XWAS 2.0

 

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.

New in XWAS version 2.0 is a sex-aware genotype calling pipeline for the X chromosome from raw intensity data based on an Affymetrix genotype array. This pipeline, based on BIRDSUITE, detects and reports genotypes and their likelihoods for each SNP-by-individual combination. It formats this information into a dataset that the user can use for continued XWAS analysis. If further allows comparing and summarizing the differences from another set of genotype calls and visualization of the genotyping intensity clusters through Evoker. XWAS 2.0 also improves on the unique gene-based tests for gene-gene interactions. New functionality allows the user to input two sets of genes (or any other loci) and test for interaction between each gene in the first set and each gene in the second. The version also features new options in the quality control pipeline, various bug fixes, and improved troubleshooting measures. See below for a full explanation of the XWAS software.

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XWAS version 2.0 begins with an optional sex-aware genotype calling pipeline for the X chromosome from raw intensity data based on an Affymetrix genotype array. The pipeline detects and reports genotypes and their likelihoods for each SNP-by-individual combination. It formats this information into a dataset that the user can use for continued XWAS analysis.

The software then provides 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. Tests include:

  1. Standard tests of association with disease risk, with male hemizygotes considered 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. Testing 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
  5. A modified epistasis test that uses a t-statistic for quantitative traits
  6. A convenient command "--run-all" to run several X-related tests at once
  7. For all tests, a version of gene-based test that combines evidence from all variants in each X-linked genes
  8. Tests for gene-gene interaction on a gene-based level, which combine SNP-by-SNP interaction tests between all pairs of SNPs in two or more genes to produce a gene-level test for interaction

The XWAS software package is implemented in C++ and scripts. It includes functions from PLINK. It is freely available for academic usage and offered open source. It is optimized for LINUX, but can be compiled for Windows and MAC OS.

To download our previous release, visit the web page of our previous version XWAS v1.1.

Contact

The Keinan Lab will continue to support and update the XWAS package on a regular basis. We will often distribute new versions with additional functionality and any needed fixes.

Specifically, we are developing functionality for sequence-based associations studies, including tailored variant discovery and powerful X-linked tests for rare variant association studies (RVAS) that build on the unique population genetics of X.

For receiving updates about future versions or any bugs, please sign up for our mailing list above.

Please also report bugs or ask questions by sending emails to keinanlab.xwas@gmail.com.

Our lab is also involved in many collaborative projects, including projects in which we apply our expertise and software for analysis of the X chromosome to existing data from genome-wide association studies (thus far we have analyzed >100 GWAS and found >20 novel X-linked risk factors or QTLs). We welcome additional such collaborations. Please email Alon Keinan (alon.keinan@cornell.edu) for exploring such opportunities.

Selected Recent Related Publications (How to Cite)

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

Gao F, Chang D, Biddanda A, Ma L, Guo Y, Zhou Z, Keinan A. "XWAS: a toolset for genetic data analysis and association studies of the X chromosome." Journal of Heredity. 2015;106(5):6.

Chang D, Gao F, Slavney A, Ma L, Waldman Y, Sams A, Billing-Ross P, Madar A, Spritz R, Keinan A. "Accounting for eXentricities: Analysis of the X Chromosome in GWAS Reveals X-Linked Genes Implicated in Autoimmune Diseases.PLoS ONE. 2014;9(12):e113684.

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

Ma L, Hoffman G, Keinan A. "X-inactivation informs variance-based testing for X-linked association of a quantitative trait." BMC Genomics. 2015;16:241.

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

Ma L, Clark AG, Keinan A. "Gene-based testing of interactions in association studies of quantitative traits."PLoS Genet.. 2013;9:e1003321