A Comprehensive Comparison of Haplotype-Based Single-Step Genomic Predictions in Livestock Populations With Different Genetic Diversity Levels: A Simulation Study.

Andre C Araujo,Paulo L S Carneiro,Luiz F Brito,Daniela A L Lourenco,Hinayah R Oliveira,Renata Veroneze,Flavio S Schenkel

doi:10.3389/fgene.2021.729867

Abstract

The level of genetic diversity in a population is inversely proportional to the linkage disequilibrium (LD) between individual single nucleotide polymorphisms (SNPs) and quantitative trait loci (QTLs), leading to lower predictive ability of genomic breeding values (GEBVs) in high genetically diverse populations. Haplotype-based predictions could outperform individual SNP predictions by better capturing the LD between SNP and QTL. Therefore, we aimed to evaluate the accuracy and bias of individual-SNP- and haplotype-based genomic predictions under the single-step-genomic best linear unbiased prediction (ssGBLUP) approach in genetically diverse populations. We simulated purebred and composite sheep populations using literature parameters for moderate and low heritability traits. The haplotypes were created based on LD thresholds of 0.1, 0.3, and 0.6. Pseudo-SNPs from unique haplotype alleles were used to create the genomic relationship matrix () in the ssGBLUP analyses. Alternative scenarios were compared in which the pseudo-SNPs were combined with non-LD clustered SNPs, only pseudo-SNPs, or haplotypes fitted in a second (two relationship matrices). The GEBV accuracies for the moderate heritability-trait scenarios fitting individual SNPs ranged from 0.41 to 0.55 and with haplotypes from 0.17 to 0.54 in the most (Ne 450) and less (Ne < 200) genetically diverse populations, respectively, and the bias fitting individual SNPs or haplotypes ranged between −0.14 and −0.08 and from −0.62 to −0.08, respectively. For the low heritability-trait scenarios, the GEBV accuracies fitting individual SNPs ranged from 0.24 to 0.32, and for fitting haplotypes, it ranged from 0.11 to 0.32 in the more (Ne 250) and less (Ne 100) genetically diverse populations, respectively, and the bias ranged between −0.36 and −0.32 and from −0.78 to −0.33 fitting individual SNPs or haplotypes, respectively. The lowest accuracies and largest biases were observed fitting only pseudo-SNPs from blocks constructed with an LD threshold of 0.3 (p < 0.05), whereas the best results were obtained using only SNPs or the combination of independent SNPs and pseudo-SNPs in one or two matrices, in both heritability levels and all populations regardless of the level of genetic diversity. In summary, haplotype-based models did not improve the performance of genomic predictions in genetically diverse populations.

Highlights

Genomic selection (GS) (Meuwissen et al, 2001) is routinely used worldwide in livestock and plant breeding programs (Lourenco et al, 2020; Moreira et al, 2020)
The total additive genetic effect variances estimated with the models that used two H matrices
Matrix and similar to the variances estimated with the model that used only the pedigree relationship matrix

Summary

Introduction

Genomic selection (GS) (Meuwissen et al, 2001) is routinely used worldwide in livestock and plant breeding programs (Lourenco et al, 2020; Moreira et al, 2020). The single-step genomic best linear unbiased prediction (ssGBLUP; Legarra et al, 2009; Aguilar et al, 2010) is widely used to perform genomic predictions in livestock This method enables the simultaneous evaluation of both genotyped and non-genotyped individuals and has similar or better statistical properties and predictive ability compared to other approaches such as pedigree-based BLUP and multi-step GBLUP (Aguilar et al, 2010; Legarra et al, 2014; Guarini et al, 2018; Piccoli et al, 2020)

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Conclusion