Characterizing pervasive biases in genome-wide association study using family health history as proxy phenotypes

项目总结 全基因组关联研究(GWAS)已经确定了与几乎所有 复杂的人类疾病。这种成功，特别是近年来加速的发现，在很大程度上要归功于 与基因组数据相匹配的深表型种群生物库的发展。然而，一个至关重要的问题 这些人口生物库的局限性是老年健康的疾病病例数量往往不足 结果，这就是为什么GWAX(GWAX)概念的引入成为 菲尔德。GWAX研究的设计基于一个简单的想法-尽管生物库参与者可能没有自己的想法 对晚年疾病结局的诊断，他们通过家庭健康为父母提供这样的诊断 历史调查；他们还(间接)提供父母的基因数据，作为他们的亲生子女。自从这项研究以来， GWAX已被广泛用于许多疾病的遗传学研究，但尤其经常用于 神经退行性疾病。最近每一位阿尔茨海默病(AD)患者都进行了Meta分析，以 将病例对照关联与GWAX代理关联相结合，以增加样本量和统计能力。 然而，GWAX中的方法学问题及其关联结果的质量并没有得到认真的考虑 调查过了。我们展示了当前GWAX方法中普遍存在的偏见，这导致了很大的分歧 GWAS和GWAX结果。此外，我们还论证了教育是一个重要的社会因素。 许多这些偏见的中心。由于认知是阿尔茨海默病的如此重要的标志，由 教育/认知在阿尔茨海默病遗传学研究中变得尤为重要，并将完全误导 如果处理不当，会导致结果。我们的建议利用了广泛的家庭健康史数据 我们的研究团队在#年开发的allfus研究计划和最新的统计进展 用多代GWA的汇总统计分解社会遗传效应。我们的目标是扩大 这些方法严格和全面地表征了当前GWAX结果中的偏差。我们的中央 假说是基于家庭健康史的GWAX关联作为疾病表型的代理 在很大程度上受到生存偏见和家庭成员的非随机多报和少报的影响 疾病，并将导致错误的结果和结论的分析天真地结合这些 与病例对照研究结果的相关性。该提案的成功完成将提高科学研究水平 了解家庭健康史的遗传基础，对设计和 分析中年生物库队列，并为未来的遗传学研究提供新的分析策略 利用人口生物库中的家庭健康史数据。

PROJECT SUMMARY Genome-wide association studies (GWAS) have identified numerous genetic loci associated with almost all complex human diseases. Much of this success, particularly the accelerated findings in recent years, is credited to the development of deeply phenotyped population biobanks with matched genomic data. However, a crucial limitation of these population biobanks is the often-insufficient number of disease cases for late-life health outcomes, which is why the introduction of the concept of GWAS-by-proxy (GWAX) served as a landmark in the field. The GWAX study design is based on a simple idea – although biobank participants may not have their own diagnosis on late-life disease outcomes, they provide such diagnosis of their parents through the family health history survey; they also (indirectly) provide parental genetic data, as their biological child. Since this study, GWAX has been widely used in genetic studies for many diseases, but particularly frequently for neurodegenerative diseases. Every recent Alzheimer’s disease (AD) GWAS performed meta-analysis to combined case-control associations with GWAX proxy associations to boost sample size and statistical power. However, methodological issues in GWAX and the quality of its association results have not been carefully investigated. We demonstrate pervasive biases in current GWAX approaches, causing substantial divergence of GWAS and GWAX results. In addition, we demonstrate that education is an important social factor at the center of many of these biases. Since cognition is such a crucial marker for AD, biases caused by education/cognition become particularly important in AD genetics research and will give completely misleading results if not handled properly. Our proposal takes advantage of extensive family health history data available in the AllofUs research program and recent statistical advances developed by our investigator team in decomposing social genetic effects with summary statistics of multi-generational GWAS. We aim to expand these methods to rigorously and comprehensively characterize the biases in current GWAX results. Our central hypothesis is that GWAX associations based on family health history as proxy for disease phenotypes are substantially affected by survival bias and non-random over- and under-report of family member’s illness, and will lead to erroneous results and conclusions for analyses that naively combine these associations with case-control GWAS results. Successful completion of this proposal will improve scientific understanding of the genetic underpinnings of family health history, shed important light on the design and analysis of mid-aged biobank cohorts, and provide novel analytical strategies for future genetic studies leveraging family health history data in population biobanks.