Genomic prediction using low density marker panels in aquaculture: performance across species, traits, and genotyping platforms

Abstract

Genomic selection increases the rate of genetic gain in breeding programmes, which results in significant cumulative improvements in commercially important traits such as disease resistance. Genomic selection currently relies on collecting genome-wide genotype data accross a large number of individuals which requires substantial economic investment. However, global aquaculture production predominantly occurs in small and medium sized enterprises for whom this technology can be prohibitively expensive. For genomic selection to benefit these aquaculture sectors more cost-efficient genotyping is necessary. In this study the utility of low and medium density SNP panels (ranging from 100 to 9000 SNPs) to accurate predict breeding values was tested and compared in four aquaculture datasets with different characteristics (species, genome size, genotyping platform, family number and size, total population size, and target trait). A consistent pattern of genomic prediction accuracy was observed across species, with little or no reduction until SNP density was reduced below 1,000 SNPs. Below this SNP density, heritability estimates and genomic prediction accuracies tended to be lower and more variable (93 % of maximum accuracy achieved with 1,000 SNPs, 89 % with 500 SNPs, and 70% with 100 SNPs). Now that a multitude of studies have highlighted the benefits of genomic over pedigree-based prediction of breeding values in aquaculture species, the results of the current study highlight that these benefits can be achieved at lower SNP densities and at lower cost, raising the possibility of a broader application of genetic improvement in smaller and more fragmented aquaculture settings.