Genome-wide Association Analysis of Body Conformation Traits in Chinese Holstein Cattle

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The present study aimed to identify gene variants associated with body conformation traits in Chinese Holstein cattle and provide marker loci for genomic selection in dairy cattle breeding. The study findings could offer robust theoretical support to optimize the health of dairy cattle and enhance their production performance. Results This study involved 586 Chinese Holstein cows, using the predicted transmitting abilities (PTAs) of 17 body conformation traits evaluated by the Council on Dairy Cattle Breeding in the USA as phenotypic values. These traits were categorized into body size traits, rump traits, feet/legs traits, udder traits, and dairy characteristic traits. Based on the genomic profiling results from the Genomic Profiler Bovine 100K SNP chip, genotype data were quality-controlled using PLINK software, retaining 586 individuals and 80,713 SNPs for further analysis. Genome-wide association studies (GWAS) were conducted using the GEMMA software, employing both univariate linear mixed models (LMM) and multivariate linear mixed models (mvLMM). The Bonferroni method was used to determine the significance threshold, identifying gene variants significantly associated with body conformation traits in Chinese Holstein cows. The single-trait GWAS identified 24 SNPs significantly associated with body conformation traits (P < 0.01), with annotation leading to the identification of 21 candidate genes. The multivariate GWAS identified 54 SNPs, which were annotated to 57 candidate genes, including 39 new SNPs not identified in the single-trait GWAS. Additionally, 14 SNPs in the 86.84–87.41 Mb region of chromosome 6 were significantly associated with multiple traits such as body size, udder, and dairy characteristics. Four genes—SLC4A4, GC, NPFFR2, and ADAMTS3—were annotated in this region. Conclusions A total of 63 SNPs were identified as significantly associated with the 17 body conformation traits in Chinese Holstein cows through both single-trait and multivariate GWAS analyses. Sixty-six candidate genes were annotated, with 12 genes identified by both methods, including SLC4A4 , GC , NPFFR2 , and ADAMTS3 , which are involved in biological processes such as active glucose transport, adipogenesis, and neural development. Thus, the study findings provided potential genetic marker information related to body conformation traits for the breeding of Chinese Holstein cattle. Chinese Holstein Cattle Body Conformation Traits Single-trait GWAS Multi-trait GWAS Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Background The Chinese Holstein cattle is the first dairy breed developed in China and is the dominant breed in the country’s dairy cattle population, accounting for over 85% of the national herd [ 1 ] . Although the body conformation traits of dairy cattle do not directly translate into economic benefits, these traits are closely related to the milk production capacity and overall health of cows [ 2 , 3 ] . These traits are an essential component for evaluating the overall performance of dairy cattle. Identifying candidate genes associated with body conformation traits in Chinese Holstein cattle is therefore crucial to provide effective molecular markers for genomic selection in breeding programs, further optimize breeding strategies, and improve the production performance of dairy cattle. The main body conformation traits in dairy cattle include (1) udder traits (such as fore udder attachment, front teat placement, teat length, rear udder height, rear udder width, and rear teat placement), (2) feet/legs traits (such as foot angle, heel depth, bone quality, rear legs side view, and rear legs rear view), (3) body size traits (such as stature, body depth, chest width, and strength), (4) rump traits (such as rump width and rump angle), and (5) dairy characteristics (such as angularity) [ 4 ] . Since 1990, many countries have incorporated body conformation traits into dairy cattle breeding programs [ 5 ] to improve the overall performance of dairy cows by optimizing these traits. To gain a deeper understanding of the genetic basis of body conformation traits, genome-wide association studies (GWAS) are commonly used to identify candidate genes for economically important traits in dairy cattle. The mixed linear model proposed by Yu et al. [ 6 ] has been widely recognized as the best GWAS analysis model currently available, as it effectively accounts for population structure and complex relationships within populations. The application of this model provides robust support for understanding the genetic mechanisms underlying body conformation traits in dairy cattle. Recently, Nazar et al. [ 7 ] conducted a GWAS analysis on Chinese Holstein cattle using the mixed linear model and identified 18 SNPs significantly associated with five udder traits. Haque et al. [ 8 ] were the first research team to report GWAS results for body conformation traits in Korean Holstein cattle, wherein they identified 24 SNPs significantly associated with 24 body conformation traits. Nazar et al. [ 9 ] also conducted a GWAS analysis on three udder traits in Chinese Holstein cattle and detected nine SNPs significantly associated with udder traits after Bonferroni correction. Čítek et al. [ 10 ] identified 32 SNPs significantly or nearly significantly associated with body conformation traits in the GWAS results of 25 body conformation traits in Czech Holstein cattle. To date, most GWAS on body conformation traits in dairy cattle have been based on independent analyses of single traits. When multiple measured traits in an individual show a correlation, the lack of linkage between causal loci (linkage disequilibrium [LD]) is often overlooked. However, multivariate GWAS can jointly analyze genetically correlated traits by simultaneously considering within-trait and between-trait variations, thereby improving detection efficiency and accuracy [ 11 ] . Numerous studies have shown that different body conformation traits are genetically correlated [ 4 , 12 , 13 ] . To more comprehensively reveal the genetic variation and underlying mechanisms of body conformation traits in dairy cattle, multivariate GWAS methods are more advantageous. The present study conducted single-trait and multivariate GWAS analyses to determine the genetic variants associated with body conformation traits in Chinese Holstein cattle and provide marker information for genomic selection in dairy cattle; the findings of this study could offer strong theoretical support to further optimize the health of dairy cattle and enhance their production performance. Materials and Methods Genotype data and quality control Genomic DNA was extracted from 586 Chinese Holstein cows by using the Tiangen Blood Genome Extraction Kit. Genotyping was performed using the GeneSeek Genomic Profiler Bovine 100K single nucleotide polymorphism (SNP) chip. Quality control of the individual and SNP data was conducted using PLINK (v1.9) software [ 14 ] , according to the following criteria: (1) individuals with an SNP missing rate of > 10% were excluded; (2) SNPs with a call rate of < 90% were excluded; (3) SNPs with a minor allele frequency (MAF) of < 5% were excluded; and (4) SNPs with a P-value of < 1.0 × 10 − 7 were excluded. LD analysis was performed using Haploview software [ 15 ] . A total of 80,713 high-quality SNP markers from 586 individuals were ultimately selected. These SNP markers were evenly distributed across the chromosomes and were suitable for the subsequent GWAS analysis of Chinese Holstein cattle (Fig. 1 ). Phenotype data collection According to the updated standard methods of the Dairy Cattle Breeding Committee [ 16 , 17 ] , the predicted transmitting abilities (PTAs) of 17 body conformation traits, as assessed by the Council on Dairy Cattle Breeding, USA, were used as the phenotypic data for this study on 586 Chinese Holstein cows. These traits were categorized into five groups: (1) body size traits: stature (STA), strength (STR), and body depth (BDE); (2) rump traits: rump angle (RPA) and rump-thurl width (RTW); (3) feet and legs traits: rear legs side view (RLS), rear legs rear view (RLR), foot angle (FTA), feet/legs score (FLS), and feet/legs composite (FLC) index; (4) udder traits: fore udder attachment (FUA), rear udder height (RUH), rear udder width (RUW), udder cleft (UCL), udder depth (UDP), and udder composite (UDC) index; and (5) dairy form traits: dairy form (DFM). Estimation of genetic parameters for body conformation traits Genetic correlations were determined using the “-reml-bivar” parameter in GCTA software [ 18 ] . Descriptive statistical analysis for the 17 body conformation traits, including maximum, minimum, mean, variance, and standard deviation, was conducted using SPSS 19 software. The frequency distribution histograms for each trait were plotted using the R package. Single-trait and multi-trait GWAS Before conducting a GWAS, principal component analysis (PCA) was performed using PLINK (v1.9) software to correct for potential false positives caused by population stratification. GWAS analysis between single conformation traits and genome-wide SNPs was performed using the univariate linear mixed model (LMM) in GEMMA software[19] by using the following model: y = Χβ + Ζκγκ + ξ + ε where y is the PTA vector, Χβ represents age and population structure effects (the first five principal components), Ζκγκ represents the effect of the marker to be tested, ξ ~ N (0,Kφ 2 ) represents the polygenic effect, and ε ~ N (0,Iσ 2 ) represents the residual effect. In the polygenic effect, K is the kinship matrix inferred from the markers. Multi-trait GWAS analysis between multiple traits and genome-wide SNPs was conducted using the multivariate linear mixed model in GEMMA software [ 20 ] by using the following model: Y = Χβ + Ζκγκ + ξ + Ε where Y is the n×d PTA matrix, n is the number of individuals in the population, and d is the number of traits analyzed; Χβ represents age and population structure effects (the first five principal components); Ζκγκ represents the effect of the marker to be tested; ξ ~ N (0,K,Vg) represents the polygenic effect; and Ε ~ N (0,In×n,Ve) represents the residual effect. In the polygenic effect, K is the kinship matrix inferred from the markers, Vg is the d×d polygenic variance-covariance matrix, and Ve is the d×d residual variance-covariance matrix. The number of independent SNPs was calculated using PLINK software with the “--indep pairs 50 5 0.2” command. The significance threshold was determined using Bonferroni correction (P_value < 0.05/number of independent SNPs). Manhattan plots and QQ plots were generated using the CMplot function in the R package. Gene annotation The SNP position information in the GeneSeek Genomic Profiler Bovine 100K SNP chip was based on Bos taurus UCD 1.2 version. The ARS-UCD 1.2 bovine reference genome information was downloaded from the ENSEMBL website. Significant SNPs within a 50-kb upstream and downstream range were annotated using ANNOVAR software [ 21 ] , and potential candidate genes related to the traits were identified. Results Descriptive statistical analysis of the phenotypic data Descriptive statistical analysis was performed on the 17 body conformation traits of 586 Chinese Holstein cows (Table 1). The phenotypic values of each trait generally followed a normal distribution(Figure S1). Table 1. Descriptive statistics of body conformation traits Traits Min Max Mean SD Var Body size STA -2.79 1.86 -0.3634 0.81616 0.666 STR -2.17 1.76 -0.3168 0.64178 0.412 BDE -2.03 1.57 -0.3865 0.67475 0.455 Rump RPA -2.83 2.01 -0.0458 0.74377 0.553 RTW -2.53 1.94 -0.3065 0.79765 0.636 Feet/Legs FLC -2.06 0.99 -0.2341 0.50674 0.257 RLS -1.78 2.75 0.1781 0.73851 0.545 RLR -2.47 1.26 -0.44 0.67612 0.457 FTA -2.46 1.62 -0.335 0.63803 0.407 FLS -2.16 1.05 -0.2268 0.52009 0.27 Udder UDC -3.13 1.71 0.1534 0.64248 0.413 FUA -4.13 2.12 0.0933 0.82106 0.674 RUH -3.54 2.48 0.2525 0.85083 0.724 RUW -3.43 2.84 0.0274 0.92465 0.855 UCL -2.12 1.79 0.0703 0.64524 0.416 UDP -3.64 2.18 -0.0003 0.89008 0.792 FTP -2.33 2.45 0.1246 0.76152 0.58 RTP -2.3 2.68 0.1985 0.82223 0.676 TLG -2.53 1.95 -0.4231 0.7033 0.495 Dairy characteristics DFM -3.01 2.46 -0.124 0.91252 0.833 Determination of genetic correlations The results of the determination of genetic correlations between the phenotypic traits indicated a strong positive correlation (r > 0.72) among the body size traits (STA, BDE, and STR; Table 2). In the rump traits, a weak negative correlation was observed between RTA and RPA (r = -0.02) (Table 3). Among the feet/legs traits, RLS showed varying degrees of negative correlation with other traits (-0.5 < r 0.6) were found between RLR, FTA, FLS, and FLC index, except for RLS (Table 4). In the udder traits, varying degrees of positive correlation (0.1 < r < 1) were noted among UDC index, FUA, RUH, RUW, central ligament, and UDP (Table 5). Table 2. Genetic correlation between body size traits STA STR BDE STA 1.000 STR 0.723 1.000 BDE 0.810 0.854 1.000 Table 3. Genetic correlation between rump traits RPA RTW RPA 1.000 RTW -0.024 1.000 Table 4. Genetic correlation between feet/legs traits FLC RLS RLR FTA FLS FLC 1.000 RLS -0.286 1.000 RLR 0.931 -0.347 1.000 FTA 0.661 -0.472 0.729 1.000 FLS 0.930 -0.220 0.912 0.760 1.000 Table 5. Genetic correlation between udder traits UDC FUA RUH RUW UCL UDP UDC 1.000 FUA 1.000 1.000 RUH 0.893 0.686 1.000 RUW 0.756 0.510 0.871 1.000 UCL 0.659 0.438 0.567 0.635 1.000 UDP 0.697 0.864 0.472 0.221 0.353 1.000 Note: r ≤ 0.3 indicates weak correlation; 0.3 0.4 indicates strong correlation. [22] PCA As shown in Figure 2, population stratification was observed. The explained variance percentage of the first 10 principal components (PCs) was calculated, and the results indicated that the first 5 PCs accounted for 80% of the variance. Therefore, in this study, the first 5 PCs were selected as covariates and included in the LMM for GWAS analysis. Results of single-trait and multi-trait GWAS analyses A single-trait GWAS was performed on the 17 body conformation traits, which identified 24 significant SNPs across 12 traits (Table 6). Figure 3 shows the QQ plots and Manhattan plots. In body size traits, one significant SNP was identified on chromosome 11, with one candidate gene, LDAH , annotated within the 50-kb upstream and downstream region of the SNP. In rump traits, four significant SNPs were identified on chromosome 7, with six candidate genes annotated, including OR2T4_1 , ELAVL1 , and LMAN2 . In feet/legs traits, two significant SNPs were identified on chromosomes 8 and 29, with two candidate genes ( PIP5K1B and NTM ) annotated. In udder traits, eight significant SNPs were identified on chromosomes 5, 6, 7, and 19, with annotation of seven candidate genes, including ADGRE5 , CCND2 , and ARAP2 . In milk production-related traits, nine significant SNPs were identified on chromosome 6, with annotation of four candidate genes, including SLC4A4 , GC , NPFFR2 , and ADAMTS3 . One SNP on chromosome 19 (BovineHD1900013254) was significantly associated with UDC index, RUH, and RUW traits in the single-trait GWAS, thus indicating that it may be a pleiotropic locus. Multi-trait GWAS was used to identify new significant SNPs, and 54 significant SNPs were identified. Figure 4 shows the QQ plots and Manhattan plots. Compared to the single-trait GWAS, 39 new SNPs were identified in the multi-trait GWAS (Table 7). In body size traits, 19 significant SNPs were identified across three combinations (STR-BDE, STA-STR, and STA-BDE), which were located on chromosomes 5, 6, 7, 8, 11, 19, and 29, and 14 candidate genes were annotated, including SLC4A4 , GC , NPFFR2 , and ADAMTS3 . Of these 19 SNPs, 18 were newly identified SNPs. In rump traits, two significant SNPs were identified in the RPA-RTW combination on chromosome 7, and four candidate genes were annotated, including OR2T4_1 , ATP8B3 , and KLF16 . Both SNPs were newly identified. In feet/legs traits, 11 significant SNPs were identified across five combinations (FLC-RLR, RLS-FTA-FLS, and FLC-RLS-FTA), which were located on chromosomes 8, 13, 21, 28, and 29, and 14 candidate genes were annotated, including GNAQ , SAMHD1 , and SOGA1 . Of these 11 SNPs, 9 were newly identified SNPs. In udder traits, 33 significant SNPs were identified across 41 combinations (FUA-RUH, FUA-RUH-RUW, and FUA-RUH-RUW-UCL), which were located on chromosomes 4, 6, 7, 11, 16, 17, 19, and 28, and 30 candidate genes were annotated, including BMT2 , IGFBP1 , and IGFBP3 . Of these 33 SNPs, 28 were newly identified SNPs. In total, 32 SNPs were repeatedly detected across multiple trait combinations in the multi-trait GWAS, with 9 SNPs associated with body size and udder traits and 2 SNPs associated with feet/legs traits and udder traits, thus suggesting these SNPs may be pleiotropic loci. A summary of the single-trait and multi-trait GWAS results for the 17 body conformation traits revealed 63 SNPs showing a significant association with body conformation traits in Chinese Holstein cows and 66 candidate genes annotated within the 50-kb region upstream and downstream of the significant loci. Additionally, the results of multi-trait GWAS showed that a region on chromosome 6 (86.84–87.41 Mb) contained 14 significant SNPs associated with udder and body size traits, and six haplotype blocks composed of 3, 2, 2, 9, 2, and 5 SNPs were observed (Figure 5). Eight of these SNPs exhibiting a significant association with milk production-related traits were also detected in the single-trait GWAS. Table 6. Significant loci and genes identified by single-trait GWAS SNP Chr Position Traits P_value Nearby genes Function area BovineHD0600024228 6 86847656 DFM 1.97E-08 SLC4A4 , GC intergenic ARS-BFGL-NGS-118182 6 86860291 2.27E-08 SLC4A4 , GC intergenic BovineHD0600024243 6 86877334 2.27E-08 SLC4A4 , GC intergenic BovineHD0600024355 6 87184768 7.02E-07 GC , NPFFR2 intergenic BovineHD0600024357 6 87187812 7.02E-07 GC , NPFFR2 intergenic chr6_89051385 6 87316810 7.29E-07 NPFFR2 exonic DB-443-seq-rs110326785 6 87324678 7.29E-07 NPFFR2 exonic DB-2033-seq-rs110186820 6 87368855 8.32E-07 NPFFR2 exonic Hapmap60852-rs29024026 6 87725832 1.97E-06 ADAMTS3 exonic ARS-BFGL-NGS-38413 11 78083413 STA 5.97E-06 LDAH exonic BovineHD0700011653 7 38822927 RPA 4.15E-06 LMAN2 exonic BovineHD0700012421 7 41261062 6.06E-06 LOC526765,OR2W3,TRIM58 exonic ARS-BFGL-NGS-13798 7 42322361 8.36E-07 OR2T4_1 exonic BovineHD0700005042 7 16748554 RTW 7.32E-06 ELAVL1 exonic BovineHD2900010725 29 34925638 RLS 3.21E-06 NTM intronic BTB-00344991 8 45049204 FLS 6.49E-06 PIP5K1B exonic Hapmap30832-BTA-144704 7 11394736 UDP 6.95E-06 ADGRE5 exonic BovineHD0500013405 5 46349963 RUH 4.38E-06 LOC112446700 , CAND1 intergenic BovineHD0500013407 5 46351738 7.13E-06 LOC112446700 , CAND1 intergenic DB-364-seq-rs378727865 5 105784987 UCL 6.80E-06 CCND2 exonic BovineHD0600015583 6 55313091 2.15E-06 ARAP2 intergenic BTA-26162-no-rs 6 55327944 1.83E-06 ARAP2 intergenic BovineHD1900012330 19 42905488 FUA 1.25E-06 LOC100138645,SAO exonic BovineHD1900013254 19 46942067 UDC 1.40E-06 TLK2 exonic RUH 6.46E-06 RUW 2.70E-06 Table 7. Significant loci and genes identified by multi-trait GWAS SNP Chr Position Nearby genes Function area BTA-16397-no-rs 4 55471733 BMT2 exonic BovineHD0400021231 4 76139944 IGFBP1,IGFBP3,LOC112446404 exonic BovineHD0600015583 6 55313091 ARAP2 intergenic BTA-26162-no-rs 6 55327944 ARAP2 intergenic BovineHD0600024228 6 86847656 SLC4A4,GC intergenic ARS-BFGL-NGS-118182 6 86860291 SLC4A4,GC intergenic BovineHD0600024243 6 86877334 SLC4A4,GC intergenic BovineHD0600024315 6 87068809 GC,NPFFR2 intergenic BovineHD0600024338 6 87130864 GC,NPFFR2 intergenic BovineHD0600024345 6 87143505 GC,NPFFR2 intergenic BovineHD0600024350 6 87153414 GC,NPFFR2 intergenic BovineHD0600024355 6 87184768 GC,NPFFR2 intergenic BovineHD0600024357 6 87187812 GC,NPFFR2 intergenic BovineHD0600024365 6 87213962 GC,NPFFR2 intergenic DB-442-seq-rs110392219 6 87314427 NPFFR2 exonic chr6_89051385 6 87316810 NPFFR2 exonic DB-443-seq-rs110326785 6 87324678 NPFFR2 exonic DB-2033-seq-rs110186820 6 87368855 NPFFR2,ADAMTS3 intergenic BovineHD0600028629 6 101050942 MAPK10 exonic Hapmap30832-BTA-144704 7 11394736 ADGRE5 exonic ARS-BFGL-NGS-13798 7 42322361 OR2T4_1 exonic BovineHD0700013141 7 44152142 ATP8B3,KLF16,REXO1 exonic ARS-BFGL-NGS-30237 8 53819922 GNAQ intronic BovineHD1100011070 11 37533658 EML6 exonic Hapmap51861-BTA-86131 11 38522047 EFEMP1 exonic chr11_38494447 11 38640411 MIR216A,MIR216B,MIR217 ncRNA_exonic BovineHD1100011351 11 38650894 MIR216A,MIR216B,MIR217 ncRNA_exonic BovineHD1100011736 11 39896854 TRNAY-AUA_2,TRNAC-GCA_141 intergenic ARS-BFGL-NGS-38413 11 78083413 LDAH exonic BovineHD1100022676 11 79037363 TTC32,LOC104973438 intergenic BovineHD1100022680 11 79049547 TTC32,LOC104973438 intergenic BovineHD1100022725 11 79217388 LOC112448820,OSR1 intergenic Hapmap47169-BTA-107308 11 80349202 LOC112448882,KCNS3 intergenic BovineHD1100029423 11 101175170 FIBCD1 exonic BovineHD1300018848 13 65929413 SAMHD1,SOGA1,TLDC2 exonic ARS-BFGL-NGS-108133 16 52327847 TMEM51 UTR5 BovineHD1700014238 17 48992546 TMEM132C,TRNAC-GCA_192 intergenic BovineHD1900012330 19 42905488 LOC100138645,SAO exonic BovineHD1900013254 19 46942067 TLK2 exonic ARS-BFGL-NGS-115719 19 48149532 CD79B,SCN4A exonic BovineHD1900013592 19 48203740 LOC616254,PRR29 exonic BovineHD1900013860 19 49052596 BPTF exonic BovineHD1900013865 19 49067122 BPTF exonic Hapmap47630-BTA-45710 19 49085782 BPTF exonic BovineHD2100019874 21 66165706 MEG9,LOC112443172 intergenic BovineHD2100019929 21 66415334 LOC101907771,LOC112443172 ncRNA_exonic ARS-BFGL-NGS-38270 21 66496288 DIO3 exonic ARS-BFGL-NGS-86477 21 66743529 PPP2R5C exonic BovineHD2400000350 24 1265748 LOC104975719,TRNAK-UUU_41 intergenic BovineHD2800005128 28 18663865 ZNF365,LOC112444734 intergenic BovineHD2800005144 28 18784655 LOC101905431 exonic BovineHD2800013715 28 19401828 JMJD1C exonic BovineHD2800005280 28 19436656 JMJD1C intronic UA-IFASA-6129 29 34835983 NTM intronic Discussion Genetic correlations among body conformation traits in Chinese Holstein cows A very strong positive genetic correlation (r > 0.72) was observed between STA, BDE, and STR in body size traits, which is consistent with the findings of Ning [ 13 ] and Degroot et al. [ 23 ] . In feet/legs traits, negative genetic correlations were observed between RLS and other leg and hoof traits (-0.47 < r < -0.22). This was similar to the genetic correlation (-0.34) between RLS and RLR reported by Huang et al. [ 24 ] in their estimation of genetic parameters for body conformation traits of dairy cows, although RLS showed a positive correlation with other leg and hoof traits in their study. Because leg and hoof traits are easily influenced by farm management and external environmental factors, the leg and hoof structure may vary between different cattle populations. In rump traits, the genetic correlation between RTA and RPA showed a weak negative correlation (r = -0.02), which is similar to the findings of Peng [ 25 ] on the genetic correlation between RTA and RPA in Holstein cows in Hebei Province. However, the genetic correlations reported by Huang et al. [ 24 ] and An et al. [ 26 ] differed significantly (0.22 < r < 0.38). In udder traits, the genetic correlations ranged from 0.22 (RUW and UDP) to 1 (UDC index and FUA); this finding is similar to the results reported by Degroot et al. [ 23 ] . The genetic correlations observed among body conformation traits suggest that the selection of one trait can indirectly influence other traits. Additionally, the positive correlations between traits indicate that these traits may share some common genetic basis, thus implying that certain genes or gene combinations may simultaneously affect multiple body conformation traits. Therefore, this genetic correlation can be used to develop more effective selection strategies. Advantages of multi-trait GWAS Body conformation traits are often controlled by multiple genes. Multi-trait GWAS can leverage the correlation between traits and combine weak genetic effects to enhance the statistical power of GWAS and improve the ability to detect new SNP loci [ 27 , 28 ] . In the present study, the body conformation traits showed genetic correlations. Compared to single-trait GWAS, 39 new SNPs were identified in the multi-trait GWAS. By using a similar strategy, Li et al. [ 28 ] conducted a multi-trait GWAS on weaning weight and yearling weight in sheep and identified 93 new SNPs. Gao et al. [ 22 ] discovered three new SNPs in carcass weight, carcass length, and chest depth in Huaxi cattle. These results suggest that when traits show genetic correlations among them, multi-trait GWAS can complement the findings of single-trait GWAS, thereby increasing the statistical power of GWAS. Critical candidate genes By using a combination of single-trait and multi-trait GWAS analyses, we detected 63 significant SNP loci and annotated 66 candidate genes. Among these, 12 genes were identified by both single-trait and multi-trait GWAS, including SLC4A4 , GC , NPFFR2 , ADAMTS3 , LDAH , OR2T4_1 , NTM , ADGRE5 , ARAP2 , TLK2 , SAO , and LOC100138645 . Four genes were located in the 86.84–87.41 Mb region of chromosome 6. Among these genes, SLC4A4 is a solute transporter and a member of a major transporter superfamily involved in active glucose transport [ 29 ] . GC is a gene encoding the vitamin D-binding protein, which is specifically expressed in tissues such as the liver [ 30 , 31 ] . NPFFR2 is a member of the G-protein-coupled neuropeptide receptor subfamily activated by neuropeptides A-18-amide and F-8-amide [ 32 ] . The ADAMTS3 gene activates vascular endothelial growth factors and promotes lymphangiogenesis [ 33 ] . SNPs in this region were associated with dairy traits in single-trait GWAS and with body size and udder traits in multi-trait GWAS. Jiang et al. [ 34 ] also found that the four genes in this region were related to milk yield and milk protein content in Holstein cows. Liang et al. [ 35 ] conducted a GWAS of over one million US Holstein cows and found that the SLC4A4 , GC , and NPFFR2 genes were related to fertility traits. Wu et al. [ 36 ] reported that SLC4A4 and NPFFR2 were candidate genes for mastitis susceptibility in Danish Holstein cows. These results suggest that the four genes in this region may exhibit pleiotropy. LDAH , a lipid droplet-associated hydrolase, is highly expressed in tissues that primarily store triacylglycerol and plays a key role in lipogenesis [ 37 ] . Previous studies have shown that it is associated with hoof and leg diseases in Danish Holstein cows [ 38 ] . NTM is a neurotrimin protein with an important role in neurodevelopment. Xu et al. [ 39 ] analyzed the imprinting status of the NTM gene in cattle and identified an SNP (rs42185569) within the NTM gene through direct sequencing of PCR products. RT-PCR amplification showed monoallelic expression of the NTM gene in bovine placenta and adult tissues, thus suggesting that NTM is an imprinted gene in cattle. ADGRE5 primarily functions in cell adhesion and transport proteins and is associated with angiogenesis in cattle [ 40 ] . ARAP2 , involved in the endocytosis pathway, could be a candidate gene affecting loin strength in Chinese Holstein cows [ 41 ] ; we also speculate that it could be a candidate gene influencing udder traits in dairy cows. TLK2 is a Tousled-like kinase, and its main function involves phosphorylation of histone chaperones ASF1a and ASF1b and promotion of DNA replication-coupled nucleosome assembly, which is crucial for genome maintenance and proper cell division in plants and animals [ 42 ] . SAO encodes a copper-containing amine oxidase that oxidizes spermine and plays an important role in polyamine metabolism in cattle [ 43 ] . LOC100138645 is a primary amine oxidase and a liver isoenzyme that functions in amine metabolism. Thus, the candidate genes identified in the GWAS are involved in various important biological functions such as active glucose transport and lipogenesis. Notably, the four genes ( SLC4A4 , GC , NPFFR2 , and ADAMTS3 ) in the 86.84–87.41 Mb region of chromosome 6 exhibit significant pleiotropy and may play roles in multiple economically important traits in dairy cattle, including milk production, body conformation, and reproductive health. These findings provide valuable genetic markers for further research on molecular breeding and functional validation in dairy cows. Conclusion In the present study, individual genotyping was performed using the Genomic Profiler Bovine 100K SNP chip, with a focus on the expected transmitting abilities of 17 body conformation traits in 586 Chinese Holstein cows. By performing single-trait and multi-trait GWAS analyses, 63 significantly associated SNP loci were identified, and 66 candidate genes were annotated, with detection of 12 genes by both methods. These genes are widely involved in various biological processes such as active glucose transport, lipogenesis, and neurodevelopment. Additionally, a genomic region significantly associated with body conformation traits was identified in the 86.84–87.41 Mb region on chromosome 6; this region included four candidate genes ( SLC4A4 , GC , NPFFR2 , and ADAMTS3 ), which may be significantly related to body conformation traits in Chinese Holstein cows. The results of this study provide potential genetic markers for genomic selection breeding and related analyses in dairy cattle. Abbreviations PTAs predicted transmitting abilities GWAS Genome-wide association study SNP Single Nucleotide Polymorphism FLC Feet/Legs Composite UDC Udder Composite STA Stature STR Strength BDE Body Depth DFM Dairy Form RPA Rump Angle RTW Rump-Thurl Width RLS Rear Legs Side View RLR Rear Legs Rear View FTA Foot Angle FLS Feet/Legs Score FUA Fore Udder Attachment RUH Rear Udder Height RUW Rear udder width UCL Udder Cleft UDP Udder Depth PCA Principal Component Analysis LD Linkage disequilibrium Declarations Acknowledgments Thanks to all the authors for their contributions to the study. Author contributions Conceptualization: SL, YC and YM; Data curation: SL, LC and YL; Formal analysis and visualization: SL and LC; Writing the paper: SL and LC; Critical revision of the manuscript: SL, LC, YL, FG, HJ, HW, YC and YM. Funding This work was supported by the Breeding Industry Special Project of Tianjin Academy of Agricultural Sciences (2023ZYCX011 and 2024ZYCX012), Tianjin Seed Industry Special Project (22ZXZYSN00020), and a special financial aid from the Xizang Autonomous Region. Data availability The data were confidential and not deposited in an official repository. Ethics approval and consent to participate The animal study protocol was approved by the Science Research Department of the Institute of Animal Science, Tianjin Academy of Agricultural Science. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References ZHANG S L, SUN D X. The past, present, and future of the dairy cattle breeding industry [J]. China Animal Husbandry Industry , 2021(15):22-26. (in Chinese) ABO-ISMAIL M K, BRITO L F, MILLER S P, et al. 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ADAMTS3 activity is mandatory for embryonic lymphangiogenesis and regulates placental angiogenesis[J]. Angiogenesis , 2016,19(1):53-65. JIANG J, MA L, PRAKAPENKA D, et al. A Large-Scale Genome-Wide Association Study in U.S. Holstein Cattle[J]. Front Genet , 2019,10:412. LIANG Z, PRAKAPENKA D, VANRADEN P M, et al. A Million-Cow Genome-Wide Association Study of Three Fertility Traits in U.S. Holstein Cows[J]. Int J Mol Sci , 2023,24(13). WU X, LUND M S, SAHANA G, et al. Association analysis for udder health based on SNP-panel and sequence data in Danish Holsteins[J]. Genet Sel Evol , 2015,47(1):50. GOO Y H, SON S H, PAUL A. Lipid Droplet-Associated Hydrolase Promotes Lipid Droplet Fusion and Enhances ATGL Degradation and Triglyceride Accumulation[J]. Sci Rep , 2017,7(1):2743. WU X, GULDBRANDTSEN B, LUND M S, et al. Association analysis for feet and legs disorders with whole-genome sequence variants in 3 dairy cattle breeds[J]. J Dairy Sci , 2016,99(9):7221-7231. DA X, JUNLIANG L, CUI Z, et al. The analysis of splice variants and genomic imprinting status of NTM gene in cattle (Bos taurus).[J]. Journal of Agricultural Biotechnology , 2018,26(10):1707-1713. TALKER S C, BARUT G T, LISCHER H, et al. Monocyte biology conserved across species: Functional insights from cattle[J]. Front Immunol , 2022,13:889175. LU X, ABDALLA I M, NAZAR M, et al. Genome-Wide Association Study on Reproduction-Related Body-Shape Traits of Chinese Holstein Cows[J]. Animals (Basel) , 2021,11(7). SIMON B, LOU H J, HUET-CALDERWOOD C, et al. Tousled-like kinase 2 targets ASF1 histone chaperones through client mimicry[J]. Nat Commun , 2022,13(1):749. CERVELLI M, LEONETTI A, CERVONI L, et al. Stability of spermine oxidase to thermal and chemical denaturation: comparison with bovine serum amine oxidase[J]. Amino Acids , 2016,48(10):2283-2291. Additional Declarations No competing interests reported. Supplementary Files FigureS1Distributionoffrequencyof17bodyconformationtraits..pdf Figure S1 Distribution of frequency of 17 body conformation traits.FLC, feet/legs composite index; UDC, udder composite index; STA, stature; STR, strength; BDE, body depth; DFM, dairy form; RPA, rump angle; RTW, rump−thurl width; RLS, rear legs side view; RLR, rear legs rear view; FTA, foot angle; FLS, feet/legs score; FUA, fore udder attachment; RUH, rear udder height; RUW, rear udder width; UCL, udder cleft; UDP, udder depth. TableS1SignificantlociandgenesidentifiedbydifferentcombinationsofmultitraitGWAS.xlsx Table S1 Significant loci and genes identified by different combinations of multi-trait GWAS. Cite Share Download PDF Status: Published Journal Publication published 03 Dec, 2024 Read the published version in BMC Genomics → Version 1 posted Editorial decision: Revision requested 05 Sep, 2024 Editor assigned by journal 05 Sep, 2024 Submission checks completed at journal 05 Sep, 2024 First submitted to journal 03 Sep, 2024 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-5024087","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":350043896,"identity":"d52badad-c32b-4f63-b6a3-5ef559b59b0a","order_by":0,"name":"Shuangshuang Li","email":"","orcid":"","institution":"Tianjin Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Shuangshuang","middleName":"","lastName":"Li","suffix":""},{"id":350043897,"identity":"ded04693-dbf9-46b6-adba-6f7df91f86da","order_by":1,"name":"Lili Chen","email":"","orcid":"","institution":"Tianjin Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Lili","middleName":"","lastName":"Chen","suffix":""},{"id":350043898,"identity":"9e676aae-4169-4e58-82b9-3871f0be04c9","order_by":2,"name":"Yuxin Liu","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yuxin","middleName":"","lastName":"Liu","suffix":""},{"id":350043899,"identity":"8c406944-ab7d-4095-a7ea-258229a6d587","order_by":3,"name":"Fei Ge","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fei","middleName":"","lastName":"Ge","suffix":""},{"id":350043900,"identity":"3c36632e-d74a-43d0-b84f-0e750e77b138","order_by":4,"name":"Hui Jiang","email":"","orcid":"","institution":"Tibet Academy of Agricultural and Animal Husbandry Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Jiang","suffix":""},{"id":350043901,"identity":"066d82ee-bd96-44c5-83d1-30dd08e47c98","order_by":5,"name":"Hongzhuang Wang","email":"","orcid":"","institution":"Tibet Academy of Agricultural and Animal Husbandry Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hongzhuang","middleName":"","lastName":"Wang","suffix":""},{"id":350043902,"identity":"a44c1513-f67c-4fcc-82a1-fe2937ec6d45","order_by":6,"name":"Yan Chen","email":"","orcid":"","institution":"Chinese Academy of Agricultural Sciences","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Chen","suffix":""},{"id":350043903,"identity":"1d952562-afaf-4aeb-ac22-f9611939a2a8","order_by":7,"name":"Yi Ma","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAr0lEQVRIiWNgGAWjYFCCAyDChoefvYFIDTwQLWkykj0HiNYCBodtDG44EKnFnvHsscc8f87zMNxgYPzwMYcoW86lG/Pw3OZhnN3ALDlzG1FazphJ80jc5mGWOcDGzEu8FoNzPGwSCSRpSTjAw0O8lgNnzCTnHEjmkeA52EycX9hnnDGTePPHzt7+ePPBDx+J0cIgcYCBCRI5jA3EqAcC/gYGxh9Eqh0Fo2AUjIIRCgC8nDDnAOjo3QAAAABJRU5ErkJggg==","orcid":"","institution":"Tianjin Academy of Agricultural Sciences","correspondingAuthor":true,"prefix":"","firstName":"Yi","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2024-09-03 10:14:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5024087/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5024087/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12864-024-11090-8","type":"published","date":"2024-12-03T15:57:36+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":66134685,"identity":"798a4ede-e4aa-4e7c-98df-9892a0b02cec","added_by":"auto","created_at":"2024-10-08 04:57:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":82453,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSNP density map following quality control with the 100K SNP chip.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5024087/v1/60db52b51c3bbbb262c0b4c7.png"},{"id":66133535,"identity":"876af7ff-aa7e-47f9-8a71-a2b6c60afa49","added_by":"auto","created_at":"2024-10-08 04:49:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35212,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCA plot after genotyping quality control.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5024087/v1/5488b953abf4a0e2404bedac.png"},{"id":66133541,"identity":"e1748688-7ebb-479a-9952-69ae5df04947","added_by":"auto","created_at":"2024-10-08 04:49:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":262259,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eManhattan and QQ plots for single-trait GWAS. (A), (C), (E), (G), and (I) represent the Manhattan plots for body size, rump, feet/legs, udder, and dairy traits, respectively. (B), (D), (F), (H), and (J) represent the QQ plots for body size, rump, feet/legs, udder, and dairy traits, respectively.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5024087/v1/5405e6ffdb5db0272d670ee3.png"},{"id":66133539,"identity":"049a64d8-e0fc-4101-8ccb-604548a48acb","added_by":"auto","created_at":"2024-10-08 04:49:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":375597,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eManhattan and QQ plots for multi-trait GWAS. (A), (C), (E), and (G) represent the Manhattan plots for body size, rump, feet/legs, and udder traits, respectively. (B), (D), (F), and (H) represent the QQ plots for body size, rump, feet/legs, and udder traits, respectively.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5024087/v1/f94bc78378ca452b2304ee80.png"},{"id":66133536,"identity":"c6b1b5a3-a78a-4131-920c-1239c297e8c0","added_by":"auto","created_at":"2024-10-08 04:49:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":298656,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHaplotypes in the 86.84-87.41 Mb region of chromosome 6.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5024087/v1/5c3370f4d77fe86b1b15fb0b.png"},{"id":70964752,"identity":"e5917498-097e-422d-814e-c86b13cf9cca","added_by":"auto","created_at":"2024-12-09 16:15:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2243681,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5024087/v1/f28cc2da-583a-4319-b502-728ec6fffe17.pdf"},{"id":66134684,"identity":"82e36aa9-ac0e-4574-84e5-2e8f88afd7d4","added_by":"auto","created_at":"2024-10-08 04:57:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":282611,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1 Distribution of frequency of 17 body conformation traits.FLC, feet/legs composite index; UDC, udder composite index; STA, stature; STR, strength; BDE, body depth; DFM, dairy form; RPA, rump angle; RTW, rump−thurl width; RLS, rear legs side view; RLR, rear legs rear view; FTA, foot angle; FLS, feet/legs score; FUA, fore udder attachment; RUH, rear udder height; RUW, rear udder width; UCL, udder cleft; UDP, udder depth.\u003c/p\u003e","description":"","filename":"FigureS1Distributionoffrequencyof17bodyconformationtraits..pdf","url":"https://assets-eu.researchsquare.com/files/rs-5024087/v1/cf6a64c32ce7ceecf4a15cfe.pdf"},{"id":66134683,"identity":"c2116581-ba2e-425b-9441-c0ba724c1caa","added_by":"auto","created_at":"2024-10-08 04:57:40","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22957,"visible":true,"origin":"","legend":"\u003cp\u003eTable S1 Significant loci and genes identified by different combinations of multi-trait GWAS.\u003c/p\u003e","description":"","filename":"TableS1SignificantlociandgenesidentifiedbydifferentcombinationsofmultitraitGWAS.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5024087/v1/d5981ba801299c98707bbcda.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide Association Analysis of Body Conformation Traits in Chinese Holstein Cattle","fulltext":[{"header":"Background","content":"\u003cp\u003eThe Chinese Holstein cattle is the first dairy breed developed in China and is the dominant breed in the country\u0026rsquo;s dairy cattle population, accounting for over 85% of the national herd\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. Although the body conformation traits of dairy cattle do not directly translate into economic benefits, these traits are closely related to the milk production capacity and overall health of cows\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. These traits are an essential component for evaluating the overall performance of dairy cattle. Identifying candidate genes associated with body conformation traits in Chinese Holstein cattle is therefore crucial to provide effective molecular markers for genomic selection in breeding programs, further optimize breeding strategies, and improve the production performance of dairy cattle. The main body conformation traits in dairy cattle include (1) udder traits (such as fore udder attachment, front teat placement, teat length, rear udder height, rear udder width, and rear teat placement), (2) feet/legs traits (such as foot angle, heel depth, bone quality, rear legs side view, and rear legs rear view), (3) body size traits (such as stature, body depth, chest width, and strength), (4) rump traits (such as rump width and rump angle), and (5) dairy characteristics (such as angularity)\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Since 1990, many countries have incorporated body conformation traits into dairy cattle breeding programs\u003csup\u003e[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e to improve the overall performance of dairy cows by optimizing these traits.\u003c/p\u003e \u003cp\u003eTo gain a deeper understanding of the genetic basis of body conformation traits, genome-wide association studies (GWAS) are commonly used to identify candidate genes for economically important traits in dairy cattle. The mixed linear model proposed by Yu et al.\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e has been widely recognized as the best GWAS analysis model currently available, as it effectively accounts for population structure and complex relationships within populations. The application of this model provides robust support for understanding the genetic mechanisms underlying body conformation traits in dairy cattle. Recently, Nazar et al.\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e conducted a GWAS analysis on Chinese Holstein cattle using the mixed linear model and identified 18 SNPs significantly associated with five udder traits. Haque et al.\u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e were the first research team to report GWAS results for body conformation traits in Korean Holstein cattle, wherein they identified 24 SNPs significantly associated with 24 body conformation traits. Nazar et al.\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e also conducted a GWAS analysis on three udder traits in Chinese Holstein cattle and detected nine SNPs significantly associated with udder traits after Bonferroni correction. Č\u0026iacute;tek et al.\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e identified 32 SNPs significantly or nearly significantly associated with body conformation traits in the GWAS results of 25 body conformation traits in Czech Holstein cattle.\u003c/p\u003e \u003cp\u003eTo date, most GWAS on body conformation traits in dairy cattle have been based on independent analyses of single traits. When multiple measured traits in an individual show a correlation, the lack of linkage between causal loci (linkage disequilibrium [LD]) is often overlooked. However, multivariate GWAS can jointly analyze genetically correlated traits by simultaneously considering within-trait and between-trait variations, thereby improving detection efficiency and accuracy\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Numerous studies have shown that different body conformation traits are genetically correlated\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e. To more comprehensively reveal the genetic variation and underlying mechanisms of body conformation traits in dairy cattle, multivariate GWAS methods are more advantageous. The present study conducted single-trait and multivariate GWAS analyses to determine the genetic variants associated with body conformation traits in Chinese Holstein cattle and provide marker information for genomic selection in dairy cattle; the findings of this study could offer strong theoretical support to further optimize the health of dairy cattle and enhance their production performance.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eGenotype data and quality control\u003c/h2\u003e \u003cp\u003eGenomic DNA was extracted from 586 Chinese Holstein cows by using the Tiangen Blood Genome Extraction Kit. Genotyping was performed using the GeneSeek Genomic Profiler Bovine 100K single nucleotide polymorphism (SNP) chip. Quality control of the individual and SNP data was conducted using PLINK (v1.9) software\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e, according to the following criteria: (1) individuals with an SNP missing rate of \u0026gt;\u0026thinsp;10% were excluded; (2) SNPs with a call rate of \u0026lt;\u0026thinsp;90% were excluded; (3) SNPs with a minor allele frequency (MAF) of \u0026lt;\u0026thinsp;5% were excluded; and (4) SNPs with a P-value of \u0026lt;\u0026thinsp;1.0 \u0026times; 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e were excluded. LD analysis was performed using Haploview software\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. A total of 80,713 high-quality SNP markers from 586 individuals were ultimately selected. These SNP markers were evenly distributed across the chromosomes and were suitable for the subsequent GWAS analysis of Chinese Holstein cattle (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003ePhenotype data collection\u003c/h2\u003e \u003cp\u003eAccording to the updated standard methods of the Dairy Cattle Breeding Committee\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e, the predicted transmitting abilities (PTAs) of 17 body conformation traits, as assessed by the Council on Dairy Cattle Breeding, USA, were used as the phenotypic data for this study on 586 Chinese Holstein cows. These traits were categorized into five groups: (1) body size traits: stature (STA), strength (STR), and body depth (BDE); (2) rump traits: rump angle (RPA) and rump-thurl width (RTW); (3) feet and legs traits: rear legs side view (RLS), rear legs rear view (RLR), foot angle (FTA), feet/legs score (FLS), and feet/legs composite (FLC) index; (4) udder traits: fore udder attachment (FUA), rear udder height (RUH), rear udder width (RUW), udder cleft (UCL), udder depth (UDP), and udder composite (UDC) index; and (5) dairy form traits: dairy form (DFM).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eEstimation of genetic parameters for body conformation traits\u003c/h2\u003e \u003cp\u003eGenetic correlations were determined using the \u0026ldquo;-reml-bivar\u0026rdquo; parameter in GCTA software\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Descriptive statistical analysis for the 17 body conformation traits, including maximum, minimum, mean, variance, and standard deviation, was conducted using SPSS 19 software. The frequency distribution histograms for each trait were plotted using the R package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eSingle-trait and multi-trait GWAS\u003c/h2\u003e \u003cp\u003eBefore conducting a GWAS, principal component analysis (PCA) was performed using PLINK (v1.9) software to correct for potential false positives caused by population stratification. GWAS analysis between single conformation traits and genome-wide SNPs was performed using the univariate linear mixed model (LMM) in GEMMA software[19] by using the following model:\u003c/p\u003e \u003cp\u003ey\u0026thinsp;=\u0026thinsp;Χβ\u0026thinsp;+\u0026thinsp;Ζκγκ\u0026thinsp;+\u0026thinsp;ξ\u0026thinsp;+\u0026thinsp;ε\u003c/p\u003e \u003cp\u003ewhere y is the PTA vector, Χβ represents age and population structure effects (the first five principal components), Ζκγκ represents the effect of the marker to be tested, ξ\u0026thinsp;~\u0026thinsp;N (0,Kφ\u003csup\u003e2\u003c/sup\u003e) represents the polygenic effect, and ε\u0026thinsp;~\u0026thinsp;N (0,Iσ\u003csup\u003e2\u003c/sup\u003e) represents the residual effect. In the polygenic effect, K is the kinship matrix inferred from the markers.\u003c/p\u003e \u003cp\u003eMulti-trait GWAS analysis between multiple traits and genome-wide SNPs was conducted using the multivariate linear mixed model in GEMMA software\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e by using the following model:\u003c/p\u003e \u003cp\u003eY\u0026thinsp;=\u0026thinsp;Χβ\u0026thinsp;+\u0026thinsp;Ζκγκ\u0026thinsp;+\u0026thinsp;ξ\u0026thinsp;+\u0026thinsp;Ε\u003c/p\u003e \u003cp\u003ewhere Y is the n\u0026times;d PTA matrix, n is the number of individuals in the population, and d is the number of traits analyzed; Χβ represents age and population structure effects (the first five principal components); Ζκγκ represents the effect of the marker to be tested; ξ\u0026thinsp;~\u0026thinsp;N (0,K,Vg) represents the polygenic effect; and Ε\u0026thinsp;~\u0026thinsp;N (0,In\u0026times;n,Ve) represents the residual effect. In the polygenic effect, K is the kinship matrix inferred from the markers, Vg is the d\u0026times;d polygenic variance-covariance matrix, and Ve is the d\u0026times;d residual variance-covariance matrix. The number of independent SNPs was calculated using PLINK software with the \u0026ldquo;--indep pairs 50 5 0.2\u0026rdquo; command. The significance threshold was determined using Bonferroni correction (P_value\u0026thinsp;\u0026lt;\u0026thinsp;0.05/number of independent SNPs). Manhattan plots and QQ plots were generated using the CMplot function in the R package.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eGene annotation\u003c/h2\u003e \u003cp\u003eThe SNP position information in the GeneSeek Genomic Profiler Bovine 100K SNP chip was based on \u003cem\u003eBos taurus\u003c/em\u003e UCD 1.2 version. The ARS-UCD 1.2 bovine reference genome information was downloaded from the ENSEMBL website. Significant SNPs within a 50-kb upstream and downstream range were annotated using ANNOVAR software\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, and potential candidate genes related to the traits were identified.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDescriptive statistical analysis of the phenotypic data\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistical analysis was performed on the 17 body conformation traits of 586 Chinese Holstein cows (Table 1). The phenotypic values of each trait generally followed a normal distribution(Figure S1).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Descriptive statistics of body conformation traits\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" style=\"width: 41%;\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003eMin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003eMax\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003eMean\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003eVar\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 23%;\"\u003e\n \u003cp\u003eBody size\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eSTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-0.3634\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.81616\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.666\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-0.3168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.64178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.412\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eBDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-0.3865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.67475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 23%;\"\u003e\n \u003cp\u003eRump\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eRPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-0.0458\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.74377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.553\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eRTW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-0.3065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.79765\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.636\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width: 23%;\"\u003e\n \u003cp\u003eFeet/Legs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eFLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-0.2341\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.50674\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eRLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e0.1781\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.73851\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eRLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.67612\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.457\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eFTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-0.335\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.63803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eFLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-0.2268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.52009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"9\" style=\"width: 23%;\"\u003e\n \u003cp\u003eUdder\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eUDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-3.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e0.1534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.64248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.413\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eFUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-4.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e0.0933\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.82106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.674\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eRUH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-3.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e0.2525\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.85083\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.724\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eRUW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e0.0274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.92465\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eUCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e0.0703\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.64524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.416\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eUDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-3.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-0.0003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.89008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.792\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eFTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e0.1246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.76152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eRTP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e0.1985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.82223\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.676\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eTLG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-0.4231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.7033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eDairy characteristics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17%;\"\u003e\n \u003cp\u003eDFM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e-3.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11%;\"\u003e\n \u003cp\u003e-0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12%;\"\u003e\n \u003cp\u003e0.91252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of genetic correlations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe results of the determination of genetic correlations between the phenotypic traits indicated a strong positive correlation (r \u0026gt; 0.72) among the body size traits (STA, BDE, and STR; Table 2). In the rump traits, a weak negative correlation was observed between RTA and RPA (r = -0.02) (Table 3). Among the feet/legs traits, RLS showed varying degrees of negative correlation with other traits (-0.5 \u0026lt; r \u0026lt; -0.2), while strong positive correlations (r \u0026gt; 0.6) were found between RLR, FTA, FLS, and FLC index, except for RLS (Table 4). In the udder traits, varying degrees of positive correlation (0.1 \u0026lt; r \u0026lt; 1) were noted among UDC index, FUA, RUH, RUW, central ligament, and UDP (Table 5).\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Genetic correlation between body size traits\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"374\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2139%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.262%;\"\u003e\n \u003cp\u003eSTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.262%;\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.262%;\"\u003e\n \u003cp\u003eBDE\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2139%;\"\u003e\n \u003cp\u003eSTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.262%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2139%;\"\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.262%;\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.262%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.262%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 30.2139%;\"\u003e\n \u003cp\u003eBDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.262%;\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.262%;\"\u003e\n \u003cp\u003e0.854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23.262%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. Genetic correlation between rump traits\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"287\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.3728%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3136%;\"\u003e\n \u003cp\u003eRPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3136%;\"\u003e\n \u003cp\u003eRTW\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.3728%;\"\u003e\n \u003cp\u003eRPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3136%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3136%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 39.3728%;\"\u003e\n \u003cp\u003eRTW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3136%;\"\u003e\n \u003cp\u003e-0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3136%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4. Genetic correlation between\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003efeet/legs\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;traits\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.6204%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003eFLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003eRLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003eRLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003eFTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003eFLS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eFLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eRLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e-0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eRLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e-0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eFTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e-0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e0.729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 20.6204%;\"\u003e\n \u003cp\u003eFLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e0.930\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e-0.220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e0.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e0.760\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.8759%;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5. Genetic correlation between udder traits\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"559\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 82px;\"\u003e\n \u003cp\u003eUDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eFUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 74px;\"\u003e\n \u003cp\u003eRUH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 74px;\"\u003e\n \u003cp\u003eRUW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 79px;\"\u003e\n \u003cp\u003eUCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003eUDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"28\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd height=\"28\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eUDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"40\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eFUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eRUH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.893\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"39\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eRUW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.871\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"39\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eUCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.659\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003eUDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0.697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.472\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0.221\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 79px;\"\u003e\n \u003cp\u003e0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e1.000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd height=\"20\" style=\"width: 0px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: r \u0026le; 0.3 indicates weak correlation; 0.3 \u0026lt; r \u0026le; 0.4 indicates moderate correlation; r \u0026gt; 0.4 indicates strong correlation.\u003csup\u003e[22]\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePCA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs shown in Figure 2, population stratification was observed. The explained variance percentage of the first 10 principal components (PCs) was calculated, and the results indicated that the first 5 PCs accounted for 80% of the variance. Therefore, in this study, the first 5 PCs were selected as covariates and included in the LMM for GWAS analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults of single-trait and multi-trait GWAS analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA single-trait GWAS was performed on the 17 body conformation traits, which identified 24 significant SNPs across 12 traits (Table 6). Figure 3 shows the QQ plots and Manhattan plots. In body size traits, one significant SNP was identified on chromosome 11, with one candidate gene, \u003cem\u003eLDAH\u003c/em\u003e, annotated within the 50-kb upstream and downstream region of the SNP. In rump traits, four significant SNPs were identified on chromosome 7, with six candidate genes annotated, including \u003cem\u003eOR2T4_1\u003c/em\u003e, \u003cem\u003eELAVL1\u003c/em\u003e, and \u003cem\u003eLMAN2\u003c/em\u003e. In feet/legs traits, two significant SNPs were identified on chromosomes 8 and 29, with two candidate genes (\u003cem\u003ePIP5K1B\u003c/em\u003e and \u003cem\u003eNTM\u003c/em\u003e) annotated. In udder traits, eight significant SNPs were identified on chromosomes 5, 6, 7, and 19, with annotation of seven candidate genes, including \u003cem\u003eADGRE5\u003c/em\u003e, \u003cem\u003eCCND2\u003c/em\u003e, and \u003cem\u003eARAP2\u003c/em\u003e. In milk production-related traits, nine significant SNPs were identified on chromosome 6, with annotation of four candidate genes, including \u003cem\u003eSLC4A4\u003c/em\u003e, \u003cem\u003eGC\u003c/em\u003e, \u003cem\u003eNPFFR2\u003c/em\u003e, and \u003cem\u003eADAMTS3\u003c/em\u003e. One SNP on chromosome 19 (BovineHD1900013254) was significantly associated with UDC index, RUH, and RUW traits in the single-trait GWAS, thus indicating that it may be a pleiotropic locus.\u003c/p\u003e\n\u003cp\u003eMulti-trait GWAS was used to identify new significant SNPs, and 54 significant SNPs were identified. Figure 4 shows the QQ plots and Manhattan plots. Compared to the single-trait GWAS, 39 new SNPs were identified in the multi-trait GWAS (Table 7). In body size traits, 19 significant SNPs were identified across three combinations (STR-BDE, STA-STR, and STA-BDE), which were located on chromosomes 5, 6, 7, 8, 11, 19, and 29, and 14 candidate genes were annotated, including \u003cem\u003eSLC4A4\u003c/em\u003e, \u003cem\u003eGC\u003c/em\u003e, \u003cem\u003eNPFFR2\u003c/em\u003e, and \u003cem\u003eADAMTS3\u003c/em\u003e. Of these 19 SNPs, 18 were newly identified SNPs. In rump traits, two significant SNPs were identified in the RPA-RTW combination on chromosome 7, and four candidate genes were annotated, including \u003cem\u003eOR2T4_1\u003c/em\u003e, \u003cem\u003eATP8B3\u003c/em\u003e, and \u003cem\u003eKLF16\u003c/em\u003e. Both SNPs were newly identified. In feet/legs traits, 11 significant SNPs were identified across five combinations (FLC-RLR, RLS-FTA-FLS, and FLC-RLS-FTA), which were located on chromosomes 8, 13, 21, 28, and 29, and 14 candidate genes were annotated, including \u003cem\u003eGNAQ\u003c/em\u003e, \u003cem\u003eSAMHD1\u003c/em\u003e, and \u003cem\u003eSOGA1\u003c/em\u003e. Of these 11 SNPs, 9 were newly identified SNPs. In udder traits, 33 significant SNPs were identified across 41 combinations (FUA-RUH, FUA-RUH-RUW, and FUA-RUH-RUW-UCL), which were located on chromosomes 4, 6, 7, 11, 16, 17, 19, and 28, and 30 candidate genes were annotated, including \u003cem\u003eBMT2\u003c/em\u003e, \u003cem\u003eIGFBP1\u003c/em\u003e, and \u003cem\u003eIGFBP3\u003c/em\u003e. Of these 33 SNPs, 28 were newly identified SNPs. In total, 32 SNPs were repeatedly detected across multiple trait combinations in the multi-trait GWAS, with 9 SNPs associated with body size and udder traits and 2 SNPs associated with feet/legs traits and udder traits, thus suggesting these SNPs may be pleiotropic loci.\u003c/p\u003e\n\u003cp\u003eA summary of the single-trait and multi-trait GWAS results for the 17 body conformation traits revealed 63 SNPs showing a significant association with body conformation traits in Chinese Holstein cows and 66 candidate genes annotated within the 50-kb region upstream and downstream of the significant loci. Additionally, the results of multi-trait GWAS showed that a region on chromosome 6 (86.84\u0026ndash;87.41 Mb) contained 14 significant SNPs associated with udder and body size traits, and six haplotype blocks composed of 3, 2, 2, 9, 2, and 5 SNPs were observed (Figure 5). Eight of these SNPs exhibiting a significant association with milk production-related traits were also detected in the single-trait GWAS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6. Significant loci and genes identified by single-trait GWAS\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"113%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eSNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003eChr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003ePosition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003eTraits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003eP_value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eNearby genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eFunction area\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eBovineHD0600024228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e86847656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"9\" style=\"width: 9%;\"\u003e\n \u003cp\u003eDFM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e1.97E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eSLC4A4\u003c/em\u003e,\u003cem\u003eGC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eARS-BFGL-NGS-118182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e86860291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e2.27E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eSLC4A4\u003c/em\u003e,\u003cem\u003eGC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eBovineHD0600024243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e86877334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e2.27E-08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eSLC4A4\u003c/em\u003e,\u003cem\u003eGC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eBovineHD0600024355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e87184768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e7.02E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eGC\u003c/em\u003e,\u003cem\u003eNPFFR2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eBovineHD0600024357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e87187812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e7.02E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eGC\u003c/em\u003e,\u003cem\u003eNPFFR2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003echr6_89051385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e87316810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e7.29E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eNPFFR2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eDB-443-seq-rs110326785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e87324678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e7.29E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eNPFFR2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eDB-2033-seq-rs110186820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e87368855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e8.32E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eNPFFR2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eHapmap60852-rs29024026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e87725832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e1.97E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eADAMTS3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eARS-BFGL-NGS-38413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e78083413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003eSTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e5.97E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eLDAH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eBovineHD0700011653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e38822927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 9%;\"\u003e\n \u003cp\u003eRPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e4.15E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eLMAN2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eBovineHD0700012421\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e41261062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e6.06E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eLOC526765,OR2W3,TRIM58\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eARS-BFGL-NGS-13798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e42322361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e8.36E-07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eOR2T4_1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eBovineHD0700005042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e16748554\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003eRTW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e7.32E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eELAVL1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eBovineHD2900010725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e34925638\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003eRLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e3.21E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eNTM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eBTB-00344991\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e45049204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003eFLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e6.49E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003ePIP5K1B\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eHapmap30832-BTA-144704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e11394736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003eUDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e6.95E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eADGRE5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eBovineHD0500013405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e46349963\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9%;\"\u003e\n \u003cp\u003eRUH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e4.38E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eLOC112446700\u003c/em\u003e,\u003cem\u003eCAND1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eBovineHD0500013407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e46351738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e7.13E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eLOC112446700\u003c/em\u003e,\u003cem\u003eCAND1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eDB-364-seq-rs378727865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e105784987\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 9%;\"\u003e\n \u003cp\u003eUCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e6.80E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eCCND2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eBovineHD0600015583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e55313091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e2.15E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eARAP2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eBTA-26162-no-rs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e55327944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e1.83E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eARAP2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003eBovineHD1900012330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e42905488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003eFUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e1.25E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eLOC100138645,SAO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 23%;\"\u003e\n \u003cp\u003eBovineHD1900013254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 8%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 10%;\"\u003e\n \u003cp\u003e46942067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003eUDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e1.40E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 23%;\"\u003e\n \u003cp\u003e\u003cem\u003eTLK2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" style=\"width: 14%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003eRUH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e6.46E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 9%;\"\u003e\n \u003cp\u003eRUW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10%;\"\u003e\n \u003cp\u003e2.70E-06\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u003c/strong\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 7. Significant loci and genes identified by multi-trait GWAS\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"589\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eSNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.50764%;\"\u003e\n \u003cp\u003eChr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.5823%;\"\u003e\n \u003cp\u003ePosition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 31.0696%;\"\u003e\n \u003cp\u003eNearby genes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eFunction area\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBTA-16397-no-rs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e55471733\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eBMT2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD0400021231\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e76139944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eIGFBP1,IGFBP3,LOC112446404\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD0600015583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e55313091\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eARAP2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBTA-26162-no-rs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e55327944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eARAP2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD0600024228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e86847656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eSLC4A4,GC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eARS-BFGL-NGS-118182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e86860291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eSLC4A4,GC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD0600024243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e86877334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eSLC4A4,GC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD0600024315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e87068809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eGC,NPFFR2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD0600024338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e87130864\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eGC,NPFFR2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD0600024345\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e87143505\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eGC,NPFFR2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD0600024350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e87153414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eGC,NPFFR2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD0600024355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e87184768\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eGC,NPFFR2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD0600024357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e87187812\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eGC,NPFFR2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD0600024365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e87213962\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eGC,NPFFR2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eDB-442-seq-rs110392219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e87314427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eNPFFR2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003echr6_89051385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e87316810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eNPFFR2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eDB-443-seq-rs110326785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e87324678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eNPFFR2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eDB-2033-seq-rs110186820\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e87368855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eNPFFR2,ADAMTS3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD0600028629\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e101050942\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eMAPK10\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eHapmap30832-BTA-144704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e11394736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eADGRE5\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eARS-BFGL-NGS-13798\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e42322361\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eOR2T4_1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD0700013141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e44152142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eATP8B3,KLF16,REXO1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eARS-BFGL-NGS-30237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e53819922\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eGNAQ\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD1100011070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e37533658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eEML6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eHapmap51861-BTA-86131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e38522047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eEFEMP1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003echr11_38494447\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e38640411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eMIR216A,MIR216B,MIR217\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003encRNA_exonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD1100011351\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e38650894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eMIR216A,MIR216B,MIR217\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003encRNA_exonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD1100011736\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e39896854\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eTRNAY-AUA_2,TRNAC-GCA_141\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eARS-BFGL-NGS-38413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e78083413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eLDAH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD1100022676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e79037363\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eTTC32,LOC104973438\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD1100022680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e79049547\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eTTC32,LOC104973438\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD1100022725\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e79217388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eLOC112448820,OSR1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eHapmap47169-BTA-107308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e80349202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eLOC112448882,KCNS3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD1100029423\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e101175170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eFIBCD1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD1300018848\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e65929413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eSAMHD1,SOGA1,TLDC2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eARS-BFGL-NGS-108133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e52327847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eTMEM51\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eUTR5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD1700014238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e48992546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eTMEM132C,TRNAC-GCA_192\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD1900012330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e42905488\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eLOC100138645,SAO\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD1900013254\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e46942067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eTLK2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eARS-BFGL-NGS-115719\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e48149532\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eCD79B,SCN4A\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD1900013592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e48203740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eLOC616254,PRR29\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD1900013860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e49052596\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eBPTF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD1900013865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e49067122\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eBPTF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eHapmap47630-BTA-45710\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e49085782\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eBPTF\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD2100019874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e66165706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eMEG9,LOC112443172\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD2100019929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e66415334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eLOC101907771,LOC112443172\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003encRNA_exonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eARS-BFGL-NGS-38270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e66496288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eDIO3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eARS-BFGL-NGS-86477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e66743529\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003ePPP2R5C\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD2400000350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e1265748\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eLOC104975719,TRNAK-UUU_41\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD2800005128\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e18663865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eZNF365,LOC112444734\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintergenic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD2800005144\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e18784655\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eLOC101905431\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD2800013715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e19401828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eJMJD1C\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eexonic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eBovineHD2800005280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e19436656\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eJMJD1C\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 28.6927%;\"\u003e\n \u003cp\u003eUA-IFASA-6129\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 9.50764%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5823%;\"\u003e\n \u003cp\u003e34835983\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.0696%;\"\u003e\n \u003cp\u003e\u003cem\u003eNTM\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17.1477%;\"\u003e\n \u003cp\u003eintronic\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGenetic correlations among body conformation traits in Chinese Holstein cows\u003c/h2\u003e \u003cp\u003eA very strong positive genetic correlation (r\u0026thinsp;\u0026gt;\u0026thinsp;0.72) was observed between STA, BDE, and STR in body size traits, which is consistent with the findings of Ning\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e and Degroot et al.\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. In feet/legs traits, negative genetic correlations were observed between RLS and other leg and hoof traits (-0.47\u0026thinsp;\u0026lt;\u0026thinsp;r \u0026lt; -0.22). This was similar to the genetic correlation (-0.34) between RLS and RLR reported by Huang et al.\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e in their estimation of genetic parameters for body conformation traits of dairy cows, although RLS showed a positive correlation with other leg and hoof traits in their study. Because leg and hoof traits are easily influenced by farm management and external environmental factors, the leg and hoof structure may vary between different cattle populations. In rump traits, the genetic correlation between RTA and RPA showed a weak negative correlation (r = -0.02), which is similar to the findings of Peng\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e on the genetic correlation between RTA and RPA in Holstein cows in Hebei Province. However, the genetic correlations reported by Huang et al.\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e and An et al.\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e differed significantly (0.22\u0026thinsp;\u0026lt;\u0026thinsp;r\u0026thinsp;\u0026lt;\u0026thinsp;0.38). In udder traits, the genetic correlations ranged from 0.22 (RUW and UDP) to 1 (UDC index and FUA); this finding is similar to the results reported by Degroot et al.\u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e. The genetic correlations observed among body conformation traits suggest that the selection of one trait can indirectly influence other traits. Additionally, the positive correlations between traits indicate that these traits may share some common genetic basis, thus implying that certain genes or gene combinations may simultaneously affect multiple body conformation traits. Therefore, this genetic correlation can be used to develop more effective selection strategies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eAdvantages of multi-trait GWAS\u003c/h2\u003e \u003cp\u003eBody conformation traits are often controlled by multiple genes. Multi-trait GWAS can leverage the correlation between traits and combine weak genetic effects to enhance the statistical power of GWAS and improve the ability to detect new SNP loci\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. In the present study, the body conformation traits showed genetic correlations. Compared to single-trait GWAS, 39 new SNPs were identified in the multi-trait GWAS. By using a similar strategy, Li et al.\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e conducted a multi-trait GWAS on weaning weight and yearling weight in sheep and identified 93 new SNPs. Gao et al.\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e discovered three new SNPs in carcass weight, carcass length, and chest depth in Huaxi cattle. These results suggest that when traits show genetic correlations among them, multi-trait GWAS can complement the findings of single-trait GWAS, thereby increasing the statistical power of GWAS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCritical candidate genes\u003c/h2\u003e \u003cp\u003eBy using a combination of single-trait and multi-trait GWAS analyses, we detected 63 significant SNP loci and annotated 66 candidate genes. Among these, 12 genes were identified by both single-trait and multi-trait GWAS, including \u003cem\u003eSLC4A4\u003c/em\u003e, \u003cem\u003eGC\u003c/em\u003e, \u003cem\u003eNPFFR2\u003c/em\u003e, \u003cem\u003eADAMTS3\u003c/em\u003e, \u003cem\u003eLDAH\u003c/em\u003e, \u003cem\u003eOR2T4_1\u003c/em\u003e, \u003cem\u003eNTM\u003c/em\u003e, \u003cem\u003eADGRE5\u003c/em\u003e, \u003cem\u003eARAP2\u003c/em\u003e, \u003cem\u003eTLK2\u003c/em\u003e, \u003cem\u003eSAO\u003c/em\u003e, and \u003cem\u003eLOC100138645\u003c/em\u003e. Four genes were located in the 86.84\u0026ndash;87.41 Mb region of chromosome 6. Among these genes, \u003cem\u003eSLC4A4\u003c/em\u003e is a solute transporter and a member of a major transporter superfamily involved in active glucose transport\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eGC\u003c/em\u003e is a gene encoding the vitamin D-binding protein, which is specifically expressed in tissues such as the liver\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eNPFFR2\u003c/em\u003e is a member of the G-protein-coupled neuropeptide receptor subfamily activated by neuropeptides A-18-amide and F-8-amide\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e. The \u003cem\u003eADAMTS3\u003c/em\u003e gene activates vascular endothelial growth factors and promotes lymphangiogenesis\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. SNPs in this region were associated with dairy traits in single-trait GWAS and with body size and udder traits in multi-trait GWAS. Jiang et al.\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e also found that the four genes in this region were related to milk yield and milk protein content in Holstein cows. Liang et al.\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e conducted a GWAS of over one million US Holstein cows and found that the \u003cem\u003eSLC4A4\u003c/em\u003e, \u003cem\u003eGC\u003c/em\u003e, and \u003cem\u003eNPFFR2\u003c/em\u003e genes were related to fertility traits. Wu et al.\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e reported that \u003cem\u003eSLC4A4\u003c/em\u003e and \u003cem\u003eNPFFR2\u003c/em\u003e were candidate genes for mastitis susceptibility in Danish Holstein cows. These results suggest that the four genes in this region may exhibit pleiotropy.\u003c/p\u003e \u003cp\u003e \u003cem\u003eLDAH\u003c/em\u003e, a lipid droplet-associated hydrolase, is highly expressed in tissues that primarily store triacylglycerol and plays a key role in lipogenesis\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Previous studies have shown that it is associated with hoof and leg diseases in Danish Holstein cows\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eNTM\u003c/em\u003e is a neurotrimin protein with an important role in neurodevelopment. Xu et al.\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e analyzed the imprinting status of the \u003cem\u003eNTM\u003c/em\u003e gene in cattle and identified an SNP (rs42185569) within the \u003cem\u003eNTM\u003c/em\u003e gene through direct sequencing of PCR products. RT-PCR amplification showed monoallelic expression of the \u003cem\u003eNTM\u003c/em\u003e gene in bovine placenta and adult tissues, thus suggesting that \u003cem\u003eNTM\u003c/em\u003e is an imprinted gene in cattle. \u003cem\u003eADGRE5\u003c/em\u003e primarily functions in cell adhesion and transport proteins and is associated with angiogenesis in cattle\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eARAP2\u003c/em\u003e, involved in the endocytosis pathway, could be a candidate gene affecting loin strength in Chinese Holstein cows\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e; we also speculate that it could be a candidate gene influencing udder traits in dairy cows. \u003cem\u003eTLK2\u003c/em\u003e is a Tousled-like kinase, and its main function involves phosphorylation of histone chaperones ASF1a and ASF1b and promotion of DNA replication-coupled nucleosome assembly, which is crucial for genome maintenance and proper cell division in plants and animals\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eSAO\u003c/em\u003e encodes a copper-containing amine oxidase that oxidizes spermine and plays an important role in polyamine metabolism in cattle\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. \u003cem\u003eLOC100138645\u003c/em\u003e is a primary amine oxidase and a liver isoenzyme that functions in amine metabolism. Thus, the candidate genes identified in the GWAS are involved in various important biological functions such as active glucose transport and lipogenesis. Notably, the four genes (\u003cem\u003eSLC4A4\u003c/em\u003e, \u003cem\u003eGC\u003c/em\u003e, \u003cem\u003eNPFFR2\u003c/em\u003e, and \u003cem\u003eADAMTS3\u003c/em\u003e) in the 86.84\u0026ndash;87.41 Mb region of chromosome 6 exhibit significant pleiotropy and may play roles in multiple economically important traits in dairy cattle, including milk production, body conformation, and reproductive health. These findings provide valuable genetic markers for further research on molecular breeding and functional validation in dairy cows.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn the present study, individual genotyping was performed using the Genomic Profiler Bovine 100K SNP chip, with a focus on the expected transmitting abilities of 17 body conformation traits in 586 Chinese Holstein cows. By performing single-trait and multi-trait GWAS analyses, 63 significantly associated SNP loci were identified, and 66 candidate genes were annotated, with detection of 12 genes by both methods. These genes are widely involved in various biological processes such as active glucose transport, lipogenesis, and neurodevelopment. Additionally, a genomic region significantly associated with body conformation traits was identified in the 86.84\u0026ndash;87.41 Mb region on chromosome 6; this region included four candidate genes (\u003cem\u003eSLC4A4\u003c/em\u003e, \u003cem\u003eGC\u003c/em\u003e, \u003cem\u003eNPFFR2\u003c/em\u003e, and \u003cem\u003eADAMTS3\u003c/em\u003e), which may be significantly related to body conformation traits in Chinese Holstein cows. The results of this study provide potential genetic markers for genomic selection breeding and related analyses in dairy cattle.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003ePTAs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 496px;\"\u003e\n \u003cp\u003epredicted transmitting abilities\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eGWAS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 496px;\"\u003e\n \u003cp\u003eGenome-wide association study\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003eSNP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 496px;\"\u003e\n \u003cp\u003eSingle Nucleotide Polymorphism\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eFLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 496px;\"\u003e\n \u003cp\u003eFeet/Legs Composite\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eUDC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 496px;\"\u003e\n \u003cp\u003eUdder Composite\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eSTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 496px;\"\u003e\n \u003cp\u003eStature\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSTR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eStrength\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBDE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eBody Depth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eDFM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eDairy Form\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRPA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRump Angle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRTW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRump-Thurl Width\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRear Legs Side View\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRLR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRear Legs Rear View\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFoot Angle\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFLS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFeet/Legs Score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eFUA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eFore Udder Attachment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRUH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRear Udder Height\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRUW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eRear udder width\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUCL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUdder Cleft\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eUDP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eUdder Depth\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003ePCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003ePrincipal Component Analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003eLinkage disequilibrium\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThanks to all the authors for their contributions to the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: SL, YC and YM; Data curation: SL, LC and YL; Formal analysis and visualization: SL and LC; Writing the paper: SL and LC; Critical revision of the manuscript: SL, LC, YL, FG, HJ, HW, YC and YM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Breeding Industry Special Project of Tianjin Academy of Agricultural Sciences (2023ZYCX011 and 2024ZYCX012), Tianjin Seed Industry Special Project (22ZXZYSN00020), and a special financial aid from the Xizang Autonomous Region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe data were confidential and not deposited in an official repository.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe animal study protocol was approved by the Science Research Department of the Institute of Animal Science, Tianjin Academy of Agricultural Science.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZHANG S L, SUN D X. 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Monocyte biology conserved across species: Functional insights from cattle[J]. \u003cem\u003eFront Immunol\u003c/em\u003e, 2022,13:889175.\u003c/li\u003e\n\u003cli\u003eLU X, ABDALLA I M, NAZAR M, et al. Genome-Wide Association Study on Reproduction-Related Body-Shape Traits of Chinese Holstein Cows[J]. \u003cem\u003eAnimals (Basel)\u003c/em\u003e, 2021,11(7).\u003c/li\u003e\n\u003cli\u003eSIMON B, LOU H J, HUET-CALDERWOOD C, et al. Tousled-like kinase 2 targets ASF1 histone chaperones through client mimicry[J]. \u003cem\u003eNat Commun\u003c/em\u003e, 2022,13(1):749.\u003c/li\u003e\n\u003cli\u003eCERVELLI M, LEONETTI A, CERVONI L, et al. Stability of spermine oxidase to thermal and chemical denaturation: comparison with bovine serum amine oxidase[J]. \u003cem\u003eAmino Acids\u003c/em\u003e, 2016,48(10):2283-2291.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Chinese Holstein Cattle, Body Conformation Traits, Single-trait GWAS, Multi-trait GWAS","lastPublishedDoi":"10.21203/rs.3.rs-5024087/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5024087/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe body conformation traits of dairy cattle are closely related to their production performance and health. The present study aimed to identify gene variants associated with body conformation traits in Chinese Holstein cattle and provide marker loci for genomic selection in dairy cattle breeding. The study findings could offer robust theoretical support to optimize the health of dairy cattle and enhance their production performance.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis study involved 586 Chinese Holstein cows, using the predicted transmitting abilities (PTAs) of 17 body conformation traits evaluated by the Council on Dairy Cattle Breeding in the USA as phenotypic values. These traits were categorized into body size traits, rump traits, feet/legs traits, udder traits, and dairy characteristic traits. Based on the genomic profiling results from the Genomic Profiler Bovine 100K SNP chip, genotype data were quality-controlled using PLINK software, retaining 586 individuals and 80,713 SNPs for further analysis. Genome-wide association studies (GWAS) were conducted using the GEMMA software, employing both univariate linear mixed models (LMM) and multivariate linear mixed models (mvLMM). The Bonferroni method was used to determine the significance threshold, identifying gene variants significantly associated with body conformation traits in Chinese Holstein cows. The single-trait GWAS identified 24 SNPs significantly associated with body conformation traits (P\u0026thinsp;\u0026lt;\u0026thinsp;0.01), with annotation leading to the identification of 21 candidate genes. The multivariate GWAS identified 54 SNPs, which were annotated to 57 candidate genes, including 39 new SNPs not identified in the single-trait GWAS. Additionally, 14 SNPs in the 86.84\u0026ndash;87.41 Mb region of chromosome 6 were significantly associated with multiple traits such as body size, udder, and dairy characteristics. Four genes\u0026mdash;SLC4A4, GC, NPFFR2, and ADAMTS3\u0026mdash;were annotated in this region.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eA total of 63 SNPs were identified as significantly associated with the 17 body conformation traits in Chinese Holstein cows through both single-trait and multivariate GWAS analyses. Sixty-six candidate genes were annotated, with 12 genes identified by both methods, including \u003cem\u003eSLC4A4\u003c/em\u003e, \u003cem\u003eGC\u003c/em\u003e, \u003cem\u003eNPFFR2\u003c/em\u003e, and \u003cem\u003eADAMTS3\u003c/em\u003e, which are involved in biological processes such as active glucose transport, adipogenesis, and neural development. Thus, the study findings provided potential genetic marker information related to body conformation traits for the breeding of Chinese Holstein cattle.\u003c/p\u003e","manuscriptTitle":"Genome-wide Association Analysis of Body Conformation Traits in Chinese Holstein Cattle","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-08 04:49:35","doi":"10.21203/rs.3.rs-5024087/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-09-05T13:36:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-05T05:50:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-05T05:33:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Genomics","date":"2024-09-03T10:11:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"gics","sideBox":"Learn more about [BMC Genomics](http://bmcgenomics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/gics","title":"BMC Genomics","twitterHandle":"#BMCGenomics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f446c21a-d23c-49a8-930b-1d36200f163b","owner":[],"postedDate":"October 8th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-09T16:03:29+00:00","versionOfRecord":{"articleIdentity":"rs-5024087","link":"https://doi.org/10.1186/s12864-024-11090-8","journal":{"identity":"bmc-genomics","isVorOnly":false,"title":"BMC Genomics"},"publishedOn":"2024-12-03 15:57:36","publishedOnDateReadable":"December 3rd, 2024"},"versionCreatedAt":"2024-10-08 04:49:35","video":"","vorDoi":"10.1186/s12864-024-11090-8","vorDoiUrl":"https://doi.org/10.1186/s12864-024-11090-8","workflowStages":[]},"version":"v1","identity":"rs-5024087","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5024087","identity":"rs-5024087","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}