Using genetic variation to disentangle the complex relationship between food intake and health outcomes.

Diet is considered as one of the most important modifiable factors influencing human health, but efforts to identify foods or dietary patterns associated with health outcomes often suffer from biases, confounding, and reverse causation. Applying Mendelian randomization in this context may provide evidence to strengthen causality in nutrition research. To this end, we first identified 283 genetic markers associated with dietary intake in 445,779 UK Biobank participants. We then converted these associations into direct genetic effects on food exposures by adjusting them for effects mediated via other traits. The SNPs which did not show evidence of mediation were then used for MR, assessing the association between genetically predicted food choices and other risk factors, health outcomes. We show that using all associated SNPs without omitting those which show evidence of mediation, leads to biases in downstream analyses (genetic correlations, causal inference), similar to those present in observational studies. However, MR analyses using SNPs which have only a direct effect on the exposure on food exposures provided unequivocal evidence of causal associations between specific eating patterns and obesity, blood lipid status, and several other risk factors and health outcomes.

Large-scale GWAS of food liking reveals genetic determinants and genetic correlations with distinct neurophysiological traits.

We present the results of a GWAS of food liking conducted on 161,625 participants from the UK-Biobank. Liking was assessed over 139 specific foods using a 9-point scale. Genetic correlations coupled with structural equation modelling identified a multi-level hierarchical map of food-liking with three main dimensions: "Highly-palatable", "Acquired" and "Low-caloric". The Highly-palatable dimension is genetically uncorrelated from the other two, suggesting that independent processes underlie liking high reward foods. This is confirmed by genetic correlations with MRI brain traits which show with distinct associations. Comparison with the corresponding food consumption traits shows a high genetic correlation, while liking exhibits twice the heritability. GWAS analysis identified 1,401 significant food-liking associations which showed substantial agreement in the direction of effects with 11 independent cohorts. In conclusion, we created a comprehensive map of the genetic determinants and associated neurophysiological factors of food-liking.

Coffee Consumption and Kidney Function: A Mendelian Randomization Study.

RATIONALE & OBJECTIVE: Chronic kidney disease (CKD) is a leading cause of morbidity and mortality worldwide, with limited strategies for prevention and treatment. Coffee is a complex mixture of chemicals, and consumption has been associated with mostly beneficial health outcomes. This work aimed to determine the impact of coffee consumption on kidney function. STUDY DESIGN: Genome-wide association study (GWAS) and Mendelian randomization. SETTING & PARTICIPANTS: UK Biobank baseline data were used for a coffee consumption GWAS and included 227,666 participants. CKDGen Consortium data were used for kidney outcomes and included 133,814 participants (12,385 cases of CKD) of mostly European ancestry across various countries. EXPOSURE: Coffee consumption. OUTCOMES: Estimated glomerular filtration rate (eGFR), CKD GFR categories 3 to 5 (G3-G5; eGFR<60mL/min/1.73m2), and albuminuria. ANALYTICAL APPROACH: GWAS to identify single-nucleotide polymorphisms (SNPs) associated with coffee consumption in UK Biobank and use of those SNPs in Mendelian randomization analyses of coffee consumption and kidney outcomes in CKDGen. RESULTS: 2,126 SNPs were associated with coffee consumption (P<5×10-8), 25 of which were independent and available in CKDGen. Drinking an extra cup of coffee per day conferred a protective effect against CKD G3-G5 (OR, 0.84; 95% CI, 0.72-0.98; P=0.03) and albuminuria (OR, 0.81; 95% CI, 0.67-0.97; P=0.02). An extra cup was also associated with higher eGFR (ß=0.022; P=1.6×10-6) after removal of 3 SNPs responsible for significant heterogeneity (Cochran Q P = 3.5×10-15). LIMITATIONS: Assays used to measure creatinine and albumin varied between studies that contributed data and a sex-specific definition was used for albuminuria rather than KDIGO guideline recommendations. CONCLUSIONS: This study provides evidence of a beneficial effect of coffee on kidney function. Given widespread coffee consumption and limited interventions to prevent CKD incidence and progression, this could have significant implications for global public health in view of the increasing burden of CKD worldwide.