The promise of composite likelihood methods for addressing computationally intensive challenges.
High-dimensional genetic data, due to its complex correlation structure, poses an enormous challenge to standard likelihood-based methods for making statistical inference. As an approximation, composite likelihood has proved to be a successful strategy for some genetic applications. It has the potential to see even wider application and much research is needed. We first give a brief description of composite likelihood. The advantage of this method and potential challenges in inference are noted. Next, its applications in genetic studies are reviewed, specifically in estimating population genetics parameters such as recombination rate, and in multi-locus linkage disequilibrium mapping of disease genes with some discussion about future research directions.
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Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN 55455, USA.
D000818:Animals; D019295:Computational Biology / Q000379:methods* / Q000706:statistics & numerical data*; D005826:Genetics, Medical / Q000379:methods / Q000706:statistics & numerical data; D006801:Humans; D016013:Likelihood Functions