Genome-wide Association Study Of Type Research Articles

Genome-wide association studies of Type 2 diabetes: are these ready to make an impact in the clinic?

SUMMARY Genome-wide association studies have facilitated a substantial and rapid rise in the number of confirmed susceptibility variants for Type 2 diabetes. This has inevitably led to widespread hope that these findings will translate into improved clinical care for the growing numbers of patients with diabetes. This article summarizes recent discoveries in the field of Type 2 diabetes genetics and will discuss their importance and the current obstacles to clinical translation.

Pathways of Distinction Analysis: A New Technique for Multi–SNP Analysis of GWAS Data

Genome-wide association studies (GWAS) have become increasingly common due to advances in technology and have permitted the identification of differences in single nucleotide polymorphism (SNP) alleles that are associated with diseases. However, while typical GWAS analysis techniques treat markers individually, complex diseases (cancers, diabetes, and Alzheimers, amongst others) are unlikely to have a single causative gene. Thus, there is a pressing need for multi–SNP analysis methods that can reveal system-level differences in cases and controls. Here, we present a novel multi–SNP GWAS analysis method called Pathways of Distinction Analysis (PoDA). The method uses GWAS data and known pathway–gene and gene–SNP associations to identify pathways that permit, ideally, the distinction of cases from controls. The technique is based upon the hypothesis that, if a pathway is related to disease risk, cases will appear more similar to other cases than to controls (or vice versa) for the SNPs associated with that pathway. By systematically applying the method to all pathways of potential interest, we can identify those for which the hypothesis holds true, i.e., pathways containing SNPs for which the samples exhibit greater within-class similarity than across classes. Importantly, PoDA improves on existing single–SNP and SNP–set enrichment analyses, in that it does not require the SNPs in a pathway to exhibit independent main effects. This permits PoDA to reveal pathways in which epistatic interactions drive risk. In this paper, we detail the PoDA method and apply it to two GWAS: one of breast cancer and the other of liver cancer. The results obtained strongly suggest that there exist pathway-wide genomic differences that contribute to disease susceptibility. PoDA thus provides an analytical tool that is complementary to existing techniques and has the power to enrich our understanding of disease genomics at the systems-level.

Genome-wide association study of type 2 diabetes in a sample from Mexico City and a meta-analysis of a Mexican-American sample from Starr County, Texas

We report a genome-wide association study of type 2 diabetes in an admixed sample from Mexico City and describe the results of a meta-analysis of this study and another genome-wide scan in a Mexican-American sample from Starr County, TX, USA. The top signals observed in this meta-analysis were followed up in the Diabetes Genetics Replication and Meta-analysis Consortium (DIAGRAM) and DIAGRAM+ datasets. We analysed 967 cases and 343 normoglycaemic controls. The samples were genotyped with the Affymetrix Genome-wide Human SNP array 5.0. Associations of genotyped and imputed markers with type 2 diabetes were tested using a missing data likelihood score test. A fixed-effects meta-analysis including 1,804 cases and 780 normoglycaemic controls was carried out by weighting the effect estimates by their inverse variances. In the meta-analysis of the two Hispanic studies, markers showing suggestive associations (p < 10(-5)) were identified in two known diabetes genes, HNF1A and KCNQ1, as well as in several additional regions. Meta-analysis of the two Hispanic studies and the recent DIAGRAM+ dataset identified genome-wide significant signals (p < 5 × 10(-8)) within or near the genes HNF1A and CDKN2A/CDKN2B, as well as suggestive associations in three additional regions, IGF2BP2, KCNQ1 and the previously unreported C14orf70. We observed numerous regions with suggestive associations with type 2 diabetes. Some of these signals correspond to regions described in previous studies. However, many of these regions could not be replicated in the DIAGRAM datasets. It is critical to carry out additional studies in Hispanic and American Indian populations, which have a high prevalence of type 2 diabetes.

Random-Effects Model Aimed at Discovering Associations in Meta-Analysis of Genome-wide Association Studies

Meta-analysis is an increasingly popular tool for combining multiple different genome-wide association studies (GWASs) in a single aggregate analysis in order to identify associations with very small effect sizes. Because the data of a meta-analysis can be heterogeneous, referring to the differences in effect sizes between the collected studies, what is often done in the literature is to apply both the fixed-effects model (FE) under an assumption of the same effect size between studies and the random-effects model (RE) under an assumption of varying effect size between studies. However, surprisingly, RE gives less significant p values than FE at variants that actually show varying effect sizes between studies. This is ironic because RE is designed specifically for the case in which there is heterogeneity. As a result, usually, RE does not discover any associations that FE did not discover. In this paper, we show that the underlying reason for this phenomenon is that RE implicitly assumes a markedly conservative null-hypothesis model, and we present a new random-effects model that relaxes the conservative assumption. Unlike the traditional RE, the new method is shown to achieve higher statistical power than FE when there is heterogeneity, indicating that the new method has practical utility for discovering associations in the meta-analysis of GWASs.

Linkage analysis without defined pedigrees

The need to collect accurate and complete pedigree information has been a drawback of family-based linkage and association studies. Even in case-control studies, investigators should be aware of, and condition on, familial relationships. In single nucleotide polymorphism (SNP) genome scans, relatedness can be directly inferred from the genetic data rather than determined through interviews. Various methods of estimating relatedness have previously been implemented, most notably in PLINK. We present new fast and accurate algorithms for estimating global and local kinship coefficients from dense SNP genotypes. These algorithms require only a single pass through the SNP genotype data. We also show that these estimates can be used to cluster individuals into pedigrees. With these estimates in hand, quantitative trait locus linkage analysis proceeds via traditional variance components methods without any prior relationship information. We demonstrate the success of our algorithms on simulated and real data sets. Our procedures make linkage analysis as easy as a typical genomewide association study.

Type 2 diabetes susceptibility single-nucleotide polymorphisms are not associated with polycystic ovary syndrome

Two cohorts of women with polycystic ovary syndrome (PCOS), comprising 400 probands and affected sisters in 365 families and a case-control group including 395 women with PCOS and 171 healthy women with regular menstrual cycles, were studied to determine whether single-nucleotide polymorphisms (SNPs) identified as susceptibility loci in genomewide association studies of type 2 diabetes are also associated with PCOS. None of the 18 allelic variants in 10 genes previously shown to be associated with type 2 diabetes were found to be associated with PCOS, but some were associated with indices of beta cell function.

Mouse models and the interpretation of human GWAS in type 2 diabetes and obesity

Within the last 3 years, genome-wide association studies (GWAS) have had unprecedented success in identifying loci that are involved in common diseases. For example, more than 35 susceptibility loci have been identified for type 2 diabetes and 32 for obesity thus far. However, the causal gene and variant at a specific linkage disequilibrium block is often unclear. Using a combination of different mouse alleles, we can greatly facilitate the understanding of which candidate gene at a particular disease locus is associated with the disease in humans, and also provide functional analysis of variants through an allelic series, including analysis of hypomorph and hypermorph point mutations, and knockout and overexpression alleles. The phenotyping of these alleles for specific traits of interest, in combination with the functional analysis of the genetic variants, may reveal the molecular and cellular mechanism of action of these disease variants, and ultimately lead to the identification of novel therapeutic strategies for common human diseases. In this Commentary, we discuss the progress of GWAS in identifying common disease loci for metabolic disease, and the use of the mouse as a model to confirm candidate genes and provide mechanistic insights.

An efficient hierarchical generalized linear mixed model for pathway analysis of genome-wide association studies.

In genome-wide association studies (GWAS) of complex diseases, genetic variants having real but weak associations often fail to be detected at the stringent genome-wide significance level. Pathway analysis, which tests disease association with combined association signals from a group of variants in the same pathway, has become increasingly popular. However, because of the complexities in genetic data and the large sample sizes in typical GWAS, pathway analysis remains to be challenging. We propose a new statistical model for pathway analysis of GWAS. This model includes a fixed effects component that models mean disease association for a group of genes, and a random effects component that models how each gene's association with disease varies about the gene group mean, thus belongs to the class of mixed effects models. The proposed model is computationally efficient and uses only summary statistics. In addition, it corrects for the presence of overlapping genes and linkage disequilibrium (LD). Via simulated and real GWAS data, we showed our model improved power over currently available pathway analysis methods while preserving type I error rate. Furthermore, using the WTCCC Type 1 Diabetes (T1D) dataset, we demonstrated mixed model analysis identified meaningful biological processes that agreed well with previous reports on T1D. Therefore, the proposed methodology provides an efficient statistical modeling framework for systems analysis of GWAS. The software code for mixed models analysis is freely available at http://biostat.mc.vanderbilt.edu/LilyWang.

The Psychiatric GWAS Consortium: Big Science Comes to Psychiatry

The Psychiatric GWAS Consortium was founded with the aim of conducting statistically rigorous and comprehensive GWAS meta-analyses for five major psychiatric disorders: ADHD, autism, bipolar disorder, major depressive disorder, and schizophrenia. In the era of GWAS and high-throughput genomics, a major trend has been the emergence of collaborative, consortia approaches. Taking advantage of the scale that collaborative consortia approaches can bring to a problem, the PGC has been a major driver in psychiatric genetics and provides a model for how similar approaches may be applied to other disease communities.

Diamonds in the Rough: Rare Variants Scratch the Surface

Diamonds in the Rough: Rare Variants Scratch the Surface

A genome-wide association study in the Japanese population identifies susceptibility loci for type 2 diabetes at UBE2E2 and C2CD4A-C2CD4B

We conducted a genome-wide association study of type 2 diabetes (T2D) using 459,359 SNPs in a Japanese population with a three-stage study design (stage 1, 4,470 cases and 3,071 controls; stage 2, 2,886 cases and 3,087 controls; stage 3, 3,622 cases and 2,356 controls). We identified new associations in UBE2E2 on chromosome 3 and in C2CD4A-C2CD4B on chromosome 15 at genome-wide significant levels (rs7612463 in UBE2E2, combined P = 2.27 × 10⁻⁹; rs7172432 in C2CD4A-C2CD4B, combined P = 3.66 × 10⁻⁹). The association of these two loci with T2D was replicated in other east Asian populations. In the European populations, the C2CD4A-C2CD4B locus was significantly associated with T2D, and a combined analysis of all populations gave P = 8.78 × 10⁻¹⁴, whereas the UBE2E2 locus did not show association to T2D. In conclusion, we identified two new loci at UBE2E2 and C2CD4A-C2CD4B associated with susceptibility to T2D.

Endocrine autoimmune disease: genetics become complex

The endocrine system is a frequent target in pathogenic autoimmune responses. Type 1 diabetes and autoimmune thyroid disease are the prevailing examples. When several diseases cluster together in one individual, the phenomenon is called autoimmune polyglandular syndrome. Progress has been made in understanding the genetic factors involved in endocrine autoimmune diseases. Studies on monogenic autoimmune diseases such as autoimmune polyglandular syndrome type 1, immunodysregulation, polyendocrinopathy, enteropathy, X-linked and primary immune deficiencies helped uncover the role of key regulators in the preservation of immune tolerance. Alleles of the major histocompatibility complex have been known to contribute to the susceptibility to most forms of autoimmunity for more than 3 decades. Furthermore, sequencing studies revealed three non-major histocompatibility complex loci and some disease specific loci, which control T lymphocyte activation or signalling. Recent genome-wide association studies (GWAS) have enabled acceleration in the identification of novel (non-HLA) loci and hence other relevant immune response pathways. Interestingly, several loci are shared between autoimmune diseases, and surprisingly some work in opposite direction. This means that the same allele which predisposes to a certain autoimmune disease can be protective in another. Well powered GWAS in type 1 diabetes has led to the uncovering of a significant number of risk variants with modest effect. These studies showed that the innate immune system may also play a role in addition to the adaptive immune system. It is anticipated that next generation sequencing techniques will uncover other (rare) variants. For other autoimmune disease (such as autoimmune thyroid disease) GWAS are clearly needed.

Common Variants in 40 Genes Assessed for Diabetes Incidence and Response to Metformin and Lifestyle Intervention in the Diabetes Prevention Program

OBJECTIVEGenome-wide association studies have begun to elucidate the genetic architecture of type 2 diabetes. We examined whether single nucleotide polymorphisms (SNPs) identified through targeted complementary approaches affect diabetes incidence in the at-risk population of the Diabetes Prevention Program (DPP) and whether they influence a response to preventive interventions.RESEARCH DESIGN AND METHODSWe selected SNPs identified by prior genome-wide association studies for type 2 diabetes and related traits, or capturing common variation in 40 candidate genes previously associated with type 2 diabetes, implicated in monogenic diabetes, encoding type 2 diabetes drug targets or drug-metabolizing/transporting enzymes, or involved in relevant physiological processes. We analyzed 1,590 SNPs for association with incident diabetes and their interaction with response to metformin or lifestyle interventions in 2,994 DPP participants. We controlled for multiple hypothesis testing by assessing false discovery rates.RESULTSWe replicated the association of variants in the metformin transporter gene SLC47A1 with metformin response and detected nominal interactions in the AMP kinase (AMPK) gene STK11, the AMPK subunit genes PRKAA1 and PRKAA2, and a missense SNP in SLC22A1, which encodes another metformin transporter. The most significant association with diabetes incidence occurred in the AMPK subunit gene PRKAG2 (hazard ratio 1.24, 95% CI 1.09–1.40, P = 7 × 10−4). Overall, there were nominal associations with diabetes incidence at 85 SNPs and nominal interactions with the metformin and lifestyle interventions at 91 and 69 mostly nonoverlapping SNPs, respectively. The lowest P values were consistent with experiment-wide 33% false discovery rates.CONCLUSIONSWe have identified potential genetic determinants of metformin response. These results merit confirmation in independent samples.

The common rs9939609 variant of the fat mass and obesity-associated gene is associated with obesity risk in children and adolescents of Beijing, China

BackgroundPrevious genome-wide association studies for type 2 diabetes susceptibility genes have confirmed that a common variant, rs9939609, in the fat mass and obesity associated (FTO) gene region is associated with body mass index (BMI) in European children and adults. A significant association of the same risk allele has been described in Asian adult populations, but the results are conflicting. In addition, no replication studies have been conducted in children and adolescents of Asian ancestry.MethodsA population-based survey was carried out among 3503 children and adolescents (6-18 years of age) in Beijing, China, including 1229 obese and 2274 non-obese subjects. We investigated the association of rs9939609 with BMI and the risk of obesity. In addition, we tested the association of rs9939609 with weight, height, waist circumference, waist-to-height ratio, fat mass percentage, birth weight, blood pressure and related metabolic traits.ResultsWe found significant associations of rs9939609 variant with weight, BMI, BMI standard deviation score (BMI-SDS), waist circumference, waist-to-height ratio, and fat mass percentage in children and adolescents (p for trend = 3.29 × 10-5, 1.39 × 10-6, 3.76 × 10-6, 2.26 × 10-5, 1.94 × 10-5, and 9.75 × 10-5, respectively). No significant associations were detected with height, birth weight, systolic and diastolic blood pressure and related metabolic traits such as total cholesterol, triglycerides, HDL-cholesterol, LDL-cholesterol and fasting plasma glucose (all p > 0.05). Each additional copy of the rs9939609 A allele was associated with a BMI increase of 0.79 [95% Confidence interval (CI) 0.47 to 1.10] kg/m2, equivalent to 0.25 (95%CI 0.14 to 0.35) BMI-SDS units. This rs9939609 variant is significantly associated with the risk of obesity under an additive model [Odds ratio (OR) = 1.29, 95% CI 1.11 to 1.50] after adjusting for age and gender. Moreover, an interaction between the FTO rs9939609 genotype and physical activity (p < 0.001) was detected on BMI levels, the effect of rs9939609-A allele on BMI being (0.95 ± 0.10), (0.77 ± 0.08) and (0.67 ± 0.05) kg/m2, for subjects who performed low, moderate and severe intensity physical activity.ConclusionThe FTO rs9939609 variant is strongly associated with BMI and the risk of obesity in a population of children and adolescents in Beijing, China.

Implication of the CD47 pathway in autoimmune diabetes

CD47 and signal regulatory protein (SIRP) interactions have been proposed to take part in autoimmune disease susceptibility. Importantly, a recent genome-wide association study for type 1 diabetes susceptibility highlighted the association of the 20p13 region comprising the SIRP cluster, where some of the SIRP proteins encode functional ligands to CD47. Using a TCR transgenic mouse model at the brink of autoimmune disease, we demonstrate that CD47-deficiency is sufficient to break the immune tolerance and provoke the onset of autoimmune diabetes. Interestingly, CD47-deficient mice show a severe reduction in the number of mature CD4 −CD8 − T cells, and passive transfer of these CD4 −CD8 − T cells is sufficient to restore immune tolerance and prevent diabetes progression. Together, these findings constitute an in vivo demonstration that CD47 is involved in diabetes susceptibility and controls the homeostatic regulation of CD4 −CD8 − T cells.

Identification of KCNJ15 as a Susceptibility Gene in Asian Patients with Type 2 Diabetes Mellitus

Recent advances in genome research have enabled the identification of new genomic variations that are associated with type 2 diabetes mellitus (T2DM). Via fine mapping of SNPs in a candidate region of chromosome 21q, the current study identifies potassium inwardly-rectifying channel, subfamily J, member 15 (KCNJ15) as a new T2DM susceptibility gene. KCNJ15 is expressed in the beta cell of the pancreas, and a synonymous SNP, rs3746876, in exon 4 (C566T) of this gene, with T allele frequency among control subjects of 3.1%, showed a significant association with T2DM affecting lean individuals in three independent Japanese sample sets (p = 2.5 x 10(-7), odds ratio [OR] = 2.54, 95% confidence interval [CI] = 1.76-3.67) and with unstratified T2DM (p = 6.7 x 10(-6), OR = 1.76, 95% CI = 1.37-2.25). The diabetes risk allele frequency was, however, very low among Europeans in whom no association between this variant and T2DM could be shown. Functional analysis in human embryonic kidney 293 cells demonstrated that the risk allele of the synonymous SNP in exon 4 increased KCNJ15 expression via increased mRNA stability, which resulted in the higher expression of protein as compared to that of the nonrisk allele. We also showed that KCNJ15 is expressed in human pancreatic beta cells. In conclusion, we demonstrated a significant association between a synonymous variant in KCNJ15 and T2DM in lean Japanese patients with T2DM, suggesting that KCNJ15 is a previously unreported susceptibility gene for T2DM among Asians.

Genome-Wide Meta-Analysis of Joint Tests for Genetic and Gene-Environment Interaction Effects

Background: There is growing interest in the study of gene-environment interactions in the context of genome-wide association studies (GWASs). These studies will likely require meta-analytic approaches to have sufficient power. Methods: We describe an approach for meta-analysis of a joint test for genetic main effects and gene-environment interaction effects. Using simulation studies based on a meta-analysis of five studies (total n = 10,161), we compare the power of this test to the meta-analysis of marginal test of genetic association and the meta-analysis of standard 1 d.f. interaction tests across a broad range of genetic main effects and gene-environment interaction effects. Results: We show that the joint meta-analysis is valid and can be more powerful than classical meta-analytic approaches, with a potential gain of power over 50% compared to the marginal test. The standard interaction test had less than 1% power in almost all the situations we considered. We also show that regardless of the test used, sample sizes far exceeding those of a typical individual GWAS will be needed to reliably detect genes with subtle gene-environment interaction patterns. Conclusion: The joint meta-analysis is an attractive approach to discover markers which may have been missed by initial GWASs focusing on marginal marker-trait associations.

A powerful approach to sub‐phenotype analysis in population‐based genetic association studies

The ultimate goal of genome-wide association (GWA) studies is to identify genetic variants contributing effects to complex phenotypes in order to improve our understanding of the biological architecture underlying the trait. One approach to allow us to meet this challenge is to consider more refined sub-phenotypes of disease, defined by pattern of symptoms, for example, which may be physiologically distinct, and thus may have different underlying genetic causes. The disadvantage of sub-phenotype analysis is that large disease cohorts are sub-divided into smaller case categories, thus reducing power to detect association. To address this issue, we have developed a novel test of association within a multinomial regression modeling framework, allowing for heterogeneity of genetic effects between sub-phenotypes. The modeling framework is extremely flexible, and can be generalized to any number of distinct sub-phenotypes. Simulations demonstrate the power of the multinomial regression-based analysis over existing methods when genetic effects differ between sub-phenotypes, with minimal loss of power when these effects are homogenous for the unified phenotype. Application of the multinomial regression analysis to a genome-wide association study of type 2 diabetes, with cases categorized according to body mass index, highlights previously recognized differential mechanisms underlying obese and non-obese forms of the disease, and provides evidence of a potential novel association that warrants follow-up in independent replication cohorts.

Diet-induced gene expression of isolated pancreatic islets from a polygenic mouse model of the metabolic syndrome

Aims/hypothesisNumerous new genes have recently been identified in genome-wide association studies for type 2 diabetes. Most are highly expressed in beta cells and presumably play important roles in their function. However, these genes account for only a small proportion of total risk and there are likely to be additional candidate genes not detected by current methodology. We therefore investigated islets from the polygenic New Zealand mouse (NZL) model of diet-induced beta cell dysfunction to identify novel genes and pathways that may play a role in the pathogenesis of diabetes.MethodsNZL mice were fed a diabetogenic high-fat diet (HF) or a diabetes-protective carbohydrate-free HF diet (CHF). Pancreatic islets were isolated by laser capture microdissection (LCM) and subjected to genome-wide transcriptome analyses.ResultsIn the prediabetic state, 2,109 islet transcripts were differentially regulated (>1.5-fold) between HF and CHF diets. Of the genes identified, 39 (e.g. Cacna1d, Chd2, Clip2, Igf2bp2, Dach1, Tspan8) correlated with data from the Diabetes Genetics Initiative and Wellcome Trust Case Control Consortium genome-wide scans for type 2 diabetes, thus validating our approach. HF diet induced early changes in gene expression associated with increased cell-cycle progression, proliferation and differentiation of islet cells, and oxidative stress (e.g. Cdkn1b, Tmem27, Pax6, Cat, Prdx4 and Txnip). In addition, pathway analysis identified oxidative phosphorylation as the predominant gene-set that was significantly upregulated in response to the diabetogenic HF diet.Conclusions/interpretationWe demonstrated that LCM of pancreatic islet cells in combination with transcriptional profiling can be successfully used to identify novel candidate genes for diabetes. Our data strongly implicate glucose-induced oxidative stress in disease progression.Electronic supplementary materialThe online version of this article (doi:10.1007/s00125-009-1576-4) contains supplementary material, which is available to authorised users.

A hidden Markov random field model for genome-wide association studies.

Genome-wide association studies (GWAS) are increasingly utilized for identifying novel susceptible genetic variants for complex traits, but there is little consensus on analysis methods for such data. Most commonly used methods include single single nucleotide polymorphism (SNP) analysis or haplotype analysis with Bonferroni correction for multiple comparisons. Since the SNPs in typical GWAS are often in linkage disequilibrium (LD), at least locally, Bonferroni correction of multiple comparisons often leads to conservative error control and therefore lower statistical power. In this paper, we propose a hidden Markov random field model (HMRF) for GWAS analysis based on a weighted LD graph built from the prior LD information among the SNPs and an efficient iterative conditional mode algorithm for estimating the model parameters. This model effectively utilizes the LD information in calculating the posterior probability that an SNP is associated with the disease. These posterior probabilities can then be used to define a false discovery controlling procedure in order to select the disease-associated SNPs. Simulation studies demonstrated the potential gain in power over single SNP analysis. The proposed method is especially effective in identifying SNPs with borderline significance at the single-marker level that nonetheless are in high LD with significant SNPs. In addition, by simultaneously considering the SNPs in LD, the proposed method can also help to reduce the number of false identifications of disease-associated SNPs. We demonstrate the application of the proposed HMRF model using data from a case-control GWAS of neuroblastoma and identify 1 new SNP that is potentially associated with neuroblastoma.