Inferring genes that escape X-Chromosome inactivation reveals important contribution of variable escape genes to sex-biased diseases

Renan Sauteraud, Jill M. Stahl, Jesica James, Marisa Englebright, Fang Chen, Xiaowei Zhan, Laura Carrel, Dajiang Liu

Research output: Contribution to journalArticlepeer-review

Abstract

The X Chromosome plays an important role in human development and disease. However, functional genomic and disease association studies of X genes greatly lag behind autosomal gene studies, in part owing to the unique biology of X-Chromosome inactivation (XCI). Because of XCI, most genes are only expressed from one allele. Yet, ∼30% of X genes “escape” XCI and are transcribed from both alleles, many only in a proportion of the population. Such interindividual differences are likely to be disease relevant, particularly for sex-biased disorders. To understand the functional biology for X-linked genes, we developed X-Chromosome inactivation for RNA-seq (XCIR), a novel approach to identify escape genes using bulk RNA-seq data. Our method, available as an R package, is more powerful than alternative approaches and is computationally efficient to handle large population-scale data sets. Using annotated XCI states, we examined the contribution of X-linked genes to the disease heritability in the United Kingdom Biobank data set. We show that escape and variable escape genes explain the largest proportion of X heritability, which is in large part attributable to X genes with Y homology. Finally, we investigated the role of each XCI state in sex-biased diseases and found that although XY homologous gene pairs have a larger overall effect size, enrichment for variable escape genes is significantly increased in female-biased diseases. Our results, for the first time, quantitate the importance of variable escape genes for the etiology of sex-biased disease, and our pipeline allows analysis of larger data sets for a broad range of phenotypes.

Original languageEnglish (US)
Pages (from-to)1629-1637
Number of pages9
JournalGenome research
Volume31
Issue number9
DOIs
StatePublished - Sep 2021

All Science Journal Classification (ASJC) codes

  • Genetics
  • Genetics(clinical)

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