Genetically harmonized dietary intake and causal relationships with diabetes-related outcomes

遗传协调的饮食摄入量及其与糖尿病相关结果的因果关系

基本信息

批准号：
10730298
负责人：
Joanne Burnette Cole
金额：
$ 24.9万
依托单位：
UNIVERSITY OF COLORADO DENVER
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-01-10 至 2025-11-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10730298
关键词：
Address Adherence Alcohols Biological Markers Body mass index Cardiometabolic Disease Career Mobility Clinical Clinical Markers Collaborations Complex Consumption Data Data Set Diabetes Mellitus Diet Dietary Intervention Dietary Practices Dietary intake Disease Education Enzymes Ethnic Origin Etiology Faculty Food Food Patterns Food Preferences Foundations Fruit Genes Genetic Genetic Determinism Genetic Markers Genetic Variation Genetic study Genomics Goals Guidelines Health Heritability Heterogeneity Human Human Genetics Individual Differences Intake Intervention Studies Intervention Trial Life Style Link Maps Mendelian randomization Mentors Meta-Analysis Metabolic Metabolic Diseases Metabolism Methodology Methods Milk Nomenclature Obesity Obesity Epidemic Observational Study Outcome Pathway Analysis Phase Phenotype Population Positioning Attribute Recommendation Research Research Personnel Risk Factors Role Series Tea Tissues Training Translations Work biobank burden of illness career cohort design dietary epidemiology study experience follow-up food consumption food environment genetic analysis genetic association genetic epidemiology genetic variant genome wide association study genome-wide genomic locus improved mortality multi-ethnic novel nutrition nutritional epidemiology nutritional genomics olfactory receptor phenome preference prevent programs skills success trait

项目摘要

ABSTRACT Unhealthful diet is a leading risk factor for diabetes and mortality worldwide. As the diabetes and obesity epidemics continue to rise, so does the contribution of dietary risk factors to global disease burden. There is an urgent need to identify which aspects of diet causally influence metabolic disease to guide more effective dietary recommendations. Teasing apart correlation from causation remains a challenge, and while numerous epidemiological studies have observationally linked diet to diabetes, there has been limited success with translation to intervention studies. Normal human genetic variation has both direct and indirect effects on dietary intake, with recent work establishing significant heritability and hundreds of genetic associations with numerous different foods and dietary patterns. However, combining dietary traits across studies for genetic analysis remains a challenge due to study differences in design, cultures, and preferences. We hypothesize shared genetic influences on dietary intake can act as the common reference to identify comparable diets across studies. In each of several cohorts, with both genetic and diet data, we will initiate new collaborations, derive quantitative food traits and dietary patterns, and conduct genome-wide association studies (GWAS) to create homologous GWAS datasets with study-specific dietary phenotypes and a common set of genetic markers. A series of genetic correlation analyses will be conducted to identify comparable foods and dietary patterns across diverse studies. Once identified, GWAS meta-analysis of comparable dietary phenotypes will improve power to detect novel and multi-ethnic genetic associations. To elucidate the direct and indirect genetic mechanisms of dietary intake at the locus and genome-wide levels we will conduct fine-mapping and gene prioritization, enrichment and pathway analysis, and genetic correlation and phenome-wide association studies (PheWAS). To address limitations with observational studies, Mendelian randomization (MR) causal inference will be performed using genetically predicted dietary intake and publicly available GWAS on diabetes-related outcomes to prioritize causal associations for intervention trials. Clustering of genetic loci by phenotypic correlations and causal effects will pinpoint genetic mechanisms of diet that causally influence metabolic disease. We will extend MR to all UK Biobank outcomes to map comprehensive causal bidirectional relationships with diet. Overall we will identify novel and multi-ethnic genetic associations with comparable dietary phenotypes across diverse studies to elucidate the mechanisms of dietary intake and uncover causal relationships between diet, diabetes, and overall human health. To achieve my goal of becoming an independent investigator in nutrigenomic and metabolic disease research, I have designed a detailed K99 plan with didactic coursework and co-mentoring by Drs. Florez, Hirschhorn, and Willett in metabolism, statistical genetics, and nutritional epidemiology. During the R00 phase, while conducting independent research and continuing to develop research skills, I will maintain and cultivate collaborations in nutrition and genetics and grow my research program.

抽象的不健康的饮食是全世界糖尿病和死亡的主要危险因素。由于糖尿病和肥胖流行病继续增加，饮食风险因素对全球疾病负担的贡献也在增加。有一个迫切需要确定饮食的哪些方面会影响代谢疾病，以指导更有效的饮食建议。区分相关性和因果关系仍然是一个挑战，尽管有很多流行病学研究已观察性地将饮食与糖尿病联系起来，但在这方面取得的成功有限转化为干预研究。正常人类遗传变异对饮食有直接和间接的影响摄入量，最近的工作建立了显着的遗传力以及与许多人的数百个遗传关联不同的食物和饮食模式。然而，结合不同研究的饮食特征进行遗传分析由于研究设计、文化和偏好的差异，这仍然是一个挑战。我们假设共享遗传对饮食摄入的影响可以作为跨研究确定可比饮食的共同参考。在每个队列中，利用遗传和饮食数据，我们将启动新的合作，得出定量的结果食物性状和饮食模式，并进行全基因组关联研究（GWAS）以创建同源具有研究特定饮食表型和一组通用遗传标记的 GWAS 数据集。一系列遗传将进行相关分析，以确定不同研究中可比较的食物和饮食模式。一旦确定，对可比较的饮食表型进行 GWAS 荟萃分析将提高检测新的和多种族遗传关联。阐明饮食摄入的直接和间接遗传机制我们将在基因座和全基因组水平上进行精细绘图和基因优先级排序、富集和通路分析、遗传相关性和全表型关联研究（PheWAS）。为了解决限制观察性研究，孟德尔随机化 (MR) 因果推断将使用遗传学方法进行预测膳食摄入量和公开可用的糖尿病相关结果的 GWAS，以优先考虑因果关系干预试验协会。通过表型相关性和因果效应对遗传位点进行聚类将查明饮食导致代谢疾病的遗传机制。我们将把 MR 扩展到全英国生物样本库的结果可绘制与饮食的全面因果双向关系。总体而言，我们将确定新的、多种族的遗传关联与可比较的饮食表型各种研究阐明饮食摄入的机制并揭示饮食之间的因果关系，糖尿病和人类整体健康。为了实现我成为一名独立调查员的目标营养基因组学和代谢疾病研究，我设计了一份详细的 K99 计划，其中包含教学课程和博士共同指导。弗洛雷斯、赫希霍恩和威利特在新陈代谢、统计遗传学和营养方面的研究流行病学。 R00阶段，在进行自主研究并持续开展研究的同时技能，我将维持和培养营养和遗传学领域的合作，并发展我的研究项目。