Integrating brain imaging endophenotypes with GWAS for Alzheimer's disease

Area of Science:

• Neurogenetics
• Neuroimaging
• Computational Biology

Background:

• Genome-wide association studies (GWAS) have identified numerous genetic variants associated with increased Alzheimer's disease (AD) risk.
• These genetic susceptibility loci may influence AD indirectly through physiological brain changes detectable by magnetic resonance imaging (MRI).

Purpose of the Study:

• To investigate the genetic etiology of Alzheimer's disease (AD) by examining brain imaging-derived phenotypes (IDPs).
• To compare the efficacy of various statistical methods, including Mendelian randomization and transcriptome-wide association studies, in identifying genetic associations with AD-related IDPs.

Main Methods:

• Utilized two-sample Mendelian randomization (2SMR), generalized summary statistics based Mendelian randomization (GSMR), transcriptome-wide association studies (TWAS), and the adaptive sum of powered score (aSPU) test.
• Employed publicly available GWAS summary statistics from the International Genomics of Alzheimer's Project (IGAP) and UK Biobank (UKBB) brain imaging initiatives, requiring only external reference panels.

Main Results:

• Identified 35 imaging-derived phenotypes (IDPs) potentially associated with Alzheimer's disease (AD).
• Many identified IDPs exhibit established or biologically plausible connections to the cognitive impairments characteristic of AD.
• Demonstrated that correlated SNP-based methods (aSPU, GSMR, TWAS) offer increased power for detecting genetic associations compared to MR methods using independent SNPs.

Conclusions:

• Correlated SNP-based methods show enhanced power in genetic association studies for Alzheimer's disease (AD) compared to traditional Mendelian randomization approaches.
• The study successfully linked genetic factors to brain imaging phenotypes relevant to AD pathogenesis.
• Findings underscore the utility of integrating neuroimaging data with advanced genetic analysis techniques for understanding complex neurological disorders like AD.