Pathogen exposure misclassification can bias association signals in GWAS of infectious diseases when using population-based common control subjects
Pathogen exposure misclassification can bias association signals in GWAS of infectious diseases when using population-based common control subjects
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DOI：
10.1016/j.ajhg.2022.12.013
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发表时间：
2023-02-02
影响因子：
9.8
通讯作者：
Duggal, Priya
中科院分区：
文献类型：
--
作者：
Duchen, Dylan;Vergara, Candelaria;Duggal, Priya
已经进行了全基因组关联研究（GWAS）以确定一系列表型的宿主遗传因素，包括感染性疾病。使用来自生物库和广泛联盟的基于人群的共同对照受试者是一种宝贵的资源，可以以最小的额外费用增加相关基因座鉴定的样本量。当对照受试者特征不明确时，已报告了结局的非差异性错误分类，这通常会削弱真实的效应量。然而，对于感染性疾病，将受影响受试者与基于人群的普通对照受试者进行比较（不考虑病原体暴露）也可能导致选择偏倚。通过模拟比较病原体暴露病例和基于人群的普通对照受试者，我们证明，不考虑病原体暴露可能导致偏倚的效应估计和虚假的全基因组显著信号。此外，观察到的关联可能会被扭曲，这取决于基因座和病原体暴露之间的关联强度以及病原体暴露的流行程度。我们还使用了来自丙型肝炎病毒（HCV）遗传联盟的真实的数据实例，将HCV自发清除与持续感染进行比较，并与来自英国生物库的具有良好特征的对照受试者和基于人群的普通对照受试者进行比较。我们发现已知的HCV清除相关位点和潜在的伪HCV清除相关性的效应估计有偏差。这些发现表明，对照受试者的选择对于感染性疾病或以环境暴露为条件的结果尤为重要。
Genome-wide association studies (GWASs) have been performed to identify host genetic factors for a range of phenotypes, including for infectious diseases. The use of population-based common control subjects from biobanks and extensive consortia is a valuable resource to increase sample sizes in the identification of associated loci with minimal additional expense. Non-differential misclassification of the outcome has been reported when the control subjects are not well characterized, which often attenuates the true effect size. However, for infectious diseases the comparison of affected subjects to population-based common control subjects regardless of pathogen exposure can also result in selection bias. Through simulated comparisons of pathogen-exposed cases and population-based common control subjects, we demonstrate that not accounting for pathogen exposure can result in biased effect estimates and spurious genome-wide significant signals. Further, the observed association can be distorted depending upon strength of the association between a locus and pathogen exposure and the prevalence of pathogen exposure. We also used a real data example from the hepatitis C virus (HCV) genetic consortium comparing HCV spontaneous clearance to persistent infection with both well-characterized control subjects and population-based common control subjects from the UK Biobank. We find biased effect estimates for known HCV clearance-associated loci and potentially spurious HCV clearance associations. These findings suggest that the choice of control subjects is especially impor-tant for infectious diseases or outcomes that are conditional upon environmental exposures.