Topical Area: Nutrient-Gene Interactions
Objectives : Many gene-diet interactions have been uncovered for obesity and other cardiometabolic risk factors, but truly personalized nutritional recommendations will require the incorporation of an individual’s full genome in predicting response to diet. Statistical genetics studies typically require thousands of individuals, limiting the ability of dietary intervention trials to answer these genome-wide nutrigenetic questions. We sought to explore a novel approach for identifying the genetic architecture of the diet-body mass index (BMI) relationship using an epidemiological dataset.
Methods : As a mathematical correlation is defined as the expected product of two standardized variables, it may be possible to estimate the genetic signal describing an underlying diet-BMI correlation by predicting their product. Statistical simulations were performed to assess the ability of this method to pick up pre-specified effects of genotype on diet response. In white women from the longitudinal Women’s Health Initiative (WHI) dataset, the product of log-transformed fat-to-carbohydrate ratio (F:C) and body mass index (BMI) (both variables standardized) was calculated both cross-sectionally at baseline (n=9357) and with respect to longitudinal changes in these variables before follow-up (n=1333). Plink and GCTA tools were used to estimate the genotype-based heritability of these products, as well as that of the change in BMI in response to a separate intervention in WHI focused partially on fat reduction.
Results : Simulations demonstrated that the method is sensitive to changes in the underlying effect sizes, but is able to detect underlying statistical correlations as intended. Genetic heritability estimates using cross-sectional data were negligible, while those using longitudinal data approached statistical significance (variance explained=14%, p=0.07). BMI changes in the dietary modification trial showed non-significant heritability (v.e.=4%), which was insufficient to validate any genetic correlation with the longitudinal results.
Conclusions : While cross-sectional data may contain too much noise, this method shows promise for the detection of genome-wide contributions to diet response in longitudinal data, and should be investigated further in larger datasets and with alternative phenotypes.
Funding Sources : This study was supported by the NHLBI T32 training grant.