A nested mixture model for genomic prediction using whole-genome SNP genotypes.

Qin Zhang,Jian Zeng,Jack Dekkers,Dorian Garrick,Rohan Fernando

doi:10.1371/journal.pone.0194683

Qin Zhang, Jian Zeng + Show 3 more

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https://doi.org/10.1371/journal.pone.0194683

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Abstract

Genomic prediction exploits single nucleotide polymorphisms (SNPs) across the whole genome for predicting genetic merit of selection candidates. In most models for genomic prediction, e.g. BayesA, B, C, R and GBLUP, independence of SNP effects is assumed. However, SNP effects are expected to be locally dependent given the presence of a nearby QTL because SNPs surrounding the QTL do not segregate independently. A consequence of ignoring this dependence is that SNPs with small effects may be overly shrunk, e.g. effects from markers with high minor allele frequencies (MAF) that flank QTL with low MAF. A nested mixture model (BayesN) is developed to account for the dependence of effects of SNPs that are closely linked, where the effects of SNPs in every non-overlapping genomic window a priori follow a point mass at zero for all SNPs or a mixture of some SNPs with nonzero effects and others with zero effects. It can be regarded as a parsimonious alternative to the existing antedependence model, antiBayesB, which allow a nonstationary dependence of SNP effects. Illumina 777K BovineHD genotypes from 948 Angus cattle were used to simulate 5,000 offspring, with 4,000 used for training and 1,000 for validation. Scenarios with 300 common (MAF > 0.05) or rare (MAF < 0.05) QTL randomly selected from segregating SNPs were replicated 8 times. SNPs corresponding to QTL were masked from a 600k panel comprising SNPs with MAF > 0.05 or a 50k evenly spaced subset of these. Compared with BayesB and a modified antiBayesB, BayesN improved the accuracy of prediction up to 2.0% with 50k SNPs and up to 7.0% with 600k SNPs, most improvements occurring in the rare QTL scenario. Computing time was reduced up to 60% with 50k SNPs and up to 75% with 600k SNPs. BayesN is an accurate and computationally efficient method for genomic prediction with whole-genome SNPs, especially for traits with rare QTL.

Highlights

Genomic prediction exploits single-nucleotide polymorphisms (SNPs) across the whole genome for predicting genetic merit of selection candidates
The 600k SNP panel resulted in an increase in the average accuracy by 0.076 (12.9%, p-value < 2e-16) for the rare quantitative trait locus (QTL) scenario and by 0.049 (6.7%, p-value < 2e-16) for the common QTL scenario
Our window selection approach that collectively considers the effects of all SNPs within that window has two favorable effects

Summary

Introduction

Genomic prediction exploits single-nucleotide polymorphisms (SNPs) across the whole genome for predicting genetic merit of selection candidates. Genomic prediction using BayesN animal breeding using panels with about 50k SNPs (medium-density, MD) [1,2,3]. High-density (HD) SNP panels, such as those with more than 500k SNPs, are expected to include a large number of SNPs that are physically proximal to every causal variant or quantitative trait locus (QTL). An HD panel must contain a subset of informative markers that provide more accurate prediction than an equivalent number of markers from an MD panel. The advantage of using HD rather than MD SNP panels has been small in practice [4,5,6], demonstrating either insufficient data or inadequacies of currently applied methods

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