Strategies for Obtaining and Pruning Imputed Whole-Genome Sequence Data for Genomic Prediction

Shaopan Ye,Xiquan Zhang,Jiaqi Li,Zhe Zhang,Zitao Chen,Rongrong Zheng,Jinyan Teng,Xiaolong Yuan,Ning Gao,Hao Zhang,Zanmou Chen

doi:10.3389/fgene.2019.00673

Abstract

Genomic prediction with imputed whole-genome sequencing (WGS) data is an attractive approach to improve predictive ability with low cost. However, high accuracy has not been realized using this method in livestock. In this study, we imputed 435 individuals from 600K single nucleotide polymorphism (SNP) chip data to WGS data using different reference panels. We also investigated the prediction accuracy of genomic best linear unbiased prediction (GBLUP) using imputed WGS data from different reference panels, linkage disequilibrium (LD)-based marker pruning, and pre-selected variants based on Genome-wide association society (GWAS) results. Results showed that the imputation accuracies from 600K to WGS data were 0.873 ± 0.038, 0.906 ± 0.036, and 0.979 ± 0.010 for the internal, external, and combined reference panels, respectively. In most traits of chickens, the prediction accuracy of imputed WGS data obtained from the internal reference panel was greater than or equal to that of the combined reference panel; the external reference panel had the lowest prediction accuracy. Compared with 600K chip data, GBLUP with imputed WGS data had only a small increase (1–3%) in prediction accuracy. Using only variants selected from imputed WGS data based on GWAS results resulted in almost no increase for most traits and even increased the bias of the regression coefficient. The impact of the degree of LD of selected and remaining variants on prediction accuracy was different. For average daily gain (ADG), residual feed intake (RFI), intestine length (IL), and body weight in 91 days (BW91), the accuracy of GBLUP increased as the degree of LD of selected variants decreased, but the opposite relationship occurred for the remaining variants. But for breast muscle weight (BMW) and average daily feed intake (ADFI), the accuracy of GBLUP increased as the degree of LD of selected variants increased, and the degree of LD of remaining variants had a small effect on prediction accuracy. Overall, the optimal imputation strategy to obtain WGS data for genomic prediction should consider the relationship between selected individuals and target population individuals to avoid heterogeneity of imputation. LD-based marker pruning can be used to improve the accuracy of genomic prediction using imputed WGS data.

Highlights

Whole genomic prediction (WGP) has been widely implemented in animal and plant breeding programs and human disease risk prediction
We found that the average imputation accuracy of the combined reference panel (0.979 ± 0.010) was higher than that of the external reference panel (0.906 ± 0.036) and the internal reference panel (0.873 ± 0.038)
We determined the impact of genotype imputation reference panels, Linkage Disequilibrium (LD)-based marker pruning, and preselected variants on prediction accuracy

Summary

Introduction

Whole genomic prediction (WGP) has been widely implemented in animal and plant breeding programs and human disease risk prediction. WGP combined with WGS data were expected to lead to higher predictive ability. This expectation was confirmed by related simulation studies (Meuwissen and Goddard, 2010; Iheshiulor et al, 2016). Using imputed WGS data to perform WGP has not led to the expected higher accuracies of genomic best linear unbiased prediction (GBLUP) or Bayesian methods in livestock. Van Binsbergen et al (2015) found no benefit for genomic prediction using imputed WGS data compared with the HD chip data in a Holstein Friesian population. Compared with the regular 54K chip data, imputed WGS data led to only a marginal increase in accuracy for nonreturn rate in heifers (NRH) and a decrease for stature and somatic cell score (SCS) in Brown Swiss cattle (Frischknecht et al, 2018). There was little or no benefit using GBLUP with imputed WGS data compared with low-density or high-density (HD) chip data (Heidaritabar et al, 2016; Ni et al, 2017)

Methods

Results

Conclusion