DOI: 10.5061/dryad.6wwpzgn2s
title: Multi-ancestry meta-analysis of host genetic susceptibility to tuberculosis identifies shared genetic architecture

description: The heritability of susceptibility to tuberculosis disease (TB) has been
 well recognized. Over one-hundred genes have been studied as candidates
 for TB susceptibility, and several variants were identified by genome-wide
 association studies (GWAS), but few replicates. We established the
 International Tuberculosis Host Genetics Consortium (ITHGC) to perform a
 multi-ancestry meta-analysis of GWAS including 14,153 cases and 19,536
 controls of African, Asian, and European ancestry. Our analyses
 demonstrate a substantial degree of heritability (pooled polygenic
 h2=26.3% 95% CI 23.7–29.0%) for susceptibility to TB that is shared across
 ancestries, highlighting an important host genetic influence on disease.
 We identified one global host genetic correlate for TB at genome-wide
 significance (p &lt; 5x10-8) in the human leukocyte antigen (HLA)-II
 region (rs28383206, p-value = 5.2x10-9). These data demonstrate the
 complex shared genetic architecture of susceptibility to TB and the
 importance of large-scale GWAS analysis across multiple ancestries
 experiencing different levels of infection pressures.
description: This analysis includes 12 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 the 17 published (and unpublished) GWAS studies of TB (with HIV-negative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. 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 four datasets with only summary statistics
 available were imputed and QC’d during the original investigations, but
 the marker names and allele orientation were checked for concordance
 between the summary statistics and the rest of the consortium’s imputed
 data.
description: Summary statistics are stored as compressed .zip files.
 <br>Unzipped files can be opened with any text editor, Excel, or
 read into R programming environment (or equivalent data analysis
 software)' note the files are space-separated and have a header row.
 Each row in the files contains information for a single SNP. 
 All authors have approved sharing the summary statistics of the
 analysis. The original individual datasets are only available from request
 from the original authors.