Genome scans with gene‐covariate interaction

Peng, Jie; Tang, Hsiu-Khuern; Siegmund, David

doi:10.1002/gepi.20100

Cited by 7 publications

(12 citation statements)

References 24 publications

(38 reference statements)

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“…However, purely for mathematical convenience, we have assumed that on the latent scale, there was no interaction between the gene at the putative location and the covariate. Actually, recent developments published by Peng et al [20] explicitly account for such interactions and these authors have derived the corresponding score test for linkage. The gene by covariate interaction could be explicitly incorporated into the GLMM model in a similar way (via the R matrix of variance-covariance of random effects) and the corresponding test would obtain analogously.…”

Section: Discussionmentioning

confidence: 99%

Score Test for Linkage in Generalized Linear Models

Lebrec

Houwelingen²

2007

Hum Hered

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We derive a test for linkage in a Generalized Linear Mixed Model (GLMM) framework which provides a natural adjustment for marginal covariate effects. The method boils down to the score test of a quasi-likelihood derived from the GLMM, it is computationally inexpensive and can be applied to arbitrary pedigrees. In particular, for binary traits, relative pairs of different nature (affected and discordant) and individuals with different covariate values can be naturally combined in a single test. The model introduced could explain a number of situations usually described as gene by covariate interaction phenomena, and offers substantial gains in efficiency compared to methods classically used in those instances.

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Section: Discussionmentioning

confidence: 99%

Score Test for Linkage in Generalized Linear Models

Lebrec

Houwelingen²

2007

Hum Hered

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“…introduced a quite different model for continuous covariates. Peng, Tang and Siegmund [2005] suggested a model for gene × covariate effects for arbitrary covariates (which appears to reduce to the model of Towne et al for binary covariates) and derived the appropriate robust score tests. In this section we discuss the score test for general pedigrees.…”

Section: Extensions To the Basic Variance Component Modelmentioning

confidence: 99%

Mapping quantitative traits in unselected families: algorithms and examples

Dupuis

Shi

Manning

et al. 2009

Genetic Epidemiology

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Linkage analysis has been widely used to identify from family data genetic variants influencing quantitative traits. Common approaches have both strengths and limitations. Likelihood ratio tests typically computed in variance component analysis can accommodate large families but are highly sensitive to departure from normality assumptions. Regression-based approaches are more robust but their use has primarily been restricted to nuclear families. In this paper, we develop methods for mapping quantitative traits in moderately large pedigrees. Our methods are based on the score statistic which in contrast to the likelihood ratio statistic, can use nonparametric estimators of variability to achieve robustness of the false positive rate against departures from the hypothesized phenotypic model. Because the score statistic is easier to calculate than the likelihood ratio statistic, our basic mapping methods utilize relatively simple computer code that performs statistical analysis on output from any program that computes estimates of identity-by-descent. This simplicity also permits development and evaluation of methods to deal with multivariate and ordinal phenotypes, and with gene-gene and gene-environment interaction. We demonstrate our methods on simulated data and on fasting insulin, a quantitative trait measured in the Framingham Heart Study.

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“…Overall, Peng et al [14] found that if the influence of the genecovariate interaction is negligible, there can be a loss in power by including the covariates, roughly equivalent to a 25% loss in sample size. This is caused by the additional degrees of freedom for the covariates.…”

Section: Coding Covariatesmentioning

confidence: 99%

“…Further work by Peng et al [14] showed that correctly modeling gene-covariate interaction in ASP studies can lead to substantially increased power to detect linkage, in contrast to analyses that ignore the covariates. In contrast to our approach that considers how covariates affect the mean IBD sharing, Peng et al derived score tests for linkage in ASP data by starting with a penetrance model for the joint effects of a genotype and a covariate on the phenotype.…”

Section: Coding Covariatesmentioning

confidence: 99%

“…Gauderman and Siegmund showed that this type of linear regression can increase the power to detect linkage when gene-environment interaction is at least moderate, and give further discussion of the advantages of linear regression in the presence of gene-environment interaction [13] . Peng et al [14] recently derived score statistics to test for linkage in the presence of gene-covariate interaction, and one of their score statistics for independent ASP s can be viewed as a score statistic resulting from a linear regression model. Because linear regression can be easier to fit, it offers a viable alternative to the non-linear approach.…”

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confidence: 99%

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Testing Genetic Linkage with Relative Pairs and Covariates by Quasi-Likelihood Score Statistics

Schaid¹,

Sinnwell²,

Thibodeau

2007

Hum Hered

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Background/Aims: Genetic linkage analysis of common diseases is complicated by the heterogeneity of genetic and environmental factors that increase disease risk, and possibly interactions among them. Most linkage methods that account for covariates are restricted to sib pairs, with the exception of the conditional logistic regression model [1] implemented in LODPAL in the S.A.G.E. software [2]. Although this model can be applied to arbitrary pedigrees, at times it can be difficult to maximize the likelihood due to model constraints, and it does not account for the dependence among the different types of relative pairs in a pedigree. Methods: To overcome these limitations, we developed a new approach based on score statistics for quasi- likelihoods, implemented as weighted least squares. Our methods can be used to test three different hypotheses: (1) a test for linkage without covariates; (2) a test for linkage with covariates, and (3) a test for effects of covariates on identity by descent sharing (i.e., heterogeneity). Furthermore, our methods are robust because they account for the dependence among different relative pairs within a pedigree. Results and Conclusion: Although application of our methods to a prostate cancer linkage study did not find any critical covariates in our data, the results illustrate the utility and interpretation of our methods, and suggest, nonetheless, that our methods will be useful for a broad range of genetic linkage heterogeneity analyses.

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Genome scans with gene‐covariate interaction

Cited by 7 publications

References 24 publications

Score Test for Linkage in Generalized Linear Models

Score Test for Linkage in Generalized Linear Models

Mapping quantitative traits in unselected families: algorithms and examples

Testing Genetic Linkage with Relative Pairs and Covariates by Quasi-Likelihood Score Statistics

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