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利用等位基因分数作为孟德尔随机化的工具变量。

Use of allele scores as instrumental variables for Mendelian randomization.

机构信息

Department of Public Health and Primary Care, Worts Causeway, Cambridge CB1 8RN, UK.

出版信息

Int J Epidemiol. 2013 Aug;42(4):1134-44. doi: 10.1093/ije/dyt093.

DOI:10.1093/ije/dyt093
PMID:24062299
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3780999/
Abstract

BACKGROUND

An allele score is a single variable summarizing multiple genetic variants associated with a risk factor. It is calculated as the total number of risk factor-increasing alleles for an individual (unweighted score), or the sum of weights for each allele corresponding to estimated genetic effect sizes (weighted score). An allele score can be used in a Mendelian randomization analysis to estimate the causal effect of the risk factor on an outcome.

METHODS

Data were simulated to investigate the use of allele scores in Mendelian randomization where conventional instrumental variable techniques using multiple genetic variants demonstrate 'weak instrument' bias. The robustness of estimates using the allele score to misspecification (for example non-linearity, effect modification) and to violations of the instrumental variable assumptions was assessed.

RESULTS

Causal estimates using a correctly specified allele score were unbiased with appropriate coverage levels. The estimates were generally robust to misspecification of the allele score, but not to instrumental variable violations, even if the majority of variants in the allele score were valid instruments. Using a weighted rather than an unweighted allele score increased power, but the increase was small when genetic variants had similar effect sizes. Naive use of the data under analysis to choose which variants to include in an allele score, or for deriving weights, resulted in substantial biases.

CONCLUSIONS

Allele scores enable valid causal estimates with large numbers of genetic variants. The stringency of criteria for genetic variants in Mendelian randomization should be maintained for all variants in an allele score.

摘要

背景

等位基因评分是一种单一变量,用于总结与风险因素相关的多个遗传变异。它是通过计算个体的风险因素增加等位基因的总数(未加权评分)或对应于估计遗传效应大小的每个等位基因的权重总和(加权评分)来计算的。等位基因评分可用于孟德尔随机化分析,以估计风险因素对结果的因果效应。

方法

模拟数据以研究等位基因评分在孟德尔随机化中的应用,其中使用多个遗传变异的传统工具变量技术显示出“弱工具”偏差。评估了使用等位基因评分对模型误设(例如非线性、效应修饰)和工具变量假设违反的稳健性。

结果

正确指定的等位基因评分的因果估计值是无偏的,具有适当的覆盖率。这些估计值通常对等位基因评分的误设具有稳健性,但对工具变量违反无稳健性,即使等位基因评分中的大多数变异都是有效的工具变量。使用加权而不是未加权的等位基因评分会增加功效,但当遗传变异具有相似的效应大小时,增加幅度很小。在等位基因评分中选择要包含的变异或推导权重时,盲目使用分析数据会导致严重的偏差。

结论

等位基因评分可以使用大量遗传变异进行有效的因果估计。在孟德尔随机化中,所有变异的等位基因评分都应保持对遗传变异的严格标准。

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