Genome-wide Scans Reveal Selection Signatures and Cross- Population Variation in South African and European Beef Cattle Breeds

Genome-wide Scans Reveal Selection Signatures and Cross- Population Variation in South African and European Beef Cattle Breeds | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Genome-wide Scans Reveal Selection Signatures and Cross- Population Variation in South African and European Beef Cattle Breeds Mamokoma Cathrine Modiba, Aletta Matshidiso Magoro, Khathutshelo Agree Nephawe, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3945698/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background In genetics and evolutionary biology, the concept of selection signatures is used to describe specific patterns in the genome that are associated with the process of natural selection. This natural selection can leave distinct genetic footprints of signatures, such as changes in allele frequencies, the presence of specific mutations, or patterns of genetic variation. Selection signatures provide information about the evolutionary forces that have shaped a population over time. Methods In this study, a total of 96 samples from four different cattle breeds, namely Nguni (n = 28), Bonsmara (n = 21), Angus (n = 22), and Simmental (n = 25) were subjected to quality control, following quality control, a total of 105,675 SNPs from 73 individuals remained for further analysis. Genomic signatures of positive selection within each breed were identified using the Integrated Haplotype Score (iHS) method, and cross-population comparison analysis was conducted using XP-EHH, Rsb, and Fst methods to assess the genetic differences between breeds. Results For the iHS analyses of individual breeds, two genomic regions identified signatures of selection for Bonsmara, six for Simmental, four for Nguni, and one for Angus. Ten regions were identified as being under selection, with BTA 12 shared between Nguni and Bonsmara. Cross-population comparisons using XP-EHH, Rsb, and FST methods revealed specific genomic regions differentially selected between breeds. Gene annotation analyses revealed candidate genes associated with several Quantitative Trait Loci (QTL). For instance, in Simmental cattle, the gene FAM110B was associated with carcass weight and body confirmation score. Bonsmara cattle had fewer candidate genes, including CDK8 and FLT1, while Angus revealed no candidate genes on BTA 18. Nguni cattle revealed the following candidate genes CRB1, PLAG2GA, and VASH2, with CDK8 shared between Bonsmara and Nguni on BTA 12. Cross population comparisons further revealed candidate genes associated with specific traits. For Bonsmara vs Nguni, genes including PLCXD3, FAM149B1, and GRIK2 were identified, whereas, for Simmental vs Angus, SLIT2 and TSPAN9 genes were identified. Furthermore, the study highlighted gene functions, revealing associations with meat quality traits, reproduction, health, diseases, fertility, and body conformation score. Gene interaction analysis using the STRING database identified a network of 63 candidate genes, revealing the structure of genetic interactions. Some genes had multiple functions, indicating multiple roles in various biological processes. Conclusion This extensive genomic study can assist in highlighting the importance of the genetic background of breed-specific traits, and in this way contributes to selective breeding and trait improvement in cattle populations. Beef Candidate genes traits and gene interaction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The genomic structure of livestock has undergone significant transformations since their domestication [ 1 ]. Over the years, evolution, mutation, selection, migration and genetic drift have enabled various breeds to survive, grow and reproduce in a specific environment [ 2 ]. South African cattle are categorised into four different groups, namely Bos taurus (e.g. Angus), B. indicus (e.g. Simmental), a local form of Sanga cattle (a cross of B. taurus and B. indicus ) known as Nguni, and a recently established breed resulting from crosses of three other breeds (two B. taurus and one Sanga) known as Bonsmara [ 3 ]. These breeds played an essential role in the cultural, economic and social aspects of South Africa [ 2 ]. The process of domestication, and artificial selection, combined with the recent rapid decrease in effective population size, has left detectable signatures of selection in numerous regions of the cattle genome [ 2 ]. Several studies have scanned whole genomes and reported on signatures of selection and genes that harbour traits of economic importance in multiple species. However, information on selection signatures for production traits in South African beef cattle breeds is limited to only two methods of analysis. Also, taking into account that domestication has contributed to changing the morphological and behavioral characteristics of modern domestic animals, along with breed formation and selection schemes, this is influenced by improving the production of specific products to achieve a certain standard [ 2 ]. Also, the commercial livestock sector is facing several new challenges, with the demand for livestock products continuously increasing, however, long-term sustainability is questionable [ 4 ]. Climate change is putting new pressures on livestock production, while livestock themselves are contributing through greenhouse emissions to climate change [ 5 ]. Given the mixed-breed nature of most African cattle populations and the presence of unique African breeds, the availability of reference genomes is important for genomic studies of African cattle. In particular, methods for obtaining genomic information on animals are developing rapidly (e.g. genome-wide sequencing and genotyping), thus opening new opportunities for genomic selection [ 6 ];[ 7 ]. The signatures of selection in South African beef cattle can be studied using various methods that are commonly used in population genetics to identify genomic regions under selection, including iHS (integrated haplotype score), Rsb (relative extended haplotype homozygosity), XP-EHH (cross-population extended haplotype homozygosity), and F ST (fixation index) [ 8 ]. [ 9 ] reported on signatures of positive selection underlying beef production traits in Korean cattle breeds, while [ 10 ] reported genomic scans for selection signatures, and revealed candidate genes for adaptation and production traits in a variety of cattle breeds in India. In South African beef cattle breeds, [ 11 ] reported several candidate genomic regions either directly or indirectly involved in tropical adaptation, immune response activation, tick and parasite resistance and reproductive performance. Several studies have explored genomic selection signature regions using high-density single nucleotide polymorphism (SNP) genotyping assays to scan cattle genomes for positions that may have been targeted by selection [ 12 ]. [ 13 ] identified candidate regions under positive selection, while another study conducted in South Africa identified several candidate genomic regions showing positive selection signatures using the Illumina High-Density Bovine SNP BeadChip [ 14 ] and also using Gene Seek Genomic Profiler 150 K (GGP Bovine 150 K) bead chip of two South African cattle breeds. The detection of signatures of selection may contribute to a better understanding of the mechanisms that underlie traits that have been exposed to intensive natural and artificial selection [ 11 ]. Such information also provides important insights into the mechanisms of evolution [ 15 ], the use of loci under selection for breeding programs and is useful for the annotation of significant functional genomic regions [ 16 ]. The recent availability of genomic information on domestic animal species, and the development of improved statistical tools, make the identification of signatures of selection in each species possible. Here, we compared (iHS, Rsb, XP-EHH, and Fst) to identify signatures of selection in South African beef cattle. Results Population and Identifying signature of selection. After quality control, 73 individuals were retained, related individuals with a PI_HAT value greater than 0.05 were removed and a subset of 105,675 SNPs remained for further analysis. The triangle population structure was used to cluster and confirm individuals within the remaining populations (Fig. 1 ). The Integrated Haplotype Score (iHS) analysis method was used to construct Manhattan plots to visually represent the genomic distribution of positive signatures of selection in all selected breeds. Positive signatures of selection were identified within each of the four populations (Bonsmara, Simmental, Nguni, and Angus). Significant genomic regions above the threshold of 4 for signatures of selection were identified. For Bonsmara (Fig. 1 a): suggestive genomic regions were detected on BTA 2 and 12. For Simmental (Fig. 1 b): significant regions were found on BTA 2, 4, 6, 13, 14, and 17. For Nguni (Fig. 1 c): significant regions were observed on BTA 6, 7, 12, and 16. For Angus (Fig. 1 d): a significant region was found on BTA 18. Ten significant regions were identified for all with BTA 12 shared between Nguni and Bonsmara. Three more analyses were incorporated using XP-EHH, Rsb and Fst methods for cross-population comparisons. XP-EHH method in Fig. 2 , Rsb Figure in 3 and Fst Figure in 4. Manhattan plots were used to visualize the distribution of signatures of selection across the genome for all methods and all four breeds. For Angus vs Simmental (Fig. 2 a): significant regions were observed on BTA 3, 6, and 13 and For Nguni vs Bonsmara (Fig. 2 b): significant regions were identified on BTA 1, 2, 11, 14, 17, and 24. The Rsb method revealed significant regions of the genome above the threshold. For Angus vs Simmental (Fig. 3 a): suggestive genomic regions were detected on BTA 3, 5, 6, and 14. For Nguni vs Bonsmara (Fig. 3 b): Suggestive genomic regions were identified on BTA 1, 4, 5, 8, 11, 14, 17, and 24. The Fst analysis distribution of regions with signatures of selection across the genome is depicted in Fig. 4 for Simmental vs Angus (Fig. 4 a) the most significant genomic regions with signatures were identified on BTA 1–29. Notably, BTA 2, 12, and 27 were excluded from the analysis. Nguni vs Bonsmara (Fig. 4 b) significant genomic regions with signatures of selection were identified on BTA 1–28 excluding BTA 2. Gene annotation Gene annotation analyses were conducted using the iHS method shown in (Table 1 ), for Simmental cattle, identification of several significant genes, including FAM110B, RBMS1, SRD5A3, TBX5, and TMEM165. Among these genes, FAM110B was found to be associated with quantitative trait loci (QTL) related to carcass weight and body conformation score. In contrast, Bonsmara cattle revealed only two candidate genes, CDK8 and FLT1, located on BTA 12, with Angus exhibiting no identified candidate gene on BTA 18. Nguni cattle, on the other hand, presented candidate genes such as CRB1, PLAG2GA, UBL3, VASH2, and ENSBTAG00000019340. Interestingly, one gene, CDK8, was shared between the Bonsmara and Nguni breeds on BTA 12, including the function of the CDK8 gene since it is common in the two indigenous breeds. Furthermore, a comprehensive examination of the cross-population between Simmental vs Angus and Bonsmara vs Nguni using the Fst methods was conducted. The Fst method applied to Bonsmara vs Nguni, identified the following candidate genes, PLCXD3, FAM149B1, TCAIM, AFG1L, FOXO3, KCNQ3, PAPPA, ENSBTAG-00000008851, HMMR, PLXDC2, ZNF704, and GRIK2. In the Simmental vs Angus comparison, genes such as MYOID, FAM172A, MIPOL1, CHMP4B, FOX13, and KCNK13 were identified. Furthermore, annotation was conducted using both the Rsb and XP-EHH methods, and the results are summarized in Table 2 across all four breeds. Cross-population comparisons, between Nguni vs Bonsmara, distinctive candidate genes revealed genes such as CHD7, CLVS1, KHDRBS4, KHDRBS5, PAG1, PRKDC, RAB2A, RSPO2, FSTL5 , KHDRBS3, and TSPAN9. Similarly, the Rsb method applied to the Simmental vs Angus cross-population comparison identified candidate genes including ADAR, ISG20L2 , SNX31, STK3, and TSPAN9 . Turning to the XP-EHH method, specific candidate genes were pinpointed for the Bonsmara vs Nguni comparison—namely, RUNX3 and NUDGT6. In the context of the Simmental vs Angus cross-population comparison using XP-EHH, the gene SLIT2 emerged as a candidate. These findings underscore distinctive genetic markers associated with cross-population differences and provide valuable insights into potential genomic factors contributing to the unique characteristics of these breeds. The study further identified gene description using ShinyGO for all candidate genes revealed through the iHS, Rsb, Fst, and XP-EHH methods, as presented in Tables 3 to 5 . Table 1 Breed, chromosome number, position, and significant genes for the iHS and Fst BREED CHR START END GENES QTL SIM 14 24381950 24382000 FAM110B Carcass weight and conformation score 2 35950750 35950800 RBMS1 - 6 70817900 70817950 SRD5A3 - 17 60241650 60241700 TBX5 - 6 70842800 70842850 TMEM165 - BON 12 33130350 33130400 CDK8, FLTI - ANG 18 38074000 38074050 - - NGU 12 33153350 33153400 CDK8 - 16 76369400 76369450 CRB1 - 12 40594100 40594150 ENSBTAG00000019340 - 16 68039450 68039500 PLA2G4A - 12 30808950 30809000 UBL3 - 16 70606700 70606750 VASH2 - NGU&BON 12 33153350 33153400 CDK8 - NGU vs BON 20 33016045 33218726 PLCXD3 - 28 29237038 29287046 FAM149B1 - 22 16271889 16309668 TCAIM - 9 41655110 41851120 AFG1L - 9 41522588 41620269 FOXO3 - 14 8738181 9029965 KCNQ3 Bovine respiratory disease susceptibility calving ease and insemination per conception 8 105351900 105612449 PAPPA Sperm count and Insemination per conception 18 57488548 57494764 ENSBTAG00000008851 - 7 75137017 75170625 HMMR - 13 21312388 21747796 PLXDC2 - 14 43837811 44085912 ZNF704 - 9 47889245 48618507 GRIK2 Bovine tuberculosis susceptibility and insemination per conception SIM vs ANG 19 17330801 17691005 MYO1D - 7 93088059 93506932 FAM172A - 21 47398496 47734138 MIPOL1 - 13 63226304 63271073 CHMP4B - 11 47351626 47386204 FOX13 - 10 101723038 101828432 KCNK13 - Table 2 :Breed, chromosome number, position, and significant genes for Rsb and XP-EHH. BREED CHR START END GENES QTL BON vs NGU 14 26457000 26457150 CHD7 Body confirmation score 26923350 26923500 CLVS1 - 6486300 6486450 KHDRBS4 6527100 6527250 KHDRBS5 6589200 6589350 PAG1 44291850 44292000 PRKDC Marbling score fat thickness at the 12 rib, fat weight and heifer pregnancy 19517850 19518000 RAB2A Carcass weight and body confirmation score 26211150 26211300 RSPO2 Calving at ease 4 22618800 22618950 FSTL5 17 36457500 36457650 KHDRBS3 5 106744050 106744200 TSPAN9 SIM vs ANG 3 15972750 15972900 ADAR 14106300 14106450 ISG20L2 6 39816000 39816150 SLIT2 Interval from fisrt to last insemination 14 63725700 63725850 SNX31 65488500 65488650 STK3 5 1,07E + 08 1,07E + 08 TSPAN9 Lactation persistency BON & NGU 2 127891800 127891950 RUNX3 17 34752450 34752600 NUDGT6 Table 3 Full gene description for the iHS methods METHOD SYMBOL OF GENES ENSEMBLE GENE ID ENTREZ TYPE SPECIES CHR POSITION (MBP) DESCRIPTION iHS RBMS1 ENSBTAG00000005180 526135 protein coding Cow 2 35.855477 RNA binding motif single stranded interacting protein 1 SRD5A3 ENSBTAG00000014913 535834 protein coding Cow 6 70.803119 steroid 5 alpha-reductase 3 TMEM165 ENSBTAG00000001269 532600 protein coding Cow 6 70.838726 transmembrane protein 165 UBL3 ENSBTAG00000012170 526950 protein coding Cow 12 30.806229 ubiquitin like 3 FLT1 ENSBTAG00000016915 503620 protein coding Cow 12 31.624125 fms related receptor tyrosine kinase 1 CDK8 ENSBTAG00000016737 507149 protein coding Cow 12 33.082022 cyclin dependent kinase 8 FAM110B ENSBTAG00000050550 767946 protein coding Cow 14 24.365744 family with sequence similarity 110 member B PLA2G4A ENSBTAG00000013298 525072 protein coding Cow 16 67.906979 phospholipase A2 group VASH2 ENSBTAG00000003701 Protein coding Cow 16 70.601757 vasohibin 2 CRB1 ENSBTAG00000008944 520406 protein coding Cow 16 76.188124 crumbs cell polarity complex component 1 TBX5 ENSBTAG00000011384 532970 protein coding Cow 17 60.228521 T-box transcription factor 5 ENSBTAG00000019340 ENSBTAG00000019340 NA NA NA NA NA NA HMMR ENSBTAG00000014773 281227 protein_coding Cow 7 75.137017 hyaluronan mediated motility receptor FAM172A ENSBTAG00000050195 617002 protein_coding Cow 7 93.088059 family with sequence similarity 172 member A PAPPA ENSBTAG00000004010 282647 protein_coding Cow 8 105.3519 pappalysin 1 FOXO3 ENSBTAG00000011234 535530 protein_coding Cow 9 41.522588 forkhead box O3 AFG1L ENSBTAG00000014592 537689 protein_coding Cow 9 41.65511 AFG1 like ATPase GRIK2 ENSBTAG00000033153 615226 protein_coding Cow 9 47.889245 glutamate ionotropic receptor kainate type subunit 2 Table 4 Full gene description for iHS, Fst and Rsb methods. METHOD SYMBOL OF GENES ENSEMBLE GENE ID ENTREZ TYPE SPECIES CHR POSITION (MBP) DESCRIPTION KCNK13 ENSBTAG00000045849 787307 protein_coding Cow 10 101.723038 potassium two pore domain channel subfamily K member 13 PLXDC2 ENSBTAG00000009475 515731 protein_coding Cow 13 21.312388 plexin domain containing 2 CHMP4B ENSBTAG00000013387 616164 protein_coding Cow 13 63.226304 charged multivesicular body protein 4B Fst KCNQ3 ENSBTAG00000020667 281884 protein_coding Cow 14 8.738181 potassium voltage-gated channel subfamily Q member 3 ZNF704 ENSBTAG00000021743 513243 protein_coding Cow 14 43.837811 zinc finger protein 704 MYO1D ENSBTAG00000015527 522967 protein_coding Cow 19 17.330801 myosin ID PLCXD3 ENSBTAG00000010822 781239 protein_coding Cow 20 33.016045 Phosphatidylinositol-specific phospholipase C X domain containing 3 MIPOL1 ENSBTAG00000000655 528380 protein_coding Cow 21 47.398496 mirror-image polydactyly 1 TCAIM ENSBTAG00000016622 515010 protein_coding Cow 22 16.271889 T-cell activation inhibitor, mitochondrial FAM149B1 ENSBTAG00000019130 533952 protein_coding Cow 28 29.237038 family with sequence similarity 149 member B1 Rsb DGKB ENSBTAG00000021905 537171 protein_coding Cow 4 22.3898 diacylglycerol kinase beta TSPAN9 ENSBTAG00000049163 540132 protein_coding Cow 5 106.5532 tetraspanin 9 KHDRBS3 ENSBTAG00000002181 615095 protein_coding Cow 14 6.4007 KH RNA binding domain containing, signal transduction associated 3 PRKDC ENSBTAG00000017019 512740 protein_coding Cow 14 19.4243 protein kinase, DNA-activated, catalytic subunit RAB2A ENSBTAG00000000948 508373 protein_coding Cow 14 26.1811 RAB2A, member RAS onco family CHD7 ENSBTAG00000021841 533175 protein_coding Cow 14 26.3612 chromodomain helicase DNA binding protein 7 Table 5 Full gene description for Rsb and XP-EHH methods. METHODS SYMBOL OF GENES ENSEMBL GENE ID ENTREZ TYPE SPECIES CHR POSITION (MBP) DESCRIPTION RSPO2 ENSBTAG00000013598 536121 protein_coding Cow 14 56.3852 R-spondin 2 CLVS1 ENSBTAG00000043978 532808 protein_coding Cow 14 26.8549 clavesin 1 FSTL5 ENSBTAG00000047595 782831 protein_coding Cow 17 36.2608 follistatin like 5 PAG1 ENSBTAG00000054180 281964 protein_coding Cow 29 38.6048 pregnancy-associated glycoprotein 1 KHDRBS4 Not mapped NA NA NA NA NA NA KHDRBS5 Not mapped NA NA NA NA NA NA DGKB ENSBTAG00000021905 537171 protein_coding Cow 4 22.3898 diacylglycerol kinase beta TSPAN9 ENSBTAG00000049163 540132 protein_coding Cow 5 106.5532 tetraspanin 9 XP-EHH SLIT2 ENSBTAG00000005108 534164 protein_coding Cow 6 39.779895 slit guidance ligand 2 NUDT6 ENSBTAG00000005695 100126047 protein_coding Cow 17 34.747124 nudix hydrolase 6 RUNX3 ENSBTAG00000050474 617389 protein_coding Cow 2 127.9983 RUNX family transcription factor 3 Gene Functions The gene symbols and IDs were accurately translated from both Ensemble and NCBI databases, providing actual gene positions and full gene names. The study further specified the function of each gene shown in Tables 6 to 8 . Some genes were multi-functional genes, meaning the same gene with different functions. The number of genes per function ranged from 1 to 15, were genes like RAB2A, TMEM165, VASH2, SLIT2, CRB1, TBX5, CHMP4B, AFG1L, MYO1D, PRKDC, FAM149B1, CHD7, DGKB, CLVS1, and FAM172A were responsible for cellular component organization or biogenesis and cellular component organization. Candidate genes like VASH2, SLIT2, CRB1, FOXO3, TBX5, RSPO2 , FLT1, PRKDC, CHD7, FSTL5 , and FAM172A were responsible in developmental processes and anatomical structure development. Genes TMEM165, SLIT2, CRB1, FOXO3, TBX5, PLA2G4A, KCNQ3, CHD7, DGKB, GRIK2 and KCNK13 were responsible regulation of biological quality. Other functions were specific to some genes, genes as FOXO3 were responsible for the multi-organism process, Autophagy, Sexual reproduction, muscle adaptation and the multi-organism reproductive process. PRKDC Somatic diversification of immune receptors, Activation of the immune response, Production of molecular mediator of the immune response. SLIT2 Cell growth and GRIK2 Behavioral defense response. Notably, PAPPA was associated with fertility traits (sperm count and insemination per conception), KCNQ3 was linked to QTL for diseases (respiratory disease susceptibility and tuberculosis susceptibility), and GRIK2 exhibited associations with fertility traits. Most of the genes in this study were associated with QTL for meat and carcass traits (body weight, marbling score fat thickness at the 12th rib and fat weight), reproduction, semen traits (sperm count), health traits, diseases (Bovine respiratory diseases susceptibility and Bovine tuberculosis susceptibility and insemination per conception), fertility traits (calving ease, insemination per conception, interval from first to last insemination, lactation persistency and heifer pregnancy) and conformation (body confirmation score). Table 6 Gene function and number of genes per function Number of genes per function High level go category Genes 15 Cellular component organization or biogenesis RAB2A TMEM165 VASH2 SLIT2 CRB1 TBX5 CHMP4B AFG1L MYO1D PRKDC FAM149B1, CHD7 DGKB CLVS1 FAM172A 15 Cellular component organization RAB2A TMEM165 VASH2 SLIT2 CRB1 TBX5 CHMP4B AFG1L MYO1D PRKDC FAM149B1 CHD7 DGKB CLVS1 FAM172A 11 Developmental process VASH2 SLIT2 CRB1 FOXO3 TBX5 RSPO2 FLT1 PRKDC CHD7 FSTL5 FAM172A 11 Anatomical structure development VASH2 SLIT2 CRB1 FOXO3 TBX5 RSPO2 FLT1 PRKDC CHD7 FSTL5 FAM172A 11 Regulation of biological quality TMEM165 SLIT2 CRB1 FOXO3 TBX5 PLA2G4A KCNQ3 CHD7 DGKB GRIK2 KCNK13 10 Positive regulation of biological process VASH2 SLIT2 FOXO3 TBX5 PLA2G4A RSPO2 FLT1 PRKDC CHD7 RUNX3 10 Negative regulation of biological process SLIT2 NUDT6 FOXO3 TBX5 MYO1D FLT1 PRKDC CHD7 GRIK2 FAM172A 9 Biosynthetic process TMEM165 FOXO3 TBX5 PLA2G4A SRD5A3 PRKDC ZNF704 CHD7 RUNX3 8 Response to external stimulus SLIT2 CRB1 FOXO3 FLT1 PRKDC CHD7 DGKB GRIK2 8 Regulation of signaling SLIT2 FOXO3 TBX5 RSPO2 FLT1 CHD7 DGKB GRIK2 7 Anatomical structure morphogenesis VASH2 SLIT2 CRB1 FOXO3 TBX5 FLT1 CHD7 7 Establishment of localization TMEM165 CHMP4B MYO1D KCNQ3 CHD7 GRIK2 KCNK13 6 System process SLIT2 CRB1 FOXO3 TBX5 CHD7 GRIK2 6 Cell population proliferation VASH2 NUDT6 FOXO3 TBX5 FLT1 PRKDC 6 Regulation of localization SLIT2 TBX5 FLT1 KCNQ3 CHD KCNK13 6 Response to chemical SLIT2 FOXO3 FLT1 PRKDC CHD7 RUNX3 6 Regulation of multicellular organismal process VASH2 SLIT2 FOXO3 TBX5 FLT1 CHD7 5 Regulation of response to stimulus SLIT2 , FOXO3, RSPO2 FLT1 PRKDC 5 Anatomical structure formation involved in morphogenesis VASH2 SLIT2 CRB1 TBX5 FLT1 5 Cellular localization CRB1 CHMP4B MYO1D FAM149B CHD7 4 Reproduction VASH2 SLIT2 FOXO3 CHD7 4 Immune system process SLIT2 FLT1 PRKDC CHD7 Table 7 Gene function and number of genes per function Number of genes per function High level go category Genes 4 Growth SLIT2 FOXO3 TBX5 CHD7 4 Reproductive process VASH2 SLIT2 FOXO3 CHD7 4 The developmental process involved in reproduction VASH2 SLIT2 FOXO3 CHD7 4 Response to stress FOXO3 PLA2G4A PRKDC GRIK2 4 Catabolic process FOXO3 PLA2G4A AFG1L SRD5A3 4 Cellular component biogenesis SLIT2 TBX5 PRKDC FAM149B1 4 Developmental growth SLIT2 FOXO3 TBX5 CHD7 4 Regulation of the developmental process VASH2 TBX5 FLT1 CHD7 3 Locomotion SLIT2 TBX5 FLT1 3 Immune system development FLT1 PRKDC CHD7 3 Response to biotic stimulus PRKDC CHD7 DGKB 3 Response to endogenous stimulus SLIT2 PRKD RUNX3 3 Regulation of growth SLIT2 TBX5 CHD7 3 Regulation of locomotion SLIT2 TBX5 FLT1 3 The biological process involved in interspecies interaction between organisms PRKDC CHD7 DGKB 3 Cell motility SLIT2 TBX5 FLT1 3 Localization of cell SLIT2 TBX5 FLT1 3 Response to other organism PRKD CHD7 DGKB 2 Behavior CHD7 GRIK2 2 Regulation of immune system process SLIT2 PRKDC 2 Response to abiotic stimulus CRB1 GRIK2 2 Ovulation cycle process, Regulation of cellular component biogenesis, Ovulation cycle, Multicellular organismal reproductive process, Multicellular organism reproduction and Rhythmic process SLIT2 FOXO3 2 Macromolecule localization CRB1 FAM149B1 Table 8 Gene function and number of genes per function Number of genes per function High level go category Genes 2 Leukocyte migration SLIT2 FLT1 2 Detection of stimulus CRB1 GRIK2 2 Regulation of molecular function MYO1D FLT1 1 Multi-organism process, Sexual reproduction, muscle adaptation multi-organism reproductive process, autophagy and maintenance of cell number FOXO3 1 Behavioral defense response GRIK2 1 Activation of immune response, production of molecular mediator of immune response, immune response, and Somatic diversification of immune receptors PRKDC 1 Locomotory behavior, leukocyte activation and maintenance of location CHD7 1 Cell growth SLIT2 Gene Interaction Gene interactions among candidate genes were elucidated through the utilization of the STRING database (Fig. 5 ). The analysis resulted in the identification of 63 nodes, each representing a candidate gene. The average node degree was calculated to be 1.84, indicating the extent of interactions each gene had within the network. A notable finding emerged with an enrichment P-value of 6.99e-05, signifying the statistical significance of the observed gene interactions. However, some genes did not exhibit any interactions which was led by underscoring the complexity of the genetic network. Discussion This study describes a comprehensive genetic analysis conducted on four South African beef cattle breeds (Bonsmara, Simmental, Nguni, and Angus) using various methods such as iHS, XP-EHH, Rsb, and Fst to identify signatures of selection and candidate genes associated with unique traits within each breed. These methods were reported in several studies, however, not all methods were incorporated in one study for South African beef cattle breeds. An example is a study by [ 13 ], using iHS and XP-EHH, to identify candidate regions that show preferential selection in the genome of South African Nguni and Bonsmara cattle. A study by [ 17 ] reported on iHS and Rsb analysis to detect selected regions in the local North African cattle. [ 18 ] identified meat quality-related gene regions that are positively selected in Ankole (Sanga) and indicus (Boran, Ogaden, and Kenana) breeds using cross-population XP-EHH and XP-CLR methods. [ 11 ] research was based on the Fst and iHS methods in six South African Bonsmara, Nguni, Afrikaner, and Sanga breeds. The filtered data was used, and the population structure was confirmed using a triangle plot, in which 73 individuals were confirmed. The study identified four specific clusters for Angus, Simmental, Nguni, and Bonsmara which highlighted distinct genetic characteristics, reflecting possible breed origins or specific genetic traits. Furthermore, crosses identified between the Nguni and Bonsmara indicated interbreeding, which could have been implications for the introduction of specific traits into the population. Significant genomic regions were identified above a threshold, with specific regions highlighted for each breed. The results obtained from the analysis using iHS identified regions of the genome that have undergone positive selection. Suggestive regions of the genome for Bonsmara on BTA 2 and 12, for Nguni significant regions on BTA 6, 7, 12, and 16, and a shared genomic region on BTA12 between Nguni and Bonsmara: This suggests that there might be common selective pressures associated with this specific genomic region for both the Nguni and Bonsmara. Similar results were also reported by [ 13 ] who highlighted shared regions on BTA 12 revealing the highest iHS score of 6.047 for Nguni and Bonsmara. In the XP-EHH method, significant regions were identified on specific chromosomes for Angus vs Simmental (BTA 3, 6, and 13) and Nguni vs Bonsmara (BTA 1, 2, 11, 14, 17, and 24). This method is useful for detecting selective sweeps and regions of the genome that have undergone recent positive selection in one population compared to another. The Rsb method, which also measures haplotype patterns, revealed genomic regions under selection in Angus vs Simmental (BTA 3, 5, 6, and 14) and Nguni vs Bonsmara (BTA 1, 4, 5, 8, 11, 14, 17, and 24). The Rsb method is particularly sensitive to recent selection events and could provide insights into the evolutionary forces shaping genetic variation. The Fst method, which calculates genetic differentiation between populations, identified significant genomic regions for Simmental vs Angus (BTA 1–29, excluding BTA 2, 12, and 27) and Nguni vs Bonsmara (BTA 1–29, excluding BTA 2, 6 and 29). The Fst method helps to identify regions of the genome that have diverged between populations, suggesting potential targets of natural selection or adaptation. Gene annotation analyses using the iHS method highlighted the association of the FAM110B gene with QTL for carcass weight and body conformation score in Simmental cattle. [ 19 ] reported the same gene on BTA 14 for carcass weight in Korean Hawoo cattle. The gene FAM110B has been previously identified to affect several traits, such as growth, birth weight, average daily gain, feed intake, meat tenderness, height, stature, and carcass weight in different beef cattle breeds ,[ 20 ],[ 21 ]. The KCNQ3 gene was identified for QTLs that are associated with important traits such as reproduction and disease traits for Bonsmara vs Nguni. The gene was also reported to be associated with milk, reproduction, and production traits in Nellore Cattle[ 21 ]. In this study, the PAPPA gene was associated with sperm count and insemination per conception in Bonsmara vs Nguni. The PAPPA gene was also reported on BTA8 for Holstein-Friesin bulls which was significant for several spermatozoa [ 22 ]. [ 23 ] reported the PAPPA gene to have a strong association with female fertility. Another gene GRIK2 was reported to be related to the nervous system in Bashan cattle [ 24 ], for the development of the nervous system in swamp buffalo and has been reported to be related to reproduction functions by its effect on gonadotropin-releasing (GnRH) secretion control [ 24 ]. In this study, the CHD7 gene was found for the body conformation score in Bonsmara vs Nguni. [ 25 ] reported the CHD7 gene detected at a 5% chromosome-wise level for carcass traits in Korean native cattle. [ 26 ] identified PRKDC to be associated with carcass traits in Hanwoo cattle, the gene was also reported to be strongly associated with subcutaneous fat deposits. Genes like RAB2A were associated with scrotal growth and male fertility in cattle [ 27 ]. RAB2A gene harbors the most significant genomic region for body confirmation, the study further reported the genes to have the highest proportion of the total additive genetic variance (3.89%) of body confirmation [ 28 ]. [ 29 ] reported SLIT2 to be associated with growth and carcass traits Simmental vs Angus, or with a function that may be related to meat production, which was different to our fundings. Tetraspanin 9 (TSPAN9) revealed an association with lactation persistency for Simmental vs Angus, which was consistent with a study by [ 30 ] who reported the gene to have an association with lactation persistency in Canadian Holstein. R-spondin 2 (RSPO2) was found for calving at ease on BTA 14 for Bonsmara vs Nguni, while [ 31 ] reported the RSPO2 gene on the same BTA14 has an association with bovine tetradysmelia in Holstein Friesian. The study also explored gene functions, revealing associations with meat and carcass traits, reproduction, health, diseases, fertility, and conformation. Gene interaction analysis through the STRING database identified a network of 63 candidate genes, highlighting the complexity of genetic interactions. Some genes exhibited multiple functions, emphasizing their multifaceted roles in various biological processes. Materials and Methods Ethics This study was approved by the Animal Research Ethics Committee of the Agricultural Research Council (APIEC17/17). This study is also reported in accordance with ARRIVE guidelines. Populations and Samples Ninety-six semen samples from Nguni: n = 28, Bonsmara: n = 21, Angus: n = 22, and Simmental: n = 25 were randomly collected from four provinces of South Africa. Following sampling, traits of the fresh bull semen were individually recorded, and the samples were then cryopreserved at the Agricultural Research Council (Animal Production Germplasm Conservation & Reproductive Biotechnologies) in Irene, South Africa. DNA extraction, genotyping, and quality control. Genomic DNA was extracted from semen samples according to the manufacturer's protocol using a NucleoMag® pathogen extraction kit. Quantification was performed using Qubit® 2.0, and quantified DNA was genotyped using the Illumina BovineSNP 150K BeadChip following the manufacturer’s protocol. Genome Studio 2.0 software was used to visualize and interpret data generated by SNPs. Plink v1.07 [ 32 ] software was used to filter the dataset following criteria: (1) eliminate SNPs with a call frequency of ≥ 90, (2) eliminate individuals with more than 5% missing genotype using (MIND) ≥ 0.05, (3) eliminate SNPs with more than 5% of missing genotype using (GENO) ≥ 0.05, (4) eliminate all SNPs with minor allele frequency of less than 5% using (MAF) ≥ 0.05 and (5) genotype frequency out of Hardy-Weinberg equilibrium (HWE > 0.00001). The study excluded 23 samples that did not meet the criteria. Then, followed by removing SNPs that were in high Linkage disequilibrium using the --indep-pairwise r2 ≥ 0.2 option, further removed related individuals using the --genome option (compute genome-wide identity by descents estimate (IBD) and --remove related.txt). StructuRly database was used to classify the remaining individuals accounting to breed type using triangle plot. Identifying signatures of selection. To assess within-population selection signatures, iHS was used to highlight regions under selection within each of the individual cattle populations. This involves identifying regions within each cattle population where genetic changes are indicative of positive selection. Methods for assessing cross-population selection signatures (Rsb, Fst and XP-EHH) in a pairwise fashion to identify regions under positive selection that are common to both cattle populations. Integrated Haplotype Score (iHS) analysis The integrated haplotype score (iHS) was used to measure the extent of extended haplotype homozygosity (EHH) around a selected allele compared. Positive iHS values indicate selection for the derived allele, while negative values suggest selection for the ancestral allele, as described by [ 33 ]. Signatures were identified within the four cattle populations and to prepare input files for the analysis, quality-controlled binary files were generated for each population using PLINK v1.9. The resulting binary files were used to create variant calling format (.vcf) files, which were sorted by base pair positions using bcftools [ 34 ], and then phased with BEAGLE v5[ 35 ]. The iHS scores were calculated in R v4.2.2 using the R package REHH [ 36 ] with the function ihs2ihs. Candidate regions were identified at MAF of < 0.05, and regions under significant selection were identified at p < 0.0001. The outputs were visualized in R v4.2.2 for all populations using Manhattan plots, and comma-separated value (.csv) files were created with significant regions and chromosomes that passed a threshold of -log (iHS) > 4 per population. XP-EHH and Rsb analysis Evidence of positive selection in populations was further investigated by comparing pairs of populations using the XP-EHH and Rsb methods. The regions with selection signatures were identified using pairwise comparisons between the four populations (Nguni & Bonsmara and Angus & Simmental). The XP-EHH is an extension of iHS and was used to measure the differences in EHH between populations. iHS It can be used to identify loci that have undergone selection in one population but not the other. The analyses consider distinct SNPs amongst populations that are monorphic for one and polymorphic for others using the comparison of the EHH score of two populations [ 37 ]. The Rsb compares the EHH of selected alleles between two populations, and it can also be used to identify loci that have undergone recent positive selection within one population compared to another. This analysis uses the formula [ 38 ] The R package REHH was used to determine the genomic regions under selection for the XP-EHH and Rsb using the functions ies2xpehh and ines2rsb Rsb, respectively. Candidate regions were identified at an MAF of < 0.05, and significant regions were identified at p < 0.001. The outputs were visualized using Manhattan plots and .csv files were created with significant regions and chromosomes (p < 0.001). Fst analysis. F st was used to measure population differentiation due to genetic structure. High Fst values suggest genetic differences between populations. While not a direct measure of selection, high FST values can indicate regions of the genome that have been under selective pressure [ 39 ]. The Fst computation was performed in PLINK v1.9 using the command --fst, while the qqman [ 40 ] package in R was used for Manhattan plots visualization. Gene Functional Annotation Gene annotation was carried out using the genomic regions identified as positive signatures of selection, from all methods used (iHS, XP-EHH, Rsb and Fst). Genes were annotated with the cattle gene assembly ARS-UCD1.2 using Bio Mart, a program in Ensemble, furthermore, ShinyGO v0.77 was used to determine the functions, pathway analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways( http://bioinformatics.sdstate.edu/go/ ) of the identified genes. String database was used to compute interaction network between genes. Conclusions In conclusion, this study presented a comprehensive genetic analysis of four distinct cattle breeds (Bonsmara, Simmental, Nguni, and Angus) encompassing a range of methodologies such as iHS, XP-EHH, Rsb, and Fst. The integration of these methods in this study offers a unique perspective on the genetic landscape of South African beef cattle breeds. While previous studies have individually applied some of these methods, this study uniquely combined them, providing a more holistic understanding of selection signatures and candidate genes in beef cattle breeds. The identification of common regions across all breeds may highlight genomic areas crucial for general cattle adaptability and fitness. Overall, these methods provide complementary information about the genetic differences and similarities between the examined cattle breeds. It's important to note that these findings can have implications for breeding strategies, conservation efforts, and our understanding of the genetic basis of important traits in cattle populations. Declarations Author Contribution M.C.M. manuscript writing; A.M. data analysis and reviewing the manuscript; K.N. reviewing the manuscript; N.J. reviewing and editing; K.H. reviewing the manuscript; T.M. reviewing the manuscript; P.T. reviewing the manuscript and B.M.reviewing the manuscript Data availability: The data for findings in this study are available at the European Variation Archive (EVA), with the accession number: PRJEB72903. References Loftus RT, MacHugh DE, Bradley DG, Sharp PM, Cunningham. 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A new F ST -based method to uncover local adaptation using environmental variables ed R B O’Hara. Methods Ecol Evol. 2015;6:1248–58. Turner SD. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. Bioinformatics; 2014. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-3945698","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276029207,"identity":"20a9fb67-501f-472b-bc22-795b464006b3","order_by":0,"name":"Mamokoma Cathrine Modiba","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACgwMMBocZGA7L8xOtxRKi5bnhzDYGxgaitNgDtTAzMPxP2HCMWC1mx5s3Hi6ouZ1gfL/H/AFDjR2DfPsBAlrOHCs4POPY7QSzYzyGDQzHkhkYexIIaLmRY3CYhw2mhe0AAzMDAS0GYC3/DicYt4G0/DvAwMb/gAgtvG2HEzawAbUwth1g4JEgZAvILzP7DhvOOJZWOCOxL5lHQoKQLcebN38u+AaMyubDGz58+GYnJ99PwBZUAFTMQ4r6UTAKRsEoGAU4AACxyExsIepj0wAAAABJRU5ErkJggg==","orcid":"","institution":"Tshwane University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Mamokoma","middleName":"Cathrine","lastName":"Modiba","suffix":""},{"id":276029208,"identity":"bbee79b8-48bb-457a-abd0-682cc59de416","order_by":1,"name":"Aletta Matshidiso Magoro","email":"","orcid":"","institution":"Tshwane University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Aletta","middleName":"Matshidiso","lastName":"Magoro","suffix":""},{"id":276029209,"identity":"ebd12238-542b-4284-a5ff-e7f235408795","order_by":2,"name":"Khathutshelo Agree Nephawe","email":"","orcid":"","institution":"Tshwane University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Khathutshelo","middleName":"Agree","lastName":"Nephawe","suffix":""},{"id":276029210,"identity":"fd9255b6-43a9-40a0-a2ac-8f930c1a0069","order_by":3,"name":"Jabulani Nkululeko Ngcobo","email":"","orcid":"","institution":"Tshwane University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Jabulani","middleName":"Nkululeko","lastName":"Ngcobo","suffix":""},{"id":276029211,"identity":"db54ab47-00ff-4b51-bba3-cb560d3eb7b4","order_by":4,"name":"Khanyisile Hadebe","email":"","orcid":"","institution":"Agricultural Research Council of South Africa","correspondingAuthor":false,"prefix":"","firstName":"Khanyisile","middleName":"","lastName":"Hadebe","suffix":""},{"id":276029212,"identity":"8a83c153-696c-40be-a553-eac532570861","order_by":5,"name":"Takalani Judas Mpofu","email":"","orcid":"","institution":"Tshwane University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Takalani","middleName":"Judas","lastName":"Mpofu","suffix":""},{"id":276029213,"identity":"6a04816d-4359-4a53-8c31-5451698a57a3","order_by":6,"name":"Peter R. 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The black line represents a threshold of 4, windows above this value were considered candidate selection signature, (\u003cstrong\u003eA\u003c/strong\u003e) Bonsmara (BON), (\u003cstrong\u003eB\u003c/strong\u003e) Simmental (SIM), (\u003cstrong\u003eC\u003c/strong\u003e) Nguni (NGU) and (\u003cstrong\u003eD\u003c/strong\u003e) Angus (ANG) depicted SNP on BTA 18.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3945698/v1/3b79bcbdee20e9470960ea43.png"},{"id":52026382,"identity":"34bae1d9-231a-46a1-b34a-58c02e77fa58","added_by":"auto","created_at":"2024-03-05 15:51:18","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":380456,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plot of genome wide distribution of EX-HPP and Rsb method in 50 windows slide for SNP dataset. The black line represents the threshold of 4, windows above this value were considered candidate selection signature, (\u003cstrong\u003eA\u003c/strong\u003e) Simmental (SIM) versus Angus (ANG); (\u003cstrong\u003eB\u003c/strong\u003e) Bonsmara (BON) versus Nguni (NGU) for EX-HPP, (\u003cstrong\u003eC\u003c/strong\u003e) Simmental (SIM) versus Angus (ANG) and (\u003cstrong\u003eD\u003c/strong\u003e) Bonsmara (BON) versus Nguni (NGU) for Rsb.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3945698/v1/509c5e1167fab6efa2b0a738.png"},{"id":52026386,"identity":"52a11f95-ae6c-4c62-b2f6-d017ef6780d6","added_by":"auto","created_at":"2024-03-05 15:51:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":380525,"visible":true,"origin":"","legend":"\u003cp\u003eManhattan plot of genome wide distribution of Fst in 50 windows for SNP dataset. The black line represents the threshold of 4, windows above this value were considered candidate selection signature, (\u003cstrong\u003eA\u003c/strong\u003e) Simmental (SIM) versus Angus (ANG) and (\u003cstrong\u003eB\u003c/strong\u003e) Bonsmara (BON) versus Nguni (NGU) depleted SNP on BTA 1 - 28.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3945698/v1/6f6d38b8950b592b23af2710.png"},{"id":52026385,"identity":"e1c3d03d-e6bb-42de-abcd-0d9cb1137713","added_by":"auto","created_at":"2024-03-05 15:51:19","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":967376,"visible":true,"origin":"","legend":"\u003cp\u003eGene interaction network associated with all traits in all breeds. Colored nodes (first shell of interaction), white nodes (second shell of interaction), empty nodes (protein with unknown structure) and filled nodes (structure known\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3945698/v1/abc88b0734a28c891edca161.png"},{"id":68269265,"identity":"5b5c2dc6-4b94-4589-ad92-91a205a78b8d","added_by":"auto","created_at":"2024-11-05 13:24:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3219256,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3945698/v1/0d49eec3-bdb2-43af-b954-173f82dbb545.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide Scans Reveal Selection Signatures and Cross- Population Variation in South African and European Beef Cattle Breeds","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe genomic structure of livestock has undergone significant transformations since their domestication [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Over the years, evolution, mutation, selection, migration and genetic drift have enabled various breeds to survive, grow and reproduce in a specific environment [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. South African cattle are categorised into four different groups, namely \u003cem\u003eBos taurus\u003c/em\u003e (e.g. Angus), \u003cem\u003eB. indicus\u003c/em\u003e (e.g. Simmental), a local form of Sanga cattle (a cross of \u003cem\u003eB. taurus\u003c/em\u003e and \u003cem\u003eB. indicus\u003c/em\u003e) known as Nguni, and a recently established breed resulting from crosses of three other breeds (two \u003cem\u003eB. taurus\u003c/em\u003e and one Sanga) known as Bonsmara [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These breeds played an essential role in the cultural, economic and social aspects of South Africa [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The process of domestication, and artificial selection, combined with the recent rapid decrease in effective population size, has left detectable signatures of selection in numerous regions of the cattle genome [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Several studies have scanned whole genomes and reported on signatures of selection and genes that harbour traits of economic importance in multiple species. However, information on selection signatures for production traits in South African beef cattle breeds is limited to only two methods of analysis. Also, taking into account that domestication has contributed to changing the morphological and behavioral characteristics of modern domestic animals, along with breed formation and selection schemes, this is influenced by improving the production of specific products to achieve a certain standard [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Also, the commercial livestock sector is facing several new challenges, with the demand for livestock products continuously increasing, however, long-term sustainability is questionable [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Climate change is putting new pressures on livestock production, while livestock themselves are contributing through greenhouse emissions to climate change [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Given the mixed-breed nature of most African cattle populations and the presence of unique African breeds, the availability of reference genomes is important for genomic studies of African cattle. In particular, methods for obtaining genomic information on animals are developing rapidly (e.g. genome-wide sequencing and genotyping), thus opening new opportunities for genomic selection [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e];[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe signatures of selection in South African beef cattle can be studied using various methods that are commonly used in population genetics to identify genomic regions under selection, including iHS (integrated haplotype score), Rsb (relative extended haplotype homozygosity), XP-EHH (cross-population extended haplotype homozygosity), and \u003cem\u003eF\u003c/em\u003eST (fixation index) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] reported on signatures of positive selection underlying beef production traits in Korean cattle breeds, while [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] reported genomic scans for selection signatures, and revealed candidate genes for adaptation and production traits in a variety of cattle breeds in India. In South African beef cattle breeds, [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] reported several candidate genomic regions either directly or indirectly involved in tropical adaptation, immune response activation, tick and parasite resistance and reproductive performance. Several studies have explored genomic selection signature regions using high-density single nucleotide polymorphism (SNP) genotyping assays to scan cattle genomes for positions that may have been targeted by selection [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] identified candidate regions under positive selection, while another study conducted in South Africa identified several candidate genomic regions showing positive selection signatures using the Illumina High-Density Bovine SNP BeadChip [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and also using Gene Seek Genomic Profiler 150 K (GGP Bovine 150 K) bead chip of two South African cattle breeds. The detection of signatures of selection may contribute to a better understanding of the mechanisms that underlie traits that have been exposed to intensive natural and artificial selection [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Such information also provides important insights into the mechanisms of evolution [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], the use of loci under selection for breeding programs and is useful for the annotation of significant functional genomic regions [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The recent availability of genomic information on domestic animal species, and the development of improved statistical tools, make the identification of signatures of selection in each species possible. Here, we compared (iHS, Rsb, XP-EHH, and Fst) to identify signatures of selection in South African beef cattle.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003ePopulation and Identifying signature of selection.\u003c/b\u003e \u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter quality control, 73 individuals were retained, related individuals with a PI_HAT value greater than 0.05 were removed and a subset of 105,675 SNPs remained for further analysis. The triangle population structure was used to cluster and confirm individuals within the remaining populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The Integrated Haplotype Score (iHS) analysis method was used to construct Manhattan plots to visually represent the genomic distribution of positive signatures of selection in all selected breeds. Positive signatures of selection were identified within each of the four populations (Bonsmara, Simmental, Nguni, and Angus). Significant genomic regions above the threshold of 4 for signatures of selection were identified. For Bonsmara (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea): suggestive genomic regions were detected on BTA 2 and 12. For Simmental (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb): significant regions were found on BTA 2, 4, 6, 13, 14, and 17. For Nguni (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec): significant regions were observed on BTA 6, 7, 12, and 16. For Angus (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed): a significant region was found on BTA 18. Ten significant regions were identified for all with BTA 12 shared between Nguni and Bonsmara. Three more analyses were incorporated using XP-EHH, Rsb and Fst methods for cross-population comparisons. XP-EHH method in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Rsb Figure in 3 and Fst Figure in 4. Manhattan plots were used to visualize the distribution of signatures of selection across the genome for all methods and all four breeds. For Angus vs Simmental (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea): significant regions were observed on BTA 3, 6, and 13 and For Nguni vs Bonsmara (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb): significant regions were identified on BTA 1, 2, 11, 14, 17, and 24. The Rsb method revealed significant regions of the genome above the threshold. For Angus vs Simmental (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea): suggestive genomic regions were detected on BTA 3, 5, 6, and 14. For Nguni vs Bonsmara (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb): Suggestive genomic regions were identified on BTA 1, 4, 5, 8, 11, 14, 17, and 24. The Fst analysis distribution of regions with signatures of selection across the genome is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e for Simmental vs Angus (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea) the most significant genomic regions with signatures were identified on BTA 1\u0026ndash;29. Notably, BTA 2, 12, and 27 were excluded from the analysis. Nguni vs Bonsmara (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) significant genomic regions with signatures of selection were identified on BTA 1\u0026ndash;28 excluding BTA 2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGene annotation\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGene annotation analyses were conducted using the iHS method shown in (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), for Simmental cattle, identification of several significant genes, including FAM110B, RBMS1, SRD5A3, TBX5, and TMEM165. Among these genes, FAM110B was found to be associated with quantitative trait loci (QTL) related to carcass weight and body conformation score. In contrast, Bonsmara cattle revealed only two candidate genes, CDK8 and FLT1, located on BTA 12, with Angus exhibiting no identified candidate gene on BTA 18. Nguni cattle, on the other hand, presented candidate genes such as CRB1, PLAG2GA, UBL3, VASH2, and ENSBTAG00000019340. Interestingly, one gene, CDK8, was shared between the Bonsmara and Nguni breeds on BTA 12, including the function of the CDK8 gene since it is common in the two indigenous breeds. Furthermore, a comprehensive examination of the cross-population between Simmental vs Angus and Bonsmara vs Nguni using the Fst methods was conducted. The Fst method applied to Bonsmara vs Nguni, identified the following candidate genes, PLCXD3, FAM149B1, TCAIM, AFG1L, FOXO3, KCNQ3, PAPPA, ENSBTAG-00000008851, HMMR, PLXDC2, ZNF704, and GRIK2. In the Simmental vs Angus comparison, genes such as MYOID, FAM172A, MIPOL1, CHMP4B, FOX13, and KCNK13 were identified. Furthermore, annotation was conducted using both the Rsb and XP-EHH methods, and the results are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e across all four breeds. Cross-population comparisons, between Nguni vs Bonsmara, distinctive candidate genes revealed genes such as \u003cem\u003eCHD7, CLVS1, KHDRBS4, KHDRBS5, PAG1, PRKDC, RAB2A, RSPO2, FSTL5\u003c/em\u003e, KHDRBS3, and TSPAN9. Similarly, the Rsb method applied to the Simmental vs Angus cross-population comparison identified candidate genes including \u003cem\u003eADAR, ISG20L2\u003c/em\u003e, \u003cem\u003eSNX31, STK3, and TSPAN9\u003c/em\u003e. Turning to the XP-EHH method, specific candidate genes were pinpointed for the Bonsmara vs Nguni comparison\u0026mdash;namely, RUNX3 and NUDGT6. In the context of the Simmental vs Angus cross-population comparison using XP-EHH, the gene SLIT2 emerged as a candidate. These findings underscore distinctive genetic markers associated with cross-population differences and provide valuable insights into potential genomic factors contributing to the unique characteristics of these breeds. The study further identified gene description using ShinyGO for all candidate genes revealed through the iHS, Rsb, Fst, and XP-EHH methods, as presented in Tables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e to \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBreed, chromosome number, position, and significant genes for the iHS and Fst\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBREED\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTART\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEND\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGENES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQTL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSIM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24381950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24382000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFAM110B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCarcass weight and conformation score\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35950750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35950800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eRBMS1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70817900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70817950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eSRD5A3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60241650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60241700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eTBX5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70842800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70842850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eTMEM165\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBON\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33130350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33130400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCDK8, FLTI\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eANG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38074000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38074050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNGU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33153350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33153400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCDK8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e76369400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e76369450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCRB1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e40594100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e40594150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eENSBTAG00000019340\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68039450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68039500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePLA2G4A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30808950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e30809000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eUBL3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70606700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e70606750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eVASH2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNGU\u0026amp;BON\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33153350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33153400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCDK8\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNGU vs BON\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33016045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e33218726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePLCXD3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29237038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e29287046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFAM149B1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16271889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16309668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eTCAIM\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41655110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41851120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eAFG1L\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41522588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41620269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFOXO3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8738181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e9029965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eKCNQ3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBovine respiratory disease susceptibility\u003c/p\u003e \u003cp\u003ecalving ease and insemination per conception\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e105351900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105612449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePAPPA\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSperm count and Insemination per conception\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57488548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57494764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eENSBTAG00000008851\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e75137017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e75170625\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eHMMR\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21312388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e21747796\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ePLXDC2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43837811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e44085912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eZNF704\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47889245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e48618507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eGRIK2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBovine tuberculosis susceptibility and insemination per conception\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSIM vs ANG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17330801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17691005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMYO1D\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93088059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93506932\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFAM172A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47398496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47734138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eMIPOL1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63226304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e63271073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eCHMP4B\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47351626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e47386204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eFOX13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e101723038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e101828432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eKCNK13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e:Breed, chromosome number, position, and significant genes for Rsb and XP-EHH.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBREED\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSTART\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEND\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGENES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQTL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBON vs NGU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26457000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26457150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCHD7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBody confirmation score\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26923350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26923500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCLVS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6486300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6486450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKHDRBS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6527100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6527250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKHDRBS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6589200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6589350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePAG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44291850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e44292000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePRKDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMarbling score fat thickness at the 12 rib, fat weight and heifer pregnancy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e19517850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19518000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRAB2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCarcass weight and body confirmation score\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26211150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26211300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRSPO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCalving at ease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22618800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22618950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFSTL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36457500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36457650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKHDRBS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e106744050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e106744200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTSPAN9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSIM vs ANG\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15972750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15972900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eADAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14106300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14106450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eISG20L2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39816000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39816150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSLIT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eInterval from fisrt to last insemination\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e63725700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63725850\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSNX31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e65488500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e65488650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSTK3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1,07E\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,07E\u0026thinsp;+\u0026thinsp;08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTSPAN9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLactation persistency\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBON \u0026amp; NGU\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e127891800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127891950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eRUNX3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34752450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34752600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eNUDGT6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFull gene description for the iHS methods\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMETHOD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSYMBOL OF GENES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSEMBLE GENE ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENTREZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTYPE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSPECIES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePOSITION (MBP)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDESCRIPTION\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eiHS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRBMS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000005180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e526135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35.855477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRNA binding motif single stranded interacting protein 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSRD5A3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000014913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e535834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e70.803119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003esteroid 5 alpha-reductase 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTMEM165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000001269\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e532600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e70.838726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003etransmembrane protein 165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUBL3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000012170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e526950\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e30.806229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eubiquitin like 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFLT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000016915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e503620\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.624125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003efms related receptor tyrosine kinase 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCDK8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000016737\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e507149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e33.082022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ecyclin dependent kinase 8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAM110B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000050550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e767946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.365744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003efamily with sequence similarity 110 member B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePLA2G4A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000013298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e525072\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e67.906979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ephospholipase A2 group\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVASH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000003701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProtein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e70.601757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003evasohibin 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCRB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000008944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e520406\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76.188124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ecrumbs cell polarity complex component 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTBX5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000011384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e532970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60.228521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eT-box transcription factor 5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eENSBTAG00000019340\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eENSBTAG00000019340\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHMMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000014773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e281227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75.137017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ehyaluronan mediated motility receptor\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAM172A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000050195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e617002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e93.088059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003efamily with sequence similarity 172 member A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePAPPA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000004010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e282647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e105.3519\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003epappalysin 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFOXO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000011234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e535530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41.522588\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eforkhead box O3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAFG1L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000014592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e537689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41.65511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eAFG1 like ATPase\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGRIK2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000033153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e615226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47.889245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eglutamate ionotropic receptor kainate type subunit 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFull gene description for iHS, Fst and Rsb methods.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMETHOD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSYMBOL OF GENES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSEMBLE GENE ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENTREZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTYPE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSPECIES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePOSITION (MBP)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDESCRIPTION\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKCNK13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000045849\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e787307\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e101.723038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003epotassium two pore domain channel subfamily K member 13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePLXDC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000009475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e515731\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e21.312388\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eplexin domain containing 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHMP4B\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000013387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e616164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e63.226304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003echarged multivesicular body protein 4B\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFst\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKCNQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000020667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e281884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.738181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003epotassium voltage-gated channel subfamily Q member 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZNF704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000021743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e513243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e43.837811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ezinc finger protein 704\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMYO1D\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000015527\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e522967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e17.330801\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003emyosin ID\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePLCXD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000010822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e781239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e33.016045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ePhosphatidylinositol-specific phospholipase C X domain containing 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMIPOL1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000000655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e528380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e47.398496\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003emirror-image polydactyly 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTCAIM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000016622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e515010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16.271889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eT-cell activation inhibitor, mitochondrial\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFAM149B1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000019130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e533952\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29.237038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003efamily with sequence similarity 149 member B1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRsb\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDGKB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000021905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e537171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22.3898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ediacylglycerol kinase beta\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSPAN9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000049163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e540132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e106.5532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003etetraspanin 9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKHDRBS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000002181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e615095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6.4007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eKH RNA binding domain containing, signal transduction associated 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePRKDC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000017019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e512740\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e19.4243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eprotein kinase, DNA-activated, catalytic subunit\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRAB2A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000000948\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e508373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26.1811\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRAB2A, member RAS onco family\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCHD7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000021841\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e533175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e26.3612\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003echromodomain helicase DNA binding protein 7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFull gene description for Rsb and XP-EHH methods.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMETHODS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSYMBOL OF GENES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSEMBL GENE ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eENTREZ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTYPE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSPECIES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eCHR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ePOSITION (MBP)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eDESCRIPTION\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRSPO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000013598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e536121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e56.3852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eR-spondin 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCLVS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000043978\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e532808\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e26.8549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eclavesin 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFSTL5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000047595\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e782831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36.2608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003efollistatin like 5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePAG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000054180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e281964\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38.6048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003epregnancy-associated glycoprotein 1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKHDRBS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot mapped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eKHDRBS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNot mapped\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDGKB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000021905\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e537171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.3898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ediacylglycerol kinase beta\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTSPAN9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000049163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e540132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e106.5532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003etetraspanin 9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eXP-EHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSLIT2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000005108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e534164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e39.779895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eslit guidance ligand 2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNUDT6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000005695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e100126047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e34.747124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003enudix hydrolase 6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRUNX3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eENSBTAG00000050474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e617389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprotein_coding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e127.9983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eRUNX family transcription factor 3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGene Functions\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe gene symbols and IDs were accurately translated from both Ensemble and NCBI databases, providing actual gene positions and full gene names. The study further specified the function of each gene shown in Tables\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e to \u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Some genes were multi-functional genes, meaning the same gene with different functions. The number of genes per function ranged from 1 to 15, were genes like RAB2A, TMEM165, VASH2, SLIT2, CRB1, TBX5, CHMP4B, AFG1L, MYO1D, PRKDC, FAM149B1, CHD7, DGKB, CLVS1, and FAM172A were responsible for cellular component organization or biogenesis and cellular component organization. Candidate genes like \u003cem\u003eVASH2, SLIT2, CRB1, FOXO3, TBX5, RSPO2\u003c/em\u003e, \u003cem\u003eFLT1, PRKDC, CHD7, FSTL5\u003c/em\u003e, and \u003cem\u003eFAM172A\u003c/em\u003e were responsible in developmental processes and anatomical structure development. Genes \u003cem\u003eTMEM165, SLIT2, CRB1, FOXO3, TBX5, PLA2G4A, KCNQ3, CHD7, DGKB, GRIK2\u003c/em\u003e and \u003cem\u003eKCNK13\u003c/em\u003e were responsible regulation of biological quality. Other functions were specific to some genes, genes as \u003cem\u003eFOXO3\u003c/em\u003e were responsible for the multi-organism process, Autophagy, Sexual reproduction, muscle adaptation and the multi-organism reproductive process. \u003cem\u003ePRKDC\u003c/em\u003e Somatic diversification of immune receptors, Activation of the immune response, Production of molecular mediator of the immune response. \u003cem\u003eSLIT2\u003c/em\u003e Cell growth and GRIK2 Behavioral defense response. Notably, PAPPA was associated with fertility traits (sperm count and insemination per conception), KCNQ3 was linked to QTL for diseases (respiratory disease susceptibility and tuberculosis susceptibility), and GRIK2 exhibited associations with fertility traits. Most of the genes in this study were associated with QTL for meat and carcass traits (body weight, marbling score fat thickness at the 12th rib and fat weight), reproduction, semen traits (sperm count), health traits, diseases (Bovine respiratory diseases susceptibility and Bovine tuberculosis susceptibility and insemination per conception), fertility traits (calving ease, insemination per conception, interval from first to last insemination, lactation persistency and heifer pregnancy) and conformation (body confirmation score).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGene function and number of genes per function\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of genes per function\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh level go category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCellular component organization or biogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRAB2A TMEM165 VASH2 SLIT2 CRB1 TBX5 CHMP4B AFG1L MYO1D PRKDC FAM149B1, CHD7 DGKB CLVS1 FAM172A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCellular component organization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eRAB2A TMEM165 VASH2 SLIT2 CRB1 TBX5 CHMP4B AFG1L MYO1D PRKDC FAM149B1 CHD7 DGKB CLVS1 FAM172A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDevelopmental process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVASH2 SLIT2 CRB1 FOXO3 TBX5 RSPO2 FLT1 PRKDC CHD7 FSTL5 FAM172A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnatomical structure development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVASH2 SLIT2 CRB1 FOXO3 TBX5 RSPO2 FLT1 PRKDC CHD7 FSTL5 FAM172A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulation of biological quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTMEM165 SLIT2 CRB1 FOXO3 TBX5 PLA2G4A KCNQ3 CHD7 DGKB GRIK2 KCNK13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePositive regulation of biological process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVASH2 SLIT2 FOXO3 TBX5 PLA2G4A RSPO2 FLT1 PRKDC CHD7 RUNX3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNegative regulation of biological process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 NUDT6 FOXO3 TBX5 MYO1D FLT1 PRKDC CHD7 GRIK2 FAM172A\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiosynthetic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTMEM165\u0026nbsp;FOXO3 TBX5 PLA2G4A SRD5A3 PRKDC ZNF704 CHD7 RUNX3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse to external stimulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 CRB1 FOXO3 FLT1 PRKDC CHD7 DGKB GRIK2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulation of signaling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 FOXO3 TBX5 RSPO2 FLT1 CHD7 DGKB GRIK2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnatomical structure morphogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVASH2 SLIT2 CRB1 FOXO3 TBX5 FLT1 CHD7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEstablishment of localization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTMEM165 CHMP4B MYO1D KCNQ3 CHD7 GRIK2 KCNK13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSystem process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 CRB1 FOXO3 TBX5 CHD7 GRIK2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell population proliferation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVASH2 NUDT6 FOXO3 TBX5 FLT1 PRKDC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulation of localization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 TBX5 FLT1 KCNQ3 CHD KCNK13\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse to chemical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 FOXO3 FLT1 PRKDC CHD7 RUNX3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulation of multicellular organismal process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVASH2 SLIT2 FOXO3 TBX5 FLT1 CHD7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulation of response to stimulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2\u003c/em\u003e, \u003cem\u003eFOXO3, RSPO2 FLT1 PRKDC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAnatomical structure formation involved in morphogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVASH2 SLIT2 CRB1 TBX5 FLT1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCellular localization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCRB1 CHMP4B MYO1D FAM149B CHD7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReproduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVASH2 SLIT2 FOXO3 CHD7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmune system process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 FLT1 PRKDC CHD7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGene function and number of genes per function\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of genes per function\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh level go category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrowth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 FOXO3 TBX5 CHD7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eReproductive process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVASH2 SLIT2 FOXO3 CHD7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe developmental process involved in reproduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVASH2 SLIT2 FOXO3 CHD7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse to stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFOXO3 PLA2G4A PRKDC GRIK2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCatabolic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFOXO3 PLA2G4A AFG1L SRD5A3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCellular component biogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 TBX5 PRKDC FAM149B1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDevelopmental growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 FOXO3 TBX5 CHD7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulation of the developmental process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eVASH2 TBX5 FLT1 CHD7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocomotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 TBX5 FLT1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImmune system development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFLT1 PRKDC CHD7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse to biotic stimulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePRKDC CHD7\u0026nbsp;DGKB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse to endogenous stimulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 PRKD RUNX3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulation of growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 TBX5 CHD7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulation of locomotion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 TBX5 FLT1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe biological process involved in interspecies interaction between organisms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePRKDC CHD7 DGKB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell motility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 TBX5 FLT1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocalization of cell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 TBX5 FLT1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse to other organism\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePRKD CHD7 DGKB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehavior\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCHD7 GRIK2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulation of immune system process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 PRKDC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eResponse to abiotic stimulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCRB1 GRIK2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOvulation cycle process, Regulation of cellular component biogenesis, Ovulation cycle, Multicellular organismal reproductive process, Multicellular organism reproduction\u0026nbsp;and Rhythmic process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 FOXO3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMacromolecule localization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCRB1 FAM149B1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGene function and number of genes per function\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of genes per function\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh level go category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGenes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLeukocyte migration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2 FLT1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDetection of stimulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCRB1 GRIK2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulation of molecular function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eMYO1D FLT1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMulti-organism process, Sexual reproduction, muscle adaptation multi-organism reproductive process, autophagy\u0026nbsp;and maintenance of cell number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eFOXO3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBehavioral defense response\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eGRIK2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActivation of immune response, production of molecular mediator of immune response, immune response,\u0026nbsp;and Somatic diversification of immune receptors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ePRKDC\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocomotory behavior, leukocyte activation and maintenance of location\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCHD7\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eSLIT2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGene Interaction\u003c/h2\u003e \u003cp\u003eGene interactions among candidate genes were elucidated through the utilization of the STRING database (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The analysis resulted in the identification of 63 nodes, each representing a candidate gene. The average node degree was calculated to be 1.84, indicating the extent of interactions each gene had within the network. A notable finding emerged with an enrichment P-value of 6.99e-05, signifying the statistical significance of the observed gene interactions. However, some genes did not exhibit any interactions which was led by underscoring the complexity of the genetic network.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study describes a comprehensive genetic analysis conducted on four South African beef cattle breeds (Bonsmara, Simmental, Nguni, and Angus) using various methods such as iHS, XP-EHH, Rsb, and Fst to identify signatures of selection and candidate genes associated with unique traits within each breed. These methods were reported in several studies, however, not all methods were incorporated in one study for South African beef cattle breeds. An example is a study by [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], using iHS and XP-EHH, to identify candidate regions that show preferential selection in the genome of South African Nguni and Bonsmara cattle. A study by [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] reported on iHS and Rsb analysis to detect selected regions in the local North African cattle. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] identified meat quality-related gene regions that are positively selected in Ankole (Sanga) and indicus (Boran, Ogaden, and Kenana) breeds using cross-population XP-EHH and XP-CLR methods. [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] research was based on the Fst and iHS methods in six South African Bonsmara, Nguni, Afrikaner, and Sanga breeds.\u003c/p\u003e \u003cp\u003eThe filtered data was used, and the population structure was confirmed using a triangle plot, in which 73 individuals were confirmed. The study identified four specific clusters for Angus, Simmental, Nguni, and Bonsmara which highlighted distinct genetic characteristics, reflecting possible breed origins or specific genetic traits. Furthermore, crosses identified between the Nguni and Bonsmara indicated interbreeding, which could have been implications for the introduction of specific traits into the population. Significant genomic regions were identified above a threshold, with specific regions highlighted for each breed. The results obtained from the analysis using iHS identified regions of the genome that have undergone positive selection. Suggestive regions of the genome for Bonsmara on BTA 2 and 12, for Nguni significant regions on BTA 6, 7, 12, and 16, and a shared genomic region on BTA12 between Nguni and Bonsmara: This suggests that there might be common selective pressures associated with this specific genomic region for both the Nguni and Bonsmara. Similar results were also reported by [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] who highlighted shared regions on BTA 12 revealing the highest iHS score of 6.047 for Nguni and Bonsmara.\u003c/p\u003e \u003cp\u003eIn the XP-EHH method, significant regions were identified on specific chromosomes for Angus vs Simmental (BTA 3, 6, and 13) and Nguni vs Bonsmara (BTA 1, 2, 11, 14, 17, and 24). This method is useful for detecting selective sweeps and regions of the genome that have undergone recent positive selection in one population compared to another. The Rsb method, which also measures haplotype patterns, revealed genomic regions under selection in Angus vs Simmental (BTA 3, 5, 6, and 14) and Nguni vs Bonsmara (BTA 1, 4, 5, 8, 11, 14, 17, and 24). The Rsb method is particularly sensitive to recent selection events and could provide insights into the evolutionary forces shaping genetic variation. The Fst method, which calculates genetic differentiation between populations, identified significant genomic regions for Simmental vs Angus (BTA 1\u0026ndash;29, excluding BTA 2, 12, and 27) and Nguni vs Bonsmara (BTA 1\u0026ndash;29, excluding BTA 2, 6 and 29). The Fst method helps to identify regions of the genome that have diverged between populations, suggesting potential targets of natural selection or adaptation.\u003c/p\u003e \u003cp\u003eGene annotation analyses using the iHS method highlighted the association of the \u003cem\u003eFAM110B\u003c/em\u003e gene with QTL for carcass weight and body conformation score in Simmental cattle. [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] reported the same gene on BTA 14 for carcass weight in Korean Hawoo cattle. The gene \u003cem\u003eFAM110B\u003c/em\u003e has been previously identified to affect several traits, such as growth, birth weight, average daily gain, feed intake, meat tenderness, height, stature, and carcass weight in different beef cattle breeds ,[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e],[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The \u003cem\u003eKCNQ3\u003c/em\u003e gene was identified for QTLs that are associated with important traits such as reproduction and disease traits for Bonsmara vs Nguni. The gene was also reported to be associated with milk, reproduction, and production traits in Nellore Cattle[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this study, the PAPPA gene was associated with sperm count and insemination per conception in Bonsmara vs Nguni. The PAPPA gene was also reported on BTA8 for Holstein-Friesin bulls which was significant for several spermatozoa [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] reported the PAPPA gene to have a strong association with female fertility. Another gene GRIK2 was reported to be related to the nervous system in Bashan cattle [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], for the development of the nervous system in swamp buffalo and has been reported to be related to reproduction functions by its effect on gonadotropin-releasing (GnRH) secretion control [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. In this study, the CHD7 gene was found for the body conformation score in Bonsmara vs Nguni. [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e] reported the CHD7 gene detected at a 5% chromosome-wise level for carcass traits in Korean native cattle. [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] identified PRKDC to be associated with carcass traits in Hanwoo cattle, the gene was also reported to be strongly associated with subcutaneous fat deposits. Genes like RAB2A were associated with scrotal growth and male fertility in cattle [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. RAB2A gene harbors the most significant genomic region for body confirmation, the study further reported the genes to have the highest proportion of the total additive genetic variance (3.89%) of body confirmation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] reported \u003cem\u003eSLIT2\u003c/em\u003e to be associated with growth and carcass traits Simmental vs Angus, or with a function that may be related to meat production, which was different to our fundings. Tetraspanin 9 (TSPAN9) revealed an association with lactation persistency for Simmental vs Angus, which was consistent with a study by [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] who reported the gene to have an association with lactation persistency in Canadian Holstein. R-spondin 2 (RSPO2) was found for calving at ease on BTA 14 for Bonsmara vs Nguni, while [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] reported the RSPO2 gene on the same BTA14 has an association with bovine tetradysmelia in Holstein Friesian. The study also explored gene functions, revealing associations with meat and carcass traits, reproduction, health, diseases, fertility, and conformation. Gene interaction analysis through the STRING database identified a network of 63 candidate genes, highlighting the complexity of genetic interactions. Some genes exhibited multiple functions, emphasizing their multifaceted roles in various biological processes.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEthics\u003c/h2\u003e \u003cp\u003eThis study was approved by the Animal Research Ethics Committee of the Agricultural Research Council (APIEC17/17). This study is also reported in accordance with ARRIVE guidelines.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003ePopulations and Samples\u003c/h2\u003e \u003cp\u003eNinety-six semen samples from Nguni: n\u0026thinsp;=\u0026thinsp;28, Bonsmara: n\u0026thinsp;=\u0026thinsp;21, Angus: n\u0026thinsp;=\u0026thinsp;22, and Simmental: n\u0026thinsp;=\u0026thinsp;25 were randomly collected from four provinces of South Africa. Following sampling, traits of the fresh bull semen were individually recorded, and the samples were then cryopreserved at the Agricultural Research Council (Animal Production Germplasm Conservation \u0026amp; Reproductive Biotechnologies) in Irene, South Africa.\u003c/p\u003e \u003cp\u003e \u003cb\u003eDNA extraction, genotyping, and quality control.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eGenomic DNA was extracted from semen samples according to the manufacturer's protocol using a NucleoMag\u0026reg; pathogen extraction kit. Quantification was performed using Qubit\u0026reg; 2.0, and quantified DNA was genotyped using the Illumina BovineSNP 150K BeadChip following the manufacturer\u0026rsquo;s protocol. Genome Studio 2.0 software was used to visualize and interpret data generated by SNPs. Plink v1.07 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] software was used to filter the dataset following criteria: (1) eliminate SNPs with a call frequency of \u0026ge;\u0026thinsp;90, (2) eliminate individuals with more than 5% missing genotype using (MIND)\u0026thinsp;\u0026ge;\u0026thinsp;0.05, (3) eliminate SNPs with more than 5% of missing genotype using (GENO)\u0026thinsp;\u0026ge;\u0026thinsp;0.05, (4) eliminate all SNPs with minor allele frequency of less than 5% using (MAF)\u0026thinsp;\u0026ge;\u0026thinsp;0.05 and (5) genotype frequency out of Hardy-Weinberg equilibrium (HWE\u0026thinsp;\u0026gt;\u0026thinsp;0.00001). The study excluded 23 samples that did not meet the criteria. Then, followed by removing SNPs that were in high Linkage disequilibrium using the --indep-pairwise r2\u0026thinsp;\u0026ge;\u0026thinsp;0.2 option, further removed related individuals using the --genome option (compute genome-wide identity by descents estimate (IBD) and --remove related.txt). StructuRly database was used to classify the remaining individuals accounting to breed type using triangle plot.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIdentifying signatures of selection.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTo assess within-population selection signatures, iHS was used to highlight regions under selection within each of the individual cattle populations. This involves identifying regions within each cattle population where genetic changes are indicative of positive selection. Methods for assessing cross-population selection signatures (Rsb, Fst and XP-EHH) in a pairwise fashion to identify regions under positive selection that are common to both cattle populations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIntegrated Haplotype Score (iHS) analysis\u003c/h2\u003e \u003cp\u003eThe integrated haplotype score (iHS) was used to measure the extent of extended haplotype homozygosity (EHH) around a selected allele compared. Positive iHS values indicate selection for the derived allele, while negative values suggest selection for the ancestral allele, as described by [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Signatures were identified within the four cattle populations and to prepare input files for the analysis, quality-controlled binary files were generated for each population using PLINK v1.9. The resulting binary files were used to create variant calling format (.vcf) files, which were sorted by base pair positions using bcftools [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e], and then phased with BEAGLE v5[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The iHS scores were calculated in R v4.2.2 using the R package REHH [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] with the function ihs2ihs. Candidate regions were identified at MAF of \u0026lt;\u0026thinsp;0.05, and regions under significant selection were identified at p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001. The outputs were visualized in R v4.2.2 for all populations using Manhattan plots, and comma-separated value (.csv) files were created with significant regions and chromosomes that passed a threshold of -log (iHS)\u0026thinsp;\u0026gt;\u0026thinsp;4 per population.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eXP-EHH and Rsb analysis\u003c/h2\u003e \u003cp\u003eEvidence of positive selection in populations was further investigated by comparing pairs of populations using the XP-EHH and Rsb methods. The regions with selection signatures were identified using pairwise comparisons between the four populations (Nguni \u0026amp; Bonsmara and Angus \u0026amp; Simmental). The XP-EHH is an extension of iHS and was used to measure the differences in EHH between populations. iHS It can be used to identify loci that have undergone selection in one population but not the other. The analyses consider distinct SNPs amongst populations that are monorphic for one and polymorphic for others using the comparison of the EHH score of two populations [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe Rsb compares the EHH of selected alleles between two populations, and it can also be used to identify loci that have undergone recent positive selection within one population compared to another. This analysis uses the formula [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eThe R package REHH was used to determine the genomic regions under selection for the XP-EHH and Rsb using the functions ies2xpehh and ines2rsb Rsb, respectively. Candidate regions were identified at an MAF of \u0026lt;\u0026thinsp;0.05, and significant regions were identified at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. The outputs were visualized using Manhattan plots and .csv files were created with significant regions and chromosomes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003e \u003cb\u003eFst analysis.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eF\u003csub\u003est\u003c/sub\u003e was used to measure population differentiation due to genetic structure. High Fst values suggest genetic differences between populations. While not a direct measure of selection, high FST values can indicate regions of the genome that have been under selective pressure [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The Fst computation was performed in PLINK v1.9 using the command --fst, while the qqman [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] package in R was used for Manhattan plots visualization.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGene Functional Annotation\u003c/h2\u003e \u003cp\u003eGene annotation was carried out using the genomic regions identified as positive signatures of selection, from all methods used (iHS, XP-EHH, Rsb and Fst). Genes were annotated with the cattle gene assembly ARS-UCD1.2 using Bio Mart, a program in Ensemble, furthermore, ShinyGO v0.77 was used to determine the functions, pathway analysis, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways(\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) of the identified genes. String database was used to compute interaction network between genes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, this study presented a comprehensive genetic analysis of four distinct cattle breeds (Bonsmara, Simmental, Nguni, and Angus) encompassing a range of methodologies such as iHS, XP-EHH, Rsb, and Fst. The integration of these methods in this study offers a unique perspective on the genetic landscape of South African beef cattle breeds. While previous studies have individually applied some of these methods, this study uniquely combined them, providing a more holistic understanding of selection signatures and candidate genes in beef cattle breeds. The identification of common regions across all breeds may highlight genomic areas crucial for general cattle adaptability and fitness. Overall, these methods provide complementary information about the genetic differences and similarities between the examined cattle breeds. It's important to note that these findings can have implications for breeding strategies, conservation efforts, and our understanding of the genetic basis of important traits in cattle populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eM.C.M. manuscript writing; A.M. data analysis and reviewing the manuscript; K.N. reviewing the manuscript; N.J. reviewing and editing; K.H. reviewing the manuscript; T.M. reviewing the manuscript; P.T. reviewing the manuscript and B.M.reviewing the manuscript\u003c/p\u003e\u003ch2\u003eData availability:\u003c/h2\u003e \u003cp\u003eThe data for findings in this study are available at the European Variation Archive (EVA), with the accession number: PRJEB72903.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLoftus RT, MacHugh DE, Bradley DG, Sharp PM, Cunningham. 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BMC Genet 18 11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlam MZ, Haque MA, Iqbal A, Lee Y-M, Ha J-J, Jin S, Park B, Kim N-Y, Won JI, Kim J-J. 2023 Genome-Wide Association Study to Identify QTL for Carcass Traits in Korean Hanwoo Cattle \u003cem\u003eAnimals\u003c/em\u003e 13 2737.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMagalh\u0026atilde;es AFB, De Camargo GMF, Fernandes GA, Gordo DGM, Tonussi RL, Costa RB, Espigolan R, Silva R M D O, Bresolin T, De Andrade WB, F, Takada L, Feitosa FLB, Baldi F, Carvalheiro R, Chardulo LAL, De Albuquerque LG. 2016 Genome-Wide Association Study of Meat Quality Traits in Nellore Cattle ed D Tesfaye \u003cem\u003ePLOS ONE\u003c/em\u003e 11 e0157845.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerakado APN, Costa RB, De Camargo GMF, Irano N, Bresolin T, Takada L, Carvalho CVD, Oliveira HN, Carvalheiro R, Baldi F, De Albuquerque LG. 2018 Genome-wide association study for growth traits in Nelore cattle \u003cem\u003eAnimal\u003c/em\u003e 12 1358\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuchocki T, Szyda J. 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J Dairy Sci. 2017;100:1955\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBecker D, Weikard R, Schulze C, Wohlsein P, K\u0026uuml;hn. C 2020 A 50-kb deletion disrupting the RSPO2 gene is associated with tetradysmelia in Holstein Friesian cattle. Genet Sel Evol 52 68.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePurcell S, Neale B, Todd-Brown K, Thomas L, Manuel AR, Ferreira, Bender D, Maller J, de Bakker W, Daly MJ, Pak C. Sham PLINK: A Tool Set for Whole-Genome Association and Population-Based Linkage Analyses.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVoight BF, Kudaravalli S, Wen X, Pritchard JK. A Map of Recent Positive Selection in the Human Genome ed L Hurst. 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Resour.\u003c/em\u003e 17 78\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThe International HapMap Consortium, Sabeti PC, Varilly P, Fry B, Lohmueller J, Hostetter E, Cotsapas C, Xie X, Byrne EH, McCarroll SA, Gaudet R, Schaffner SF, Lander ES. Genome-wide detection and characterization of positive selection in human populations. Nature. 2007;449:913\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang K, Thornton KR, Stoneking M. A New Approach for Using Genome Scans to Detect Recent Positive Selection in the Human Genome ed K H Wolfe. PLoS Biol. 2007;5:e171.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Villemereuil P, Gaggiotti OE. A new F \u003csub\u003eST\u003c/sub\u003e -based method to uncover local adaptation using environmental variables ed R B O\u0026rsquo;Hara. Methods Ecol Evol. 2015;6:1248\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurner SD. qqman: an R package for visualizing GWAS results using Q-Q and manhattan plots. Bioinformatics; 2014.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Beef, Candidate genes, traits and gene interaction","lastPublishedDoi":"10.21203/rs.3.rs-3945698/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3945698/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn genetics and evolutionary biology, the concept of selection signatures is used to describe specific patterns in the genome that are associated with the process of natural selection. This natural selection can leave distinct genetic footprints of signatures, such as changes in allele frequencies, the presence of specific mutations, or patterns of genetic variation. Selection signatures provide information about the evolutionary forces that have shaped a population over time.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, a total of 96 samples from four different cattle breeds, namely Nguni (n = 28), Bonsmara (n = 21), Angus (n = 22), and Simmental (n = 25) were subjected to quality control, following quality control, a total of 105,675 SNPs from 73 individuals remained for further analysis. Genomic signatures of positive selection within each breed were identified using the Integrated Haplotype Score (iHS) method, and cross-population comparison analysis was conducted using XP-EHH, Rsb, and Fst methods to assess the genetic differences between breeds.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor the iHS analyses of individual breeds, two genomic regions identified signatures of selection for Bonsmara, six for Simmental, four for Nguni, and one for Angus. Ten regions were identified as being under selection, with BTA 12 shared between Nguni and Bonsmara. Cross-population comparisons using XP-EHH, Rsb, and FST methods revealed specific genomic regions differentially selected between breeds. Gene annotation analyses revealed candidate genes associated with several Quantitative Trait Loci (QTL). For instance, in Simmental cattle, the gene FAM110B was associated with carcass weight and body confirmation score. Bonsmara cattle had fewer candidate genes, including CDK8 and FLT1, while Angus revealed no candidate genes on BTA 18. Nguni cattle revealed the following candidate genes CRB1, PLAG2GA, and VASH2, with CDK8 shared between Bonsmara and Nguni on BTA 12. Cross population comparisons further revealed candidate genes associated with specific traits. For Bonsmara vs Nguni, genes including PLCXD3, FAM149B1, and GRIK2 were identified, whereas, for Simmental vs Angus, SLIT2 and TSPAN9 genes were identified. Furthermore, the study highlighted gene functions, revealing associations with meat quality traits, reproduction, health, diseases, fertility, and body conformation score. Gene interaction analysis using the STRING database identified a network of 63 candidate genes, revealing the structure of genetic interactions. Some genes had multiple functions, indicating multiple roles in various biological processes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis extensive genomic study can assist in highlighting the importance of the genetic background of breed-specific traits, and in this way contributes to selective breeding and trait improvement in cattle populations.\u003c/p\u003e","manuscriptTitle":"Genome-wide Scans Reveal Selection Signatures and Cross- Population Variation in South African and European Beef Cattle Breeds","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-05 15:51:12","doi":"10.21203/rs.3.rs-3945698/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":"88ca5efd-70f1-439a-8d97-37d949755b5c","owner":[],"postedDate":"March 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-11-05T13:24:16+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-05 15:51:12","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3945698","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3945698","identity":"rs-3945698","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}