Mendelian Randomization using Public Data from Genetic Consortia
Mendelian Randomization using Public Data from Genetic Consortia
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DOI：
10.1515/ijb-2015-0074
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发表时间：
2016-11-01
影响因子：
1.2
通讯作者：
Del Greco, Fabiola M.
中科院分区：
文献类型：
--
作者：
Thompson, John R.;Minelli, Cosetta;Del Greco, Fabiola M.
孟德尔随机化（MR）是一种利用观测数据建立暴露与结果之间因果关系的技术。它是一种工具变量分析，其中遗传变异被用作工具。许多协会对变异和特定性状之间的全基因组关联进行了荟萃分析，并公开了他们的结果。利用这些数据，就有可能推导出一种性状的遗传风险评分，并推断出同样的风险评分与另一种性状的关联。通过模拟和评估出生体重对成人血糖水平的潜在因果影响，研究了这种方法的特性。在这样的分析中，决定一个人是对基于一组估计的回归系数的风险评分感兴趣，还是对基于真实的潜在系数的评分感兴趣，这一点很重要。MR主要关注的是后者。为前一个问题设计的方法如果用于mr，会低估方差。这种方差是可以纠正的，但需要小心，以避免引入偏差。基于公共数据源的MR是有用且易于执行的，但必须注意避免错误的精度或偏差。
Mendelian randomization (MR) is a technique that seeks to establish causation between an exposure and an outcome using observational data. It is an instrumental variable analysis in which genetic variants are used as the instruments. Many consortia have meta-analysed genome-wide associations between variants and specific traits and made their results publicly available. Using such data, it is possible to derive genetic risk scores for one trait and to deduce the association of that same risk score with a second trait. The properties of this approach are investigated by simulation and by evaluating the potentially causal effect of birth weight on adult glucose level. In such analyses, it is important to decide whether one is interested in the risk score based on a set of estimated regression coefficients or the score based on the true underlying coefficients. MR is primarily concerned with the latter. Methods designed for the former question will under-estimate the variance if used for MR. This variance can be corrected but it needs to be done with care to avoid introducing bias. MR based on public data sources is useful and easy to perform, but care must be taken to avoid false precision or bias.