Abstract
Our aim is to develop methods for mapping genes related to age at onset in general pedigrees. We propose two score tests, one derived from a gamma frailty model with pairwise likelihood and one derived from a log-normal frailty model with approximated likelihood around the null random effect. The score statistics are weighted nonparametric linkage statistics, with weights depending on the age at onset. These tests are correct under the null hypothesis irrespective of the weight used. They are simple, robust, computationally fast, and can be applied to large, complex pedigrees. We apply these methods to simulated data and to the Genetic Analysis Workshop 16 Framingham Heart Study data set. We investigate the time to the first of three events: hard coronary heart disease, diabetes, or death from any cause. We use a two-step procedure. In the first step, we estimate the population parameters under the null hypothesis of no linkage. In the second step, we apply the score tests, using the population parameters estimated in the first step.
Highlights
It is well known that heterogeneity results in loss of statistical power when studying genetic factors of complex genetic diseases
Gamma frailty models are attractive because the gamma-distributed random effect can be integrated out and it allows the use of observable marginal survival functions [1,2,3,4]
Application to the Framingham Heart Study (FHS) dataset We performed a genome-wide linkage analysis using the unweighted nonparametric linkage (NPL) test with variance of the allele shared identically by descent (IBD) estimated by simulations [8]
Summary
It is well known that heterogeneity results in loss of statistical power when studying genetic factors of complex genetic diseases. To deal with heterogeneity additional data such as covariates (e.g., age at onset, known genetic factors) are collected. In this paper we are interested in adjusting linkage for age at onset. Frailty models have been proposed for age-at-onset linkage analysis [1,2,3,4,5]. Gamma frailty models are attractive because the gamma-distributed random effect can be integrated out and it allows the use of observable marginal survival functions [1,2,3,4]. A drawback of these models is that their corresponding likelihood becomes very complex for large pedigrees
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