Abstract
Olson's conditional-logistic model retains the nice property of the LOD score formulation and has advantages over other methods that make it an appropriate choice for complex trait linkage mapping. However, the asymptotic distribution of the conditional-logistic likelihood-ratio (CL-LR) statistic with genetic constraints on the model parameters is unknown for some analysis models, even in the case of samples comprising only independent sib pairs. We derive approximations to the asymptotic null distributions of the CL-LR statistics and compare them with the empirical null distributions by simulation using independent affected sib pairs. Generally, the empirical null distributions of the CL-LR statistics match well the known or approximated asymptotic distributions for all analysis models considered except for the covariate model with a minimum-adjusted binary covariate. This work will provide useful guidelines for linkage analysis of real data sets for the genetic analysis of complex traits, thereby contributing to the identification of genes for disease traits.
Highlights
In the study of human data by genetic linkage analysis, the traditional LOD score method, called a “parametric” or “modelbased” method because it requires information about an assumed genetic model, is efficient for single-gene Mendelian traits but is much less well suited for the analysis of traits with complex nonMendelian modes of inheritance
One such approach that has been extremely useful in the analysis of human genetic diseases is the affected sib pair (ASP) study design, as in tests based on the mean proportion of identity-bydescent (IBD) sharing (Blackwelder and Elston, 1985) or tests based on the likelihood-ratio (LR) defined by Risch (1990a,b) that uses the same one-parameter model to analyze ASPs or any other affected unilineal relative pairs by producing a LOD score
Olson (1999) proposed a general conditional-logistic (CL) model that combines several extensions and modifications (Cordell et al, 1995; Rogus and Krolewski, 1996; Greenwood and Bull, 1997, 1999; Olson, 1997; Lunetta and Rogus, 1998) into a unified framework: the likelihood is conditioned on sampling affected relative pairs (ARPs) and the parameterization is done in terms of the logarithm of allele sharing specific relative risks, instead of allele sharing probabilities as in the Risch and Holmans (RH) model
Summary
In the study of human data by genetic linkage analysis, the traditional LOD score method, called a “parametric” or “modelbased” method because it requires information about an assumed genetic model, is efficient for single-gene Mendelian traits but is much less well suited for the analysis of traits with complex nonMendelian modes of inheritance. In the absence of a well-defined disease inheritance model, alternative robust “non-parametric,” “weakly-parametric” or “model-free” linkage methods, which do not require the specification of a disease model, have been used for deciphering the genetic basis of complex traits One such approach that has been extremely useful in the analysis of human genetic diseases is the affected sib pair (ASP) study design, as in tests based on the mean proportion of identity-bydescent (IBD) sharing (Blackwelder and Elston, 1985) or tests based on the likelihood-ratio (LR) defined by Risch (1990a,b) that uses the same one-parameter model to analyze ASPs or any other affected unilineal relative pairs by producing a LOD score. Linkage analysis using the CL model has been proven to be an effective tool for evaluating genetic linkage (Goddard et al, 2001; ArcosBurgos et al, 2004; Reck et al, 2005; Doan et al, 2006; Rybicki et al, 2007; Stein et al, 2007; Zandi et al, 2007; Song et al, 2011)
Sign-in/Register to view highlights & summary 
Published Version (
Free)
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call