Document Type

Technical Report


Computer Science and Engineering

Publication Date






Technical Report Number



Alzheimer’s disease (AD) is a progressive neurodegenerative disorder currently with no cure. Understanding the pathogenesis in the early stages of late-onset AD can help gain important mechanistic insights into this disease as well as aid in effective drug development. The analysis of incipient AD is steeped in difficulties due to its slight pathological and genetic differences from normal ageing. The difficulty also lies in the choice of analysis techniques as statistical power to analyse incipient AD with a small sample size, as is common in pilot studies, can be low if the proper analytical tool is not employed. In this study, we propose the use of a new method of significant genes selection, multiple linear regression, which uses the cognitive index (MiniMental Status Examination (MMSE)) and pathological characteristic (neurofibrillary tangles (NFT)), along with gene expression profiles, to select genes. The data consists of 7 incipient AD affected subjects and 9 age-matched normal controls. The analysis resulted in 686 significant genes with a false discovery rate of 0.2. Among the various biological processes previously known to be associated with AD, we discovered a set of 14 DNA repair genes that had statistically elevated or lowered levels of mRNA expression. Many key players involved in the defense against DNA damage were present in this list of 14 genes. In this article we report the status of DNA repair activity in incipient AD. From this study we conclude that the much observed apoptosis in AD may also be due to the activity of DNA repair genes. These findings have not been previously reported with respect to incipient AD and may shed new light onto its pathogenesis. This is the first study that has incorporated multiple clinical phenotypes of AD affected individuals in order to select statistically significant genes. It is also the first in analysing DNA repair genes in the context of AD via microarray gene expression analysis.


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