Incorporation of covariates in simultaneous localization of two linked loci using affected relative pairs

Yen-Feng Chiu,Chun-Yi Lee,Jeng-Min Chiou,Kung-Yee Liang

doi:10.1186/1471-2156-11-67

Abstract

BackgroundMany dichotomous traits for complex diseases are often involved more than one locus and/or associated with quantitative biomarkers or environmental factors. Incorporating these quantitative variables into linkage analysis as well as localizing two linked disease loci simultaneously could therefore improve the efficiency in mapping genes. We extended the robust multipoint Identity-by-Descent (IBD) approach with incorporation of covariates developed previously to simultaneously estimate two linked loci using different types of affected relative pairs (ARPs).ResultsWe showed that the efficiency was enhanced by incorporating a quantitative covariate parametrically or non-parametrically while localizing two disease loci using ARPs. In addition to its help in identifying factors associated with the disease and in improving the efficiency in estimating disease loci, this extension also allows investigators to account for heterogeneity in risk-ratios for different ARPs. Data released from the collaborative study on the genetics of alcoholism (COGA) for Genetic Analysis Workshop 14 (GAW 14) were used to illustrate the application of this extended method.ConclusionsThe simulation studies and example illustrated that the efficiency in estimating disease loci was demonstratively enhanced by incorporating a quantitative covariate and by using all relative pairs while mapping two linked loci simultaneously.

Highlights

Many dichotomous traits for complex diseases are often involved more than one locus and/or associated with quantitative biomarkers or environmental factors
To incorporate relevant covariate information while simultaneously estimate the locations of two genes using all types of relative pairs in linkage analysis, we proposed the following linkage approaches
We studied the enhancement of efficiency incurred by the incorporation of a quantitative covariate and by the usage of relative pairs in place of using sib pairs alone within a one-locus model

Summary

Introduction

Many dichotomous traits for complex diseases are often involved more than one locus and/or associated with quantitative biomarkers or environmental factors Incorporating these quantitative variables into linkage analysis as well as localizing two linked disease loci simultaneously could improve the efficiency in mapping genes. Olson (1999) [10] proposed a unified framework for model-free linkage analysis that can handle the separate inclusion of other ARPs, discordant relative pairs, covariates, or additional disease loci through a conditional-logistic parameterization. These regression-based approaches can be generalized to include all covariates; they assume either one disease locus or multiple unlinked loci and are not applicable to analyses of multiple linked loci. The problem of multiple testing may arise when researchers perform multiple tests or analyses using various combinations of multiple loci or covariates using these approaches

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Conclusion