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1.  Genetic Predisposition to an Impaired Metabolism of the Branched-Chain Amino Acids and Risk of Type 2 Diabetes: A Mendelian Randomisation Analysis 
PLoS Medicine  2016;13(11):e1002179.
Background
Higher circulating levels of the branched-chain amino acids (BCAAs; i.e., isoleucine, leucine, and valine) are strongly associated with higher type 2 diabetes risk, but it is not known whether this association is causal. We undertook large-scale human genetic analyses to address this question.
Methods and Findings
Genome-wide studies of BCAA levels in 16,596 individuals revealed five genomic regions associated at genome-wide levels of significance (p < 5 × 10−8). The strongest signal was 21 kb upstream of the PPM1K gene (beta in standard deviations [SDs] of leucine per allele = 0.08, p = 3.9 × 10−25), encoding an activator of the mitochondrial branched-chain alpha-ketoacid dehydrogenase (BCKD) responsible for the rate-limiting step in BCAA catabolism. In another analysis, in up to 47,877 cases of type 2 diabetes and 267,694 controls, a genetically predicted difference of 1 SD in amino acid level was associated with an odds ratio for type 2 diabetes of 1.44 (95% CI 1.26–1.65, p = 9.5 × 10−8) for isoleucine, 1.85 (95% CI 1.41–2.42, p = 7.3 × 10−6) for leucine, and 1.54 (95% CI 1.28–1.84, p = 4.2 × 10−6) for valine. Estimates were highly consistent with those from prospective observational studies of the association between BCAA levels and incident type 2 diabetes in a meta-analysis of 1,992 cases and 4,319 non-cases. Metabolome-wide association analyses of BCAA-raising alleles revealed high specificity to the BCAA pathway and an accumulation of metabolites upstream of branched-chain alpha-ketoacid oxidation, consistent with reduced BCKD activity. Limitations of this study are that, while the association of genetic variants appeared highly specific, the possibility of pleiotropic associations cannot be entirely excluded. Similar to other complex phenotypes, genetic scores used in the study captured a limited proportion of the heritability in BCAA levels. Therefore, it is possible that only some of the mechanisms that increase BCAA levels or affect BCAA metabolism are implicated in type 2 diabetes.
Conclusions
Evidence from this large-scale human genetic and metabolomic study is consistent with a causal role of BCAA metabolism in the aetiology of type 2 diabetes.
Claudia Langenberg and colleagues show that high circulating branched chain amino acids associate with future risk of type 2 diabetes.
Author Summary
Why Was This Study Done?
Higher circulating levels of isoleucine, leucine, and valine, i.e., the branched-chain amino acids (BCAAs), are strongly associated with the risk of future type 2 diabetes.
It is not known if this association reflects a causal relationship.
It is important to assess the aetiologic nature of this relationship. If it is causal, then intervening on BCAA levels or metabolism may reduce the risk of diabetes.
What Did the Researchers Do and Find?
We used a human genetics framework known as “Mendelian randomisation” to study this question. Mendelian randomisation postulates that if a biomarker is causally implicated in a disease, then genetic variants specifically associated with that biomarker should also be associated with the disease.
In a meta-analysis of 1,992 incident cases of type 2 diabetes and 4,319 non-cases, we found strong associations between higher levels of each of the BCAAs and a higher risk of type 2 diabetes.
In a genome-wide meta-analysis of 16,596 individuals, we identified five genomic regions where common genetic variants were associated with BCAA levels.
In a meta-analysis of genetic association studies including 47,877 cases of type 2 diabetes and 267,694 controls, a genetically predicted difference of one standard deviation in amino acid level was associated with an odds ratio of type 2 diabetes of 1.44 (95% confidence interval 1.26–1.65) for isoleucine, 1.85 (1.41–2.42) for leucine, and 1.54 (1.28–1.84) for valine.
What Do These Findings Mean?
Evidence from this large-scale human genetic and metabolomic study is consistent with a causal role of BCAA metabolism in the aetiology of type 2 diabetes.
Possible limitations of the study included the possibility of non-specific associations of the genetic variants included in the study (i.e., “pleiotropy”) and the relatively low proportion of heritability in BCAA levels explained by the identified genetic variants.
doi:10.1371/journal.pmed.1002179
PMCID: PMC5127513  PMID: 27898682
2.  A principal component meta-analysis on multiple anthropometric traits identifies novel loci for body shape 
Ried, Janina S. | Jeff M., Janina | Chu, Audrey Y. | Bragg-Gresham, Jennifer L. | van Dongen, Jenny | Huffman, Jennifer E. | Ahluwalia, Tarunveer S. | Cadby, Gemma | Eklund, Niina | Eriksson, Joel | Esko, Tõnu | Feitosa, Mary F. | Goel, Anuj | Gorski, Mathias | Hayward, Caroline | Heard-Costa, Nancy L. | Jackson, Anne U. | Jokinen, Eero | Kanoni, Stavroula | Kristiansson, Kati | Kutalik, Zoltán | Lahti, Jari | Luan, Jian'an | Mägi, Reedik | Mahajan, Anubha | Mangino, Massimo | Medina-Gomez, Carolina | Monda, Keri L. | Nolte, Ilja M. | Pérusse, Louis | Prokopenko, Inga | Qi, Lu | Rose, Lynda M. | Salvi, Erika | Smith, Megan T. | Snieder, Harold | Stančáková, Alena | Ju Sung, Yun | Tachmazidou, Ioanna | Teumer, Alexander | Thorleifsson, Gudmar | van der Harst, Pim | Walker, Ryan W. | Wang, Sophie R. | Wild, Sarah H. | Willems, Sara M. | Wong, Andrew | Zhang, Weihua | Albrecht, Eva | Couto Alves, Alexessander | Bakker, Stephan J. L. | Barlassina, Cristina | Bartz, Traci M. | Beilby, John | Bellis, Claire | Bergman, Richard N. | Bergmann, Sven | Blangero, John | Blüher, Matthias | Boerwinkle, Eric | Bonnycastle, Lori L. | Bornstein, Stefan R. | Bruinenberg, Marcel | Campbell, Harry | Chen, Yii-Der Ida | Chiang, Charleston W. K. | Chines, Peter S. | Collins, Francis S | Cucca, Fracensco | Cupples, L Adrienne | D'Avila, Francesca | de Geus, Eco J .C. | Dedoussis, George | Dimitriou, Maria | Döring, Angela | Eriksson, Johan G. | Farmaki, Aliki-Eleni | Farrall, Martin | Ferreira, Teresa | Fischer, Krista | Forouhi, Nita G. | Friedrich, Nele | Gjesing, Anette Prior | Glorioso, Nicola | Graff, Mariaelisa | Grallert, Harald | Grarup, Niels | Gräßler, Jürgen | Grewal, Jagvir | Hamsten, Anders | Harder, Marie Neergaard | Hartman, Catharina A. | Hassinen, Maija | Hastie, Nicholas | Hattersley, Andrew Tym | Havulinna, Aki S. | Heliövaara, Markku | Hillege, Hans | Hofman, Albert | Holmen, Oddgeir | Homuth, Georg | Hottenga, Jouke-Jan | Hui, Jennie | Husemoen, Lise Lotte | Hysi, Pirro G. | Isaacs, Aaron | Ittermann, Till | Jalilzadeh, Shapour | James, Alan L. | Jørgensen, Torben | Jousilahti, Pekka | Jula, Antti | Marie Justesen, Johanne | Justice, Anne E. | Kähönen, Mika | Karaleftheri, Maria | Tee Khaw, Kay | Keinanen-Kiukaanniemi, Sirkka M. | Kinnunen, Leena | Knekt, Paul B. | Koistinen, Heikki A. | Kolcic, Ivana | Kooner, Ishminder K. | Koskinen, Seppo | Kovacs, Peter | Kyriakou, Theodosios | Laitinen, Tomi | Langenberg, Claudia | Lewin, Alexandra M. | Lichtner, Peter | Lindgren, Cecilia M. | Lindström, Jaana | Linneberg, Allan | Lorbeer, Roberto | Lorentzon, Mattias | Luben, Robert | Lyssenko, Valeriya | Männistö, Satu | Manunta, Paolo | Leach, Irene Mateo | McArdle, Wendy L. | Mcknight, Barbara | Mohlke, Karen L. | Mihailov, Evelin | Milani, Lili | Mills, Rebecca | Montasser, May E. | Morris, Andrew P. | Müller, Gabriele | Musk, Arthur W. | Narisu, Narisu | Ong, Ken K. | Oostra, Ben A. | Osmond, Clive | Palotie, Aarno | Pankow, James S. | Paternoster, Lavinia | Penninx, Brenda W. | Pichler, Irene | Pilia, Maria G. | Polašek, Ozren | Pramstaller, Peter P. | Raitakari, Olli T | Rankinen, Tuomo | Rao, D. C. | Rayner, Nigel W. | Ribel-Madsen, Rasmus | Rice, Treva K. | Richards, Marcus | Ridker, Paul M. | Rivadeneira, Fernando | Ryan, Kathy A. | Sanna, Serena | Sarzynski, Mark A. | Scholtens, Salome | Scott, Robert A. | Sebert, Sylvain | Southam, Lorraine | Sparsø, Thomas Hempel | Steinthorsdottir, Valgerdur | Stirrups, Kathleen | Stolk, Ronald P. | Strauch, Konstantin | Stringham, Heather M. | Swertz, Morris A. | Swift, Amy J. | Tönjes, Anke | Tsafantakis, Emmanouil | van der Most, Peter J. | Van Vliet-Ostaptchouk, Jana V. | Vandenput, Liesbeth | Vartiainen, Erkki | Venturini, Cristina | Verweij, Niek | Viikari, Jorma S. | Vitart, Veronique | Vohl, Marie-Claude | Vonk, Judith M. | Waeber, Gérard | Widén, Elisabeth | Willemsen, Gonneke | Wilsgaard, Tom | Winkler, Thomas W. | Wright, Alan F. | Yerges-Armstrong, Laura M. | Hua Zhao, Jing | Carola Zillikens, M. | Boomsma, Dorret I. | Bouchard, Claude | Chambers, John C. | Chasman, Daniel I. | Cusi, Daniele | Gansevoort, Ron T. | Gieger, Christian | Hansen, Torben | Hicks, Andrew A. | Hu, Frank | Hveem, Kristian | Jarvelin, Marjo-Riitta | Kajantie, Eero | Kooner, Jaspal S. | Kuh, Diana | Kuusisto, Johanna | Laakso, Markku | Lakka, Timo A. | Lehtimäki, Terho | Metspalu, Andres | Njølstad, Inger | Ohlsson, Claes | Oldehinkel, Albertine J. | Palmer, Lyle J. | Pedersen, Oluf | Perola, Markus | Peters, Annette | Psaty, Bruce M. | Puolijoki, Hannu | Rauramaa, Rainer | Rudan, Igor | Salomaa, Veikko | Schwarz, Peter E. H. | Shudiner, Alan R. | Smit, Jan H. | Sørensen, Thorkild I. A. | Spector, Timothy D. | Stefansson, Kari | Stumvoll, Michael | Tremblay, Angelo | Tuomilehto, Jaakko | Uitterlinden, André G. | Uusitupa, Matti | Völker, Uwe | Vollenweider, Peter | Wareham, Nicholas J. | Watkins, Hugh | Wilson, James F. | Zeggini, Eleftheria | Abecasis, Goncalo R. | Boehnke, Michael | Borecki, Ingrid B. | Deloukas, Panos | van Duijn, Cornelia M. | Fox, Caroline | Groop, Leif C. | Heid, Iris M. | Hunter, David J. | Kaplan, Robert C. | McCarthy, Mark I. | North, Kari E. | O'Connell, Jeffrey R. | Schlessinger, David | Thorsteinsdottir, Unnur | Strachan, David P. | Frayling, Timothy | Hirschhorn, Joel N. | Müller-Nurasyid, Martina | Loos, Ruth J. F.
Nature Communications  2016;7:13357.
Large consortia have revealed hundreds of genetic loci associated with anthropometric traits, one trait at a time. We examined whether genetic variants affect body shape as a composite phenotype that is represented by a combination of anthropometric traits. We developed an approach that calculates averaged PCs (AvPCs) representing body shape derived from six anthropometric traits (body mass index, height, weight, waist and hip circumference, waist-to-hip ratio). The first four AvPCs explain >99% of the variability, are heritable, and associate with cardiometabolic outcomes. We performed genome-wide association analyses for each body shape composite phenotype across 65 studies and meta-analysed summary statistics. We identify six novel loci: LEMD2 and CD47 for AvPC1, RPS6KA5/C14orf159 and GANAB for AvPC3, and ARL15 and ANP32 for AvPC4. Our findings highlight the value of using multiple traits to define complex phenotypes for discovery, which are not captured by single-trait analyses, and may shed light onto new pathways.
Past genome-wide associate studies have identified hundreds of genetic loci that influence body size and shape when examined one trait at a time. Here, Jeff and colleagues develop an aggregate score of various body traits, and use meta-analysis to find new loci linked to body shape.
doi:10.1038/ncomms13357
PMCID: PMC5114527  PMID: 27876822
3.  The common p.R114W HNF4A mutation causes a distinct clinical subtype of monogenic diabetes 
Diabetes  2016;65(10):3212-3217.
HNF4A mutations cause increased birth weight, transient neonatal hypoglycaemia and maturity onset diabetes of the young (MODY). The most frequently reported HNF4A mutation is p.R114W (previously p.R127W) but functional studies have shown inconsistent results, there is lack of co-segregation in some pedigrees and an unexpectedly high frequency in public variant databases. We confirm that p.R114W is a pathogenic mutation with an odds ratio of 30.4 (95% CI: 9.79 – 125, P=2x10-21) for diabetes in our MODY cohort compared to controls. p.R114W heterozygotes do not have the increased birth weight of patients with other HNF4A mutations (3476g vs. 4147g, P=0.0004) and fewer patients responded to sulfonylurea treatment (48% vs. 73%, P=0.038). p.R114W has reduced penetrance; only 54% of heterozygotes developed diabetes by age 30 compared to 71% for other HNF4A mutations. We re-define p.R114W as a pathogenic mutation causing a distinct clinical subtype of HNF4A MODY with reduced penetrance, reduced sensitivity to sulfonylurea treatment and no effect on birth weight. This has implications for diabetes treatment, management of pregnancy and predictive testing of at-risk relatives. The increasing availability of large-scale sequence data is likely to reveal similar examples of rare, low-penetrance MODY mutations.
doi:10.2337/db16-0628
PMCID: PMC5035684  PMID: 27486234
4.  Genetic evidence for causal relationships between maternal obesity-related traits and birth weight 
JAMA  2016;315(11):1129-1140.
Structured abstract
Importance
Neonates born to overweight/obese women are larger and at higher risk of birth complications. Many maternal obesity-related traits are observationally associated with birth weight, but the causal nature of these associations is uncertain.
Objective
To test for genetic evidence of causal associations of maternal body mass index (BMI) and related traits with birth weight.
Design, Setting and Participants
We used Mendelian randomization to test whether maternal BMI and obesity-related traits are causally related to offspring birth weight. Mendelian randomization makes use of the fact that genotypes are randomly determined at conception and are thus not confounded by non-genetic factors. Data were analysed on 30,487 women from 18 studies. Participants were of European ancestry from population- or community-based studies located in Europe, North America or Australia and participating in the Early Growth Genetics (EGG) Consortium. Live, term, singleton offspring born between 1929 and 2013 were included. We tested associations between a genetic score of 30 BMI-associated single nucleotide polymorphisms (SNPs) and (i) maternal BMI and (ii) birth weight, to estimate the causal relationship between BMI and birth weight. Analyses were repeated for other obesity-related traits.
Exposures
Genetic scores for BMI, fasting glucose level, type 2 diabetes, systolic blood pressure (SBP), triglyceride level, HDL-cholesterol level, vitamin D status and adiponectin level.
Main Outcome(s) and Measure(s)
Offspring birth weight measured by trained study personnel (n=2 studies), from medical records (n= 10 studies) or from maternal report (n=6 studies).
Results
Among the 30,487 newborns the mean birth weight in the various cohorts ranged from 3325 g to 3679 g. The genetic score for BMI was associated with a 2g (95%CI: 0, 3g) higher offspring birth weight per maternal BMI-raising allele (P=0.008). The maternal genetic scores for fasting glucose and SBP were also associated with birth weight with effect sizes of 8g (95%CI: 6, 10g) per glucose-raising allele (P=7×10−14) and −4g (95%CI: −6, −2g) per SBP-raising allele (P=1×10−5), respectively. A 1 standard deviation (1 SD ≈ 4kg/m2) genetically higher maternal BMI was associated with a 55g (95% CI: 17, 93g) higher birth weight. A 1-SD genetically higher maternal fasting glucose (≈ 0.4mmol/L) or SBP (10mmHg) were associated with a 114g (95%CI: 80, 147g) higher or −208g (95% CI: −394, −21g) lower birth weight, respectively. For BMI and fasting glucose these genetic associations were consistent with the observational associations, but for SBP, the genetic and observational associations were in opposite directions.
Conclusions and Relevance
In this Mendelian randomization study of more than 30,000 women with singleton offspring from 18 studies, genetically elevated maternal BMI and blood glucose levels were potentially causally associated with higher offspring birth weight, whereas genetically elevated maternal systolic blood pressure was shown to be potentially causally related to lower birth weight. If replicated, these findings may have implications for counseling and managing pregnancies to avoid adverse weight-related birth outcomes.
doi:10.1001/jama.2016.1975
PMCID: PMC4811305  PMID: 26978208
5.  Variation in the glucose transporter gene SLC2A2 is associated with glycemic response to metformin 
Nature genetics  2016;48(9):1055-1059.
Metformin is the first-line antidiabetic drug with over 100 million users worldwide, yet its mechanism of action remains unclear1. Here the Metformin Genetics (MetGen) Consortium reports a three-stage genome-wide association study (GWAS), consisting of 13,123 participants of different ancestries. The C allele of rs8192675 in the intron of SLC2A2, which encodes the facilitated glucose transporter GLUT2, was associated with a 0.17% (p=6.6×10−14) greater metformin-induced in haemoglobin A1c (HbA1c) in 10,577 participants of European ancestry. rs8192675 is the top cis expression quantitative trait locus (cis-eQTL) for SLC2A2 in 1,226 human liver samples, suggesting a key role for hepatic GLUT2 in regulation of metformin action. Among obese individuals, C-allele homozygotes at rs8192675 had a 0.33% (3.6 mmol/mol) greater absolute HbA1c reduction than T-allele homozygotes. This was about half the effect seen with the addition of a DPP-4 inhibitor, and equated to a dose difference of 550mg of metformin, suggesting rs8192675 as a potential biomarker for stratified medicine.
doi:10.1038/ng.3632
PMCID: PMC5007158  PMID: 27500523
6.  Burden of Diabetes and First Evidence for the Utility of HbA1c for Diagnosis and Detection of Diabetes in Urban Black South Africans: The Durban Diabetes Study 
PLoS ONE  2016;11(8):e0161966.
Objective
Glycated haemoglobin (HbA1c) is recommended as an additional tool to glucose-based measures (fasting plasma glucose [FPG] and 2-hour plasma glucose [2PG] during oral glucose tolerance test [OGTT]) for the diagnosis of diabetes; however, its use in sub-Saharan African populations is not established. We assessed prevalence estimates and the diagnosis and detection of diabetes based on OGTT, FPG, and HbA1c in an urban black South African population.
Research Design and Methods
We conducted a population-based cross-sectional survey using multistage cluster sampling of adults aged ≥18 years in Durban (eThekwini municipality), KwaZulu-Natal. All participants had a 75-g OGTT and HbA1c measurements. Receiver operating characteristic (ROC) analysis was used to assess the overall diagnostic accuracy of HbA1c, using OGTT as the reference, and to determine optimal HbA1c cut-offs.
Results
Among 1190 participants (851 women, 92.6% response rate), the age-standardised prevalence of diabetes was 12.9% based on OGTT, 11.9% based on FPG, and 13.1% based on HbA1c. In participants without a previous history of diabetes (n = 1077), using OGTT as the reference, an HbA1c ≥48 mmol/mol (6.5%) detected diabetes with 70.3% sensitivity (95%CI 52.7–87.8) and 98.7% specificity (95%CI 97.9–99.4) (AUC 0.94 [95%CI 0.89–1.00]). Additional analyses suggested the optimal HbA1c cut-off for detection of diabetes in this population was 42 mmol/mol (6.0%) (sensitivity 89.2% [95%CI 78.6–99.8], specificity 92.0% [95%CI: 90.3–93.7]).
Conclusions
In an urban black South African population, we found a high prevalence of diabetes and provide the first evidence for the utility of HbA1c for the diagnosis and detection of diabetes in black Africans in sub-Saharan Africa.
doi:10.1371/journal.pone.0161966
PMCID: PMC4999239  PMID: 27560687
7.  Assessing allele-specific expression across multiple tissues from RNA-seq read data 
Bioinformatics  2015;31(15):2497-2504.
Motivation: RNA sequencing enables allele-specific expression (ASE) studies that complement standard genotype expression studies for common variants and, importantly, also allow measuring the regulatory impact of rare variants. The Genotype-Tissue Expression (GTEx) project is collecting RNA-seq data on multiple tissues of a same set of individuals and novel methods are required for the analysis of these data.
Results: We present a statistical method to compare different patterns of ASE across tissues and to classify genetic variants according to their impact on the tissue-wide expression profile. We focus on strong ASE effects that we are expecting to see for protein-truncating variants, but our method can also be adjusted for other types of ASE effects. We illustrate the method with a real data example on a tissue-wide expression profile of a variant causal for lipoid proteinosis, and with a simulation study to assess our method more generally.
Availability and implementation: http://www.well.ox.ac.uk/~rivas/mamba/. R-sources and data examples http://www.iki.fi/mpirinen/
Contact: matti.pirinen@helsinki.fi or rivas@well.ox.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btv074
PMCID: PMC4514921  PMID: 25819081
8.  Loss-of-Function Mutations in the Cell-Cycle Control Gene CDKN2A Impact on Glucose Homeostasis in Humans 
Diabetes  2015;65(2):527-533.
At the CDKN2A/B locus, three independent signals for type 2 diabetes risk are located in a non-coding region near CDKN2A. The disease-associated alleles have been implicated in reduced β-cell function, but the underlying mechanism remains elusive. In mice, β-cell specific loss of Cdkn2a causes hyperplasia whilst overexpression leads to diabetes, highlighting CDKN2A as a candidate effector transcript. Rare CDKN2A loss-of-function mutations are a cause of familial melanoma and offer the opportunity to determine the impact of CDKN2A haploinsufficiency on glucose homeostasis in humans. To test the hypothesis that such individuals have improved β-cell function, we performed oral and intravenous glucose tolerance tests on mutation carriers and matched controls. Compared with controls, carriers displayed increased insulin secretion, impaired insulin sensitivity and reduced hepatic insulin clearance. These results are consistent with a model whereby CDKN2A-loss affects a range of different tissues, including pancreatic β-cells and liver. To test for direct effects of CDKN2A-loss on β-cell function, we performed knockdown in a human β-cell line, EndoC-bH1. This revealed increased insulin secretion independent of proliferation. Overall, we demonstrate that CDKN2A is an important regulator of glucose homeostasis in humans, thus supporting its candidacy as an effector transcript for type 2 diabetes-associated alleles in the region.
doi:10.2337/db15-0602
PMCID: PMC4724950  PMID: 26542317
9.  Correction: The Influence of Age and Sex on Genetic Associations with Adult Body Size and Shape: A Large-Scale Genome-Wide Interaction Study 
Winkler, Thomas W. | Justice, Anne E. | Graff, Mariaelisa | Barata, Llilda | Feitosa, Mary F. | Chu, Su | Czajkowski, Jacek | Esko, Tõnu | Fall, Tove | Kilpeläinen, Tuomas O. | Lu, Yingchang | Mägi, Reedik | Mihailov, Evelin | Pers, Tune H. | Rüeger, Sina | Teumer, Alexander | Ehret, Georg B. | Ferreira, Teresa | Heard-Costa, Nancy L. | Karjalainen, Juha | Lagou, Vasiliki | Mahajan, Anubha | Neinast, Michael D. | Prokopenko, Inga | Simino, Jeannette | Teslovich, Tanya M. | Jansen, Rick | Westra, Harm-Jan | White, Charles C. | Absher, Devin | Ahluwalia, Tarunveer S. | Ahmad, Shafqat | Albrecht, Eva | Alves, Alexessander Couto | Bragg-Gresham, Jennifer L. | de Craen, Anton J. M. | Bis, Joshua C. | Bonnefond, Amélie | Boucher, Gabrielle | Cadby, Gemma | Cheng, Yu-Ching | Chiang, Charleston W. K. | Delgado, Graciela | Demirkan, Ayse | Dueker, Nicole | Eklund, Niina | Eiriksdottir, Gudny | Eriksson, Joel | Feenstra, Bjarke | Fischer, Krista | Frau, Francesca | Galesloot, Tessel E. | Geller, Frank | Goel, Anuj | Gorski, Mathias | Grammer, Tanja B. | Gustafsson, Stefan | Haitjema, Saskia | Hottenga, Jouke-Jan | Huffman, Jennifer E. | Jackson, Anne U. | Jacobs, Kevin B. | Johansson, Åsa | Kaakinen, Marika | Kleber, Marcus E. | Lahti, Jari | Mateo Leach, Irene | Lehne, Benjamin | Liu, Youfang | Lo, Ken Sin | Lorentzon, Mattias | Luan, Jian'an | Madden, Pamela A. F. | Mangino, Massimo | McKnight, Barbara | Medina-Gomez, Carolina | Monda, Keri L. | Montasser, May E. | Müller, Gabriele | Müller-Nurasyid, Martina | Nolte, Ilja M. | Panoutsopoulou, Kalliope | Pascoe, Laura | Paternoster, Lavinia | Rayner, Nigel W. | Renström, Frida | Rizzi, Federica | Rose, Lynda M. | Ryan, Kathy A. | Salo, Perttu | Sanna, Serena | Scharnagl, Hubert | Shi, Jianxin | Smith, Albert Vernon | Southam, Lorraine | Stančáková, Alena | Steinthorsdottir, Valgerdur | Strawbridge, Rona J. | Sung, Yun Ju | Tachmazidou, Ioanna | Tanaka, Toshiko | Thorleifsson, Gudmar | Trompet, Stella | Pervjakova, Natalia | Tyrer, Jonathan P. | Vandenput, Liesbeth | van der Laan, Sander W | van der Velde, Nathalie | van Setten, Jessica | van Vliet-Ostaptchouk, Jana V. | Verweij, Niek | Vlachopoulou, Efthymia | Waite, Lindsay L. | Wang, Sophie R. | Wang, Zhaoming | Wild, Sarah H. | Willenborg, Christina | Wilson, James F. | Wong, Andrew | Yang, Jian | Yengo, Loïc | Yerges-Armstrong, Laura M. | Yu, Lei | Zhang, Weihua | Zhao, Jing Hua | Andersson, Ehm A. | Bakker, Stephan J. L. | Baldassarre, Damiano | Banasik, Karina | Barcella, Matteo | Barlassina, Cristina | Bellis, Claire | Benaglio, Paola | Blangero, John | Blüher, Matthias | Bonnet, Fabrice | Bonnycastle, Lori L. | Boyd, Heather A. | Bruinenberg, Marcel | Buchman, Aron S | Campbell, Harry | Chen, Yii-Der Ida | Chines, Peter S. | Claudi-Boehm, Simone | Cole, John | Collins, Francis S. | de Geus, Eco J. C. | de Groot, Lisette C. P. G. M. | Dimitriou, Maria | Duan, Jubao | Enroth, Stefan | Eury, Elodie | Farmaki, Aliki-Eleni | Forouhi, Nita G. | Friedrich, Nele | Gejman, Pablo V. | Gigante, Bruna | Glorioso, Nicola | Go, Alan S. | Gottesman, Omri | Gräßler, Jürgen | Grallert, Harald | Grarup, Niels | Gu, Yu-Mei | Broer, Linda | Ham, Annelies C. | Hansen, Torben | Harris, Tamara B. | Hartman, Catharina A. | Hassinen, Maija | Hastie, Nicholas | Hattersley, Andrew T. | Heath, Andrew C. | Henders, Anjali K. | Hernandez, Dena | Hillege, Hans | Holmen, Oddgeir | Hovingh, Kees G | Hui, Jennie | Husemoen, Lise L. | Hutri-Kähönen, Nina | Hysi, Pirro G. | Illig, Thomas | De Jager, Philip L. | Jalilzadeh, Shapour | Jørgensen, Torben | Jukema, J. Wouter | Juonala, Markus | Kanoni, Stavroula | Karaleftheri, Maria | Khaw, Kay Tee | Kinnunen, Leena | Kittner, Steven J. | Koenig, Wolfgang | Kolcic, Ivana | Kovacs, Peter | Krarup, Nikolaj T. | Kratzer, Wolfgang | Krüger, Janine | Kuh, Diana | Kumari, Meena | Kyriakou, Theodosios | Langenberg, Claudia | Lannfelt, Lars | Lanzani, Chiara | Lotay, Vaneet | Launer, Lenore J. | Leander, Karin | Lindström, Jaana | Linneberg, Allan | Liu, Yan-Ping | Lobbens, Stéphane | Luben, Robert | Lyssenko, Valeriya | Männistö, Satu | Magnusson, Patrik K. | McArdle, Wendy L. | Menni, Cristina | Merger, Sigrun | Milani, Lili | Montgomery, Grant W. | Morris, Andrew P. | Narisu, Narisu | Nelis, Mari | Ong, Ken K. | Palotie, Aarno | Pérusse, Louis | Pichler, Irene | Pilia, Maria G. | Pouta, Anneli | Rheinberger, Myriam | Ribel-Madsen, Rasmus | Richards, Marcus | Rice, Kenneth M. | Rice, Treva K. | Rivolta, Carlo | Salomaa, Veikko | Sanders, Alan R. | Sarzynski, Mark A. | Scholtens, Salome | Scott, Robert A. | Scott, William R. | Sebert, Sylvain | Sengupta, Sebanti | Sennblad, Bengt | Seufferlein, Thomas | Silveira, Angela | Slagboom, P. Eline | Smit, Jan H. | Sparsø, Thomas H. | Stirrups, Kathleen | Stolk, Ronald P. | Stringham, Heather M. | Swertz, Morris A | Swift, Amy J. | Syvänen, Ann-Christine | Tan, Sian-Tsung | Thorand, Barbara | Tönjes, Anke | Tremblay, Angelo | Tsafantakis, Emmanouil | van der Most, Peter J. | Völker, Uwe | Vohl, Marie-Claude | Vonk, Judith M. | Waldenberger, Melanie | Walker, Ryan W. | Wennauer, Roman | Widén, Elisabeth | Willemsen, Gonneke | Wilsgaard, Tom | Wright, Alan F. | Zillikens, M. Carola | van Dijk, Suzanne C. | van Schoor, Natasja M. | Asselbergs, Folkert W. | de Bakker, Paul I. W. | Beckmann, Jacques S. | Beilby, John | Bennett, David A. | Bergman, Richard N. | Bergmann, Sven | Böger, Carsten A. | Boehm, Bernhard O. | Boerwinkle, Eric | Boomsma, Dorret I. | Bornstein, Stefan R. | Bottinger, Erwin P. | Bouchard, Claude | Chambers, John C. | Chanock, Stephen J. | Chasman, Daniel I. | Cucca, Francesco | Cusi, Daniele | Dedoussis, George | Erdmann, Jeanette | Eriksson, Johan G. | Evans, Denis A. | de Faire, Ulf | Farrall, Martin | Ferrucci, Luigi | Ford, Ian | Franke, Lude | Franks, Paul W. | Froguel, Philippe | Gansevoort, Ron T. | Gieger, Christian | Grönberg, Henrik | Gudnason, Vilmundur | Gyllensten, Ulf | Hall, Per | Hamsten, Anders | van der Harst, Pim | Hayward, Caroline | Heliövaara, Markku | Hengstenberg, Christian | Hicks, Andrew A | Hingorani, Aroon | Hofman, Albert | Hu, Frank | Huikuri, Heikki V. | Hveem, Kristian | James, Alan L. | Jordan, Joanne M. | Jula, Antti | Kähönen, Mika | Kajantie, Eero | Kathiresan, Sekar | Kiemeney, Lambertus A. L. M. | Kivimaki, Mika | Knekt, Paul B. | Koistinen, Heikki A. | Kooner, Jaspal S. | Koskinen, Seppo | Kuusisto, Johanna | Maerz, Winfried | Martin, Nicholas G | Laakso, Markku | Lakka, Timo A. | Lehtimäki, Terho | Lettre, Guillaume | Levinson, Douglas F. | Lind, Lars | Lokki, Marja-Liisa | Mäntyselkä, Pekka | Melbye, Mads | Metspalu, Andres | Mitchell, Braxton D. | Moll, Frans L. | Murray, Jeffrey C. | Musk, Arthur W. | Nieminen, Markku S. | Njølstad, Inger | Ohlsson, Claes | Oldehinkel, Albertine J. | Oostra, Ben A. | Palmer, Lyle J | Pankow, James S. | Pasterkamp, Gerard | Pedersen, Nancy L. | Pedersen, Oluf | Penninx, Brenda W. | Perola, Markus | Peters, Annette | Polašek, Ozren | Pramstaller, Peter P. | Psaty, Bruce M. | Qi, Lu | Quertermous, Thomas | Raitakari, Olli T. | Rankinen, Tuomo | Rauramaa, Rainer | Ridker, Paul M. | Rioux, John D. | Rivadeneira, Fernando | Rotter, Jerome I. | Rudan, Igor | den Ruijter, Hester M. | Saltevo, Juha | Sattar, Naveed | Schunkert, Heribert | Schwarz, Peter E. H. | Shuldiner, Alan R. | Sinisalo, Juha | Snieder, Harold | Sørensen, Thorkild I. A. | Spector, Tim D. | Staessen, Jan A. | Stefania, Bandinelli | Thorsteinsdottir, Unnur | Stumvoll, Michael | Tardif, Jean-Claude | Tremoli, Elena | Tuomilehto, Jaakko | Uitterlinden, André G. | Uusitupa, Matti | Verbeek, André L. M. | Vermeulen, Sita H. | Viikari, Jorma S. | Vitart, Veronique | Völzke, Henry | Vollenweider, Peter | Waeber, Gérard | Walker, Mark | Wallaschofski, Henri | Wareham, Nicholas J. | Watkins, Hugh | Zeggini, Eleftheria | Chakravarti, Aravinda | Clegg, Deborah J. | Cupples, L. Adrienne | Gordon-Larsen, Penny | Jaquish, Cashell E. | Rao, D. C. | Abecasis, Goncalo R. | Assimes, Themistocles L. | Barroso, Inês | Berndt, Sonja I. | Boehnke, Michael | Deloukas, Panos | Fox, Caroline S. | Groop, Leif C. | Hunter, David J. | Ingelsson, Erik | Kaplan, Robert C. | McCarthy, Mark I. | Mohlke, Karen L. | O'Connell, Jeffrey R. | Schlessinger, David | Strachan, David P. | Stefansson, Kari | van Duijn, Cornelia M. | Hirschhorn, Joel N. | Lindgren, Cecilia M. | Heid, Iris M. | North, Kari E. | Borecki, Ingrid B. | Kutalik, Zoltán | Loos, Ruth J. F.
PLoS Genetics  2016;12(6):e1006166.
doi:10.1371/journal.pgen.1006166
PMCID: PMC4927064  PMID: 27355579
10.  Contribution of common non-synonymous variants in PCSK1 to body mass index variation and risk of obesity: a systematic review and meta-analysis with evidence from up to 331 175 individuals 
Human Molecular Genetics  2015;24(12):3582-3594.
Polymorphisms rs6232 and rs6234/rs6235 in PCSK1 have been associated with extreme obesity [e.g. body mass index (BMI) ≥ 40 kg/m2], but their contribution to common obesity (BMI ≥ 30 kg/m2) and BMI variation in a multi-ethnic context is unclear. To fill this gap, we collected phenotypic and genetic data in up to 331 175 individuals from diverse ethnic groups. This process involved a systematic review of the literature in PubMed, Web of Science, Embase and the NIH GWAS catalog complemented by data extraction from pre-existing GWAS or custom-arrays in consortia and single studies. We employed recently developed global meta-analytic random-effects methods to calculate summary odds ratios (OR) and 95% confidence intervals (CIs) or beta estimates and standard errors (SE) for the obesity status and BMI analyses, respectively. Significant associations were found with binary obesity status for rs6232 (OR = 1.15, 95% CI 1.06–1.24, P = 6.08 × 10−6) and rs6234/rs6235 (OR = 1.07, 95% CI 1.04–1.10, P = 3.00 × 10−7). Similarly, significant associations were found with continuous BMI for rs6232 (β = 0.03, 95% CI 0.00–0.07; P = 0.047) and rs6234/rs6235 (β = 0.02, 95% CI 0.00–0.03; P = 5.57 × 10−4). Ethnicity, age and study ascertainment significantly modulated the association of PCSK1 polymorphisms with obesity. In summary, we demonstrate evidence that common gene variation in PCSK1 contributes to BMI variation and susceptibility to common obesity in the largest known meta-analysis published to date in genetic epidemiology.
doi:10.1093/hmg/ddv097
PMCID: PMC4498155  PMID: 25784503
11.  Insights into islet development and biology through characterization of a human iPSC-derived endocrine pancreas model 
Islets  2016;8(3):83-95.
ABSTRACT
Directed differentiation of stem cells offers a scalable solution to the need for human cell models recapitulating islet biology and T2D pathogenesis. We profiled mRNA expression at 6 stages of an induced pluripotent stem cell (iPSC) model of endocrine pancreas development from 2 donors, and characterized the distinct transcriptomic profiles associated with each stage. Established regulators of endodermal lineage commitment, such as SOX17 (log2 fold change [FC] compared to iPSCs = 14.2, p-value = 4.9 × 10−5) and the pancreatic agenesis gene GATA6 (log2 FC = 12.1, p-value = 8.6 × 10−5), showed transcriptional variation consistent with their known developmental roles. However, these analyses highlighted many other genes with stage-specific expression patterns, some of which may be novel drivers or markers of islet development. For example, the leptin receptor gene, LEPR, was most highly expressed in published data from in vivo-matured cells compared to our endocrine pancreas-like cells (log2 FC = 5.5, p-value = 2.0 × 10−12), suggesting a role for the leptin pathway in the maturation process. Endocrine pancreas-like cells showed significant stage-selective expression of adult islet genes, including INS, ABCC8, and GLP1R, and enrichment of relevant GO-terms (e.g. “insulin secretion”; odds ratio = 4.2, p-value = 1.9 × 10−3): however, principal component analysis indicated that in vitro-differentiated cells were more immature than adult islets. Integration of the stage-specific expression information with genetic data from T2D genome-wide association studies revealed that 46 of 82 T2D-associated loci harbor genes present in at least one developmental stage, facilitating refinement of potential effector transcripts. Together, these data show that expression profiling in an iPSC islet development model can further understanding of islet biology and T2D pathogenesis.
doi:10.1080/19382014.2016.1182276
PMCID: PMC4987020  PMID: 27246810
diabetes; differentiation; endocrine pancreas; pluripotent stem cells; transcriptional profiling
12.  Genetic fine-mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci 
Gaulton, Kyle J | Ferreira, Teresa | Lee, Yeji | Raimondo, Anne | Mägi, Reedik | Reschen, Michael E | Mahajan, Anubha | Locke, Adam | Rayner, N William | Robertson, Neil | Scott, Robert A | Prokopenko, Inga | Scott, Laura J | Green, Todd | Sparso, Thomas | Thuillier, Dorothee | Yengo, Loic | Grallert, Harald | Wahl, Simone | Frånberg, Mattias | Strawbridge, Rona J | Kestler, Hans | Chheda, Himanshu | Eisele, Lewin | Gustafsson, Stefan | Steinthorsdottir, Valgerdur | Thorleifsson, Gudmar | Qi, Lu | Karssen, Lennart C | van Leeuwen, Elisabeth M | Willems, Sara M | Li, Man | Chen, Han | Fuchsberger, Christian | Kwan, Phoenix | Ma, Clement | Linderman, Michael | Lu, Yingchang | Thomsen, Soren K | Rundle, Jana K | Beer, Nicola L | van de Bunt, Martijn | Chalisey, Anil | Kang, Hyun Min | Voight, Benjamin F | Abecasis, Goncalo R | Almgren, Peter | Baldassarre, Damiano | Balkau, Beverley | Benediktsson, Rafn | Blüher, Matthias | Boeing, Heiner | Bonnycastle, Lori L | Borringer, Erwin P | Burtt, Noël P | Carey, Jason | Charpentier, Guillaume | Chines, Peter S | Cornelis, Marilyn C | Couper, David J | Crenshaw, Andrew T | van Dam, Rob M | Doney, Alex SF | Dorkhan, Mozhgan | Edkins, Sarah | Eriksson, Johan G | Esko, Tonu | Eury, Elodie | Fadista, João | Flannick, Jason | Fontanillas, Pierre | Fox, Caroline | Franks, Paul W | Gertow, Karl | Gieger, Christian | Gigante, Bruna | Gottesman, Omri | Grant, George B | Grarup, Niels | Groves, Christopher J | Hassinen, Maija | Have, Christian T | Herder, Christian | Holmen, Oddgeir L | Hreidarsson, Astradur B | Humphries, Steve E | Hunter, David J | Jackson, Anne U | Jonsson, Anna | Jørgensen, Marit E | Jørgensen, Torben | Kao, Wen-Hong L | Kerrison, Nicola D | Kinnunen, Leena | Klopp, Norman | Kong, Augustine | Kovacs, Peter | Kraft, Peter | Kravic, Jasmina | Langford, Cordelia | Leander, Karin | Liang, Liming | Lichtner, Peter | Lindgren, Cecilia M | Lindholm, Eero | Linneberg, Allan | Liu, Ching-Ti | Lobbens, Stéphane | Luan, Jian’an | Lyssenko, Valeriya | Mӓnnistö, Satu | McLeod, Olga | Meyer, Julia | Mihailov, Evelin | Mirza, Ghazala | Mühleisen, Thomas W | Müller-Nurasyid, Martina | Navarro, Carmen | Nöthen, Markus M | Oskolkov, Nikolay N | Owen, Katharine R | Palli, Domenico | Pechlivanis, Sonali | Peltonen, Leena | Perry, John RB | Platou, Carl GP | Roden, Michael | Ruderfer, Douglas | Rybin, Denis | van der Schouw, Yvonne T | Sennblad, Bengt | Sigurđsson, Gunnar | Stančáková, Alena | Steinbach, Gerald | Storm, Petter | Strauch, Konstantin | Stringham, Heather M | Sun, Qi | Thorand, Barbara | Tikkanen, Emmi | Tonjes, Anke | Trakalo, Joseph | Tremoli, Elena | Tuomi, Tiinamaija | Wennauer, Roman | Wiltshire, Steven | Wood, Andrew R | Zeggini, Eleftheria | Dunham, Ian | Birney, Ewan | Pasquali, Lorenzo | Ferrer, Jorge | Loos, Ruth JF | Dupuis, Josée | Florez, Jose C | Boerwinkle, Eric | Pankow, James S | van Duijn, Cornelia | Sijbrands, Eric | Meigs, James B | Hu, Frank B | Thorsteinsdottir, Unnur | Stefansson, Kari | Lakka, Timo A | Rauramaa, Rainer | Stumvoll, Michael | Pedersen, Nancy L | Lind, Lars | Keinanen-Kiukaanniemi, Sirkka M | Korpi-Hyövӓlti, Eeva | Saaristo, Timo E | Saltevo, Juha | Kuusisto, Johanna | Laakso, Markku | Metspalu, Andres | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Ripatti, Samuli | Salomaa, Veikko | Ingelsson, Erik | Boehm, Bernhard O | Bergman, Richard N | Collins, Francis S | Mohlke, Karen L | Koistinen, Heikki | Tuomilehto, Jaakko | Hveem, Kristian | Njølstad, Inger | Deloukas, Panagiotis | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | de Faire, Ulf | Hamsten, Anders | Illig, Thomas | Peters, Annette | Cauchi, Stephane | Sladek, Rob | Froguel, Philippe | Hansen, Torben | Pedersen, Oluf | Morris, Andrew D | Palmer, Collin NA | Kathiresan, Sekar | Melander, Olle | Nilsson, Peter M | Groop, Leif C | Barroso, Inês | Langenberg, Claudia | Wareham, Nicholas J | O’Callaghan, Christopher A | Gloyn, Anna L | Altshuler, David | Boehnke, Michael | Teslovich, Tanya M | McCarthy, Mark I | Morris, Andrew P
Nature genetics  2015;47(12):1415-1425.
We performed fine-mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in/near KCNQ1. “Credible sets” of variants most likely to drive each distinct signal mapped predominantly to non-coding sequence, implying that T2D association is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine-mapping implicated rs10830963 as driving T2D association. We confirmed that this T2D-risk allele increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D-risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease.
doi:10.1038/ng.3437
PMCID: PMC4666734  PMID: 26551672
13.  Genetic fine-mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci 
Gaulton, Kyle J | Ferreira, Teresa | Lee, Yeji | Raimondo, Anne | Mägi, Reedik | Reschen, Michael E | Mahajan, Anubha | Locke, Adam | Rayner, N William | Robertson, Neil | Scott, Robert A | Prokopenko, Inga | Scott, Laura J | Green, Todd | Sparso, Thomas | Thuillier, Dorothee | Yengo, Loic | Grallert, Harald | Wahl, Simone | Frånberg, Mattias | Strawbridge, Rona J | Kestler, Hans | Chheda, Himanshu | Eisele, Lewin | Gustafsson, Stefan | Steinthorsdottir, Valgerdur | Thorleifsson, Gudmar | Qi, Lu | Karssen, Lennart C | van Leeuwen, Elisabeth M | Willems, Sara M | Li, Man | Chen, Han | Fuchsberger, Christian | Kwan, Phoenix | Ma, Clement | Linderman, Michael | Lu, Yingchang | Thomsen, Soren K | Rundle, Jana K | Beer, Nicola L | van de Bunt, Martijn | Chalisey, Anil | Kang, Hyun Min | Voight, Benjamin F | Abecasis, Goncalo R | Almgren, Peter | Baldassarre, Damiano | Balkau, Beverley | Benediktsson, Rafn | Blüher, Matthias | Boeing, Heiner | Bonnycastle, Lori L | Borringer, Erwin P | Burtt, Noël P | Carey, Jason | Charpentier, Guillaume | Chines, Peter S | Cornelis, Marilyn C | Couper, David J | Crenshaw, Andrew T | van Dam, Rob M | Doney, Alex SF | Dorkhan, Mozhgan | Edkins, Sarah | Eriksson, Johan G | Esko, Tonu | Eury, Elodie | Fadista, João | Flannick, Jason | Fontanillas, Pierre | Fox, Caroline | Franks, Paul W | Gertow, Karl | Gieger, Christian | Gigante, Bruna | Gottesman, Omri | Grant, George B | Grarup, Niels | Groves, Christopher J | Hassinen, Maija | Have, Christian T | Herder, Christian | Holmen, Oddgeir L | Hreidarsson, Astradur B | Humphries, Steve E | Hunter, David J | Jackson, Anne U | Jonsson, Anna | Jørgensen, Marit E | Jørgensen, Torben | Kao, Wen-Hong L | Kerrison, Nicola D | Kinnunen, Leena | Klopp, Norman | Kong, Augustine | Kovacs, Peter | Kraft, Peter | Kravic, Jasmina | Langford, Cordelia | Leander, Karin | Liang, Liming | Lichtner, Peter | Lindgren, Cecilia M | Lindholm, Eero | Linneberg, Allan | Liu, Ching-Ti | Lobbens, Stéphane | Luan, Jian’an | Lyssenko, Valeriya | Männistö, Satu | McLeod, Olga | Meyer, Julia | Mihailov, Evelin | Mirza, Ghazala | Mühleisen, Thomas W | Müller-Nurasyid, Martina | Navarro, Carmen | Nöthen, Markus M | Oskolkov, Nikolay N | Owen, Katharine R | Palli, Domenico | Pechlivanis, Sonali | Peltonen, Leena | Perry, John RB | Platou, Carl GP | Roden, Michael | Ruderfer, Douglas | Rybin, Denis | van der Schouw, Yvonne T | Sennblad, Bengt | Sigurðsson, Gunnar | Stančáková, Alena | Steinbach, Gerald | Storm, Petter | Strauch, Konstantin | Stringham, Heather M | Sun, Qi | Thorand, Barbara | Tikkanen, Emmi | Tonjes, Anke | Trakalo, Joseph | Tremoli, Elena | Tuomi, Tiinamaija | Wennauer, Roman | Wiltshire, Steven | Wood, Andrew R | Zeggini, Eleftheria | Dunham, Ian | Birney, Ewan | Pasquali, Lorenzo | Ferrer, Jorge | Loos, Ruth JF | Dupuis, Josée | Florez, Jose C | Boerwinkle, Eric | Pankow, James S | van Duijn, Cornelia | Sijbrands, Eric | Meigs, James B | Hu, Frank B | Thorsteinsdottir, Unnur | Stefansson, Kari | Lakka, Timo A | Rauramaa, Rainer | Stumvoll, Michael | Pedersen, Nancy L | Lind, Lars | Keinanen-Kiukaanniemi, Sirkka M | Korpi-Hyövälti, Eeva | Saaristo, Timo E | Saltevo, Juha | Kuusisto, Johanna | Laakso, Markku | Metspalu, Andres | Erbel, Raimund | Jöckel, Karl-Heinz | Moebus, Susanne | Ripatti, Samuli | Salomaa, Veikko | Ingelsson, Erik | Boehm, Bernhard O | Bergman, Richard N | Collins, Francis S | Mohlke, Karen L | Koistinen, Heikki | Tuomilehto, Jaakko | Hveem, Kristian | Njølstad, Inger | Deloukas, Panagiotis | Donnelly, Peter J | Frayling, Timothy M | Hattersley, Andrew T | de Faire, Ulf | Hamsten, Anders | Illig, Thomas | Peters, Annette | Cauchi, Stephane | Sladek, Rob | Froguel, Philippe | Hansen, Torben | Pedersen, Oluf | Morris, Andrew D | Palmer, Collin NA | Kathiresan, Sekar | Melander, Olle | Nilsson, Peter M | Groop, Leif C | Barroso, Inês | Langenberg, Claudia | Wareham, Nicholas J | O’Callaghan, Christopher A | Gloyn, Anna L | Altshuler, David | Boehnke, Michael | Teslovich, Tanya M | McCarthy, Mark I | Morris, Andrew P
Nature genetics  2015;47(12):1415-1425.
We performed fine-mapping of 39 established type 2 diabetes (T2D) loci in 27,206 cases and 57,574 controls of European ancestry. We identified 49 distinct association signals at these loci, including five mapping in/near KCNQ1. “Credible sets” of variants most likely to drive each distinct signal mapped predominantly to non-coding sequence, implying that T2D association is mediated through gene regulation. Credible set variants were enriched for overlap with FOXA2 chromatin immunoprecipitation binding sites in human islet and liver cells, including at MTNR1B, where fine-mapping implicated rs10830963 as driving T2D association. We confirmed that this T2D-risk allele increases FOXA2-bound enhancer activity in islet- and liver-derived cells. We observed allele-specific differences in NEUROD1 binding in islet-derived cells, consistent with evidence that the T2D-risk allele increases islet MTNR1B expression. Our study demonstrates how integration of genetic and genomic information can define molecular mechanisms through which variants underlying association signals exert their effects on disease.
doi:10.1038/ng.3437
PMCID: PMC4666734  PMID: 26551672
14.  Impact of predicted protein-truncating genetic variants on the human transcriptome 
Science (New York, N.Y.)  2015;348(6235):666-669.
Accurate prediction of the functional impact of genetic variation is critical for clinical genome interpretation. We systematically characterized the transcriptome effects of protein-truncating variants (PTVs), a class of variants expected to have profound impacts on gene function, using data from the Genotype-Tissue Expression (GTEx) and Geuvadis projects. We quantitate tissue-specific and positional effects on nonsense-mediated transcript decay, and present an improved predictive model for this decay. We directly measure the impact of variants both proximal and distal to splice junctions. Furthermore, we find that robustness to heterozygous gene inactivation is not due to dosage compensation. Our results illustrate the value of transcriptome data in the functional interpretation of genetic variants.
doi:10.1126/science.1261877
PMCID: PMC4537935  PMID: 25954003
15.  Age- and Sex-Specific Causal Effects of Adiposity on Cardiovascular Risk Factors 
Fall, Tove | Hägg, Sara | Ploner, Alexander | Mägi, Reedik | Fischer, Krista | Draisma, Harmen H.M. | Sarin, Antti-Pekka | Benyamin, Beben | Ladenvall, Claes | Åkerlund, Mikael | Kals, Mart | Esko, Tõnu | Nelson, Christopher P. | Kaakinen, Marika | Huikari, Ville | Mangino, Massimo | Meirhaeghe, Aline | Kristiansson, Kati | Nuotio, Marja-Liisa | Kobl, Michael | Grallert, Harald | Dehghan, Abbas | Kuningas, Maris | de Vries, Paul S. | de Bruijn, Renée F.A.G. | Willems, Sara M. | Heikkilä, Kauko | Silventoinen, Karri | Pietiläinen, Kirsi H. | Legry, Vanessa | Giedraitis, Vilmantas | Goumidi, Louisa | Syvänen, Ann-Christine | Strauch, Konstantin | Koenig, Wolfgang | Lichtner, Peter | Herder, Christian | Palotie, Aarno | Menni, Cristina | Uitterlinden, André G. | Kuulasmaa, Kari | Havulinna, Aki S. | Moreno, Luis A. | Gonzalez-Gross, Marcela | Evans, Alun | Tregouet, David-Alexandre | Yarnell, John W.G. | Virtamo, Jarmo | Ferrières, Jean | Veronesi, Giovanni | Perola, Markus | Arveiler, Dominique | Brambilla, Paolo | Lind, Lars | Kaprio, Jaakko | Hofman, Albert | Stricker, Bruno H. | van Duijn, Cornelia M. | Ikram, M. Arfan | Franco, Oscar H. | Cottel, Dominique | Dallongeville, Jean | Hall, Alistair S. | Jula, Antti | Tobin, Martin D. | Penninx, Brenda W. | Peters, Annette | Gieger, Christian | Samani, Nilesh J. | Montgomery, Grant W. | Whitfield, John B. | Martin, Nicholas G. | Groop, Leif | Spector, Tim D. | Magnusson, Patrik K. | Amouyel, Philippe | Boomsma, Dorret I. | Nilsson, Peter M. | Järvelin, Marjo-Riitta | Lyssenko, Valeriya | Metspalu, Andres | Strachan, David P. | Salomaa, Veikko | Ripatti, Samuli | Pedersen, Nancy L. | Prokopenko, Inga | McCarthy, Mark I. | Ingelsson, Erik
Diabetes  2015;64(5):1841-1852.
Observational studies have reported different effects of adiposity on cardiovascular risk factors across age and sex. Since cardiovascular risk factors are enriched in obese individuals, it has not been easy to dissect the effects of adiposity from those of other risk factors. We used a Mendelian randomization approach, applying a set of 32 genetic markers to estimate the causal effect of adiposity on blood pressure, glycemic indices, circulating lipid levels, and markers of inflammation and liver disease in up to 67,553 individuals. All analyses were stratified by age (cutoff 55 years of age) and sex. The genetic score was associated with BMI in both nonstratified analysis (P = 2.8 × 10−107) and stratified analyses (all P < 3.3 × 10−30). We found evidence of a causal effect of adiposity on blood pressure, fasting levels of insulin, C-reactive protein, interleukin-6, HDL cholesterol, and triglycerides in a nonstratified analysis and in the <55-year stratum. Further, we found evidence of a smaller causal effect on total cholesterol (P for difference = 0.015) in the ≥55-year stratum than in the <55-year stratum, a finding that could be explained by biology, survival bias, or differential medication. In conclusion, this study extends previous knowledge of the effects of adiposity by providing sex- and age-specific causal estimates on cardiovascular risk factors.
doi:10.2337/db14-0988
PMCID: PMC4407863  PMID: 25712996
16.  A Protein Domain and Family Based Approach to Rare Variant Association Analysis 
PLoS ONE  2016;11(4):e0153803.
Background
It has become common practice to analyse large scale sequencing data with statistical approaches based around the aggregation of rare variants within the same gene. We applied a novel approach to rare variant analysis by collapsing variants together using protein domain and family coordinates, regarded to be a more discrete definition of a biologically functional unit.
Methods
Using Pfam definitions, we collapsed rare variants (Minor Allele Frequency ≤ 1%) together in three different ways 1) variants within single genomic regions which map to individual protein domains 2) variants within two individual protein domain regions which are predicted to be responsible for a protein-protein interaction 3) all variants within combined regions from multiple genes responsible for coding the same protein domain (i.e. protein families). A conventional collapsing analysis using gene coordinates was also undertaken for comparison. We used UK10K sequence data and investigated associations between regions of variants and lipid traits using the sequence kernel association test (SKAT).
Results
We observed no strong evidence of association between regions of variants based on Pfam domain definitions and lipid traits. Quantile-Quantile plots illustrated that the overall distributions of p-values from the protein domain analyses were comparable to that of a conventional gene-based approach. Deviations from this distribution suggested that collapsing by either protein domain or gene definitions may be favourable depending on the trait analysed.
Conclusion
We have collapsed rare variants together using protein domain and family coordinates to present an alternative approach over collapsing across conventionally used gene-based regions. Although no strong evidence of association was detected in these analyses, future studies may still find value in adopting these approaches to detect previously unidentified association signals.
doi:10.1371/journal.pone.0153803
PMCID: PMC4851355  PMID: 27128313
18.  Evaluation of type 2 diabetes genetic risk variants in Chinese adults: findings from 93,000 individuals from the China Kadoorie Biobank 
Diabetologia  2016;59:1446-1457.
Aims/hypothesis
Genome-wide association studies (GWAS) have discovered many risk variants for type 2 diabetes. However, estimates of the contributions of risk variants to type 2 diabetes predisposition are often based on highly selected case–control samples, and reliable estimates of population-level effect sizes are missing, especially in non-European populations.
Methods
The individual and cumulative effects of 59 established type 2 diabetes risk loci were measured in a population-based China Kadoorie Biobank (CKB) study of 93,000 Chinese adults, including >7,100 diabetes cases.
Results
Association signals were directionally consistent between CKB and the original discovery GWAS: of 56 variants passing quality control, 48 showed the same direction of effect (binomial test, p = 2.3 × 10−8). We observed a consistent overall trend towards lower risk variant effect sizes in CKB than in case–control samples of GWAS meta-analyses (mean 19–22% decrease in log odds, p ≤ 0.0048), likely to reflect correction of both ‘winner’s curse’ and spectrum bias effects. The association with risk of diabetes of a genetic risk score, based on lead variants at 25 loci considered to act through beta cell function, demonstrated significant interactions with several measures of adiposity (BMI, waist circumference [WC], WHR and percentage body fat [PBF]; all pinteraction < 1 × 10−4), with a greater effect being observed in leaner adults.
Conclusions/interpretation
Our study provides further evidence of shared genetic architecture for type 2 diabetes between Europeans and East Asians. It also indicates that even very large GWAS meta-analyses may be vulnerable to substantial inflation of effect size estimates, compared with those observed in large-scale population-based cohort studies.
Access to research materials
Details of how to access China Kadoorie Biobank data and details of the data release schedule are available from www.ckbiobank.org/site/Data+Access.
Electronic supplementary material
The online version of this article (doi:10.1007/s00125-016-3920-9) contains peer-reviewed but unedited supplementary material, which is available to authorised users.
doi:10.1007/s00125-016-3920-9
PMCID: PMC4901105  PMID: 27053236
Biobank; Chinese; Genetic risk score; Population-based cohort studies; Type 2 diabetes; Winner’s curse
19.  Adiposity as a cause of cardiovascular disease: a Mendelian randomization study 
Background: Adiposity, as indicated by body mass index (BMI), has been associated with risk of cardiovascular diseases in epidemiological studies. We aimed to investigate if these associations are causal, using Mendelian randomization (MR) methods.
Methods: The associations of BMI with cardiovascular outcomes [coronary heart disease (CHD), heart failure and ischaemic stroke], and associations of a genetic score (32 BMI single nucleotide polymorphisms) with BMI and cardiovascular outcomes were examined in up to 22 193 individuals with 3062 incident cardiovascular events from nine prospective follow-up studies within the ENGAGE consortium. We used random-effects meta-analysis in an MR framework to provide causal estimates of the effect of adiposity on cardiovascular outcomes.
Results: There was a strong association between BMI and incident CHD (HR = 1.20 per SD-increase of BMI, 95% CI, 1.12–1.28, P = 1.9·10−7), heart failure (HR = 1.47, 95% CI, 1.35–1.60, P = 9·10−19) and ischaemic stroke (HR = 1.15, 95% CI, 1.06–1.24, P = 0.0008) in observational analyses. The genetic score was robustly associated with BMI (β = 0.030 SD-increase of BMI per additional allele, 95% CI, 0.028–0.033, P = 3·10−107). Analyses indicated a causal effect of adiposity on development of heart failure (HR = 1.93 per SD-increase of BMI, 95% CI, 1.12–3.30, P = 0.017) and ischaemic stroke (HR = 1.83, 95% CI, 1.05–3.20, P = 0.034). Additional cross-sectional analyses using both ENGAGE and CARDIoGRAMplusC4D data showed a causal effect of adiposity on CHD.
Conclusions: Using MR methods, we provide support for the hypothesis that adiposity causes CHD, heart failure and, previously not demonstrated, ischaemic stroke.
doi:10.1093/ije/dyv094
PMCID: PMC4553708  PMID: 26016847
Cardiovascular disease; epidemiology; body mass index; Mendelian randomization
20.  Harmonising and linking biomedical and clinical data across disparate data archives to enable integrative cross-biobank research 
A wealth of biospecimen samples are stored in modern globally distributed biobanks. Biomedical researchers worldwide need to be able to combine the available resources to improve the power of large-scale studies. A prerequisite for this effort is to be able to search and access phenotypic, clinical and other information about samples that are currently stored at biobanks in an integrated manner. However, privacy issues together with heterogeneous information systems and the lack of agreed-upon vocabularies have made specimen searching across multiple biobanks extremely challenging. We describe three case studies where we have linked samples and sample descriptions in order to facilitate global searching of available samples for research. The use cases include the ENGAGE (European Network for Genetic and Genomic Epidemiology) consortium comprising at least 39 cohorts, the SUMMIT (surrogate markers for micro- and macro-vascular hard endpoints for innovative diabetes tools) consortium and a pilot for data integration between a Swedish clinical health registry and a biobank. We used the Sample avAILability (SAIL) method for data linking: first, created harmonised variables and then annotated and made searchable information on the number of specimens available in individual biobanks for various phenotypic categories. By operating on this categorised availability data we sidestep many obstacles related to privacy that arise when handling real values and show that harmonised and annotated records about data availability across disparate biomedical archives provide a key methodological advance in pre-analysis exchange of information between biobanks, that is, during the project planning phase.
doi:10.1038/ejhg.2015.165
PMCID: PMC4929882  PMID: 26306643
21.  The FTO/obesity associated locus and dietary intake in children 
Background
A region of chromosome 16 containing the fat mass/obesity associated gene (FTO) is reproducibly associated with fat mass and body mass index, risk of obesity and adiposity.
Objectives
To assess the possibility that appetite plays a role in the association between FTO and BMI.
Design
Detailed dietary report information from the Avon Longitudinal Study of Parents and Children allowed relationships between FTO variation and dietary intake to be explored. Analyses were performed to interrogate possible associations between variation at the FTO locus and a range of micro and macro-nutrients, taking into account the bias often found within dietary report data when assessing factors related to BMI. We also assessed associations between FTO and dietary intake independent of BMI in order to test the hypothesis that FTO may be influencing appetite directly as opposed to indirectly via BMI and altered intake requirement.
Results
Relationships between a single nucleotide polymorphism characterising the FTO signal (rs9939609) and dietary variables were found and can be summarised by per allele effects on total energy and total fat (both p=<0.001). These were attenuated, however persisted specifically for fat and energy consumption after adjustment for BMI (total daily fat consumption approximately 1.5g/day; difference per allele p=0.02, total daily energy consumption approximately 25kj/day; difference per allele p=0.03).
Conclusion
These associations suggest that individuals carrying minor variants at rs9939609 were consuming more fat and total energy, and that this was not simply dependent upon them having higher average BMI levels.
PMCID: PMC4773885  PMID: 18842783
ALSPAC; FTO; APPETITE
22.  Transancestral fine-mapping of four type 2 diabetes susceptibility loci highlights potential causal regulatory mechanisms 
Human Molecular Genetics  2016;25(10):2070-2081.
To gain insight into potential regulatory mechanisms through which the effects of variants at four established type 2 diabetes (T2D) susceptibility loci (CDKAL1, CDKN2A-B, IGF2BP2 and KCNQ1) are mediated, we undertook transancestral fine-mapping in 22 086 cases and 42 539 controls of East Asian, European, South Asian, African American and Mexican American descent. Through high-density imputation and conditional analyses, we identified seven distinct association signals at these four loci, each with allelic effects on T2D susceptibility that were homogenous across ancestry groups. By leveraging differences in the structure of linkage disequilibrium between diverse populations, and increased sample size, we localised the variants most likely to drive each distinct association signal. We demonstrated that integration of these genetic fine-mapping data with genomic annotation can highlight potential causal regulatory elements in T2D-relevant tissues. These analyses provide insight into the mechanisms through which T2D association signals are mediated, and suggest future routes to understanding the biology of specific disease susceptibility loci.
doi:10.1093/hmg/ddw048
PMCID: PMC5062576  PMID: 26911676
23.  The impact of low-frequency and rare variants on lipid levels 
Surakka, Ida | Horikoshi, Momoko | Mägi, Reedik | Sarin, Antti-Pekka | Mahajan, Anubha | Lagou, Vasiliki | Marullo, Letizia | Ferreira, Teresa | Miraglio, Benjamin | Timonen, Sanna | Kettunen, Johannes | Pirinen, Matti | Karjalainen, Juha | Thorleifsson, Gudmar | Hägg, Sara | Hottenga, Jouke-Jan | Isaacs, Aaron | Ladenvall, Claes | Beekman, Marian | Esko, Tõnu | Ried, Janina S | Nelson, Christopher P | Willenborg, Christina | Gustafsson, Stefan | Westra, Harm-Jan | Blades, Matthew | de Craen, Anton JM | de Geus, Eco J | Deelen, Joris | Grallert, Harald | Hamsten, Anders | Havulinna, Aki S. | Hengstenberg, Christian | Houwing-Duistermaat, Jeanine J | Hyppönen, Elina | Karssen, Lennart C | Lehtimäki, Terho | Lyssenko, Valeriya | Magnusson, Patrik KE | Mihailov, Evelin | Müller-Nurasyid, Martina | Mpindi, John-Patrick | Pedersen, Nancy L | Penninx, Brenda WJH | Perola, Markus | Pers, Tune H | Peters, Annette | Rung, Johan | Smit, Johannes H | Steinthorsdottir, Valgerdur | Tobin, Martin D | Tsernikova, Natalia | van Leeuwen, Elisabeth M | Viikari, Jorma S | Willems, Sara M | Willemsen, Gonneke | Schunkert, Heribert | Erdmann, Jeanette | Samani, Nilesh J | Kaprio, Jaakko | Lind, Lars | Gieger, Christian | Metspalu, Andres | Slagboom, P Eline | Groop, Leif | van Duijn, Cornelia M | Eriksson, Johan G | Jula, Antti | Salomaa, Veikko | Boomsma, Dorret I | Power, Christine | Raitakari, Olli T | Ingelsson, Erik | Järvelin, Marjo-Riitta | Stefansson, Kari | Franke, Lude | Ikonen, Elina | Kallioniemi, Olli | Pietiäinen, Vilja | Lindgren, Cecilia M | Thorsteinsdottir, Unnur | Palotie, Aarno | McCarthy, Mark I | Morris, Andrew P | Prokopenko, Inga | Ripatti, Samuli
Nature genetics  2015;47(6):589-597.
Using a genome-wide screen of 9.6 million genetic variants achieved through 1000 Genomes imputation in 62,166 samples, we identify association to lipids in 93 loci including 79 previously identified loci with new lead-SNPs, 10 new loci, 15 loci with a low-frequency and 10 loci with missense lead-SNPs, and, 2 loci with an accumulation of rare variants. In six loci, SNPs with established function in lipid genetics (CELSR2, GCKR, LIPC, and APOE), or candidate missense mutations with predicted damaging function (CD300LG and TM6SF2), explained the locus associations. The low-frequency variants increased the proportion of variance explained, particularly for LDL-C and TC. Altogether, our results highlight the impact of low-frequency variants in complex traits and show that imputation offers a cost-effective alternative to re-sequencing.
doi:10.1038/ng.3300
PMCID: PMC4757735  PMID: 25961943
25.  Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels 
Nature communications  2015;6:7208.
Metabolites are small molecules involved in cellular metabolism, which can be detected in biological samples using metabolomic techniques. Here we present the results of genome-wide association and meta-analyses for variation in the blood serum levels of 129 metabolites as measured by the Biocrates metabolomic platform. In a discovery sample of 7,478 individuals of European descent, we find 4,068 genome- and metabolome-wide significant (Z-test, P < 1.09 × 10−9) associations between single nucleotide polymorphisms (SNPs) and metabolites, involving 59 independent SNPs and 85 metabolites. Five of the fifty-nine independent SNPs are new for serum metabolite levels, and were followed-up for replication in an independent sample (N=1,182). The novel SNPs are located in or near genes encoding metabolite transporter proteins or enzymes (SLC22A16, ARG1, AGPS and ACSL1) that have demonstrated biomedical or pharmaceutical importance. The further characterization of genetic influences on metabolic phenotypes is important for progress in biological and medical research.
doi:10.1038/ncomms8208
PMCID: PMC4745136  PMID: 26068415

Results 1-25 (237)