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1.  Meta-Analysis Investigating Associations Between Healthy Diet and Fasting Glucose and Insulin Levels and Modification by Loci Associated With Glucose Homeostasis in Data From 15 Cohorts 
American Journal of Epidemiology  2012;177(2):103-115.
Whether loci that influence fasting glucose (FG) and fasting insulin (FI) levels, as identified by genome-wide association studies, modify associations of diet with FG or FI is unknown. We utilized data from 15 US and European cohort studies comprising 51,289 persons without diabetes to test whether genotype and diet interact to influence FG or FI concentration. We constructed a diet score using study-specific quartile rankings for intakes of whole grains, fish, fruits, vegetables, and nuts/seeds (favorable) and red/processed meats, sweets, sugared beverages, and fried potatoes (unfavorable). We used linear regression within studies, followed by inverse-variance-weighted meta-analysis, to quantify 1) associations of diet score with FG and FI levels and 2) interactions of diet score with 16 FG-associated loci and 2 FI-associated loci. Diet score (per unit increase) was inversely associated with FG (β = −0.004 mmol/L, 95% confidence interval: −0.005, −0.003) and FI (β = −0.008 ln-pmol/L, 95% confidence interval: −0.009, −0.007) levels after adjustment for demographic factors, lifestyle, and body mass index. Genotype variation at the studied loci did not modify these associations. Healthier diets were associated with lower FG and FI concentrations regardless of genotype at previously replicated FG- and FI-associated loci. Studies focusing on genomic regions that do not yield highly statistically significant associations from main-effect genome-wide association studies may be more fruitful in identifying diet-gene interactions.
doi:10.1093/aje/kws297
PMCID: PMC3707424  PMID: 23255780
diabetes; dietary pattern; gene-environment interaction; glucose; insulin
2.  Bayesian refinement of association signals for 14 loci in 3 common diseases 
Nature genetics  2012;44(12):1294-1301.
To further investigate susceptibility loci identified by genome-wide association studies, we genotyped 5,500 SNPs across 14 associated regions in 8,000 samples from a control group and 3 diseases: type 2 diabetes (T2D), coronary artery disease (CAD) and Graves’ disease. We defined, using Bayes theorem, credible sets of SNPs that were 95% likely, based on posterior probability, to contain the causal disease-associated SNPs. In 3 of the 14 regions, TCF7L2 (T2D), CTLA4 (Graves’ disease) and CDKN2A-CDKN2B (T2D), much of the posterior probability rested on a single SNP, and, in 4 other regions (CDKN2A-CDKN2B (CAD) and CDKAL1, FTO and HHEX (T2D)), the 95% sets were small, thereby excluding most SNPs as potentially causal. Very few SNPs in our credible sets had annotated functions, illustrating the limitations in understanding the mechanisms underlying susceptibility to common diseases. Our results also show the value of more detailed mapping to target sequences for functional studies.
doi:10.1038/ng.2435
PMCID: PMC3791416  PMID: 23104008
3.  Human β Cell Transcriptome Analysis Uncovers lncRNAs That Are Tissue-Specific, Dynamically Regulated, and Abnormally Expressed in Type 2 Diabetes 
Cell metabolism  2012;16(4):435-448.
SUMMARY
A significant portion of the genome is transcribed as long non-coding RNAs (lncRNAs), several of which are known to control gene expression. The repertoire and regulation of lncRNAs in disease-relevant tissues, however, has not been systematically explored. We report a comprehensive strand-specific transcriptome map of human pancreatic islets and β-cells, and uncover >1100 intergenic and antisense islet-cell lncRNA genes. We find islet lncRNAs that are dynamically regulated, and show that they are an integral component of the β-cell differentiation and maturation program. We sequenced the mouse islet transcriptome, and identify lncRNA orthologs that are regulated like their human counterparts. Depletion of HI-LNC25, a β-cell specific lncRNA, downregulated GLIS3 mRNA, thus exemplifying a gene regulatory function of islet lncRNAs. Finally, selected islet lncRNAs were dysregulated in type 2 diabetes or mapped to genetic loci underlying diabetes susceptibility. These findings reveal a new class of islet-cell genes relevant to β-cell programming and diabetes pathophysiology.
doi:10.1016/j.cmet.2012.08.010
PMCID: PMC3475176  PMID: 23040067
4.  Mapping cis- and trans-regulatory effects across multiple tissues in twins 
Nature genetics  2012;44(10):1084-1089.
Sequence-based variation in gene expression is a key driver of disease risk. Common variants regulating expression in cis have been mapped in many eQTL studies typically in single tissues from unrelated individuals. Here, we present a comprehensive analysis of gene expression across multiple tissues conducted in a large set of mono- and dizygotic twins that allows systematic dissection of genetic (cis and trans) and non-genetic effects on gene expression. Using identity-by-descent estimates, we show that at least 40% of the total heritable cis-effect on expression cannot be accounted for by common cis-variants, a finding which exposes the contribution of low frequency and rare regulatory variants with respect to both transcriptional regulation and complex trait susceptibility. We show that a substantial proportion of gene expression heritability is trans to the structural gene and identify several replicating trans-variants which act predominantly in a tissue-restricted manner and may regulate the transcription of many genes.
doi:10.1038/ng.2394
PMCID: PMC3784328  PMID: 22941192
5.  Rare MTNR1B variants impairing melatonin receptor 1B function contribute to type 2 diabetes 
Nature genetics  2012;44(3):297-301.
Genome-wide association studies revealed that common non-coding variants in MTNR1B (encoding melatonin receptor 1B, also known as MT2) increase type 2 diabetes (T2D) risk1,2. Although the strongest association signal was highly significant (P<10−20), its contribution to T2D risk was modest (odds ratio, OR~1.10-1.15)1-3. We performed large-scale exon resequencing in 7,632 Europeans including 2,186 T2D patients and identified 40 non-synonymous variants, including 36 very rare variants (minor allele frequency, MAF<0.1%) associated with T2D (OR=3.31[1.78;6.18]95%); P=1.64×10−4. A four-tier functional investigation of all 40 mutants revealed that 14 were non-functional and rare (MAF<1%); four were very rare with complete loss of melatonin binding and signaling capabilities. Among the very rare variants, the partial or total loss-of-function variants, but not the neutral ones, contributed to T2D (OR=5.67[2.17;14.82]95%; P=4.09×10−4). Genotyping the four complete loss-of-function variants in 11,854 additional individuals revealed their association with T2D risk (Ncases=8,153/Ncontrols=10,100; OR=3.88[1.49;10.07]95%; P=5.37×10−3). This study establishes a firm functional link between MTNR1B and T2D risk.
doi:10.1038/ng.1053
PMCID: PMC3773908  PMID: 22286214
6.  Common variants at 12q15 and 12q24 are associated with infant head circumference 
Taal, H Rob | Pourcain, Beate St | Thiering, Elisabeth | Das, Shikta | Mook-Kanamori, Dennis O | Warrington, Nicole M | Kaakinen, Marika | Kreiner-Møller, Eskil | Bradfield, Jonathan P | Freathy, Rachel M | Geller, Frank | Guxens, Mònica | Cousminer, Diana L | Kerkhof, Marjan | Timpson, Nicholas J | Ikram, M Arfan | Beilin, Lawrence J | Bønnelykke, Klaus | Buxton, Jessica L | Charoen, Pimphen | Chawes, Bo Lund Krogsgaard | Eriksson, Johan | Evans, David M | Hofman, Albert | Kemp, John P | Kim, Cecilia E | Klopp, Norman | Lahti, Jari | Lye, Stephen J | McMahon, George | Mentch, Frank D | Müller, Martina | O’Reilly, Paul F | Prokopenko, Inga | Rivadeneira, Fernando | Steegers, Eric A P | Sunyer, Jordi | Tiesler, Carla | Yaghootkar, Hanieh | Breteler, Monique M B | Debette, Stephanie | Fornage, Myriam | Gudnason, Vilmundur | Launer, Lenore J | van der Lugt, Aad | Mosley, Thomas H | Seshadri, Sudha | Smith, Albert V | Vernooij, Meike W | Blakemore, Alexandra IF | Chiavacci, Rosetta M | Feenstra, Bjarke | Fernandez-Benet, Julio | Grant, Struan F A | Hartikainen, Anna-Liisa | van der Heijden, Albert J | Iñiguez, Carmen | Lathrop, Mark | McArdle, Wendy L | Mølgaard, Anne | Newnham, John P | Palmer, Lyle J | Palotie, Aarno | Pouta, Annneli | Ring, Susan M | Sovio, Ulla | Standl, Marie | Uitterlinden, Andre G | Wichmann, H-Erich | Vissing, Nadja Hawwa | DeCarli, Charles | van Duijn, Cornelia M | McCarthy, Mark I | Koppelman, Gerard H. | Estivill, Xavier | Hattersley, Andrew T | Melbye, Mads | Bisgaard, Hans | Pennell, Craig E | Widen, Elisabeth | Hakonarson, Hakon | Smith, George Davey | Heinrich, Joachim | Jarvelin, Marjo-Riitta | Jaddoe, Vincent W V
Nature genetics  2012;44(5):532-538.
To identify genetic variants associated with head circumference in infancy, we performed a meta-analysis of seven genome-wide association (GWA) studies (N=10,768 from European ancestry enrolled in pregnancy/birth cohorts) and followed up three lead signals in six replication studies (combined N=19,089). Rs7980687 on chromosome 12q24 (P=8.1×10−9), and rs1042725 on chromosome 12q15 (P=2.8×10−10) were robustly associated with head circumference in infancy. Although these loci have previously been associated with adult height1, their effects on infant head circumference were largely independent of height (P=3.8×10−7 for rs7980687, P=1.3×10−7 for rs1042725 after adjustment for infant height). A third signal, rs11655470 on chromosome 17q21, showed suggestive evidence of association with head circumference (P=3.9×10−6). SNPs correlated to the 17q21 signal show genome-wide association with adult intra cranial volume2, Parkinson’s disease and other neurodegenerative diseases3-5, indicating that a common genetic variant in this region might link early brain growth with neurological disease in later life.
doi:10.1038/ng.2238
PMCID: PMC3773913  PMID: 22504419
7.  Mosaic PPM1D mutations are associated with predisposition to breast and ovarian cancer 
Nature  2012;493(7432):406-410.
Improved sequencing technologies offer unprecedented opportunities for investigating the role of rare genetic variation in common disease. However, there are considerable challenges with respect to study design, data analysis and replication1. Here, using pooled next-generation sequencing of 507 genes implicated in the repair of DNA in 1,150 samples, an analytical strategy focussed on protein truncating variants (PTVs) and a large-scale sequencing case-control replication experiment in 13,642 individuals, we show that rare PTVs in the p53 inducible protein phosphatase PPM1D are associated with predisposition to breast cancer and to ovarian cancer. PPM1D PTV mutations were present in 25/7781 cases vs 1/5861 controls; P=1.12×10−5, which included 18 mutations in 6,912 individuals with breast cancer; P = 2.42×10−4 and 12 mutations in 1,121 individuals with ovarian cancer; P = 3.10×10−9. Notably, all the identified PPM1D PTVs were mosaic in lymphocyte DNA and clustered within a 370 bp region in the final exon of the gene, C-terminal to the phosphatase catalytic domain. Functional studies demonstrated that the mutations result in enhanced suppression of p53 in response to ionising radiation exposure, suggesting the mutant alleles encode hyperactive PPM1D isoforms. Thus, although the mutations cause premature protein truncation, they do not result in the simple loss-of-function typically associated with this class of variant, but instead likely have a gain-of-function effect. Our results have implications for the detection and management of breast and ovarian cancer risk. More generally, these data provide new insights into the role of rare and of mosaic genetic variants in common conditions, and the utility of sequencing in their identification.
doi:10.1038/nature11725
PMCID: PMC3759028  PMID: 23242139
8.  Reduced Insulin Exocytosis in Human Pancreatic β-Cells With Gene Variants Linked to Type 2 Diabetes 
Diabetes  2012;61(7):1726-1733.
The majority of genetic risk variants for type 2 diabetes (T2D) affect insulin secretion, but the mechanisms through which they influence pancreatic islet function remain largely unknown. We functionally characterized human islets to determine secretory, biophysical, and ultrastructural features in relation to genetic risk profiles in diabetic and nondiabetic donors. Islets from donors with T2D exhibited impaired insulin secretion, which was more pronounced in lean than obese diabetic donors. We assessed the impact of 14 disease susceptibility variants on measures of glucose sensing, exocytosis, and structure. Variants near TCF7L2 and ADRA2A were associated with reduced glucose-induced insulin secretion, whereas susceptibility variants near ADRA2A, KCNJ11, KCNQ1, and TCF7L2 were associated with reduced depolarization-evoked insulin exocytosis. KCNQ1, ADRA2A, KCNJ11, HHEX/IDE, and SLC2A2 variants affected granule docking. We combined our results to create a novel genetic risk score for β-cell dysfunction that includes aberrant granule docking, decreased Ca2+ sensitivity of exocytosis, and reduced insulin release. Individuals with a high risk score displayed an impaired response to intravenous glucose and deteriorating insulin secretion over time. Our results underscore the importance of defects in β-cell exocytosis in T2D and demonstrate the potential of cellular phenotypic characterization in the elucidation of complex genetic disorders.
doi:10.2337/db11-1516
PMCID: PMC3379663  PMID: 22492527
9.  New loci associated with birth weight identify genetic links between intrauterine growth and adult height and metabolism 
Horikoshi, Momoko | Yaghootkar, Hanieh | Mook-Kanamori, Dennis O. | Sovio, Ulla | Taal, H. Rob | Hennig, Branwen J. | Bradfield, Jonathan P. | St. Pourcain, Beate | Evans, David M. | Charoen, Pimphen | Kaakinen, Marika | Cousminer, Diana L. | Lehtimäki, Terho | Kreiner-Møller, Eskil | Warrington, Nicole M. | Bustamante, Mariona | Feenstra, Bjarke | Berry, Diane J. | Thiering, Elisabeth | Pfab, Thiemo | Barton, Sheila J. | Shields, Beverley M. | Kerkhof, Marjan | van Leeuwen, Elisabeth M. | Fulford, Anthony J. | Kutalik, Zoltán | Zhao, Jing Hua | den Hoed, Marcel | Mahajan, Anubha | Lindi, Virpi | Goh, Liang-Kee | Hottenga, Jouke-Jan | Wu, Ying | Raitakari, Olli T. | Harder, Marie N. | Meirhaeghe, Aline | Ntalla, Ioanna | Salem, Rany M. | Jameson, Karen A. | Zhou, Kaixin | Monies, Dorota M. | Lagou, Vasiliki | Kirin, Mirna | Heikkinen, Jani | Adair, Linda S. | Alkuraya, Fowzan S. | Al-Odaib, Ali | Amouyel, Philippe | Andersson, Ehm Astrid | Bennett, Amanda J. | Blakemore, Alexandra I.F. | Buxton, Jessica L. | Dallongeville, Jean | Das, Shikta | de Geus, Eco J. C. | Estivill, Xavier | Flexeder, Claudia | Froguel, Philippe | Geller, Frank | Godfrey, Keith M. | Gottrand, Frédéric | Groves, Christopher J. | Hansen, Torben | Hirschhorn, Joel N. | Hofman, Albert | Hollegaard, Mads V. | Hougaard, David M. | Hyppönen, Elina | Inskip, Hazel M. | Isaacs, Aaron | Jørgensen, Torben | Kanaka-Gantenbein, Christina | Kemp, John P. | Kiess, Wieland | Kilpeläinen, Tuomas O. | Klopp, Norman | Knight, Bridget A. | Kuzawa, Christopher W. | McMahon, George | Newnham, John P. | Niinikoski, Harri | Oostra, Ben A. | Pedersen, Louise | Postma, Dirkje S. | Ring, Susan M. | Rivadeneira, Fernando | Robertson, Neil R. | Sebert, Sylvain | Simell, Olli | Slowinski, Torsten | Tiesler, Carla M.T. | Tönjes, Anke | Vaag, Allan | Viikari, Jorma S. | Vink, Jacqueline M. | Vissing, Nadja Hawwa | Wareham, Nicholas J. | Willemsen, Gonneke | Witte, Daniel R. | Zhang, Haitao | Zhao, Jianhua | Wilson, James F. | Stumvoll, Michael | Prentice, Andrew M. | Meyer, Brian F. | Pearson, Ewan R. | Boreham, Colin A.G. | Cooper, Cyrus | Gillman, Matthew W. | Dedoussis, George V. | Moreno, Luis A | Pedersen, Oluf | Saarinen, Maiju | Mohlke, Karen L. | Boomsma, Dorret I. | Saw, Seang-Mei | Lakka, Timo A. | Körner, Antje | Loos, Ruth J.F. | Ong, Ken K. | Vollenweider, Peter | van Duijn, Cornelia M. | Koppelman, Gerard H. | Hattersley, Andrew T. | Holloway, John W. | Hocher, Berthold | Heinrich, Joachim | Power, Chris | Melbye, Mads | Guxens, Mònica | Pennell, Craig E. | Bønnelykke, Klaus | Bisgaard, Hans | Eriksson, Johan G. | Widén, Elisabeth | Hakonarson, Hakon | Uitterlinden, André G. | Pouta, Anneli | Lawlor, Debbie A. | Smith, George Davey | Frayling, Timothy M. | McCarthy, Mark I. | Grant, Struan F.A. | Jaddoe, Vincent W.V. | Jarvelin, Marjo-Riitta | Timpson, Nicholas J. | Prokopenko, Inga | Freathy, Rachel M.
Nature genetics  2012;45(1):76-82.
Birth weight within the normal range is associated with a variety of adult-onset diseases, but the mechanisms behind these associations are poorly understood1. Previous genome-wide association studies identified a variant in the ADCY5 gene associated both with birth weight and type 2 diabetes, and a second variant, near CCNL1, with no obvious link to adult traits2. In an expanded genome-wide association meta-analysis and follow-up study (up to 69,308 individuals of European descent from 43 studies), we have now extended the number of genome-wide significant loci to seven, accounting for a similar proportion of variance to maternal smoking. Five of the loci are known to be associated with other phenotypes: ADCY5 and CDKAL1 with type 2 diabetes; ADRB1 with adult blood pressure; and HMGA2 and LCORL with adult height. Our findings highlight genetic links between fetal growth and postnatal growth and metabolism.
doi:10.1038/ng.2477
PMCID: PMC3605762  PMID: 23202124
10.  Systematic Assessment of Etiology in Adults With a Clinical Diagnosis of Young-Onset Type 2 Diabetes Is a Successful Strategy for Identifying Maturity-Onset Diabetes of the Young 
Diabetes Care  2012;35(6):1206-1212.
OBJECTIVE
Misdiagnosis of maturity-onset diabetes of the young (MODY) remains widespread, despite the benefits of optimized management. This cross-sectional study examined diagnostic misclassification of MODY in subjects with clinically labeled young adult-onset type 1 and type 2 diabetes by extending genetic testing beyond current guidelines.
RESEARCH DESIGN AND METHODS
Individuals were selected for diagnostic sequencing if they displayed features atypical for their diagnostic label. From 247 case subjects with clinically labeled type 1 diabetes, we sequenced hepatocyte nuclear factor 1 α (HNF1A) and hepatocyte nuclear factor 4 α (HNF4A) in 20 with residual β-cell function ≥3 years from diagnosis (random or glucagon-stimulated C-peptide ≥0.2 nmol/L). From 322 with clinically labeled type 2 diabetes, we sequenced HNF1A and HNF4A in 80 with diabetes diagnosed ≤30 years and/or diabetes diagnosed ≤45 years without metabolic syndrome. We also sequenced the glucokinase (GCK) in 40 subjects with mild fasting hyperglycemia.
RESULTS
In the type 1 diabetic group, two HNF1A mutations were found (0.8% prevalence). In type 2 diabetic subjects, 10 HNF1A, two HNF4A, and one GCK mutation were identified (4.0%). Only 47% of MODY case subjects identified met current guidelines for diagnostic sequencing. Follow-up revealed a further 12 mutation carriers among relatives. Twenty-seven percent of newly identified MODY subjects changed treatment, all with improved glycemic control (HbA1c 8.8 vs. 7.3% at 3 months; P = 0.02).
CONCLUSIONS
The systematic use of widened diagnostic testing criteria doubled the numbers of MODY case subjects identified compared with current clinical practice. The yield was greatest in young adult-onset type 2 diabetes. We recommend that all patients diagnosed before age 30 and with presence of C-peptide at 3 years' duration are considered for molecular diagnostic analysis.
doi:10.2337/dc11-1243
PMCID: PMC3357216  PMID: 22432108
11.  Common variants at 6q22 and 17q21 are associated with intracranial volume 
Nature genetics  2012;44(5):539-544.
During aging, intracranial volume remains unchanged and represents maximally attained brain size, while various interacting biological phenomena lead to brain volume loss. Consequently, intracranial volume and brain volume in late life reflect different genetic influences. Our genome-wide association study in 8,175 community-dwelling elderly did not reveal any genome-wide significant associations (p<5*10−8) for brain volume. In contrast, intracranial volume was significantly associated with two loci: rs4273712 (p=3.4*10−11), a known height locus on chromosome 6q22, and rs9915547, tagging the inversion on chromosome 17q21 (p=1.5*10−12). We replicated the associations of these loci with intracranial volume in a separate sample of 1,752 older persons (p=1.1*10−3 for 6q22 and p=1.2*10−3 for 17q21). Furthermore, we also found suggestive associations of the 17q21 locus with head circumference in 10,768 children (mean age 14.5 months). Our data identify two loci associated with head size, with the inversion on 17q21 also likely involved in attaining maximal brain size.
doi:10.1038/ng.2245
PMCID: PMC3618290  PMID: 22504418
12.  FTO genotype is associated with phenotypic variability of body mass index 
Yang, Jian | Loos, Ruth J. F. | Powell, Joseph E. | Medland, Sarah E. | Speliotes, Elizabeth K. | Chasman, Daniel I. | Rose, Lynda M. | Thorleifsson, Gudmar | Steinthorsdottir, Valgerdur | Mägi, Reedik | Waite, Lindsay | Smith, Albert Vernon | Yerges-Armstrong, Laura M. | Monda, Keri L. | Hadley, David | Mahajan, Anubha | Li, Guo | Kapur, Karen | Vitart, Veronique | Huffman, Jennifer E. | Wang, Sophie R. | Palmer, Cameron | Esko, Tõnu | Fischer, Krista | Zhao, Jing Hua | Demirkan, Ayşe | Isaacs, Aaron | Feitosa, Mary F. | Luan, Jian’an | Heard-Costa, Nancy L. | White, Charles | Jackson, Anne U. | Preuss, Michael | Ziegler, Andreas | Eriksson, Joel | Kutalik, Zoltán | Frau, Francesca | Nolte, Ilja M. | Van Vliet-Ostaptchouk, Jana V. | Hottenga, Jouke-Jan | Jacobs, Kevin B. | Verweij, Niek | Goel, Anuj | Medina-Gomez, Carolina | Estrada, Karol | Bragg-Gresham, Jennifer Lynn | Sanna, Serena | Sidore, Carlo | Tyrer, Jonathan | Teumer, Alexander | Prokopenko, Inga | Mangino, Massimo | Lindgren, Cecilia M. | Assimes, Themistocles L. | Shuldiner, Alan R. | Hui, Jennie | Beilby, John P. | McArdle, Wendy L. | Hall, Per | Haritunians, Talin | Zgaga, Lina | Kolcic, Ivana | Polasek, Ozren | Zemunik, Tatijana | Oostra, Ben A. | Junttila, M. Juhani | Grönberg, Henrik | Schreiber, Stefan | Peters, Annette | Hicks, Andrew A. | Stephens, Jonathan | Foad, Nicola S. | Laitinen, Jaana | Pouta, Anneli | Kaakinen, Marika | Willemsen, Gonneke | Vink, Jacqueline M. | Wild, Sarah H. | Navis, Gerjan | Asselbergs, Folkert W. | Homuth, Georg | John, Ulrich | Iribarren, Carlos | Harris, Tamara | Launer, Lenore | Gudnason, Vilmundur | O’Connell, Jeffrey R. | Boerwinkle, Eric | Cadby, Gemma | Palmer, Lyle J. | James, Alan L. | Musk, Arthur W. | Ingelsson, Erik | Psaty, Bruce M. | Beckmann, Jacques S. | Waeber, Gerard | Vollenweider, Peter | Hayward, Caroline | Wright, Alan F. | Rudan, Igor | Groop, Leif C. | Metspalu, Andres | Khaw, Kay Tee | van Duijn, Cornelia M. | Borecki, Ingrid B. | Province, Michael A. | Wareham, Nicholas J. | Tardif, Jean-Claude | Huikuri, Heikki V. | Cupples, L. Adrienne | Atwood, Larry D. | Fox, Caroline S. | Boehnke, Michael | Collins, Francis S. | Mohlke, Karen L. | Erdmann, Jeanette | Schunkert, Heribert | Hengstenberg, Christian | Stark, Klaus | Lorentzon, Mattias | Ohlsson, Claes | Cusi, Daniele | Staessen, Jan A. | Van der Klauw, Melanie M. | Pramstaller, Peter P. | Kathiresan, Sekar | Jolley, Jennifer D. | Ripatti, Samuli | Jarvelin, Marjo-Riitta | de Geus, Eco J. C. | Boomsma, Dorret I. | Penninx, Brenda | Wilson, James F. | Campbell, Harry | Chanock, Stephen J. | van der Harst, Pim | Hamsten, Anders | Watkins, Hugh | Hofman, Albert | Witteman, Jacqueline C. | Zillikens, M. Carola | Uitterlinden, André G. | Rivadeneira, Fernando | Zillikens, M. Carola | Kiemeney, Lambertus A. | Vermeulen, Sita H. | Abecasis, Goncalo R. | Schlessinger, David | Schipf, Sabine | Stumvoll, Michael | Tönjes, Anke | Spector, Tim D. | North, Kari E. | Lettre, Guillaume | McCarthy, Mark I. | Berndt, Sonja I. | Heath, Andrew C. | Madden, Pamela A. F. | Nyholt, Dale R. | Montgomery, Grant W. | Martin, Nicholas G. | McKnight, Barbara | Strachan, David P. | Hill, William G. | Snieder, Harold | Ridker, Paul M. | Thorsteinsdottir, Unnur | Stefansson, Kari | Frayling, Timothy M. | Hirschhorn, Joel N. | Goddard, Michael E. | Visscher, Peter M.
Nature  2012;490(7419):267-272.
There is evidence across several species for genetic control of phenotypic variation of complex traits1–4, such that the variance among phenotypes is genotype dependent. Understanding genetic control of variability is important in evolutionary biology, agricultural selection programmes and human medicine, yet for complex traits, no individual genetic variants associated with variance, as opposed to the mean, have been identified. Here we perform a meta-analysis of genome-wide association studies of phenotypic variation using 170,000 samples on height and body mass index (BMI) in human populations. We report evidence that the single nucleotide polymorphism (SNP) rs7202116 at the FTO gene locus, which is known to be associated with obesity (as measured by mean BMI for each rs7202116 genotype)5–7, is also associated with phenotypic variability. We show that the results are not due to scale effects or other artefacts, and find no other experiment-wise significant evidence for effects on variability, either at loci other than FTO for BMI or at any locus for height. The difference in variance for BMI among individuals with opposite homozygous genotypes at the FTO locus is approximately 7%, corresponding to a difference of 0.5 kilograms in the standard deviation of weight. Our results indicate that genetic variants can be discovered that are associated with variability, and that between-person variability in obesity can partly be explained by the genotype at the FTO locus. The results are consistent with reported FTO by environment interactions for BMI8, possibly mediated by DNA methylation9,10. Our BMI results for other SNPs and our height results for all SNPs suggest that most genetic variants, including those that influence mean height or mean BMI, are not associated with phenotypic variance, or that their effects on variability are too small to detect even with samples sizes greater than 100,000.
doi:10.1038/nature11401
PMCID: PMC3564953  PMID: 22982992
13.  Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits 
Nature genetics  2012;44(4):369-S3.
We present an approximate conditional and joint association analysis that can use summary-level statistics from a meta-analysis of genome-wide association studies (GWAS) and estimated linkage disequilibrium (LD) from a reference sample with individual-level genotype data. Using this method, we analyzed meta-analysis summary data from the GIANT Consortium for height and body mass index (BMI), with the LD structure estimated from genotype data in two independent cohorts. We identified 36 loci with multiple associated variants for height (38 leading and 49 additional SNPs, 87 in total) via a genome-wide SNP selection procedure. The 49 new SNPs explain approximately 1.3% of variance, nearly doubling the heritability explained at the 36 loci. We did not find any locus showing multiple associated SNPs for BMI. The method we present is computationally fast and is also applicable to case-control data, which we demonstrate in an example from meta-analysis of type 2 diabetes by the DIAGRAM Consortium.
doi:10.1038/ng.2213
PMCID: PMC3593158  PMID: 22426310
14.  A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance 
Manning, Alisa K. | Hivert, Marie-France | Scott, Robert A. | Grimsby, Jonna L. | Bouatia-Naji, Nabila | Chen, Han | Rybin, Denis | Liu, Ching-Ti | Bielak, Lawrence F. | Prokopenko, Inga | Amin, Najaf | Barnes, Daniel | Cadby, Gemma | Hottenga, Jouke-Jan | Ingelsson, Erik | Jackson, Anne U. | Johnson, Toby | Kanoni, Stavroula | Ladenvall, Claes | Lagou, Vasiliki | Lahti, Jari | Lecoeur, Cecile | Liu, Yongmei | Martinez-Larrad, Maria Teresa | Montasser, May E. | Navarro, Pau | Perry, John R. B. | Rasmussen-Torvik, Laura J. | Salo, Perttu | Sattar, Naveed | Shungin, Dmitry | Strawbridge, Rona J. | Tanaka, Toshiko | van Duijn, Cornelia M. | An, Ping | de Andrade, Mariza | Andrews, Jeanette S. | Aspelund, Thor | Atalay, Mustafa | Aulchenko, Yurii | Balkau, Beverley | Bandinelli, Stefania | Beckmann, Jacques S. | Beilby, John P. | Bellis, Claire | Bergman, Richard N. | Blangero, John | Boban, Mladen | Boehnke, Michael | Boerwinkle, Eric | Bonnycastle, Lori L. | Boomsma, Dorret I. | Borecki, Ingrid B. | Böttcher, Yvonne | Bouchard, Claude | Brunner, Eric | Budimir, Danijela | Campbell, Harry | Carlson, Olga | Chines, Peter S. | Clarke, Robert | Collins, Francis S. | Corbatón-Anchuelo, Arturo | Couper, David | de Faire, Ulf | Dedoussis, George V | Deloukas, Panos | Dimitriou, Maria | Egan, Josephine M | Eiriksdottir, Gudny | Erdos, Michael R. | Eriksson, Johan G. | Eury, Elodie | Ferrucci, Luigi | Ford, Ian | Forouhi, Nita G. | Fox, Caroline S | Franzosi, Maria Grazia | Franks, Paul W | Frayling, Timothy M | Froguel, Philippe | Galan, Pilar | de Geus, Eco | Gigante, Bruna | Glazer, Nicole L. | Goel, Anuj | Groop, Leif | Gudnason, Vilmundur | Hallmans, Göran | Hamsten, Anders | Hansson, Ola | Harris, Tamara B. | Hayward, Caroline | Heath, Simon | Hercberg, Serge | Hicks, Andrew A. | Hingorani, Aroon | Hofman, Albert | Hui, Jennie | Hung, Joseph | Jarvelin, Marjo Riitta | Jhun, Min A. | Johnson, Paul C.D. | Jukema, J Wouter | Jula, Antti | Kao, W.H. | Kaprio, Jaakko | Kardia, Sharon L. R. | Keinanen-Kiukaanniemi, Sirkka | Kivimaki, Mika | Kolcic, Ivana | Kovacs, Peter | Kumari, Meena | Kuusisto, Johanna | Kyvik, Kirsten Ohm | Laakso, Markku | Lakka, Timo | Lannfelt, Lars | Lathrop, G Mark | Launer, Lenore J. | Leander, Karin | Li, Guo | Lind, Lars | Lindstrom, Jaana | Lobbens, Stéphane | Loos, Ruth J. F. | Luan, Jian’an | Lyssenko, Valeriya | Mägi, Reedik | Magnusson, Patrik K. E. | Marmot, Michael | Meneton, Pierre | Mohlke, Karen L. | Mooser, Vincent | Morken, Mario A. | Miljkovic, Iva | Narisu, Narisu | O’Connell, Jeff | Ong, Ken K. | Oostra, Ben A. | Palmer, Lyle J. | Palotie, Aarno | Pankow, James S. | Peden, John F. | Pedersen, Nancy L. | Pehlic, Marina | Peltonen, Leena | Penninx, Brenda | Pericic, Marijana | Perola, Markus | Perusse, Louis | Peyser, Patricia A | Polasek, Ozren | Pramstaller, Peter P. | Province, Michael A. | Räikkönen, Katri | Rauramaa, Rainer | Rehnberg, Emil | Rice, Ken | Rotter, Jerome I. | Rudan, Igor | Ruokonen, Aimo | Saaristo, Timo | Sabater-Lleal, Maria | Salomaa, Veikko | Savage, David B. | Saxena, Richa | Schwarz, Peter | Seedorf, Udo | Sennblad, Bengt | Serrano-Rios, Manuel | Shuldiner, Alan R. | Sijbrands, Eric J.G. | Siscovick, David S. | Smit, Johannes H. | Small, Kerrin S. | Smith, Nicholas L. | Smith, Albert Vernon | Stančáková, Alena | Stirrups, Kathleen | Stumvoll, Michael | Sun, Yan V. | Swift, Amy J. | Tönjes, Anke | Tuomilehto, Jaakko | Trompet, Stella | Uitterlinden, Andre G. | Uusitupa, Matti | Vikström, Max | Vitart, Veronique | Vohl, Marie-Claude | Voight, Benjamin F. | Vollenweider, Peter | Waeber, Gerard | Waterworth, Dawn M | Watkins, Hugh | Wheeler, Eleanor | Widen, Elisabeth | Wild, Sarah H. | Willems, Sara M. | Willemsen, Gonneke | Wilson, James F. | Witteman, Jacqueline C.M. | Wright, Alan F. | Yaghootkar, Hanieh | Zelenika, Diana | Zemunik, Tatijana | Zgaga, Lina | Wareham, Nicholas J. | McCarthy, Mark I. | Barroso, Ines | Watanabe, Richard M. | Florez, Jose C. | Dupuis, Josée | Meigs, James B. | Langenberg, Claudia
Nature genetics  2012;44(6):659-669.
Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and beta-cell dysfunction, but contributed little to our understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways may be uncovered by accounting for differences in body mass index (BMI) and potential interaction between BMI and genetic variants. We applied a novel joint meta-analytical approach to test associations with fasting insulin (FI) and glucose (FG) on a genome-wide scale. We present six previously unknown FI loci at P<5×10−8 in combined discovery and follow-up analyses of 52 studies comprising up to 96,496non-diabetic individuals. Risk variants were associated with higher triglyceride and lower HDL cholesterol levels, suggestive of a role for these FI loci in insulin resistance pathways. The localization of these additional loci will aid further characterization of the role of insulin resistance in T2D pathophysiology.
doi:10.1038/ng.2274
PMCID: PMC3613127  PMID: 22581228
15.  Genome-wide association study identifies multiple loci influencing human serum metabolite levels 
Nature genetics  2012;44(3):269-276.
Nuclear magnetic resonance assays allow for measurement of a wide range of metabolic phenotypes. We report here the results of a GWAS on 8,330 Finnish individuals genotyped and imputed at 7.7 million SNPs for a range of 216 serum metabolic phenotypes assessed by NMR of serum samples. We identified significant associations (P < 2.31 × 10−10) at 31 loci, including 11 for which there have not been previous reports of associations to a metabolic trait or disorder. Analyses of Finnish twin pairs suggested that the metabolic measures reported here show higher heritability than comparable conventional metabolic phenotypes. In accordance with our expectations, SNPs at the 31 loci associated with individual metabolites account for a greater proportion of the genetic component of trait variance (up to 40%) than is typically observed for conventional serum metabolic phenotypes. The identification of such associations may provide substantial insight into cardiometabolic disorders.
doi:10.1038/ng.1073
PMCID: PMC3605033  PMID: 22286219
16.  Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways 
Scott, Robert A | Lagou, Vasiliki | Welch, Ryan P | Wheeler, Eleanor | Montasser, May E | Luan, Jian’an | Mägi, Reedik | Strawbridge, Rona J | Rehnberg, Emil | Gustafsson, Stefan | Kanoni, Stavroula | Rasmussen-Torvik, Laura J | Yengo, Loïc | Lecoeur, Cecile | Shungin, Dmitry | Sanna, Serena | Sidore, Carlo | Johnson, Paul C D | Jukema, J Wouter | Johnson, Toby | Mahajan, Anubha | Verweij, Niek | Thorleifsson, Gudmar | Hottenga, Jouke-Jan | Shah, Sonia | Smith, Albert V | Sennblad, Bengt | Gieger, Christian | Salo, Perttu | Perola, Markus | Timpson, Nicholas J | Evans, David M | Pourcain, Beate St | Wu, Ying | Andrews, Jeanette S | Hui, Jennie | Bielak, Lawrence F | Zhao, Wei | Horikoshi, Momoko | Navarro, Pau | Isaacs, Aaron | O’Connell, Jeffrey R | Stirrups, Kathleen | Vitart, Veronique | Hayward, Caroline | Esko, Tönu | Mihailov, Evelin | Fraser, Ross M | Fall, Tove | Voight, Benjamin F | Raychaudhuri, Soumya | Chen, Han | Lindgren, Cecilia M | Morris, Andrew P | Rayner, Nigel W | Robertson, Neil | Rybin, Denis | Liu, Ching-Ti | Beckmann, Jacques S | Willems, Sara M | Chines, Peter S | Jackson, Anne U | Kang, Hyun Min | Stringham, Heather M | Song, Kijoung | Tanaka, Toshiko | Peden, John F | Goel, Anuj | Hicks, Andrew A | An, Ping | Müller-Nurasyid, Martina | Franco-Cereceda, Anders | Folkersen, Lasse | Marullo, Letizia | Jansen, Hanneke | Oldehinkel, Albertine J | Bruinenberg, Marcel | Pankow, James S | North, Kari E | Forouhi, Nita G | Loos, Ruth J F | Edkins, Sarah | Varga, Tibor V | Hallmans, Göran | Oksa, Heikki | Antonella, Mulas | Nagaraja, Ramaiah | Trompet, Stella | Ford, Ian | Bakker, Stephan J L | Kong, Augustine | Kumari, Meena | Gigante, Bruna | Herder, Christian | Munroe, Patricia B | Caulfield, Mark | Antti, Jula | Mangino, Massimo | Small, Kerrin | Miljkovic, Iva | Liu, Yongmei | Atalay, Mustafa | Kiess, Wieland | James, Alan L | Rivadeneira, Fernando | Uitterlinden, Andre G | Palmer, Colin N A | Doney, Alex S F | Willemsen, Gonneke | Smit, Johannes H | Campbell, Susan | Polasek, Ozren | Bonnycastle, Lori L | Hercberg, Serge | Dimitriou, Maria | Bolton, Jennifer L | Fowkes, Gerard R | Kovacs, Peter | Lindström, Jaana | Zemunik, Tatijana | Bandinelli, Stefania | Wild, Sarah H | Basart, Hanneke V | Rathmann, Wolfgang | Grallert, Harald | Maerz, Winfried | Kleber, Marcus E | Boehm, Bernhard O | Peters, Annette | Pramstaller, Peter P | Province, Michael A | Borecki, Ingrid B | Hastie, Nicholas D | Rudan, Igor | Campbell, Harry | Watkins, Hugh | Farrall, Martin | Stumvoll, Michael | Ferrucci, Luigi | Waterworth, Dawn M | Bergman, Richard N | Collins, Francis S | Tuomilehto, Jaakko | Watanabe, Richard M | de Geus, Eco J C | Penninx, Brenda W | Hofman, Albert | Oostra, Ben A | Psaty, Bruce M | Vollenweider, Peter | Wilson, James F | Wright, Alan F | Hovingh, G Kees | Metspalu, Andres | Uusitupa, Matti | Magnusson, Patrik K E | Kyvik, Kirsten O | Kaprio, Jaakko | Price, Jackie F | Dedoussis, George V | Deloukas, Panos | Meneton, Pierre | Lind, Lars | Boehnke, Michael | Shuldiner, Alan R | van Duijn, Cornelia M | Morris, Andrew D | Toenjes, Anke | Peyser, Patricia A | Beilby, John P | Körner, Antje | Kuusisto, Johanna | Laakso, Markku | Bornstein, Stefan R | Schwarz, Peter E H | Lakka, Timo A | Rauramaa, Rainer | Adair, Linda S | Smith, George Davey | Spector, Tim D | Illig, Thomas | de Faire, Ulf | Hamsten, Anders | Gudnason, Vilmundur | Kivimaki, Mika | Hingorani, Aroon | Keinanen-Kiukaanniemi, Sirkka M | Saaristo, Timo E | Boomsma, Dorret I | Stefansson, Kari | van der Harst, Pim | Dupuis, Josée | Pedersen, Nancy L | Sattar, Naveed | Harris, Tamara B | Cucca, Francesco | Ripatti, Samuli | Salomaa, Veikko | Mohlke, Karen L | Balkau, Beverley | Froguel, Philippe | Pouta, Anneli | Jarvelin, Marjo-Riitta | Wareham, Nicholas J | Bouatia-Naji, Nabila | McCarthy, Mark I | Franks, Paul W | Meigs, James B | Teslovich, Tanya M | Florez, Jose C | Langenberg, Claudia | Ingelsson, Erik | Prokopenko, Inga | Barroso, Inês
Nature genetics  2012;44(9):991-1005.
Through genome-wide association meta-analyses of up to 133,010 individuals of European ancestry without diabetes, including individuals newly genotyped using the Metabochip, we have raised the number of confirmed loci influencing glycemic traits to 53, of which 33 also increase type 2 diabetes risk (q < 0.05). Loci influencing fasting insulin showed association with lipid levels and fat distribution, suggesting impact on insulin resistance. Gene-based analyses identified further biologically plausible loci, suggesting that additional loci beyond those reaching genome-wide significance are likely to represent real associations. This conclusion is supported by an excess of directionally consistent and nominally significant signals between discovery and follow-up studies. Functional follow-up of these newly discovered loci will further improve our understanding of glycemic control.
doi:10.1038/ng.2385
PMCID: PMC3433394  PMID: 22885924
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.  Epigenetic silencing of HNF1A associates with changes in the composition of the human plasma N-glycome 
Epigenetics  2012;7(2):164-172.
Protein glycosylation is a ubiquitous modification that affects the structure and function of proteins. Our recent genome wide association study identified transcription factor HNF1A as an important regulator of plasma protein glycosylation. To evaluate the potential impact of epigenetic regulation of HNF1A on protein glycosylation we analyzed CpG methylation in 810 individuals. The association between methylation of four CpG sites and the composition of plasma and IgG glycomes was analyzed. Several statistically significant associations were observed between HNF1A methylation and plasma glycans, while there were no significant associations with IgG glycans. The most consistent association with HNF1A methylation was observed with the increase in the proportion of highly branched glycans in the plasma N-glycome. The hypothesis that inactivation of HNF1A promotes branching of glycans was supported by the analysis of plasma N-glycomes in 61 patients with inactivating mutations in HNF1A, where the increase in plasma glycan branching was also observed. This study represents the first demonstration of epigenetic regulation of plasma glycome composition, suggesting a potential mechanism by which epigenetic deregulation of the glycome may contribute to disease development.
doi:10.4161/epi.7.2.18918
PMCID: PMC3335910  PMID: 22395466
protein glycosylation; plasma glycome; HNF1A; CpG methylation; epigenetics
20.  The Metabochip, a Custom Genotyping Array for Genetic Studies of Metabolic, Cardiovascular, and Anthropometric Traits 
PLoS Genetics  2012;8(8):e1002793.
Genome-wide association studies have identified hundreds of loci for type 2 diabetes, coronary artery disease and myocardial infarction, as well as for related traits such as body mass index, glucose and insulin levels, lipid levels, and blood pressure. These studies also have pointed to thousands of loci with promising but not yet compelling association evidence. To establish association at additional loci and to characterize the genome-wide significant loci by fine-mapping, we designed the “Metabochip,” a custom genotyping array that assays nearly 200,000 SNP markers. Here, we describe the Metabochip and its component SNP sets, evaluate its performance in capturing variation across the allele-frequency spectrum, describe solutions to methodological challenges commonly encountered in its analysis, and evaluate its performance as a platform for genotype imputation. The metabochip achieves dramatic cost efficiencies compared to designing single-trait follow-up reagents, and provides the opportunity to compare results across a range of related traits. The metabochip and similar custom genotyping arrays offer a powerful and cost-effective approach to follow-up large-scale genotyping and sequencing studies and advance our understanding of the genetic basis of complex human diseases and traits.
Author Summary
Recent genetic studies have identified hundreds of regions of the human genome that contribute to risk for type 2 diabetes, coronary artery disease and myocardial infarction, and to related quantitative traits such as body mass index, glucose and insulin levels, blood lipid levels, and blood pressure. These results motivate two central questions: (1) can further genetic investigation identify additional associated regions?; and (2) can more detailed genetic investigation help us identify the causal variants (or variants more strongly correlated with the causal variants) in the regions identified so far? Addressing these questions requires assaying many genetic variants in DNA samples from thousands of individuals, which is expensive and timeconsuming when done a few SNPs at a time. To facilitate these investigations, we designed the “Metabochip,” a custom genotyping array that assays variation in nearly 200,000 sites in the human genome. Here we describe the Metabochip, evaluate its performance in assaying human genetic variation, and describe solutions to methodological challenges commonly encountered in its analysis.
doi:10.1371/journal.pgen.1002793
PMCID: PMC3410907  PMID: 22876189
21.  Metabolic Profiling in Maturity-Onset Diabetes of the Young (MODY) and Young Onset Type 2 Diabetes Fails to Detect Robust Urinary Biomarkers 
PLoS ONE  2012;7(7):e40962.
It is important to identify patients with Maturity-onset diabetes of the young (MODY) as a molecular diagnosis determines both treatment and prognosis. Genetic testing is currently expensive and many patients are therefore not assessed and are misclassified as having either type 1 or type 2 diabetes. Biomarkers could facilitate the prioritisation of patients for genetic testing. We hypothesised that patients with different underlying genetic aetiologies for their diabetes could have distinct metabolic profiles which may uncover novel biomarkers. The aim of this study was to perform metabolic profiling in urine from patients with MODY due to mutations in the genes encoding glucokinase (GCK) or hepatocyte nuclear factor 1 alpha (HNF1A), type 2 diabetes (T2D) and normoglycaemic control subjects. Urinary metabolic profiling by Nuclear Magnetic Resonance (NMR) and ultra performance liquid chromatography hyphenated to Q-TOF mass spectrometry (UPLC-MS) was performed in a Discovery set of subjects with HNF1A-MODY (n = 14), GCK-MODY (n = 17), T2D (n = 14) and normoglycaemic controls (n = 34). Data were used to build a valid partial least squares discriminate analysis (PLS-DA) model where HNF1A-MODY subjects could be separated from the other diabetes subtypes. No single metabolite contributed significantly to the separation of the patient groups. However, betaine, valine, glycine and glucose were elevated in the urine of HNF1A-MODY subjects compared to the other subgroups. Direct measurements of urinary amino acids and betaine in an extended dataset did not support differences between patients groups. Elevated urinary glucose in HNF1A-MODY is consistent with the previously reported low renal threshold for glucose in this genetic subtype. In conclusion, we report the first metabolic profiling study in monogenic diabetes and show that, despite the distinct biochemical pathways affected, there are unlikely to be robust urinary biomarkers which distinguish monogenic subtypes from T2D. Our results have implications for studies investigating metabolic profiles in complex traits including T2D.
doi:10.1371/journal.pone.0040962
PMCID: PMC3408469  PMID: 22859960
22.  Evidence of Inbreeding Depression on Human Height 
McQuillan, Ruth | Eklund, Niina | Pirastu, Nicola | Kuningas, Maris | McEvoy, Brian P. | Esko, Tõnu | Corre, Tanguy | Davies, Gail | Kaakinen, Marika | Lyytikäinen, Leo-Pekka | Kristiansson, Kati | Havulinna, Aki S. | Gögele, Martin | Vitart, Veronique | Tenesa, Albert | Aulchenko, Yurii | Hayward, Caroline | Johansson, Åsa | Boban, Mladen | Ulivi, Sheila | Robino, Antonietta | Boraska, Vesna | Igl, Wilmar | Wild, Sarah H. | Zgaga, Lina | Amin, Najaf | Theodoratou, Evropi | Polašek, Ozren | Girotto, Giorgia | Lopez, Lorna M. | Sala, Cinzia | Lahti, Jari | Laatikainen, Tiina | Prokopenko, Inga | Kals, Mart | Viikari, Jorma | Yang, Jian | Pouta, Anneli | Estrada, Karol | Hofman, Albert | Freimer, Nelson | Martin, Nicholas G. | Kähönen, Mika | Milani, Lili | Heliövaara, Markku | Vartiainen, Erkki | Räikkönen, Katri | Masciullo, Corrado | Starr, John M. | Hicks, Andrew A. | Esposito, Laura | Kolčić, Ivana | Farrington, Susan M. | Oostra, Ben | Zemunik, Tatijana | Campbell, Harry | Kirin, Mirna | Pehlic, Marina | Faletra, Flavio | Porteous, David | Pistis, Giorgio | Widén, Elisabeth | Salomaa, Veikko | Koskinen, Seppo | Fischer, Krista | Lehtimäki, Terho | Heath, Andrew | McCarthy, Mark I. | Rivadeneira, Fernando | Montgomery, Grant W. | Tiemeier, Henning | Hartikainen, Anna-Liisa | Madden, Pamela A. F. | d'Adamo, Pio | Hastie, Nicholas D. | Gyllensten, Ulf | Wright, Alan F. | van Duijn, Cornelia M. | Dunlop, Malcolm | Rudan, Igor | Gasparini, Paolo | Pramstaller, Peter P. | Deary, Ian J. | Toniolo, Daniela | Eriksson, Johan G. | Jula, Antti | Raitakari, Olli T. | Metspalu, Andres | Perola, Markus | Järvelin, Marjo-Riitta | Uitterlinden, André | Visscher, Peter M. | Wilson, James F.
PLoS Genetics  2012;8(7):e1002655.
Stature is a classical and highly heritable complex trait, with 80%–90% of variation explained by genetic factors. In recent years, genome-wide association studies (GWAS) have successfully identified many common additive variants influencing human height; however, little attention has been given to the potential role of recessive genetic effects. Here, we investigated genome-wide recessive effects by an analysis of inbreeding depression on adult height in over 35,000 people from 21 different population samples. We found a highly significant inverse association between height and genome-wide homozygosity, equivalent to a height reduction of up to 3 cm in the offspring of first cousins compared with the offspring of unrelated individuals, an effect which remained after controlling for the effects of socio-economic status, an important confounder (χ2 = 83.89, df = 1; p = 5.2×10−20). There was, however, a high degree of heterogeneity among populations: whereas the direction of the effect was consistent across most population samples, the effect size differed significantly among populations. It is likely that this reflects true biological heterogeneity: whether or not an effect can be observed will depend on both the variance in homozygosity in the population and the chance inheritance of individual recessive genotypes. These results predict that multiple, rare, recessive variants influence human height. Although this exploratory work focuses on height alone, the methodology developed is generally applicable to heritable quantitative traits (QT), paving the way for an investigation into inbreeding effects, and therefore genetic architecture, on a range of QT of biomedical importance.
Author Summary
Studies investigating the extent to which genetics influences human characteristics such as height have concentrated mainly on common variants of genes, where having one or two copies of a given variant influences the trait or risk of disease. This study explores whether a different type of genetic variant might also be important. We investigate the role of recessive genetic variants, where two identical copies of a variant are required to have an effect. By measuring genome-wide homozygosity—the phenomenon of inheriting two identical copies at a given point of the genome—in 35,000 individuals from 21 European populations, and by comparing this to individual height, we found that the more homozygous the genome, the shorter the individual. The offspring of first cousins (who have increased homozygosity) were predicted to be up to 3 cm shorter on average than the offspring of unrelated parents. Height is influenced by the combined effect of many recessive variants dispersed across the genome. This may also be true for other human characteristics and diseases, opening up a new way to understand how genetic variation influences our health.
doi:10.1371/journal.pgen.1002655
PMCID: PMC3400549  PMID: 22829771
23.  A Genome-Wide Association Meta-Analysis of Circulating Sex Hormone–Binding Globulin Reveals Multiple Loci Implicated in Sex Steroid Hormone Regulation 
Coviello, Andrea D. | Haring, Robin | Wellons, Melissa | Vaidya, Dhananjay | Lehtimäki, Terho | Keildson, Sarah | Lunetta, Kathryn L. | He, Chunyan | Fornage, Myriam | Lagou, Vasiliki | Mangino, Massimo | Onland-Moret, N. Charlotte | Chen, Brian | Eriksson, Joel | Garcia, Melissa | Liu, Yong Mei | Koster, Annemarie | Lohman, Kurt | Lyytikäinen, Leo-Pekka | Petersen, Ann-Kristin | Prescott, Jennifer | Stolk, Lisette | Vandenput, Liesbeth | Wood, Andrew R. | Zhuang, Wei Vivian | Ruokonen, Aimo | Hartikainen, Anna-Liisa | Pouta, Anneli | Bandinelli, Stefania | Biffar, Reiner | Brabant, Georg | Cox, David G. | Chen, Yuhui | Cummings, Steven | Ferrucci, Luigi | Gunter, Marc J. | Hankinson, Susan E. | Martikainen, Hannu | Hofman, Albert | Homuth, Georg | Illig, Thomas | Jansson, John-Olov | Johnson, Andrew D. | Karasik, David | Karlsson, Magnus | Kettunen, Johannes | Kiel, Douglas P. | Kraft, Peter | Liu, Jingmin | Ljunggren, Östen | Lorentzon, Mattias | Maggio, Marcello | Markus, Marcello R. P. | Mellström, Dan | Miljkovic, Iva | Mirel, Daniel | Nelson, Sarah | Morin Papunen, Laure | Peeters, Petra H. M. | Prokopenko, Inga | Raffel, Leslie | Reincke, Martin | Reiner, Alex P. | Rexrode, Kathryn | Rivadeneira, Fernando | Schwartz, Stephen M. | Siscovick, David | Soranzo, Nicole | Stöckl, Doris | Tworoger, Shelley | Uitterlinden, André G. | van Gils, Carla H. | Vasan, Ramachandran S. | Wichmann, H.-Erich | Zhai, Guangju | Bhasin, Shalender | Bidlingmaier, Martin | Chanock, Stephen J. | De Vivo, Immaculata | Harris, Tamara B. | Hunter, David J. | Kähönen, Mika | Liu, Simin | Ouyang, Pamela | Spector, Tim D. | van der Schouw, Yvonne T. | Viikari, Jorma | Wallaschofski, Henri | McCarthy, Mark I. | Frayling, Timothy M. | Murray, Anna | Franks, Steve | Järvelin, Marjo-Riitta | de Jong, Frank H. | Raitakari, Olli | Teumer, Alexander | Ohlsson, Claes | Murabito, Joanne M. | Perry, John R. B.
PLoS Genetics  2012;8(7):e1002805.
Sex hormone-binding globulin (SHBG) is a glycoprotein responsible for the transport and biologic availability of sex steroid hormones, primarily testosterone and estradiol. SHBG has been associated with chronic diseases including type 2 diabetes (T2D) and with hormone-sensitive cancers such as breast and prostate cancer. We performed a genome-wide association study (GWAS) meta-analysis of 21,791 individuals from 10 epidemiologic studies and validated these findings in 7,046 individuals in an additional six studies. We identified twelve genomic regions (SNPs) associated with circulating SHBG concentrations. Loci near the identified SNPs included SHBG (rs12150660, 17p13.1, p = 1.8×10−106), PRMT6 (rs17496332, 1p13.3, p = 1.4×10−11), GCKR (rs780093, 2p23.3, p = 2.2×10−16), ZBTB10 (rs440837, 8q21.13, p = 3.4×10−09), JMJD1C (rs7910927, 10q21.3, p = 6.1×10−35), SLCO1B1 (rs4149056, 12p12.1, p = 1.9×10−08), NR2F2 (rs8023580, 15q26.2, p = 8.3×10−12), ZNF652 (rs2411984, 17q21.32, p = 3.5×10−14), TDGF3 (rs1573036, Xq22.3, p = 4.1×10−14), LHCGR (rs10454142, 2p16.3, p = 1.3×10−07), BAIAP2L1 (rs3779195, 7q21.3, p = 2.7×10−08), and UGT2B15 (rs293428, 4q13.2, p = 5.5×10−06). These genes encompass multiple biologic pathways, including hepatic function, lipid metabolism, carbohydrate metabolism and T2D, androgen and estrogen receptor function, epigenetic effects, and the biology of sex steroid hormone-responsive cancers including breast and prostate cancer. We found evidence of sex-differentiated genetic influences on SHBG. In a sex-specific GWAS, the loci 4q13.2-UGT2B15 was significant in men only (men p = 2.5×10−08, women p = 0.66, heterogeneity p = 0.003). Additionally, three loci showed strong sex-differentiated effects: 17p13.1-SHBG and Xq22.3-TDGF3 were stronger in men, whereas 8q21.12-ZBTB10 was stronger in women. Conditional analyses identified additional signals at the SHBG gene that together almost double the proportion of variance explained at the locus. Using an independent study of 1,129 individuals, all SNPs identified in the overall or sex-differentiated or conditional analyses explained ∼15.6% and ∼8.4% of the genetic variation of SHBG concentrations in men and women, respectively. The evidence for sex-differentiated effects and allelic heterogeneity highlight the importance of considering these features when estimating complex trait variance.
Author Summary
Sex hormone-binding globulin (SHBG) is the key protein responsible for binding and transporting the sex steroid hormones, testosterone and estradiol, in the circulatory system. SHBG regulates their bioavailability and therefore their effects in the body. SHBG has been linked to chronic diseases including type 2 diabetes and to hormone-sensitive cancers such as breast and prostate cancer. SHBG concentrations are approximately 50% heritable in family studies, suggesting SHBG concentrations are under significant genetic control; yet, little is known about the specific genes that influence SHBG. We conducted a large study of the association of SHBG concentrations with markers in the human genome in ∼22,000 white men and women to determine which loci influence SHBG concentrations. Genes near the identified genomic markers in addition to the SHBG protein coding gene included PRMT6, GCKR, ZBTB10, JMJD1C, SLCO1B1, NR2F2, ZNF652, TDGF3, LHCGR, BAIAP2L1, and UGT2B15. These genes represent a wide range of biologic pathways that may relate to SHBG function and sex steroid hormone biology, including liver function, lipid metabolism, carbohydrate metabolism and type 2 diabetes, and the development and progression of sex steroid hormone-responsive cancers.
doi:10.1371/journal.pgen.1002805
PMCID: PMC3400553  PMID: 22829776
24.  A genome-wide association meta-analysis identifies new childhood obesity loci 
Bradfield, Jonathan P. | Taal, H. Rob | Timpson, Nicholas J. | Scherag, André | Lecoeur, Cecile | Warrington, Nicole M. | Hypponen, Elina | Holst, Claus | Valcarcel, Beatriz | Thiering, Elisabeth | Salem, Rany M. | Schumacher, Fredrick R. | Cousminer, Diana L. | Sleiman, Patrick M.A. | Zhao, Jianhua | Berkowitz, Robert I. | Vimaleswaran, Karani S. | Jarick, Ivonne | Pennell, Craig E. | Evans, David M. | St. Pourcain, Beate | Berry, Diane J. | Mook-Kanamori, Dennis O | Hofman, Albert | Rivadeinera, Fernando | Uitterlinden, André G. | van Duijn, Cornelia M. | van der Valk, Ralf J.P. | de Jongste, Johan C. | Postma, Dirkje S. | Boomsma, Dorret I. | Gauderman, William J. | Hassanein, Mohamed T. | Lindgren, Cecilia M. | Mägi, Reedik | Boreham, Colin A.G. | Neville, Charlotte E. | Moreno, Luis A. | Elliott, Paul | Pouta, Anneli | Hartikainen, Anna-Liisa | Li, Mingyao | Raitakari, Olli | Lehtimäki, Terho | Eriksson, Johan G. | Palotie, Aarno | Dallongeville, Jean | Das, Shikta | Deloukas, Panos | McMahon, George | Ring, Susan M. | Kemp, John P. | Buxton, Jessica L. | Blakemore, Alexandra I.F. | Bustamante, Mariona | Guxens, Mònica | Hirschhorn, Joel N. | Gillman, Matthew W. | Kreiner-Møller, Eskil | Bisgaard, Hans | Gilliland, Frank D. | Heinrich, Joachim | Wheeler, Eleanor | Barroso, Inês | O'Rahilly, Stephen | Meirhaeghe, Aline | Sørensen, Thorkild I.A. | Power, Chris | Palmer, Lyle J. | Hinney, Anke | Widen, Elisabeth | Farooqi, I. Sadaf | McCarthy, Mark I. | Froguel, Philippe | Meyre, David | Hebebrand, Johannes | Jarvelin, Marjo-Riitta | Jaddoe, Vincent W.V. | Smith, George Davey | Hakonarson, Hakon | Grant, Struan F.A.
Nature Genetics  2012;44(5):526-531.
Multiple genetic variants have been associated with adult obesity and a few with severe obesity in childhood; however, less progress has been made to establish genetic influences on common early-onset obesity. We performed a North American-Australian-European collaborative meta-analysis of fourteen studies consisting of 5,530 cases (≥95th percentile of body mass index (BMI)) and 8,318 controls (<50th percentile of BMI) of European ancestry. Taking forward the eight novel signals yielding association with P < 5×10−6 in to nine independent datasets (n = 2,818 cases and 4,083 controls) we observed two loci that yielded a genome wide significant combined P-value, namely near OLFM4 on 13q14 (rs9568856; P=1.82×10−9; OR=1.22) and within HOXB5 on 17q21 (rs9299; P=3.54×10−9; OR=1.14). Both loci continued to show association when including two extreme childhood obesity cohorts (n = 2,214 cases and 2,674 controls). Finally, these two loci yielded directionally consistent associations in the GIANT meta-analysis of adult BMI1.
doi:10.1038/ng.2247
PMCID: PMC3370100  PMID: 22484627
25.  Stratifying Type 2 Diabetes Cases by BMI Identifies Genetic Risk Variants in LAMA1 and Enrichment for Risk Variants in Lean Compared to Obese Cases 
Perry, John R. B. | Voight, Benjamin F. | Yengo, Loïc | Amin, Najaf | Dupuis, Josée | Ganser, Martha | Grallert, Harald | Navarro, Pau | Li, Man | Qi, Lu | Steinthorsdottir, Valgerdur | Scott, Robert A. | Almgren, Peter | Arking, Dan E. | Aulchenko, Yurii | Balkau, Beverley | Benediktsson, Rafn | Bergman, Richard N. | Boerwinkle, Eric | Bonnycastle, Lori | Burtt, Noël P. | Campbell, Harry | Charpentier, Guillaume | Collins, Francis S. | Gieger, Christian | Green, Todd | Hadjadj, Samy | Hattersley, Andrew T. | Herder, Christian | Hofman, Albert | Johnson, Andrew D. | Kottgen, Anna | Kraft, Peter | Labrune, Yann | Langenberg, Claudia | Manning, Alisa K. | Mohlke, Karen L. | Morris, Andrew P. | Oostra, Ben | Pankow, James | Petersen, Ann-Kristin | Pramstaller, Peter P. | Prokopenko, Inga | Rathmann, Wolfgang | Rayner, William | Roden, Michael | Rudan, Igor | Rybin, Denis | Scott, Laura J. | Sigurdsson, Gunnar | Sladek, Rob | Thorleifsson, Gudmar | Thorsteinsdottir, Unnur | Tuomilehto, Jaakko | Uitterlinden, Andre G. | Vivequin, Sidonie | Weedon, Michael N. | Wright, Alan F. | Hu, Frank B. | Illig, Thomas | Kao, Linda | Meigs, James B. | Wilson, James F. | Stefansson, Kari | van Duijn, Cornelia | Altschuler, David | Morris, Andrew D. | Boehnke, Michael | McCarthy, Mark I. | Froguel, Philippe | Palmer, Colin N. A. | Wareham, Nicholas J. | Groop, Leif | Frayling, Timothy M. | Cauchi, Stéphane
PLoS Genetics  2012;8(5):e1002741.
Common diseases such as type 2 diabetes are phenotypically heterogeneous. Obesity is a major risk factor for type 2 diabetes, but patients vary appreciably in body mass index. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m2) compared to obese cases (BMI≥30 Kg/m2). We performed two case-control genome-wide studies using two accepted cut-offs for defining individuals as overweight or obese. We used 2,112 lean type 2 diabetes cases (BMI<25 kg/m2) or 4,123 obese cases (BMI≥30 kg/m2), and 54,412 un-stratified controls. Replication was performed in 2,881 lean cases or 8,702 obese cases, and 18,957 un-stratified controls. To assess the effects of known signals, we tested the individual and combined effects of SNPs representing 36 type 2 diabetes loci. After combining data from discovery and replication datasets, we identified two signals not previously reported in Europeans. A variant (rs8090011) in the LAMA1 gene was associated with type 2 diabetes in lean cases (P = 8.4×10−9, OR = 1.13 [95% CI 1.09–1.18]), and this association was stronger than that in obese cases (P = 0.04, OR = 1.03 [95% CI 1.00–1.06]). A variant in HMG20A—previously identified in South Asians but not Europeans—was associated with type 2 diabetes in obese cases (P = 1.3×10−8, OR = 1.11 [95% CI 1.07–1.15]), although this association was not significantly stronger than that in lean cases (P = 0.02, OR = 1.09 [95% CI 1.02–1.17]). For 36 known type 2 diabetes loci, 29 had a larger odds ratio in the lean compared to obese (binomial P = 0.0002). In the lean analysis, we observed a weighted per-risk allele OR = 1.13 [95% CI 1.10–1.17], P = 3.2×10−14. This was larger than the same model fitted in the obese analysis where the OR = 1.06 [95% CI 1.05–1.08], P = 2.2×10−16. This study provides evidence that stratification of type 2 diabetes cases by BMI may help identify additional risk variants and that lean cases may have a stronger genetic predisposition to type 2 diabetes.
Author Summary
Individuals with Type 2 diabetes (T2D) can present with variable clinical characteristics. It is well known that obesity is a major risk factor for type 2 diabetes, yet patients can vary considerably—there are many lean diabetes patients and many overweight people without diabetes. We hypothesized that the genetic predisposition to the disease may be different in lean (BMI<25 Kg/m2) compared to obese cases (BMI≥30 Kg/m2). Specifically, as lean T2D patients had lower risk than obese patients, they must have been more genetically susceptible. Using genetic data from multiple genome-wide association studies, we tested genetic markers across the genome in 2,112 lean type 2 diabetes cases (BMI<25 kg/m2), 4,123 obese cases (BMI≥30 kg/m2), and 54,412 healthy controls. We confirmed our results in an additional 2,881 lean cases, 8,702 obese cases, and 18,957 healthy controls. Using these data we found differences in genetic enrichment between lean and obese cases, supporting our original hypothesis. We also searched for genetic variants that may be risk factors only in lean or obese patients and found two novel gene regions not previously reported in European individuals. These findings may influence future study design for type 2 diabetes and provide further insight into the biology of the disease.
doi:10.1371/journal.pgen.1002741
PMCID: PMC3364960  PMID: 22693455

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