Evolution of the Simulated Data Problem

Thomas, Duncan C.; Borecki, Ingrid B.; Thomson, Glenys; Weiss, Ken; Almasy, Laura; Blangero, John; Nielsen, Dahlia M.; Terwilliger, Joseph D.; Zaykin, Dmitri V.; MacCluer, Jean W.

doi:10.1002/gepi.2001.21.s1.s325

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Less extensive, but more realistic simulations assumed a drift with admixture population-genetic model that we used to form the isolated population described in [2,33]. In the model used in the present study, we considered two starting populations (100 and 10,000 individuals for the isolate and the general populations) with no ancestral LD.…”

Section: Statistical Power Assuming An Explicit Population-genetic Momentioning

confidence: 99%

Testing Association of Statistically Inferred Haplotypes with Discrete and Continuous Traits in Samples of Unrelated Individuals

Zaykin¹,

et al. 2002

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There have been increasing efforts to relate drug efficacy and disease predisposition with genetic polymorphisms. We present statistical tests for association of haplotype frequencies with discrete and continuous traits in samples of unrelated individuals. Haplotype frequencies are estimated through the expectation-maximization algorithm, and each individual in the sample is expanded into all possible haplotype configurations with corresponding probabilities, conditional on their genotype. A regression-based approach is then used to relate inferred haplotype probabilities to the response. The relationship of this technique to commonly used approaches developed for case-control data is discussed. We confirm the proper size of the test under H₀ and find an increase in power under the alternative by comparing test results using inferred haplotypes with single-marker tests using simulated data. More importantly, analysis of real data comprised of a dense map of single nucleotide polymorphisms spaced along a 12-cM chromosomal region allows us to confirm the utility of the haplotype approach as well as the validity and usefulness of the proposed statistical technique. The method appears to be successful in relating data from multiple, correlated markers to response.

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Section: Statistical Power Assuming An Explicit Population-genetic Momentioning

confidence: 99%

Testing Association of Statistically Inferred Haplotypes with Discrete and Continuous Traits in Samples of Unrelated Individuals

Zaykin¹,

et al. 2002

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“…We simulated a disease on the genetic data simulated for the Genetic Analysis workshop 12 (GAW12) (Almasy et al, 2001; Thomas et al, 2001; Wijsman et al, 2001). This data set consists of microsatellite markers simulated without influence from selection, about 1 cM apart, with LD specifically simulated among the markers, described in Thomas et al (2001). The number of alleles for each microsatellite ranged from 4 to 16.…”

Section: Analyzing Simulated Datamentioning

confidence: 99%

Stochastic Search Gene Suggestion: A Bayesian Hierarchical Model for Gene Mapping

et al. 2005

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Mapping the genes for a complex disease, such as diabetes or rheumatoid arthritis (RA), involves finding multiple genetic loci that may contribute to the onset of the disease. Pairwise testing of the loci leads to the problem of multiple testing. Looking at haplotypes, or linear sets of loci, avoids multiple tests but results in a contingency table with sparse counts, especially when using marker loci with multiple alleles. We propose a hierarchical Bayesian model for case-parent triad data that uses a conditional logistic regression likelihood to model the probability of transmission to a diseased child. We define hierarchical prior distributions on the allele main effects to model the genetic dependencies present in the human leukocyte antigen (HLA) region of chromosome 6. First, we add a hierarchical level for model selection that accounts for both locus and allele selection. This allows us to cast the problem of identifying genetic loci relevant to the disease into a problem of Bayesian variable selection. Second, we attempt to include linkage disequilibrium as a covariance structure in the prior for model coefficients. We evaluate the performance of the procedure with some simulated examples and then apply our procedure to identifying genetic markers in the HLA region that influence risk for RA. Our software is available on the website http://www.epigenetic.org/Linkage/ssgs-public/.

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“…In the remainder of this paper, we outline traditional variance components methods and use this outline to motivate the development of parallel techniques for the proportional hazards model. We then illustrate the methods using data collected from the Minnesota Breast Cancer Family Resource [Anderson et al, 1958;Sellers et al, 1995] and simulated for the Twelfth Genetics Analysis Workshop (GAW12) [Thomas et al, 2001;Almasy et al, 2001].…”

Section: Introductionmentioning

confidence: 99%

Random‐effects Cox proportional hazards model: General variance components methods for time‐to‐event data

Pankratz

Andrade

Therneau

2004

Genetic Epidemiology

109

133

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Proportional hazards regression models are commonly used to study factors associated with time-to-event data. Because many complex genetic diseases exhibit variation in age at onset, it is important to have the capability to perform survival analyses on data collected from individuals whose observations are correlated due to shared genes or environment. While there are widely accepted methods for variance components analysis for simple quantitative traits, a parallel methodology for survival data has not been available. This manuscript outlines a method to perform variance component analyses under general random effects proportional hazards models. This method is based on a Laplace approximation, and makes computation for correlated time-to-event data feasible. The correlated frailty models described here can be used to perform genetic analyses, and other analyses with structured random effects, on age-at-onset data in a manner analogous to standard variance components methods for quantitative traits. We illustrate the use of the method by examining the heritability of breast cancer in a large familial cohort study. We also perform variance components linkage analyses on data simulated for the Twelfth Genetic Analysis Workshop (GAW12), and further examine the performance of this method for linkage analysis in a simulation study. The breast cancer analyses support significant heritability of disease age-at-onset that is of moderate size. The variance component linkage analyses successfully identify the location of the disease genes that were simulated to have a direct impact on age-at-onset. The methods outlined here make it possible to perform general variance components analyses on time-to-event endpoints, even on large data sets, in a computationally efficient manner.

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Evolution of the Simulated Data Problem

Cited by 5 publications

References 3 publications

Testing Association of Statistically Inferred Haplotypes with Discrete and Continuous Traits in Samples of Unrelated Individuals

Testing Association of Statistically Inferred Haplotypes with Discrete and Continuous Traits in Samples of Unrelated Individuals

Stochastic Search Gene Suggestion: A Bayesian Hierarchical Model for Gene Mapping

Random‐effects Cox proportional hazards model: General variance components methods for time‐to‐event data

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