Elucidating the Role of the Genetic and Environmental Determinants of Preterm Birth Using Integrative Computational Approaches

摘要 鉴于基因组和环境暴露数据的丰富性和可用性，计算方法提供了 这是一个确定特定人群疾病决定因素的有力机会。正确处理数据类型 从一组不同的分子和环境分析技术中产生的， 需要传统的统计程序和新的计算方法。根据总统的 精准医学倡议，该提案的目标是开发计算方法，并整合大型 规模遗传和环境暴露数据集，以阐明影响早产（PTB）的因素， 不同的人群。早产，或在怀孕37周之前分娩婴儿，是一种主要的健康问题， 关心早产儿占美国新生儿的12%， 新生儿死亡率和一系列健康问题。早产率在不同的种族群体中各不相同， 非裔美国人的频率显著升高，西班牙裔美国人的频率中度升高， 与欧洲人相比。环境和社会经济因素本身可能无法解释这些差异 尽管有证据表明早产有遗传基础，但迄今为止， 鉴定在这个建议中，我的目标是利用丰富的遗传和环境变异数据， 计算方法，以促进我们对早产生物学的理解，因为它涉及到所有 人口数量。为此，我提出三个目标。在目标1中，我将开发计算方法来识别和 通过全基因组关联（GWA）研究在不同种族中验证早产的新遗传因素 人口数量。我获得了一套全面的公开可用的PTB病例和对照数据集，包括： 不同种族的母亲和婴儿，包括来自dbGAP的3,500例病例和近16,000例对照， 开展基于祖先的病例对照GWA研究，以确定影响PTB的遗传因素。在目标2中，我将 制定分析方法，以确定影响早产的环境和社会经济因素 在不同的种族人群中。我建议将覆盖300多万个数据库的链接加州州数据库整合在一起 不同人口的出生情况，以及地理位置数据、污染水平和紫外线照射数据 为了确定这些暴露是否在以下方面发挥作用， 导致人群特异性PTB风险。在目标3中，我将进行综合数据分析， 计算模型，以确定遗传和 影响肺结核风险的环境因素。我假设基因与环境的相互作用 环境暴露后早产风险的人口差异。这项工作将使我们 了解更多关于PTB的病因，但也可以扩展到其他感兴趣的表型。这个项目 是研究疾病背景下遗传和环境相互作用的合理下一步， 其可用于告知精确的人群特异性诊断和治疗策略。

ABSTRACT Given the wealth and availability of genomic and environmental exposure data, computational methods provide a powerful opportunity to identify population-specific determinants of disease. Proper treatment of data types emerging from a diverse set of molecular and environmental profiling technologies cannot be analyzed using traditional statistical routines and new computational approaches are needed. In line with the President's Precision Medicine Initiative, the goal of this proposal is to develop computational methods and integrate large- scale genetic and environmental exposure datasets to elucidate factors that affect preterm birth (PTB) in diverse populations. Preterm birth, or the delivery of an infant prior to 37 weeks of gestation, is a major health concern. Infants born prematurely, comprising of about 12% of the US newborns, have elevated risks of neonatal mortality and a wide array of health problems. Preterm birth rates vary among different ethnic groups, with frequencies significantly elevated in African Americans and moderately elevated in Hispanics in comparison to Europeans. Environmental and socioeconomic factors alone may not explain these disparities and despite the evidence for a genetic basis to preterm birth, to date no causal genetic variants have been identified. In this proposal I aim to leverage the rich genetic and environmental variation data and develop computational approaches to advance our understanding of biology of preterm birth as it relates to all populations. To that extent, I propose three aims. In aim 1, I will develop computational methods to identify and validate novel genetic factors for preterm birth by genome-wide association (GWA) study in diverse ethnic populations. I obtained a comprehensive set of publicly available PTB case and control datasets consisting of ethnically diverse mothers and babies including 3,500 cases and nearly 16,000 controls from dbGAP and will carry out an ancestry-based case-control GWA study to identify genetic factors influencing PTB. In aim 2, I will develop analytical methodology to identify environmental and socioeconomic factors that impact preterm birth in diverse ethnic populations. I propose to integrate linked California State databases covering over 3 million births across diverse populations with geographical location data and pollution levels and UV exposure data from the Environmental Protection Agency in order to identify whether these exposures play a role in contributing to population-specific PTB risk. In aim 3, I will carry out integrative data analysis and build computational models in order to identify population specific interactions between the genetic and environmental factors affecting PTB risk. I hypothesize that gene-environment interactions contribute to population differences in preterm birth risk following environmental exposures. The proposed work will allow us to learn more about the etiology PTB, but could also be extended to other phenotypes of interest. This project is the logical next step for the study of the interaction of genetics and environment in the context of disease, which can be used to inform precise population-specific diagnostic and therapeutic strategies.