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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.

37Despite food choices being one of the most important factors influencing health, efforts to 38 identify individual food groups and dietary patterns that cause disease have been 39 challenging, with traditional nutritional epidemiological approaches plagued by biases and 40 confounding. After identifying 302 (289 novel) individual genetic determinants of dietary 41 intake in 445,779 individuals in the UK Biobank study, we develop a statistical genetics 42 framework that enables us, for the first time, to directly assess the impact of food choices 43 on health outcomes. We show that the biases which affect observational studies extend 44 also to GWAS, genetic correlations and causal inference through genetics, which can be 45 corrected by applying our methods. Finally, by applying Mendelian Randomization 46 approaches to the corrected results we identify some of the first robust causal associations 47 between eating patterns and risks of cancer, heart disease and obesity, distinguishing 48 between the effects of specific foods or dietary patterns. 49 50 Recently, causal inference has been improved by a large number of studies which use Mendelian 61 Randomization (MR) to assess the causal relationship between one or more exposures and 62 outcomes. In MR, genetic variants are used as instrumental variables to measure the "life-long 63 exposure" to a risk factor 5 . This technique has proven to be extremely powerful, not influenced by 64 represents the unadjusted GWAS associations while the lower panel represents the association with food choices, after 87 adjustment for mediating traits, such as health status. 88 89 90 91 92 Replication for 23 of the 29 traits was sought in two additional UK based cohorts (EPIC-Norfolk 15 93 and Fenland 16 ) totalling up to 32,779 subjects. Despite relatively limited power, we could nominally 94 replicate 104/325 associations at p<0.05 (one-sided test) (32%; p=9.47x10 -54 ). The direction of 95 effect was consistent with that for discovery in 268 of the 325 associations (82%; p=7.82x10 -35 , 96 Binomial test; see Table S5). After prioritization of the genes in each locus (see Methods for details 97 and Supp. Table S4 for the prioritized genes), we noticed that for many genes associated with 98 BMI, the BMI-raising allele was associated with lower reported consumption of energy-dense foods 99 such as meat or fat and with higher consumption of lower-calorie foods. Although the exact 100 mechanism of action of many of these genes is unknown, in the case of MC4R in mice loss-of-101 function K314X mutants show an increase in weight, higher intake of calories and higher 102 preference for a high fat diet 17 , while we observe a lower intake of fat and higher intake of fresh 103 fruit. We thus wondered if this could be due to the effect of higher BMI on food choices instead of 104 the reverse and if this effect might also occur for a broader range of health-related traits. 105 106 Detecting the effects of potential confounders on food frequency data 107To test this hypothesis, we first se...

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.