Graduate School of Education, Stanford University, 520 Galvez Mall, Stanford, CA 94305, USA.
Department of Medicine, Duke University School of Medicine; Duke University Population Research Institute, Duke University, 2020 W. Main St., Durham NC, 27705.
Int J Epidemiol. 2017 Aug 1;46(4):1285-1294. doi: 10.1093/ije/dyx041.
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).
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).
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.
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.
当感兴趣的人群中一个非随机的子集在数据收集之前死亡且在数据中未被观察到时，就会发生死亡率选择。死亡率选择在社会科学和健康科学中普遍受到关注，但在遗传流行病学中却很少受到关注。我们使用来自美国健康与退休研究（HRS）的数据检验了死亡率选择可能会偏倚遗传关联估计的假设。
我们通过比较在 2006 年遗传数据收集前存活至 HRS 的受访者与未存活至 HRS 的受访者，来检验死亡率选择进入 HRS 遗传数据库的情况。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。在应用逆概率加权法来纠正死亡率选择之前，我们对多基因评分与几个性状的关联进行了分析。我们在考虑死亡率选择的情况下分析了简单关联和时变遗传关联（即基因与队列的交互作用）。
我们观察到死亡率选择与人口统计学、健康和社会特征有关。使用逆概率加权法进行死亡率选择校正并未改变简单关联的估计值。然而，使用这些方法确实改变了基因与队列的交互作用效果的估计值。基于对 2012 年之前存活的 HRS 受访者的分析，死亡率选择校正改变了基因与队列的交互作用估计值，与死亡率选择增加的方向相反。
死亡率选择可能会偏倚基因与队列的交互作用效果的估计值。通过包括概率权重，HRS 数据分析可以调整与可观察因素相关的死亡率选择。死亡率选择是遗传关联研究的一个潜在混杂因素，但混杂的程度因性状而异。
