STATISTICAL ANALYSES TO DETECT AND REFINE GENETIC ASSOCIATIONS WITH NEURODEGENERATIVE DISEASES
Author ORCID Identifier
Year of Publication
Doctor of Philosophy (PhD)
Epidemiology and Biostatistics
Dr. David W. Fardo
Dementia is a clinical state caused by neurodegeneration and characterized by a loss of function in cognitive domains and behavior. Alzheimer’s disease (AD) is the most common form of dementia. Although the amyloid β (Aβ) protein and hyperphosphorylated tau aggregates in the brain are considered to be the key pathological hallmarks of AD, the exact cause of AD is yet to be identified. In addition, clinical diagnoses of AD can be error prone. Many previous studies have compared the clinical diagnosis of AD against the gold standard of autopsy confirmation and shown substantial AD misdiagnosis Hippocampal sclerosis of aging (HS-Aging) is one type of dementia that is often clinically misdiagnosed as AD. AD and HS-Aging are controlled by different genetic architectures. Familial AD, which often occurs early in life, is linked to mainly mutations in three genes: APP, PSEN1, and PSEN2. Late-onset AD (LOAD) is strongly associated with the ε4 allele of apolipoprotein E (APOE) gene. In addition to the APOE gene, genome-wide association studies (GWAS) have identified several single nucleotide polymorphisms (SNPs) in or close to some genes associated with LOAD. On the other hand, GRN, TMEM106B, ABCC9, and KCNMB2 have been reported to harbor risk alleles associated with HS-Aging pathology. Although GWAS have succeeded in revealing numerous susceptibility variants for dementias, it is an ongoing challenge to identify functional loci and to understand how they contribute to dementia pathogenesis.
Until recently, rare variants were not investigated comprehensively. GWAS rely on genotype imputation which is not reliable for rare variants. Therefore, imputed rare variants are typically removed from GWAS analysis. Recent advances in sequencing technologies enable accurate genotyping of rare variants, thus potentially improving our understanding the role of rare variants on disease. There are significant computational and statistical challenges for these sequencing studies. Traditional single variant-based association tests are underpowered to detect rare variant associations. Instead, more powerful and computationally efficient approaches for aggregating the effects of rare variants have become a standard approach for association testing. The sequence-kernel association test (SKAT) is one of the most powerful rare variant analysis methods. A recently-proposed scan-statistic-based test is another approach to detect the location of rare variant clusters influencing disease.
In the first study, we examined the gene-based associations of the four putative risk genes, GRN, TMEM106B, ABCC9, and KCNMB2 with HS-aging pathology. We analyzed haplotype associations of a targeted ABCC9 region with HS-Aging pathology and with ABCC9 gene expression. In the second study, we elucidated the role of the non-coding SNPs identified in the International Genomics of Alzheimer’s Project (IGAP) consortium GWAS within a systems genetics framework to understand the flow of biological information underlying AD. In the last study, we identified genetic regions which contain rare variants associated with AD using a scan-statistic-based approach.
Digital Object Identifier (DOI)
Katsumata, Yuriko, "STATISTICAL ANALYSES TO DETECT AND REFINE GENETIC ASSOCIATIONS WITH NEURODEGENERATIVE DISEASES" (2017). Theses and Dissertations--Epidemiology and Biostatistics. 17.
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