Increasing the accuracy of genomic prediction in pure‐bred Limousin beef cattle by including cross‐bred Limousin data and accounting for an F94L variant in MSTN

Explicitly fitting effects for major genes or QTL that account for a large percentage of variation in a whole genomic prediction model may increase prediction accuracy. This study compared approaches to account for a major effect of an F94L variant in the MSTN gene within the genomic prediction using bovine whole‐genomic SNP markers. Among the beef cattle breeds, Limousin have been known to have an F94L variant that is not present in Angus. The reference population in this study consisted of 3060 beef cattle including pure‐bred Limousin (PL), cross‐bred Limousin with Angus (LF) and pure‐bred Angus, genotyped using a BovineSNP50 BeadChip and directly for the MSTN‐F94L variant. We compared prediction accuracies in PL animals using the three datasets from only the PL population, admixed PL and LF (AL) or multibreed analysis using all of the PL, LF and Angus (MB) population according to four‐fold cross‐validation after K‐means clustering. The MSTN‐F94L variant was the most strongly associated with five traits (birth weight, calving ease direct, milk, weaning weight and yield grade) among the 13 measured traits in PL and AL populations. Fitting the MSTN‐F94L variant as a random effect, the genomic prediction accuracies for birth weight increased by 2.7% in PL, by 2.2% in AL and by 3.2% in MB. Prediction accuracies for five traits increased in the MB analysis. Fitting MSTN‐F94L as a fixed effect in PL, AL and MB analyses resulted in increased prediction accuracy in PL for eight traits. Prediction accuracies can be improved by including a causal variant in genomic evaluation compared with simply using whole‐genome SNP markers. Fitting the causal variant as a fixed effect along with markers fitted as random effects resulted in greater prediction accuracies for most traits. Causal variants should be genotyped along with SNP markers.     

Design of a low‐density SNP chip for the main Australian sheep breeds and its effect on imputation and genomic prediction accuracy

Genotyping sheep for genome‐wide SNPs at lower density and imputing to a higher density would enable cost‐effective implementation of genomic selection, provided imputation was accurate enough. Here, we describe the design of a low‐density (12k) SNP chip and evaluate the accuracy of imputation from the 12k SNP genotypes to 50k SNP genotypes in the major Australian sheep breeds. In addition, the impact of imperfect imputation on genomic predictions was evaluated by comparing the accuracy of genomic predictions for 15 novel meat traits including carcass and meat quality and omega fatty acid traits in sheep, from 12k SNP genotypes, imputed 50k SNP genotypes and real 50k SNP genotypes. The 12k chip design included 12 223 SNPs with a high minor allele frequency that were selected with intermarker spacing of 50–475 kb. SNPs for parentage and horned or polled tests also were represented. Chromosome ends were enriched with SNPs to reduce edge effects on imputation. The imputation performance of the 12k SNP chip was evaluated using 50k SNP genotypes of 4642 animals from six breeds in three different scenarios: (1) within breed, (2) single breed from multibreed reference and (3) multibreed from a single‐breed reference. The highest imputation accuracies were found with scenario 2, whereas scenario 3 was the worst, as expected. Using scenario 2, the average imputation accuracy in Border Leicester, Polled Dorset, Merino, White Suffolk and crosses was 0.95, 0.95, 0.92, 0.91 and 0.93 respectively. Imputation scenario 2 was used to impute 50k genotypes for 10 396 animals with novel meat trait phenotypes to compare genomic prediction accuracy using genomic best linear unbiased prediction (GBLUP) with real and imputed 50k genotypes. The weighted mean imputation accuracy achieved was 0.92. The average accuracy of genomic estimated breeding values (GEBVs) based on only 12k data was 0.08 across traits and breeds, but accuracies varied widely. The mean GBLUP accuracies with imputed 50k data more than doubled to 0.21. Accuracies of genomic prediction were very similar for imputed and real 50k genotypes. There was no apparent impact on accuracy of GEBVs as a result of using imputed rather than real 50k genotypes, provided imputation accuracy was >90%.     

Epistasis between calpain 1 and its inhibitor calpastatin within breeds of cattle

The calpain gene family and its inhibitors have diverse effects, many related to protein turnover, which appear to affect a range of phenotypes such as diabetes, exercise-induced muscle injury, and pathological events associated with degenerative neural diseases in humans, fertility, longevity, and postmortem effects on meat tenderness in livestock species. The calpains are inhibited by calpastatin, which binds directly to calpain. Here we report the direct measurement of epistatic interactions of causative mutations for quantitative trait loci (QTL) at calpain 1 (CAPN1), located on chromosome 29, with causative mutations for QTL variation at calpastatin (CAST), located on chromosome 7, in cattle. First we identified potential causative mutations at CAST and then genotyped these along with putative causative mutations at CAPN1 in >1500 cattle of seven breeds. The maximum allele substitution effect on the phenotype of the CAPN1:c.947G>C single nucleotide polymorphism (SNP) was 0.14 sigma(p) (P = 0.0003) and of the CAST:c.155C>T SNP was also 0.14 sigma(p) (P = 0.0011) when measured across breeds. We found significant epistasis between SNPs at CAPN1 and CAST in both taurine and zebu derived breeds. There were more additive x dominance components of epistasis than additive x additive and dominance x dominance components combined. A minority of breed comparisons did not show epistasis, suggesting that genetic variation at other genes may influence the degree of epistasis found in this system.     

Accuracy of genomic breeding values in multi-breed dairy cattle populations

Background

Two key findings from genomic selection experiments are 1) the reference population used must be very large to subsequently predict accurate genomic estimated breeding values (GEBV), and 2) prediction equations derived in one breed do not predict accurate GEBV when applied to other breeds. Both findings are a problem for breeds where the number of individuals in the reference population is limited. A multi-breed reference population is a potential solution, and here we investigate the accuracies of GEBV in Holstein dairy cattle and Jersey dairy cattle when the reference population is single breed or multi-breed. The accuracies were obtained both as a function of elements of the inverse coefficient matrix and from the realised accuracies of GEBV.

Methods

Best linear unbiased prediction with a multi-breed genomic relationship matrix (GBLUP) and two Bayesian methods (BAYESA and BAYES_SSVS) which estimate individual SNP effects were used to predict GEBV for 400 and 77 young Holstein and Jersey bulls respectively, from a reference population of 781 and 287 Holstein and Jersey bulls, respectively. Genotypes of 39,048 SNP markers were used. Phenotypes in the reference population were de-regressed breeding values for production traits. For the GBLUP method, expected accuracies calculated from the diagonal of the inverse of coefficient matrix were compared to realised accuracies.

Results

When GBLUP was used, expected accuracies from a function of elements of the inverse coefficient matrix agreed reasonably well with realised accuracies calculated from the correlation between GEBV and EBV in single breed populations, but not in multi-breed populations. When the Bayesian methods were used, realised accuracies of GEBV were up to 13% higher when the multi-breed reference population was used than when a pure breed reference was used. However no consistent increase in accuracy across traits was obtained.

Conclusion

Predicting genomic breeding values using a genomic relationship matrix is an attractive approach to implement genomic selection as expected accuracies of GEBV can be readily derived. However in multi-breed populations, Bayesian approaches give higher accuracies for some traits. Finally, multi-breed reference populations will be a valuable resource to fine map QTL.     

Runs of homozygosity and signatures of selection: a comparison among eight local Swiss sheep breeds

A dataset consisting of 787 animals with high‐density SNP chip genotypes (346 774 SNPs) and 939 animals with medium‐density SNP chip genotypes (33 828 SNPs) from eight indigenous Swiss sheep breeds was analyzed to characterize population structure, quantify genomic inbreeding based on runs of homozygosity and identify selection signatures. In concordance with the recent known history of these breeds, the highest genetic diversity was observed in Engadine Red sheep and the lowest in Valais Blacknose sheep. Correlation between F_PED and F_ROH was around 0.50 and thereby lower than that found in similar studies in cattle. Mean F_ROH estimates from medium‐density data and HD data were highly correlated (0.95). Signatures of selection and candidate gene analysis revealed that the most prominent signatures of selection were found in the proximity of genes associated with body size (NCAPG, LCORL, LAP3, SPP1, PLAG1, ALOX12, TP53), litter size (SPP1), milk production (ABCG2, SPP1), coat color (KIT, ASIP, TBX3) and horn status (RXFP2). For the Valais Blacknose sheep, the private signatures in proximity of genes/QTL influencing body size, coat color and fatty acid composition were confirmed based on runs of homozygosity analysis. These private signatures underline the genetic uniqueness of the Valais Blacknose sheep breed. In conclusion, we identified differences in the genetic make‐up of Swiss sheep breeds, and we present relevant candidate genes responsible for breed differentiation in locally adapted breeds.     

Genetic diversity and genomic signatures of selection among cattle breeds from Siberia,eastern and northern Europe

Domestication in the near eastern region had a major impact on the gene pool of humpless taurine cattle (Bos taurus). As a result of subsequent natural and artificial selection, hundreds of different breeds have evolved, displaying a broad range of phenotypic traits. Here, 10 Eurasian B. taurus breeds from different biogeographic and production conditions, which exhibit different demographic histories and have been under artificial selection at various intensities, were investigated using the Illumina BovineSNP50 panel to understand their genetic diversity and population structure. In addition, we scanned genomes from eight breeds for signatures of diversifying selection. Our population structure analysis indicated six distinct breed groups, the most divergent being the Yakutian cattle from Siberia. Selection signals were shared (experimental P‐value < 0.01) with more than four breeds on chromosomes 6, 7, 13, 16 and 22. The strongest selection signals in the Yakutian cattle were found on chromosomes 7 and 21, where a miRNA gene and genes related to immune system processes are respectively located. In general, genomic regions indicating selection overlapped with known QTL associated with milk production (e.g. on chromosome 19), reproduction (e.g. on chromosome 24) and meat quality (e.g. on chromosome 7). The selection map created in this study shows that native cattle breeds and their genetic resources represent unique material for future breeding.     

Improved precision of QTL mapping using a nonlinear Bayesian method in a multi-breed population leads to greater accuracy of across-breed genomic predictions

Background

Genomic selection is increasingly widely practised, particularly in dairy cattle. However, the accuracy of current predictions using GBLUP (genomic best linear unbiased prediction) decays rapidly across generations, and also as selection candidates become less related to the reference population. This is likely caused by the effects of causative mutations being dispersed across many SNPs (single nucleotide polymorphisms) that span large genomic intervals. In this paper, we hypothesise that the use of a nonlinear method (BayesR), combined with a multi-breed (Holstein/Jersey) reference population will map causative mutations with more precision than GBLUP and this, in turn, will increase the accuracy of genomic predictions for selection candidates that are less related to the reference animals.

Results

BayesR improved the across-breed prediction accuracy for Australian Red dairy cattle for five milk yield and composition traits by an average of 7% over the GBLUP approach (Australian Red animals were not included in the reference population). Using the multi-breed reference population with BayesR improved accuracy of prediction in Australian Red cattle by 2 – 5% compared to using BayesR with a single breed reference population. Inclusion of 8478 Holstein and 3917 Jersey cows in the reference population improved accuracy of predictions for these breeds by 4 and 5%. However, predictions for Holstein and Jersey cattle were similar using within-breed and multi-breed reference populations. We propose that the improvement in across-breed prediction achieved by BayesR with the multi-breed reference population is due to more precise mapping of quantitative trait loci (QTL), which was demonstrated for several regions. New candidate genes with functional links to milk synthesis were identified using differential gene expression in the mammary gland.

Conclusions

QTL detection and genomic prediction are usually considered independently but persistence of genomic prediction accuracies across breeds requires accurate estimation of QTL effects. We show that accuracy of across-breed genomic predictions was higher with BayesR than with GBLUP and that BayesR mapped QTL more precisely. Further improvements of across-breed accuracy of genomic predictions and QTL mapping could be achieved by increasing the size of the reference population, including more breeds, and possibly by exploiting pleiotropic effects to improve mapping efficiency for QTL with small effects.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-014-0074-4) contains supplementary material, which is available to authorized users.     

Association of polymorphisms in calpain 1, (mu/I) large subunit,calpastatin, and cathepsin D genes with meat quality traits in double‐muscled Piemontese cattle

Five single‐nucleotide polymorphisms (SNPs) located in the calpain 1, (mu/I) large subunit (CAPN1), calpastatin (CAST), and cathepsin D (CTSD) genes were analyzed in a large sample of Piemontese cattle. The aim of this study was to evaluate allele and genotype frequencies of these SNPs and to investigate associations of CAPN1, CAST, and CTSD gene variants with meat quality traits. Minor allele frequencies ranged from 30 to 48%. The presence of the A allele at CAPN530 increased yellowness and drip loss. The CAST282 G allele was associated with an increased drip loss compared to the C allele, and the CAST2959 A allele decreased redness compared to the G allele.     

Insights into the genetic history of Green‐legged Partridgelike fowl: mtDNA and genome‐wide SNP analysis

The Green‐legged Partridgelike (GP) fowl, an old native Polish breed, is characterised by reseda green‐coloured shanks rather than yellow, white, slate or black commonly observed across most domestic breeds of chicken. Here, we investigate the origin, genetic relationships and structure of the GP fowl using mtDNA D‐loop sequencing and genome‐wide SNP analysis. Genome‐wide association analysis between breeds enables us to verify the genetic control of the reseda green shank phenotype, a defining trait for the breed. Two mtDNA D‐loop haplogroups and three autosomal genetic backgrounds are revealed. Significant associations of SNPs on chromosomes GGA24 and GGAZ indicate that the reseda green leg phenotype is associated with recessive alleles linked to the W and Id loci. Our results provide new insights into the genetic history of European chicken, indicating an admixd origin of East European traditional breeds of chicken on the continent, as supported by the presence of the reseda green phenotype and the knowledge that the GP fowl as a breed was developed before the advent of commercial stocks.     

Genome‐wide population structure and admixture analysis reveals weak differentiation among Ugandan goat breeds

Uganda has a large population of goats, predominantly from indigenous breeds reared in diverse production systems, whose existence is threatened by crossbreeding with exotic Boer goats. Knowledge about the genetic characteristics and relationships among these Ugandan goat breeds and the potential admixture with Boer goats is still limited. Using a medium‐density single nucleotide polymorphism (SNP) panel, we assessed the genetic diversity, population structure and admixture in six goat breeds in Uganda: Boer, Karamojong, Kigezi, Mubende, Small East African and Sebei. All the animals had genotypes for about 46 105 SNPs after quality control. We found high proportions of polymorphic SNPs ranging from 0.885 (Kigezi) to 0.928 (Sebei). The overall mean observed (H_O) and expected (H_E) heterozygosity across breeds was 0.355 ± 0.147 and 0.384 ± 0.143 respectively. Principal components, genetic distances and admixture analyses revealed weak population sub‐structuring among the breeds. Principal components separated Kigezi and weakly Small East African from other indigenous goats. Sebei and Karamojong were tightly entangled together, whereas Mubende occupied a more central position with high admixture from all other local breeds. The Boer breed showed a unique cluster from the Ugandan indigenous goat breeds. The results reflect common ancestry but also some level of geographical differentiation. admixture and f₄ statistics revealed gene flow from Boer and varying levels of genetic admixture among the breeds. Generally, moderate to high levels of genetic variability were observed. Our findings provide useful insights into maintaining genetic diversity and designing appropriate breeding programs to exploit within‐breed diversity and heterozygote advantage in crossbreeding schemes.     

Empirical and deterministic accuracies of across-population genomic prediction

Background

Differences in linkage disequilibrium and in allele substitution effects of QTL (quantitative trait loci) may hinder genomic prediction across populations. Our objective was to develop a deterministic formula to estimate the accuracy of across-population genomic prediction, for which reference individuals and selection candidates are from different populations, and to investigate the impact of differences in allele substitution effects across populations and of the number of QTL underlying a trait on the accuracy.

Methods

A deterministic formula to estimate the accuracy of across-population genomic prediction was derived based on selection index theory. Moreover, accuracies were deterministically predicted using a formula based on population parameters and empirically calculated using simulated phenotypes and a GBLUP (genomic best linear unbiased prediction) model. Phenotypes of 1033 Holstein-Friesian, 105 Groninger White Headed and 147 Meuse-Rhine-Yssel cows were simulated by sampling 3000, 300, 30 or 3 QTL from the available high-density SNP (single nucleotide polymorphism) information of three chromosomes, assuming a correlation of 1.0, 0.8, 0.6, 0.4, or 0.2 between allele substitution effects across breeds. The simulated heritability was set to 0.95 to resemble the heritability of deregressed proofs of bulls.

Results

Accuracies estimated with the deterministic formula based on selection index theory were similar to empirical accuracies for all scenarios, while accuracies predicted with the formula based on population parameters overestimated empirical accuracies by ~25 to 30%. When the between-breed genetic correlation differed from 1, i.e. allele substitution effects differed across breeds, empirical and deterministic accuracies decreased in proportion to the genetic correlation. Using a multi-trait model, it was possible to accurately estimate the genetic correlation between the breeds based on phenotypes and high-density genotypes. The number of QTL underlying the simulated trait did not affect the accuracy.

Conclusions

The deterministic formula based on selection index theory estimated the accuracy of across-population genomic predictions well. The deterministic formula using population parameters overestimated the across-population genomic accuracy, but may still be useful because of its simplicity. Both formulas could accommodate for genetic correlations between populations lower than 1. The number of QTL underlying a trait did not affect the accuracy of across-population genomic prediction using a GBLUP method.     

Polymorphisms in calpastatin and mu‐calpain genes are associated with beef iron content

The objective of this study was to assess the association of markers in the calpastatin and mu‐calpain loci with iron in beef cattle muscle. The population consisted of 259 cross‐bred steers from Beefmaster, Brangus, Bonsmara, Romosinuano, Hereford and Angus sires. Total iron and heme iron concentrations were measured. Markers in the calpastatin (referred to as CAST) and mu‐calpain (referred to as CAPN4751) genes were used to assess their association with iron levels. The mean and standard error for iron and heme iron content in the population was 35.6 ± 1.3 μg and 27.1 ± 1.4 μg respectively. Significant associations (P < 0.01) of markers were observed for both iron and heme iron content. For CAST, animals with the CC genotype had higher levels of iron and heme iron in longissimus dorsi muscle. For CAPN4751, individuals with the TT genotype had higher concentrations of iron and heme iron than did animals with the CC and CT genotypes. Genotypes known to be associated with tougher meat were associated with higher levels of iron concentration.     

Impact of QTL properties on the accuracy of multi-breed genomic prediction

Background

Although simulation studies show that combining multiple breeds in one reference population increases accuracy of genomic prediction, this is not always confirmed in empirical studies. This discrepancy might be due to the assumptions on quantitative trait loci (QTL) properties applied in simulation studies, including number of QTL, spectrum of QTL allele frequencies across breeds, and distribution of allele substitution effects. We investigated the effects of QTL properties and of including a random across- and within-breed animal effect in a genomic best linear unbiased prediction (GBLUP) model on accuracy of multi-breed genomic prediction using genotypes of Holstein-Friesian and Jersey cows.

Methods

Genotypes of three classes of variants obtained from whole-genome sequence data, with moderately low, very low or extremely low average minor allele frequencies (MAF), were imputed in 3000 Holstein-Friesian and 3000 Jersey cows that had real high-density genotypes. Phenotypes of traits controlled by QTL with different properties were simulated by sampling 100 or 1000 QTL from one class of variants and their allele substitution effects either randomly from a gamma distribution, or computed such that each QTL explained the same variance, i.e. rare alleles had a large effect. Genomic breeding values for 1000 selection candidates per breed were estimated using GBLUP modelsincluding a random across- and a within-breed animal effect.

Results

For all three classes of QTL allele frequency spectra, accuracies of genomic prediction were not affected by the addition of 2000 individuals of the other breed to a reference population of the same breed as the selection candidates. Accuracies of both single- and multi-breed genomic prediction decreased as MAF of QTL decreased, especially when rare alleles had a large effect. Accuracies of genomic prediction were similar for the models with and without a random within-breed animal effect, probably because of insufficient power to separate across- and within-breed animal effects.

Conclusions

Accuracy of both single- and multi-breed genomic prediction depends on the properties of the QTL that underlie the trait. As QTL MAF decreased, accuracy decreased, especially when rare alleles had a large effect. This demonstrates that QTL properties are key parameters that determine the accuracy of genomic prediction.

Electronic supplementary material

The online version of this article (doi:10.1186/s12711-015-0124-6) contains supplementary material, which is available to authorized users.     

Genome‐wide variability and selection signatures in Italian island cattle breeds

In the present study, a sample of 88 animals belonging to four local (Modicana, Sarda, Sardo‐Bruna and Sardo‐Modicana) and one cosmopolitan (Italian Brown Swiss) cattle breeds were genotyped with a medium density SNP beadchip and compared to investigate their genetic diversity and the existence of selection signatures. A total of 43 012 SNPs distributed across all 29 autosomal chromosomes were retained after data quality control. Basic population statistics, Wright fixation index and runs of homozygosity (ROH) analyses confirmed that the Italian Brown Swiss genome was shaped mainly by selection, as underlined by the low values of heterozygosity and minor allele frequency. As expected, local cattle exhibited a large within‐breed genetic heterogeneity. The F_ST comparison revealing the largest number of significant SNPs was Sardo‐Bruna vs. Sardo‐Modicana, whereas the smallest was observed for Italian Brown Swiss vs. Sardo‐Modicana. Modicana exhibited the largest number of detected ROHs, whereas the smallest was observed for Sardo‐Modicana. Signatures of selection were detected in genomic regions that harbor genes involved in milk production traits for Italian Brown Swiss and fitness traits for local breeds. According to the results of multi‐dimensional scaling and the admixture analysis the Sardo‐Bruna is more similar to the Sarda than to the Italian Brown Swiss breed. Moreover, the Sardo‐Modicana is genetically closer to the Modicana than to the Sarda breed. Results of the present work confirm the usefulness of single nucleotide polymorphisms in deciphering the genetic architecture of livestock breeds.     

A genome‐wide association study for a proxy of intermuscular fat level in the Italian Large White breed identifies genomic regions affecting an important quality parameter for dry‐cured hams

Intermuscular fat content in protected designations of origin dry‐cured hams is a very important meat quality trait that affects the acceptability of the product by the consumers. An excess in intermuscular fat (defined as the level of fat deposition between leg muscles) is a defect that depreciates the final product. In this study we carried out a genome‐wide association study for visible intermuscular fat (VIF) of hams in the Italian Large White pig breed. This trait was evaluated on the exposed muscles of green legs in 1122 performance‐tested gilts by trained personnel, according to a classification scale useful for routine and cheap evaluation. All animals were genotyped with the Illumina PorcineSNP60 BeadChip. The genome‐wide association study identified three QTL regions on porcine chromosome 1 (SSC1; accounting for ~79% of the SNPs below the 5.0E?04 threshold) and SSC2, two on SSC7 and one each on SSC3, SSC6, SSC9, SSC11, SSC13, SSC15, SSC16 and SSC17. The most significant SNP (ALGA0004143 on SSC1 at 77.3 Mb; P_FDR < 0.05), included in the largest QTL region which spanned about 6.8 Mb on SSC1, is located within the glutamate ionotropic receptor kainate type subunit 2 (GRIK2) gene. Functional annotation of all genes included in QTL regions for VIF suggested that intermuscular fat in the Italian Large White breed is a complex trait apparently influenced by complex biological mechanisms also involving obesity‐related processes. These QTL target mainly chromosome regions different from those affecting subcutaneous and intramuscular fat deposition.     

Genome‐wide association study identifies variants in the CAPN9 gene associated with umbilical hernia in pigs

Pig umbilical hernia (UH) affects pig welfare and brings considerable economic loss to the pig industry. To date, the molecular mechanisms underlying pig UH are still poorly understood. To identify potential loci for susceptibility to this disease, we performed a genome‐wide association study in an Erhualian × Shaziling F₂ intercross population. A total of 45 animals were genotyped using Illumina Porcine SNP60 BeadChips. We observed a SNP (rs80993347) located in the calpain‐9 (CAPN9) gene on Sus scrofa chromosome 14 that was significantly associated with UH (P = 1.97 × 10^?10). Then, we identified a synonymous mutation rs321865883 (g.20164T>C) in exon 10 of the CAPN9 gene that distinguished two affected individuals (CC) from their normal full‐sibs (TC). Finally, quantitative polymerase chain reaction was explored to investigate the mRNA expression profile of the CAPN9 gene in 12 tissues in Yorkshire pigs at different developmental stages (3, 90 and 180 days). CAPN9 showed high expression levels in the gastrointestinal tract at these three growth stages. The results of this study indicate that the CAPN9 gene might be implicated in UH. Further studies are required to establish a role of CAPN9 in pig UH.     

Genetic Variability and Linkage Disequilibrium Patterns in the Bovine <Emphasis Type="Italic">DNAJA1</Emphasis> Gene

Correlation between expression level of the bovine DNAJA1 gene and meat tenderness was recently found in Charolais longissimus thoracis muscle samples, suggesting that this gene could play an important role in meat tenderness. Here, we report the validation of polymorphisms within the bovine DNAJA1 gene, and the haplotype variability and extent of linkage disequilibrium in the three main French beef breeds (Blonde d’Aquitaine, Charolais, Limousin). Genotyping 18 putative SNPs revealed that 16 SNPs were polymorphic within the breeds tested. Two SNPs were removed from further analyses as one SNP had a low genotyping call rate, while the other SNP was not in Hardy–Weinberg equilibrium. The degree of heterozygosity observed for the remaining 14 SNPs varied between breeds, with Charolais being the breed with the highest genetic variation and Blonde d’Aquitaine the lowest. Linkage disequilibrium and haplotype structure of DNAJA1 were different between breeds. Eighteen different haplotypes, including three shared by all breeds, were discovered, and two to three tag SNPs (depending on the breed) are sufficient to capture all the genetic variability seen in these haplotypes. The results of this study will facilitate the design of optimal future association studies evaluating the role of the DNAJA1 gene in meat tenderness.