Abstract
BackgroundGenome-wide association studies have been deemed successful for identifying statistically associated genetic variants of large effects on complex traits. Past studies have found enrichment of trait-associated SNPs in functionally annotated regions, while depletion was reported for intergenic regions (IGR). However, no systematic examination of connections between genomic regions and predictive ability of complex phenotypes has been carried out.ResultsIn this study, we partitioned SNPs based on their annotation to characterize genomic regions that deliver low and high predictive power for three broiler traits in chickens using a whole-genome approach. Additive genomic relationship kernels were constructed for each of the genic regions considered, and a kernel-based Bayesian ridge regression was employed as prediction machine. We found that the predictive performance for ultrasound area of breast meat from using genic regions marked by SNPs was consistently better than that from SNPs in IGR, while IGR tagged by SNPs were better than the genic regions for body weight and hen house egg production. We also noted that predictive ability delivered by the whole battery of markers was close to the best prediction achieved by one of the genomic regions.ConclusionsWhole-genome regression methods use all available quality filtered SNPs into a model, contrary to accommodating only validated SNPs from exonic or coding regions. Our results suggest that, while differences among genomic regions in terms of predictive ability were observed, the whole-genome approach remains as a promising tool if interest is on prediction of complex traits.
Highlights
Genome-wide association studies have been deemed successful for identifying statistically associated genetic variants of large effects on complex traits
Hindorff et al [4] found that nonsynonymous sites and 5Kb promoter regions were overrepresented in trait-associated SNPs, while depletion was observed for intergenic regions (IGR)
We examined partitioning SNPs based on their annotation, to characterize genomic regions that convey low or high predictive power
Summary
Genome-wide association studies have been deemed successful for identifying statistically associated genetic variants of large effects on complex traits. Past studies have found enrichment of trait-associated SNPs in functionally annotated regions, while depletion was reported for intergenic regions (IGR). Genome-wide association studies (GWAS) have been deemed successful for identifying statistically associated allelic substitution effects in known protein-coding genes. About 90% of trait-associated SNPs reported in humans do not lie within coding regions [4,5]. Hindorff et al [4] found that nonsynonymous sites and 5Kb promoter regions were overrepresented in trait-associated SNPs, while depletion was observed for intergenic regions (IGR). A recent release of the ENCyclopedia of DNA Elements (ENCODE) includes evidence of biochemical activity of the human genome [9]. About 62% of the genome is transcribed into RNA, and together with evidence such as transcription-factor-binding, specific chromatin structure and histone modification, the picture suggests that 80%
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