Comprehensive genomic analysis of dietary habits in UK Biobank identifies hundreds of genetic loci and establishes causal relationships between educational attainment and healthy eating

Cole, Joanne B.; Jc, Florez; Jn, Hirschhorn

doi:10.1101/662239

Cited by 7 publications

(5 citation statements)

References 57 publications

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“…Our results are in contradiction to some previous studies in which no evidence of reverse causation influencing genetic susceptibility for dietary patterns was reported. 28,29 We believe that this difference is due to our novel approach, which is not based on using the potential mediators as covariates, but rather exploits MR, which should be able to distinguish the forward and reverse effects when the causal relationship is bidirectional. We have thus shown that it is possible, through the use of available data and methods, to disentangle these different colliding effects and to select the instrumental variables which show a non-mediated effect, thus enabling the use of MR for the assessment of causal relationships between food and health.…”

Section: Results In Contextmentioning

confidence: 99%

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

Pirastu

McDonnell

Grzeszkowiak

et al. 2019

Preprint

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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...

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Section: Results In Contextmentioning

confidence: 99%

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

Pirastu

McDonnell

Grzeszkowiak

et al. 2019

Preprint

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“…However, the strongest results were observed with respect to dietary habits: rs9291211*A was positively associated with reduced OE risk in the PGC-SUD cohorts and with increased propensity to use dietary supplements, such as vitamin and mineral supplements in the UK Biobank. A recent GWAS identified several loci associated with dietary habits and indicated a causal relationship between educational attainment and healthy eating 45 . With respect to rs9291211, we also observed a nominally significant association with traits related to educational attainment (e.g., UK Biobank Field ID: 6138 Qualification [College or University degree], p=0.033).…”

Section: Discussionmentioning

confidence: 99%

Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium

et al. 2020

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To provide novel insights into the biology of opioid dependence (OD) and opioid use (i.e., exposure, OE), we completed a genome-wide analysis comparing up to 4,503 OD cases, 4,173 opioid-exposed controls, and 32,500 opioid-unexposed controls. Among the variants identified, rs9291211 was associated with OE (a comparison of exposed vs. unexposed controls; z=-5.39, p=7.2×10 -8 ). This variant regulates the transcriptomic profiles of SLC30A9 and BEND4 in multiple brain tissues and was previously associated with depression, alcohol consumption, and neuroticism. A phenome-wide scan of rs9291211 in the UK Biobank (N>360,000) found association of this variant with propensity to use dietary supplements (p=1.68×10 -8 ). With respect to the same OE phenotype in the gene-based analysis, we identified SDCCAG8 (z=4.69, p=10 -6 ), which was previously associated with educational attainment, risk-taking behaviors, and schizophrenia. In addition, rs201123820 showed a genome-wide significant difference between OD cases and unexposed controls (z=5.55, p=2.9×10 -8 ) and a significant association with musculoskeletal disorders in the UK Biobank (p=4.88×10 -7 ). A polygenic risk score (PRS) based on a GWAS of risk-tolerance (N=466,571) was positively associated with OD (OD cases vs. unexposed controls, p=8.1×10 -5 ; OD cases vs. exposed controls, p=0.054) and OE (exposed controls vs. unexposed controls, p=3.6×10 -5 ). A PRS based on a GWAS of neuroticism (N=390,278) was positively associated with OD (OD cases vs. unexposed controls, p=3.2×10 -5 ; OD cases vs. exposed controls, p=0.002) but not with OE (p=0.671). Our analyses highlight the difference between dependence and exposure and the importance of considering the definition of controls (exposed vs. unexposed) in studies of addiction.

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“…Our results showing that genetic variations associated with food phenotypes could be influenced by reverse causation and confounding are in contradiction to some previous studies, in which no evidence of reverse causation was reported. [ 34 , 35 ] We believe that this difference is due to our novel approach, which does not correct for potential mediators based on their correlation (through linear regression), but rather based on their causal effect (through MR), which should be able to distinguish the forward and reverse effects when the causal relationship is bidirectional. Our study suggests that it is possible to disentangle these different colliding effects, and identify genetic instruments with a non-mediated effect.…”

Section: Discussionmentioning

confidence: 99%

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

et al. 2022

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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.

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Comprehensive genomic analysis of dietary habits in UK Biobank identifies hundreds of genetic loci and establishes causal relationships between educational attainment and healthy eating

Cited by 7 publications

References 57 publications

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

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

Leveraging genome-wide data to investigate differences between opioid use vs. opioid dependence in 41,176 individuals from the Psychiatric Genomics Consortium

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

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