Novel statistical genetics methods to unravel polygenic interactions in complex traits

项目摘要/摘要 复杂的性状是许多遗传和环境因素相互作用的结果。尽管如此，大多数人 复杂性状研究采用加性模型，其中遗传效应与环境无关 还有彼此。这个简单的模型已经成功地识别了许多与性状相关的基因座，这些基因座可以是 结合成多基因评分(PGS)来预测疾病。然而，这些结果并没有普遍地确定 新的疾病生物学或治疗方法。更糟糕的是，PGS偏向欧洲血统--个人，因此 目前PGS的临床应用将加剧现有的健康差距。 我假设，在我们理解复杂的性状生物学时，遗传交互作用是缺失的一环。遗传 相互作用是生物学许多领域的核心，复杂的人类特征不太可能从根本上 不一样。然而，以前对遗传相互作用的研究通常都不成功。我认为这一结果 不受我们现有模型的限制。在接下来的五年里，我将开发复杂的遗传交互作用模型 特征来解决这些限制。 首先，我将开发模型，在建立的途径-途径相互作用的水平上识别基因-基因相互作用 关于我最近提出的上位主义的“协调”框架。协调在生物学上是可信的，在统计学上也是如此。 很强大。我将扩展我的协调模型，分解多个性状的多效性效应，并解开 常见病的亚型。 其次，我将开发适用于新领域的严格而强大的基因-环境相互作用模型 复杂的性状遗传学。我将在单细胞组学数据中研究特定细胞类型的遗传性，我将合并 改善PGS的能力和便携性的特定环境效应，我将量化治疗的遗传性 来自生物库数据的响应。 我的方法将在数学上是严格的，在计算上是有效的。他们将以我过去的记录为基础 发展稳健和自由分布的统计遗传学方法。我将把我的方法应用于全社会 在不同的队列中进行扫描，特别是为了量化PGS在不同祖先之间的便携性。我也会学习专业 抑郁障碍的细节，一个典型的不同种类的复杂障碍与糟糕的混合 了解遗传和环境原因。我的交互方法将缩小统计学上的差距 讲解和生物学理解，揭示了造福所有人的精准医学的新路径。

PROJECT SUMMARY/ABSTRACT Complex traits result from interactions between many genetic and environmental factors. Nonetheless, most complex trait studies assume an additive model, in which genetic effects are independent of the environment and each other. This simple model has successfully identified many trait-associated loci, and these loci can be combined into Polygenic Scores (PGS) to predict disease. However, these results have not generally identified novel disease biology or therapies. Worse yet, PGS are biased toward European ancestry-individuals, hence clinical use of current PGS will exacerbate existing health disparities. I hypothesize that genetic interactions are the missing link in our understanding of complex trait biology. Genetic interactions are central to many fields of biology, and it is not likely that complex human traits are fundamentally different. However, prior studies of genetic interactions have generally been unsuccessful. I argue this results from limitations in our current models. In the next five years, I will develop genetic interaction models for complex traits to address these limitations. First, I will develop models to identify gene-gene interaction at the level of pathway-pathway interaction that build on my recent “Coordinated” framework for epistasis. Coordination is biologically plausible and statistically powerful. I will extend my Coordinated models to decompose pleiotropic effects on multiple traits and to unravel subtypes of common diseases. Second, I will develop rigorous and powerful models of gene-environment interaction that apply to novel areas of complex trait genetics. I will study cell type-specific heritability in single cell ‘omics data, I will incorporate context-specific effects to improve power and portability in PGS, and I will quantify the heritability of treatment response from biobank data. My methods will be mathematically rigorous and computationally efficient. They will build on my track record of developing robust and freely-distributed statistical genetics methods. I will apply my methods to phenome-wide scans in diverse cohorts, especially to quantify the portability of PGS across ancestries. I will also study Major Depressive Disorder in detail, a classic example of a heterogeneous complex disorder with a mix of poorly understood genetic and environmental causes. My interaction methods will close the gap between statistical explanation and biological understanding, revealing new paths to precision medicine that benefit everyone.