Genetic aspects of type 2 diabetes and related traits The past, present and future. PhD dissertation. Niels Grarup, MD

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1 Genetic aspects of type 2 diabetes and related traits The past, present and future PhD dissertation Niels Grarup, MD Faculty of Health Sciences Aarhus University 2009

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3 Genetic aspects of type 2 diabetes and related traits The past, present and future PhD dissertation Niels Grarup, MD Faculty of Health Sciences Aarhus University Steno Diabetes Center, Gentofte & Hagedorn Research Institute, Gentofte & Department of Pharmacology, Aarhus University A colour pdf-file of the thesis can be found in the Danish Medical Bulletin PhD database at

4 Preface and acknowledgements PREFACE AND ACKNOWLEDGEMENTS This PhD thesis is submitted for evaluation, with the purpose of obtaining the degree of Doctor of Philosophy at Aarhus University. The experimental work has been carried out at Steno Diabetes Center and Hagedorn Research Institute in Gentofte and at Aarhus University between 2005 and 2009 under the supervision of Professor Ole Schmitz MD, DMSc, Professor Oluf Pedersen MD, DMSc and Professor Torben Hansen, MD, PhD. The thesis consists of a review of the current knowledge of approaches and methods applied for investigating the genetic background of complex diseases in relation to the current status of the genetic susceptibility of type 2 diabetes, obesity and dyslipidemia with special focus on the experimental results obtained during this PhD study. Furthermore a part of the thesis is dedicated to a discussion of the possible future directions of this field of scientific research. The experimental work included in this thesis has all been published in international peer-review journals. The PhD thesis is based on the following manuscripts which are thus enclosed in Appendix II: Grarup N, Urhammer SA, Ek J, Albrechtsen A, Glümer C, Borch-Johnsen K, Jørgensen T, Hansen T, Pedersen O. Studies of the relationship between the ENPP1 K121Q polymorphism and type 2 diabetes, insulin resistance and obesity in 7,333 Danish white subjects. Diabetologia 49: , 2006 Grarup N, Albrechtsen A, Ek J, Borch-Johnsen K, Jørgensen T, Schmitz O, Hansen T, Pedersen O. Variation in the peroxisome proliferator-activated receptor δ gene in relation to common metabolic traits in 7495 middleaged white people. Diabetologia 50: , 2007 Grarup N*, Stender-Petersen KL*, Andersson EA, Jørgensen T, Borch-Johnsen K, Sandbæk A, Lauritzen T, Schmitz O, Hansen T, Pedersen O. Association of variants in the sterol regulatory element-binding factor 1 gene (SREBF1) with type 2 diabetes, glycemia, and insulin resistance - A study of 15,734 Danish subjects. Diabetes 57: , 2008; *, the authors contributed equally to this work Grarup N, Andreasen CH, Andersen MK, Albrechtsen A, Sandbæk A, Lauritzen T, Borch-Johnsen K, Jørgensen T, Schmitz O, Hansen T, Pedersen O. The -250G>A promoter variant in hepatic lipase (LIPC) associates with elevated fasting serum high-density lipoprotein cholesterol modulated by interaction with physical activity in a study of 16,156 Danish subjects. J Clin Endocrinol Metab 93: , 2008 I would like to express my sincere gratitude to everyone who has helped me in the PhD project and during the writing process. First of all I am indebted to my mentor, tutor and supervisor Oluf Borbye Pedersen for giving me the opportunity to work in the inspiring research facilities at Steno and Hagedorn. His ever-lasting devotion to research and science, his enormous energy, scientific inspiration and full support has been an essential part of my work and this thesis would not have been possible without him. I would also like to give my thanks to my daily supervisor Torben Hansen for his encouragement, motivation, skills and undying optimism. Also, I would like to express my gratitude to my main supervisor Ole Schmitz for support and dedication. Without the scientific and social support and fellowship from the entire Olufgruppe at Steno and Hagedorn this thesis would not have been possible. It is most of all you who make this a wonderful place to work. In particular I will thank Jakob Ek for introducing me to research without you I would probably never have got started. In addition I appreciate the support from room mates Christian S. Rose and Trine Nielsen for bearing with my paper-mess in the office and the skilful assistance from Thomas Sparsø and Camilla H. Andreasen. Also aid from master and medical students Mette K. Andersen, Camilla H. Andreasen, Ehm A. Andersson and Nikolaj T. Krarup is much appreciated. A great support and help with data analysis has come from Anders Albrechtsen I have really enjoyed your expertise, enthusiasm and innovation. I would like to thank all coauthors on the papers on which this thesis is based for their help and constructive criticism: Søren A. Urhammer, Jakob Ek, Anders Albrechtsen, Kirstine L. Stender-Petersen, Ehm A. Andersson, Camilla H. Andreasen, Mette K. Andersen, Charlotte Glümer, Knut Borch-Johnsen, Torben Jørgensen, Ole Schmitz, Annelli Sandbæk, Torsten Lauritzen, Torben Hansen and Oluf Pedersen. I am forever deeply grateful to my kids Hugo and Alberte for your infinite spontaneous joyful attitude to life you always make me smile and to Cecilie for her patience and support. Substantial financial support of this thesis has come from Aarhus University, the Danish Clinical Intervention Research Academy, the Danish Diabetes Association, and the Sehested-Hansen Foundation. Farum, March 2009 Niels Grarup

5 Table of contents TABLE OF CONTENTS SUMMARY...2 INTRODUCTION AND AIMS...3 GENETIC SUSCEPTIBILITY GENES FOR TYPE 2 DIABETES AND METABOLISM...4 Linkage studies and positional cloning...4 Biological candidate gene approach and genetic association studies...4 Candidate genes inspired by monogenic forms of diabetes...7 Candidate genes inspired by drug targets for type 2 diabetes...7 Candidate genes inspired by biological functionality..8 Technology- and HapMap-driven breakthrough discoveries in the genetics of common forms of type 2 diabetes, obesity and lipid traits...13 Validated genetic loci in common type 2 diabetes...13 Examples of putative risk genes in type 2 diabetes which are below genome-wide significance threshold...14 Genetics of obesity...15 Genetics of lipid traits...16 THE FUTURE OF THE CANDIDATE GENE APPROACH IN TYPE 2 DIABETES GENETICS...29 How to define a candidate gene in the pre-wholegenome sequencing era?...29 How to study the genomic variation in candidate regions in the future?...30 Combining multiple disciplines integrative genetics...31 REFERENCES...32 DANSK RESUMÉ...49 ABBREVIATIONS...50 Abbreviations...50 Gene name abbreviations...50 APPENDIX I - STUDY POPULATIONS...52 Inter ADDITION...52 Steno Diabetes Center - glucose-tolerant individuals53 Steno Diabetes Center - type 2 diabetic cases...53 APPENDIX II STUDIES I-IV...54 A CLOSER LOOK AT THE GENETIC ARCHITECTURE OF COMMON DISEASES SUCH AS TYPE 2 DIABETES...19 Heritability of type 2 diabetes and related traits - has it been overestimated?...19 What are identified type 2 diabetes susceptibility variants like?...19 What do the identified gene variants for metabolic phenotypes explain?...20 More low-impact, high-frequency variants...22 Low-frequency and structural variants in common disease...23 Epigenetic modifications in the inheritance of type 2 diabetes and metabolism...25 Beyond main effects the search for gene-gene and gene-environment interaction in type 2 diabetes...25 Defining the genetic background of intermediary diabetes-related phenotypes taking type 2 diabetes genetics to the general population

6 Summary SUMMARY Type 2 diabetes and obesity are major global health problems with increasing incidence and prevalence in both the western world and in the developing countries. Type 2 diabetes is primarily characterized by obesity, insulin resistance and a relative deficient insulin secretion by the pancreatic β-cell and is influenced by lifestyle, environment and genetic factors. This PhD thesis focuses on the genetic background of type 2 diabetes and related metabolic phenotypes. The PhD thesis is submitted for evaluation at the Faculty of Health Sciences at Aarhus University and is based on four published original papers. Until spring 2007 the progress in finding genes responsible for the genetic predisposition to type 2 diabetes and related traits was sparse. Studies were primarily done by investigating biological candidate genes thought to be involved in disease pathogenesis or by family-based linkage analysis. Only a few candidate genes associated with type 2 diabetes have since been widely replicated in large-scale studies. This is the case for variations in two genes inspired by known drug targets for anti-diabetic medicine, PPARG and KCNJ11, and for a candidate gene inspired by monogenic forms of diabetes, HNF1B. However, several other type 2 diabetes candidate genes have potential but disputed effects on risk of disease. We investigated the common amino-acid changing K121Q variant (rs ) in ENPP1 in relation to type 2 diabetes and insulin resistance (Study I). ENPP1 encodes a protein involved in insulin signalling in the target tissues. In a case-control study of ~6,000 individuals we found no association with type 2 diabetes. However, when combining all published data in a metaanalysis we demonstrated a modest impact on risk of type 2 diabetes. A subsequent, updated meta-analysis confirmed an 8% increase in risk of type 2 diabetes. Other examples of candidate genes in type 2 diabetes were investigated in studies II and III. PPARD encodes a multipotent regulator of glucose and lipid metabolism and insulin sensitivity expressed both in the liver and in skeletal muscles. We investigated 12 variants in PPARD and found no robust impact on type 2 diabetes, lipid traits or insulin sensitivity. In the thesis a meta-analysis is included combining data from study II and from published studies. This meta-analysis indicates an 8% increase in risk of type 2 diabetes per risk-allele of rs As well, variation in SREBF1 may siginificantly impact risk of type 2 diabetes as demonstrated in a metaanalysis of Danish and published data presented in study III. Also, this impact is modest. In the population-based Inter99 cohort and the ADDITION Denmark screening sample we found associations of SREBF1 variation with quantitative levels of glycemia (Study III) Since the development of genome-wide association studies, in which approximately 500,000 genome-wide variants are investigated simultaneously, many gene variants have been convincingly shown to influence risk of type 2 diabetes and related traits. For instance, at present 19 validated risk-loci for type 2 diabetes and ~36 loci influencing lipid traits have been found. We investigated one of the lipid-associated variants in hepatic lipase, LIPC, in two large cohorts and found a highly statistically significant mmol/l increase in high-density lipoprotein (HDL) cholesterol levels in both cohorts (Study IV). In addition, we showed that this effect may be modulated by physical activity showing a higher increase in HDL-cholesterol in vigorously physically active individuals. Despite the high number of validated common variants, the explained proportion of the genetic contribution and variation in metabolic phenotypes is modest. In the Inter99 cohort the validated variants explain from 1 to 3.5% of variation in fasting glucose, body mass index, lipid traits and estimates of insulin sensitivity and insulin response. Similarly, validated type 2 diabetes variants explain below 10% of the genetic contribution to risk of disease which is underlined by the poor ability of genetic variants to predict type 2 diabetes. Reasons for the large residual variation are many. First, many more common, low-impact variants probably exist. Second, variants with a frequency below 5% have not been thoroughly investigated and preliminary reports indicate that these variants may cause larger impacts. Simulation analyses presented in the PhD thesis show that a high number of both common, low-impact variants and variants with lower frequencies and higher impacts are necessary to obtain a useful ability to predict type 2 diabetes based on genetic information. Third, also factors like structural variation, gene-gene and geneenvironment interaction and epigenetic modifications may explain parts of the residual variation in metabolic traits. In summary, the studies performed in the PhD project contribute to the ongoing exploration of the molecular genetics of type 2 diabetes and related phenotypes and may provide further basis for the improvement of diagnosis and prevention of this complex disease. 2

7 Introduction and aims INTRODUCTION AND AIMS Type 2 diabetes, obesity and the inflicted complications are major global health problems due to dramatically increasing prevalence in both the western world and in the developing countries (1). The total number of people worldwide with diabetes is projected to rise from 171 million in 2000 to 366 million in 2030 corresponding to a predicted increase in prevalence from 6.0% in 2000 to 7.3% in 2030 (2). Type 2 diabetes is primarily caused by obesity, insulin resistance in liver, skeletal muscle and adipose tissue and a relative deficient insulin secretion by the pancreatic β-cell (3,4). Moreover type 2 diabetes clusters with dyslipidemia and hypertension; which together with insulin resistance, glucose intolerance and visceral obesity are hallmarks of the metabolic syndrome (5). Type 2 diabetes is often accompanied by severe complications of the cardiovascular system, eyes and kidneys leading to increased morbidity and mortality from cardiovascular disease (CVD) and end-stage renal failure. The large increase in incidence and prevalence of type 2 diabetes and obesity seems to be largely due to lifestyle changes such as high-fat diet and physical inactivity, yet, several genetic epidemiological studies demonstrate that both obesity and type 2 diabetes are highly inherited traits (6-9). However, despite much research, the dissection of the specific causes of these common disorders at the molecular level is still in its infancy. More detailed insights into the molecular mechanisms of the metabolic diseases are thought to improve the chances for a more targeted treatment and in some cases also for prevention of disease development. Thus the overall aim of this PhD project was to contribute to the continuing exploration of the genetic risk factors of type 2 diabetes and related phenotypes as obesity, insulin resistance and dyslipidemia. The aim of the PhD thesis is to review this field of research in relation to past findings, the current status and future inventions. The review is divided in three parts. The purpose of the first part is to give a brief review of past and current findings in the dissection of type 2 diabetes, insulin resistance, obesity and dyslipidemia with incorporation of methodological issues. The second part elaborates on the relatively little part of the genetic contribution explained by findings thus far and discusses possible future directions of research. In the third part, the future of the candidate gene approach in studies of complex diseases is discussed. In particular, four candidate genes for type 2 diabetes, insulin resistance and dyslipidemia were investigated and are discussed in the review. I) ENPP1 is involved in insulin signalling, insulin resistance, obesity and type 2 diabetes (10); II) PPARD is a biological candidate and treatment target for type 2 diabetes, insulin resistance and dyslipidemia (11); III) SREBF1 is involved in signalling of insulin action (12) and IV) LIPC encodes a liver-enzyme important for metabolism of high-density lipoprotein (HDL) cholesterol (13). For full details of the studies the reader is referred to Appendix II. 3

8 Genetic susceptibility genes for type 2 diabetes and metabolism GENETIC SUSCEPTIBILITY GENES FOR TYPE 2 DIABETES AND METABOLISM For years it has been well-known that genetic factors are crucially important for the development of type 2 diabetes (14). Despite a great effort in seeking to understand the molecular genetic basis, until a few years ago, only a handful of genes responsible for relatively rare monogenic and syndromic subsets of diabetes were detected and progress in finding genetic predispositions to common type 2 diabetes was lacking. However, the last couple of years have brought by a revolution in genetics of common, complex traits leading to renewed optimism for the validity of this research. Despite this great success major tasks are still undone to fully understand the genetic architecture of type 2 diabetes and related phenotypes. Linkage studies and positional cloning Before the existence of genome-wide association (GWA) studies the investigation of genetic origins for the polygenic heritability of common metabolic diseases and phenotypes were primarily done by candidate gene approach or by genetic mapping by linkage analysis. Linkage analysis seeks evidence of co-segregation between genomic markers and the phenotype of interest. These two methods are fundamentally different in the prior assumptions made. Linkage analysis requires no prior hypothesis of the genomic regions investigated while in the candidate gene method a gene of interest is selected based the a priori biological involvement in disease pathogenesis leading to obvious limitations by the dependence on the relatively sparse knowledge of disease pathology. In principle, linkage studies allow the entire genome to be screened for susceptibility loci using a limited number of highly polymorphic microsatelitte markers (15) and has proven very successful in detecting genes involved in Mendelian monogenic diseases (16). However, since risk for relatives is lower in common complex diseases the statistical power of this method in studies of polygenic traits is limited due to the low penetrance of polygenic risk-alleles (17). Even for loci with substantial effects on susceptibility at the population level, the number of families needed to offer reasonable power to detect linkage is prohibitive (18). Since no high-impact risk-alleles seems to exist in type 2 diabetes all linkage scans performed in type 2 diabetes have, in retrospect, been statistically underpowered which also explains the meagre findings and the lack of replication of regions putatively linked to disease. A major obstacle, besides lack of statistical power, is that even if evidence of linkage is observed the genomic region linked to disease is often very large. The process of fine-mapping a disease-linked region to identify a causative gene or even a polymorphism has proved to be a major challenge. The success of linkage and subsequent fine-mapping in identifying genes with a putative influence on type 2 diabetes has been restricted to calpain 10 (CAPN10) and in some sense transcription factor 7-like 2 (TCF7L2). The region on chromosome 2q37 containing CAPN10 was initially found in a linkage scan of type 2 diabetes in Mexican Americans (19). By large efforts in positional cloning, a haplotype of three single-nucleotide polymorphisms (SNPs) in an intron of CAPN10 was associated with risk of type 2 diabetes and could explain most of the linkage to type 2 diabetes (20). Despite subsequent inconstant replication, two meta-analyses have in 2006 shown a modest 9-15% increased risk of type 2 diabetes in carriers of CAPN10 SNP43 and SNP44 (21,22). These variants are also associated with an insulin resistance phenotype (23). One of the major lessens learned from CAPN10 has been that intronic variation may contribute to risk of complex diseases. To date the strongest type 2 diabetes susceptibility gene is TCF7L2 with an increase in risk of ~40% per minor T-allele (Table 1, page 14). TCF7L2 was discovered by typing of microsatellite markers under a previously identified linkage peak (24) and one of the genotyped microsatellite markers associated strongly with type 2 diabetes and could be tagged by nearby SNPs (25). Unprecedented in 2006, the association of variants in TCF7L2 with type 2 diabetes has since been widely replicated in populations of a range of different ethnic origins (26,27). Risk variants in TCF7L2 associate with impaired insulin response possibly due to an impaired effect of the incretin hormones, GLP-1 and GIP; although the more exact diabetogenic mechanism has not been elucidated (28-30). Biological candidate gene approach and genetic association studies Different approaches have been taken in the search for genetic contributions to complex disease. The development of approaches has mainly been guided by technological advances in genotyping and sequencing techniques, statistical handling of data and also by collection of larger cohorts suitable for genetic studies. Before the advent of GWA the major approaches for 4

9 Genetic susceptibility genes for type 2 diabetes and metabolism finding susceptibility variation were the candidate gene approach and genetic linkage analysis (31-33). In the biological candidate gene approach the gene of interest is selected based on biological knowledge of relation of the gene with the particular disease or trait, for instance based on in vivo genetically engineered animal models or in vitro cell experiments. Variation in the gene of interest is then investigated in genetic association studies to find polymorphisms with an influence on disease risk. The biological candidate gene approach has generally had limited success in finding susceptibility genes for common diseases. Several reasons for this exist and while some are related to the method itself others are more a reflection of the era in genetic research in which this approach was widely used, i.e. limitations of genetic association studies in the early phases of molecular genetic epidemiology. First, a major and somewhat crucial limitation of the candidate gene approach is the fundamental need to have a detailed knowledge of the disease of interest to be able to pick a reasonable candidate gene. As the pathophysiology of type 2 diabetes and related traits is extremely complex involving thousands of proteins, any single candidate gene will have a low prior probability to affect disease susceptibility. Second, genetic association studies performed in a case-control design of unrelated individuals induce a risk that differences in population structure give rise to false positive associations, i.e. that undisclosed or unknown differences in ethnical origin imposes differences in frequency of genetic variants or disease occurrence mimicking the signal of association (34,35). This phenomenon is also termed population stratification. In case-control studies of a few selected variants there is no widely accepted way of statistically assessing or accounting for population structure. Several methods to detect and correct for population stratification have been suggested but they all demand genotyping of a large panel of unrelated markers and were not widely used before the GWA studies (36-39). This is opposed to GWA studies for which validated methods for accounting for this problem are at hand and easily incorporated in the analyses (40,41). Although appropriate and detailed sampling of cases and controls may diminish problems of population stratification, even in well-designed studies modest amounts of population stratification can be detected (42). The effect of population stratification on the results of association analyses are potentially more severe when small effects are studied in very large sample sets (43). Third, a major obstacle in most reported candidate gene studies is the study design in relation to size and phenotypic characterization of the studied sample and genomic coverage of the gene of interest. Sample sizes have over the years generally increased tremendously in recognition of the very modest effect sizes inflicted by most common variants related to common disease. Statistical power analyses are important in order to assess the limitations of the study and for small effect sizes thousands of samples are needed to ensure validity of the interpretation. In figure 1 is shown estimates of the statistical power of different sample sizes, allele frequencies and effect sizes for two different significance thresholds; a conventional P-value of 0.05 and a genomewide significance level of (44). It is evident that for effect sizes of odds ratio (OR) below ~1.2 large samples are needed to have power to detect association even at a liberal significance threshold of At genome-wide significance, around 10,000 cases and 10,000 controls are needed to have 80% power to detect effect sizes of ~1.2 (Figure 1). In order to circumvent problems with lack of statistical power, meta-analysis has been increasing applied in genetic epidemiology (45,46). Although larger sample sizes in theory deliver more confident estimates of association there are several pitfalls related to metaanalysis. One problem is publication bias which refers to the fact that negative reports are often not published and a meta-analysis may therefore tend to overestimate association. Several statistical methods exists to uncover publication bias (47,48) but they generally have low power (49,50). These tests can possibly also detect heterogeneity between studies because of overestimation of effect sizes in small studies of low quality (51,52). Also, other biases (such as time lag bias and language bias) can influence the outcome of metaanalyses. Heterogeneity between studies may also be introduced by confounding by ethnic origin, age, sex, or other, measured or unmeasured, variables and should be thoroughly evaluated (52,53). Therefore, answers from meta-analysis in genetic epidemiology should be carefully considered and interpreted. 5

10 Genetic susceptibility genes for type 2 diabetes and metabolism MAF 1% MAF 5% MAF 10% MAF 20% MAF 40% Figure 1. Statistical power for case-control studies of variants with a minor allele frequency (MAF) of 1-40% and relative risk (RR) below 1.5. In the upper three panels are shown power as a function of effect size per allele for a conventional significance level of alpha = 0.05 in 4,000; 10,000 and 20,000 individuals, respectively. Similarly, in the lower three panels are shown power as a function of effect size per allele for a genome-wide significance level of alpha = for three different sample sizes. Black line: MAF 1%, Red line: MAF 5%, Green line: MAF 10%, Blue line: MAF 20%, Yellow line: MAF 40%. The power analyses were performed using RGui (54) by 5,000 simulations for each of 50 points for each line assuming an additive genetic model and a disease prevalence of 8%. Furthermore, the selection of variants to large-scale genotype in the gene of interest has also changed over time. Some years ago, thorough investigations were done by performing mutation screening of the coding region to detect common variants for genotyping. Other studies included only a single or a few candidate polymorphisms either thought to be causal, most often variants changing an amino acid of the encoded protein, to study direct associations. However, the disclosure of the human genome sequence (55) led to an international collaboration with the ambition to develop an extensive catalogue of common SNPs and to describe linkage disequilibrium (LD) and haplotype structure across the human genome in four populations (HapMap) (56,57). By 2007 more than 3 million SNPs were genotyped, and supported by sequence data the outcome of these studies showed that the vast majority of common SNPs are correlated to one or more nearby SNPs which can serve as proxies (58). In genetic association studies this has led to a trend for candidate genes to be investigated by selection of haplotype tagging SNPs (tag SNPs) normally with the aim that all common, HapMap-covered variation in the gene region should be correlated with a genotyped SNP at an r 2 of at least 0.8. Theoretically, this allows for testing of indirect association with markers correlated to an unmeasured causal variant without severely impeding statistical power to pick up the true association signal (59,60). Finally, thorough phenotype characterization of study subjects is of importance in case-control designs to ensure appropriate categorical classifications and also to allow for statistical adjustment for confounding factors, especially related to environmental exposures. Besides the first point on how to choose a candidate gene, issues discussed here are not related directly to the biological candidate gene approach but are more general limitations in genetic association studies. Although many associations in biological candidate gene studies have been published only few associations have since been replicated (45,61). An obvious reason for non-replication is lack of statistical power in follow-up studies to detect 6

11 Genetic susceptibility genes for type 2 diabetes and metabolism or more crucially to exclude an initial true finding (type II error). Other reasons relate to false positive findings (type I error) in the initial report due to failure to properly correct for multiple testing, spurious association because of population stratification or by random. Also, true population-specific differences in allele frequencies or in LD between the genotyped marker and the causal variant as well as differences in sampling and phenotypic characteristics of cases and controls may obscure replication (31,62,63). In addition, unmeasured population-specific environmental exposures may confound association. Numerous candidate genes have been investigated in relation to risk of type 2 diabetes and metabolic phenotypes. In the early days these studies were performed in drastically underpowered study samples severely impeding the detection and replication of true associations. Since effect sizes for common variants contributing to risk of common metabolic diseases are modest with risk increments below 40% per copy of allele, high sample sizes are crucial to obtain statistically power to detect and exclude association of specific loci. Despite limitations this method has been successful in finding genes for both monogenic and polygenic forms of type 2 diabetes and metabolism. Candidate genes inspired by monogenic forms of diabetes Progress in finding disease-causing gene variants for the relatively rare monogenic subsets of diabetes has been impressive (64), and around half of all genetic contributions to maturity-onset diabetes of the young (MODY), which is the most common form of monogenic diabetes, has been unravelled (65). Early it was hypothesized that more common and non-deleterious variants in genes causing monogenic diabetes could give rise to susceptibility to common complex types of the disease and this has proved a successful line of enquiry. Variations in several MODY genes have been shown to impact type 2 diabetes or type 2 diabetes-related phenotypes. A promoter variant in the MODY2 gene, glucokinase (GCK), has been repeatedly associated with variation in fasting plasma glucose and glucose levels after an oral glucose challenge, however it does not associate with type 2 diabetes (66,67). In addition, two variants in the distant P2 promoter of the MODY1 gene, hepatocyte nuclear factor 4α (HNF4A), have been associated with risk of type 2 diabetes although the impact is disputed (68-72). Statistically most confidence in association with common type 2 diabetes has been reported for variants in HNF1B (MODY5) which is now an accepted type 2 diabetes risk gene (Table 1). Initially, this gene has been tested as a candidate gene for common type 2 diabetes (73) but is was not until sample sizes increased that the modest effect (~10% risk-increase per allele) of variation in HNF1B was detected (74,75). Simultaneously in 2007, HNF1B was also detected as a type 2 diabetes risk gene in a GWA study of prostate cancer (76). Similarly, common variants in two genes causing syndromic forms of diabetes, wolfram syndrome 1 (WFS1) and lamin A/C (LMNA), have been implicated in type 2 diabetes pathogenesis by candidate gene analysis. Variants in WFS1 were identified in a large evaluation of 84 candidate genes and have a modest but statistically robust impact on risk of type 2 diabetes (77,78). The increase in risk for WFS1 carriers seems to be transmitted through an impaired β-cell function although data are complex and of modest statistical significance (79,80). A potential modest effect of LMNA variants is still disputed (81-84). Also, for obesity and lipid disorders there are several examples which underline this relationship between rare mutations causing monogenic forms of disease and more common at-risk variants in the same gene (85). Candidate genes inspired by drug targets for type 2 diabetes Besides inspiration from monogenic subsets of diabetes, stimulation for selection of candidate genes for common type 2 diabetes has come from the known drug targets of anti-diabetic medicine. Peroxisome proliferator-activated receptor γ (PPARG) emerged as a type 2 diabetes candidate gene from the knowledge that a class of antidiabetic drugs, the thiazolidinediones, are high-affinity ligands for the PPARγ receptor (86). Subsequent association studies found a modest, protective impact of the PPARG P12A variant on risk of type 2 diabetes (87,88) which has been confirmed in some GWA studies (89-91). The P12A variant seems to influence type 2 diabetes by changing insulin sensitivity (87,92). Another known drug target is the sulphonylurea receptor in the β-cell which is encoded by ATP-binding cassette, sub-family C, member 8 (ABCC8) and potassium inwardly-rectifying channel, subfamily J, member 11 (KCNJ11). Variations in both genes have been widely studied and a common E23K variant in KCNJ11 has been shown to increase risk of type 2 diabetes in association studies (93,94) and replicated in GWA studies (89-91). As anticipated from the function of the gene product, this variant influences insulin secretion from the pancreatic 7

12 Genetic susceptibility genes for type 2 diabetes and metabolism β-cell (94). The role of variation in ABCC8 is not yet clarified although a major impact has been excluded (61,95,96). Interestingly, mutations in both ABCC8 and KCNJ11 are responsible for cases of permanent neonatal diabetes mellitus (97-99) and familial hyperinsulinemic hypoglycemia (100,101) underlining the importance in glucose regulation. Candidate genes inspired by biological functionality Candidate genes for genetic association studies can also be chosen from the existing knowledge of disease pathogenesis. Given the incomplete knowledge of disease pathogenesis it is very difficult to select biological candidate genes and the a priori probability of association is low. If this approach is to be successful it is crucial to carefully evaluate the gene of interest to improve chances of positive findings. Numerous examples of such genes and investigations exists in the field of type 2 diabetes and related traits. Most positive associations obtained in such studies have not been widely replicated; this is a problem in all genetic association studies. However, this may pose more profound problems in biological candidate gene studies as the low prior probability of association may increase the risk that positive findings are false positive. Below are discussions of three examples of putative type 2 diabetes risk genes, ectonucleotide pyrophosphatasephosphodiesterase 1 (ENPP1), peroxisome proliferatoractivated receptor δ (PPARD) and sterol regulatory element binding transcription factor 1 (SREBF1), selected based on this approach as illustrations of some the pros and cons of this selection method. Interestingly, for each of these genes it is evident that increasing sample size in meta-analyses of type 2 diabetes may elucidate lowimpact effects on type 2 diabetes making them putative susceptibility genes. ENPP1 encodes a protein with inhibitory effect on insulin receptor function and subsequent intracellular insulin signalling ( ) and overexpression of ENPP1 in mice leads to insulin resistance and hyperglycemia (106). ENPP1 is widely expressed including the liver, adipose tissue and skeletal muscle (107). A common K121Q polymorphism (rs ) may result in gain-offunction leading to enhanced inhibition of the insulin receptor (105,108). Since initial publications in 1999 it has been discussed whether the K121Q variant in ENPP1 has diabetogenic potential. The first report indicated association with increased insulin resistance (108) which also seems plausible given the biological role in insulin signalling in the target tissues ( ). In 2005, Meyre et al. published a report finding association of K121Q and a three SNP haplotype including K121Q with childhood and adult obesity and with type 2 diabetes in several cohorts of French and Austrian origin (109). Since then this polymorphism has been widely studied. In a subsequent study in the Danish population we found no association of ENPP1 K121Q with type 2 diabetes in a case-control study of 1,386 type 2 diabetic cases and 4,770 glucose-tolerant individuals (10). The association with type 2 diabetes has in fact been investigated in numerous studies coming to highly inconsistent results. The positive association found in the first large study (109) has been replicated in some of the subsequent studies (110,111) while other well-powered studies have failed to associate the codon 121 Q-allele with type 2 diabetes (10, ). Several authors have compiled data to increase statistical power in combined collaborative meta-analysis (10,116,122). The most recent update of a meta-analysis demonstrated a marginal 8% increase in risk of type 2 diabetes (OR 1.08, 95% confidence interval (CI) ; P=0.01) per copy of the rare Q-allele when combining data from more than 35,000 individuals of European ancestry (Figure 2); however the statistical evidence for association was not very convincing (122). This meta-analysis also indicated that K121Q may transfer its risk by a recessive genetic model for the rare Q-allele (recessive model: OR 1.38, 95% CI ; P=0.005) (122), yet this finding is not in agreement the meta-analysis performed in the Danish study which could statistically exclude the recessive genetic model in favour of an additive mode of risk transmission (10). The online database for results of the Diabetes Genetics Replication And Meta-analysis (DIAGRAM) consortium GWA meta-analysis of ~10,000 individuals show no association of K121Q with type 2 diabetes (P=0.8) (123); however many samples are overlapping with the study by Weedon et al. (116) and data can therefore not be directly included in the metaanalysis. 8

13 Genetic susceptibility genes for type 2 diabetes and metabolism Figure 2. Meta-analysis of the association of ENPP1 K121Q with type 2 diabetes in 35,326 individuals from populations of European ancestry. In combined analysis the Q-allele associates with type 2 diabetes with an OR of 1.08 (95% CI , P=0.01). References for individual studies: Pizzuti et al. (108), Gu et al. (112), Barroso et al. (114), Abate et al. (110), Meyre et al. (109), Bacci et al. (124), Bochenski et al. (118), Grarup et al. (10), Kubaszek et al. (125), Lyon et al. (117), Weedon et al. (116), Chandalia et al. (119), Willer et al. (126), Meyre et al. (120). The meta-analysis was published by McAteer et al. in 2008 (122). Many explanations for the inconsistent association results for type 2 diabetes exist. None of the individual case-control studies have had sufficient statistical power to detect modest effect sizes with any certainty (Figure 1, page 6; K121Q minor allele frequency (MAF) is ~15%). As discussed, the objective of meta-analyses is to circumvent this major problem but meta-analyses have other methodological limitations; primarily relating to publication bias and heterogeneity between studies. In general, false-positive reports of association can be due to publication bias, bias by population stratification, or initial overfitting of genotype-phenotype correlations (127). In regard to the former, it is notable that two of the published meta-analyses indicate signs of publication bias (116,122). Additionally, positive results may be confounded by underlying population substructure within association studies, especially when both allele frequencies and rates of disease vary among different ethnic groups. The Q-allele frequencies of ENPP1 K121Q differ substantially among African Americans (~78%), Hispanics (~22%) and Whites (~13%) (119), why cryptic differences in population ancestry between case and control individuals may very well lead to spurious findings and confound the true effect size of association. Also the most recent meta-analysis observes residual heterogeneity which may be partly explained by modification of the association by body mass index (BMI). The question of a possible modifying effect of BMI on the association with type 2 diabetes is currently being addressed by an ongoing international collaboration (J.C. Florez, personal communication). Of interest in this regard is a recent report showing dramatic differences in the specific genetic associations obtained by changing BMI case ascertainment criteria in a post hoc analysis of GWA data indicating the importance of assessment of this correlated modifier (128). None of the GWA studies published within the last couple of years have reported ENPP1 as a type 2 diabetes gene (89-91, ). This comes as no surprise if the ENPP1 K121Q variant indeed acts in a recessive manner partly modified by obesity as all GWA studies so far have only reported results of unadjusted additive models and are probably severely statistically underpowered to detect recessive effects of this size. A potential association with insulin resistance phenotypes has also been very inconsistently reported with findings of association with measures of insulin resistance in some (108,110,112, ) but far from all studies (10,136). In 5,863 participants of the population-based Inter99 cohort (137) we investigated fasting and post-ogtt plasma glucose, serum insulin and serum C-peptide levels along with several indices of insulin resistance but observed no robust associations with the ENPP1 K121Q variant (10). Most of the quantitative trait studies of the 9

14 Genetic susceptibility genes for type 2 diabetes and metabolism ENPP1 K121Q variant have been small and probably underpowered to detect modest effects. Our Danish study investigated the impact on obesity and insulin resistance in a large population-based sample (10). Of importance this allows for assessment of the effect in the general population, yet it is also likely that the genetic impact is higher when analysing selected, more extreme individuals maybe making it easier to identify associations with quantitative phenotypes. Besides differences in the study population and general lack of statistical power, disparities in the methods for estimation of insulin resistance, i.e. hyperinsulinamic euglycemic clamp technique or intravenous glucose tolerance test as opposed to indices based on fasting and/or post OGTT measurements, may explain some of the inconsistencies. In our report we are admittedly restricted to analyse surrogate OGTT-based estimates of insulin resistance, although these show high correlation with more exact physiological measures ( ). In addition, in 2008 a study showed association with early phase insulin response in codon 121 QQ-carriers (141) suggesting an inefficient interplay between insulin resistance and insulin secretion. Differences in this interplay among studies may also obscure association with insulin resistance. Other SNPs or haplotypes in ENPP1 have been associated with both obesity and type 2 diabetes. Meyre et al. found association of a three SNP haplotype (rs (K121Q), rs and rs ) with obesity and type 2 diabetes while a Polish study observed some evidence of type 2 diabetes association of rs among seven ENPP1 SNPs (118). Stolerman and coworkers investigated 39 tag SNPs in ENPP1 in the Framingham Heart Study and in that study K121Q (rs ) seemed to be the most important genetic determinant of glucose homeostasis (143). A recent comprehensive candidate gene study investigated more than 3,000 SNPs in 222 candidate genes and found a highly significant association with type 2 diabetes for the rs variant in ENPP1 in a two-stage design. Yet, no association with K121Q (rs ) was found, although the point estimate was in same direction as previous reports (121). Interestingly, rs and rs are not in LD (r 2 = 0.05 in the Danish Inter99 cohort). In the DIAGRAM online database of GWA meta-analysis results (123,132) four of 78 SNPs in the ENPP1 region show nominal (P<0.05) association with type 2 diabetes which is consistent with chance findings. At present there is no robust evidence for a stronger effect of any other ENPP1 SNP or haplotype than for K121Q. Since it has been reported that K121Q seems to have a functional impact on inhibition of insulin signalling (105,108) it is likely that this polymorphism represents a causal variant in this locus, however it cannot be excluded that two separate signals of association emerge from this region. Despite inconsistencies, the persistent reports of associations between variation in ENPP1 and metabolic phenotypes combined with the molecular and physiological evidence for an implication in insulin resistance and putatively in type 2 diabetes still situate this gene as a highly plausible candidate. The story of the ENPP1 K121Q variant represents a classical example of the problems with inconsistencies and non-replication in genetic epidemiology. Also it serves as an endorsement of the complex nature of the human genetic predisposition to metabolic phenotypes. Some points are important for future studies of such genes and variants. First, huge well-defined study samples are needed to obtain confident estimates of associations and for practical reasons these studies demand close international collaborations in which detailed phenotypes and genotypes are shared. Second, these studies underline the need for integrative statistical approaches which take environmental variation into consideration to understand the complex biology behind such associations. In particular in order to get a deeper understanding of genetic susceptibility of insulin resistance, analyses should be performed across different BMI strata. Third, evidence of publication bias in the meta-analyses emphasizes the need for a forum where high-quality negative association results can be easily and freely communicated. Figure 3. Schematic overview of the multi-organ consequences of PPARδ activation in vivo. From Barish et al. (142) 10

15 Genetic susceptibility genes for type 2 diabetes and metabolism The rapidly growing rates of obesity, the metabolic syndrome and type 2 diabetes combined with the current insufficient treatment options makes it increasingly important to pursue novel drug targets. In particular it has proven very difficult to sufficiently treat the decreased insulin sensitivity observed as a hallmark of the metabolic syndrome and type 2 diabetes (4). One such novel drug target is PPARδ which has emerged as a key regulator of metabolism with the potential to therapeutically target multiple aspects of the metabolic syndrome (Figure 3) (142,144). Several different compounds have been investigated, both alone or in combination with PPARγ and PPARα agonists (145), yet none have so far been approved for clinical use (146), although clinical studies showing attractive efficacy and safety profiles have been published (147). Due to the receptor-specific treatment effects illustrated in figure 3, to its role in lipid oxidation in adipose tissue and skeletal muscle and the function as a regulator of glucose metabolism and insulin sensitivity in both skeletal muscle and the liver ( ), PPARD is an excellent candidate gene for type 2 diabetes and other cardiovascular risk factors. Owing to this obvious candidacy several studies have investigated variation in PPARD in relation to metabolic traits. In the population-based Inter99 cohort we investigated 12 PPARD tag SNPs for association with measures of insulin sensitivity and obesity as well as levels of fasting serum lipids. We found no associations in the full Inter99 cohort but in a subgroup consisting of participants with treatment-naïve impaired glucose regulation (impaired fasting glycemia (IFG), impaired glucose tolerance (IGT) or screen-detected type 2 diabetes) (n=1,448) we found a nominally statistically significant impact of rs and two haplotypes consisting of rs and rs inflicting a ~7-8% decrease per allele in insulin sensitivity as measured by homeostasis model assessment of insulin resistance (HOMA-IR) (139). Although these associations were not statistically significant after correction for multiple testing, it is biologically plausible that variation in PPARD influences insulin sensitivity in diabetes-prone subjects. Supportive of an influence on insulin sensitivity, a Finnish study of 129 young, healthy individuals found an association with increased whole-body and skeletal muscle insulin sensitivity assessed by hyperinsulinaemic euglycaemic clamp technique in carriers of three variants in PPARD (154). In addition, results of a German study of 136 subjects indicated decreased improvement in insulin sensitivity during 9-month lifestyle intervention in carriers of either of three PPARD SNPs (rs , rs , and rs ) (155). Minor allele carriers of the same SNPs also had a decreased effect of lifestyle intervention on measures of adiposity (156). Although these study cohorts are small and probably severely statistically underpowered, the former has the advantage of applying the hyperinsulinamic-euglycemic clamp technique to measure insulin sensitivity. A study of 679 individuals demonstrated an increased risk of conversion from IGT to overt type 2 diabetes in carriers of the PPARD rs variant (157); however, a small cross-sectional study of 838 individuals observed no association of rs with type 2 diabetes (158). In the Danish population we found no associations of PPARD tag SNPs with type 2 diabetes and we concluded that we with confidence can exclude an effect above 27% on risk of type 2 diabetes (11). However, in light of the risk-estimates associated with validated type 2 diabetes variants (Table 1, page 14), we are under no circumstances able to truly exclude PPARD as a type 2 diabetes susceptibility gene. In order to be more confident in the estimation of a possible impact on type 2 diabetes meta-analysis is an option. In figure 4 data from two GWA studies are extracted and combined with all published data in a meta-analysis. From the Wellcome Trust Case Control Consortium (WTCCC) GWA study (89,131,160) genotyped or imputed data were available for 11 of the tag SNPs genotyped in the Danish study (11) and from the Diabetes Genetics Initiative (DGI) GWA study (91,161) data were available German [402 vs. 436] CC TC Chinese [287 vs. 376] CC TC Danish [1161 vs. 4648] CC TC British (WTCCC) [1923 vs. 2936] CC TC Scandinavian (DGI) [1449 vs. 1448] CC TC All studies [5222 vs. 9844] CC TC Odds ratio vs. TT Figure 4. Meta-analysis of PPARD rs in relation to type 2 diabetes. The analysis was performed by the Mantel-Haenszel method. No heterogeneity between studies was observed (P=0.7). Estimate of overall effect is: OR 1.08 (95% CI ; P=0.02) per C- allele. based on imputation. Numbers in square brackets designate numbers of type 2 diabetic patients and control subjects. References: German (158), Chinese (159), Danish (11), British (131,160), Scandinavian (91,161). Unpublished data. 11

16 Genetic susceptibility genes for type 2 diabetes and metabolism for two SNPs (rs and rs ) and for two SNPs by LD with genotyped variants (HapMap: rs : r 2 = 0.85 with rs and rs : r 2 = 1 with rs ). Meta-analysis of rs in 13,595 individuals showed a nominally significant increased susceptibility to type 2 diabetes (OR 1.08, 95% CI ; P=0.02) (Figure 4). Similar results were observed for rs and rs , which are all in some LD (r 2 ~ ). Although the significance level for an association is unimpressive we cannot exclude that variants in PPARD have a modest impact on risk of type 2 diabetes and only larger studies or more thorough metaanalyses will be able to clarify this potential association. Another example of a biological candidate gene for type 2 diabetes which has shown association in several studies but does not conform to the stringent consensus criteria of genome-wide significance levels to be considered a validated risk gene is SREBF1. In brief, SREBF1 encodes isoforms sterol regulatory element binding protein (SREBP)-1a and SREBP-1c (162). SREBP-1c is upregulated by insulin and is a mediator of insulin action in liver, adipose tissue and skeletal muscle ( ). SREBP-1c is also implicated in the regulation of de novo lipogenesis by mediation of the insulin-dependent upregulation of key enzymes in liver, adipose and muscle tissues (165,167,168). Interestingly, an overexpression of SREBP-1c in the liver of diabetic mice decreases hyperglycaemia by mimicking insulin action suggesting a major role in glucose homeostasis (169). This is also supported by SREBP-1c specific knockout mice showing a rs French [711 vs. 589] (12) GG CG Austrian [446 vs. 1,524] (15) GG CG British [1,919 vs. 2,933] (19) GG CG Danish [2,842 vs. 4,276] (present study) GG CG All studies [5,918 vs. 9,322] GG CG OR 1.08 ( ) per allele, P= Odds ratio vs. CC Figure 5. Meta-analysis of the SREBF1 rs variant in relation to type 2 diabetes. In combined analysis rs associated with type 2 diabetes with an OR of 1.08 (95% CI ; P=0.001). Numbers in square brackets designate numbers of type 2 diabetic patients and control subjects. Genotypes were based on imputation. References for individual studies: French (172), Austrian (173), British (89,160), Danish (12). Adapted from Grarup et al. (12). mild hyperglycaemic phenotype (170); thus SREBF1 is an excellent biological candidate gene for insulin resistance and type 2 diabetes. Furthermore, genome-linkage scans have linked a large region comprising SREBF1 on chromosomal 17p11 to type 2 diabetes (171). Association studies investigating a possible impact of SREBF1 on type 2 diabetes have shown an impact of various variants in SREBF1 on risk of type 2 diabetes ( ). In the Danish study we reported associations of sequence variation in SREBF1 with a modest increase in type 2 diabetes risk in a case-control study of 15,240 individuals (rs : OR 1.17 [95% CI ], P=0.003) and in a meta-analysis of all available data (OR 1.08 [95% CI ; P=0.001) (Figure 5). All the associated variants are in substantial LD (HapMap: r 2 : ) making it probable that these associations represent a single association signal. Despite limitations, based on the sample size the current meta-analysis represents the most valid estimation of association between variation in SREBF1 and type 2 diabetes. Furthermore, association with glycaemia has been indicated in a British study reporting borderline associations with glucose levels at fasting and 2 hours after an oral glucose load (175). In concordance we showed higher plasma glucose and serum insulin after an oral glucose load as well as a slight 0.5% increase in HbA 1C per allele for the diabetes risk-allele carriers in the population-based Inter99 sample of middle-aged individuals. The latter was supported by association with HbA 1C in the Danish ADDITION screening cohort (176) (Table 2 in (12)). Also, variants in SREBF1 associated with insulin resistance as measured by BIGTT-Si (138). The association of SREBF1 variants with increased insulin resistance and risk of type 2 diabetes is in line with a subtle loss-of-function variant affecting SREBP-1c function. Although these associations fit well with biological knowledge, some limitations need consideration to enable a more precise interpretation of the role of SREBF1 variation in type 2 diabetes. First, several studies have analysed multiple SNPs and phenotypes without properly correcting for multiple testing thereby increasing the risk of spurious associations. Second, in the meta-analysis it is recognized that not all data from all the GWA studies were available and included; a notion which definitely has the potential to change the results. Therefore, in conclusion, variation in SREBF1 may well increase risk of type 2 diabetes probably through a decreased insulin sensitivity which is already present in the normoglycemic state; however, more replication studies are needed to verify this. 12

17 Genetic susceptibility genes for type 2 diabetes and metabolism Technology- and HapMap-driven breakthrough discoveries in the genetics of common forms of type 2 diabetes, obesity and lipid traits A GWA study utilizes chip-based high-throughput genotyping of 300,000-1,000,000 SNPs across the entire genome to assess association for each SNP with casecontrol status or a quantitative trait. Since SNPs for genotyping are selected as genome-wide tag SNPs based on HapMap or are randomly and evenly spaced across the genome, the study design assumes no prior hypothesis for each single SNP. There is no doubt the GWA studies have advanced the field of genetics and led to a tremendous progress in understanding the genetic basis of numerous complex diseases. Nevertheless, analysing the vast quantity of data, validating true positive findings as well as identifying causative genetic variants have proved to be challenging. Also, it appears from the early GWA studies that, even though it seems less critical, non-replication and inconsistency still remain. The majority of the identified disease susceptibility variants show modest effect sizes. Thus, a true association might be missed due to lack of statistical power. The power not only depends on the magnitude of the effect size but is also highly dependent upon the required level of statistical significance, the frequency of the disease causing allele, the sample size, and the degree of LD of the examined variant with an actual disease susceptibility marker ( ). To overcome this problem meta-analyses are increasingly applied to genome-wide genotype data improving the power to detect genetic loci harbouring disease susceptibility alleles with only modest effect sizes, by increasing the effective sample size (46,132,180,181). It is important which platform is used in a GWA study as the coverage differs between available platforms. Both Affymetrix (182) and Illumina (183) provide platforms which offer high coverage of common genetic variation across different populations. According to the HapMap phase II, the Affymetrix 500K GeneChip platform is estimated to capture ~65% of the common genetic variation in the HapMap CEU study sample of U.S. residents with northern and western European ancestry with a mean r 2 > 0.96 by genotyping more than 500,000 SNPs, distributed at random throughout the genome. Genotyping over 318,000 tag SNPs thoroughly selected using LD-structures obtained from the HapMap project phase II, the Illumina HumanMap300 platform offers ~75% coverage of common genetic variability in the CEU study sample with a mean r 2 > 0.96 (184,185). Newer arrays such as Affymetrix SNP Array 6.0 or Illumina Human 1M Beadchip have somewhat higher coverage compared with previous arrays. As a result of the vast amount of genetic variants analysed in a GWA study a high number of statistical tests are performed increasing risk of false positives due to multiple testing. The crucial need for controlling this problem has resulted in the general use of a more stringent genome-wide significance level before an association is considered statistically significant. Current consensus has, based on a simulation study, defined a genome-wide significance level of P< to account for 10 6 independent genome-wide hypotheses tested in a dense GWA in populations of European origin (44) even though also P<10-7 has been suggested (186,187). Comparisons of statistical power using a genome-wide threshold of P< and a conventional threshold of 0.05 are shown in figure 1. These are of course arbitrary cut-offs and shall, as other thresholds for statistical significance, be interpreted cautiously. Validated genetic loci in common type 2 diabetes In spring 2007 the first GWA studies investigating type 2 diabetes were published. Since then, the number of validated type 2 diabetes susceptibility genes have increased to 19 (Table 1). For all these variants the statistical evidence of association (the genome-wide P- value) is lower than the genome-wide significance level of P< Five independent GWA studies have been published (89-91, ). All these studies have had relatively small sample sizes and are therefore essentially statistically underpowered to detect variants with modest effect sizes. In recognition of this, data from three GWA studies were combined by the DIAGRAM consortium and by thorough imputation of ~2 million SNPs, meta-analyses and extensive replication six novel type 2 diabetes susceptibility genes were detected (132). Of interest most type 2 diabetes variants have been shown to have an impact on pancreatic β-cell function which seems to be the case for variants in KCNJ11, TCF7L2, WFS1, CDKN2A, HHEX, CDKAL1, SLC30A8, JAZF1, CDC123, TSPAN8, MTNR1B and KCNQ1 (26,79,80,94,130, ). Indeed, only the PPARG P12A variant has so far displayed a diabetogenic potential through affecting peripheral insulin sensitivity (87) and variants in FTO by increased obesity ( ). The validated 19 susceptibility loci are summarized in Table 1. 13

18 Genetic susceptibility genes for type 2 diabetes and metabolism Chr. Regional gene(s) SNP ID RAF Discovery method Cellular function Putative intermediary mechanism OR References 1p13 NOTCH2 rs GWA Regulator of cell differentiation Unknown 1.13 (132) 2p21 THADA rs GWA Apoptosis Unknown 1.15 (132) 3p14 ADAMTS9 rs GWA Proteolytic enzyme Unknown 1.09 (132) 3p25 PPARG rs Candidate Adipocyte function and Insulin resistance 1.14 (87-89) differentiation 3q27 IGF2BP2 rs GWA Developmental growth and Insulin response 1.14 (89-91,188) stimulation of insulin action 4p16 WFS1 rs Candidate Endoplasmic reticulum stress and Insulin response 1.11 (77-79) β-cell apoptosis 6p22 CDKAL1 rs GWA Cell cycle regulation in the β-cell Insulin response 1.14 (89-91,130,189) 7p15 JAZF1 rs GWA Zinc-finger protein with unknown Insulin response 1.10 (132,192) function 8q24 SLC30A8 rs GWA Zinc transporter in β-cell insulin Insulin response 1.15 (90,129,190) granules 9p21 CDKN2A, rs GWA Cell cycle regulators Insulin response 1.20 (89-91) CDKN2B 10q25 TCF7L2 rs Linkage Transcription factor influencing insulin and glucagon secretion Insulin response 1.37 (25,26,198) 10p13 CDC123, CAMK1D rs GWA CDC123: Cell cycle regulation CAMK1D: Regulator of granulocyte function rs GWA HHEX: pancreatic development; rs IDE: cellular processing of insulin Insulin response 1.09 (132,192) 10q23 HHEX, IDE Insulin response 1.15 (89-91,129, ) 11p15 KCNQ1 rs GWA Electrical depolarisation of the cell Insulin response 1.25 (194,199) membrane 11p15 KCNJ11 rs Candidate Subunit of the β-cell K + channel, Insulin response 1.14 (89-91,93,94) involved in insulin secretion 11q21 MTNR1B rs GWA (QT) Rreceptor for melatonin Insulin response 1.15 (193,200,201) 12q14 TSPAN8, rs GWA TSPAN8: Cell surface glycoprotein Insulin response 1.09 (132,192) LGR5 LGR5: G protein-coupled receptor 16q12 FTO rs GWA Possible hypothalamic effect Obesity 1.17 (89-91, ) 17q21 HNF1B rs Candidate Transcription factor influencing Unknown 1.10 (75,76) (TCF2) pancreatic development Table 1. Validated type 2 diabetes susceptibility loci. All loci have shown genome-wide statistical significance. Effect sizes are presented as odds ratio per allele and are based on the currently available data. OR, odds ratio; RAF, risk allele frequency. Examples of putative risk genes in type 2 diabetes which are below genome-wide significance threshold The emergence of GWA studies has put intensified focus on the validity and confidence in the statistical estimates of association studies. This has been a necessary part of the development of the GWA approach in which correction for multiple testing is of crucial importance to avoid false positive findings. Genetic variants identified and investigated in the pre-gwa era by candidate gene studies have for the most part not been replicated in GWA studies of type 2 diabetes. Although the genome-wide significance threshold is in principle not important when analysing few variants in a gene of interest the need for increased statistical confidence is recognized and therefore this ultimate target of statistical evidence is still coveted. Several genes exist for which association with type 2 diabetes has been replicated or meta-analysis have indicated a potential modest impact on type 2 diabetes susceptibility while the statistical evidence is not that convincing. For instance, associations of variation in CAPN10, LMNA, ENPP1, SREBF1 and PPARGC1A (12,22,84,122,202,203) with type 2 diabetes have these characteristics. Current GWA studies are underpowered in the initial stage of genome-wide genotyping to detect modest size effects (177,178). Even though meta-analysis of GWA data, like performed by the DIAGRAM consortium (132) and the upcoming second-phase GWA meta-analysis in type 2 diabetes, seeks to improve power by increasing sample size and to improve genomic coverage by imputing all HapMap SNPs (204), these studies have low ability to exclude a locus from having a modest impact. To achieve the level of statistical confidence suggested as genomewide significance threshold, huge sample sizes are needed in particular in case the association is modified by other factors increasing the complexity. As outlined above, besides PPARG and FTO, most of the validated type 2 diabetes variants associate with an insulin response defect implying a β-cell dysfunction as the cause of type 2 diabetes. Curiously, many of the 14

19 Genetic susceptibility genes for type 2 diabetes and metabolism variants showing some but not definitive evidence of association with type 2 diabetes, as CAPN10, LMNA, ENPP1, SREBF1 and PPARGC1A, are likely to inflict an insulin resistance phenotype. This seems to underline the lack of insulin resistance genes in the genetics of type 2 diabetes validated to date. Reasons for this could be that variants affecting insulin resistance generally have lower effect sizes making detecting difficult (205). Also, the study design of first wave GWA studies somewhat disfavoured finding insulin resistance genes by deliberately matching cases and controls for obesity (91) or by selecting lean cases with low age at diagnosis (129). Possibly coming GWA studies of quantitative estimates of insulin resistance may to some extent clarify the role of common variants as determinants of insulin resistance phenotypes. Finally, one might speculate that insulin resistance variants are more prone to interaction with correlated phenotypes or environmental factors, such as obesity, physical activity or dietary intake, thus obscuring association and replication in crude studies of genetic main effects. For instance, regarding ENPP1 some evidence of BMI gene interaction exists (118,122) and it has been suggested that a genetic predisposition to insulin resistance may initially have a BMI-lowering effect, which balances the decrease in insulin sensitivity. If BMI increases, as a result of lifestyle or other genetic factors, the higher level of insulin resistance caused by a combination of both factors severely predisposes to diabetes (205,206). Nevertheless, nominal association with type 2 diabetes supported by association with relevant quantitative metabolic traits in the general population sustain the notion of a true pathogenic role for a number of these genes in type 2 diabetes; however, the final evidence is missing and can only be unravelled by efforts of international collaborations. Genetics of obesity GWA studies have similarly boosted the knowledge of the influence of common variants on obesity and quantitative measures of adiposity. In spring 2007 three independent reports demonstrated highly significant association of variants in FTO with BMI and other measures of obesity ( ) and have since been widely replicated in various ethnicities (197, ). The association was initially seen in a study of type 2 diabetes but this effect proved to be mediated through adiposity. Each copy of the FTO rs A-allele increase body weight by 1.6 kg and BMI by 0.51 kg/m 2 in the general Danish population (211), and is the most influential obesity gene identified to date. Subsequently, several other loci with a modest impact on measures of obesity have been revealed through a number of large GWA studies including more than 50,000 participants in replication stages ( ) (Table 2). Chr. Regional gene(s) SNP ID SNP type RAF Effect size BMI (percent of SD) Effect size weight (percent of SD) Effect size Obesity (OR) References 1p31 NEGR1 rs Intronic (214,215) 1q25 SEC16B, RASAL2 rs Intronic (214) 2p25 TMEM18 rs Intergenic (214,215) 3q27 ETV5, SFRS10, DGKG rs Intergenic (214) 4p13 GNPDA2 rs Intergenic kg/m 2 (215) 5q15 PCSK1 rs6232 Coding (216) 6p22 a PRL rs Intergenic kg/m (217) 6p21 NCR3, AIF1, BAT2 rs Intergenic (214) 10p12 a PTER rs Intergenic kg/m (217) 11p14 BDNF rs Intergenic b (214) 11p11 MTCH2 rs Intronic kg/m 2 (215) 12q13 FAIM2, BCDIN3D rs Intergenic (214) 16p11 SH2B1 rs Coding (214,215) 16q22 MAF rs Intergenic kg/m (217) 16q22 FTO, RPGRIP1L rs Intronic ( ) 18q11 NPC1 rs Coding kg/m (217) 18q22 MC4R rs Intergenic (212,213) 19q13 KCTD15 rs29941 Intergenic (214,215) Table 2. Validated loci influencing BMI, weight or obesity. All loci have shown genome-wide statistical significance except ( a ) which have a P- values of ~10-7. Effect sizes are based on the currently available data and are shown as percent of a standard deviation (SD) or odds ratio per allele unless otherwise stated. OR, odds ratio; RAF, risk allele frequency; SD, standard deviation. b obesity association for rs6265 in the same locus. 15

20 Genetic susceptibility genes for type 2 diabetes and metabolism Genetics of lipid traits The heritability of lipid traits is generally high ranging from 0.5 to 0.8 ( ). Besides genetic factors, lipid levels are in particular influenced by gender and modifiable risk factors like dietary components and to some extent by levels of physical activity. Progress in finding gene variants with an impact on total cholesterol, low-density lipoprotein (LDL)-cholesterol, HDLcholesterol and triglyceride has been tremendous. Already before the emerging of GWA studies some genetic contributions to the interindividual variation in lipid traits were widely replicated and the GWA studies have reported a range of novel loci (Table 3) (180,181, ). All loci showing association with lipid traits at a genome-wide significance level are summarized in Table 3. Of interest, as many as 12 of the identified loci containing common variants with a low impact on lipid traits shown in Table 3 (ABCA1, ABCG5, ABCG8, APOA1/APOA5, APOB, APOC2/APOE, CETP, LCAT, LDLR, LIPC, LDL, and PCSK9) also house rare mutations responsible for monogenic Mendelian forms of dyslipidemia (85). High LDL- and low HDL-cholesterol are well-known risk factors for CVD ( ) and several reports have shown that individual gene variants associated with lipid levels also influence risk of CVD ( ) which has also been demonstrated in the genetic overlap between findings of GWA studies of CVD-related outcomes and lipid traits (222,234,235). A study published in 2008 combined 11 variants with validated influences on LDLand/or HDL-cholesterol in a genotype risk score in a study of ~6,000 participants with ~10 years of follow-up for incident CVD events. The study demonstrated a statistically significant 15% increase in incidence of CVD per copy of risk allele after adjustment for clinical risk factors (236). Such advances are important in order to close the gap between basic genetic epidemiological research and clinical application. It is evident that although GWA studies for lipid traits have generated multiple novel loci with no prior implication in the regulation of cholesterol and triglyceride, also genes and variants originally found by candidate gene studies have been widely replicated and validated. Already before the advent of GWA studies it was recognized that variation in the hepatic lipase (HL) gene, LIPC, influences the level of HDL-cholesterol (237,238). HL is an enzyme primarily synthesized by hepatocytes with the capacity to catalyze the hydrolysis of triglycerides and phospholipids in most lipoprotein subtypes, thereby altering lipoprotein size and density (239). HL is involved in reverse cholesterol transport by its ability to stimulate hepatic HDL-cholesterol ester uptake and hydrolysis (240). However, it is also implicated in the remodeling of triglyceride-rich lipoprotein particles and thereby the formation of atherogenic small dense low-density lipoprotein (LDL) (241,242). Studies in genetically modified rodents have demonstrated that overexpression of Lipc leads to a marked reduction in plasma HDL-cholesterol (243,244). Numerous genetic association studies have investigated the impact of variation in LIPC on particularly lipid levels but also with other components of the metabolic syndrome, type 2 diabetes and CVD. Mostly two promoter variants, -514C>T (rs ) and -250G>A (rs ), in high LD (r 2 >0.96 (245)) have been investigated. An impact of these promoter variants on HDL-cholesterol was first indicated in 1997 (237) and the association was later firmly established in a metaanalysis of almost 24,000 individuals (238). In 5,585 participants of the population-based Inter99 cohort we found a highly statistically significant association of the -250 A-allele with a mmol/l increased HDLcholesterol per allele (95% CI mmol/l per allele, P= ) (13). Also several GWA studies of lipid traits have replicated the impact of variation in LIPC on HDL-cholesterol (180,181,221,222,224,225) (Table 3). The increase in HDL-cholesterol inflicted by these variants is ~ mmol/l per copy of minor -514 T-allele or -250 A-allele, equivalent to an increase of 12-15% of a standard deviation (SD) (13,180,181,238). In the Danish Inter99 cohort the LIPC -250G>A genotype explains ~0.5% of the total variation in serum HDL-cholesterol in the general middle-aged Danish population. Interestingly, the latest GWA studies indicated that two independent association signals in LIPC may influence HDL-cholesterol as one block of associated SNPs (rs , rs and rs ) is not in LD (r 2 ~ 0) with the described promoter variants (rs and rs ) (180,181). 16