Background: Genome-wide association studies (GWAS) uncovered a wealth of associations between common variants and human phenotypes. These results, widely shared across the scientific community as summary statistics, fostered a flurry of secondary analysis: heritability and genetic correlation assessment, pleiotropy characterization and multitrait association test. Amongst these secondary analyses, a rising new field is the decomposition of multitrait genetic effects into distinct profiles of pleiotropy. Results: We conducted an integrative analysis of GWAS summary statistics from 36 phenotypes to decipher multitrait genetic architecture and its link to biological mechanisms. We started by benchmarking multitrait association tests on a large panel of phenotype sets and established the Omnibus test as the most powerful in practice. We detected 322 new associations that were not previously reported by univariate screening. Using independent significant associations, we investigated the breakdown of genetic association into clusters of variants harboring similar multitrait association profile. Focusing on two subsets of immunity and metabolism phenotypes, we then demonstrate how SNPs within clusters can be mapped to biological pathways and disease mechanisms, providing a putative insight for numerous SNPs with unknown biological function. Finally, for the metabolism set, we investigate the link between gene cluster assignment and success of drug targets in random control trials. We report additional uninvestigated drug targets classified by clusters. Conclusions: Multitrait genetic signals can be decomposed into distinct pleiotropy profiles that reveal consistent with pathways databases and random control trials. We propose this method for the mapping of unannotated SNPs to putative pathways.

中文翻译：

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多特征基因表型关联，将疾病变异与生物学机制联系起来

背景：全基因组关联研究（GWAS）发现了常见变异与人类表型之间的大量关联。这些结果作为摘要统计在整个科学界广泛共享，促成了一系列的次要分析：遗传性和遗传相关性评估，多效性表征和多性状关联测试。在这些次要分析中，一个新出现的领域是将多性状遗传效应分解为不同的多效性。结果：我们对来自36个表型的GWAS汇总统计数据进行了综合分析，以破译多特征遗传结构及其与生物学机制的联系。我们首先在大量表型集上对多特征关联测试进行基准测试，然后将Omnibus测试确定为实际上最强大的测试。我们检测到322个新的关联，这些关联以前未通过单变量筛选报告。使用独立的重要协会，我们调查了遗传协会细分成具有相似的多性状协会概况的变异簇。然后针对免疫和代谢表型的两个子集，我们演示如何将簇中的SNPs定位到生物学途径和疾病机制，为生物学功能未知的许多SNPs提供推定的见解。最后，对于代谢组，我们在随机对照试验中研究了基因簇分配与药物靶标成功之间的联系。我们报告了按类别分类的其他未调查的药物靶标。结论：多性状遗传信号可以分解为独特的多效性图谱，揭示出与途径数据库和随机对照试验一致。我们提出此方法用于将未注释的SNP映射到推定的途径。