Quantitative genetics theory for genomic selection and efficiency of genotypic value prediction in open-pollinated populations

Abstract

Quantitative genetics theory for genomic selection has mainly focused on additive effects. This study presents quantitative genetics theory applied to genomic selection aiming to prove that prediction of genotypic value based on thousands of single nucleotide polymorphisms (SNPs) depends on linkage disequilibrium (LD) between markers and QTLs, assuming dominance and epistasis. Based on simulated data, we provided information on dominance and genotypic value prediction accuracy, assuming mass selection in an open-pollinated population, all quantitative trait loci (QTLs) of lower effect, and reduced sample size. We show that the predictor of dominance value is proportional to the square of the LD value and to the dominance deviation for each QTL that is in LD with each marker. The weighted (by the SNP frequencies) dominance value predictor has greater accuracy than the unweighted predictor. The linear × linear, linear × quadratic, quadratic × linear, and quadratic × quadratic SNP effects are proportional to the corresponding linear combinations of epistatic effects for QTLs and the LD values. LD between two markers with a common QTL causes a bias in the prediction of epistatic values. Compared to phenotypic selection, the efficiency of genomic selection for genotypic value prediction increases as trait heritability decreases. The degree of dominance did not affect the genotypic value prediction accuracy and the approach to maximum accuracy is asymptotic with increases in SNP density. The decrease in the sample size from 500 to 200 did not markedly reduce the genotypic value prediction accuracy.

Weighted single-step genomic BLUP improves accuracy of genomic breeding values for protein content in French dairy goats: a quantitative trait influenced by a major gene

“…Several studies have reported that the accuracy of genomic prediction obtained with these methods is higher than with genetic evaluation using pedigree-based BLUP [ 16 – 18 ]. However, the accuracy obtained from genomic information depends on several parameters including reference population size [ 19 , 20 ], extent of linkage disequilibrium (LD), heritability of the trait [ 20 , 21 ], relationship between training and validation populations [ 10 ] and the genetic architecture of the trait, which relates to the relative size of allele substitution effects at quantitative trait loci (QTL) [ 10 , 22 ].…”

Subset selection of markers for genome-enabled prediction of genetic val-ues using radial basis function neural networks

“…Due to the importance of epistasis in studies of quantitative traits in plants [32,33,34,35,36,37], explicit (in the model) or implicit (in hidden layers) inclusion of epistatic interactions may increase the accuracy of prediction [38]. Furthermore, the frequency variation of the epistatic allele between populations may cause the gene-of-interest effect to be significant in one population but not in another, and the effect may even be inverse on the character in different environments [5], which reinforces the importance of using computational intelligence methods that easily incorporate interactions between linear effects through their hidden layers.…”

Efficiency of genomic prediction of non-assessed single crosses

“…The SNP and QTL genotypic data for DH lines, the QTL genotypic data of single crosses, and the phenotypic data for DH lines and single crosses were simulated using the software REALbreeding. The program has been developed by the first author using the software REALbasic 2009 (Viana et al 2017a;Viana et al 2017b;Viana et al 2016;Azevedo et al 2015;Viana et al 2013). Based on our input, the software distributed 10,000 SNPs and 400 QTLs in ten chromosomes (1,000 SNPs and 40 QTLs by chromosome).…”