Improving accuracy of genomic predictions within and between dairy cattle breeds with imputed high-density single nucleotide polymorphism panels.

By Malena Erbe, Ben J. Hayes, Lakshmi K. Matukumalli, S. Goswami, P. J. Bowman, C. Reich, B. Mason, Michael E. Goddard. Journal of Dairy Science, 2012

614 citations recorded by the source corpus.

Achieving accurate genomic estimated breeding values for dairy cattle requires a very large reference population of genotyped and phenotyped individuals. Assembling such reference populations has been achieved for breeds such as Holstein, but is challenging for breeds with fewer individuals. An alternative is to use a multi-breed reference population, such that smaller breeds gain some advantage in accuracy of genomic estimated breeding values (GEBV) from information from larger breeds. However, this requires that marker-quantitative trait loci associations persist across breeds. Here, we assessed the gain in accuracy of GEBV in Jersey cattle as a result of using a combined Holstein and Jersey reference population, with either 39,745 or 624,213 single nucleotide polymorphism (SNP) markers. The surrogate used for accuracy was the correlation of GEBV with daughter trait deviations in a validation population. Two methods were used to predict breeding values, either a genomic BLUP (GBLUP_mod), or a new method, BayesR, which used a mixture of normal distributions as the prior for SNP effects, including one distribution that set SNP effects to zero. The GBLUP_mod method scaled both the genomic relationship matrix and the additive relationship matrix to a base at the time the breeds diverged, and regressed the genomic relationship matrix to account for sampling errors in estimating relationship coefficients due to a finite number of markers, before combining the 2 matrices. Although these modifications did result in less biased breeding values for Jerseys compared with an unmodified genomic relationship matrix, BayesR gave the highest accuracies of GEBV for the 3 traits investigated (milk yield, fat yield, and protein yield), with an average increase in accuracy compared with GBLUP_mod across the 3 traits of 0.05 for both Jerseys and Holsteins. The advantage was limited for either Jerseys or Holsteins in using 624,213 SNP rather than 39,745 SNP (0.01 for Holsteins and 0.03 for Jerseys, averaged across traits). Even this limited and nonsignificant advantage was only observed when BayesR was used. An alternative panel, which extracted the SNP in the transcribed part of the bovine genome from the 624,213 SNP panel (to give 58,532 SNP), performed better, with an increase in accuracy of 0.03 for Jerseys across traits. This panel captures much of the increased genomic content of the 624,213 SNP panel, with the advantage of a greatly reduced number of SNP effects to estimate. Taken together, using this panel, a combined breed reference and using BayesR rather than GBLUP_mod increased the accuracy of GEBV in Jerseys from 0.43 to 0.52, averaged across the 3 traits.

Subfields: Animal Science and Zoology, Genetics

View Paper: https://www.semanticscholar.org/paper/550b39d7b8170933855296008755fc6749b7d536

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In Genetics

Citations in the corpus

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores (2015, result): “, a mixture of normal, were used by other studies.(23,26,28) One recent study proposed an elegant approach for handling case-control ascertainment while including genome-wide-significant associations as fixed effects;(56) however, the correlations between distal causal SNPs induced by case-control a”
Perspectives for Genomic Selection Applications and Research in Plants (2015, background): “This disjunction between the relationship at causal loci and its whole genome approximation is a potential explanation of why prediction across breeds in dairy cattle does not seem to work with GBLUP (Erbe et al., 2012) and why sometimes adding more individuals to the training set actually decreases”
Pitfalls of predicting complex traits from SNPs (2013, methodology): “We use cattle data 44 to illustrate the inflation in variance explained by a SNP predictor when the validation sample is included in discovery steps (Fig 2c) The remedy to this pitfall is to use external validation.”
Polygenic prediction via Bayesian regression and continuous shrinkage priors (2018, methodology): “Given that a majority of the existing Bayesian genomic prediction models, including Bayes alphabetic methods [Habier et al., 2011; Hayes et al., 2010; Meuwissen and Goddard, 2004; Meuwissen et al., 2001; Verbyla et al., 2009, 2010; Yi et al., 2003], BayesR [Erbe et al., 2012; Moser et al., 2015], BV”
Polygenic Modeling with Bayesian Sparse Linear Mixed Models (2012, methodology): “Among these, the models most closely related to ours are BayesCp [34] and BayesR [35].”
Linkage disequilibrium levels in Bos indicus and Bos taurus cattle using medium and high density SNP chip data and different minor allele frequency distributions (2014, background): “The increase of density from the 50K chip to the HD chip has been shown to only slightly increase the accuracy of genomic predictions in dairy taurine populations using GS (Erbe et al., 2012; Su et al., 2012; VanRaden et al., 2013), but this impact should be higher for indicine populations with lowe”
Genomic evaluation of cattle in a multi-breed context ☆ (2014, methodology): “However, when using denser SNP panels, functional subsets of markers or Bayesian methods, increases in accuracy for the Jersey animals have been observed when adding HF to the reference (Erbe et al., 2012).”