IMPROVEMENT OF MAPPING ACCURACY BY UNIFYING LINKAGE AND ASSOCIATION ANALYSIS

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. It is well known that pedigree/family data record information on the coexistence in founder haplotypes of alleles at nearby loci and the cotransmission from parent to offspring that reveal different, but complementary, profiles of the genetic architecture. Either conventional linkage analysis that assumes linkage equilibrium or family-based association tests capture only partial information, leading to inefficiency. Using a data augmentation technique and the EM algorithm, we developed a likelihood-based approach that embeds both linkage and association analyses into a unified framework for general pedigree data. Relative to either linkage or association analysis, the proposed approach has greater estimation accuracy and power. The new approach holds the theoretical advantage that it can extract statistical information to the maximum extent and thereby improve mapping accuracy and power because it integrates multilocus population-based association and pedigree-based linkage analysis into a coherent framework. Furthermore, extensive simulation studies, including application to the data of Genetic Analysis Workshop 15, demonstrated the practical advantage of this general likelihood framework for model-based linkage and association analysis.