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

使用综合计算方法阐明早产的遗传和环境决定因素的作用

• 批准号: 9324358
• 负责人: Marina Sirota
• 金额: $ 17.69万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2019-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9324358
• 关键词: 37 weeks gestation; Advisory Committees; Affect; African American; Air Pollution; Animals; Behavior; Bioinformatics; Biological; Biology; Birth; Birth Rate; California; Candidate Disease Gene; Child; Computer Simulation; Computing Methodologies; Data; Data Analyses; Data Set; Databases; Diagnostic; Disease; Drug usage; Ecology; Environment; Environmental Exposure; Environmental Risk Factor; Ethnic group; Etiology; European; Family; Food; Frequencies; Genetic; Genetic Determinism; Genetic study; Genome; Genomics; Genotype; Geographic Locations; Goals; Health; Heritability; High Prevalence; Hispanics; Immunity; Individual; Infant; Inflammation; Investigation; Knowledge; Latino; Learning; Life Style; Link; Machine Learning; Mass Spectrum Analysis; Mentors; Methodology; Methods; Molecular; Mothers; Neonatal Mortality; Neonatology; Newborn Infant; Nutrient; Particulate; Pathway interactions; Phenotype; Play; Pollution; Population; Population Heterogeneity; Precision Medicine Initiative; Pregnancy; Premature Birth; Premature Infant; Prevalence; Research Personnel; Risk; Role; Signal Transduction; Smoking; Societies; Socioeconomic Factors; Stress; Subgroup; Technology; Therapeutic; Training Activity; Twin Studies; UV Radiation Exposure; United States Environmental Protection Agency; Variant; Work; admixture mapping; base; case control; cohort; database of Genotypes and Phenotypes; economic cost; ethnic diversity; gene environment interaction; genetic variant; genome wide association study; improved; interest; non-genetic; novel; precision medicine; racial and ethnic; statistics

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.

摘要 鉴于基因组和环境暴露数据的财富和可用性，计算方法提供了 这是确定特定人群疾病决定因素的一个强有力的机会。正确处理数据类型 从一系列不同的分子和环境分析技术中脱颖而出不能用 需要传统的统计程序和新的计算方法。与总统的 精准医学倡议，该提案的目标是开发计算方法并整合大型 衡量遗传和环境暴露数据集，以阐明影响早产的因素 不同的人群。早产，或在怀孕37周前分娩的婴儿，是一种主要的健康状况。 担忧。早产婴儿，约占美国新生儿的12%，患心脏病的风险增加 新生儿死亡率和一系列健康问题。不同种族的早产率有所不同， 年，非洲裔美国人的频率显著上升，西班牙裔美国人的频率中等上升 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，但到目前为止，还没有因果遗传变异 确认身份。在这项提议中，我的目标是利用丰富的遗传和环境变异数据，并开发 促进我们对早产生物学的理解的计算方法，因为它与所有人有关 人口。在这个意义上，我提出了三个目标。在目标1中，我将开发计算方法来识别和 通过基因组全关联(GWA)研究在不同种族中验证早产的新遗传因素 人口。我获得了一套全面的可公开获得的结核病病例和对照数据集，包括 不同种族的母亲和婴儿，包括3,500例和近16,000名来自DBGaP和Will的对照 开展一项以家族为基础的病例对照GWA研究，以确定影响肺结核的遗传因素。在《目标2》中，我会 制定分析方法以确定影响早产的环境和社会经济因素 在不同种族的人群中。我建议整合覆盖300多万个链接的加利福尼亚州数据库 通过地理位置数据、污染水平和紫外线暴露数据在不同人群中出生 以确定这些暴露是否在 增加了人群特有的肺结核风险。在目标3中，我将进行综合数据分析和构建 计算模型，以确定遗传和基因之间特定于群体的交互作用 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有助于 环境暴露后早产风险的人群差异。拟议的工作将使我们能够 以了解更多关于肺结核的病因，但也可以推广到其他感兴趣的表型。这个项目 是在疾病背景下研究遗传和环境相互作用的合乎逻辑的下一步， 这可用于提供针对特定人群的精确诊断和治疗策略。