Mendelian randomization (MR) is a valuable tool for detecting causal effects by using genetic variant associations. Opportunities to apply MR are growing rapidly with the increasing number of genome-wide association studies (GWAS). However, existing MR methods rely on strong assumptions that are often violated, leading to false positives. Correlated horizontal pleiotropy, which arises when variants affect both traits through a heritable shared factor, remains a particularly challenging problem. We propose a new MR method, Causal Analysis Using Summary Effect estimates (CAUSE), that accounts for correlated and uncorrelated horizontal pleiotropic effects. We demonstrate, in simulations, that CAUSE avoids more false positives induced by correlated horizontal pleiotropy than other methods. Applied to traits studied in recent GWAS studies, we find that CAUSE detects causal relationships that have strong literature support and avoids identifying most unlikely relationships. Our results suggest that shared heritable factors are common and may lead to many false positives using alternative methods.

孟德尔随机化 (MR) 是一种利用遗传变异关联检测因果效应的宝贵工具。随着全基因组关联研究 (GWAS) 数量的增加，应用 MR 的机会正在迅速增加。然而，现有的 MR 方法依赖于经常被违反的强假设，导致误报。当变异通过可遗传的共享因素影响两个性状时出现的相关水平多效性仍然是一个特别具有挑战性的问题。我们提出了一种新的 MR 方法，即使用汇总效应估计的因果分析 (CAUSE)，它解释了相关和不相关的水平多效性效应。我们在模拟中证明，与其他方法相比，CAUSE 避免了更多由相关水平多效性引起的误报。应用于最近 GWAS 研究中研究的性状，我们发现 CAUSE 检测具有强大文献支持的因果关系，并避免识别最不可能的关系。我们的结果表明，共享的遗传因素很常见，使用替代方法可能会导致许多误报。