Development of SNP markers for individual identification and parentage test in a Japanese Black cattle population

This study describes the development of efficient single nucleotide polymorphism (SNP) markers for individual identification and parentage tests in a Japanese Black cattle population. An amplified fragment length polymorphism method was employed to detect informative candidate markers, and yielded 44 SNP markers from 220 primer combinations. 29 unlinked SNPs were finally selected as diagnostic markers. The allelic frequencies for each marker were estimated by using PCR‐RFLP in the Japanese Black population. Based on the frequency data, the estimated identity power of these markers was 2.73 × 10^?12. Parentage exclusion probabilities, when both suspected parents' genotypes were known and when only one suspected parent was genotyped, were estimated as 0.96929 and 0.99693, respectively. This panel of SNP markers is theoretically sufficient for individual identification, and would also be a powerful tool for a parentage test in Japanese Black cattle. The markers could contribute to the management of the beef industry in Japan.     

How many markers are enough? Factors influencing parentage testing in different livestock populations

Reliability of parentage test panels is usually based on its power to exclude wrong parentage assignments based on allele frequencies. We evaluated the rates of false exclusions and inclusions in parentage assignments, and how these results are affected by allele frequencies, panel sizes and the number of allowed mismatches. We also evaluated the reliability of parentage testing by comparing populations with distinct genetic backgrounds using pure and composite families of cattle and sheep. Allowing for 1% genotype mismatches in true parent–offspring relations provided the best compromise between false‐positive and false‐negative assignments. Pure breeds needed at least 200–210 single‐nucleotide polymorphism (SNP) markers to correctly assign relations, but between 700 and 890 markers to avoid assigning incorrect relationships. Composite breeds needed between 220 (sheep) and 500 (cattle) markers for correct assignment; 680 (cattle) to 4400 (sheep) SNPs were needed to eliminate false‐positive assignments. Allowing 0% genotype mismatches decreased false‐positive but increased false‐negative assignments, whilst a higher threshold of 2% showed the opposite effects. Panels with high minor allele frequencies (0.35–0.45) provided the best chance for correct parentage resolutions requiring fewer markers. Further, we propose that a dynamic threshold would allow adapting to population specific error rates. A comparison to the performance of the official International Society for Animal Genetics SNP panel for cattle and a recently published SNP panel for sheep showed that randomly selected markers performed only slightly worse for the applied parentage test based on opposing homozygotes. This suggests that even with carefully selected panels, only marginal assignment improvements are obtainable for a particular number of SNPs. The main point for improvement is the number of markers used. We recommend using at least 200 SNP markers for parentage testing if the aim is to reduce false‐negative results. To fully exclude false positives at least 700 markers are required.     

Combined use of principal component analysis and random forests identify population‐informative single nucleotide polymorphisms: application in cattle breeds

The genetic identification of the population of origin of individuals, including animals, has several practical applications in forensics, evolution, conservation genetics, breeding and authentication of animal products. Commercial high‐density single nucleotide polymorphism (SNP) genotyping tools that have been recently developed in many species provide information from a large number of polymorphic sites that can be used to identify population‐/breed‐informative markers. In this study, starting from Illumina BovineSNP50 v1 BeadChip array genotyping data available from 3711 cattle of four breeds (2091 Italian Holstein, 738 Italian Brown, 475 Italian Simmental and 407 Marchigiana), principal component analysis (PCA) and random forests (RFs) were combined to identify informative SNP panels useful for cattle breed identification. From a PCA preselected list of 580 SNPs, RFs were computed using ranking methods (Mean Decrease in the Gini Index and Mean Accuracy Decrease) to identify the most informative 48 and 96 SNPs for breed assignment. The out‐of‐bag (OOB) error rate for both ranking methods and SNP densities ranged from 0.0 to 0.1% in the reference population. Application of this approach in a test population (10% of individuals pre‐extracted from the whole data set) achieved 100% of correct assignment with both classifiers. Linkage disequilibrium between selected SNPs was relevant (r² > 0.6) only in few pairs of markers indicating that most of the selected SNPs captured different fractions of variance. Several informative SNPs were in genes/QTL regions that affect or are associated with phenotypes or production traits that might differentiate the investigated breeds. The combination of PCA and RF to perform SNP selection and breed assignment can be easily implemented and is able to identify subsets of informative SNPs useful for population assignment starting from a large number of markers derived by high‐throughput genotyping platforms.     

Single and composite influence of growth-related candidate gene polymorphisms on additive genetic variation of birth weight in charolais beef cattle

The objective of the present experiment work was to evaluate the effect of the inclusion of genomic information on the additive genetic variance of birth weight (BW) of Charolais cattle in Mexico. Variance components and heritability were estimated using four linear models. The first model was the base model (BM) from which single and composite effects of selected single-nucleotide polymorphism (SNP) markers were evaluated (BM1, BM2, and a composite BM3). Genetic markers were included in a regression model and analyzed by stepwise regression against adjusted BW from a panel of growth-related traits candidate gene markers. After two regression rounds, two SNPs (R ²?>?0.02) were chosen to include into the animal models as fixed effects. Growth hormone receptor gene GHR 4.2 and GHR 6.1 SNPs were selected from a panel of 39 SNPs. GHR 4.2 had a negligible effect on BW, whilst GHR6.1, interestingly, explained ～9 % of genetic variance (p?=?0.0877) with an α_G>A?=?0.509. The inclusion of markers in M2 and M3 reduced 19 and 15 % of the additive genetic variance, respectively. Both adjusted significantly better the linear model (LRT?=?p?<?0.01). Results obtained suggest that the previous selection of markers in a candidate gene approach and subsequent inclusion of selected SNPs into animal model might provide a better fit, avoiding the overestimation of genetic variance components and breeding values for BW.     

Incidence of the mask phenotype M264V mutation in Labrador Retrievers

The introduction of SNP (Single Nucleotide Polymorphism) chips allows for the rapid typing of multiple markers for many individuals at one time. Our lab routinely types dogs using a custom designed combined panel of SNPs for parentage verification and a number of genes for diagnostic tests using an OpenArray platform manufactured by BioTrove (Woburn, MA, USA). By utilizing the same SNP panel across a wide array of canine breeds it is possible to detect trait-associated SNPs in breeds not thought to carry those traits. We genotyped 245 Labrador Retrievers on the canine SNP chip and found 13 animals heterozygous for the M264V mutation associated with autosomal dominant mask trait, and one animal homozygous for this trait. The color genotypes for these animals were further examined. In standard colored Labradors (black, chocolate, and yellow), the mask phenotype would never be distinguishable. As illustrated by this example, we feel this SNP panel is a valuable method for discovering traits not known to exist in a breed.     

Estimation of variance and genomic prediction using genotypes imputed from low‐density marker subsets for carcass traits in Japanese black cattle

The influence of genotype imputation using low‐density single nucleotide polymorphism (SNP) marker subsets on the genomic relationship matrix (G matrix), genetic variance explained, and genomic prediction (GP) was investigated for carcass weight and marbling score in Japanese Black fattened steers, using genotype data of approximately 40,000 SNPs. Genotypes were imputed using equally spaced SNP subsets of different densities. Two different linear models were used. The first (model 1) incorporated one G matrix, while the second (model 2) used two different G matrices constructed using the selected and remaining SNPs. When using model 1, the estimated additive genetic variance was always larger when using all SNPs obtained via genotype imputation than when using only equally spaced SNP subsets. The correlations between the genomic estimated breeding values obtained using genotype imputation with at least 3,000 SNPs and those using all available SNPs without imputation were higher than 0.99 for both traits. While additive genetic variance was likely to be partitioned with model 2, it did not enhance the accuracy of GP compared with model 1. These results indicate that genotype imputation using an equally spaced low‐density panel of an appropriate size can be used to produce a cost‐effective, valid GP.     

Incidence of the mask phenotype M264V mutation in Labrador Retrievers

The introduction of SNP (Single Nucleotide Polymorphism) chips allows for the rapid typing of multiple markers for many individuals at one time. Our lab routinely types dogs using a custom designed combined panel of SNPs for parentage verification and a number of genes for diagnostic tests using an OpenArray platform manufactured by BioTrove (Woburn, MA, USA). By utilizing the same SNP panel across a wide array of canine breeds it is possible to detect trait-associated SNPs in breeds not thought to carry those traits. We genotyped 245 Labrador Retrievers on the canine SNP chip and found 13 animals heterozygous for the M264V mutation associated with autosomal dominant mask trait, and one animal homozygous for this trait. The color genotypes for these animals were further examined. In standard colored Labradors (black, chocolate, and yellow), the mask phenotype would never be distinguishable. As illustrated by this example, we feel this SNP panel is a valuable method for discovering traits not known to exist in a breed.     

Identification of divergently selected regions between Japanese Black and Holstein cattle using bovine 50k SNP array

Differences between average allelic frequencies of genes that relate to traits suggest that it would be evidence of artificial selections. Sliding window approach is a useful method to identify genomic regions that have been differently selected between two breeds. The objective of this study was to identify the divergently selected regions between Japanese Black (JB) and Japanese Holstein (JH) cattle based on genotypic information obtained through a high‐density single nucleotide polymorphism (SNP) panel. After genotyping of 54 001 SNP markers on 100 animals (50 JB and 50 JH), 40 635 SNPs were suitable for the analysis. For each of these SNPs, the absolute difference between allelic frequencies of JB and JH was calculated. In the current study, 10 consecutive SNPs were defined as components of a window. For each window, the average difference in allelic frequency was calculated. This was termed sliding window average difference (SWAD). Among 40 055 windows, we focused on 39 windows with the largest SWAD. This was equivalent to 0.1% of all windows and the SWAD was more than 0.435. Some of these windows overlapped and were distributed in 11 regions. These regions were in good agreement with reported quantitative trait locus, therefore would be selection signatures and good candidates that harbor the causative mutations.     

Genotypes and allele frequencies of buried SNPs in a bovine single‐nucleotide polymorphism array in Japanese Black cattle

Single nucleotide polymorphism (SNP) arrays are widely used for genetic and genomic analyses in cattle breeding; thus, data derived from SNP arrays have accumulated on a large scale nationwide. Commercial SNP arrays contain a considerable number of unassigned SNPs on the chromosome/position on the genome; these SNPs are excluded in subsequent analyses. Notably, the position‐unassigned SNPs, or “buried SNPs” include some of the markers associated with genetic disease. In this study, we identified the position of buried SNPs using the Basic Local Alignment Search Tool against the surrounding sequences and characterized the relationship between SNPs and genetic diseases in Online Mendelian Inheritance in Animals based on the genomic position. We determined the position of 285 buried SNPs on the genome and surveyed the genotype and allele frequencies of these SNPs in 5,955 individual Japanese Black cattle. Eleven SNPs associated with genetic disease, which contained five buried SNPs, were found in the population with the risk allele frequency ranging from 0.00008396 to 0.46. These results indicate that buried SNPs in the bovine SNP array can be utilized to identify associations with genetic disorders from large scale accumulated SNP genotype data in Japanese Black cattle.     

Accuracy of imputation of single nucleotide polymorphism marker genotypes from low‐density panels in Japanese Black cattle

Using target and reference fattened steer populations, the performance of genotype imputation using lower‐density marker panels in Japanese Black cattle was evaluated. Population imputation was performed using BEAGLE software. Genotype information for approximately 40 000 single nucleotide polymorphism (SNP) markers by Illumina BovineSNP50 BeadChip was available, and imputation accuracy was assessed based on the average concordance rates of the genotypes, varying equally spaced SNP densities, and the number of individuals in the reference population. Two additional statistics were also calculated as indicators of imputation performance. The concordance rates tended to be lower for SNPs with greater minor allele frequencies, or those located near the ends of the chromosomes. Longer autosomes yielded greater imputation accuracies than shorter ones. When SNPs were selected based on linkage disequilibrium information, relative imputation accuracy was slightly improved. When 3000 and 10 000 equally spaced SNPs were used, the imputation accuracies were greater than 90% and approximately 97%, respectively. These results indicate that combining genotyping using a lower‐density SNP chip with genotype imputation based on a population of individuals genotyped using a higher‐density SNP chip is a cost‐effective and valid approach for genomic prediction.     

Characterization and validation of bovine gonadotripin releasing hormone receptor (GNRHR) polymorphisms

Gonadotropin releasing hormone and its receptor (GNRHR) play a critical role in sexual differentiation and reproduction. Available evidence shows a strong genetic component in the timing of puberty. In bovines, there are significant differences within and among beef breeds in the time when bulls reach puberty. Despite its economic importance, there are not many SNPs or genetic markers associated with this characteristic. The aims of the study were to identify DNA polymorphism in the bovine GNRHR by re-sequencing analysis, determine haplotype phases, and perform a population study in a selected tag SNP in six breeds. Eight SNPs were detected, including: one in the Upstream Regulatory Region (URR), five in the coding regions, and two in non-coding regions. This polymorphism level corresponds to one variant every 249.4 bp and a global nucleotide diversity of 0.385. Two haplogroups comprising nine haplotypes and two linkage blocks were detected. Despite 5 tag SNPs were required to capture all variability, just one SNP allowed to define both haplogroups, and only two SNPs were needed to differentiate the most common haplotypes. An additional taq SNP was necessary to identify both URR variants. Allele-frequency analysis of a selected taq SNP among breeds showed a geographical cline. European Bos taurus breeds had lower frequencies of the C allele than B. indicus type cattle, while Creole cattle and Wagyu breeds had intermediate frequency. There was a significant correlation between frequency profile and timing of puberty among the studied breeds, which seems to suggest that genetic variation within bovine GNRHR gene could explain at least part of the reported variability.     

Characterization and validation of bovine Gonadotripin releasing hormone receptor (GNRHR) polymorphisms

Gonadotropin releasing hormone and its receptor (GNRHR) play a critical role in sexual differentiation and reproduction. Available evidence shows a strong genetic component in the timing of puberty. In bovines, there are significant differences within and among beef breeds in the time when bulls reach puberty. Despite its economic importance, there are not many SNPs or genetic markers associated with this characteristic. The aims of the study were to identify DNA polymorphism in the bovine GNRHR by re-sequencing analysis, determine haplotype phases, and perform a population study in a selected tag SNP in six breeds. Eight SNPs were detected, including: one in the Upstream Regulatory Region (URR), five in the coding regions, and two in non-coding regions. This polymorphism level corresponds to one variant every 249.4 bp and a global nucleotide diversity of 0.385. Two haplogroups comprising nine haplotypes and two linkage blocks were detected. Despite 5 tag SNPs were required to capture all variability, just one SNP allowed to define both haplogroups, and only two SNPs were needed to differentiate the most common haplotypes. An additional taq SNP was necessary to identify both URR variants. Allele-frequency analysis of a selected taq SNP among breeds showed a geographical cline. European Bos taurus breeds had lower frequencies of the C allele than B. indicus type cattle, while Creole cattle and Wagyu breeds had intermediate frequency. There was a significant correlation between frequency profile and timing of puberty among the studied breeds, which seems to suggest that genetic variation within bovine GNRHR gene could explain at least part of the reported variability.     

Assessment of single nucleotide polymorphisms in genes residing on chromosomes 14 and 29 for association with carcass composition traits in Bos indicus cattle

Objective of this study was to assess the association of SNP in the diacylglycerol O-acyltransferase 1 (DGAT1), thyroglobulin (TG), and micromolar calcium-activated neutral protease (CAPN1) genes with carcass composition and meat quality traits in Bos indicus cattle. A population of Brahman calves (n = 479) was developed in central Florida from 1996 to 2000. Traits analyzed were ADG, hip height, slaughter weight, fat thickness, HCW, marbling score, LM area, estimated KPH fat, yield grade, retail yield, sensory panel tenderness score, carcass hump height, and cooked meat tenderness measured as Warner-Bratzler shear force at 7, 14, and 21 d postmortem. Single nucleotide polymorphisms previously reported in the TG and DGAT1 genes were used as markers on chromosome 14. Two previously reported and two new SNP in the CAPN1 gene were used as markers on chromosome 29. One SNP in CAPN1 was uninformative, and another one was associated with tenderness score (P < 0.05), suggesting the presence of variation affecting meat tenderness. All three informative SNP at the CAPN1 gene were associated with hump height (P < 0.02). The TG marker was associated with fat thickness and LMA (P < 0.05), but not with marbling score. No significant associations of the SNP in the DGAT1 gene were observed for any trait. Allele frequencies of the SNP in TG and CAPN1 were different in this Brahman population than in reported allele frequencies in Bos taurus populations. The results suggest that the use of molecular marker information developed in Bos taurus populations to Bos indicus populations may require development of appropriate additional markers.     

Use of molecular markers to improve relationship information in the genetic evaluation of beef cattle tick resistance under pedigree‐based models

The selection of genetically superior individuals is conditional upon accurate breeding value predictions which, in turn, are highly depend on how precisely relationship is represented by pedigree. For that purpose, the numerator relationship matrix is essential as a priori information in mixed model equations. The presence of pedigree errors and/or the lack of relationship information affect the genetic gain because it reduces the correlation between the true and estimated breeding values. Thus, this study aimed to evaluate the effects of correcting the pedigree relationships using single‐nucleotide polymorphism (SNP) markers on genetic evaluation accuracies for resistance of beef cattle to ticks. Tick count data from Hereford and Braford cattle breeds were used as phenotype. Genotyping was carried out using a high‐density panel (BovineHD ‐ Illumina^® bead chip with 777 962 SNPs) for sires and the Illumina BovineSNP50 panel (54 609 SNPs) for their progenies. The relationship between the parents and progenies of genotyped animals was evaluated, and mismatches were based on the Mendelian conflicts counts. Variance components and genetic parameters estimates were obtained using a Bayesian approach via Gibbs sampling, and the breeding values were predicted assuming a repeatability model. A total of 460 corrections in relationship definitions were made (Table 1) corresponding to 1018 (9.5%) tick count records. Among these changes, 97.17% (447) were related to the sire's information, and 2.8% (13) were related to the dam's information. We observed 27.2% (236/868) of Mendelian conflicts for sire–progeny genotyped pairs and 14.3% (13/91) for dam–progeny genotyped pairs. We performed 2174 new definitions of half‐siblings according to the correlation coefficient between the coancestry and molecular coancestry matrices. It was observed that higher‐quality genetic relationships did not result in significant differences of variance components estimates; however, they resulted in more accurate breeding values predictions. Using SNPs to assess conflicts between parents and progenies increases certainty in relationships and consequently the accuracy of breeding value predictions of candidate animals for selection. Thus, higher genetic gains are expected when compared to the traditional non‐corrected relationship matrix.     

Development of discrimination markers between Japanese domestic and imported beef

In the meat industry, correct labeling of beef origins or breed is required to assure quality and safety. This paper describes the development of discrimination markers between Japanese domestic and imported beef from the United States (US) and Australia (AUS) based on a bovine 50K single nucleotide polymorphism (SNP) array using a total of 110 samples: Japanese Black (n = 50), Japanese Holstein (n = 50) and US cattle (n = 10). Genotyping information revealed 1081 SNPs as candidate markers that were polymorphic only in US cattle. The genotyping results by PCR – restriction length polymorphism in Japanese Black (n = 300) and Holstein cattle (n = 146) revealed that 11 SNPs had alleles specific to US cattle. Their allelic frequencies in US cattle (n = 108) ranged from 0.097 to 0.250 with an average of 0.178 and the combined identification probability of US cattle was 0.987. In addition, we also verified the applicability of these US‐specific markers to AUS cattle. Their allelic frequencies in AUS cattle (n = 280) ranged from 0.063 to 0.224 with an average of 0.137 and the combined identification probability of AUS cattle was 0.963. In conclusion, a set of these markers could be useful for discriminating between Japanese domestic and imported beef and would contribute to identify origins and prevent falsified labeling of beef.     

Fine-mapping of quantitative trait loci for body weight and bone traits and positional cloning of the RB1 gene in chicken

Previously, a quantitative trait locus (QTL) that affects body weight (BW) at 4-12 weeks of age and carcass weight at 12 weeks of age had been mapped on chicken chromosome 1. After including more markers and individuals, the confidence interval was narrowed down to approximately 5.5 Mbps and located this QTL near a microsatellite marker (ADL328). This QTL is the same as the QTL for 12 bone traits, including metatarsus length and metatarsus circumference at 4, 6, 8, 10 and 12 weeks of age and keel length and metatarsus claw weight at 12 weeks of age, that was identified using the same population. In the current study, 1010 individuals from the Northeast Agricultural University F(2) resource population were used and 14 single-nucleotide polymorphism (SNPs) around ADL328 were developed to construct haplotypes, and an association analysis was performed to fine-map the QTL. The haplotypes were constructed on the basis of a sliding 'window', with three SNP markers included in each 'window'. The association analysis results indicated that the haplotypes in 'windows' 6-12 were significantly associated with BW and bone traits and suggested that the QTL for BW and bone traits was located between SNP8 and SNP14 or was in linkage disequilibrium with this region. The interval from SNP8 to SNP14 was approximately 400 kbps. This region contained five RefSeq genes (RB1, P2RY5, FNDC3A, MLNR and CAB39L) on the University of California Santa Cruz website. The RB1 gene was selected as a candidate gene and five SNPs were identified in the gene. The association results indicated that the RB1 gene was a major gene for BW and bone traits. The SNPs g.39692 G>A and g.77260 A>G in RB1 gene might be two quantitative trait nucleotides for BW and bone traits.     

Genomic prediction in a numerically small breed population using prioritized genetic markers from whole-genome sequence data

The objective of this study was to investigate the accuracy of genomic prediction of body weight and eating quality traits in a numerically small sheep population (Dorper sheep). Prediction was based on a large multi-breed/admixed reference population and using (a) 50k or 500k single nucleotide polymorphism (SNP) genotypes, (b) imputed whole-genome sequencing data (~31 million), (c) selected SNPs from whole genome sequence data and (d) 50k SNP genotypes plus selected SNPs from whole-genome sequence data. Furthermore, the impact of using a breed-adjusted genomic relationship matrix on accuracy of genomic breeding value was assessed. The selection of genetic variants was based on an association study performed on imputed whole-genome sequence data in an independent population, which was chosen either randomly from the base population or according to higher genetic proximity to the target population. Genomic prediction was based on genomic best linear unbiased prediction (GBLUP), and the accuracy of genomic prediction was assessed according to the correlation between genomic breeding value and corrected phenotypes divided by the square root of trait heritability. The accuracy of genomic prediction was between 0.20 and 0.30 across different traits based on common 50k SNP genotypes, which improved on average by 0.06 (absolute value) on average based on using prioritized genetic markers from whole-genome sequence data. Using prioritized genetic markers from a genetically more related GWAS population resulted in slightly higher prediction accuracy (0.02 absolute value) compared to genetic markers derived from a random GWAS population. Using high-density SNP genotypes or imputed whole-genome sequence data in GBLUP showed almost no improvement in genomic prediction accuracy however, accounting for different marker allele frequencies in reference population according to a breed-adjusted GRM resulted to on average 0.024 (absolute value) increase in accuracy of genomic prediction.     

Performance of using opposing homozygotes for paternity testing in Japanese Black cattle

Genome-wide single nucleotide polymorphism (SNP) markers in Japanese Black cattle enable genomic prediction and verifying parent–offspring relationships. We assessed the performance of opposing homozygotes (OH) for paternity testing in Japanese Black cattle, using SNP genotype information of 50 sires and 3,420 fattened animals, 1,945 of which were fathered by the 50 genotyped sires. The number of OH was counted for each sire–progeny pair in 28,764 SNPs with minor allele frequencies of ≥0.05 in this population. Across all pairs of animals, the number of OH tended to increase as the pedigree-based coefficient of relationship decreased. With a threshold of 288 (1% of SNPs) for paternity testing, most sire–progeny pairs were detected as true relationships. The frequency of Mendelian inconsistencies was 2.4%, reflecting the high accuracy of pedigree information in Japanese Black cattle population. The results indicate the utility of OH for paternity testing in Japanese Black cattle.     

Single Nuclease Polymorphism Analysis of Leptin Gene in Buffalo

This study was aimed to detect the polymorphisms of Leptin gene in buffalo that provided a fundamental for further study on marker assisted breeding in buffaloes.The single nuclease polymorphisms (SNP) of Leptin gene were identified and genotyped by using DNA pooled sequencing and high-resolution melting (HRM) method in three buffalo breeds with 182 buffalo individuals,respectively.The results showed that seven SNPs of Leptin gene were identified in studied population that were located in the intron 1,intron 2 and exon 3 regions,respectively.All the SNPs loci were moderate polymorphism except for the SNP5 and SNP6.The χ²-test indicated that all the SNPs loci were in agreement with Hardy-Weinberg equilibrium in Nili-Ravi population (P>0.05).Our findings revealed that seven SNPs of Leptin gene in buffalo were identified and genotyped in population,which provide the data support for further analyzing the associations between these polymorphism and production traits in buffaloes.     

A genome‐wide SNP scan in a porcine Large White × Minzhu intercross population reveals a locus influencing muscle mass on chromosome 2

A high‐density single nucleotide polymorphism (SNP) array containing 62 163 markers was employed for a genome‐wide association study (GWAS) to identify variants associated with lean meat in ham (LMH, %) and lean meat percentage (LMP, %) within a porcine Large White × Minzhu intercross population. For each individual, LMH and LMP were measured after slaughter at the age of 240 ± 7 days. A total of 557 F2 animals were genotyped. The GWAS revealed that 21 SNPs showed significant genome‐wide or chromosome‐wide associations with LMH and LMP by the Genome‐wide Rapid Association using Mixed Model and Regression‐Genomic Control approach. Nineteen significant genome‐wide SNPs were mapped to the distal end of Sus Scrofa Chromosome (SSC) 2, where a major known gene responsible for muscle mass, IGF2 is located. A conditioned analysis, in which the genotype of the strongest associated SNP is included as a fixed effect in the model, showed that those significant SNPs on SSC2 were derived from a single quantitative trait locus. The two chromosome‐wide association SNPs on SSC1 disappeared after conditioned analysis suggested the association signal is a false association derived from using a F2 population. The present result is expected to lead to novel insights into muscle mass in different pig breeds and lays a preliminary foundation for follow‐up studies for identification of causal mutations for subsequent application in marker‐assisted selection programs for improving muscle mass in pigs.