Dataset Information

Investigating the Role of Gene-Gene Interactions in TB Susceptibility.

ABSTRACT: Tuberculosis (TB) is the second leading cause of mortality from infectious disease worldwide. One of the factors involved in developing disease is the genetics of the host, yet the field of TB susceptibility genetics has not yielded the answers that were expected. A commonly posited explanation for the missing heritability of complex disease is gene-gene interactions, also referred to as epistasis. In this study we investigate the role of gene-gene interactions in genetic susceptibility to TB using a cohort recruited from a high TB incidence community from Cape Town, South Africa. Our discovery data set incorporates genotypes from a large a number of candidate gene studies as well as genome-wide data. After limiting our search space to pairs of putative TB susceptibility genes, as well as pairs of genes that have been curated in online databases as potential interactors, we use statistical modelling to identify pairs of interacting SNPs. We attempt to validate the top models identified in our discovery data set using an independent genome-wide TB case-control data set from The Gambia. A number of models were successfully validated, indicating that interplay between the NRG1 - NRG3, GRIK1 - GRIK3 and IL23R - ATG4C gene pairs may modify susceptibility to TB. Gene pairs involved in the NF-?B pathway were also identified in the discovery data set (SFTPD - NOD2, ISG15 - TLR8 and NLRC5 - IL12RB1), but could not be tested in the Gambian study group due to lack of overlapping data.

SUBMITTER: Daya M

PROVIDER: S-EPMC4412713 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Investigating the Role of Gene-Gene Interactions in TB Susceptibility.

Daya Michelle M van der Merwe Lize L van Helden Paul D PD Möller Marlo M Hoal Eileen G EG

PloS one 20140101 4

Tuberculosis (TB) is the second leading cause of mortality from infectious disease worldwide. One of the factors involved in developing disease is the genetics of the host, yet the field of TB susceptibility genetics has not yielded the answers that were expected. A commonly posited explanation for the missing heritability of complex disease is gene-gene interactions, also referred to as epistasis. In this study we investigate the role of gene-gene interactions in genetic susceptibility to TB us ...[more]

PMID: 25919455

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Project description:Mycobacterium tuberculosis (Mtb) is extremely recalcitrant to antimicrobial chemotherapy requiring 6 months to treat drug-sensitive tuberculosis (TB). Despite this, 4-10% of cured patients will develop recurrent disease within 12 months after completing therapy. Reasons for relapse in cured TB patients remains speculative, attributed to both pathogen and host factors. Populations of dormant bacilli are hypothesized to cause relapse in initially cured TB patients however, development of tests to convincingly demonstrate their presence at the end of anti-TB treatment has been challenging. Previous studies have indicated the utility of culture filtrate supplemented media (CFSM) to detect differentially culturable tubercle bacilli (DCTB). Here, we show that 3/22 of clinically cured patients retained DCTB in induced sputum and bronchoalveolar lavage fluid (BALF), with one DCTB positive patient relapsing within the first year of completing therapy. We also show a correlation of DCTB status with "unresolved" end of treatment FDG PET-CT imaging. Additionally, 19 end of treatment induced sputum samples from patients not undergoing bronchoscopy were assessed for DCTB, identifying a further relapse case with DCTB. We further show that induced sputum is a less reliable source for the DCTB assay at the end of treatment, limiting the utility of this assay in a clinical setting. We next investigated the host proteome at the site of disease (BALF) using multiplexed proteomic analysis and compared these to active TB cases to identify host-specific factors indicative of cure. Distinct signatures stratified active from cured TB patients into distinct groups, with a DCTB positive, subsequently relapsing, end of treatment patient showing a proteomic signature closer to active TB disease than cure. This exploratory study offers evidence of live Mtb, undetectable with conventional culture methods, at the end of clinically successful treatment and putative host protein biomarkers of active disease and cure. These findings have implications for the assessment of true sterilizing cure in TB patients and opens new avenues for targeted approaches to monitor treatment response.

Project description:BackgroundThe risk of common diseases is likely determined by the complex interplay between environmental and genetic factors, including single nucleotide polymorphisms (SNPs). Traditional methods of data analysis are poorly suited for detecting complex interactions due to sparseness of data in high dimensions, which often occurs when data are available for a large number of SNPs for a relatively small number of samples. Validation of associations observed using multiple methods should be implemented to minimize likelihood of false-positive associations. Moreover, high-throughput genotyping methods allow investigators to genotype thousands of SNPs at one time. Investigating associations for each individual SNP or interactions between SNPs using traditional approaches is inefficient and prone to false positives.ResultsWe developed the Polymorphism Interaction Analysis tool (PIA version 2.0) to include different approaches for ranking and scoring SNP combinations, to account for imbalances between case and control ratios, stratify on particular factors, and examine associations of user-defined pathways (based on SNP or gene) with case status. PIA v. 2.0 detected 2-SNP interactions as the highest ranking model 77% of the time, using simulated data sets of genetic models of interaction (minor allele frequency = 0.2; heritability = 0.01; N = 1600) generated previously [Velez DR, White BC, Motsinger AA, Bush WS, Ritchie MD, Williams SM, Moore JH: A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction. Genet Epidemiol 2007, 31:306-315.]. Interacting SNPs were detected in both balanced (20 SNPs) and imbalanced data (case:control 1:2 and 1:4, 10 SNPs) in the context of non-interacting SNPs.ConclusionPIA v. 2.0 is a useful tool for exploring gene*gene or gene*environment interactions and identifying a small number of putative associations which may be investigated further using other statistical methods and in replication study populations.

Dataset Information

Investigating the Role of Gene-Gene Interactions in TB Susceptibility.

Publications

Investigating the Role of Gene-Gene Interactions in TB Susceptibility.

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