Daniel J Schaid1, Jason P Sinnwell, Stephen N Thibodeau. 1. Division of Biostatistics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, MN 55905, USA. schaid@mayo.edu
Abstract
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. Copyright 2007 S. Karger AG, Basel.
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. Copyright 2007 S. Karger AG, Basel.
Authors: Chris Wallace; Ming-Zhan Xue; Stephen J Newhouse; Ana Carolina B Marcano; Abiodun K Onipinla; Beverley Burke; Johannie Gungadoo; Richard J Dobson; Morris Brown; John M Connell; Anna Dominiczak; G Mark Lathrop; John Webster; Martin Farrall; Charles Mein; Nilesh J Samani; Mark J Caulfield; David G Clayton; Patricia B Munroe Journal: Am J Hum Genet Date: 2006-06-19 Impact factor: 11.025