Showing papers by "Michael Boehnke published in 2008"
Wellcome Trust Centre for Human Genetics1, University of Michigan2, University of Oxford3, Massachusetts Institute of Technology4, Brigham and Women's Hospital5, Harvard University6, Lund University7, Steno Diabetes Center8, University of Southern California9, National Institutes of Health10, Health Science University11, Novartis12, Ninewells Hospital13, University of Exeter14, University of Düsseldorf15, Queen Mary University of London16, Glostrup Hospital17, deCODE genetics18, University of Eastern Finland19, University of Cambridge20, Aarhus University21, University of North Carolina at Chapel Hill22, Norwegian University of Science and Technology23, Wellcome Trust Sanger Institute24, University of Bristol25, University of Helsinki26, Newcastle University27
TL;DR: The results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D, and detect at least six previously unknown loci with robust evidence for association.
Abstract: Genome-wide association (GWA) studies have identified multiple loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D). Established associations to common and rare variants explain only a small proportion of the heritability of T2D. As previously published analyses had limited power to identify variants with modest effects, we carried out meta-analysis of three T2D GWA scans comprising 10,128 individuals of European descent and approximately 2.2 million SNPs (directly genotyped and imputed), followed by replication testing in an independent sample with an effective sample size of up to 53,975. We detected at least six previously unknown loci with robust evidence for association, including the JAZF1 (P = 5.0 x 10(-14)), CDC123-CAMK1D (P = 1.2 x 10(-10)), TSPAN8-LGR5 (P = 1.1 x 10(-9)), THADA (P = 1.1 x 10(-9)), ADAMTS9 (P = 1.2 x 10(-8)) and NOTCH2 (P = 4.1 x 10(-8)) gene regions. Our results illustrate the value of large discovery and follow-up samples for gaining further insights into the inherited basis of T2D.
1,872 citations
University of Michigan1, National Institutes of Health2, University of Oxford3, University of Bristol4, National Research Council5, University of Maryland, Baltimore6, French Institute of Health and Medical Research7, University of Paris8, National Institute for Health and Welfare9, University of Southern California10, University of London11, University of Helsinki12, University of North Carolina at Chapel Hill13
TL;DR: In this paper, the authors used genotype imputation and meta-analysis to identify genetic variants influencing plasma lipid concentrations, using three genome-wide scans totaling 8,816 individuals and comprising 6,068 individuals specific to their study.
Abstract: To identify genetic variants influencing plasma lipid concentrations, we first used genotype imputation and meta-analysis to combine three genome-wide scans totaling 8,816 individuals and comprising 6,068 individuals specific to our study (1,874 individuals from the FUSION study of type 2 diabetes and 4,184 individuals from the SardiNIA study of aging-associated variables) and 2,758 individuals from the Diabetes Genetics Initiative, reported in a companion study in this issue. We subsequently examined promising signals in 11,569 additional individuals. Overall, we identify strongly associated variants in eleven loci previously implicated in lipid metabolism (ABCA1, the APOA5-APOA4-APOC3-APOA1 and APOE-APOC clusters, APOB, CETP, GCKR, LDLR, LPL, LIPC, LIPG and PCSK9) and also in several newly identified loci (near MVK-MMAB and GALNT2, with variants primarily associated with high-density lipoprotein (HDL) cholesterol; near SORT1, with variants primarily associated with low-density lipoprotein (LDL) cholesterol; near TRIB1, MLXIPL and ANGPTL3, with variants primarily associated with triglycerides; and a locus encompassing several genes near NCAN, with variants strongly associated with both triglycerides and LDL cholesterol). Notably, the 11 independent variants associated with increased LDL cholesterol concentrations in our study also showed increased frequency in a sample of coronary artery disease cases versus controls.
1,616 citations
Broad Institute1, Boston Children's Hospital2, University of Michigan3, Ludwig Maximilian University of Munich4, Harvard University5, Brigham and Women's Hospital6, National Institutes of Health7, Lund University8, United States Department of Health and Human Services9, Helsinki University Central Hospital10, University of Helsinki11, Wellcome Trust Sanger Institute12, University of North Carolina at Chapel Hill13
TL;DR: A meta-analysis of genome-wide association study data of height from 15,821 individuals at 2.2 million SNPs found 10 newly identified and two previously reported loci were strongly associated with variation in height, and highlight several pathways as important regulators of human stature.
Abstract: Identification of ten loci associated with height highlights new biological pathways in human growth
598 citations
University of Pennsylvania1, Massachusetts Institute of Technology2, Boston Children's Hospital3, Scripps Health4, University of Washington5, King's College London6, Illumina7, University of Oxford8, Broad Institute9, National Institutes of Health10, Beijing Institute of Genomics11, Brigham and Women's Hospital12, McGill University13, Cedars-Sinai Medical Center14, University of California, Los Angeles15, University of Texas Health Science Center at Houston16, Lund University17, University of Michigan18, University of Leeds19, Harvard University20, University of Cambridge21, Queen Mary University of London22, University of Leicester23, Merck & Co.24, University of Mississippi25, University of Ulm26, McMaster University27
TL;DR: A gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes and it is demonstrated that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related lociAcross all major HapMap populations.
Abstract: A wealth of genetic associations for cardiovascular and metabolic phenotypes in humans has been accumulating over the last decade, in particular a large number of loci derived from recent genome wide association studies (GWAS) True complex disease-associated loci often exert modest effects, so their delineation currently requires integration of diverse phenotypic data from large studies to ensure robust meta-analyses We have designed a gene-centric 50 K single nucleotide polymorphism (SNP) array to assess potentially relevant loci across a range of cardiovascular, metabolic and inflammatory syndromes The array utilizes a ‘‘cosmopolitan’’ tagging approach to capture the genetic diversity across ,2,000 loci in populations represented in the HapMap and SeattleSNPs projects The array content is informed by GWAS of vascular and inflammatory disease, expression quantitative trait loci implicated in atherosclerosis, pathway based approaches and comprehensive literature searching The custom flexibility of the array platform facilitated interrogation of loci at differing stringencies, according to a gene prioritization strategy that allows saturation of high priority loci with a greater density of markers than the existing GWAS tools, particularly in African HapMap samples We also demonstrate that the IBC array can be used to complement GWAS, increasing coverage in high priority CVD-related loci across all major HapMap populations DNA from over 200,000 extensively phenotyped individuals will be genotyped with this array with a significant portion of the generated data being released into the academic domain facilitating in silico replication attempts, analyses of rare variants and cross-cohort meta-analyses in diverse populations These datasets will also facilitate more robust secondary analyses, such as explorations with alternative genetic models, epistasis and gene-environment interactions
400 citations
University of Michigan1, National Institutes of Health2, University of Virginia3, University of Maryland, Baltimore4, University of Bristol5, Broad Institute6, Johns Hopkins University7, University of London8, University of Southern California9, University of Helsinki10, National Institute for Health and Welfare11, Veterans Health Administration12, University of Texas Health Science Center at Houston13, University of North Carolina at Chapel Hill14
TL;DR: It is shown that common variants in the osteoarthritis-associated locus GDF5-UQCC contribute to variation in height with an estimated additive effect of 0.44 cm (overall P < 10−15).
Abstract: Identifying genetic variants that influence human height will advance our understanding of skeletal growth and development. Several rare genetic variants have been convincingly and reproducibly associated with height in mendelian syndromes, and common variants in the transcription factor gene HMGA2 are associated with variation in height in the general population. Here we report genome-wide association analyses, using genotyped and imputed markers, of 6,669 individuals from Finland and Sardinia, and follow-up analyses in an additional 28,801 individuals. We show that common variants in the osteoarthritis-associated locus GDF5-UQCC contribute to variation in height with an estimated additive effect of 0.44 cm (overall P < 10(-15)). Our results indicate that there may be a link between the genetic basis of height and osteoarthritis, potentially mediated through alterations in bone growth and development.
399 citations
University of Virginia1, National Institutes of Health2, University of Michigan3, Broad Institute4, National Research Council5, University of Maryland, Baltimore6, University of Bristol7, Novo Nordisk8, University of Eastern Finland9, University of London10, University of Copenhagen11, University of Helsinki12, National Institute for Health and Welfare13, University of Southern California14, Aarhus University15, University of North Carolina at Chapel Hill16
TL;DR: The results in combination with data reported in the literature suggest that G6PC2, a glucose-6-phosphatase almost exclusively expressed in pancreatic islet cells, may underlie variation in fasting glucose, though it is possible that ABCB11, which is expressed primarily in liver, may also contribute to such variation.
Abstract: Identifying the genetic variants that regulate fasting glucose concentrations may further our understanding of the pathogenesis of diabetes. We therefore investigated the association of fasting glucose levels with SNPs in 2 genome-wide scans including a total of 5,088 nondiabetic individuals from Finland and Sardinia. We found a significant association between the SNP rs563694 and fasting glucose concentrations (P = 3.5 x 10(-7)). This association was further investigated in an additional 18,436 nondiabetic individuals of mixed European descent from 7 different studies. The combined P value for association in these follow-up samples was 6.9 x 10(-26), and combining results from all studies resulted in an overall P value for association of 6.4 x 10(-33). Across these studies, fasting glucose concentrations increased 0.01-0.16 mM with each copy of the major allele, accounting for approximately 1% of the total variation in fasting glucose. The rs563694 SNP is located between the genes glucose-6-phosphatase catalytic subunit 2 (G6PC2) and ATP-binding cassette, subfamily B (MDR/TAP), member 11 (ABCB11). Our results in combination with data reported in the literature suggest that G6PC2, a glucose-6-phosphatase almost exclusively expressed in pancreatic islet cells, may underlie variation in fasting glucose, though it is possible that ABCB11, which is expressed primarily in liver, may also contribute to such variation.
173 citations
TL;DR: This study provides an effective gene-based approach to association study design and analysis and implicated novel genes, including RAPGEF1 and TP53, in type 2 diabetes susceptibility.
Abstract: Objective: Type 2 diabetes (T2D) is a common complex disorder with environmental and genetic components. We used a candidate gene-based approach to identify single nucleotide polymorphism (SNP) variants in 222 candidate genes that influence susceptibility to T2D.
Research Design and Method: In a case-control study of 1,161 T2D and 1,174 normal glucose tolerant (NGT) control Finns, we genotyped 3,531 tagSNPs and annotation-based SNPs and imputed an additional 7,498 SNPs, providing 99.9% coverage of common HapMap variants in the 222 candidate genes. Selected SNPs were genotyped in an additional 1,211 T2D cases and 1,259 NGT controls, also from Finland.
Results: Using SNP and gene-based analysis methods, we replicated previously reported SNP- T2D associations in PPARG, KCNJ11 , and SLC2A2 , identified significant SNPs in genes with previously reported associations, ENPP1 (rs2021966, p=.00026) and NRF1 (rs1882095, p=.00096), and implicated novel genes in T2D susceptibility including RAPGEF1 (rs4740283, p=.00013) and TP53 (rs1042522; Arg72Pro, p=.00086).
Conclusion: Our study provides an effective gene-based approach to association study design and analysis. One or more of the newly implicated genes may contribute to T2D pathogenesis; analysis of additional samples will be necessary to determine their effect on susceptibility.
112 citations
TL;DR: Genome-wide association studies are providing new insights into the genetic basis of metabolic and cardiovascular traits by identifying common variants in approximately 50 loci and determining the functional impact of the underlying alleles and genes.
Abstract: Genome-wide association studies are providing new insights into the genetic basis of metabolic and cardiovascular traits. In the past 3 years, common variants in approximately 50 loci have been strongly associated with metabolic and cardiovascular traits. Several of these loci have implicated genes without a previously known connection with metabolism. Further studies will be required to characterize the full impact of these loci on metabolism. Many of the identified loci include multiple independent variants that influence the same metabolic or cardiovascular trait and a few loci harbor independent variants that each influence distinct traits. The total proportion of trait heritability explained by variants identified so far is still modest (typically <10%). Future studies will build on these successes by identifying additional common and rare variants and by determining the functional impact of the underlying alleles and genes.
87 citations
TL;DR: The simulation results suggested that uneven marker density across studies results in substantial variation in empirical type I error rates for all meta-analysis methods, but that 2 cM bins and scores that make more explicit use of linkage evidence, especially the truncated p values, reduce this problem.
Abstract: Background: The International Type 2 Diabetes Linkage Analysis Consortium was formed to localize type 2 diabetes predisposing variants based on 23 autosomal linkage scans. Methods: We carried out meta-analysis using the genome scan meta-analysis (GSMA) method which divides the genome into bins of ∼30 cM, ranks the best linkage results in each bin for each sample, and then sums the ranks across samples. We repeated the meta-analysis using 2 cM bins, and/or replacing bin ranks with measures of linkage evidence: bin maximum LOD score or bin minimum p value for bins with p value Results: Our analyses provided modest evidence for type 2 diabetes-predisposing variants on chromosomes 4, 10, and 14 (using LOD scores or truncated p values), or chromosome 10 and 16 (using ranks). Our simulation results suggested that uneven marker density across studies results in substantial variation in empirical type I error rates for all meta-analysis methods, but that 2 cM bins and scores that make more explicit use of linkage evidence, especially the truncated p values, reduce this problem. Conclusion: We identified regions modestly linked with type 2 diabetes by summarizing results from 23 autosomal genome scans.
41 citations