XWAS 1.1


XWAS (chromosome X-Wide Analysis toolSet) is a software suite for the analysis of the X chromosome in association analyses and similar studies.

The X chromosome plays an important role in complex human traits and diseases, especially those with sexually dimorphic characteristics. Special attention needs to be given to analysis of X due to its unique inheritance pattern and X-inactivation. These lead to several analytical complications that have resulted in the majority of genome-wide association studies (GWAS) to date having excluded the X chromosome or otherwise mishandled it by applying the same tools designed for non-sex chromosomes. With XWAS, we hope to provide the tools and incentive to incorporate the X chromosome into GWAS, hence enabling discoveries of novel loci implicated in many diseases and in their sexual dimorphism.


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  • Software Usage Manual (Stand alone file in PDF format. Size 186 Kb)

  • Package (Binaries, manual, auxiliary files, and examples as a gzip compressed tar archive. Size 13 Mb)

  • Source Code (XWAS code in C++ as gzip compressed tar archive. Size 584 Kb)

  • Legend file for imputation (Updated legend file with MAF for multiple continental populations in 1000G Phase III. Size 67 Mb)

XWAS version 1.1 starts with extensive QC procedures that are tailored to X (e.g. filter variants with artifactual sex difference in allele frequency or level of missingness in control; apply QC separately to males and females and consider only the intersection of remaining SNPs; remove pseudoautosomal regions). It then removes related individuals in the sample in a fine-grained resolution to preserve as many individuals as possible. Followed is an optional imputation pipeline based on IMPUTE2.

The software offers an array of statistical tests of association of X-linked markers, while correcting for population stratification based on EIGENSTRAT and controlling for any other covariates, including (1) standard tests of association with disease risk, with male hemizygotes as equivalent to female homozygotes by counting each male allele twice (0/2 coding), including the variant of this test implemented by Clayton, or by considering each male allele as a single copy (0/1 coding), (2) testing for association in each sex separately and combining into a single statistic using one of two methods: Fisher’s method that accommodates the possibility of sex-differential effect size/direction and is not affected by allele coding (0/2 or 0/1), and Stouffer’s method, which weighs evidence from each sex proportionally to sample size, (3) test for sex-differences in effect size/direction based on the above sex-stratified tests, (4) motivated by X-inactivation, a test for higher variance of a trait in heterozygous females as compared to homozygous females, as well as a weighted regression test of the mean that accounts for this potential inflation in phenotypic variance due to X-inactivation, and a test that combines the two tests. (7) a modified epistasis test that uses a t statistic for quantitative traits, (8) a convenient command "--run-all" to run several X-related tests at once, (9) for all these tests, a version of gene-based test that combines evidence from all variants in each X-linked genes, and (10) tests for gene-gene interaction on a gene-based level, which combine SNP-by-SNP interaction tests between all pairs of SNPs in two genes to produce a gene-level test for interaction between the two.

The XWAS software package is implemented in C++ and scripts that includes in part functions from PLINK. It is freely available for research usage, including open source. It is optimized for LINUX, but can be compiled for Windows and MAC OS. Please find download options below.


The Keinan Lab is continually developing and updating XWAS and new features/fixes will be distributed often. Future versions of the software will also consider sequence-based association studies, with tests designed for testing rare variants on the X chromosome. Please join our mailing list by signing up above. Please report bugs or ask questions by sending emails to keinanlab.xwas@gmail.com. We are also collaborating with several groups by running desired features of XWAS on their data, and we welcome additional such collaborations. Please email Alon Keinan (alon.keinan@cornell.edu) regarding such collaborative opportunities.

Selected Recent Related Publications (How to Cite)

1. Please cite these papers if you use the basic QC or association methods:

Gao FChang DBiddanda A, et al. "XWAS: a toolset for genetic data analysis and association studies of the X chromosome." Journal of Heredity. 2015;106(5):6.

Chang DGao FSlavney A, et al. "Accounting for eXentricities: Analysis of the X Chromosome in GWAS Reveals X-Linked Genes Implicated in Autoimmune Diseases.PLoS ONE. 2014;9(12):e113684.

2. Please cite this paper if you use the X-inactivation-based methods:

Ma LHoffman GKeinan A. "X-inactivation informs variance-based testing for X-linked association of a quantitative trait." BMC Genomics. 2015;16:241.

3. Please cite this paper if you use gene-based tests for interaction:

Ma LClark AGKeinan A. "Gene-based testing of interactions in association studies of quantitative traits."PLoS Genet.. 2013;9:e1003321