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Abstract Background Approximately 5-10% of persons infected with M. tuberculosis develop tuberculosis, but the factors associated with disease progression are incompletely understood. Both linkage and association studies have identified human genetic variants associated with susceptibility to pulmonary tuberculosis, but few genetic studies have evaluated extrapulmonary disease. Because extrapulmonary and pulmonary tuberculosis likely have different underlying pathophysiology, identification of genetic mutations associated with extrapulmonary disease is important. Findings We performed a pilot genome-wide association study among 24 persons with previous extrapulmonary tuberculosis and well-characterized immune defects; 24 pulmonary tuberculosis patients and 57 patients with M. tuberculosis infection served as controls. The Affymetrix GeneChip Human Mapping Xba Array was used for genotyping; after careful quality control, genotypes at 44,175 single nucleotide polymorphisms (SNPs) were available for analysis. Eigenstrat quantified population stratification within our sample; logistic regression, using results of the Eigenstrat analysis as a covariate, identified significant associations between groups. Permutation testing controlled the family-wise error rate for each comparison between groups. Four SNPs were significantly associated with extrapulmonary tuberculosis compared to controls with M. tuberculosis infection; one (rs4893980) in the gene PDE11A, one (rs10488286) in KCND2, and one (rs2026414) in PCDH15; one was in chromosome 7 but not associated with a known gene. Two additional variants were significantly associated with extrapulmonary tuberculosis compared with pulmonary tuberculosis; one (rs340708) in the gene FAM135B and one in chromosome 13 but not associated with a known gene. The function of all four genes affects cell signaling and activity, including in the brain. Conclusions In this pilot study, we identified 6 novel variants not previously known to be associated with extrapulmonary tuberculosis, including two SNPs more common in persons with extrapulmonary than pulmonary tuberculosis. This provides some support for the hypothesis that the pathogenesis and genetic predisposition to extrapulmonary tuberculosis differs from pulmonary tuberculosis. Further study of these novel SNPs, and more well-powered genome-wide studies of extrapulmonary tuberculosis, is warranted.

Abstract Background Multifactor Dimensionality Reduction (MDR) is a novel method developed to detect gene-gene interactions in case-control association analysis by exhaustively searching multi-locus combinations. While the end-goal of analysis is hypothesis generation, significance testing is employed to indicate statistical interest in a resulting model. Because the underlying distribution for the null hypothesis of no association is unknown, non-parametric permutation testing is used. Lately, there has been more emphasis on selecting all statistically significant models at the end of MDR analysis in order to avoid missing a true signal. This approach opens up questions about the permutation testing procedure. Traditionally omnibus permutation testing is used, where one permutation distribution is generated for all models. An alternative is n-locus permutation testing, where a separate distribution is created for each n-level of interaction tested. Findings In this study, we show that the false positive rate for the MDR method is at or below a selected alpha level, and demonstrate the conservative nature of omnibus testing. We compare the power and false positive rates of both permutation approaches and find omnibus permutation testing optimal for preserving power while protecting against false positives. Conclusion Omnibus permutation testing should be used with the MDR method.

Abstract Background With the advent of increasingly efficient means to obtain genetic information, a great insurgence of data has resulted, leading to the need for methods for analyzing this data beyond that of traditional parametric statistical approaches. Recently we introduced Grammatical Evolution Neural Network (GENN), a machine-learning approach to detect gene-gene or gene-environment interactions, also known as epistasis, in high dimensional genetic epidemiological data. GENN has been shown to be highly successful in a range of simulated data, but the impact of error common to real data is unknown. In the current study, we examine the power of GENN to detect interesting interactions in the presence of noise due to genotyping error, missing data, phenocopy, and genetic heterogeneity. Additionally, we compare the performance of GENN to that of another computational method – Multifactor Dimensionality Reduction (MDR). Findings GENN is extremely robust to missing data and genotyping error. Phenocopy in a dataset reduces the power of both GENN and MDR. GENN is reasonably robust to genetic heterogeneity and find that in some cases GENN has substantially higher power than MDR to detect functional loci in the presence of genetic heterogeneity. Conclusion GENN is a promising method to detect gene-gene interaction, even in the presence of common types of error found in real data.