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1.  Common genetic variants highlight the role of insulin resistance and body fat distribution in type 2 diabetes, independently of obesity 
Diabetes  2014;63(12):4378-4387.
We aimed to validate genetic variants as instruments for insulin resistance and secretion, to characterise their association with intermediate phenotypes, and to investigate their role in T2D risk among normal-weight, overweight and obese individuals.We investigated the association of genetic scores with euglycaemic-hyperinsulinaemic clamp- and OGTT-based measures of insulin resistance and secretion, and a range of metabolic measures in up to 18,565 individuals. We also studied their association with T2D risk among normal-weight, overweight and obese individuals in up to 8,124 incident T2D cases. The insulin resistance score was associated with lower insulin sensitivity measured by M/I value (β in SDs-per-allele [95%CI]:−0.03[−0.04,−0.01];p=0.004). This score was associated with lower BMI (−0.01[−0.01,−0.0;p=0.02) and gluteofemoral fat-mass (−0.03[−0.05,−0.02;p=1.4×10−6), and with higher ALT (0.02[0.01,0.03];p=0.002) and gamma-GT (0.02[0.01,0.03];p=0.001). While the secretion score had a stronger association with T2D in leaner individuals (pinteraction=0.001), we saw no difference in the association of the insulin resistance score with T2D among BMI- or waist-strata(pinteraction>0.31). While insulin resistance is often considered secondary to obesity, the association of the insulin resistance score with lower BMI and adiposity and with incident T2D even among individuals of normal weight highlights the role of insulin resistance and ectopic fat distribution in T2D, independently of body size.
doi:10.2337/db14-0319
PMCID: PMC4241116  PMID: 24947364
Genetics; type 2 diabetes; insulin resistance; insulin secretion; adipose expandability
2.  Heritability of variation in glycaemic response to metformin: a genome-wide complex trait analysis 
Summary
Background
Metformin is a first-line oral agent used in the treatment of type 2 diabetes, but glycaemic response to this drug is highly variable. Understanding the genetic contribution to metformin response might increase the possibility of personalising metformin treatment. We aimed to establish the heritability of glycaemic response to metformin using the genome-wide complex trait analysis (GCTA) method.
Methods
In this GCTA study, we obtained data about HbA1c concentrations before and during metformin treatment from patients in the Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS) study, which includes a cohort of patients with type 2 diabetes and is linked to comprehensive clinical databases and genome-wide association study data. We applied the GCTA method to estimate heritability for four definitions of glycaemic response to metformin: absolute reduction in HbA1c; proportional reduction in HbA1c; adjusted reduction in HbA1c; and whether or not the target on-treatment HbA1c of less than 7% (53 mmol/mol) was achieved, with adjustment for baseline HbA1c and known clinical covariates. Chromosome-wise heritability estimation was used to obtain further information about the genetic architecture.
Findings
5386 individuals were included in the final dataset, of whom 2085 had enough clinical data to define glycaemic response to metformin. The heritability of glycaemic response to metformin varied by response phenotype, with a heritability of 34% (95% CI 1–68; p=0·022) for the absolute reduction in HbA1c, adjusted for pretreatment HbA1c. Chromosome-wise heritability estimates suggest that the genetic contribution is probably from individual variants scattered across the genome, which each have a small to moderate effect, rather than from a few loci that each have a large effect.
Interpretation
Glycaemic response to metformin is heritable, thus glycaemic response to metformin is, in part, intrinsic to individual biological variation. Further genetic analysis might enable us to make better predictions for stratified medicine and to unravel new mechanisms of metformin action.
Funding
Wellcome Trust.
doi:10.1016/S2213-8587(14)70050-6
PMCID: PMC4038749  PMID: 24731673
3.  Transcriptome and genome sequencing uncovers functional variation in humans 
Nature  2013;501(7468):506-511.
Summary
Genome sequencing projects are discovering millions of genetic variants in humans, and interpretation of their functional effects is essential for understanding the genetic basis of variation in human traits. Here we report sequencing and deep analysis of mRNA and miRNA from lymphoblastoid cell lines of 462 individuals from the 1000 Genomes Project – the first uniformly processed RNA-seq data from multiple human populations with high-quality genome sequences. We discovered extremely widespread genetic variation affecting regulation of the majority of genes, with transcript structure and expression level variation being equally common but genetically largely independent. Our characterization of causal regulatory variation sheds light on cellular mechanisms of regulatory and loss-of-function variation, and allowed us to infer putative causal variants for dozens of disease-associated loci. Altogether, this study provides a deep understanding of the cellular mechanisms of transcriptome variation and of the landscape of functional variants in the human genome.
doi:10.1038/nature12531
PMCID: PMC3918453  PMID: 24037378
4.  Data sharing in large research consortia: experiences and recommendations from ENGAGE 
Data sharing is essential for the conduct of cutting-edge research and is increasingly required by funders concerned with maximising the scientific yield from research data collections. International research consortia are encouraged to share data intra-consortia, inter-consortia and with the wider scientific community. Little is reported regarding the factors that hinder or facilitate data sharing in these different situations. This paper provides results from a survey conducted in the European Network for Genetic and Genomic Epidemiology (ENGAGE) that collected information from its participating institutions about their data-sharing experiences. The questionnaire queried about potential hurdles to data sharing, concerns about data sharing, lessons learned and recommendations for future collaborations. Overall, the survey results reveal that data sharing functioned well in ENGAGE and highlight areas that posed the most frequent hurdles for data sharing. Further challenges arise for international data sharing beyond the consortium. These challenges are described and steps to help address these are outlined.
doi:10.1038/ejhg.2013.131
PMCID: PMC3925260  PMID: 23778872
biobanks; data sharing; consortia; genetic research
5.  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
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.  Genome-wide association study in people of South Asian ancestry identifies six novel susceptibility loci for type 2 diabetes 
Nature genetics  2011;43(10):984-989.
We carried out a genome wide association study of type-2 diabetes (T2D) amongst 20,119 people of South Asian ancestry (5,561 with T2D); we identified 20 independent SNPs associated with T2D at P<10−4 for testing amongst a further 38,568 South Asians (13,170 with T2D). In combined analysis, common genetic variants at six novel loci (GRB14, ST6GAL1, VPS26A, HMG20A, AP3S2 and HNF4A) were associated with T2D (P=4.1×10−8 to P=1.9×10−11); SNPs at GRB14 were also associated with insulin sensitivity, and at ST6GAL1 and HNF4A with pancreatic beta-cell function respectively. Our findings provide additional insight into mechanisms underlying T2D, and demonstrate the potential for new discovery from genetic association studies in South Asians who have increased susceptibility to T2D.
doi:10.1038/ng.921
PMCID: PMC3773920  PMID: 21874001
8.  Genetic loci influencing kidney function and chronic kidney disease in man 
Chambers, John C | Zhang, Weihua | Lord, Graham M | van der Harst, Pim | Lawlor, Debbie A | Sehmi, Joban S | Gale, Daniel P | Wass, Mark N | Ahmadi, Kourosh R | Bakker, Stephan JL | Beckmann, Jacqui | Bilo, Henk JG | Bochud, Murielle | Brown, Morris J | Caulfield, Mark J | Connell, John M C | Cook, Terence | Cotlarciuc, Ioana | Smith, George Davey | de Silva, Ranil | Deng, Guohong | Devuyst, Olivier | Dikkeschei, Lambert D. | Dimkovic, Nada | Dockrell, Mark | Dominiczak, Anna | Ebrahim, Shah | Eggermann, Thomas | Farrall, Martin | Ferrucci, Luigi | Floege, Jurgen | Forouhi, Nita G | Gansevoort, Ron T | Han, Xijin | Hedblad, Bo | van der Heide, Jaap J Homan | Hepkema, Bouke G | Hernandez-Fuentes, Maria | Hypponen, Elina | Johnson, Toby | de Jong, Paul E | Kleefstra, Nanne | Lagou, Vasiliki | Lapsley, Marta | Li, Yun | Loos, Ruth J F | Luan, Jian'an | Luttropp, Karin | Maréchal, Céline | Melander, Olle | Munroe, Patricia B | Nordfors, Louise | Parsa, Afshin | Penninx, Brenda W. | Perucha, Esperanza | Pouta, Anneli | Prokopenko, Inga | Roderick, Paul J | Ruokonen, Aimo | Samani, Nilesh | Sanna, Serena | Schalling, Martin | Schlessinger, David | Schlieper, Georg | Seelen, Marc AJ | Shuldiner, Alan R | Sjögren, Marketa | Smit, Johannes H. | Snieder, Harold | Soranzo, Nicole | Spector, Timothy D | Stenvinkel, Peter | Sternberg, Michael JE | Swaminathan, Ramasamyiyer | Tanaka, Toshiko | Ubink-Veltmaat, Lielith J. | Uda, Manuela | Vollenweider, Peter | Wallace, Chris | Waterworth, Dawn | Zerres, Klaus | Waeber, Gerard | Wareham, Nicholas J | Maxwell, Patrick H | McCarthy, Mark I | Jarvelin, Marjo-Riitta | Mooser, Vincent | Abecasis, Goncalo R | Lightstone, Liz | Scott, James | Navis, Gerjan | Elliott, Paul | Kooner., Jaspal S
Nature genetics  2010;42(5):373-375.
Chronic kidney disease (CKD), the result of permanent loss of kidney function, is a major global problem. We identify common genetic variants at chr2p12-p13, chr6q26, chr17q23 and chr19q13 associated with serum creatinine, a marker of kidney function (P=10−10 to 10−15). SNPs rs10206899 (near NAT8, chr2p12-p13) and rs4805834 (near SLC7A9, chr19q13) were also associated with CKD. Our findings provide new insight into metabolic, solute and drug-transport pathways underlying susceptibility to CKD.
doi:10.1038/ng.566
PMCID: PMC3748585  PMID: 20383145
9.  Gene expression changes with age in skin, adipose tissue, blood and brain 
Genome Biology  2013;14(7):R75.
Background
Previous studies have demonstrated that gene expression levels change with age. These changes are hypothesized to influence the aging rate of an individual. We analyzed gene expression changes with age in abdominal skin, subcutaneous adipose tissue and lymphoblastoid cell lines in 856 female twins in the age range of 39-85 years. Additionally, we investigated genotypic variants involved in genotype-by-age interactions to understand how the genomic regulation of gene expression alters with age.
Results
Using a linear mixed model, differential expression with age was identified in 1,672 genes in skin and 188 genes in adipose tissue. Only two genes expressed in lymphoblastoid cell lines showed significant changes with age. Genes significantly regulated by age were compared with expression profiles in 10 brain regions from 100 postmortem brains aged 16 to 83 years. We identified only one age-related gene common to the three tissues. There were 12 genes that showed differential expression with age in both skin and brain tissue and three common to adipose and brain tissues.
Conclusions
Skin showed the most age-related gene expression changes of all the tissues investigated, with many of the genes being previously implicated in fatty acid metabolism, mitochondrial activity, cancer and splicing. A significant proportion of age-related changes in gene expression appear to be tissue-specific with only a few genes sharing an age effect in expression across tissues. More research is needed to improve our understanding of the genetic influences on aging and the relationship with age-related diseases.
doi:10.1186/gb-2013-14-7-r75
PMCID: PMC4054017  PMID: 23889843
Aging; gene expression; skin; adipose; brain; microarrays
10.  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
11.  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
12.  Ethnic variation in the activity of lipid desaturases and their relationships with cardiovascular risk factors in control women and an at-risk group with previous gestational diabetes mellitus: a cross-sectional study 
Background
Lipid desaturase enzymes mediate the metabolism of fatty acids to long chain polyunsaturated fatty acids and their activities are related to metabolic risk factors for Type 2 diabetes (T2DM) and coronary heart disease (CHD). There are marked ethnic differences in risks of CHD and T2DM but little is known about ethnic differences in desaturase activities.
Methods
Samples from a study of CVD risk in women with previous gestational diabetes were analysed for percentage fatty acids in plasma free fatty acid, triglyceride, cholesterol ester and phospholipid pools for 89 white European, 53 African Caribbean and 56 Asian Indian women. The fatty acid desaturase activities, stearoyl-CoA desaturase (SCD, calculated separately for C16 and C18 fatty acids), delta 6 desaturase (D6D) and delta 5 desaturase (D5D) were estimated from precursor-to-product ratios and their relationships with adiposity, blood pressure, cholesterol, triglycerides, HDL cholesterol and insulin sensitivity explored. Ethnic differences in desaturase activities independent of ethnic variation in risk factor correlates of desaturase activities were then identified.
Results
There was significant ethnic variation in age, BMI, waist circumference, blood pressure, serum triglycerides and HDL cholesterol concentrations and insulin resistance. Desaturase activities showed significant correlations, independent of ethnicity, with BMI, waist circumference, triglycerides and HDL cholesterol. Independent of ethnic variation in BMI, waist circumference, triglycerides and HDL cholesterol, SCD-16 activity, calculated from each of the four lipid pools measured, was 18–35 percent higher in white Europeans than in African Caribbeans or Asian Indians (all p < 0.001). Similar, though less consistent differences were apparent for SCD-18 activity. Also independently of risk factor variation, but specifically when calculated from the cholesterol ester and phospholipid, pools, D6D activity was significantly lower in Asian Indians, and D5D activity higher in African Caribbeans.
Conclusions
Significant ethnic differences exist in desaturase activities, independently of ethnic variation in other risk factors. These characteristics did not accord with higher risk of T2DM among African Caribbeans and Asian Indians nor with lower risk of CHD among African Caribbeans but did accord with the higher risk of CHD in Asian Indians.
doi:10.1186/1476-511X-12-25
PMCID: PMC3605319  PMID: 23496836
Ethnicity; Lipids; Blood pressure; Insulin resistance; Stearoyl-CoA desaturase; Delta 6 desaturase; Delta 5 desaturase; Fatty acids; Desaturase activities; Triglycerides; HDL cholesterol; Insulin resistance
13.  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
14.  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
15.  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
16.  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
17.  Variants in MTNR1B influence fasting glucose levels 
Prokopenko, Inga | Langenberg, Claudia | Florez, Jose C | Saxena, Richa | Soranzo, Nicole | Thorleifsson, Gudmar | Loos, Ruth J F | Manning, Alisa K | Jackson, Anne U | Aulchenko, Yurii | Potter, Simon C | Erdos, Michael R | Sanna, Serena | Hottenga, Jouke-Jan | Wheeler, Eleanor | Kaakinen, Marika | Lyssenko, Valeriya | Chen, Wei-Min | Ahmadi, Kourosh | Beckmann, Jacques S | Bergman, Richard N | Bochud, Murielle | Bonnycastle, Lori L | Buchanan, Thomas A | Cao, Antonio | Cervino, Alessandra | Coin, Lachlan | Collins, Francis S | Crisponi, Laura | de Geus, Eco J C | Dehghan, Abbas | Deloukas, Panos | Doney, Alex S F | Elliott, Paul | Freimer, Nelson | Gateva, Vesela | Herder, Christian | Hofman, Albert | Hughes, Thomas E | Hunt, Sarah | Illig, Thomas | Inouye, Michael | Isomaa, Bo | Johnson, Toby | Kong, Augustine | Krestyaninova, Maria | Kuusisto, Johanna | Laakso, Markku | Lim, Noha | Lindblad, Ulf | Lindgren, Cecilia M | McCann, Owen T | Mohlke, Karen L | Morris, Andrew D | Naitza, Silvia | Orrù, Marco | Palmer, Colin N A | Pouta, Anneli | Randall, Joshua | Rathmann, Wolfgang | Saramies, Jouko | Scheet, Paul | Scott, Laura J | Scuteri, Angelo | Sharp, Stephen | Sijbrands, Eric | Smit, Jan H | Song, Kijoung | Steinthorsdottir, Valgerdur | Stringham, Heather M | Tuomi, Tiinamaija | Tuomilehto, Jaakko | Uitterlinden, André G | Voight, Benjamin F | Waterworth, Dawn | Wichmann, H-Erich | Willemsen, Gonneke | Witteman, Jacqueline C M | Yuan, Xin | Zhao, Jing Hua | Zeggini, Eleftheria | Schlessinger, David | Sandhu, Manjinder | Boomsma, Dorret I | Uda, Manuela | Spector, Tim D | Penninx, Brenda WJH | Altshuler, David | Vollenweider, Peter | Jarvelin, Marjo Riitta | Lakatta, Edward | Waeber, Gerard | Fox, Caroline S | Peltonen, Leena | Groop, Leif C | Mooser, Vincent | Cupples, L Adrienne | Thorsteinsdottir, Unnur | Boehnke, Michael | Barroso, Inês | Van Duijn, Cornelia | Dupuis, Josée | Watanabe, Richard M | Stefansson, Kari | McCarthy, Mark I | Wareham, Nicholas J | Meigs, James B | Abecasis, Gonçalo R
Nature genetics  2008;41(1):77-81.
To identify previously unknown genetic loci associated with fasting glucose concentrations, we examined the leading association signals in ten genome-wide association scans involving a total of 36,610 individuals of European descent. Variants in the gene encoding melatonin receptor 1B (MTNR1B) were consistently associated with fasting glucose across all ten studies. The strongest signal was observed at rs10830963, where each G allele (frequency 0.30 in HapMap CEU) was associated with an increase of 0.07 (95% CI = 0.06-0.08) mmol/l in fasting glucose levels (P = 3.2 = × 10−50) and reduced beta-cell function as measured by homeostasis model assessment (HOMA-B, P = 1.1 × 10−15). The same allele was associated with an increased risk of type 2 diabetes (odds ratio = 1.09 (1.05-1.12), per G allele P = 3.3 × 10−7) in a meta-analysis of 13 case-control studies totaling 18,236 cases and 64,453 controls. Our analyses also confirm previous associations of fasting glucose with variants at the G6PC2 (rs560887, P = 1.1 × 10−57) and GCK (rs4607517, P = 1.0 × 10−25) loci.
doi:10.1038/ng.290
PMCID: PMC2682768  PMID: 19060907
18.  Genome-wide association study identifies loci influencing concentrations of liver enzymes in plasma 
Chambers, John C | Zhang, Weihua | Sehmi, Joban | Li, Xinzhong | Wass, Mark N | Van der Harst, Pim | Holm, Hilma | Sanna, Serena | Kavousi, Maryam | Baumeister, Sebastian E | Coin, Lachlan J | Deng, Guohong | Gieger, Christian | Heard-Costa, Nancy L | Hottenga, Jouke-Jan | Kühnel, Brigitte | Kumar, Vinod | Lagou, Vasiliki | Liang, Liming | Luan, Jian’an | Vidal, Pedro Marques | Leach, Irene Mateo | O’Reilly, Paul F | Peden, John F | Rahmioglu, Nilufer | Soininen, Pasi | Speliotes, Elizabeth K | Yuan, Xin | Thorleifsson, Gudmar | Alizadeh, Behrooz Z | Atwood, Larry D | Borecki, Ingrid B | Brown, Morris J | Charoen, Pimphen | Cucca, Francesco | Das, Debashish | de Geus, Eco J C | Dixon, Anna L | Döring, Angela | Ehret, Georg | Eyjolfsson, Gudmundur I | Farrall, Martin | Forouhi, Nita G | Friedrich, Nele | Goessling, Wolfram | Gudbjartsson, Daniel F | Harris, Tamara B | Hartikainen, Anna-Liisa | Heath, Simon | Hirschfield, Gideon M | Hofman, Albert | Homuth, Georg | Hyppönen, Elina | Janssen, Harry L A | Johnson, Toby | Kangas, Antti J | Kema, Ido P | Kühn, Jens P | Lai, Sandra | Lathrop, Mark | Lerch, Markus M | Li, Yun | Liang, T Jake | Lin, Jing-Ping | Loos, Ruth J F | Martin, Nicholas G | Moffatt, Miriam F | Montgomery, Grant W | Munroe, Patricia B | Musunuru, Kiran | Nakamura, Yusuke | O’Donnell, Christopher J | Olafsson, Isleifur | Penninx, Brenda W | Pouta, Anneli | Prins, Bram P | Prokopenko, Inga | Puls, Ralf | Ruokonen, Aimo | Savolainen, Markku J | Schlessinger, David | Schouten, Jeoffrey N L | Seedorf, Udo | Sen-Chowdhry, Srijita | Siminovitch, Katherine A | Smit, Johannes H | Spector, Timothy D | Tan, Wenting | Teslovich, Tanya M | Tukiainen, Taru | Uitterlinden, Andre G | Van der Klauw, Melanie M | Vasan, Ramachandran S | Wallace, Chris | Wallaschofski, Henri | Wichmann, H-Erich | Willemsen, Gonneke | Würtz, Peter | Xu, Chun | Yerges-Armstrong, Laura M | Abecasis, Goncalo R | Ahmadi, Kourosh R | Boomsma, Dorret I | Caulfield, Mark | Cookson, William O | van Duijn, Cornelia M | Froguel, Philippe | Matsuda, Koichi | McCarthy, Mark I | Meisinger, Christa | Mooser, Vincent | Pietiläinen, Kirsi H | Schumann, Gunter | Snieder, Harold | Sternberg, Michael J E | Stolk, Ronald P | Thomas, Howard C | Thorsteinsdottir, Unnur | Uda, Manuela | Waeber, Gérard | Wareham, Nicholas J | Waterworth, Dawn M | Watkins, Hugh | Whitfield, John B | Witteman, Jacqueline C M | Wolffenbuttel, Bruce H R | Fox, Caroline S | Ala-Korpela, Mika | Stefansson, Kari | Vollenweider, Peter | Völzke, Henry | Schadt, Eric E | Scott, James | Järvelin, Marjo-Riitta | Elliott, Paul | Kooner, Jaspal S
Nature genetics  2011;43(11):1131-1138.
Concentrations of liver enzymes in plasma are widely used as indicators of liver disease. We carried out a genome-wide association study in 61,089 individuals, identifying 42 loci associated with concentrations of liver enzymes in plasma, of which 32 are new associations (P = 10−8 to P = 10−190). We used functional genomic approaches including metabonomic profiling and gene expression analyses to identify probable candidate genes at these regions. We identified 69 candidate genes, including genes involved in biliary transport (ATP8B1 and ABCB11), glucose, carbohydrate and lipid metabolism (FADS1, FADS2, GCKR, JMJD1C, HNF1A, MLXIPL, PNPLA3, PPP1R3B, SLC2A2 and TRIB1), glycoprotein biosynthesis and cell surface glycobiology (ABO, ASGR1, FUT2, GPLD1 and ST3GAL4), inflammation and immunity (CD276, CDH6, GCKR, HNF1A, HPR, ITGA1, RORA and STAT4) and glutathione metabolism (GSTT1, GSTT2 and GGT), as well as several genes of uncertain or unknown function (including ABHD12, EFHD1, EFNA1, EPHA2, MICAL3 and ZNF827). Our results provide new insight into genetic mechanisms and pathways influencing markers of liver function.
doi:10.1038/ng.970
PMCID: PMC3482372  PMID: 22001757
19.  Genomic inflation factors under polygenic inheritance 
Population structure, including population stratification and cryptic relatedness, can cause spurious associations in genome-wide association studies (GWAS). Usually, the scaled median or mean test statistic for association calculated from multiple single-nucleotide-polymorphisms across the genome is used to assess such effects, and ‘genomic control' can be applied subsequently to adjust test statistics at individual loci by a genomic inflation factor. Published GWAS have clearly shown that there are many loci underlying genetic variation for a wide range of complex diseases and traits, implying that a substantial proportion of the genome should show inflation of the test statistic. Here, we show by theory, simulation and analysis of data that in the absence of population structure and other technical artefacts, but in the presence of polygenic inheritance, substantial genomic inflation is expected. Its magnitude depends on sample size, heritability, linkage disequilibrium structure and the number of causal variants. Our predictions are consistent with empirical observations on height in independent samples of ∼4000 and ∼133 000 individuals.
doi:10.1038/ejhg.2011.39
PMCID: PMC3137506  PMID: 21407268
genome-wide association study; genomic inflation factor; polygenic inheritance
20.  Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes 
Zeggini, Eleftheria | Scott, Laura J. | Saxena, Richa | Voight, Benjamin F. | Marchini, Jonathan L | Hu, Tainle | de Bakker, Paul IW | Abecasis, Gonçalo R | Almgren, Peter | Andersen, Gitte | Ardlie, Kristin | Boström, Kristina Bengtsson | Bergman, Richard N | Bonnycastle, Lori L | Borch-Johnsen, Knut | Burtt, Noël P | Chen, Hong | Chines, Peter S | Daly, Mark J | Deodhar, Parimal | Ding, Charles | Doney, Alex S F | Duren, William L | Elliott, Katherine S | Erdos, Michael R | Frayling, Timothy M | Freathy, Rachel M | Gianniny, Lauren | Grallert, Harald | Grarup, Niels | Groves, Christopher J | Guiducci, Candace | Hansen, Torben | Herder, Christian | Hitman, Graham A | Hughes, Thomas E | Isomaa, Bo | Jackson, Anne U | Jørgensen, Torben | Kong, Augustine | Kubalanza, Kari | Kuruvilla, Finny G | Kuusisto, Johanna | Langenberg, Claudia | Lango, Hana | Lauritzen, Torsten | Li, Yun | Lindgren, Cecilia M | Lyssenko, Valeriya | Marvelle, Amanda F | Meisinger, Christa | Midthjell, Kristian | Mohlke, Karen L | Morken, Mario A | Morris, Andrew D | Narisu, Narisu | Nilsson, Peter | Owen, Katharine R | Palmer, Colin NA | Payne, Felicity | Perry, John RB | Pettersen, Elin | Platou, Carl | Prokopenko, Inga | Qi, Lu | Qin, Li | Rayner, Nigel W | Rees, Matthew | Roix, Jeffrey J | Sandbæk, Anelli | Shields, Beverley | Sjögren, Marketa | Steinthorsdottir, Valgerdur | Stringham, Heather M | Swift, Amy J | Thorleifsson, Gudmar | Thorsteinsdottir, Unnur | Timpson, Nicholas J | Tuomi, Tiinamaija | Tuomilehto, Jaakko | Walker, Mark | Watanabe, Richard M | Weedon, Michael N | Willer, Cristen J | Illig, Thomas | Hveem, Kristian | Hu, Frank B | Laakso, Markku | Stefansson, Kari | Pedersen, Oluf | Wareham, Nicholas J | Barroso, Inês | Hattersley, Andrew T | Collins, Francis S | Groop, Leif | McCarthy, Mark I | Boehnke, Michael | Altshuler, David
Nature genetics  2008;40(5):638-645.
Genome-wide association (GWA) studies have identified multiple new genomic loci at which common variants modestly but reproducibly influence risk of type 2 diabetes (T2D)1-11. Established associations to common and rare variants explain only a small proportion of the heritability of T2D. As previously published analyses had limited power to discover loci at which common alleles have modest effects, we performed meta-analysis of three T2D GWA scans encompassing 10,128 individuals of European-descent and ~2.2 million SNPs (directly genotyped and imputed). Replication testing was performed in an independent sample with an effective sample size of up to 53,975. At least six new loci with robust evidence for association were detected, including the JAZF1 (p=5.0×10−14), CDC123/CAMK1D (p=1.2×10−10), TSPAN8/LGR5 (p=1.1×10−9), THADA (p=1.1×10−9), ADAMTS9 (p=1.2×10−8), and NOTCH2 (p=4.1×10−8) gene regions. The large number of loci with relatively small effects indicates the value of large discovery and follow-up samples in identifying additional clues about the inherited basis of T2D.
doi:10.1038/ng.120
PMCID: PMC2672416  PMID: 18372903
22.  Genome-wide association study identifies variants in TMPRSS6 associated with hemoglobin levels 
Nature genetics  2009;41(11):1170-1172.
We carried out a genome-wide association study of hemoglobin levels in 16,001 individuals of European and Indian Asian ancestry. The most closely associated SNP (rs855791) results in nonsynonymous (V736A) change in the serine protease domain of TMPRSS6 and a blood hemoglobin concentration 0.13 (95% CI 0.09–0.17) g/dl lower per copy of allele A (P = 1.6 × 10−13). Our findings suggest that TMPRSS6, a regulator of hepcidin synthesis and iron handling, is crucial in hemoglobin level maintenance.
doi:10.1038/ng.462
PMCID: PMC3178047  PMID: 19820698
23.  Mendelian Randomization Studies do not Support a Role for Raised Circulating Triglyceride Levels influencing Type 2 Diabetes, Glucose Levels, or Insulin Resistance 
Diabetes  2011;60(3):1008-1018.
Objective
The causal nature of associations between circulating triglycerides, insulin resistance and type 2 diabetes is unclear. We aimed to use Mendelian randomization to test the hypothesis that raised circulating triglyceride levels causally influence the risk of type 2 diabetes, raised normal fasting glucose levels, and hepatic insulin resistance.
Research design and methods
We tested 10 common genetic variants robustly associated with circulating triglyceride levels against type 2 diabetes status in 5637 cases, 6860 controls, and four continuous outcomes (reflecting glycemia and hepatic insulin resistance) in 8271 non-diabetic individuals from four studies.
Results
Individuals carrying greater numbers of triglyceride-raising alleles had increased circulating triglyceride levels (0.59 SD [95% CI: 0.52, 0.65] difference between the 20% of individuals with the most alleles and the 20% with the fewest alleles). There was no evidence that carriers of greater numbers of triglyceride-raising alleles were at increased risk of type 2 diabetes (per weighted allele odds ratio (OR) 0.99 [95% CI: 0.97, 1.01]; P = 0.26). In non-diabetic individuals, there was no evidence that carriers of greater numbers of triglyceride-raising alleles had increased fasting insulin levels (0.00 SD per weighted allele [95% CI: −0.01, 0.02]; P = 0.72) or increased fasting glucose levels (0.00 SD per weighted allele [95% CI: −0.01, 0.01]; P = 0.88). Instrumental variable analyses confirmed that genetically raised circulating triglyceride levels were not associated with increased diabetes risk, fasting glucose or fasting insulin, and, for diabetes, showed a trend towards a protective association (OR per 1 SD increase in log10-triglycerides: 0.61 [95% CI: 0.45, 0.83]; P = 0.002).
Conclusion
Genetically raised circulating triglyceride levels do not increase the risk of type 2 diabetes, or raise fasting glucose or fasting insulin levels in non-diabetic individuals. One explanation for our results is that raised circulating triglycerides are predominantly secondary to the diabetes disease process rather than causal.
doi:10.2337/db10-1317
PMCID: PMC3046819  PMID: 21282362
24.  Human metabolic profiles are stably controlled by genetic and environmental variation 
A comprehensive variation map of the human metabolome identifies genetic and stable-environmental sources as major drivers of metabolite concentrations. The data suggest that sample sizes of a few thousand are sufficient to detect metabolite biomarkers predictive of disease.
We designed a longitudinal twin study to characterize the genetic, stable-environmental, and longitudinally fluctuating influences on metabolite concentrations in two human biofluids—urine and plasma—focusing specifically on the representative subset of metabolites detectable by 1H nuclear magnetic resonance (1H NMR) spectroscopy.We identified widespread genetic and stable-environmental influences on the (urine and plasma) metabolomes, with (30 and 42%) attributable on average to familial sources, and (47 and 60%) attributable to longitudinally stable sources.Ten of the metabolites annotated in the study are estimated to have >60% familial contribution to their variation in concentration.Our findings have implications for the design and interpretation of 1H NMR-based molecular epidemiology studies. On the basis of the stable component of variation quantified in the current paper, we specified a model of disease association under which we inferred that sample sizes of a few thousand should be sufficient to detect disease-predictive metabolite biomarkers.
Metabolites are small molecules involved in biochemical processes in living systems. Their concentration in biofluids, such as urine and plasma, can offer insights into the functional status of biological pathways within an organism, and reflect input from multiple levels of biological organization—genetic, epigenetic, transcriptomic, and proteomic—as well as from environmental and lifestyle factors. Metabolite levels have the potential to indicate a broad variety of deviations from the ‘normal' physiological state, such as those that accompany a disease, or an increased susceptibility to disease. A number of recent studies have demonstrated that metabolite concentrations can be used to diagnose disease states accurately. A more ambitious goal is to identify metabolite biomarkers that are predictive of future disease onset, providing the possibility of intervention in susceptible individuals.
If an extreme concentration of a metabolite is to serve as an indicator of disease status, it is usually important to know the distribution of metabolite levels among healthy individuals. It is also useful to characterize the sources of that observed variation in the healthy population. A proportion of that variation—the heritable component—is attributable to genetic differences between individuals, potentially at many genetic loci. An effective, molecular indicator of a heritable, complex disease is likely to have a substantive heritable component. Non-heritable biological variation in metabolite concentrations can arise from a variety of environmental influences, such as dietary intake, lifestyle choices, general physical condition, composition of gut microflora, and use of medication. Variation across a population in stable-environmental influences leads to long-term differences between individuals in their baseline metabolite levels. Dynamic environmental pressures lead to short-term fluctuations within an individual about their baseline level. A metabolite whose concentration changes substantially in response to short-term pressures is relatively unlikely to offer long-term prediction of disease. In summary, the potential suitability of a metabolite to predict disease is reflected by the relative contributions of heritable and stable/unstable-environmental factors to its variation in concentration across the healthy population.
Studies involving twins are an established technique for quantifying the heritable component of phenotypes in human populations. Monozygotic (MZ) twins share the same DNA genome-wide, while dizygotic (DZ) twins share approximately half their inherited DNA, as do ordinary siblings. By comparing the average extent of phenotypic concordance within MZ pairs to that within DZ pairs, it is possible to quantify the heritability of a trait, and also to quantify the familiality, which refers to the combination of heritable and common-environmental effects (i.e., environmental influences shared by twins in a pair). In addition to incorporating twins into the study design, it is useful to quantify the phenotype in some individuals at multiple time points. The longitudinal aspect of such a study allows environmental effects to be decomposed into those that affect the phenotype over the short term and those that exert stable influence.
For the current study, urine and blood samples were collected from a cohort of MZ and DZ twins, with some twins donating samples on two occasions several months apart. Samples were analysed by 1H nuclear magnetic resonance (1H NMR) spectroscopy—an untargeted, discovery-driven technique for quantifying metabolite concentrations in biological samples. The application of 1H NMR to a biological sample creates a spectrum, made up of multiple peaks, with each peak's size quantitatively representing the concentration of its corresponding hydrogen-containing metabolite.
In each biological sample in our study, we extracted a full set of peaks, and thereby quantified the concentrations of all common plasma and urine metabolites detectable by 1H NMR. We developed bespoke statistical methods to decompose the observed concentration variation at each metabolite peak into that originating from familial, individual-environmental, and unstable-environmental sources.
We quantified the variability landscape across all common metabolite peaks in the urine and plasma 1H NMR metabolomes. We annotated a subset of peaks with a total of 65 metabolites; the variance decompositions for these are shown in Figure 1. Ten metabolites' concentrations were estimated to have familial contributions in excess of 60%. The average proportion of stable variation across all extracted metabolite peaks was estimated to be 47% in the urine samples and 60% in the plasma samples; the average estimated familiality was 30% for urine and 42% for plasma. These results comprise the first quantitative variation map of the 1H NMR metabolome. The identification and quantification of substantive widespread stability provides support for the use of these biofluids in molecular epidemiology studies. On the basis of our findings, we performed power calculations for a hypothetical study searching for predictive disease biomarkers among 1H NMR-detectable urine and plasma metabolites. Our calculations suggest that sample sizes of 2000–5000 should allow reliable identification of disease-predictive metabolite concentrations explaining 5–10% of disease risk, while greater sample sizes of 5000–20 000 would be required to identify metabolite concentrations explaining 1–2% of disease risk.
1H Nuclear Magnetic Resonance spectroscopy (1H NMR) is increasingly used to measure metabolite concentrations in sets of biological samples for top-down systems biology and molecular epidemiology. For such purposes, knowledge of the sources of human variation in metabolite concentrations is valuable, but currently sparse. We conducted and analysed a study to create such a resource. In our unique design, identical and non-identical twin pairs donated plasma and urine samples longitudinally. We acquired 1H NMR spectra on the samples, and statistically decomposed variation in metabolite concentration into familial (genetic and common-environmental), individual-environmental, and longitudinally unstable components. We estimate that stable variation, comprising familial and individual-environmental factors, accounts on average for 60% (plasma) and 47% (urine) of biological variation in 1H NMR-detectable metabolite concentrations. Clinically predictive metabolic variation is likely nested within this stable component, so our results have implications for the effective design of biomarker-discovery studies. We provide a power-calculation method which reveals that sample sizes of a few thousand should offer sufficient statistical precision to detect 1H NMR-based biomarkers quantifying predisposition to disease.
doi:10.1038/msb.2011.57
PMCID: PMC3202796  PMID: 21878913
biomarker; 1H nuclear magnetic resonance spectroscopy; metabolome-wide association study; top-down systems biology; variance decomposition
25.  Common variants near ATM are associated with glycemic response to metformin in type 2 diabetes 
Nature genetics  2010;43(2):117-120.
Metformin is the most commonly used pharmacological therapy for type 2 diabetes. We carried out a GWA study on glycaemic response to metformin in 1024 Scottish patients with type 2 diabetes. Replication was in two cohorts consisting of 1783 Scottish patients and 1113 patients from the UK Prospective Diabetes Study. In a meta-analysis (n=3920) we observed an association (P=2.9 *10−9) for a SNP rs11212617 at a locus containing the ataxia telangiectasia mutated (ATM) gene with an odds ratio of 1.35 (95% CI 1.22 to 1.49) for treatment success. In a rat hepatoma cell line, inhibition of ATM with KU-55933 attenuated the phosphorylation and activation of AMPK in response to metformin. We conclude that ATM, a gene known to be involved in DNA repair and cell cycle control, plays a role in the effect of metformin upstream of AMPK, and variation in this gene alters glycaemic response to metformin.
doi:10.1038/ng.735
PMCID: PMC3030919  PMID: 21186350

Results 1-25 (56)