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

基本信息

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
项目类别：
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

项目摘要

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.

抽象的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确定特定人群疾病决定因素的绝佳机会。正确处理数据类型无法使用不同的分子和环境分析技术来分析需要传统的统计程序和新的计算方法。按照总统的指示精准医学倡议，该提案的目标是开发计算方法并集成大规模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，即妊娠 37 周之前分娩婴儿，是一项重大健康问题忧虑。早产婴儿（约占美国新生儿的 12%）的风险较高新生儿死亡率和一系列健康问题。不同种族的早产率存在差异，非洲裔美国人的频率显着升高，西班牙裔美国人的频率适度升高与欧洲人相比。仅环境和社会经济因素可能无法解释这些差异尽管有证据表明早产有遗传基础，但迄今为止还没有发现因果遗传变异确定。在本提案中，我的目标是利用丰富的遗传和环境变异数据并开发计算方法可以增进我们对早产生物学的理解，因为它与所有疾病都有关系人口。就此而言，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不同种族的全基因组关联（GWA）研究验证早产的新遗传因素人口。我获得了一套全面的公开可用的 PTB 病例和对照数据集，其中包括不同种族的母亲和婴儿，包括来自 dbGAP 和 will 的 3,500 例病例和近 16,000 例对照开展基于血统的病例对照 GWA 研究，以确定影响 PTB 的遗传因素。在目标2中，我会开发分析方法来确定影响早产的环境和社会经济因素在不同种族人群中。我建议整合覆盖超过 300 万个的加州州立数据库不同人群的出生情况以及地理位置数据、污染水平和紫外线暴露数据环境保护局，以确定这些暴露是否在导致特定人群的肺结核风险。在目标3中，我将进行综合数据分析并构建计算模型，以识别遗传和群体之间特定的相互作用影响 PTB 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了解有关 PTB 病因学的更多信息，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与环境相互作用的合乎逻辑的下一步，它可用于为特定人群提供精确的诊断和治疗策略。