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Joint Analysis (joint + analysis)
Selected AbstractsSemiparametric Transformation Models with Random Effects for Joint Analysis of Recurrent and Terminal EventsBIOMETRICS, Issue 3 2009Donglin Zeng Summary We propose a broad class of semiparametric transformation models with random effects for the joint analysis of recurrent events and a terminal event. The transformation models include proportional hazards/intensity and proportional odds models. We estimate the model parameters by the nonparametric maximum likelihood approach. The estimators are shown to be consistent, asymptotically normal, and asymptotically efficient. Simple and stable numerical algorithms are provided to calculate the parameter estimators and to estimate their variances. Extensive simulation studies demonstrate that the proposed inference procedures perform well in realistic settings. Applications to two HIV/AIDS studies are presented. [source] Joint Analysis of Time-to-Event and Multiple Binary Indicators of Latent ClassesBIOMETRICS, Issue 1 2004Klaus Larsen Summary. Multiple categorical variables are commonly used in medical and epidemiological research to measure specific aspects of human health and functioning. To analyze such data, models have been developed considering these categorical variables as imperfect indicators of an individual's "true" status of health or functioning. In this article, the latent class regression model is used to model the relationship between covariates, a latent class variable (the unobserved status of health or functioning), and the observed indicators (e.g., variables from a questionnaire). The Cox model is extended to encompass a latent class variable as predictor of time-to-event, while using information about latent class membership available from multiple categorical indicators. The expectation-maximization (EM) algorithm is employed to obtain maximum likelihood estimates, and standard errors are calculated based on the profile likelihood, treating the nonparametric baseline hazard as a nuisance parameter. A sampling-based method for model checking is proposed. It allows for graphical investigation of the assumption of proportional hazards across latent classes. It may also be used for checking other model assumptions, such as no additional effect of the observed indicators given latent class. The usefulness of the model framework and the proposed techniques are illustrated in an analysis of data from the Women's Health and Aging Study concerning the effect of severe mobility disability on time-to-death for elderly women. [source] A Genome-wide Association Study of Autism Reveals a Common Novel Risk Locus at 5p14.1ANNALS OF HUMAN GENETICS, Issue 3 2009Deqiong Ma Summary Although autism is one of the most heritable neuropsychiatric disorders, its underlying genetic architecture has largely eluded description. To comprehensively examine the hypothesis that common variation is important in autism, we performed a genome-wide association study (GWAS) using a discovery dataset of 438 autistic Caucasian families and the Illumina Human 1M beadchip. 96 single nucleotide polymorphisms (SNPs) demonstrated strong association with autism risk (p-value < 0.0001). The validation of the top 96 SNPs was performed using an independent dataset of 487 Caucasian autism families genotyped on the 550K Illumina BeadChip. A novel region on chromosome 5p14.1 showed significance in both the discovery and validation datasets. Joint analysis of all SNPs in this region identified 8 SNPs having improved p-values (3.24E-04 to 3.40E-06) than in either dataset alone. Our findings demonstrate that in addition to multiple rare variations, part of the complex genetic architecture of autism involves common variation. [source] Effect of genotype and environment on branching in weedy green millet (Setaria viridis) and domesticated foxtail millet (Setaria italica) (Poaceae)MOLECULAR ECOLOGY, Issue 5 2006ANDREW N. DOUST Abstract Many domesticated crops are derived from species whose life history includes weedy characteristics, such as the ability to vary branching patterns in response to environmental conditions. However, domesticated crop plants are characterized by less variable plant architecture, as well as by a general reduction in vegetative branching compared to their progenitor species. Here we examine weedy green millet and its domesticate foxtail millet that differ in the number of tillers (basal branches) and axillary branches along each tiller. Branch number in F2:3 progeny of a cross between the two species varies with genotype, planting density, and other environmental variables, with significant genotype,environment interactions (GEI). This is shown by a complex pattern of reaction norms and by variation in the pattern of significant quantitative trait loci (QTL) amongst trials. Individual and joint analyses of high and low density trials indicate that most QTL have significant GEI. Dominance and epistasis also explain some variation in branching. Likely candidate genes underlying the QTL (based on map position and phenotypic effect) include teosinte branched1 and barren stalk1. Phytochrome B, which has been found to affect response to shading in other plants, explains little or no variation. Much variation in branching is explained by QTL that do not have obvious candidate genes from maize or rice. [source] APOE is not Associated with Alzheimer Disease: a Cautionary tale of Genotype ImputationANNALS OF HUMAN GENETICS, Issue 3 2010Gary W. Beecham Summary With the advent of publicly available genome-wide genotyping data, the use of genotype imputation methods is becoming increasingly common. These methods are of particular use in joint analyses, where data from different genotyping platforms are imputed to a reference set and combined in a single analysis. We show here that such an analysis can miss strong genetic association signals, such as that of the apolipoprotein-e gene in late-onset Alzheimer disease. This can occur in regions of weak to moderate LD; unobserved SNPs are not imputed with confidence so there is no consensus SNP set on which to perform association tests. Both IMPUTE and Mach software are tested, with similar results. Additionally, we show that a meta-analysis that properly accounts for the genotype uncertainty can recover association signals that were lost under a joint analysis. This shows that joint analyses of imputed genotypes, particularly failure to replicate strong signals, should be considered critically and examined on a case-by-case basis. [source] GENETIC STUDY: FULL ARTICLE: Incorporating age at onset of smoking into genetic models for nicotine dependence: evidence for interaction with multiple genesADDICTION BIOLOGY, Issue 3 2010Richard A. Grucza ABSTRACT Nicotine dependence is moderately heritable, but identified genetic associations explain only modest portions of this heritability. We analyzed 3369 SNPs from 349 candidate genes and investigated whether incorporation of SNP-by-environment interaction into association analyses might bolster gene discovery efforts and prediction of nicotine dependence. Specifically, we incorporated the interaction between allele count and age at onset of regular smoking (AOS) into association analyses of nicotine dependence. Subjects were from the Collaborative Genetic Study of Nicotine Dependence and included 797 cases ascertained for Fagerström nicotine dependence and 811 non-nicotine-dependent smokers as controls, all of European descent. Compared with main effect models, SNP × AOS interaction models resulted in higher numbers of nominally significant tests, increased predictive utility at individual SNPs and higher predictive utility in a multi-locus model. Some SNPs previously documented in main effect analyses exhibited improved fits in the joint analysis, including rs16969968 from CHRNA5 and rs2314379 from MAP3K4. CHRNA5 exhibited larger effects in later-onset smokers, in contrast with a previous report that suggested the opposite interaction (Weiss et al. 2008). However, a number of SNPs that did not emerge in main effect analyses were among the strongest findings in the interaction analyses. These include SNPs located in GRIN2B (P = 1.5 × 10,5), which encodes a subunit of the N-methyl-D-aspartate receptor channel, a key molecule in mediating age-dependent synaptic plasticity. Incorporation of logically chosen interaction parameters, such as AOS, into genetic models of substance use disorders may increase the degree of explained phenotypic variation and constitutes a promising avenue for gene discovery. [source] Long-Term Effects of Fiscal Policy on the Size and Distribution of the Pie in the UK,FISCAL STUDIES, Issue 3 2008Xavier Ramos C5; E6; H3 Abstract. This paper provides a joint analysis of the output and distributional long-term effects of various fiscal policies in the UK, using a vector autoregression (VAR) approach. Our findings suggest that the long-term impact on GDP of increasing public spending and taxes is negative, and especially strong in the case of current expenditure. We also find significant distributional effects associated with fiscal policies, indicating that an increase in public spending reduces inequality while a rise in indirect taxes increases income inequality. [source] Effect of including environmental data in investigations of gene-disease associations in the presence of qualitative interactionsGENETIC EPIDEMIOLOGY, Issue 6 2010Elizabeth Williamson Abstract Complex diseases are likely to be caused by the interplay of genetic and environmental factors. Despite this, gene-disease associations are frequently investigated using models that focus solely on a marginal gene effect, ignoring environmental factors entirely. Failing to take into account a gene-environment interaction can weaken the apparent gene-disease association, leading to loss in statistical power and, potentially, inability to identify genuine risk factors. If a gene-environment interaction exists, therefore, a joint analysis allowing the effect of the gene to differ between groups defined by the environmental exposure can have greater statistical power than a marginal gene-disease model. However, environmental data are subject to measurement error. Substantial losses in statistical power for detecting gene-environment interactions can arise from measurement error in the environmental exposure. It is unclear, however, what effect measurement error may have on the power of the joint analysis. We consider the potential benefits, in terms of statistical power, of collecting concurrent environmental data within large cohorts in order to enhance gene detection. We further consider whether these benefits remain in the presence of misclassification in both the gene and the environmental exposure. We find that when an effect of the gene is apparent only in the presence of the environmental exposure, the joint analysis has greater power than a marginal gene-disease analysis. This comparative increase in power remains in the presence of likely levels of misclassification of either the gene or environmental exposure. Genet. Epidemiol. 34:552,560, 2010. © 2010 Wiley-Liss, Inc. [source] A common cortactin gene variation confers differential susceptibility to severe asthmaGENETIC EPIDEMIOLOGY, Issue 8 2008Shwu-Fan Ma Abstract Genomic regions with replicated linkage to asthma-related phenotypes likely harbor multiple susceptibility loci with relatively minor effects on disease susceptibility. The 11q13 chromosomal region has repeatedly been linked to asthma with five genes residing in this region with reported replicated associations. Cortactin, an actin-binding protein encoded by the CTTN gene in 11q13, constitutes a key regulator of cytoskeletal dynamics and contractile cell machinery, events facilitated by interaction with myosin light chain kinase; encoded by MYLK, a gene we recently reported as associated with severe asthma in African Americans. To evaluate potential association of CTTN gene variation with asthma susceptibility, CTTN exons and flanking regions were re-sequenced in 48 non-asthmatic multiethnic samples, leading to selection of nine tagging polymorphisms for case-control association studies in individuals of European and African descent. After ancestry adjustments, an intronic variant (rs3802780) was significantly associated with severe asthma (odds ratio [OR]: 1.71; 95% confidence interval [CI]: 1.20,2.43; p=0.003) in a joint analysis. Further analyses evidenced independent and additive effects of CTTN and MYLK risk variants for severe asthma susceptibility in African Americans (accumulated OR: 2.93, 95% CI: 1.40,6.13, p=0.004). These data suggest that CTTN gene variation may contribute to severe asthma and that the combined effects of CTTN and MYLK risk polymorphisms may further increase susceptibility to severe asthma in African Americans harboring both genetic variants. Genet. Epidemiol. 2008. © 2008 Wiley-Liss, Inc. [source] Multistage designs in the genomic era: Providing balance in complex disease studiesGENETIC EPIDEMIOLOGY, Issue S1 2007Marie-Pierre Dubé Abstract In this summary paper, we describe the contributions included in the Multistage Design group (Group 14) at the Genetic Analysis Workshop 15, which was held during November 12,14, 2006. Our group contrasted and compared different approaches to reducing complexity in a genetic study through implementation of staged designs. Most groups used the simulated dataset (problem 3), which provided ample opportunities for evaluating various staged designs. A wide range of multistage designs that targeted different aspects of complexity were explored. We categorized these approaches as reducing phenotypic complexity, model complexity, analytic complexity or genetic complexity. In general we learned that: (1) when staged designs are carefully planned and implemented, the power loss compared to a single-stage analysis can be minimized and study cost is greatly reduced; (2) a joint analysis of the results from each stage is generally more powerful than treating the second stage as a replication analysis. Genet. Epidemiol. 31 (Suppl. 1):S118,S123, 2007. © 2007 Wiley-Liss, Inc. [source] Joint full-waveform analysis of off-ground zero-offset ground penetrating radar and electromagnetic induction synthetic data for estimating soil electrical propertiesGEOPHYSICAL JOURNAL INTERNATIONAL, Issue 3 2010D. Moghadas SUMMARY A joint analysis of full-waveform information content in ground penetrating radar (GPR) and electromagnetic induction (EMI) synthetic data was investigated to reconstruct the electrical properties of multilayered media. The GPR and EMI systems operate in zero-offset, off-ground mode and are designed using vector network analyser technology. The inverse problem is formulated in the least-squares sense. We compared four approaches for GPR and EMI data fusion. The two first techniques consisted of defining a single objective function, applying different weighting methods. As a first approach, we weighted the EMI and GPR data using the inverse of the data variance. The ideal point method was also employed as a second weighting scenario. The third approach is the naive Bayesian method and the fourth technique corresponds to GPR,EMI and EMI,GPR sequential inversions. Synthetic GPR and EMI data were generated for the particular case of a two-layered medium. Analysis of the objective function response surfaces from the two first approaches demonstrated the benefit of combining the two sources of information. However, due to the variations of the GPR and EMI model sensitivities with respect to the medium electrical properties, the formulation of an optimal objective function based on the weighting methods is not straightforward. While the Bayesian method relies on assumptions with respect to the statistical distribution of the parameters, it may constitute a relevant alternative for GPR and EMI data fusion. Sequential inversions of different configurations for a two layered medium show that in the case of high conductivity or permittivity for the first layer, the inversion scheme can not fully retrieve the soil hydrogeophysical parameters. But in the case of low permittivity and conductivity for the first layer, GPR,EMI inversion provides proper estimation of values compared to the EMI,GPR inversion. [source] Using DC resistivity tomography to detect and characterize mountain permafrostGEOPHYSICAL PROSPECTING, Issue 4 2003Christian Hauck ABSTRACT Direct-current (DC) resistivity tomography has been applied to different mountain permafrost regions. Despite problems with the very high resistivities of the frozen material, plausible results were obtained. Inversions with synthetic data revealed that an appropriate choice of regularization constraints was important, and that a joint analysis of several tomograms computed with different constraints was required to judge the reliability of individual features. The theoretical results were verified with three field experiments conducted in the Swiss and the Italian Alps. At the first site, near Zermatt, Switzerland, the location and the approximate lateral and vertical extent of an ice core within a moraine could be delineated. On the Murtel rock glacier, eastern Swiss Alps, a steeply dipping boundary at its frontal part was observed, and extremely high resistivities of several M, indicated a high ice content. The base of the rock glacier remained unresolved by the DC resistivity measurements, but it could be constrained with transient EM soundings. On another rock glacier near the Stelvio Pass, eastern Italian Alps, DC resistivity tomography allowed delineation of the rock glacier base, and the only moderately high resistivities within the rock glacier body indicated that the ice content must be lower compared with the Murtel rock glacier. [source] c-MYC Asn11Ser is associated with increased risk for familial breast cancerINTERNATIONAL JOURNAL OF CANCER, Issue 4 2005Michael Wirtenberger Abstract c-MYC is a multifaceted protein that regulates cell proliferation, differentiation and apoptosis. Its crucial role in diverse cancers has been demonstrated in several studies. Here, we analysed the influence of the rare c-MYC Asn11Ser polymorphism on familial breast cancer risk by performing a case-control study with a Polish (cases n = 349; controls n = 441) and a German (cases n = 356; controls n = 655) study population. All cases have been tested negative for mutations in the BRCA1 and BRCA2 genes. A joint analysis of the Polish and the German study population revealed a 54% increased risk for breast cancer associated with the heterozygous Asn11Ser variant (OR = 1.54, 95% CI 1.05,2.26, p = 0.028). The breast cancer risk associated with this genotype increases above the age of 50 years (OR = 2.24, 95% CI 1.20,4.21, p = 0.012). The wild-type amino acid Asn of this polymorphism is located in the N-terminal MYC transactivation domain and is highly conserved not only among most diverse species but also in the N-MYC homologue. Due to the pivotal role of c-MYC in diverse tumours, this variant might affect the genetic susceptibility of other cancers as well. © 2005 Wiley-Liss, Inc. [source] Joint spectrum and power optimization in the design of the UMTS satellite componentINTERNATIONAL JOURNAL OF SATELLITE COMMUNICATIONS AND NETWORKING, Issue 6 2001Ernestina Cianca Abstract The paper provides a power and spectrum joint analysis of the universal mobile telecommunications system (UMTS) satellite component, based on the wideband code division multiple access (W-CDMA) air interface. In fact, power and spectral efficiency may become highly conflicting requirements in a satellite system and a trade-off analysis is needed to drive a proper dimensioning of the satellite. The proposed approach allows a dimensioning of the satellite component either in terms of orbit and power budget or in terms of additional capacity for the terrestrial section, for specified orbit and power limitations. The impact of candidate frequency bands, orbit type and diversity on both spectral and power requirements of the satellite component is evaluated. For given traffic requirements, power-vs-spectrum trade-off is proposed which ensures a proper resources utilization. The efficiency evaluation accounts for: beams overlapping, ortho gonality, voice activity factor, diversity and cross-polarization frequency reuse. Perfect power control is assumed and the effect of the excess power required by the shadowed users is accounted for in the interference calculation. Furthermore, still in the frame of a proper resource exploitation, a possible optimization of capacity through the use of unpaired bands in the two link directions is analysed. Copyright © 2001 John Wiley & Sons, Ltd. [source] Selection of the relevant information set for predictive relationships analysis between time seriesJOURNAL OF FORECASTING, Issue 8 2002Umberto Triacca Abstract In time series analysis, a vector Y is often called causal for another vector X if the former helps to improve the k -step-ahead forecast of the latter. If this holds for k=1, vector Y is commonly called Granger-causal for X. It has been shown in several studies that the finding of causality between two (vectors of) variables is not robust to changes of the information set. In this paper, using the concept of Hilbert spaces, we derive a condition under which the predictive relationships between two vectors are invariant to the selection of a bivariate or trivariate framework. In more detail, we provide a condition under which the finding of causality (improved predictability at forecast horizon 1) respectively non-causality of Y for X is unaffected if the information set is either enlarged or reduced by the information in a third vector Z. This result has a practical usefulness since it provides a guidance to validate the choice of the bivariate system {X, Y} in place of {X, Y, Z}. In fact, to test the ,goodness' of {X, Y} we should test whether Z Granger cause X not requiring the joint analysis of all variables in {X, Y, Z}. Copyright © 2002 John Wiley & Sons, Ltd. [source] Sparse partial least squares regression for simultaneous dimension reduction and variable selectionJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES B (STATISTICAL METHODOLOGY), Issue 1 2010Hyonho Chun Summary., Partial least squares regression has been an alternative to ordinary least squares for handling multicollinearity in several areas of scientific research since the 1960s. It has recently gained much attention in the analysis of high dimensional genomic data. We show that known asymptotic consistency of the partial least squares estimator for a univariate response does not hold with the very large p and small n paradigm. We derive a similar result for a multivariate response regression with partial least squares. We then propose a sparse partial least squares formulation which aims simultaneously to achieve good predictive performance and variable selection by producing sparse linear combinations of the original predictors. We provide an efficient implementation of sparse partial least squares regression and compare it with well-known variable selection and dimension reduction approaches via simulation experiments. We illustrate the practical utility of sparse partial least squares regression in a joint analysis of gene expression and genomewide binding data. [source] Analysis of longitudinal multiple-source binary data using generalized estimating equationsJOURNAL OF THE ROYAL STATISTICAL SOCIETY: SERIES C (APPLIED STATISTICS), Issue 1 2004Liam M. O'Brien Summary., We present a multivariate logistic regression model for the joint analysis of longitudinal multiple-source binary data. Longitudinal multiple-source binary data arise when repeated binary measurements are obtained from two or more sources, with each source providing a measure of the same underlying variable. Since the number of responses on each subject is relatively large, the empirical variance estimator performs poorly and cannot be relied on in this setting. Two methods for obtaining a parsimonious within-subject association structure are considered. An additional complication arises with estimation, since maximum likelihood estimation may not be feasible without making unrealistically strong assumptions about third- and higher order moments. To circumvent this, we propose the use of a generalized estimating equations approach. Finally, we present an analysis of multiple-informant data obtained longitudinally from a psychiatric interventional trial that motivated the model developed in the paper. [source] THE STATE OF THE FIELD: Combining contemporary and ancient DNA in population genetic and phylogeographical studiesMOLECULAR ECOLOGY RESOURCES, Issue 5 2010MIGUEL NAVASCUÉS Abstract The analysis of ancient DNA in a population genetic or phylogeographical framework is an emerging field, as traditional analytical tools were largely developed for the purpose of analysing data sampled from a single time point. Markov chain Monte Carlo approaches have been successfully developed for the analysis of heterochronous sequence data from closed panmictic populations. However, attributing genetic differences between temporal samples to mutational events between time points requires the consideration of other factors that may also result in genetic differentiation. Geographical effects are an obvious factor for species exhibiting geographical structuring of genetic variation. The departure from a closed panmictic model require researchers to either exploit software developed for the analysis of isochronous data, take advantage of simulation approaches using algorithms developed for heterochronous data, or explore approximate Bayesian computation. Here, we review statistical approaches employed and available software for the joint analysis of ancient and modern DNA, and where appropriate we suggest how these may be further developed. [source] Combining maximum-entropy and the Mexican hat wavelet to reconstruct the microwave skyMONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY, Issue 1 2001P. Vielva We present a maximum-entropy method (MEM) and ,Mexican hat' wavelet (MHW) joint analysis to recover the different components of the microwave sky from simulated observations by the ESA Planck Surveyor satellite in a small patch of the sky . This combined method allows one to improve the CMB, Sunyaev,Zel'dovich and Galactic foregrounds separation achieved by the MEM technique alone. In particular, the reconstructed CMB map is free from any bright point-source contamination. The joint analysis also produces point-source catalogues at each Planck frequency that are more complete and accurate than those obtained by either method on its own. The results are especially improved at high frequencies where infrared galaxies dominate the point-source contribution. Although this joint technique has been performed on simulated Planck data, it could easily be applied to other multifrequency CMB experiments, such as the forthcoming NASA MAP satellite or the recently-performed BOOMERANG and MAXIMA experiments. [source] Refined localization of the Escherichia coli F4ab/F4ac receptor locus on pig chromosome 13ANIMAL GENETICS, Issue 5 2009D. Joller Summary Diarrhoea in newborn and weaned pigs caused by enterotoxigenic Escherichia coli (ETEC) expressing F4 fimbriae leads to considerable losses in pig production. In this study, we refined the mapping of the receptor locus for ETEC F4ab/F4ac adhesion (F4bcR) by joint analysis of Nordic and Swiss data. A total of 236 pigs from a Nordic experimental herd, 331 pigs from a Swiss experimental herd and 143 pigs from the Swiss performing station were used for linkage analysis. Genotyping data of six known microsatellite markers, two newly developed markers (MUC4gt and HSA125gt) and an intronic SNP in MUC4 (MUC4-8227) were used to create the linkage map. The region for F4bcR was refined to the interval SW207,S0075 on pig chromosome 13. The most probable position of F4bcR was in the SW207,MUC4 region. The order of six markers was supported by physical mapping on the BAC fingerprint contig from the Wellcome Trust Sanger Institute. Thus, the region for F4bcR could be reduced from 26 to 14 Mb. [source] APOE is not Associated with Alzheimer Disease: a Cautionary tale of Genotype ImputationANNALS OF HUMAN GENETICS, Issue 3 2010Gary W. Beecham Summary With the advent of publicly available genome-wide genotyping data, the use of genotype imputation methods is becoming increasingly common. These methods are of particular use in joint analyses, where data from different genotyping platforms are imputed to a reference set and combined in a single analysis. We show here that such an analysis can miss strong genetic association signals, such as that of the apolipoprotein-e gene in late-onset Alzheimer disease. This can occur in regions of weak to moderate LD; unobserved SNPs are not imputed with confidence so there is no consensus SNP set on which to perform association tests. Both IMPUTE and Mach software are tested, with similar results. Additionally, we show that a meta-analysis that properly accounts for the genotype uncertainty can recover association signals that were lost under a joint analysis. This shows that joint analyses of imputed genotypes, particularly failure to replicate strong signals, should be considered critically and examined on a case-by-case basis. [source] Genome-Wide Association Study Confirms SNPs in SNCA and the MAPT Region as Common Risk Factors for Parkinson DiseaseANNALS OF HUMAN GENETICS, Issue 2 2010Todd L. Edwards Summary Parkinson disease (PD) is a chronic neurodegenerative disorder with a cumulative prevalence of greater than one per thousand. To date three independent genome-wide association studies (GWAS) have investigated the genetic susceptibility to PD. These studies implicated several genes as PD risk loci with strong, but not genome-wide significant, associations. In this study, we combined data from two previously published GWAS of Caucasian subjects with our GWAS of 604 cases and 619 controls for a joint analysis with a combined sample size of 1752 cases and 1745 controls. SNPs in SNCA (rs2736990, p-value = 6.7 × 10,8; genome-wide adjusted p = 0.0109, odds ratio (OR) = 1.29 [95% CI: 1.17,1.42] G vs. A allele, population attributable risk percent (PAR%) = 12%) and the MAPT region (rs11012, p-value = 5.6 × 10,8; genome-wide adjusted p = 0.0079, OR = 0.70 [95% CI: 0.62,0.79] T vs. C allele, PAR%= 8%) were genome-wide significant. No other SNPs were genome-wide significant in this analysis. This study confirms that SNCA and the MAPT region are major genes whose common variants are influencing risk of PD. [source] CALHM1 Polymorphism is not Associated with Late-onset Alzheimer DiseaseANNALS OF HUMAN GENETICS, Issue 3 2009Gary W. Beecham Summary Data suggests that the P86L polymorphism (rs2986017) in the calcium homeostasis modulator 1 (CALHM1) gene interferes with CALHM1 functionality, increases A, levels, and is associated with late-onset Alzheimer's disease (LOAD). Previous studies have demonstrated association with P86L and LOAD in three of five case-control cohorts, and a joint analysis of all datasets showed association with a p-value of 2 × 10,10 and an allele-specific odds ratio of 1.44 (2,043 cases, 1,361 controls total). In this short communication we attempt to replicate these results in our case-control cohort (510 cases, 524 controls). We show no association between P86L and LOAD despite having sufficient power to detect at the reported odds ratios, and briefly discuss potential reasons for the discrepancy. [source] Genetic variation at the IRF7/PHRF1 locus is associated with autoantibody profile and serum interferon-, activity in lupus patientsARTHRITIS & RHEUMATISM, Issue 2 2010Rafah Salloum Objective Interferon-, (IFN,) is a heritable risk factor for systemic lupus erythematosus (SLE). Genetic variation near IRF7 is implicated in SLE susceptibility. SLE-associated autoantibodies can stimulate IFN, production through the Toll-like receptor/IRF7 pathway. This study was undertaken to determine whether variants of IRF7 act as risk factors for SLE by increasing IFN, production and whether autoantibodies are important to this phenomenon. Methods We studied 492 patients with SLE (236 African American, 162 European American, and 94 Hispanic American subjects). Serum levels of IFN, were measured using a reporter cell assay, and single-nucleotide polymorphisms (SNPs) in the IRF7/PHRF1 locus were genotyped. Results In a joint analysis of European American and Hispanic American subjects, the rs702966 C allele was associated with the presence of anti,double-stranded DNA (anti-dsDNA) antibodies (odds ratio [OR] 1.83, P = 0.0069). The rs702966 CC genotype was only associated with higher serum levels of IFN, in European American and Hispanic American patients with anti-dsDNA antibodies (joint analysis P = 4.1 × 10,5 in anti-dsDNA,positive patients and P = 0.99 in anti-dsDNA,negative patients). In African American subjects, anti-Sm antibodies were associated with the rs4963128 SNP near IRF7 (OR 1.95, P = 0.0017). The rs4963128 CT and TT genotypes were associated with higher serum levels of IFN, only in African American patients with anti-Sm antibodies (P = 0.0012). In African American patients lacking anti-Sm antibodies, an effect of anti-dsDNA,rs702966 C allele interaction on serum levels of IFN, was observed, similar to the other patient groups (overall joint analysis P = 1.0 × 10,6). In European American and Hispanic American patients, the IRF5 SLE risk haplotype showed an additive effect with the rs702966 C allele on IFN, level in anti-dsDNA,positive patients. Conclusion Our findings indicate that IRF7/PHRF1 variants in combination with SLE-associated autoantibodies result in higher serum levels of IFN,, providing a biologic relevance for this locus at the protein level in human SLE in vivo. [source] Hierarchical Bayesian modeling of random and residual variance,covariance matrices in bivariate mixed effects modelsBIOMETRICAL JOURNAL, Issue 3 2010Nora M. Bello Abstract Bivariate mixed effects models are often used to jointly infer upon covariance matrices for both random effects (u) and residuals (e) between two different phenotypes in order to investigate the architecture of their relationship. However, these (co)variances themselves may additionally depend upon covariates as well as additional sets of exchangeable random effects that facilitate borrowing of strength across a large number of clusters. We propose a hierarchical Bayesian extension of the classical bivariate mixed effects model by embedding additional levels of mixed effects modeling of reparameterizations of u- level and e -level (co)variances between two traits. These parameters are based upon a recently popularized square-root-free Cholesky decomposition and are readily interpretable, each conveniently facilitating a generalized linear model characterization. Using Markov Chain Monte Carlo methods, we validate our model based on a simulation study and apply it to a joint analysis of milk yield and calving interval phenotypes in Michigan dairy cows. This analysis indicates that the e -level relationship between the two traits is highly heterogeneous across herds and depends upon systematic herd management factors. [source] Score Test for Conditional Independence Between Longitudinal Outcome and Time to Event Given the Classes in the Joint Latent Class ModelBIOMETRICS, Issue 1 2010Hélène Jacqmin-Gadda Summary Latent class models have been recently developed for the joint analysis of a longitudinal quantitative outcome and a time to event. These models assume that the population is divided in,G,latent classes characterized by different risk functions for the event, and different profiles of evolution for the markers that are described by a mixed model for each class. However, the key assumption of conditional independence between the marker and the event given the latent classes is difficult to evaluate because the latent classes are not observed. Using a joint model with latent classes and shared random effects, we propose a score test for the null hypothesis of independence between the marker and the outcome given the latent classes versus the alternative hypothesis that the risk of event depends on one or several random effects from the mixed model in addition to the latent classes. A simulation study was performed to compare the behavior of the score test to other previously proposed tests, including situations where the alternative hypothesis or the baseline risk function are misspecified. In all the investigated situations, the score test was the most powerful. The methodology was applied to develop a prognostic model for recurrence of prostate cancer given the evolution of prostate-specific antigen in a cohort of patients treated by radiation therapy. [source] Semiparametric Transformation Models with Random Effects for Joint Analysis of Recurrent and Terminal EventsBIOMETRICS, Issue 3 2009Donglin Zeng Summary We propose a broad class of semiparametric transformation models with random effects for the joint analysis of recurrent events and a terminal event. The transformation models include proportional hazards/intensity and proportional odds models. We estimate the model parameters by the nonparametric maximum likelihood approach. The estimators are shown to be consistent, asymptotically normal, and asymptotically efficient. Simple and stable numerical algorithms are provided to calculate the parameter estimators and to estimate their variances. Extensive simulation studies demonstrate that the proposed inference procedures perform well in realistic settings. Applications to two HIV/AIDS studies are presented. [source] | |||||||||||||||||||||||||||||||||||||