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1.  Impact of Type 2 Diabetes Susceptibility Variants on Quantitative Glycemic Traits Reveals Mechanistic Heterogeneity 
Diabetes  2014;63(6):2158-2171.
Patients with established type 2 diabetes display both β-cell dysfunction and insulin resistance. To define fundamental processes leading to the diabetic state, we examined the relationship between type 2 diabetes risk variants at 37 established susceptibility loci, and indices of proinsulin processing, insulin secretion, and insulin sensitivity. We included data from up to 58,614 nondiabetic subjects with basal measures and 17,327 with dynamic measures. We used additive genetic models with adjustment for sex, age, and BMI, followed by fixed-effects, inverse-variance meta-analyses. Cluster analyses grouped risk loci into five major categories based on their relationship to these continuous glycemic phenotypes. The first cluster (PPARG, KLF14, IRS1, GCKR) was characterized by primary effects on insulin sensitivity. The second cluster (MTNR1B, GCK) featured risk alleles associated with reduced insulin secretion and fasting hyperglycemia. ARAP1 constituted a third cluster characterized by defects in insulin processing. A fourth cluster (TCF7L2, SLC30A8, HHEX/IDE, CDKAL1, CDKN2A/2B) was defined by loci influencing insulin processing and secretion without a detectable change in fasting glucose levels. The final group contained 20 risk loci with no clear-cut associations to continuous glycemic traits. By assembling extensive data on continuous glycemic traits, we have exposed the diverse mechanisms whereby type 2 diabetes risk variants impact disease predisposition.
PMCID: PMC4030103  PMID: 24296717
2.  Pharmacogenetic meta-analysis of genome-wide association studies of LDL cholesterol response to statins 
Postmus, Iris | Trompet, Stella | Deshmukh, Harshal A. | Barnes, Michael R. | Li, Xiaohui | Warren, Helen R. | Chasman, Daniel I. | Zhou, Kaixin | Arsenault, Benoit J. | Donnelly, Louise A. | Wiggins, Kerri L. | Avery, Christy L. | Griffin, Paula | Feng, QiPing | Taylor, Kent D. | Li, Guo | Evans, Daniel S. | Smith, Albert V. | de Keyser, Catherine E. | Johnson, Andrew D. | de Craen, Anton J. M. | Stott, David J. | Buckley, Brendan M. | Ford, Ian | Westendorp, Rudi G. J. | Eline Slagboom, P. | Sattar, Naveed | Munroe, Patricia B. | Sever, Peter | Poulter, Neil | Stanton, Alice | Shields, Denis C. | O’Brien, Eoin | Shaw-Hawkins, Sue | Ida Chen, Y.-D. | Nickerson, Deborah A. | Smith, Joshua D. | Pierre Dubé, Marie | Matthijs Boekholdt, S. | Kees Hovingh, G. | Kastelein, John J. P. | McKeigue, Paul M. | Betteridge, John | Neil, Andrew | Durrington, Paul N. | Doney, Alex | Carr, Fiona | Morris, Andrew | McCarthy, Mark I. | Groop, Leif | Ahlqvist, Emma | Bis, Joshua C. | Rice, Kenneth | Smith, Nicholas L. | Lumley, Thomas | Whitsel, Eric A. | Stürmer, Til | Boerwinkle, Eric | Ngwa, Julius S. | O’Donnell, Christopher J. | Vasan, Ramachandran S. | Wei, Wei-Qi | Wilke, Russell A. | Liu, Ching-Ti | Sun, Fangui | Guo, Xiuqing | Heckbert, Susan R | Post, Wendy | Sotoodehnia, Nona | Arnold, Alice M. | Stafford, Jeanette M. | Ding, Jingzhong | Herrington, David M. | Kritchevsky, Stephen B. | Eiriksdottir, Gudny | Launer, Leonore J. | Harris, Tamara B. | Chu, Audrey Y. | Giulianini, Franco | MacFadyen, Jean G. | Barratt, Bryan J. | Nyberg, Fredrik | Stricker, Bruno H. | Uitterlinden, André G. | Hofman, Albert | Rivadeneira, Fernando | Emilsson, Valur | Franco, Oscar H. | Ridker, Paul M. | Gudnason, Vilmundur | Liu, Yongmei | Denny, Joshua C. | Ballantyne, Christie M. | Rotter, Jerome I. | Adrienne Cupples, L. | Psaty, Bruce M. | Palmer, Colin N. A. | Tardif, Jean-Claude | Colhoun, Helen M. | Hitman, Graham | Krauss, Ronald M. | Wouter Jukema, J | Caulfield, Mark J.
Nature Communications  2014;5:5068.
Statins effectively lower LDL cholesterol levels in large studies and the observed interindividual response variability may be partially explained by genetic variation. Here we perform a pharmacogenetic meta-analysis of genome-wide association studies (GWAS) in studies addressing the LDL cholesterol response to statins, including up to 18,596 statin-treated subjects. We validate the most promising signals in a further 22,318 statin recipients and identify two loci, SORT1/CELSR2/PSRC1 and SLCO1B1, not previously identified in GWAS. Moreover, we confirm the previously described associations with APOE and LPA. Our findings advance the understanding of the pharmacogenetic architecture of statin response.
Statins are effectively used to prevent and manage cardiovascular disease, but patient response to these drugs is highly variable. Here, the authors identify two new genes associated with the response of LDL cholesterol to statins and advance our understanding of the genetic basis of drug response.
PMCID: PMC4220464  PMID: 25350695
3.  Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation 
Horikoshi, Momoko | Mӓgi, Reedik | van de Bunt, Martijn | Surakka, Ida | Sarin, Antti-Pekka | Mahajan, Anubha | Marullo, Letizia | Thorleifsson, Gudmar | Hӓgg, Sara | Hottenga, Jouke-Jan | Ladenvall, Claes | Ried, Janina S. | Winkler, Thomas W. | Willems, Sara M. | Pervjakova, Natalia | Esko, Tõnu | Beekman, Marian | Nelson, Christopher P. | Willenborg, Christina | Wiltshire, Steven | Ferreira, Teresa | Fernandez, Juan | Gaulton, Kyle J. | Steinthorsdottir, Valgerdur | Hamsten, Anders | Magnusson, Patrik K. E. | Willemsen, Gonneke | Milaneschi, Yuri | Robertson, Neil R. | Groves, Christopher J. | Bennett, Amanda J. | Lehtimӓki, Terho | Viikari, Jorma S. | Rung, Johan | Lyssenko, Valeriya | Perola, Markus | Heid, Iris M. | Herder, Christian | Grallert, Harald | Müller-Nurasyid, Martina | Roden, Michael | Hypponen, Elina | Isaacs, Aaron | van Leeuwen, Elisabeth M. | Karssen, Lennart C. | Mihailov, Evelin | Houwing-Duistermaat, Jeanine J. | de Craen, Anton J. M. | Deelen, Joris | Havulinna, Aki S. | Blades, Matthew | Hengstenberg, Christian | Erdmann, Jeanette | Schunkert, Heribert | Kaprio, Jaakko | Tobin, Martin D. | Samani, Nilesh J. | Lind, Lars | Salomaa, Veikko | Lindgren, Cecilia M. | Slagboom, P. Eline | Metspalu, Andres | van Duijn, Cornelia M. | Eriksson, Johan G. | Peters, Annette | Gieger, Christian | Jula, Antti | Groop, Leif | Raitakari, Olli T. | Power, Chris | Penninx, Brenda W. J. H. | de Geus, Eco | Smit, Johannes H. | Boomsma, Dorret I. | Pedersen, Nancy L. | Ingelsson, Erik | Thorsteinsdottir, Unnur | Stefansson, Kari | Ripatti, Samuli | Prokopenko, Inga | McCarthy, Mark I. | Morris, Andrew P.
PLoS Genetics  2015;11(7):e1005230.
Reference panels from the 1000 Genomes (1000G) Project Consortium provide near complete coverage of common and low-frequency genetic variation with minor allele frequency ≥0.5% across European ancestry populations. Within the European Network for Genetic and Genomic Epidemiology (ENGAGE) Consortium, we have undertaken the first large-scale meta-analysis of genome-wide association studies (GWAS), supplemented by 1000G imputation, for four quantitative glycaemic and obesity-related traits, in up to 87,048 individuals of European ancestry. We identified two loci for body mass index (BMI) at genome-wide significance, and two for fasting glucose (FG), none of which has been previously reported in larger meta-analysis efforts to combine GWAS of European ancestry. Through conditional analysis, we also detected multiple distinct signals of association mapping to established loci for waist-hip ratio adjusted for BMI (RSPO3) and FG (GCK and G6PC2). The index variant for one association signal at the G6PC2 locus is a low-frequency coding allele, H177Y, which has recently been demonstrated to have a functional role in glucose regulation. Fine-mapping analyses revealed that the non-coding variants most likely to drive association signals at established and novel loci were enriched for overlap with enhancer elements, which for FG mapped to promoter and transcription factor binding sites in pancreatic islets, in particular. Our study demonstrates that 1000G imputation and genetic fine-mapping of common and low-frequency variant association signals at GWAS loci, integrated with genomic annotation in relevant tissues, can provide insight into the functional and regulatory mechanisms through which their effects on glycaemic and obesity-related traits are mediated.
Author Summary
Human genetic studies have demonstrated that quantitative human anthropometric and metabolic traits, including body mass index, waist-hip ratio, and plasma concentrations of glucose and insulin, are highly heritable, and are established risk factors for type 2 diabetes and cardiovascular diseases. Although many regions of the genome have been associated with these traits, the specific genes responsible have not yet been identified. By making use of advanced statistical “imputation” techniques applied to more than 87,000 individuals of European ancestry, and publicly available “reference panels” of more than 37 million genetic variants, we have been able to identify novel regions of the genome associated with these glycaemic and obesity-related traits and localise genes within these regions that are most likely to be causal. This improved understanding of the biological mechanisms underlying glycaemic and obesity-related traits is extremely important because it may advance drug development for downstream disease endpoints, ultimately leading to public health benefits.
PMCID: PMC4488845  PMID: 26132169
4.  Age-Related Clonal Hematopoiesis Associated with Adverse Outcomes 
The New England journal of medicine  2014;371(26):2488-2498.
The incidence of hematologic cancers increases with age. These cancers are associated with recurrent somatic mutations in specific genes. We hypothesized that such mutations would be detectable in the blood of some persons who are not known to have hematologic disorders.
We analyzed whole-exome sequencing data from DNA in the peripheral-blood cells of 17,182 persons who were unselected for hematologic phenotypes. We looked for somatic mutations by identifying previously characterized single-nucleotide variants and small insertions or deletions in 160 genes that are recurrently mutated in hematologic cancers. The presence of mutations was analyzed for an association with hematologic phenotypes, survival, and cardiovascular events.
Detectable somatic mutations were rare in persons younger than 40 years of age but rose appreciably in frequency with age. Among persons 70 to 79 years of age, 80 to 89 years of age, and 90 to 108 years of age, these clonal mutations were observed in 9.5% (219 of 2300 persons), 11.7% (37 of 317), and 18.4% (19 of 103), respectively. The majority of the variants occurred in three genes: DNMT3A, TET2, and ASXL1. The presence of a somatic mutation was associated with an increase in the risk of hematologic cancer (hazard ratio, 11.1; 95% confidence interval [CI], 3.9 to 32.6), an increase in all-cause mortality (hazard ratio, 1.4; 95% CI, 1.1 to 1.8), and increases in the risks of incident coronary heart disease (hazard ratio, 2.0; 95% CI, 1.2 to 3.4) and ischemic stroke (hazard ratio, 2.6; 95% CI, 1.4 to 4.8).
Age-related clonal hematopoiesis is a common condition that is associated with increases in the risk of hematologic cancer and in all-cause mortality, with the latter possibly due to an increased risk of cardiovascular disease. (Funded by the National Institutes of Health and others.)
PMCID: PMC4306669  PMID: 25426837
5.  Human islet function following 20 years of cryogenic biobanking 
Diabetologia  2015;58(7):1503-1512.
There are potential advantages to the low-temperature (−196°C) banking of isolated islets, including the maintenance of viable islets for future research. We therefore assessed the in vitro and in vivo function of islets cryopreserved for nearly 20 years.
Human islets were cryopreserved from 1991 to 2001 and thawed between 2012 and 2014. These were characterised by immunostaining, patch-clamp electrophysiology, insulin secretion, transcriptome analysis and transplantation into a streptozotocin (STZ)-induced mouse model of diabetes.
The cryopreservation time was 17.6 ± 0.4 years (n = 43). The thawed islets stained positive with dithizone, contained insulin-positive and glucagon-positive cells, and displayed levels of apoptosis and transcriptome profiles similar to those of freshly isolated islets, although their insulin content was lower. The cryopreserved beta cells possessed ion channels and exocytotic responses identical to those of freshly isolated beta cells. Cells from a subset of five donors demonstrated similar perifusion insulin secretion profiles pre- and post-cryopreservation. The transplantation of cryopreserved islets into the diabetic mice improved their glucose tolerance but did not completely normalise their blood glucose levels. Circulating human insulin and insulin-positive grafts were detectable at 10 weeks post-transplantation.
We have demonstrated the potential for long-term banking of human islets for research, which could enable the use of tissue from a large number of donors with future technologies to gain new insight into diabetes.
PMCID: PMC4472956  PMID: 25930156
Cryopreservation; Exocytosis; Human; Insulin; Ion channels; Islets; Secretion; Transplantation
6.  The Power of Gene-Based Rare Variant Methods to Detect Disease-Associated Variation and Test Hypotheses About Complex Disease 
PLoS Genetics  2015;11(4):e1005165.
Genome and exome sequencing in large cohorts enables characterization of the role of rare variation in complex diseases. Success in this endeavor, however, requires investigators to test a diverse array of genetic hypotheses which differ in the number, frequency and effect sizes of underlying causal variants. In this study, we evaluated the power of gene-based association methods to interrogate such hypotheses, and examined the implications for study design. We developed a flexible simulation approach, using 1000 Genomes data, to (a) generate sequence variation at human genes in up to 10K case-control samples, and (b) quantify the statistical power of a panel of widely used gene-based association tests under a variety of allelic architectures, locus effect sizes, and significance thresholds. For loci explaining ~1% of phenotypic variance underlying a common dichotomous trait, we find that all methods have low absolute power to achieve exome-wide significance (~5-20% power at α=2.5×10-6) in 3K individuals; even in 10K samples, power is modest (~60%). The combined application of multiple methods increases sensitivity, but does so at the expense of a higher false positive rate. MiST, SKAT-O, and KBAC have the highest individual mean power across simulated datasets, but we observe wide architecture-dependent variability in the individual loci detected by each test, suggesting that inferences about disease architecture from analysis of sequencing studies can differ depending on which methods are used. Our results imply that tens of thousands of individuals, extensive functional annotation, or highly targeted hypothesis testing will be required to confidently detect or exclude rare variant signals at complex disease loci.
Author Summary
Re-sequencing technologies allow for a more complete interrogation of the role of human variation in complex disease. The inadequate power of single variant methods to assess the role of less common variation has led to the development of numerous statistical methods for testing aggregate groups of variants for association with disease. Such endeavors pose substantial analytical challenges, however, due to the diverse array of genetic hypotheses that need to be considered. In this work, we systematically quantify and compare the performance of a panel of commonly used gene-based association methods under a range of allelic architectures, significance thresholds, locus effect sizes, sample sizes, and filters for neutral variation. We find that MiST, SKAT-O, and KBAC have the highest mean power across simulated datasets. Across all methods, however, the power to detect even loci of relatively large effect is very low at exome-wide significance thresholds for sample sizes comparable with those of ongoing sequencing studies; as such, the absence of signal in studies of a few thousand individuals does not exclude a role for rare variation in complex traits. Finally, we directly compare the results reported by different gene-based methods in order to identify their comparative advantages and disadvantages under distinct locus architectures. Our findings have implications for meaningful interpretation of both positive and negative findings in ongoing and future sequencing studies.
PMCID: PMC4407972  PMID: 25906071
7.  Parent-of-origin specific allelic associations among 106 genomic loci for age at menarche 
Perry, John RB | Day, Felix | Elks, Cathy E | Sulem, Patrick | Thompson, Deborah J | Ferreira, Teresa | He, Chunyan | Chasman, Daniel I | Esko, Tõnu | Thorleifsson, Gudmar | Albrecht, Eva | Ang, Wei Q | Corre, Tanguy | Cousminer, Diana L | Feenstra, Bjarke | Franceschini, Nora | Ganna, Andrea | Johnson, Andrew D | Kjellqvist, Sanela | Lunetta, Kathryn L | McMahon, George | Nolte, Ilja M | Paternoster, Lavinia | Porcu, Eleonora | Smith, Albert V | Stolk, Lisette | Teumer, Alexander | Tšernikova, Natalia | Tikkanen, Emmi | Ulivi, Sheila | Wagner, Erin K | Amin, Najaf | Bierut, Laura J | Byrne, Enda M | Hottenga, Jouke-Jan | Koller, Daniel L | Mangino, Massimo | Pers, Tune H | Yerges-Armstrong, Laura M | Zhao, Jing Hua | Andrulis, Irene L | Anton-Culver, Hoda | Atsma, Femke | Bandinelli, Stefania | Beckmann, Matthias W | Benitez, Javier | Blomqvist, Carl | Bojesen, Stig E | Bolla, Manjeet K | Bonanni, Bernardo | Brauch, Hiltrud | Brenner, Hermann | Buring, Julie E | Chang-Claude, Jenny | Chanock, Stephen | Chen, Jinhui | Chenevix-Trench, Georgia | Collée, J. Margriet | Couch, Fergus J | Couper, David | Coveillo, Andrea D | Cox, Angela | Czene, Kamila | D’adamo, Adamo Pio | Smith, George Davey | De Vivo, Immaculata | Demerath, Ellen W | Dennis, Joe | Devilee, Peter | Dieffenbach, Aida K | Dunning, Alison M | Eiriksdottir, Gudny | Eriksson, Johan G | Fasching, Peter A | Ferrucci, Luigi | Flesch-Janys, Dieter | Flyger, Henrik | Foroud, Tatiana | Franke, Lude | Garcia, Melissa E | García-Closas, Montserrat | Geller, Frank | de Geus, Eco EJ | Giles, Graham G | Gudbjartsson, Daniel F | Gudnason, Vilmundur | Guénel, Pascal | Guo, Suiqun | Hall, Per | Hamann, Ute | Haring, Robin | Hartman, Catharina A | Heath, Andrew C | Hofman, Albert | Hooning, Maartje J | Hopper, John L | Hu, Frank B | Hunter, David J | Karasik, David | Kiel, Douglas P | Knight, Julia A | Kosma, Veli-Matti | Kutalik, Zoltan | Lai, Sandra | Lambrechts, Diether | Lindblom, Annika | Mägi, Reedik | Magnusson, Patrik K | Mannermaa, Arto | Martin, Nicholas G | Masson, Gisli | McArdle, Patrick F | McArdle, Wendy L | Melbye, Mads | Michailidou, Kyriaki | Mihailov, Evelin | Milani, Lili | Milne, Roger L | Nevanlinna, Heli | Neven, Patrick | Nohr, Ellen A | Oldehinkel, Albertine J | Oostra, Ben A | Palotie, Aarno | Peacock, Munro | Pedersen, Nancy L | Peterlongo, Paolo | Peto, Julian | Pharoah, Paul DP | Postma, Dirkje S | Pouta, Anneli | Pylkäs, Katri | Radice, Paolo | Ring, Susan | Rivadeneira, Fernando | Robino, Antonietta | Rose, Lynda M | Rudolph, Anja | Salomaa, Veikko | Sanna, Serena | Schlessinger, David | Schmidt, Marjanka K | Southey, Mellissa C | Sovio, Ulla | Stampfer, Meir J | Stöckl, Doris | Storniolo, Anna M | Timpson, Nicholas J | Tyrer, Jonathan | Visser, Jenny A | Vollenweider, Peter | Völzke, Henry | Waeber, Gerard | Waldenberger, Melanie | Wallaschofski, Henri | Wang, Qin | Willemsen, Gonneke | Winqvist, Robert | Wolffenbuttel, Bruce HR | Wright, Margaret J | Boomsma, Dorret I | Econs, Michael J | Khaw, Kay-Tee | Loos, Ruth JF | McCarthy, Mark I | Montgomery, Grant W | Rice, John P | Streeten, Elizabeth A | Thorsteinsdottir, Unnur | van Duijn, Cornelia M | Alizadeh, Behrooz Z | Bergmann, Sven | Boerwinkle, Eric | Boyd, Heather A | Crisponi, Laura | Gasparini, Paolo | Gieger, Christian | Harris, Tamara B | Ingelsson, Erik | Järvelin, Marjo-Riitta | Kraft, Peter | Lawlor, Debbie | Metspalu, Andres | Pennell, Craig E | Ridker, Paul M | Snieder, Harold | Sørensen, Thorkild IA | Spector, Tim D | Strachan, David P | Uitterlinden, André G | Wareham, Nicholas J | Widen, Elisabeth | Zygmunt, Marek | Murray, Anna | Easton, Douglas F | Stefansson, Kari | Murabito, Joanne M | Ong, Ken K
Nature  2014;514(7520):92-97.
Age at menarche is a marker of timing of puberty in females. It varies widely between individuals, is a heritable trait and is associated with risks for obesity, type 2 diabetes, cardiovascular disease, breast cancer and all-cause mortality1. Studies of rare human disorders of puberty and animal models point to a complex hypothalamic-pituitary-hormonal regulation2,3, but the mechanisms that determine pubertal timing and underlie its links to disease risk remain unclear. Here, using genome-wide and custom-genotyping arrays in up to 182,416 women of European descent from 57 studies, we found robust evidence (P<5×10−8) for 123 signals at 106 genomic loci associated with age at menarche. Many loci were associated with other pubertal traits in both sexes, and there was substantial overlap with genes implicated in body mass index and various diseases, including rare disorders of puberty. Menarche signals were enriched in imprinted regions, with three loci (DLK1/WDR25, MKRN3/MAGEL2 and KCNK9) demonstrating parent-of-origin specific associations concordant with known parental expression patterns. Pathway analyses implicated nuclear hormone receptors, particularly retinoic acid and gamma-aminobutyric acid-B2 receptor signaling, among novel mechanisms that regulate pubertal timing in humans. Our findings suggest a genetic architecture involving at least hundreds of common variants in the coordinated timing of the pubertal transition.
PMCID: PMC4185210  PMID: 25231870
8.  Expression of Phosphofructokinase in Skeletal Muscle Is Influenced by Genetic Variation and Associated With Insulin Sensitivity 
Diabetes  2014;63(3):1154-1165.
Using an integrative approach in which genetic variation, gene expression, and clinical phenotypes are assessed in relevant tissues may help functionally characterize the contribution of genetics to disease susceptibility. We sought to identify genetic variation influencing skeletal muscle gene expression (expression quantitative trait loci [eQTLs]) as well as expression associated with measures of insulin sensitivity. We investigated associations of 3,799,401 genetic variants in expression of >7,000 genes from three cohorts (n = 104). We identified 287 genes with cis-acting eQTLs (false discovery rate [FDR] <5%; P < 1.96 × 10−5) and 49 expression–insulin sensitivity phenotype associations (i.e., fasting insulin, homeostasis model assessment–insulin resistance, and BMI) (FDR <5%; P = 1.34 × 10−4). One of these associations, fasting insulin/phosphofructokinase (PFKM), overlaps with an eQTL. Furthermore, the expression of PFKM, a rate-limiting enzyme in glycolysis, was nominally associated with glucose uptake in skeletal muscle (P = 0.026; n = 42) and overexpressed (Bonferroni-corrected P = 0.03) in skeletal muscle of patients with T2D (n = 102) compared with normoglycemic controls (n = 87). The PFKM eQTL (rs4547172; P = 7.69 × 10−6) was nominally associated with glucose uptake, glucose oxidation rate, intramuscular triglyceride content, and metabolic flexibility (P = 0.016–0.048; n = 178). We explored eQTL results using published data from genome-wide association studies (DIAGRAM and MAGIC), and a proxy for the PFKM eQTL (rs11168327; r2 = 0.75) was nominally associated with T2D (DIAGRAM P = 2.7 × 10−3). Taken together, our analysis highlights PFKM as a potential regulator of skeletal muscle insulin sensitivity.
PMCID: PMC3931395  PMID: 24306210
9.  A coherent approach for analysis of the Illumina HumanMethylation450 BeadChip improves data quality and performance in epigenome-wide association studies 
Genome Biology  2015;16(1):37.
DNA methylation plays a fundamental role in the regulation of the genome, but the optimal strategy for analysis of genome-wide DNA methylation data remains to be determined. We developed a comprehensive analysis pipeline for epigenome-wide association studies (EWAS) using the Illumina Infinium HumanMethylation450 BeadChip, based on 2,687 individuals, with 36 samples measured in duplicate. We propose new approaches to quality control, data normalisation and batch correction through control-probe adjustment and establish a null hypothesis for EWAS using permutation testing. Our analysis pipeline outperforms existing approaches, enabling accurate identification of methylation quantitative trait loci for hypothesis driven follow-up experiments.
Electronic supplementary material
The online version of this article (doi:10.1186/s13059-015-0600-x) contains supplementary material, which is available to authorized users.
PMCID: PMC4365767  PMID: 25853392
10.  LRP5 Regulates Human Body Fat Distribution by Modulating Adipose Progenitor Biology in a Dose- and Depot-Specific Fashion 
Cell Metabolism  2015;21(2):262-272.
Common variants in WNT pathway genes have been associated with bone mass and fat distribution, the latter predicting diabetes and cardiovascular disease risk. Rare mutations in the WNT co-receptors LRP5 and LRP6 are similarly associated with bone and cardiometabolic disorders. We investigated the role of LRP5 in human adipose tissue. Subjects with gain-of-function LRP5 mutations and high bone mass had enhanced lower-body fat accumulation. Reciprocally, a low bone mineral density-associated common LRP5 allele correlated with increased abdominal adiposity. Ex vivo LRP5 expression was higher in abdominal versus gluteal adipocyte progenitors. Equivalent knockdown of LRP5 in both progenitor types dose-dependently impaired β-catenin signaling and led to distinct biological outcomes: diminished gluteal and enhanced abdominal adipogenesis. These data highlight how depot differences in WNT/β-catenin pathway activity modulate human fat distribution via effects on adipocyte progenitor biology. They also identify LRP5 as a potential pharmacologic target for the treatment of cardiometabolic disorders.
Graphical Abstract
•Carriers of LRP5 variants display altered body fat distribution•LRP5 is more highly expressed in abdominal versus gluteal fat progenitor cells•LRP5 knockdown in both progenitor types leads to different biological responses•LRP5 modulates fat progenitor biology by controlling β-catenin signaling dosage
Loh et al. identify the WNT co-receptor LRP5 as a regulator of human body fat distribution, an independent predictor of diabetes and cardiovascular disease risk. Studying LRP5 gene variant carriers and human fat progenitors, they show that LRP5 differentially modulates regional adipose progenitor biology by titrating WNT/β-catenin signaling dosage.
PMCID: PMC4321886  PMID: 25651180
11.  Identification and Functional Characterization of G6PC2 Coding Variants Influencing Glycemic Traits Define an Effector Transcript at the G6PC2-ABCB11 Locus 
Mahajan, Anubha | Sim, Xueling | Ng, Hui Jin | Manning, Alisa | Rivas, Manuel A. | Highland, Heather M. | Locke, Adam E. | Grarup, Niels | Im, Hae Kyung | Cingolani, Pablo | Flannick, Jason | Fontanillas, Pierre | Fuchsberger, Christian | Gaulton, Kyle J. | Teslovich, Tanya M. | Rayner, N. William | Robertson, Neil R. | Beer, Nicola L. | Rundle, Jana K. | Bork-Jensen, Jette | Ladenvall, Claes | Blancher, Christine | Buck, David | Buck, Gemma | Burtt, Noël P. | Gabriel, Stacey | Gjesing, Anette P. | Groves, Christopher J. | Hollensted, Mette | Huyghe, Jeroen R. | Jackson, Anne U. | Jun, Goo | Justesen, Johanne Marie | Mangino, Massimo | Murphy, Jacquelyn | Neville, Matt | Onofrio, Robert | Small, Kerrin S. | Stringham, Heather M. | Syvänen, Ann-Christine | Trakalo, Joseph | Abecasis, Goncalo | Bell, Graeme I. | Blangero, John | Cox, Nancy J. | Duggirala, Ravindranath | Hanis, Craig L. | Seielstad, Mark | Wilson, James G. | Christensen, Cramer | Brandslund, Ivan | Rauramaa, Rainer | Surdulescu, Gabriela L. | Doney, Alex S. F. | Lannfelt, Lars | Linneberg, Allan | Isomaa, Bo | Tuomi, Tiinamaija | Jørgensen, Marit E. | Jørgensen, Torben | Kuusisto, Johanna | Uusitupa, Matti | Salomaa, Veikko | Spector, Timothy D. | Morris, Andrew D. | Palmer, Colin N. A. | Collins, Francis S. | Mohlke, Karen L. | Bergman, Richard N. | Ingelsson, Erik | Lind, Lars | Tuomilehto, Jaakko | Hansen, Torben | Watanabe, Richard M. | Prokopenko, Inga | Dupuis, Josee | Karpe, Fredrik | Groop, Leif | Laakso, Markku | Pedersen, Oluf | Florez, Jose C. | Morris, Andrew P. | Altshuler, David | Meigs, James B. | Boehnke, Michael | McCarthy, Mark I. | Lindgren, Cecilia M. | Gloyn, Anna L.
PLoS Genetics  2015;11(1):e1004876.
Genome wide association studies (GWAS) for fasting glucose (FG) and insulin (FI) have identified common variant signals which explain 4.8% and 1.2% of trait variance, respectively. It is hypothesized that low-frequency and rare variants could contribute substantially to unexplained genetic variance. To test this, we analyzed exome-array data from up to 33,231 non-diabetic individuals of European ancestry. We found exome-wide significant (P<5×10-7) evidence for two loci not previously highlighted by common variant GWAS: GLP1R (p.Ala316Thr, minor allele frequency (MAF)=1.5%) influencing FG levels, and URB2 (p.Glu594Val, MAF = 0.1%) influencing FI levels. Coding variant associations can highlight potential effector genes at (non-coding) GWAS signals. At the G6PC2/ABCB11 locus, we identified multiple coding variants in G6PC2 (p.Val219Leu, p.His177Tyr, and p.Tyr207Ser) influencing FG levels, conditionally independent of each other and the non-coding GWAS signal. In vitro assays demonstrate that these associated coding alleles result in reduced protein abundance via proteasomal degradation, establishing G6PC2 as an effector gene at this locus. Reconciliation of single-variant associations and functional effects was only possible when haplotype phase was considered. In contrast to earlier reports suggesting that, paradoxically, glucose-raising alleles at this locus are protective against type 2 diabetes (T2D), the p.Val219Leu G6PC2 variant displayed a modest but directionally consistent association with T2D risk. Coding variant associations for glycemic traits in GWAS signals highlight PCSK1, RREB1, and ZHX3 as likely effector transcripts. These coding variant association signals do not have a major impact on the trait variance explained, but they do provide valuable biological insights.
Author Summary
Understanding how FI and FG levels are regulated is important because their derangement is a feature of T2D. Despite recent success from GWAS in identifying regions of the genome influencing glycemic traits, collectively these loci explain only a small proportion of trait variance. Unlocking the biological mechanisms driving these associations has been challenging because the vast majority of variants map to non-coding sequence, and the genes through which they exert their impact are largely unknown. In the current study, we sought to increase our understanding of the physiological pathways influencing both traits using exome-array genotyping in up to 33,231 non-diabetic individuals to identify coding variants and consequently genes associated with either FG or FI levels. We identified novel association signals for both traits including the receptor for GLP-1 agonists which are a widely used therapy for T2D. Furthermore, we identified coding variants at several GWAS loci which point to the genes underlying these association signals. Importantly, we found that multiple coding variants in G6PC2 result in a loss of protein function and lower fasting glucose levels.
PMCID: PMC4307976  PMID: 25625282
12.  An integrated epigenomic analysis for type 2 diabetes susceptibility loci in monozygotic twins 
Nature Communications  2014;5:5719.
DNA methylation has a great potential for understanding the aetiology of common complex traits such as Type 2 diabetes (T2D). Here we perform genome-wide methylated DNA immunoprecipitation sequencing (MeDIP-seq) in whole-blood-derived DNA from 27 monozygotic twin pairs and follow up results with replication and integrated omics analyses. We identify predominately hypermethylated T2D-related differentially methylated regions (DMRs) and replicate the top signals in 42 unrelated T2D cases and 221 controls. The strongest signal is in the promoter of the MALT1 gene, involved in insulin and glycaemic pathways, and related to taurocholate levels in blood. Integrating the DNA methylome findings with T2D GWAS meta-analysis results reveals a strong enrichment for DMRs in T2D-susceptibility loci. We also detect signals specific to T2D-discordant twins in the GPR61 and PRKCB genes. These replicated T2D associations reflect both likely causal and consequential pathways of the disease. The analysis indicates how an integrated genomics and epigenomics approach, utilizing an MZ twin design, can provide pathogenic insights as well as potential drug targets and biomarkers for T2D and other complex traits.
Type 2 diabetes (T2D) is a highly heterogeneous disease with a strong genetic component. Here the authors examine genome-wide methylation patterns in T2D-discordant, T2D-concordant and healthy concordant monozygotic twin pairs, and identify DNA methylation signals that may represent new biomarkers or drug targets for T2D.
PMCID: PMC4284644  PMID: 25502755
13.  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.
PMCID: PMC4241116  PMID: 24947364
Genetics; type 2 diabetes; insulin resistance; insulin secretion; adipose expandability
14.  Multiple type 2 diabetes susceptibility genes following genome-wide association scan in UK samples 
Science (New York, N.Y.)  2007;316(5829):1336-1341.
The molecular mechanisms involved in the development of type 2 diabetes are poorly understood. Starting from genome-wide genotype data for 1,924 diabetic cases and 2,938 population controls generated by the Wellcome Trust Case Control Consortium, we set out to detect replicated diabetes association signals through analysis of 3,757 additional cases and 5,346 controls, and by integration of our findings with equivalent data from other international consortia. We detected diabetes susceptibility loci in and around the genes CDKAL1, CDKN2A/CDKN2B and IGF2BP2 and confirmed the recently described associations at HHEX/IDE and SLC30A8. Our findings provide insights into the genetic architecture of type 2 diabetes, emphasizing the contribution of multiple variants of modest effect. The regions identified underscore the importance of pathways influencing pancreatic beta cell development and function in the etiology of type 2 diabetes.
PMCID: PMC3772310  PMID: 17463249
15.  Whole-genome sequencing to understand the genetic architecture of common gene expression and biomarker phenotypes 
Human Molecular Genetics  2014;24(5):1504-1512.
Initial results from sequencing studies suggest that there are relatively few low-frequency (<5%) variants associated with large effects on common phenotypes. We performed low-pass whole-genome sequencing in 680 individuals from the InCHIANTI study to test two primary hypotheses: (i) that sequencing would detect single low-frequency–large effect variants that explained similar amounts of phenotypic variance as single common variants, and (ii) that some common variant associations could be explained by low-frequency variants. We tested two sets of disease-related common phenotypes for which we had statistical power to detect large numbers of common variant–common phenotype associations—11 132 cis-gene expression traits in 450 individuals and 93 circulating biomarkers in all 680 individuals. From a total of 11 657 229 high-quality variants of which 6 129 221 and 5 528 008 were common and low frequency (<5%), respectively, low frequency–large effect associations comprised 7% of detectable cis-gene expression traits [89 of 1314 cis-eQTLs at P < 1 × 10−06 (false discovery rate ∼5%)] and one of eight biomarker associations at P < 8 × 10−10. Very few (30 of 1232; 2%) common variant associations were fully explained by low-frequency variants. Our data show that whole-genome sequencing can identify low-frequency variants undetected by genotyping based approaches when sample sizes are sufficiently large to detect substantial numbers of common variant associations, and that common variant associations are rarely explained by single low-frequency variants of large effect.
PMCID: PMC4321449  PMID: 25378555
16.  A novel common variant in DCST2 is associated with length in early life and height in adulthood 
van der Valk, Ralf J.P. | Kreiner-Møller, Eskil | Kooijman, Marjolein N. | Guxens, Mònica | Stergiakouli, Evangelia | Sääf, Annika | Bradfield, Jonathan P. | Geller, Frank | Hayes, M. Geoffrey | Cousminer, Diana L. | Körner, Antje | Thiering, Elisabeth | Curtin, John A. | Myhre, Ronny | Huikari, Ville | Joro, Raimo | Kerkhof, Marjan | Warrington, Nicole M. | Pitkänen, Niina | Ntalla, Ioanna | Horikoshi, Momoko | Veijola, Riitta | Freathy, Rachel M. | Teo, Yik-Ying | Barton, Sheila J. | Evans, David M. | Kemp, John P. | St Pourcain, Beate | Ring, Susan M. | Davey Smith, George | Bergström, Anna | Kull, Inger | Hakonarson, Hakon | Mentch, Frank D. | Bisgaard, Hans | Chawes, Bo | Stokholm, Jakob | Waage, Johannes | Eriksen, Patrick | Sevelsted, Astrid | Melbye, Mads | van Duijn, Cornelia M. | Medina-Gomez, Carolina | Hofman, Albert | de Jongste, Johan C. | Taal, H. Rob | Uitterlinden, André G. | Armstrong, Loren L. | Eriksson, Johan | Palotie, Aarno | Bustamante, Mariona | Estivill, Xavier | Gonzalez, Juan R. | Llop, Sabrina | Kiess, Wieland | Mahajan, Anubha | Flexeder, Claudia | Tiesler, Carla M.T. | Murray, Clare S. | Simpson, Angela | Magnus, Per | Sengpiel, Verena | Hartikainen, Anna-Liisa | Keinanen-Kiukaanniemi, Sirkka | Lewin, Alexandra | Da Silva Couto Alves, Alexessander | Blakemore, Alexandra I. | Buxton, Jessica L. | Kaakinen, Marika | Rodriguez, Alina | Sebert, Sylvain | Vaarasmaki, Marja | Lakka, Timo | Lindi, Virpi | Gehring, Ulrike | Postma, Dirkje S. | Ang, Wei | Newnham, John P. | Lyytikäinen, Leo-Pekka | Pahkala, Katja | Raitakari, Olli T. | Panoutsopoulou, Kalliope | Zeggini, Eleftheria | Boomsma, Dorret I. | Groen-Blokhuis, Maria | Ilonen, Jorma | Franke, Lude | Hirschhorn, Joel N. | Pers, Tune H. | Liang, Liming | Huang, Jinyan | Hocher, Berthold | Knip, Mikael | Saw, Seang-Mei | Holloway, John W. | Melén, Erik | Grant, Struan F.A. | Feenstra, Bjarke | Lowe, William L. | Widén, Elisabeth | Sergeyev, Elena | Grallert, Harald | Custovic, Adnan | Jacobsson, Bo | Jarvelin, Marjo-Riitta | Atalay, Mustafa | Koppelman, Gerard H. | Pennell, Craig E. | Niinikoski, Harri | Dedoussis, George V. | Mccarthy, Mark I. | Frayling, Timothy M. | Sunyer, Jordi | Timpson, Nicholas J. | Rivadeneira, Fernando | Bønnelykke, Klaus | Jaddoe, Vincent W.V.
Human Molecular Genetics  2014;24(4):1155-1168.
Common genetic variants have been identified for adult height, but not much is known about the genetics of skeletal growth in early life. To identify common genetic variants that influence fetal skeletal growth, we meta-analyzed 22 genome-wide association studies (Stage 1; N = 28 459). We identified seven independent top single nucleotide polymorphisms (SNPs) (P < 1 × 10−6) for birth length, of which three were novel and four were in or near loci known to be associated with adult height (LCORL, PTCH1, GPR126 and HMGA2). The three novel SNPs were followed-up in nine replication studies (Stage 2; N = 11 995), with rs905938 in DC-STAMP domain containing 2 (DCST2) genome-wide significantly associated with birth length in a joint analysis (Stages 1 + 2; β = 0.046, SE = 0.008, P = 2.46 × 10−8, explained variance = 0.05%). Rs905938 was also associated with infant length (N = 28 228; P = 5.54 × 10−4) and adult height (N = 127 513; P = 1.45 × 10−5). DCST2 is a DC-STAMP-like protein family member and DC-STAMP is an osteoclast cell-fusion regulator. Polygenic scores based on 180 SNPs previously associated with human adult stature explained 0.13% of variance in birth length. The same SNPs explained 2.95% of the variance of infant length. Of the 180 known adult height loci, 11 were genome-wide significantly associated with infant length (SF3B4, LCORL, SPAG17, C6orf173, PTCH1, GDF5, ZNFX1, HHIP, ACAN, HLA locus and HMGA2). This study highlights that common variation in DCST2 influences variation in early growth and adult height.
PMCID: PMC4447786  PMID: 25281659
17.  Mendelian Randomization Studies Do Not Support a Causal Role for Reduced Circulating Adiponectin Levels in Insulin Resistance and Type 2 Diabetes 
Yaghootkar, Hanieh | Lamina, Claudia | Scott, Robert A. | Dastani, Zari | Hivert, Marie-France | Warren, Liling L. | Stancáková, Alena | Buxbaum, Sarah G. | Lyytikäinen, Leo-Pekka | Henneman, Peter | Wu, Ying | Cheung, Chloe Y.Y. | Pankow, James S. | Jackson, Anne U. | Gustafsson, Stefan | Zhao, Jing Hua | Ballantyne, Christie M. | Xie, Weijia | Bergman, Richard N. | Boehnke, Michael | el Bouazzaoui, Fatiha | Collins, Francis S. | Dunn, Sandra H. | Dupuis, Josee | Forouhi, Nita G. | Gillson, Christopher | Hattersley, Andrew T. | Hong, Jaeyoung | Kähönen, Mika | Kuusisto, Johanna | Kedenko, Lyudmyla | Kronenberg, Florian | Doria, Alessandro | Assimes, Themistocles L. | Ferrannini, Ele | Hansen, Torben | Hao, Ke | Häring, Hans | Knowles, Joshua W. | Lindgren, Cecilia M. | Nolan, John J. | Paananen, Jussi | Pedersen, Oluf | Quertermous, Thomas | Smith, Ulf | Lehtimäki, Terho | Liu, Ching-Ti | Loos, Ruth J.F. | McCarthy, Mark I. | Morris, Andrew D. | Vasan, Ramachandran S. | Spector, Tim D. | Teslovich, Tanya M. | Tuomilehto, Jaakko | van Dijk, Ko Willems | Viikari, Jorma S. | Zhu, Na | Langenberg, Claudia | Ingelsson, Erik | Semple, Robert K. | Sinaiko, Alan R. | Palmer, Colin N.A. | Walker, Mark | Lam, Karen S.L. | Paulweber, Bernhard | Mohlke, Karen L. | van Duijn, Cornelia | Raitakari, Olli T. | Bidulescu, Aurelian | Wareham, Nick J. | Laakso, Markku | Waterworth, Dawn M. | Lawlor, Debbie A. | Meigs, James B. | Richards, J. Brent | Frayling, Timothy M.
Diabetes  2013;62(10):3589-3598.
Adiponectin is strongly inversely associated with insulin resistance and type 2 diabetes, but its causal role remains controversial. We used a Mendelian randomization approach to test the hypothesis that adiponectin causally influences insulin resistance and type 2 diabetes. We used genetic variants at the ADIPOQ gene as instruments to calculate a regression slope between adiponectin levels and metabolic traits (up to 31,000 individuals) and a combination of instrumental variables and summary statistics–based genetic risk scores to test the associations with gold-standard measures of insulin sensitivity (2,969 individuals) and type 2 diabetes (15,960 case subjects and 64,731 control subjects). In conventional regression analyses, a 1-SD decrease in adiponectin levels was correlated with a 0.31-SD (95% CI 0.26–0.35) increase in fasting insulin, a 0.34-SD (0.30–0.38) decrease in insulin sensitivity, and a type 2 diabetes odds ratio (OR) of 1.75 (1.47–2.13). The instrumental variable analysis revealed no evidence of a causal association between genetically lower circulating adiponectin and higher fasting insulin (0.02 SD; 95% CI −0.07 to 0.11; N = 29,771), nominal evidence of a causal relationship with lower insulin sensitivity (−0.20 SD; 95% CI −0.38 to −0.02; N = 1,860), and no evidence of a relationship with type 2 diabetes (OR 0.94; 95% CI 0.75–1.19; N = 2,777 case subjects and 13,011 control subjects). Using the ADIPOQ summary statistics genetic risk scores, we found no evidence of an association between adiponectin-lowering alleles and insulin sensitivity (effect per weighted adiponectin-lowering allele: −0.03 SD; 95% CI −0.07 to 0.01; N = 2,969) or type 2 diabetes (OR per weighted adiponectin-lowering allele: 0.99; 95% CI 0.95–1.04; 15,960 case subjects vs. 64,731 control subjects). These results do not provide any consistent evidence that interventions aimed at increasing adiponectin levels will improve insulin sensitivity or risk of type 2 diabetes.
PMCID: PMC3781444  PMID: 23835345
18.  The South Asian Genome 
PLoS ONE  2014;9(8):e102645.
The genetic sequence variation of people from the Indian subcontinent who comprise one-quarter of the world's population, is not well described. We carried out whole genome sequencing of 168 South Asians, along with whole-exome sequencing of 147 South Asians to provide deeper characterisation of coding regions. We identify 12,962,155 autosomal sequence variants, including 2,946,861 new SNPs and 312,738 novel indels. This catalogue of SNPs and indels amongst South Asians provides the first comprehensive map of genetic variation in this major human population, and reveals evidence for selective pressures on genes involved in skin biology, metabolism, infection and immunity. Our results will accelerate the search for the genetic variants underlying susceptibility to disorders such as type-2 diabetes and cardiovascular disease which are highly prevalent amongst South Asians.
PMCID: PMC4130493  PMID: 25115870
19.  Pancreatic islet enhancer clusters enriched in type 2 diabetes risk–associated variants 
Nature genetics  2014;46(2):136-143.
Type 2 diabetes affects over 300 million people, causing severe complications and premature death, yet the underlying molecular mechanisms are largely unknown. Pancreatic islet dysfunction is central for type 2 diabetes pathogenesis, and therefore understanding islet genome regulation could provide valuable mechanistic insights. We have now mapped and examined the function of human islet cis-regulatory networks. We identify genomic sequences that are targeted by islet transcription factors to drive islet-specific gene activity, and show that most such sequences reside in clusters of enhancers that form physical 3D chromatin domains. We find that sequence variants associated with type 2 diabetes and fasting glycemia are enriched in these clustered islet enhancers, and identify trait-associated variants that disrupt DNA-binding and islet enhancer activity. Our studies illustrate how islet transcription factors interact functionally with the epigenome, and provide systematic evidence that dysregulation of islet enhancers is relevant to the mechanisms underlying type 2 diabetes.
PMCID: PMC3935450  PMID: 24413736
20.  Distribution and Medical Impact of Loss-of-Function Variants in the Finnish Founder Population 
PLoS Genetics  2014;10(7):e1004494.
Exome sequencing studies in complex diseases are challenged by the allelic heterogeneity, large number and modest effect sizes of associated variants on disease risk and the presence of large numbers of neutral variants, even in phenotypically relevant genes. Isolated populations with recent bottlenecks offer advantages for studying rare variants in complex diseases as they have deleterious variants that are present at higher frequencies as well as a substantial reduction in rare neutral variation. To explore the potential of the Finnish founder population for studying low-frequency (0.5–5%) variants in complex diseases, we compared exome sequence data on 3,000 Finns to the same number of non-Finnish Europeans and discovered that, despite having fewer variable sites overall, the average Finn has more low-frequency loss-of-function variants and complete gene knockouts. We then used several well-characterized Finnish population cohorts to study the phenotypic effects of 83 enriched loss-of-function variants across 60 phenotypes in 36,262 Finns. Using a deep set of quantitative traits collected on these cohorts, we show 5 associations (p<5×10−8) including splice variants in LPA that lowered plasma lipoprotein(a) levels (P = 1.5×10−117). Through accessing the national medical records of these participants, we evaluate the LPA finding via Mendelian randomization and confirm that these splice variants confer protection from cardiovascular disease (OR = 0.84, P = 3×10−4), demonstrating for the first time the correlation between very low levels of LPA in humans with potential therapeutic implications for cardiovascular diseases. More generally, this study articulates substantial advantages for studying the role of rare variation in complex phenotypes in founder populations like the Finns and by combining a unique population genetic history with data from large population cohorts and centralized research access to National Health Registers.
Author Summary
We explored the coding regions of 3,000 Finnish individuals with 3,000 non-Finnish Europeans (NFEs) using whole-exome sequence data, in order to understand how an individual from a bottlenecked population might differ from an individual from an out-bred population. We provide empirical evidence that there are more rare and low-frequency deleterious alleles in Finns compared to NFEs, such that an average Finn has almost twice as many low-frequency complete knockouts of a gene. As such, we hypothesized that some of these low-frequency loss-of-function variants might have important medical consequences in humans and genotyped 83 of these variants in 36,000 Finns. In doing so, we discovered that completely knocking out the TSFM gene might result in inviability or a very severe phenotype in humans and that knocking out the LPA gene might confer protection against coronary heart diseases, suggesting that LPA is likely to be a good potential therapeutic target.
PMCID: PMC4117444  PMID: 25078778
21.  Genome-wide association and longitudinal analyses reveal genetic loci linking pubertal height growth, pubertal timing and childhood adiposity 
Human Molecular Genetics  2013;22(13):2735-2747.
The pubertal height growth spurt is a distinctive feature of childhood growth reflecting both the central onset of puberty and local growth factors. Although little is known about the underlying genetics, growth variability during puberty correlates with adult risks for hormone-dependent cancer and adverse cardiometabolic health. The only gene so far associated with pubertal height growth, LIN28B, pleiotropically influences childhood growth, puberty and cancer progression, pointing to shared underlying mechanisms. To discover genetic loci influencing pubertal height and growth and to place them in context of overall growth and maturation, we performed genome-wide association meta-analyses in 18 737 European samples utilizing longitudinally collected height measurements. We found significant associations (P < 1.67 × 10−8) at 10 loci, including LIN28B. Five loci associated with pubertal timing, all impacting multiple aspects of growth. In particular, a novel variant correlated with expression of MAPK3, and associated both with increased prepubertal growth and earlier menarche. Another variant near ADCY3-POMC associated with increased body mass index, reduced pubertal growth and earlier puberty. Whereas epidemiological correlations suggest that early puberty marks a pathway from rapid prepubertal growth to reduced final height and adult obesity, our study shows that individual loci associating with pubertal growth have variable longitudinal growth patterns that may differ from epidemiological observations. Overall, this study uncovers part of the complex genetic architecture linking pubertal height growth, the timing of puberty and childhood obesity and provides new information to pinpoint processes linking these traits.
PMCID: PMC3674797  PMID: 23449627
22.  Evaluation of Common Type 2 Diabetes Risk Variants in a South Asian Population of Sri Lankan Descent 
PLoS ONE  2014;9(6):e98608.
Most studies seeking common variant associations with type 2 diabetes (T2D) have focused on individuals of European ancestry. These discoveries need to be evaluated in other major ancestral groups, to understand ethnic differences in predisposition, and establish whether these contribute to variation in T2D prevalence and presentation. This study aims to establish whether common variants conferring T2D-risk in Europeans contribute to T2D-susceptibility in the South Asian population of Sri Lanka.
Lead single nucleotide polymorphism (SNPs) at 37 T2D-risk loci attaining genome-wide significance in Europeans were genotyped in 878 T2D cases and 1523 normoglycaemic controls from Sri Lanka. Association testing was performed by logistic regression adjusting for age and sex and by the Cochran-Mantel-Haenszel test after stratifying according to self-identified ethnolinguistic subgroup. A weighted genetic risk score was generated to examine the combined effect of these SNPs on T2D-risk in the Sri Lankan population.
Of the 36 SNPs passing quality control, sixteen showed nominal (p<0.05) association in Sri Lankan samples, fifteen of those directionally-consistent with the original signal. Overall, these association findings were robust to analyses that accounted for membership of ethnolinguistic subgroups. Overall, the odds ratios for 31 of the 36 SNPs were directionally-consistent with those observed in Europeans (p = 3.2×10−6). Allelic odds ratios and risk allele frequencies in Sri Lankan subjects were not systematically different to those reported in Europeans. Genetic risk score and risk of T2D were strongly related in Sri Lankans (per allele OR 1.10 [95%CI 1.08–1.13], p = 1.2×10−17).
Our data indicate that most T2D-risk variants identified in Europeans have similar effects in South Asians from Sri Lanka, and that systematic difference in common variant associations are unlikely to explain inter-ethnic differences in prevalence or presentation of T2D.
PMCID: PMC4057178  PMID: 24926958
23.  Loss-of-function mutations in SLC30A8 protect against type 2 diabetes 
Flannick, Jason | Thorleifsson, Gudmar | Beer, Nicola L. | Jacobs, Suzanne B. R. | Grarup, Niels | Burtt, Noël P. | Mahajan, Anubha | Fuchsberger, Christian | Atzmon, Gil | Benediktsson, Rafn | Blangero, John | Bowden, Don W. | Brandslund, Ivan | Brosnan, Julia | Burslem, Frank | Chambers, John | Cho, Yoon Shin | Christensen, Cramer | Douglas, Desirée A. | Duggirala, Ravindranath | Dymek, Zachary | Farjoun, Yossi | Fennell, Timothy | Fontanillas, Pierre | Forsén, Tom | Gabriel, Stacey | Glaser, Benjamin | Gudbjartsson, Daniel F. | Hanis, Craig | Hansen, Torben | Hreidarsson, Astradur B. | Hveem, Kristian | Ingelsson, Erik | Isomaa, Bo | Johansson, Stefan | Jørgensen, Torben | Jørgensen, Marit Eika | Kathiresan, Sekar | Kong, Augustine | Kooner, Jaspal | Kravic, Jasmina | Laakso, Markku | Lee, Jong-Young | Lind, Lars | Lindgren, Cecilia M | Linneberg, Allan | Masson, Gisli | Meitinger, Thomas | Mohlke, Karen L | Molven, Anders | Morris, Andrew P. | Potluri, Shobha | Rauramaa, Rainer | Ribel-Madsen, Rasmus | Richard, Ann-Marie | Rolph, Tim | Salomaa, Veikko | Segrè, Ayellet V. | Skärstrand, Hanna | Steinthorsdottir, Valgerdur | Stringham, Heather M. | Sulem, Patrick | Tai, E Shyong | Teo, Yik Ying | Teslovich, Tanya | Thorsteinsdottir, Unnur | Trimmer, Jeff K. | Tuomi, Tiinamaija | Tuomilehto, Jaakko | Vaziri-Sani, Fariba | Voight, Benjamin F. | Wilson, James G. | Boehnke, Michael | McCarthy, Mark I. | Njølstad, Pål R. | Pedersen, Oluf | Groop, Leif | Cox, David R. | Stefansson, Kari | Altshuler, David
Nature genetics  2014;46(4):357-363.
Loss-of-function mutations protective against human disease provide in vivo validation of therapeutic targets1,2,3, yet none are described for type 2 diabetes (T2D). Through sequencing or genotyping ~150,000 individuals across five ethnicities, we identified 12 rare protein-truncating variants in SLC30A8, which encodes an islet zinc transporter (ZnT8)4 and harbors a common variant (p.Trp325Arg) associated with T2D risk, glucose, and proinsulin levels5–7. Collectively, protein-truncating variant carriers had 65% reduced T2D risk (p=1.7×10−6), and non-diabetic Icelandic carriers of a frameshift variant (p.Lys34SerfsX50) demonstrated reduced glucose levels (−0.17 s.d., p=4.6×10−4). The two most common protein-truncating variants (p.Arg138X and p.Lys34SerfsX50) individually associate with T2D protection and encode unstable ZnT8 proteins. Previous functional study of SLC30A8 suggested reduced zinc transport increases T2D risk8,9, yet phenotypic heterogeneity was observed in rodent Slc30a8 knockouts10–15. Contrastingly, loss-of-function mutations in humans provide strong evidence that SLC30A8 haploinsufficiency protects against T2D, proposing ZnT8 inhibition as a therapeutic strategy in T2D prevention.
PMCID: PMC4051628  PMID: 24584071
24.  Heritability of variation in glycaemic response to metformin: a genome-wide complex trait analysis 
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.
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.
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.
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.
Wellcome Trust.
PMCID: PMC4038749  PMID: 24731673
25.  Gene-Lifestyle Interaction and Type 2 Diabetes: The EPIC InterAct Case-Cohort Study 
PLoS Medicine  2014;11(5):e1001647.
In this study, Wareham and colleagues quantified the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention. The authors found that the relative effect of a type 2 diabetes genetic risk score is greater in younger and leaner participants, and the high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Please see later in the article for the Editors' Summary
Understanding of the genetic basis of type 2 diabetes (T2D) has progressed rapidly, but the interactions between common genetic variants and lifestyle risk factors have not been systematically investigated in studies with adequate statistical power. Therefore, we aimed to quantify the combined effects of genetic and lifestyle factors on risk of T2D in order to inform strategies for prevention.
Methods and Findings
The InterAct study includes 12,403 incident T2D cases and a representative sub-cohort of 16,154 individuals from a cohort of 340,234 European participants with 3.99 million person-years of follow-up. We studied the combined effects of an additive genetic T2D risk score and modifiable and non-modifiable risk factors using Prentice-weighted Cox regression and random effects meta-analysis methods. The effect of the genetic score was significantly greater in younger individuals (p for interaction  = 1.20×10−4). Relative genetic risk (per standard deviation [4.4 risk alleles]) was also larger in participants who were leaner, both in terms of body mass index (p for interaction  = 1.50×10−3) and waist circumference (p for interaction  = 7.49×10−9). Examination of absolute risks by strata showed the importance of obesity for T2D risk. The 10-y cumulative incidence of T2D rose from 0.25% to 0.89% across extreme quartiles of the genetic score in normal weight individuals, compared to 4.22% to 7.99% in obese individuals. We detected no significant interactions between the genetic score and sex, diabetes family history, physical activity, or dietary habits assessed by a Mediterranean diet score.
The relative effect of a T2D genetic risk score is greater in younger and leaner participants. However, this sub-group is at low absolute risk and would not be a logical target for preventive interventions. The high absolute risk associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Please see later in the article for the Editors' Summary
Editors' Summary
Worldwide, more than 380 million people currently have diabetes, and the condition is becoming increasingly common. Diabetes is characterized by high levels of glucose (sugar) in the blood. Blood sugar levels are usually controlled by insulin, a hormone released by the pancreas after meals (digestion of food produces glucose). In people with type 2 diabetes (the commonest type of diabetes), blood sugar control fails because the fat and muscle cells that normally respond to insulin by removing excess sugar from the blood become less responsive to insulin. Type 2 diabetes can often initially be controlled with diet and exercise (lifestyle changes) and with antidiabetic drugs such as metformin and sulfonylureas, but patients may eventually need insulin injections to control their blood sugar levels. Long-term complications of diabetes, which include an increased risk of heart disease and stroke, reduce the life expectancy of people with diabetes by about ten years compared to people without diabetes.
Why Was This Study Done?
Type 2 diabetes is thought to originate from the interplay between genetic and lifestyle factors. But although rapid progress is being made in understanding the genetic basis of type 2 diabetes, it is not known whether the consequences of adverse lifestyles (for example, being overweight and/or physically inactive) differ according to an individual's underlying genetic risk of diabetes. It is important to investigate this question to inform strategies for prevention. If, for example, obese individuals with a high level of genetic risk have a higher risk of developing diabetes than obese individuals with a low level of genetic risk, then preventative strategies that target lifestyle interventions to obese individuals with a high genetic risk would be more effective than strategies that target all obese individuals. In this case-cohort study, researchers from the InterAct consortium quantify the combined effects of genetic and lifestyle factors on the risk of type 2 diabetes. A case-cohort study measures exposure to potential risk factors in a group (cohort) of people and compares the occurrence of these risk factors in people who later develop the disease with those who remain disease free.
What Did the Researchers Do and Find?
The InterAct study involves 12,403 middle-aged individuals who developed type 2 diabetes after enrollment (incident cases) into the European Prospective Investigation into Cancer and Nutrition (EPIC) and a sub-cohort of 16,154 EPIC participants. The researchers calculated a genetic type 2 diabetes risk score for most of these individuals by determining which of 49 gene variants associated with type 2 diabetes each person carried, and collected baseline information about exposure to lifestyle risk factors for type 2 diabetes. They then used various statistical approaches to examine the combined effects of the genetic risk score and lifestyle factors on diabetes development. The effect of the genetic score was greater in younger individuals than in older individuals and greater in leaner participants than in participants with larger amounts of body fat. The absolute risk of type 2 diabetes, expressed as the ten-year cumulative incidence of type 2 diabetes (the percentage of participants who developed diabetes over a ten-year period) increased with increasing genetic score in normal weight individuals from 0.25% in people with the lowest genetic risk scores to 0.89% in those with the highest scores; in obese people, the ten-year cumulative incidence rose from 4.22% to 7.99% with increasing genetic risk score.
What Do These Findings Mean?
These findings show that in this middle-aged cohort, the relative association with type 2 diabetes of a genetic risk score comprised of a large number of gene variants is greatest in individuals who are younger and leaner at baseline. This finding may in part reflect the methods used to originally identify gene variants associated with type 2 diabetes, and future investigations that include other genetic variants, other lifestyle factors, and individuals living in other settings should be undertaken to confirm this finding. Importantly, however, this study shows that young, lean individuals with a high genetic risk score have a low absolute risk of developing type 2 diabetes. Thus, this sub-group of individuals is not a logical target for preventative interventions. Rather, suggest the researchers, the high absolute risk of type 2 diabetes associated with obesity at any level of genetic risk highlights the importance of universal rather than targeted approaches to lifestyle intervention.
Additional Information
Please access these websites via the online version of this summary at
The US National Diabetes Information Clearinghouse provides information about diabetes for patients, health-care professionals and the general public, including detailed information on diabetes prevention (in English and Spanish)
The UK National Health Service Choices website provides information for patients and carers about type 2 diabetes and about living with diabetes; it also provides people's stories about diabetes
The charity Diabetes UK provides detailed information for patients and carers in several languages, including information on healthy lifestyles for people with diabetes
The UK-based non-profit organization Healthtalkonline has interviews with people about their experiences of diabetes
The Genetic Landscape of Diabetes is published by the US National Center for Biotechnology Information
More information on the InterAct study is available
MedlinePlus provides links to further resources and advice about diabetes and diabetes prevention (in English and Spanish)
PMCID: PMC4028183  PMID: 24845081

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