A substantial body of evidence points to the heritability of dietary preferences. While vegetarianism has been practiced for millennia in various societies, its practitioners remain a small minority of people worldwide, and the role of genetics in choosing a vegetarian diet is not well understood. Dietary choices involve an interplay between the physiologic effects of dietary items, their metabolism, and taste perception, all of which are strongly influenced by genetics. In this study, we used a genome-wide association study (GWAS) to identify loci associated with strict vegetarianism in UK Biobank participants. Comparing 5,324 strict vegetarians to 329,455 controls, we identified one SNP on chromosome 18 that is associated with vegetarianism at the genome-wide significant level (rs72884519, β = -0.11, P = 4.997 x 10−8), and an additional 201 suggestively significant variants. Four genes are associated with rs72884519: TMEM241, RIOK3, NPC1, and RMC1. Using the Functional Mapping and Annotation (FUMA) platform and the Multi-marker Analysis of GenoMic Annotation (MAGMA) tool, we identified 34 genes with a possible role in vegetarianism, 3 of which are GWAS-significant based on gene-level analysis: RIOK3, RMC1, and NPC1. Several of the genes associated with vegetarianism, including TMEM241, NPC1, and RMC1, have important functions in lipid metabolism and brain function, raising the possibility that differences in lipid metabolism and their effects on the brain may underlie the ability to subsist on a vegetarian diet. These results support a role for genetics in choosing a vegetarian diet and open the door to future studies aimed at further elucidating the physiologic pathways involved in vegetarianism.

A substantial body of evidence points to the heritability of dietary preferences. While vegetarianism has been practiced for millennia in various societies, its practitioners remain a small minority of people worldwide, and the role of genetics in choosing a vegetarian diet is not well understood. Dietary choices involve an interplay between the physiologic effects of dietary items, their metabolism, and taste perception, all of which are strongly influenced by genetics. In this study, we used a genome-wide association study (GWAS) to identify loci associated with strict vegetarianism in UK Biobank participants. Comparing 5,324 strict vegetarians to 329,455 controls, we identified one SNP on chromosome 18 that is associated with vegetarianism at the genome-wide significant level (rs72884519, β = -0.11, P = 4.997 x 10−8), and an additional 201 suggestively significant variants. Four genes are associated with rs72884519: TMEM241, RIOK3, NPC1, and RMC1. Using the Functional Mapping and Annotation (FUMA) platform and the Multi-marker Analysis of GenoMic Annotation (MAGMA) tool, we identified 34 genes with a possible role in vegetarianism, 3 of which are GWAS-significant based on gene-level analysis: RIOK3, RMC1, and NPC1. Several of the genes associated with vegetarianism, including TMEM241, NPC1, and RMC1, have important functions in lipid metabolism and brain function, raising the possibility that differences in lipid metabolism and their effects on the brain may underlie the ability to subsist on a vegetarian diet. These results support a role for genetics in choosing a vegetarian diet and open the door to future studies aimed at further elucidating the physiologic pathways involved in vegetarianism.

A substantial body of evidence points to the heritability of dietary preferences. While vegetarianism has been practiced for millennia in various societies, its practitioners remain a small minority of people worldwide, and the role of genetics in choosing a vegetarian diet is not well understood. Dietary choices involve an interplay between the physiologic effects of dietary items, their metabolism, and taste perception, all of which are strongly influenced by genetics. In this study, we used a genome-wide association study (GWAS) to identify loci associated with strict vegetarianism in UK Biobank participants. Comparing 5,324 strict vegetarians to 329,455 controls, we identified one SNP on chromosome 18 that is associated with vegetarianism at the genome-wide significant level (rs72884519, β = -0.11, P = 4.997 x 10−8), and an additional 201 suggestively significant variants. Four genes are associated with rs72884519: TMEM241, RIOK3, NPC1, and RMC1. Using the Functional Mapping and Annotation (FUMA) platform and the Multi-marker Analysis of GenoMic Annotation (MAGMA) tool, we identified 34 genes with a possible role in vegetarianism, 3 of which are GWAS-significant based on gene-level analysis: RIOK3, RMC1, and NPC1. Several of the genes associated with vegetarianism, including TMEM241, NPC1, and RMC1, have important functions in lipid metabolism and brain function, raising the possibility that differences in lipid metabolism and their effects on the brain may underlie the ability to subsist on a vegetarian diet. These results support a role for genetics in choosing a vegetarian diet and open the door to future studies aimed at further elucidating the physiologic pathways involved in vegetarianism.

Characteristics of vegetarian and control populations.

The NIHR IBD Bioresource comprises ~34k participants with Inflammatory Bowel Disease (IBD). SNP chip data can be used to impute many of the (non-rare) SNPs not included on the chips. The NIHR BioResource is using a modified version of the UK Biobank protocol to improve the options for recall.

Correction: Frequency and distribution of corneal astigmatism and keratometry features in adult life: Methodology and findings of the UK Biobank study

The dataset contains results of genome-wide association study of human blood plasma glycome. The 113 files contain association summary statistics for 113 glycome traits, of which 36 were directly measured by UPLC technology and 77 were derived glycome traits. Description of each glycome trait can be found in the Additional notes section. This dataset is also available for graphical exploration in the genomic context at http://gwasarchive.org.

The data are provided on an "AS-IS" basis, without warranty of any type, expressed or implied, including but not limited to any warranty as to their performance, merchantability, or fitness for any particular purpose. If investigators use these data, any and all consequences are entirely their responsibility. By downloading and using these data, you agree that you will cite the appropriate publication in any communications or publications arising directly or indirectly from these data; for utilisation of data available prior to publication, you agree to respect the requested responsibilities of resource users under 2003 Fort Lauderdale principles; you agree that you will never attempt to identify any participant. This research has been conducted using the UK Biobank Resource and the use of the data is guided by the principles formulated by the UK Biobank.

When using downloaded data, please cite corresponding paper and this repository:

1. Sharapov, S. Z., Tsepilov, Y. A., Klaric, L., Mangino, M., Thareja, G., Shadrina, A. S., … Aulchenko, Y. (2019). Defining the genetic control of human blood plasma N-glycome using genome-wide association study. Human Molecular Genetics. http://doi.org/10.1093/hmg/ddz054
2. Sodbo Sharapov, Yakov Tsepilov, Lucija Klaric, Massimo Mangino, Gaurav Thareja, Mirna Simurina, Concetta Dagostino, Julia Dmitrieva, Marija Vilaj, FranoVuckovic, Tamara Pavic, Jerko Stambuk, Irena Trbojevic-Akmacic, Jasminka Kristic, Jelena Simunovic, Ana Momcilovic, Harry Campbell, Malcolm Dunlop, Susan Farrington, Maria Pucic-Bakovic, Christian Gieger, Massimo Allegri, Edouard Louis, Michel Georges, Karsten Suhre, Tim Spector, Frances MK Williams, Gordan Lauc, Yurii Aulchenko. (2018). Genome-wide association summary statistics for human blood plasma glycome (Version 1) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1298406

Funding

This work was supported by the European Community’s Seventh Framework Programme funded project PainOmics (Grant agreement # 602736) and by the European Structural and Investments funding for the "Croatian National Centre of Research Excellence in Personalized Healthcare" (contract #KK.01.1.1.01.0010).

The work of SSh was supported by the Russian Ministry of Science and Education under the 5-100 Excellence Programme.

The work of YT was supported by the Federal Agency of Scientific Organizations via the Institute of Cytology and Genetics (project #0324-2018-0017).

Karsten Suhre and Gaurav Thareja are supported by ‘Biomedical Research Program’ funds at Weill Cornell Medicine - Qatar, a program funded by the Qatar Foundation. We thank all staff at Weill Cornell Medicine - Qatar and Hamad Medical Corporation, and especially all study participants who made the QMDiab study possible.

The SOCCS study was supported by grants from Cancer Research UK (C348/A3758, C348/A8896, C348/ A18927); Scottish Government Chief Scientist Office (K/OPR/2/2/D333, CZB/4/94); Medical Research Council (G0000657-53203, MR/K018647/1); Centre Grant from CORE as part of the Digestive Cancer Campaign (http://www.corecharity.org.uk).

TwinsUK is funded by the Wellcome Trust, Medical Research Council, European Union, the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust in partnership with King’s College London.

Column headers:

1. SNP: SNP rsID
2. CHR: chromosome
3. POS: position (GRCh37 build)
4. OTHER_ALLELE: reference allele (coded as "0")
5. EFFECT_ALLELE: effective allele (coded as "1")
6. EAF: effective allele frequency
7. N: sample size
8. BETA: effect size of effective allele
9. SE: standard error of effect size
10. PVAL: P-value of association (without GC correction)
11. IMPUTATION: imputation quality

Genome wide association summary statistics for predicted VO2max (ml O2 per minute per kg fat free mass) in the UK Biobank study. VO2max was estimated from a submaximal ramped cycle ergometer test. Participants of non-European ancestry were excluded from the genetic analysis. Results for this publication used UK Biobank applications 408, 12871 and 44448.

The NIHR Bioresource consists of several groups of participants: ~70k from the general population and blood donors (COMPARE, INTERVAL and STRIDES studies); ~19k with one of ~50 rare diseases (RD) including a ~5k pilot for GEL; ~30k with Inflammatory Bowel Disease (IBD) which include the members of Gut Reaction, the Health Data Research Hub for IBD; and ~20k with Anxiety or depression (GLAD study). It intends to extend recruitment in all areas, and to other rare and common disease groups, with a target of ~300k by 2022. SNP chip data can be used to impute many of the (non-rare) SNPs not included on the chips. The NIHR BioResource is using a modified version of the UK Biobank protocol to improve the options for recall.

Genome-wide summary statistics from the GWAS analysis of the Frailty Index in participants of European descent aged 60+ from UK Biobank and TwinGene.If used please cite paper Atkins et al. 2021 "A Genome-Wide Association Study of the Frailty Index Highlights Brain Pathways in Healthy Aging"Included are two files: 1. meta-analysis results, 2. individual SNP associations in UK Biobank and TwinGeneREADME file included with each summary stats file. All base pair coordinates are GRCh37AbstractFrailty is a common geriatric syndrome, strongly associated with disability, mortality and hospitalisation. Frailty is commonly measured using the frailty index (FI), based on the accumulation of a number of health deficits during the life course. The mechanisms underlying FI are multifactorial and not well understood, but a genetic basis has been suggested with heritability estimates between 30 and 45%. Understanding the genetic determinants and biological mechanisms underpinning FI may help to delay or even prevent frailty. We performed a genome-wide association study (GWAS) meta-analysis of a frailty index in European descent UK Biobank participants (n=164,610, aged 60-70 years) and Swedish TwinGene participants (n=10,616, aged 41-87 years). FI calculation was based on 49 or 44 self-reported items on symptoms, disabilities and diagnosed diseases for UK Biobank and TwinGene respectively. 14 loci were associated with the FI (p

The NIHR Bioresource consists of several groups of participants: ~70k from the general population and blood donors (COMPARE, INTERVAL and STRIDES studies); ~19k with one of ~50 rare diseases (RD) including a ~5k pilot for GEL; ~30k with Inflammatory Bowel Disease (IBD) which include the members of Gut Reaction, the Health Data Research Hub for IBD; and ~20k with Anxiety or depression (GLAD study). It intends to extend recruitment in all areas, and to other rare and common disease groups, with a target of ~300k by 2022. The NIHR BioResource extracts DNA from blood and saliva samples taken at recruitment, and measures a panel of SNPs on each DNA sample, using a commodity SNP genotyping array from e.g. Illumina or Affymetrix (now Thermofisher). This is used to pre-screen or match participants when inviting them to take part in experimental medicine studies. De-identified versions of this data is available to researchers investigating the feasibility of future studies. The Technical Metadata describes a SNP annotation file – i.e. what the chip is measuring. The file itself has as many rows as there are SNPs represented on the chip, and is proprietary to the manufacturer, although deeply familiar to researchers.

Background: Proton pump inhibitors (PPIs) are widely used and have been linked to kidney diseases. However, the role of PPI use in the development of chronic kidney disease (CKD) remains unclear. We undertook this study to examine the association between PPI use and the subsequent risk of CKD.Methods: This is a prospective analysis of 462,421 participants free of cancer diagnosis or chronic kidney disease from the United Kingdom Biobank. Self-reported PPI use was recorded using an electronic questionnaire and confirmed by a trained staff. Incident CKD was identified based on the medical history. Overlap propensity score weighting with the Cox model was used to calculate the effect of PPI use on CKD risk. The number needed to harm (NNH) was calculated at 5 and 10 years of follow-up.Results: We documented 7,031 cases of CKD over a median follow-up of 8.1 years. Overlap propensity score weighting analysis showed that regular PPI users had a 37% higher risk of CKD incident than non-users (HR 1.37, 95% CI 1.28–1.47). The association persisted across subgroup analyses, different types of PPIs, and several sensitivity analyses. Quantitative bias analysis indicated that the result was robust to unmeasured confounding (E-value 2.08, lower 95% CI 1.88). The NNH was 147.9 and 78.6 for 5 and 10 years of follow-up, respectively. A head-to-head comparison showed that PPI users had a 19% higher risk of CKD than H2RA users (HR 1.19, 95% CI 1.02–1.39).Conclusion: The regular use of PPI is associated with a higher risk of CKD. Healthcare providers should carefully weigh up the potential benefits against the risk in prescribing PPIs, particularly for patients requiring long-term treatment.

IntroductionUnderstanding how socioeconomic markers interact could inform future policies aimed at increasing adherence to a healthy diet.MethodsThis cross-sectional study included 437,860 participants from the UK Biobank. Dietary intake was self-reported. Were used as measures socioeconomic education level, income and Townsend deprivation index. A healthy diet score was defined using current dietary recommendations for nine food items and one point was assigned for meeting the recommendation for each. Good adherence to a healthy diet was defined as the top 75th percentile, while poor adherence was defined as the lowest 25th percentile. Poisson regression was used to investigate adherence to dietary recommendations.ResultsThere were significant trends whereby diet scores tended to be less healthy as deprivation markers increased. The diet score trends were greater for education compared to area deprivation and income. Compared to participants with the highest level of education, those with the lowest education were found to be 48% less likely to adhere to a healthy diet (95% Confidence Interval [CI]: 0.60–0.64). Additionally, participants with the lowest income level were 33% less likely to maintain a healthy diet (95% CI: 0.73–0.81), and those in the most deprived areas were 13% less likely (95% CI: 0.84–0.91).Discussion/conclussionAmong the three measured proxies of socioeconomic status – education, income, and area deprivation – low education emerged as the strongest factor associated with lower adherence to a healthy diet.