Abstract
BackgroundA better understanding of the genetic architecture underlying complex traits (e.g., the distribution of causal variants and their effects) may aid in the genomic prediction. Here, we hypothesized that the genomic variants of complex traits might be enriched in a subset of genomic regions defined by genes grouped on the basis of “Gene Ontology” (GO), and that incorporating this independent biological information into genomic prediction models might improve their predictive ability.ResultsFour complex traits (i.e., milk, fat and protein yields, and mastitis) together with imputed sequence variants in Holstein (HOL) and Jersey (JER) cattle were analysed. We first carried out a post-GWAS analysis in a HOL training population to assess the degree of enrichment of the association signals in the gene regions defined by each GO term. We then extended the genomic best linear unbiased prediction model (GBLUP) to a genomic feature BLUP (GFBLUP) model, including an additional genomic effect quantifying the joint effect of a group of variants located in a genomic feature. The GBLUP model using a single random effect assumes that all genomic variants contribute to the genomic relationship equally, whereas GFBLUP attributes different weights to the individual genomic relationships in the prediction equation based on the estimated genomic parameters. Our results demonstrate that the immune-relevant GO terms were more associated with mastitis than milk production, and several biologically meaningful GO terms improved the prediction accuracy with GFBLUP for the four traits, as compared with GBLUP. The improvement of the genomic prediction between breeds (the average increase across the four traits was 0.161) was more apparent than that it was within the HOL (the average increase across the four traits was 0.020).ConclusionsOur genomic feature modelling approaches provide a framework to simultaneously explore the genetic architecture and genomic prediction of complex traits by taking advantage of independent biological knowledge.
Highlights
A better understanding of the genetic architecture underlying complex traits may aid in the genomic prediction
The objectives of this study were 1) to explore the genetic and biological basis underlying milk production and mastitis by using post-Genome-wide association study (GWAS) analysis in the HOL training population (n = 4002), 2) to improve the prediction accuracy for these complex traits within and between breeds by using genomic feature BLUP (GFBLUP) instead of genomic best linear unbiased prediction (GBLUP), and 3) to investigate the relationship between the degree of enrichment of association signals (i.e., P-values) in a genomic feature based on post-GWAS in the HOL training population and its predictive ability with GFBLUP in the HOL validation population
Association signals of genomic variants from singlemarker GWAS Single-marker GWAS was separately conducted for milk production traits and mastitis in a HOL training population using imputed sequence variants
Summary
A better understanding of the genetic architecture underlying complex traits (e.g., the distribution of causal variants and their effects) may aid in the genomic prediction. Studying the genetic architecture (e.g., the distribution of causal variants and their effects) and predicting future individual phenotypes for complex traits and diseases on the basis of genomic polymorphism data are very important in the fields of human medicine, adaptive evolution, and plant and animal breeding. The genomic variation of complex traits has always been treated as a “black box” that neither generates nor utilizes biological knowledge of the genetic architecture and the underlying biological mechanisms This type of model performs well in populations with a large amount of LD (linkage disequilibrium), such as selectively bred plants and animals [3,4,5]. The accuracy of the estimated genomic breeding values with GBLUP ranges from zero to very low in between-breed prediction in dairy cattle [3, 4, 7]
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