Genome-wide association studies (GWASs) across thousands of traits have revealed the pervasive pleiotropy of trait-associated genetic variants. While methods have been proposed to characterize pleiotropic components across groups of phenotypes, scaling these approaches to ultra-large-scale biobanks has been challenging. Here, we propose FactorGo, a scalable variational factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data. In extensive simulations, we observe that FactorGo outperforms the state-of-the-art (model-free) approach tSVD in capturing latent pleiotropic factors across phenotypes while maintaining a similar computational cost. We apply FactorGo to estimate 100 latent pleiotropic factors from GWAS summary data of 2,483 phenotypes measured in European-ancestry Pan-UK BioBank individuals (N = 420,531). Next, we find that factors from FactorGo are more enriched with relevant tissue-specific annotations than those identified by tSVD (p = 2.58E−10) and validate our approach by recapitulating brain-specific enrichment for BMI and the height-related connection between reproductive system and muscular-skeletal growth. Finally, our analyses suggest shared etiologies between rheumatoid arthritis and periodontal condition in addition to alkaline phosphatase as a candidate prognostic biomarker for prostate cancer. Overall, FactorGo improves our biological understanding of shared etiologies across thousands of GWASs.

中文翻译：

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使用 GWAS 摘要统计来描述数千种人类疾病和复杂性状的多效性的可扩展方法

涵盖数千个性状的全基因组关联研究（GWAS）揭示了性状相关遗传变异的普遍多效性。虽然已经提出了一些方法来表征跨表型组的多效性成分，但将这些方法扩展到超大规模生物库一直具有挑战性。在这里，我们提出了 FactorGo，一种可扩展的变分因子分析模型，用于使用生物库 GWAS 摘要数据来识别和表征多效性成分。在广泛的模拟中，我们观察到 FactorGo 在捕获跨表型的潜在多效性因子方面优于最先进的（无模型）方法 tSVD，同时保持相似的计算成本。我们应用 FactorGo 从欧洲血统 Pan-UK BioBank 个体 (N = 420,531) 测量的 2,483 个表型的 GWAS 摘要数据中估计 100 个潜在多效性因子。接下来，我们发现来自 FactorGo 的因子比 tSVD 识别的因子更丰富地具有相关组织特异性注释（p = 2.58E−10），并通过概括 BMI 的大脑特异性富集和生殖与身高之间的身高相关联系来验证我们的方法。系统和肌肉骨骼生长。最后，我们的分析表明，除了碱性磷酸酶作为前列腺癌的候选预后生物标志物之外，类风湿性关节炎和牙周病之间还有共同的病因。总体而言，FactorGo 提高了我们对数千个 GWAS 的共同病因的生物学理解。