Transferability of type 2 diabetes implicated loci in multi-ethnic cohorts from Southeast Asia

Recent large genome-wide association studies (GWAS) have identified multiple loci which harbor genetic variants associated with type 2 diabetes mellitus (T2D), many of which encode proteins not previously suspected to be involved in the pathogenesis of T2D. Most GWAS for T2D have focused on populations of European descent, and GWAS conducted in other populations with different ancestry offer a unique opportunity to study the genetic architecture of T2D. We performed genome-wide association scans for T2D in 3,955 Chinese (2,010 cases, 1,945 controls), 2,034 Malays (794 cases, 1,240 controls), and 2,146 Asian Indians (977 cases, 1,169 controls). In addition to the search for novel variants implicated in T2D, these multi-ethnic cohorts serve to assess the transferability and relevance of the previous findings from European descent populations in the three major ethnic populations of Asia, comprising half of the world's population. Of the SNPs associated with T2D in previous GWAS, only variants at CDKAL1 and HHEX/IDE/KIF11 showed the strongest association with T2D in the meta-analysis including all three ethnic groups. However, consistent direction of effect was observed for many of the other SNPs in our study and in those carried out in European populations. Close examination of the associations at both the CDKAL1 and HHEX/IDE/KIF11 loci provided some evidence of locus and allelic heterogeneity in relation to the associations with T2D. We also detected variation in linkage disequilibrium between populations for most of these loci that have been previously identified. These factors, combined with limited statistical power, may contribute to the failure to detect associations across populations of diverse ethnicity. These findings highlight the value of surveying across diverse racial/ethnic groups towards the fine-mapping efforts for the casual variants and also of the search for variants, which may be population-specific.     

血脂异常遗传性疾病的研究现状

血脂异常(Dyslipidemia)是指血浆中胆固醇和(或)甘油三酯水平升高, 可导致严重的心血管疾病, 常以冠心病和脑中风为首发表现, 该类疾病严重危害着人们的健康。一些血脂异常疾病具有遗传性, 主要包括孟德尔遗传和多基因遗传。传统检测血脂异常相关基因的方法主要有DNA测序和连锁分析, 适合于孟德尔遗传性血脂异常疾病。最近几年兴起的新一代测序技术(Next-generation sequencing)不仅适用于孟德尔遗传性血脂异常疾病的研究, 同样适用于复杂性血脂异常疾病。2006年至今, 运用全基因组关联分析(Genome wide association study, GWAS)筛出许多与血脂异常疾病相关的基因, 这些基因和早期孟德尔遗传家系确定的基因多数相同。GWAS频谱分析发现, 复杂性疾病相关的基因变异频率存在差异, 并且几乎所有筛查出的与血脂异常疾病相关的单核苷酸多态性(Single nucleotide polymorphisms, SNPs)变异均位于非编码区, 使得人们逐渐对非编码区基因变异展开了研究。血脂异常致病基因的发现和基因变异致病机制的阐明, 为血脂异常疾病提供新的治疗靶点, 并为新一代药物筛选提供新思路。文章对血脂异常遗传性疾病的研究现状进行了综述。     

GWAS在2型糖尿病相关基因研究中的应用

2型糖尿病(type 2 diabetes,T2D)是一种常见的复杂疾病,其发病受到遗传和环境因素的共同作用.全基因组关联研究(genome-wide association study,GWAS)是一种可在全基因组范围筛查疾病相关的序列变异的新型群体关联研究方法.近年来,采用GWAS以及在此基础上展开的meta分析,已分别在TCF7L2、HHEX-IDE、SLC30A8、CDKAL1、CDKN2A-CDKN2B、IGF2BP2、NOTCH2、CDC123-CAMK1D、ADAMTS9、THADA、TSPAN8-LGR5、JAZF1等12个基因区域鉴定出多个T2D相关的多态位点.已有的研究提示,上述多个基因可能在胰岛β细胞发育和功能维持方面扮演着重要角色.本文集中介绍了GWAS的原理及其在T2D研究中的优势;回顾了GWAS在T2D研究中的主要发现;并对运用GWAS在T2D研究中尚需解决的问题进行了总结和展望.     

Genome-wide association studies in multiple sclerosis: lessons and future prospects

Multiple sclerosis (MS) is an inflammatory neurodegenerative disease with complex aetiology. A haplotype within the major histocompatibility region is the major risk factor for MS, but despite clear evidence for a genetic component additional risk variants were not identified until the recent advent of genome-wide association studies (GWAS). At present, 10 GWAS have been conducted in MS, and together with follow-up studies these have confirmed 16 loci with genome-wide significance. Many of these common risk variants are located at or near genes with central immunological functions and the majority are associated with other autoimmune diseases. However, evidence from pathway analyses on more modestly associated variants also supports the involvement of neurological genes. Although the mechanisms by which the associated variants exert their effects are still poorly understood, some have been shown to correlate with expression of nearby genes. Further studies are required to define the functionally relevant variants in the identified regions and to investigate their effects at the molecular and cellular level. Finally, many genetic risk variants for MS remain to be identified. In order to expose some of the loci with more modest effects, a GWAS in nearly 10,000 MS patients has recently been completed.     

Parkinson Disease from Mendelian Forms to Genetic Susceptibility: New Molecular Insights into the Neurodegeneration Process

Parkinson disease (PD) is known as a common progressive neurodegenerative disease which is clinically diagnosed by the manifestation of numerous motor and nonmotor symptoms. PD is a genetically heterogeneous disorder with both familial and sporadic forms. To date, researches in the field of Parkinsonism have identified 23 genes or loci linked to rare monogenic familial forms of PD with Mendelian inheritance. Biochemical studies revealed that the products of these genes usually play key roles in the proper protein and mitochondrial quality control processes, as well as synaptic transmission and vesicular recycling pathways within neurons. Despite this, large number of patients affected with PD typically tends to show sporadic forms of disease with lack of a clear family history. Recent genome-wide association studies (GWAS) meta-analyses on the large sporadic PD case–control samples from European populations have identified over 12 genetic risk factors. However, the genetic etiology that underlies pathogenesis of PD is also discussed, since it remains unidentified in 40% of all PD-affected cases. Nowadays, with the emergence of new genetic techniques, international PD genomics consortiums and public online resources such as PDGene, there are many hopes that future large-scale genetics projects provide further insights into the genetic etiology of PD and improve diagnostic accuracy and therapeutic clinical trial designs.     

Pathway Analysis of Smoking Quantity in Multiple GWAS Identifies Cholinergic and Sensory Pathways

Cigarette smoking is a common addiction that increases the risk for many diseases, including lung cancer and chronic obstructive pulmonary disease. Genome-wide association studies (GWAS) have successfully identified and validated several susceptibility loci for nicotine consumption and dependence. However, the trait variance explained by these genes is only a small fraction of the estimated genetic risk. Pathway analysis complements single marker methods by including biological knowledge into the evaluation of GWAS, under the assumption that causal variants lie in functionally related genes, enabling the evaluation of a broad range of signals. Our approach to the identification of pathways enriched for multiple genes associated with smoking quantity includes the analysis of two studies and the replication of common findings in a third dataset. This study identified pathways for the cholinergic receptors, which included SNPs known to be genome-wide significant; as well as novel pathways, such as genes involved in the sensory perception of smell, that do not contain any single SNP that achieves that stringent threshold.     

Effects of GWAS-Associated Genetic Variants on lncRNAs within IBD and T1D Candidate Loci

Long non-coding RNAs are a new class of non-coding RNAs that are at the crosshairs in many human diseases such as cancers, cardiovascular disorders, inflammatory and autoimmune disease like Inflammatory Bowel Disease (IBD) and Type 1 Diabetes (T1D). Nearly 90% of the phenotype-associated single-nucleotide polymorphisms (SNPs) identified by genome-wide association studies (GWAS) lie outside of the protein coding regions, and map to the non-coding intervals. However, the relationship between phenotype-associated loci and the non-coding regions including the long non-coding RNAs (lncRNAs) is poorly understood. Here, we systemically identified all annotated IBD and T1D loci-associated lncRNAs, and mapped nominally significant GWAS/ImmunoChip SNPs for IBD and T1D within these lncRNAs. Additionally, we identified tissue-specific cis-eQTLs, and strong linkage disequilibrium (LD) signals associated with these SNPs. We explored sequence and structure based attributes of these lncRNAs, and also predicted the structural effects of mapped SNPs within them. We also identified lncRNAs in IBD and T1D that are under recent positive selection. Our analysis identified putative lncRNA secondary structure-disruptive SNPs within and in close proximity (+/−5 kb flanking regions) of IBD and T1D loci-associated candidate genes, suggesting that these RNA conformation-altering polymorphisms might be associated with diseased-phenotype. Disruption of lncRNA secondary structure due to presence of GWAS SNPs provides valuable information that could be potentially useful for future structure-function studies on lncRNAs.     

Systematic identification of interaction effects between genome- and environment-wide associations in type 2 diabetes mellitus

Diseases such as type 2 diabetes (T2D) result from environmental and genetic factors, and risk varies considerably in the population. T2D-related genetic loci discovered to date explain only a small portion of the T2D heritability. Some heritability may be due to gene–environment interactions. However, documenting these interactions has been difficult due to low availability of concurrent genetic and environmental measures, selection bias, and challenges in controlling for multiple hypothesis testing. Through genome-wide association studies (GWAS), investigators have identified over 90 single nucleotide polymorphisms (SNPs) associated to T2D. Using a method analogous to GWAS [environment-wide association study (EWAS)], we found five environmental factors associated with the disease. By focusing on risk factors that emerge from GWAS and EWAS, it is possible to overcome difficulties in uncovering gene–environment interactions. Using data from the National Health and Nutrition Examination Survey (NHANES), we screened 18 SNPs and 5 serum-based environmental factors for interaction in association to T2D. We controlled for multiple hypotheses using false discovery rate (FDR) and Bonferroni correction and found four interactions with FDR <20 %. The interaction between rs13266634 (SLC30A8) and trans-β-carotene withstood Bonferroni correction (corrected p = 0.006, FDR <1.5 %). The per-risk-allele effect sizes in subjects with low levels of trans-β-carotene were 40 % greater than the marginal effect size [odds ratio (OR) 1.8, 95 % CI 1.3–2.6]. We hypothesize that impaired function driven by rs13266634 increases T2D risk when combined with serum levels of nutrients. Unbiased consideration of environmental and genetic factors may help identify larger and more relevant effect sizes for disease associations.     

Meta-Analysis of Genome-Wide Association Studies in African Americans Provides Insights into the Genetic Architecture of Type 2 Diabetes

Type 2 diabetes (T2D) is more prevalent in African Americans than in Europeans. However, little is known about the genetic risk in African Americans despite the recent identification of more than 70 T2D loci primarily by genome-wide association studies (GWAS) in individuals of European ancestry. In order to investigate the genetic architecture of T2D in African Americans, the MEta-analysis of type 2 DIabetes in African Americans (MEDIA) Consortium examined 17 GWAS on T2D comprising 8,284 cases and 15,543 controls in African Americans in stage 1 analysis. Single nucleotide polymorphisms (SNPs) association analysis was conducted in each study under the additive model after adjustment for age, sex, study site, and principal components. Meta-analysis of approximately 2.6 million genotyped and imputed SNPs in all studies was conducted using an inverse variance-weighted fixed effect model. Replications were performed to follow up 21 loci in up to 6,061 cases and 5,483 controls in African Americans, and 8,130 cases and 38,987 controls of European ancestry. We identified three known loci (TCF7L2, HMGA2 and KCNQ1) and two novel loci (HLA-B and INS-IGF2) at genome-wide significance (4.15×10⁻⁹⁴<P<5×10⁻⁸, odds ratio (OR) = 1.09 to 1.36). Fine-mapping revealed that 88 of 158 previously identified T2D or glucose homeostasis loci demonstrated nominal to highly significant association (2.2×10⁻²³ < locus-wide P<0.05). These novel and previously identified loci yielded a sibling relative risk of 1.19, explaining 17.5% of the phenotypic variance of T2D on the liability scale in African Americans. Overall, this study identified two novel susceptibility loci for T2D in African Americans. A substantial number of previously reported loci are transferable to African Americans after accounting for linkage disequilibrium, enabling fine mapping of causal variants in trans-ethnic meta-analysis studies.     

Breakthroughs in the genetics of orofacial clefting

Nonsyndromic orofacial clefts have a multifactorial etiology, involving both genetic and environmental factors. Although linkage and candidate gene studies have attempted to elucidate the underlying genetic architecture, only the interferon regulatory factor 6 (IRF6) gene has been identified as causative. The recent introduction of high-throughput genotyping technologies has enabled researchers to perform genome-wide association studies (GWAS). Four GWAS of nonsyndromic cleft lip with or without cleft palate have been conducted, and these have identified five new chromosomal loci. One locus, located in an intergenic region of chromosome 8q24, has been implicated in all GWAS and constitutes a major susceptibility locus. This review describes the latest genetic findings for nonsyndromic orofacial clefts and discusses their biological and functional implications.     

The New Perspectives on Genetic Studies of Type 2 Diabetes and Thyroid Diseases

Recently, genome-wide association studies (GWAS) have led to the discovery of hundreds of susceptibility loci that are associated with complex metabolic diseases, such as type 2 diabetes and hyperthyroidism. The majority of the susceptibility loci are common across different races or populations; while some of them show ethnicity-specific distribution. Though the abundant novel susceptibility loci identified by GWAS have provided insight into biology through the discovery of new genes or pathways that were previously not known, most of them are in introns and the associated variants cumulatively explain only a small fraction of total heritability. Here we reviewed the genetic studies on the metabolic disorders, mainly type 2 diabetes and hyperthyroidism, including candidate genes-based findings and more recently the GWAS discovery; we also included the clinical relevance of these novel loci and the gene-environmental interactions. Finally, we discussed the future direction about the genetic study on the exploring of the pathogenesis of the metabolic diseases.     

2型糖尿病易感候选基因在世界不同人群中的多样性比较分析

近10年来兴起的全基因组关联分析（Genome-wide association study, GWAS）相关研究结果获得了大量与2型糖尿病相关的候选易感基因,了解这些候选基因在正常人群中的遗传多样性程度以及在不同人群间的遗传差异,不但有助于阐明2型糖尿病的遗传机理,而且对于今后在特定人群中进行2型糖尿病发病机制的深入研究具有指导意义。本研究通过对GWAS数据库和相关文献的搜索和整理确定了170个与2型糖尿病相关的基因或基因区域;随后基于千人基因组计划的全基因组测序数据对这些候选基因在世界范围内14个人群间的遗传多样性进行了比较分析;进一步确定了在人群间存在显著差异的易感基因,并分析了这些基因的多样性特征。在所研究的14个世界人群中,2型糖尿病候选易感基因的遗传多样性与基因组范围的平均水平没有显著差异;但其中8个易感基因IL20RA、RNMTL1-NXN、NOTCH2、ADRA2A-BTBD7P2、TBC1D4、RBM38-HMGB1P1、UBE2E2和PPARD在群体间呈现显著差异,其中最明显的是IL20RA基因 (FST=0.152),该易感基因在非洲人群和非非洲人群间存在显著等位基因频率和单倍型频率差异。14个人群中易感基因遗传结构差异的主要原因是由于非洲人群与非非洲人群之间的群体遗传结构的不同所造成的。进一步比较东西方人群间的2型糖尿病候选基因遗传结构差异,发现在东西方人群中同样存在明显的群体遗传结构差别,其中DGKB-AGMO(FST=0.173)和JAZF1(FST=0.182)是差异最显著的易感基因。本研究通过对群体间2型糖尿病易感基因遗传结构进行比较,鉴别出一些差异特别显著的易感基因,对今后2型糖尿病易感基因与不同人群间发病率和易感性差异的相关研究提供重要参考。     

冠心病全基因组关联研究进展

近年来全基因组关联研究在世界范围内发展迅猛,研究者应用全基因组关联研究策略发现了一系列疾病的相关基因或变异,将疾病的基因组研究推向一个新的阶段。冠心病是一种由环境因素和遗传因素共同作用导致的复杂疾病,且是世界范围内死亡和致残的首要原因之一,世界各地的研究者应用此策略发现了候选基因关联研究未曾发现的多个冠心病相关易感区域。文章对近年来世界范围内针对冠心病的全基因组关联研究取得的重要进展进行简要总结,然后就现阶段全基因组关联研究所面临的挑战以及对未来研究的发展趋势进行分析阐述,为进一步探究冠心病的遗传机制提供指导。     

Integrated analysis of genome-wide genetic and epigenetic association data for identification of disease mechanisms

Many human diseases are multifactorial, involving multiple genetic and environmental factors impacting on one or more biological pathways. Much of the environmental effect is believed to be mediated through epigenetic changes. Although many genome-wide genetic and epigenetic association studies have been conducted for different diseases and traits, it is still far from clear to what extent the genomic loci and biological pathways identified in the genetic and epigenetic studies are shared. There is also a lack of statistical tools to assess these important aspects of disease mechanisms. In the present study, we describe a protocol for the integrated analysis of genome-wide genetic and epigenetic data based on permutation of a sum statistic for the combined effects in a locus or pathway. The method was then applied to published type 1 diabetes (T1D) genome-wide- and epigenome-wide-association studies data to identify genomic loci and biological pathways that are associated with T1D genetically and epigenetically. Through combined analysis, novel loci and pathways were also identified, which could add to our understanding of disease mechanisms of T1D as well as complex diseases in general.     

Genetic determinants of plasma triglycerides

Plasma triglyceride (TG) concentration is reemerging as an important cardiovascular disease risk factor. More complete understanding of the genes and variants that modulate plasma TG should enable development of markers for risk prediction, diagnosis, prognosis, and response to therapies and might help specify new directions for therapeutic interventions. Recent genome-wide association studies (GWAS) have identified both known and novel loci associated with plasma TG concentration. However, genetic variation at these loci explains only ～10% of overall TG variation within the population. As the GWAS approach may be reaching its limit for discovering genetic determinants of TG, alternative genetic strategies, such as rare variant sequencing studies and evaluation of animal models, may provide complementary information to flesh out knowledge of clinically and biologically important pathways in TG metabolism. Herein, we review genes recently implicated in TG metabolism and describe how some of these genes likely modulate plasma TG concentration. We also discuss lessons regarding plasma TG metabolism learned from various genomic and genetic experimental approaches. Treatment of patients with moderate to severe hypertriglyceridemia with existing therapies is often challenging; thus, gene products and pathways found in recent genetic research studies provide hope for development of more effective clinical strategies.     

Large-scale gene-centric meta-analysis across 39 studies identifies type 2 diabetes loci

To identify genetic factors contributing to type 2 diabetes (T2D), we performed large-scale meta-analyses by using a custom ～50,000 SNP genotyping array (the ITMAT-Broad-CARe array) with ～2000 candidate genes in 39 multiethnic population-based studies, case-control studies, and clinical trials totaling 17,418 cases and 70,298 controls. First, meta-analysis of 25 studies comprising 14,073 cases and 57,489 controls of European descent confirmed eight established T2D loci at genome-wide significance. In silico follow-up analysis of putative association signals found in independent genome-wide association studies (including 8,130 cases and 38,987 controls) performed by the DIAGRAM consortium identified a T2D locus at genome-wide significance (GATAD2A/CILP2/PBX4; p = 5.7 × 10(-9)) and two loci exceeding study-wide significance (SREBF1, and TH/INS; p < 2.4 × 10(-6)). Second, meta-analyses of 1,986 cases and 7,695 controls from eight African-American studies identified study-wide-significant (p = 2.4 × 10(-7)) variants in HMGA2 and replicated variants in TCF7L2 (p = 5.1 × 10(-15)). Third, conditional analysis revealed multiple known and novel independent signals within five T2D-associated genes in samples of European ancestry and within HMGA2 in African-American samples. Fourth, a multiethnic meta-analysis of all 39 studies identified T2D-associated variants in BCL2 (p = 2.1 × 10(-8)). Finally, a composite genetic score of SNPs from new and established T2D signals was significantly associated with increased risk of diabetes in African-American, Hispanic, and Asian populations. In summary, large-scale meta-analysis involving a dense gene-centric approach has uncovered additional loci and variants that contribute to T2D risk and suggests substantial overlap of T2D association signals across multiple ethnic groups.     

Functional genomics complements quantitative genetics in identifying disease-gene associations

An ultimate goal of genetic research is to understand the connection between genotype and phenotype in order to improve the diagnosis and treatment of diseases. The quantitative genetics field has developed a suite of statistical methods to associate genetic loci with diseases and phenotypes, including quantitative trait loci (QTL) linkage mapping and genome-wide association studies (GWAS). However, each of these approaches have technical and biological shortcomings. For example, the amount of heritable variation explained by GWAS is often surprisingly small and the resolution of many QTL linkage mapping studies is poor. The predictive power and interpretation of QTL and GWAS results are consequently limited. In this study, we propose a complementary approach to quantitative genetics by interrogating the vast amount of high-throughput genomic data in model organisms to functionally associate genes with phenotypes and diseases. Our algorithm combines the genome-wide functional relationship network for the laboratory mouse and a state-of-the-art machine learning method. We demonstrate the superior accuracy of this algorithm through predicting genes associated with each of 1157 diverse phenotype ontology terms. Comparison between our prediction results and a meta-analysis of quantitative genetic studies reveals both overlapping candidates and distinct, accurate predictions uniquely identified by our approach. Focusing on bone mineral density (BMD), a phenotype related to osteoporotic fracture, we experimentally validated two of our novel predictions (not observed in any previous GWAS/QTL studies) and found significant bone density defects for both Timp2 and Abcg8 deficient mice. Our results suggest that the integration of functional genomics data into networks, which itself is informative of protein function and interactions, can successfully be utilized as a complementary approach to quantitative genetics to predict disease risks. All supplementary material is available at http://cbfg.jax.org/phenotype.