In livestock, genome-wide association studies (GWAS) are usually conducted in a single population (single-GWAS) with limited sample size and detection power. To enhance the detection power of GWAS, meta-analysis of GWAS (meta-GWAS) and mega-analysis of GWAS (mega-GWAS) have been proposed to integrate data from multiple populations at the level of summary statistics or individual data, respectively. However, there is a lack of comparison for these different strategies, which makes it difficult to guide the best practice of GWAS integrating data from multiple study populations. To maximize the comparison of different association analysis strategies across multiple populations, we conducted single-GWAS, meta-GWAS, and mega-GWAS for the backfat thickness of 100 kg (BFT_100) and days to 100 kg (DAYS_100) within each of the three commercial pig breeds (Duroc, Yorkshire, and Landrace). Based on controlling the genome inflation factor to one, we calculated corrected p-values (p_C). In Yorkshire, with the largest sample size, mega-GWAS, meta-GWAS and single-GWAS detected 149, 38 and 20 significant SNPs (p_C < 1E-5) associated with BFT_100, as well as 26, four, and one QTL, respectively. Among them, p_C of SNPs from mega-GWAS was the lowest, followed by meta-GWAS and single-GWAS. The correlation of p_C among the three GWAS strategies ranged from 0.60 to 0.75 and the correlation of SNP effect values between meta-GWAS and mega-GWAS was 0.74, all showing good agreement. Collectively, even though there are differences in the integration of individual data or summary statistics, integrating data from multiple populations is an effective means of genetic argument for complex traits, especially mega-GWAS versus single-GWAS.

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评估用于整合多个群体数据的全基因组关联分析的三种策略

在家畜中，全基因组关联研究（GWAS）通常在样本量和检测能力有限的单一群体（单 GWAS）中进行。为了增强GWAS的检测能力，人们提出了GWAS的荟萃分析（meta-GWAS）和GWAS的巨量分析（mega-GWAS），分别在汇总统计或个体数据层面整合来自多个人群的数据。然而，这些不同策略缺乏比较，这使得很难指导 GWAS 整合多个研究人群数据的最佳实践。为了最大限度地比较多个人群的不同关联分析策略，我们对三个群体中的每一个中的背膘厚度为 100 公斤 (BFT_100) 和达到 100 公斤的天数 (DAYS_100) 进行了单 GWAS、元 GWAS 和兆 GWAS商业猪品种（杜洛克、约克夏和长白猪）。基于将基因组膨胀因子控制为 1，我们计算了校正的p值 ( p _C )。在约克郡，样本量最大，mega-GWAS、meta-GWAS和single-GWAS检测到149、38和20个与BFT_100相关的显着SNP（p _C  < 1E-5），以及26、4和1个QTL ， 分别。其中， mega-GWAS的SNP p _C最低，其次是meta-GWAS和single-GWAS。三种GWAS策略之间的p _C相关性范围为0.60至0.75，meta-GWAS和mega-GWAS之间的SNP效应值相关性为0.74，均表现出良好的一致性。总的来说，尽管个体数据的整合或汇总统计数据存在差异，但整合多个群体的数据是复杂性状遗传论证的有效手段，尤其是大型 GWAS 与单 GWAS 的比较。