Genome-wide association studies and meta-analysis uncovers new candidate genes for growth and carcass traits in pigs

Abstract

Genome-wide association studies (GWAS) have been widely used in the genetic dissection of complex traits. As more genomic data is being generated within different commercial or resource pig populations, the challenge which arises is how to collectively investigate the data with the purpose to increase sample size and implicitly the statistical power. This study performs an individual population GWAS, a joint population GWAS and a meta-analysis in three pig F2 populations. D1 is derived from European type breeds (Piétrain, Large White and Landrace), D2 is obtained from an Asian breed (Meishan) and Piétrain, and D3 stems from a European Wild Boar and Piétrain, which is the common founder breed. The traits investigated are average daily gain, backfat thickness, meat to fat ratio and carcass length. The joint and the meta-analysis did not identify additional genomic clusters besides the ones discovered via the individual population GWAS. However, the benefit was an increased mapping resolution which pinpointed to narrower clusters harboring causative variants. The joint analysis identified a higher number of clusters as compared to the meta-analysis; nevertheless, the significance levels and the number of significant variants in the meta-analysis were generally higher. Both types of analysis had similar outputs suggesting that the two strategies can complement each other and that the meta-analysis approach can be a valuable tool whenever access to raw datasets is limited. Overall, a total of 20 genomic clusters that predominantly overlapped at various extents, were identified on chromosomes 2, 7 and 17, many confirming previously identified quantitative trait loci. Several new candidate genes are being proposed and, among them, a strong candidate gene to be taken into account for subsequent analysis is BMP2 (bone morphogenetic protein 2).

GWAS for Meat and Carcass Traits Using Imputed Sequence Level Genotypes in Pooled F2-Designs in Pigs

“…Most of the clusters overlapped or were in the vicinity of the previously reported QTL. This was expected as the database has been recently updated and also includes our previous results (Blaj et al 2018) using SNP chip data and three out of the four pig populations which were taken into account here. Some of the earlier reported QTL in the database spread over large genomic regions ( e.g.…”

“…, stable, slaughter month) and for the effect of RYR1 gene (Fujii et al 1991) using a general linear model. Trait definition, descriptive statistics and information about the pre-adjustment and the fixed effects used per cross can be found in (Blaj et al 2018).…”

“…In a recent simulation study, it was shown to be possible by pooling F2 designs, particularly when founder breeds are closely related and QTL are segregating in one founder breed (Schmid et al 2018). This approach has already been successfully applied based on medium density SNP chip data (Blaj et al 2018; Stratz et al 2018).…”

Meta-analyses of genome wide association studies in lines of laying hens divergently selected for feather pecking using imputed sequence level genotypes

“…In the present study, we combined the results of GWAS from two resource populations: an F2 design and one half-sib (HS) population divergently selected for feather pecking behavior. This type of meta-analysis was already established in pigs [11] and applied in an FP analysis, which in addition included selection signatures [8]. The same strategy enabled us to identify 15 genome-wide significant variants associated with FP behavior in the present study.…”

Genome-wide association and evolutionary analyses reveal the formation of swine facial wrinkles in Chinese Erhualian pigs

“…The most significant SNP (rs80983858) located at the 3255-bp downstream of positional candidate gene GRM4 , and the G allele was of benefit to increase facial wrinkles. Previously at around 34.8 Mb on SSC7, we have also identified QTLs affecting ear size [7], meat moisture content [17], skin thickness [18], fatness deposition [19], the weight of internal organs [12], limb bone lengths [20], head weight [21], and carcass length [22] in divergent pig populations, which indicated that this candidate region plays important roles in the development of many traits in pigs. This region is a gene-rich region, for example, there were 35 protein-coding genes from 34.0 to 35.5 Mb on SSC7, which suggested that the biological functions of this locus would be very important and complicate.…”