The detection of loci contributing effects to complex human traits, and their subsequent fine-mapping for the location of causal variants, remains a considerable challenge for the genetics research community. Meta-analyses of genomewide association studies, primarily ascertained from European-descent populations, have made considerable advances in our understanding of complex trait genetics, although much of their heritability is still unexplained. With the increasing availability of genomewide association data from diverse populations, transethnic meta-analysis may offer an exciting opportunity to increase the power to detect novel complex trait loci and to improve the resolution of fine-mapping of causal variants by leveraging differences in local linkage disequilibrium structure between ethnic groups. However, we might also expect there to be substantial genetic heterogeneity between diverse populations, both in terms of the spectrum of causal variants and their allelic effects, which cannot easily be accommodated through traditional approaches to meta-analysis. In order to address this challenge, I propose novel transethnic meta-analysis methodology that takes account of the expected similarity in allelic effects between the most closely related populations, while allowing for heterogeneity between more diverse ethnic groups. This approach yields substantial improvements in performance, compared to fixed-effects meta-analysis, both in terms of power to detect association, and localization of the causal variant, over a range of models of heterogeneity between ethnic groups. Furthermore, when the similarity in allelic effects between populations is well captured by their relatedness, this approach has increased power and mapping resolution over random-effects meta-analysis. Genet. Epidemiol. 2011. © 2011 Wiley Periodicals, Inc.35: 809–;822, 2011.
meta-analysis; transethnic; genomewide association study; diverse populations; Bayesian partition model; fine-mapping
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.
Genome wide association study; Gene-environmental interaction; Hyperthyroidism; Risk prediction; Type 2 diabetes.
Cutaneous malignancies, especially malignant melanoma, exhibit great genetic heterogeneity. As a result, some individuals and families have particularly increased risk due to genetic predisposition to the disease. The susceptibility alleles range from rarely occurring, heritable, high-risk variants to ubiquitously occurring low-risk variants. Although until now the focus has been mostly towards the familial high-risk genes, the development of genome-wide association studies has uncovered a number of moderate- to low-risk predisposition alleles. The ability to specifically identify genetic variation associated with visible pigmentation traits and disease risk has provided a much richer view of the genetics of cutaneous malignancies. In this review, we provide an update on the recently identified risk loci. Existing clinical data, combined with vast genome information, will provide a better understanding of the biology of disease, and increased accuracy in risk prediction.
In recent years, genome wide association studies have revolutionized the understanding of the genetic architecture of complex disease, particularly in the context of disorders that present in old age, such as type 2 diabetes and cardiovascular disease. This new era is made all the more compelling by the fact that, through extensive validation efforts, there is now very strong consensus among human geneticists on what the key loci are that contribute to the pathogenesis of these traits. However, as these variants have been almost exclusively uncovered in an adult setting, there is the question of when these genetic variants start exerting their effects; indeed many may start setting up an individual’s predisposition to a disease of old age very early on in life. To this end, we review what breakthroughs have been made in elucidating which of these genetic factors are operating in childhood and conversely what discoveries have actually been made in the pediatric setting that have then been found subsequently to increase one’s risk of a late-onset disease. After all, it well known that complex traits like obesity, type 2 diabetes and inflammatory bowel disease are strongly determined by genetic factors, but the isolation of genes in these complex phenotypes in adults has been impeded by interaction with strong environmental factors. Distillation of the genetic component in these complex traits, which will at least partially have origins in childhood, should be easier to determine in a pediatric setting, where the relatively short period of a child’s lifetime limits the impact of environmental exposure.
Disease; late-onset; childhood; genetic; association
For the past two decades, genetics has been widely explored as a tool for unraveling the pathogenesis of diabetes. Many risk alleles for type 2 diabetes and hyperglycemia have been detected in recent years through massive genome-wide association studies and evidence exists that most of these variants influence pancreatic β-cell function. However, risk alleles in five loci seem to have a primary impact on insulin sensitivity. Investigations of more detailed physiologic phenotypes, such as the insulin response to intravenous glucose or the incretion hormones, are now emerging and give indications of more specific pathologic mechanisms for diabetes-related risk variants. Such studies have shed light on the function of some loci but also underlined the complex nature of disease mechanism. In the future, sequencing-based discovery of low-frequency variants with higher impact on intermediate diabetes-related traits is a likely scenario and identification of new pathways involved in type 2 diabetes predisposition will offer opportunities for the development of novel therapeutic and preventative approaches.
Type 2 diabetes; Glycemia; β-cell function; Insulin sensitivity; Genetic epidemiology; Physiology
Despite years of investigation, very little is known about the genetic predisposition for gestational diabetes mellitus (GDM). However, the advent of genome-wide association and identification of loci contributing to susceptibility to type 2 diabetes mellitus has opened a small window into the genetics of GDM. More importantly, the study of the genetics of GDM has not only illuminated potential new biology underlying diabetes in pregnancy, but has also provided insights into fetal outcomes. Here, I review some of the insights into GDM and fetal outcomes gained through the study of both rare and common genetic variation. I also discuss whether recent testing of type 2 diabetes mellitus susceptibility loci in GDM case-control samples changes views of whether GDM is a distinct form of diabetes. Finally, I examine how the study of susceptibility loci can be used to influence clinical care, one of the great promises of the new era of human genome analysis.
Gestational diabetes mellitus (GDM) is a complex metabolic disorder of pregnancy that is suspected to have a strong genetic predisposition. It is associated with poor perinatal outcome, and both GDM women and their offspring are at increased risk of future development of type 2 diabetes mellitus (T2DM). During the past several years, there has been progress in finding the genetic risk factors of GDM in relation to T2DM. Some of the genetic variants that were proven to be significantly associated with T2DM are also genetic risk factors of GDM. Recently, a genome-wide association study of GDM was performed and reported that genetic variants in CDKAL1 and MTNR1B were associated with GDM at a genome-wide significance level. Current investigations using next-generation sequencing will improve our insight into the pathophysiology of GDM. It would be important to know whether genetic information revealed from these studies could improve our prediction of GDM and the future development of T2DM. We hope further research on the genetics of GDM would ultimately lead us to personalized genomic medicine and improved patient care.
genetics; gestational diabetes; type 2 diabetes
A great majority of genetic markers discovered in recent genome-wide association studies have small effect sizes, and they explain only a small fraction of the genetic contribution to the diseases. How many more variants can we expect to discover and what study sizes are needed? We derive the connection between the cumulative risk of the SNP variants to the latent genetic risk model and heritability of the disease. We determine the sample size required for case-control studies in order to achieve a certain expected number of discoveries in a collection of most significant SNPs. Assuming similar allele frequencies and effect sizes of the currently validated SNPs, complex phenotypes such as type-2 diabetes would need approximately 800 variants to explain its 40% heritability. Much smaller numbers of variants are needed if we assume rare-variants but higher penetrance models. We estimate that up to 50,000 cases and an equal number of controls are needed to discover 800 common low-penetrant variants among the top 5000 SNPs. Under common and rare low-penetrance models, the very large studies required to discover the numerous variants are probably at the limit of practical feasibility. Under rare-variant with medium- to high-penetrance models (odds-ratios between 1.6 and 4.0), studies comparable in size to many existing studies are adequate provided the genotyping technology can interrogate more and rarer variants.
Despite numerous candidate gene and linkage studies, the field of type 2 diabetes (T2D) genetics had until recently succeeded in identifying few genuine disease-susceptibility loci. The advent of genome-wide association (GWA) scans has transformed the situation, leading to an expansion in the number of established, robustly replicating T2D loci to almost 20. These novel findings offer unique insights into the pathogenesis of T2D and in the main point towards the etiological importance of disorders of beta-cell development and function. All associated variants have common allele frequencies in the discovery populations, and exert modest to small effects on the risk of disease, characteristics which limit their prognostic and diagnostic potential. However, ongoing studies focussing on the role of copy number variation and targeting low frequency polymorphisms should identify additional T2D-susceptibility loci, some of which may have larger effect sizes and offer better individual prediction of disease risk.
Epidemiologic evidence supports a genetic predisposition to stroke. Recent advances, primarily using the genome-wide association study approach, are transforming what we know about the genetics of multifactorial stroke, and are identifying novel stroke genes. The current findings are consistent with different stroke subtypes having different genetic architecture. These discoveries may identify novel pathways involved in stroke pathogenesis, and suggest new treatment approaches. However, the already identified genetic variants explain only a small proportion of overall stroke risk, and therefore are not currently useful in predicting risk for the individual patient. Such risk prediction may become a reality as identification of a greater number of stroke risk variants that explain the majority of genetic risk proceeds, and perhaps when information on rare variants, identified by whole-genome sequencing, is also incorporated into risk algorithms. Pharmacogenomics may offer the potential for earlier implementation of 'personalized genetic' medicine. Genetic variants affecting clopidogrel and warfarin metabolism may identify non-responders and reduce side-effects, but these approaches have not yet been widely adopted in clinical practice.
The successes of genome-wide association (GWA) studies have mainly come from studies performed in populations of European descent. Since complex traits are characterized by marked genetic heterogeneity, the findings so far may provide an incomplete picture of the genetic architecture of complex traits. However, the recent GWA studies performed on East Asian populations now allow us to globally assess the heterogeneity of association signals between populations of European ancestry and East Asians, and the possible obstacles for multi-ethnic GWA studies. We focused on four different traits that represent a broad range of complex phenotypes, which have been studied in both Europeans and East Asians: type 2 diabetes, systemic lupus erythematosus, ulcerative colitis and height. For each trait, we observed that most of the risk loci identified in East Asians were shared with Europeans. However, we also observed that a significant part of the association signals at these shared loci seems to be independent between populations. This suggests that disease aetiology is common between populations, but that risk variants are often population specific. These variants could be truly population specific and result from natural selection, genetic drift and recent mutations, or they could be spurious, caused by the limitations of the method of analysis employed in the GWA studies. We therefore propose a three-stage framework for multi-ethnic GWA analyses, starting with the commonly used single-nucleotide polymorphism-based analysis, and followed by a gene-based approach and a pathway-based analysis, which will take into account the heterogeneity of association between populations at different levels.
Development of Type 2 diabetes, like obesity, is promoted by a genetic predisposition. Although several genetic variants have been identified they only account for a small proportion of risk. We have asked if genetic risk is associated with abnormalities in storing excess lipids in the abdominal subcutaneous adipose tissue.
We recruited 164 lean and 500 overweight/obese individuals with or without a genetic predisposition for Type 2 diabetes or obesity. Adipose cell size was measured in biopsies from the abdominal adipose tissue as well as insulin sensitivity (HOMA index), HDL-cholesterol and Apo AI and Apo B. 166 additional non-obese individuals with a genetic predisposition for Type 2 diabetes underwent a euglycemic hyperinsulinemic clamp to measure insulin sensitivity. Genetic predisposition for Type 2 diabetes, but not for overweight/obesity, was associated with inappropriate expansion of the adipose cells, reduced insulin sensitivity and a more proatherogenic lipid profile in non-obese individuals. However, obesity per se induced a similar expansion of adipose cells and dysmetabolic state irrespective of genetic predisposition.
Genetic predisposition for Type 2 diabetes, but not obesity, is associated with an impaired ability to recruit new adipose cells to store excess lipids in the subcutaneous adipose tissue, thereby promoting ectopic lipid deposition. This becomes particularly evident in non-obese individuals since obesity per se promotes a dysmetabolic state irrespective of genetic predisposition. These results identify a novel susceptibility factor making individuals with a genetic predisposition for Type 2 diabetes particularly sensitive to the environment and caloric excess.
Type 2 diabetes (T2D) has become a leading health problem throughout the world. It is caused by both environmental and genetic factors and interactions between them. However, until very recently, the T2D susceptibility genes have been poorly understood. During the past 5 years, with the advent of genome-wide association studies (GWAS), a total of 58 T2D susceptibility loci have been associated with T2D risk at a genome-wide significance level (P <5×10−8) and evidence shows that most of these genetic variants influence pancreatic β-cell function. Most novel T2D susceptibility loci were identified through GWAS in European populations and later confirmed in other ethnic groups. Although the recent discovery of novel T2D susceptibility loci has contributed substantially to our understanding of the pathophysiology of the disease, clinical utility of these loci in disease prediction and prognosis is limited. More studies using multiethnic meta-analysis, gene-environment interaction analysis, sequencing analysis, epigenetic analysis, and functional experiments are needed to identify new susceptibility T2D loci and causal variants and to establish biological mechanisms.
Europeans; Genetics; Genome-wide association studies; Type 2 diabetes
Recent genome-wide association studies (GWASs) have reported several genetic variants to be reproducibly associated with type 2 diabetes. Additional variants have also been detected from a metaanalysis of three GWASs, performed in populations of European ancestry. In the present study, we evaluated the influence of 17 genetic variants from 15 candidate loci, identified in type 2 diabetes GWASs and the metaanalysis, in a Han Chinese cohort.
Selected type 2 diabetes–associated genetic variants were genotyped in 1,165 type 2 diabetic patients and 1,136 normoglycemic control individuals of Southern Han Chinese ancestry. The OR for risk of developing type 2 diabetes was calculated using a logistic regression model adjusted for age, sex, and BMI. Genotype-phenotype associations were tested using a multivariate linear regression model. Genetic variants in CDKN2A/B, CDKAL1, TCF7L2, TCF2, MC4R, and PPARG showed a nominal association with type 2 diabetes (P≤0.05), of whom the three first would stand correction for multiple testing: CDKN2A/B rs10811661, OR: 1.26 (1.12–1.43) P = 1.8*10−4; CDKAL1 rs10946398, OR: 1.23 (1.09–1.39); P = 7.1*10−4, and TCF7L2 rs7903146, OR: 1.61 (1.19–2.18) P = 2.3 * 10−3. Only nominal phenotype associations were observed, notably for rs8050136 in FTO and fasting plasma glucose (P = 0.002), postprandial plasma glucose (P = 0.002), and fasting C-peptide levels (P = 0.006) in the diabetic patients, and with BMI in controls (P = 0.033).
We have identified significant association between variants in CDKN2A/B, CDKAL1 and TCF7L2, and type 2 diabetes in a Han Chinese cohort, indicating these genes as strong candidates conferring susceptibility to type 2 diabetes across different ethnicities.
Dyslipidemia has been associated with type 2 diabetes, but it remains unclear whether dyslipidemia plays a causal role in type 2 diabetes. We aimed to examine the association between the genetic predisposition to dyslipdemia and type 2 diabetes risk. The current study included 2,447 patients with type 2 diabetes and 3,052 control participants of European ancestry from the Nurses’ Health Study and the Health Professionals Follow-up Study. Genetic predisposition to dyslipidemia was estimated by three genotype scores of lipids (LDL cholesterol, HDL cholesterol, and triglycerides) on the basis of the established loci for blood lipids. Linear relation analysis indicated that the HDL cholesterol and triglyceride genotype scores, but not the LDL cholesterol genotype score, were linearly related to elevated type 2 diabetes risk. Each point of the HDL cholesterol and triglyceride genotype scores was associated with a 3% (odds ratio [OR] 1.03 [95% CI 1.01–1.04]) and a 2% (1.02 [1.00–1.04]) increased risk of developing type 2 diabetes, respectively. The ORs were 1.39 (1.17–1.65) and 1.19 (1.01–1.41) for type 2 diabetes by comparing extreme quartiles of the HDL cholesterol genotype score and triglyceride genotype score, respectively. In conclusion, genetic predisposition to low HDL cholesterol or high triglycerides is related to elevated type 2 diabetes risk.
A multi-ethnic study demonstrates that the extrapolation of genetic disease risk models from European populations to other ethnicities is compromised more strongly by genetic structure than by environmental or global genetic background in differential genetic risk associations across ethnicities.
The vast majority of genome-wide association study (GWAS) findings reported to date are from populations with European Ancestry (EA), and it is not yet clear how broadly the genetic associations described will generalize to populations of diverse ancestry. The Population Architecture Using Genomics and Epidemiology (PAGE) study is a consortium of multi-ancestry, population-based studies formed with the objective of refining our understanding of the genetic architecture of common traits emerging from GWAS. In the present analysis of five common diseases and traits, including body mass index, type 2 diabetes, and lipid levels, we compare direction and magnitude of effects for GWAS-identified variants in multiple non-EA populations against EA findings. We demonstrate that, in all populations analyzed, a significant majority of GWAS-identified variants have allelic associations in the same direction as in EA, with none showing a statistically significant effect in the opposite direction, after adjustment for multiple testing. However, 25% of tagSNPs identified in EA GWAS have significantly different effect sizes in at least one non-EA population, and these differential effects were most frequent in African Americans where all differential effects were diluted toward the null. We demonstrate that differential LD between tagSNPs and functional variants within populations contributes significantly to dilute effect sizes in this population. Although most variants identified from GWAS in EA populations generalize to all non-EA populations assessed, genetic models derived from GWAS findings in EA may generate spurious results in non-EA populations due to differential effect sizes. Regardless of the origin of the differential effects, caution should be exercised in applying any genetic risk prediction model based on tagSNPs outside of the ancestry group in which it was derived. Models based directly on functional variation may generalize more robustly, but the identification of functional variants remains challenging.
The number of known associations between human diseases and common genetic variants has grown dramatically in the past decade, most being identified in large-scale genetic studies of people of Western European origin. But because the frequencies of genetic variants can differ substantially between continental populations, it's important to assess how well these associations can be extended to populations with different continental ancestry. Are the correlations between genetic variants, disease endpoints, and risk factors consistent enough for genetic risk models to be reliably applied across different ancestries? Here we describe a systematic analysis of disease outcome and risk-factor–associated variants (tagSNPs) identified in European populations, in which we test whether the effect size of a tagSNP is consistent across six populations with significant non-European ancestry. We demonstrate that although nearly all such tagSNPs have effects in the same direction across all ancestries (i.e., variants associated with higher risk in Europeans will also be associated with higher risk in other populations), roughly a quarter of the variants tested have significantly different magnitude of effect (usually lower) in at least one non-European population. We therefore advise caution in the use of tagSNP-based genetic disease risk models in populations that have a different genetic ancestry from the population in which original associations were first made. We then show that this differential strength of association can be attributed to population-dependent variations in the correlation between tagSNPs and the variant that actually determines risk—the so-called functional variant. Risk models based on functional variants are therefore likely to be more robust than tagSNP-based models.
Genetic variants associated with fasting glucose in European ancestry populations are increasingly well understood. However, the nature of the associations between these SNPs and fasting glucose in other racial and ethnic groups is unclear. We sought to examine regions previously identified to be associated with fasting glucose in Caucasian GWAS across multiple ethnicities in the Multi-Ethnic Study of Atherosclerosis (MESA). Non-diabetic MESA participants with fasting glucose measured at the baseline exam and with GWAS genotyping were included; 2349 Caucasians, 664 individuals of Chinese descent, 1366 African Americans, and 1171 Hispanics. Genotype data was generated from the Affymetrix 6.0 array and imputation in IMPUTE. Fasting glucose was regressed on SNP dosage data in each ethnic group adjusting for age, gender, MESA study center, and ethnic-specific principal components. SNPs from the three gene regions with the strongest associations to fasting glucose in previous Caucasian GWAS (MTNR1B / GCK / G6PC2) were examined in depth. There was limited power to replicate associations in other ethnic groups due to smaller allele frequencies and limited sample size; SNP associations may also have differed across ethnic groups due to differing LD patterns with causal variants. rs10830963 in MTNR1B and rs4607517 in GCK demonstrated consistent magnitude and direction of association with fasting glucose across ethnic groups, although the associations were often not nominally significant. In conclusion, certain SNPs in MTNR1B and GCK demonstrate consistent effects across four racial and ethnic groups, narrowing the putative region for these causal variants.
GWAS; fasting glucose; SNP
Genome-wide association studies (GWAS) in populations of European ancestry have mapped a type 2 diabetes susceptibility region to chromosome 10q23.33 containing IDE, KIF11 and HHEX genes (IDE-KIF11-HHEX), which has also been replicated in Chinese populations. However, the functional relevance for genetic variants at this locus is still unclear. It is critical to systematically assess the relationship of genetic variants in this region with the risk of type 2 diabetes.
A fine-mapping study was conducted by genotyping fourteen tagging single-nucleotide polymorphisms (SNPs) in a 290-kb linkage disequilibrium (LD) region using a two-stage case-control study of type 2 diabetes in a Chinese Han population. Suggestive associations (P<0.05) observed from 1,200 cases and 1,200 controls in the first stage were further replicated in 1,725 cases and 2,081 controls in the second stage. Seven tagging SNPs were consistently associated with type 2 diabetes in both stages (P<0.05), with combined odds ratios (ORs) ranging from 1.14 to 1.33 in the combined analysis. The most significant locus was rs7923837 [OR = 1.33, 95% confidence interval (CI): 1.21–1.47] at the 3′-flanking region of HHEX gene. SNP rs1111875 was found to be another partially independent locus (OR = 1.23, 95% CI: 1.13–1.35) in this region that was associated with type 2 diabetes risk. A cumulative effect of rs7923837 and rs1111875 was observed with individuals carrying 1, 2, and 3 or 4 risk alleles having a 1.27, 1.44, and 1.73-fold increased risk, respectively, for type 2 diabetes (P for trend = 4.1E-10).
Our results confirm that genetic variants of the IDE-KIF11-HHEX region at 10q23.33 contribute to type 2 diabetes susceptibility and suggest that rs7923837 may represent the strongest signal related to type 2 diabetes risk in the Chinese Han population.
Meta-analysis has proven a useful tool in genetic association studies. Allelic heterogeneity can arise from ethnic background differences across populations being meta-analyzed (for example, in search of common frequency variants through genome-wide association studies), and through the presence of multiple low frequency and rare associated variants in the same functional unit of interest (for example, within a gene or a regulatory region). The latter challenge will be increasingly relevant in whole-genome and whole-exome sequencing studies investigating association with complex traits. Here, we evaluate the performance of different approaches to meta-analysis in the presence of allelic heterogeneity. We simulate allelic heterogeneity scenarios in three populations and examine the performance of current approaches to the analysis of these data. We show that current approaches can detect only a small fraction of common frequency causal variants. We also find that for low-frequency variants with large effects (odds ratios 2–3), single-point tests have high power, but also high false-positive rates. P-value based meta-analysis of summary results from allele-matching locus-wide tests outperforms collapsing approaches. We conclude that current strategies for the combination of genetic association data in the presence of allelic heterogeneity are insufficiently powered.
genetic association; trans-ethnic mapping; multiple rare variants
This meta-analysis assessed the pooled effect of each genetic variant reproducibly associated with diabetic nephropathy.
PubMed, EMBASE and Web of Science were searched for articles assessing the association between genes and diabetic nephropathy. All genetic variants statistically associated with diabetic nephropathy in an initial study, then independently reproduced in at least one additional study, were selected. Subsequently, all studies assessing these variants were included. The association between these variants and diabetic nephropathy (defined as macroalbuminuria/proteinuria or end-stage renal disease [ESRD]) was calculated at the allele level and the main measure of effect was a pooled odds ratio. Pre-specified subgroup analyses were performed, stratifying for type 1/type 2 diabetes mellitus, proteinuria/ESRD and ethnic group.
The literature search yielded 3,455 citations, of which 671 were genetic association studies investigating diabetic nephropathy. We identified 34 replicated genetic variants. Of these, 21 remained significantly associated with diabetic nephropathy in a random-effects meta-analysis. These variants were in or near the following genes: ACE, AKR1B1 (two variants), APOC1, APOE, EPO, NOS3 (two variants), HSPG2, VEGFA, FRMD3 (two variants), CARS (two variants), UNC13B, CPVL and CHN2, and GREM1, plus four variants not near genes. The odds ratios of associated genetic variants ranged from 0.48 to 1.70. Additional variants were detected in subgroup analyses: ELMO1 (Asians), CCR5 (Asians) and CNDP1 (type 2 diabetes).
This meta-analysis found 24 genetic variants associated with diabetic nephropathy. The relative contribution and relevance of the identified genes in the pathogenesis of diabetic nephropathy should be the focus of future studies.
Electronic supplementary material
The online version of this article (doi:10.1007/s00125-010-1996-1) contains supplementary material, which is available to authorised users.
Diabetes mellitus; Diabetic nephropathy; Genetic association studies; Genetic polymorphism; Meta-analysis
Allergic rhinitis (AR) is an atopic disease which affects about 600 million people worldwide and results from a complex interplay between genetic and environmental factors. However genetic association studies on known candidate genes yielded variable results. The aim of this study is to identify the genetic variants that influence predisposition towards allergic rhinitis in an ethnic Chinese population in Singapore using a genome-wide association study (GWAS) approach. A total of 4461 ethnic Chinese volunteers were recruited in Singapore and classified according to their allergic disease status. The GWAS included a discovery stage comparing 515 atopic cases (including 456 AR cases) and 486 non-allergic non-rhinitis (NANR) controls. The top SNPs were then validated in a replication cohort consisting of a separate 2323 atopic cases (including 676 AR cases) and 511 NANR controls. Two SNPs showed consistent association in both discovery and replication phases; MRPL4 SNP rs8111930 on 19q13.2 (OR = 0.69, Pcombined = 4.46×10−05) and BCAP SNP rs505010 on chromosome 10q24.1 (OR = 0.64, Pcombined = 1.10×10−04). In addition, we also replicated multiple associations within known candidates regions such as HLA-DQ and NPSR1 locus in the discovery phase. Our study suggests that MRPL4 and BCAP, key components of the HIF-1α and PI3K/Akt signaling pathways respectively, are two novel candidate genes for atopy and allergic rhinitis. Further study on these molecules and their signaling pathways would help in understanding of the pathogenesis of allergic rhinitis and identification of targets for new therapeutic intervention.
Variation in the transcription factor 7-like 2 (TCF7L2) locus is associated with type 2 diabetes across multiple ethnicities. The aim of this study was to elucidate which variant in TCF7L2 confers diabetes susceptibility in African Americans.
RESEARCH DESIGN AND METHODS
Through the evaluation of tagging single nucleotide polymorphisms (SNPs), type 2 diabetes susceptibility was limited to a 4.3-kb interval, which contains the YRI (African) linkage disequilibrium (LD) block containing rs7903146. To better define the relationship between type 2 diabetes risk and genetic variation we resequenced this 4.3-kb region in 96 African American DNAs. Thirty-three novel and 13 known SNPs were identified: 20 with minor allele frequencies (MAF) >0.05 and 12 with MAF >0.10. These polymorphisms and the previously identified DG10S478 microsatellite were evaluated in African American type 2 diabetic cases (n = 1,033) and controls (n = 1,106).
Variants identified from direct sequencing and databases were genotyped or imputed. Fifteen SNPs showed association with type 2 diabetes (P < 0.05) with rs7903146 being the most significant (P = 6.32 × 10−6). Results of imputation, haplotype, and conditional analysis of SNPs were consistent with rs7903146 being the trait-defining SNP. Analysis of the DG10S478 microsatellite, which is outside the 4.3-kb LD block, revealed consistent association of risk allele 8 with type 2 diabetes (odds ratio [OR] = 1.33; P = 0.022) as reported in European populations; however, allele 16 (MAF = 0.016 cases and 0.032 controls) was strongly associated with reduced risk (OR = 0.39; P = 5.02 × 10−5) in contrast with previous studies.
In African Americans, these observations suggest that rs7903146 is the trait-defining polymorphism associated with type 2 diabetes risk. Collectively, these results support ethnic differences in type 2 diabetes associations.
An interaction between genes and the environment is a critical component underlying the pathogenesis of the hyperglycemia of type 2 diabetes. The development of more sophisticated techniques for studying gene variants and for analyzing genetic data has led to the discovery of some 40 genes associated with type 2 diabetes. Most of these genes are related to changes in β-cell function, with a few associated with decreased insulin sensitivity and obesity. Interestingly, using quantitative traits based on continuous measures rather than dichotomous ones, it has become evident that not all genes associated with changes in fasting or post-prandial glucose are also associated with a diagnosis of type 2 diabetes. Identification of these gene variants has provided novel insights into the physiology and pathophysiology of the β-cell, including the identification of molecules involved in β-cell function that were not previously recognized as playing a role in this critical cell.
incretins; insulin secretion; insulin sensitivity; islet; proinsulin; zinc; glucokinase; TCF7L2; KCNJ11; ABCC8; SLC30A8
In the book Epidemiology of Diabetes and Its Vascular Lesions (1978), Kelly West summarized extant knowledge of the distribution and causes of diabetes. The 30 years of epidemiological research that followed have seen remarkable advances in the understanding of obesity as a risk factor for type 2 diabetes, and diabetes and pre-diabetes as risk factors for cardiovascular disease. Increasingly detailed understanding of these relationships has, unfortunately, been accompanied by an alarming increase in the prevalence of obesity, diabetes, and cardiovascular disease. West recognized that pre-diabetes is recognizable as what we now call metabolic syndrome. He predicted that novel insight into diabetes pathogenesis would come from biochemical and genetic epidemiology studies. He predicted that type 2 diabetes could be prevented by healthy lifestyle change. The challenge now is for us to translate these insights into effective strategies for the prevention of the modern epidemic of diabetes and vascular disease.
Following years of linear gains in the genetic dissection of human disease, we are now in a period of exponential discovery. This is particularly apparent for complex disease. Genome-wide association studies have provided myriad associations between common variability and disease, and shown that common genetic variability is unlikely to explain the entire genetic predisposition to disease. Here, we detail how one can expand on this success and systematically identify genetic risks that lead or predispose to disease using next generation sequencing. Geneticists have had for many years a protocol to identify Mendelian disease. Now we have available a similar set of tools for the identification of rare moderate risk loci and common low risk variants. While undoubtedly major challenges remain, particularly with data handling and the functional classification of variants, we suggest that these will be largely practical and not conceptual.