Mortality selection in a genetic sample and implications for association studies
Mortality selection in a genetic sample and implications for association studies
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DOI：
10.1093/ije/dyx041
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发表时间：
2017-08-01
影响因子：
7.7
通讯作者：
Boardman, Jason D.
中科院分区：
文献类型：
--
作者：
Domingue, Benjamin W.;Belsky, Daniel W.;Boardman, Jason D.
背景资料：当感兴趣的群体的非随机子集在数据收集之前已经死亡并且在数据中未观察到时，发生死亡率选择。死亡率选择在社会和健康科学中受到普遍关注，但在遗传流行病学中却很少受到关注。我们测试的假设，死亡率的选择可能会偏向遗传关联估计，使用来自美国的健康和退休研究（HRS）的数据：我们测试的死亡率选择到HRS的遗传数据库比较HRS受访者谁生存，直到2006年的遗传数据收集与那些谁不。接下来，我们根据人口统计学、健康和社会特征对死亡率选择进行建模，以计算死亡率选择概率权重。我们分析了多基因得分协会与几个性状之前和之后应用逆概率加权占死亡率选择。我们测试了简单的协会和随时间变化的遗传协会（即基因与队列的相互作用）。结果：我们观察到的HRS的人口统计学，健康和社会特征的遗传数据库中的死亡率选择。使用逆概率加权法校正死亡率选择并没有改变简单的关联估计。然而，使用这些方法确实改变了对基因与队列相互作用效应的估计。基于2012年HRS受试者生存分析，对死亡率选择的校正改变了基因与队列交互作用的估计，与增加死亡率选择的方向相反。结论：死亡率选择可能会使基因与队列交互作用的估计产生偏差。HRS数据的分析可以通过包括概率权重来调整与可观察量相关的死亡率选择。死亡率选择是遗传关联研究的潜在混杂因素，但混杂程度因性状而异。
Background: Mortality selection occurs when a non-random subset of a population of interest has died before data collection and is unobserved in the data. Mortality selection is of general concern in the social and health sciences, but has received little attention in genetic epidemiology. We tested the hypothesis that mortality selection may bias genetic association estimates, using data from the US-based Health and Retirement Study (HRS).Methods: We tested mortality selection into the HRS genetic database by comparing HRS respondents who survive until genetic data collection in 2006 with those who do not. We next modelled mortality selection on demographic, health and social characteristics to calculate mortality selection probability weights. We analysed polygenic score associations with several traits before and after applying inverse-probability weighting to account for mortality selection. We tested simple associations and time-varying genetic associations (i.e. gene-by-cohort interactions).Results: We observed mortality selection into the HRS genetic database on demographic, health and social characteristics. Correction for mortality selection using inverse probability weighting methods did not change simple association estimates. However, using these methods did change estimates of gene-by-cohort interaction effects. Correction for mortality selection changed gene-by-cohort interaction estimates in the opposite direction from increased mortality selection based on analysis of HRS respondents surviving through 2012.Conclusions: Mortality selection may bias estimates of gene-by-cohort interaction effects. Analyses of HRS data can adjust for mortality selection associated with observables by including probability weights. Mortality selection is a potential confounder of genetic association studies, but the magnitude of confounding varies by trait.