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The natural history of tuberculosis (TB) is characterized by a large inter-individual outcome variability after exposure to Mycobacterium tuberculosis. Specifically, some highly exposed individuals remain resistant to M. tuberculosis infection, as inferred by tuberculin skin test (TST) or interferon-gamma release assays (IGRAs). We performed a genome-wide association study of resistance to M. tuberculosis infection in an endemic region of Southern Vietnam. We enrolled household contacts (HHC) of pulmonary TB cases and compared subjects who were negative for both TST and IGRA (n = 185) with infected individuals (n = 353) who were either positive for both TST and IGRA or had a diagnosis of TB. We found a genome-wide significant locus on chromosome 10q26.2 with a cluster of variants associated with strong protection against M. tuberculosis infection (OR = 0.42, 95%CI 0.35–0.49, P = 3.71×10−8, for the genotyped variant rs17155120). The locus was replicated in a French multi-ethnic HHC cohort and a familial admixed cohort from a hyper-endemic area of South Africa, with an overall OR for rs17155120 estimated at 0.50 (95%CI 0.45–0.55, P = 1.26×10−9). The variants are located in intronic regions and upstream of C10orf90, a tumor suppressor gene which encodes an ubiquitin ligase activating the transcription factor p53. In silico analysis showed that the protective alleles were associated with a decreased expression in monocytes of the nearby gene ADAM12 which could lead to an enhanced response of Th17 lymphocytes. Our results reveal a novel locus controlling resistance to M. tuberculosis infection across different populations., Author summary There is strong epidemiological evidence that a proportion of highly exposed individuals remain resistant to M. tuberculosis infection, as shown by a negative result for Tuberculin Skin Test (TST) or IFN-γ Release Assays (IGRAs). We performed a genome-wide association study between resistant and infected individuals, which were carefully selected employing a household contact design to maximize exposure by infectious index patients. We employed stringently defined concordant results for both TST and IGRA assays to avoid misclassifications. We discovered a locus at 10q26.2 associated with resistance to M. tuberculosis infection in a Vietnamese discovery cohort. This locus could be replicated in two independent cohorts from different epidemiological settings and of diverse ancestries enrolled in France and South Africa.

Complex traits are known to be influenced by a combination of environmental factors and rare and common genetic variants. However, detection of such multivariate associations can be compromised by low statistical power and confounding by population structure. Linear mixed effects models (LMM) can account for correlations due to relatedness but have not been applicable in high-dimensional (HD) settings where the number of fixed effect predictors greatly exceeds the number of samples. False positives or false negatives can result from two-stage approaches, where the residuals estimated from a null model adjusted for the subjects’ relationship structure are subsequently used as the response in a standard penalized regression model. To overcome these challenges, we develop a general penalized LMM with a single random effect called ggmix for simultaneous SNP selection and adjustment for population structure in high dimensional prediction models. We develop a blockwise coordinate descent algorithm with automatic tuning parameter selection which is highly scalable, computationally efficient and has theoretical guarantees of convergence. Through simulations and three real data examples, we show that ggmix leads to more parsimonious models compared to the two-stage approach or principal component adjustment with better prediction accuracy. Our method performs well even in the presence of highly correlated markers, and when the causal SNPs are included in the kinship matrix. ggmix can be used to construct polygenic risk scores and select instrumental variables in Mendelian randomization studies. Our algorithms are available in an R package available on CRAN (https://cran.r-project.org/package=ggmix)., Author summary This work addresses a recurring challenge in the analysis and interpretation of genetic association studies: which genetic variants can best predict and are independently associated with a given phenotype in the presence of population structure? Not controlling confounding due to geographic population structure, family and/or cryptic relatedness can lead to spurious associations. Much of the existing research has therefore focused on modeling the association between a phenotype and a single genetic variant in a linear mixed model with a random effect. However, this univariate approach may miss true associations due to the stringent significance thresholds required to reduce the number of false positives and also ignores the correlations between markers. We propose an alternative method for fitting high-dimensional multivariable models, which selects SNPs that are independently associated with the phenotype while also accounting for population structure. We provide an efficient implementation of our algorithm and show through simulation studies and real data examples that our method outperforms existing methods in terms of prediction accuracy and controlling the false discovery rate.

Leprosy Type-1 Reactions (T1Rs) are pathological inflammatory responses that afflict a sub-group of leprosy patients and result in peripheral nerve damage. Here, we employed a family-based GWAS in 221 families with 229 T1R-affect offspring with stepwise replication to identify risk factors for T1R. We discovered, replicated and validated T1R-specific associations with SNPs located in chromosome region 10p21.2. Combined analysis across the three independent samples resulted in strong evidence of association of rs1875147 with T1R (p = 4.5x10-8; OR = 1.54, 95% CI = 1.32–1.80). The T1R-risk locus was restricted to a lncRNA-encoding genomic interval with rs1875147 being an eQTL for the lncRNA. Since a genetic overlap between leprosy and inflammatory bowel disease (IBD) has been detected, we evaluated if the shared genetic control could be traced to the T1R endophenotype. Employing the results of a recent IBD GWAS meta-analysis we found that 10.6% of IBD SNPs available in our dataset shared a common risk-allele with T1R (p = 2.4x10-4). This finding points to a substantial overlap in the genetic control of clinically diverse inflammatory disorders., Author summary Leprosy still affects approximately 200,000 new victims each year. A major challenge of leprosy control is the prevention of permanent disability due to nerve damage. Nerve damage occurs if leprosy remains undiagnosed for extended periods or when patients undergo pathological inflammatory responses termed Type-1 Reactions (T1R). T1R is a rare example where beneficial inflammatory responses are temporal separated from host pathological responses. There is strong experimental evidence that supports a role of host genetic factors in T1R susceptibility. Here, we employed a genome-wide association study (GWAS) to investigate susceptibility factors for T1R in Vietnamese families. We followed up the initial GWAS findings in independent population samples from Vietnam and Brazil and identified a set of cis-eQTL genetic variants for the ENSG00000235140 lncRNA as global risk factors for T1R. To test our proposal that T1R is a strong model for pathological inflammatory responses we evaluated if inflammatory bowel disease (IBD) genetic risk-factors were enriched among T1R risk factors. We observed that more than 10% of IBD-risk loci were nominally associated with risk for T1R suggesting a shared mechanism of excessive inflammatory response in the both disease etiologies.

Leprosy reversal reactions type 1 (T1R) are acute immune episodes that affect a subset of leprosy patients and remain a major cause of nerve damage. Little is known about the relative importance of innate versus environmental factors in the pathogenesis of T1R. In a retrospective design, we evaluated innate differences in response to Mycobacterium leprae between healthy individuals and former leprosy patients affected or free of T1R by analyzing the transcriptome response of whole blood to M. leprae sonicate. Validation of results was conducted in a subsequent prospective study. We observed the differential expression of 581 genes upon exposure of whole blood to M. leprae sonicate in the retrospective study. We defined a 44 T1R gene set signature of differentially regulated genes. The majority of the T1R set genes were represented by three functional groups: i) pro-inflammatory regulators; ii) arachidonic acid metabolism mediators; and iii) regulators of anti-inflammation. The validity of the T1R gene set signature was replicated in the prospective arm of the study. The T1R genetic signature encompasses genes encoding pro- and anti-inflammatory mediators of innate immunity. This suggests an innate defect in the regulation of the inflammatory response to M. leprae antigens. The identified T1R gene set represents a critical first step towards a genetic profile of leprosy patients who are at increased risk of T1R and concomitant nerve damage., Author Summary Leprosy type 1 reversal reactions (T1R) are an important cause of nerve damage in leprosy patients and accurate prediction of patients at increased risk of T1R is a major challenge of current leprosy control. The incidence of T1R differs widely from 6% to 67% of leprosy patients in different leprosy endemic settings. Whether or not this reflects the impact of unknown environmental triggers or differences in the genetic background across ethnicities is not known. We performed a comparative transcriptome analysis between leprosy patients affected and free of T1R in response to M. leprae antigens. As the discovery sample we enrolled cured leprosy patients who had been diagnosed with T1R at the time of leprosy diagnosis and leprosy patients who had never undergone T1R (retrospective arm). Whole genome transcriptome analysis after stimulation of blood with M. leprae antigen resulted in the definition of a T1R signature gene set. We validated the T1R gene set in RNA samples obtained from T1R-free patients at the time of leprosy diagnosis and followed for 3 years for development of T1R (prospective arm). These results confirm the role of innate factors in T1R and are a first step towards a predictive genetic T1R signature.