NG00187 – Stability of general cognitive ability from infancy to adulthood: A combined twin and genomic investigation – Gustavson et al., 2025

We have been developing more powerful statistical methods to detect common variant (CV)- or rare variant (RV)-complex trait associations and/or putative causal relationships for GWAS and DNA sequencing data. Here we propose applying our new methods, along with other suitable existing methods, to the existing ADSP sequencing data and other AD GWAS data provided by NIA, hence requesting approval for accessing the ADSP sequencing and other related GWAS/genetic data. We have the following two specific Aims: Aim1. Association testing under genetic heterogeneity: For complex traits, genetic heterogeneity, especially of RVs, is ubiquitous as well acknowledged in the literature, however there is barely any existing methodology to explicitly account for genetic heterogeneity in association analysis of RVs based on a single sample/cohort. We propose using secondary and other omic data, such as transcriptomic or metabolomic data, to stratify the given sample, then apply a weighted test to the resulting strata, explicitly accounting for genetic heterogeneity that causal RVs may be different (with varying effect sizes) across unknown and hidden subpopulations. Some preliminary analyses have confirmed power gains of the proposed approach over the standard analysis. Aim 2. Meta analysis of RV tests: Although it has been well appreciated that it is necessary to account for varying association effect sizes and directions in meta analysis of RVs for multi-ethnic cohorts, existing tests are not highly adaptive to varying association patterns across the cohorts and across the RVs, leading to power loss. We propose a highly adaptive test based on a family of SPU tests, which cover many existing meta-analysis tests as special cases. Our preliminary results demonstrated possibly substantial power gains. Aim 3. Inferring (putative) causal genes/proteins/metabolites for AD. We will apply TWA/PWAS/MWAS/xWAS methods to ADSP, AD GWAS and other omic data to identify (putative) causal genes, proteins, metabolites and other molecular traits for AD. These methods may be based on standard linear models or emerging ML/AI methods.

We propose applying our newly developed statistical and computational methods, along with other suitable existing methods, to the existing ADSP sequencing data, other AD GWAS data and other omic data to detect common or rare genetic variants and other molecular traits, such as genes/proteins/metabolites, associated with and/pr (putative) causal to Alzheimer’s disease (AD). The novelty and power of our new methods are in three aspects: first, we consider and account for possible genetic heterogeneity with several subcategories of AD; second, we apply powerful meta-analysis methods to combine the association analyses across multiple subcategories of AD; third, we will develop and apply standard linear model- and emerging ML/AI-based causal inference methods to infer causal genes, proteins, metabolites and other traits for AD. In addition, our proposed analyses of the existing large amount of ADSP sequencing data and other AD GWAS data with our developed new methods are novel, powerful and cost-effective.

This project will examine how genetic variants, gene expression profiles, transcriptomic patterns, APOE status where available, and biological pathways are associated with Alzheimer’s disease and related cognitive, functional, and neuropathological phenotypes. The goal is to identify genomic and pathway-level factors that may help explain cognitive decline, daily functional changes, neuropathological burden, and vulnerability or resilience in Alzheimer’s disease and related neurodegenerative or cognitive aging conditions. This study is a secondary analysis of existing coded/de-identified controlled-access data obtained through NIAGADS DSS. No new participants will be recruited, no participant contact will occur, and no new biospecimens or data will be collected. The requested datasets include Alzheimer’s disease sequencing data, oldest-old clinical and pathological data, microglia single-nuclei RNA-seq data, multi-brain-region mRNA-seq data from sporadic ALS, and genomic data related to cognitive ability. All analyses will comply with NIAGADS data use limitations, NIH Genomic Data Sharing Policy, NIA requirements, and Messiah University institutional oversight. Analyses will evaluate associations between genetic or gene-level features and approved phenotypes, including AD diagnosis, case-control status, cognitive performance, general cognitive ability, functional status, daily activity measures, neuropathological findings, microglia-related expression profiles, brain-region-specific expression patterns, and disease stage where available. Methods may include quality control, phenotype harmonization, regression-based association testing, differential gene expression analysis, variant-to-gene annotation, gene-level aggregation, pathway enrichment, and aggregate or polygenic measures if appropriate. Models will adjust for relevant covariates such as age, sex, ancestry, relatedness, brain region, cell type, disease stage, batch effects, and technical factors. Only aggregate or summary-level results will be reported.

This project will use existing de-identified research data to study why some people develop Alzheimer’s disease or cognitive decline while others remain more resilient. The study will look at genetic information, gene activity, and biological pathways that may be related to memory, thinking ability, daily function, and brain changes seen in Alzheimer’s disease and related conditions. No new participants will be recruited, and no new samples will be collected. The project will only use approved research data from NIAGADS. The goal is to better understand how inherited genetic differences and changes in gene expression may contribute to Alzheimer’s disease, cognitive decline, and functional changes in daily life. This research may help identify biological patterns that could support future studies on earlier detection, risk prediction, and better understanding of neurodegenerative diseases. All data used in this project will be coded or de-identified. The study will not attempt to identify any individual participant, and results will only be reported in summary form.