Allele-specific analysis reveals exon- and cell-type-specific regulatory effects of Alzheimer's disease-associated genetic variants.
Liang HeYury LoikaAlexander M KulminskiPublished in: Translational psychiatry (2022)
Elucidating regulatory effects of Alzheimer's disease (AD)-associated genetic variants is critical for unraveling their causal pathways and understanding the pathology. However, their cell-type-specific regulatory mechanisms in the brain remain largely unclear. Here, we conducted an analysis of allele-specific expression quantitative trait loci (aseQTLs) for 33 AD-associated variants in four brain regions and seven cell types using ~3000 bulk RNA-seq samples and >0.25 million single nuclei. We first develop a flexible hierarchical Poisson mixed model (HPMM) and demonstrate its superior statistical power to a beta-binomial model achieved by unifying samples in both allelic and genotype-level expression data. Using the HPMM, we identified 24 (~73%) aseQTLs in at least one brain region, including three new eQTLs associated with CA12, CHRNE, and CASS4. Notably, the APOE ε4 variant reduces APOE expression across all regions, even in AD-unaffected controls. Our results reveal region-dependent and exon-specific effects of multiple aseQTLs, such as rs2093760 with CR1, rs7982 with CLU, and rs3865444 with CD33. In an attempt to pinpoint the cell types responsible for the observed tissue-level aseQTLs using the snRNA-seq data, we detected many aseQTLs in microglia or monocytes associated with immune-related genes, including HLA-DQB1, HLA-DQA2, CD33, FCER1G, MS4A6A, SPI1, and BIN1, highlighting the regulatory role of AD-associated variants in the immune response. These findings provide further insights into potential causal pathways and cell types mediating the effects of the AD-associated variants.
Keyphrases
- single cell
- rna seq
- poor prognosis
- cognitive decline
- immune response
- transcription factor
- genome wide
- copy number
- resting state
- white matter
- electronic health record
- stem cells
- functional connectivity
- binding protein
- machine learning
- gene expression
- type diabetes
- spinal cord
- inflammatory response
- multiple sclerosis
- cerebral ischemia
- long non coding rna
- dna methylation
- brain injury
- mild cognitive impairment
- human health
- deep learning