Ant Colony Optimisation for Exploring Logical Gene-Gene Associations in Genome Wide Association Studies.

In this paper a search for the logical variants of gene-gene interactions in genome-wide association study (GWAS) data using ant colony optimisation is proposed. The method based on stochastic algorithms is tested on a large established database from the Wellcome Trust Case Control Consortium and is shown to discover logical operations between combinations of single nucleotide polymorphisms that can discriminate Type II diabetes. A variety of logical combinations are explored and the best discovered associations are found within reasonable computational time and are shown to be statistically significantThis study makes use of data generated by the Wellcome Trust Case Control Consortium. A full list of the investigators who contributed to the generation of the data is available from http://www.wtccc.org.uk. Funding for the project was provided by the Wellcome Trust under award 076113. The work contained in this paper was funded by an EPSRC First Grant (EP/J007439/1) and we acknowledge their kind support

Broad changes in body mass index between age 10 and adulthood are associated with type 2 diabetes risk independently of adult body mass index

 This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record Diabetes Research and Wellness FoundationDiabetes UKEuropean Foundation for the Study of Diabete

Simulated distributions from negative experiments highlight the importance of the body mass index distribution in explaining depression–body mass index genetic risk score interactions

This is the final version. Available on open access from Oxford University Press via the DOI in this record. Data availability: All data from UK Biobank are publicly available; the negative experiments algorithm can be found here https://github.com/drar wood/gags.Abstract. Background: Depression and obesity are complex global health problems. Recent studies suggest a genetic predisposition to obesity might be accentuated in people with depression, but these analyses are prone to bias. Here, we tested the hypothesis that depression accentuates genetic susceptibility to obesity and applied negative control experiments to test whether any observed interactions were real or driven by confounding and statistical biases. Methods: We used data from upto 378,000 Europeans in UK Biobank, a 73 variant Body Mass Index (BMI) genetic risk score, 2 depression measures (depressive symptoms (DS), major depression (MD)) and an antidepressant usage variable available. We tested whether a) depression and b) antidepressant treatment accentuated genetic susceptibility to obesity. Finally, we performed negative control experiments by sampling individuals at random so that they had BMI distributions identical to depression cases and controls. Results: Depression was associated with an accentuation of an individuals genetic risk of obesity with evidence of interactions for both DS and MD (Pinteraction=7x10-4 and 7x10-5 respectively). Antidepressant usage within DS cases accentuated genetic obesity risk (Pinteraction=9x10-4), but not for MD (Pinteraction=0.13). Negative control experiments suggested that the observed interactions for MD (empirical-P =0.067) may be driven by statistical biases or confounding factors but were not possible with the larger DS groups. Antidepressant usage interaction also appears to be driven by statistical artefacts (empirical-P=0.510 using MD and 0.162 using DS). Conclusion: We have highlighted the importance of running negative experiments to confirm putative interactions in gene-environment studies. We provide some tentative evidence that depression accentuates an individual’s genetic susceptibility to higher BMI but demonstrated that the BMI distributions within cases and controls might drive these interactions.Academy of Medical SciencesEuropean Research Council (ERC

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

On the Futility of Screening for Genes That Make You Fat

J. Lennert Veerman discusses the implications for genetic screening of findings showing that physical activity substantially attenuates the effects of genetic variants which predispose towards obesity

Mediation and moderation of genetic risk of obesity through eating behaviours in two UK cohorts

This is the final version. Available on open access from Oxford University Press (OUP) via the DOI in this recordData availability: The data underlying this article are available upon request to the ALSPAC data portal. The ALSPAC data management plan describes in detail the policy regarding data sharing, which is through a system of managed open access. Full instructions for applying for data access can be found here: [http://www.bristol.ac.uk/alspac/researchers/access/].Background The mechanisms underlying genetic predisposition to higher body mass index (BMI) remain unclear. Methods We hypothesized that the relationship between BMI-genetic risk score (BMI-GRS) and BMI was mediated via disinhibition, emotional eating and hunger, and moderated by flexible (but not rigid) restraint within two UK cohorts: the Genetics of Appetite Study (GATE) (n = 2101, 2010–16) and the Avon Longitudinal Study of Parents and Children (ALSPAC) (n = 1679, 2014–18). Eating behaviour was measured by the Adult Eating Behaviour Questionnaire and Three-Factor Eating Questionaire-51. Results The association between BMI-GRS and BMI were partially mediated by habitual, emotional and situational disinhibition in the GATE/ALSPAC meta-mediation [standardized betaindirect 0.04, 95% confidence interval (CI) 0.02–0.06; 0.03, 0.01–0.04; 0.03, 0.01–0.04, respectively] external hunger and internal hunger in the GATE study (0.02, 0.01–0.03; 0.01, 0.001–0.02, respectively). There was evidence of mediation by emotional over/undereating and hunger in the ALSPAC study (0.02, 0.01–0.03; 0.01, 0.001–0.02; 0.01, 0.002–0.01, respectively). Rigid or flexible restraint did not moderate the direct association between BMI-GRS and BMI, but high flexible restraint moderated the effect of disinhibition subscales on BMI (reduction of the indirect mediation by -5% to -11% in GATE/ALSPAC) and external hunger (-5%) in GATE. High rigid restraint reduced the mediation via disinhibition subscales in GATE/ALSPAC (-4% to -11%) and external hunger (-3%) in GATE. Conclusions Genetic predisposition to a higher BMI was partly explained by disinhibition and hunger in two large cohorts. Flexible/rigid restraint may play an important role in moderating the impact of predisposition to higher BMI.Medical Research Council (MRC)National Institute for Health and Care Research (NIHR)University of BristolEuropean Research Council (ERC

Is the thrifty genotype hypothesis supported by evidence based on confirmed type 2 diabetes- and obesity-susceptibility variants?

AIMS/HYPOTHESIS: According to the thrifty genotype hypothesis, the high prevalence of type 2 diabetes and obesity is a consequence of genetic variants that have undergone positive selection during historical periods of erratic food supply. The recent expansion in the number of validated type 2 diabetes- and obesity-susceptibility loci, coupled with access to empirical data, enables us to look for evidence in support (or otherwise) of the thrifty genotype hypothesis using proven loci. METHODS: We employed a range of tests to obtain complementary views of the evidence for selection: we determined whether the risk allele at associated 'index' single-nucleotide polymorphisms is derived or ancestral, calculated the integrated haplotype score (iHS) and assessed the population differentiation statistic fixation index (F (ST)) for 17 type 2 diabetes and 13 obesity loci. RESULTS: We found no evidence for significant differences for the derived/ancestral allele test. None of the studied loci showed strong evidence for selection based on the iHS score. We find a high F (ST) for rs7901695 at TCF7L2, the largest type 2 diabetes effect size found to date. CONCLUSIONS/INTERPRETATION: Our results provide some evidence for selection at specific loci, but there are no consistent patterns of selection that provide conclusive confirmation of the thrifty genotype hypothesis. Discovery of more signals and more causal variants for type 2 diabetes and obesity is likely to allow more detailed examination of these issues

Genetic variation at CHRNA5-CHRNA3-CHRNB4 interacts with smoking status to influence body mass index

Cigarette smoking is associated with lower body mass index (BMI), and a commonly cited reason for unwillingness to quit smoking is a concern about weight gain. Common variation in the CHRNA5-CHRNA3-CHRNB4 gene region (chromosome 15q25) is robustly associated with smoking quantity in smokers, but its association with BMI is unknown. We hypothesized that genotype would accurately reflect smoking exposure and that, if smoking were causally related to weight, it would be associated with BMI in smokers, but not in never smokers

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