Genome-enabled prediction of quantitative traits in chickens using genomic annotation

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.

Whole-genome sequence-based genomic prediction in laying chickens with different genomic relationship matrices to account for genetic architecture

“…Similarly, Do et al [44] reported that predictive ability increased only when SNPs in genes were considered for residual feed intake based on 1272 Duroc pigs, which were genotyped with the 60 K SNP chip, although the increase was not significantly different from that obtained with 1000 randomly SNPs. In chicken, Morota et al [12] studied predictive ability with 1351 commercial broiler chickens genotyped with the Affymetrix 600 K chip, and found that prediction based on SNPs in or around genes did not result in a higher accuracy using kernel-based Bayesian ridge regression. In our dataset, predictive ability with HD_genic data was slightly higher than that with all HD data.…”

“…For instance, Morota et al [12] reported that GP accuracy was higher when using all available SNPs than when using only validated SNPs from a partial genome (e.g. coding regions), based on the 600 K SNP array data of 1351 commercial broiler chicken.…”

Marker-Based Estimates Reveal Significant Nonadditive Effects in Clonally Propagated Cassava (Manihot esculenta): Implications for the Prediction of Total Genetic Value and the Selection of Varieties

“…Aside from cassava, breeding of other noninbred, clonally propagated species also identifies and makes use of nonadditive effects, including in potato (Killick 1977), Eucalyptus (Costa et al 2004), and loblolly pine (Muñoz et al 2014). More recently, marker-based and GRM-based models have identified significant nonadditive effects in pigs (Su et al 2012; Nishio and Satoh 2014), mice (Vitezica et al 2013), beef cattle (Bolormaa et al 2015), dairy cows (Morota et al 2014), maize (Dudley and Johnson 2009), soy (Hu et al 2011), loblolly pine (Muñoz et al 2014), and apple (Kumar et al 2015). Results from the present study suggest that accounting for nonadditive effects in the variety development pipeline should increase the value of hybrids released by cassava breeding programs.…”

Marker-based estimates reveal significant non-additive effects in clonally propagated cassava (Manihot esculenta): implications for the prediction of total genetic value and the selection of varieties

“…Aside from cassava, breeding of other noninbred, clonally propagated species also identifies and makes use of nonadditive effects, including in potato (Killick 1977), Eucalyptus (Costa et al 2004), and loblolly pine (Muñoz et al 2014). More recently, marker-based and GRM-based models have identified significant nonadditive effects in pigs (Su et al 2012;Nishio and Satoh 2014), mice (Vitezica et al 2013), beef cattle (Bolormaa et al 2015), dairy cows (Morota et al 2014), maize (Dudley and Johnson 2009), soy (Hu et al 2011), loblolly pine (Muñoz et al 2014), and apple (Kumar et al 2015). Results from the present study suggest that accounting for nonadditive effects in the variety development pipeline should increase the value of hybrids released by cassava breeding programs.…”