Advent Of Genome-wide Association Studies Research Articles

What are genome-wide association studies telling us about B-cell tumor development?

It has long been speculated that common genetic variation influences the development of B-cell malignancy, however until recently evidence for this assertion was lacking. The advent of genome-wide association studies (GWAS) has allowed the search for this class of susceptibility allele to be conducted on a genome-wide basis. Recent GWAS of chronic lymphocytic leukemia (CLL) and acute lymphoblastic leukemia (ALL) have identified novel disease genes for CLL and ALL and underscore the importance of polymorphic variation in B-cell development genes as determinants of leukemia risk.

Combined effects of 17 common genetic variants on type 2 diabetes risk in a Han Chinese population

The recent advent of genome-wide association studies has considerably accelerated the identification of type 2 diabetes loci. We aimed to investigate the combined effects of multiple genetic variants, alone or in combination with conventional risk factors, on type 2 diabetes and diabetes-related traits in Han Chinese. We genotyped 17 variants in 17 loci in a population-based Han Chinese cohort including 3,210 unrelated individuals. A genetic risk score (GRS) was calculated on the basis of these variants. The discriminatory ability was assessed by the area under the receiver operating characteristics curve. The odds ratio for type 2 diabetes and hyperglycaemia with each GRS point (per risk allele) was 1.18 (95% CI 1.12-1.23, p = 1.3 x 10(-12)) and 1.12 (95% CI 1.09-1.16, p = 7.5 x 10(-14)), respectively. Compared with participants with GRS < or =11.0 (7.63%), those with GRS > or =19.0 (8.87%) had a 4.58-fold higher risk (95% CI 2.49-8.42) of type 2 diabetes. The GRS also showed a significant association with lower beta cell function estimated by HOMA of beta cell function (p = 8.4 x 10(-10)). In addition, we observed significant interactive effects between GRS and BMI on fasting glucose and HbA(1c) levels (p = 0.04 and p = 0.03 for interaction, respectively). Discrimination of diabetes risk was improved (p < 0.001) when the GRS was added to a model including clinical risk factors. The AUCs were 0.62 and 0.77, respectively, for the GRS and conventional clinic risk factors alone, and 0.79 when the GRS was added. In this Han Chinese population, the GRS of 17 combined variants modestly but significantly improved discrimination of the conventional risk factors for type 2 diabetes.

The genetics of affective disorder and suicide

Suicidal behaviour shows evidence of familial clustering and the twin data on completed suicide suggest moderate heritability. The extent to which the genetics of suicidal behaviour overlaps with the genetics of affective disorders is unclear but there is overwhelming evidence that both bipolar and unipolar disorder are substantially influenced by genes. So far, candidate gene studies of suicidality have provoked much interest, but recently, attention has also turned to candidate gene approaches to suicidal ideation emerging during antidepressant treatment. The advent of genome-wide association studies (GWAS) has had a major impact on studies of affective disorder with some provocative new findings. The GWAS approach is also beginning to be applied in the search for genes that underlie suicidal ideation and behaviour.

Atrial Fibrillation Susceptibility Alleles on Chromosome 4q25 Modulate Response to Catheter Ablation

Atrial Fibrillation Susceptibility Alleles on Chromosome 4q25 Modulate Response to Catheter Ablation

Replication of past candidate loci for common diseases and phenotypes in 100 genome-wide association studies

Genome-wide association studies (GWASs) have created a paradigm shift in discovering genetic associations for common diseases and phenotypes, but it is unclear whether the thousands of candidate genetic association studies performed in the pre-GWAS era had found any reliable associations for common diseases and phenotypes. We aimed to systematically evaluate whether loci proposed to harbor candidate associations before the advent of GWASs are replicated in GWASs. The GWAS data published through August, 2008 and included in the NHGRI catalog were screened and variants in candidate loci were selected on the basis of statistical significance (P<0.05) to create a list of independent, non-redundant associations. Altogether, 159 articles on GWASs were evaluated, 100 of which addressed past proposed candidate loci. A total of 291 independent, nominally significant (P<0.05) candidate gene associations were assembled after keeping only the SNP with lowest P-value for each locus and each phenotype; 108 of those had P<10(-3) for association and 41 had P<10(-7). A total of 22 of these 41 candidate gene associations pertained to binary phenotypes with a median odds ratio=2.91 (IQR: 1.82-4.6) and median minor allele frequency=0.17 (IQR: 0.12-0.29) in Caucasians; for comparison, 60 new associations of binary outcomes with P<10(-7) discovered in the same GWASs had much smaller effects (median odds ratio 1.30, IQR: 1.18-1.58) and modestly larger minor allele frequencies (median 0.27, IQR: 0.15-0.43). Overall, few of the numerous genetic associations proposed in the candidate gene era have been replicated in GWASs, but those that have been conclusively replicated have large genetic effects that should not be discarded.

Advances in genome-wide association study of coronary heart disease

As a new effective strategy for researching complex diseases, genome-wide association study (GWAS) has being developed rapidly in recent years worldwide. The world genomic study has been taken into a new stage, since a series of disease related genes or variants have been identified by GWAS strategy. Coronary heart disease (CHD) is a complex disease that is caused by both environmental and genetic factors, and has become one of the leading causes of death and disability worldwide. With the application of GWAS strategy, researchers from all over the world have identified many susceptibility loci or regions of CHD that were unable to be identified by candidate gene case-control study. The present paper reviewed the important progresses worldwide attained in GWAS of CHD in recent years. Then it is also expounded the challenges we are facing nowadays in GWAS as well as the future study direction. The information outlined in this paper provides us a valuable guidance upon further exploration into genetic mechanism of CHD.

The Genetics of Juvenile Idiopathic Arthritis: What Is New in 2010?

Juvenile idiopathic arthritis (JIA), the most common cause of chronic arthritis in children, is believed to be influenced by genetic factors. Recent studies on the genetics of JIA have not only validated proposed genetic associations but have also led to the recognition of novel genetic associations. Studies of specific genes have been modeled on the premise of shared autoimmunity, wherein genetic variants that predispose to other autoimmune phenotypes may also confer susceptibility to JIA. The advent of genome-wide association studies has accelerated the detection of non-HLA susceptibility loci in other autoimmune phenotypes and is likely to uncover novel JIA-associated variants as well. This review highlights recent genetic investigations of JIA.

Functional intronic polymorphisms: Buried treasure awaiting discovery within our genes

Functional intronic polymorphisms: Buried treasure awaiting discovery within our genes

Laplacian eigenfunctions learn population structure.

Principal components analysis has been used for decades to summarize genetic variation across geographic regions and to infer population migration history. More recently, with the advent of genome-wide association studies of complex traits, it has become a commonly-used tool for detection and correction of confounding due to population structure. However, principal components are generally sensitive to outliers. Recently there has also been concern about its interpretation. Motivated from geometric learning, we describe a method based on spectral graph theory. Regarding each study subject as a node with suitably defined weights for its edges to close neighbors, one can form a weighted graph. We suggest using the spectrum of the associated graph Laplacian operator, namely, Laplacian eigenfunctions, to infer population structure. In simulations and real data on a ring species of birds, Laplacian eigenfunctions reveal more meaningful and less noisy structure of the underlying population, compared with principal components. The proposed approach is simple and computationally fast. It is expected to become a promising and basic method for population genetics and disease association studies.

Re: Discriminatory Accuracy From Single-Nucleotide Polymorphisms in Models to Predict Breast Cancer Risk

The advent of genome-wide association studies to identify low-penetrance common susceptibility alleles heralds the possibility of incorporating panels of gene variants into existing risk prediction models and of assessing improvement in model performance. However, to date, the updated models have shown only modest improvements in discrimination. Gail (1) had previously shown that adding seven single-nucleotide polymorphisms (SNPs) identified from genome-wide association analyses to the original Breast Cancer Risk Assessment Tool had yielded only a modest improvement in area under the curve (AUC) from 0.607 to 0.632. Gail (2) now reports that inclusion of 11 SNPs exhibits an even smaller improvement in the AUC (0.637) than that of the BRACTplus 7 model. The receiver operating characteristics curve may not be sensitive to differences in probabilities between models and, therefore, may be insufficient to assess the impact of adding new predictors. A very large independent association of the new marker is required for a meaningful improvement in AUC, and a substantial gain in performance may not yield a substantial increase in AUC. One suggested statistic for comparing nested models is the net reclassification index that is useful when risk categories are defined, and there is a consensus as to clinically meaningful cut points (3). The net reclassification index quantifies overall improvement in model sensitivity and specificity. A net improvement in risk classification implies upward reclassification of case patients and downward reclassification of control subjects. We evaluated these metrics in our own internally validated risk prediction model for lung cancer that incorporated easily attainable epidemiological and clinical variables (4). In a genome-wide association analysis of 315 450 tagging SNPs in 1154 patients with lung cancer who were current and former smokers and were of European ancestry and 1137 frequency-matched control subjects (5), two SNPs, rs1051730 and rs8034191, that mapped to a region within 15q25.1 (which encompasses the nicotinic acetylcholine receptor subunit genes CHRNA3 and CHRNA5) were strongly associated with risk (odds ratio [OR] = 1.32, 95% confidence interval [CI] = 1.24 to 1.41, P = 3.15 × 10−18 for rs8034191; and OR = 1.32, 95% CI = 1.23 to 1.39, P = 7.00 × 10−18 for rs1051730). In a subsequent meta-analysis (6) involving the UK genome-wide association study, the International Agency for Research on Cancer genome-wide association study, and our Texas genome-wide association study, the strongest associations remained for SNPs mapping to 15q25.1 (ie, rs1051730, P = 2.83 × 10−19; and rs8034191, P = 4.03 × 10−19). There was also consistent evidence for a new disease locus at 5p15.33 (ie, rs401681, P = 4.40 × 10−6). This locus contains two known genes: TERT (human telomerase reverse transcriptase) gene and CLPTM1L (cleft lip and palate transmembrane 1-like) gene. We therefore added one SNP from the 15q25.1 locus (ie, rs1051730, which was used because it was in strong linkage disequilibrium with rs8034191) and two SNPs from the 5p15.33 region (ie, rs2736100 and rs401681) to the baseline model and assessed discrimination improvement. Our AUC for the baseline epidemiological–clinical model including 1016 case patients and 1111 control subjects was 0.661 (95% CI = 0.64 to 0.68). With addition of the three SNPs, the AUC showed modest, yet statistically significant, improvement to 0.673 (95% CI = 0.65 to 0.70, P = .01). We defined risk categories on the basis of the lower and upper quartiles of predicted risk from our baseline model as proposed by Bach et al. (7): low (predicted risk 50%). The resulting net reclassification indices were 0.152 (95% CI = 0.112 to 0.193) overall, 0.089 (95% CI = 0.048 to 0.130) for case patients, and 0.064 (95% CI = 0.023 to 0.105) for control subjects (all statistically significant at the 0.2% level), indicating that the SNPs modestly improved both sensitivity (9%) and specificity (6%). Although it could be argued that models providing a continuous score are more appropriate in the clinical setting, it is likely that a variety of additional summary measures evaluating model performance will be needed to assess these multigenic models.

Genetic basis of myocardial infarction: Novel insights from genome-wide association studies

Inheritance plays an important role in the pathogenesis of coronary artery disease (CAD) and myocardial infarction (MI). Yet, for decades, gene discovery proved to be difficult, as variants in candidate genes failed to show robust association with the disease. However, the advent of genome-wide association studies allowed the identification of multiple genetic loci associated with risk of CAD/MI. The discoveries revealed several unexpected findings. First, the strongest association with CAD/MI in the genome was located to chromosome 9p21.3, with poor annotation of genes. Even more surprisingly, of the multiple regions identified for CAD/MI risk, only a few relate to traditional risk factors such as low-density lipoprotein cholesterol. Moreover, all currently identified loci confer a rather modest risk increase, with a risk per allele of about 10–25%. Together, these surprising findings brought about by genome-wide association studies suggest significant gaps in the traditional understanding of the pathophysiology of CAD/MI.

Evaluation of genetic loci influencing adult height in the Japanese population

Adult height is a highly heritable, classic polygenic trait. The recent advent of genome-wide association studies has led to the identification of robust association at common variants influencing the normal variation of height; the number of loci has now risen to 50. The present study tested the potential overlap of height associations at 46 loci that were previously reported in European-descent populations with those among 1530 Japanese subjects and also attempted to replicate the suggestive association signals that were previously reported in East Asians alone. We found a total of 20 independent loci to be significantly (P<0.05) associated with height among the Japanese subjects (N< or =6814); 19 loci were originally identified in Europeans and the remaining 1 locus (ZFAT) was previously reported in Koreans (P=3.8 x 10(-9) with the Japanese and Korean data combined for ZFAT). Although 41% (19 of 46) of the loci were nominally replicated, a higher proportion of the tested loci seemed to overlap between the Japanese and Europeans; 83% (38 of 46) of the loci showed a concordant direction of association between the two ethnic groups. Despite the substantial inter-population overlap observed in the present study, further investigation is warranted to search for loci with enhanced genetic impacts in East Asians than in Europeans.

It Takes Two to Tango: Genotyping and Phenotyping in Genome-Wide Association Studies

In this article we examine the “phenotype” concept in light of recent technological advances in Genome-Wide Association Studies (GWAS). By observing the technology and its presuppositions, we put forward the thesis that at least in this case genotype and phenotype are effectively coidentifled one by means of the other. We suggest that the coidentiflcation of genotype-phenotype couples in expression-based GWAS also indicates a conceptual dependence, which we call “co-dennition.” We note that viewing these terms as codeflned runs against possible expectations, viz., that genotypes and phenotypes could ultimately be expressed independently from one another. In addition, the co-definition of genotypes and phenotypes in this context emphasizes the correlative (rather than mechanistic) character of both genotypes and phenotypes in GWAS.

Meta-analysis in genome-wide association studies.

The advent of genome-wide association studies has allowed considerable progress in the identification and robust replication of common gene variants that confer susceptibility to common diseases and other phenotypes of interest. These genetic effect sizes are almost invariably moderate to small in magnitude and single studies, even if large, are underpowered to detect them with confidence. Meta-analysis of many genome-wide association studies improves the power to detect more associations, and to investigate the consistency or heterogeneity of these associations across diverse datasets and study populations. In this review, we discuss the key methodological issues in the set-up, information gathering and processing, and analysis of meta-analyses of genome-wide association datasets. We illustrate, as an example, the application of meta-analysis methods in the elucidation of common genetic variants associated with Type 2 diabetes. Finally, we discuss the prospects and caveats for future application of meta-analysis methods in the genome-wide setting.

Population stratification and patterns of linkage disequilibrium

Although the importance of selecting cases and controls from the same population has been recognized for decades, the recent advent of genome-wide association studies has heightened awareness of this issue. Because these studies typically deal with large samples, small differences in allele frequencies between cases and controls can easily reach statistical significance. When, unbeknownst to a researcher, cases and controls have different substructures, the number of false-positive findings is inflated. There have been three recent developments of purely statistical approaches to assessing the ancestral comparability of case and control samples: genomic control, structured association, and multivariate reduction analyses. The widespread use of high-throughput technology has allowed the quick and accurate genotyping of the large number of markers required by these methods. Group 13 dealt with four population stratification issues: single-nucleotide polymorphism marker selection, association testing, nonstandard methods, and linkage disequilibrium calculations in stratified or mixed ethnicity samples. We demonstrated that there are continuous axes of ethnic variation in both data sets of Genetic Analysis Workshop 16. Furthermore, ignoring this structure created P-value inflation for a variety of phenotypes. Principal-components analysis (or multidimensional scaling) can control inflation as covariates in a logistic regression. One can weigh for local ancestry estimation and allow the use of related individuals. Problems arise in the presence of extremely high association or unusually strong linkage disequilibrium (e.g., in chromosomal inversions). Our group also reported a method for performing an association test controlling for substructure, when genome-wide markers are not available, to explicitly compute stratification.

Genetic susceptibility to psoriasis: an emerging picture

Psoriasis is recognized as a complex disease for which multiple genetic and non-genetic factors influence susceptibility. The major susceptibility locus resides in the MHC class I region and, until relatively recently, evidence for non-MHC loci was inconsistent. Like many common diseases, knowledge of the genetic basis of this condition has been advanced dramatically in recent times with the advent of genome-wide association studies using single nucleotide polymorphisms. Here, we give an overview of current knowledge of genetic risk factors for psoriasis and consider emerging studies that may further add to our understanding of the genetic basis of the disease.

Paneth cells and inflammation dance together in Crohn's disease

Inflammatory Bowel Disease (IBD) is a dysregulated response of the immune system associated with intestinal tissues to the commensal microbiota in a genetically susceptible host. The understanding of the genetic basis of IBD has been significantly expanded with the advent of genome-wide association studies (GWAS) which has resulted in the identification of more than 30 loci that are associated with Crohn's disease (CD) and ulcerative colitis (UC). As anticipated, this unbiased approach of gene discovery has both confirmed previously established disease related pathways and led to new hypotheses of IBD pathogenesis that are currently being tested in biologic model systems to establish the precise mechanisms of action involved. One such potential pathway that was unexpectedly identified is that associated with autophagy; a cell biologic pathway important in the clearance of microbes and cellular proteins and organelles.

Genes in asthma: new genes and new ways

Asthma is a disease of unknown aetiology characterized by intermittent inflammation of the small airways of the lung. Asthma is familial due to an interaction between strong genetic and environmental factors. This article aims to review the current understanding of the genetic factors underlying asthma, looking historically as well as highlighting the latest developments in the field. Findings from recent candidate gene studies and microsatellite genome screens have continued to highlight the importance of the epithelial barrier and its defence mechanisms in asthma. Completion of the human genome sequence and the advent of genome-wide association studies have resulted in the identification of two novel asthma susceptibility genes, ORMDL3 and CHI3L1, in the past year. With the advances in genetics and genomics substantial steps have been taken in the last decade in understanding the genetic factors underlying asthma. Studies have highlighted the importance of the role of the epithelium with many of the genes so far identified being expressed in this key barrier. With the application of genome-wide expression, microRNA studies, metagenomics, proteomics and metabolomics the next decade will undoubtedly result in a further substantial increment in our understanding of the mechanisms underlying asthma.

Monogenic Diabetes in the Young, Pharmacogenetics and Relevance to Multifactorial Forms of Type 2 Diabetes

Most valuable breakthroughs in the genetics of type 2 diabetes for the past two decades have arisen from candidate gene studies and familial linkage analysis of maturity-onset diabetes of the young (MODY), an autosomal dominant form of diabetes typically occurring before 25 years of age caused by primary insulin secretion defects. Despite its low prevalence, MODY is not a single entity but presents genetic, metabolic and clinical heterogeneity. MODY can result from mutations in at least six different genes encoding the glucose sensor enzyme glucokinase and transcription factors that participate in a regulatory network essential for adult beta-cell function. Additional genes have been described in other discrete phenotypes or syndromic forms of diabetes. Whereas common variants in the MODY genes contribute very modestly to type 2 diabetes susceptibility in adults, major findings emerging from the advent of genome-wide association studies will deliver an increasing number of genes and new pathways for the pathological events of the disease.

Classification of genetic profiles of Crohn’s disease: a focus on the ATG16L1 gene

Inflammatory bowel disease constitutes two related clinical entities, Crohn’s disease (CD) and ulcerative colitis (UC), both of which have increased in prevalence over the last decade. Family and twin studies have strongly indicated that genetic factors play a large role in an individual’s risk of developing inflammatory bowel disease. Despite this, it has proven difficult to isolate disease genes that confer susceptibility to this disease using classical candidate gene and linkage approaches, with the notable exception of the isolation of the caspase recruitment domain family, member 15 (CARD15) gene. However, over the last 2 years, genome-wide association (GWA) studies have become feasible, where modern high-throughput single nucleotide polymorphism (SNP) genotyping technologies can be applied to large and comprehensively phenotyped patient cohorts. Such approaches have enabled scientists to robustly associate specific variants with many complex diseases, including age-related macular degeneration, Type 2 diabetes, breast cancer and asthma. In the inflammatory bowel disease field, positive associations with CD and UC coming from GWA studies have been reported for an ever increasing number of genes. The most consistently and strongly associated variants have been in the CARD15, the interleukin 23 receptor (IL23R) and autophagy-related 16-like 1 (ATG16L1) genes. With respect to ATG16L1, the G allele of SNP rs2241880 has been shown in multiple association studies to confer strong risk for CD, although its association with UC remains more debatable. This SNP is in fact a common coding variant, specifically a threonine-to-alanine substitution at amino acid position 300 of the ATG16L1 protein (T300A), and appears to account for all of the disease risk conferred by this locus. This review addresses recent advances in GWA studies of inflammatory bowel disease, with specific focus on the growing evidence of the ATG16L1 gene’s role in CD and how its protein product operating within the autophagic pathway makes autophagy an attractive therapeutic target for this debilitating disorder.