Making Genomic Prediction of Complex Disease Equitable

项目摘要 全基因组关联研究是以前所未有的规模产生的，目的是将遗传变异与疾病和其他特征的易感性联系起来。但顾名思义，GWAS只生成相关数据--随之而来的是许多挑战。将Gwas数据与先进的人口遗传学工具结合起来，为了解复杂人类特征的遗传学和进化提供了一个巨大的机会；但我们需要新的统计方法来实现可能的好处。我的实验室将直接利用我之前在种群遗传学、统计遗传学和计算方面的工作和专业知识，开发这样的统计方法，并将它们应用于复杂的生物医学特征。首先，我们将解决阻碍采用多基因评分的一个主要问题：它们在不同于GWAS样本的群体中表现较差--无论是在遗传祖先、环境还是社会暴露方面--多基因评分是复杂性状的遗传预测因子。我们将从机制上理解多基因得分预测准确性的决定因素，从而推动复杂性状遗传学研究，造福所有人--特别是历史上服务不足和代表性不足的群体。其次，我们将描述复杂性状中基因与环境的交互作用。有充分的证据表明，这种相互作用是常见的，但来自GWAS的证据并不令人印象深刻。我们提出了一种新的方法来描述基因与环境之间的相互作用，这可能会解决这种明显的差异：一个模型，该模型预计一大组变量的影响大小会发生协调变化，例如，对环境提示的反应。在这个模型适用的地方，它将与表型预测和多基因分数跨组可移植的问题密切相关。最后，我们将利用上述研究的结果来研究近代人类历史上复杂性状的自然选择。我们将集中在两个方向上：(I)开发一种新的方法来研究复杂人类特征的选择--建立在标准GWAS的统计能力和家系研究对各种GWAS混杂因素的免疫力之间的结合；以及(Ii)了解具有遗传效应的变种是如何根据环境进化的。环境特定选择的历史被认为对人类健康具有非常重要的影响。我们将开发新的理论和匹配的统计推断工具，以了解对基因与环境相互作用的选择性限制，以及它们对当代复杂疾病的遗传结构的影响。

Project Summary Genome-Wide Association Studies (GWAS) are generated at unprecedented scale in order to link genetic variation to susceptibility to disease and other traits. But as the name suggests, GWAS only generate correlation data-and numerous challenges follow. Combining GWAS data with advanced population genetics tools provides a tremendous opportunity to learn about the genetics and evolution of complex human traits; but we need new statistical methods to realize the possible benefits. My lab will develop such statistical methods and apply them to complex biomedical traits, drawing directly on my previous work and expertise in population genetics, statistical genetics, and computation. First, we will tackle a major concern hindering the adoption of polygenic scores-genetic predictors of complex traits, derived from GWAS: Their poorer performance in groups that differ-whether in genetic ancestry, environmental or social exposures-from the samples in which the GWAS was performed. We will develop a mechanistic understanding of the determinants of the prediction accuracy of polygenic scores, thereby advancing complex trait genetics research for the benefit of all people-in particular historically underserved and underrepresented groups. Second, we will characterize gene-by-environment interactions in complex traits. There is ample evidence that such interactions are common but evidence from GWAS has been underwhelming. We propose a new approach for characterizing gene-by-environment interactions, that might solve this apparent discrepancy: A model that expects concerted changes in magnitude of effects across a large set of variants, e.g. in response to an environmental cue. Where this model fits, it will be germane to phenotypic prediction and to the problem of polygenic score portability across groups. Finally, we will leverage the results of the research described above to study natural selection on complex traits in recent human history. We will focus on two directions: (i) Developing a new method to the study of selection on complex human traits-built upon a marriage between the statistical power of standard GWAS and the immunity of family studies to various GWAS confounders; and (ii) Understanding how variants with genetic effects contingent on the environment evolve. The history of environment-specific selection is hypothesized to have been highly consequential for human health. We will develop new theory and a matching statistical inference tool to understand selective constraint on gene-by- environment interactions, and their consequences for contemporary genetic architecture of complex diseases.