bioRxivEvolutionary Biology
Sex differences in inter-individual gene expression variability across human tissues
S. Khodursky, C. S. Jiang, E. B. Zheng, R. Vaughan, D. R. Schrider, L. Zhao
Abstract
Understanding phenotypic sex differences has long been a goal of biology from both a medical and evolutionary perspective. Although much attention has been paid to mean differences in phenotype between the sexes, little is known about sex differences in phenotypic variability. To gain insight into sex differences in inter-individual variability at the molecular level, we analyzed RNA-seq data from 43 tissues from the Genotype-Tissue Expression project (GTEx). Within each tissue, we identified genes that show sex differences in gene expression variability. We found that these sex-differentially variable (SDV) genes are associated with various important biological functions, including sex hormone response, immune response, and other signaling pathways. By analyzing single-cell RNA sequencing data collected from breast epithelial cells, we found that genes with sex differences in gene expression variability in breast tissue tend to be expressed in a cell-type-specific manner. We looked for an association between SDV expression and Graves disease, a well-known heavily female-biased disease, and found a significant enrichment of Graves associated genes among genes with higher variability in females in thyroid tissue. This suggests a possible role for SDV expression in the context of sex-biased disease. We then examined the evolutionary constraints acting on genes with sex differences in variability and found that they exhibit evidence of increased selective constraint. Through analysis of sex-biased eQTL data, we found evidence that SDV expression may have a genetic basis. Finally, we propose a simple evolutionary model for the emergence of sex-differentially variable expression from sex-specific constraints.
- organism: human
- assay: bulk RNA-seq
- assay: scRNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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