\u003e55\u003c/strong\u003e(10): p. 792-799.\u003c/li\u003e\n\u003cli\u003eKleinjan, D.A., et al., \u003cem\u003eSubfunctionalization of duplicated zebrafish pax6 genes by cis-regulatory divergence.\u003c/em\u003e PLoS genetics, 2008. \u003cstrong\u003e4\u003c/strong\u003e(2): p. e29.\u003c/li\u003e\n\u003cli\u003eTang, H., et al., \u003cem\u003eGenome-wide association study reveals the genetic determinism of serum biochemical indicators in ducks.\u003c/em\u003e BMC genomics, 2022. \u003cstrong\u003e23\u003c/strong\u003e(1): p. 856.\u003c/li\u003e\n\u003cli\u003eMaurer, A., et al., \u003cem\u003eEstimating parent-specific QTL effects through cumulating linked identity-by-state SNP effects in multiparental populations.\u003c/em\u003e Heredity (Edinb), 2017. \u003cstrong\u003e118\u003c/strong\u003e(5): p. 477-485.\u003c/li\u003e\n\u003cli\u003eAhmad, A., et al., \u003cem\u003eThe piRNA protein Asz1 is essential for germ cell and gonad development in in zebrafish and exhibits differential necessities in distinct types of RNP granules.\u003c/em\u003e bioRxiv, 2023: p. 2023.07. 18.549483.\u003c/li\u003e\n\u003cli\u003eGanjavi, H. and C.M. Shapiro, \u003cem\u003eHypocretin/Orexin: a molecular link between sleep, energy regulation, and pleasure.\u003c/em\u003e The Journal of neuropsychiatry and clinical neurosciences, 2007. \u003cstrong\u003e19\u003c/strong\u003e(4): p. 413-419.\u003c/li\u003e\n\u003cli\u003eTuziak, S.M., M.L. Rise, and H. Volkoff, \u003cem\u003eAn investigation of appetite-related peptide transcript expression in Atlantic cod (Gadus morhua) brain following a Camelina sativa meal-supplemented feeding trial.\u003c/em\u003e Gene, 2014. \u003cstrong\u003e550\u003c/strong\u003e(2): p. 253-263.\u003c/li\u003e\n\u003cli\u003eLarson, S.R., et al., \u003cem\u003eLeymus EST linkage maps identify 4NsL-5NsL reciprocal translocation, wheat-Leymus chromosome introgressions, and functionally important gene loci.\u003c/em\u003e Theor Appl Genet, 2012. \u003cstrong\u003e124\u003c/strong\u003e(1): p. 189-206.\u003c/li\u003e\n\u003cli\u003eJia, J.-Y., et al., \u003cem\u003eAndrogen signaling inhibits de novo lipogenesis to alleviate lipid deposition in zebrafish.\u003c/em\u003e Zoological Research, 2024. \u003cstrong\u003e45\u003c/strong\u003e(2): p. 355.\u003c/li\u003e\n\u003cli\u003eXiao, N., et al., \u003cem\u003eFine mapping of qRC10-2, a quantitative trait locus for cold tolerance of rice roots at seedling and mature stages.\u003c/em\u003e PLoS One, 2014. \u003cstrong\u003e9\u003c/strong\u003e(5): p. e96046.\u003c/li\u003e\n\u003cli\u003eOtis, J.P., et al., \u003cem\u003eIntestinal epithelial cell caveolin 1 regulates fatty acid and lipoprotein cholesterol plasma levels.\u003c/em\u003e Disease models \u0026amp; mechanisms, 2017. \u003cstrong\u003e10\u003c/strong\u003e(3): p. 283-295.\u003c/li\u003e\n\u003cli\u003eGuo, R., et al., \u003cem\u003eEffects of Fat and Carnitine on the Expression of Carnitine Acetyltransferase and Enoyl-CoA Hydratase Short-Chain 1 in the Liver of Juvenile GIFT (Oreochromis niloticus).\u003c/em\u003e Genes, 2024. \u003cstrong\u003e15\u003c/strong\u003e(4): p. 480.\u003c/li\u003e\n\u003cli\u003eJersin, R.\u0026Aring;., L.R. Jonassen, and S.N. Dankel, \u003cem\u003eThe neutral amino acid transporter SLC7A10 in adipose tissue, obesity and insulin resistance.\u003c/em\u003e Frontiers in cell and developmental biology, 2022. \u003cstrong\u003e10\u003c/strong\u003e: p. 974338.\u003c/li\u003e\n\u003cli\u003eLi, Y.-H. and H.-P. Wang, \u003cem\u003eAdvances of genotyping-by-sequencing in fisheries and aquaculture.\u003c/em\u003e Reviews in Fish Biology and Fisheries, 2017. \u003cstrong\u003e27\u003c/strong\u003e(3): p. 535-559.\u003c/li\u003e\n\u003cli\u003eHan, P., et al., \u003cem\u003eIdentification and functional analysis of dual-specificity phosphatases (DUSP) genes in Japanese flounder (Paralichthys olivaceus) against temperature and Edwardsiella tarda stress.\u003c/em\u003e Fish \u0026amp; Shellfish Immunology, 2022. \u003cstrong\u003e130\u003c/strong\u003e: p. 453-461.\u003c/li\u003e\n\u003cli\u003eLiang, Z., et al., \u003cem\u003eFunctional characterization of BAG3 in orange-spotted grouper (Epinephelus coioides) during viral infection.\u003c/em\u003e Fish \u0026amp; Shellfish Immunology, 2022. \u003cstrong\u003e122\u003c/strong\u003e: p. 465-475.\u003c/li\u003e\n\u003cli\u003eYou, X., et al., \u003cem\u003eConstruction of high-density genetic linkage maps for orange-spotted grouper Epinephelus coioides using multiplexed shotgun genotyping.\u003c/em\u003e BMC genetics, 2013. \u003cstrong\u003e14\u003c/strong\u003e: p. 1-8.\u003c/li\u003e\n\u003cli\u003eToubanaki, D.K., A. Efstathiou, and E. Karagouni, \u003cem\u003eTranscriptomic analysis of fish hosts responses to nervous necrosis virus.\u003c/em\u003e Pathogens, 2022. \u003cstrong\u003e11\u003c/strong\u003e(2): p. 201.\u003c/li\u003e\n\u003cli\u003eSarais, F., et al., \u003cem\u003eCharacterisation of the teleostean \u0026kappa;B-Ras family: The two members NKIRAS1 and NKIRAS2 from rainbow trout influence the activity of NF-\u0026kappa;B in opposite ways.\u003c/em\u003e Fish \u0026amp; Shellfish Immunology, 2020. \u003cstrong\u003e106\u003c/strong\u003e: p. 1004-1013. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003eClassification of the growth and fillet quality attributes extracted from the selected studies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.697%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrait\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription of subclasses\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.9394%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviation\u003c/strong\u003e \u003cstrong\u003eof\u003c/strong\u003e \u003cstrong\u003esubclasses\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.697%;\"\u003e\n \u003cp\u003eGrowth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.9394%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3636%;\"\u003e\n \u003cp\u003eBody weight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.9394%;\"\u003e\n \u003cp\u003eBW\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3636%;\"\u003e\n \u003cp\u003eFork length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.9394%;\"\u003e\n \u003cp\u003eFL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3636%;\"\u003e\n \u003cp\u003elength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.9394%;\"\u003e\n \u003cp\u003eL\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3636%;\"\u003e\n \u003cp\u003eCondition factor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.9394%;\"\u003e\n \u003cp\u003eCF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3636%;\"\u003e\n \u003cp\u003eCentroid size\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.9394%;\"\u003e\n \u003cp\u003eCS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.697%;\"\u003e\n \u003cp\u003eFillet quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3636%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.9394%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3636%;\"\u003e\n \u003cp\u003eProtein content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.9394%;\"\u003e\n \u003cp\u003ePC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3636%;\"\u003e\n \u003cp\u003eFat content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.9394%;\"\u003e\n \u003cp\u003eFC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3636%;\"\u003e\n \u003cp\u003eMoisture content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.9394%;\"\u003e\n \u003cp\u003eMC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3636%;\"\u003e\n \u003cp\u003eMuscle yield\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.9394%;\"\u003e\n \u003cp\u003eMY\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 19.697%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 36.3636%;\"\u003e\n \u003cp\u003eShear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 43.9394%;\"\u003e\n \u003cp\u003eSF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"465\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003eSummary of characterization of the selected QTL studies\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1075%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.0968%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarkers\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.4731%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubclasses of two traits\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3226%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eReferences\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1075%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.0968%;\"\u003e\n \u003cp\u003eRAD, Microsatellite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.4731%;\"\u003e\n \u003cp\u003eL\u003csup\u003e1\u003c/sup\u003e, CF\u003csup\u003e2\u003c/sup\u003e,FL\u003csup\u003e3\u003c/sup\u003e,CS\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3226%;\"\u003e\n \u003cp\u003eHecht et al 2012\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1075%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.0968%;\"\u003e\n \u003cp\u003eSNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.4731%;\"\u003e\n \u003cp\u003eBW\u003csup\u003e5\u003c/sup\u003e, MC\u003csup\u003e6\u003c/sup\u003e,FC\u003csup\u003e7\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3226%;\"\u003e\n \u003cp\u003eAliAli et al 2020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1075%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.0968%;\"\u003e\n \u003cp\u003eSNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.4731%;\"\u003e\n \u003cp\u003ePC\u003csup\u003e8\u003c/sup\u003e,SF\u003csup\u003e9\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3226%;\"\u003e\n \u003cp\u003eAliAli et al 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1075%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.0968%;\"\u003e\n \u003cp\u003eSNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.4731%;\"\u003e\n \u003cp\u003eMY\u003csup\u003e10\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3226%;\"\u003e\n \u003cp\u003eSalem et al 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1075%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.0968%;\"\u003e\n \u003cp\u003eSNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.4731%;\"\u003e\n \u003cp\u003eBW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3226%;\"\u003e\n \u003cp\u003eReis Neto et al 2019\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 10.1075%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 27.0968%;\"\u003e\n \u003cp\u003eMicrosatellite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 32.4731%;\"\u003e\n \u003cp\u003eCF, BW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3226%;\"\u003e\n \u003cp\u003eWringe et al 2010\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e: length; \u003csup\u003e2\u0026nbsp;\u003c/sup\u003e: Condition factor; \u003csup\u003e3\u0026nbsp;\u003c/sup\u003e: Fork length; \u003csup\u003e4\u0026nbsp;\u003c/sup\u003e: Centroid size; \u003csup\u003e5\u003c/sup\u003e : Body weight; \u003csup\u003e6\u0026nbsp;\u003c/sup\u003e: Moisture content; \u003csup\u003e7\u0026nbsp;\u003c/sup\u003e: Fat content; \u003csup\u003e8\u0026nbsp;\u003c/sup\u003e: Protein content; \u003csup\u003e9\u0026nbsp;\u003c/sup\u003e: Shear force; \u003csup\u003e10\u0026nbsp;\u003c/sup\u003e: Muscle yield\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" valign=\"top\" style=\"width: 99.8355%;\"\u003e\n \u003cp\u003eTable3. Information related to the linkage groups in which meta-QTLs were identified.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" rowspan=\"2\" valign=\"top\" style=\"width: 247px;\"\u003e\n \u003cp\u003eMaps\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 258px;\"\u003e\n \u003cp\u003elinkage groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eReferences\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eConsensus map\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eNumber of markers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003epresent study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMap Length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e433.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e1219.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e144.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eRexroad et al\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eNumber of markers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e[52]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMap Length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e96.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e128.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e94.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003eHecht et al\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eNumber of markers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e[20]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMap Length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e189.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e107.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e47.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 112px;\"\u003e\n \u003cp\u003ePalti et al\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eNumber of markers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e[53]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 135px;\"\u003e\n \u003cp\u003eMap Length\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e140.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e134.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e119.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\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\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"673\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99.8514%;\" colspan=\"8\"\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Characteristics of gm-QTLs obtained in this study\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChr\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003egm-QTL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI\u003csup\u003e2\u003c/sup\u003e (cM)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePeak position (cM )\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL.Marker\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR-Marker\u003csup\u003e4\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQTLS\u003csup\u003e5\u003c/sup\u003e(AVGLOD\u003csup\u003e6\u003c/sup\u003e-AVGR2\u003csup\u003e7\u003c/sup\u003e)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI QTLs/CIgmQTL\u003csup\u003e8\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eGM-1-1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e208.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eR29784\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eSSAF43NUIG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e10(7.2-12.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e11.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp; GM-1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e216.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOMM1005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eR39679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e25(7.34-15.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e4.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp; GM-1-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e237.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOMM5147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eSSA16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e5(6.49-9.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e9.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp; GM-7-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e708.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOMM1574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eOMM3048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e13(3.81-8.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e6.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;GM-13-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e5.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e51.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eR30335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eR43229\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e7(4.47-7.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e2.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eGM-13-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003e65.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eOMM1671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 78px;\"\u003e\n \u003cp\u003eR42910\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003e33(5.6-9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 115px;\"\u003e\n \u003cp\u003e4.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e: Chromosome, \u003csup\u003e2\u003c/sup\u003e: Confidence interval, \u003csup\u003e3\u003c/sup\u003e: Left Marker, \u003csup\u003e4\u003c/sup\u003e: Right Marker, \u003csup\u003e5\u003c/sup\u003e: The number of QTLs participating in each meta-QTL, \u003csup\u003e6\u003c/sup\u003e: Average of LOD, \u003csup\u003e7\u003c/sup\u003e: Average of phenotypic variance explained by QTLs, \u003csup\u003e8\u003c/sup\u003e: The ratio of confidence interval QTLs to meta-QTLs. *: In the second column:G is genetic map based, M is the meta-QTL and the first and second numbers are the number of chromosomes and meta-QTLs,\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"768\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 100%;\" colspan=\"7\"\u003e\u003cstrong\u003eTable5.\u003c/strong\u003e Summary of sequence-based meta-QTL (smQTL) intervals\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChr\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmQTL\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eL.Marker\u003csup\u003e3\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMarkers in sm-QTLs interval (bp )\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eR-Marker\u003csup\u003e4\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u003cstrong\u003esmQTL intervals\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e(bp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCI (bp)\u003csup\u003e5\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eSM-1-1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eSSAF43NUIG(30055974-30055865)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAX-171607624 (49520794)\u003c/p\u003e\n \u003cp\u003eAX-171620793(54958459)\u003c/p\u003e\n \u003cp\u003eAX-89924960(54948051)\u003c/p\u003e\n \u003cp\u003eOMM5237(39398251-39398189)\u003c/p\u003e\n \u003cp\u003eOMM1205(30442542 - 30443013)\u003c/p\u003e\n \u003cp\u003eOMM1780(48034627-48034470)\u003c/p\u003e\n \u003cp\u003eR43999(46508750 - 46508817)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eR29784(66054734-66054801)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e30055974-66054801\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e35998827\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eOMM1302(34983177-34982915)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eOMM1454(52583557 - 52583809)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eR42795(52797894 - 52797961)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eR13377(62949046 - 62949113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eOMM1046(62887269 - 62887847)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eR01951(63207125 - 63207192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eR39919(63350009 - 63350076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eSM-1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eR39679(48579512-48579579)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAX-171607624 (49520794)\u003c/p\u003e\n \u003cp\u003eAX-171620793(54958459)\u003c/p\u003e\n \u003cp\u003eAX-89924960(54948051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eOMM1005(79240016-79240371)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e48579512-79240371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e30660859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eOMM1454(52583557 - 52583809)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eR42795(52797894 - 52797961)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eR13377(62949046 - 62949113)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eOMM1046(62887269 - 62887847)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eR01951(63207125 - 63207192)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eR39919(63350009 - 63350076)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eSM-1-3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eSSA16(42287756-42287823)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eOMM5147(45344622-45344689)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e42287756-45344689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3056933\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eSM-7-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eOMM3048(48121823-48122077)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eOMM1574(48634713-48635051)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e48121823-48635051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e513228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eSM-13-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eR43229(45650041-45650108)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAX-171603464(45731245)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eR30335(49582869-49582936)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e45650108-49582936\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3932895\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAX-171603124(45744877)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAX-171606422(45826267)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAX-171606423(45826307)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAX-171604417(45826604)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAX-172555500(45900926)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eAX-171603120(45785431)\u003c/p\u003e\n \u003cp\u003eAX-172553249(45795312)\u003c/p\u003e\n \u003cp\u003eAX-174104388(45825907)\u003c/p\u003e\n \u003cp\u003eAX-171606420(45826199)\u003c/p\u003e\n \u003cp\u003eAX-171604416(45826876)\u003c/p\u003e\n \u003cp\u003eAX-171602066(45857332)\u003c/p\u003e\n \u003cp\u003eAX-172555502(45864000)\u003c/p\u003e\n \u003cp\u003eAX-172555501(45876868)\u003c/p\u003e\n \u003cp\u003eAX-171604665(45913986)\u003c/p\u003e\n \u003cp\u003eAX-172550441(45982532)\u003c/p\u003e\n \u003cp\u003eAX-172557872(46017811)\u003c/p\u003e\n \u003cp\u003eAX-172548069(46017924)\u003c/p\u003e\n \u003cp\u003eAX-171609134(46017963)\u003c/p\u003e\n \u003cp\u003eAX-171609135(46018191)\u003c/p\u003e\n \u003cp\u003eAX-171609138(46019477)\u003c/p\u003e\n \u003cp\u003eAX-171609139(46046944)\u003c/p\u003e\n \u003cp\u003eAX-171634202(46060142)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eSM-13-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eOMM1671(34913635-34914072)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eR36287(35.676.065 - 35676132)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eR42910(38542963-38543030)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e34913635-38543030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3629395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003eOMM1646(36275479 - 36276000)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 180px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 126px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\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\u003cbr\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 99.8442%;\" colspan=\"7\"\u003e\u003cstrong\u003eTable6.\u003c/strong\u003e Characteristics of SNPs identified in smeta-QTL intervals\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChr\u003csup\u003e1\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003esm-QTL\u003csup\u003e2\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSNP ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubclass\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene ID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene annotation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSM-1-1*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171607624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eFat content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110524937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003erab GDP dissociation inhibitor beta-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171620793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eMoisture content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110525667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eserum response factor-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-89924960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eMoisture content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003efam160b1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003efamily with sequence similarity 160 member B1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; SM-1-2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171607624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eFat content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110524937\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003erab GDP dissociation inhibitor beta-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171620793\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eMoisture content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110525667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eserum response factor-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-89924960\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eMoisture content\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003efam160b1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003efamily with sequence similarity 160 member B1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eSM-13-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171603464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003ecytochrome c oxidase subunit 6A, mitochondrial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171606422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003emyosin regulatory light chain 2, skeletal muscle isoform-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171606423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eTBC1 domain family member 10B-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171604417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eTBC1 domain family member 10B-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171606423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eTBC1 domain family member 10B-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171604417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eTBC1 domain family member 10B-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171604416\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eTBC1 domain family member 10B-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171604417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eTBC1 domain family member 10B-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-172555500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eDDB1- and CUL4-associated factor 7-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171604665\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eDDB1- and CUL4-associated factor 7-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171604665A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eDDB1- and CUL4-associated factor 7-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171602066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003e3 beta-hydroxysteroid dehydrogenase type 7-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-172555502\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003e3 beta-hydroxysteroid dehydrogenase type 7-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171609139\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eATP-dependent RNA helicase DDX42-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171609139B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eATP-dependent RNA helicase DDX42-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171609139A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486688\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eATP-dependent RNA helicase DDX42-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171603120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003echromosome 13 C16orf58 homolog\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-172550441\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC100136168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eglial fibrillary acidic protein\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-172555501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110485207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003ehistone-lysine N-methyltransferase SETD1A-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-172557872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eLIM domain-containing protein 2-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-172548069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eLIM domain-containing protein 2-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171609134\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eLIM domain-containing protein 2-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171609135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eLIM domain-containing protein 2-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171609138\u003c/p\u003e\n \u003cp\u003eAX-172557872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486687\u003c/p\u003e\n \u003cp\u003eLOC110486687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eLIM domain-containing protein 2-like\u003c/p\u003e\n \u003cp\u003eLIM domain-containing protein 2-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-172548069\u003c/p\u003e\n \u003cp\u003eAX-171609134\u003c/p\u003e\n \u003cp\u003eAX-171609135\u003c/p\u003e\n \u003cp\u003eAX-172548069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486687\u003c/p\u003e\n \u003cp\u003eLOC110486687\u003c/p\u003e\n \u003cp\u003eLOC110486687\u003c/p\u003e\n \u003cp\u003eLOC110486687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eLIM domain-containing protein 2-like\u003c/p\u003e\n \u003cp\u003eLIM domain-containing protein 2-like\u003c/p\u003e\n \u003cp\u003eLIM domain-containing protein 2-like\u003c/p\u003e\n \u003cp\u003eLIM domain-containing protein 2-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171609138A\u003c/p\u003e\n \u003cp\u003eAX-171609138B\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486687\u003c/p\u003e\n \u003cp\u003eLOC110486687\u003c/p\u003e\n \u003cp\u003eLOC110486687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eLIM domain-containing protein 2-like\u003c/p\u003e\n \u003cp\u003eLIM domain-containing protein 2-like\u003c/p\u003e\n \u003cp\u003eLIM domain-containing protein 2-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-174104388\u003c/p\u003e\n \u003cp\u003eAX-171606420\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486680\u003c/p\u003e\n \u003cp\u003eLOC110486680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003emyosin regulatory light chain 2, skeletal muscle isoform-like\u003c/p\u003e\n \u003cp\u003emyosin regulatory light chain 2, skeletal muscle isoform-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171606422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003emyosin regulatory light chain 2, skeletal muscle isoform-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171634202\u003c/p\u003e\n \u003cp\u003eAX-171606423\u003c/p\u003e\n \u003cp\u003eAX-171604417\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486690\u003c/p\u003e\n \u003cp\u003eLOC110486679\u003c/p\u003e\n \u003cp\u003eLOC110486679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eSTE20-related kinase adapter protein alpha-like\u003c/p\u003e\n \u003cp\u003eTBC1 domain family member 10B-like\u003c/p\u003e\n \u003cp\u003eTBC1 domain family member 10B-like\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eAX-171604416\u003c/p\u003e\n \u003cp\u003eAX-171604417A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003cp\u003eshear force\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 96px;\"\u003e\n \u003cp\u003eLOC110486679\u003c/p\u003e\n \u003cp\u003eLOC110486679\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 248px;\"\u003e\n \u003cp\u003eTBC1 domain family member 10B-like\u003c/p\u003e\n \u003cp\u003eTBC1 domain family member 10B-like\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\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":"meta-QTL analysis, rainbow trout, growth, fillet quality","lastPublishedDoi":"10.21203/rs.3.rs-6178047/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6178047/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eRainbow trout (\u003cem\u003eOncorhynchus mykiss\u003c/em\u003e) is one of the most frequently farmed cold water fish species. The effective identification of quantitative trait loci (QTL) associated with the growth and fillet quality of rainbow trout can play an effective role in genetic improvement in global aquaculture. In this study, we used the strategy of meta-QTL analysis and identified the position of consensus genetic map markers on the reference genome of rainbow trout, to identify breeders\u0026rsquo; sequence based meta-QTLs (sm-QTL). After reviewing QTL studies over the past 22 years, we collected 308 QTLs distributed on 14 chromosomes. Based on the distribution of QTLs related to different studies on each chromosome, six genetic map meta-QTLs (gm-QTL) and four sm-QTL were detected on three chromosomes. Confidence interval (CI) of gm-QTLs decreased on average 6.39 times compared to initial QTLs. The results showed the most promising areas for controlling two traits (breeders\u0026rsquo; smQTL) are located on Chromosome 1 and 13. So probably the flanking and interval markers with these genomic regions, have effective applications for marker-assisted selection (MAS).\u003c/p\u003e \u003cp\u003eOur study will be effective in investigating diversity and MAS with the aim of molecular breeding of rainbow trout due to the identification of markers close to the consensus of QTLs of growth and fillet quality.\u003c/p\u003e","manuscriptTitle":"Identification of breeders’ smQTL and candidate genes associated with growth and fillet quality in Rainbow Trout (Oncorhynchus mykiss) with meta-analysis of QTLs","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 10:24:55","doi":"10.21203/rs.3.rs-6178047/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":"e60c3449-cc42-4f2e-ad49-ffa366dca58b","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":47211844,"name":"Biological sciences/Biotechnology"},{"id":47211845,"name":"Biological sciences/Genetics"},{"id":47211846,"name":"Health sciences/Biomarkers"}],"tags":[],"updatedAt":"2025-05-02T04:08:37+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-17 10:24:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6178047","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6178047","identity":"rs-6178047","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}