Infectious and inflammatory diseases have repeatedly shown strong genetic associations within the major histocompatibility complex (MHC); however, the basis for these associations remains elusive. To define host genetic effects on the outcome of a chronic viral infection, we performed genome-wide association analysis in a multiethnic cohort of HIV-1 controllers and progressors, and we analyzed the effects of individual amino acids within the classical human leukocyte antigen (HLA) proteins. We identified >300 genome-wide significant single-nucleotide polymorphisms (SNPs) within the MHC and none elsewhere. Specific amino acids in the HLA-B peptide binding groove, as well as an independent HLA-C effect, explain the SNP associations and reconcile both protective and risk HLA alleles. These results implicate the nature of the HLA–viral peptide interaction as the major factor modulating durable control of HIV infection.

Susceptibility to primary biliary cirrhosis (PBC) is strongly associated with HLA region polymorphisms. To determine if associations can be explained by classical HLA determinants we studied Italian 676 cases and 1440 controls with genotyped with dense single nucleotide polymorphisms (SNPs) for which classical HLA alleles and amino acids were imputed. Although previous genome-wide association studies and our results show stronger SNP associations near DQB1, we demonstrate that the HLA signals can be attributed to classical DRB1 and DPB1 genes. Strong support for the predominant role of DRB1 is provided by our conditional analyses. We also demonstrate an independent association of DPB1. Specific HLA-DRB1 genes (*08, *11 and *14) account for most of the DRB1 association signal. Consistent with previous studies, DRB1*08 (p = 1.59 × 10−11) was the strongest predisposing allele where as DRB1*11 (p = 1.42 × 10−10) was protective. Additionally DRB1*14 and the DPB1 association (DPB1*03:01) (p = 9.18 × 10−7) were predisposing risk alleles. No signal was observed in the HLA class 1 or class 3 regions. These findings better define the association of PBC with HLA and specifically support the role of classical HLA-DRB1 and DPB1 genes and alleles in susceptibility to PBC.

Objective

To perform a one-stage meta-analysis of genome-wide association studies (GWAS) of multiple sclerosis (MS) susceptibility and explore functional consequences of new susceptibility loci.

Methods

We synthesized 7 MS GWAS. Each dataset was imputed using HapMap phase II and a per-SNP meta-analysis was performed across the 7 datasets. We explored RNA expression data using a quantitative trait analysis in peripheral blood mononuclear cells (PBMCs) of 228 subjects with demyelinating disease.

Results

We meta-analyzed 2,529,394 unique SNPs in 5,545 cases and 12,153 controls. We identified three novel susceptibility alleles: rs170934T at 3p24.1 (OR=1.17, P = 1.6 × 10−8) near EOMES, rs2150702G in the second intron of MLANA on chromosome 9p24.1 (OR = 1.16, P = 3.3 × 10−8), and rs6718520A in an intergenic region on chromosome 2p21, with THADA as the nearest flanking gene (OR = 1.17, P = 3.4 × 10−8). The three new loci do not have a strong “cis” effect on RNA expression in PBMCs. Ten other susceptibility loci had a suggestive P<1×10−6, some of which have evidence of association in other inflammatory diseases, i.e. IL12B, TAGAP, PLEK, and ZMIZ1.

Interpretation

We have performed a meta-analysis of GWAS in MS that more than doubles the size of previous gene discovery efforts and highlights three novel MS susceptibility loci. These and additional loci with suggestive evidence of association are excellent candidates for further investigations to refine and validate their role in the genetic architecture of MS.

The major histocompatibility complex (MHC) on chromosome 6p is an established risk locus for ulcerative colitis (UC) and Crohn’s disease (CD). We aimed to better define MHC association signals in UC and CD by combining data from dense single nucleotide polymorphism (SNP) genotyping and from imputation of classical HLA types, their constituent SNPs and corresponding amino acids in 562 UC, 611 CD, and 1,428 control subjects. Univariate and multivariate association analyses were performed, controlling for ancestry. In univariate analyses, absence of the rs9269955 C allele was strongly associated with risk for UC (P = 2.67×10−13). rs9269955 is a SNP in the codon for amino acid position 11 of HLA-DRβ1, located in the P6 pocket of the HLA-DR antigen binding cleft. This amino acid position was also the most significantly UC-associated amino acid in omnibus tests (P = 2.68×10−13). Multivariate modeling identified rs9269955-C and 13 other variants in best predicting UC versus control status. In contrast, there was only suggestive association evidence between the MHC and CD. Taken together, these data demonstrate that variation at HLA-DRβ1, amino acid 11 in the P6 pocket of the HLA-DR complex antigen binding cleft is a major determinant of chromosome 6p association with ulcerative colitis.

The genetic association of the major histocompatibility complex (MHC) to rheumatoid arthritis risk has commonly been attributed to HLA-DRB1 alleles. Yet controversy persists about the causal variants in HLA-DRB1 and the presence of independent effects elsewhere in the MHC. Using existing genome-wide SNP data in 5,018 seropositive cases and 14,974 controls, we imputed and tested classical alleles and amino acid polymorphisms for HLA-A, B, C, DPA1, DPB1, DQA1, DQB1, and DRB1 along with 3,117 SNPs across the MHC. Conditional and haplotype analyses reveal that three amino acid positions (11, 71 and 74) in HLA-DRβ1, and single amino acid polymorphisms in HLA-B (position 9) and HLA-DPβ1 (position 9), all located in the peptide-binding grooves, almost completely explain the MHC association to disease risk. This study illustrates how imputation of functional variation from large reference panels can help fine-map association signals in the MHC.

Weight-loss interventions generally improve lipid profiles and reduce cardiovascular disease risk, but effects are variable and may depend on genetic factors. We performed a genetic association analysis of data from 2,993 participants in the Diabetes Prevention Program to test the hypotheses that a genetic risk score (GRS) based on deleterious alleles at 32 lipid-associated single-nucleotide polymorphisms modifies the effects of lifestyle and/or metformin interventions on lipid levels and nuclear magnetic resonance (NMR) lipoprotein subfraction size and number. Twenty-three loci previously associated with fasting LDL-C, HDL-C, or triglycerides replicated (P = 0.04–1×10−17). Except for total HDL particles (r = −0.03, P = 0.26), all components of the lipid profile correlated with the GRS (partial |r| = 0.07–0.17, P = 5×10−5–1×10−19). The GRS was associated with higher baseline-adjusted 1-year LDL cholesterol levels (β = +0.87, SEE±0.22 mg/dl/allele, P = 8×10−5, Pinteraction = 0.02) in the lifestyle intervention group, but not in the placebo (β = +0.20, SEE±0.22 mg/dl/allele, P = 0.35) or metformin (β = −0.03, SEE±0.22 mg/dl/allele, P = 0.90; Pinteraction = 0.64) groups. Similarly, a higher GRS predicted a greater number of baseline-adjusted small LDL particles at 1 year in the lifestyle intervention arm (β = +0.30, SEE±0.012 ln nmol/L/allele, P = 0.01, Pinteraction = 0.01) but not in the placebo (β = −0.002, SEE±0.008 ln nmol/L/allele, P = 0.74) or metformin (β = +0.013, SEE±0.008 nmol/L/allele, P = 0.12; Pinteraction = 0.24) groups. Our findings suggest that a high genetic burden confers an adverse lipid profile and predicts attenuated response in LDL-C levels and small LDL particle number to dietary and physical activity interventions aimed at weight loss.

Author Summary

The study included 2,993 participants from the Diabetes Prevention Program, a randomized clinical trial of intensive lifestyle intervention, metformin treatment, and placebo control. We examined associations between 32 gene variants that have been reproducibly associated with dyslipidemia and concentrations of lipids and NMR lipoprotein particle sizes and numbers. We also examined whether genetic background influences a person's response to cardioprotective interventions on lipid levels. Our analysis, which focused on determining whether common genetic variants impact the effects of cardioprotective interventions on lipid and lipoprotein particle size, shows that in persons with a high genetic risk score the benefit of intensive lifestyle intervention on LDL and small LDL particle levels is substantially diminished; this information may be informative for the targeted prevention of dyslipidemia, as it suggests that genetics might help identify persons in whom lifestyle intervention is likely to be an effective treatment for elevated lipids and lipoproteins. The NMR subfraction analyses provide novel insight into the biology of dyslipidemia by illustrating how numerous genetic variants that have previously been associated with lipid levels also modulate NMR lipoprotein particle sizes and number. This information may be informative for the targeted prevention of cardiovascular disease.

QRS interval on the electrocardiogram reflects ventricular depolarization and conduction time, and is a risk factor for mortality, sudden death, and heart failure. We performed a genome-wide association meta-analysis in 40,407 European-descent individuals from 14 studies, with further genotyping in 7170 additional Europeans, and identified 22 loci associated with QRS duration (P < 5 × 10−8). These loci map in or near genes in pathways with established roles in ventricular conduction such as sodium channels, transcription factors, and calcium-handling proteins, but also point to novel biologic processes, such as kinase inhibitors and genes related to tumorigenesis. We demonstrate that SCN10A, a gene at our most significant locus, is expressed in the mouse ventricular conduction system, and treatment with a selective SCN10A blocker prolongs QRS duration. These findings extend our current knowledge of ventricular depolarization and conduction.

Personalized medicine is a high priority for the future of health care. The idea of tailoring an individual’s wellness plan to their unique genetic code is one which we hope to realize through the use of pharmacogenomics. There have been examples of tremendous success in pharmacogenomic associations however there are many such examples in which only a small proportion of trait variance has been explained by the genetic variation. Although the increased use of GWAS could help explain more of this variation, it is likely that a significant proportion of the genetic architecture of these pharmacogenomic traits are due to complex genetic effects such as epistasis, also known as gene-gene interactions, as well as gene-drug interactions. In this study, we utilize the Biofilter software package to look for candidate epistasis contributing to risk for virologic failure with efavirenz-containing antiretroviral therapy (ART) regimens in treatment-naïve participants of AIDS Clinical Trials Group (ACTG) randomized clinical trials. A total of 904 individuals from three ACTG trials with data on efavirenz treatment are analyzed after race-stratification into white, black, and Hispanic ethnic groups. Biofilter was run considering 245 candidate ADME (absorption, distribution, metabolism, and excretion) genes and using database knowledge of gene and protein interaction networks to produce approximately 2 million SNP-SNP interaction models within each ethnic group. These models were evaluated within the PLATO software package using pair wise logistic regression models. Although no interaction model remained significant after correction for multiple comparisons, an interaction between SNPs in the TAP1 and ABCC9 genes was one of the top models before correction. The TAP1 protein is responsible for intracellular transport of antigen to MHC class I molecules, while ABCC9 codes for a transporter which is part of the subfamily of ABC transporters associated with multi-drug resistance. This study demonstrates the utility of the Biofilter method to prioritize the search for gene-gene interactions in large-scale genomic datasets, although replication in a larger cohort is required to confirm the validity of this particular TAP1-ABCC9 finding.

OBJECTIVE

Genome-wide association studies have begun to elucidate the genetic architecture of type 2 diabetes. We examined whether single nucleotide polymorphisms (SNPs) identified through targeted complementary approaches affect diabetes incidence in the at-risk population of the Diabetes Prevention Program (DPP) and whether they influence a response to preventive interventions.

RESEARCH DESIGN AND METHODS

We selected SNPs identified by prior genome-wide association studies for type 2 diabetes and related traits, or capturing common variation in 40 candidate genes previously associated with type 2 diabetes, implicated in monogenic diabetes, encoding type 2 diabetes drug targets or drug-metabolizing/transporting enzymes, or involved in relevant physiological processes. We analyzed 1,590 SNPs for association with incident diabetes and their interaction with response to metformin or lifestyle interventions in 2,994 DPP participants. We controlled for multiple hypothesis testing by assessing false discovery rates.

RESULTS

We replicated the association of variants in the metformin transporter gene SLC47A1 with metformin response and detected nominal interactions in the AMP kinase (AMPK) gene STK11, the AMPK subunit genes PRKAA1 and PRKAA2, and a missense SNP in SLC22A1, which encodes another metformin transporter. The most significant association with diabetes incidence occurred in the AMPK subunit gene PRKAG2 (hazard ratio 1.24, 95% CI 1.09–1.40, P = 7 × 10−4). Overall, there were nominal associations with diabetes incidence at 85 SNPs and nominal interactions with the metformin and lifestyle interventions at 91 and 69 mostly nonoverlapping SNPs, respectively. The lowest P values were consistent with experiment-wide 33% false discovery rates.

CONCLUSIONS

We have identified potential genetic determinants of metformin response. These results merit confirmation in independent samples.

BACKGROUND

Carbamazepine causes various forms of hypersensitivity reactions, ranging from maculopapular exanthema to severe blistering reactions. The HLA-B★1502 allele has been shown to be strongly correlated with carbamazepine-induced Stevens–Johnson syndrome and toxic epidermal necrolysis (SJS–TEN) in the Han Chinese and other Asian populations but not in European populations.

METHODS

We performed a genomewide association study of samples obtained from 22 subjects with carbamazepine-induced hypersensitivity syndrome, 43 subjects with carbamazepine-induced maculopapular exanthema, and 3987 control subjects, all of European descent. We tested for an association between disease and HLA alleles through proxy single-nucleotide polymorphisms and imputation, confirming associations by high-resolution sequence-based HLA typing. We replicated the associations in samples from 145 subjects with carbamazepine-induced hypersensitivity reactions.

RESULTS

The HLA-A★3101 allele, which has a prevalence of 2 to 5% in Northern European populations, was significantly associated with the hypersensitivity syndrome (P = 3.5×10−8). An independent genomewide association study of samples from subjects with maculopapular exanthema also showed an association with the HLA-A★3101 allele (P = 1.1×10−6). Follow-up genotyping confirmed the variant as a risk factor for the hypersensitivity syndrome (odds ratio, 12.41; 95% confidence interval [CI], 1.27 to 121.03), maculopapular exanthema (odds ratio, 8.33; 95% CI, 3.59 to 19.36), and SJS–TEN (odds ratio, 25.93; 95% CI, 4.93 to 116.18).

CONCLUSIONS

The presence of the HLA-A★3101 allele was associated with carbamazepine-induced hypersensitivity reactions among subjects of Northern European ancestry. The presence of the allele increased the risk from 5.0% to 26.0%, whereas its absence reduced the risk from 5.0% to 3.8%. (Funded by the U.K. Department of Health and others.)

To date, genome-wide association studies have focused almost exclusively on populations of European ancestry. These studies continue with the advent of next-generation sequencing, designed to systematically catalog and test low-frequency variation for a role in disease. A complementary approach would be to focus further efforts on cohorts of multiple ethnicities. This leverages the idea that population genetic drift may have elevated some variants to higher allele frequency in different populations, boosting statistical power to detect an association. Based on empirical allele frequency distributions from eleven populations represented in HapMap Phase 3 and the 1000 Genomes Project, we simulate a range of genetic models to quantify the power of association studies in multiple ethnicities relative to studies that exclusively focus on samples of European ancestry. In each of these simulations, a first phase of GWAS in exclusively European samples is followed by a second GWAS phase in any of the other populations (including a multiethnic design). We find that nontrivial power gains can be achieved by conducting future whole-genome studies in worldwide populations, where, in particular, African populations contribute the largest relative power gains for low-frequency alleles (<5%) of moderate effect that suffer from low power in samples of European descent. Our results emphasize the importance of broadening genetic studies to worldwide populations to ensure efficient discovery of genetic loci contributing to phenotypic trait variability, especially for those traits for which large numbers of samples of European ancestry have already been collected and tested.

Atrial fibrillation (AF) is the most common sustained arrhythmia. A subset of patients with lone AF have no overt heart disease and an increased heritability of AF. We sought to identify common genetic variants underlying lone AF. Cases were from the German AF Network, Heart and Vascular Health Study, Atherosclerosis Risk in Communities Study, Cleveland Clinic, and Massachusetts General Hospital. Subjects were genotyped, HapMap SNPs imputed, and age- sex- and hypertension-adjusted analyses performed. A meta-analysis was conducted using 1,335 cases of lone AF and 12,844 referents. A novel locus on chromosome 1q21 was identified, and the most significant SNP, rs13376333, had an adjusted odds ratio of 1.56 (P=6.3×10−12). This association was replicated in two cohorts with lone AF for an overall odds ratio of 1.52 (P=1.83×10−21). Rs13376333 is intronic to KCNN3, a potassium channel involved in atrial repolarization. KCNN3 represents a novel potential therapeutic target in the treatment of AF.

Ma’am – We read with great interest the article by He et al. [2008] describing the effects on HIV acquisition and disease progression of a single-nucleotide polymorphism (SNP, rs2814778, -46T→C) that disrupts the promoter region of the Duffy antigen receptor for chemokines (DARC) gene and abolishes gene expression in red blood cells. He et al. reported that HIV-infected African Americans have a frequency of the null homozygous genotype (-46C/C) of 70% while non-HIV infected individuals have a null genotype frequency of 60%. Based on this frequency difference they argued that the null allele confers susceptibility to infection with HIV-1. They also reported that the null genotype is associated with better outcomes amongst those who do become infected, including longer survival, slower loss of CD4+ T-lymphocytes, and delayed progression to HIV-associated dementia.

Summary: The interpretation of genome-wide association results is confounded by linkage disequilibrium between nearby alleles. We have developed a flexible bioinformatics query tool for single-nucleotide polymorphisms (SNPs) to identify and to annotate nearby SNPs in linkage disequilibrium (proxies) based on HapMap. By offering functionality to generate graphical plots for these data, the SNAP server will facilitate interpretation and comparison of genome-wide association study results, and the design of fine-mapping experiments (by delineating genomic regions harboring associated variants and their proxies).

Availability: SNAP server is available at http://www.broad.mit.edu/mpg/snap/.

Contact: debakker@broad.mit.edu

OBJECTIVE—Using the genome-wide association approach, we recently identified the glucokinase regulatory protein gene (GCKR, rs780094) region as a novel quantitative trait locus for plasma triglyceride concentration in Europeans. Here, we sought to study the association of GCKR variants with metabolic phenotypes, including measures of glucose homeostasis, to evaluate the GCKR locus in samples of non-European ancestry and to fine- map across the associated genomic interval.

RESEARCH DESIGN AND METHODS—We performed association studies in 12 independent cohorts comprising >45,000 individuals representing several ancestral groups (whites from Northern and Southern Europe, whites from the U.S., African Americans from the U.S., Hispanics of Caribbean origin, and Chinese, Malays, and Asian Indians from Singapore). We conducted genetic fine-mapping across the ∼417-kb region of linkage disequilibrium spanning GCKR and 16 other genes on chromosome 2p23 by imputing untyped HapMap single nucleotide polymorphisms (SNPs) and genotyping 104 SNPs across the associated genomic interval.

RESULTS—We provide comprehensive evidence that GCKR rs780094 is associated with opposite effects on fasting plasma triglyceride (Pmeta = 3 × 10−56) and glucose (Pmeta = 1 × 10−13) concentrations. In addition, we confirmed recent reports that the same SNP is associated with C-reactive protein (CRP) level (P = 5 × 10−5). Both fine-mapping approaches revealed a common missense GCKR variant (rs1260326, Pro446Leu, 34% frequency, r2 = 0.93 with rs780094) as the strongest association signal in the region.

CONCLUSIONS—These findings point to a molecular mechanism in humans by which higher triglycerides and CRP can be coupled with lower plasma glucose concentrations and position GCKR in central pathways regulating both hepatic triglyceride and glucose metabolism.

Motivated by the overwhelming success of genome-wide association studies, droves of researchers are working vigorously to exchange and to combine genetic data to expediently discover genetic risk factors for common human traits. The primary tools that fuel these new efforts are imputation, allowing researchers who have collected data on a diversity of genotype platforms to share data in a uniformly exchangeable format, and meta-analysis for pooling statistical support for a genotype–phenotype association. As many groups are forming collaborations to engage in these efforts, this review collects a series of guidelines, practical detail and learned experiences from a variety of individuals who have contributed to the subject.

We report the results of a meta-analysis of genome-wide association scans for multiple sclerosis (MS) susceptibility that includes 2,624 subjects with MS and 7,220 control subjects. Replication in an independent set of 2,215 subjects with MS and 2,116 control subjects validates new MS susceptibility loci at TNFRSF1A (combined P = 1.59 × 10−11), IRF8 (P = 3.73 × 10−9) and CD6 (P = 3.79 × 10−9). TNFRSF1A harbors two independent susceptibility alleles: rs1800693 is a common variant with modest effect (odds ratio = 1.2), whereas rs4149584 is a nonsynonymous coding polymorphism of low frequency but with stronger effect (allele frequency = 0.02; odds ratio = 1.6). We also report that the susceptibility allele near IRF8, which encodes a transcription factor known to function in type I interferon signaling, is associated with higher mRNA expression of interferon-response pathway genes in subjects with MS.

QT interval duration reflecting myocardial repolarization on the electrocardiogram is a heritable risk factor for sudden cardiac death and drug-induced arrhythmias. We conducted a meta-analysis of 3 genome-wide association studies in 13,685 individuals of European ancestry from the Framingham Heart Study, the Rotterdam Study and the Cardiovascular Health Study. We observed associations at P < 5×10−8 with variants in NOS1AP, KCNQ1, KCNE1, KCNH2 and SCN5A, known to be involved in myocardial repolarization and Mendelian Long QT Syndromes. Associations at five novel loci included 16q21 near NDRG4 and GINS3, 6q22 near PLN, 1p36 near RNF207, 16p13 near LITAF and 17q12 near LIG3 and RIFFL. Collectively, the 14 independent variants at these 10 loci explain 5.4–6.5% of variation in QT interval. Identifying the causal variants and defining their impact on myocardial repolarization may add incrementally to the prevention of sudden cardiac death and drug-induced arrhythmias.

The proteins encoded by the classical HLA class I and class II genes in the major histocompatibility complex (MHC) are highly polymorphic and play an essential role in self/non-self immune recognition. HLA variation is a crucial determinant of transplant rejection and susceptibility to a large number of infectious and autoimmune disease1. Yet identification of causal variants is problematic due to linkage disequilibrium (LD) that extends across multiple HLA and non-HLA genes in the MHC2,3. We therefore set out to characterize the LD patterns between the highly polymorphic HLA genes and background variation by typing the classical HLA genes and >7,500 common single nucleotide polymorphisms (SNPs) and deletion/insertion polymorphisms (DIPs) across four population samples. The analysis provides informative tag SNPs that capture some of the variation in the MHC region and that could be used in initial disease association studies, and provides new insight into the evolutionary dynamics and ancestral origins of the HLA loci and their haplotypes.

To identify susceptibility alleles associated with rheumatoid arthritis, we genotyped 397 individuals with rheumatoid arthritis for 116,204 SNPs and carried out an association analysis in comparison to publicly available genotype data for 1,211 related individuals from the Framingham Heart Study1. After evaluating and adjusting for technical and population biases, we identified a SNP at 6q23 (rs10499194, ∼150 kb from TNFAIP3 and OLIG3) that was reproducibly associated with rheumatoid arthritis both in the genome-wide association (GWA) scan and in 5,541 additional case-control samples (P = 10−3, GWA scan; P < 10−6, replication; P = 10−9, combined). In a concurrent study, the Wellcome Trust Case Control Consortium (WTCCC) has reported strong association of rheumatoid arthritis susceptibility to a different SNP located 3.8 kb from rs10499194 (rs6920220; P = 5 × 10−6 in WTCCC)2. We show that these two SNP associations are statistically independent, are each reproducible in the comparison of our data and WTCCC data, and define risk and protective haplotypes for rheumatoid arthritis at 6q23.

BACKGROUND

Rheumatoid arthritis is a chronic inflammatory disease with a substantial genetic component. Susceptibility to disease has been linked with a region on chromosome 2q.

METHODS

We tested single-nucleotide polymorphisms (SNPs) in and around 13 candidate genes within the previously linked chromosome 2q region for association with rheumatoid arthritis. We then performed fine mapping of the STAT1-STAT4 region in a total of 1620 case patients with established rheumatoid arthritis and 2635 controls, all from North America. Implicated SNPs were further tested in an independent case-control series of 1529 patients with early rheumatoid arthritis and 881 controls, all from Sweden, and in a total of 1039 case patients and 1248 controls from three series of patients with systemic lupus erythematosus.

RESULTS

A SNP haplotype in the third intron of STAT4 was associated with susceptibility to both rheumatoid arthritis and systemic lupus erythematosus. The minor alleles of the haplotype-defining SNPs were present in 27% of chromosomes of patients with established rheumatoid arthritis, as compared with 22% of those of controls (for the SNP rs7574865, P = 2.81×10-7; odds ratio for having the risk allele in chromosomes of patients vs. those of controls, 1.32). The association was replicated in Swedish patients with recent-onset rheumatoid arthritis (P = 0.02) and matched controls. The haplotype marked by rs7574865 was strongly associated with lupus, being present on 31% of chromosomes of case patients and 22% of those of controls (P = 1.87×10-9; odds ratio for having the risk allele in chromosomes of patients vs. those of controls, 1.55). Homozygosity of the risk allele, as compared with absence of the allele, was associated with a more than doubled risk for lupus and a 60% increased risk for rheumatoid arthritis.

CONCLUSIONS

A haplotype of STAT4 is associated with increased risk for both rheumatoid arthritis and systemic lupus erythematosus, suggesting a shared pathway for these illnesses.

Background

The HLA genes, located in the MHC region on chromosome 6p21.3, play an important role in many autoimmune disorders, such as celiac disease (CD), type 1 diabetes (T1D), rheumatoid arthritis, multiple sclerosis, psoriasis and others. Known HLA variants that confer risk to CD, for example, include DQA1*05/DQB1*02 (DQ2.5) and DQA1*03/DQB1*0302 (DQ8). To diagnose the majority of CD patients and to study disease susceptibility and progression, typing these strongly associated HLA risk factors is of utmost importance. However, current genotyping methods for HLA risk factors involve many reactions, and are complicated and expensive. We sought a simple experimental approach using tagging SNPs that predict the CD-associated HLA risk factors.

Methodology

Our tagging approach exploits linkage disequilibrium between single nucleotide polymorphism (SNPs) and the CD-associated HLA risk factors DQ2.5 and DQ8 that indicate direct risk, and DQA1*0201/DQB1*0202 (DQ2.2) and DQA1*0505/DQB1*0301 (DQ7) that attribute to the risk of DQ2.5 to CD. To evaluate the predictive power of this approach, we performed an empirical comparison of the predicted DQ types, based on these six tag SNPs, with those executed with current validated laboratory typing methods of the HLA-DQA1 and -DQB1 genes in three large cohorts. The results were validated in three European celiac populations.

Conclusion

Using this method, only six SNPs were needed to predict the risk types carried by >95% of CD patients. We determined that for this tagging approach the sensitivity was >0.991, specificity >0.996 and the predictive value >0.948. Our results show that this tag SNP method is very accurate and provides an excellent basis for population screening for CD. This method is broadly applicable in European populations.

Background

Common polymorphisms of the transcription factor 7–like 2 gene (TCF7L2) have recently been associated with type 2 diabetes. We examined whether the two most strongly associated variants (rs12255372 and rs7903146) predict the progression to diabetes in persons with impaired glucose tolerance who were enrolled in the Diabetes Prevention Program, in which lifestyle intervention or treatment with metformin was compared with placebo.

Methods

We genotyped these variants in 3548 participants and performed Cox regression analysis using genotype, intervention, and their interactions as predictors. We assessed the effect of genotype on measures of insulin secretion and insulin sensitivity at baseline and at one year.

Results

Over an average period of three years, participants with the risk-conferring TT genotype at rs7903146 were more likely to have progression from impaired glucose tolerance to diabetes than were CC homozygotes (hazard ratio, 1.55; 95 percent confidence interval, 1.20 to 2.01; P<0.001). The effect of genotype was stronger in the placebo group (hazard ratio, 1.81; 95 percent confidence interval, 1.21 to 2.70; P = 0.004) than in the metformin and lifestyle-intervention groups (hazard ratios, 1.62 and 1.15, respectively; P for the interaction between genotype and intervention not significant). The TT genotype was associated with decreased insulin secretion but not increased insulin resistance at baseline. Similar results were obtained for rs12255372.

Conclusions

Common variants in TCF7L2 seem to be associated with an increased risk of diabetes among persons with impaired glucose tolerance. The risk-conferring genotypes in TCF7L2 are associated with impaired beta-cell function but not with insulin resistance. (ClinicalTrials.gov number, NCT00004992.)

PANDIT is a database of homologous sequence alignments accompanied by estimates of their corresponding phylogenetic trees. It provides a valuable resource to those studying phylogenetic methodology and the evolution of coding-DNA and protein sequences. Currently in version 17.0, PANDIT comprises 7738 families of homologous protein domains; for each family, DNA and corresponding amino acid sequence multiple alignments are available together with high quality phylogenetic tree estimates. Recent improvements include expanded methods for phylogenetic tree inference, assessment of alignment quality and a redesigned web interface, available at the URL .