• A Corrigendum to this article was published on 07 March 2018

This article has been updated


X chromosome inactivation (XCI) silences transcription from one of the two X chromosomes in female mammalian cells to balance expression dosage between XX females and XY males. XCI is, however, incomplete in humans: up to one-third of X-chromosomal genes are expressed from both the active and inactive X chromosomes (Xa and Xi, respectively) in female cells, with the degree of ‘escape’ from inactivation varying between genes and individuals1,2. The extent to which XCI is shared between cells and tissues remains poorly characterized3,4, as does the degree to which incomplete XCI manifests as detectable sex differences in gene expression5 and phenotypic traits6. Here we describe a systematic survey of XCI, integrating over 5,500 transcriptomes from 449 individuals spanning 29 tissues from GTEx (v6p release) and 940 single-cell transcriptomes, combined with genomic sequence data. We show that XCI at 683 X-chromosomal genes is generally uniform across human tissues, but identify examples of heterogeneity between tissues, individuals and cells. We show that incomplete XCI affects at least 23% of X-chromosomal genes, identify seven genes that escape XCI with support from multiple lines of evidence and demonstrate that escape from XCI results in sex biases in gene expression, establishing incomplete XCI as a mechanism that is likely to introduce phenotypic diversity6,7. Overall, this updated catalogue of XCI across human tissues helps to increase our understanding of the extent and impact of the incompleteness in the maintenance of XCI.


Mammalian female tissues consist of two mixed cell populations, each with either the maternally or paternally inherited X chromosome marked for inactivation. To overcome this heterogeneity, assessments of human XCI have often been confined to the use of artificial cell systems1 or to samples that have skewed XCI1,2, that is, preferential inactivation of one of the two X chromosomes; this is common in clonal cell lines but rare in karyotypically normal, primary human tissues8 (Extended Data Fig. 1 and Supplementary Note). Others have used bias in DNA methylation3,4,9 or in gene expression5,10 between males and females as a proxy for XCI status. Surveys of XCI are powerful in engineered model organisms, for example, mouse models with completely skewed XCI11, but the degree to which these discoveries are generalizable to human XCI remains unclear given marked differences in XCI initiation and the extent of escape across species7. Here we describe a systematic survey of the landscape of human XCI using three complementary RNA sequencing (RNA-seq)-based approaches (Fig. 1) that together enable the assessment of XCI from individual cells to population level across a diverse range of human tissues.

Figure 1: Schematic overview of the study.
Figure 1

Previous expression-based surveys of XCI1,2 have established the incomplete and variable nature of XCI, but these studies have been limited in the tissue types and samples assessed. To investigate the landscape of XCI across human tissues, we combined three approaches: (1) sex biases in expression using population-level GTEx data across 29 tissue types; (2) allelic expression in 16 tissue samples from a female GTEx donor with fully skewed XCI, and (3) validation using scRNA-seq by combining allelic expression and genotype phasing. WGS, whole-genome sequencing; WES, whole-exome sequencing.

Given the limited accessibility of most human tissues, particularly in large sample sizes, no global investigation into the impact of incomplete XCI on X-chromosomal expression has been conducted in datasets spanning multiple tissue types. We used the Genotype-Tissue Expression (GTEx) project12,13 dataset (v6p release), which includes high-coverage RNA-seq data from diverse human tissues, to investigate male–female differences in the expression of 681 X-chromosomal genes that encode proteins or long non-coding RNA in 29 adult tissues (Extended Data Table 1), hypothesizing that escape from XCI should typically result in higher female expression of these genes. Previous work5,10,14 has indicated that some of the genes that escape XCI (hereafter referred to as escape genes) show female bias in expression, but our analysis benefits from a larger set of profiled tissues and individuals, as well as the high sensitivity of RNA-seq.

To confirm that male–female expression differences reflect incomplete XCI, we assessed the enrichment of sex-biased expression in known XCI categories using 561 genes with previously assigned XCI status, defined as escape (n = 82), variable escape (n = 89) or inactive (n = 390) (Fig. 1 and Supplementary Table 1). Sex-biased expression is enriched in escape genes compared to both inactive genes (two-sided paired Wilcoxon rank-sum test, P = 3.73 × 10−9) and variable escape genes (P = 3.73 × 10−9) (Fig. 2b and Extended Data Fig. 2), with 74% of escape genes showing significant (false-discovery rate (FDR) q < 0.01) male–female differences in at least one tissue (Fig. 2a, Extended Data Figs 3, 4 and Supplementary Table 2). In line with two active X-chromosomal copies in females, escape genes in the non-pseudoautosomal, that is, the X-specific, region (nonPAR) predominantly show female-biased expression across tissues (52 out of 67 assessed genes, binomial P = 6.46 × 10−6). However, genes in the pseudoautosomal region PAR1, are expressed more highly in males (14 out of 15 genes, binomial P = 9.77 × 10−6) (Fig. 2a), suggesting that combined Xa and Xi expression in females fails to reach the expression arising from X and Y chromosomes in males (discussed below).

Figure 2: Assessment of tissue-sharing and population-level impacts of incomplete XCI in GTEx data.
Figure 2

a, Male–female expression differences in reported XCI-escaping genes (n = 82) across 29 GTEx tissues. Definitions for the abbreviations can be found in Extended Data Table 1. b, Proportion of significantly biased (FDR <1%) genes in each tissue by reported XCI status. c, Proportion of tissues where the bias direction is shared with the reported XCI status. Genes expressed in at least five tissues are included. d, Sex bias pattern of nine genes not classified as full escape genes that follow a similar profile to established escape genes. e, Chromatin state enrichment between escape and inactive genes in the Roadmap Epigenomics31 female samples.

Sex bias of escape genes is often shared across tissues; these genes show a higher number of tissues with sex-biased expression than genes in other XCI categories (Fig. 2a and Extended Data Fig. 2c), a result that is not driven by differences in the breadth of expression of escape and inactive genes (Extended Data Fig. 2e). Also, the direction of sex bias across tissues is consistent (Fig. 2a, c and Extended Data Fig. 2b). Together, these observations indicate that there is global and tight control of XCI, that potentially arises from early lockdown of the epigenetic marks regulating XCI. Previous reports have identified several epigenetic signatures associated with XCI escape in humans and mice15; in agreement with these discoveries we show that escape genes are enriched in chromatin states that are related to active transcription (Fig. 2e).

Although sex bias on the X chromosome is broadly specific to escape genes, some genes show unexpected patterns. Eight genes with some previous evidence for inactivation show >90% concordance in effect direction and significant sex bias (Fig. 2d and Supplementary Table 3), suggesting that variable escape can also have considerable population-level effects. For example, CHM demonstrates such concordance in sex bias and escape at this gene is confirmed when using single-cell RNA-seq (scRNA-seq; see below). One gene (RP11-706O15.3) without an assigned XCI status shows a similar sex bias pattern to escape genes. RP11-706O15.3 resides between escape and variable escape genes PRKX and NLGN4X (Fig. 2d), consistent with known clustering of escape genes1,2. Some escape genes show more heterogeneous sex bias, for example, ACE2 (Fig. 2a and Supplementary Discussion). Many such genes lie in the evolutionarily older region of the chromosome16, in Xq, where escape genes also show higher tissue-specificity and lower expression levels (Extended Data Fig. 5), characteristics that have been linked with higher protein evolutionary rates17,18.

Although sex bias serves as a proxy for XCI status, it provides only an indirect measurement of XCI. We identified a GTEx female donor with an unusual degree of skewing of XCI (Fig. 3a), in whom the same copy of chromosome X was silenced in approximately 100% of cells across all tissues, but with no X-chromosomal abnormality detected by whole-genome sequencing (Extended Data Fig. 6 and Supplementary Note), providing an opportunity to analyse allele-specific expression (ASE) across 16 tissues to investigate XCI. This approach is analogous to previous surveys in mouse11 or in human cell lines with skewed XCI2, but extends the assessment to a larger number of tissues and avoids biases arising from genetic heterogeneity between tissue samples.

Figure 3: Assessment of tissue-sharing of XCI in a GTEx donor with a highly skewed XCI.
Figure 3

a, Distribution of the skewness of XCI in GTEx female samples (n = 62, v3 release). Each data point shows the mean skew in XCI across tissue samples per individual. b, Classification of X-chromosomal genes (n = 186) into full or incomplete and tissue-shared or heterogeneous XCI based on the analysis of ASE patterns across tissues. Error bars show the 95% credible interval. ce, Examples of genes where the ASE-based assessment of XCI status match previously reported assignments (TSR2, inactive; XIST, escape; ZBED1, escape). Note that XIST is only expressed monoallelically from Xi, which is unusual for an escape gene. f, KAL1 shows strong evidence for tissue-specific escape. gk, Genes without previous or conclusive evidence for escape from XCI that are classified as incompletely inactivated in this sample. In ck asterisks indicate that the Xi expression in the given tissue was significant at FDR <1% (one-sided binomial test) and error bars show the 95% confidence interval.

Analysis of the X-chromosomal allelic counts (Supplementary Tables 4–6) from this GTEx donor highlights the incompleteness and consistency of XCI across tissues (Fig. 3b). Approximately 23% of the 186 X-chromosomal genes that were assessed show expression from both alleles, indicative of incomplete XCI, matching previous estimates of the extent of escape1,2. For 43% of the genes that were expressed from both alleles in this sample, Xi expression is of a similar magnitude between tissues, therefore supporting the observation of a general global and tight control of XCI. However, suggesting some tissue dependence in XCI, the rest of the genes that were expressed from both alleles show variability in Xi expression, including a subset of genes (5.8% of all genes) that appear biallelic in only one of the multiple tissues assayed. While tissue-specific escape is common in mouse11, limited evidence exists for such a pattern in human tissues other than for neurons3,4,9. In our data, one of the genes with the strongest evidence for tissue-specific escape is KAL1 (Fig. 3f and Supplementary Table 6), the causal gene for X-linked Kallmann syndrome. We show that KAL1 shows biallelic expression exclusively in the lung (Fig. 3f), in line with the strong female bias detected specifically in lung expression in the analysis described above and in Fig. 2a, suggesting that tissue differences in escape can directly translate into tissue-specific sex biases in gene expression. The predictions of XCI status in this sample not only align with previous assignments (Fig. 3c–f and Supplementary Table 7, for example, TSR2, XIST and ZBED1) but also suggest five new incompletely inactivated genes (Fig. 3g–k and Supplementary Table 5), three of which act in a tissue-specific manner. For instance, CLIC2, which in previous studies was shown to either be subject to2 or variably escape from1 XCI, shows considerable Xi expression only in skin tissue. Such specific patterns illustrate the need to assay multiple tissue types to fully uncover the diversity in XCI.

The emergence of scRNA-seq methods19 presents an opportunity to directly assess XCI without the complication of cellular heterogeneity in bulk tissue samples (Fig. 1), as demonstrated recently in mouse studies20,21,22,23 and in human fibroblasts24 and preimplantation development25. To directly profile XCI in human samples, we examined scRNA-seq data in combination with deep genotype sequences from 940 immune-related cells from four females: 198 cells from lymphoblastoid cell lines (LCLs) sampled from three females of African (Yoruba) ancestry, and 742 blood dendritic cells from a female of Asian ancestry26 (Fig. 1 and Extended Data Table 2). We used ASE to distinguish the expression coming from each of the two X-chromosomal haplotypes in a given cell (Supplementary Table 4). Because the inference of allele-specific phenomena in single cells is complicated by widespread monoallelic expression21,27,28,29, besides searching for X-chromosomal sites with biallelic expression (Extended Data Fig. 7), we leveraged genotype phase information to detect sites for which the expressed allele was discordant with the active X chromosome in that cell.

Only 129 (78%) out of the 165 assayed genes (41–98 per sample) were fully inactivated in these data whereas the rest showed incomplete XCI in one or more samples (Fig. 4a, b and Supplementary Tables 8, 9); this is mostly consistent with previous assignments of XCI status to these genes (Fig. 4a and Supplementary Table 10). For instance, single-cell data reveal consistent expression from both X-chromosomal alleles for eleven genes in PAR1, in line with their known escape from XCI (for example, ZBED1, Fig. 4c), and replicate the known expression of XIST exclusively from Xi (Fig. 4d).

Figure 4: Analysis of XCI using scRNA-seq.
Figure 4

a, Proportion of genes demonstrating full and partial XCI in the ASE analysis in scRNA-seq data, and the concordance with previously reported XCI status. bl, Examples of genes with different XCI patterns in scRNA-seq: previously reported inactive gene (b), known escape gene in PAR1 (c), escape gene with known exclusive expression from Xi (d), new candidates for escape genes that demonstrate incomplete XCI in only a subset of samples (ek), and a known escape gene that shows escape of varying degrees in the three samples (Pearson’s χ2 test for equal proportions, P = 3.80 × 10−7) (l). b–l, x axis labels are sample identifiers. Asterisk above a bar indicates that the proportion of Xi expression, that is, blue bar, in a given sample is significantly greater than the expected baseline (FDR <1%, one-sided binomial test). Error bars show the 95% confidence interval.

We next assessed whether our approach could extend the spectrum of escape from XCI. For seven genes that have previously been reported as inactivated, the data from single cells pointed to incomplete XCI (Fig. 4e–k and Supplementary Table 11), including FHL1, which was also highlighted as a candidate escape gene in the GTEx ASE analysis (Fig. 4e), and ATP6AP2, which displays predominantly female-biased expression across GTEx tissues (Fig. 4h). Both of these genes demonstrate significant Xi expression in only a subset of the scRNA-seq samples, a pattern that is consistent with variable escape1,2. Between-individual variability exists not only in the presence but also in the degree of expression from Xi (for example, MSL3, Fig. 4l). Highlighting the capacity of scRNA-seq to provide information beyond bulk RNA-seq, we identify examples where Xi expression varies considerably between the two X-chromosomal haplotypes within an individual (for example, ASMTL; Supplementary Table 12), suggesting cis-acting variation as one of the determinants for the level of Xi expression3. As a further layer of heterogeneity in Xi expression, we find a unique pattern for TIMP1. For this gene, the level of Xi expression across cells is not significant, but exclusive to a subset of cells that express the gene biallelically (Extended Data Fig. 7), pointing to cell-to-cell variability in escape.

Using the ASE estimates from the scRNA-seq and GTEx analyses to infer the magnitude of the incompleteness of XCI, we find that expression from Xi at escape genes rarely reaches levels equal to expression from Xa, Xi expression remaining on average at 33% of Xa expression. However, there is a lot of variability along the chromosome (Extended Data Fig. 8a and Supplementary Discussion), as has previously been demonstrated in specific tissue types1,2. Balanced expression dosage between males and females in PAR1 requires full escape from XCI, however, Xi expression remains below Xa expression also in this region (mean Xi to Xa ratio is around 0.80), pointing to partial spreading of XCI beyond nonPAR. In further support that the consistent male bias in PAR1 expression (Fig. 2a) is due to the incompleteness of escape, we observe no systematic up- or downregulation of Y chromosome expression in PAR1 (Extended Data Fig. 8b and Supplementary Discussion). As another consequence of the partial Xi expression, several of the X–Y homologous genes in nonPAR30 become male-biased when expression from the Y chromosome counterpart is accounted for (Extended Data Fig. 8c).

By combining diverse types and analyses of high-throughput RNA-seq data, we have systematically assessed the incompleteness and heterogeneity in XCI across 29 human tissues (Supplementary Table 13). We establish that scRNA-seq is suitable for surveys of human XCI and present the first steps towards understanding the cellular-level variability in the maintenance of XCI. Our phasing-based approach enables the full use of low-coverage scRNA-seq, however, because any single individual and cell type is only informative for restricted number of genes, larger datasets with more diverse cell types and conditions are required to fully profile XCI. We have therefore used the multi-tissue GTEx dataset to explore XCI in a larger number of X-chromosomal genes and to assess the tissue heterogeneity and impacts of XCI on gene expression differences between the sexes.

These analyses show that incomplete XCI is mostly shared between individuals and tissues, and extend previous surveys by pinpointing several examples of variability in the degree of XCI escape between cells, chromosomes, and tissues. In addition, our data demonstrate that escape from XCI results in sex-biased expression of at least 60 genes, potentially contributing to sex-specific differences in health and disease (Supplementary Discussion). As a whole, these results highlight the between-female and male–female diversity introduced by incomplete XCI, the biological implications of which remain to be fully explored.


GTEx data

The GTEx project12 collected tissue samples from 554 postmortem donors (187 females, 357 males; age range, 20–70), carried out RNA-seq on 8,555 tissue samples and generated genotyping data for up to 449 donors (GTEx analysis v6p release). More detailed methods can be found in ref. 13. All GTEx data, including RNA, genome and exome sequencing data, used in the analyses described here are available through dbGaP under accession number phs000424.v6.p1, unless otherwise stated. Summary data and details on data production and processing are also available from the GTEx Portal (http://gtexportal.org).

Single-cell samples

For the human dendritic cells samples profiled, the healthy donor (ID: 24A) was recruited from the Boston-based PhenoGenetic project, a resource of healthy subjects that are re-contactable by genotype32. The donor was a female Asian individual from China, 25 years of age at the time of blood collection. She was a non-smoker, had a normal BMI (height: 168.7 cm; weight: 56.45 kg; BMI: 19.8), and normal blood pressure (108/74). The donor had no family history of cancer, allergies, inflammatory disease, autoimmune disease, chronic metabolic disorders or infectious disorders. She provided written informed consent for the genetic research studies and molecular testing, as previously reported26.

Daughters of three parent–child Yoruba trios from Ibadan, Nigeria (that is, YRI trios), collected as part of the International HapMap Project, were chosen for single-cell profiling, both to maximize heterozygosity and due to availability of parental genotypes enabling phasing. DNA and LCLs were ordered from the NHGRI Sample Repository for Human Genetic Research (Coriell Institute for Medical Research): LCLs from B lymphocytes for the three daughters (catalogue numbers: GM19240, GM19199 and GM18518) and DNA extracted from LCLs for all members of the three trios (catalogue numbers for DNA: NA19240, NA19238, NA19239, NA19199, NA19197, NA19198, NA18518, NA18519 and NA18520). These YRI samples are referred to by their family IDs: Y014, Y035 and Y117.

Clinical muscle samples

To assess whether PAR1 genes are equally expressed from X and Y chromosomes, a combination of skeletal muscle RNA-seq data and trio genotyping data from eight male patients with muscular dystrophy, sequenced as part of an unrelated study, was used. Patient cases with available muscle biopsies were referred from clinicians starting April 2013 until June 2016. All patients included for RNA-seq had previously available trio whole-exome sequencing (WES) data, with one sample having additional trio whole-genome sequencing (WGS). Muscle biopsies were shipped frozen from clinical centres by liquid nitrogen dry shipping and, where possible, frozen muscle was sectioned on a cryostat and stained with haematoxylin and eosin to assess muscle quality as well as the presence of overt freeze–thaw artefacts.


The GTEx v6p release includes WGS data for 148 donors, including GTEX-UPIC. WGS libraries were sequenced on the Illumina HiSeqX or Illumina HiSeq2000. WGS data was processed through a Picard-based pipeline, using base quality score recalibration and local realignment at known indels. BWA-MEM aligner was used for mapping reads to the human genome build 37 (hg19). Single-nucleotide polymorphisms (SNPs) and indels (insertions and deletions) were jointly called across all 148 samples and additional reference genomes using HaplotypeCaller v.3.1 of GATK. Default filters were applied to SNP and indel calls using the variant quality score recalibration (VQSR) approach of GATK. An additional hard filter InbreedingCoeff ≤−0.3 was applied to remove sites that VQSR failed to filter.

WGS for one of the clinical muscle samples was performed on 500 ng to 1.5 μg of genomic DNA using a PCR-Free protocol that substantially increases the uniformity of genome coverage. These libraries were sequenced on the Illumina HiSeq X10 with 151-bp paired-end reads and a target mean coverage of >30×, and were processed similarly to the above description.

The Y117 trio (sample IDs NA19240 (daughter), NA19238 (mother), and NA19239 (father)) was whole-genome-sequenced as part of the 1000 Genomes Project as described previously33. The VCF file containing the WGS-based genotypes for SNPs (YRI.trio.2010_09.genotypes.vcf.gz) was downloaded from the FTP site of the project. The genotype coordinates (in human genome build 36) in the original VCF were converted to hg19 using the liftover script (liftOverVCF.pl) and chain files provided as part of the GATK package.

WES was performed using Illumina’s capture Exome (ICE) technology (Y035, Y014, 24A) or Agilent SureSelect Human All Exon Kit v.2 exome capture (clinical muscle samples) with a mean target coverage of >80×. WES data was aligned with BWA, processed with Picard, and SNPs and indels were jointly called with other samples using GATK HaplotypeCaller package v.3.1 (24A, clinical muscle samples) or v.3.4 (Y035, Y014). Default filters were applied to SNP and indel calls using the VQSR approach. A modified version of the Ensembl variant effect predictor was used for variant annotation for all WES and WGS data. For trio WES or WGS data the genotypes of the proband were phased using the PhaseByTransmission tool of the GATK toolkit.

Single-cell data preparation and sequencing

For profiling of healthy dendritic cells (DCs), peripheral blood mononuclear cells (PBMCs) were first isolated from fresh blood within 2 h of collection, using Ficoll–Paque density gradient centrifugation as previously described34. Single-cell suspensions were stained as per the manufacturer’s recommendations with an antibody panel designed to enrich for all known blood DC population for single-cell sorting and scRNA-seq profiling26. A total of 24 single cells from four loosely gated populations were sorted per 96-well plate, with each well containing 10 μl of lysis buffer. A total of eight plates were analysed by scRNA-seq.

All LCL cell lines were cultured according to Coriell’s recommendations (medium: RPMI 1640, 2 mM l-glutamine, 15% fetal bovine serum (all three from ThermoFisher Scientific)) in T25 tissue culture flask with 10–20 ml medium at 37 °C in 5% carbon dioxide. Cells were split upon reaching a cell density of approximately 300,000–400,000 viable cells per ml. All three lymphoblast cultures were split once before single-cell sorting. Cells were washed with 1× PBS, the pellet was resuspended and stained with DAPI (Biolegend) for viability according to the manufacturer’s recommendations.

All single live cells (for both DCs and LCL cell lines) were sorted into a 96-well full-skirted Eppendorf plate chilled to 4 °C, that were pre-prepared with 10 μl TCL buffer (Qiagen) supplemented with 1% β-mercaptoethanol (lysis buffer), using a BD FACS Fusion instrument. Single-cell lysates were sealed, vortexed, spun down at 300g at 4 °C for 1 min, immediately placed on dry ice and transferred for storage at −80 °C.

The Smart-Seq2 protocol was performed on single-sorted cells as described35,36, with some modifications as described in ref. 26 (Supplementary Methods). A total of 768 single DCs isolated from a healthy Asian female individual, along with 96 single cells from GM19240, 48 single cells from GM19199 and 48 single cells from GM18518 were profiled. In brief, single-cell lysates were thawed on ice, purified and reverse-transcribed using Maxima H Minus Reverse Transcriptase. PCR was performed with KAPA HiFi HotStart ReadyMix (KAPA Biosystems) and purified with Agencourt AMPureXP SPRI beads (Beckman-Coulter). The concentration of amplified cDNA was measured on the Synergy H1 Hybrid Microplate Reader (BioTek) using High-Sensitivity Qubit reagent (Life Technologies) and the size distribution of select wells was checked on a High-Sensitivity Bioanalyzer Chip (Agilent). The expected concentration was around 0.5−2 ng μl−1 with a size distribution that sharply peaked around 2 kb.

Library preparation was carried out using the Nextera XT DNA Sample Kit (Illumina) with custom indexing adapters, allowing up to 384 libraries to be simultaneously generated in a 384-well PCR plate (note that DCs were processed in a 384-well plate whereas LCLs were processed in 96-well plate format). The concentration of the final pooled libraries was measured using the High-Sensitivity DNA Qubit (Life Technologies) and the size distribution was measured on a High-Sensitivity Bioanalyzer Chip (Agilent). The expected concentration of the pooled libraries was 10–30 ng μl−1 with a size distribution of 300–700 bp. For the DCs, we created pools of 384 cells, whereas 96 LCL samples were pooled at the time. We sequenced one library pool per lane as paired-end 25-bp reads on a HiSeq2500 (Illumina). Barcodes used for indexing are listed in the Supplementary Methods.

RNA-seq in GTEx

RNA sequencing was performed using a non-strand-specific RNA-seq protocol with polyA selection of RNA using the Illumina TruSeq protocol with sequence coverage goal of 50 million 76-bp paired-end reads as has been previously described in detail12. The RNA-seq data, except for GTEX-UPIC, was aligned with TopHat v.1.4.1 to the UCSC human genome release version hg19 using the Gencode v.19 annotations as the transcriptome reference. Gene level read counts and reads per kilobase per million reads (RPKMs) were derived using the RNA-SeQC tool37 using the Gencode v.19 transcriptome annotation. The transcript model was collapsed into a gene model as described previously12. Read count and RPKM quantification include only uniquely mapped and properly paired reads contained within exon boundaries.

RNA-seq alignment to personalized genomes

For the four single-cell samples and for GTEX-UPIC RNA-seq, data were processed using a modification of the AlleleSeq pipeline38,39 to minimize reference allele bias in alignment. A diploid personal reference genome for each of the samples was generated with the vcf2diploid tool38 including all heterozygous biallelic single-nucleotide variants identified in WES or WGS either together with (YRI samples) or without (GTEX-UPIC, 24A) maternal and paternal genotype information. The RNA-seq reads were then aligned to both parental references using STAR40 v.2.4.1a in a per-sample two-pass mode (GTEX-UPIC and YRI samples) or v.2.3.0e (24A) using hg19 as the reference. The alignments were combined by comparing the quality of alignment between the two references: for reads aligning uniquely to both references the alignment with the higher alignment score was chosen and reads aligning uniquely to only one reference were kept as such.

RNA-seq of clinical muscle samples

Patient RNA samples derived from primary muscle were sequenced using the GTEx sequencing protocol12 with sequence coverage of 50 million or 100 million 76-bp paired-end reads. RNA-seq reads were aligned using STAR40 2-pass version v.2.4.2a using hg19 as the reference genome. Junctions were filtered after first pass alignment to exclude junctions with less than 5 uniquely mapped reads supporting the event and junctions found on the mitochondrial genome. The value for unique mapping quality was assigned to 60 and duplicate reads were marked with Picard MarkDuplicates (v.1.1099).

Catalogue of X-inactivation status

To compare results from the ASE and GTEx analyses with previous observations on genic XCI status we collated findings from two earlier studies1,2 that represent systematic expression-based surveys into XCI. Each study catalogues hundreds of X-linked genes and together the data span two tissue types.

Carrel and Willard1 surveyed in total 624 X-chromosomal transcripts expressed in primary fibroblasts in nine cell hybrids each containing a different human Xi. In order to find the gene corresponding to each transcript, the primer sequences designed to test the expression of the transcripts in the original study were aligned to reference databases based on the Gencode v.19 transcriptome and hg19 using in-house software (unpublished) (Supplementary Methods). In total 553 transcripts primer pairs were successfully matched to X-chromosomal Gencode v.19 reference mapping together with 470 unique X-chromosomal genes (Supplementary Methods). These 470 genes were split into three XCI status categories (escape, variable, inactive) based on the level of Xi expression (that is, the number of cell lines expressing the gene from Xi) resulting in 75 escape, 51 variable escape and 344 inactive genes.

Cotton et al.2 surveyed XCI using allelic imbalance in clonal or near-clonal female LCL and fibroblast cell lines and provided XCI statuses for 508 genes (68 escape, 146 variable escape, 294 subject genes). The data were mapped to Gencode v.19 using the reported gene names and their known aliases (Supplementary Methods), resulting in a list of XCI statuses for 506 X-chromosomal genes.

The results were combined by retaining the XCI status in the original study where possible (that is, same status in both studies or gene unique to one study) and for genes where the reported XCI statuses were in conflict the following rules were applied: (1) a gene was considered ‘escape’ if it was called escape in one study and variable in the other; (2) ‘variable escape’ if classified as escape and inactive; and (3) ‘inactive’ if classified as inactive in one study and variable escape in the other. The final combined list of XCI statuses consisted of 631 X-chromosomal genes including 99 escape, 101 variable escape and 431 inactive genes.

Analysis of sex-biased expression

Differential expression analyses were conducted to identify genes that are expressed at significantly different levels between male and female samples using 29 GTEx v6p tissues with RNA-seq and genotype data available from more than 70 individuals after excluding samples flagged in QC and sex-specific, outlier (that is, breast tissue) and highly correlated tissues14. Only autosomal and X-chromosomal protein-coding or long non-coding RNA genes in Gencode v.19 were included, and all lowly expressed genes were removed (Extended Data Table 1 and Supplementary Methods).

Differential expression analysis between male and female samples was conducted following the voom-limma pipeline41,42,43 available as an R package through Bioconductor (https://bioconductor.org/packages/release/bioc/html/limma.html) using the gene-level read counts as input. The analyses were adjusted for age, three principal components inferred from genotype data using EIGENSTRAT44, sample ischaemic time, surrogate variables45,46 built using the sva R package47, and the cause of death classified into five categories based on the four-point Hardy scale (Supplementary Methods).

To control the FDR, the qvalue R package was used to obtain q values applying the adjustment separately for the differential expression results from each tissue. The null hypothesis was rejected for tests with q values below 0.01.

XY homologue analysis

A list of Y-chromosomal genes with functional counterparts in the X chromosome, that is, X–Y gene pairs, was obtained from ref. 30, which lists 19 ancestral Y chromosome genes that have been retained in the human Y chromosome. After excluding two of the genes (MXRA5Y and OFD1Y), which were annotated as pseudogenes in ref 30, and a further four genes (SRY, RBMY, TSPY and HSFY) that according to ref. 30 have clearly diverged in function from their X-chromosomal homologues, the remaining 13 Y-chromosomal genes were matched with their X-chromosome counterparts using gene-pair annotations given in ref. 30 or by searching for known paralogues of the Y-chromosomal genes. To test for completeness of dosage compensation of the X–Y homologous genes, the sex-bias analysis in GTEx data was repeated replacing the expression of the X-chromosomal counterpart with the combined expression of the X and Y homologues.

Chromatin state analysis

To study the relationship between chromatin states and XCI, we used chromatin state calls from the Roadmap Epigenomics Consortium31. Specifically, we used the chromatin state annotations from the core 15-state model, publicly available at http://egg2.wustl.edu/roadmap/web_portal/chr_state_learning.html#core_15state. We followed our previously published method48 to calculate the covariate-corrected percentage of each gene body assigned to each chromatin state. After pre-processing, we filtered down to the 399 inactive and 86 escape genes on the X chromosome and down to 38 female epigenomes.

To compare the chromatin state profiles of the escape and inactive genes in female samples, we used the one-sided Wilcoxon rank-sum test. Specifically, for each chromatin state, we averaged the chromatin state coverage across the 38 female samples for each gene, and compared that average chromatin state coverage for all 86 escape genes to the average chromatin state coverage for all 399 inactive genes. We performed both one-sided tests, to test for enrichment in escape genes, as well as for enrichment in inactive genes.

Next, we performed simulations to account for possible chromatin state biases, such as the fact that the escape and inactive genes are all from the X chromosome. Specifically, we generated 10,000 randomized simulations where we randomly shuffled the escape or inactive labels on the combined set of 485 genes, while retaining the sizes of each gene set. For each of these simulated escape and inactive gene sets, we calculated both one-sided Wilcoxon rank-sum test P values as described above, and then, we calculated a permutation P value for the real gene sets based on these 10,000 random simulations (Supplementary Methods). Finally, we used Bonferroni multiple hypothesis corrections for our significance thresholds to correct for our 30 tests, one for each of 15 chromatin states, and both possible test directions.

Allele-specific expression

For ASE analysis the allele counts for biallelic heterozygous variants were retrieved from RNA-seq data using GATK ASEReadCounter (v.3.6)39. Heterozygous variants that passed VQSR filtering were first extracted for each sample from WES or WGS VCFs using GATK SelectVariants. The analysis was restricted to biallelic SNPs owing to known issues in mapping bias in RNA-seq against indels39. Sample-specific VCFs and RNA-seq BAMs were inputted to ASEReadCounter requiring minimum base quality of 13 in the RNA-seq data (scRNA-seq samples, GTEX-UPIC) or requiring coverage in the RNA-seq data of each variant to be at least 10 reads, with a minimum base quality of 10 and counting only reads with unique mapping quality (MQ = 60) (clinical muscle samples).

For downstream processing of the scRNA-seq and GTEX-UPIC ASE data, we applied further filters to the data to focus on exonic variation only and to conservatively remove potentially spurious sites (Supplementary Methods), for example, sites with non-unique mappability were removed, and furthermore, after an initial analysis of the ASE data, we subjected 22 of the X-chromosomal ASE sites to manual investigation. For GTEX-UPIC the X-chromosomal ASE data was limited to only one site per gene in case of multiple ASE sites, by selecting the site with coverage >7 reads in the largest number of tissues, to have equal representation of each gene for downstream analyses.

Assessing ASE across tissues

For the GTEX-UPIC individual, for whom ASE data from up to 16 tissues per each ASE site was available, we applied the two-sided hierarchical grouped tissue model (GTM*) implemented in MAMBA v.1.0.0 (refs 49, 50) to ASE data. The Gibbs sampler was run for 200 iterations with a burn-in of 50 iterations.

GTM* is a Bayesian hierarchical model that borrows information across tissues and across variants, and provides parameter estimates that are useful for interpreting global properties of variants. It classifies the sites into ASE states according to their tissue-wide ASE profiles and provides an estimate of the proportion of variants in each of the five different ASE states (strong ASE across all tissues (SNGASE), moderate ASE across all tissues (MODASE), no ASE across all tissues (NOASE) and heterogeneous ASE across tissues (HET1 and HET0)).

To summarize the GTM* output in the context of XCI, SNGASE was considered to reflect full XCI, MODASE and NOASE were taken together to represent partial XCI with similar effects across tissues, and HET1 and HET0 were considered to reflect partial yet heterogeneous patterns of XCI across tissues. To combine estimates from two ASE states, we summed the estimated proportions in each class and subsequently calculated the 95% confidence intervals for each remaining ASE state using Jeffreys’ prior.

Determining XCI status in GTEX-UPIC

In addition to the ASE states provided by the above MAMBA analysis, genic XCI status was assessed by comparing the allelic ratios at each X-chromosomal ASE site in each tissue individually. For each ASE site, the alleles were first mapped to Xa and Xi; the allele with lower combined relative expression across tissues was assumed to be the Xi allele. As an exception, at XIST the higher expressing allele was assumed to be the Xi allele. The significance of Xi expression at each ASE observation was tested using a one-sided binomial test, where the hypothesized probability of success was set at 0.025, that is, the fraction of Xi expression from total expression was expected to be significantly greater than 0.025. To account for multiple testing, a FDR correction was applied, using the qvalue R package, to the P values from the binomial test for each of the 16 tissues separately. Observations with q values <0.01 were considered significant, that is, indicative of incomplete XCI at the given ASE site and tissue.

Biallelic expression in single cells

Biallelic expression in individual cells in the X chromosome was assessed only at ASE sites covered by the minimum of eight reads. A site was considered biallelically expressed when (1) allelic expression >0.05 and (2) the one-sided binomial test indicated allelic expression to be at least nominally significantly greater than 0.025. Only genes with at least two observations of biallelic expression across all cells within a sample were counted as biallelic.

Phasing scRNA-seq data

We assigned each cell to either of two cell populations distinguished by the parental X-chromosome designated for inactivation using genotype phasing. For the YRI samples, where parental genotype data was available, the assignment to the two parental cell populations was unambiguous for all cells where X-chromosomal sites outside PAR1 or frequently biallelic sites were expressed. For 24A, no parental genotype data were available, and we therefore used the correlation structure of the expressed X-chromosomal alleles across the 948 cells to infer the two parental haplotypes using the fact that in individual cells the expressed alleles at the chrX sites subject to full inactivation (that is, the majority chrX ASE sites), are from the X chromosome active in each cell (Supplementary Methods). In other words, while monoallelic expression in scRNA-seq in the autosomes is largely stochastic in origin, in the X chromosome the pattern of monoallelic expression is consistent across cells with the same parental X chromosome active22, unless a gene is expressed also from the inactive X. As such, for the phase inference calculations, we excluded all PAR1 sites and all additional sites that were frequently biallelic, to minimize the contribution of escape genes to the phase estimation. After assigning each informative cell to either of the parental cell populations, the reference and alternate allele reads for each ASE site were mapped to active and inactive allele reads within each sample using the actual or inferred parental haplotypes. The data were first combined per variant by taking the sum of active and inactive counts separately across cells, and further similarly combined per gene, if multiple SNPs per gene were available. For 24A the allele expressed at XIST was assumed the Xi allele, in line with the exclusive Xi expression in the Yoruba samples confirmed using the information on parental haplotypes.

Determining XCI status from scRNA-seq ASE

Before calling XCI status using the Xa and Xi read counts from the phased data aggregated across cells, we excluded all sites without fewer than five cells contributing ASE data at each gene and also all sites with coverage lower than eight reads across cells within each sample. To determine whether the observed Xi expression is significantly different from zero, and therefore indicative of incomplete XCI at the site or gene, we required the Xi to total expression ratio to be significantly (q value <0.01) greater than the hypothesized upper bound for error, 0.025. This threshold was determined using the proportion of miscalled alleles at XIST ASE sites (by definition, XIST should express only alleles from the inactive chrX) in the two YRI samples, which presented with fully skewed XCI, that is, the same active X chromosome across all assessed cells. The median proportion of miscalled XIST alleles was 0, yet one site in one of the samples showed up to 2.5% of other allele calls, and therefore this was chosen as the error margin. FDR correction, conducted using the qvalue R package, was applied to each sample individually. Genes where at least one of the samples showed significant Xi expression were considered partially inactivated, while the remaining were classified as subject to full XCI. Allelic dropout, which is extensive in scRNA-seq19,28, can lead to biases in allelic ratios in individual cells, that is, in our case resulting in false negatives where true escape genes are classified as inactivated, the used approach is based on using aggregate data across several cells and therefore the XCI status estimates are robust to such errors.

ChrX and chrY expression in PAR1

Using the parental origin of each allele reference and alternate allele read counts at PAR1 ASE sites were assigned to X and Y chromosomes (that is, maternally and paternally inherited alleles, respectively). For each sample, the PAR1 ASE data was summarized by gene by taking the sum of X and Y chromosome reads across all informative ASE sites within each gene. Significance of deviation from equal expression was assessed using a two-sided binomial test.

Manual curation of heterozygous variants from ASE analyses

Twenty-two heterozygous variants assessed in chrX ASE analysis were subjected to manual curation because of results in the XCI analysis that were in conflict with previous assignment of the underlying gene to be subject to full XCI. For each sample, BWA-aligned germline BAM files were viewed in IGV using either WGS or WES data. The presence of a number of characteristics called into question the confidence of the variant read alignments and thus the variant itself (Supplementary Methods). Allele balance that deviated significantly from 50:50 was considered suspect and often coincided with the existence of homology between the reference sequence in the region surrounding the variant and another area of the genome, as ascertained using the UCSC browser self-chain track and/or BLAT alignment of variant reads from within IGV. Other sequence-based annotations added to the VCF by HaplotypeCaller were also evaluated in the interests of examining other signatures of ambiguous mapping. The phasing of nearby variants was also considered. If phased variants occurred in the DNA sequencing data that were not assessed in the ASE analysis, those variants were considered suspect.

Data availability

Gene expression and genotype data from the GTEx v6p release are available in dbGaP (study accession phs000424.v6.p1; http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs000424.v6.p1). Raw RNA-seq data for 24A is available through dbGaP accession number phs001294.v1.p1 (https://www.ncbi.nlm.nih.gov/bioproject/?term=phs001294.v1.p1). The authors declare that all data supporting the findings of this study are available within the paper and its Supplementary Information. Source Data for Figs 2, 3, 4 are provided with the paper.

Change history

  • 07 March 2018

    Please see accompanying Corrigendum (http://doi.org/10.1038/nature25993). The Source Data associated with Fig. 2a and d have been replaced. See Supplementary Information to the Corrigendum for the original Source Data for Fig. 2.


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We thank J. Maller, F. Zhao and M. Lek for technical assistance and P. J. Siponen for assistance with figure design. T.T. was supported by the Academy of Finland (285725), Finnish Cultural Foundation, Orion-Farmos Research Foundation and Emil Aaltonen Foundation. K.J.K. is supported by a NIGMS Fellowship (F32GM115208). This work was supported by NIH grants U54DK105566, R01MH101820 and R01GM104371 to D.G.M. The Genotype-Tissue Expression (GTEx) project was supported by the Common Fund of the Office of the Director of the National Institutes of Health. Additional funds were provided by the NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS. Donors were enrolled at Biospecimen Source Sites funded by NCI\SAIC-Frederick, Inc. (SAIC-F) subcontracts to the National Disease Research Interchange (10XS170), Roswell Park Cancer Institute (10XS171) and Science Care, Inc. (X10S172). The Laboratory, Data Analysis, and Coordinating Center (LDACC) was funded through a contract (HHSN268201000029C) to The Broad Institute; this grant also provided funding to D.G.M. and T.T. Biorepository operations were funded through an SAIC-F subcontract to the Van Andel Institute (10ST1035). Additional data repository and project management were provided by SAIC-F (HHSN261200800001E). The Brain Bank was supported by supplements to University of Miami grants DA006227 and DA033684 and to contract N01MH000028. Statistical Methods development grants were made to the University of Geneva (MH090941 and MH101814), the University of Chicago (MH090951, MH090937, MH101820 and MH101825), the University of North Carolina, Chapel Hill (MH090936 and MH101819), Harvard University (MH090948), Stanford University (MH101782), Washington University St. Louis (MH101810) and the University of Pennsylvania (MH101822).

Author information

Author notes


  1. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA

    • Taru Tukiainen
    • , Manuel A. Rivas
    • , Jamie L. Marshall
    • , Matt Aguirre
    • , Laura Gauthier
    • , Andrew Kirby
    • , Beryl B. Cummings
    • , Konrad J. Karczewski
    • , Andrea Byrnes
    • , Gad Getz
    •  & Daniel G. MacArthur
  2. Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA

    • Taru Tukiainen
    • , Alexandra-Chloé Villani
    • , Angela Yen
    • , Manuel A. Rivas
    • , Jamie L. Marshall
    • , Rahul Satija
    • , Matt Aguirre
    • , Laura Gauthier
    • , Mark Fleharty
    • , Andrew Kirby
    • , Beryl B. Cummings
    • , Konrad J. Karczewski
    • , François Aguet
    • , Andrea Byrnes
    • , Aviv Regev
    • , Kristin G. Ardlie
    • , Nir Hacohen
    •  & Daniel G. MacArthur
  3. Center for Immunology and Inflammatory Diseases, Massachusetts General Hospital, Charlestown, Massachusetts 02129, USA

    • Alexandra-Chloé Villani
    •  & Nir Hacohen
  4. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

    • Angela Yen
  5. Department of Biomedical Data Science, Stanford University, Stanford, California 94305, USA

    • Manuel A. Rivas
  6. New York Genome Center, New York, New York 10013, USA

    • Rahul Satija
    • , Stephane E. Castel
    •  & Tuuli Lappalainen
  7. Center for Genomics and Systems Biology, Department of Biology, New York University, New York, New York 10003, USA

    • Rahul Satija
  8. Department of Systems Biology, Columbia University, New York, New York 10032, USA

    • Stephane E. Castel
    •  & Tuuli Lappalainen
  9. Department of Biology, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

    • Aviv Regev
  10. The Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts 02142, USA

    • François Aguet
    • , Kristin G. Ardlie
    • , Beryl B. Cummings
    • , Ellen T. Gelfand
    • , Kane Hadley
    • , Robert E. Handsaker
    • , Katherine H. Huang
    • , Seva Kashin
    • , Konrad J. Karczewski
    • , Monkol Lek
    • , Xiao Li
    • , Daniel G. MacArthur
    • , Jared L. Nedzel
    • , Duyen T. Nguyen
    • , Michael S. Noble
    • , Ayellet V. Segrè
    • , Casandra A. Trowbridge
    • , Taru Tukiainen
    • , Melina Claussnitzer
    • , Lei Hou
    • , Manolis Kellis
    • , Yaping Liu
    • , Benoit Molinie
    • , Yongjin Park
    • , Nicola J. Rinaldi
    • , Li Wang
    •  & Nicholas Van Wittenberghe
  11. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA

    • Beryl B. Cummings
    • , Konrad J. Karczewski
    • , Monkol Lek
    • , Daniel G. MacArthur
    •  & Taru Tukiainen
  12. Massachusetts General Hospital Cancer Center and Department of Pathology, Massachusetts General Hospital, Boston, Massachusetts 02114, USA

    • Gad Getz
  13. Department of Genetics, Harvard Medical School, Boston, Massachusetts 02114, USA

    • Robert E. Handsaker
    •  & Seva Kashin
  14. Department of Genetics, Stanford University, Stanford, California 94305, USA

    • Nathan S. Abell
    • , Joe R. Davis
    • , Laure Frésard
    • , Michael J. Gloudemans
    • , Boxiang Liu
    • , Stephen B. Montgomery
    • , Zachary Zappala
    • , Joanne Chan
    • , Ruiqi Jian
    • , Lihua Jiang
    • , Jin Billy Li
    • , Qin Li
    • , Xiao Li
    • , Jessica Lin
    • , Shin Lin
    • , Sandra Linder
    • , Kevin S. Smith
    • , Michael P. Snyder
    • , Hua Tang
    • , Meng Wang
    •  & Rui Zhang
  15. Department of Pathology, Stanford University, Stanford, California 94305, USA

    • Nathan S. Abell
    • , Brunilda Balliu
    • , Joe R. Davis
    • , Laure Frésard
    • , Michael J. Gloudemans
    • , Xin Li
    • , Boxiang Liu
    • , Stephen B. Montgomery
    • , Emily K. Tsang
    • , Zachary Zappala
    • , Sandra Linder
    •  & Kevin S. Smith
  16. Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel

    • Ruth Barshir
    • , Omer Basha
    •  & Esti Yeger-Lotem
  17. Department of Computer Science, Johns Hopkins University, Baltimore, Maryland 21218, USA

    • Alexis Battle
    • , Farhan N. Damani
    • , Yungil Kim
    • , Princy Parsana
    • , Ashis Saha
    •  & Lin S. Chen
  18. Centre for Genomic Regulation (CRG), The Barcelona Institute for Science and Technology, 08003 Barcelona, Spain

    • Gireesh K. Bogu
    • , Diego Garrido-Martín
    • , Roderic Guigo
    • , Jean Monlong
    • , Manuel Muñoz-Aguirre
    • , Panagiotis Papasaikas
    • , Ferran Reverter
    •  & Reza Sodaei
  19. Universitat Pompeu Fabra (UPF), 08002 Barcelona, Spain

    • Gireesh K. Bogu
    • , Diego Garrido-Martín
    • , Roderic Guigo
    • , Jean Monlong
    • , Manuel Muñoz-Aguirre
    • , Panagiotis Papasaikas
    • , Ferran Reverter
    •  & Reza Sodaei
  20. Department of Genetic Medicine and Development, University of Geneva Medical School, 1211 Geneva, Switzerland

    • Andrew Brown
    • , Olivier Delaneau
    • , Emmanouil T. Dermitzakis
    • , Cedric Howald
    • , Halit Ongen
    •  & Nikolaos Panousis
  21. Institute for Genetics and Genomics in Geneva (iG3), University of Geneva, 1211 Geneva, Switzerland

    • Andrew Brown
    • , Olivier Delaneau
    • , Emmanouil T. Dermitzakis
    • , Cedric Howald
    • , Halit Ongen
    •  & Nikolaos Panousis
  22. Swiss Institute of Bioinformatics, 1211 Geneva, Switzerland

    • Andrew Brown
    • , Olivier Delaneau
    • , Emmanouil T. Dermitzakis
    • , Cedric Howald
    • , Halit Ongen
    •  & Nikolaos Panousis
  23. Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, USA

    • Christopher D. Brown
    •  & YoSon Park
  24. New York Genome Center, New York, New York 10013, USA

    • Stephane E. Castel
    • , Sarah Kim-Hellmuth
    • , Tuuli Lappalainen
    •  & Pejman Mohammadi
  25. Department of Systems Biology, Columbia University Medical Center, New York, New York 10032, USA

    • Stephane E. Castel
    • , Sarah Kim-Hellmuth
    • , Tuuli Lappalainen
    •  & Pejman Mohammadi
  26. Department of Public Health Sciences, The University of Chicago, Chicago, Illinois 60637, USA

    • Lin S. Chen
    • , Kathryn Demanelis
    • , Farzana Jasmine
    • , Muhammad G. Kibriya
    •  & Brandon L. Pierce
  27. McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri 63108, USA

    • Colby Chiang
    • , Ira M. Hall
    •  & Alexandra J. Scott
  28. Department of Genetics, Washington University School of Medicine, St. Louis, Missouri 63108, USA

    • Donald F. Conrad
    •  & Ira M. Hall
  29. Department of Pathology & Immunology, Washington University School of Medicine, St. Louis, Missouri 63108, USA

    • Donald F. Conrad
  30. Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA

    • Nancy J. Cox
    •  & Eric R. Gamazon
  31. Department of Computer Science, Center for Statistics and Machine Learning, Princeton University, Princeton, New Jersey 08540, USA

    • Barbara E. Engelhardt
  32. Department of Computer Science, University of California, Los Angeles, California 90095, USA

    • Eleazar Eskin
    • , Farhad Hormozdiari
    • , Eun Yong Kang
    •  & Serghei Mangul
  33. Department of Human Genetics, University of California, Los Angeles, California 90095, USA

    • Eleazar Eskin
  34. Instituto de Investigação e Inovação em Saúde (i3S), Universidade do Porto, 4200-135 Porto, Portugal

    • Pedro G. Ferreira
  35. Institute of Molecular Pathology and Immunology (IPATIMUP), University of Porto, 4200-625 Porto, Portugal

    • Pedro G. Ferreira
  36. Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands

    • Eric R. Gamazon
  37. Department of Psychiatry, Academic Medical Center, University of Amsterdam, 1105 AZ Amsterdam, The Netherlands

    • Eric R. Gamazon
  38. Lewis Sigler Institute, Princeton University, Princeton, New Jersey 08540, USA

    • Ariel D. H. Gewirtz
    • , Brian Jo
    •  & Joshua M. Akey
  39. Department of Operations Research and Financial Engineering, Princeton University, Princeton, New Jersey 08540, USA

    • Genna Gliner
  40. Biomedical Informatics Program, Stanford University, Stanford, California 94305, USA

    • Michael J. Gloudemans
    •  & Emily K. Tsang
  41. Institut Hospital del Mar d’Investigacions Mèdiques (IMIM), 08003 Barcelona, Spain

    • Roderic Guigo
  42. Department of Medicine, Washington University School of Medicine, St. Louis, Missouri 63108, USA

    • Ira M. Hall
  43. Department of Convergence Medicine, University of Ulsan College of Medicine, Asan Medical Center, Seoul 138-736, South Korea

    • Buhm Han
  44. Department of Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland 21218, USA

    • Yuan He
    • , Benjamin J. Strober
    •  & Andrew P. Feinberg
  45. Section of Genetic Medicine, Department of Medicine, The University of Chicago, Chicago, Illinois 60637, USA

    • Hae Kyung Im
    • , Dan L. Nicolae
    • , Meritxell Oliva
    • , Barbara E. Stranger
    • , Marian S. Fernando
    • , Caroline Linke
    • , Andrew Skol
    •  & Fan Wu
  46. Department of Biostatistics, Mailman School of Public Health, Columbia University, New York, New York 10032, USA

    • Gen Li
  47. Department of Biology, Stanford University, Stanford, California 94305, USA

    • Boxiang Liu
  48. Wellcome Trust Centre for Human Genetics, Nuffield Department of Medicine, University of Oxford, Oxford OX3 7BN, UK

    • Mark I. McCarthy
    • , Anne W. Ndungu
    • , Anthony J. Payne
    •  & Martijn van de Bunt
  49. Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Churchill Hospital, Oxford OX3 7LE, UK

    • Mark I. McCarthy
    •  & Martijn van de Bunt
  50. Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford OX3 7LJ, UK

    • Mark I. McCarthy
  51. Computational Biology & Bioinformatics Graduate Program, Duke University, Durham, North Carolina 27708, USA

    • Ian C. McDowell
  52. Human Genetics Department, McGill University, Montreal, Quebec H3A 0G1, Canada

    • Jean Monlong
  53. Departament d’Estadística i Investigació Operativa, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain

    • Manuel Muñoz-Aguirre
  54. Department of Statistics, The University of Chicago, Chicago, Illinois 60637, USA

    • Dan L. Nicolae
    •  & Matthew Stephens
  55. Department of Human Genetics, The University of Chicago, Chicago, Illinois 60637, USA

    • Dan L. Nicolae
    • , Matthew Stephens
    • , Sarah Urbut
    •  & Gao Wang
  56. Department of Statistics and Operations Research, University of North Carolina, Chapel Hill, North Carolina 27599, USA

    • Andrew B. Nobel
  57. Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina 27599, USA

    • Andrew B. Nobel
  58. Institute for Genomics and Systems Biology, The University of Chicago, Chicago, Illinois 60637, USA

    • Meritxell Oliva
    • , John J. Palowitch
    • , Barbara E. Stranger
    • , Marian S. Fernando
    • , Caroline Linke
    • , Andrew Skol
    •  & Fan Wu
  59. Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas 77030, USA

    • Christine B. Peterson
  60. Computational Sciences, Pfizer Inc, Cambridge, Massachusetts 02139, USA

    • Jie Quan
    •  & Hualin S. Xi
  61. Universitat de Barcelona, 08028 Barcelona, Spain

    • Ferran Reverter
  62. Department of Biomedical Data Science, Stanford University, Stanford, California 94305, USA

    • Chiara Sabatti
  63. Department of Statistics, Stanford University, Stanford, California 94305, USA

    • Chiara Sabatti
  64. Institute of Biophysics Carlos Chagas Filho (IBCCF), Federal University of Rio de Janeiro (UFRJ), 21941902 Rio de Janeiro, Brazil

    • Michael Sammeth
  65. Department of Psychiatry, University of Utah, Salt Lake City, Utah 84108, USA

    • Andrey A. Shabalin
  66. Center for Data Intensive Science, The University of Chicago, Chicago, Illinois 60637, USA

    • Barbara E. Stranger
    •  & Andrew Skol
  67. Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, California 90095, USA

    • Jae Hoon Sul
  68. Department of Biostatistics, University of Michigan, Ann Arbor, Michigan 48109, USA

    • Xiaoquan Wen
  69. Bioinformatics Research Center and Departments of Statistics and Biological Sciences, North Carolina State University, Raleigh, North Carolina 27695, USA

    • Fred A. Wright
    •  & Yi-Hui Zhou
  70. National Institute for Biotechnology in the Negev, Beer-Sheva, 84105, Israel

    • Esti Yeger-Lotem
  71. European Molecular Biology Laboratory, 69117 Heidelberg, Germany

    • Judith B. Zaugg
  72. Department of Ecology and Evolutionary Biology, Princeton University, Princeton, New Jersey 08540, USA

    • Joshua M. Akey
  73. Altius Institute for Biomedical Sciences, Seattle, Washington 98121, USA

    • Daniel Bates
    • , Morgan Diegel
    • , Jessica Halow
    • , Eric Haugen
    • , Audra Johnson
    • , Rajinder Kaul
    • , Kristen Lee
    • , Jemma Nelson
    • , Fidencio J. Neri
    • , Richard Sandstrom
    •  & John Stamatoyannopoulos
  74. Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts 02215, USA

    • Melina Claussnitzer
  75. University of Hohenheim, 70599 Stuttgart, Germany

    • Melina Claussnitzer
  76. Huntsman Cancer Institute, Department of Population Health Sciences, University of Utah, Salt Lake City, Utah 84112, USA

    • Jennifer A. Doherty
  77. Center for Epigenetics, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA

    • Andrew P. Feinberg
    • , Kasper D. Hansen
    •  & Lindsay F. Rizzardi
  78. Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA

    • Andrew P. Feinberg
  79. Department of Mental Health, Johns Hopkins University School of Public Health, Baltimore, Maryland 21205, USA

    • Andrew P. Feinberg
  80. McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, Maryland 21205, USA

    • Kasper D. Hansen
  81. Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland 21205, USA

    • Kasper D. Hansen
    •  & Peter F. Hickey
  82. Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA

    • Lei Hou
    • , Manolis Kellis
    • , Yaping Liu
    • , Yongjin Park
    •  & Nicola J. Rinaldi
  83. Department of Medicine, University of Washington, Seattle, Washington 98195, USA

    • Jessica Lin
    •  & John Stamatoyannopoulos
  84. Division of Cardiology, University of Washington, Seattle, Washington 98195, USA

    • Shin Lin
  85. Institute for Systems Genetics, New York University Langone Medical Center, New York, New York 10016, USA

    • Matthew T. Maurano
  86. Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA

    • John Stamatoyannopoulos
  87. Office of Strategic Coordination, Division of Program Coordination, Planning and Strategic Initiatives, Office of the Director, NIH, Rockville, Maryland 20852, USA

    • Concepcion R. Nierras
  88. Biorepositories and Biospecimen Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, Maryland 20892, USA

    • Philip A. Branton
    • , Latarsha J. Carithers
    • , Ping Guan
    • , Helen M. Moore
    • , Abhi Rao
    •  & Jimmie B. Vaught
  89. National Institute of Dental and Craniofacial Research, Bethesda, Maryland 20892, USA

    • Latarsha J. Carithers
  90. Division of Genomic Medicine, National Human Genome Research Institute, Rockville, Maryland 20852, USA

    • Sarah E. Gould
    • , Nicole C. Lockart
    • , Casey Martin
    • , Jeffery P. Struewing
    •  & Simona Volpi
  91. Division of Neuroscience and Basic Behavioral Science, National Institute of Mental Health, NIH, Bethesda, Maryland 20892, USA

    • Anjene M. Addington
    •  & Susan E. Koester
  92. Division of Neuroscience and Behavior, National Institute on Drug Abuse, NIH, Bethesda, Maryland 20892, USA

    • A. Roger Little
  93. Washington Regional Transplant Community, Falls Church, Virginia 22003, USA

    • Lori E. Brigham
  94. Gift of Life Donor Program, Philadelphia, Pennsylvania 19103, USA

    • Richard Hasz
  95. LifeGift, Houston, Texas 77055, USA

    • Marcus Hunter
    • , Kevin Myer
    •  & Brian Roe
  96. Center for Organ Recovery and Education, Pittsburgh, Pennsylvania 15238, USA

    • Christopher Johns
    •  & Joseph Wheeler
  97. LifeNet Health, Virginia Beach, Virginia 23453, USA

    • Mark Johnson
    •  & Michael Washington
  98. National Disease Research Interchange, Philadelphia, Pennsylvania 19103, USA

    • Gene Kopen
    • , William F. Leinweber
    • , John T. Lonsdale
    • , Alisa McDonald
    • , Bernadette Mestichelli
    • , Michael Salvatore
    • , Saboor Shad
    • , Jeffrey A. Thomas
    •  & Gary Walters
  99. Unyts, Buffalo, New York 14203, USA

    • Jason Bridge
    •  & Mark Miklos
  100. Pharmacology and Therapeutics, Roswell Park Cancer Institute, Buffalo, New York 14263, USA

    • Barbara A. Foster
    • , Bryan M. Gillard
    • , Ellen Karasik
    • , Rachna Kumar
    •  & Michael T. Moser
  101. Van Andel Research Institute, Grand Rapids, Michigan 49503, USA

    • Scott D. Jewell
    • , Robert G. Montroy
    • , Daniel C. Rohrer
    •  & Dana R. Valley
  102. Brain Endowment Bank, Miller School of Medicine, University of Miami, Miami, Florida 33136, USA

    • David A. Davis
    •  & Deborah C. Mash
  103. National Institute of Allergy and Infectious Diseases, NIH, Rockville, Maryland 20852, USA

    • Anita H. Undale
  104. Biospecimen Research Group, Clinical Research Directorate, Leidos Biomedical Research, Inc., Rockville, Maryland 20852, USA

    • Anna M. Smith
    • , David E. Tabor
    • , Nancy V. Roche
    • , Jeffrey A. McLean
    • , Negin Vatanian
    • , Karna L. Robinson
    • , Leslie Sobin
    • , Kimberly M. Valentino
    • , Liqun Qi
    • , Steven Hunter
    • , Pushpa Hariharan
    • , Shilpi Singh
    • , Ki Sung Um
    • , Takunda Matose
    •  & Maria M. Tomaszewski
  105. Leidos Biomedical Research, Inc., Frederick, Maryland 21701, USA

    • Mary E. Barcus
  106. Temple University, Philadelphia, Pennsylvania 19122, USA

    • Laura K. Barker
    • , Laura A. Siminoff
    •  & Heather M. Traino
  107. Department of Health Behavior and Policy, School of Medicine, Virginia Commonwealth University, Richmond, Virginia 23298, USA

    • Maghboeba Mosavel
  108. European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton CB10 1SD, UK

    • Paul Flicek
    • , Thomas Juettemann
    • , Magali Ruffier
    • , Dan Sheppard
    • , Kieron Taylor
    • , Stephen J. Trevanion
    •  & Daniel R. Zerbino
  109. UCSC Genomics Institute, University of California Santa Cruz, Santa Cruz, California 95064, USA

    • Brian Craft
    • , Mary Goldman
    • , Maximilian Haeussler
    • , W. James Kent
    • , Christopher M. Lee
    • , Benedict Paten
    • , Kate R. Rosenbloom
    • , John Vivian
    •  & Jingchun Zhu


  1. GTEx Consortium

    Laboratory, Data Analysis & Coordinating Center (LDACC)—Analysis Working Group

    Statistical Methods groups—Analysis Working Group

    Enhancing GTEx (eGTEx) groups

    NIH Common Fund





    Biospecimen Collection Source Site—NDRI

    Biospecimen Collection Source Site—RPCI

    Biospecimen Core Resource—VARI

    Brain Bank Repository—University of Miami Brain Endowment Bank

    Leidos Biomedical—Project Management

    ELSI Study

    Genome Browser Data Integration & Visualization—EBI

    Genome Browser Data Integration & Visualization—UCSC Genomics Institute, University of California Santa Cruz


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T.T. and D.G.M. designed the study. A.-C.V. designed and conducted the scRNA-seq experiments. T.T., A.Y., M.A.R., M.A., L.G., M.F. and B.B.C. analysed the data. J.L.M., R.S., S.E.C., A.K., K.J.K., F.A., A.B., T.L., A.R., K.G.A., N.H. and D.G.M. provided tools and reagents. T.T. and D.G.M. wrote the manuscript with input from other authors.

Competing interests

D.G.M. is a founder with equity in Goldfinch Bio. The authors declare no other competing financial interests.

Corresponding authors

Correspondence to Taru Tukiainen or Daniel G. MacArthur.

Reviewer Information Nature thanks A. Clark and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Supplementary information

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  1. 1.

    Reporting Summary

  2. 2.

    Supplementary Information

    This file contains Supplementary Methods, a Supplementary Discussion, the Supplementary Table guide and a Supplementary Note describing the analysis of skew in XCI in GTEx female samples.

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  1. 1.

    Supplementary Tables

    This file contains Supplementary Tables 1-14.

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