An association study using imputed whole‐genome sequence data identifies novel significant loci for growth‐related traits in a Duroc × Erhualian F2 population

• Published in: Journal of animal breeding and genetics (1986) Vol. 136; no. 3; pp. 217 - 228
• Main Authors: Ji, Jiuxiu, Yan, Guorong, Chen, Dong, Xiao, Shijun, Gao, Jun, Zhang, Zhiyan
• Format: Journal Article
• Language: English
• Published: Oxford Blackwell Publishing Ltd 01.05.2019
• Subjects: Algorithms; Animal models; Annotations; average daily gain; Bioinformatics; Body weight; Breeding; Gene mapping; Genes; Genomes; GWAS; imputation; Livestock breeding; Meat; Meat production; Model accuracy; Mutation; Nucleotide sequence; pigs; QTL; Quantitative trait loci
• ISSN: 0931-2668; 1439-0388
• DOI: 10.1111/jbg.12389

The average daily gain (ADG) and body weight (BW) are very important traits for breeding programs and for the meat production industry, which have attracted many researchers to delineate the genetic architecture behind these traits. In the present study, single‐ and multi‐trait genome‐wide association studies (GWAS) were performed between imputed whole‐genome sequence data and the traits of the ADG and BW at different stages in a large‐scale White Duroc × Erhualian F2 population. A bioinformatics annotation analysis was used to assist in the identification of candidate genes that are associated with these traits. Five and seven genome‐wide significant quantitative trait loci (QTLs) were identified by single‐ and multi‐trait GWAS, respectively. Furthermore, more than 40 genome‐wide suggestive loci were detected. On the basis of the whole‐genome sequence association study and the bioinformatics analysis, NDUFAF6, TNS1 and HMGA1 stood out as the strongest candidate genes. The presented single‐ and multi‐trait GWAS analysis using imputed whole‐genome sequence data identified several novel QTLs for pig growth‐related traits. Integrating the GWAS with bioinformatics analysis can facilitate the more accurate identification of candidate genes. Higher imputation accuracy, time‐saving algorithms, improved models and comprehensive databases will accelerate the identification of causal genes or mutations, which will contribute to genomic selection and pig breeding in the future.