Retrospective Association Analysis of Longitudinal Binary Traits Identifies Important Loci and Pathways in Cocaine Use
Retrospective Association Analysis of Longitudinal Binary Traits Identifies Important Loci and Pathways in Cocaine Use
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DOI：
10.1534/genetics.119.302598
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发表时间：
2019-05
期刊：
影响因子：
3.3
通讯作者：
Weimiao Wu;Zhong Wang;Ke Xu;Xinyu Zhang;Amei Amei-Amei;J. Gelernter;Hongyu Zhao;Amy C. Justice;Zuoheng Wang
中科院分区：
文献类型：
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作者：
Weimiao Wu;Zhong Wang;Ke Xu;Xinyu Zhang;Amei Amei-Amei;J. Gelernter;Hongyu Zhao;Amy C. Justice;Zuoheng Wang
在全基因组关联研究（GWAS）和基于电子健康记录的研究中，纵向表型越来越多地用于识别随时间推移影响复杂性状的遗传变异。对于纵向二进制数据，在基因定位方面仍然存在重大挑战，包括由于确定而导致表型分布模型的错误说明。在此，我们提出L-BRAT（纵向二元性状回顾性关联检验），这是一种回顾性的、基于广义估计方程的纵向二元结果遗传关联分析方法。我们还开发了RGMMAT，一个回顾性的，广义的线性混合模型为基础的关联检验。这两种测试都是回顾性评分方法，其中基因型被视为随机条件的表型和协变量。它们允许在分析中包含静态和时变协变量。通过模拟，我们证明了回顾性关联测试对于确定和其他类型的表型模型错误规范是稳健的，并且比以前的关联方法更强大。我们将L-BRAT和RGMMAT应用于纵向队列中可卡因使用重复测量的全基因组关联分析。通路分析暗示与阿片信号和轴突引导信号通路有关。最后，我们在一个独立的可卡因依赖病例对照GWAS中复制了重要的通路。我们的研究结果表明，L-BRAT能够在基因组扫描中检测到重要的位点和途径，并为可卡因使用的遗传结构提供见解。
Longitudinal phenotypes have been increasingly available in genome-wide association studies (GWAS) and electronic health record-based studies for identification of genetic variants that influence complex traits over time. For longitudinal binary data, there remain significant challenges in gene mapping, including misspecification of the model for phenotype distribution due to ascertainment. Here, we propose L-BRAT (Longitudinal Binary-trait Retrospective Association Test), a retrospective, generalized estimating equation-based method for genetic association analysis of longitudinal binary outcomes. We also develop RGMMAT, a retrospective, generalized linear mixed model-based association test. Both tests are retrospective score approaches in which genotypes are treated as random conditional on phenotype and covariates. They allow both static and time-varying covariates to be included in the analysis. Through simulations, we illustrated that retrospective association tests are robust to ascertainment and other types of phenotype model misspecification, and gain power over previous association methods. We applied L-BRAT and RGMMAT to a genome-wide association analysis of repeated measures of cocaine use in a longitudinal cohort. Pathway analysis implicated association with opioid signaling and axonal guidance signaling pathways. Lastly, we replicated important pathways in an independent cocaine dependence case-control GWAS. Our results illustrate that L-BRAT is able to detect important loci and pathways in a genome scan and to provide insights into genetic architecture of cocaine use.