Density-based clustering in haplotype analysis for association mapping

Igo, Robert P.; Londoño, Douglas; Miller, Kathleen L.; Parrado, Antonio; Quade, Shannon R E; Sinha, Moumita; Kim, Sulgi; Won, Sungho; Li, Jing; Goddard, Katrina A.B.

doi:10.1186/1753-6561-1-s1-s27

Cited by 5 publications

(10 citation statements)

References 18 publications

(29 reference statements)

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“…It has also been suggested that haplotype information that accounts for correlations across gene loci offers greater power in characterizing gene-trait associations. Methods for clustering subjects according to similarity in haplotype pairs have been proposed [Igo et al, 2007;Waldron et al, 2006]. Another approach that is suitable for the case where a small number of low-polymorphic loci are involved is patterning.…”

Section: Methods Complex Traits and Genotype Groupingmentioning

confidence: 99%

A variance components factor model for genetic association studies: A Bayesian analysis

Whittaker

2010

Genetic Epidemiology

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Studies of gene-trait associations for complex diseases often involve multiple traits that may vary by genotype groups or patterns. Such traits are usually manifestations of lower-dimensional latent factors or disease syndromes. We illustrate the use of a variance components factor (VCF) model to model the association between multiple traits and genotype groups as well as any other existing patient-level covariates. This model characterizes the correlations between traits as underlying latent factors that can be used in clinical decision-making. We apply it within the Bayesian framework and provide a straightforward implementation using the WinBUGS software. The VCF model is illustrated with simulated data and an example that comprises changes in plasma lipid measurements of patients who were treated with statins to lower low-density lipoprotein cholesterol, and polymorphisms from the apolipoprotein-E gene. The simulation shows that this model clearly characterizes existing multiple trait manifestations across genotype groups where individuals' group assignments are fully observed or can be deduced from the observed data. It also allows one to investigate covariate by genotype group interactions that may explain the variability in the traits. The flexibility to characterize such multiple trait manifestations makes the VCF model more desirable than the univariate variance components model, which is applied to each trait separately. The Bayesian framework offers a flexible approach that allows one to incorporate prior information.

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Section: Methods Complex Traits and Genotype Groupingmentioning

confidence: 99%

A variance components factor model for genetic association studies: A Bayesian analysis

Whittaker

2010

Genetic Epidemiology

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“…Several studies [Bardel et al, 2007;Igo et al, 2007;Pankratz, 2007;Yoo et al, 2007] incorporated haplotypes into association analyses, albeit in differing ways and in the pursuit of diverse goals. Pankratz [2007] and Yoo et al [2007] carried out haplotype-based tests of association specifically to compare significance of results between single SNPs and haplotypes.…”

Section: Combining Information From Multiple Correlated Snpsmentioning

confidence: 99%

“…Because the genotype data were complete, three studies were able to evaluate the effect of missing data under investigator-specified conditions on the performance of association analysis [Bardel et al, 2007;Croiseau et al, 2007a;Guo, 2007]. Finally, the availability of a dense marker map spanning three trait loci encouraged the study of haplotype-based analyses and other schemes to capture higher-order interactions among nearby markers [Bardel et al, 2007;Charoen et al, 2007;Gu et al, 2007;Igo et al, 2007;Pankratz, 2007;Ye et al, 2007;Yoo et al, 2007;Zhang et al, 2007].…”

Section: Introductionmentioning

confidence: 99%

“…The problem 3 data are explained in detail by Miller et al [2007]. Several groups took advantage of all 100 replicate samples to evaluate the power and validity of association mapping techniques [Bardel et al, 2007;Croiseau et al, 2007a;Gu et al, 2007;Igo et al, 2007]. Because the genotype data were complete, three studies were able to evaluate the effect of missing data under investigator-specified conditions on the performance of association analysis [Bardel et al, 2007;Croiseau et al, 2007a;Guo, 2007].…”

Section: Introductionmentioning

confidence: 99%

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Issues in association mapping with high-density SNP data and diverse family structures

et al. 2007

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Genetic association studies have the potential to identify causative genetic variants with small effects in complex diseases, but it is not at all clear which study designs best balance power with sample size, especially when taking into account the difficulty of obtaining a sample of the necessary structure. The 14 contributions from the Genetic Analysis Workshop 15 group 3 used data sets with rheumatoid arthritis as primary phenotype from problem 2 (real data) and Problem 3 (simulated data) to investigate design and analysis problems that arise in candidate-gene, candidate-region, and genome-wide association studies. We identified three major themes that were addressed by multiple groups: (1) comparing family-based and case-control study designs with each other and with hybrid designs incorporating both related and unrelated individuals; (2) exploring and comparing techniques of combining information from multiple, correlated single-nucleotide polymorphisms; and (3) comparing analyses that select the model(s) of best fit with the ultimate aim of detecting the joint effects of several unlinked single-nucleotide polymorphisms. These contributions achieved some success in improving upon existing methods. For example, tests using related cases and unrelated controls can achieve higher power than the tests using unrelated cases and unrelated controls. Aside from these successes, the group 3 contributions highlight some interesting areas for future research.

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“…Consequently methods have been developed to reduce the number of haplotype groups at a marker locus, using hierarchical clustering and Bayesian partitioning algorithms (Molitor et al 2003;Durrant et al 2004;Bardel et al 2006;Morris 2006;Tzeng et al 2006;Waldron et al 2006;Igo et al 2007;Liu et al 2007;Tachmazidou et al 2007;Knight et al 2008).…”

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confidence: 99%

Bayesian Quantitative Trait Locus Mapping Using Inferred Haplotypes

Durrant

Mott

2010

Genetics

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We describe a fast hierarchical Bayesian method for mapping quantitative trait loci by haplotype-based association, applicable when haplotypes are not observed directly but are inferred from multiple marker genotypes. The method avoids the use of a Monte Carlo Markov chain by employing priors for which the likelihood factorizes completely. It is parameterized by a single hyperparameter, the fraction of variance explained by the quantitative trait locus, compared to the frequentist fixed-effects model, which requires a parameter for the phenotypic effect of each combination of haplotypes; nevertheless it still provides estimates of haplotype effects. We use simulation to show that the method matches the power of the frequentist regression model and, when the haplotypes are inferred, exceeds it for small QTL effect sizes. The Bayesian estimates of the haplotype effects are more accurate than the frequentist estimates, for both known and inferred haplotypes, which indicates that this advantage is independent of the effect of uncertainty in haplotype inference and will hold in comparison with frequentist methods in general. We apply the method to data from a panel of recombinant inbred lines of Arabidopsis thaliana, descended from 19 inbred founders.

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Density-based clustering in haplotype analysis for association mapping

Cited by 5 publications

References 18 publications

A variance components factor model for genetic association studies: A Bayesian analysis

A variance components factor model for genetic association studies: A Bayesian analysis

Issues in association mapping with high-density SNP data and diverse family structures

Bayesian Quantitative Trait Locus Mapping Using Inferred Haplotypes

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