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. 2016 Aug 19;7:151.
doi: 10.3389/fgene.2016.00151. eCollection 2016.

Weighting Strategies for Single-Step Genomic BLUP: An Iterative Approach for Accurate Calculation of GEBV and GWAS

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Free PMC article

Weighting Strategies for Single-Step Genomic BLUP: An Iterative Approach for Accurate Calculation of GEBV and GWAS

Xinyue Zhang et al. Front Genet. .
Free PMC article

Abstract

Genomic Best Linear Unbiased Predictor (GBLUP) assumes equal variance for all single nucleotide polymorphisms (SNP). When traits are influenced by major SNP, Bayesian methods have the advantage of SNP selection. To overcome the limitation of GBLUP, unequal variance or weights for all SNP are applied in a method called weighted GBLUP (WGBLUP). If only a fraction of animals is genotyped, single-step WGBLUP (WssGBLUP) can be used. Default weights in WGBLUP or WssGBLUP are obtained iteratively based on single SNP effect squared (u (2)) and/or heterozygosity. When the weights are optimal, prediction accuracy, and ability to detect major SNP are maximized. The objective was to develop optimal weights for WGBLUP-based methods. We evaluated 5 new procedures that accounted for locus-specific or windows-specific variance to maximize accuracy of predicting genomic estimated breeding value (GEBV) and SNP effect. Simulated datasets consisted of phenotypes for 13,000 animals, including 1540 animals genotyped for 45,000 SNP. Scenarios with 5, 100, and 500 simulated quantitative trait loci (QTL) were considered. The 5 new procedures for SNP weighting were: (1) u (2) plus a constant equal to the weight of the top SNP; (2) from a heavy-tailed distribution (similar to BayesA); (3) for every 20 SNP in a window along the whole genome, the largest effect (u (2)) among them; (4) the mean effect of every 20 SNP; and (5) the summation of every 20 SNP. Those methods were compared to the default WssGBLUP, GBLUP, BayesB, and BayesC. WssGBLUP methods were evaluated over 10 iterations. The accuracy of predicting GEBV was the correlation between true and estimated genomic breeding values for 300 genotyped animals from the last generation. The ability to detect the simulated QTL was also investigated. For most of the QTL scenarios, the accuracies obtained with all WssGBLUP procedures were higher compared to those from BayesB and BayesC, partly due to automatic inclusion of parent average in the former. Manhattan plots had higher resolution with 5 and 100 QTL. Using a common weight for a window of 20 SNP that sums or averages the SNP variance enhances accuracy of predicting GEBV and provides accurate estimation of marker effects.

Keywords: BayesB; BayesC; SNP window; WssGBLUP; genome-wide association.

Figures

Figure 1
Figure 1
Proportion of variance (%) explained by simulated QTL and SNP for different methods under 5-QTL simulation. (A) (a), true QTL; (b), default; (c), constant; (d), nonlinear A: weights as ν|s−2|, where ν is a scale standing for the departure from normality, and s is number of standard deviation from mean for each ui2; (e), largest window; (f), mean window; (g), sum window. (B) (a), true QTL; (b), BayesC π = 0.5; (c), BayesC π = 0.9; (d), BayesC π = 0.99; (e), BayesB π = 0.5; (f), BayesB π = 0.9; (g), BayesB π = 0.99.
Figure 2
Figure 2
Proportion of variance (%) explained by simulated QTL and SNP for different methods under 500-QTL simulation. (A) (a), true QTL; (b), default; (c), constant; (d), nonlinear A: weights as ν|s−2|, where ν is a scale standing for the departure from normality, and s0 is number of standard deviation from mean for each ui2; (e), largest window; (f), mean window; (g), sum window. (B) (a), true QTL; (b), BayesC π = 0.5; (c), BayesC π = 0.9; (d), BayesC π = 0.99; (e), BayesB π = 0.5; (f), BayesB π = 0.9; (g), BayesB π = 0.99.
Figure 3
Figure 3
Proportion of variance (%) explained by simulated QTL and SNP for different methods under 100-QTL simulation. (A) (a), true QTL; (b), default; (c), constant; (d), nonlinear A: weights as ν|s−2|, where ν is a scale standing for the departure from normality, and s is number of standard deviation from mean for each ui2; (e), largest window; (f), mean window; (g), sum window. (B) (a), true QTL; (b), BayesC π = 0.5; (c), BayesC π = 0.9; (d), BayesC π = 0.99; (e), BayesB π = 0.5; (f), BayesB π = 0.9; (g), BayesB π = 0.99.

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