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

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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.