DOI: 10.5061/dryad.2z34tmpv5
title: X-linked multi-ancestry meta-analysis reveals tuberculosis susceptibility variants

description: Globally, tuberculosis (TB) presents with a clear male bias that cannot be
 completely accounted for by environment, behaviour, socioeconomic factors,
 or the impact of sex hormones on the immune system. This suggests that
 genetic and biological differences, which may be mediated by the X
 chromosome, further influence the observed male sex bias. The X chromosome
 is heavily implicated in immune function and yet has largely been ignored
 in previous association studies. Here we report the first multi-ancestry X
 chromosome specific meta-analysis on TB susceptibility. We identified
 X-linked TB susceptibility variants using seven genotyping data sets and
 20,255 individuals from diverse genetic ancestries. Sex-specific effects
 were also identified in polygenic heritability between males and females
 along with enhanced concordance in direction of genetic effects for males
 but not females. These sex-specific genetic effects were supported by a
 sex-stratified and combined meta-analysis conducted using the X chromosome
 specific XWAS software and a multi-ancestry analysis using the MR-MEGA
 software. Seven significant associations were identified. Two in the
 overall analysis (rs6610096, rs7888114) and a second for the female
 specific analysis (rs4465088) including all data sets. For the ancestry
 specific meta-analysis three significant associations were identified for
 males in the Asian cohorts (rs1726176, rs5939510, rs1726203) and one in
 females for the African cohort (rs2428212). Several genomic regions
 previously associated with TB susceptibility were reproduced in this
 study, along with strong ancestry-specific effects. These results support
 the hypothesis that the X chromosome and sex-specific effects could
 significantly impact the observed male bias in TB incidence rates
 globally.   
description: This analysis includes 7 of the 17 published (and unpublished)
 GWAS studies of TB (with HIV-negative cohorts) prior to 2022. It excludes
 data from Iceland and Vietnam, as they declined to share data. It excludes
 data from China, Korea, Peru, and Japan, as data-sharing agreements could
 not be finalized in time for this analysis. The Indonesian data was not
 suitable for reliable imputation, and the Moroccan data was family-based
 and thus also not suitable for this meta-analysis. Data from Thailand,
 Japan, Estonia and Germany were excluded as they did not have X chromosome
 genotyping. Finally, genotyped TB cases and controls are also available in
 the UK Biobank, but this data was not included in this analysis as genetic
 association studies on such highly selected datasets need to be undertaken
 with caution, and to not bias results, were excluded for this
 analysis. Included individuals were genotyped on a
 variety of genotyping arrays, and raw genotyping data were available for
 eight datasets, and for the remainder, association testing summary
 statistics were obtained to use in the meta-analysis. Quality control (QC)
 and imputation of the data with raw genotyping information available was
 done using Plink (v1.9), followed by pre-phasing using SHAPEIT and Impute2
 with the 1000 genomes phase 3 reference panel. QC and imputation were done
 as described previously; briefly we used a minor allele filter of 0.025
 and an individual and SNP missingness filter of 0.1. Hardy–- equilibrium
 threshold was set at a Bonferroni corrected p-value according to the
 number of SNPs testes (0.05/number of SNPs) and samples where sex could
 not be determined from genotyping were also removed. Imputed data were
 filtered at a quality score of 0.3, prior to individual and genotype
 filtration steps. Prior to QC and imputation, allele orientation was
 corrected using Genotype Harmoniser version 1.4.15, and the genome build
 of all datasets was checked for consistency (GRCh37) and updated if
 necessary, using the liftOver software from the UCSC genome browser. The
 final imputed datasets again went through a QC process, but this time in a
 sex-stratified manner using the XWAS software and XWAS pipeline.
 Sex-stratified GWAS analysis of the individual datasets was then done
 using the XWAS software. The results from the sex-stratified GWAS were
 then combined in a sex-stratified and combined meta-analysis using the
 XWAS software. The results of the sex-stratified and combined
 meta-analysis are uploaded here.
description: # X-linked multi-ancestry meta-analysis reveals tuberculosis
 susceptibility variants
 [https://doi.org/10.5061/dryad.2z34tmpv5](https://doi.org/10.5061/dryad.2z34tmpv5) For this X-chromosome specific sex-stratified meta-analysis multiple analysis were conducted. First, we did sex-stratified association analysis on all the individual datasets using the XWAS software and then the results were combined in multiple meta-analysis (also using XWAS). The results for the individual datasets are not available in this repository but can be requested through the corresponding authors in the published manuscript. The results for the meta-analysis are available in this repository. Multiple meta-analysis was conducted, a combined meta-analysis and a sex stratified meta-analysis, which were also further stratified by the source population subgroup. The following meta-analysis were conducted: 1.      A combined meta-analysis using data across all populations for males and females.    2.      A sex-stratified meta-analysis including data across all populations. 3.      A combined meta-analysis for the Asian, Euroasian and African populations 4.      Sex-stratified meta-analysis for the Asian, Euroasian and African populations. The results from this analysis identified novel genetic variants with strong sex-specific effects. While previous X-linked associations were not duplicated in this study the analysis revealed associations in genomic regions that overlap with previous studies.   ## Description of the data and file structure Files included in this repository: 1.      Plink_male_female_combined_meta_analysis_all_cohorts.meta a.      Meta-analysis containing results from all datasets from males and females (not stratified by sex or ancestry). b.      Produced using PLINK software 2.      XWAS_female_ALL_cohorts_meta_analysis.meta a.      Meta-analysis containing results from the females of all datasets. (not stratified by ancestry) b.      Produced using the XWAS software 3.      XWAS_male_ALL_cohorts_meta_analysis.meta a.      Meta-analysis containing results from the males of all datasets. (not stratified by ancestry) b.      Produced using the XWAS software 4.      XWAS_female_chinese_cohorts_meta_analysis.meta a.      Meta-analysis containing results from the females of all datasets of Asian ancestry. b.      Produced using the XWAS software 5.      XWAS_male_chinese_cohorts_meta_analysis.meta a.      Meta-analysis containing results from the males of all datasets of Asian ancestry. b.      Produced using the XWAS software 6.      XWAS_female_euroasian_cohorts_meta_analysis.meta a.      Meta-analysis containing results from the females of all datasets of Asian and European ancestry. b.      Produced using the XWAS software 7.      XWAS_male_euroasian_cohorts_meta_analysis.meta a.      Meta-analysis containing results from the males of all datasets of Asian and European ancestry. b.      Produced using the XWAS software 8.      XWAS_female_african_cohorts_meta_analysis.meta a.      Meta-analysis containing results from the females of all datasets of African ancestry. b.      Produced using the XWAS software 9.      XWAS_male_african_cohorts_meta_analysis.meta a.      Meta-analysis containing results from the males of all datasets of African ancestry. b.      Produced using the XWAS software The files contain the association testing results for all variants on the X chromosome.   For the meta-analysis output files the column descriptions are as follows: **CHR:** Chromosome number ‘23’ representing the X chromosome **BP:** The base pair position of the genetic variants (build 37 locations) **SNP:** SNP names presented as ‘CHR:BP’ **A1:** Major allele **A2:** Minor allele **N:** Number of studies in the meta-analysis for each variant **P:** P-value of the association testing **P(R):** P-value of the residual **OR:** Odds ratio of the association testing for each variant **OR(R):** Odds ratio of the residual **Q:** Cochran’s Q measure of heterogeneity, which is calculated as the weighted sum of squared differences between individual study effects and the pooled effect across studies **I:** The I² statistic describes the percentage of variation across studies that is due to heterogeneity rather than chance ## Sharing/Access information The meta-analysis results can be downloaded from this repository. The original raw data and summary statistics of the association testing of the individual files are not available due to ethical and data sharing constraints. These files can be requested through the corresponding authors listed in the published manuscript. Data was derived from the following sources: * International Tuberculosis Host Genetics Consortium ## Code/Software Imputation of the individual data was done using the Impute2 software. Quality control of the data was done using PLINK and XWAS software. Association testing of the individual files and subsequent meta-analysis were also performed using the PLINK and XWAS software.