Biology of Sex DifferencesGenetic & Genomic MedicinePeer-reviewed journal article
Relationship between sex biases in gene expression and sex biases in autism and Alzheimer's disease
Stuart B. FassiD, Bernard MulveyiD, Rebecca ChaseiD, Wei Cheng YangiD, Din SelmanoviciD, Sneha M. ChaturvediiD, Eric TyckseniD, Lauren A. WeissiD, Joseph D. DoughertyiD
Abstract
Sex differences in the brain may play an important role in sex-differential prevalence of neuropsychiatric conditions. In order to understand the transcriptional basis of sex differences, we analyzed multiple, large-scale, human postmortem brain RNA-seq datasets using both within-region and pan-regional frameworks. We find evidence of sex-biased transcription in many autosomal genes, some of which provide evidence for pathways and cell population differences between chromosomally male and female individuals. These analyses also highlight regional differences in the extent of sex-differential gene expression. We observe an increase in specific neuronal transcripts in male brains and an increase in immune and glial function-related transcripts in female brains. Integration with single-cell data suggests this corresponds to sex differences in cellular states rather than cell abundance. Integration with case-control gene expression studies suggests a female molecular predisposition towards Alzheimers disease, a female-biased disease. Autism, a male-biased diagnosis, does not exhibit a male predisposition pattern in our analysis. Finally, we provide region specific analyses of sex differences in brain gene expression to enable additional studies at the interface of gene expression and diagnostic differences. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=137 SRC="FIGDIR/small/23294773v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1399eccorg.highwire.dtl.DTLVardef@1e8762borg.highwire.dtl.DTLVardef@a6375forg.highwire.dtl.DTLVardef@7d4caa_HPS_FORMAT_FIGEXP M_FIG C_FIG
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- approach: statistical model
- Citations
- 23
- FWCI
- 9.48
- Field percentile
- top 1%
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