A meta-analysis of genome-wide association studies for average daily gain and lean meat percentage in two Duroc pig populations

Abstract

Background Average daily gain (ADG) and lean meat percentage (LMP) are the main production performance indicators of pigs. Nevertheless, the genetic architecture of ADG and LMP is still elusive. Here, we conducted genome-wide association studies (GWAS) and meta-analysis for ADG and LMP in 3770 American and 2090 Canadian Duroc pigs. Results In the American Duroc pigs, one novel pleiotropic quantitative trait locus (QTL) on Sus scrofa chromosome 1 (SSC1) was identified to be associated with ADG and LMP, which spans 2.53 Mb (from 159.66 to 162.19 Mb). In the Canadian Duroc pigs, two novel QTLs on SSC1 were detected for LMP, which were situated in 3.86 Mb (from 157.99 to 161.85 Mb) and 555 kb (from 37.63 to 38.19 Mb) regions. The meta-analysis identified ten and 20 additional SNPs for ADG and LMP, respectively. Finally, four genes (PHLPP1, STC1, DYRK1B, and PIK3C2A) were detected to be associated with ADG and/or LMP. Further bioinformatics analysis showed that the candidate genes for ADG are mainly involved in bone growth and development, whereas the candidate genes for LMP mainly participated in adipose tissue and muscle tissue growth and development. Conclusions We performed GWAS and meta-analysis for ADG and LMP based on a large sample size consisting of two Duroc pig populations. One pleiotropic QTL that shared a 2.19 Mb haplotype block from 159.66 to 161.85 Mb on SSC1 was found to affect ADG and LMP in the two Duroc pig populations. Furthermore, the combination of single-population and meta-analysis of GWAS improved the efficiency of detecting additional SNPs for the analyzed traits. Our results provide new insights into the genetic architecture of ADG and LMP traits in pigs. Moreover, some significant SNPs associated with ADG and/or LMP in this study may be useful for marker-assisted selection in pig breeding.

Genome-Wide Association Analysis Reveals Genetic Loci and Candidate Genes for Chest, Abdominal, and Waist Circumferences in Two Duroc Pig Populations

“…Further demonstrated that the uniformities of these three conformation traits in AOD pigs were higher than that of COD pigs. Interestingly, our previous study showed that compared to the COD population, the coefficient of variation for the lean meat percentage (LMP) and average daily gain (ADG) was smaller in the AOD population ( 25 ). In addition, compared to the COD population, the mean of LMP and ADG were higher in the AOD population.…”

“…Thus, PCA was conducted for experimental populations before GWAS analysis to detect the potential population stratification. The PCA plot was presented in our previous paper ( 25 ). The PCA plot showed that AOD and COD pigs were classified clearly into two clusters, indicating that these two populations have different genetic backgrounds.…”

Assessment of Heterozygosity and Genome-Wide Analysis of Heterozygosity Regions in Two Duroc Pig Populations

“…In this study, we identified ROHet in two Duroc populations and found that the distribution of ROHet island and the were different in two Duroc populations. According to our previous study ( Zhou et al, 2021 ), the genetic background of the two Duroc populations are different due to the different intensity and direction of selection, which may lead to diverse heterozygosity-rich regions in the genome. Additionally, Williams et al (2016) found that heterozygosity-rich regions were not randomly distributed in the Chillingham cattle genome, but rather than clustered in specific chromosomal regions, in analogy to ROH and defined as ROHet.…”

Genome-wide association study and transcriptome analysis dissect the genetic control of silique length in Brassica napus L.

“…When the population is stratified, the conventional method is to use a fixed effect covariate matrix ( Q matrix) or PCA to control the false positive caused by population structure. Meta-analysis can increase power, reduce false-positive findings, and even identify new genetic loci, so it can solve the shortcomings of single-population GWAS [ 28 ]. In this study, we tried to perform independent GWAS by subgroups and then perform meta-analysis on the significant SNPs obtained by independent analysis.…”