IMPROVING THE POWER OF LINKAGE DISEQULIBRIUM MAPPING

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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. Association studies offer an exciting approach to finding underlying genetic variants of complex human diseases. However, identification of genetic variants still includes difficult challenges, and it is important to develop powerful new statistical methods. Currently, association methods may depend on single-locus analysis--that is, analysis of the association of one locus, which is typically a single-nucleotide polymorphism (SNP), at a time--or on multilocus analysis, in which multiple SNPs are used to allow extraction of maximum information about linkage disequilibrium (LD). It has been shown that single-locus analysis may have low power because a single SNP often has limited LD information. Multilocus analysis, which is more informative, can be performed on the basis of either haplotypes or genotypes. It may lose power because of the often large number of degrees of freedom involved. The ideal method must make full use of important information from multiple loci but avoid increasing the degrees of freedom. Therefore, we have developed two methods to capture information from multiple SNPs. We developed a test based on weighted Fourier transformation coefficients, with more weight given to the low-frequency components. We developed an association mapping method for complex diseaes by mining the sharing of haplotype segments (i.e. phased genotype pairs) in affected individuals that are rarely present in normal individuals, now extended to address the problem of quantitative trait mapping from unrelated individuals. The effectiveness of the approaches was demonstrated by extensive experimental studies using both simulated and real data sets. Our simulation results demonstrate the validity and substantially higher power of the proposed method compared with other common methods. These methods provide additional tools for ithe identifiying of causative genetic variants underlying complex diseases.