Large genome-wide association studies (GWAS) have identified loci that are associated with complex traits and diseases, but index variants are often not causal and reside in non-coding regions of the genome. To gain a better understanding of the relevant biological mechanisms, intermediate traits such as gene expression and protein levels are increasingly being investigated because these are likely mediators between genetic variants and disease outcome. Genetic variants associated with intermediate traits, termed molecular quantitative trait loci (molQTLs), can then be used as instrumental variables in a Mendelian randomization (MR) approach to identify the causal features and mechanisms of complex traits. Challenges such as pleiotropy and the non-specificity of molQTLs remain, and further approaches and methods need to be developed.

中文翻译：

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使用分子数量性状位点加强复杂疾病的因果推断。

大型全基因组关联研究 (GWAS) 已经确定了与复杂性状和疾病相关的位点，但索引变异通常不是因果关系，而是位于基因组的非编码区域。为了更好地了解相关的生物学机制，越来越多地研究基因表达和蛋白质水平等中间性状，因为它们可能是遗传变异和疾病结果之间的中介。与中间性状相关的遗传变异，称为分子数量性状基因座 (molQTL)，可以用作孟德尔随机化 (MR) 方法中的工具变量，以确定复杂性状的因果特征和机制。molQTLs 的多效性和非特异性等挑战仍然存在，需要开发进一步的方法和方法。