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.     

Lung eQTLs to Help Reveal the Molecular Underpinnings of Asthma

Genome-wide association studies (GWAS) have identified loci reproducibly associated with pulmonary diseases; however, the molecular mechanism underlying these associations are largely unknown. The objectives of this study were to discover genetic variants affecting gene expression in human lung tissue, to refine susceptibility loci for asthma identified in GWAS studies, and to use the genetics of gene expression and network analyses to find key molecular drivers of asthma. We performed a genome-wide search for expression quantitative trait loci (eQTL) in 1,111 human lung samples. The lung eQTL dataset was then used to inform asthma genetic studies reported in the literature. The top ranked lung eQTLs were integrated with the GWAS on asthma reported by the GABRIEL consortium to generate a Bayesian gene expression network for discovery of novel molecular pathways underpinning asthma. We detected 17,178 cis- and 593 trans- lung eQTLs, which can be used to explore the functional consequences of loci associated with lung diseases and traits. Some strong eQTLs are also asthma susceptibility loci. For example, rs3859192 on chr17q21 is robustly associated with the mRNA levels of GSDMA (P = 3.55×10⁻¹⁵¹). The genetic-gene expression network identified the SOCS3 pathway as one of the key drivers of asthma. The eQTLs and gene networks identified in this study are powerful tools for elucidating the causal mechanisms underlying pulmonary disease. This data resource offers much-needed support to pinpoint the causal genes and characterize the molecular function of gene variants associated with lung diseases.     

Metabolic and cardiovascular genes in polycystic ovary syndrome: a candidate-wide association study (CWAS)

The role of metabolic disturbance in polycystic ovary syndrome (PCOS) has been well established, with insulin resistance and the resulting compensatory hyperinsulinemia thought to promote hyperandrogenemia. Genome-wide association studies (GWAS) have established a large number of loci for metabolic conditions such as type 2 diabetes and obesity. A subset of these loci has been investigated for a role in PCOS; these studies generally have not revealed a confirmed role for these loci in PCOS risk. However, a large scale investigation of genes related to these pathways has not previously been performed. We conducted a two stage case control association study of 121,715 single nucleotide polymorphisms (SNPs) selected to represent susceptibility loci associated with traits such as type 2 diabetes, obesity measures, lipid levels and cardiovascular function using the Cardio-Metabochip in 847 PCOS cases and 845 controls. Several hypothesis-generating associations with PCOS were observed (top SNP rs2129107, P=3.8×10(-6)). We did not find any loci definitively associated with PCOS after strict correction for multiple testing, suggesting that cardio-metabolic loci are not major risk factors underlying the susceptibility to PCOS.     

环境和遗传因素与慢性代谢性疾病的人群研究

几十年来我国居民经历了快速的营养转型,与不健康的膳食和生活方式相关的＂致肥环境＂,以及遗传倾向是导致我国慢性代谢性疾病如代谢综合征和2型糖尿病快速流行的主要推手。然而,我国目前非常缺乏针对导致慢性代谢性疾病的主要遗传和环境危险因素而开展的系统研究。在过去若干年中,通过开展基于社区人群的流行病学研究,本课题组发现了多个与代谢性疾病相关的基因变异、环境因素和生物标记物。与此同时,通过对代谢综合征或2型糖尿病患者进行的营养干预,发现添加亚麻子或其衍生物木酚素、核桃,以及用糙米替代白米能不同程度地改善代谢综合征或血糖控制。总之,所有努力旨在增进对导致中国人代谢性疾病高易感性相关的病因和机制的理解,同时也希望为疾病的预测和预防提供新的思路和线索。     

Genetic Insights into Cardiometabolic Risk Factors

Many biochemical traits are recognised as risk factors, which contribute to or predict the development of disease. Only a few are in widespread use, usually to assist with treatment decisions and motivate behavioural change. The greatest effort has gone into evaluation of risk factors for cardiovascular disease and/or diabetes, with substantial overlap as ‘cardiometabolic’ risk. Over the past few years many genome-wide association studies (GWAS) have sought to account for variation in risk factors, with the expectation that identifying relevant polymorphisms would improve our understanding or prediction of disease; others have taken the direct approach of genomic case-control studies for the corresponding diseases. Large GWAS have been published for coronary heart disease and Type 2 diabetes, and also for associated biomarkers or risk factors including body mass index, lipids, C-reactive protein, urate, liver function tests, glucose and insulin. Results are not encouraging for personal risk prediction based on genotyping, mainly because known risk loci only account for a small proportion of risk. Overlap of allelic associations between disease and marker, as found for low density lipoprotein cholesterol and heart disease, supports a causal association, but in other cases genetic studies have cast doubt on accepted risk factors. Some loci show unexpected effects on multiple markers or diseases. An intriguing feature of risk factors is the blurring of categories shown by the correlation between them and the genetic overlap between diseases previously thought of as distinct. GWAS can provide insight into relationships between risk factors, biomarkers and diseases, with potential for new approaches to disease classification.     

A comprehensive analysis of shared loci between systemic lupus erythematosus (SLE) and sixteen autoimmune diseases reveals limited genetic overlap

In spite of the well-known clustering of multiple autoimmune disorders in families, analyses of specific shared genes and polymorphisms between systemic lupus erythematosus (SLE) and other autoimmune diseases (ADs) have been limited. Therefore, we comprehensively tested autoimmune variants for association with SLE, aiming to identify pleiotropic genetic associations between these diseases. We compiled a list of 446 non–Major Histocompatibility Complex (MHC) variants identified in genome-wide association studies (GWAS) of populations of European ancestry across 17 ADs. We then tested these variants in our combined Caucasian SLE cohorts of 1,500 cases and 5,706 controls. We tested a subset of these polymorphisms in an independent Caucasian replication cohort of 2,085 SLE cases and 2,854 controls, allowing the computation of a meta-analysis between all cohorts. We have uncovered novel shared SLE loci that passed multiple comparisons adjustment, including the VTCN1 (rs12046117, P = 2.02×10⁻⁰⁶) region. We observed that the loci shared among the most ADs include IL23R, OLIG3/TNFAIP3, and IL2RA. Given the lack of a universal autoimmune risk locus outside of the MHC and variable specificities for different diseases, our data suggests partial pleiotropy among ADs. Hierarchical clustering of ADs suggested that the most genetically related ADs appear to be type 1 diabetes with rheumatoid arthritis and Crohn''s disease with ulcerative colitis. These findings support a relatively distinct genetic susceptibility for SLE. For many of the shared GWAS autoimmune loci, we found no evidence for association with SLE, including IL23R. Also, several established SLE loci are apparently not associated with other ADs, including the ITGAM-ITGAX and TNFSF4 regions. This study represents the most comprehensive evaluation of shared autoimmune loci to date, supports a relatively distinct non–MHC genetic susceptibility for SLE, provides further evidence for previously and newly identified shared genes in SLE, and highlights the value of studies of potentially pleiotropic genes in autoimmune diseases.     

Meta-analysis of new genome-wide association studies of colorectal cancer risk

Colorectal cancer is the second leading cause of cancer death in developed countries. Genome-wide association studies (GWAS) have successfully identified novel susceptibility loci for colorectal cancer. To follow up on these findings, and try to identify novel colorectal cancer susceptibility loci, we present results for GWAS of colorectal cancer (2,906 cases, 3,416 controls) that have not previously published main associations. Specifically, we calculated odds ratios and 95% confidence intervals using log-additive models for each study. In order to improve our power to detect novel colorectal cancer susceptibility loci, we performed a meta-analysis combining the results across studies. We selected the most statistically significant single nucleotide polymorphisms (SNPs) for replication using ten independent studies (8,161 cases and 9,101 controls). We again used a meta-analysis to summarize results for the replication studies alone, and for a combined analysis of GWAS and replication studies. We measured ten SNPs previously identified in colorectal cancer susceptibility loci and found eight to be associated with colorectal cancer (p value range 0.02 to 1.8?×?10(-8)). When we excluded studies that have previously published on these SNPs, five SNPs remained significant at p?相似文献   

遗传风险评分在复杂疾病遗传学研究中的应用

心血管疾病、2型糖尿病、原发性高血压、哮喘、肥胖、肿瘤等复杂疾病在全球范围内流行,并成为人类死亡的主要原因。越来越多的人开始关注遗传易感性在复杂疾病发病机制中的作用。至今,与复杂疾病相关的易感基因和基因序列变异仍未完全清楚。人们希望通过遗传关联研究来阐明复杂疾病的遗传基础。近年来,全基因组关联研究和候选基因研究发现了大量与复杂疾病有关的基因序列变异。这些与复杂疾病有因果和(或)关联关系的基因序列变异的发现促进了复杂疾病预测和防治方法的产生和发展。遗传风险评分(Genetic risk score,GRS)作为探索单核苷酸多态(Single nucleotide polymorphisms,SNPs)与复杂疾病临床表型之间关系的新兴方法,综合了若干SNPs的微弱效应,使基因多态对疾病的预测性大幅度提升。该方法在许多复杂疾病遗传学研究中得到成功应用。本文重点介绍了GRS的计算方法和评价标准,简要列举了运用GRS取得的系列成果,并对运用过程中所存在的局限性进行了探讨,最后对遗传风险评分的未来发展方向进行了展望。     

Review: a meta-analysis of GWAS and age-associated diseases

Genome‐Wide Association studies (GWAS) offer an unbiased means to understand the genetic basis of traits by identifying single nucleotide polymorphisms (SNPs) linked to causal variants of complex phenotypes. GWAS have identified a host of susceptibility SNPs associated with many important human diseases, including diseases associated with aging. In an effort to understand the genetics of broad resistance to age‐associated diseases (i.e., ‘wellness’), we performed a meta‐analysis of human GWAS. Toward that end, we compiled 372 GWAS that identified 1775 susceptibility SNPs to 105 unique diseases and used these SNPs to create a genomic landscape of disease susceptibility. This map was constructed by partitioning the genome into 200 kb ‘bins’ and mapping the 1775 susceptibility SNPs to bins based on their genomic location. Investigation of these data revealed significant heterogeneity of disease association within the genome, with 92% of bins devoid of disease‐associated SNPs. In contrast, 10 bins (0.06%) were significantly (P < 0.05) enriched for susceptibility to multiple diseases, 5 of which formed two highly significant peaks of disease association (P < 0.0001). These peaks mapped to the Major Histocompatibility (MHC) locus on 6p21 and the INK4/ARF (CDKN2a/b) tumor suppressor locus on 9p21.3. Provocatively, all 10 significantly enriched bins contained genes linked to either inflammation or cellular senescence pathways, and SNPs near regulators of senescence were particularly associated with disease of aging (e.g., cancer, atherosclerosis, type 2 diabetes, glaucoma). This analysis suggests that germline genetic heterogeneity in the regulation of immunity and cellular senescence influences the human healthspan.     

Multiple common susceptibility variants near BMP pathway loci GREM1, BMP4, and BMP2 explain part of the missing heritability of colorectal cancer

Genome-wide association studies (GWAS) have identified 14 tagging single nucleotide polymorphisms (tagSNPs) that are associated with the risk of colorectal cancer (CRC), and several of these tagSNPs are near bone morphogenetic protein (BMP) pathway loci. The penalty of multiple testing implicit in GWAS increases the attraction of complementary approaches for disease gene discovery, including candidate gene- or pathway-based analyses. The strongest candidate loci for additional predisposition SNPs are arguably those already known both to have functional relevance and to be involved in disease risk. To investigate this proposition, we searched for novel CRC susceptibility variants close to the BMP pathway genes GREM1 (15q13.3), BMP4 (14q22.2), and BMP2 (20p12.3) using sample sets totalling 24,910 CRC cases and 26,275 controls. We identified new, independent CRC predisposition SNPs close to BMP4 (rs1957636, P = 3.93×10(-10)) and BMP2 (rs4813802, P = 4.65×10(-11)). Near GREM1, we found using fine-mapping that the previously-identified association between tagSNP rs4779584 and CRC actually resulted from two independent signals represented by rs16969681 (P = 5.33×10(-8)) and rs11632715 (P = 2.30×10(-10)). As low-penetrance predisposition variants become harder to identify-owing to small effect sizes and/or low risk allele frequencies-approaches based on informed candidate gene selection may become increasingly attractive. Our data emphasise that genetic fine-mapping studies can deconvolute associations that have arisen owing to independent correlation of a tagSNP with more than one functional SNP, thus explaining some of the apparently missing heritability of common diseases.     

Insight from genome-wide association studies in rheumatoid arthritis and multiple sclerosis

Autoimmune diseases are caused by multiple genes and environmental effects. In addition, genetic contributions and the number of associated genes differ among different diseases and ethnic populations. Genome-wide association studies (GWAS) on rheumatoid arthritis (RA) and multiple sclerosis (MS) show that these diseases share many genetic factors. Recently, in addition to the major histocompatibility complex (MHC) gene, other genetic loci have been found to be associated with the risk for autoimmune diseases. This review focuses on the search for genetic variants that influence the susceptibility to RA and MS as typical autoimmune diseases and discusses the future of GWAS.     

A Single Nucleotide Polymorphism within DUSP9 Is Associated with Susceptibility to Type 2 Diabetes in a Japanese Population

Aims

The DUSP9 locus on chromosome X was identified as a susceptibility locus for type 2 diabetes in a meta-analysis of European genome-wide association studies (GWAS), and GWAS in South Asian populations identified 6 additional single nucleotide polymorphism (SNP) loci for type 2 diabetes. However, the association of these loci with type 2 diabetes have not been examined in the Japanese. We performed a replication study to investigate the association of these 7 susceptibility loci with type 2 diabetes in the Japanese population.

Methods

We genotyped 11,319 Japanese participants (8,318 with type 2 diabetes and 3,001 controls) for each of the 7 SNPs–rs5945326 near DUSP9, rs3923113 near GRB14, rs16861329 in ST6GAL1, rs1802295 in VPS26A, rs7178572 in HMG20A, rs2028299 near AP3S2, and rs4812829 in HNF4A–and examined the association of each of these 7 SNPs with type 2 diabetes by using logistic regression analysis.

Results

All SNPs had the same direction of effect (odds ratio [OR]>1.0) as in the original reports. One SNP, rs5945326 near DUSP9, was significantly associated with type 2 diabetes at a genome-wide significance level (p = 2.21×10⁻⁸; OR 1.39, 95% confidence interval [CI]: 1.24−1.56). The 6 SNPs derived from South Asian GWAS were not significantly associated with type 2 diabetes in the Japanese population by themselves (p≥0.007). However, a genetic risk score constructed from 6 South Asian GWAS derived SNPs was significantly associated with Japanese type 2 diabetes (p = 8.69×10⁻⁴, OR  = 1.06. 95% CI; 1.03−1.10).

Conclusions/interpretation

These results indicate that the DUSP9 locus is a common susceptibility locus for type 2 diabetes across different ethnicities, and 6 loci identified in South Asian GWAS also have significant effect on susceptibility to Japanese type 2 diabetes.     

From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes

Genome-wide association studies (GWAS) have been fruitful in identifying disease susceptibility loci for common and complex diseases. A remaining question is whether we can quantify individual disease risk based on genotype data, in order to facilitate personalized prevention and treatment for complex diseases. Previous studies have typically failed to achieve satisfactory performance, primarily due to the use of only a limited number of confirmed susceptibility loci. Here we propose that sophisticated machine-learning approaches with a large ensemble of markers may improve the performance of disease risk assessment. We applied a Support Vector Machine (SVM) algorithm on a GWAS dataset generated on the Affymetrix genotyping platform for type 1 diabetes (T1D) and optimized a risk assessment model with hundreds of markers. We subsequently tested this model on an independent Illumina-genotyped dataset with imputed genotypes (1,008 cases and 1,000 controls), as well as a separate Affymetrix-genotyped dataset (1,529 cases and 1,458 controls), resulting in area under ROC curve (AUC) of ∼0.84 in both datasets. In contrast, poor performance was achieved when limited to dozens of known susceptibility loci in the SVM model or logistic regression model. Our study suggests that improved disease risk assessment can be achieved by using algorithms that take into account interactions between a large ensemble of markers. We are optimistic that genotype-based disease risk assessment may be feasible for diseases where a notable proportion of the risk has already been captured by SNP arrays.     

Sex differences in disease risk from reported genome-wide association study findings

Men and women differ in susceptibility to many diseases and in responses to treatment. Recent advances in genome-wide association studies (GWAS) provide a wealth of data for associating genetic profiles with disease risk; however, in general, these data have not been systematically probed for sex differences in gene-disease associations. Incorporating sex into the analysis of GWAS results can elucidate new relationships between single nucleotide polymorphisms (SNPs) and human disease. In this study, we performed a sex-differentiated analysis on significant SNPs from GWAS data of the seven common diseases studied by the Wellcome Trust Case Control Consortium. We employed and compared three methods: logistic regression, Woolf’s test of heterogeneity, and a novel statistical metric that we developed called permutation method to assess sex effects (PMASE). After correction for false discovery, PMASE finds SNPs that are significantly associated with disease in only one sex. These sexually dimorphic SNP-disease associations occur in Coronary Artery Disease and Crohn’s Disease. GWAS analyses that fail to consider sex-specific effects may miss discovering sexual dimorphism in SNP-disease associations that give new insights into differences in disease mechanism between men and women.     

Abundant Genetic Overlap between Blood Lipids and Immune-Mediated Diseases Indicates Shared Molecular Genetic Mechanisms

Epidemiological studies suggest a relationship between blood lipids and immune-mediated diseases, but the nature of these associations is not well understood. We used genome-wide association studies (GWAS) to investigate shared single nucleotide polymorphisms (SNPs) between blood lipids and immune-mediated diseases. We analyzed data from GWAS (n~200,000 individuals), applying new False Discovery Rate (FDR) methods, to investigate genetic overlap between blood lipid levels [triglycerides (TG), low density lipoproteins (LDL), high density lipoproteins (HDL)] and a selection of archetypal immune-mediated diseases (Crohn’s disease, ulcerative colitis, rheumatoid arthritis, type 1 diabetes, celiac disease, psoriasis and sarcoidosis). We found significant polygenic pleiotropy between the blood lipids and all the investigated immune-mediated diseases. We discovered several shared risk loci between the immune-mediated diseases and TG (n = 88), LDL (n = 87) and HDL (n = 52). Three-way analyses differentiated the pattern of pleiotropy among the immune-mediated diseases. The new pleiotropic loci increased the number of functional gene network nodes representing blood lipid loci by 40%. Pathway analyses implicated several novel shared mechanisms for immune pathogenesis and lipid biology, including glycosphingolipid synthesis (e.g. FUT2) and intestinal host-microbe interactions (e.g. ATG16L1). We demonstrate a shared genetic basis for blood lipids and immune-mediated diseases independent of environmental factors. Our findings provide novel mechanistic insights into dyslipidemia and immune-mediated diseases and may have implications for therapeutic trials involving lipid-lowering and anti-inflammatory agents.     

Type 2 Diabetes Mellitus: New Genetic Insights will Lead to New Therapeutics

Type 2 diabetes is a disorder of dysregulated glucose homeostasis. Normal glucose homeostasis is a complex process involving several interacting mechanisms, such as insulin secretion, insulin sensitivity, glucose production, and glucose uptake. The dysregulation of one or more of these mechanisms due to environmental and/or genetic factors, can lead to a defective glucose homeostasis. Hyperglycemia is managed by augmenting insulin secretion and/or interaction with hepatic glucose production, as well as by decreasing dietary caloric intake and raising glucose metabolism through exercise. Although these interventions can delay disease progression and correct blood glucose levels, they are not able to cure the disease or stop its progression entirely. Better management of type 2 diabetes is sorely needed. Advances in genotyping techniques and the availability of large patient cohorts have made it possible to identify common genetic variants associated with type 2 diabetes through genome-wide association studies (GWAS). So far, genetic variants on 19 loci have been identified. Most of these loci contain or lie close to genes that were not previously linked to diabetes and they may thus harbor targets for new drugs. It is also hoped that further genetic studies will pave the way for predictive genetic screening. The newly discovered type 2 diabetes genes can be classified based on their presumed molecular function, and we discuss the relation between these gene classes and current treatments. We go on to consider whether the new genes provide opportunities for developing alternative drug therapies.Key Words: Type 2 diabetes, drug targets, genetics, personalized medicine.     

Prediction of individual genetic risk of complex disease

Most common diseases are caused by multiple genetic and environmental factors. In the last 2 years, genome-wide association studies (GWAS) have identified polymorphisms that are associated with risk to common disease, but the effect of any one risk allele is typically small. By combining information from many risk variants, will it be possible to predict accurately each individual person's genetic risk for a disease? In this review we consider the lessons from GWAS and the implications for genetic risk prediction to common disease. We conclude that with larger GWAS sample sizes or by combining studies, accurate prediction of genetic risk will be possible, even if the causal mutations or the mechanisms by which they affect susceptibility are unknown.     

Do Variants Associated with Susceptibility to Pancreatic Cancer and Type 2 Diabetes Reciprocally Affect Risk?

ObjectivesAlthough type 2 diabetes mellitus is a known risk factor for pancreatic cancer, the existence of shared genetic susceptibility is largely unknown. We evaluated whether any reported genetic risk variants of either disease found by genome-wide association studies reciprocally confer susceptibility.MethodsData that were generated in previous genome-wide association studies (GENEVA Type 2 Diabetes; PanScan) were obtained through the National Institutes of Health database of Genotypes and Phenotypes (dbGaP). Using the PanScan datasets, we tested for association of 38 variants within 37 genomic regions known to be susceptibility factors for type 2 diabetes. We further examined whether type 2 diabetes variants predispose to pancreatic cancer risk stratified by diabetes status. Correspondingly, we examined the association of fourteen pancreatic cancer susceptibility variants within eight genomic regions in the GENEVA Type 2 Diabetes dataset.ResultsFour plausible associations of diabetes variants and pancreatic cancer risk were detected at a significance threshold of p = 0.05, and one pancreatic cancer susceptibility variant was associated with diabetes risk at threshold of p = 0.05, but none remained significant after correction for multiple comparisons.ConclusionCurrently identified GWAS susceptibility variants are unlikely to explain the potential shared genetic etiology between Type 2 diabetes and pancreatic cancer.     

Association of new loci identified in European genome-wide association studies with susceptibility to type 2 diabetes in the Japanese

Background

Several novel susceptibility loci for type 2 diabetes have been identified through genome-wide association studies (GWAS) for type 2 diabetes or quantitative traits related to glucose metabolism in European populations. To investigate the association of the 13 new European GWAS-derived susceptibility loci with type 2 diabetes in the Japanese population, we conducted a replication study using 3 independent Japanese case-control studies.

Methodology/Principal Findings

We examined the association of single nucleotide polymorphisms (SNPs) within 13 loci (MTNR1B, GCK, IRS1, PROX1, BCL11A, ZBED3, KLF14, TP53INP1, KCNQ1, CENTD2, HMGA2, ZFAND6 and PRC1) with type 2 diabetes using 4,964 participants (2,839 cases and 2,125 controls) from 3 independent Japanese samples. The association of each SNP with type 2 diabetes was analyzed by logistic regression analysis. Further, we performed combined meta-analyses for the 3 studies and previously performed Japanese GWAS data (4,470 cases vs. 3,071 controls). The meta-analysis revealed that rs2943641 in the IRS1 locus was significantly associated with type 2 diabetes, (P = 0.0034, OR = 1.15 95% confidence interval; 1.05–1.26) and 3 SNPs, rs10930963 in the MTNR1B locus, rs972283 in the KLF14 locus, and rs231362 in the KCNQ1 locus, had nominal association with type 2 diabetes in the present Japanese samples (P<0.05).

Conclusions

These results indicate that IRS1 locus may be common locus for type 2 diabetes across different ethnicities.