With the exception of ApoE gene, no universally accepted genetic association has been identified with the complex Late-onset Alzheimer Disease (LOAD). A broad region of chromosome 10 has engendered continued interest generated from both preliminary genetic linkage and candidate gene studies.
To better examine this region, we applied the genomic convergence approach by combining unbiased genetic linkage with candidate gene association studies. We genotyped 36 SNPs across 80.2 Mb in 567 multiplex families to narrow the peak region of linkage using both covariate and subset analyses. Simultaneously, we examined seven functional candidate genes that also fell within the broad area of linkage. Although a two point LOD score of 2.69 was obtained in the linkage analysis, the associated candidate genes were not under the linkage peak, suggesting a more extensive heterogeneity on chromosome10 than previously expected.
We then converged linkage analysis and gene expression data to identify genes that were under linkage peaks and also differentially expressed in AD cases and controls based on the rationale that genes showing positive results in multiple studies are more likelihood to be involved in AD. We identified and examined 28 genes on chromosome 10 for the association with AD. Both single marker and haplotypic associations were tested in overall and eight subsets that were stratified by age, gender, ApoE status and clinical diagnosis. Gene-gene interaction was tested to detect important genes in this complex disease. PTPLA gene showed allelic, genotypic and haplotypic association in the overall dataset. The SORCS1 gene showed very significant association in the female dataset (allelic association p=0.00002, a 3-locus haplotype has p=0.00098). Two SNPs in CACNB2 gene showed gene-gene interaction in overall dataset using Multifactor Dimensionality Reduction (MDR).
The work presented in this dissertation applied a multifactorial, multistep approach, genomic convergence, which combined linkage analysis, gene expression data, and candidate gene association analysis to identify and prioritize candidate susceptibility genes for AD. This study suggests that genetic variations in PTPLA, SORCS1 and CACNB2 genes might alter the risk for Alzheimer disease by affecting multiple pathways.