Genetically determined height and coronary artery disease.

BACKGROUND: The nature and underlying mechanisms of an inverse association between adult height and the risk of coronary artery disease (CAD) are unclear. METHODS: We used a genetic approach to investigate the association between height and CAD, using 180 height-associated genetic variants. We tested the association between a change in genetically determined height of 1 SD (6.5 cm) with the risk of CAD in 65,066 cases and 128,383 controls. Using individual-level genotype data from 18,249 persons, we also examined the risk of CAD associated with the presence of various numbers of height-associated alleles. To identify putative mechanisms, we analyzed whether genetically determined height was associated with known cardiovascular risk factors and performed a pathway analysis of the height-associated genes. RESULTS: We observed a relative increase of 13.5% (95% confidence interval [CI], 5.4 to 22.1; P<0.001) in the risk of CAD per 1-SD decrease in genetically determined height. There was a graded relationship between the presence of an increased number of height-raising variants and a reduced risk of CAD (odds ratio for height quartile 4 versus quartile 1, 0.74; 95% CI, 0.68 to 0.84; P<0.001). Of the 12 risk factors that we studied, we observed significant associations only with levels of low-density lipoprotein cholesterol and triglycerides (accounting for approximately 30% of the association). We identified several overlapping pathways involving genes associated with both development and atherosclerosis. CONCLUSIONS: There is a primary association between a genetically determined shorter height and an increased risk of CAD, a link that is partly explained by the association between shorter height and an adverse lipid profile. Shared biologic processes that determine achieved height and the development of atherosclerosis may explain some of the association. (Funded by the British Heart Foundation and others.)

Genome-wide association study of nevirapine hypersensitivity in a sub-Saharan African HIV-infected population

The initial GWAS was funded by the International Serious Adverse Events Consortium (iSAEC). The iSAEC is a non-profit organization dedicated to identifying and validating DNA variants useful in predicting the risk of drug-related serious adverse events. The Consortium brings together the pharmaceutical industry, regulatory authorities and academic centres to address clinical and scientific issues associated with the genetics of drug-related serious adverse events. The iSAEC’s current funding members include: Abbott, Amgen, AstraZeneca, Daiichi Sankyo, GlaxoSmithKline, Merck, Novartis, Pfizer, Takeda and the Wellcome Trust. Mas Chaponda was funded by a 3 year Wellcome Trust training fellowship WT078857MA administered through the University of Liverpool. Malawi-Liverpool-Wellcome Trust Clinical Research Programme is funded through a Core Programme Grant award from the Wellcome Trust. Munir Pirmohamed is a National Institute for Health Research Senior Investigator, and also wishes to thank the MRC Centre for Drug Safety Science for support. The DART study was supported by the UK Medical Research Council (grant number G0600344), the UK Department for International Development and the Rockefeller Foundation. Andrew P. Morris is a Wellcome Trust Senior Research Fellow in Basic Biomedical Science (grant number WT098017). Louise Y. Takeshita is funded by a PhD fellowship from CNPq (National Council for Scientific and Technological Development, Brazil). Panos Deloukas’ work forms part of the research themes contributing to the translational research portfolio of Barts Cardiovascular Biomedical Research Unit which is supported and funded by the National Institute for Health Research

Fifteen new risk loci for coronary artery disease highlight arterial-wall-specific mechanisms

Coronary artery disease (CAD) is a leading cause of morbidity and mortality worldwide. Although 58 genomic regions have been associated with CAD thus far, most of the heritability is unexplained, indicating that additional susceptibility loci await identification. An efficient discovery strategy may be larger-scale evaluation of promising associations suggested by genome-wide association studies (GWAS). Hence, we genotyped 56,309 participants using a targeted gene array derived from earlier GWAS results and performed meta-analysis of results with 194,427 participants previously genotyped, totaling 88,192 CAD cases and 162,544 controls. We identified 25 new SNP-CAD associations (P &lt; 5 × 10(-8), in fixed-effects meta-analysis) from 15 genomic regions, including SNPs in or near genes involved in cellular adhesion, leukocyte migration and atherosclerosis (PECAM1, rs1867624), coagulation and inflammation (PROCR, rs867186 (p.Ser219Gly)) and vascular smooth muscle cell differentiation (LMOD1, rs2820315). Correlation of these regions with cell-type-specific gene expression and plasma protein levels sheds light on potential disease mechanisms

Sequencing and association analysis of the type 1 diabetes - linked region on chromosome 10p12-q11

Background: In an effort to locate susceptibility genes for type I diabetes (TID) several genome-wide linkage scans have been undertaken. A chromosomal region designated IDDM10 retained genome-wide significance in a combined analysis of the main linkage scans. Here, we studied sequence polymorphisms in 23 Mb on chromosome 10p12-q11, including the putative IDDM10 region, to identify genes associated with TID. Results: Initially, we resequenced the functional candidate genes, CREM and SDF1, located in this region, genotyped 13 tag single nucleotide polymorphisms (SNPs) and found no association with TID. We then undertook analysis of the whole 23 Mb region. We constructed and sequenced a contig tile path from two bacterial artificial clone libraries. By comparison with a clone library from an unrelated person used in the Human Genome Project, we identified 12,058 SNPs. We genotyped 303 SNPs and 25 polymorphic microsatellite markers in 765 multiplex TID families and followed up 22 associated polymorphisms in up to 2,857 families. We found nominal evidence of association in six loci (P = 0.05-0.0026), located near the PAPDI gene. Therefore, we resequenced 38.8 kb in this region, found 147 SNPs and genotyped 84 of them in the TID families. We also tested 13 polymorphisms in the PAPDI gene and in five other loci in 1,612 TID patients and 1,828 controls from the UK. Overall, only the D10S193 microsatellite marker located 28 kb downstream of PAPDI showed nominal evidence of association in both TID families and in the case-control sample (P = 0.037 and 0.03, respectively). Conclusion: We conclude that polymorphisms in the CREM and SDFI genes have no major effect on TID. The weak TID association that we detected in the association scan near the PAPDI gene may be either false or due to a small genuine effect, and cannot explain linkage at the IDDM10 region

Common Variants at 10 Genomic Loci Influence Hemoglobin A(1C) Levels via Glycemic and Nonglycemic Pathways

OBJECTIVE Glycated hemoglobin (HbA1c), used to monitor and diagnose diabetes, is influenced by average glycemia over a 2- to 3-month period. Genetic factors affecting expression, turnover, and abnormal glycation of hemoglobin could also be associated with increased levels of HbA1c. We aimed to identify such genetic factors and investigate the extent to which they influence diabetes classification based on HbA1c levels. RESEARCH DESIGN AND METHODS We studied associations with HbA1c in up to 46,368 nondiabetic adults of European descent from 23 genome-wide association studies (GWAS) and 8 cohorts with de novo genotyped single nucleotide polymorphisms (SNPs). We combined studies using inverse-variance meta-analysis and tested mediation by glycemia using conditional analyses. We estimated the global effect of HbA1c loci using a multilocus risk score, and used net reclassification to estimate genetic effects on diabetes screening. RESULTS Ten loci reached genome-wide significant association with HbA1c, including six new loci near FN3K (lead SNP/P value, rs1046896/P = 1.6 × 10−26), HFE (rs1800562/P = 2.6 × 10−20), TMPRSS6 (rs855791/P = 2.7 × 10−14), ANK1 (rs4737009/P = 6.1 × 10−12), SPTA1 (rs2779116/P = 2.8 × 10−9) and ATP11A/TUBGCP3 (rs7998202/P = 5.2 × 10−9), and four known HbA1c loci: HK1 (rs16926246/P = 3.1 × 10−54), MTNR1B (rs1387153/P = 4.0 × 10−11), GCK (rs1799884/P = 1.5 × 10−20) and G6PC2/ABCB11 (rs552976/P = 8.2 × 10−18). We show that associations with HbA1c are partly a function of hyperglycemia associated with 3 of the 10 loci (GCK, G6PC2 and MTNR1B). The seven nonglycemic loci accounted for a 0.19 (% HbA1c) difference between the extreme 10% tails of the risk score, and would reclassify ∼2% of a general white population screened for diabetes with HbA1c. CONCLUSIONS GWAS identified 10 genetic loci reproducibly associated with HbA1c. Six are novel and seven map to loci where rarer variants cause hereditary anemias and iron storage disorders. Common variants at these loci likely influence HbA1c levels via erythrocyte biology, and confer a small but detectable reclassification of diabetes diagnosis by HbA1c