Genetic evidence that higher central adiposity causes gastro-oesophageal reflux disease: a Mendelian-randomization study

Background: Gastro-oesophageal reflux disease (GORD) is associated with multiple risk factors but determining causality is difficult. We used a genetic approach [Mendelian randomization (MR)] to identify potential causal modifiable risk factors for GORD. Methods: We used data from 451 097 European participants in the UK Biobank and defined GORD using hospital-defined ICD10 and OPCS4 codes and self-report data (N = 41 024 GORD cases). We tested observational and MR-based associations between GORD and four adiposity measures [body mass index (BMI), waist-hip ratio (WHR), a metabolically favourable higher body-fat percentage and waist circumference], smoking status, smoking frequency and caffeine consumption. Results: Observationally, all adiposity measures were associated with higher odds of GORD. Ever and current smoking were associated with higher odds of GORD. Coffee consumption was associated with lower odds of GORD but, among coffee drinkers, more caffeinated-coffee consumption was associated with higher odds of GORD. Using MR, we provide strong evidence that higher WHR and higher WHR adjusted for BMI lead to GORD. There was weak evidence that higher BMI, body-fat percentage, coffee drinking or smoking caused GORD, but only the observational effects for BMI and body-fat percentage could be excluded. This MR estimated effect for WHR equates to a 1.23-fold higher odds of GORD per 5-cm increase in waist circumference. Conclusions: These results provide strong evidence that a higher waist-hip ratio leads to GORD. Our study suggests that central fat distribution is crucial in causing GORD rather than overall weight.This article is freely available via Open Access. Click on the Publisher URL to access it via the publisher's site.S.E.J. is funded by the Medical Research Council (grant: MR/M005070/1). A.R.W., T.M.F and H.Y. are supported by the European Research Council grants: SZ-245 50371-GLUCOSEGENES-FP7-IDEAS-ERC and 323195. H.Y. is also funded by the Diabetes UK RD Lawrence fellowship (grant: 17/0005594). R.N.B. is funded by the Wellcome Trust and Royal Society, grant 104150/Z/14/Z. J.T. is supported by an Academy of Medical Sciences (AMS) Springboard award, which is supported by the AMS, the Wellcome Trust, GCRF, the Government Department of Business, Energy and Industrial strategy, the British Heart Foundation and Diabetes UK (SBF004\1079). N.A.K. declares personal fees from Falk, Takeda and Pharmacosmos; other fees from Janssen; and non-financial support from Janssen, AbbVie and Celltrion outside the submitted work. J.R.G. received honoraria from Falk, AbbVie and Shield therapeutics, outside the submitted work for unrelated topics. T.A. reports grants from AbbVie, MSD, Napp Pharmaceuticals, Celltrion, Pfizer, Janssen and Celgene during this study; personal fees and non-financial support from Immunodiagnostik; personal fees and non-financial support from Napp Pharmaceuticals, AbbVie and MSD; personal fees from Celltrion and Pfizer; grants and personal fees from Takeda; and grants and non-financial support from Tillotts, outside the submitted work.published version, accepted version (12 month embargo), submitted versio

The functional "KL-VS" variant of KLOTHO is not associated with type 2 diabetes in 5028 UK Caucasians

BACKGROUND: Klotho has an important role in insulin signalling and the development of ageing-like phenotypes in mice. The common functional "KL-VS" variant in the KLOTHO (KL) gene is associated with longevity in humans but its role in type 2 diabetes is not known. We performed a large case-control and family-based study to test the hypothesis that KL-VS is associated with type 2 diabetes in a UK Caucasian population. METHODS: We genotyped 1793 cases, 1619 controls and 1616 subjects from 509 families for the single nucleotide polymorphism (SNP) F352V (rs9536314) that defines the KL-VS variant. Allele and genotype frequencies were compared between cases and controls. Family-based analysis was used to test for over- or under-transmission of V352 to affected offspring. RESULTS: Despite good power to detect odds ratios of 1.2, there were no significant associations between alleles or genotypes and type 2 diabetes (V352 allele: odds ratio = 0.96 (0.84–1.09)). Additional analysis of quantitative trait data in 1177 healthy control subjects showed no association of the variant with fasting insulin, glucose, triglycerides, HDL- or LDL-cholesterol (all P > 0.05). However, the HDL-cholesterol levels observed across the genotype groups showed a similar, but non-significant, pattern to previously reported data. CONCLUSION: This is the first large-scale study to examine the association between common functional variation in KL and type 2 diabetes risk. We have found no evidence that the functional KL-VS variant is a risk factor for type 2 diabetes in a large UK Caucasian case-control and family-based study

Gene variants influencing measures of inflammation or predisposing to autoimmune and inflammatory diseases are not associated with the risk of type 2 diabetes.

AIMS/HYPOTHESIS: There are strong associations between measures of inflammation and type 2 diabetes, but the causal directions of these associations are not known. We tested the hypothesis that common gene variants known to alter circulating levels of inflammatory proteins, or known to alter autoimmune-related disease risk, influence type 2 diabetes risk. METHODS: We selected 46 variants: (1) eight variants known to alter circulating levels of inflammatory proteins, including those in the IL18, IL1RN, IL6R, MIF, PAI1 (also known as SERPINE1) and CRP genes; and (2) 38 variants known to predispose to autoimmune diseases, including type 1 diabetes. We tested the associations of these variants with type 2 diabetes using a meta-analysis of 4,107 cases and 5,187 controls from the Wellcome Trust Case Control Consortium, the Diabetes Genetics Initiative, and the Finland-United States Investigation of NIDDM studies. We followed up associated variants (p < 0.01) in a further set of 3,125 cases and 3,596 controls from the UK. RESULTS: We found no evidence that inflammatory or autoimmune disease variants are associated with type 2 diabetes (at p <or= 0.01). The OR observed between the variant altering IL-18 levels, rs2250417, and type 2 diabetes (OR 1.00 [95% CI 0.99-1.03]), is much lower than that expected given (1) the effect of the variant on IL-18 levels (0.28 SDs per allele); and (2) estimates, based on other studies, of the correlation between IL-18 levels and type 2 diabetes risk (approximate OR 1.15 [95% CI 1.09-1.21] per 0.28 SD increase in IL-18 levels). CONCLUSIONS/INTERPRETATION: Our study provided no evidence that variants known to alter measures of inflammation, autoimmune or inflammatory disease risk, including type 1 diabetes, alter type 2 diabetes risk

Estimating sleep parameters using an accelerometer without sleep diary

This is the final version. Available from the publisher via the DOI in this record.Wrist worn raw-data accelerometers are used increasingly in large-scale population research. We examined whether sleep parameters can be estimated from these data in the absence of sleep diaries. Our heuristic algorithm uses the variance in estimated z-axis angle and makes basic assumptions about sleep interruptions. Detected sleep period time window (SPT-window) was compared against sleep diary in 3752 participants (range = 60–82 years) and polysomnography in sleep clinic patients (N = 28) and in healthy good sleepers (N = 22). The SPT-window derived from the algorithm was 10.9 and 2.9 minutes longer compared with sleep diary in men and women, respectively. Mean C-statistic to detect the SPT-window compared to polysomnography was 0.86 and 0.83 in clinic-based and healthy sleepers, respectively. We demonstrated the accuracy of our algorithm to detect the SPT-window. The value of this algorithm lies in studies such as UK Biobank where a sleep diary was not used.Medical Research Council (MRC)National Institute of Health (NIH

Large copy number variants in UK Biobank caused by clonal haematopoiesis may confound penetrance estimates

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this recordThis study did not generate new datasets or code. The code used during this study is available at https://github.com/WGLab/PennCNV. All bona fide researchers can apply to use the UK Biobank resource for health related research that is in the public interest, https://www.ukbiobank.ac.uk/.Large copy number variants (CNVs) are strongly associated with both developmental delay and cancer, but the type of disease depends strongly on when and where the mutation occurred, i.e. germline versus somatic. We used microarray data from UK Biobank to investigate the prevalence and penetrance of large autosomal CNVs and chromosomal aneuploidies using a standard CNV detection algorithm not designed for detecting mosaic variants. We found 160 individuals that carry >10Mb copy number changes, including 56 with whole chromosome aneuploidies. Nineteen (12%) individuals had a diagnosis of Down’s syndrome or other developmental disorder, while 84 (52.5%) individuals had a diagnosis of haematological malignancies or chronic myeloproliferative disorders. Notably, there was no evidence of mosaicism in the blood for many of these large CNVs, so they could easily be mistaken for germline alleles even when caused by somatic mutations. We therefore suggest that somatic mutations associated with blood cancers may result in false estimates of rare variant penetrance from population biobanks

Gene-obesogenic environment interactions in the UK Biobank study

This is the final version of the article. Available from the publisher via the DOI in this record.BACKGROUND: Previous studies have suggested that modern obesogenic environments accentuate the genetic risk of obesity. However, these studies have proven controversial as to which, if any, measures of the environment accentuate genetic susceptibility to high body mass index (BMI). METHODS: We used up to 120 000 adults from the UK Biobank study to test the hypothesis that high-risk obesogenic environments and behaviours accentuate genetic susceptibility to obesity. We used BMI as the outcome and a 69-variant genetic risk score (GRS) for obesity and 12 measures of the obesogenic environment as exposures. These measures included Townsend deprivation index (TDI) as a measure of socio-economic position, TV watching, a 'Westernized' diet and physical activity. We performed several negative control tests, including randomly selecting groups of different average BMIs, using a simulated environment and including sun-protection use as an environment. RESULTS: We found gene-environment interactions with TDI (Pinteraction = 3 × 10(-10)), self-reported TV watching (Pinteraction = 7 × 10(-5)) and self-reported physical activity (Pinteraction = 5 × 10(-6)). Within the group of 50% living in the most relatively deprived situations, carrying 10 additional BMI-raising alleles was associated with approximately 3.8 kg extra weight in someone 1.73 m tall. In contrast, within the group of 50% living in the least deprivation, carrying 10 additional BMI-raising alleles was associated with approximately 2.9 kg extra weight. The interactions were weaker, but present, with the negative controls, including sun-protection use, indicating that residual confounding is likely. CONCLUSIONS: Our findings suggest that the obesogenic environment accentuates the risk of obesity in genetically susceptible adults. Of the factors we tested, relative social deprivation best captures the aspects of the obesogenic environment responsible.J.T. is funded by a Diabetes Research and Wellness Foundation Fellowship. S.E.J. is funded by the Medical Research Council (grant: MR/M005070/1). M.A.T., M.N.W. and A.M. are supported by the Wellcome Trust Institutional Strategic Support Award (WT097835MF). A.R.W., H.Y. and T.M.F. are supported by the European Research Council grant: 323195:SZ-245 50371- GLUCOSEGENES-FP7-IDEAS-ERC. R.M.F. is a Sir Henry Dale Fellow (Wellcome Trust and Royal Society grant: 104150/Z/14/Z). R.B. is funded by the Wellcome Trust and Royal Society grant: 104150/Z/14/Z. R.M.A is supported by the Wellcome Trust Institutional Strategic Support Award (WT105618MA). Z.K. is funded by Swiss National Science Foundation (31003A-143914). The funders had no influence on study design, data collection and analysis, decision to publish or preparation of the manuscript. The data reported in this paper are available via application directly to the UK Biobank

Red blood cell distribution width: Genetic evidence for aging pathways in 116,666 volunteers

This is the final version of the article. Available from Public Library of Science via the DOI in this record.INTRODUCTION: Variability in red blood cell volumes (distribution width, RDW) increases with age and is strongly predictive of mortality, incident coronary heart disease and cancer. We investigated inherited genetic variation associated with RDW in 116,666 UK Biobank human volunteers. RESULTS: A large proportion RDW is explained by genetic variants (29%), especially in the older group (60+ year olds, 33.8%, <50 year olds, 28.4%). RDW was associated with 194 independent genetic signals; 71 are known for conditions including autoimmune disease, certain cancers, BMI, Alzheimer's disease, longevity, age at menopause, bone density, myositis, Parkinson's disease, and age-related macular degeneration. Exclusion of anemic participants did not affect the overall findings. Pathways analysis showed enrichment for telomere maintenance, ribosomal RNA, and apoptosis. The majority of RDW-associated signals were intronic (119 of 194), including SNP rs6602909 located in an intron of oncogene GAS6, an eQTL in whole blood. CONCLUSIONS: Although increased RDW is predictive of cardiovascular outcomes, this was not explained by known CVD or related lipid genetic risks, and a RDW genetic score was not predictive of incident disease. The predictive value of RDW for a range of negative health outcomes may in part be due to variants influencing fundamental pathways of aging.This work was supported by an award to DM, TF, AM and LH by the UK Medical Research Council (grant number MR/M023095/1). SEJ is funded by the Medical Research Council (grant: MR/M005070/1). JT is funded by a Diabetes Research and Wellness Foundation Fellowship. RB is funded by the Wellcome Trust and Royal Society grant: 104150/Z/14/Z. MAT, MNW and AM are supported by the Wellcome Trust Institutional Strategic Support Award (WT097835MF). ARW, HY, and TF are supported by the European Research Council grant: 323195:GLUCOSEGENES-FP7-IDEAS-ERC. LF is supported by the Intramural Research Program of the National Institute on Aging, U.S. National Institutes of Health. Input from MD, CLK and GK was supported by the University of Connecticut Health Center. This research has been conducted using the UK Biobank Resource under Application Number 14631. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Using genetics to understand the causal influence of higher BMI on depression

 This is the final version. Available on open access from OUP via the DOI in this record.Background: Depression is more common in obese than non-obese individuals, especially in women, but the causal relationship between obesity and depression is complex and uncertain. Previous studies have used genetic variants associated with BMI to provide evidence that higher body mass index (BMI) causes depression, but have not tested whether this relationship is driven by the metabolic consequences of BMI nor for differences between men and women. Methods: We performed a Mendelian randomization study using 48 791 individuals with depression and 291 995 controls in the UK Biobank, to test for causal effects of higher BMI on depression (defined using self-report and Hospital Episode data). We used two genetic instruments, both representing higher BMI, but one with and one without its adverse metabolic consequences, in an attempt to 'uncouple' the psychological component of obesity from the metabolic consequences. We further tested causal relationships in men and women separately, and using subsets of BMI variants from known physiological pathways. Results: Higher BMI was strongly associated with higher odds of depression, especially in women. Mendelian randomization provided evidence that higher BMI partly causes depression. Using a 73-variant BMI genetic risk score, a genetically determined one standard deviation (1 SD) higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals [odds ratio (OR): 1.18, 95% confidence interval (CI): 1.09, 1.28, P = 0.00007) and women only (OR: 1.24, 95% CI: 1.11, 1.39, P = 0.0001). Meta-analysis with 45 591 depression cases and 97 647 controls from the Psychiatric Genomics Consortium (PGC) strengthened the statistical confidence of the findings in all individuals. Similar effect size estimates were obtained using different Mendelian randomization methods, although not all reached P < 0.05. Using a metabolically favourable adiposity genetic risk score, and meta-analysing data from the UK biobank and PGC, a genetically determined 1 SD higher BMI (4.9 kg/m2) was associated with higher odds of depression in all individuals (OR: 1.26, 95% CI: 1.06, 1.50], P = 0.010), but with weaker statistical confidence. Conclusions: Higher BMI, with and without its adverse metabolic consequences, is likely to have a causal role in determining the likelihood of an individual developing depression.Diabetes Research and Wellness FoundationAustralian Research Training ProgramMedical Research CouncilWellcome TrustEuropean Research CouncilRoyal SocietyGillings Family FoundationDiabetes UKNational Institute for Health Research (NIHR

A genome-wide association study identifies protein quantitative trait loci (pQTLs)

There is considerable evidence that human genetic variation influences gene expression. Genome-wide studies have revealed that mRNA levels are associated with genetic variation in or close to the gene coding for those mRNA transcripts - cis effects, and elsewhere in the genome - trans effects. The role of genetic variation in determining protein levels has not been systematically assessed. Using a genome-wide association approach we show that common genetic variation influences levels of clinically relevant proteins in human serum and plasma. We evaluated the role of 496,032 polymorphisms on levels of 42 proteins measured in 1200 fasting individuals from the population based InCHIANTI study. Proteins included insulin, several interleukins, adipokines, chemokines, and liver function markers that are implicated in many common diseases including metabolic, inflammatory, and infectious conditions. We identified eight Cis effects, including variants in or near the IL6R (p = 1.8×10 -57), CCL4L1 (p = 3.9×10-21), IL18 (p = 6.8×10-13), LPA (p = 4.4×10-10), GGT1 (p = 1.5×10-7), SHBG (p = 3.1×10-7), CRP (p = 6.4×10-6) and IL1RN (p = 7.3×10-6) genes, all associated with their respective protein products with effect sizes ranging from 0.19 to 0.69 standard deviations per allele. Mechanisms implicated include altered rates of cleavage of bound to unbound soluble receptor (IL6R), altered secretion rates of different sized proteins (LPA), variation in gene copy number (CCL4L1) and altered transcription (GGT1). We identified one novel trans effect that was an association between ABO blood group and tumour necrosis factor alpha (TNF-alpha) levels (p = 6.8×10-40), but this finding was not present when TNF-alpha was measured using a different assay , or in a second study, suggesting an assay-specific association. Our results show that protein levels share some of the features of the genetics of gene expression. These include the presence of strong genetic effects in cis locations. The identification of protein quantitative trait loci (pQTLs) may be a powerful complementary method of improving our understanding of disease pathways. © 2008 Melzer et al

Identification and analysis of individuals who deviate from their genetically-predicted phenotype

This is the final version. Available from Public Library of Science via the DOI in this record. Data Availability: The research utilised data from the UK Biobank resource carried out under UK Biobank application number 9072. UK Biobank protocols were approved by the National Research Ethics Service Committee. Individual-level data cannot be shared publicly because of data access policies of the UK Biobank. Data are available from the UK Biobank for researchers who meet the criteria for access to datasets to UK Biobank (http://www.ukbiobank.ac.uk). The weights used to calculate the polygenic score for height is available in Table C in S1 Data. The weights used to calculate the polygenic score for LDL-cholesterol, calculated in a meta-analysis excluding UK Biobank, are available from the Global Lipids Genetics Consortium at https://csg.sph.umich.edu/willer/public/glgc-lipids2021/.Findings from genome-wide association studies have facilitated the generation of genetic predictors for many common human phenotypes. Stratifying individuals misaligned to a genetic predictor based on common variants may be important for follow-up studies that aim to identify alternative causal factors. Using genome-wide imputed genetic data, we aimed to classify 158,951 unrelated individuals from the UK Biobank as either concordant or deviating from two well-measured phenotypes. We first applied our methods to standing height: our primary analysis classified 244 individuals (0.15%) as misaligned to their genetically predicted height. We show that these individuals are enriched for self-reporting being shorter or taller than average at age 10, diagnosed congenital malformations, and rare loss-of-function variants in genes previously catalogued as causal for growth disorders. Secondly, we apply our methods to LDL cholesterol (LDL-C). We classified 156 (0.12%) individuals as misaligned to their genetically predicted LDL-C and show that these individuals were enriched for both clinically actionable cardiovascular risk factors and rare genetic variants in genes previously shown to be involved in metabolic processes. Individuals whose LDL-C was higher than expected based on the genetic predictor were also at higher risk of developing coronary artery disease and type-two diabetes, even after adjustment for measured LDL-C, BMI and age, suggesting upward deviation from genetically predicted LDL-C is indicative of generally poor health. Our results remained broadly consistent when performing sensitivity analysis based on a variety of parametric and non-parametric methods to define individuals deviating from polygenic expectation. Our analyses demonstrate the potential importance of quantitatively identifying individuals for further follow-up based on deviation from genetic predictions.Innovative Medicines Initiative 2 Joint UndertakingAcademy of Medical SciencesMedical Research Council (MRC)Australian Research Council (ARC