Comparison of two multi-trait association testing methods and sequence-based fine mapping of six additive QTL in Swiss Large White pigs

Abstract

Background: Genetic correlations between complex traits suggest that pleiotropic variants contribute to trait variation. Genome-wide association studies (GWAS) aim to uncover the genetic underpinnings of traits. Multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS enable detecting variants associated with multiple phenotypes. In this study, we used array-derived genotypes and phenotypes for 24 reproduction, production, and conformation traits to explore differences between the two methods and used imputed sequence variant genotypes to fine-map six quantitative trait loci (QTL).

Results: We considered genotypes at 44,733 SNPs for 5,753 pigs from the Swiss Large White breed that had deregressed breeding values for 24 traits. Single-trait association analyses revealed eleven QTL that affected 15 traits. Multi-trait association testing and the meta-analysis of the single-trait GWAS revealed between 3 and 6 QTL, respectively, in three groups of traits. The multi-trait methods revealed three loci that were not detected in the single-trait GWAS. Four QTL that were identified in the single-trait GWAS, remained undetected in the multi-trait analyses. To pinpoint candidate causal variants for the QTL, we imputed the array-derived genotypes to the sequence level using a sequenced reference panel consisting of 421 pigs. This approach provided genotypes at 16 million imputed sequence variants with a mean accuracy of imputation of 0.94. The fine-mapping of six QTL with imputed sequence variant genotypes revealed four previously proposed causal mutations among the top variants.

Conclusions: Our findings in a medium-size cohort of pigs suggest that multivariate association testing and the meta-analysis of summary statistics from single-trait GWAS provide very similar results. Although multi-trait association methods provide a useful overview of pleiotropic loci segregating in mapping populations, the investigation of single-trait association studies is still advised, as multi-trait methods may miss QTL that are uncovered in single-trait GWAS.

Keywords: Genome-wide association study; Imputation; Meta-analyses; Multivariate analyses; Pleiotropy.

Fig. 1

Comparison of variants associated with 24 traits from 3 multi-trait GWAS methods. Multivariate (mtGWAS), meta-analyses with complete dataset (metaGWAS¹), and meta-analyses including samples with missing trait records (metaGWAS²) were based on array-derived genotypes. A Proportions of significantly associated variants discovered across chromosomes, groups of traits and multi-trait methods. B Overlaps between the associated variants revealed by each of the methods. Sum across all four trait-groups. C QQ plot between -log10(P) of variants (N = 41) associated in both mtGWAS and metaGWAS². The line denotes a correlation of 1. D QTL detected by different methods across all trait groups

Fig. 2

Fine mapping of six QTL detected by metaGWAS. A Manhattan plots from array (upper) and imputed sequence (bottom) variants in metaGWAS with 24 traits. B Linkage disequilibrium between the lead SNPs and all other variants. Black circles mark array SNPs, arrows point to previously proposed causal variants. The red line indicates the genome-wide Bonferroni-corrected significance threshold. C Variation explained (in % of the drEBV variance) by alternative alleles of the lead SNPs in the single traits. Production traits are in blue scale (ADFI—Average daily feed intake; DWG—Daily weight gain on test; LDWG—Lifetime daily weight gain; LMC—Lean meat content; BFT—Back fat thickness; IMF—Intramuscular fat content in loin), and conformation traits in red scale (CL—Carcass length; NT—Number of teats—both sides; NUT—Number of underdeveloped teats; BFL—Bent to pre-bent, front legs; XOH—X- to O-legged)