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: 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
• 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

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展的关键因素，但饮食因素对疾病的风险有所不同 跨个人。基因 - 迪埃特相互作用（GXD）可以帮助解决饮食反应中的这个个体间变异性 通过识别遗传变异，以改变饮食行为与代谢风险因素之间的关联 （例如，血糖特征和腰围）。确定许多这样的相互作用将使基因组引导 精确营养建议（例如，地中海风格的饮食（Meddiet）与低脂饮食模式 降低糖尿病风险）。但是，自我报告的饮食行为和小的不重点和偏见 特异性效应在很大程度上阻碍了鲁棒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来改善GXD的技术来解决障碍 鉴别。具体而言，可以通过（1）纵向饮食来改善饮食测量 测量更稳定的摄入量估计值和（2）饮食的客观代谢组合 自我报告的数据。通过调整多基因风险评分（PRS），可以改善小效应大小的问题 创建多基因相互作用得分（IPR）的方法，该分数结合了许多变体的相互作用效应。 这些策略可以在具有精密医学的跨词（TopMed）同类中探索，这些策略具有 在多个时间点和血清代谢组学上进行自我报告的饮食。 Meddiet是一个合理的曝光 要探讨这些方法：通常被消耗，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签名。要探讨的假设是改进的 饮食测量方法和知识产权将允许复制已知的新型GXD和发现。这 首席调查员肯尼斯·韦斯特曼（Kenneth Westerman）博士的唯一位置可以完成这项工作，作为早期 具有营养背景的职业科学家，GXD分析方面的专业知识以及先前的饮食经验 数据。他需要额外的培训来解决纵向数据分析，代谢组学的当前技能差距， PR和领导力，促进以下研究的完成。目标1：改善 通过结合纵向，用于代谢危险因素的标准基因 - 迪埃特相互作用模型的可重复性 测量和代谢组饮食代理。 AIM 2：改进用于交互测试的标准PR 通过开发一种多基因分数方法，该方法将汇总在遗传相互作用效应上的目标3：发现 新的遗传因素改变了地中海风格饮食与代谢危险因素之间的关联。 拟议的研究将验证改进的GXD研究的饮食建模方法，并创建遗传 预测饮食反应的分数。相关的培训将帮助Westerman博士在 精确营养领域，并在未来补助金的饮食试验中进一步探索这些想法。