Novel statistical genetics methods to unravel polygenic interactions in complex traits

项目概要/摘要 复杂的性状是许多遗传和环境因素相互作用的结果。然而，大多数 复杂性状的研究假设一个加性模型，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鉴定了许多与性状相关的基因座，这些基因座可以 多基因评分（PGS）预测疾病。然而，这些结果并没有普遍确定 新的疾病生物学或疗法。更糟糕的是，PGS偏向于欧洲血统的个人，因此 目前PGS的临床使用将加剧现有的健康差距。 我假设遗传相互作用是我们理解复杂性状生物学中缺失的一环。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 不同.然而，以前的遗传相互作用的研究通常是不成功的。我认为这个结果 从我们现有模型的局限性。在接下来的五年里，我将开发复杂的遗传相互作用模型， 以克服这些局限性。 首先，我将开发模型，以确定基因-基因相互作用的水平上的途径-途径相互作用，建立 关于我最近的上位论的“协调”框架协调在生物学上是合理的， 强大.我将扩展我的协调模型来分解多个性状的多效性效应， 常见疾病的亚型。 第二，我将开发适用于新领域的基因-环境相互作用的严格而强大的模型 复杂的遗传特征。我将在单细胞组学数据中研究细胞类型特异性遗传率，我将结合 特定环境的影响，以提高功率和便携性PGS，我将量化的遗传性治疗 来自biobank数据的回复 我的方法在数学上是严谨的，在计算上是高效的。他们将建立在我的记录， 开发强大的和自由分布的统计遗传学方法。我会把我的方法应用到整个现象组 在不同的队列中进行扫描，特别是为了量化PGS在不同祖先中的可移植性。我也会学习专业 抑郁症的细节，一个典型的例子，一个异质性复杂的疾病与混合的不良 了解遗传和环境原因。我的交互方法将缩小统计数据之间的差距 解释和生物学理解，揭示了使每个人受益的精准医学的新途径。

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.