Invited review: Genomic selection in dairy cattle: Progress and challenges
ABSTRACT A new technology called genomic selection is revolutionizing dairy cattle breeding. Genomic selection refers to selection decisions based on genomic breeding values (GEBV). The GEBV are calculated as the sum of the effects of dense genetic markers, or haplotypes of these markers, across the entire genome, thereby potentially capturing all the quantitative trait loci (QTL) that contribute to variation in a trait. The QTL effects, inferred from either haplotypes or individual single nucleotide polymorphism markers, are first estimated in a large reference population with phenotypic information. In subsequent generations, only marker information is required to calculate GEBV. The reliability of GEBV predicted in this way has already been evaluated in experiments in the United States, New Zealand, Australia, and the Netherlands. These experiments used reference populations of between 650 and 4,500 progeny-tested Holstein-Friesian bulls, genotyped for approximately 50,000 genome-wide markers. Reliabilities of GEBV for young bulls without progeny test results in the reference population were between 20 and 67%. The reliability achieved depended on the heritability of the trait evaluated, the number of bulls in the reference population, the statistical method used to estimate the single nucleotide polymorphism effects in the reference population, and the method used to calculate the reliability. A common finding in 3 countries (United States, New Zealand, and Australia) was that a straightforward BLUP method for estimating the marker effects gave reliabilities of GEBV almost as high as more complex methods. The BLUP method is attractive because the only prior information required is the additive genetic variance of the trait. All countries included a polygenic effect (parent average breeding value) in their GEBV calculation. This inclusion is recommended to capture any genetic variance not associated with the markers, and to put some selection pressure on low-frequency QTL that may not be captured by the markers. The reliabilities of GEBV achieved were significantly greater than the reliability of parental average breeding values, the current criteria for selection of bull calves to enter progeny test teams. The increase in reliability is sufficiently high that at least 2 dairy breeding companies are already marketing bull teams for commercial use based on their GEBV only, at 2 yr of age. This strategy should at least double the rate of genetic gain in the dairy industry. Many challenges with genomic selection and its implementation remain, including increasing the accuracy of GEBV, integrating genomic information into national and international genetic evaluations, and managing long-term genetic gain.
SourceAvailable from: C. S. Mukhopadhyay21 Days CATF Training Program on “Advanced Tools for Analysis of Phenomic and Genomic Data”, NDRI, Karnal, Haryana, India; 03/2015
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ABSTRACT: Reggiana is a local dairy cattle breed that currently counts about 2,000 cows reared mainly in the province of Reggio Emilia (North of Italy). Reggiana cows are less productive than Holstein cows opening questions about the profitability of this breed whose milk is used to produce a niche brand of Parmigiano-Reggiano cheese. For these reasons, protein content and yield of the Reggiana milk are very important traits. With the aim to identify markers that could be useful to describe genetic variability in this breed and eventually to use them in conservation and marker assisted selection programs, we genotyped 22 DNA polymorphisms of 17 candidate genes (ABCG2, CRH, CSN3, CYP11B1, DGAT1, FGF2, GH1, GHR, KIT, LEP, LGB, ORL1, POU1F1, PRLR, SPP1, STAT1 and TG) in 128 Reggiana sires for which semen was available and we analysed association between 20 polymorphic markers and milk yield, protein yield (PY), fat yield (FY), protein percentage (PP) and fat percentage (FP) estimated breeding values. Five markers, four of which on Bos taurus chromosome (BTA) 14 (DGAT1 p.K232A, CYP11B1 p.A30V, TG g.9509279 C>T, CRH p.S45D) and one on BTA19 (GH1 p.L153V), were highly associated (P<0.01) with several production traits (PP, FY and PY; FP; PY; FY; PP and FP; respectively). This study is the largest investigation of molecular markers carried out in this breed so far and represents one of the few attempts to identify DNA markers affecting production traits in a cattle breed constituted by a very small population. Obtained results could open new possibilities for conservation and breeding programs in Reggiana cattle.
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ABSTRACT: Background The reliability of whole-genome prediction models (WGP) based on using high-density single nucleotide polymorphism (SNP) panels critically depends on proper specification of key hyperparameters. A currently popular WGP model labeled BayesB specifies a hyperparameter π, that is `loosely used to describe the proportion of SNPs that are in linkage disequilibrium (LD) with causal variants. The remaining markers are specified to be random draws from a Student t distribution with key hyperparameters being degrees of freedom v and scale s2. Methods We consider three alternative Markov chain Monte Carlo (MCMC) approaches based on the use of Metropolis-Hastings (MH) to estimate these key hyperparameters. The first approach, termed DFMH, is based on a previously published strategy for which s2 is drawn by a Gibbs step and v is drawn by a MH step. The second strategy, termed UNIMH, substitutes MH for Gibbs when drawing s2 and further collapses or marginalizes the full conditional density of v. The third strategy, termed BIVMH, is based on jointly drawing the two hyperparameters in a bivariate MH step. We also tested the effect of misspecification of s2 for its effect on accuracy of genomic estimated breeding values (GEBV), yet allowing for inference on the other hyperparameters. Results The UNIMH and BIVMH strategies had significantly greater (P < 0.05) computational efficiencies for estimating v and s2 than DFMH in BayesA (π = 1) and BayesB implementations. We drew similar conclusions based on an analysis of the public domain heterogeneous stock mice data. We also determined significant drops (P < 0.01) in accuracies of GEBV under BayesA by overspecifying s2, whereas BayesB was more robust to such misspecifications. However, understating s2 was compensated by counterbalancing inferences on v in BayesA and BayesB, and on π in BayesB. Conclusions Sampling strategies based solely on MH updates of v and s2, and collapsed representations of full conditional densities can improve the computational efficiency of MCMC relative to the use of Gibbs updates. We believe that proper inferences on s2, v and π are vital to ensure that the accuracy of GEBV is maximized when using parametric WGP models. Electronic supplementary material The online version of this article (doi:10.1186/s12711-015-0092-x) contains supplementary material, which is available to authorized users.Genetics Selection Evolution 03/2015; 47(1). DOI:10.1186/s12711-015-0092-x · 3.75 Impact Factor