Improved detection of gene-diet interactions via longitudinal data, metabolomic proxies, and polygenic scores

通过纵向数据、代谢组代理和多基因评分改进对基因-饮食相互作用的检测

基本信息

批准号：
10653260
负责人：
Kenneth E Westerman
金额：
$ 15.28万
依托单位：
MASSACHUSETTS GENERAL HOSPITAL
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-07-01 至 2027-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10653260
关键词：
Address Affect Alleles Area Awareness Behavior Complement Consumption Data Data Analyses Data Reporting Detection Development Diabetes Mellitus Diet Dietary Assessment Dietary Factors Dietary Fats Dietary Practices Doctor of Philosophy Fellowship Foundations Future Genes Genetic Genetic Carriers Genome Grant Individual Intake Leadership Life Style Link Literature Measurement Mentors Mentorship Metabolic Metabolic Diseases Methods Modeling Modification National Heart, Lung, and Blood Institute National Institute of Diabetes and Digestive and Kidney Diseases Non-Insulin-Dependent Diabetes Mellitus Nutritional Study Outcome Output Patient Self-Report Pattern Performance Persons Population Heterogeneity Positioning Attribute Principal Investigator Proxy Publications Recommendation Reporting Reproducibility Research Research Personnel Risk Risk Factors Scientist Scoring Method Serum Statistical Methods Strategic Planning TCF7L2 gene Techniques Testing Training Training Programs Trans-Omics for Precision Medicine United States National Institutes of Health Variant Work analytical method biobank career catalyst cohort diabetes risk dietary disorder risk experience fasting glucose genetic makeup genetic variant genome sequencing genome-wide improved inter-individual variation metabolomics novel nutrition nutritional epidemiology nutritional genomics polygenic risk score precision nutrition prediction algorithm prevent rare variant response simulation skills statistics success tool trait waist circumference whole genome

项目摘要

SUMMARY Diet is a critical factor in the development of metabolic diseases, but dietary factors affect disease risk differently across individuals. Gene-diet interactions (GxDs) can help resolve this inter-individual variability in diet response by identifying genetic variants that modify the association between dietary behaviors and metabolic risk factors (e.g., glycemic traits and waist circumference). Identifying many such interactions would enable genome-guided precision nutrition recommendations (e.g., Mediterranean-style diet (MedDiet) vs. lower-fat dietary pattern to decrease diabetes risk). However, imprecision and bias in self-reported dietary behaviors as well as small variant-specific effects have largely impeded the identification of robust GxD interactions. These two key obstacles can be addressed by using techniques from nutritional epidemiology and genomics to improve GxD identification. Specifically, diet measurement can be improved by incorporating (1) longitudinal diet measurements for more stable intake estimates and (2) objective metabolomic proxies for diet to complement self-reported data. The problem of small effect sizes can be improved by adapting the polygenic risk score (PRS) approach to create polygenic interaction scores (iPRS) that combine interaction effects across many variants. These strategies can be explored in the Trans-Omics for Precision Medicine (TOPMed) cohorts, which have self-reported diet at multiple timepoints and serum metabolomics. The MedDiet is a well-suited exposure with which to explore these approaches: it is commonly consumed, impacts metabolic disease risk, has evidence of GxD interactions, and has a validated metabolomic signature. The hypothesis to be explored is that improved dietary measurement approaches and iPRS will allow replication of known and discovery of novel GxDs. The principal investigator, Dr. Kenneth Westerman, Ph.D., is uniquely positioned to complete this work as an early- career scientist with a nutrition background, expertise in GxD analysis, and prior experience with TOPMed dietary data. He requires additional training to address current skill gaps in longitudinal data analysis, metabolomics, PRS, and leadership, facilitating the completion of the following research aims. Aim 1: To improve the reproducibility of standard gene-diet interaction models for metabolic risk factors by combining longitudinal measurements and metabolomic dietary proxies. Aim 2: To improve upon standard PRS for interaction testing by developing a polygenic score method that aggregates over genetic interaction effects Aim 3: To discover novel genetic factors modifying the association between a Mediterranean-style diet and metabolic risk factors. The proposed research will validate improved dietary modeling methods for GxD studies and create genetic scores that predict diet response. The associated training will help Dr. Westerman achieve independence in the field of precision nutrition and explore these ideas further in dietary trials in future grants.

概括饮食是代谢性疾病发生的关键因素，但饮食因素对疾病风险的影响不同跨个人。基因-饮食相互作用（GxD）可以帮助解决饮食反应的个体间差异通过识别改变饮食行为和代谢危险因素之间关联的基因变异（例如，血糖特征和腰围）。识别许多此类相互作用将使基因组引导成为可能精准营养建议（例如地中海式饮食 (MedDiet) 与低脂饮食模式）降低糖尿病风险）。然而，自我报告的饮食行为存在不精确性和偏差，而且存在较小的偏差。变体特异性效应在很大程度上阻碍了稳健的 GxD 相互作用的识别。这两个关键可以通过使用营养流行病学和基因组学技术来解决障碍，以改善 GxD 鉴别。具体来说，饮食测量可以通过纳入（1）纵向饮食来改进用于更稳定的摄入量估计的测量和（2）饮食补充的客观代谢组代理自我报告的数据。效应量小的问题可以通过调整多基因风险评分（PRS）来改善创建多基因相互作用评分（iPRS）的方法，该评分结合了许多变异之间的相互作用效应。这些策略可以在精准医学跨组学 (TOPMed) 队列中进行探索，该队列已多个时间点的自我报告饮食和血清代谢组学。 MedDiet 是一种非常适合的暴露方式探索这些方法：它被普遍食用，影响代谢疾病风险，有证据表明 GxD 相互作用，并具有经过验证的代谢组学特征。要探讨的假设是改进饮食测量方法和 iPRS 将允许复制已知的 GxD 并发现新的 GxD。这首席研究员 Kenneth Westerman 博士作为早期研究人员，具有独特的优势来完成这项工作。具有营养背景、GxD 分析专业知识以及 TOPMed 饮食经验的职业科学家数据。他需要额外的培训来解决目前在纵向数据分析、代谢组学、 PRS 和领导力，促进完成以下研究目标。目标 1：改善通过结合纵向研究，代谢危险因素的标准基因-饮食相互作用模型的可重复性测量和代谢组膳食代理。目标 2：改进交互测试的标准 PRS 通过开发一种聚合遗传相互作用效应的多基因评分方法目标 3：发现新的遗传因素改变了地中海式饮食与代谢危险因素之间的关联。拟议的研究将验证 GxD 研究的改进饮食模型方法，并创建遗传模型预测饮食反应的分数。相关培训将帮助 Westerman 博士实现独立工作精准营养领域，并在未来的资助中在饮食试验中进一步探索这些想法。