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1.  Exome array analysis identifies novel loci and low-frequency variants for insulin processing and secretion 
Nature genetics  2012;45(2):197-201.
Insulin secretion plays a critical role in glucose homeostasis, and failure to secrete sufficient insulin is a hallmark of type 2 diabetes. Genome-wide association studies (GWAS) have identified loci contributing to insulin processing and secretion1,2; however, a substantial fraction of the genetic contribution remains undefined. To examine low-frequency (minor allele frequency (MAF) 0.5% to 5%) and rare (MAF<0.5%) nonsynonymous variants, we analyzed exome array data in 8,229 non-diabetic Finnish males. We identified low-frequency coding variants associated with fasting proinsulin levels at the SGSM2 and MADD GWAS loci and three novel genes with low-frequency variants associated with fasting proinsulin or insulinogenic index: TBC1D30, KANK1, and PAM. We also demonstrate that the interpretation of single-variant and gene-based tests needs to consider the effects of noncoding SNPs nearby and megabases (Mb) away. This study demonstrates that exome array genotyping is a valuable approach to identify low-frequency variants that contribute to complex traits.
doi:10.1038/ng.2507
PMCID: PMC3727235  PMID: 23263489
2.  No Interactions Between Previously Associated 2-Hour Glucose Gene Variants and Physical Activity or BMI on 2-Hour Glucose Levels 
Scott, Robert A. | Chu, Audrey Y. | Grarup, Niels | Manning, Alisa K. | Hivert, Marie-France | Shungin, Dmitry | Tönjes, Anke | Yesupriya, Ajay | Barnes, Daniel | Bouatia-Naji, Nabila | Glazer, Nicole L. | Jackson, Anne U. | Kutalik, Zoltán | Lagou, Vasiliki | Marek, Diana | Rasmussen-Torvik, Laura J. | Stringham, Heather M. | Tanaka, Toshiko | Aadahl, Mette | Arking, Dan E. | Bergmann, Sven | Boerwinkle, Eric | Bonnycastle, Lori L. | Bornstein, Stefan R. | Brunner, Eric | Bumpstead, Suzannah J. | Brage, Soren | Carlson, Olga D. | Chen, Han | Chen, Yii-Der Ida | Chines, Peter S. | Collins, Francis S. | Couper, David J. | Dennison, Elaine M. | Dowling, Nicole F. | Egan, Josephine S. | Ekelund, Ulf | Erdos, Michael R. | Forouhi, Nita G. | Fox, Caroline S. | Goodarzi, Mark O. | Grässler, Jürgen | Gustafsson, Stefan | Hallmans, Göran | Hansen, Torben | Hingorani, Aroon | Holloway, John W. | Hu, Frank B. | Isomaa, Bo | Jameson, Karen A. | Johansson, Ingegerd | Jonsson, Anna | Jørgensen, Torben | Kivimaki, Mika | Kovacs, Peter | Kumari, Meena | Kuusisto, Johanna | Laakso, Markku | Lecoeur, Cécile | Lévy-Marchal, Claire | Li, Guo | Loos, Ruth J.F. | Lyssenko, Valeri | Marmot, Michael | Marques-Vidal, Pedro | Morken, Mario A. | Müller, Gabriele | North, Kari E. | Pankow, James S. | Payne, Felicity | Prokopenko, Inga | Psaty, Bruce M. | Renström, Frida | Rice, Ken | Rotter, Jerome I. | Rybin, Denis | Sandholt, Camilla H. | Sayer, Avan A. | Shrader, Peter | Schwarz, Peter E.H. | Siscovick, David S. | Stančáková, Alena | Stumvoll, Michael | Teslovich, Tanya M. | Waeber, Gérard | Williams, Gordon H. | Witte, Daniel R. | Wood, Andrew R. | Xie, Weijia | Boehnke, Michael | Cooper, Cyrus | Ferrucci, Luigi | Froguel, Philippe | Groop, Leif | Kao, W.H. Linda | Vollenweider, Peter | Walker, Mark | Watanabe, Richard M. | Pedersen, Oluf | Meigs, James B. | Ingelsson, Erik | Barroso, Inês | Florez, Jose C. | Franks, Paul W. | Dupuis, Josée | Wareham, Nicholas J. | Langenberg, Claudia
Diabetes  2012;61(5):1291-1296.
Gene–lifestyle interactions have been suggested to contribute to the development of type 2 diabetes. Glucose levels 2 h after a standard 75-g glucose challenge are used to diagnose diabetes and are associated with both genetic and lifestyle factors. However, whether these factors interact to determine 2-h glucose levels is unknown. We meta-analyzed single nucleotide polymorphism (SNP) × BMI and SNP × physical activity (PA) interaction regression models for five SNPs previously associated with 2-h glucose levels from up to 22 studies comprising 54,884 individuals without diabetes. PA levels were dichotomized, with individuals below the first quintile classified as inactive (20%) and the remainder as active (80%). BMI was considered a continuous trait. Inactive individuals had higher 2-h glucose levels than active individuals (β = 0.22 mmol/L [95% CI 0.13–0.31], P = 1.63 × 10−6). All SNPs were associated with 2-h glucose (β = 0.06–0.12 mmol/allele, P ≤ 1.53 × 10−7), but no significant interactions were found with PA (P > 0.18) or BMI (P ≥ 0.04). In this large study of gene–lifestyle interaction, we observed no interactions between genetic and lifestyle factors, both of which were associated with 2-h glucose. It is perhaps unlikely that top loci from genome-wide association studies will exhibit strong subgroup-specific effects, and may not, therefore, make the best candidates for the study of interactions.
doi:10.2337/db11-0973
PMCID: PMC3331745  PMID: 22415877
3.  Use of microarray hybrid capture and next-generation sequencing to identify the anatomy of a transgene 
Nucleic Acids Research  2013;41(6):e70.
Transgenic animals are extensively used to model human disease. Typically, the transgene copy number is estimated, but the exact integration site and configuration of the foreign DNA remains uncharacterized. When transgenes have been closely examined, some unexpected configurations have been found. Here, we describe a method to recover transgene insertion sites and assess structural rearrangements of host and transgene DNA using microarray hybridization and targeted sequence capture. We used information about the transgene insertion site to develop a polymerase chain reaction genotyping assay to distinguish heterozygous from homozygous transgenic animals. Although we worked with a bacterial artificial chromosome transgenic mouse line, this method can be used to analyse the integration site and configuration of any foreign DNA in a sequenced genome.
doi:10.1093/nar/gks1463
PMCID: PMC3616733  PMID: 23314155
4.  The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits 
PLoS Genetics  2012;8(8):e1002793.
Genome-wide association studies have identified hundreds of loci for type 2 diabetes, coronary artery disease and myocardial infarction, as well as for related traits such as body mass index, glucose and insulin levels, lipid levels, and blood pressure. These studies also have pointed to thousands of loci with promising but not yet compelling association evidence. To establish association at additional loci and to characterize the genome-wide significant loci by fine-mapping, we designed the “Metabochip,” a custom genotyping array that assays nearly 200,000 SNP markers. Here, we describe the Metabochip and its component SNP sets, evaluate its performance in capturing variation across the allele-frequency spectrum, describe solutions to methodological challenges commonly encountered in its analysis, and evaluate its performance as a platform for genotype imputation. The metabochip achieves dramatic cost efficiencies compared to designing single-trait follow-up reagents, and provides the opportunity to compare results across a range of related traits. The metabochip and similar custom genotyping arrays offer a powerful and cost-effective approach to follow-up large-scale genotyping and sequencing studies and advance our understanding of the genetic basis of complex human diseases and traits.
Author Summary
Recent genetic studies have identified hundreds of regions of the human genome that contribute to risk for type 2 diabetes, coronary artery disease and myocardial infarction, and to related quantitative traits such as body mass index, glucose and insulin levels, blood lipid levels, and blood pressure. These results motivate two central questions: (1) can further genetic investigation identify additional associated regions?; and (2) can more detailed genetic investigation help us identify the causal variants (or variants more strongly correlated with the causal variants) in the regions identified so far? Addressing these questions requires assaying many genetic variants in DNA samples from thousands of individuals, which is expensive and timeconsuming when done a few SNPs at a time. To facilitate these investigations, we designed the “Metabochip,” a custom genotyping array that assays variation in nearly 200,000 sites in the human genome. Here we describe the Metabochip, evaluate its performance in assaying human genetic variation, and describe solutions to methodological challenges commonly encountered in its analysis.
doi:10.1371/journal.pgen.1002793
PMCID: PMC3410907  PMID: 22876189
5.  Evaluation of common genetic variants in 82 candidate genes as risk factors for neural tube defects 
BMC Medical Genetics  2012;13:62.
Background
Neural tube defects (NTDs) are common birth defects (~1 in 1000 pregnancies in the US and Europe) that have complex origins, including environmental and genetic factors. A low level of maternal folate is one well-established risk factor, with maternal periconceptional folic acid supplementation reducing the occurrence of NTD pregnancies by 50-70%. Gene variants in the folate metabolic pathway (e.g., MTHFR rs1801133 (677 C > T) and MTHFD1 rs2236225 (R653Q)) have been found to increase NTD risk. We hypothesized that variants in additional folate/B12 pathway genes contribute to NTD risk.
Methods
A tagSNP approach was used to screen common variation in 82 candidate genes selected from the folate/B12 pathway and NTD mouse models. We initially genotyped polymorphisms in 320 Irish triads (NTD cases and their parents), including 301 cases and 341 Irish controls to perform case–control and family based association tests. Significantly associated polymorphisms were genotyped in a secondary set of 250 families that included 229 cases and 658 controls. The combined results for 1441 SNPs were used in a joint analysis to test for case and maternal effects.
Results
Nearly 70 SNPs in 30 genes were found to be associated with NTDs at the p < 0.01 level. The ten strongest association signals (p-value range: 0.0003–0.0023) were found in nine genes (MFTC, CDKN2A, ADA, PEMT, CUBN, GART, DNMT3A, MTHFD1 and T (Brachyury)) and included the known NTD risk factor MTHFD1 R653Q (rs2236225). The single strongest signal was observed in a new candidate, MFTC rs17803441 (OR = 1.61 [1.23-2.08], p = 0.0003 for the minor allele). Though nominally significant, these associations did not remain significant after correction for multiple hypothesis testing.
Conclusions
To our knowledge, with respect to sample size and scope of evaluation of candidate polymorphisms, this is the largest NTD genetic association study reported to date. The scale of the study and the stringency of correction are likely to have contributed to real associations failing to survive correction. We have produced a ranked list of variants with the strongest association signals. Variants in the highest rank of associations are likely to include true associations and should be high priority candidates for further study of NTD risk.
doi:10.1186/1471-2350-13-62
PMCID: PMC3458983  PMID: 22856873
Neural tube defects; Spina bifida; Folic acid; One-carbon metabolism; Candidate gene
6.  A Genome-Wide Association Study of Type 2 Diabetes in Finns Detects Multiple Susceptibility Variants 
Science (New York, N.Y.)  2007;316(5829):1341-1345.
Identifying the genetic variants that increase the risk of type 2 diabetes (T2D) in humans has been a formidable challenge. Adopting a genome-wide association strategy, we genotyped 1161 Finnish T2D cases and 1174 Finnish normal glucose-tolerant (NGT) controls with >315,000 single-nucleotide polymorphisms (SNPs) and imputed genotypes for an additional >2 million autosomal SNPs. We carried out association analysis with these SNPs to identify genetic variants that predispose to T2D, compared our T2D association results with the results of two similar studies, and genotyped 80 SNPs in an additional 1215 Finnish T2D cases and 1258 Finnish NGT controls. We identify T2D-associated variants in an intergenic region of chromosome 11p12, contribute to the identification of T2D-associated variants near the genes IGF2BP2 and CDKAL1 and the region of CDKN2A and CDKN2B, and confirm that variants near TCF7L2, SLC30A8, HHEX, FTO, PPARG, and KCNJ11 are associated with T2D risk. This brings the number of T2D loci now confidently identified to at least 10.
doi:10.1126/science.1142382
PMCID: PMC3214617  PMID: 17463248
7.  Global epigenomic analysis of primary human pancreatic islets provides insights into type 2 diabetes susceptibility loci 
Cell metabolism  2010;12(5):443-455.
Summary
Identifying cis-regulatory elements is important to understand how human pancreatic islets modulate gene expression in physiologic or pathophysiologic (e.g., diabetic) conditions. We conducted genome-wide analysis of DNase I hypersensitive sites, histone H3 lysine methylation modifications (K4me1, K4me3, K79me2), and CCCTC factor (CTCF) binding in human islets. This identified ~18,000 putative promoters (several hundred unannotated and islet-active). Surprisingly, active promoter modifications were absent at genes encoding islet-specific hormones, suggesting a distinct regulatory mechanism. Of 34,039 distal (non-promoter) regulatory elements, 47% are islet-unique and 22% are CTCF-bound. In the 18 type 2 diabetes (T2D)-associated loci, we identified 118 putative regulatory elements and confirmed enhancer activity for 12/33 tested. Among 6 regulatory elements harboring T2D-associated variants, 2 exhibit significant allele-specific differences in activity. These findings present a global snapshot of the human islet epigenome and should provide functional context for non-coding variants emerging from genetic studies of T2D and other islet disorders.
doi:10.1016/j.cmet.2010.09.012
PMCID: PMC3026436  PMID: 21035756
8.  Detailed Physiologic Characterization Reveals Diverse Mechanisms for Novel Genetic Loci Regulating Glucose and Insulin Metabolism in Humans 
Diabetes  2010;59(5):1266-1275.
OBJECTIVE
Recent genome-wide association studies have revealed loci associated with glucose and insulin-related traits. We aimed to characterize 19 such loci using detailed measures of insulin processing, secretion, and sensitivity to help elucidate their role in regulation of glucose control, insulin secretion and/or action.
RESEARCH DESIGN AND METHODS
We investigated associations of loci identified by the Meta-Analyses of Glucose and Insulin-related traits Consortium (MAGIC) with circulating proinsulin, measures of insulin secretion and sensitivity from oral glucose tolerance tests (OGTTs), euglycemic clamps, insulin suppression tests, or frequently sampled intravenous glucose tolerance tests in nondiabetic humans (n = 29,084).
RESULTS
The glucose-raising allele in MADD was associated with abnormal insulin processing (a dramatic effect on higher proinsulin levels, but no association with insulinogenic index) at extremely persuasive levels of statistical significance (P = 2.1 × 10−71). Defects in insulin processing and insulin secretion were seen in glucose-raising allele carriers at TCF7L2, SCL30A8, GIPR, and C2CD4B. Abnormalities in early insulin secretion were suggested in glucose-raising allele carriers at MTNR1B, GCK, FADS1, DGKB, and PROX1 (lower insulinogenic index; no association with proinsulin or insulin sensitivity). Two loci previously associated with fasting insulin (GCKR and IGF1) were associated with OGTT-derived insulin sensitivity indices in a consistent direction.
CONCLUSIONS
Genetic loci identified through their effect on hyperglycemia and/or hyperinsulinemia demonstrate considerable heterogeneity in associations with measures of insulin processing, secretion, and sensitivity. Our findings emphasize the importance of detailed physiological characterization of such loci for improved understanding of pathways associated with alterations in glucose homeostasis and eventually type 2 diabetes.
doi:10.2337/db09-1568
PMCID: PMC2857908  PMID: 20185807
9.  Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis 
Voight, Benjamin F | Scott, Laura J | Steinthorsdottir, Valgerdur | Morris, Andrew P | Dina, Christian | Welch, Ryan P | Zeggini, Eleftheria | Huth, Cornelia | Aulchenko, Yurii S | Thorleifsson, Gudmar | McCulloch, Laura J | Ferreira, Teresa | Grallert, Harald | Amin, Najaf | Wu, Guanming | Willer, Cristen J | Raychaudhuri, Soumya | McCarroll, Steve A | Langenberg, Claudia | Hofmann, Oliver M | Dupuis, Josée | Qi, Lu | Segrè, Ayellet V | van Hoek, Mandy | Navarro, Pau | Ardlie, Kristin | Balkau, Beverley | Benediktsson, Rafn | Bennett, Amanda J | Blagieva, Roza | Boerwinkle, Eric | Bonnycastle, Lori L | Boström, Kristina Bengtsson | Bravenboer, Bert | Bumpstead, Suzannah | Burtt, Noisël P | Charpentier, Guillaume | Chines, Peter S | Cornelis, Marilyn | Couper, David J | Crawford, Gabe | Doney, Alex S F | Elliott, Katherine S | Elliott, Amanda L | Erdos, Michael R | Fox, Caroline S | Franklin, Christopher S | Ganser, Martha | Gieger, Christian | Grarup, Niels | Green, Todd | Griffin, Simon | Groves, Christopher J | Guiducci, Candace | Hadjadj, Samy | Hassanali, Neelam | Herder, Christian | Isomaa, Bo | Jackson, Anne U | Johnson, Paul R V | Jørgensen, Torben | Kao, Wen H L | Klopp, Norman | Kong, Augustine | Kraft, Peter | Kuusisto, Johanna | Lauritzen, Torsten | Li, Man | Lieverse, Aloysius | Lindgren, Cecilia M | Lyssenko, Valeriya | Marre, Michel | Meitinger, Thomas | Midthjell, Kristian | Morken, Mario A | Narisu, Narisu | Nilsson, Peter | Owen, Katharine R | Payne, Felicity | Perry, John R B | Petersen, Ann-Kristin | Platou, Carl | Proença, Christine | Prokopenko, Inga | Rathmann, Wolfgang | Rayner, N William | Robertson, Neil R | Rocheleau, Ghislain | Roden, Michael | Sampson, Michael J | Saxena, Richa | Shields, Beverley M | Shrader, Peter | Sigurdsson, Gunnar | Sparsø, Thomas | Strassburger, Klaus | Stringham, Heather M | Sun, Qi | Swift, Amy J | Thorand, Barbara | Tichet, Jean | Tuomi, Tiinamaija | van Dam, Rob M | van Haeften, Timon W | van Herpt, Thijs | van Vliet-Ostaptchouk, Jana V | Walters, G Bragi | Weedon, Michael N | Wijmenga, Cisca | Witteman, Jacqueline | Bergman, Richard N | Cauchi, Stephane | Collins, Francis S | Gloyn, Anna L | Gyllensten, Ulf | Hansen, Torben | Hide, Winston A | Hitman, Graham A | Hofman, Albert | Hunter, David J | Hveem, Kristian | Laakso, Markku | Mohlke, Karen L | Morris, Andrew D | Palmer, Colin N A | Pramstaller, Peter P | Rudan, Igor | Sijbrands, Eric | Stein, Lincoln D | Tuomilehto, Jaakko | Uitterlinden, Andre | Walker, Mark | Wareham, Nicholas J | Watanabe, Richard M | Abecasis, Gonçalo R | Boehm, Bernhard O | Campbell, Harry | Daly, Mark J | Hattersley, Andrew T | Hu, Frank B | Meigs, James B | Pankow, James S | Pedersen, Oluf | Wichmann, H-Erich | Barroso, Inês | Florez, Jose C | Frayling, Timothy M | Groop, Leif | Sladek, Rob | Thorsteinsdottir, Unnur | Wilson, James F | Illig, Thomas | Froguel, Philippe | van Duijn, Cornelia M | Stefansson, Kari | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2010;42(7):579-589.
By combining genome-wide association data from 8,130 individuals with type 2 diabetes (T2D) and 38,987 controls of European descent and following up previously unidentified meta-analysis signals in a further 34,412 cases and 59,925 controls, we identified 12 new T2D association signals with combinedP < 5 × 10−8. These include a second independent signal at the KCNQ1 locus; the first report, to our knowledge, of an X-chromosomal association (near DUSP9); and a further instance of overlap between loci implicated in monogenic and multifactorial forms of diabetes (at HNF1A). The identified loci affect both beta-cell function and insulin action, and, overall, T2D association signals show evidence of enrichment for genes involved in cell cycle regulation. We also show that a high proportion of T2D susceptibility loci harbor independent association signals influencing apparently unrelated complex traits.
doi:10.1038/ng.609
PMCID: PMC3080658  PMID: 20581827
10.  FOXE1 association with both isolated cleft lip with or without cleft palate, and isolated cleft palate 
Human Molecular Genetics  2009;18(24):4879-4896.
Nonsyndromic orofacial clefts are a common complex birth defect caused by genetic and environmental factors and/or their interactions. A previous genome-wide linkage scan discovered a novel locus for cleft lip with or without cleft palate (CL/P) at 9q22–q33. To identify the etiologic gene, we undertook an iterative and complementary fine mapping strategy using family-based CL/P samples from Colombia, USA and the Philippines. Candidate genes within 9q22–q33 were sequenced, revealing 32 new variants. Concurrently, 397 SNPs spanning the 9q22–q33 2-LOD-unit interval were tested for association. Significant SNP and haplotype association signals (P = 1.45E − 08) narrowed the interval to a 200 kb region containing: FOXE1, C9ORF156 and HEMGN. Association results were replicated in CL/P families of European descent and when all populations were combined the two most associated SNPs, rs3758249 (P = 5.01E − 13) and rs4460498 (P = 6.51E − 12), were located inside a 70 kb high linkage disequilibrium block containing FOXE1. Association signals for Caucasians and Asians clustered 5′ and 3′ of FOXE1, respectively. Isolated cleft palate (CP) was also associated, indicating that FOXE1 plays a role in two phenotypes thought to be genetically distinct. Foxe1 expression was found in the epithelium undergoing fusion between the medial nasal and maxillary processes. Mutation screens of FOXE1 identified two family-specific missense mutations at highly conserved amino acids. These data indicate that FOXE1 is a major gene for CL/P and provides new insights for improved counseling and genetic interaction studies.
doi:10.1093/hmg/ddp444
PMCID: PMC2778374  PMID: 19779022
11.  Common variants in the GDF5-BFZB region are associated with variation in human height 
Nature genetics  2008;40(2):198-203.
Identifying genetic variants that influence human height will further our understanding of skeletal growth and development. A number of rare genetic variants have been convincingly and reproducibly associated with height in Mendelian syndromes, and common variants in HMGA2 were recently found to be associated with variation in height in the general population1. Here, we report genome-wide association analyses of 6,669 individuals from Finland and Sardinia, using genotyped and imputed markers, and follow-up in an additional 28,801 individuals. We show that common variants in the osteoarthritis-associated2 GDF5-BFZB locus are responsible for variation in height (estimated additive effect of 0.44 cm, overall p<10−15). Our results suggest a link between the genetic basis of height and osteoarthritis, potentially mediated through alterations in bone growth and development.
doi:10.1038/ng.74
PMCID: PMC2914680  PMID: 18193045
12.  LocusZoom: regional visualization of genome-wide association scan results 
Bioinformatics  2010;26(18):2336-2337.
Summary: Genome-wide association studies (GWAS) have revealed hundreds of loci associated with common human genetic diseases and traits. We have developed a web-based plotting tool that provides fast visual display of GWAS results in a publication-ready format. LocusZoom visually displays regional information such as the strength and extent of the association signal relative to genomic position, local linkage disequilibrium (LD) and recombination patterns and the positions of genes in the region.
Availability: LocusZoom can be accessed from a web interface at http://csg.sph.umich.edu/locuszoom. Users may generate a single plot using a web form, or many plots using batch mode. The software utilizes LD information from HapMap Phase II (CEU, YRI and JPT+CHB) or 1000 Genomes (CEU) and gene information from the UCSC browser, and will accept SNP identifiers in dbSNP or 1000 Genomes format. Single plots are generated in ∼20 s. Source code and associated databases are available for download and local installation, and full documentation is available online.
Contact: cristen@umich.edu
doi:10.1093/bioinformatics/btq419
PMCID: PMC2935401  PMID: 20634204
13.  Comprehensive Association Study of Type 2 Diabetes and Related Quantitative Traits With 222 Candidate Genes 
Diabetes  2008;57(11):3136-3144.
OBJECTIVE—Type 2 diabetes 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 type 2 diabetes.
RESEARCH DESIGN AND METHODS—In a case-control study of 1,161 type 2 diabetic subjects and 1,174 control Finns who are normal glucose tolerant, 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 type 2 diabetic case subjects and 1,259 control subjects who are normal glucose tolerant, also from Finland.
RESULTS—Using SNP- and gene-based analysis methods, we replicated previously reported SNP-type 2 diabetes associations in PPARG, KCNJ11, and SLC2A2; identified significant SNPs in genes with previously reported associations (ENPP1 [rs2021966, P = 0.00026] and NRF1 [rs1882095, P = 0.00096]); and implicated novel genes, including RAPGEF1 (rs4740283, P = 0.00013) and TP53 (rs1042522, Arg72Pro, P = 0.00086), in type 2 diabetes susceptibility.
CONCLUSIONS—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 type 2 diabetes pathogenesis. Analysis of additional samples will be necessary to determine their effect on susceptibility.
doi:10.2337/db07-1731
PMCID: PMC2570412  PMID: 18678618
14.  Correction: Genome-Wide Association Scan Meta-Analysis Identifies Three Loci Influencing Adiposity and Fat Distribution 
Lindgren, Cecilia M. | Heid, Iris M. | Randall, Joshua C. | Lamina, Claudia | Steinthorsdottir, Valgerdur | Qi, Lu | Speliotes, Elizabeth K. | Thorleifsson, Gudmar | Willer, Cristen J. | Herrera, Blanca M. | Jackson, Anne U. | Lim, Noha | Scheet, Paul | Soranzo, Nicole | Amin, Najaf | Aulchenko, Yurii S. | Chambers, John C. | Drong, Alexander | Luan, Jian'an | Lyon, Helen N. | Rivadeneira, Fernando | Sanna, Serena | Timpson, Nicholas J. | Zillikens, M. Carola | Zhao, Jing Hua | Almgren, Peter | Bandinelli, Stefania | Bennett, Amanda J. | Bergman, Richard N. | Bonnycastle, Lori L. | Bumpstead, Suzannah J. | Chanock, Stephen J. | Cherkas, Lynn | Chines, Peter | Coin, Lachlan | Cooper, Cyrus | Crawford, Gabriel | Doering, Angela | Dominiczak, Anna | Doney, Alex S. F. | Ebrahim, Shah | Elliott, Paul | Erdos, Michael R. | Estrada, Karol | Ferrucci, Luigi | Fischer, Guido | Forouhi, Nita G. | Gieger, Christian | Grallert, Harald | Groves, Christopher J. | Grundy, Scott | Guiducci, Candace | Hadley, David | Hamsten, Anders | Havulinna, Aki S. | Hofman, Albert | Holle, Rolf | Holloway, John W. | Illig, Thomas | Isomaa, Bo | Jacobs, Leonie C. | Jameson, Karen | Jousilahti, Pekka | Karpe, Fredrik | Kuusisto, Johanna | Laitinen, Jaana | Lathrop, G. Mark | Lawlor, Debbie A. | Mangino, Massimo | McArdle, Wendy L. | Meitinger, Thomas | Morken, Mario A. | Morris, Andrew P. | Munroe, Patricia | Narisu, Narisu | Nordström, Anna | Nordström, Peter | Oostra, Ben A. | Palmer, Colin N. A. | Payne, Felicity | Peden, John F. | Prokopenko, Inga | Renström, Frida | Ruokonen, Aimo | Salomaa, Veikko | Sandhu, Manjinder S. | Scott, Laura J. | Scuteri, Angelo | Silander, Kaisa | Song, Kijoung | Yuan, Xin | Stringham, Heather M. | Swift, Amy J. | Tuomi, Tiinamaija | Uda, Manuela | Vollenweider, Peter | Waeber, Gerard | Wallace, Chris | Walters, G. Bragi | Weedon, Michael N. | Witteman, Jacqueline C. M. | Zhang, Cuilin | Zhang, Weihua | Caulfield, Mark J. | Collins, Francis S. | Davey Smith, George | Day, Ian N. M. | Franks, Paul W. | Hattersley, Andrew T. | Hu, Frank B. | Jarvelin, Marjo-Riitta | Kong, Augustine | Kooner, Jaspal S. | Laakso, Markku | Lakatta, Edward | Mooser, Vincent | Morris, Andrew D. | Peltonen, Leena | Samani, Nilesh J. | Spector, Timothy D. | Strachan, David P. | Tanaka, Toshiko | Tuomilehto, Jaakko | Uitterlinden, André G. | van Duijn, Cornelia M. | Wareham, Nicholas J. | Watkins for the PROCARDIS consortia, Hugh | Waterworth, Dawn M. | Boehnke, Michael | Deloukas, Panos | Groop, Leif | Hunter, David J. | Thorsteinsdottir, Unnur | Schlessinger, David | Wichmann, H.-Erich | Frayling, Timothy M. | Abecasis, Gonçalo R. | Hirschhorn, Joel N. | Loos, Ruth J. F. | Stefansson, Kari | Mohlke, Karen L. | Barroso, Inês | McCarthy for the GIANT consortium, Mark I.
PLoS Genetics  2009;5(7):10.1371/annotation/b6e8f9f6-2496-4a40-b0e3-e1d1390c1928.
doi:10.1371/annotation/b6e8f9f6-2496-4a40-b0e3-e1d1390c1928
PMCID: PMC2722420
15.  Six new loci associated with body mass index highlight a neuronal influence on body weight regulation 
Willer, Cristen J | Speliotes, Elizabeth K | Loos, Ruth J F | Li, Shengxu | Lindgren, Cecilia M | Heid, Iris M | Berndt, Sonja I | Elliott, Amanda L | Jackson, Anne U | Lamina, Claudia | Lettre, Guillaume | Lim, Noha | Lyon, Helen N | McCarroll, Steven A | Papadakis, Konstantinos | Qi, Lu | Randall, Joshua C | Roccasecca, Rosa Maria | Sanna, Serena | Scheet, Paul | Weedon, Michael N | Wheeler, Eleanor | Zhao, Jing Hua | Jacobs, Leonie C | Prokopenko, Inga | Soranzo, Nicole | Tanaka, Toshiko | Timpson, Nicholas J | Almgren, Peter | Bennett, Amanda | Bergman, Richard N | Bingham, Sheila A | Bonnycastle, Lori L | Brown, Morris | Burtt, Noël P | Chines, Peter | Coin, Lachlan | Collins, Francis S | Connell, John M | Cooper, Cyrus | Smith, George Davey | Dennison, Elaine M | Deodhar, Parimal | Elliott, Paul | Erdos, Michael R | Estrada, Karol | Evans, David M | Gianniny, Lauren | Gieger, Christian | Gillson, Christopher J | Guiducci, Candace | Hackett, Rachel | Hadley, David | Hall, Alistair S | Havulinna, Aki S | Hebebrand, Johannes | Hofman, Albert | Isomaa, Bo | Jacobs, Kevin B | Johnson, Toby | Jousilahti, Pekka | Jovanovic, Zorica | Khaw, Kay-Tee | Kraft, Peter | Kuokkanen, Mikko | Kuusisto, Johanna | Laitinen, Jaana | Lakatta, Edward G | Luan, Jian'an | Luben, Robert N | Mangino, Massimo | McArdle, Wendy L | Meitinger, Thomas | Mulas, Antonella | Munroe, Patricia B | Narisu, Narisu | Ness, Andrew R | Northstone, Kate | O'Rahilly, Stephen | Purmann, Carolin | Rees, Matthew G | Ridderstråle, Martin | Ring, Susan M | Rivadeneira, Fernando | Ruokonen, Aimo | Sandhu, Manjinder S | Saramies, Jouko | Scott, Laura J | Scuteri, Angelo | Silander, Kaisa | Sims, Matthew A | Song, Kijoung | Stephens, Jonathan | Stevens, Suzanne | Stringham, Heather M | Tung, Y C Loraine | Valle, Timo T | Van Duijn, Cornelia M | Vimaleswaran, Karani S | Vollenweider, Peter | Waeber, Gerard | Wallace, Chris | Watanabe, Richard M | Waterworth, Dawn M | Watkins, Nicholas | Witteman, Jacqueline C M | Zeggini, Eleftheria | Zhai, Guangju | Zillikens, M Carola | Altshuler, David | Caulfield, Mark J | Chanock, Stephen J | Farooqi, I Sadaf | Ferrucci, Luigi | Guralnik, Jack M | Hattersley, Andrew T | Hu, Frank B | Jarvelin, Marjo-Riitta | Laakso, Markku | Mooser, Vincent | Ong, Ken K | Ouwehand, Willem H | Salomaa, Veikko | Samani, Nilesh J | Spector, Timothy D | Tuomi, Tiinamaija | Tuomilehto, Jaakko | Uda, Manuela | Uitterlinden, André G | Wareham, Nicholas J | Deloukas, Panagiotis | Frayling, Timothy M | Groop, Leif C | Hayes, Richard B | Hunter, David J | Mohlke, Karen L | Peltonen, Leena | Schlessinger, David | Strachan, David P | Wichmann, H-Erich | McCarthy, Mark I | Boehnke, Michael | Barroso, Inês | Abecasis, Gonçalo R | Hirschhorn, Joel N
Nature genetics  2008;41(1):25-34.
Common variants at only two loci, FTO and MC4R, have been reproducibly associated with body mass index (BMI) in humans. To identify additional loci, we conducted meta-analysis of 15 genome-wide association studies for BMI (n > 32,000) and followed up top signals in 14 additional cohorts (n > 59,000). We strongly confirm FTO and MC4R and identify six additional loci (P < 5 × 10−8): TMEM18, KCTD15, GNPDA2, SH2B1, MTCH2 and NEGR1 (where a 45-kb deletion polymorphism is a candidate causal variant). Several of the likely causal genes are highly expressed or known to act in the central nervous system (CNS), emphasizing, as in rare monogenic forms of obesity, the role of the CNS in predisposition to obesity.
doi:10.1038/ng.287
PMCID: PMC2695662  PMID: 19079261
16.  Genome-Wide Association Scan Meta-Analysis Identifies Three Loci Influencing Adiposity and Fat Distribution 
Lindgren, Cecilia M. | Heid, Iris M. | Randall, Joshua C. | Lamina, Claudia | Steinthorsdottir, Valgerdur | Qi, Lu | Speliotes, Elizabeth K. | Thorleifsson, Gudmar | Willer, Cristen J. | Herrera, Blanca M. | Jackson, Anne U. | Lim, Noha | Scheet, Paul | Soranzo, Nicole | Amin, Najaf | Aulchenko, Yurii S. | Chambers, John C. | Drong, Alexander | Luan, Jian'an | Lyon, Helen N. | Rivadeneira, Fernando | Sanna, Serena | Timpson, Nicholas J. | Zillikens, M. Carola | Zhao, Jing Hua | Almgren, Peter | Bandinelli, Stefania | Bennett, Amanda J. | Bergman, Richard N. | Bonnycastle, Lori L. | Bumpstead, Suzannah J. | Chanock, Stephen J. | Cherkas, Lynn | Chines, Peter | Coin, Lachlan | Cooper, Cyrus | Crawford, Gabriel | Doering, Angela | Dominiczak, Anna | Doney, Alex S. F. | Ebrahim, Shah | Elliott, Paul | Erdos, Michael R. | Estrada, Karol | Ferrucci, Luigi | Fischer, Guido | Forouhi, Nita G. | Gieger, Christian | Grallert, Harald | Groves, Christopher J. | Grundy, Scott | Guiducci, Candace | Hadley, David | Hamsten, Anders | Havulinna, Aki S. | Hofman, Albert | Holle, Rolf | Holloway, John W. | Illig, Thomas | Isomaa, Bo | Jacobs, Leonie C. | Jameson, Karen | Jousilahti, Pekka | Karpe, Fredrik | Kuusisto, Johanna | Laitinen, Jaana | Lathrop, G. Mark | Lawlor, Debbie A. | Mangino, Massimo | McArdle, Wendy L. | Meitinger, Thomas | Morken, Mario A. | Morris, Andrew P. | Munroe, Patricia | Narisu, Narisu | Nordström, Anna | Nordström, Peter | Oostra, Ben A. | Palmer, Colin N. A. | Payne, Felicity | Peden, John F. | Prokopenko, Inga | Renström, Frida | Ruokonen, Aimo | Salomaa, Veikko | Sandhu, Manjinder S. | Scott, Laura J. | Scuteri, Angelo | Silander, Kaisa | Song, Kijoung | Yuan, Xin | Stringham, Heather M. | Swift, Amy J. | Tuomi, Tiinamaija | Uda, Manuela | Vollenweider, Peter | Waeber, Gerard | Wallace, Chris | Walters, G. Bragi | Weedon, Michael N. | Witteman, Jacqueline C. M. | Zhang, Cuilin | Zhang, Weihua | Caulfield, Mark J. | Collins, Francis S. | Davey Smith, George | Day, Ian N. M. | Franks, Paul W. | Hattersley, Andrew T. | Hu, Frank B. | Jarvelin, Marjo-Riitta | Kong, Augustine | Kooner, Jaspal S. | Laakso, Markku | Lakatta, Edward | Mooser, Vincent | Morris, Andrew D. | Peltonen, Leena | Samani, Nilesh J. | Spector, Timothy D. | Strachan, David P. | Tanaka, Toshiko | Tuomilehto, Jaakko | Uitterlinden, André G. | van Duijn, Cornelia M. | Wareham, Nicholas J. | Watkins for the PROCARDIS consortia, Hugh | Waterworth, Dawn M. | Boehnke, Michael | Deloukas, Panos | Groop, Leif | Hunter, David J. | Thorsteinsdottir, Unnur | Schlessinger, David | Wichmann, H.-Erich | Frayling, Timothy M. | Abecasis, Gonçalo R. | Hirschhorn, Joel N. | Loos, Ruth J. F. | Stefansson, Kari | Mohlke, Karen L. | Barroso, Inês | McCarthy for the GIANT consortium, Mark I. | Allison, David B.
PLoS Genetics  2009;5(6):e1000508.
To identify genetic loci influencing central obesity and fat distribution, we performed a meta-analysis of 16 genome-wide association studies (GWAS, N = 38,580) informative for adult waist circumference (WC) and waist–hip ratio (WHR). We selected 26 SNPs for follow-up, for which the evidence of association with measures of central adiposity (WC and/or WHR) was strong and disproportionate to that for overall adiposity or height. Follow-up studies in a maximum of 70,689 individuals identified two loci strongly associated with measures of central adiposity; these map near TFAP2B (WC, P = 1.9×10−11) and MSRA (WC, P = 8.9×10−9). A third locus, near LYPLAL1, was associated with WHR in women only (P = 2.6×10−8). The variants near TFAP2B appear to influence central adiposity through an effect on overall obesity/fat-mass, whereas LYPLAL1 displays a strong female-only association with fat distribution. By focusing on anthropometric measures of central obesity and fat distribution, we have identified three loci implicated in the regulation of human adiposity.
Author Summary
Here, we describe a meta-analysis of genome-wide association data from 38,580 individuals, followed by large-scale replication (in up to 70,689 individuals) designed to uncover variants influencing anthropometric measures of central obesity and fat distribution, namely waist circumference (WC) and waist–hip ratio (WHR). This work complements parallel efforts that have been successful in defining variants impacting overall adiposity and focuses on the visceral fat accumulation which has particularly strong relationships to metabolic and cardiovascular disease. Our analyses have identified two loci (TFAP2B and MSRA) associated with WC, and a further locus, near LYPLAL1, which shows gender-specific relationships with WHR (all to levels of genome-wide significance). These loci vary in the strength of their associations with overall adiposity, and LYPLAL1 in particular appears to have a specific effect on patterns of fat distribution. All in all, these three loci provide novel insights into human physiology and the development of obesity.
doi:10.1371/journal.pgen.1000508
PMCID: PMC2695778  PMID: 19557161
17.  Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes 
Zeggini, Eleftheria | Scott, Laura J. | Saxena, Richa | Voight, Benjamin F. | Marchini, Jonathan L | Hu, Tainle | de Bakker, Paul IW | Abecasis, Gonçalo R | Almgren, Peter | Andersen, Gitte | Ardlie, Kristin | Boström, Kristina Bengtsson | Bergman, Richard N | Bonnycastle, Lori L | Borch-Johnsen, Knut | Burtt, Noël P | Chen, Hong | Chines, Peter S | Daly, Mark J | Deodhar, Parimal | Ding, Charles | Doney, Alex S F | Duren, William L | Elliott, Katherine S | Erdos, Michael R | Frayling, Timothy M | Freathy, Rachel M | Gianniny, Lauren | Grallert, Harald | Grarup, Niels | Groves, Christopher J | Guiducci, Candace | Hansen, Torben | Herder, Christian | Hitman, Graham A | Hughes, Thomas E | Isomaa, Bo | Jackson, Anne U | Jørgensen, Torben | Kong, Augustine | Kubalanza, Kari | Kuruvilla, Finny G | Kuusisto, Johanna | Langenberg, Claudia | Lango, Hana | Lauritzen, Torsten | Li, Yun | Lindgren, Cecilia M | Lyssenko, Valeriya | Marvelle, Amanda F | Meisinger, Christa | Midthjell, Kristian | Mohlke, Karen L | Morken, Mario A | Morris, Andrew D | Narisu, Narisu | Nilsson, Peter | Owen, Katharine R | Palmer, Colin NA | Payne, Felicity | Perry, John RB | Pettersen, Elin | Platou, Carl | Prokopenko, Inga | Qi, Lu | Qin, Li | Rayner, Nigel W | Rees, Matthew | Roix, Jeffrey J | Sandbæk, Anelli | Shields, Beverley | Sjögren, Marketa | Steinthorsdottir, Valgerdur | Stringham, Heather M | Swift, Amy J | Thorleifsson, Gudmar | Thorsteinsdottir, Unnur | Timpson, Nicholas J | Tuomi, Tiinamaija | Tuomilehto, Jaakko | Walker, Mark | Watanabe, Richard M | Weedon, Michael N | Willer, Cristen J | Illig, Thomas | Hveem, Kristian | Hu, Frank B | Laakso, Markku | Stefansson, Kari | Pedersen, Oluf | Wareham, Nicholas J | Barroso, Inês | Hattersley, Andrew T | Collins, Francis S | Groop, Leif | McCarthy, Mark I | Boehnke, Michael | Altshuler, David
Nature genetics  2008;40(5):638-645.
Genome-wide association (GWA) studies have identified multiple new genomic loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D)1-11. 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 discover loci at which common alleles have modest effects, we performed meta-analysis of three T2D GWA scans encompassing 10,128 individuals of European-descent and ~2.2 million SNPs (directly genotyped and imputed). Replication testing was performed in an independent sample with an effective sample size of up to 53,975. At least six new loci with robust evidence for association were detected, including the JAZF1 (p=5.0×10−14), CDC123/CAMK1D (p=1.2×10−10), TSPAN8/LGR5 (p=1.1×10−9), THADA (p=1.1×10−9), ADAMTS9 (p=1.2×10−8), and NOTCH2 (p=4.1×10−8) gene regions. The large number of loci with relatively small effects indicates the value of large discovery and follow-up samples in identifying additional clues about the inherited basis of T2D.
doi:10.1038/ng.120
PMCID: PMC2672416  PMID: 18372903
18.  Investigation of altering single-nucleotide polymorphism density on the power to detect trait loci and frequency of false positive in nonparametric linkage analyses of qualitative traits 
BMC Genetics  2005;6(Suppl 1):S20.
Genome-wide linkage analysis using microsatellite markers has been successful in the identification of numerous Mendelian and complex disease loci. The recent availability of high-density single-nucleotide polymorphism (SNP) maps provides a potentially more powerful option. Using the simulated and Collaborative Study on the Genetics of Alcoholism (COGA) datasets from the Genetics Analysis Workshop 14 (GAW14), we examined how altering the density of SNP marker sets impacted the overall information content, the power to detect trait loci, and the number of false positive results. For the simulated data we used SNP maps with density of 0.3 cM, 1 cM, 2 cM, and 3 cM. For the COGA data we combined the marker sets from Illumina and Affymetrix to create a map with average density of 0.25 cM and then, using a sub-sample of these markers, created maps with density of 0.3 cM, 0.6 cM, 1 cM, 2 cM, and 3 cM. For each marker set, multipoint linkage analysis using MERLIN was performed for both dominant and recessive traits derived from marker loci. Our results showed that information content increased with increased map density. For the homogeneous, completely penetrant traits we created, there was only a modest difference in ability to detect trait loci. Additionally, as map density increased there was only a slight increase in the number of false positive results when there was linkage disequilibrium (LD) between markers. The presence of LD between markers may have led to an increased number of false positive regions but no clear relationship between regions of high LD and locations of false positive linkage signals was observed.
doi:10.1186/1471-2156-6-S1-S20
PMCID: PMC1866766  PMID: 16451629
19.  Identification of tag single-nucleotide polymorphisms in regions with varying linkage disequilibrium 
BMC Genetics  2005;6(Suppl 1):S73.
We compared seven different tagging single-nucleotide polymorphism (SNP) programs in 10 regions with varied amounts of linkage disequilibrium (LD) and physical distance. We used the Collaborative Studies on the Genetics of Alcoholism dataset, part of the Genetic Analysis Workshop 14. We show that in regions with moderate to strong LD these programs are relatively consistent, despite different parameters and methods. In addition, we compared the selected SNPs in a multipoint linkage analysis for one region with strong LD. As the number of selected SNPs increased, the LOD score, mean information content, and type I error also increased.
doi:10.1186/1471-2156-6-S1-S73
PMCID: PMC1866708  PMID: 16451687

Results 1-19 (19)