Genetic variants associated with Alzheimer’s disease confer different cerebral cortex cell-type population structure

• Main Authors: Zeran Li, Jorge L. Del-Aguila, Umber Dube, John Budde, Rita Martinez, Kathleen Black, Qingli Xiao, Nigel J. Cairns, The Dominantly Inherited Alzheimer Network (DIAN), Joseph D. Dougherty, Jin-Moo Lee, John C. Morris, Randall J. Bateman, Celeste M. Karch, Carlos Cruchaga, Oscar Harari
• Format: Article
• Language: English
• Published: BMC 2018-06-01
• Series: Genome Medicine
• Subjects: Digital deconvolution; Alzheimer’s disease; Brain cellular composition; Bulk RNA-sequencing; Autosomal dominant AD; TREM2
• Online Access: http://link.springer.com/article/10.1186/s13073-018-0551-4

Abstract Background Alzheimer’s disease (AD) is characterized by neuronal loss and astrocytosis in the cerebral cortex. However, the specific effects that pathological mutations and coding variants associated with AD have on the cellular composition of the brain are often ignored. Methods We developed and optimized a cell-type-specific expression reference panel and employed digital deconvolution methods to determine brain cellular distribution in three independent transcriptomic studies. Results We found that neuronal and astrocyte relative proportions differ between healthy and diseased brains and also among AD cases that carry specific genetic risk variants. Brain carriers of pathogenic mutations in APP, PSEN1, or PSEN2 presented lower neuron and higher astrocyte relative proportions compared to sporadic AD. Similarly, the APOE ε4 allele also showed decreased neuronal and increased astrocyte relative proportions compared to AD non-carriers. In contrast, carriers of variants in TREM2 risk showed a lower degree of neuronal loss compared to matched AD cases in multiple independent studies. Conclusions These findings suggest that genetic risk factors associated with AD etiology have a specific imprinting in the cellular composition of AD brains. Our digital deconvolution reference panel provides an enhanced understanding of the fundamental molecular mechanisms underlying neurodegeneration, enabling the analysis of large bulk RNA-sequencing studies for cell composition and suggests that correcting for the cellular structure when performing transcriptomic analysis will lead to novel insights of AD.