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

• 批准号: 10506410
• 负责人: Kenneth E Westerman
• 金额: $ 15.53万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 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/10506410
• 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; 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; Research Training; 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; base; 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

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.

总结 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 在不同的个体之间。基因-饮食相互作用（GxDs）可以帮助解决饮食反应中的个体间差异 通过识别改变饮食行为和代谢危险因素之间关联的遗传变异， (e.g.,血糖特征和腰围）。识别许多这样的相互作用将使基因组引导的 精确的营养建议（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的不精确性和偏差以及小的 变异体特异性效应在很大程度上阻碍了对强GxD相互作用的鉴定。这两个关键 这些障碍可以通过使用营养流行病学和基因组学的技术来改善GxD 识别.具体地说，饮食测量可以通过以下方式得到改善：（1）纵向饮食 测量更稳定的摄入量估计和（2）客观代谢组学代理饮食补充 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体之间的联合收割机相互作用效应。 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是一种非常适合的接触， 探索这些方法：它通常被消费，影响代谢疾病的风险，有证据表明， GxD相互作用，并具有经验证的代谢组学特征。要探讨的假设是， 膳食测量方法和iPRS将允许复制已知的和发现新的GxD。的 首席研究员肯尼斯·韦斯特曼博士是唯一能够完成这项工作的早期- 具有营养学背景的职业科学家，GxD分析专业知识，以及TOPMed膳食经验 数据他需要额外的培训，以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，研究代谢危险因素的标准基因-饮食相互作用模型的重现性 测量和代谢组学饮食代理。目标2：改进交互测试的标准PRS 通过开发一种多基因评分方法，该方法聚集了遗传互作效应。 新的遗传因素改变地中海式饮食和代谢危险因素之间的关联。 拟议的研究将验证GxD研究的改进饮食建模方法，并创建遗传模型。 预测饮食反应的分数。相关的培训将帮助韦斯特曼博士实现独立， 精确营养领域，并在未来的赠款中进一步探索这些想法。