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

Abstract

BackgroundWith the availability of next-generation sequencing technologies, genomic prediction based on whole-genome sequencing (WGS) data is now feasible in animal breeding schemes and was expected to lead to higher predictive ability, since such data may contain all genomic variants including causal mutations. Our objective was to compare prediction ability with high-density (HD) array data and WGS data in a commercial brown layer line with genomic best linear unbiased prediction (GBLUP) models using various approaches to weight single nucleotide polymorphisms (SNPs).MethodsA total of 892 chickens from a commercial brown layer line were genotyped with 336 K segregating SNPs (array data) that included 157 K genic SNPs (i.e. SNPs in or around a gene). For these individuals, genome-wide sequence information was imputed based on data from re-sequencing runs of 25 individuals, leading to 5.2 million (M) imputed SNPs (WGS data), including 2.6 M genic SNPs. De-regressed proofs (DRP) for eggshell strength, feed intake and laying rate were used as quasi-phenotypic data in genomic prediction analyses. Four weighting factors for building a trait-specific genomic relationship matrix were investigated: identical weights, −(log10 P) from genome-wide association study results, squares of SNP effects from random regression BLUP, and variable selection based weights (known as BLUP|GA). Predictive ability was measured as the correlation between DRP and direct genomic breeding values in five replications of a fivefold cross-validation.ResultsAveraged over the three traits, the highest predictive ability (0.366 ± 0.075) was obtained when only genic SNPs from WGS data were used. Predictive abilities with genic SNPs and all SNPs from HD array data were 0.361 ± 0.072 and 0.353 ± 0.074, respectively. Prediction with −(log10 P) or squares of SNP effects as weighting factors for building a genomic relationship matrix or BLUP|GA did not increase accuracy, compared to that with identical weights, regardless of the SNP set used.ConclusionsOur results show that little or no benefit was gained when using all imputed WGS data to perform genomic prediction compared to using HD array data regardless of the weighting factors tested. However, using only genic SNPs from WGS data had a positive effect on prediction ability.Electronic supplementary materialThe online version of this article (doi:10.1186/s12711-016-0277-y) contains supplementary material, which is available to authorized users.

Genomic prediction of bull fertility in US Jersey dairy cattle

“…The bottom and top of the box represent first and third quartiles; the horizontal line denotes the median; the whiskers correspond to 1.5× interquartile distance; and dark dots are outliers. dissimilar results (Yang et al, 2011;Li et al, 2012;Koufariotis et al, 2014;Do et al, 2015;Abdollahi-Arpanahi et al, 2016;Ni et al, 2017). For instance, Ni et al (2017) reported slightly higher predictive ability using SNP within or around genes compared with whole-genome SNP data in laying chickens.…”

“…dissimilar results (Yang et al, 2011;Li et al, 2012;Koufariotis et al, 2014;Do et al, 2015;Abdollahi-Arpanahi et al, 2016;Ni et al, 2017). For instance, Ni et al (2017) reported slightly higher predictive ability using SNP within or around genes compared with whole-genome SNP data in laying chickens. Contrary, and Abdollahi-Arpanahi et al (2016) found that prediction based on genomic regions is trait dependent in broiler chickens, and in general the whole-genome approach provided better predictive ability than functional classes considered individually.…”

High-Throughput Sequencing With the Preselection of Markers Is a Good Alternative to SNP Chips for Genomic Prediction in Broilers

“…Iheshiulor et al (2016) showed that the accuracy of genomic selection using sequencing data can be increased by up to 92% in a simulation study. However, when using real data, researchers can hardly achieve such attractive results (Heidaritabar et al, 2016;Ni et al, 2017;Elbasyoni et al, 2018). In plant breeding, Elbasyoni et al (2018) studied four traits in a winter wheat population and showed that high-throughput sequencing could achieve only comparable or even better accuracy than an SNP chip.…”

“…In plant breeding, Elbasyoni et al (2018) studied four traits in a winter wheat population and showed that high-throughput sequencing could achieve only comparable or even better accuracy than an SNP chip. In a commercial brown layer line, Ni et al (2017) compared genomic predictions for three egg-laying traits using genome-wide sequencing and a 336K SNP chip and reported that little or no benefit was gained when using all sequencing SNPs for genomic prediction.…”

“…The accuracy of genomic prediction does not increase dramatically as the number of markers increases exponentially (Heidaritabar et al, 2016;Ni et al, 2017). Heidaritabar et al (Heidaritabar et al, 2016) compared the differences of the accuracy of genomic prediction for the number of eggs between whole genome sequencing data and 60K gene chip data in a commercial white layers line.…”