Weighting Strategies for Single-Step Genomic BLUP: An Iterative Approach for Accurate Calculation of GEBV and GWAS
نویسندگان
چکیده
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.
منابع مشابه
Genome-wide Association Study (GWAS) and Its Application for Improving the Genomic Estimated Breeding Values (GEBV) of the Berkshire Pork Quality Traits
The missing heritability has been a major problem in the analysis of best linear unbiased prediction (BLUP). We introduced the traditional genome-wide association study (GWAS) into the BLUP to improve the heritability estimation. We analyzed eight pork quality traits of the Berkshire breeds using GWAS and BLUP. GWAS detects the putative quantitative trait loci regions given traits. The single n...
متن کاملPredictive Ability of Statistical Genomic Prediction Methods When Underlying Genetic Architecture of Trait Is Purely Additive
A simulation study was conducted to address the issue of how purely additive (simple) genetic architecture might impact on the efficacy of parametric and non-parametric genomic prediction methods. For this purpose, we simulated a trait with narrow sense heritability h2= 0.3, with only additive genetic effects for 300 loci in order to compare the predictive ability of 14 more practically used ge...
متن کاملComparison of Single and Multi-Step Bayesian Methods for Predicting Genomic Breeding Values in Genotyped and Non-Genotyped Animals- A Simulation Study
The purpose of this study was to compare the accuracy of genomic evaluation for Bayes A, Bayes B, Bayes C and Bayes L multi-step methods and SSBR-C and SSBR-A single-step methods in the different values of π for predicting genomic breeding values of the genotyped and non-genotyped animals. A genome with 40000 SNPs on the 20 chromosom was simulated with the same distance (100cM). The π valu...
متن کاملHot topic: Use of genomic recursions in single-step genomic best linear unbiased predictor (BLUP) with a large number of genotypes.
The purpose of this study was to evaluate the accuracy of genomic selection in single-step genomic BLUP (ssGBLUP) when the inverse of the genomic relationship matrix (G) is derived by the "algorithm for proven and young animals" (APY). This algorithm implements genomic recursions on a subset of "proven" animals. Only a relationship matrix for animals treated as "proven" needs to be inverted, an...
متن کاملGenetic evaluation using single-step genomic BLUP in American Angus
Introduction Genomic selection in beef cattle has currently been performed with multistep methods, which uses deregressed EBV to estimate SNP effects and then direct genomic value (DGV) for selection candidates based on their genotypes (Meuwissen et al., 2001; Garrick et al., 2009). The main advantage of this approach is that the traditional BLUP evaluation is kept unchanged and genomic selecti...
متن کامل