The effect of survival bias on case-control genetic association studies of highly lethal diseases.
The effect of survival bias on case-control genetic association studies of highly lethal diseases.
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DOI：
10.1161/circgenetics.110.957928
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发表时间：
2011-04
期刊：
影响因子：
--
通讯作者：
Rosand J
中科院分区：
文献类型：
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作者：
Anderson CD;Nalls MA;Biffi A;Rost NS;Greenberg SM;Singleton AB;Meschia JF;Rosand J
生存偏差是指个体被排除在某一特征的分析之外，因为与该特征的表达有关的死亡率。在遗传关联研究中，在遗传关联病例对照设计中，增加疾病发病风险以及疾病相关死亡(致命性)风险的变异可能很难检测到，这可能导致低估变异对疾病风险的影响。我们利用现有的纵向数据为三种高致死性疾病(脑出血、缺血性中风和心肌梗死)建立了队列模型。基于这些模型，我们模拟了不同效应大小、致死率和次要等位基因频率(MAF)的遗传风险因素的病例对照遗传关联研究。对于每种疾病，对于高龄(年龄75岁)和/或具有非常高的事件相关死亡率(事件相关死亡的基因类型相对风险>；2.0)的个体的病例对照研究，检测到的效应大小的侵蚀更大。我们发现，对于平均年龄为75岁的队列，生存偏差导致的效应大小侵蚀不超过20%，即使是死亡风险增加一倍的变异也是如此。此外，我们发现，在病例群体中，伴随着效应大小侵蚀的增加伴随着MAF的耗尽，产生了存在生存偏见的“特征”。我们的模拟提供了公式，以允许估计影响大小侵蚀给定的变种的优势比(OR)的疾病，OR的致命性，和MAF。这些公式将增加病例对照基因研究的计算和复制工作的精确度。我们的方法需要使用预期数据进行验证。
Survival bias is the phenomenon by which individuals are excluded from analysis of a trait because of mortality related to the expression of that trait. In genetic association studies, variants increasing risk for disease onset as well as risk of disease-related mortality (lethality) could be difficult to detect in genetic association case-control designs, possibly leading to underestimation of a variant's effect on disease risk. We modeled cohorts for three diseases of high lethality (intracerebral hemorrhage, ischemic stroke, and myocardial infarction) using existing longitudinal data. Based on these models, we simulated case-control genetic association studies for genetic risk factors of varying effect sizes, lethality, and minor allele frequencies (MAF). For each disease, erosion of detected effect size was larger for case-control studies of individuals of advanced age (age > 75 years) and/or variants with very high event-associated lethality (Genotype Relative Risk for event-related death > 2.0). We found that survival bias results in no more than 20% effect size erosion for cohorts with mean age < 75 years, even for variants that double lethality risk. Furthermore, we found that increasing effect size erosion was accompanied by depletion of MAF in the case population, yielding a “signature” of the presence of survival bias. Our simulation provides formulas to allow estimation of effect size erosion given a variant's odds-ratio (OR) of disease, OR of lethality, and MAF. These formulas will add precision to power calculation and replication efforts for case-control genetic studies. Our approach requires validation using prospective data.