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

Abstract

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 multitrait 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.

Genome-wide association study using haplotype libraries and repeated-measures model to identify candidate genomic regions for stillbirth in Holstein cattle

“…In addition, GLDC, IFT57, AHI1, CACNA1C, GFER, RAB35, and PXN were previously associated with the regulation of the embryonic development (Diagne et al, 2003;Valente et al, 2006;Kaneko et al, 2008;Todd et al, 2010;Narisawa et al, 2012;Fukuyama et al, 2014;Thevenon et al, 2016;Zhang et al, 2019;Strand et al, 2020). Interestingly, among the functional candidate genes, KDM4C, SYNJ2, DISC1, SLC9A3R2, TSC2, SIRT4, CYP7A1, and ACADM are associated with abortion, bad pregnancy outcomes, and early live death in different species (McConkie-Rosell and Iafolla, 1993;Glantz et al, 2004;Jablensky et al, 2005;Maier et al, 2005;Lappas et al, 2011;Lipina et al, 2013;Moscovitz et al, 2016;Lim et al, 2016;Sankar et al, 2017;Krieg et al, 2018;Kazmi et al, 2019;Jiang et al, 2020;Zhao et al, 2021).…”

GWAS of Reproductive Traits in Large White Pigs on Chip and Imputed Whole-Genome Sequencing Data

“…In addition, the meta-analysis improved the power of detection for SNPs by combining different populations. The advantage of meta-analyses has been reported in pigs [ 10 , 33 , 34 ]. In our study, we detected novel significant SNPs in the meta-analysis compared with single-breed analyses.…”

“…Genome-wide association studies (GWAS) have emerged as an efficient method for dissecting the genetic mechanisms of complex traits. Recently, GWAS have been widely employed to identify the candidate genes on traits in pigs, such as growth [ 7 , 8 , 9 ], reproduction [ 10 , 11 , 12 ], meat quality [ 13 , 14 , 15 ], and disease resistance [ 16 , 17 ]. The variant detection power of GWAS is affected by marker density.…”

Genome-wide association study identifying genetic variants associated with carcass backfat thickness, lean percentage and fat percentage in a four-way crossbred pig population using SLAF-seq technology

“…For complex traits, such as BFT, CLP and CFP, large-scale analysis is necessary to detect trait-associated SNPs. Genome-wide association study (GWAS) [13] represents a powerful approach to correlate SNPs and functional genes with quantitative traits, and has been widely applied in important economic traits of pigs, including carcass [14][15][16][17][18][19], meat quality [14,15,19,20], growth [15,21], immunity [22], and reproductive traits [23]. For species with larger genomes, such as pigs, the cost of GWAS using whole-genome sequencing (WGS) is still high at present.…”