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Single-cell RNA sequencing data reveals rewiring of transcriptional relationships in Alzheimer's Disease associated with risk variants.

ABSTRACT: Understanding how genetic risk variants contribute to Alzheimer's Disease etiology remains a challenge. Single-cell RNA sequencing (scRNAseq) allows for the investigation of cell type specific effects of genomic risk loci on gene expression. Using seven scRNAseq datasets totalling >1.3 million cells, we investigated differential correlation of genes between healthy individuals and individuals diagnosed with Alzheimer's Disease. Using the number of differential correlations of a gene to estimate its involvement and potential impact, we present a prioritization scheme for identifying probable causal genes near genomic risk loci. Besides prioritizing genes, our approach pin-points specific cell types and provides insight into the rewiring of gene-gene relationships associated with Alzheimer's.

SUBMITTER: Bouland GA

PROVIDER: S-EPMC10246028 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Single-cell RNA sequencing data reveals rewiring of transcriptional relationships in Alzheimer's Disease associated with risk variants.

Bouland Gerard A GA Marinus Kevin I KI van Kesteren Ronald E RE Smit August B AB Mahfouz Ahmed A Reinders Marcel J T MJT

medRxiv : the preprint server for health sciences 20230516

Understanding how genetic risk variants contribute to Alzheimer's Disease etiology remains a challenge. Single-cell RNA sequencing (scRNAseq) allows for the investigation of cell type specific effects of genomic risk loci on gene expression. Using seven scRNAseq datasets totalling >1.3 million cells, we investigated differential correlation of genes between healthy individuals and individuals diagnosed with Alzheimer's Disease. Using the number of differential correlations of a gene to estimate ...[more]

PMID: 37292975

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