Mendelian randomization and colocalization are two statistical approaches that can be applied to summarized data from genome-wide association studies (GWASs) to understand relationships between traits and diseases. However, despite similarities in scope, they are different in their objectives, implementation, and interpretation, in part because they were developed to serve different scientific communities. Mendelian randomization assesses whether genetic predictors of an exposure are associated with the outcome and interprets an association as evidence that the exposure has a causal effect on the outcome, whereas colocalization assesses whether two traits are affected by the same or distinct causal variants. When considering genetic variants in a single genetic region, both approaches can be performed. While a positive colocalization finding typically implies a non-zero Mendelian randomization estimate, the reverse is not generally true: there are several scenarios which would lead to a non-zero Mendelian randomization estimate but lack evidence for colocalization. These include the existence of distinct but correlated causal variants for the exposure and outcome, which would violate the Mendelian randomization assumptions, and a lack of strong associations with the outcome. As colocalization was developed in the GWAS tradition, typically evidence for colocalization is concluded only when there is strong evidence for associations with both traits. In contrast, a non-zero estimate from Mendelian randomization can be obtained despite only nominally significant genetic associations with the outcome at the locus. In this review, we discuss how the two approaches can provide complementary information on potential therapeutic targets.

孟德尔随机化和共定位是两种统计方法，可应用于全基因组关联研究 (GWAS) 的汇总数据，以了解性状与疾病之间的关系。然而，尽管范围相似，但它们的目标、实施和解释不同，部分原因是它们是为服务不同的科学界而开发的。孟德尔随机化评估暴露的遗传预测因素是否与结果相关，并将关联解释为暴露对结果有因果影响的证据，而共定位评估两个性状是否受到相同或不同因果变异的影响。当考虑单个遗传区域的遗传变异时，两种方法都可以执行。虽然正共定位发现通常意味着非零孟德尔随机化估计，但反之通常并非如此：有几种情况会导致非零孟德尔随机化估计，但缺乏共定位的证据。其中包括暴露和结果存在不同但相关的因果变异，这将违反孟德尔随机化假设，以及与结果缺乏强关联。由于共定位是在 GWAS 传统中发展起来的，因此通常只有当有强有力的证据表明与这两个特征相关时，才能得出共定位的证据。相比之下，尽管与该基因座的结果仅有名义上显着的遗传关联，但仍可以获得孟德尔随机化的非零估计值。在这篇综述中，我们讨论了这两种方法如何提供有关潜在治疗靶点的补充信息。