RT Journal
A1 Sauteraud, Renan
A1 Stahl, Jill M.
A1 James, Jesica
A1 Englebright, Marisa
A1 Chen, Fang
A1 Zhan, Xiaowei
A1 Carrel, Laura
A1 Liu, Dajiang J.
T1 Inferring genes that escape X-Chromosome inactivation reveals important contribution of variable escape genes to sex-biased diseases
JF Genome Research 
JO Genome Research 
YR 2021 
FD September 01 
VO 31 
IS 9 
SP 1629 
OP 1637 
DO 10.1101/gr.275677.121 
UL http://genome.cshlp.org/content/31/9/1629.abstract 
AB 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.