GWAS on Imputed Whole-Genome Resequencing From Genotyping-by-Sequencing Data for Farrowing Interval of Different Parities in Pigs

Abstract

The whole-genome sequencing (WGS) data can potentially discover all genetic variants. Studies have shown the power of WGS for genome-wide association study (GWAS) lies in the ability to identify quantitative trait loci and nucleotides (QTNs). However, the resequencing of thousands of target individuals is expensive. Genotype imputation is a powerful approach for WGS and to identify causal mutations. This study aimed to evaluate the imputation accuracy from genotyping-by-sequencing (GBS) to WGS in two pig breeds using a resequencing reference population and to detect single-nucleotide polymorphisms (SNPs) and candidate genes for farrowing interval (FI) of different parities using the data before and after imputation for GWAS. Six hundred target pigs, 300 Landrace and 300 Large White pigs, were genotyped by GBS, and 60 reference pigs, 20 Landrace and 40 Large White pigs, were sequenced by whole-genome resequencing. Imputation for pigs was conducted using Beagle software. The average imputation accuracy (allelic R 2) from GBS to WGS was 0.42 for Landrace pigs and 0.45 for Large White pigs. For Landrace pigs (Large White pigs), 4,514,934 (5,533,290) SNPs had an accuracy >0.3, resulting an average accuracy of 0.73 (0.72), and 2,093,778 (2,468,645) SNPs had an accuracy >0.8, resulting an average accuracy of 0.94 (0.93). Association studies with data before and after imputation were performed for FI of different parities in two populations. Before imputation, 18 and 128 significant SNPs were detected for FI in Landrace and Large White pigs, respectively. After imputation, 125 and 27 significant SNPs were identified for dataset with an accuracy >0.3 and 0.8 in Large White pigs, and 113 and 18 SNPs were found among imputed sequence variants. Among these significant SNPs, six top SNPs were detected in both GBS data and imputed WGS data, namely, SSC2: 136127645, SSC5: 103426443, SSC6: 27811226, SSC10: 3609429, SSC14: 15199253, and SSC15: 150297519. Overall, many candidate genes could be involved in FI of different parities in pigs. Although imputation from GBS to WGS data resulted in a low imputation accuracy, association analyses with imputed WGS data were optimized to detect QTNs for complex trait. The obtained results provide new insight into genotype imputation, genetic architecture, and candidate genes for FI of different parities in Landrace and Large White pigs.

Genome-Wide Analyses Reveal Genetic Convergence of Prolificacy between Goats and Sheep

“…The mRNA level of CHST11 increases upon the interaction of BMP4 with a phospho-Smad3 binding site in the CHST11 promoter [ 31 ]. Furthermore, CHST11 was linked to ovarian cancer [ 32 ], endometrial carcinomas [ 33 ], and porcine farrowing interval [ 34 ]. However, the CHST11 transcripts were not differentially expressed in goat ovary ( Table S5 ).…”

Multi-trait GWAS using imputed high-density genotypes from whole-genome sequencing identifies genes associated with body traits in Nile tilapia

“…The use of ultra-high-density SNPs or WGS can improve the accuracy and power of GWAS to detect QTLs associated with complex traits [27][28][29][30]. Although the cost of WGS is rapidly decreasing, it is still expensive to sequence all available phenotyped individuals in a GWAS design.…”

“…To solve this, genotype imputation to WGS data can be successfully implemented to detect putative causal loci in a cost-efficient manner. Previous studies using imputed genotypes from WGS for GWAS have been reported in cattle [27,28], pigs [29,30] and sheep [31]. In addition, new strategies such as multi-trait GWAS (mtGWAS) analysis are required to increase the power to detect QTL through GWAS [32].…”

An association study on imputed whole‐genome resequencing from high‐throughput sequencing data for body traits in crossbred pigs

“…Genome‐wide association studies (GWASs) are an effective method for identifying QTL involved in body traits. Several studies have reported that some QTL may be associated with body traits by GWASs; however, they were carried out in pure‐breed livestock populations using low‐density microarray data, which limits the capture of a large number of genetic variations (Wu et al, 2019).…”