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1.  Identification and Functional Characterization of G6PC2 Coding Variants Influencing Glycemic Traits Define an Effector Transcript at the G6PC2-ABCB11 Locus 
Mahajan, Anubha | Sim, Xueling | Ng, Hui Jin | Manning, Alisa | Rivas, Manuel A. | Highland, Heather M. | Locke, Adam E. | Grarup, Niels | Im, Hae Kyung | Cingolani, Pablo | Flannick, Jason | Fontanillas, Pierre | Fuchsberger, Christian | Gaulton, Kyle J. | Teslovich, Tanya M. | Rayner, N. William | Robertson, Neil R. | Beer, Nicola L. | Rundle, Jana K. | Bork-Jensen, Jette | Ladenvall, Claes | Blancher, Christine | Buck, David | Buck, Gemma | Burtt, Noël P. | Gabriel, Stacey | Gjesing, Anette P. | Groves, Christopher J. | Hollensted, Mette | Huyghe, Jeroen R. | Jackson, Anne U. | Jun, Goo | Justesen, Johanne Marie | Mangino, Massimo | Murphy, Jacquelyn | Neville, Matt | Onofrio, Robert | Small, Kerrin S. | Stringham, Heather M. | Syvänen, Ann-Christine | Trakalo, Joseph | Abecasis, Goncalo | Bell, Graeme I. | Blangero, John | Cox, Nancy J. | Duggirala, Ravindranath | Hanis, Craig L. | Seielstad, Mark | Wilson, James G. | Christensen, Cramer | Brandslund, Ivan | Rauramaa, Rainer | Surdulescu, Gabriela L. | Doney, Alex S. F. | Lannfelt, Lars | Linneberg, Allan | Isomaa, Bo | Tuomi, Tiinamaija | Jørgensen, Marit E. | Jørgensen, Torben | Kuusisto, Johanna | Uusitupa, Matti | Salomaa, Veikko | Spector, Timothy D. | Morris, Andrew D. | Palmer, Colin N. A. | Collins, Francis S. | Mohlke, Karen L. | Bergman, Richard N. | Ingelsson, Erik | Lind, Lars | Tuomilehto, Jaakko | Hansen, Torben | Watanabe, Richard M. | Prokopenko, Inga | Dupuis, Josee | Karpe, Fredrik | Groop, Leif | Laakso, Markku | Pedersen, Oluf | Florez, Jose C. | Morris, Andrew P. | Altshuler, David | Meigs, James B. | Boehnke, Michael | McCarthy, Mark I. | Lindgren, Cecilia M. | Gloyn, Anna L.
PLoS Genetics  2015;11(1):e1004876.
Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights.
Author Summary
Understanding how FI and FG levels are regulated is important because their derangement is a feature of T2D. Despite recent success from GWAS in identifying regions of the genome influencing glycemic traits, collectively these loci explain only a small proportion of trait variance. Unlocking the biological mechanisms driving these associations has been challenging because the vast majority of variants map to non-coding sequence, and the genes through which they exert their impact are largely unknown. In the current study, we sought to increase our understanding of the physiological pathways influencing both traits using exome-array genotyping in up to 33,231 non-diabetic individuals to identify coding variants and consequently genes associated with either FG or FI levels. We identified novel association signals for both traits including the receptor for GLP-1 agonists which are a widely used therapy for T2D. Furthermore, we identified coding variants at several GWAS loci which point to the genes underlying these association signals. Importantly, we found that multiple coding variants in G6PC2 result in a loss of protein function and lower fasting glucose levels.
PMCID: PMC4307976  PMID: 25625282
2.  Blood Pressure Levels in Male Carriers of Arg82Cys in CD300LG 
PLoS ONE  2014;9(10):e109646.
The genetics of hypertension has been scrutinized in large-scale genome-wide association studies (GWAS) with a large number of common genetic variants identified, each exerting subtle effects on disease susceptibility. An amino acid polymorphism, p.Arg82Cys, in CD300LG was recently found to be associated with fasting HDL-cholesterol and triglyceride levels. The polymorphism has not been detected in hypertension GWAS potentially due to its low frequency, but CD300LG has been linked to blood pressure as CD300LG knockout mice have changes in blood pressure. Twenty-four-hour ambulatory blood pressure was obtained in human CD300LG CT-carriers to follow up on these observations.
Twenty healthy male CD300LG rs72836561 CT-carriers matched for age and BMI with 20 healthy male CC-carriers. Office blood pressure, 24-hour ambulatory blood pressure, carotid intima-media thickness (CIMT), and fasting blood samples were evaluated. The clinical study was combined with a genetic-epidemiological study to replicate the association between blood pressure and CD300LG Arg82Cys in 2,637 men and 3,249 women.
CT-carriers had a higher 24-hour ambulatory systolic blood pressure (122 mmHg versus 115; p = 0.01) and diastolic blood pressure (77 mmHg versus 72; p<0.01) compared with CC-carriers. There were no differences in CIMT between the two groups. Metalloproteinase-9 level was higher in CT-carriers than in CC-carriers (P<0.01). However, no association between office blood pressure and CD300LG genotype was detected in the genetic-epidemiological study.
Although 24-hour blood pressure, measured with a sensitive method, in a small sample of CD300LG rs72836561 CT-carriers was higher than in CC-carriers, this did not translate into significant differences in office blood pressure in a larger cohort. This discrepancy which may reflect differences in methodological approach, underlines the importance of performing replication studies in a larger clinical context, but a formal rejection of a relation between blood pressure and CD300LG requires measurement of 24-hour ambulatory blood pressure in a larger cohort.
PMCID: PMC4196928  PMID: 25314291
3.  Genome-wide association study identifies a sequence variant within the DAB2IP gene conferring susceptibility to abdominal aortic aneurysm 
Gretarsdottir, Solveig | Baas, Annette F | Thorleifsson, Gudmar | Holm, Hilma | den Heijer, Martin | de Vries, Jean-Paul P M | Kranendonk, Steef E | Zeebregts, Clark J A M | van Sterkenburg, Steven M | Geelkerken, Robert H | van Rij, Andre M | Williams, Michael J A | Boll, Albert P M | Kostic, Jelena P | Jonasdottir, Adalbjorg | Jonasdottir, Aslaug | Walters, G Bragi | Masson, Gisli | Sulem, Patrick | Saemundsdottir, Jona | Mouy, Magali | Magnusson, Kristinn P | Tromp, Gerard | Elmore, James R | Sakalihasan, Natzi | Limet, Raymond | Defraigne, Jean-Olivier | Ferrell, Robert E | Ronkainen, Antti | Ruigrok, Ynte M | Wijmenga, Cisca | Grobbee, Diederick E | Shah, Svati H | Granger, Christopher B | Quyyumi, Arshed A | Vaccarino, Viola | Patel, Riyaz S | Zafari, A Maziar | Levey, Allan I | Austin, Harland | Girelli, Domenico | Pignatti, Pier Franco | Olivieri, Oliviero | Martinelli, Nicola | Malerba, Giovanni | Trabetti, Elisabetta | Becker, Lewis C | Becker, Diane M | Reilly, Muredach P | Rader, Daniel J | Mueller, Thomas | Dieplinger, Benjamin | Haltmayer, Meinhard | Urbonavicius, Sigitas | Lindblad, Bengt | Gottsäter, Anders | Gaetani, Eleonora | Pola, Roberto | Wells, Philip | Rodger, Marc | Forgie, Melissa | Langlois, Nicole | Corral, Javier | Vicente, Vicente | Fontcuberta, Jordi | España, Francisco | Grarup, Niels | Jørgensen, Torben | Witte, Daniel R | Hansen, Torben | Pedersen, Oluf | Aben, Katja K | de Graaf, Jacqueline | Holewijn, Suzanne | Folkersen, Lasse | Franco-Cereceda, Anders | Eriksson, Per | Collier, David A | Stefansson, Hreinn | Steinthorsdottir, Valgerdur | Rafnar, Thorunn | Valdimarsson, Einar M | Magnadottir, Hulda B | Sveinbjornsdottir, Sigurlaug | Olafsson, Isleifur | Magnusson, Magnus Karl | Palmason, Robert | Haraldsdottir, Vilhelmina | Andersen, Karl | Onundarson, Pall T | Thorgeirsson, Gudmundur | Kiemeney, Lambertus A | Powell, Janet T | Carey, David J | Kuivaniemi, Helena | Lindholt, Jes S | Jones, Gregory T | Kong, Augustine | Blankensteijn, Jan D | Matthiasson, Stefan E | Thorsteinsdottir, Unnur | Stefansson, Kari
Nature genetics  2010;42(8):692-697.
We performed a genome-wide association study on 1,292 individuals with abdominal aortic aneurysms (AAAs) and 30,503 controls from Iceland and The Netherlands, with a follow-up of top markers in up to 3,267 individuals with AAAs and 7,451 controls. The A allele of rs7025486 on 9q33 was found to associate with AAA, with an odds ratio (OR) of 1.21 and P = 4.6 × 10−10. In tests for association with other vascular diseases, we found that rs7025486[A] is associated with early onset myocardial infarction (OR = 1.18, P = 3.1 × 10−5), peripheral arterial disease (OR = 1.14, P = 3.9 × 10−5) and pulmonary embolism (OR = 1.20, P = 0.00030), but not with intracranial aneurysm or ischemic stroke. No association was observed between rs7025486[A] and common risk factors for arterial and venous diseases—that is, smoking, lipid levels, obesity, type 2 diabetes and hypertension. Rs7025486 is located within DAB2IP, which encodes an inhibitor of cell growth and survival.
PMCID: PMC4157066  PMID: 20622881
4.  Loss-of-function mutations in SLC30A8 protect against type 2 diabetes 
Flannick, Jason | Thorleifsson, Gudmar | Beer, Nicola L. | Jacobs, Suzanne B. R. | Grarup, Niels | Burtt, Noël P. | Mahajan, Anubha | Fuchsberger, Christian | Atzmon, Gil | Benediktsson, Rafn | Blangero, John | Bowden, Don W. | Brandslund, Ivan | Brosnan, Julia | Burslem, Frank | Chambers, John | Cho, Yoon Shin | Christensen, Cramer | Douglas, Desirée A. | Duggirala, Ravindranath | Dymek, Zachary | Farjoun, Yossi | Fennell, Timothy | Fontanillas, Pierre | Forsén, Tom | Gabriel, Stacey | Glaser, Benjamin | Gudbjartsson, Daniel F. | Hanis, Craig | Hansen, Torben | Hreidarsson, Astradur B. | Hveem, Kristian | Ingelsson, Erik | Isomaa, Bo | Johansson, Stefan | Jørgensen, Torben | Jørgensen, Marit Eika | Kathiresan, Sekar | Kong, Augustine | Kooner, Jaspal | Kravic, Jasmina | Laakso, Markku | Lee, Jong-Young | Lind, Lars | Lindgren, Cecilia M | Linneberg, Allan | Masson, Gisli | Meitinger, Thomas | Mohlke, Karen L | Molven, Anders | Morris, Andrew P. | Potluri, Shobha | Rauramaa, Rainer | Ribel-Madsen, Rasmus | Richard, Ann-Marie | Rolph, Tim | Salomaa, Veikko | Segrè, Ayellet V. | Skärstrand, Hanna | Steinthorsdottir, Valgerdur | Stringham, Heather M. | Sulem, Patrick | Tai, E Shyong | Teo, Yik Ying | Teslovich, Tanya | Thorsteinsdottir, Unnur | Trimmer, Jeff K. | Tuomi, Tiinamaija | Tuomilehto, Jaakko | Vaziri-Sani, Fariba | Voight, Benjamin F. | Wilson, James G. | Boehnke, Michael | McCarthy, Mark I. | Njølstad, Pål R. | Pedersen, Oluf | Groop, Leif | Cox, David R. | Stefansson, Kari | Altshuler, David
Nature genetics  2014;46(4):357-363.
Loss-of-function mutations protective against human disease provide in vivo validation of therapeutic targets1,2,3, yet none are described for type 2 diabetes (T2D). Through sequencing or genotyping ~150,000 individuals across five ethnicities, we identified 12 rare protein-truncating variants in SLC30A8, which encodes an islet zinc transporter (ZnT8)4 and harbors a common variant (p.Trp325Arg) associated with T2D risk, glucose, and proinsulin levels5–7. Collectively, protein-truncating variant carriers had 65% reduced T2D risk (p=1.7×10−6), and non-diabetic Icelandic carriers of a frameshift variant (p.Lys34SerfsX50) demonstrated reduced glucose levels (−0.17 s.d., p=4.6×10−4). The two most common protein-truncating variants (p.Arg138X and p.Lys34SerfsX50) individually associate with T2D protection and encode unstable ZnT8 proteins. Previous functional study of SLC30A8 suggested reduced zinc transport increases T2D risk8,9, yet phenotypic heterogeneity was observed in rodent Slc30a8 knockouts10–15. Contrastingly, loss-of-function mutations in humans provide strong evidence that SLC30A8 haploinsufficiency protects against T2D, proposing ZnT8 inhibition as a therapeutic strategy in T2D prevention.
PMCID: PMC4051628  PMID: 24584071
5.  Studies of association of AGPAT6 variants with type 2 diabetes and related metabolic phenotypes in 12,068 Danes 
BMC Medical Genetics  2013;14:113.
Type 2 diabetes, obesity and insulin resistance are characterized by hypertriglyceridemia and ectopic accumulation of lipids in liver and skeletal muscle. AGPAT6 encodes a novel glycerol-3 phosphate acyltransferase, GPAT4, which catalyzes the first step in the de novo triglyceride synthesis. AGPAT6-deficient mice show lower weight and resistance to diet- and genetically induced obesity. Here, we examined whether common or low-frequency variants in AGPAT6 associate with type 2 diabetes or related metabolic traits in a Danish population.
Eleven variants selected by a candidate gene approach capturing the common and low-frequency variation of AGPAT6 were genotyped in 12,068 Danes from four study populations of middle-aged individuals. The case–control study involved 4,638 type 2 diabetic and 5,934 glucose-tolerant individuals, while studies of quantitative metabolic traits were performed in 5,645 non-diabetic participants of the Inter99 Study.
None of the eleven AGPAT6 variants were robustly associated with type 2 diabetes in the Danish case–control study. Moreover, none of the AGPAT6 variants showed association with measures of obesity (waist circumference and BMI), serum lipid concentrations, fasting or 2-h post-glucose load levels of plasma glucose and serum insulin, or estimated indices of insulin secretion or insulin sensitivity.
Common and low-frequency variants in AGPAT6 do not significantly associate with type 2 diabetes susceptibility, or influence related phenotypic traits such as obesity, dyslipidemia or indices of insulin sensitivity or insulin secretion in the population studied.
PMCID: PMC4231429  PMID: 24156295
Type 2 diabetes; Genetics; Insulin resistance; Human; Lipid droplets; AGPAT6; GPAT4
6.  Gene × Physical Activity Interactions in Obesity: Combined Analysis of 111,421 Individuals of European Ancestry 
PLoS Genetics  2013;9(7):e1003607.
Numerous obesity loci have been identified using genome-wide association studies. A UK study indicated that physical activity may attenuate the cumulative effect of 12 of these loci, but replication studies are lacking. Therefore, we tested whether the aggregate effect of these loci is diminished in adults of European ancestry reporting high levels of physical activity. Twelve obesity-susceptibility loci were genotyped or imputed in 111,421 participants. A genetic risk score (GRS) was calculated by summing the BMI-associated alleles of each genetic variant. Physical activity was assessed using self-administered questionnaires. Multiplicative interactions between the GRS and physical activity on BMI were tested in linear and logistic regression models in each cohort, with adjustment for age, age2, sex, study center (for multicenter studies), and the marginal terms for physical activity and the GRS. These results were combined using meta-analysis weighted by cohort sample size. The meta-analysis yielded a statistically significant GRS × physical activity interaction effect estimate (Pinteraction = 0.015). However, a statistically significant interaction effect was only apparent in North American cohorts (n = 39,810, Pinteraction = 0.014 vs. n = 71,611, Pinteraction = 0.275 for Europeans). In secondary analyses, both the FTO rs1121980 (Pinteraction = 0.003) and the SEC16B rs10913469 (Pinteraction = 0.025) variants showed evidence of SNP × physical activity interactions. This meta-analysis of 111,421 individuals provides further support for an interaction between physical activity and a GRS in obesity disposition, although these findings hinge on the inclusion of cohorts from North America, indicating that these results are either population-specific or non-causal.
Author Summary
We undertook analyses in 111,421 adults of European descent to examine whether physical activity diminishes the genetic risk of obesity predisposed by 12 single nucleotide polymorphisms, as previously reported in a study of 20,000 UK adults (Li et al, PLoS Med. 2010). Although the study by Li et al is widely cited, the original report has not been replicated to our knowledge. Therefore, we sought to confirm or refute the original study's findings in a combined analysis of 111,421 adults. Our analyses yielded a statistically significant interaction effect (Pinteraction = 0.015), confirming the original study's results; we also identified an interaction between the FTO locus and physical activity (Pinteraction = 0.003), verifying previous analyses (Kilpelainen et al, PLoS Med., 2010), and we detected a novel interaction between the SEC16B locus and physical activity (Pinteraction = 0.025). We also examined the power constraints of interaction analyses, thereby demonstrating that sources of within- and between-study heterogeneity and the manner in which data are treated can inhibit the detection of interaction effects in meta-analyses that combine many cohorts with varying characteristics. This suggests that combining many small studies that have measured environmental exposures differently may be relatively inefficient for the detection of gene × environment interactions.
PMCID: PMC3723486  PMID: 23935507
7.  Genetic Architecture of Vitamin B12 and Folate Levels Uncovered Applying Deeply Sequenced Large Datasets 
PLoS Genetics  2013;9(6):e1003530.
Genome-wide association studies have mainly relied on common HapMap sequence variations. Recently, sequencing approaches have allowed analysis of low frequency and rare variants in conjunction with common variants, thereby improving the search for functional variants and thus the understanding of the underlying biology of human traits and diseases. Here, we used a large Icelandic whole genome sequence dataset combined with Danish exome sequence data to gain insight into the genetic architecture of serum levels of vitamin B12 (B12) and folate. Up to 22.9 million sequence variants were analyzed in combined samples of 45,576 and 37,341 individuals with serum B12 and folate measurements, respectively. We found six novel loci associating with serum B12 (CD320, TCN2, ABCD4, MMAA, MMACHC) or folate levels (FOLR3) and confirmed seven loci for these traits (TCN1, FUT6, FUT2, CUBN, CLYBL, MUT, MTHFR). Conditional analyses established that four loci contain additional independent signals. Interestingly, 13 of the 18 identified variants were coding and 11 of the 13 target genes have known functions related to B12 and folate pathways. Contrary to epidemiological studies we did not find consistent association of the variants with cardiovascular diseases, cancers or Alzheimer's disease although some variants demonstrated pleiotropic effects. Although to some degree impeded by low statistical power for some of these conditions, these data suggest that sequence variants that contribute to the population diversity in serum B12 or folate levels do not modify the risk of developing these conditions. Yet, the study demonstrates the value of combining whole genome and exome sequencing approaches to ascertain the genetic and molecular architectures underlying quantitative trait associations.
Author Summary
Genome-wide association studies have in recent years revealed a wealth of common variants associated with common diseases and phenotypes. We took advantage of the advances in sequencing technologies to study the association of low frequency and rare variants in conjunction with common variants with serum levels of vitamin B12 (B12) and folate in Icelanders and Danes. We found 18 independent signals in 13 loci associated with serum B12 or folate levels. Interestingly, 13 of the 18 identified variants are coding and 11 of the 13 target genes have known functions related to B12 and folate pathways. These data indicate that the target genes at all of the loci have been identified. Epidemiological studies have shown a relationship between serum B12 and folate levels and the risk of cardiovascular diseases, cancers, and Alzheimer's disease. We investigated association between the identified variants and these diseases but did not find consistent association.
PMCID: PMC3674994  PMID: 23754956
9.  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.
PMCID: PMC3331745  PMID: 22415877
10.  What Is the Contribution of Two Genetic Variants Regulating VEGF Levels to Type 2 Diabetes Risk and to Microvascular Complications? 
PLoS ONE  2013;8(2):e55921.
Vascular endothelial growth factor (VEGF) is a key chemokine involved in tissue growth and organ repair processes, particularly angiogenesis. Elevated circulating VEGF levels are believed to play a role in type 2 diabetes (T2D) microvascular complications, especially diabetic retinopathy. Recently, a genome-wide association study identified two common single nucleotide polymorphisms (SNPs; rs6921438 and rs10738760) explaining nearly half of the variance in circulating VEGF levels. Considering the putative contribution of VEGF to T2D and its complications, we aimed to assess the effect of these VEGF-related SNPs on the risk of T2D, nephropathy and retinopathy, as well as on variation in related traits.
SNPs were genotyped in several case-control studies: French and Danish T2D studies (Ncases = 6,920-Ncontrols = 3,875 and Ncases = 3,561-Ncontrols = 2,623; respectively), two French studies one for diabetic nephropathy (Ncases = 1,242-Ncontrols = 860) and the other for diabetic retinopathy (Ncases = 1,336-Ncontrols = 1,231). The effects of each SNP on quantitative traits were analyzed in a French general population-based cohort (N = 4,760) and two French T2D studies (N = 3,480). SNP associations were assessed using logistic or linear regressions.
In the French population, we found an association between the G-allele of rs6921438, shown to increase circulating VEGF levels, and increased T2D risk (OR = 1.15; P = 3.7×10−5). Furthermore, the same allele was associated with higher glycated hemoglobin levels (β = 0.02%; P = 9.2×10−3). However, these findings were not confirmed in the Danes. Conversely, the SNP rs10738760 was not associated with T2D in the French or Danish populations. Despite having adequate statistical power, we did not find any significant effects of rs6921438 or rs10738760 on diabetic microvascular complications or the variation in related traits in T2D patients.
In spite of their impact on the variance in circulating VEGF, we did not find any association between SNPs rs6921438 and rs10738760, and the risk of T2D, diabetic nephropathy or retinopathy. The link between VEGF and T2D and its complications might be indirect and more complex than expected.
PMCID: PMC3566098  PMID: 23405237
11.  Genetic Variant SCL2A2 Is Associated with Risk of Cardiovascular Disease – Assessing the Individual and Cumulative Effect of 46 Type 2 Diabetes Related Genetic Variants 
PLoS ONE  2012;7(11):e50418.
To assess the individual and combined effect of 46 type 2 diabetes related risk alleles on incidence of a composite CVD endpoint.
Data from the first Danish MONICA study (N = 3523) and the Inter99 study (N = 6049) was used. Using Cox proportional hazard regression the individual effect of each risk allele on incident CVD was analyzed. Risk was presented as hazard ratios (HR) per risk allele.
During 80,859 person years 1441 incident cases of CVD (fatal and non-fatal) occurred in the MONICA study. In Inter99 942 incident cases were observed during 61,239 person years.
In the Danish MONICA study four gene variants were significantly associated with incident CVD independently of known diabetes status at baseline; SLC2A2 rs11920090 (HR 1.147, 95% CI 1.027–1.283 , P = 0.0154), C2CD4A rs7172432 (1.112, 1.027–1.205 , P = 0.0089), GCKR rs780094 (1.094, 1.007–1.188 , P = 0.0335) and C2CD4B rs11071657 (1.092, 1.007–1.183 , P = 0.0323). The genetic score was significantly associated with increased risk of CVD (1.025, 1.010–1.041, P = 0.0016). In Inter99 two gene variants were associated with risk of CVD independently of diabetes; SLC2A2 (HR 1.180, 95% CI 1.038–1.341 P = 0.0116) and FTO (0.909, 0.827–0.998, P = 0.0463). Analysing the two populations together we found SLC2A2 rs11920090 (HR 1.164, 95% CI 1.070–1.267, P = 0.0004) meeting the Bonferroni corrected threshold for significance. GCKR rs780094 (1.076, 1.010–1.146, P = 0.0229), C2CD4B rs11071657 (1.067, 1.003–1.135, P = 0.0385) and NOTCH2 rs10923931 (1.104 (1.001 ; 1.217 , P = 0.0481) were found associated with CVD without meeting the corrected threshold. The genetic score was significantly associated with increased risk of CVD (1.018, 1.006–1.031, P = 0.0043).
This study showed that out of the 46 genetic variants examined only the minor risk allele of SLC2A2 rs11920090 was significantly (P = 0.0005) associated with a composite endpoint of incident CVD below the threshold for statistical significance corrected for multiple testing. This potential pathway needs further exploration.
PMCID: PMC3503928  PMID: 23185617
13.  The PNPLA3 rs738409 G-Allele Associates with Reduced Fasting Serum Triglyceride and Serum Cholesterol in Danes with Impaired Glucose Regulation 
PLoS ONE  2012;7(7):e40376.
Background and Aim
Non-alcoholic fatty liver disease (NAFLD) is a common condition, associated with hepatic insulin resistance and the metabolic syndrome including hyperglycaemia and dyslipidemia. We aimed at studying the potential impact of the NAFLD-associated PNPLA3 rs738409 G-allele on NAFLD-related metabolic traits in hyperglycaemic individuals.
The rs738409 variant was genotyped in the population-based Inter99 cohort examined by an oral glucose-tolerance test, and a combined study-sample consisting of 192 twins (96 twin pairs) and a sub-set of the Inter99 population (n = 63) examined by a hyperinsulinemic euglycemic clamp (ntotal = 255). In Inter99, we analyzed associations of rs738409 with components of the WHO-defined metabolic syndrome (n = 5,847) and traits related to metabolic disease (n = 5,663). In the combined study sample we elucidated whether the rs738409 G-allele altered hepatic or peripheral insulin sensitivity. Study populations were divided into individuals with normal glucose-tolerance (NGT) and with impaired glucose regulation (IGR).
The case-control study showed no associations with components of the metabolic syndrome or the metabolic syndrome. Among 1,357 IGR individuals, the rs738409 G-allele associated with decreased fasting serum triglyceride levels (per allele effect(β) = −9.9% [−14.4%;−4.0% (95% CI)], p = 5.1×10−5) and fasting total cholesterol (β = −0.2 mmol/l [−0.3;−0.01 mmol/l(95% CI)], p = 1.5×10−4). Meta-analyses showed no impact on hepatic or peripheral insulin resistance in carriers of the rs738409 G-allele.
Our findings suggest that the G-allele of PNPLA3 rs738409 associates with reduced fasting levels of cholesterol and triglyceride in individuals with IGR.
PMCID: PMC3390392  PMID: 22792295
14.  The Birth Weight Lowering C-Allele of rs900400 Near LEKR1 and CCNL1 Associates with Elevated Insulin Release following an Oral Glucose Challenge 
PLoS ONE  2011;6(11):e27096.
Background and Aim
The first genome-wide association study on birth weight was recently published and the most significant associated birth weight lowering variant was the rs900400 C-allele located near LEKR1 and CCNL1. We aimed to replicate the association with birth weight in the Danish Inter99 study and furthermore to evaluate associations between rs900400 and indices of insulin secretion and insulin sensitivity obtained by oral glucose tolerance tests in adults from the Danish Inter99 study and the Finnish, Metabolic Syndrome in Men (METSIM) sample.
For 4,744 of 6,784 Inter99 participants, midwife journals were traced through the Danish State Archives and association of rs900400 with birth weight was examined. Associations between rs900400 and fasting serum insulin, fasting plasma glucose, insulinogenic index, homeostasis model assessment of insulin resistance (HOMA-IR) and disposition index were studied in 5,484 Danish and 6,915 Finnish non-diabetic individuals and combined in meta-analyses.
The C-allele of rs900400 was associated with a 22.1 g lower birth weight ([−41.3;−3.0], P = 0.024) per allele. Moreover, in combined analyses of the Danish Inter99 study and the Finnish METSIM study we found that the birth weight lowering allele was associated with increased insulin release measured by the insulinogenic index (β = 2.25% [0.59; 3.91], P = 0.008) and with an increased disposition index (β = 1.76% [0.04; 3.49], P = 0.05).
The birth weight lowering effect of the C-allele of rs900400 located near LEKR1 and CCNL1 was replicated in the Danish population. Furthermore the C-allele was associated with increased insulin response following oral glucose stimulation in a meta-analysis based on Danish and Finnish non-diabetic individuals.
PMCID: PMC3208566  PMID: 22073261
15.  Natural Selection Affects Multiple Aspects of Genetic Variation at Putatively Neutral Sites across the Human Genome 
PLoS Genetics  2011;7(10):e1002326.
A major question in evolutionary biology is how natural selection has shaped patterns of genetic variation across the human genome. Previous work has documented a reduction in genetic diversity in regions of the genome with low recombination rates. However, it is unclear whether other summaries of genetic variation, like allele frequencies, are also correlated with recombination rate and whether these correlations can be explained solely by negative selection against deleterious mutations or whether positive selection acting on favorable alleles is also required. Here we attempt to address these questions by analyzing three different genome-wide resequencing datasets from European individuals. We document several significant correlations between different genomic features. In particular, we find that average minor allele frequency and diversity are reduced in regions of low recombination and that human diversity, human-chimp divergence, and average minor allele frequency are reduced near genes. Population genetic simulations show that either positive natural selection acting on favorable mutations or negative natural selection acting against deleterious mutations can explain these correlations. However, models with strong positive selection on nonsynonymous mutations and little negative selection predict a stronger negative correlation between neutral diversity and nonsynonymous divergence than observed in the actual data, supporting the importance of negative, rather than positive, selection throughout the genome. Further, we show that the widespread presence of weakly deleterious alleles, rather than a small number of strongly positively selected mutations, is responsible for the correlation between neutral genetic diversity and recombination rate. This work suggests that natural selection has affected multiple aspects of linked neutral variation throughout the human genome and that positive selection is not required to explain these observations.
Author Summary
While researchers have identified candidate genes that have evolved under positive Darwinian natural selection, less is known about how much of the human genome has been affected by natural selection or whether positive selection has had a greater role at shaping patterns of variation across the human genome than negative selection acting against deleterious mutations. To address these questions, we have combined patterns of genetic variation in three genome-wide resequencing datasets with population genetic models of natural selection. We find that genetic diversity and average minor allele frequency are reduced in regions of the genome with low recombination rate. Additionally, genetic diversity, human-chimp divergence, and average minor allele frequency have been reduced near genes. Overall, while we cannot exclude positive selection at a fraction of mutations, models that include many weakly deleterious mutations throughout the human genome better explain multiple aspects of the genome-wide resequencing data. This work points to negative selection as an important force for shaping patterns of variation and suggests that there are many weakly deleterious mutations at both coding and noncoding sites throughout the human genome. Understanding such mutations will be important for learning about human evolution and the genetic basis of common disease.
PMCID: PMC3192825  PMID: 22022285
16.  Genome-Wide Population-Based Association Study of Extremely Overweight Young Adults – The GOYA Study 
PLoS ONE  2011;6(9):e24303.
Thirty-two common variants associated with body mass index (BMI) have been identified in genome-wide association studies, explaining ∼1.45% of BMI variation in general population cohorts. We performed a genome-wide association study in a sample of young adults enriched for extremely overweight individuals. We aimed to identify new loci associated with BMI and to ascertain whether using an extreme sampling design would identify the variants known to be associated with BMI in general populations.
Methodology/Principal Findings
From two large Danish cohorts we selected all extremely overweight young men and women (n = 2,633), and equal numbers of population-based controls (n = 2,740, drawn randomly from the same populations as the extremes, representing ∼212,000 individuals). We followed up novel (at the time of the study) association signals (p<0.001) from the discovery cohort in a genome-wide study of 5,846 Europeans, before attempting to replicate the most strongly associated 28 SNPs in an independent sample of Danish individuals (n = 20,917) and a population-based cohort of 15-year-old British adolescents (n = 2,418). Our discovery analysis identified SNPs at three loci known to be associated with BMI with genome-wide confidence (P<5×10−8; FTO, MC4R and FAIM2). We also found strong evidence of association at the known TMEM18, GNPDA2, SEC16B, TFAP2B, SH2B1 and KCTD15 loci (p<0.001), and nominal association (p<0.05) at a further 8 loci known to be associated with BMI. However, meta-analyses of our discovery and replication cohorts identified no novel associations.
Our results indicate that the detectable genetic variation associated with extreme overweight is very similar to that previously found for general BMI. This suggests that population-based study designs with enriched sampling of individuals with the extreme phenotype may be an efficient method for identifying common variants that influence quantitative traits and a valid alternative to genotyping all individuals in large population-based studies, which may require tens of thousands of subjects to achieve similar power.
PMCID: PMC3174168  PMID: 21935397
17.  Combined Analyses of 20 Common Obesity Susceptibility Variants 
Diabetes  2010;59(7):1667-1673.
Genome-wide association studies and linkage studies have identified 20 validated genetic variants associated with obesity and/or related phenotypes. The variants are common, and they individually exhibit small-to-modest effect sizes.
In this study we investigate the combined effect of these variants and their ability to discriminate between normal weight and overweight/obese individuals. We applied receiver operating characteristics (ROC) curves, and estimated the area under the ROC curve (AUC) as a measure of the discriminatory ability. The analyses were performed cross-sectionally in the population-based Inter99 cohort where 1,725 normal weight, 1,519 overweight, and 681 obese individuals were successfully genotyped for all 20 variants.
When combining all variants, the 10% of the study participants who carried more than 22 risk-alleles showed a significant increase in probability of being both overweight with an odds ratio of 2.00 (1.47–2.72), P = 4.0 × 10−5, and obese with an OR of 2.62 (1.76–3.92), P = 6.4 × 10−7, compared with the 10% of the study participants who carried less than 14 risk-alleles. Discrimination ability for overweight and obesity, using the 20 single nucleotide polymorphisms (SNPs), was determined to AUCs of 0.53 and 0.58, respectively. When combining SNP data with conventional nongenetic risk factors of obesity, the discrimination ability increased to 0.64 for overweight and 0.69 for obesity. The latter is significantly higher (P < 0.001) than for the nongenetic factors alone (AUC = 0.67).
The discriminative value of the 20 validated common obesity variants is at present time sparse and too weak for clinical utility, however, they add to increase the discrimination ability of conventional nongenetic risk factors.
PMCID: PMC2889766  PMID: 20110568
18.  Estimation of allele frequency and association mapping using next-generation sequencing data 
BMC Bioinformatics  2011;12:231.
Estimation of allele frequency is of fundamental importance in population genetic analyses and in association mapping. In most studies using next-generation sequencing, a cost effective approach is to use medium or low-coverage data (e.g., < 15X). However, SNP calling and allele frequency estimation in such studies is associated with substantial statistical uncertainty because of varying coverage and high error rates.
We evaluate a new maximum likelihood method for estimating allele frequencies in low and medium coverage next-generation sequencing data. The method is based on integrating over uncertainty in the data for each individual rather than first calling genotypes. This method can be applied to directly test for associations in case/control studies. We use simulations to compare the likelihood method to methods based on genotype calling, and show that the likelihood method outperforms the genotype calling methods in terms of: (1) accuracy of allele frequency estimation, (2) accuracy of the estimation of the distribution of allele frequencies across neutrally evolving sites, and (3) statistical power in association mapping studies. Using real re-sequencing data from 200 individuals obtained from an exon-capture experiment, we show that the patterns observed in the simulations are also found in real data.
Overall, our results suggest that association mapping and estimation of allele frequencies should not be based on genotype calling in low to medium coverage data. Furthermore, if genotype calling methods are used, it is usually better not to filter genotypes based on the call confidence score.
PMCID: PMC3212839  PMID: 21663684
19.  MTNR1B G24E Variant Associates With BMI and Fasting Plasma Glucose in the General Population in Studies of 22,142 Europeans 
Diabetes  2010;59(6):1539-1548.
Common variants in the melatonin receptor type 1B (MTNR1B) locus have been shown to increase fasting plasma glucose (FPG) and the risk of type 2 diabetes. The aims of this study were to evaluate whether nonsynonymous variants in MTNR1B associate with monogenic forms of hyperglycemia, type 2 diabetes, or related metabolic traits.
MTNR1B was sequenced in 47 probands with clinical maturity-onset diabetes of the young (MODY), in 51 probands with early-onset familial type 2 diabetes, and in 94 control individuals. Six nonsynonymous variants (G24E, L60R, V124I, R138C, R231H, and K243R) were genotyped in up to 22,142 Europeans. Constitutive and melatonin-induced signaling was characterized for the wild-type melatonin receptor type 1B (MT2) and the 24E, 60R, and 124I MT2 mutants in transfected COS-7 cells.
No mutations in MTNR1B were MODY specific, and none of the investigated MTNR1B variants associated with type 2 diabetes. The common 24E variant associated with increased prevalence of obesity (odds ratio 1.20 [1.08–1.34]; P = 8.3 × 10−4) and increased BMI (β = 0.5 kg/m2; P = 1.2 × 10−5) and waist circumference (β = 1.2 cm; P = 9 × 10−6) in combined Danish and French study samples. 24E also associated with decreased FPG (β = −0.08 mmol/l; P = 9.2 × 10−4) in the Danish Inter99 population. Slightly decreased constitutive activity was observed for the MT2 24E mutant, while the 124I and 60R mutants displayed considerably decreased or completely disrupted signaling, respectively.
Nonsynonymous mutations in MTNR1B are not a common cause of MODY or type 2 diabetes among Danes. MTNR1B 24E associates with increased body mass and decreased FPG. Decreased MT2 signaling does apparently not directly associate with FPG or type 2 diabetes.
PMCID: PMC2874716  PMID: 20200315
20.  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.
PMCID: PMC3080658  PMID: 20581827
21.  Impact of rs361072 in the Phosphoinositide 3-Kinase p110β Gene on Whole-Body Glucose Metabolism and Subunit Protein Expression in Skeletal Muscle 
Diabetes  2010;59(4):1108-1112.
Phosphoinositide 3-kinase (PI3K) is a major effector in insulin signaling. rs361072, located in the promoter of the gene (PIK3CB) for the p110β subunit, has previously been found to be associated with homeostasis model assessment for insulin resistance (HOMA-IR) in obese subjects. The aim was to investigate the influence of rs361072 on in vivo glucose metabolism, skeletal muscle PI3K subunit protein levels, and type 2 diabetes.
The functional role of rs361072 was studied in 196 Danish healthy adult twins. Peripheral and hepatic insulin sensitivity was assessed by a euglycemic-hyperinsulinemic clamp. Basal and insulin-stimulated biopsies were taken from the vastus lateralis muscle, and tissue p110β and p85α proteins were measured by Western blotting. The genetic association with type 2 diabetes and quantitative metabolic traits was investigated in 9,316 Danes with glucose tolerance ranging from normal to overt type 2 diabetes.
While hepatic insulin resistance was similar in the fasting state, carriers of the minor G allele had lower hepatic glucose output (per-allele effect: −16%, Padd = 0.004) during high physiological insulin infusion. rs361072 did not associate with insulin-stimulated peripheral glucose disposal despite a decreased muscle p85α:p110β protein ratio (Padd = 0.03) in G allele carriers. No association with HOMA-IR or type 2 diabetes (odds ratio 1.07, P = 0.5) was identified, and obesity did not interact with rs361072 on these traits.
Our study suggests that the minor G allele of PIK3CB rs361072 associates with decreased muscle p85α:p110β ratio and lower hepatic glucose production at high plasma insulin levels. However, no impact on type 2 diabetes prevalence was found.
PMCID: PMC2844820  PMID: 20107106
22.  Bioinformatics-Driven Identification and Examination of Candidate Genes for Non-Alcoholic Fatty Liver Disease 
PLoS ONE  2011;6(1):e16542.
Candidate genes for non-alcoholic fatty liver disease (NAFLD) identified by a bioinformatics approach were examined for variant associations to quantitative traits of NAFLD-related phenotypes.
Research Design and Methods
By integrating public database text mining, trans-organism protein-protein interaction transferal, and information on liver protein expression a protein-protein interaction network was constructed and from this a smaller isolated interactome was identified. Five genes from this interactome were selected for genetic analysis. Twenty-one tag single-nucleotide polymorphisms (SNPs) which captured all common variation in these genes were genotyped in 10,196 Danes, and analyzed for association with NAFLD-related quantitative traits, type 2 diabetes (T2D), central obesity, and WHO-defined metabolic syndrome (MetS).
273 genes were included in the protein-protein interaction analysis and EHHADH, ECHS1, HADHA, HADHB, and ACADL were selected for further examination. A total of 10 nominal statistical significant associations (P<0.05) to quantitative metabolic traits were identified. Also, the case-control study showed associations between variation in the five genes and T2D, central obesity, and MetS, respectively. Bonferroni adjustments for multiple testing negated all associations.
Using a bioinformatics approach we identified five candidate genes for NAFLD. However, we failed to provide evidence of associations with major effects between SNPs in these five genes and NAFLD-related quantitative traits, T2D, central obesity, and MetS.
PMCID: PMC3029374  PMID: 21339799
23.  Studies of the Association of Arg72Pro of Tumor Suppressor Protein p53 with Type 2 Diabetes in a Combined Analysis of 55,521 Europeans 
PLoS ONE  2011;6(1):e15813.
A study of 222 candidate genes in type 2 diabetes reported association of variants in RAPGEF1, ENPP1, TP53, NRF1, SLC2A2, SLC2A4 and FOXC2 with type 2 diabetes in 4,805 Finnish individuals. We aimed to replicate these associations in a Danish case-control study and to substantiate any replicated associations in meta-analyses. Furthermore, we evaluated the impact on diabetes-related intermediate traits in a population-based sample of middle-aged Danes.
We genotyped nine lead variants in the seven genes in 4,973 glucose-tolerant and 3,612 type 2 diabetes Danish individuals. In meta-analyses we combined case-control data from the DIAGRAM+ Consortium (n = 47,117) and the present genotyping results. The quantitative trait studies involved 5,882 treatment-naive individuals from the Danish Inter99 study.
None of the nine investigated variants were significantly associated with type 2 diabetes in the Danish samples. However, for all nine variants the estimate of increase in type 2 diabetes risk was observed for the same allele as previously reported. In a meta-analysis of published and online data including 55,521 Europeans the G-allele of rs1042522 in TP53 showed significant association with type 2 diabetes (OR = 1.06 95% CI 1.02–1.11, p = 0.0032). No substantial associations with diabetes-related intermediary phenotypes were found.
The G-allele of TP53 rs1042522 is associated with an increased prevalence of type 2 diabetes in a combined analysis of 55,521 Europeans.
PMCID: PMC3024396  PMID: 21283750
24.  New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk 
Dupuis, Josée | Langenberg, Claudia | Prokopenko, Inga | Saxena, Richa | Soranzo, Nicole | Jackson, Anne U | Wheeler, Eleanor | Glazer, Nicole L | Bouatia-Naji, Nabila | Gloyn, Anna L | Lindgren, Cecilia M | Mägi, Reedik | Morris, Andrew P | Randall, Joshua | Johnson, Toby | Elliott, Paul | Rybin, Denis | Thorleifsson, Gudmar | Steinthorsdottir, Valgerdur | Henneman, Peter | Grallert, Harald | Dehghan, Abbas | Hottenga, Jouke Jan | Franklin, Christopher S | Navarro, Pau | Song, Kijoung | Goel, Anuj | Perry, John R B | Egan, Josephine M | Lajunen, Taina | Grarup, Niels | Sparsø, Thomas | Doney, Alex | Voight, Benjamin F | Stringham, Heather M | Li, Man | Kanoni, Stavroula | Shrader, Peter | Cavalcanti-Proença, Christine | Kumari, Meena | Qi, Lu | Timpson, Nicholas J | Gieger, Christian | Zabena, Carina | Rocheleau, Ghislain | Ingelsson, Erik | An, Ping | O’Connell, Jeffrey | Luan, Jian'an | Elliott, Amanda | McCarroll, Steven A | Payne, Felicity | Roccasecca, Rosa Maria | Pattou, François | Sethupathy, Praveen | Ardlie, Kristin | Ariyurek, Yavuz | Balkau, Beverley | Barter, Philip | Beilby, John P | Ben-Shlomo, Yoav | Benediktsson, Rafn | Bennett, Amanda J | Bergmann, Sven | Bochud, Murielle | Boerwinkle, Eric | Bonnefond, Amélie | Bonnycastle, Lori L | Borch-Johnsen, Knut | Böttcher, Yvonne | Brunner, Eric | Bumpstead, Suzannah J | Charpentier, Guillaume | Chen, Yii-Der Ida | Chines, Peter | Clarke, Robert | Coin, Lachlan J M | Cooper, Matthew N | Cornelis, Marilyn | Crawford, Gabe | Crisponi, Laura | Day, Ian N M | de Geus, Eco | Delplanque, Jerome | Dina, Christian | Erdos, Michael R | Fedson, Annette C | Fischer-Rosinsky, Antje | Forouhi, Nita G | Fox, Caroline S | Frants, Rune | Franzosi, Maria Grazia | Galan, Pilar | Goodarzi, Mark O | Graessler, Jürgen | Groves, Christopher J | Grundy, Scott | Gwilliam, Rhian | Gyllensten, Ulf | Hadjadj, Samy | Hallmans, Göran | Hammond, Naomi | Han, Xijing | Hartikainen, Anna-Liisa | Hassanali, Neelam | Hayward, Caroline | Heath, Simon C | Hercberg, Serge | Herder, Christian | Hicks, Andrew A | Hillman, David R | Hingorani, Aroon D | Hofman, Albert | Hui, Jennie | Hung, Joe | Isomaa, Bo | Johnson, Paul R V | Jørgensen, Torben | Jula, Antti | Kaakinen, Marika | Kaprio, Jaakko | Kesaniemi, Y Antero | Kivimaki, Mika | Knight, Beatrice | Koskinen, Seppo | Kovacs, Peter | Kyvik, Kirsten Ohm | Lathrop, G Mark | Lawlor, Debbie A | Le Bacquer, Olivier | Lecoeur, Cécile | Li, Yun | Lyssenko, Valeriya | Mahley, Robert | Mangino, Massimo | Manning, Alisa K | Martínez-Larrad, María Teresa | McAteer, Jarred B | McCulloch, Laura J | McPherson, Ruth | Meisinger, Christa | Melzer, David | Meyre, David | Mitchell, Braxton D | Morken, Mario A | Mukherjee, Sutapa | Naitza, Silvia | Narisu, Narisu | Neville, Matthew J | Oostra, Ben A | Orrù, Marco | Pakyz, Ruth | Palmer, Colin N A | Paolisso, Giuseppe | Pattaro, Cristian | Pearson, Daniel | Peden, John F | Pedersen, Nancy L. | Perola, Markus | Pfeiffer, Andreas F H | Pichler, Irene | Polasek, Ozren | Posthuma, Danielle | Potter, Simon C | Pouta, Anneli | Province, Michael A | Psaty, Bruce M | Rathmann, Wolfgang | Rayner, Nigel W | Rice, Kenneth | Ripatti, Samuli | Rivadeneira, Fernando | Roden, Michael | Rolandsson, Olov | Sandbaek, Annelli | Sandhu, Manjinder | Sanna, Serena | Sayer, Avan Aihie | Scheet, Paul | Scott, Laura J | Seedorf, Udo | Sharp, Stephen J | Shields, Beverley | Sigurðsson, Gunnar | Sijbrands, Erik J G | Silveira, Angela | Simpson, Laila | Singleton, Andrew | Smith, Nicholas L | Sovio, Ulla | Swift, Amy | Syddall, Holly | Syvänen, Ann-Christine | Tanaka, Toshiko | Thorand, Barbara | Tichet, Jean | Tönjes, Anke | Tuomi, Tiinamaija | Uitterlinden, André G | van Dijk, Ko Willems | van Hoek, Mandy | Varma, Dhiraj | Visvikis-Siest, Sophie | Vitart, Veronique | Vogelzangs, Nicole | Waeber, Gérard | Wagner, Peter J | Walley, Andrew | Walters, G Bragi | Ward, Kim L | Watkins, Hugh | Weedon, Michael N | Wild, Sarah H | Willemsen, Gonneke | Witteman, Jaqueline C M | Yarnell, John W G | Zeggini, Eleftheria | Zelenika, Diana | Zethelius, Björn | Zhai, Guangju | Zhao, Jing Hua | Zillikens, M Carola | Borecki, Ingrid B | Loos, Ruth J F | Meneton, Pierre | Magnusson, Patrik K E | Nathan, David M | Williams, Gordon H | Hattersley, Andrew T | Silander, Kaisa | Salomaa, Veikko | Smith, George Davey | Bornstein, Stefan R | Schwarz, Peter | Spranger, Joachim | Karpe, Fredrik | Shuldiner, Alan R | Cooper, Cyrus | Dedoussis, George V | Serrano-Ríos, Manuel | Morris, Andrew D | Lind, Lars | Palmer, Lyle J | Hu, Frank B. | Franks, Paul W | Ebrahim, Shah | Marmot, Michael | Kao, W H Linda | Pankow, James S | Sampson, Michael J | Kuusisto, Johanna | Laakso, Markku | Hansen, Torben | Pedersen, Oluf | Pramstaller, Peter Paul | Wichmann, H Erich | Illig, Thomas | Rudan, Igor | Wright, Alan F | Stumvoll, Michael | Campbell, Harry | Wilson, James F | Hamsten, Anders | Bergman, Richard N | Buchanan, Thomas A | Collins, Francis S | Mohlke, Karen L | Tuomilehto, Jaakko | Valle, Timo T | Altshuler, David | Rotter, Jerome I | Siscovick, David S | Penninx, Brenda W J H | Boomsma, Dorret | Deloukas, Panos | Spector, Timothy D | Frayling, Timothy M | Ferrucci, Luigi | Kong, Augustine | Thorsteinsdottir, Unnur | Stefansson, Kari | van Duijn, Cornelia M | Aulchenko, Yurii S | Cao, Antonio | Scuteri, Angelo | Schlessinger, David | Uda, Manuela | Ruokonen, Aimo | Jarvelin, Marjo-Riitta | Waterworth, Dawn M | Vollenweider, Peter | Peltonen, Leena | Mooser, Vincent | Abecasis, Goncalo R | Wareham, Nicholas J | Sladek, Robert | Froguel, Philippe | Watanabe, Richard M | Meigs, James B | Groop, Leif | Boehnke, Michael | McCarthy, Mark I | Florez, Jose C | Barroso, Inês
Nature genetics  2010;42(2):105-116.
Circulating glucose levels are tightly regulated. To identify novel glycemic loci, we performed meta-analyses of 21 genome-wide associations studies informative for fasting glucose (FG), fasting insulin (FI) and indices of β-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 non-diabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with FG/HOMA-B and two associated with FI/HOMA-IR. These include nine new FG loci (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and FAM148B) and one influencing FI/HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB/TMEM195 with type 2 diabetes (T2D). Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify T2D risk loci, as well as loci that elevate FG modestly, but do not cause overt diabetes.
PMCID: PMC3018764  PMID: 20081858
25.  Do Gene Variants Influencing Adult Adiposity Affect Birth Weight? A Population-Based Study of 24 Loci in 4,744 Danish Individuals 
PLoS ONE  2010;5(12):e14190.
Several obesity risk alleles affecting adult adiposity have been identified by the recent wave of genome wide association studies. We aimed to examine the potential effect of these variants on fetal body composition by investigating the variants in relation to birth weight and ponderal index of the newborn.
Methodology/Principal Findings
Midwife records from the Danish State Archives provided information on mother's age, parity, as well as birth weight, birth length and prematurity of the newborn in 4,744 individuals of the population-based Inter99 study. Twenty-four risk alleles showing genome-wide associations with adult BMI and/or waist circumference were genotyped. None of the 24 risk variants tested showed an association with birth weight or ponderal index after correction for multiple testing. Birth weight was divided into three categories low (≤10th percentile), normal (10th–90th percentile) and high birth weight (≥90th percentile) to allow for non-linear associations. There was no difference in the number of risk alleles between the groups (p = 0.57). No interactions between each risk allele and birth weight in the prediction of adult BMI were observed. An obesity risk score was created by summing up risk alleles. The risk score did not associate with fetal body composition. Moreover there was no interaction between the risk score and birth weight/ponderal index in the prediction of adult BMI.
24 common variants associated with adult adiposity did not affect or interact with birth weight among Danes suggesting that the effects of these variants predominantly arise in the post-natal life.
PMCID: PMC2995733  PMID: 21152014

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