Polygenic type 2 diabetes prediction at the limit of common variant detection.

Genome-wide association studies (GWAS) may have reached their limit of detecting common type 2 diabetes (T2D)-associated genetic variation. We evaluated the performance of current polygenic T2D prediction. Using data from the Framingham Offspring (FOS) and the Coronary Artery Risk Development in Young Adults (CARDIA) studies, we tested three hypotheses: 1) a 62-locus genotype risk score (GRSt) improves T2D prediction compared with previous less inclusive GRSt; 2) separate GRS for \u3b2-cell (GRS\u3b2) and insulin resistance (GRSIR) independently predict T2D; and 3) the relationships between T2D and GRSt, GRS\u3b2, or GRSIR do not differ between blacks and whites. Among 1,650 young white adults in CARDIA, 820 young black adults in CARDIA, and 3,471 white middle-aged adults in FOS, cumulative T2D incidence was 5.9%, 14.4%, and 12.9%, respectively, over 25 years. The 62-locus GRSt was significantly associated with incident T2D in all three groups. In FOS but not CARDIA, the 62-locus GRSt improved the model C statistic (0.698 and 0.726 for models without and with GRSt, respectively; P < 0.001) but did not materially improve risk reclassification in either study. Results were similar among blacks compared with whites. The GRS\u3b2 but not GRSIR predicted incident T2D among FOS and CARDIA whites. At the end of the era of common variant discovery for T2D, polygenic scores can predict T2D in whites and blacks but do not outperform clinical models. Further optimization of polygenic prediction may require novel analytic methods, including less common as well as functional variants

Overweight across the life course and adipokines, inflammatory and endothelial markers at age 60-64 years: evidence from the 1946 birth cohort.

BACKGROUND/OBJECTIVES: There is growing evidence that early development of obesity increases cardiovascular risk later in life, but less is known about whether there are effects of long-term excess body weight on the biological drivers associated with the atherosclerotic pathway, particularly adipokines, inflammatory and endothelial markers. This paper therefore investigates the influence of overweight across the life course on levels of these markers at retirement age. SUBJECTS/METHODS: Data from the Medical Research Council National Survey of Health and Development (n=1784) were used to examine the associations between overweight status at 2, 4, 6, 7, 11, 15, 20, 26, 36, 43, 53 and 60-64 years (body mass index (BMI)⩾25 kg m(-2) for adult ages and gender-specific cut-points for childhood ages equivalent to BMI⩾25 kg m(-2)) and measurements of adipokines (leptin and adiponectin), inflammatory markers (C-reactive protein (CRP), interleukin-6 (IL-6)) and endothelial markers (E-selectin, tissue plasminogen activator (t-PA) and von Willebrand factor) at 60-64 years. In addition, the fit of different life course models (sensitive periods/accumulation) were compared using partial F-tests. RESULTS: In age- and sex-adjusted models, overweight at 11 years and onwards was associated with higher leptin, CRP and IL-6 and lower adiponectin; overweight at 15 years and onwards was associated with higher E-selectin and t-PA. Associations between overweight at all ages earlier than 60-64 with leptin, adiponectin, CRP and IL-6 were reduced but remained apparent after adjustment for overweight at 60-64 years; whereas those with E-selectin and t-PA were entirely explained. An accumulation model best described the associations between overweight across the life course with adipokines and inflammatory markers, whereas for the endothelial markers, the sensitive period model for 60-64 years provided a slightly better fit than the accumulation model. CONCLUSIONS: Overweight across the life course has a cumulative influence on adipokines, inflammatory and possibly endothelial markers. Avoidance of overweight from adolescence onwards is likely important for cardiovascular disease prevention

TCF7L2 polymorphisms and inflammatory markers before and after treatment with fenofibrate

<p>Abstract</p> <p>Background</p> <p>Inflammation is implicated in causing diabetes. We tested whether transcription factor 7 like-2 (TCF7L2) gene polymorphisms (rs12255372 and rs7903146), consistently associated with type 2 diabetes, are associated with plasma concentrations of inflammatory markers before and after three weeks of daily treatment with fenofibrate.</p> <p>Methods</p> <p>Men and women in the Genetics of Lipid-Lowering Drugs and Diet Network study (n = 1025, age 49 ± 16 y) were included. All participants suspended use of lipid-lowering drugs for three weeks and were then given 160 mg/day of fenofibrate for three weeks. Inflammatory markers and lipids were measured before and after fenofibrate. ANOVA was used to test for differences across TCF7L2 genotypes.</p> <p>Results</p> <p>Under the additive or dominant model, there were no significant differences (<it>P </it>> 0.05) in the concentrations of inflammatory markers (hsCRP, IL-2, IL-6, TNF-α and MCP-1) across TCF7L2 genotypes in the period before or after treatment. For both rs12255372 and rs7903146, homozygote T-allele carriers had significantly higher (<it>P </it>< 0.05) post-fenofibrate concentrations of MCP-1 in the recessive model. No other significant associations were detected.</p> <p>Conclusion</p> <p>Overall these data show no association between TCF7L2 polymorphisms and the inflammatory markers suggesting that the effects of TCF7L2 on diabetes may not be via inflammation.</p

Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR)

© 2015 De et al. Background: Despite heritability estimates of 40-70 for obesity, less than 2 of its variation is explained by Body Mass Index (BMI) associated loci that have been identified so far. Epistasis, or gene-gene interactions are a plausible source to explain portions of the missing heritability of BMI. Methods: Using genotypic data from 18,686 individuals across five study cohorts - ARIC, CARDIA, FHS, CHS, MESA - we filtered SNPs (Single Nucleotide Polymorphisms) using two parallel approaches. SNPs were filtered either on the strength of their main effects of association with BMI, or on the number of knowledge sources supporting a specific SNP-SNP interaction in the context of BMI. Filtered SNPs were specifically analyzed for interactions that are highly associated with BMI using QMDR (Quantitative Multifactor Dimensionality Reduction). QMDR is a nonparametric, genetic model-free method that detects non-linear interactions associated with a quantitative trait. Results: We identified seven novel, epistatic models with a Bonferroni corrected p-value of association < 0.1. Prior experimental evidence helps explain the plausible biological interactions highlighted within our results and their relationship with obesity. We identified interactions between genes involved in mitochondrial dysfunction (POLG2), cholesterol metabolism (SOAT2), lipid metabolism (CYP11B2), cell adhesion (EZR), cell proliferation (MAP2K5), and insulin resistance (IGF1R). Moreover, we found an 8.8 increase in the variance in BMI explained by these seven SNP-SNP interactions, beyond what is explained by the main effects of an index FTO SNP and the SNPs within these interactions. We also replicated one of these interactions and 58 proxy SNP-SNP models representing it in an independent dataset from the eMERGE study. Conclusion: This study highlights a novel approach for discovering gene-gene interactions by combining methods such as QMDR with traditional statistics.National Institutes of Health; National Heart, Lung and Blood Institute; National Heart, Lung and Blood Institute; Netherlands Heart Foundation; NHGR

Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals

Background The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). Results Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. We used two different filters: 1. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of p < 0.001 in a previous association study. 2. A filter that only tested interactions identified by Biofilter 2.0. Pairwise models that reached an interaction significance level of p < 0.001 in the discovery dataset were tested for replication. We identified thirteen SNP-SNP models that were significant in more than one replication cohort after accounting for multiple testing. Conclusions These results may reveal novel insights into the genetic etiology of lipid levels. Furthermore, we developed a pipeline to perform a computationally efficient interaction analysis with multi-cohort replication

Pleiotropic genes for metabolic syndrome and inflammation

Metabolic syndrome (MetS) has become a health and financial burden worldwide. The MetS definition captures clustering of risk factors that predict higher risk for diabetes mellitus and cardiovascular disease. Our study hypothesis is that additional to genes influencing individual MetS risk factors, genetic variants exist that influence MetS and inflammatory markers forming a predisposing MetS genetic network. To test this hypothesis a staged approach was undertaken. (a) We analyzed 17 metabolic and inflammatory traits in more than 85,500 participants from 14 large epidemiological studies within the Cross Consortia Pleiotropy Group. Individuals classified with MetS (NCEP definition), versus those without, showed on average significantly different levels for most inflammatory markers studied. (b) Paired average correlations between 8 metabolic traits and 9 inflammatory markers from the same studies as above, estimated with two methods, and factor analyses on large simulated data, helped in identifying 8 combinations of traits for follow-up in meta-analyses, out of 130,305 possible combinations between metabolic traits and inflammatory markers studied. (c) We performed correlated meta-analyses for 8 metabolic traits and 6 inflammatory markers by using existing GWAS published genetic summary results, with about 2.5 million SNPs from twelve predominantly largest GWAS consortia. These analyses yielded 130 unique SNPs/genes with pleiotropic associations (a SNP/gene associating at least one metabolic trait and one inflammatory marker). Of them twenty-five variants (seven loci newly reported) are proposed as MetS candidates. They map to genes MACF1, KIAA0754, GCKR, GRB14, COBLL1, LOC646736-IRS1, SLC39A8, NELFE, SKIV2L, STK19, TFAP2B, BAZ1B, BCL7B, TBL2, MLXIPL, LPL, TRIB1, ATXN2, HECTD4, PTPN11, ZNF664, PDXDC1, FTO, MC4R and TOMM40. Based on large data evidence, we conclude that inflammation is a feature of MetS and several gene variants show pleiotropic genetic associations across phenotypes and might explain a part of MetS correlated genetic architecture. These findings warrant further functional investigation. (C) 2014 Elsevier Inc. All rights reserved

The use of race, ethnicity and ancestry in human genetic research

Post-Human Genome Project progress has enabled a new wave of population genetic research, and intensified controversy over the use of race/ethnicity in this work. At the same time, the development of methods for inferring genetic ancestry offers more empirical means of assigning group labels. Here, we provide a systematic analysis of the use of race/ethnicity and ancestry in current genetic research. We base our analysis on key published recommendations for the use and reporting of race/ethnicity which advise that researchers: explain why the terms/categories were used and how they were measured, carefully define them, and apply them consistently. We studied 170 population genetic research articles from high impact journals, published 2008–2009. A comparative perspective was obtained by aligning study metrics with similar research from articles published 2001–2004. Our analysis indicates a marked improvement in compliance with some of the recommendations/guidelines for the use of race/ethnicity over time, while showing that important shortfalls still remain: no article using ‘race’, ‘ethnicity’ or ‘ancestry’ defined or discussed the meaning of these concepts in context; a third of articles still do not provide a rationale for their use, with those using ‘ancestry’ being the least likely to do so. Further, no article discussed potential socio-ethical implications of the reported research. As such, there remains a clear imperative for highlighting the importance of consistent and comprehensive reporting on human populations to the genetics/genomics community globally, to generate explicit guidelines for the uses of ancestry and genetic ancestry, and importantly, to ensure that guidelines are followed

Meta-Analysis of Genome-Wide Association Studies for Abdominal Aortic Aneurysm Identifies Four New Disease-Specific Risk Loci

Rationale: Abdominal aortic aneurysm (AAA) is a complex disease with both genetic and environmental risk factors. Together, 6 previously identified risk loci only explain a small proportion of the heritability of AAA. Objective: To identify additional AAA risk loci using data from all available genome-wide association studies (GWAS). Methods and Results: Through a meta-analysis of 6 GWAS datasets and a validation study totalling 10,204 cases and 107,766 controls we identified 4 new AAA risk loci: 1q32.3 (SMYD2), 13q12.11 (LINC00540), 20q13.12 (near PCIF1/MMP9/ZNF335), and 21q22.2 (ERG). In various database searches we observed no new associations between the lead AAA SNPs and coronary artery disease, blood pressure, lipids or diabetes. Network analyses identified ERG, IL6R and LDLR as modifiers of MMP9, with a direct interaction between ERG and MMP9. Conclusions: The 4 new risk loci for AAA appear to be specific for AAA compared with other cardiovascular diseases and related traits suggesting that traditional cardiovascular risk factor management may only have limited value in preventing the progression of aneurysmal disease

Genome-wide physical activity interactions in adiposity. A meta-analysis of 200,452 adults

Physical activity (PA) may modify the genetic effects that give rise to increased risk of obesity. To identify adiposity loci whose effects are modified by PA, we performed genome-wide interaction meta-analyses of BMI and BMI-adjusted waist circumference and waist-hip ratio from up to 200,452 adults of European (n = 180,423) or other ancestry (n = 20,029). We standardized PA by categorizing it into a dichotomous variable where, on average, 23% of participants were categorized as inactive and 77% as physically active. While we replicate the interaction with PA for the strongest known obesity-risk locus in the FTO gene, of which the effect is attenuated by similar to 30% in physically active individuals compared to inactive individuals, we do not identify additional loci that are sensitive to PA. In additional genome-wide meta-analyses adjusting for PA and interaction with PA, we identify 11 novel adiposity loci, suggesting that accounting for PA or other environmental factors that contribute to variation in adiposity may facilitate gene discovery.Peer reviewe