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1.  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
2.  Identifying Plausible Genetic Models Based on Association and Linkage Results: Application to Type 2 Diabetes 
Genetic epidemiology  2012;10.1002/gepi.21668.
When planning re-sequencing studies for complex diseases, previous association and linkage studies can constrain the range of plausible genetic models for a given locus. Here, we explore the combinations of causal risk allele frequency RAFC and genotype relative risk GRRC consistent with no or limited evidence for affected sibling pair (ASP) linkage and strong evidence for case-control association. We find that significant evidence for case-control association combined with no or moderate evidence for ASP linkage can define a lower bound for the plausible RAFC. Using data from large type 2 diabetes (T2D) linkage and genome-wide association study meta-analyses, we find that under reasonable model assumptions, 23 of 36 autosomal T2D risk loci are unlikely to be due to causal variants with combined RAFC < .005, and four of the 23 are unlikely to be due to causal variants with combined RAFC < .05.
doi:10.1002/gepi.21668
PMCID: PMC3578091  PMID: 22865662
gene mapping; genetics; genetic structure; complex diseases
3.  What Will Diabetes Genomes Tell Us? 
Current diabetes reports  2012;12(6):643-650.
A new generation of genetic studies of diabetes is underway. Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes. Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk. Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants. We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.
doi:10.1007/s11892-012-0321-4
PMCID: PMC3489976  PMID: 22983892
genotyping; genome-wide association; sequencing; imputation; exome; genome; fine-mapping; diabetes; quantitative traits; metabochip; single nucleotide polymorphism
4.  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
5.  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
6.  Allelic expression imbalance at high-density lipoprotein cholesterol locus MMAB-MVK 
Human Molecular Genetics  2010;19(10):1921-1929.
Genome-wide association studies (GWAS) have identified numerous loci associated with various complex traits for which the underlying susceptibility gene(s) remain unknown. In a GWAS for high-density lipoprotein-cholesterol (HDL-C) level, one strongly associated locus contains at least two biologically compelling candidates, methylmalonic aciduria cblB type (MMAB) and mevalonate kinase (MVK). To detect evidence of cis-acting regulation at this locus, we measured relative allelic expression of transcribed SNPs in five genes using human hepatocyte samples heterozygous for the transcribed SNP. If an HDL-C-associated SNP allele differentially regulates mRNA level in cis, samples heterozygous both for a transcribed SNP and an HDL-C-associated SNP should display allelic expression imbalance (AEI) of the transcribed SNP. We designed statistical tests to detect AEI in a comprehensive set of linkage disequilibrium (LD) scenarios between the transcribed SNP and an HDL-C-associated SNP (rs7298565) in phase unknown samples. We observed significant AEI of 22% in MMAB (P = 1.4 × 10−13, transcribed SNP rs11067231), and the allele associated with lower HDL-C level was associated with greater MMAB transcript level. The same rs7298565 allele was also associated with higher MMAB mRNA level (P = 0.0081) and higher MMAB protein level (P = 0.0020). In contrast, MVK, UBE3B, KCTD10 and ACACB did not show significant AEI (P ≥ 0.05). These data suggest MMAB is the most likely gene influencing HDL-C levels at this locus and demonstrate that measuring AEI at loci containing more than one candidate gene can prioritize genes for functional studies.
doi:10.1093/hmg/ddq067
PMCID: PMC2860891  PMID: 20159775
7.  Circulating β-carotene levels and Type 2 diabetes: Cause or effect? 
Diabetologia  2009;52(10):2117-2121.
Aims and Hypothesis
Circulating β-carotene levels are inversely associated with type 2 diabetes risk, but the causal direction of this association is not certain. In this study we used a Mendelian Randomization approach to provide evidence for or against the causal role of the anti-oxidant vitamin β-carotene in type 2 diabetes.
Methods
We used a common polymorphism (rs6564851) near the β-carotene 15,15'-Monooxygenase 1 (BCMO1) gene that is strongly associated with circulating β-carotene levels (P = 2×10−24) - each G allele is associated with a 0.27 standard deviation increase in levels. We used data from the InCHIANTI study and the ULSAM study to estimate the association between β-carotene levels and type 2 diabetes. We next used a triangulation approach to estimate the expected effect of rs6564851 on type 2 diabetes risk, and compared this to the observed effect using data from 4549 type 2 diabetes cases and 5579 controls from the DIAGRAM consortium.
Results
A 0.27 standard deviation increase in β-carotene levels is associated with an odds ratio of 0.90 (0.86–0.95) for type 2 diabetes in the InCHIANTI study. This association is similar to that of the ULSAM study, OR (0.90 (0.84–0.97)). In contrast there was no association between rs6564851 and type 2 diabetes (OR 0.98 (0.93–1.04, P = 0.58), and this effect size was smaller than that expected given the known associations between rs6564851 and β-carotene levels and the associations between β-carotene levels and type 2 diabetes.
Conclusion
Our Mendelian Randomization studies are in keeping with randomized controlled trials that suggest β-carotene is not causally protective against type 2 diabetes.
doi:10.1007/s00125-009-1475-8
PMCID: PMC2746424  PMID: 19662379
type 2 diabetes; β-carotene; mendelian randomization
8.  Eight blood pressure loci identified by genome-wide association study of 34,433 people of European ancestry 
Newton-Cheh, Christopher | Johnson, Toby | Gateva, Vesela | Tobin, Martin D | Bochud, Murielle | Coin, Lachlan | Najjar, Samer S | Zhao, Jing Hua | Heath, Simon C | Eyheramendy, Susana | Papadakis, Konstantinos | Voight, Benjamin F | Scott, Laura J | Zhang, Feng | Farrall, Martin | Tanaka, Toshiko | Wallace, Chris | Chambers, John C | Khaw, Kay-Tee | Nilsson, Peter | van der Harst, Pim | Polidoro, Silvia | Grobbee, Diederick E | Onland-Moret, N Charlotte | Bots, Michiel L | Wain, Louise V | Elliott, Katherine S | Teumer, Alexander | Luan, Jian’an | Lucas, Gavin | Kuusisto, Johanna | Burton, Paul R | Hadley, David | McArdle, Wendy L | Brown, Morris | Dominiczak, Anna | Newhouse, Stephen J | Samani, Nilesh J | Webster, John | Zeggini, Eleftheria | Beckmann, Jacques S | Bergmann, Sven | Lim, Noha | Song, Kijoung | Vollenweider, Peter | Waeber, Gerard | Waterworth, Dawn M | Yuan, Xin | Groop, Leif | Orho-Melander, Marju | Allione, Alessandra | Di Gregorio, Alessandra | Guarrera, Simonetta | Panico, Salvatore | Ricceri, Fulvio | Romanazzi, Valeria | Sacerdote, Carlotta | Vineis, Paolo | Barroso, Inês | Sandhu, Manjinder S | Luben, Robert N | Crawford, Gabriel J. | Jousilahti, Pekka | Perola, Markus | Boehnke, Michael | Bonnycastle, Lori L | Collins, Francis S | Jackson, Anne U | Mohlke, Karen L | Stringham, Heather M | Valle, Timo T | Willer, Cristen J | Bergman, Richard N | Morken, Mario A | Döring, Angela | Gieger, Christian | Illig, Thomas | Meitinger, Thomas | Org, Elin | Pfeufer, Arne | Wichmann, H Erich | Kathiresan, Sekar | Marrugat, Jaume | O’Donnell, Christopher J | Schwartz, Stephen M | Siscovick, David S | Subirana, Isaac | Freimer, Nelson B | Hartikainen, Anna-Liisa | McCarthy, Mark I | O’Reilly, Paul F | Peltonen, Leena | Pouta, Anneli | de Jong, Paul E | Snieder, Harold | van Gilst, Wiek H | Clarke, Robert | Goel, Anuj | Hamsten, Anders | Peden, John F | Seedorf, Udo | Syvänen, Ann-Christine | Tognoni, Giovanni | Lakatta, Edward G | Sanna, Serena | Scheet, Paul | Schlessinger, David | Scuteri, Angelo | Dörr, Marcus | Ernst, Florian | Felix, Stephan B | Homuth, Georg | Lorbeer, Roberto | Reffelmann, Thorsten | Rettig, Rainer | Völker, Uwe | Galan, Pilar | Gut, Ivo G | Hercberg, Serge | Lathrop, G Mark | Zeleneka, Diana | Deloukas, Panos | Soranzo, Nicole | Williams, Frances M | Zhai, Guangju | Salomaa, Veikko | Laakso, Markku | Elosua, Roberto | Forouhi, Nita G | Völzke, Henry | Uiterwaal, Cuno S | van der Schouw, Yvonne T | Numans, Mattijs E | Matullo, Giuseppe | Navis, Gerjan | Berglund, Göran | Bingham, Sheila A | Kooner, Jaspal S | Paterson, Andrew D | Connell, John M | Bandinelli, Stefania | Ferrucci, Luigi | Watkins, Hugh | Spector, Tim D | Tuomilehto, Jaakko | Altshuler, David | Strachan, David P | Laan, Maris | Meneton, Pierre | Wareham, Nicholas J | Uda, Manuela | Jarvelin, Marjo-Riitta | Mooser, Vincent | Melander, Olle | Loos, Ruth JF | Elliott, Paul | Abecasis, Goncalo R | Caulfield, Mark | Munroe, Patricia B
Nature genetics  2009;41(6):666-676.
Elevated blood pressure is a common, heritable cause of cardiovascular disease worldwide. To date, identification of common genetic variants influencing blood pressure has proven challenging. We tested 2.5m genotyped and imputed SNPs for association with systolic and diastolic blood pressure in 34,433 subjects of European ancestry from the Global BPgen consortium and followed up findings with direct genotyping (N≤71,225 European ancestry, N=12,889 Indian Asian ancestry) and in silico comparison (CHARGE consortium, N=29,136). We identified association between systolic or diastolic blood pressure and common variants in 8 regions near the CYP17A1 (P=7×10−24), CYP1A2 (P=1×10−23), FGF5 (P=1×10−21), SH2B3 (P=3×10−18), MTHFR (P=2×10−13), c10orf107 (P=1×10−9), ZNF652 (P=5×10−9) and PLCD3 (P=1×10−8) genes. All variants associated with continuous blood pressure were associated with dichotomous hypertension. These associations between common variants and blood pressure and hypertension offer mechanistic insights into the regulation of blood pressure and may point to novel targets for interventions to prevent cardiovascular disease.
doi:10.1038/ng.361
PMCID: PMC2891673  PMID: 19430483
9.  Heritable Individual-Specific and Allele-Specific Chromatin Signatures in Humans 
Science (New York, N.Y.)  2010;328(5975):235-239.
The extent to which variation in chromatin structure and transcription factor binding may influence gene expression, and thus underlie or contribute to variation in phenotype, is unknown. To address this question, we cataloged both individual-to-individual variation and differences between homologous chromosomes within the same individual (allele-specific variation) in chromatin structure and transcription factor binding in lymphoblastoid cells derived from individuals of geographically diverse ancestry. Ten percent of active chromatin sites were individual-specific; a similar proportion were allele-specific. Both individual-specific and allele-specific sites were commonly transmitted from parent to child, which suggests that they are heritable features of the human genome. Our study shows that heritable chromatin status and transcription factor binding differ as a result of genetic variation and may underlie phenotypic variation in humans.
doi:10.1126/science.1184655
PMCID: PMC2929018  PMID: 20299549
10.  Subsets of Finns with High HDL to Total Cholesterol Ratio Show Evidence for Linkage to Type 2 Diabetes on Chromosome 6q 
Human heredity  2006;63(1):17-25.
Objectives
The purpose of this study was to examine carefully heterogeneity underlying evidence for linkage to type 2 diabetes (T2DM) on chromosome 6q from two sets of FUSION families.
Methods
Ordered subsets analysis (OSA) was performed on two sets of FUSION families. For OSA results showing significant improvement in evidence for linkage, T2DM-related phenotypes were compared between individuals with T2DM within the subset versus the complement.
Results
OSA analysis revealed 105 families with the highest average HDL to total cholesterol ratio (HDL ratio) that had strongly increased evidence for linkage (MLS = 7.91 at 78.0 cM; uncorrected p = 0.00002). Subjects with T2DM within this subset were significantly leaner, had lower fasting glucose, insulin, and C-peptide, and more favorable cardiovascular risk profile compared to the complement set of subjects with T2DM. OSA also revealed 33 families with the lowest average fasting insulin that had increased evidence for linkage at a second locus (MLS = 3.45 at 128 cM; uncorrected p = 0.017) coincident with quantitative trait locus linkage analysis results for fasting and 2-hour insulin in subjects without T2DM.
Conclusions
These results suggest two diabetes susceptibility loci on chromosome 6q that may affect subsets of individuals with a milder form of T2DM.
doi:10.1159/000097927
PMCID: PMC2923439  PMID: 17179727
Linkage analysis; Heterogeneity; Type 2 diabetes; HDL cholesterol; Ordered subsets analysis; Chromosome 6q
11.  Linkage Disequilibrium Mapping of the Replicated Type 2 Diabetes Linkage Signal on Chromosome 1q 
Diabetes  2009;58(7):1704-1709.
OBJECTIVE
Linkage of the chromosome 1q21–25 region to type 2 diabetes has been demonstrated in multiple ethnic groups. We performed common variant fine-mapping across a 23-Mb interval in a multiethnic sample to search for variants responsible for this linkage signal.
RESEARCH DESIGN AND METHODS
In all, 5,290 single nucleotide polymorphisms (SNPs) were successfully genotyped in 3,179 type 2 diabetes case and control subjects from eight populations with evidence of 1q linkage. Samples were ascertained using strategies designed to enhance power to detect variants causal for 1q linkage. After imputation, we estimate ∼80% coverage of common variation across the region (r 2 > 0.8, Europeans). Association signals of interest were evaluated through in silico replication and de novo genotyping in ∼8,500 case subjects and 12,400 control subjects.
RESULTS
Association mapping of the 23-Mb region identified two strong signals, both of which were restricted to the subset of European-descent samples. The first mapped to the NOS1AP (CAPON) gene region (lead SNP: rs7538490, odds ratio 1.38 [95% CI 1.21–1.57], P = 1.4 × 10−6, in 999 case subjects and 1,190 control subjects); the second mapped within an extensive region of linkage disequilibrium that includes the ASH1L and PKLR genes (lead SNP: rs11264371, odds ratio 1.48 [1.18–1.76], P = 1.0 × 10−5, under a dominant model). However, there was no evidence for association at either signal on replication, and, across all data (>24,000 subjects), there was no indication that these variants were causally related to type 2 diabetes status.
CONCLUSIONS
Detailed fine-mapping of the 23-Mb region of replicated linkage has failed to identify common variant signals contributing to the observed signal. Future studies should focus on identification of causal alleles of lower frequency and higher penetrance.
doi:10.2337/db09-0081
PMCID: PMC2699860  PMID: 19389826
12.  Linkage disequilibrium mapping of the replicated type 2 diabetes linkage signal on chromosome 1q 
Diabetes  2009;58(7):1704-1709.
Objective
Linkage of the chromosome 1q21-25 region to type 2 diabetes has been demonstrated in multiple ethnic groups. We performed common variant fine-mapping across a 23Mb interval in a multiethnic sample to search for variants responsible for this linkage signal.
Research Design and Methods
In all, 5,290 SNPs were successfully genotyped in 3,179 T2D cases and controls from eight populations with evidence of 1q linkage. Samples were ascertained using strategies designed to enhance power to detect variants causal for 1q-linkage. Following imputation, we estimate ~80% coverage of common variation across the region (r2>0.8, Europeans). Association signals of interest were evaluated through in silico replication and de novo genotyping in approximately 8,500 cases and 12,400 controls.
Results
Association mapping of the 23Mb region identified two strong signals, both restricted to the subset of European-descent samples. The first mapped to the NOS1AP (CAPON) gene region (lead SNP: rs7538490, OR 1.38 (95% CI, 1.21-1.57), p=1.4×10-6 in 999 cases and 1,190 controls): the second within an extensive region of linkage disequilibrium that includes the ASH1L and PKLR genes (lead SNP: rs11264371, OR 1.48 [1.18-1.76], p=1.0×10-5, under a dominant model). However, there was no evidence for association at either signal on replication, and, across all data (>24,000 subjects), no indication that these variants were causally-related to T2D status.
Conclusion
Detailed fine-mapping of the 23Mb region of replicated linkage has failed to identify common variant signals contributing to the observed signal. Future studies should focus on identification of causal alleles of lower frequency and higher penetrance.
doi:10.2337/db09-0081
PMCID: PMC2699860  PMID: 19389826
chromosome 1q; linkage; association
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.  Tissue-specific alternative splicing of TCF7L2 
Human Molecular Genetics  2009;18(20):3795-3804.
Common variants in the transcription factor 7-like 2 (TCF7L2) gene have been identified as the strongest genetic risk factors for type 2 diabetes (T2D). However, the mechanisms by which these non-coding variants increase risk for T2D are not well-established. We used 13 expression assays to survey mRNA expression of multiple TCF7L2 splicing forms in up to 380 samples from eight types of human tissue (pancreas, pancreatic islets, colon, liver, monocytes, skeletal muscle, subcutaneous adipose tissue and lymphoblastoid cell lines) and observed a tissue-specific pattern of alternative splicing. We tested whether the expression of TCF7L2 splicing forms was associated with single nucleotide polymorphisms (SNPs), rs7903146 and rs12255372, located within introns 3 and 4 of the gene and most strongly associated with T2D. Expression of two splicing forms was lower in pancreatic islets with increasing counts of T2D-associated alleles of the SNPs: a ubiquitous splicing form (P = 0.018 for rs7903146 and P = 0.020 for rs12255372) and a splicing form found in pancreatic islets, pancreas and colon but not in other tissues tested here (P = 0.009 for rs12255372 and P = 0.053 for rs7903146). Expression of this form in glucose-stimulated pancreatic islets correlated with expression of proinsulin (r2 = 0.84–0.90, P < 0.00063). In summary, we identified a tissue-specific pattern of alternative splicing of TCF7L2. After adjustment for multiple tests, no association between expression of TCF7L2 in eight types of human tissue samples and T2D-associated genetic variants remained significant. Alternative splicing of TCF7L2 in pancreatic islets warrants future studies. GenBank Accession Numbers: FJ010164–FJ010174.
doi:10.1093/hmg/ddp321
PMCID: PMC2748888  PMID: 19602480
15.  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
16.  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
17.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes 
Morris, Andrew P | Voight, Benjamin F | Teslovich, Tanya M | Ferreira, Teresa | Segrè, Ayellet V | Steinthorsdottir, Valgerdur | Strawbridge, Rona J | Khan, Hassan | Grallert, Harald | Mahajan, Anubha | Prokopenko, Inga | Kang, Hyun Min | Dina, Christian | Esko, Tonu | Fraser, Ross M | Kanoni, Stavroula | Kumar, Ashish | Lagou, Vasiliki | Langenberg, Claudia | Luan, Jian'an | Lindgren, Cecilia M | Müller-Nurasyid, Martina | Pechlivanis, Sonali | Rayner, N William | Scott, Laura J | Wiltshire, Steven | Yengo, Loic | Kinnunen, Leena | Rossin, Elizabeth J | Raychaudhuri, Soumya | Johnson, Andrew D | Dimas, Antigone S | Loos, Ruth J F | Vedantam, Sailaja | Chen, Han | Florez, Jose C | Fox, Caroline | Liu, Ching-Ti | Rybin, Denis | Couper, David J | Kao, Wen Hong L | Li, Man | Cornelis, Marilyn C | Kraft, Peter | Sun, Qi | van Dam, Rob M | Stringham, Heather M | Chines, Peter S | Fischer, Krista | Fontanillas, Pierre | Holmen, Oddgeir L | Hunt, Sarah E | Jackson, Anne U | Kong, Augustine | Lawrence, Robert | Meyer, Julia | Perry, John RB | Platou, Carl GP | Potter, Simon | Rehnberg, Emil | Robertson, Neil | Sivapalaratnam, Suthesh | Stančáková, Alena | Stirrups, Kathleen | Thorleifsson, Gudmar | Tikkanen, Emmi | Wood, Andrew R | Almgren, Peter | Atalay, Mustafa | Benediktsson, Rafn | Bonnycastle, Lori L | Burtt, Noël | Carey, Jason | Charpentier, Guillaume | Crenshaw, Andrew T | Doney, Alex S F | Dorkhan, Mozhgan | Edkins, Sarah | Emilsson, Valur | Eury, Elodie | Forsen, Tom | Gertow, Karl | Gigante, Bruna | Grant, George B | Groves, Christopher J | Guiducci, Candace | Herder, Christian | Hreidarsson, Astradur B | Hui, Jennie | James, Alan | Jonsson, Anna | Rathmann, Wolfgang | Klopp, Norman | Kravic, Jasmina | Krjutškov, Kaarel | Langford, Cordelia | Leander, Karin | Lindholm, Eero | Lobbens, Stéphane | Männistö, Satu | Mirza, Ghazala | Mühleisen, Thomas W | Musk, Bill | Parkin, Melissa | Rallidis, Loukianos | Saramies, Jouko | Sennblad, Bengt | Shah, Sonia | Sigurðsson, Gunnar | Silveira, Angela | Steinbach, Gerald | Thorand, Barbara | Trakalo, Joseph | Veglia, Fabrizio | Wennauer, Roman | Winckler, Wendy | Zabaneh, Delilah | Campbell, Harry | van Duijn, Cornelia | Uitterlinden89-, Andre G | Hofman, Albert | Sijbrands, Eric | Abecasis, Goncalo R | Owen, Katharine R | Zeggini, Eleftheria | Trip, Mieke D | Forouhi, Nita G | Syvänen, Ann-Christine | Eriksson, Johan G | Peltonen, Leena | Nöthen, Markus M | Balkau, Beverley | Palmer, Colin N A | Lyssenko, Valeriya | Tuomi, Tiinamaija | Isomaa, Bo | Hunter, David J | Qi, Lu | Shuldiner, Alan R | Roden, Michael | Barroso, Ines | Wilsgaard, Tom | Beilby, John | Hovingh, Kees | Price, Jackie F | Wilson, James F | Rauramaa, Rainer | Lakka, Timo A | Lind, Lars | Dedoussis, George | Njølstad, Inger | Pedersen, Nancy L | Khaw, Kay-Tee | Wareham, Nicholas J | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Korpi-Hyövälti, Eeva | Saltevo, Juha | Laakso, Markku | Kuusisto, Johanna | Metspalu, Andres | Collins, Francis S | Mohlke, Karen L | Bergman, Richard N | Tuomilehto, Jaakko | Boehm, Bernhard O | Gieger, Christian | Hveem, Kristian | Cauchi, Stephane | Froguel, Philippe | Baldassarre, Damiano | Tremoli, Elena | Humphries, Steve E | Saleheen, Danish | Danesh, John | Ingelsson, Erik | Ripatti, Samuli | Salomaa, Veikko | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Peters, Annette | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Morris, Andrew D | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | Boerwinkle, Eric | Melander, Olle | Kathiresan, Sekar | Nilsson, Peter M | Deloukas, Panos | Thorsteinsdottir, Unnur | Groop, Leif C | Stefansson, Kari | Hu, Frank | Pankow, James S | Dupuis, Josée | Meigs, James B | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2012;44(9):981-990.
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
doi:10.1038/ng.2383
PMCID: PMC3442244  PMID: 22885922
18.  Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes 
Morris, Andrew P | Voight, Benjamin F | Teslovich, Tanya M | Ferreira, Teresa | Segré, Ayellet V | Steinthorsdottir, Valgerdur | Strawbridge, Rona J | Khan, Hassan | Grallert, Harald | Mahajan, Anubha | Prokopenko, Inga | Kang, Hyun Min | Dina, Christian | Esko, Tonu | Fraser, Ross M | Kanoni, Stavroula | Kumar, Ashish | Lagou, Vasiliki | Langenberg, Claudia | Luan, Jian’an | Lindgren, Cecilia M | Müller-Nurasyid, Martina | Pechlivanis, Sonali | Rayner, N William | Scott, Laura J | Wiltshire, Steven | Yengo, Loic | Kinnunen, Leena | Rossin, Elizabeth J | Raychaudhuri, Soumya | Johnson, Andrew D | Dimas, Antigone S | Loos, Ruth J F | Vedantam, Sailaja | Chen, Han | Florez, Jose C | Fox, Caroline | Liu, Ching-Ti | Rybin, Denis | Couper, David J | Kao, Wen Hong L | Li, Man | Cornelis, Marilyn C | Kraft, Peter | Sun, Qi | van Dam, Rob M | Stringham, Heather M | Chines, Peter S | Fischer, Krista | Fontanillas, Pierre | Holmen, Oddgeir L | Hunt, Sarah E | Jackson, Anne U | Kong, Augustine | Lawrence, Robert | Meyer, Julia | Perry, John R B | Platou, Carl G P | Potter, Simon | Rehnberg, Emil | Robertson, Neil | Sivapalaratnam, Suthesh | Stančáková, Alena | Stirrups, Kathleen | Thorleifsson, Gudmar | Tikkanen, Emmi | Wood, Andrew R | Almgren, Peter | Atalay, Mustafa | Benediktsson, Rafn | Bonnycastle, Lori L | Burtt, Noël | Carey, Jason | Charpentier, Guillaume | Crenshaw, Andrew T | Doney, Alex S F | Dorkhan, Mozhgan | Edkins, Sarah | Emilsson, Valur | Eury, Elodie | Forsen, Tom | Gertow, Karl | Gigante, Bruna | Grant, George B | Groves, Christopher J | Guiducci, Candace | Herder, Christian | Hreidarsson, Astradur B | Hui, Jennie | James, Alan | Jonsson, Anna | Rathmann, Wolfgang | Klopp, Norman | Kravic, Jasmina | Krjutškov, Kaarel | Langford, Cordelia | Leander, Karin | Lindholm, Eero | Lobbens, Stéphane | Männistö, Satu | Mirza, Ghazala | Mühleisen, Thomas W | Musk, Bill | Parkin, Melissa | Rallidis, Loukianos | Saramies, Jouko | Sennblad, Bengt | Shah, Sonia | Sigurðsson, Gunnar | Silveira, Angela | Steinbach, Gerald | Thorand, Barbara | Trakalo, Joseph | Veglia, Fabrizio | Wennauer, Roman | Winckler, Wendy | Zabaneh, Delilah | Campbell, Harry | van Duijn, Cornelia | Uitterlinden, Andre G | Hofman, Albert | Sijbrands, Eric | Abecasis, Goncalo R | Owen, Katharine R | Zeggini, Eleftheria | Trip, Mieke D | Forouhi, Nita G | Syvänen, Ann-Christine | Eriksson, Johan G | Peltonen, Leena | Nöthen, Markus M | Balkau, Beverley | Palmer, Colin N A | Lyssenko, Valeriya | Tuomi, Tiinamaija | Isomaa, Bo | Hunter, David J | Qi, Lu | Shuldiner, Alan R | Roden, Michael | Barroso, Ines | Wilsgaard, Tom | Beilby, John | Hovingh, Kees | Price, Jackie F | Wilson, James F | Rauramaa, Rainer | Lakka, Timo A | Lind, Lars | Dedoussis, George | Njølstad, Inger | Pedersen, Nancy L | Khaw, Kay-Tee | Wareham, Nicholas J | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Korpi-Hyövälti, Eeva | Saltevo, Juha | Laakso, Markku | Kuusisto, Johanna | Metspalu, Andres | Collins, Francis S | Mohlke, Karen L | Bergman, Richard N | Tuomilehto, Jaakko | Boehm, Bernhard O | Gieger, Christian | Hveem, Kristian | Cauchi, Stephane | Froguel, Philippe | Baldassarre, Damiano | Tremoli, Elena | Humphries, Steve E | Saleheen, Danish | Danesh, John | Ingelsson, Erik | Ripatti, Samuli | Salomaa, Veikko | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Peters, Annette | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Morris, Andrew D | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | Boerwinkle, Eric | Melander, Olle | Kathiresan, Sekar | Nilsson, Peter M | Deloukas, Panos | Thorsteinsdottir, Unnur | Groop, Leif C | Stefansson, Kari | Hu, Frank | Pankow, James S | Dupuis, Josée | Meigs, James B | Altshuler, David | Boehnke, Michael | McCarthy, Mark I
Nature genetics  2012;44(9):981-990.
To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
doi:10.1038/ng.2383
PMCID: PMC3442244  PMID: 22885922
19.  Underlying Genetic Models of Inheritance in Established Type 2 Diabetes Associations 
American Journal of Epidemiology  2009;170(5):537-545.
For most associations of common single nucleotide polymorphisms (SNPs) with common diseases, the genetic model of inheritance is unknown. The authors extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations with type 2 diabetes. For 13 SNPs, the data fitted very well to an additive model of inheritance for the diabetes risk allele; for 4 SNPs, the data were consistent with either an additive model or a dominant model; and for 2 SNPs, the data were consistent with an additive or recessive model. Results were robust to the use of different priors and after exclusion of data for which index SNPs had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that were very similar to those previously reported based on fixed- or random-effects models, but uncertainty about several of the effects was substantially larger. The authors also examined the extent of between-study heterogeneity in the genetic model and found generally small between-study deviation values for the genetic model parameter. Heterosis could not be excluded for 4 SNPs. Information on the genetic model of robustly replicated association signals derived from genome-wide association studies may be useful for predictive modeling and for designing biologic and functional experiments.
doi:10.1093/aje/kwp145
PMCID: PMC2732984  PMID: 19602701
Bayes theorem; diabetes mellitus, type 2; meta-analysis; models, genetic; polymorphism, genetic; population characteristics
20.  Common variants at 30 loci contribute to polygenic dyslipidemia 
Nature genetics  2008;41(1):56-65.
Blood low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol and triglyceride levels are risk factors for cardiovascular disease. To dissect the polygenic basis of these traits, we conducted genome-wide association screens in 19,840 individuals and replication in up to 20,623 individuals. We identified 30 distinct loci associated with lipoprotein concentrations (each with P < 5 × 10-8), including 11 loci that reached genome-wide significance for the first time. The 11 newly defined loci include common variants associated with LDL cholesterol near ABCG8, MAFB, HNF1A and TIMD4; with HDL cholesterol near ANGPTL4, FADS1-FADS2-FADS3, HNF4A, LCAT, PLTP and TTC39B; and with triglycerides near AMAC1L2, FADS1-FADS2-FADS3 and PLTP. The proportion of individuals exceeding clinical cut points for high LDL cholesterol, low HDL cholesterol and high triglycerides varied according to an allelic dosage score (P < 10-15 for each trend). These results suggest that the cumulative effect of multiple common variants contributes to polygenic dyslipidemia.
doi:10.1038/ng.291
PMCID: PMC2881676  PMID: 19060906
21.  Underlying genetic models of inheritance in established type 2 diabetes associations 
American journal of epidemiology  2009;170(5):537-545.
For most associations of common polymorphisms with common diseases, the genetic model of inheritance is unknown. We extended and applied a Bayesian meta-analysis approach to data from 19 studies on 17 replicated associations for type 2 diabetes. For 13 polymorphisms, the data fit very well to an additive model, for 4 polymorphisms the data were consistent with either an additive or dominant model, and for 2 polymorphisms with an additive or recessive model of inheritance for the diabetes risk allele. Results were robust to using different priors and after excluding data where index polymorphisms had been examined indirectly through proxy markers. The Bayesian meta-analysis model yielded point estimates for the genetic effects that are very similar to those previously reported based on fixed or random effects models, but uncertainty about several of the effects was substantially larger. We also examined the extent of between-study heterogeneity in the genetic model and found generally small values of the between-study deviation for the genetic model parameter. Heterosis could not be excluded in 4 SNPs. Information on the genetic model of robustly replicated GWA-derived association signals may be useful for predictive modeling, and for designing biological and functional experiments.
doi:10.1093/aje/kwp145
PMCID: PMC2732984  PMID: 19602701
22.  Genetic evidence that raised sex hormone binding globulin (SHBG) levels reduce the risk of type 2 diabetes 
Human Molecular Genetics  2009;19(3):535-544.
Epidemiological studies consistently show that circulating sex hormone binding globulin (SHBG) levels are lower in type 2 diabetes patients than non-diabetic individuals, but the causal nature of this association is controversial. Genetic studies can help dissect causal directions of epidemiological associations because genotypes are much less likely to be confounded, biased or influenced by disease processes. Using this Mendelian randomization principle, we selected a common single nucleotide polymorphism (SNP) near the SHBG gene, rs1799941, that is strongly associated with SHBG levels. We used data from this SNP, or closely correlated SNPs, in 27 657 type 2 diabetes patients and 58 481 controls from 15 studies. We then used data from additional studies to estimate the difference in SHBG levels between type 2 diabetes patients and controls. The SHBG SNP rs1799941 was associated with type 2 diabetes [odds ratio (OR) 0.94, 95% CI: 0.91, 0.97; P = 2 × 10−5], with the SHBG raising allele associated with reduced risk of type 2 diabetes. This effect was very similar to that expected (OR 0.92, 95% CI: 0.88, 0.96), given the SHBG-SNP versus SHBG levels association (SHBG levels are 0.2 standard deviations higher per copy of the A allele) and the SHBG levels versus type 2 diabetes association (SHBG levels are 0.23 standard deviations lower in type 2 diabetic patients compared to controls). Results were very similar in men and women. There was no evidence that this variant is associated with diabetes-related intermediate traits, including several measures of insulin secretion and resistance. Our results, together with those from another recent genetic study, strengthen evidence that SHBG and sex hormones are involved in the aetiology of type 2 diabetes.
doi:10.1093/hmg/ddp522
PMCID: PMC2798726  PMID: 19933169
23.  Variations in the G6PC2/ABCB11 genomic region are associated with fasting glucose levels  
The Journal of Clinical Investigation  2008;118(7):2620-2628.
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 × 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 × 10–26, and combining results from all studies resulted in an overall P value for association of 6.4 × 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.
doi:10.1172/JCI34566
PMCID: PMC2398737  PMID: 18521185

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