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Search Results: 1 - 10 of 425507 matches for " Cornelia M. van Duijn "
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A Genomic Background Based Method for Association Analysis in Related Individuals
Najaf Amin, Cornelia M. van Duijn, Yurii S. Aulchenko
PLOS ONE , 2007, DOI: 10.1371/journal.pone.0001274
Abstract: Background Feasibility of genotyping of hundreds and thousands of single nucleotide polymorphisms (SNPs) in thousands of study subjects have triggered the need for fast, powerful, and reliable methods for genome-wide association analysis. Here we consider a situation when study participants are genetically related (e.g. due to systematic sampling of families or because a study was performed in a genetically isolated population). Of the available methods that account for relatedness, the Measured Genotype (MG) approach is considered the ‘gold standard’. However, MG is not efficient with respect to time taken for the analysis of genome-wide data. In this context we proposed a fast two-step method called Genome-wide Association using Mixed Model and Regression (GRAMMAR) for the analysis of pedigree-based quantitative traits. This method certainly overcomes the drawback of time limitation of the measured genotype (MG) approach, but pays in power. One of the major drawbacks of both MG and GRAMMAR, is that they crucially depend on the availability of complete and correct pedigree data, which is rarely available. Methodology In this study we first explore type 1 error and relative power of MG, GRAMMAR, and Genomic Control (GC) approaches for genetic association analysis. Secondly, we propose an extension to GRAMMAR i.e. GRAMMAR-GC. Finally, we propose application of GRAMMAR-GC using the kinship matrix estimated through genomic marker data, instead of (possibly missing and/or incorrect) genealogy. Conclusion Through simulations we show that MG approach maintains high power across a range of heritabilities and possible pedigree structures, and always outperforms other contemporary methods. We also show that the power of our proposed GRAMMAR-GC approaches to that of the ‘gold standard’ MG for all models and pedigrees studied. We show that this method is both feasible and powerful and has correct type 1 error in the context of genome-wide association analysis in related individuals.
ProbABEL package for genome-wide association analysis of imputed data
Yurii S Aulchenko, Maksim V Struchalin, Cornelia M van Duijn
BMC Bioinformatics , 2010, DOI: 10.1186/1471-2105-11-134
Abstract: We developed the ProbABEL software package for the analysis of genome-wide imputed SNP data and quantitative, binary, and time-till-event outcomes under linear, logistic, and Cox proportional hazards models, respectively. For quantitative traits, the package also implements a fast two-step mixed model-based score test for association in samples with differential relationships, facilitating analysis in family-based studies, studies performed in human genetically isolated populations and outbred animal populations.ProbABEL package provides fast efficient way to analyze imputed data in genome-wide context and will facilitate future identification of complex trait loci.Genome-wide association (GWA) studies became the tool of choice for the identification of loci associated with complex traits. In GWA analyses, association between a trait of interest and genetic polymorphisms (usually single nucleotide polymorphisms, SNPs) is studied using thousands of people typed for hundreds of thousands of polymorphisms. Several hundred loci for dozens of complex human disease and quantitative traits have been discovered thus far using this method [1].For any given genetic polymorphism, association can be studied using standard statistical analysis methodology, such as fixed and mixed effects models. However, because of the large number of tests to be performed and the quantity of data to be stored in GWA studies, computational throughput and effective data handling are essential features of statistical analysis software to be used in this context. A number of specialized software packages, such as PLINK[2], GenABEL[3], SNPTEST[4] and snpMatrix[5] were developed for the statistical analysis of GWA data. Most of these packages were designed, and are fit for, the analysis of directly typed SNPs. When directly typed markers are studied, genotype calling is performed with a high degree of confidence for the vast majority of markers, resulting in four possible genotypes ("AA", "AB", "BB",
Genome-based prediction of common diseases: methodological considerations for future research
A Cecile JW Janssens, Cornelia M van Duijn
Genome Medicine , 2009, DOI: 10.1186/gm20
Abstract: The past decade has seen rapid developments in our understanding of the genetic etiology of various common multifactorial diseases, such as age-related macular degeneration (AMD), type 1 and type 2 diabetes, cardiovascular diseases, Crohn's disease and various cancers [1]. Further developments in genomic research, such as the growing number of genome-wide association studies, the large-scale consortia that are pooling data from various studies, and the advances in statistical genomics and genotype technology, are drastically improving the chances of identifying common low risk variants and rare high risk variants. It is beyond doubt that many more genetic susceptibility variants will be discovered in the next few years.Expectations are high that increasing knowledge of the genetic bases of disease will eventually lead to personalized medicine, that is, to preventive and therapeutic interventions for complex diseases that are tailored to individuals on the basis of their genetic profiles [2,3]. Genome-based personalized medicine already exists for monogenic disorders. For example, female carriers of BRCA1 or BRCA2 mutations are offered biannual mammography screening or provided the opportunity of preventive surgery. Potential applications of genetic profiling in multifactorial diseases include tailoring of prevention programs to at-risk individuals, determining the starting age of participation in screening programs [4] and, when profiles predict treatment success, tailoring treatment modalities and starting doses.As we have reviewed recently [5], the predictive value of genetic profiling is still limited at present, with a few promising exceptions. The area under the receiver operating characteristic curve (AUC) gives an assessment of the discriminative accuracy of a prediction model, that is, the degree to which the test results can discriminate between persons who will develop the disease and those who will not. AUC ranges from 0.50 (equal to tossing a coin) to 1.
An epidemiological perspective on the future of direct-to-consumer personal genome testing
A Cecile JW Janssens, Cornelia M van Duijn
Investigative Genetics , 2010, DOI: 10.1186/2041-2223-1-10
Abstract: An increasing number of companies are offering health-related personal genome testing via the internet directly to consumers. Over time, these products have evolved from testing a few variants for a single health outcome to testing hundreds of thousands genetic variants genome-wide for multiple outcomes simultaneously. These tests provide information about predisposition to drug response and risk predictions for a variety of diseases. For example, DeCODEme is currently predicting risks for 50 different diseases, traits and medication responses, Navigenics for 40 and 23 and Me for 66. The outcomes predicted range from various cancers, to Alzheimer disease, to Warfarin response and eye color (accessed 14 July 2010). The utility of these tests is far from clear, not in the least because the predictive ability is still limited for most diseases, and risk predictions remain subject to change as long as new variants are being discovered [1-4].To facilitate the discovery of new variants, next-generation whole genome sequencing is increasingly utilized in genetic research. The arrays used for genome wide scans include a very large but finite number of common variants covering the genome based on the principle of linkage disequilibrium. In contrast, whole genome sequencing documents the entire genome, base pair by base pair, and thus comprises more DNA variations, such as rare variants, copy number and structural variations with potentially larger effects on clinically relevant outcomes. Whole genome sequencing will be instrumental to discover more common variants implicated in complex outcomes, but may also reveal rare causal genetic variants for monogenic diseases that are private to specific populations or even to persons. Because whole genome sequencing gives a more complete coverage, it is beyond doubt that companies will consider the technique to predict predisposition to drug response and risks of complex and monogenic diseases. Whole genome sequencing is still rather
Region-Based Association Analysis of Human Quantitative Traits in Related Individuals
Nadezhda M. Belonogova, Gulnara R. Svishcheva, Cornelia M. van Duijn, Yurii S. Aulchenko, Tatiana I. Axenovich
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0065395
Abstract: Regional-based association analysis instead of individual testing of each SNP was introduced in genome-wide association studies to increase the power of gene mapping, especially for rare genetic variants. For regional association tests, the kernel machine-based regression approach was recently proposed as a more powerful alternative to collapsing-based methods. However, the vast majority of existing algorithms and software for the kernel machine-based regression are applicable only to unrelated samples. In this paper, we present a new method for the kernel machine-based regression association analysis of quantitative traits in samples of related individuals. The method is based on the GRAMMAR+ transformation of phenotypes of related individuals, followed by use of existing kernel machine-based regression software for unrelated samples. We compared the performance of kernel-based association analysis on the material of the Genetic Analysis Workshop 17 family sample and real human data by using our transformation, the original untransformed trait, and environmental residuals. We demonstrated that only the GRAMMAR+ transformation produced type I errors close to the nominal value and that this method had the highest empirical power. The new method can be applied to analysis of related samples by using existing software for kernel-based association analysis developed for unrelated samples.
Perspectives on the Use of Multiple Sclerosis Risk Genes for Prediction
Naghmeh Jafari, Linda Broer, Cornelia M. van Duijn, A. Cecile J. W. Janssens, Rogier Q. Hintzen
PLOS ONE , 2011, DOI: 10.1371/journal.pone.0026493
Abstract: Objective A recent collaborative genome-wide association study replicated a large number of susceptibility loci and identified novel loci. This increase in known multiple sclerosis (MS) risk genes raises questions about clinical applicability of genotyping. In an empirical set we assessed the predictive power of typing multiple genes. Next, in a modelling study we explored current and potential predictive performance of genetic MS risk models. Materials and Methods Genotype data on 6 MS risk genes in 591 MS patients and 600 controls were used to investigate the predictive value of combining risk alleles. Next, the replicated and novel MS risk loci from the recent and largest international genome-wide association study were used to construct genetic risk models simulating a population of 100,000 individuals. Finally, we assessed the required numbers, frequencies, and ORs of risk SNPs for higher discriminative accuracy in the future. Results Individuals with 10 to 12 risk alleles had a significantly increased risk compared to individuals with the average population risk for developing MS (OR 2.76 (95% CI 2.02–3.77)). In the simulation study we showed that the area under the receiver operating characteristic curve (AUC) for a risk score based on the 6 SNPs was 0.64. The AUC increases to 0.66 using the well replicated 24 SNPs and to 0.69 when including all replicated and novel SNPs (n = 53) in the risk model. An additional 20 SNPs with allele frequency 0.30 and ORs 1.1 would be needed to increase the AUC to a slightly higher level of 0.70, and at least 50 novel variants with allele frequency 0.30 and ORs 1.4 would be needed to obtain an AUC of 0.85. Conclusion Although new MS risk SNPs emerge rapidly, the discriminatory ability in a clinical setting will be limited.
An R package "VariABEL" for genome-wide searching of potentially interacting loci by testing genotypic variance heterogeneity
Maksim V Struchalin, Najaf Amin, Paul HC Eilers, Cornelia M van Duijn, Yurii S Aulchenko
BMC Genetics , 2012, DOI: 10.1186/1471-2156-13-4
Abstract: We and Pare with colleagues (2010) developed a method allowing to overcome such difficulties. The method is based on the fact that loci which are involved in interactions can show genotypic variance heterogeneity of a trait. Genome-wide testing of such heterogeneity can be a fast scanning approach which can point to the interacting genetic variants.In this work we present a new method, SVLM, allowing for variance heterogeneity analysis of imputed genetic variation. Type I error and power of this test are investigated and contracted with these of the Levene's test. We also present an R package, VariABEL, implementing existing and newly developed tests.Variance heterogeneity analysis is a promising method for detection of potentially interacting loci. New method and software package developed in this work will facilitate such analysis in genome-wide context.Genome-wide association studies (GWAS) have been instrumental in identifying genetic variants involved in complex diseases. In GWAS, the relation between a trait of interest and genetic variation (usually a single nuclear polymorphism -- a SNP) is studied by assessing hundreds of thousands of polymorphisms in thousands of individuals. Several hundreds of loci for dozens of complex human diseases and quantitative traits have been discovered using GWAS [1].Though GWASs were successful in finding single loci associated with a trait, complex genetic models which include many interacting loci and environmental factors are of interest as they may help finding new loci and improve our understanding of the genetics of complex traits. A search for genetic interactions by direct analysis, in which all possible genetic models are examined, meets substantial computational and methodological difficulties. When millions of SNPs are considered, which nowadays has become routine in GWAS, testing for interaction for all possible pairwise combinations of SNPs becomes cumbersome requiring parallel computations using hundreds or thous
A critical appraisal of epidemiological studies comes from basic knowledge: a reader's guide to assess potential for biases
Stefania Boccia, Giuseppe La Torre, Roberto Persiani, Domenico D'Ugo, Cornelia M van Duijn, Gualtiero Ricciardi
World Journal of Emergency Surgery , 2007, DOI: 10.1186/1749-7922-2-7
Abstract: Epidemiology is largely a non-experimental discipline and although this has often been perceived as a weakness, probably its most important strength is in that observations are made on samples of real-world populations. In fact, the limited opportunity for experiments in epidemiological research has lead to a very critical theoretical understanding of the different types and sources of error [1].Clinicians and surgeons face two important questions as they read medical research: is the report believable, and, if so, is it relevant to my practice? Uncritical acceptance of published research has led to serious errors and squandered resources [2,3]. In this review, we will examine these questions in terms of study validity; describing a simple checklist for readers to judge reported associations.Additionally, we will describe another kind of bias, one that does not affect the validity of a single study but rather the dissemination of research findings: publication bias, which can be described as the selective submission or publication of study results based on the direction or strength of the study's findings.Important discussions on bias took place as the concept of study design in modern epidemiology was refined. In the 1950s, the introduction of the randomised controlled trial (RCT) and its assigned role as a 'gold standard' for medical research lead to the anticipation that the validity of a study could be improved in circumstances where randomization was feasible. In the 1970s, during general discussions on the sources of biases, taxonomies of bias were proposed by Murphy and Sackett [4,5], with the latter proposing a 'catalogue' of 35 different types of bias. Modern definitions of bias, however, tend to be restricted to those categories that have a logical basis. According to Rothman, bias is a systematic error that afflicts study design, thus affecting the validity of the study itself [6]. An epidemiologic study can be viewed as an attempt to obtain an epidemiolo
Candidate Gene-Based Association Study of Antipsychotic-Induced Movement Disorders in Long-Stay Psychiatric Patients: A Prospective Study
P. Roberto Bakker, Egbert Bakker, Najaf Amin, Cornelia M. van Duijn, Jim van Os, Peter N. van Harten
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0036561
Abstract: Objective Four types of antipsychotic-induced movement disorders: tardive dyskinesia (TD), parkinsonism, akathisia and tardive dystonia, subtypes of TD (orofacial and limb truncal dyskinesia), subtypes of parkinsonism (rest tremor, rigidity, and bradykinesia), as well as a principal-factor of the movement disorders and their subtypes, were examined for association with variation in 10 candidate genes (PPP1R1B, BDNF, DRD3, DRD2, HTR2A, HTR2C, COMT, MnSOD, CYP1A2, and RGS2). Methods Naturalistic study of 168 white long-stay patients with chronic mental illness requiring long-term antipsychotic treatment, examined by the same rater at least two times over a 4-year period, with a mean follow-up time of 1.1 years, with validated scales for TD, parkinsonism, akathisia, and tardive dystonia. The authors genotyped 31 SNPs, associated with movement disorders or schizophrenia in previous studies. Genotype and allele frequency comparisons were performed with multiple regression methods for continuous movement disorders. Results Various SNPs reached nominal significance: TD and orofacial dyskinesia with rs6265 and rs988748, limb truncal dyskinesia with rs6314, rest tremor with rs6275, rigidity with rs6265 and rs4680, bradykinesia with rs4795390, akathisia with rs4680, tardive dystonia with rs1799732, rs4880 and rs1152746. After controlling for multiple testing, no significant results remained. Conclusions The findings suggest that selected SNPs are not associated with a susceptibility to movement disorders. However, as the sample size was small and previous studies show inconsistent results, definite conclusions cannot be made. Replication is needed in larger study samples, preferably in longitudinal studies which take the fluctuating course of movement disorders and gene-environment interactions into account.
Lack of association of two common polymorphisms on 9p21 with risk of coronary heart disease and myocardial infarction; results from a prospective cohort study
Abbas Dehghan, Mandy van Hoek, Eric JG Sijbrands, Ben A Oostra, Albert Hofman, Cornelia M van Duijn, Jacqueline CM Witteman
BMC Medicine , 2008, DOI: 10.1186/1741-7015-6-30
Abstract: The Rotterdam Study is a population-based, prospective cohort study among 7983 participants aged 55 years and older. Associations of the polymorphisms with CHD and MI were assessed by use of Cox proportional hazards analyses.In an additive model, the age and sex adjusted hazard ratios (HRs) (95% confidence interval) for CHD and MI were 1.03 (0.90, 1.18) and 0.94 (0.82, 1.08) per copy of the G allele of rs10757274. The corresponding HRs were 1.03 (0.90, 1.18) and 0.93 (0.81, 1.06) for the G allele of rs10757278. The association of the SNPs with CHD and MI was not significant in any of the subgroups of CHD risk factors.we were not able to show an association of the studied SNPs with risks of CHD and MI. This may be due to differences in genes involved in the occurrence of CHD in young and older people.It has been considered for long that genes play a substantial role in susceptibility to coronary heart disease (CHD) [1]. Up to now, a limited number of these genes have been identified through the candidate gene approach and genome wide linkage studies. Recently a number of genome wide association (GWA) studies have identified several genetic variants on chromosome 9p21 associated with the risk of CHD. McPherson et al. found a Single Nucleotide Polymorphism (SNP), rs10757274, on chromosome 9p21 associated with the risk of CHD [2]. Helgadottir et al. found a close-by SNP, rs10757278, in the same 9p21 region associated with the risk of myocardial infarction (MI) [3]. These findings were followed by another GWA study by Samani et al [4], which found rs1333049 to be associated with the risk of coronary artery disease [4]. All three SNPs are located within the same Linkage Disequilibrium (LD) block on chromosome 9 approximately 22 million base pairs from the 9p telomere, adjacent to two tumor suppressor genes, CDKN2A and CDKN2B. These genes are involved in regulation of cell proliferation. Abnormal proliferation is one of the characteristics of atherosclerosis, one of the pa
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