Genome-wide association study in Brazil identifies risk factor-adjusted genetic susceptibility to pulmonary tuberculosis with cell-specific gene expression effects
Dill-McFarland, K. A.; Andrade, B. B.; Figueiredo, M. C.; Andrade, A. M.; Avendano-Rangel, F.; Cordeiro-Santos, M.; Kritski, A. L.; Rolla, V. C.; Cubillos-Angulo, J. M.; Kalams, S. A.; Simmons, J. D.; Oakes, J. M.; Pena Avila, J.; Nakaya, H. I.; Gangula, R. D.; Rebeiro, P. F.; Amorim, G.; Mallal, S. A.; Regional Prospective Observational Research in Tuberculosis (RePORT)-Brazil Consortium, ; Sterling, T. R.; Hawn, T. R.
Show abstract
Although genetic factors contribute to tuberculosis (TB) risk, no cross-population causal variants have been identified by genome-wide association studies (GWAS). Here, we utilized low-pass whole genome sequencing (lpWGS) with imputation plus detailed epidemiologic risk factors and single-cell expression quantitative loci (sceQTL) to address prior GWAS limitations. Using 947 pulmonary tuberculosis (PTB) cases and 1807 close contact controls in the Regional Prospective Observational Research in TB (RePORT) study in Brazil, we estimated PTB heritability to be 47.7%. We identified 19 SNPs associated with PTB (P<5E-8) after adjustment for major risk factors (HIV, diabetes, smoking). Seven of these SNPs were associated with peripheral blood cell-specific sceQTLs in controls. Specifically, SNPs cis to transcription factors ZNF717 and MAML3 were associated with PTB disease and gene expression in monocytes, T cells, or B cells. Overall, this study utilized lpWGS, in-depth epidemiology, and single-cell analyses to detect population-specific genetic risk factors for PTB in Brazil. SUMMARYRobust correction for tuberculosis risk factors in GWAS in combination with paired single-cell transcriptomics reveals novel genetic risk of pulmonary tuberculosis with measurable consequences for baseline gene expression in multiple cell types.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Multivariate adaptive shrinkage improves cross-population transcriptome prediction for transcriptome-wide association studies in underrepresented populations 94%
- A year of COVID-19 GWAS results from the GRASP portal reveals potential SARS-CoV-2 modifiers 93%
- Polygenic risk score prediction accuracy convergence 93%
Similar papers in this journal
- Widespread recessive effects on common diseases in a cohort of 44,000 British Pakistanis and Bangladeshis with high autozygosity 94%
- A phenome-wide association study identifies effects of copy number variation of VNTRs and multicopy genes on multiple human traits 94%
- Disentangling mechanisms behind the pleiotropic effects of proximal 16p11.2 BP4-5 CNVs 94%
Similar papers in this journal
- Circulating Cell-Free RNA in Blood as a Host Response Biomarker for the Detection of Tuberculosis 95%
- Accounting for diverse evolutionary forces reveals the mosaic nature of selection on genomic regions associated with human preterm birth 94%
- USP18 modulates lupus risk via negative regulation of interferon response 94%
Similar papers in this journal
- Genome-wide analysis in 756,646 individuals provides first genetic evidence that ACE2 expression influences COVID-19 risk and yields genetic risk scores predictive of severe disease 95%
- Large scale genome-wide association study in a Japanese population identified 45 novel susceptibility loci for 22 diseases 94%
- Sequencing of over 100,000 individuals identifies multiple genes and rare variants associated with Crohns disease susceptibility 94%
Similar papers in this journal
- Large Registry Based Analysis of Genetic Predisposition to Tuberculosis Identifies Genetic Risk Factors at HLA 94%
- Genetic risk factors and Covid-19 severity in Brazil: results from BRACOVID Study 93%
- GWAS in Africans identifies novel lipids loci and demonstrates heterogenous association within Africa 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.