Date Published: October 11, 2018
Publisher: Public Library of Science
Author(s): Iulia Blaj, Jens Tetens, Siegfried Preuß, Jörn Bennewitz, Georg Thaller, Cristina Óvilo.
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).
In pig breeding, the search for quantitative trait loci (QTLs) and the underlying causative mutations has been in progress for more than two decades. The onset was the landmark publication on genetic mapping of QTL for growth and fatness by . Up to date, according to the latest release of the AnimalQTLdb (Release 35, 29th April, 2018), the Pig QTL database stores 27,465 pig QTLs curated from 620 publications and representing a wide range of economically important phenotypes (PigQTLdb; https://www.animalgenome.org/cgi-bin/QTLdb/SS/summary).
Linkage disequilibrium between markers and quantitative trait loci is fundamental for conducting a successful genome-wide association study. In order to disentangle variants associated with complex traits, the LD pattern in the populations under investigation must be evaluated. This particular analysis was carried out by  for the D1, D2 and D3 populations included in this study. The main findings were that there is a faster LD decay in the European type breeds cross (D1) as compared to the Asian/Wild Boar and European breeds cross (D2 and D3), while the fastest breakdown of LD is observed by pooling the data. The latter finding is supportive of the fact that the joint design (D1D2D3) could have a positive impact on the mapping resolution. Also in accordance to this study were the results by  and  obtained via stochastic simulations of populations with a similar phylogeny as D1, D2 and D3.
A genome-wide association study was conducted for growth and carcass traits using SNP-chip information from three populations sharing a common founder breed (Piétrain). An individual population GWAS was conducted and two strategies for combining the datasets were employed: a joint population GWAS and a meta-analysis of the individual population GWAS summary statistics. While the joint population GWAS and the meta-analysis did not identify new associated regions besides the ones identified in the individual populations, both approaches had a positive impact on the mapping resolution which implies that causative mutations can be identified with higher accuracy. Depending on the access to the complete original datasets, the strategies can complement or substitute each other. A total of 20 genomic clusters were pinpointed and they contained genes previously associated with the traits (e.g. IGF2, VRTN and TGFB3). Finally, among the additional candidate genes being suggested, BMP2 is being proposed as a strong candidate gene for carcass length. The findings of this study provide novel insights into approaches of dissecting the genetic basis of growth and carcass traits and indicate directions of further research which will lead to the identification of causal mutations affecting traits relevant in pig breeding programs.