Accuracy of genomic prediction within and across populations for nematode resistance and body weight traits in sheep

Abstract

Genomic prediction utilizes single nucleotide polymorphism (SNP) chip data to predict animal genetic merit. It has the advantage of potentially capturing the effects of the majority of loci that contribute to genetic variation in a trait, even when the effects of the individual loci are very small. To implement genomic prediction, marker effects are estimated with a training set, including individuals with marker genotypes and trait phenotypes; subsequently, genomic estimated breeding values (GEBV) for any genotyped individual in the population can be calculated using the estimated marker effects. In this study, we aimed to: (i) evaluate the potential of genomic prediction to predict GEBV for nematode resistance traits and BW in sheep, within and across populations; (ii) evaluate the accuracy of these predictions through within-population cross-validation; and (iii) explore the impact of population structure on the accuracy of prediction. Four data sets comprising 752 lambs from a Scottish Blackface population, 2371 from a Sarda × Lacaune backcross population, 1000 from a Martinik Black-Belly × Romane backcross population and 64 from a British Texel population were used in this study. Traits available for the analysis were faecal egg count for Nematodirus and Strongyles and BW at different ages or as average effect, depending on the population. Moreover, immunoglobulin A was also available for the Scottish Blackface population. Results show that GEBV had moderate to good within-population predictive accuracy, whereas across-population predictions had accuracies close to zero. This can be explained by our finding that in most cases the accuracy estimates were mostly because of additive genetic relatedness between animals, rather than linkage disequilibrium between SNP and quantitative trait loci. Therefore, our results suggest that genomic prediction for nematode resistance and BW may be of value in closely related animals, but that with the current SNP chip genomic predictions are unlikely to work across breeds.

Genome-Wide Association and Genomic Prediction for Fry Color in Potato

“…This resulted in a different parental profile and lower rate of selection in this material. This is similar to previous studies in plants [20,21] and predictions across breeds in animals [22,23], and emphasises the importance of a good relationship between training set and selection candidates.…”

Immunogenomics of gastrointestinal nematode infection in ruminants – breeding for resistance to produce food sustainably and safely

“…In this methodology, panels of SNPs are evaluated concurrently. The number of SNPs used per panel (window size) has varied between studies, ranging from 5 SNPs to 100 SNPs . The genomic architecture of the QTL region should influence the window size, with smaller windows more appropriate for single causative variants and larger windows for the more complex architectures.…”

“…The major histocompatibility complex (MHC), which is an incredibly diverse region, is on this chromosome and encompasses several hundred genes known to be involved in immune regulation. This region was highlighted is a meta‐analysis of 3 diverse sheep breeds (Scottish Blackface, Sarda × Lacaune and Martinik Black‐Belly × Romane) using RHM methodologies with a 100‐SNP window . The most studied gene of the MHC complex is DRB1 .…”

“…The number of SNPs used per panel (window size) has varied between studies, ranging from 5 SNPs 71 to 100 SNPs. 48,105 The genomic architecture of the QTL region should influence the window size, with smaller windows more appropriate for single causative variants and larger windows for the more complex architectures. In the face of unknown genetic architecture, it is difficult to offer firm conclusions on optimal window size.…”

“…This region was highlighted is a meta-analysis of 3 diverse sheep breeds (Scottish Blackface, Sarda × Lacaune and Martinik Black-Belly × Romane) using RHM methodologies with a 100-SNP window. 105 The most studied gene of the MHC complex is DRB1. 61,79,107,121 Conversely, the highly polymorphic nature of this region (over 80 known alleles) and the significant role that it plays in the spectrum of immune-mediated genetic disease are likely to make it difficult to identify SNPs that will be useful for selecting for GIN resistance without affecting susceptibility to other traits.…”