👤 Diane Gilbert-Diamond

The objective of this study is to evaluate obesity-related genetic factors in relation to excess consumption and assess if food cues modify associations. Children (9-12 years) completed a randomized crossover experiment. During two visits, children ate a preload and then snacks ad libitum while watching television, embedded with food or non-food advertisements to assess eating in the absence of hunger (EAH). Primary exposures were obesity-associated genotypes, FTO rs9939609 and MC4R rs571312, and a paediatric-specific polygenic risk score (PRS). Outcomes included consumption of all snacks (total EAH) and gummy candy only (gummy candy EAH). Linear mixed-effects models tested whether genetic exposures related to EAH outcomes. We tested for effect modification by food cues using multiplicative interaction terms. Among 177 children, each FTO risk allele was associated with a 30% increase in gummy candy EAH (p = 0.025) in adjusted models. Food cue exposure exacerbated associations between the FTO variant with gummy candy EAH (p = 0.046). No statistically significant associations were found between MC4R and EAH. The results suggest children with the FTO rs9939609 risk allele may be predisposed to excess consumption of candy and that this association may be exacerbated by food cues. Show less

To test associations of candidate obesity-related single nucleotide polymorphisms (SNPs) and obesity polygenic risk scores (PRS) with neural reward reactivity to food cues. After consuming a pre-load meal, 9-12-year-old children completed a functional magnetic resonance imaging (fMRI) paradigm with exposure to food and non-food commercials. Genetic exposures included Analyses included 151 children ( Children genetically at risk for obesity, as indicated by the https://clinicaltrials.gov/study/NCT03766191, identifier NCT03766191. Show less

Despite heritability estimates of 40-70 % for obesity, less than 2 % of its variation is explained by Body Mass Index (BMI) associated loci that have been identified so far. Epistasis, or gene-gene interactions are a plausible source to explain portions of the missing heritability of BMI. Using genotypic data from 18,686 individuals across five study cohorts - ARIC, CARDIA, FHS, CHS, MESA - we filtered SNPs (Single Nucleotide Polymorphisms) using two parallel approaches. SNPs were filtered either on the strength of their main effects of association with BMI, or on the number of knowledge sources supporting a specific SNP-SNP interaction in the context of BMI. Filtered SNPs were specifically analyzed for interactions that are highly associated with BMI using QMDR (Quantitative Multifactor Dimensionality Reduction). QMDR is a nonparametric, genetic model-free method that detects non-linear interactions associated with a quantitative trait. We identified seven novel, epistatic models with a Bonferroni corrected p-value of association < 0.1. Prior experimental evidence helps explain the plausible biological interactions highlighted within our results and their relationship with obesity. We identified interactions between genes involved in mitochondrial dysfunction (POLG2), cholesterol metabolism (SOAT2), lipid metabolism (CYP11B2), cell adhesion (EZR), cell proliferation (MAP2K5), and insulin resistance (IGF1R). Moreover, we found an 8.8 % increase in the variance in BMI explained by these seven SNP-SNP interactions, beyond what is explained by the main effects of an index FTO SNP and the SNPs within these interactions. We also replicated one of these interactions and 58 proxy SNP-SNP models representing it in an independent dataset from the eMERGE study. This study highlights a novel approach for discovering gene-gene interactions by combining methods such as QMDR with traditional statistics. Show less

Recent findings have reemphasized the importance of epistasis, or gene-gene interactions, as a contributing factor to the unexplained heritability of obesity. Network-based methods such as statistical epistasis networks (SEN), present an intuitive framework to address the computational challenge of studying pairwise interactions between thousands of genetic variants. In this study, we aimed to analyze pairwise interactions that are associated with Body Mass Index (BMI) between SNPs from twelve genes robustly associated with obesity (BDNF, ETV5, FAIM2, FTO, GNPDA2, KCTD15, MC4R, MTCH2, NEGR1, SEC16B, SH2B1, and TMEM18). We used information gain measures to identify all SNP-SNP interactions among and between these genes that were related to obesity (BMI > 30 kg/m(2)) within the Framingham Heart Study Cohort; interactions exceeding a certain threshold were used to build an SEN. We also quantified whether interactions tend to occur more between SNPs from the same gene (dyadicity) or between SNPs from different genes (heterophilicity). We identified a highly connected SEN of 709 SNPs and 1241 SNP-SNP interactions. Combining the SEN framework with dyadicity and heterophilicity analyses, we found 1 dyadic gene (TMEM18, P-value = 0.047) and 3 heterophilic genes (KCTD15, P-value = 0.045; SH2B1, P-value = 0.003; and TMEM18, P-value = 0.001). We also identified a lncRNA SNP (rs4358154) as a key node within the SEN using multiple network measures. This study presents an analytical framework to characterize the global landscape of genetic interactions from genome-wide arrays and also to discover nodes of potential biological significance within the identified network. Show less