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

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.

The human inactive X chromosome modulates expression of the active X chromosome

“…To identify the NPX genes most likely to drive copy-number-dependent phenotypes, we considered those with both 1) expression constraints ranking in the top quartile and 2) |ΔEX ≥ 0.1| (FDR<0.05) in LCLs or fibroblasts. We found 13 genes that fit these criteria ( (Cotton et al, 2013, Tukiainen et al, 2017, Garieri et al, 2018, Sauteraud et al, 2021). b Gene constraint percentile ranking is calculated for NPX genes relative to all annotated NPX genes, and for PAR1 genes relative to all PAR and autosomal genes.…”

“…The fifth dataset was derived from allele-specific bulk RNA-seq performed on 136 samples from the set of GEUVADIS LCLs (Table S4 in (Sauteraud et al, 2021)). We used their data for "Mean.Xi.Expression" as the AR values.…”

“…Early studies assayed expression from Xi directly, using human-rodent hybrid cell lines to genetically isolate Xi [for example, (Mohandas et al, 1980, Brown et al, 1997, Carrel et al, 1999)]. Subsequently, allele-specific methods in human cells were developed to distinguish transcripts deriving from Xa and Xi – either in cell lines with skewed XCI or in single cells (Carrel and Willard, 2005, Cotton et al, 2013, Tukiainen et al, 2017, Garieri et al, 2018, Wainer Katsir and Linial, 2019, Sauteraud et al, 2021). While conceptually superior to hybrid cell lines, allele-specific methods yield sparse data because they require the fortuitous presence of heterozygous SNPs to differentiate between alleles.…”

“…Early studies assayed expression in human-rodent hybrid cell lines that retained Xi but not Xa; detection of Chr X expression in these cells indicates that a gene is expressed from Xi [for example, (Brown et al, 1997; Carrel et al, 1999; Mohandas et al, 1980)]. Subsequent allele-specific methods distinguished transcripts from Xa and Xi in human cell lines with skewed XCI, or in single cells (Carrel and Willard, 2005; Cotton et al, 2013; Garieri et al, 2018; Sauteraud et al, 2021; Tukiainen et al, 2017; Wainer Katsir and Linial, 2019). While conceptually superior to hybrid cell lines, allele-specific methods yielded sparse data because they require the fortuitous presence of heterozygous SNPs to differentiate between alleles.…”

Genetic and hormonal mechanisms underlying sex-specific immune responses in tuberculosis

“…including TLR7, TLR8, CYBB, IL-13RA1/RA2, CXCR3, BTK, KDM6a, CD99, CD40L, IRAK1, DDX3X, LAMP-2, and KDM5c are known to escape XCI (Figure 2) [95,96], thereby generating a female-specific heterogeneous population with biallelic expression. The resulting cellular mosaicism is likely to impart functional plasticity within female immune cells.…”