Taye H. Hamza

Common genetic variation in the HLA region is associated with late-onset sporadic Parkinson's disease

Parkinsons disease is a common disorder that leads to motor and cognitive disability. We performed a genome-wide association study of 2,000 individuals with Parkinsons disease (cases) and 1,986 unaffected controls from the NeuroGenetics Research Consortium (NGRC). We confirmed associations with SNCA and MAPT, replicated an association with GAK (using data from the NGRC and a previous study, P = 3.2 × 10−9) and detected a new association with the HLA region (using data from the NGRC only, P = 2.9 × 10−8), which replicated in two datasets (meta-analysis P = 1.9 × 10−10). The HLA association was uniform across all genetic and environmental risk strata and was strong in sporadic (P = 5.5 × 10−10) and late-onset (P = 2.4 × 10−8) disease. The association peak we found was at rs3129882, a noncoding variant in HLA-DRA. Two studies have previously suggested that rs3129882 influences expression of HLA-DR and HLA-DQ. The brains of individuals with Parkinsons disease show upregulation of DR antigens and the presence of DR-positive reactive microglia, and nonsteroidal anti-inflammatory drugs reduce Parkinsons disease risk. The genetic association with HLA supports the involvement of the immune system in Parkinsons disease and offers new targets for drug development.

Comprehensive research synopsis and systematic meta-analyses in Parkinson's disease genetics : The PDGene database

More than 800 published genetic association studies have implicated dozens of potential risk loci in Parkinsons disease (PD). To facilitate the interpretation of these findings, we have created a dedicated online resource, PDGene, that comprehensively collects and meta-analyzes all published studies in the field. A systematic literature screen of ∼27,000 articles yielded 828 eligible articles from which relevant data were extracted. In addition, individual-level data from three publicly available genome-wide association studies (GWAS) were obtained and subjected to genotype imputation and analysis. Overall, we performed meta-analyses on more than seven million polymorphisms originating either from GWAS datasets and/or from smaller scale PD association studies. Meta-analyses on 147 SNPs were supplemented by unpublished GWAS data from up to 16,452 PD cases and 48,810 controls. Eleven loci showed genome-wide significant (P<5×10−8) association with disease risk: BST1, CCDC62/HIP1R, DGKQ/GAK, GBA, LRRK2, MAPT, MCCC1/LAMP3, PARK16, SNCA, STK39, and SYT11/RAB25. In addition, we identified novel evidence for genome-wide significant association with a polymorphism in ITGA8 (rs7077361, OR 0.88, P = 1.3×10−8). All meta-analysis results are freely available on a dedicated online database (www.pdgene.org), which is cross-linked with a customized track on the UCSC Genome Browser. Our study provides an exhaustive and up-to-date summary of the status of PD genetics research that can be readily scaled to include the results of future large-scale genetics projects, including next-generation sequencing studies.

Bivariate random effects meta-analysis of ROC curves.

Meta-analysis of receiver operating characteristic (ROC)-curve data is often done with fixed-effects models, which suffer many shortcomings. Some random-effects models have been proposed to execute a meta-analysis of ROC-curve data, but these models are not often used in practice. Straightforward modeling techniques for multivariate random-effects meta-analysis of ROC-curve data are needed. The 1st aim of this article is to present a practical method that addresses the drawbacks of the fixedeffects summary ROC (SROC) method of Littenberg and Moses. Sensitivities and specificities are analyzed simultaneously using a bivariate random-effects model. The 2nd aim is to show that other SROC curves can also be derived from the bivariate model through different characterizations of the estimated bivariate normal distribution. Thereby the authors show that the bivariate random-effects approach not only extends the SROC approach but also provides a unifying framework for other approaches. The authors bring the statistical meta-analysis of ROC-curve data back into a framework of relatively standard multivariate meta-analysis with random effects. The analyses were carried out using the software package SAS (Proc NLMIXED).

Random effects meta‐analysis of event outcome in the framework of the generalized linear mixed model with applications in sparse data

We consider random effects meta-analysis where the outcome variable is the occurrence of some event of interest. The data structures handled are where one has one or more groups in each study, and in each group either the number of subjects with and without the event, or the number of events and the total duration of follow-up is available. Traditionally, the meta-analysis follows the summary measures approach based on the estimates of the outcome measure(s) and the corresponding standard error(s). This approach assumes an approximate normal within-study likelihood and treats the standard errors as known. This approach has several potential disadvantages, such as not accounting for the standard errors being estimated, not accounting for correlation between the estimate and the standard error, the use of an (arbitrary) continuity correction in case of zero events, and the normal approximation being bad in studies with few events. We show that these problems can be overcome in most cases occurring in practice by replacing the approximate normal within-study likelihood by the appropriate exact likelihood. This leads to a generalized linear mixed model that can be fitted in standard statistical software. For instance, in the case of odds ratio meta-analysis, one can use the non-central hypergeometric distribution likelihood leading to mixed-effects conditional logistic regression. For incidence rate ratio meta-analysis, it leads to random effects logistic regression with an offset variable. We also present bivariate and multivariate extensions. We present a number of examples, especially with rare events, among which an example of network meta-analysis.

Meta‐analysis of Parkinson's Disease: Identification of a novel locus, RIT2

Genome‐wide association (GWAS) methods have identified genes contributing to Parkinsons disease (PD); we sought to identify additional genes associated with PD susceptibility.

Genome-Wide Gene-Environment Study Identifies Glutamate Receptor Gene GRIN2A as a Parkinson's Disease Modifier Gene via Interaction with Coffee

Our aim was to identify genes that influence the inverse association of coffee with the risk of developing Parkinsons disease (PD). We used genome-wide genotype data and lifetime caffeinated-coffee-consumption data on 1,458 persons with PD and 931 without PD from the NeuroGenetics Research Consortium (NGRC), and we performed a genome-wide association and interaction study (GWAIS), testing each SNPs main-effect plus its interaction with coffee, adjusting for sex, age, and two principal components. We then stratified subjects as heavy or light coffee-drinkers and performed genome-wide association study (GWAS) in each group. We replicated the most significant SNP. Finally, we imputed the NGRC dataset, increasing genomic coverage to examine the region of interest in detail. The primary analyses (GWAIS, GWAS, Replication) were performed using genotyped data. In GWAIS, the most significant signal came from rs4998386 and the neighboring SNPs in GRIN2A. GRIN2A encodes an NMDA-glutamate-receptor subunit and regulates excitatory neurotransmission in the brain. Achieving P2df = 10−6, GRIN2A surpassed all known PD susceptibility genes in significance in the GWAIS. In stratified GWAS, the GRIN2A signal was present in heavy coffee-drinkers (OR = 0.43; P = 6×10−7) but not in light coffee-drinkers. The a priori Replication hypothesis that “Among heavy coffee-drinkers, rs4998386_T carriers have lower PD risk than rs4998386_CC carriers” was confirmed: ORReplication = 0.59, PReplication = 10−3; ORPooled = 0.51, PPooled = 7×10−8. Compared to light coffee-drinkers with rs4998386_CC genotype, heavy coffee-drinkers with rs4998386_CC genotype had 18% lower risk (P = 3×10−3), whereas heavy coffee-drinkers with rs4998386_TC genotype had 59% lower risk (P = 6×10−13). Imputation revealed a block of SNPs that achieved P2df<5×10−8 in GWAIS, and OR = 0.41, P = 3×10−8 in heavy coffee-drinkers. This study is proof of concept that inclusion of environmental factors can help identify genes that are missed in GWAS. Both adenosine antagonists (caffeine-like) and glutamate antagonists (GRIN2A-related) are being tested in clinical trials for treatment of PD. GRIN2A may be a useful pharmacogenetic marker for subdividing individuals in clinical trials to determine which medications might work best for which patients.

Differences in characteristics and risk of cardiovascular disease in familial hypercholesterolemia patients with and without tendon xanthomas: a systematic review and meta-analysis.

BACKGROUND Tendon xanthomas are characteristic of familial hypercholesterolemia (FH). It is not clear whether FH patients with xanthomas have higher risk of cardiovascular disease (CVD) than those without xanthomas. The clinical diagnosis of FH in patients without xanthomas, namely requires the presence of CVD in the patient or in a first-degree relative. This may have masked the association between xanthomas and CVD in a number of studies. A diagnosis of FH based on the presence of a mutation in the low-density lipoprotein receptor (LDLR) gene is free from this selection on CVD. In this systematic review and meta-analysis, we therefore compared the risk of CVD between patients heterozygous for LDLR mutation with and without xanthomas. METHODS AND RESULTS We conducted a literature search with PubMed and the Web of Science up to January 14, 2009. We selected all articles examining more than 25 human heterozygous FH patients, that provided information about xanthomas. Articles had to be written in a Western European language. A total of 22 articles suited for analyses. A genetic confirmation of FH was compulsory to correctly assess the risk of CVD with presence of xanthomas. Age, male gender, LDL-cholesterol and triglyceride level were associated with the presence of xanthomas (p<0.05 for all). In patients with genetically confirmed FH, xanthomas were associated with a 3.20-fold higher risk of CVD (95% CI 2.12-4.82, p<0.01). CONCLUSIONS Xanthomas are associated with a 3 times higher risk of CVD among FH patients, suggesting that xanthomas and CVD may share etiology.

Multivariate random effects meta-analysis of diagnostic tests with multiple thresholds

BackgroundBivariate random effects meta-analysis of diagnostic tests is becoming a well established approach when studies present one two-by-two table or one pair of sensitivity and specificity. When studies present multiple thresholds for test positivity, usually meta-analysts reduce the data to a two-by-two table or take one threshold value at a time and apply the well developed meta-analytic approaches. However, this approach does not fully exploit the data.MethodsIn this paper we generalize the bivariate random effects approach to the situation where test results are presented with k thresholds for test positivity, resulting in a 2 by (k+1) table per study. The model can be fitted with standard likelihood procedures in statistical packages such as SAS (Proc NLMIXED). We follow a multivariate random effects approach; i.e., we assume that each study estimates a study specific ROC curve that can be viewed as randomly sampled from the population of all ROC curves of such studies. In contrast to the bivariate case, where nothing can be said about the shape of study specific ROC curves without additional untestable assumptions, the multivariate model can be used to describe study specific ROC curves. The models are easily extended with study level covariates.ResultsThe method is illustrated using published meta-analysis data. The SAS NLMIXED syntax is given in the appendix.ConclusionWe conclude that the multivariate random effects meta-analysis approach is an appropriate and convenient framework to meta-analyse studies with multiple threshold without losing any information by dichotomizing the test results.

A comprehensive analysis of deletions, multiplications, and copy number variations in PARK2

Objectives: To perform a comprehensive population genetic study of PARK2. PARK2 mutations are associated with juvenile parkinsonism, Alzheimer disease, cancer, leprosy, and diabetes mellitus, yet ironically, there has been no comprehensive study of PARK2 in control subjects; and to resolve controversial association of PARK2 heterozygous mutations with Parkinson disease (PD) in a well-powered study. Methods: We studied 1,686 control subjects (mean age 66.1 ± 13.1 years) and 2,091 patients with PD (mean onset age 58.3 ± 12.1 years). We tested for PARK2 deletions/multiplications/copy number variations (CNV) using semiquantitative PCR and multiplex ligation-dependent probe amplification, and validated the mutations by real-time quantitative PCR. Subjects were tested for point mutations previously. Association with PD was tested as PARK2 main effect, and in combination with known PD risk factors: SNCA, MAPT, APOE, smoking, and coffee intake. Results: A total of 0.95% of control subjects and 0.86% of patients carried a heterozygous CNV mutation. CNV mutations found in 16 control subjects were all in exons 1–4, sparing exons that encode functionally critical protein domains. Thirteen patients had 2 CNV mutations, 5 had 1 CNV and 1 point mutation, and 18 had 1 CNV mutation. Mutations found in patients spanned exons 2–9. In whites, having 1 CNV was not associated with increased risk (odds ratio 1.05, p = 0.89) or earlier onset of PD (64.7 ± 8.6 heterozygous vs 58.5 ± 11.8 normal). Conclusions: This comprehensive population genetic study in control subjects fills the void for a PARK2 reference dataset. There is no compelling evidence for association of heterozygous PARK2 mutations, by themselves or in combination with known risk factors, with PD.

Meta-Analysis of Diagnostic Studies: A Comparison of Random Intercept, Normal-Normal, and Binomial-Normal Bivariate Summary ROC Approaches:

Background . The authors compared 3 recently introduced refinements of the Littenberg and Moses summary receiver operating characteristic (ROC) method for pooling studies of a diagnostic test: the random intercept (RI) linear meta-regression model, the approximate normal distribution (normal-normal [NN] model), and the binomial distribution (binomial-normal [BN] model). Methods . Using data from a published meta-analysis of magnetic resonance imaging of the menisci and cruciate ligaments, the authors varied the overall sensitivity and specificity, the between-studies variance, the within-study sample size, and the number of studies to evaluate the performances of the 3 methods in a simulation study. The parameters to be compared are the associated intercept, slope, and residual variance, using bias, mean squared error, and coverage probabilities. Results . The BN method always gave unbiased estimates of the intercept and slope parameter. The coverage probabilities were also reasonably acceptable, unless the number of studies was very small. In contrast, the RI and NN methods could produce large biases with poor coverage probabilities, especially when sample sizes of individual studies were small or when sensitivities or specificities were close to 1. Although this was rare in the simulations, the bivariate methods can suffer from nonconvergence mostly due to the correlation being close to ± 1. Conclusion . The binomial-normal model performed better than the other recently introduced methods for meta-analysis of data from studies of test performance.