Sequence-based genome-wide association studies (GWAS) provide high statistical power to identify candidate causal mutations when a large number of individuals with both sequence variant genotypes and phenotypes is available. A meta-analysis combines summary statistics from multiple GWAS and increases the power to detect trait-associated variants without requiring access to data at the individual level of the GWAS mapping cohorts. Because linkage disequilibrium between adjacent markers is conserved only over short distances across breeds, a multi-breed meta-analysis can improve mapping precision. To maximise the power to identify quantitative trait loci (QTL), we combined the results of nine within-population GWAS that used imputed sequence variant genotypes of 94,321 cattle from eight breeds, to perform a large-scale meta-analysis for fat and protein percentage in cattle. The meta-analysis detected (p ≤ 10−8) 138 QTL for fat percentage and 176 QTL for protein percentage. This was more than the number of QTL detected in all within-population GWAS together (124 QTL for fat percentage and 104 QTL for protein percentage). Among all the lead variants, 100 QTL for fat percentage and 114 QTL for protein percentage had the same direction of effect in all within-population GWAS. This indicates either persistence of the linkage phase between the causal variant and the lead variant across breeds or that some of the lead variants might indeed be causal or tightly linked with causal variants. The percentage of intergenic variants was substantially lower for significant variants than for non-significant variants, and significant variants had mostly moderate to high minor allele frequencies. Significant variants were also clustered in genes that are known to be relevant for fat and protein percentages in milk. Our study identified a large number of QTL associated with fat and protein percentage in dairy cattle. We demonstrated that large-scale multi-breed meta-analysis reveals more QTL at the nucleotide resolution than within-population GWAS. Significant variants were more often located in genic regions than non-significant variants and a large part of them was located in potentially regulatory regions.

中文翻译：

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使用估算的序列变异基因型对来自八个牛种的94,321头牛的乳脂和蛋白质百分比进行荟萃分析。

当有大量具有序列变异基因型和表型的个体时，基于序列的全基因组关联研究（GWAS）提供了很高的统计能力，可以识别候选因果突变。荟萃分析结合了来自多个GWAS的摘要统计信息，并提高了检测与性状相关的变体的能力，而无需访问GWAS映射队列中各个级别的数据。由于相邻标记之间的连锁不平衡仅在跨品种的短距离内得以保留，因此多品种荟萃分析可以提高绘图精度。为了最大化识别数量性状基因座（QTL）的能力，我们结合了9个种群内GWAS的结果，这些结果使用了来自8个品种的94321头牛的推算序列变异基因型，对牛的脂肪和蛋白质百分比进行大规模的荟萃分析。荟萃分析检测到（p≤10-8）脂肪百分比为138 QTL，蛋白质百分比为176 QTL。这超过了在所有种群内GWAS中检测到的QTL数量（脂肪百分比为124 QTL，蛋白质百分比为104 QTL）。在所有先导变体中，脂肪百分比的100 QTL和蛋白质百分比的114 QTL在所有种群内GWAS中具有相同的作用方向。这表明，因果变体和跨品种的先导变体之间的连接阶段持续存在，或者某些先导变体可能确实是因果关系或与因果变体紧密联系。对于显着变异，基因间变异的百分比显着低于非显着变异，显着变异体的中位等位基因频率大多为中到高。显着的变异也聚集在已知与牛奶中脂肪和蛋白质百分比相关的基因中。我们的研究确定了与奶牛脂肪和蛋白质百分比相关的大量QTL。我们证明，大规模的多元荟萃分析显示，在核苷酸分辨率上比人群内部GWAS具有更多的QTL。显着的变体比非显着的变体更常位于基因区，并且它们的很大一部分位于潜在的调控区。我们的研究确定了与奶牛脂肪和蛋白质百分比相关的大量QTL。我们证明，大规模的多元荟萃分析显示，在核苷酸分辨率上比人群内部GWAS具有更多的QTL。显着的变体比非显着的变体更常位于基因区，并且它们的很大一部分位于潜在的调控区。我们的研究确定了与奶牛脂肪和蛋白质百分比相关的大量QTL。我们证明，大规模的多元荟萃分析显示，在核苷酸分辨率上比人群内部GWAS具有更多的QTL。显着的变体比非显着的变体更常位于基因区，并且它们的很大一部分位于潜在的调控区。