Association studies for untyped markers with TUNA

Wen, Xiaoquan; Nicolae, Dan L.

doi:10.1093/bioinformatics/btm603

Cited by 13 publications

(12 citation statements)

References 12 publications

(7 reference statements)

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“…Several methods have been proposed previously for multilocus association in which information from untyped markers has been shown to be useful in inferring or imputing the missing genotypes [Browning, 2008; Marchini et al, 2007; Nicolae, 2006; Wen and Nicolae, 2008]. However, these methods require the complete genotype for each individual in order to infer the genotype of the missing marker.…”

Section: Discussionmentioning

confidence: 99%

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A multipoint method for meta‐analysis of genetic association studies

Bagos

Liakopoulos

2010

Genetic Epidemiology

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Meta-analyses of genetic association studies are usually performed using a single polymorphism at a time, even though in many cases the individual studies report results from partially overlapping sets of polymorphisms. We present here a multipoint (or multilocus) method for multivariate meta-analysis of published population-based case-control association studies. The method is derived by extending the general method for multivariate meta-analysis and allows for multivariate modelling of log(odds ratios (OR)) derived from several polymorphisms that are in linkage disequilibrium (LD). The method is presented in a genetic model-free approach, although it can also be used by assuming a genetic model of inheritance beforehand. Furthermore, the method is presented in a unified framework and is easily applied to both discrete outcomes (using the OR), as well as to meta-analyses of a continuous outcome (using the mean difference). The main innovation of the method is the analytical calculation of the within-studies covariances between estimates derived from linked polymorphisms. The only requirement is that of an external estimate for the degree of pairwise LD between the polymorphisms under study, which can be obtained from the same published studies, from the literature or from HapMap. Thus, the method is quite simple and fast, it can be extended to an arbitrary set of polymorphisms and can be fitted in nearly all statistical packages (Stata, R/Splus and SAS). Applications in two already published meta-analyses provide encouraging results concerning the robustness and the usefulness of the method and we expect that it would be widely used in the future.

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Section: Discussionmentioning

confidence: 99%

“…This structure, i.e. the degree of linkage disequilibrium (LD) [Devlin and Risch, 1995; Guo, 1997; Pritchard and Przeworski, 2001] between various polymorphisms, has been shown to be very useful in genetic association studies [Gu et al, 2008; Marchini et al, 2007; Wen and Nicolae, 2008; Zaitlen et al, 2007].…”

Section: Introductionmentioning

confidence: 99%

A multipoint method for meta‐analysis of genetic association studies

Bagos

Liakopoulos

2010

Genetic Epidemiology

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“…Our approach for untyped variation (TUNA) is described elsewhere (Nicolae, 2006; Wen and Nicolae, 2008), and relies on estimates of joint haplotype frequencies for both typed and untyped markers in order to perform tests of association for the untyped markers. The frequencies are used to build linear combinations of observed haplotype frequencies that are used as proxies for untyped alleles.…”

Section: Untyped Markersmentioning

confidence: 99%

Gene, region and pathway level analyses in whole‐genome studies

Cruz

Wen

et al. 2009

Genetic Epidemiology

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In the setting of genome-wide association studies, we propose a method for assigning a measure of significance to pre-defined sets of markers in the genome. The sets can be genes, conserved regions, or groups of genes such as pathways. Using the proposed methods and algorithms, evidence for association between a particular functional unit and a disease status can be obtained not just by the presence of a strong signal from a SNP within it, but also by the combination of several simultaneous weaker signals that are not strongly correlated. This approach has several advantages. First, moderately strong signals from different SNPs are combined to obtain a much stronger signal for the set, therefore increasing power. Second, in combination with methods that provide information on untyped markers, it leads to results that can be readily combined across studies and platforms that might use different SNPs. Third, the results are easy to interpret, since they refer to functional sets of markers that are likely to behave as a unit in their phenotypic effect. Finally, the availability of gene-level p-values for association is the first step in developing methods that integrate information from pathways and networks with genome-wide association data, and these can lead to a better understanding of the complex traits genetic architecture. The power of the approach is investigated in simulated and real datasets. Novel Crohn's disease associations are found using the WTCCC data.

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“…The methods for imputation generally take similar approaches, relying on LD relationships between SNPs in the HapMap, and are relatively computer intensive. Popular imputation programs include MACH29, IMPUTE30, PLINK27, BEAGLE31, BimBam32 and TUNA33. Application of these programs to most genome-wide genotyping datasets currently results in estimates for more than 2 million SNPs, increasing genomic coverage and allowing groups with distinct starting genotyping platforms to compare results or conduct meta-analysis.…”

Section: Software For the Conduct And Interpretation Of Genetic Analymentioning

confidence: 99%