Imputation to whole-genome sequence using multiple pig populations and its use in genome-wide association studies

BackgroundUse of whole-genome sequence data (WGS) is expected to improve identification of quantitative trait loci (QTL). However, this requires imputation to WGS, often with a limited number of sequenced animals for the target population. The objective of this study was to investigate imputation to WGS in two pig lines using a multi-line reference population and, subsequently, to investigate the effect of using these imputed WGS (iWGS) for GWAS.MethodsPhenotypes and genotypes were available on 12,184 Large White pigs (LW-line) and 4943 Dutch Landrace pigs (DL-line). Imputed 660K and 80K genotypes for the LW-line and DL-line, respectively, were imputed to iWGS using Beagle v.4.1. Since only 32 LW-line and 12 DL-line boars were sequenced, 142 animals from eight commercial lines were added. GWAS were performed for each line using the 80K and 660K SNPs, the genotype scores of iWGS SNPs that had an imputation accuracy (Beagle R-2) higher than 0.6, and the dosage scores of all iWGS SNPs.ResultsFor the DL-line (LW-line), imputation of 80K genotypes to iWGS resulted in an average Beagle R-2 of 0.39 (0.49). After quality control, 2.5x10(6) (3.5x10(6)) SNPs had a Beagle R-2 higher than 0.6, resulting in an average Beagle R-2 of 0.83 (0.93). Compared to the 80K and 660K genotypes, using iWGS led to the identification of 48.9 and 64.4% more QTL regions, for the DL-line and LW-line, respectively, and the most significant SNPs in the QTL regions explained a higher proportion of phenotypic variance. Using dosage instead of genotype scores improved the identification of QTL, because the model accounted for uncertainty of imputation, and all SNPs were used in the analysis.ConclusionsImputation to WGS using the multi-line reference population resulted in relatively poor imputation, especially when imputing from 80K (DL-line). In spite of the poor imputation accuracies, using iWGS instead of a lower density SNP chip increased the number of detected QTL and the estimated proportion of phenotypic variance explained by these QTL, especially when dosage scores were used instead of genotype scores. Thus, iWGS, even with poor imputation accuracy, can be used to identify possible interesting regions for fine mapping.