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Search Results: 1 - 10 of 325714 matches for " Yurii S. Aulchenko "
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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",
High-throughput Genome-wide Association Analysis for Single and Multiple Phenotypes
Diego Fabregat-Traver,Yurii S. Aulchenko,Paolo Bientinesi
Computer Science , 2012,
Abstract: The variance component tests used in genomewide association studies of thousands of individuals become computationally exhaustive when multiple traits are analysed in the context of omics studies. We introduce two high-throughput algorithms -- CLAK-CHOL and CLAK-EIG -- for single and multiple phenotype genome-wide association studies (GWAS). The algorithms, generated with the help of an expert system, reduce the computational complexity to the point that thousands of traits can be analyzed for association with millions of polymorphisms in a course of days on a standard workstation. By taking advantage of problem specific knowledge, CLAK-CHOL and CLAK-EIG significantly outperform the current state-of-the-art tools in both single and multiple trait analysis.
The Empirical Power of Rare Variant Association Methods: Results from Sanger Sequencing in 1,998 Individuals
Martin Ladouceur,Zari Dastani,Yurii S. Aulchenko,Celia M. T. Greenwood,J. Brent Richards
PLOS Genetics , 2012, DOI: 10.1371/journal.pgen.1002496
Abstract: The role of rare genetic variation in the etiology of complex disease remains unclear. However, the development of next-generation sequencing technologies offers the experimental opportunity to address this question. Several novel statistical methodologies have been recently proposed to assess the contribution of rare variation to complex disease etiology. Nevertheless, no empirical estimates comparing their relative power are available. We therefore assessed the parameters that influence their statistical power in 1,998 individuals Sanger-sequenced at seven genes by modeling different distributions of effect, proportions of causal variants, and direction of the associations (deleterious, protective, or both) in simulated continuous trait and case/control phenotypes. Our results demonstrate that the power of recently proposed statistical methods depend strongly on the underlying hypotheses concerning the relationship of phenotypes with each of these three factors. No method demonstrates consistently acceptable power despite this large sample size, and the performance of each method depends upon the underlying assumption of the relationship between rare variants and complex traits. Sensitivity analyses are therefore recommended to compare the stability of the results arising from different methods, and promising results should be replicated using the same method in an independent sample. These findings provide guidance in the analysis and interpretation of the role of rare base-pair variation in the etiology of complex traits and diseases.
ParallABEL: an R library for generalized parallelization of genome-wide association studies
Unitsa Sangket, Surakameth Mahasirimongkol, Wasun Chantratita, Pichaya Tandayya, Yurii S Aulchenko
BMC Bioinformatics , 2010, DOI: 10.1186/1471-2105-11-217
Abstract: Most components of GWA analysis can be divided into four groups based on the types of input data and statistical outputs. The first group contains statistics computed for a particular Single Nucleotide Polymorphism (SNP), or trait, such as SNP characterization statistics or association test statistics. The input data of this group includes the SNPs/traits. The second group concerns statistics characterizing an individual in a study, for example, the summary statistics of genotype quality for each sample. The input data of this group includes individuals. The third group consists of pair-wise statistics derived from analyses between each pair of individuals in the study, for example genome-wide identity-by-state or genomic kinship analyses. The input data of this group includes pairs of SNPs/traits. The final group concerns pair-wise statistics derived for pairs of SNPs, such as the linkage disequilibrium characterisation. The input data of this group includes pairs of individuals. We developed the ParallABEL library, which utilizes the Rmpi library, to parallelize these four types of computations. ParallABEL library is not only aimed at GenABEL, but may also be employed to parallelize various GWA packages in R. The data set from the North American Rheumatoid Arthritis Consortium (NARAC) includes 2,062 individuals with 545,080, SNPs' genotyping, was used to measure ParallABEL performance. Almost perfect speed-up was achieved for many types of analyses. For example, the computing time for the identity-by-state matrix was linearly reduced from approximately eight hours to one hour when ParallABEL employed eight processors.Executing genome-wide association analysis using the ParallABEL library on a computer cluster is an effective way to boost performance, and simplify the parallelization of GWA studies. ParallABEL is a user-friendly parallelization of GenABEL.GWA analysis [1] is a well established and powerful method for identifying loci associated with variations of c
Variance heterogeneity analysis for detection of potentially interacting genetic loci: method and its limitations
Maksim V Struchalin, Abbas Dehghan, Jacqueline CM Witteman, Cornelia van Duijn, Yurii S Aulchenko
BMC Genetics , 2010, DOI: 10.1186/1471-2156-11-92
Abstract: Through simulations, we investigated type I error for Bartlett's test, Bartlett's test with prior rank transformation of a trait to normality, and Levene's test for different genetic models. Additionally, we derived an analytical expression for power estimation. We showed that Bartlett's test has acceptable type I error in the case of trait following a normal distribution, whereas Levene's test kept nominal Type I error under all scenarios investigated. For the power of variance homogeneity test, we showed (as opposed to the power of direct test which uses information about known interacting factor) that, given the same interaction effect, the power can vary widely depending on the non-estimable direct effect of the unobserved interacting variable. Thus, for a given interaction effect, only very wide limits of power of the variance homogeneity test can be estimated. Also we applied Levene's approach to test genome-wide homogeneity of variances of the C-reactive protein in the Rotterdam Study population (n = 5959). In this analysis, we replicate previous results of Pare and colleagues (2010) for the SNP rs12753193 (n = 21, 799).Screening for differences in variances among genotypes of a SNP is a promising approach as a number of biologically interesting models may lead to the heterogeneity of variances. However, it should be kept in mind that the absence of variance heterogeneity for a SNP can not be interpreted as the absence of involvement of the SNP in the interaction network.Genome-wide association (GWA) study has become the tool of choice for the identification of loci associated with complex traits. In GWA analysis, the association between a trait of interest and genetic variation is studied by using thousands of subjects typed for hundreds of thousands of polymorphisms. Thus several hundred loci for dozens of complex human disease and quantitative traits have been discovered utilizing this method [1].However, it has become clear that for most complex traits, l
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
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.
Development and Application of Genomic Control Methods for Genome-Wide Association Studies Using Non-Additive Models
Yakov A. Tsepilov, Janina S. Ried, Konstantin Strauch, Harald Grallert, Cornelia M. van Duijn, Tatiana I. Axenovich, Yurii S. Aulchenko
PLOS ONE , 2013, DOI: 10.1371/journal.pone.0081431
Abstract: Genome-wide association studies (GWAS) comprise a powerful tool for mapping genes of complex traits. However, an inflation of the test statistic can occur because of population substructure or cryptic relatedness, which could cause spurious associations. If information on a large number of genetic markers is available, adjusting the analysis results by using the method of genomic control (GC) is possible. GC was originally proposed to correct the Cochran-Armitage additive trend test. For non-additive models, correction has been shown to depend on allele frequencies. Therefore, usage of GC is limited to situations where allele frequencies of null markers and candidate markers are matched. In this work, we extended the capabilities of the GC method for non-additive models, which allows us to use null markers with arbitrary allele frequencies for GC. Analytical expressions for the inflation of a test statistic describing its dependency on allele frequency and several population parameters were obtained for recessive, dominant, and over-dominant models of inheritance. We proposed a method to estimate these required population parameters. Furthermore, we suggested a GC method based on approximation of the correction coefficient by a polynomial of allele frequency and described procedures to correct the genotypic (two degrees of freedom) test for cases when the model of inheritance is unknown. Statistical properties of the described methods were investigated using simulated and real data. We demonstrated that all considered methods were effective in controlling type 1 error in the presence of genetic substructure. The proposed GC methods can be applied to statistical tests for GWAS with various models of inheritance. All methods developed and tested in this work were implemented using R language as a part of the GenABEL package.
Association between Type 2 Diabetes Loci and Measures of Fatness
Slavica Pecioska,M. Carola Zillikens,Peter Henneman,Pieter J. Snijders,Ben A. Oostra,Cornelia M. van Duijn,Yurii S. Aulchenko
PLOS ONE , 2012, DOI: 10.1371/journal.pone.0008541
Abstract: Type 2 diabetes (T2D) is a metabolic disorder characterized by disturbances of carbohydrate, fat and protein metabolism and insulin resistance. The majority of T2D patients are obese and obesity by itself may be a cause of insulin resistance. Our aim was to evaluate whether the recently identified T2D risk alleles are associated with human measures of fatness as characterized with Dual Energy X-ray Absorptiometry (DEXA).
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