New blood pressure associated loci identified in meta-analyses of 475,000 individuals

Background: Genome-wide association studies have recently identified over 400 loci that harbor DNA sequence variants that influence blood pressure (BP). Our earlier work identified and validated 56 single nucleotide variants (SNVs) associated with BP from meta-analyses of Exome Chip genotype data. An additional 100 variants yielded suggestive evidence of association. Methods and Results: Here, we augment the sample with 140,886 European individuals from the UK Biobank, in whom 77 of the 100 suggestive SNVs were available for association analysis with systolic or diastolic blood pressure (SBP, DBP) or pulse pressure (PP). We performed two meta-analyses, one in individuals of European, South Asian, African and Hispanic descent (pan-ancestry, ~475,000), and the other in the subset of individuals of European descent (~423,000). Twenty-one SNVs were genome-wide significant (P < 5x10⁻⁸) for BP, of which four are new BP loci: rs9678851 (missense, SLC4A1AP), rs7437940 (AFAP1), rs13303 (missense, STAB1) and rs1055144 (7p15.2). In addition, we identified a potentially independent novel BP-associated SNV (rs3416322 (missense, SYNPO2L) at a known locus, uncorrelated with the previously reported SNVs. Two SNVs are associated with expression levels of nearby genes, and SNVs at three loci are associated with other traits. One SNV with a minor allele frequency < 0.01, (rs3025380 at DBH) was genome-wide significant. Conclusions: We report four novel loci associated with BP regulation, and one independent variant at an established BP locus. This analysis highlights several candidate genes with variation that alter protein function or gene expression for potential follow-up.A detailed list of acknowledgments is presented in the Online Appendix, together with the full list of members of the contributing consortia

Evidence of association with type 1 diabetes in the SLC11A1 gene region

BackgroundLinkage and congenic strain analyses using the nonobese diabetic (NOD) mouse as a model for human type 1 autoimmune diabetes (T1D) have identified several NOD mouse Idd (insulin dependent diabetes) loci, including Slc11a1 (formerly known as Nramp1). Genetic variants in the orthologous region encompassing SLC11A1 in human chromosome 2q35 have been reported to be associated with various immune-related diseases including T1D. Here, we have conducted association analysis of this candidate gene region, and then investigated potential correlations between the most T1D-associated variant and RNA expression of the SLC11A1 gene and its splice isoform.MethodsNine SNPs (rs2276631, rs2279015, rs1809231, rs1059823, rs17235409 (D543N), rs17235416 (3'UTR), rs3731865 (INT4), rs7573065 (-237 C → T) and rs4674297) were genotyped using TaqMan genotyping assays and the polymorphic promoter microsatellite (GT)n was genotyped using PCR and fragment length analysis. A maximum of 8,863 T1D British cases and 10,841 British controls, all of white European descent, were used to test association using logistic regression. A maximum of 5,696 T1D families were also tested for association using the transmission/disequilibrium test (TDT). We considered P ≤ 0.005 as evidence of association given that we tested nine variants in total. Upon identification of the most T1D-associated variant, we investigated the correlation between its genotype and SLC11A1 expression overall or with splice isoform ratio using 42 PAXgene whole blood samples from healthy donors by quantitative PCR (qPCR).ResultsUsing the case-control collection, rs3731865 (INT4) was identified to be the variant most associated with T1D (P = 1.55 × 10-6). There was also some evidence of association at rs4674297 (P = 1.57 × 10-4). No evidence of disease association was obtained at any of the loci using the family collections (PTDT ≥ 0.13). We also did not observe a correlation between rs3731865 genotypes and SLC11A1 expression overall or with splice isoform expression.ConclusionWe conclude that rs3731685 (INT4) in the SLC11A1 gene may be associated with T1D susceptibility in the European ancestry population studied. We did not observe a difference in SLC11A1 expression at the RNA level based on the genotypes of rs3731865 in whole blood samples. However, a potential correlation cannot be ruled out in purified cell subsets especially monocytes or macrophages

A comparison of Cox and logistic regression for use in genome-wide association studies of cohort and case-cohort design

Logistic regression is often used instead of Cox regression to analyse genome-wide association studies (GWAS) of single-nucleotide polymorphisms (SNPs) and disease outcomes with cohort and case-cohort designs, as it is less computationally expensive. Although Cox and logistic regression models have been compared previously in cohort studies, this work does not completely cover the GWAS setting nor extend to the case-cohort study design. Here, we evaluated Cox and logistic regression applied to cohort and case-cohort genetic association studies using simulated data and genetic data from the EPIC-CVD study. In the cohort setting, there was a modest improvement in power to detect SNP-disease associations using Cox regression compared with logistic regression, which increased as the disease incidence increased. In contrast, logistic regression had more power than (Prentice weighted) Cox regression in the case-cohort setting. Logistic regression yielded inflated effect estimates (assuming the hazard ratio is the underlying measure of association) for both study designs, especially for SNPs with greater effect on disease. Given logistic regression is substantially more computationally efficient than Cox regression in both settings, we propose a two-step approach to GWAS in cohort and case-cohort studies. First to analyse all SNPs with logistic regression to identify associated variants below a pre-defined P-value threshold, and second to fit Cox regression (appropriately weighted in case-cohort studies) to those identified SNPs to ensure accurate estimation of association with disease.This work was supported by the UK Medical Research Council (G66840) and Pfizer (G73632). EPIC-CVD was funded by grants awarded to the University of Cambridge from the EU Framework Programme 7 (HEALTH-F2-2012- 279233), the UK Medical Research Council (G0800270), British Heart Foundation (SP/09/002) and the European Research Council (268834). EPIC InterAct project was funded by the EU FP6 programme (LSHM_CT_2006_037197) and is also supported by MC_UU_12015/1 and MC_UU_12015/5

Leveraging human genetic data to investigate the cardiometabolic effects of glucose-dependent insulinotropic polypeptide signalling.

AIMS/HYPOTHESIS: The aim of this study was to leverage human genetic data to investigate the cardiometabolic effects of glucose-dependent insulinotropic polypeptide (GIP) signalling. METHODS: Data were obtained from summary statistics of large-scale genome-wide association studies. We examined whether genetic associations for type 2 diabetes liability in the GIP and GIPR genes co-localised with genetic associations for 11 cardiometabolic outcomes. For those outcomes that showed evidence of co-localisation (posterior probability >0.8), we performed Mendelian randomisation analyses to estimate the association of genetically proxied GIP signalling with risk of cardiometabolic outcomes, and to test whether this exceeded the estimate observed when considering type 2 diabetes liability variants from other regions of the genome. RESULTS: Evidence of co-localisation with genetic associations of type 2 diabetes liability at both the GIP and GIPR genes was observed for five outcomes. Mendelian randomisation analyses provided evidence for associations of lower genetically proxied type 2 diabetes liability at the GIP and GIPR genes with lower BMI (estimate in SD units -0.16, 95% CI -0.30, -0.02), C-reactive protein (-0.13, 95% CI -0.19, -0.08) and triacylglycerol levels (-0.17, 95% CI -0.22, -0.12), and higher HDL-cholesterol levels (0.19, 95% CI 0.14, 0.25). For all of these outcomes, the estimates were greater in magnitude than those observed when considering type 2 diabetes liability variants from other regions of the genome. CONCLUSIONS/INTERPRETATION: This study provides genetic evidence to support a beneficial role of sustained GIP signalling on cardiometabolic health greater than that expected from improved glycaemic control alone. Further clinical investigation is warranted. DATA AVAILABILITY: All data used in this study are publicly available. The scripts for the analysis are available at: https://github.com/vkarhune/GeneticallyProxiedGIP

A method for identifying genetic heterogeneity within phenotypically defined disease subgroups.

Many common diseases show wide phenotypic variation. We present a statistical method for determining whether phenotypically defined subgroups of disease cases represent different genetic architectures, in which disease-associated variants have different effect sizes in two subgroups. Our method models the genome-wide distributions of genetic association statistics with mixture Gaussians. We apply a global test without requiring explicit identification of disease-associated variants, thus maximizing power in comparison to standard variant-by-variant subgroup analysis. Where evidence for genetic subgrouping is found, we present methods for post hoc identification of the contributing genetic variants. We demonstrate the method on a range of simulated and test data sets, for which expected results are already known. We investigate subgroups of individuals with type 1 diabetes (T1D) defined by autoantibody positivity, establishing evidence for differential genetic architecture with positivity for thyroid-peroxidase-specific antibody, driven generally by variants in known T1D-associated genomic regions.We acknowledge the help of the Diabetes and Inflammation Laboratory Data Service for access and quality control procedures on the data sets used in this study. The JDRF/Wellcome Trust Diabetes and Inflammation Laboratory is in receipt of a Wellcome Trust Strategic Award (107212; J.A.T.) and receives funding from the NIHR Cambridge Biomedical Research Centre. J.L. is funded by the NIHR Cambridge Biomedical Research Centre and is on the Wellcome Trust PhD program in Mathematical Genomics and Medicine at the University of Cambridge. C.W. is funded by the MRC (grant MC_UP_1302/5). We thank M. Simmonds, S. Gough, J. Franklyn, and O. Brand for sharing their AITD genetic association data set and all patients with AITD and control subjects for participating in this study. The AITD UK national collection was funded by the Wellcome Trust. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Mendelian randomization for studying the effects of perturbing drug targets [version 1; peer review: awaiting peer review]

Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline

Mendelian randomization for studying the effects of perturbing drug targets [version 2; peer review: 3 approved, 1 approved with reservations]

Drugs whose targets have genetic evidence to support efficacy and safety are more likely to be approved after clinical development. In this paper, we provide an overview of how natural sequence variation in the genes that encode drug targets can be used in Mendelian randomization analyses to offer insight into mechanism-based efficacy and adverse effects. Large databases of summary level genetic association data are increasingly available and can be leveraged to identify and validate variants that serve as proxies for drug target perturbation. As with all empirical research, Mendelian randomization has limitations including genetic confounding, its consideration of lifelong effects, and issues related to heterogeneity across different tissues and populations. When appropriately applied, Mendelian randomization provides a useful empirical framework for using population level data to improve the success rates of the drug development pipeline

Assessing the causal association of glycine with risk of cardio-metabolic diseases

Circulating levels of glycine have previously been associated with lower incidence of coronary heart disease (CHD) and type 2 diabetes (T2D) but it remains uncertain if glycine plays an aetiological role. We present a meta-analysis of genome-wide association studies for glycine in 80,003 participants and investigate the causality and potential mechanisms of the association between glycine and cardio-metabolic diseases using genetic approaches. We identify 27 genetic loci, of which 22 have not previously been reported for glycine. We show that glycine is genetically associated with lower CHD risk and find that this may be partly driven by blood pressure. Evidence for a genetic association of glycine with T2D is weaker, but we find a strong inverse genetic effect of hyperinsulinaemia on glycine. Our findings strengthen evidence for a protective effect of glycine on CHD and show that the glycine-T2D association may be driven by a glycine-lowering effect of insulin resistance

Genetic Evidence for Repurposing of GLP1R (Glucagon-Like Peptide-1 Receptor) Agonists to Prevent Heart Failure.

Background This study was designed to investigate the genetic evidence for repurposing of GLP1R (glucagon-like peptide-1 receptor) agonists to prevent heart failure (HF) and whether the potential benefit exceeds the benefit conferred by more general glycemic control. Methods and Results We applied 2-sample Mendelian randomization of genetically proxied GLP1R agonism on HF as the main outcome and left ventricular ejection fraction as the secondary outcome. The associations were compared with those of general glycemic control on the same outcomes. Genetic associations were obtained from genome-wide association study summary statistics of type 2 diabetes mellitus (228 499 cases and 1 178 783 controls), glycated hemoglobin (n=344 182), HF (47,309 cases and 930 014 controls), and left ventricular ejection fraction (n=16 923). Genetic proxies for GLP1R agonism associated with reduced risk of HF (odds ratio per 1 mmol/mol decrease in glycated hemoglobin 0.75; 95% CI, 0.64-0.87; P=1.69×10-4), and higher left ventricular ejection fraction (SD change in left ventricular ejection fraction per 1 mmol/mol decrease in glycated hemoglobin 0.22%; 95% CI, 0.03-0.42; P=0.03). The magnitude of these benefits exceeded those expected from improved glycemic control more generally. The results were similar in sensitivity analyses, and we did not find evidence to suggest that these associations were mediated by reduced coronary artery disease risk. Conclusions This genetic evidence supports the repurposing of GLP1R agonists for preventing HF

Rare and common genetic determinants of metabolic individuality and their effects on human health

Garrod’s concept of ‘chemical individuality’ has contributed to comprehension of the molecular origins of human diseases. Untargeted high-throughput metabolomic technologies provide an in-depth snapshot of human metabolism at scale. We studied the genetic architecture of the human plasma metabolome using 913 metabolites assayed in 19,994 individuals and identified 2,599 variant–metabolite associations (P < 1.25 × 10−11) within 330 genomic regions, with rare variants (minor allele frequency ≤ 1%) explaining 9.4% of associations. Jointly modeling metabolites in each region, we identified 423 regional, co-regulated, variant–metabolite clusters called genetically influenced metabotypes. We assigned causal genes for 62.4% of these genetically influenced metabotypes, providing new insights into fundamental metabolite physiology and clinical relevance, including metabolite-guided discovery of potential adverse drug effects (DPYD and SRD5A2). We show strong enrichment of inborn errors of metabolism-causing genes, with examples of metabolite associations and clinical phenotypes of non-pathogenic variant carriers matching characteristics of the inborn errors of metabolism. Systematic, phenotypic follow-up of metabolite-specific genetic scores revealed multiple potential etiological relationships