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