Back

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.

2025-03-14 genetic and genomic medicine
10.1101/2025.03.13.25323932 medRxiv
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.

50% of probability mass above

"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.