A genome-wide association study of reproduction traits in four pig populations with different genetic backgrounds

• Main Authors: Yao Jiang, Shaoqing Tang, Wei Xiao, Peng Yun, Xiangdong Ding
• Format: Article
• Language: English
• Published: Asian-Australasian Association of Animal Production Societies 2020-09-01
• Series: Asian-Australasian Journal of Animal Sciences
• Subjects: genome-wide association study; total number born; number born alive; meta-analysis
• Online Access: http://www.ajas.info/upload/pdf/ajas-19-0411.pdf
• ISSN: 1011-2367; 1976-5517

Objective Genome-wide association study and two meta-analysis based on GWAS performed to explore the genetic mechanism underlying variation in pig number born alive (NBA) and total number born (TNB). Methods Single trait GWAS and two meta-analysis (single-trait meta analysis and multi-trait meta analysis) were used in our study for NBA and TNB on 3,121 Yorkshires from 4 populations, including three different American Yorkshire populations (n = 2,247) and one British Yorkshire populations (n = 874). Results The result of single trait GWAS showed that no significant associated single nucleotide polymorphisms (SNPs) were identified. Using single-trait meta analysis and multi-trait meta analysis within populations, 11 significant loci were identified associated with target traits. Spindlin 1, vascular endothelial growth factor A, forkhead box Q1, msh homeobox 1, and LHFPL tetraspan submily member 3 are five functionally plausible candidate genes for NBA and TNB. Compared to the single population GWAS, single-trait Meta analysis can improve the detection power to identify SNPs by integrating information of multiple populations. The multiple-trait analysis reduced the power to detect trait-specific loci but enhanced the power to identify the common loci across traits. Conclusion In total, our findings identified novel genes to be validated as candidates for NBA and TNB in pigs. Also, it enabled us to enlarge population size by including multiple populations with different genetic backgrounds and increase the power of GWAS by using meta analysis.