Identifying Alzheimer's disease-related pathways based on whole-genome sequencing data

Abstract

Alzheimers disease (AD) is a highly inheritable neurodegenerative disorder for which pathway-specific genetic profiling provides insights into its key biological mechanisms and potential treatment targets. Traditional disease-pathway analyses for AD have certain limitations, such as environmental interference and arbitrary sample division. We present a comprehensive framework that starts with genome data, avoiding these drawbacks and offering intrinsic pathway-specific genetic profiling for AD. Whole genome sequencing data from 173 individuals were used to quantify transcriptomes in 14 brain regions, estimate individual-level pathway variant scores, and analyze AD risk for each patient. These results were combined to identify AD-related pathways and quantify their interactions. The predicted expression levels were consistent with previous findings, and the estimated AD risk showed a significant correlation with Braak/Thal scores. A total of 3,798 pathways were identified as potentially associated with AD, with about 19.7% previously reported. Key pathways, including NF-{kappa}B signaling and GSK3{beta} activation, were linked to AD pathogenesis. The interactions among pathways highlighted shared gene functions in AD. In summary, we provided an effective framework for disease-pathway analysis, revealing the interdependence or compensatory effects of pathways in AD.