Genome‐wide association study and genomic prediction for intramuscular fat content in Suhuai pigs using imputed whole‐genome sequencing data

作者：Binbin Wang, Pinghua Li, Liming Hou, Wuduo Zhou, Tao Wei, Chenxi Liu, Kaiyue Liu, Peipei Niu, Zongping Zhang, Qiang Li, Guosheng Su, Ruihua Huang · 发表于：Evolutionary Applications · 年份：2022 · DOI：10.1111/eva.13496 · 被引用次数：16 · 研究领域：Genetic and phenotypic traits in livestock、Cancer-related molecular mechanisms research、Genetic Mapping and Diversity in Plants and Animals

Abstract Integrating the single‐nucleotide polymorphisms (SNPs) significantly affecting target traits from imputed whole‐genome sequencing (iWGS) data into the genomic prediction (GP) model is an economic, efficient, and feasible strategy to improve prediction accuracy. The objective was to dissect the genetic architecture of intramuscular fat content (IFC) by genome wide association studies (GWAS) and to investigate the accuracy of GP based on pedigree‐based BLUP (PBLUP) model, genomic best linear unbiased prediction (GBLUP) models and Bayesian mixture (BayesMix) models under different strategies. A total of 482 Suhuai pigs were genotyped using an 80 K SNP chip. Furthermore, 30 key samples were selected for resequencing and were used as a reference panel to impute the 80 K chip data to the WGS dataset. The 80 K data and iWGS data were used to perform GWAS and test GP accuracies under different scenarios. GWAS results revealed that there were four major regions affecting IFC. Two important functional candidate genes were found in the two most significant regions, including protein kinase C epsilon ( PRKCE ) and myosin light chain 2 ( MYL2 ). The results of the predictions showed that the PBLUP model had the lowest reliability (0.096 ± 0.032). The reliability (0.229 ± 0.035) was improved by replacing pedigree information with 80 K chip data. Compared with using 80 K SNPs alone, pruning iWGS SNPs with the R‐squared cutoff of linkage disequilibrium (0.55) led to a slight improve...