The 26.2 Data Release introduces the FunGen-xQTL study from the Alzheimer’s Disease Functional Genomics Consortium and provides updates to other human studies.

Proper acknowledgment of data sources is not only mandatory for users of the AD Knowledge Portal, it also enhances the visibility of your work and elevates recognition of datasets used. All data use must be acknowledged with the following: the AD Knowledge Portal Acknowledgement Statement; and an Acknowledgement Statement specific to the study/dataset used, from the relevant Study page; and a Data Availability statement that includes a direct link to access relevant resources, known as a dataset DOI. For more information and pre-written acknowledgement statements, see Data Use & Acknowledgement.

Human

  • The DivCo_HS Study

    The DivCo_HS study presents reprocessed datasets from the Diverse Cohorts project.

    • This release provides harmonized RNASeq data including raw RNA expression counts as well as normalized matrices and QC results. All data was processed through a common pipeline for consistency.

  • The FunGen-xQTL study

    This study presents FunGen-xQTL, a flagship multi-omics functional genomics resource from the Alzheimer’s Disease Functional Genomics Consortium designed to link genetic variation to molecular cascades underlying Alzheimer’s disease. The study profiles genetic regulation across 6 major brain cell types and 67 brain cell subtypes, plus two immune-related myeloid cell types, in 2,328 individuals—1,769 of whom have multi-layer omics data—encompassing eight molecular modalities: transcriptomic, proteomic, metabolomic, and epigenomic data including chromatin accessibility.

    • This release provides single-nucleus RNA-seq data comprising over 3.1 million high-quality nuclei from 530 unique donors across 722 specimens sourced from the Religious Orders Study and Rush Memory and Aging Project (ROSMAP), whole-genome sequencing genotypes, cell-type-specific eQTL and sQTL summary statistics, fine-mapped regulatory variant annotations, and colocalization results with neurological GWAS traits. It includes results from two analytical frameworks: scEEMs, a machine learning method for prioritizing causal regulatory variants underlying brain eQTLs, and ISSAC, a statistical framework for mapping genetic regulation of alternative splicing at single-cell resolution including Alzheimer’s disease-biased and cell state-dependent effects.

  • The Zhao_USF_Study generated genome-wide N6-methyladenine (6mA) methylation data from diverse human cohorts to investigate epigenetic modifications in aging and Alzheimer’s disease. This study provides postmortem brain 6mA data from 1,000 individuals, including samples from the Religious Orders Study and Memory and Aging Project (ROSMAP), the Minority Aging Research Study (MARS), the African American Clinical Core (AA Core), and the Latino CORE Study (LATC). Data was contributed through the Alzheimer’s Disease Community Data Contribution Program.

    • This release provides processed genome-wide 6mA methylation data for 1,000 Zhao_USF_Study samples (dorsolateral prefrontal cortex). This includes an RDS file of the processed data from all 1,000 samples, genomic positions for 6mA regions, and associated individual identifiers from cohort participants.