Comparison of two multi-trait association testing methods and sequence-based fine mapping of six 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.

Genome-wide association study and regional heritability mapping of protein efficiency and performance traits in Swiss Large White pigs

“…The RHM approach identified two regions for PE at the suggestive threshold on SSC9 and SSC2, located at the 2–3 Mb and at 31–32Mb position, encompassing 13,440 and 5,399 SNPs, respectively. Similar to our study, Shirali et al [23] found associations on SSC2 and SSC9 for total nitrogen excretion and average daily nitrogen excretion, respectively, during the 60 – 140kg growth stage, but these associations were not confirmed in our study. Our study also found four suggestive regions associated with ADFI located at 270–271Mb, 272–273Mb and 273–274Mb on SSC1, and at 133–134Mb on SSC14.…”

“…Our study also found four suggestive regions associated with ADFI located at 270–271Mb, 272–273Mb and 273–274Mb on SSC1, and at 133–134Mb on SSC14. Nosková et al [23] conducted a multi-trait meta-GWAS in 5,109 Swiss Large White pigs with 60K array data imputed to sequence level and found QTL associated with ADFI on SSC1 at the 270 Mb – 272Mb position, confirming that this region is important for production traits in pigs. The associated chromosomal region also harbours QTL for ADFI in other pig populations [44, 45], possibly suggesting an ancestral origin of the QTL.…”

“…Thus, the three genotyping/sequencing platforms comprised of (i) 258 pigs genotyped at 600K obtained with the Axiom Porcine Genotyping array; (ii) 297 pigs sequenced at an intended read depth of 4×; and (iii) 492 pigs sequenced at an intended read depth of 1×. The array genotyping data was imputed to whole genome sequence level with a reference panel consisting of 421 pigs (Landrace and Large White) that were sequenced at a coverage ranging between 4× and 37.5× [23, 36]. For pigs sequenced at 4× coverage, imputation of sporadically missing genotypes was done using Beagle (v 4.1 [36]).…”

“…As FCR, ADG, and ADFI have either direct or indirect impacts on efficiency and production costs, a number of studies have identified QTL for FCR, ADG and ADFI [19, 20, 21, 22]. The majority of these QTL were found in Duroc and Landrace pigs, with only a few QTL identified in Large White pigs [23]. However, although FCR and RFI may be correlated with PE as reported in the studies by Ewaoluwagbemiga et al [8] and Saintilan et al [11], it has been suggested that selection for improved FCR and RFI with the aim of reducing nutrient excretion is clearly less efficient than direct selection for the nutrient efficiency trait itself (e.g., PE) in poultry [24].…”