Accuracy of high‐density genotype imputation in Japanese Black cattle

Genotype imputation facilitates the identification of missing genotypes on a high‐density array using low‐density arrays and has great potential for reducing genotyping costs for cattle populations. However, the imputation quality varies across breeds, which have different effective population sizes. Therefore, the accuracy of genotype imputation must be evaluated in each breed. The Japanese Black cattle population has a unique genetic background, and this study aimed to investigate different factors affecting imputation quality in this population. A total of 1368 animals were genotyped using the Illumina BovineHD BeadChip, and the accuracy of imputation was evaluated using information from four lower density arrays. The extent of linkage disequilibrium for this population was relatively higher than that in other beef breeds but lower than that in dairy breeds. The accuracy of arrays with more than 20 000 single nucleotide polymorphisms (SNPs) was similar to or higher than that of lower density arrays. In addition, the minor allele frequency of SNPs in the reference population affected the accuracy. The accuracy increased as the size of the reference population increased, up to 400 animals, beyond which there was little increase. A higher genetic relationship between the reference and test populations increased imputation accuracy. These results indicate that high imputation accuracy can be achieved using high‐density arrays, having enough reference animals and including relatives in the reference population.     

Inferring the population structure of the Maghreb sheep breeds using a medium‐density SNP chip

North Africa has a great diversity of indigenous sheep breeds whose origin is linked to its environmental characteristics and to certain historical events that took place in the region. To date, few genome‐wide studies have been conducted to investigate the population structure of North African indigenous sheep. The objective of the present study was to provide a detailed assessment of the genetic structure and admixture patterns of six Maghreb sheep populations using the Illumina 50K Ovine BeadChip and comparisons with 22 global populations of sheep and mouflon. Regardless of the method of analysis used, patterns of multiple hybridization events were observed within all North African populations, leading to a heterogeneous genetic architecture that varies according to the breed. The Barbarine population showed the lowest genetic heterogeneity and major southwest Asian ancestry, providing additional support to the Asian origin of the North African fat‐tailed sheep. All other breeds presented substantial Merino introgression ranging from 15% for D'man to 31% for Black Thibar. We highlighted several signals of ancestral introgression between North African and southern European sheep. In addition, we identified two opposite gradients of ancestry, southwest Asian and central European, occurring between North Africa and central Europe. Our results provide further evidence of the weak global population structure of sheep resulting from high levels of gene flow among breeds occurring worldwide. At the regional level, signs of recent admixture among North African populations, resulting in a change of the original genomic architecture of minority breeds, were also detected.     

Genotype imputation from various low-density SNP panels and its impact on accuracy of genomic breeding values in pigs

The uptake of genomic selection (GS) by the swine industry is still limited by the costs of genotyping. A feasible alternative to overcome this challenge is to genotype animals using an affordable low-density (LD) single nucleotide polymorphism (SNP) chip panel followed by accurate imputation to a high-density panel. Therefore, the main objective of this study was to screen incremental densities of LD panels in order to systematically identify one that balances the tradeoffs among imputation accuracy, prediction accuracy of genomic estimated breeding values (GEBVs), and genotype density (directly associated with genotyping costs). Genotypes using the Illumina Porcine60K BeadChip were available for 1378 Duroc (DU), 2361 Landrace (LA) and 3192 Yorkshire (YO) pigs. In addition, pseudo-phenotypes (de-regressed estimated breeding values) for five economically important traits were provided for the analysis. The reference population for genotyping imputation consisted of 931 DU, 1631 LA and 2103 YO animals and the remainder individuals were included in the validation population of each breed. A LD panel of 3000 evenly spaced SNPs (LD3K) yielded high imputation accuracy rates: 93.78% (DU), 97.07% (LA) and 97.00% (YO) and high correlations (>0.97) between the predicted GEBVs using the actual 60 K SNP genotypes and the imputed 60 K SNP genotypes for all traits and breeds. The imputation accuracy was influenced by the reference population size as well as the amount of parental genotype information available in the reference population. However, parental genotype information became less important when the LD panel had at least 3000 SNPs. The correlation of the GEBVs directly increased with an increase in imputation accuracy. When genotype information for both parents was available, a panel of 300 SNPs (imputed to 60 K) yielded GEBV predictions highly correlated (⩾0.90) with genomic predictions obtained based on the true 60 K panel, for all traits and breeds. For a small reference population size with no parents on reference population, it is recommended the use of a panel at least as dense as the LD3K and, when there are two parents in the reference population, a panel as small as the LD300 might be a feasible option. These findings are of great importance for the development of LD panels for swine in order to reduce genotyping costs, increase the uptake of GS and, therefore, optimize the profitability of the swine industry.     

Toward genomic prediction from whole-genome sequence data: impact of sequencing design on genotype imputation and accuracy of predictions

Genomic prediction from whole-genome sequence data is attractive, as the accuracy of genomic prediction is no longer bounded by extent of linkage disequilibrium between DNA markers and causal mutations affecting the trait, given the causal mutations are in the data set. A cost-effective strategy could be to sequence a small proportion of the population, and impute sequence data to the rest of the reference population. Here, we describe strategies for selecting individuals for sequencing, based on either pedigree relationships or haplotype diversity. Performance of these strategies (number of variants detected and accuracy of imputation) were evaluated in sequence data simulated through a real Belgian Blue cattle pedigree. A strategy (AHAP), which selected a subset of individuals for sequencing that maximized the number of unique haplotypes (from single-nucleotide polymorphism panel data) sequenced gave good performance across a range of variant minor allele frequencies. We then investigated the optimum number of individuals to sequence by fold coverage given a maximum total sequencing effort. At 600 total fold coverage (x 600), the optimum strategy was to sequence 75 individuals at eightfold coverage. Finally, we investigated the accuracy of genomic predictions that could be achieved. The advantage of using imputed sequence data compared with dense SNP array genotypes was highly dependent on the allele frequency spectrum of the causative mutations affecting the trait. When this followed a neutral distribution, the advantage of the imputed sequence data was small; however, when the causal mutations all had low minor allele frequencies, using the sequence data improved the accuracy of genomic prediction by up to 30%.     

Impact of genotyping strategy on the accuracy of genomic prediction in simulated populations of purebred swine

Single-step genomic BLUP (ssGBLUP) has been widely used in genomic evaluation due to relatively higher prediction accuracy and simplicity of use. The prediction accuracy from ssGBLUP depends on the amount of information available concerning both genotype and phenotype. This study investigated how information on genotype and phenotype that had been acquired from previous generations influences the prediction accuracy of ssGBLUP, and thus we sought an optimal balance about genotypic and phenotypic information to achieve a cost-effective and computationally efficient genomic evaluation. We generated two genetically correlated traits (h² = 0.35 for trait A, h² = 0.10 for trait B and genetic correlation 0.20) as well as two distinct populations mimicking purebred swine. Phenotypic and genotypic information in different numbers of previous generations and different genotyping rates for each litter were set to generate different datasets. Prediction accuracy was evaluated by correlating genomic estimated breeding values with true breeding values for genotyped animals in the last generation. The results revealed a negligible impact of previous generations that lacked genotyped animals on the prediction accuracy. Phenotypic and genotypic data, including the most recent three to four generations with a genotyping rate of 40% or 50% for each litter, could lead to asymptotic maximum prediction accuracy for genotyped animals in the last generation. Single-step genomic best linear unbiased prediction yielded an optimal balance about genotypic and phenotypic information to ensure a cost-effective and computationally efficient genomic evaluation of populations of polytocous animals such as purebred pigs.     

A new genomic tool for walnut (Juglans regia L.): development and validation of the high‐density Axiom™ J. regia 700K SNP genotyping array

Over the last 20 years, global production of Persian walnut (Juglans regia L.) has grown enormously, likely reflecting increased consumption due to its numerous benefits to human health. However, advances in genome‐wide association (GWA) studies and genomic selection (GS) for agronomically important traits in walnut remain limited due to the lack of powerful genomic tools. Here, we present the development and validation of a high‐density 700K single nucleotide polymorphism (SNP) array in Persian walnut. Over 609K high‐quality SNPs have been thoroughly selected from a set of 9.6 m genome‐wide variants, previously identified from the high‐depth re‐sequencing of 27 founders of the Walnut Improvement Program (WIP) of University of California, Davis. To validate the effectiveness of the array, we genotyped a collection of 1284 walnut trees, including 1167 progeny of 48 WIP families and 26 walnut cultivars. More than half of the SNPs (55.7%) fell in the highest quality class of ‘Poly High Resolution’ (PHR) polymorphisms, which were used to assess the WIP pedigree integrity. We identified 151 new parent‐offspring relationships, all confirmed with the Mendelian inheritance test. In addition, we explored the genetic variability among cultivars of different origin, revealing how the varieties from Europe and California were differentiated from Asian accessions. Both the reconstruction of the WIP pedigree and population structure analysis confirmed the effectiveness of the Applied Biosystems? Axiom? J. regia 700K SNP array, which initiates a novel genomic and advanced phase in walnut genetics and breeding.     

Accuracy of genomic selection for age at puberty in a multi‐breed population of tropically adapted beef cattle

Genomic selection is becoming a standard tool in livestock breeding programs, particularly for traits that are hard to measure. Accuracy of genomic selection can be improved by increasing the quantity and quality of data and potentially by improving analytical methods. Adding genotypes and phenotypes from additional breeds or crosses often improves the accuracy of genomic predictions but requires specific methodology. A model was developed to incorporate breed composition estimated from genotypes into genomic selection models. This method was applied to age at puberty data in female beef cattle (as estimated from age at first observation of a corpus luteum) from a mix of Brahman and Tropical Composite beef cattle. In this dataset, the new model incorporating breed composition did not increase the accuracy of genomic selection. However, the breeding values exhibited slightly less bias (as assessed by deviation of regression of phenotype on genomic breeding values from the expected value of 1). Adding additional Brahman animals to the Tropical Composite analysis increased the accuracy of genomic predictions and did not affect the accuracy of the Brahman predictions.     

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.     

A high‐density SNP chip for genotyping great tit (Parus major) populations and its application to studying the genetic architecture of exploration behaviour

High‐density SNP microarrays (“SNP chips”) are a rapid, accurate and efficient method for genotyping several hundred thousand polymorphisms in large numbers of individuals. While SNP chips are routinely used in human genetics and in animal and plant breeding, they are less widely used in evolutionary and ecological research. In this article, we describe the development and application of a high‐density Affymetrix Axiom chip with around 500,000 SNPs, designed to perform genomics studies of great tit (Parus major) populations. We demonstrate that the per‐SNP genotype error rate is well below 1% and that the chip can also be used to identify structural or copy number variation. The chip is used to explore the genetic architecture of exploration behaviour (EB), a personality trait that has been widely studied in great tits and other species. No SNPs reached genomewide significance, including at DRD4, a candidate gene. However, EB is heritable and appears to have a polygenic architecture. Researchers developing similar SNP chips may note: (i) SNPs previously typed on alternative platforms are more likely to be converted to working assays; (ii) detecting SNPs by more than one pipeline, and in independent data sets, ensures a high proportion of working assays; (iii) allele frequency ascertainment bias is minimized by performing SNP discovery in individuals from multiple populations; and (iv) samples with the lowest call rates tend to also have the greatest genotyping error rates.     

Impacts of both reference population size and inclusion of a residual polygenic effect on the accuracy of genomic prediction

Background

The purpose of this work was to study the impact of both the size of genomic reference populations and the inclusion of a residual polygenic effect on dairy cattle genetic evaluations enhanced with genomic information.

Methods

Direct genomic values were estimated for German Holstein cattle with a genomic BLUP model including a residual polygenic effect. A total of 17,429 genotyped Holstein bulls were evaluated using the phenotypes of 44 traits. The Interbull genomic validation test was implemented to investigate how the inclusion of a residual polygenic effect impacted genomic estimated breeding values.

Results

As the number of reference bulls increased, both the variance of the estimates of single nucleotide polymorphism effects and the reliability of the direct genomic values of selection candidates increased. Fitting a residual polygenic effect in the model resulted in less biased genome-enhanced breeding values and decreased the correlation between direct genomic values and estimated breeding values of sires in the reference population.

Conclusions

Genetic evaluation of dairy cattle enhanced with genomic information is highly effective in increasing reliability, as well as using large genomic reference populations. We found that fitting a residual polygenic effect reduced the bias in genome-enhanced breeding values, decreased the correlation between direct genomic values and sire''s estimated breeding values and made genome-enhanced breeding values more consistent in mean and variance as is the case for pedigree-based estimated breeding values.     

The easy road to genome‐wide medium density SNP screening in a non‐model species: development and application of a 10 K SNP‐chip for the house sparrow (Passer domesticus)

With the advent of next generation sequencing, new avenues have opened to study genomics in wild populations of non‐model species. Here, we describe a successful approach to a genome‐wide medium density Single Nucleotide Polymorphism (SNP) panel in a non‐model species, the house sparrow (Passer domesticus), through the development of a 10 K Illumina iSelect HD BeadChip. Genomic DNA and cDNA derived from six individuals were sequenced on a 454 GS FLX system and generated a total of 1.2 million sequences, in which SNPs were detected. As no reference genome exists for the house sparrow, we used the zebra finch (Taeniopygia guttata) reference genome to determine the most likely position of each SNP. The 10 000 SNPs on the SNP‐chip were selected to be distributed evenly across 31 chromosomes, giving on average one SNP per 100 000 bp. The SNP‐chip was screened across 1968 individual house sparrows from four island populations. Of the original 10 000 SNPs, 7413 were found to be variable, and 99% of these SNPs were successfully called in at least 93% of all individuals. We used the SNP‐chip to demonstrate the ability of such genome‐wide marker data to detect population sub‐division, and compared these results to similar analyses using microsatellites. The SNP‐chip will be used to map Quantitative Trait Loci (QTL) for fitness‐related phenotypic traits in natural populations.     

Tool for genomic selection and breeding to evolutionary adaptation: Development of a 100K single nucleotide polymorphism array for the honey bee

High‐throughput high‐density genotyping arrays continue to be a fast, accurate, and cost‐effective method for genotyping thousands of polymorphisms in high numbers of individuals. Here, we have developed a new high‐density SNP genotyping array (103,270 SNPs) for honey bees, one of the most ecologically and economically important pollinators worldwide. SNPs were detected by conducting whole‐genome resequencing of 61 honey bee drones (haploid males) from throughout Europe. Selection of SNPs for the chip was done in multiple steps using several criteria. The majority of SNPs were selected based on their location within known candidate regions or genes underlying a range of honey bee traits, including hygienic behavior against pathogens, foraging, and subspecies. Additionally, markers from a GWAS of hygienic behavior against the major honey bee parasite Varroa destructor were brought over. The chip also includes SNPs associated with each of three major breeding objectives—honey yield, gentleness, and Varroa resistance. We validated the chip and make recommendations for its use by determining error rates in repeat genotypings, examining the genotyping performance of different tissues, and by testing how well different sample types represent the queen's genotype. The latter is a key test because it is highly beneficial to be able to determine the queen's genotype by nonlethal means. The array is now publicly available and we suggest it will be a useful tool in genomic selection and honey bee breeding, as well as for GWAS of different traits, and for population genomic, adaptation, and conservation questions.     

Development and evaluation of a genome‐wide Coffee 8.5K SNP array and its application for high‐density genetic mapping and for investigating the origin of Coffea arabica L.

Coffee species such as Coffea canephora P. (Robusta) and C. arabica L. (Arabica) are important cash crops in tropical regions around the world. C. arabica is an allotetraploid (2n = 4x = 44) originating from a hybridization event of the two diploid species C. canephora and C. eugenioides (2n = 2x = 22). Interestingly, these progenitor species harbour a greater level of genetic variability and are an important source of genes to broaden the narrow Arabica genetic base. Here, we describe the development, evaluation and use of a single‐nucleotide polymorphism (SNP) array for coffee trees. A total of 8580 unique and informative SNPs were selected from C. canephora and C. arabica sequencing data, with 40% of the SNP located in annotated genes. In particular, this array contains 227 markers associated to 149 genes and traits of agronomic importance. Among these, 7065 SNPs (~82.3%) were scorable and evenly distributed over the genome with a mean distance of 54.4 Kb between markers. With this array, we improved the Robusta high‐density genetic map by adding 1307 SNP markers, whereas 945 SNPs were found segregating in the Arabica mapping progeny. A panel of C. canephora accessions was successfully discriminated and over 70% of the SNP markers were transferable across the three species. Furthermore, the canephora‐derived subgenome of C. arabica was shown to be more closely related to C. canephora accessions from northern Uganda than to other current populations. These validated SNP markers and high‐density genetic maps will be useful to molecular genetics and for innovative approaches in coffee breeding.     

N‐acetylcysteine prevents oxidized low‐density lipoprotein‐induced reduction of MG53 and enhances MG53 protective effect on bone marrow stem cells

MG53 is an important membrane repair protein and partially protects bone marrow multipotent adult progenitor cells (MAPCs) against oxidized low‐density lipoprotein (ox‐LDL). The present study was to test the hypothesis that the limited protective effect of MG53 on MAPCs was due to ox‐LDL‐induced reduction of MG53. MAPCs were cultured with and without ox‐LDL (0‐20 μg/mL) for up to 48 hours with or without MG53 and antioxidant N‐acetylcysteine (NAC). Serum MG53 level was measured in ox‐LDL‐treated mice with or without NAC treatment. Ox‐LDL induced significant membrane damage and substantially impaired MAPC survival with selective inhibition of Akt phosphorylation. NAC treatment effectively prevented ox‐LDL‐induced reduction of Akt phosphorylation without protecting MAPCs against ox‐LDL. While having no effect on Akt phosphorylation, MG53 significantly decreased ox‐LDL‐induced membrane damage and partially improved the survival, proliferation and apoptosis of MAPCs in vitro. Ox‐LDL significantly decreased MG53 level in vitro and serum MG53 level in vivo without changing MG53 clearance. NAC treatment prevented ox‐LDL‐induced MG53 reduction both in vitro and in vivo. Combined NAC and MG53 treatment significantly improved MAPC survival against ox‐LDL. These data suggested that NAC enhanced the protective effect of MG53 on MAPCs against ox‐LDL through preventing ox‐LDL‐induced reduction of MG53.     

Pool‐hmm: a Python program for estimating the allele frequency spectrum and detecting selective sweeps from next generation sequencing of pooled samples

Due to its cost effectiveness, next generation sequencing of pools of individuals (Pool‐Seq) is becoming a popular strategy for genome‐wide estimation of allele frequencies in population samples. As the allele frequency spectrum provides information about past episodes of selection, Pool‐seq is also a promising design for genomic scans for selection. However, no software tool has yet been developed for selection scans based on Pool‐Seq data. We introduce Pool‐hmm, a Python program for the estimation of allele frequencies and the detection of selective sweeps in a Pool‐Seq sample. Pool‐hmm includes several options that allow a flexible analysis of Pool‐Seq data, and can be run in parallel on several processors. Source code and documentation for Pool‐hmm is freely available at https://qgsp.jouy.inra.fr/ .     

Development of iFOX‐hunting as a functional genomic tool and demonstration of its use to identify early senescence‐related genes in the polyploid Brassica napus

Functional genomic studies of many polyploid crops, including rapeseed (Brassica napus), are constrained by limited tool sets. Here we report development of a gain‐of‐function platform, termed ‘iFOX (inducible Full‐length cDNA OvereXpressor gene)‐Hunting’, for inducible expression of B. napus seed cDNAs in Arabidopsis. A Gateway‐compatible plant gene expression vector containing a methoxyfenozide‐inducible constitutive promoter for transgene expression was developed. This vector was used for cloning of random cDNAs from developing B. napus seeds and subsequent Agrobacterium‐mediated transformation of Arabidopsis. The inducible promoter of this vector enabled identification of genes upon induction that are otherwise lethal when constitutively overexpressed and to control developmental timing of transgene expression. Evaluation of a subset of the resulting ~6000 Arabidopsis transformants revealed a high percentage of lines with full‐length B. napus transgene insertions. Upon induction, numerous iFOX lines with visible phenotypes were identified, including one that displayed early leaf senescence. Phenotypic analysis of this line (rsl‐1327) after methoxyfenozide induction indicated high degree of leaf chlorosis. The integrated B. napuscDNA was identified as a homolog of an Arabidopsis acyl‐CoA binding protein (ACBP) gene designated BnACBP1‐like. The early senescence phenotype conferred by BnACBP1‐like was confirmed by constitutive expression of this gene in Arabidopsis and B. napus. Use of the inducible promoter in the iFOX line coupled with RNA‐Seq analyses allowed mechanistic clues and a working model for the phenotype associated with BnACBP1‐like expression. Our results demonstrate the utility of iFOX‐Hunting as a tool for gene discovery and functional characterization of Brassica napus genome.     

Genome‐wide association and genomic prediction for biomass yield in a genetically diverse Miscanthus sinensis germplasm panel phenotyped at five locations in Asia and North America

To improve the efficiency of breeding of Miscanthus for biomass yield, there is a need to develop genomics‐assisted selection for this long‐lived perennial crop by relating genotype to phenotype and breeding value across a broad range of environments. We present the first genome‐wide association (GWA) and genomic prediction study of Miscanthus that utilizes multilocation phenotypic data. A panel of 568 Miscanthus sinensis accessions was genotyped with 46,177 single nucleotide polymorphisms (SNPs) and evaluated at one subtropical and five temperate locations over 3 years for biomass yield and 14 yield‐component traits. GWA and genomic prediction were performed separately for different years of data in order to assess reproducibility. The analyses were also performed for individual field trial locations, as well as combined phenotypic data across groups of locations. GWA analyses identified 27 significant SNPs for yield, and a total of 504 associations across 298 unique SNPs across all traits, sites, and years. For yield, the greatest number of significant SNPs was identified by combining phenotypic data across all six locations. For some of the other yield‐component traits, greater numbers of significant SNPs were obtained from single site data, although the number of significant SNPs varied greatly from site to site. Candidate genes were identified. Accounting for population structure, genomic prediction accuracies for biomass yield ranged from 0.31 to 0.35 across five northern sites and from 0.13 to 0.18 for the subtropical location, depending on the estimation method. Genomic prediction accuracies of all traits were similar for single‐location and multilocation data, suggesting that genomic selection will be useful for breeding broadly adapted M. sinensis as well as M. sinensis optimized for specific climates. All of our data, including DNA sequences flanking each SNP, are publicly available. By facilitating genomic selection in M. sinensis and Miscanthus × giganteus, our results will accelerate the breeding of these species for biomass in diverse environments.     

Evidence for a third allele at the β-lactoglobulin (β-Lg) locus of sheep milk and its occurrence in different breeds

Summary. Milk samples from 189 Merinoland Sheep, 145 Black Faced Mutton Sheep, 89 East Friesian Milk Sheep, 36 Rhön, 36 Pleven, 23 Tsigaja, 25 Black Razka and 86 Hungarian Merino x Pleven (F1) sheep were analysed by polyacrylamide gel electrophoresis under acid conditions and isoelectric focusing in ultrathin layer polyacrylamide gels with carrier ampholytes. Six different β-lactoglobulin (β-Lg) phenotypes (A, AB, B, AC, BC and C) were observed by both methods. The occurrence of three codominant alleles (β-Lg^A, β-Lg^B, β-Lg^c) at an autosomal locus (β-Lg) was supported by family and population data on genetic equilibrium. Differences in gene frequencies between the breeds were observed.