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Pauci- and Multibacillary Leprosy: Two Distinct, Genetically Neglected Diseases

  • Jean Gaschignard,

    Affiliations Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, INSERM, Paris, France, EU, Paris Descartes University, Imagine Institute, Paris, France, EU

  • Audrey Virginia Grant,

    Affiliations Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, INSERM, Paris, France, EU, Paris Descartes University, Imagine Institute, Paris, France, EU, Unité de Génétique fonctionnelle des maladies infectieuses, Institut Pasteur, Paris, France, EU

  • Nguyen Van Thuc,

    Affiliation Hospital for Dermato-Venerology, Ho Chi Minh City, Vietnam

  • Marianna Orlova,

    Affiliation Program in Infectious Diseases and Immunity in Global Health, The Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada

  • Aurélie Cobat,

    Affiliations Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, INSERM, Paris, France, EU, Paris Descartes University, Imagine Institute, Paris, France, EU

  • Nguyen Thu Huong,

    Affiliation Hospital for Dermato-Venerology, Ho Chi Minh City, Vietnam

  • Nguyen Ngoc Ba,

    Affiliation Hospital for Dermato-Venerology, Ho Chi Minh City, Vietnam

  • Vu Hong Thai,

    Affiliation Hospital for Dermato-Venerology, Ho Chi Minh City, Vietnam

  • Laurent Abel,

    Affiliations Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, INSERM, Paris, France, EU, Paris Descartes University, Imagine Institute, Paris, France, EU

  • Erwin Schurr,

    Affiliations Program in Infectious Diseases and Immunity in Global Health, The Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada, The McGill International TB Centre, Departments of Human Genetics and Medicine, McGill University, Montreal, Quebec, Canada

  • Alexandre Alcaïs

    alexandre.alcais@inserm.fr

    Affiliations Laboratory of Human Genetics of Infectious Diseases, Necker Branch, INSERM U1163, Necker Hospital for Sick Children, INSERM, Paris, France, EU, Paris Descartes University, Imagine Institute, Paris, France, EU, URC, CIC, Necker and Cochin Hospitals, Paris, France, EU

Pauci- and Multibacillary Leprosy: Two Distinct, Genetically Neglected Diseases

  • Jean Gaschignard, 
  • Audrey Virginia Grant, 
  • Nguyen Van Thuc, 
  • Marianna Orlova, 
  • Aurélie Cobat, 
  • Nguyen Thu Huong, 
  • Nguyen Ngoc Ba, 
  • Vu Hong Thai, 
  • Laurent Abel, 
  • Erwin Schurr
PLOS
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Abstract

After sustained exposure to Mycobacterium leprae, only a subset of exposed individuals develops clinical leprosy. Moreover, leprosy patients show a wide spectrum of clinical manifestations that extend from the paucibacillary (PB) to the multibacillary (MB) form of the disease. This “polarization” of leprosy has long been a major focus of investigation for immunologists because of the different immune response in these two forms. But while leprosy per se has been shown to be under tight human genetic control, few epidemiological or genetic studies have focused on leprosy subtypes.

Using PubMed, we collected available data in English on the epidemiology of leprosy polarization and the possible role of human genetics in its pathophysiology until September 2015. At the genetic level, we assembled a list of 28 genes from the literature that are associated with leprosy subtypes or implicated in the polarization process. Our bibliographical search revealed that improved study designs are needed to identify genes associated with leprosy polarization. Future investigations should not be restricted to a subanalysis of leprosy per se studies but should instead contrast MB to PB individuals. We show the latter approach to be the most powerful design for the identification of genetic polarization determinants. Finally, we bring to light the important resource represented by the nine-banded armadillo model, a unique animal model for leprosy.

Introduction

Leprosy is a chronic infectious disease caused by Mycobacterium leprae, with over 200,000 new cases detected each year, often leading to severe disabilities and social stigma. After sustained exposure to M. leprae, only a subset of individuals develops clinical leprosy. From the early observations of familial aggregation of leprosy cases to the most recent genome-wide association studies identifying genetic polymorphisms associated with leprosy, there is strong evidence that the development of the disease is under tight human genetic control [1,2]. The interest in the genetics of leprosy is reflected by the continuous increase in the number of genetic publications (host and pathogen), while publications on immunology of leprosy have decreased significantly after 1980 [3].

There is considerable clinical variability among leprosy patients, with a clinical spectrum that extends from the polar “tuberculoid” to the polar “lepromatous” form of the disease [4,5]. The tuberculoid form is characterized by a small number of hypopigmented, well-bordered, anesthetic skin lesions with a low bacillary load (therefore this form is commonly referred to as “paucibacillary” [PB]), early peripheral nerve impairment, and a T-helper 1 (Th1)–mediated immune response. In contrast, the lepromatous form is characterized by numerous infiltrated skin lesions displaying high bacillary loads (therefore referred to as “multibacillary” [MB]), impaired peripheral nerves, possible involvement of internal organs, and a Th2-mediated immune response [6]. Leprosy has long been a major focus of investigation for immunologists because it is an excellent example in natura of the clinical impact of a differential immune response of B and T lymphocytes on the form of disease, but it has not been thoroughly considered by geneticists. Some genetic studies have focused specifically on either PB leprosy or MB leprosy; however, very few have investigated the process that we call “polarization,” which is the process that drives the differentiation to different subtypes and therefore impacts on all or at least multiple subtypes of leprosy.

From a genetic perspective, polarization can thus be regarded as a neglected phenotype. To address this shortcoming, we have conducted a review of epidemiological studies addressing leprosy subtypes and polarization. We argue that the poor understanding of the genetic architecture of leprosy polarization is mainly caused by inappropriate study designs. We provide suggestions for future research on PB and MB leprosy and propose that the use of the armadillo model will be of pivotal importance for the study of these phenotypes.

Methodology

Review of the literature

On September 30th, 2015, we searched PubMed with the search terms “(leprosy OR Mycobacterium leprae OR multibacillary OR paucibacillary OR lepromatous OR tuberculoid NOT tuberculosis) AND (genetic OR association OR risk factors)” and identified ~1,500 publications on leprosy (~1,000 on genetics and ~500 on epidemiology). Epidemiological data were also gathered from the Weekly Epidemiological Records released by the World Health Organization (WHO).

Results and Discussion

Leprosy classification

As early as 600 b.c., the Sushruta-Samhita (the most ancient Indian medical book), distinguished kushta, a lepromatous form of the disease, from rakta, a neural form [7]. In 1881, Danielssen and Boeck distinguished a nodular and an anesthetic leprosy [8], and in 1895, Hansen and Looft used the terms “anesthetic” and “maculo-anesthetic” [9]. In 1935, Wade noticed that some patients could move from a tuberculoid state to a lepromatous one, and named “borderline” the form of leprosy of these patients [10]. Nowadays, once the diagnosis of leprosy is made, the identification of the clinical form is critical because it will determine the length of multidrug therapy [11]. In addition, the clinical determination of leprosy subtype provides major pathophysiological information, which was explored early on by immunologists but, as already mentioned, was somewhat disregarded by the genetics community.

International Leprosy Conferences in Cairo (1938), La Havana (1948), and Madrid (1953) paved the way for the subclassification of the disease into three classes, based on clinical (aspect of lesions), bacteriological (identification of bacilli), immunological (lepromin test), and histological (nature of the infiltrate, grouping of bacilli) criteria, known as the Madrid definition. The three Madrid classes are: Tuberculoid (T), Borderline (or Dimorphic) (B), and Lepromatous (or Virchowian) (L). The Madrid classification scheme is still used in some countries like Brazil [12]. A major development came with the leprosy classification by Ridley and Jopling in 1966, which aimed to better characterize the borderline group that could comprise patients with different clinical, immunological, bacteriological, or histological findings. Ridley and Jopling proposed five subtypes: Tuberculoid Tuberculoid (TT), Borderline Tuberculoid (BT), Borderline Borderline (BB), Borderline Lepromatous (BL), and Lepromatous Lepromatous (LL) [13]. The latter form may itself be refined in “subpolar” (LLs) and “polar” (LLp) on a clinicopathologic basis [14].

To facilitate diagnosis and treatment of leprosy in the field, WHO proposed in 1982 a simplified classification. It relied on clinical findings and the bacteriological index (BI), calculated by counting 6 to 8 stained smears under the 100x oil immersion lens in a smear made by nicking the skin with a sharp scalpel and scraping it. The results are expressed on a logarithmic scale from 0 to 6+ [15]. The WHO-82 classification distinguishes PB leprosy, which includes TT and BT patients with BI ≤ 1, from MB leprosy, which comprises BB, BL, and LL patients, as well as those with a BI ≥ 2 [11]. In 1988, WHO changed its bacteriological criteria and any patient with a positive BI was now classified as MB (“WHO-88” definition) [16]. Finally, in 1996, WHO switched for an operational definition to optimize treatment allocation: patients with less than five skin lesions are diagnosed as PB, while all others are diagnosed as MB (“WHO-96” definition). A side effect is that this operationally driven classification has eclipsed pathophysiological aspects differentiating the leprosy forms. A summary of all classifications is given in Box 1.

Box 1. Different Classifications of Leprosy

Madrid (1953): T (Tuberculoid), B (Borderline) and L (Lepromatous), according to clinical (skin and nerve lesions), bacteriological (bacteriological index [BI]—a measure of the number of acid-fast bacilli in the dermis expressed in a logarithmic scale), immunological (lepromin reaction), and histological (infiltrate’s nature) criteria.

Ridley–Jopling (1966): TT (Tuberculoid Tuberculoid), BT (Borderline Tuberculoid), BB (Borderline Borderline), BL (Borderline Lepromatous), and LL (Lepromatous Lepromatous), defined according to clinical, bacteriological, immunological, and histological criteria.

WHO (1982): Paucibacillary (PB) or Multibacillary (MB) according to Ridley–Jopling (or Madrid) classifications and the BI. PB = TT + BT (or T) and BI ≤ 1; MB = BB + BL + LL (or B + L) or BI ≥ 2.

WHO (1988): PB = TT + BT and BI = 0; MB = BB + BL + LL or BI ≥ 1.

WHO (1996): PB or MB according to the sole number of skin lesions (≤/>5).

*****

Polarization: process that drives an infected individual towards the PB or the MB form (in our manuscript, irrespective of the classification used to define PB and MB cases).

To which extent the evolution of the WHO classification system impacted on the counts of PB and MB forms is unclear. In a Vietnamese sample of 1,182 leprosy cases, 1,127, 1,074, and 1,051 could be classified according to WHO-82 (mainly relying on Ridley and Jopling criteria), WHO-96 (which only relies on the number of lesions), and the physicians, respectively (S1 Appendix). The physician classification was the one that in practice determined which multidrug regimen each patient received. The proportion of MB patients was 66% (695/1,051), 60% (672/1,127), and 41% (435/1,074) with the final physicians’, WHO-82, and WH0-96 classifications, respectively. Fig 1 shows the Venn diagram of MB and PB patients classified according to the three definitions. The concordance analysis of the three classifications was quantified by means of the Kappa coefficient for the subset of 943 individuals classified for all three definitions: it was higher between “WHO-82” and “physician” than between “WHO-82” and “WHO-96” or between “WHO-96” and “physician” (0.88 [0.85–0.91], 0.62 [0.58–0.67], and 0.59 [0.54–0.64], respectively—a common interpretation of the Kappa coefficient is: poor [0–0.4], medium [0.4–0.6], good [0.6–0.8], and excellent [0.8–1]).

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Fig 1. Concordance of leprosy polarization phenotypes according to WHO-82, WHO-96, or physician definitions in a Vietnamese sample.

Venn diagram for the number of patients with PB leprosy (left) and MB leprosy (right) according to three definitions: WHO-82, WHO-96, and physician in a Vietnamese sample. The surfaces of overlap are approximately proportional to the number of individuals identically classified by one, two, or three definitions. There was no individual classified PB by a physician and MB by WHO-82 + WHO-96 as well as no individual classified MB by a physician and PB by WHO-82 + WHO-96.

https://doi.org/10.1371/journal.pntd.0004345.g001

By defining MB and PB forms, an implicit phenotype is the contrast between these two forms. We define this implicit phenotype as leprosy polarization, i.e., the process that drives the development of leprosy subtypes, which needs to be differentiated from factors that impact on only one subtype. Here, we will refer to polarization whenever PB and MB forms are opposed, irrespective of the criteria used to define PB and MB leprosy.

Epidemiology of Leprosy Subtypes

No epidemiological study specifically dealing with MB or PB leprosy has been published. Therefore, we based our approach on more general epidemiological studies of leprosy per se that included information on MB and PB forms. The vast majority of recent epidemiological studies focused on the contacts of an index leprosy case to identify risk factors for the occurrence of secondary cases [1721]. In this setting, subgroup analysis of clinical forms is not very powerful, given the low number of such secondary cases: indeed, the detection rate of new cases is often less than 1% after 4 years of follow-up, requiring 10,000 contacts to ultimately study 100 secondary leprosy cases [18,19]. Since there is minimal variability within the genome of M. leprae, [2224], pathogen factors are unlikely to contribute to clinical variability. Therefore, we restricted our explorations to host-related risk factors: sex, age, geographical origin, and bacille Calmette-Guérin (BCG) vaccination status.

Sex

The number of leprosy cases reported in adults is consistently higher among men, with a male to female sex ratio ranging between 1.5 and 2 [25]. Few exceptions of a sex ratio <1 have been described in some African countries such as Burkina Faso, Kenya, or Uganda [26]. Exposure, reporting, or access to care biases cannot fully explain such a difference: during a massive outbreak on the island of Nauru in the Pacific (where leprosy affected a third of the population during the 1920s and where these biases are expected to play a minor role), the sex ratio was still 1.5 [27]. Concerning the sex ratio for MB or PB forms of the disease, a very large Brazilian study that used census data from the Ministry of Health between 2006 and 2010 (40,544 PB and 29,764 MB cases) showed a sex ratio larger than 2 for MB patients but slightly lower than 1 for PB cases (Madrid classification) [28]. This might explain why, according to the relative proportion of MB and PB forms in a population, the overall sex ratio varies widely and why in some African countries, where the PB form is predominant, women are overrepresented among leprosy cases.

Age

Generally, the mean age of MB cases is higher than the one of PB cases, the common explanation being one of a longer period of incubation for the MB form (reviewed in [29]). A study in Andhra Pradesh (India) in 1962 showed that the proportion of MB cases—corresponding to L/(L+T) with the Madrid classification—increased from less than 5% between 5 and 9 years of age to over 25% for patients 25–64 years of age [30]. These proportions were similar to those observed during the same period in another region from India (Tamil Nadu) [31] and in Burma (both using the Madrid definition) (Fig 2) [28]. An independent study conducted in Uttar Pradesh (India) between 2005 and 2010 found the proportion of MB cases to be 54% in children against 77% in adults (p < 0.001) [32]. Finally, we used the data of the Brazilian study mentioned above [28] to show that the proportion of MB leprosy continuously increased with age, from less than 10% before 10 years of age to more than 50% for patients older than 60 years (Fig 2) [28]. The influence of age on the proportion of MB patients was greater in men than in women, and multivariate analysis showed that the effects of these two factors (age and sex) on leprosy polarization were independent (Fig 2). In a Vietnamese sample previously mentioned, we also observed that age was correlated with a higher proportion of MB patients, and this correlation was stronger for males than for females (S1 Fig).

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Fig 2. Proportion of multibacillary patients according to age and sex.

Proportion of multibacillary patients according to age and sex are given for two distinct geographic areas: (1) in Brazil, 40,544 PB and 29,764 MB cases (Madrid classification) were declared over the 2006–2010 period (national data) [28]; (2) in Tamil Nadu (India), during the 1962–1970 period, 3,963 PB and 1,258 MB cases were collected out of a total of 276,568 persons screened for leprosy (Madrid classification) [31].

https://doi.org/10.1371/journal.pntd.0004345.g002

BCG

Two meta-analyses have shown that BCG vaccination significantly reduced the risk of developing clinical leprosy [33,34]. They included 26 and 28 studies, respectively, that used various classifications of MB and PB leprosy (Madrid, Ridley–Jopling, WHO-82, WHO-88, and WHO-96); 22 studies were common to both meta-analyses. The proportion of the observed protection ranged from 26% to 61%, depending on the studies included. In the first meta-analysis, the protective effect was greater for MB than for PB patients (78% versus 59%, p = 0.04). In contrast, in the second study, in which an adjustment was made on the number of immunizations with BCG and on the type of study (observational versus interventional), the impact of BCG was not significantly different on the onset of one leprosy form or the other. A few reports suggested that the LL form is insensitive to vaccination (as these patients are not able to develop a granulomatous response) [35], while others suggested that BCG vaccination may lead exposed individuals to preferentially develop a PB rather than a MB leprosy [36,37].

Geography

The proportion of MB cases varies greatly across continents but also within each continent, between neighboring countries: among the countries that reported more than 1,000 cases in 2012, the proportion of MB cases (WHO-96) varied between 45% (Bangladesh) and 92% (Philippines) [25]. It is unlikely that demographic data such as age and sex explain the observed variability, given that population sex ratios are similar across countries, and the patient age distribution is relatively homogeneous within a continent.

Endemicity

In 1966, Kenneth Newell, an epidemiologist self-defined as “naïve” about leprosy, published a seminal paper entitled “An Epidemiologist's View of Leprosy.” He focused on a largely disregarded observation that he considered to be very informative: in countries of high endemicity, the prevalence of lepromatous cases (similar to MB cases as defined by Madrid classification) was relatively stable (between 5 and 10/1,000). This observation enabled him to disregard some debated hypotheses about leprosy pathogenesis: (1) the lepromatous form is caused by a more virulent strain of M. leprae, (2) lepromatous cases are associated with a greater inoculum and/or greater exposure, and (3) polarization is linked to an environmental factor common to all human populations. Conversely, the data were consistent with the hypothesis that a fixed proportion of the infected population presents one or several characteristics that make certain persons more likely to develop a lepromatous form of leprosy. It is therefore reasonable to consider host genetic factors as a plausible explanation of leprosy polarization [38].

Human Genetics of Leprosy Subtypes

There are many methodological tools in genetic epidemiology that can be used according to the hypotheses to be tested and the nature of the data. Their main characteristics are summarized in S1 Table [39]. A number of approaches do not require any biological material and rely only on the information and the knowledge of the familial relationships. These DNA-independent studies are of particular interest in the context of neglected tropical diseases and knowledge transfer to endemic countries: they are easier to implement and intrinsically technology-independent (i.e., their cost has remained low). The following section illustrates how such DNA-independent methods have contributed to our current understanding of the genetics of leprosy subtypes.

DNA-independent analyses: familial correlations, twin and segregation studies

Numerous studies have established that among close contacts of index cases (e.g., individuals living in the same house or sharing the same bed as the case), those biologically related to the index case are significantly more at risk of developing leprosy per se [17,18,21]. Intriguingly, no study has focused specifically on the subtype of leprosy developed by these secondary cases according to their demographics (age, sex, etc.,) or those of the index case, further substantiating that these phenotypes are neglected. Conversely, twin studies considered subtypes in addition to leprosy per se [40,41]. Among them, a study of twin pairs in which at least one twin suffered from the MB subtype of leprosy (Madrid classification) found a higher concordance rate of the MB subtype in monozygotic (MZ) as compared to dizygotic (DZ) pairs (70% in MZ versus 20% in DZ) [40]. On the opposite, for the PB form, concordance rates between MZ and DZ twins were very close. As we mentioned earlier, there is a greater proportion of MB leprosy among males [25], and this probably marginally increased the concordance rate in MZ twins versus DZ ones (which may be of opposite sex). Finally, several familial segregation studies in the Philippines, La Desirade Island, Thailand, and Brazil support a significant host genetic contribution to both MB and PB forms of the disease, although the genetic models were not always concordant across studies [4144].

DNA-based analyses: linkage and association studies

Candidate-region or genome-wide linkage studies.

Early linkage studies using DNA were performed on small chromosomal segments around a particular gene of interest chosen for its known function (“candidate-gene,” or more precisely, “candidate-region” hypothesis). While the vast majority of these studies focused on leprosy per se, one analyzed the genetic control of leprosy polarization as defined by Ridley and Jopling [45]. It included 118 sibs (i.e., brothers and/or sisters) from 20 Vietnamese families and covered 6 chromosomal regions of interest: 1p36 around tumor necrosis factor receptor 2 (TNFR2), 5q31–q33 around a group of cytokines, 6p21 around TNF, 12p13 around TNFR1, 12q13 around vitamin D receptor (VDR), and 10p13. Polarization was coded as a “quantitative” trait, giving an increasing weight throughout the Ridley–Jopling spectrum (TT<BT<BB<BL<LL). The study detected significant linkage of the 6p21 region, including TNF, with leprosy polarization [45].

Studies from Jamieson and Miller in a Brazilian sample reported linkage of leprosy per se with chromosomal regions 17q11–q21 [46], 6p21, 17q22, and 20p13 [47]. These studies subsequently defined two groups, T* and L*. The T* group included patients classified as TT, BT, and BB according to the Ridley–Jopling scale, while the L* group comprised BL and LL patients. Using this uncommon classification (BB usually comes along with BL and LL in the MB group), significant linkage of the 17q11 and 20p13 chromosomal regions was detected for the L* phenotype but not for the T* group. Finally, a linkage study from Malawi found a significant linkage signal between polarization and genetic markers on the 21q22 [48] chromosomal region (WHO-88 definition). However, as for most regions identified by linkage, the functional importance of specific genes located in this region awaits confirmation.

The first genome-wide (i.e., no restriction to a particular genomic region) linkage study of leprosy was performed in India in 2001. The sample included 224 families comprising 245 sib pairs affected by leprosy [49]. A significant linkage hit (p < 2·10−5) for leprosy was observed with genetic markers located on chromosomal region 10p13. However, because of the very high proportion of PB cases (98%; Madrid definition), it was not possible to decide whether the mapped locus influenced leprosy per se or was specific for the PB form. A strong argument for the impact of the 10p13 region on leprosy subtypes rather than leprosy per se is the significant heterogeneity found in a Vietnamese sample between PB and MB individuals (WHO-88, p = 0.03) [50]. A second study in India added some families to the sample of the first linkage study (243 families in total, including 233 with only PB children) and identified a second linkage signal on the 20p12 chromosomal region [51]. Overall, the results of the linkage studies in Indian and Vietnamese leprosy patients suggest that the 10p13 and 20p12 regions are differentially implicated according to the subtype considered.

Candidate-gene, candidate-region, and genome-wide association studies.

Numerous studies have tested the association between leprosy per se and genetic variants located in biologically relevant candidate genes. These studies were mainly focused on genes from the major histocompatibility complex (MHC) region, the Toll-like receptors (TLRs), and the cytokine family [1,2]. Similarly to linkage studies, only a subset of the candidate-gene studies has considered the clinical form of the disease. Their results are summarized in Table 1.

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Table 1. Studies identifying 28 genes associated with clinical forms of leprosy or its polarization.

https://doi.org/10.1371/journal.pntd.0004345.t001

The strategy of association studies with a candidate gene faces the classical problem of “multiple testing” (several genes tested and several variants within each gene), which can result in inflated type I errors if not properly corrected. To avoid inflated type I errors in genetic association studies, replication (i.e., confirmation in an independent sample from the same population) and validation studies (confirmation in a sample from a different population) are critical. The problem of inflated type I errors in candidate gene association studies is not specific to leprosy but extends to the whole field of complex traits. The only validated studies for leprosy subtype (whether PB, MB, or polarization) are provided by single nucleotide polymorphisms (SNPs) in genes for Toll-like receptor 2 (TLR2) in Ethiopia [52] and Malawi [53], TNF in India [54] and Thailand [55], mannose-binding lectin 2 (MBL2) in Brazil [56] and Nepal [57], mannose receptor C type 1 (MRC1) in Brazil and Vietnam [58], and MHC class I chain-related gene A (MICA) in China [59] and Brazil [60]. Finally, the association of interleukin 10 (IL-10) with polarization has been replicated in two independent Brazilian samples [61,62].

Other associations have only been found once, and the implication of the tested genes in leprosy pathogenesis must be considered with caution: IFNγ and HLA-G in Brazil [63,64]; TLR4 and leukotriene A4 hydrolase (LTA4H) in Ethiopia [65,66]; vitamin D receptor (VDR) [67,68], killer cell immunoglobulin like receptor, two Ig domains and short cytoplasmic tail 3 (KIR2DS3) [69], heat shock protein 1A (HSPA1A), and IL-23R in India [70,71]; lymphotoxin-α (LTA) in Malawi [53]; complement fraction 4 (C4A) in Thailand [72]; IL-12B in Mexico [73]; IL-12RB2 in Japan [74]; natural resistance-associated macrophage protein 1 (NRAMP1) in Mali [75]; laminin 2 (LAMA2) in Indonesia [76]; defensin-β1 (DEFB1) in Mexico [77]; and optic atrophy 1 (OPA1) in China [78]. Interestingly, for VDR, one study identified a polymorphism associated with leprosy when comparing MB to PB [67], while a second study identified a haplotype when comparing MB to controls and another haplotype when comparing PB to controls [68]. Finally, a Brazilian study illustrated the impact of clinical classification. Here, the association of genetic variants in the gene coding for IL-17F was significant for leprosy polarization based on the WHO-88 classification (88 PB and 52 MB patients, odds-ratio [OR] 3.2, p < 0.05) but not for the WHO-96 definition (53 PB and 87 MB individuals) [79]. As mentioned above, linkage of the 10p13 chromosomal region with the PB form of leprosy in India [49] has been confirmed in Vietnam [50]. To characterize the molecular identity of the genes responsible for the signal in the region, a fine-mapping association study of this candidate region was conducted using 1,522 markers and spanned over nine megabases (Mb), excluding MRC1 [80]. The primary sample included 294 nuclear families from Southern Vietnam with 303 sibs affected by leprosy, of which 63% were classified as MB (WHO-96 definition). The replication sample from the same area consisted of 192 trios (2 parents and 1 child with leprosy), with 55% MB children. Three SNPs located in the genes coding for nebulette (NEBL, actin-binding Z-disk protein) and cubilin (CUBN, intrinsic factor-cobalamin receptor), located 3 Mb apart, were specifically and independently associated with the MB form [80].

The first genome-wide association study (GWAS) for leprosy was published in 2009 [81]. It included 706 cases and 1,225 controls from China; 4,367 additional controls were added in a subsequent study published in 2011 [82]. 500,000 SNPs spanned over the entire genome were analyzed in this sample. The most promising signals were tested in a replication sample including 3,254 cases and 5,955 controls, mainly from Eastern China but also from Chinese ethnic minorities. In total, significant association of leprosy was detected with 18 polymorphisms assigned to eight genes: HLA-DR-DQ, receptor-interacting protein kinase 2 (RIPK2), tumor necrosis factor superfamily member 15 (TNFSF15), coiled-coil domain containing 122 (CCDC122), LACcase containing domain 1 (LACC1), nucleotide-binding oligomerization domain containing 2 (NOD2), Rab32 protein (RAB32), and IL-23R [81,82]. Heterogeneity in strength of association between the MB and PB groups (WHO-88) was observed for four polymorphisms assigned to four genes (LACC1, LRRK2, NOD2, and RIPK2). Validation studies of the observed associations were conducted in Vietnamese and Indian samples. In a familial sample from Vietnam including 286 MB and 188 PB leprosy patients (WHO-88), signals located in HLA-DR-DQ, RIPK2, CCDC122, LACC1, and NOD2 were validated (p < 0.05), and the effect was stronger in MB patients for HLA-DR-DQ, CCDC122, and LACC1 [83]. In the Indian case-control study using a smaller population (137 MB and 74 PB cases—WHO-88), signals in LRRK2 and RIPK2 genes were validated, and the observed effect was consistently more significant for PB patients [84]. A second study using 48,000 markers located in 2,092 genes was performed in an Indian population in 2010. The primary sample included only 258 cases and 300 controls from North India. Markers in the TLR1 and HLA-DRB1/DQA1 genes were significantly associated with leprosy per se. The test for heterogeneity between MB and PB groups (WHO-88) was not significant for the association with markers of these two genes [85]. Nonetheless, in both GWAS, leprosy per se was the main phenotype, and only SNPs that were significant with this phenotype were subsequently tested for heterogeneity between MB and PB groups. This is not an optimal design (see next section) and may well have missed SNPs with significant impact on polarization.

Study Design: Benefits of Optimal Hypothesis Testing

The number of genes for which a variant has been associated in one of leprosy’s subtypes or its polarization (39 associations in 28 genes) (Table 1) is shorter than for leprosy per se (82 associations in 50 genes). Fig 3 summarizes the distribution of the associations according to leprosy phenotypes. Several scenarios may explain the difference. First, since most studies were conducted in samples used for leprosy per se studies, by design patient numbers for subtype analysis were smaller than for leprosy per se, resulting in lower power to detect genetic variants impacting on leprosy subtype. This is consistent with the fact that most reported associations concerned the MB form, which is more common in endemic countries except India. Second, an inadequate definition of MB and PB forms can also lead to a loss of power, as illustrated by the study of IL-17F [79]. Third, most studies analyzing leprosy polarization did not account for demographic risk factors: yet, the study of leprosy per se has revealed that the inclusion of these covariates was essential (e.g., the association of leprosy per se with PARK2 or LTA was three to five times stronger in young patients [86,87]).

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Fig 3. Distribution of 91 published associations between a genetic variant and leprosy per se, any leprosy subtype (i.e., MB or PB), or polarization.

The + and–symbols refer to the presence or the absence of association of a given gene with one of the leprosy subtypes in the study. The red quadrant refers to studies for which the primary outcome was the association with leprosy polarization or the PB subtypes; the blue quadrant refers to studies that could not conclude an association with leprosy per se but did conclude an association with one of its subtypes; the green quadrant refers to studies that identified an association for both leprosy per se and one of its subtypes; and the grey quadrant refers to studies for which an association with only leprosy per se was reported.

https://doi.org/10.1371/journal.pntd.0004345.g003

Schematically, most studies first look for association by comparing leprosy cases to controls, then derive a p-value for a given genetic marker and a given genetic model (pper se). Only in a second step are additional tests for significance of association by comparing MB to all controls (pMB) and PB to all controls (pPB) conducted. If pMB < pper se < pPB, one might conclude that this SNP is associated with leprosy and has a major effect on MB patients (if pMB < pper se < threshold) or that this SNP is associated with MB leprosy only (if pMB < threshold < pper se). Such a general strategy has several major shortcomings, including the well-known fact that p-values contain little meaningful information: odds ratios should be compared instead. Second, correction for multiple testing should be performed when testing for leprosy subtypes after testing for leprosy per se. Third, heterogeneity tests (used to compare PB versus MB) are generally not very powerful.

The optimal strategy to identify variants that specifically differentiate MB from PB patients requires (1) refining the phenotype, (2) balancing the number of patients in each class, and (3) testing the polarization phenotype as a whole. In Fig 4, we give an example in which this optimal strategy can reveal a significantly associated SNP that the classical strategy (testing leprosy per se first and second its subtypes) could not highlight because of a lack of power.

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Fig 4. Statistical impact of the hypothesis used to test for an association between a genetic variant and leprosy polarization.

In the common case-control study design, a standard test of association between a genetic variant and the phenotype under study is to compare allelic frequencies (f) between the group of cases and the group of controls. Here, we consider a sample including an even number of controls and leprosy cases, equally distributed between PB and MB. In addition, we fixed the frequency of the allele of interest to 0.5 among the controls (fC = 0.5). In each panel, fPB and fMB (x and y axis) are the allele frequencies among PB and MB individuals, respectively. The first strategy directly compares MB to PB cases (Panel A). The second strategy first tests PB versus controls (Panel B) and MB versus controls (Panel C) before performing a heterogeneity test MB versus PB, possibly after testing leprosy per se versus controls (panel D). The red SNP is a variant for which fMB ≠ fPB and for which the association is significant with the first strategy but not with the second (the red SNP is in the gray area where the null hypothesis H0 cannot be rejected). This second strategy is indeed hampered by correcting for multiple testing (testing MB versus PB after testing PB versus controls and MB versus controls) as well as the usual low power of heterogeneity tests.

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Clearly, it is vital to use specific experimental designs according to the hypotheses to be tested and not to limit future investigations to the simple subanalysis of leprosy per se studies. In particular, comparing MB (or PB) individuals with controls does not allow determining if tagged genes are implicated in the infection, the occurrence of the particular form of leprosy, or even leprosy per se. By contrast, testing MB versus PB individuals is a powerful strategy to identify “specific” polarization genetic determinants. Another part of the study design is the choice of the genetic variants to be tested: it can either be DNA (SNP or any structural variant, such as copy number variant), RNA, or epigenetics (methylation or glycosylation status, for example). Finally, the size of the genotyping effort (candidate gene, candidate region, GWAS) is critical, since the multiple testing will be much less an issue when testing few strong variants in a candidate gene as opposed to several millions of SNPs in a GWAS. In the face of limited resources and relatively small sample sizes, a focus on strong candidates can provide a very substantial increase in power. In the next section, we argue that several powerful strategies able to generate appealing candidate genes have been surprisingly overlooked in the case of leprosy and its subphenotypes.

Generation of Candidate Genes

The human track

Because M. leprae cannot be cultivated in vitro, human studies have emerged as the only source for genetic studies. Candidate genes, are mainly chosen from a list of genes involved in the human immune system: MHC, TLR, and cytokine family genes have been the most popular choices [1,2]. By contrast, “candidate pathways” have never been tested. Definitions of pathways can be derived from the screening of in silico databases such as the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Ontology (GO), or Search Tool for the Retrieval of Interacting Genes (STRING). Pathway-based approaches allow extending the search for genetic markers (most typically SNPs) significantly associated with a phenotype from a single gene (~2–10 SNPs) to the pathway level (~100–1,000 SNPs, depending on the size of the pathway). Similarly, pathway-based approaches can be used in the context of GWAS. Such strategies have been used successfully in cancer research [88,89], but have not been reported yet for leprosy.

Large GWAS remain a promising approach for the study of leprosy [90]. However, GWAS rarely have sufficient power to identify genetic variants that are rare at the population level. Such rare variants may potentially have strong biological effects and contribute strongly to the onset of the disease in a given individual. Such variants, although by themselves of minor importance at the population level, can be critical to highlight pathways, as illustrated by the implication of the IFNγ–IL-12/23 pathway in tuberculosis through the detection of private mutations causing susceptibility to mycobacterial diseases in a limited number of individuals. The knowledge of such “Mendelian” or “monogenic” causes of leprosy is currently lacking. Yet, the identification of such cases should be of high priority because of the dearth of relevant in vitro and in vivo experimental models for leprosy.

The armadillo track

The nine-banded armadillo is a natural host of M. leprae that can be used for the study of the in vivo propagation of the bacilli [9193]. Armadillos are cat-sized mammals found only in the Americas that can live for up to 12 years and have a cool body temperature (32–35°C). The majority of nine-banded armadillos inoculated intravenously with M. leprae develop a disseminated form of the disease that is similar to multibacillary leprosy in man and, in particular, exhibits severe nerve lesions [94]. A study that included 392 wild armadillos affected by the disease in the United States showed that more than 80% were MB according to Ridley–Jopling (72% LL and 11% BL), regardless of their geographic origin (either from Louisiana, an enzootic region, or from Florida, a nonzoonotic area). Still, 17% were classified as PB. Hence, while armadillo is usually considered as a model for multibacillary leprosy, it can also be used to study polarization. The armadillo genome has been sequenced and annotated [95,96]. Although to date only one armadillo has been fully sequenced, additional sequencing will enable comparison of genetic variants between mammals that develop leprosy (only armadillo and human) and those that do not. Any variants unique to armadillo and humans might offer valuable insight into susceptibility, while genes tagged for comparisons of MB and PB armadillos might provide useful insight into leprosy polarization.

Armadillos also have a unique characteristic that makes them particularly interesting for genetic studies, which is polyembryony (always giving birth to monozygotic quadruplets). This peculiarity can help distinguish between environmental and genetic factors that favor the phenotype. Adams et al. observed that within each set of sibling armadillos experimentally infected with M. leprae and bred in the same environment, the results of bacteria harvests were similar: this is a strong argument in favor of a major role of host genetics in the ability to fight the bacteria [91].

The pleiotropic track

In the absence of functional studies specific to leprosy, some genes have been explored in other diseases that share a common genetic background with leprosy. Inflammatory bowel disease (IBD) is a striking example. Genetic studies have revealed common signaling pathways between leprosy and Crohn’s disease (CD) [97,83,2], fostering an ongoing debate about a possible contribution of mycobacteria to CD [98101]. The analogy can be pursued further by comparing the two main forms of IBD, CD and ulcerative colitis (UC), with the two clinical forms of leprosy. Indeed, PB leprosy and CD preferably involve Th1 signaling, while MB leprosy and UC involve a Th2 response [102]. GWAS on IBD have identified 163 loci associated with IBD: 110 were common to both diseases, 30 specific for CD, and 23 for UC [103]. Among the genes specific for CD, RIPK2, LACC1, and NOD2 have been shown to be associated in leprosy studies but not specifically with the PB form (NOD2 and RIPK2 with leprosy per se and LACC1 with MB leprosy). To systematically test our hypothesis that CD and PB leprosy on the one hand and UC and MB leprosy on the other hand share some specific susceptibility genes, it will be necessary to replicate CD- and UC-specific genes in PB and MB subsets, respectively. If correct, this approach will allow scientists working on leprosy to use advances in the genetics of IBD to derive an architecture for the genetic determinants of leprosy.

Conclusion

Here, we summarized the accumulated evidence for susceptibility genes acting at the polarization level that do not influence the onset of leprosy disease. We highlighted that precise phenotype characterization is crucial for the identification of genes driving polarization. Since there is no conventional animal model for leprosy while armadillo is not widely available, the “reverse genetics” approach through genetic epidemiology remains an attractive strategy to decipher the physiopathology of this neglected tropical disease, of which polarization is a major aspect. Importantly, such progress can only come from a close collaboration between physicians, biologists, epidemiologists, and geneticists. Leprosy, so long a cause of social isolation and stigmatization of those who suffer the disease, will only be eradicated by a joint effort of all components of the scientific community.

Key Learning Points

  • Age and sex are two independent risk factors for leprosy polarization.
  • Males who develop leprosy at an older age display a higher proportion of the multibacillary form as compared to younger affected individuals.
  • The most powerful method to identify genes or markers associated with leprosy polarization is to directly compare multibacillary with paucibacillary patients (a design used by a minority of leprosy genetic studies).
  • The nine-band armadillo model may prove very helpful in the search for genetic determinants of leprosy and, particularly, its polarization.

Top Five Papers

  1. Misch EA, Berrington WR, Vary JC Jr, Hawn TR (2010) Leprosy and the human genome. Microbiol Mol Biol Rev MMBR 74: 589–620. doi:10.1128/MMBR.00025-10.
  2. Mira MT, Alcais A, Di Pietrantonio T, Thuc NV, Phuong MC, et al. (2003) Segregation of HLA/TNF region is linked to leprosy clinical spectrum in families displaying mixed leprosy subtypes. Genes Immun 4: 67–73.
  3. Zhang F-R, Huang W, Chen S-M, Sun L-D, Liu H, et al. (2009) Genomewide association study of leprosy. N Engl J Med 361: 2609–2618. doi:10.1056/NEJMoa0903753.
  4. Guerra-Silveira F, Abad-Franch F (2013) Sex bias in infectious disease epidemiology: patterns and processes. PLoS ONE 8: e62390. doi:10.1371/journal.pone.0062390.
  5. Kirchheimer WF, Storrs EE (1971) Attempts to establish the armadillo (Dasypus novemcinctus Linn.) as a model for the study of leprosy. I. Report of lepromatoid leprosy in an experimentally infected armadillo. Int J Lepr Mycobact Dis Off Organ Int Lepr Assoc 39: 693–702

Supporting Information

S1 Appendix. Methodology for the collection of leprosy cases in Vietnam.

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(DOCX)

S1 Table. Main types of analyses used in genetic epidemiology.

https://doi.org/10.1371/journal.pntd.0004345.s002

(DOCX)

S1 Fig. Proportion of multibacillary patients according to age and sex in a Vietnamese sample.

Proportion of multibacillary patients according to age and sex among 1,127 leprosy cases (WHO-82 classification).

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(TIF)

Acknowledgments

We thank Dr. Abad-Franch from the Instituto Leonidas e Maria Deane (Manaus, Brazil) for sharing his data, as well as Jean-Laurent Casanova and all members of the Epidemiological Team for their support and constructive criticism. Special thanks go to Frédégonde About and Fabienne Jabot-Hanin for their assistance in designing the figures and their careful reading of the manuscript.

References

  1. 1. Misch EA, Berrington WR, Vary JC Jr, Hawn TR. Leprosy and the human genome. Microbiol Mol Biol Rev MMBR. 2010;74: 589–620. pmid:21119019
  2. 2. Gaschignard J, Scurr E, Alcaïs A. La lèpre, pilier de la génétique des maladies infectieuses. Pathol Biol. 2013;61: 120–128.
  3. 3. Scollard DM. Leprosy research declines, but most of the basic questions remain unanswered. Int J Lepr Mycobact Dis Off Organ Int Lepr Assoc. 2005;73: 25–27.
  4. 4. Scollard DM, Adams LB, Gillis TP, Krahenbuhl JL, Truman RW, Williams DL. The continuing challenges of leprosy. Clin Microbiol Rev. 2006;19: 338–381. pmid:16614253
  5. 5. Scollard DM. Classification of leprosy: a full color spectrum, or black and white? Int J Lepr Mycobact Dis Off Organ Int Lepr Assoc. 2004;72: 166–168.
  6. 6. Walker SL, Lockwood DNJ. The clinical and immunological features of leprosy. Br Med Bull. 2006;77–78: 103–121. pmid:17090777
  7. 7. Sharma PV. Sushruta-Samhita: with the English Translation of Text and Dalhana’s Commentary Along with Critical Notes. Varanasi, India; 1999.
  8. 8. Languillon J. Précis de léprologie. In: Languillon J, Bourrel P, Saint-André P, Discamps G, Baquillon G, editors. Leprosy, 1st ed. Pré Presse-Pastel Créations, Lavaur, France, 1999.
  9. 9. Hansen GA, Looft C. Leprosy in its clinical and pathological aspects. Bristol, UK: John Wright;1895.
  10. 10. Wade HW. Tuberculoid changes in leprosy IV. Classification of Tuberculoid leprosy. Int J Lepr. 1935;3: 121–136.
  11. 11. WHO. Chemotherapy of leprosy for control programmes. WHO, Technical Report Series; 1982. http://apps.who.int/iris/bitstream/10665/38984/1/WHO_TRS_675.pdf.
  12. 12. Wade HW et al. The technical resolution on classification at the 6th International Congress of Leprosy, Madrid, 1953. Internat. J. Leprosy 1953;21: 504–516.
  13. 13. Ridley DS, Jopling WH. Classification of leprosy according to immunity. A five-group system. Int J Lepr Mycobact Dis Off Organ Int Lepr Assoc. 1966;34: 255–273.
  14. 14. Ridley DS, Waters MF. Significance of variations within the lepromatous group. Lepr Rev. 1969;40: 143–152. pmid:5351801
  15. 15. Ridley DS. Therapeutic trials in leprosy using serial biopsies. Lepr Rev. 1958;29: 45–52. pmid:13515393
  16. 16. WHO Expert Comittee on Leprosy. Sixth Report. WHO Technical Report Series; 1988. http://apps.who.int/iris/bitstream/10665/37409/1/WHO_TRS_768.pdf.
  17. 17. Moet FJ, Meima A, Oskam L, Richardus JH. Risk factors for the development of clinical leprosy among contacts, and their relevance for targeted interventions. Lepr Rev. 2004;75: 310–326. pmid:15682969
  18. 18. Moet FJ, Pahan D, Schuring RP, Oskam L, Richardus JH. Physical distance, genetic relationship, age, and leprosy classification are independent risk factors for leprosy in contacts of patients with leprosy. J Infect Dis. 2006;193: 346–353. pmid:16388481
  19. 19. Bakker MI, Hatta M, Kwenang A, Van Mosseveld P, Faber WR, Klatser PR, et al. Risk factors for developing leprosy—a population-based cohort study in Indonesia. Lepr Rev. 2006;77: 48–61. pmid:16715690
  20. 20. Antunes DE, Araujo S, Ferreira GP, Cunha ACSR da, Costa AV da, Gonçalves MA, et al. Identification of clinical, epidemiological and laboratory risk factors for leprosy reactions during and after multidrug therapy. Mem Inst Oswaldo Cruz. 2013;108: 901–908. pmid:24271045
  21. 21. Dos Santos DS, Duppre NC, Sales AM, Nery JA da C, Sarno EN, Hacker MA. Kinship and Leprosy in the Contacts of Leprosy Patients: Cohort at the Souza Araújo Outpatient Clinic, Rio de Janeiro, RJ, 1987–2010. J Trop Med. 2013;2013: 596316. pmid:23690793
  22. 22. Cole ST, Eiglmeier K, Parkhill J, James KD, Thomson NR, Wheeler PR, et al. Massive gene decay in the leprosy bacillus. Nature. 2001;409: 1007–1011. pmid:11234002
  23. 23. Monot M, Honoré N, Garnier T, Zidane N, Sherafi D, Paniz-Mondolfi A, et al. Comparative genomic and phylogeographic analysis of Mycobacterium leprae. Nat Genet. 2009;41: 1282–1289. pmid:19881526
  24. 24. Singh P, Cole ST. Mycobacterium leprae: genes, pseudogenes and genetic diversity. Future Microbiol. 2011;6: 57–71. pmid:21162636
  25. 25. WHO. Global leprosy: update on the 2012 situation. Weekly epidemiological record. No. 35. 2013: 365–380.
  26. 26. Le Grand A. Women and leprosy: a review. Lepr Rev. 1997;68: 203–211. pmid:9364820
  27. 27. Wade HW, Ledowsky V. The leprosy epidemic at Nauru; a review, with data on the status since 1937. Int J Lepr. 1952;20: 1–29. pmid:14927239
  28. 28. Guerra-Silveira F, Abad-Franch F. Sex bias in infectious disease epidemiology: patterns and processes. PLoS ONE. 2013;8: e62390. pmid:23638062
  29. 29. Fine PE. Leprosy: the epidemiology of a slow bacterium. Epidemiol Rev. 1982;4: 161–188. pmid:6754406
  30. 30. de Vries JL, Perry BH. Leprosy case detection rates by age, sex, and polar type under leprosy control conditions. Am J Epidemiol. 1985;121: 403–413. pmid:4014130
  31. 31. Rao PS, Karat AB, Kaliaperumal VG, Karat S. Prevalence of leprosy in Gudiyatham Taluk, South India. I. Specific rates with reference to age, sex and type. Int J Lepr Mycobact Dis Off Organ Int Lepr Assoc. 1972;40: 157–163.
  32. 32. Mukherjee PK, Das P, Rao PSS. Time trends in MB-PB ratio among untreated leprosy patients attending a referral hospital in UP, India during 2001 to 2010. Indian J Lepr. 2013;85: 59–64. pmid:24236364
  33. 33. Setia MS, Steinmaus C, Ho CS, Rutherford GW. The role of BCG in prevention of leprosy: a meta-analysis. Lancet Infect Dis. 2006;6: 162–170. pmid:16500597
  34. 34. Merle CSC, Cunha SS, Rodrigues LC. BCG vaccination and leprosy protection: review of current evidence and status of BCG in leprosy control. Expert Rev Vaccines. 2010;9: 209–222. pmid:20109030
  35. 35. Thuc NV, Abel L, Lap VD, Oberti J, Lagrange PH. Protective effect of BCG against leprosy and its subtypes: a case-control study in southern Vietnam. Int J Lepr Mycobact Dis Off Organ Int Lepr Assoc. 1994;62: 532–538.
  36. 36. Muliyil J, Nelson KE, Diamond EL. Effect of BCG on the risk of leprosy in an endemic area: a case control study. Int J Lepr Mycobact Dis Off Organ Int Lepr Assoc. 1991;59: 229–236.
  37. 37. Schuring RP, Richardus JH, Pahan D, Oskam L. Protective effect of the combination BCG vaccination and rifampicin prophylaxis in leprosy prevention. Vaccine. 2009;27: 7125–7128. pmid:19786134
  38. 38. Newell KW. A epidemiologist’s view of leprosy. Bull World Health Organ. 1966;34: 827. pmid:5330347
  39. 39. Ranque B, Alter A, Schurr E, Abel L, Alcais A. [Leprosy: a paradigm for the study of human genetic susceptibility to infectious diseases]. Médecine Sci MS. 2008;24: 491–497.
  40. 40. Chakravarrti MR, Vogel F. A twin study on leprosy. Georg Thieme Publishers, Stuttgart, Germany; 1973.
  41. 41. Smith DG. The genetic hypothesis for susceptibility to lepromatous leprosy. Hum Genet. 1979;50: 163–177. pmid:511131
  42. 42. Abel L, Demenais F. Detection of major genes for susceptibility to leprosy and its subtypes in a Caribbean island: Desirade island. Am J Hum Genet. 1988;42: 256–266. pmid:3341381
  43. 43. Wagener DK, Schauf V, Nelson KE, Scollard D, Brown A, Smith T. Segregation analysis of leprosy in families of northern Thailand. Genet Epidemiol. 1988;5: 95–105. pmid:3402727
  44. 44. Feitosa MF, Borecki I, Krieger H, Beiguelman B, Rao DC. The genetic epidemiology of leprosy in a Brazilian population. Am J Hum Genet. 1995;56: 1179–1185. pmid:7726175
  45. 45. Mira MT, Alcais A, Di Pietrantonio T, Thuc NV, Phuong MC, Abel L, et al. Segregation of HLA/TNF region is linked to leprosy clinical spectrum in families displaying mixed leprosy subtypes. Genes Immun. 2003;4: 67–73. pmid:12595904
  46. 46. Jamieson SE, Miller EN, Black GF, Peacock CS, Cordell HJ, Howson JMM, et al. Evidence for a cluster of genes on chromosome 17q11-q21 controlling susceptibility to tuberculosis and leprosy in Brazilians. Genes Immun. 2004;5: 46–57. pmid:14735149
  47. 47. Miller EN, Jamieson SE, Joberty C, Fakiola M, Hudson D, Peacock CS, et al. Genome-wide scans for leprosy and tuberculosis susceptibility genes in Brazilians. Genes Immun. 2004;5: 63–67. pmid:14735151
  48. 48. Wallace C, Fitness J, Hennig B, Sichali L, Mwaungulu L, Pönnighaus JM, et al. Linkage analysis of susceptibility to leprosy type using an IBD regression method. Genes Immun. 2004;5: 221–225. pmid:15014432
  49. 49. Siddiqui MR, Meisner S, Tosh K, Balakrishnan K, Ghei S, Fisher SE, et al. A major susceptibility locus for leprosy in India maps to chromosome 10p13. Nat Genet. 2001;27: 439–41. pmid:11279529
  50. 50. Mira MT, Alcaïs A, Van Thuc N, Thai VH, Huong NT, Ba NN, et al. Chromosome 6q25 is linked to susceptibility to leprosy in a Vietnamese population. Nat Genet. 2003;33: 412–415. pmid:12577057
  51. 51. Tosh K, Meisner S, Siddiqui MR, Balakrishnan K, Ghei S, Golding M, et al. A region of chromosome 20 is linked to leprosy susceptibility in a South Indian population. J Infect Dis. 2002;186: 1190–1193. pmid:12355375
  52. 52. Bochud P- Y, Hawn TR, Siddiqui MR, Saunderson P, Britton S, Abraham I, et al. Toll-like receptor 2 (TLR2) polymorphisms are associated with reversal reaction in leprosy. J Infect Dis. 2008;197: 253–261. pmid:18177245
  53. 53. Fitness J, Floyd S, Warndorff DK, Sichali L, Mwaungulu L, Crampin AC, et al. Large-scale candidate gene study of leprosy susceptibility in the Karonga district of northern Malawi. Am J Trop Med Hyg. 2004;71: 330–340. pmid:15381816
  54. 54. Roy S, McGuire W, Mascie-Taylor CG, Saha B, Hazra SK, Hill AV, et al. Tumor necrosis factor promoter polymorphism and susceptibility to lepromatous leprosy. J Infect Dis. 1997;176: 530–2. pmid:9237725
  55. 55. Vejbaesya S, Mahaisavariya P, Luangtrakool P, Sermduangprateep C. TNF alpha and NRAMP1 polymorphisms in leprosy. J Med Assoc Thail Chotmaihet Thangphaet. 2007;90: 1188–1192.
  56. 56. de Messias-Reason IJ, Boldt ABW, Moraes Braga AC, Von Rosen Seeling Stahlke E, Dornelles L, Pereira-Ferrari L, et al. The association between mannan-binding lectin gene polymorphism and clinical leprosy: new insight into an old paradigm. J Infect Dis. 2007;196: 1379–1385. pmid:17922403
  57. 57. Sapkota BR, Macdonald M, Berrington WR, Misch EA, Ranjit C, Siddiqui MR, et al. Association of TNF, MBL, and VDR polymorphisms with leprosy phenotypes. Hum Immunol. 2010;71: 992–998. pmid:20650301
  58. 58. Alter A, de Léséleuc L, Van Thuc N, Thai VH, Huong NT, Ba NN, et al. Genetic and functional analysis of common MRC1 exon 7 polymorphisms in leprosy susceptibility. Hum Genet. 2010;127: 337–348. pmid:20035344
  59. 59. Wang LM, Kimura A, Satoh M, Mineshita S. HLA linked with leprosy in southern China: HLA-linked resistance alleles to leprosy. Int J Lepr Mycobact Dis Off Organ Int Lepr Assoc. 1999;67: 403–408.
  60. 60. do Sacramento WS, Mazini PS, Franceschi DAS, de Melo FC, Braga MA, Sell AM, et al. Frequencies of MICA alleles in patients from southern Brazil with multibacillary and paucibacillary leprosy. Int J Immunogenet. 2012;39: 210–215. pmid:22168250
  61. 61. Moraes MO, Pacheco AG, Schonkeren JJM, Vanderborght PR, Nery JAC, Santos AR, et al. Interleukin-10 promoter single-nucleotide polymorphisms as markers for disease susceptibility and disease severity in leprosy. Genes Immun. 2004;5: 592–595. pmid:15306847
  62. 62. Santos AR, Suffys PN, Vanderborght PR, Moraes MO, Vieira LMM, Cabello PH, et al. Role of tumor necrosis factor-alpha and interleukin-10 promoter gene polymorphisms in leprosy. J Infect Dis. 2002;186: 1687–1691. pmid:12447749
  63. 63. Reynard MP, Turner D, Junqueira-Kipnis AP, Ramos de Souza M, Moreno C, Navarrete CV. Allele frequencies for an interferon-gamma microsatellite in a population of Brazilian leprosy patients. Eur J Immunogenetics Off J Br Soc Histocompat Immunogenetics. 2003;30: 149–151.
  64. 64. Lucena-Silva N, Teixeira MAG, Ramos A de L, de Albuquerque RS, Diniz GTN, Mendes-Junior CT, et al. The +3187A/G HLA-G polymorphic site is associated with polar forms and reactive reaction in leprosy. Mol Genet Genomic Med. 2013;1: 123–130. pmid:24498610
  65. 65. Bochud P- Y, Sinsimer D, Aderem A, Siddiqui MR, Saunderson P, Britton S, et al. Polymorphisms in Toll-like receptor 4 (TLR4) are associated with protection against leprosy. Eur J Clin Microbiol Infect Dis Off Publ Eur Soc Clin Microbiol. 2009;28: 1055–1065.
  66. 66. Tobin DM, Vary JC Jr, Ray JP, Walsh GS, Dunstan SJ, Bang ND, et al. The lta4h locus modulates susceptibility to mycobacterial infection in zebrafish and humans. Cell. 2010;140: 717–730. pmid:20211140
  67. 67. Roy S, Frodsham A, Saha B, Hazra SK, Mascie-Taylor CG, Hill AV. Association of vitamin D receptor genotype with leprosy type. J Infect Dis. 1999;179: 187–191. pmid:9841838
  68. 68. Neela VSK, Suryadevara NC, Shinde VG, Pydi SS, Jain S, Jonnalagada S, et al. Association of Taq I, Fok I and Apa I polymorphisms in Vitamin D Receptor (VDR) gene with leprosy. Hum Immunol. 2015;76: 402–405. pmid:25890006
  69. 69. Franceschi DSA, Mazini PS, Rudnick CCC, Sell AM, Tsuneto LT, de Melo FC, et al. Association between killer-cell immunoglobulin-like receptor genotypes and leprosy in Brazil. Tissue Antigens. 2008;72: 478–482. pmid:18778326
  70. 70. Rajalingam R, Mehra NK, Singal DP. Polymorphism in heat-shock protein 70–1 (HSP70-1) gene promoter region and susceptibility to tuberculoid leprosy and pulmonary tuberculosis in Asian Indians. Indian J Exp Biol. 2000;38: 658–662. pmid:11215307
  71. 71. Ali S, Srivastava AK, Chopra R, Aggarwal S, Garg VK, Bhattacharya SN, et al. IL12B SNPs and copy number variation in IL23R gene associated with susceptibility to leprosy. J Med Genet. 2013;50: 34–42. pmid:23240095
  72. 72. Greiner J, Weber FJ, Mauff G, Baur M. Genetic polymorphisms of properdin factor B(Bf), the second component (C2), and the fourth component (C4) of complement in leprosy patients and healthy controls from Thailand. Immunobiology. 1980;158: 134–138. pmid:6907169
  73. 73. Alvarado-Navarro A, Montoya-Buelna M, Muñoz-Valle JF, López-Roa RI, Guillén-Vargas C, Fafutis-Morris M. The 3’UTR 1188 A/C polymorphism in the interleukin-12p40 gene (IL-12B) is associated with lepromatous leprosy in the west of Mexico. Immunol Lett. 2008;118: 148–151. pmid:18485489
  74. 74. Ohyama H, Ogata K, Takeuchi K, Namisato M, Fukutomi Y, Nishimura F, et al. Polymorphism of the 5’ flanking region of the IL-12 receptor beta2 gene partially determines the clinical types of leprosy through impaired transcriptional activity. J Clin Pathol. 2005;58: 740–743. pmid:15976343
  75. 75. Meisner SJ, Mucklow S, Warner G, Sow SO, Lienhardt C, Hill AV. Association of NRAMP1 polymorphism with leprosy type but not susceptibility to leprosy per se in west Africans. Am J Trop Med Hyg. 2001;65: 733–735. pmid:11791966
  76. 76. Wibawa T, Soebono H, Matsuo M. Association of a missense mutation of the laminin alpha2 gene with tuberculoid type of leprosy in Indonesian patients. Trop Med Int Health TM IH. 2002;7: 631–636. pmid:12100448
  77. 77. Prado-Montes de Oca E, Velarde-Félix JS, Ríos-Tostado JJ, Picos-Cárdenas VJ, Figuera LE. SNP 668C (-44) alters a NF-kappaB1 putative binding site in non-coding strand of human beta-defensin 1 (DEFB1) and is associated with lepromatous leprosy. Infect Genet Evol J Mol Epidemiol Evol Genet Infect Dis. 2009;9: 617–625.
  78. 78. Xiang Y-L, Zhang D-F, Wang D, Li Y-Y, Yao Y-G. Common variants of OPA1 conferring genetic susceptibility to leprosy in Han Chinese from Southwest China. J Dermatol Sci. 2015; 80(2):133–41. pmid:26360011
  79. 79. Chaitanya VS, Jadhav RS, Lavania M, Singh M, Valluri V, Sengupta U. Interleukin-17F single-nucleotide polymorphism (7488T>C) and its association with susceptibility to leprosy. Int J Immunogenet. 2013; 41(2):131–7. pmid:24164796
  80. 80. Grant AV, Cobat A, Van Thuc N, Orlova M, Huong NT, Gaschignard J, et al. CUBN and NEBL common variants in the chromosome 10p13 linkage region are associated with multibacillary leprosy in Vietnam. Hum Genet. 2014; 133(7):883–93. pmid:24563210
  81. 81. Zhang F-R, Huang W, Chen S-M, Sun L-D, Liu H, Li Y, et al. Genomewide association study of leprosy. N Engl J Med. 2009;361: 2609–2618. pmid:20018961
  82. 82. Zhang F, Liu H, Chen S, Low H, Sun L, Cui Y, et al. Identification of two new loci at IL23R and RAB32 that influence susceptibility to leprosy. Nat Genet. 2011;43: 1247–1251. pmid:22019778
  83. 83. Grant AV, Alter A, Huong NT, Orlova M, Van Thuc N, Ba NN, et al. Crohn’s disease susceptibility genes are associated with leprosy in the Vietnamese population. J Infect Dis. 2012;206: 1763–1767. pmid:22984114
  84. 84. Marcinek P, Jha AN, Shinde V, Sundaramoorthy A, Rajkumar R, Suryadevara NC, et al. LRRK2 and RIPK2 Variants in the NOD 2-Mediated Signaling Pathway Are Associated with Susceptibility to Mycobacterium leprae in Indian Populations. PLoS ONE. 2013;8: e73103. pmid:24015287
  85. 85. Wong SH, Gochhait S, Malhotra D, Pettersson FH, Teo YY, Khor CC, et al. Leprosy and the adaptation of human toll-like receptor 1. PLoS Pathog. 2010;6: e1000979. pmid:20617178
  86. 86. Alcais A, Alter A, Antoni G, Orlova M, Van Thuc N, Singh M, et al. Stepwise replication identifies a low-producing lymphotoxin-alpha allele as a major risk factor for early-onset leprosy. Nat Genet. 2007;39: 517–522. pmid:17353895
  87. 87. Alter A, Fava VM, Huong NT, Singh M, Orlova M, Van Thuc N, et al. Linkage disequilibrium pattern and age-at-diagnosis are critical for replicating genetic associations across ethnic groups in leprosy. Hum Genet. 2013; 132(1):107–16. pmid:23052943
  88. 88. Vinayagamoorthy N, Hu H-J, Yim S-H, Jung S-H, Jo J, Jee SH, et al. New Variants Including ARG1 Polymorphisms Associated with C-Reactive Protein Levels Identified by Genome-Wide Association and Pathway Analysis. PLoS ONE. 2014;9: e95866. pmid:24763700
  89. 89. Lee YH, Kim J-H, Song GG. Genome-wide pathway analysis of breast cancer. Tumour Biol J Int Soc Oncodevelopmental Biol Med. 2014; 35(8):7699–705.
  90. 90. Stranger BE, Stahl EA, Raj T. Progress and promise of genome-wide association studies for human complex trait genetics. Genetics. 2011;187: 367–383. pmid:21115973
  91. 91. Adams LB, Pena MT, Sharma R, Hagge DA, Schurr E, Truman RW. Insights from animal models on the immunogenetics of leprosy: a review. Mem Inst Oswaldo Cruz. 2012;107 Suppl 1: 197–208. pmid:23283472
  92. 92. Kirchheimer WF, Storrs EE. Attempts to establish the armadillo (Dasypus novemcinctus Linn.) as a model for the study of leprosy. I. Report of lepromatoid leprosy in an experimentally infected armadillo. Int J Lepr Mycobact Dis Off Organ Int Lepr Assoc. 1971;39: 693–702.
  93. 93. Truman RW, Ebenezer GJ, Pena MT, Sharma R, Balamayooran G, Gillingwater TH, et al. The armadillo as a model for peripheral neuropathy in leprosy. ILAR J Natl Res Counc Inst Lab Anim Resour. 2014;54: 304–314.
  94. 94. Sharma R, Lahiri R, Scollard DM, Pena M, Williams DL, Adams LB, et al. The armadillo: a model for the neuropathy of leprosy and potentially other neurodegenerative diseases. Dis Model Mech. 2013;6: 19–24. pmid:23223615
  95. 95. Lindblad-Toh K, Garber M, Zuk O, Lin MF, Parker BJ, Washietl S, et al. A high-resolution map of human evolutionary constraint using 29 mammals. Nature. 2011;478: 476–482. pmid:21993624
  96. 96. Lynch VJ, Leclerc RD, May G, Wagner GP. Transposon-mediated rewiring of gene regulatory networks contributed to the evolution of pregnancy in mammals. Nat Genet. 2011;43: 1154–1159. pmid:21946353
  97. 97. Alter A, Grant A, Abel L, Alcaïs A, Schurr E. Leprosy as a genetic disease. Mamm Genome. 2011;22: 19–31. pmid:20936290
  98. 98. Greenstein RJ. Is Crohn’s disease caused by a mycobacterium? Comparisons with leprosy, tuberculosis, and Johne’s disease. Lancet Infect Dis. 2003;3: 507–514. pmid:12901893
  99. 99. Sartor RB. Does Mycobacterium avium subspecies paratuberculosis cause Crohn’s disease? Gut. 2005;54: 896–898. pmid:15951529
  100. 100. Uzoigwe JC, Khaitsa ML, Gibbs PS. Epidemiological evidence for Mycobacterium avium subspecies paratuberculosis as a cause of Crohn’s disease. Epidemiol Infect. 2007;135: 1057–1068. pmid:17445316
  101. 101. Pierce ES. Ulcerative colitis and Crohn’s disease: is Mycobacterium avium subspecies paratuberculosis the common villain? Gut Pathog. 2010;2: 21. pmid:21167058
  102. 102. Brand S. Crohn’s disease: Th1, Th17 or both? The change of a paradigm: new immunological and genetic insights implicate Th17 cells in the pathogenesis of Crohn’s disease. Gut. 2009;58: 1152–1167. pmid:19592695
  103. 103. Jostins L, Ripke S, Weersma RK, Duerr RH, McGovern DP, Hui KY, et al. Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature. 2012;491: 119–124. pmid:23128233
  104. 104. Ohyama H, Kato-Kogoe N, Nishimura F, Takeuchi-Hatanaka K, Matsushita S, Yamanegi K, et al. Differential effects of polymorphisms in the 5’ flanking region of IL12RB2 on NK- and T-cell activity. J Interferon Cytokine Res Off J Int Soc Interferon Cytokine Res. 2008;28: 563–569.
  105. 105. Franceschi DSA, Mazini PS, Rudnick CCC, Sell AM, Tsuneto LT, Ribas ML, et al. Influence of TNF and IL10 gene polymorphisms in the immunopathogenesis of leprosy in the south of Brazil. Int J Infect Dis IJID Off Publ Int Soc Infect Dis. 2009;13: 493–498.