Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2012 Feb 1.
Published in final edited form as: Mamm Genome. 2010 Nov 23;22(1-2):91–99. doi: 10.1007/s00335-010-9301-7

Tuberculosis as a complex trait: impact of genetic epidemiological study design

Catherine M Stein 1,✉, Allison R Baker 2
PMCID: PMC3043369  NIHMSID: NIHMS272313  PMID: 21104256

Abstract

Several studies have suggested a role for human genetic risk factors in the susceptibility to developing tuberculosis (TB). However, results of these studies have been inconsistent, and one potential reason for these inconsistencies is variation in aspects of study design. Specifically, phenotype definitions and population genetic factors have varied dramatically. Since TB is a complex trait, there are many challenges in designing studies to assess appropriately human genetic risk factors for the development of TB as opposed to the acquisition of latent M. tuberculosis infection. In this review we summarize these important study design differences, with illustrations from the TB genetics literature. We cite specific examples of studies of the NRAMP1 (SLC11A1) gene and present Fisher’s combined p values for different stratifications of these studies to further illustrate the impact of study design differences. Finally, we provide suggestions for the design of future genetic epidemiological studies of TB.

Introduction

Tuberculosis (TB), caused by Mycobacterium tuberculosis, is a significant global public health problem, especially with the rise of the HIV pandemic. Among the one-third of the world infected by M. tuberculosis, almost 8 million incident cases of TB are diagnosed annually, and 2 million deaths are attributed to the disease each year.

Support for genetic susceptibility to TB in humans was first provided by twin studies, which have estimated that the concordance rate of TB is higher in monozygotic than in dizygotic twins (Comstock 1978; Kallmann and Reisner 1943). Differences in susceptibility between racial groups have been demonstrated and further support a genetic component; macrophages in blacks are more permissive to tuberculosis infection than those in whites (Crowle and Elkins 1990). A segregation analysis by Shaw et al. (1997) suggested that a general two-locus model was marginally favored over a single-gene codominant model for TB. Several animal models have also provided evidence for a role of genetic factors in TB susceptibility (Blackwell et al. 1995; Flynn et al. 1995; Kramnik et al. 2000; Lurie 1941). Furthermore, candidate gene studies have been conducted in a variety of world populations, though the results have been equivocal (Bellamy 2003).

As discussed in our previous work (Stein et al. 2003), one inherent complication in previous genetic studies of human TB has been the definition of the disease phenotype; as stated by Möller et al. (2010), studies of TB are “exquisitely sensitive to phenotype definition.” TB is heterogeneous in its presentation, severity, and duration. TB most often presents with pulmonary disease, but can also affect nearly all organ systems. Even for pulmonary TB, the presentation may differ among people with progressive primary disease and those with reactivation of a latent form of infection. Since TB is such a heterogeneous disease, it is difficult to define a reliable phenotype for genetic analysis. As we discuss below, diagnostic criteria for TB vary considerably based on available resources. Moreover, TB may develop at any time after infection, and active disease may not present during the data collection phase of the study but rather develop years later. The objective of this review is to examine closely the spectrum of criteria that have been used for clinical characterization of subjects in TB genetics studies and illustrate how these varying criteria may affect study results.

Furthermore, complex traits are characterized by polygenic effects, gene–gene and gene–environment interactions, unclear phenotype definitions, pleiotropy, and genetic heterogeneity. In addition to the aforementioned inconsistency in phenotype definition, many of these genetic epidemiological principles also apply to TB; throughout this review we illustrate examples in the current literature and emphasize the need for ongoing study.

Impact of phenotype definition

TB natural history and disease: factors affecting diagnosis

The pathogenesis of TB can be thought of as a two-stage process (Comstock 1982). In the first stage, exposed individuals can acquire latent M. tuberculosis infection (LTBI), in which M. tuberculosis establishes a productive infection but does not produce symptoms. LTBI is diagnosed by a positive tuberculin skin test (TST) and/or positive interferon-γ sresponse assay (IGRA) and the absence of clinical signs and symptoms of full-blown disease (ATS/CDC 2000; Nyendak et al. 2008). Approximately 10% of individuals with LTBI progress to develop active TB disease, which is characterized by growth of M. tuberculosis in sputum and cultivation in culture or positive acid-fast bacilli (AFB) smear, plus characteristic radiological signs on chest X-ray and hallmark symptoms, including persistent productive cough, fever, and weight loss (ATS/CDC 2000; Garay 2004).

Some studies have provided evidence that these different stages of TB natural history and disease have different genetic influences. Two whole-genome scans (Cobat et al. 2009; Stein et al. 2008) and a candidate gene study (Thye et al. 2009) examined traits related to LTBI and found distinct regions linked to these phenotypes compared to those linked to TB disease. Other studies (Flores-Villanueva et al. 2005; Motsinger-Reif et al. 2010; Stein et al. 2007) have contrasted TB cases to LTBI controls and concluded that observed genetic associations were related to progression from LTBI to active TB disease. However, these studies are in the minority. There are two issues that make the examination of TB genetics literature difficult: (1) characterization of controls and (2) diagnosis of TB. These two issues relate to the two stages of TB pathogenesis, respectively, in which we go into detail below.

In case–control studies, controls should be similar to cases in every way, only nondiseased. This is why case– control studies can be used to establish a relationship between a (genetic) risk factor and disease. For studies of TB, this means that controls should have had the opportunity to become cases and were therefore exposed to other infectious TB cases. The difficulty with many published reports is that there is no characterization of exposure in controls. Some studies assume that in TB-endemic communities, all individuals are exposed (Hoal et al. 2004; Taype et al. 2006), thereby assuming that all individuals who do not have clinical TB disease are latently infected (LTBI). However, other research has demonstrated that individuals may be persistently exposed to M. tuberculosis but never develop LTBI (ATS/CDC 2000; Stein et al. 2008). Some studies characterize exposure in controls by conducting TST on unaffected individuals (e.g., Dubaniewicz et al. 2005; Flores-Villanueva et al. 2005; Motsinger-Reif et al. 2010). Other studies utilize unaffected household members as controls (Cervino et al. 2000; El Baghdadi et al. 2003; Stein et al. 2007; Velez et al. 2009a). However, many other studies have used population controls, but this design runs the danger of possible misclassification bias (Edwards et al. 2005).

These issues have an impact on the interpretation of results, depending on study design. If a study used population controls and it is not known whether these individuals have LTBI, a positive association between a polymorphism and TB could mean that the polymorphism is associated with LTBI (since all TB cases must first develop LTBI) or progression to active TB disease. A second disadvantage is that individuals who are never exposed to infectious TB cases may actually carry the risk polymorphism, but because they are unexposed, they never develop TB. In this case, misclassification bias results in a bias toward the null hypothesis, and this could be problematic when the disease is common (McCarthy et al. 2008).

Second is the issue of TB diagnosis, which varies widely by study site. The gold standard for TB diagnosis is growth of M. tuberculosis in culture (ATS/CDC 2000). Many research sites lack facilities available for culture confirmation of TB and thus base diagnosis of TB on a positive AFB smear. Research has shown that an AFB smear is less sensitive than culture and that the AFB smear grade could reflect differences in disease severity (Garay 2004). Furthermore, smear-negative, culture-positive TB is a problem in developing countries because of HIV coinfection (Garay 2004). Because diagnostic criteria for TB vary by study, this could reflect underlying differences in disease severity. If host genes influence severity of TB, these differences in diagnostic criteria become very important. Furthermore, because sensitivity and specificity differ between culture and AFB smear, the problem of potential misclassification arises, which could introduce further bias into genetic studies.

Another factor related to differences in TB diagnosis is the different M. tuberculosis strains. Researchers have categorized M. tuberculosis into six main strain lineages that are associated with particular geographical regions (Gagneux and Small 2007) as well as clinical presentation (Thwaites et al. 2008) and rate of progression to active TB disease (de Jong et al. 2008). Therefore, not only do different diagnostic criteria potentially reflect differences in disease severity, as discussed above, but specific M. tuberculosis strains may also influence disease severity. The potential impact of strain lineage on genetic epidemiological studies was demonstrated by a recent study that observed an interaction between host genotype and M. tuberculosis genotype whereby the TLR2 genotype is associated with TB caused by the Beijing strain (Caws et al. 2008).

Lastly, another contributing factor to the differential diagnosis, clinical presentation, and pathogenesis of TB is age of onset. Diagnosis of TB in children is complicated by the absence of positive M. tuberculosis cultures in the vast majority of cases (Lewinsohn et al. 2004) and is often delayed (Iriso et al. 2005). Furthermore, children may be asymptomatic (Iriso et al. 2005). The immune response is different in young children versus adults (Lewinsohn et al. 2008). An association study of NRAMP1 in pediatric TB cases suggests that this gene may be associated with primary TB disease as opposed to reactivation disease (Malik et al. 2005). Leung et al. (2007) also found a more significant association between NRAMP1 and TB in younger individuals, with age cutoff of 65 years and youner. Besides these few studies, the confounding influence of age of onset on TB genetics studies has not been explored, but it may have an impact on the heterogeneity among published studies.

Studies of NRAMP1 as examples

So far we have discussed how phenotype definition matters in the analysis of genetic susceptibility to TB. Next we illustrate these issues using studies of the NRAMP1 gene, aka SLC11A1, as examples. Table 1 summarizes each publication by population, diagnosis method used, information provided about study controls, and the most significant p value observed for any polymorphism within the gene. Note that this table includes only those studies that focused on pulmonary TB in all age groups.

Table 1.

Summary of TB association genetic studies of NRAMP1/SLC11A1a

Population (Ref.) TB diagnostic criteriab Characterization of controls M. tuberculosis lineage groupc Association p value
Gambia (Bellamy et al. 1998) Smear + Healthy blood donors 6, 4 < 0.001 unadjusted, 0.004 adjusted
Gambia (Awomoyi et al. 2002) Smear + Healthy blood donors 6, 4 0.024
Malawi (Fitness et al. 2004) Smear + OR culture + OR histology Unrelated with no history of infectious disease 4, 2, 1 0.014 (HIV− cases) 0.046 (HIV+ cases)
Morrocco (El Baghdadi et al. 2003) Culture + Healthy family members 4 0.33
Tanzania (Søborg et al. 2007) Culture + Blood donors 1, 3 0.010
Guinea (Cervino et al. 2000) Microscopy (smear +? Culture +?) Unaffected relatives 6 0.036
South Africa (Hoal et al. 2004) Smear + OR culture + Unrelated healthy 4, 2 0.002
Caucasian and African American (Velez et al. 2009a) Culture + OR past diagnosis Household members in close contact 4 0.03
Caucasian (Ma et al. 2002) Culture + OR response to TB treatment Clinic patients without infectious disease 4 < 0.010
Caucasian, African-American, and Asian (Motsinger-Reif et al. 2010) Culture + Tuberculin skin test positive 4 Not reported
Cambodia (Delgado et al. 2002) Smear + Hospital/clinic patients 2, 4 0.009
China (Liu et al. 2004) Smear + OR culture + OR symptoms and radiological evidence; Males only Unrelated healthy males 2 0.030
Japan (Abe et al. 2003) Smear + OR culture + No history of TB disease 2 Not reported
Japan (Gao et al. 2000) Smear + Random clinic patients 2 < 0.0001
Taiwanese (Liaw et al. 2002) Culture + Clinic patients without pulmonary disease 4 0.114
Japan (Akahoshi et al. 2004) Smear + Healthy blood donors without history of pulmonary or inflammatory disease 2 0.144
Thai (Vejbaesya et al. 2007) Culture + Healthy blood bank donors 2, 1 0.54
China (Leung et al. 2007) Culture + Hospital patients and healthy blood donors 2 0.0163
Korea (Ryu et al. 2000) Culture + (unclear) No history of TB disease 2 0.020
Japan (Kusuhara et al. 2007) Smear + OR culture + Unrelated healthy 2 0.096
Poland (Dubaniewicz et al. 2005) Culture + TST negative 4 0.63
Open in a new tab
a

This table is limited to studies published in English so that case and control definitions could be determined. It is also limited to studies of pulmonary TB in all age groups

b

Smear + refers to AFB smear positive. Culture + could include more stringent definitions such as culture positive, smear positive, and radiological evidence consistent with TB

c

M. tuberculosis strain lineage based on Gagneux et al. (2006). 1 = Indo-Oceanic, 2 = East-Asian, 3 = East-African-Indian, 4 = Euro-American, 5 = West-African lineage 1 (not detected in these above populations), 6 = West-African lineage 2

First, we can see how different studies diagnosed TB. Some studies used the gold-standard definition of growth of M. tuberculosis in culture, while other studies used only positive AFB smear. Other studies had more heterogeneous criteria; for example, many studies diagnosed TB based on growth in culture or smear, whereas others also included individuals with response to TB treatment or evidence of TB disease on chest X-ray. Although they did not conduct an association analysis of NRAMP1, another approach to classifying TB cases is illustrated by the first genome-wide linkage scan of TB (Bellamy et al. 2000), where patients diagnosed based on positive AFB smear were categorized together with individuals with a history of TB treatment. One problem in using TB treatment as a diagnostic criterion for TB disease is that some research sites may be unable to distinguish TB from disease due to nontuberculous mycobacteria, but standard TB therapy cures both diseases. It is also possible that some research sites “overtreat” cases to make sure that patients recover from whatever ails them. Furthermore, some studies included both pulmonary and extrapulmonary TB and analyzed them together in the analysis (Ma et al. 2003; Motsinger-Reif et al. 2010; Rossouw et al. 2003; Taype et al. 2006; Velez et al. 2009a). Thus, the classification of TB cases is not comparable across studies.

Second, we can see the wide variety of designs used to categorize individuals as controls. Some studies utilized unaffected individuals within households who were clearly exposed to TB and therefore had the opportunity to develop disease but did not. Other studies conducted TST in unaffected individuals. Not only does a positive TST indicate past exposure to a TB case, it also allows the authors to deduce that the genetic association observed is with progression to active disease. However, the clinical status of other controls is unclear. For example, many studies utilized blood donors, so nothing is known about the past exposure or LTBI status of these individuals. Other studies recruited individuals presenting at clinics for diseases other than TB, so again, both past exposure and LTBI classification are unknown.

Notice that of the 12 studies that demonstrated an association between NRAMP1 and TB, only four used culture positivity as their diagnosis method. Also note that only one of the NRAMP1 associations has been observed in studies in which exposure has been characterized in some way, by either utilizing household members as controls or evaluating TST in nondiseased individuals. To investigate differences in a more quantitative fashion, we used Fisher’s method for combining p values (Fisher 1950). When we combined p values, we looked for the most significant p value for any marker within the gene. This answers the question of whether the gene is associated with TB at all; as we point out later, with the variation seen in polymorphisms analyzed in this gene, this may actually be the most appropriate question. Although not nearly as rigorous as a full meta-analysis, this method provides a quick look at these different groupings of studies. We do not present a proper meta-analysis here because of the various stratifications we are interested in; we refer the interested reader to published meta-analysis of this gene (Li et al. 2006) for additional insight.

First, to assess the impact of the TB diagnosis method used, we grouped studies with culture positivity as the only diagnostic criterion versus all other studies. There was a significant association with TB in both categories (p = 0.0008 and p < 10−5, respectively), so it does not appear that the TB diagnosis method has a significant impact on the overall conclusion of association between TB and NRAMP1.

Second, we analyzed the impact of characterization of controls on association results. Here, we grouped together studies in which controls had been clinically characterized, in terms of either exposure to an infectious TB case or TST (Cervino et al. 2000; Dubaniewicz et al. 2005; El Baghdadi et al. 2003; Motsinger-Reif et al. 2010; Velez et al. 2009a). When p values across these studies were combined, the result was still statistically significant (p = 0.012), although it must be interpreted with caution. Because a p value was not reported by Motsinger-Reif et al. (2010), we conservatively inferred a nonsignificant p value of 0.055. However, if we assumed a higher p value of, say, 0.3, the pooled p value was less significant (p = 0.04). If the studies were restricted to those with family-based controls, the result is significant, but not overwhelmingly so (p = 0.014). When we consider that these gene-centric analyses utilized the smallest p value across the polymorphisms analyzed for NRAMP1, one might argue that the more appropriate significant cutoff would account for multiple comparisons. In this case, if we consider that most studies analyzed four polymorphisms within NRAMP1, a conservative Bonferroni-like significance threshold would be α* = 0.05/4 = 0.0125; then, the latter analysis that restricted studies to those with family-based controls would not show a statistically significant association between TB and NRAMP1. By contrast, the combined p value in the remaining studies was highly significant (p < 10−10). Does this contrast suggest an influence of misclassification of controls because of undocumented exposure? There are not enough studies to firmly conclude this. In addition, there are not enough studies with documented TST to conclude whether associations with NRAMP1 are with progression from LTBI to active TB disease or simply susceptibility to LTBI.

Third, we examined the potential impact of the M. tuberculosis strain. Using the global phylogeography of M. tuberculosis strain lineage presented by Gagneux et al. (2006), we grouped studies by the M. tuberculosis lineage found in the study population (Table 1). Malawi was not studied by Gagneux et al., so we conservatively assumed that the Indo-Oceanic (present in Tanzania), East African (present in Tanzania and South Africa), and Euro-American (in South Africa) lineages were prevalent there. Note that there was a great ecological simplification made here; we assumed that all of the cases analyzed in articles from in a given geographic region were caused by the same strain(s). However, it presents an interesting hypothesis that genetic association studies of NRAMP1 may differ based on the endemic M. tuberculosis strain. It is not trivial to analyze these data because many populations were infected by more than one lineage, and populations that have unique combinations (e.g., Tanzania has both Indo-Oceanic and East-African-Indian lineages) cannot be pooled with others. Studies conducted in populations with the East-Asian strain were highly statistically significant (p < 10−5). When populations infected by Euro-American and West-African 2 strains were analyzed together, there also was a statistically significant association (p < 10−4). When populations with only the Euro-American strain were analyzed, the result was less significant (p = 0.033), although strong conclusions cannot be drawn about this. Again, if we apply the adjustment for multiple comparisons described above (α* = 0.0125), then this analysis of populations with Euro-American lineage were not statistically significant. Thus, we do not have evidence that there is a strain lineage by NRAMP1 interaction, although specific studies investigating this hypothesis are warranted. These results must be interpreted with caution because our assumption that the results of Gagneux et al. (2006) adequately capture the variation within these study populations is likely an oversimplification. Indeed, recent research has suggested that the distribution of the bacterial population structure has changed significantly over time (van der Spuy et al. 2009), so it is possible that strain diversity in study populations has changed.

Other complicating factors

Population genetics

Another explanation for differing results by population includes genetic heterogeneity or inestimable polygenic effects (Deng 2001; Möller et al. 2010). Polygenic effects may be caused by the combined effects of several rare variants (Bodmer and Bonilla 2008). Rare variants in TB have been much understudied. One study has done extensive resequencing of Toll-like receptor (TLR) genes and found association with TB (Ma et al. 2007). We have also conducted full-exon resequencing of TLR genes and identified novel polymorphisms in Ugandan and South African populations (Baker et al. 2009). Copy number variants, which have been associated with HIV acquisition and progression as well as autoimmune diseases (McCarroll and Altshuler 2007), may also prove to be important to TB susceptibility. Because these variants have low frequency, they must be treated as rare genetic variants in analysis, and thus they require either large sample sizes or novel analytical approaches to detect trait associations. Analysis of large extended pedigrees would be ideal for the detection and analysis of rare variants (Manolio et al. 2009).

Linkage disequilibrium (LD) is another population genetic factor that may explain differences between studies. LD differs widely by population (Jakobsson et al. 2008), and since trait-associated polymorphisms may actually be untyped and in LD with genotyped markers, variations in LD may confound the ability to detect and replicate marker–trait associations. This is illustrated by the NRAMP1 association study by Velez et al. (2009a, 2009b); this study did not find association between TB and the four polymorphisms in NRAMP1 that were examined in previous studies, but it did find association between TB and other single nucleotide polymorphisms (SNPs) in the gene. If these additional SNPs had not been analyzed, the association between TB and NRAMP1 in that population likely would have been missed. In terms of understanding how specific genes convey risk for developing disease, it may be more important to first understand which gene is associated with disease risk and then identify the specific polymorphism( s). Since TB is a complex trait, there is likely genetic heterogeneity, implying that different variants influence disease risk in different populations. Thus, it is important to conduct thorough genotyping when conducting genetic epidemiological studies rather than focus on a few promoter or nonsynonymous SNPs.

Complex genetic effects

Complex traits are often characterized by gene–gene and gene–environment interactions, but such interaction effects in TB have been understudied. One study suggested interaction between the NOS2A gene and IFNGR1 and TLR4 (Velez et al. 2009b), though neither IFNGR1 nor TLR4 had significant main effects in this analysis. A second study by the same research group found interaction between NRAMP1 and TLR2, but TLR2 did not have a significant main effect (Velez et al. 2009a). This suggests that many important genes may influence TB in combination with other genes, but these interactions may be over-looked because their individual effects did not meet criteria for statistical significance. Alternative statistical approaches may be used to discover epistatic effects; such an analysis was conducted by Motsinger-Reif et al. (2010) who used multifactor dimensionality reduction to identify a potential gene–gene interaction between the TLR4 and TNFα genes.

Epidemiological factors also may modify the effects of genes. M. tuberculosis gene by human gene interaction, as discussed above, is one example of this. Another important effect modifier is HIV seropositivity. The effect of HIV on TB genetics has been understudied because most research studies exclude HIV-infected individuals from both their case and control populations, and HIV is a strong confounding factor of TB immune response. However, our previous work has shown an interaction between HIV serostatus and the TNF receptor 1 gene (Stein et al. 2007). Future studies may suggest that certain genetic effects are important only in HIV-uninfected individuals, while other genes modify TB susceptibility in HIV-infected individuals. On the other hand, studies examining interaction effects require large sample sizes (Velez et al. 2009b).

Summary and conclusions

Although numerous studies examining genetic risk factors for TB have been conducted, we are only beginning to scratch the surface of understanding the role of host genetics in TB susceptibility. Here we have described several principles of genetic epidemiology—phenotype definition, population genetics, and complex genetic effects—and we have illustrated how these factors cloud the current TB genetics literature. These factors should all be considered when synthesizing the literature. Although many of our combined p values attained statistical significance when considering NRAMP1 studies as examples, we do not wish to imply that this is true of studies of other genes. Many of these p values are highly significant due to the large number of p values that were combined. Moreover, these p values are all significant because we analyzed the most significant published p value. If we had conducted these analyses separately for each polymorphism, we likely would have had different results. For genes that have been studied less extensively (see Möler and Hoal 2010; Möller et al. 2010 for recent reviews), the impact of study design on study results is unknown. Although it is impossible to change these study design components retrospectively, future studies should consider these issues when developing the study’s design.

Seven genome-wide linkage scans for TB have been published. Recently, the first genome-wide association study (GWAS) of TB was published (Thye et al. 2010). Interestingly, this GWAS detected statistically significant association between TB and a novel genomic region on chromosome 18 (p < 10−8), but this region appears to be a gene desert, so no new candidate genes were immediately proposed by this study. Because of the increased power of association analysis over linkage analysis for detection of common variants with smaller effect sizes (Ardlie et al. 2002; Risch and Merikangas 1996), a GWAS analysis of TB should certainly provide new clues to the genetic underpinnings of TB risk. However, in order to provide sufficient statistical power for a GWAS, thousands of study subjects are needed. In the Thye et al. (2010) GWAS, populations from Ghana, The Gambia, and Malawi were analyzed using meta-analysis. As we show in this review, merging data across studies should be done with extreme caution because of differences in study design. Other GWAS analyses have merged data across studies, using meta-analysis techniques, and have successfully identified loci associated with Crohn’s disease and type II diabetes (Barrett et al. 2008; Zeggini et al. 2008); however, heterogeneity in study design may mask underlying associations when unaccounted for (Heid et al. 2009). However, if we are interested in detecting rare variants underlying TB risk, linkage analysis may be more powerful (Ardlie et al. 2002), so family studies will be advantageous, as stated above.

The ultimate goal in understanding TB genetics is to understand which factors make individuals more susceptible to developing disease in order to facilitate the development of better vaccines and other therapeutics. Another field of study involved in reaching this goal is immunology. Very few studies have examined genetic influences on the TB immune response (Hawn et al. 2007; Shey et al. 2010; Stein et al. 2007, 2008; Wheeler et al. 2006), and more studies are needed. Furthermore, the observation that some genes are associated with more than one phenotype demonstrates pleiotropic effects. For example, studies have shown association with both immunological traits and TB disease outcomes (Shey et al. 2010; Stein et al. 2007). These results could reflect the fact that these traits are on the same pathway (immune response influencing disease outcome) or that the traits themselves are correlated. In addition, some researchers have advocated the study of the “genetics of health” for a different perspective (Nadeau and Topol 2006), which would be useful in vaccine development. The few studies on TB that have focused on resistance to M. tuberculosis infection (Stein et al. 2008), or on LTBI and not TB disease (Cobat et al. 2009; Stein et al. 2007; Thye et al. 2009), are beginning to offer insight into the genetics of individuals who infected with M. tuberculosis and remain healthy.

Finally, unlike most genetic epidemiological studies that are conducted in developed countries, studies of infectious diseases like TB must focus on developing countries in Africa and Asia where the disease is endemic. Studies in Africa offer a variety of challenges (Sirugo et al. 2008), including limited resources for diagnosis of TB (Gustafson et al. 2001), geographic variations in M. tuberculosis lineage, and population-level variations in LD. Future studies must be mindful of these issues. From a public health perspective, these populations will gain the most from genetic epidemiological studies of TB, though the challenges are great.

Acknowledgments

This work was funded in part by National Institutes of Health (NIH) grant R01-HL096811 from NHLBI Tuberculosis Research Unit, grant HHSN266200700022C/N01-AI70022 from NIAID, and grant T32-HL0756 from NHLBI. We also thank Dr. W. Henry Boom for helpful discussions.

Contributor Information

Catherine M. Stein, Email: catherine.stein@case.edu, Department of Epidemiology and Biostatistics, Case Western Reserve University, Wolstein Research Building, Room 1316, 2103 Cornell Rd., Cleveland, OH 44106, USA. Tuberculosis Research Unit, Case Western Reserve University, Cleveland, OH 44106, USA

Allison R. Baker, Department of Epidemiology and Biostatistics, Case Western Reserve University, Wolstein Research Building, Room 1316, 2103 Cornell Rd., Cleveland, OH 44106, USA

References

  1. Abe T, Iinuma Y, Ando M, Yokoyama T, Yamamoto T, et al. NRAMP1 polymorphisms, susceptibility and clinical features of tuberculosis. J Infect. 2003;46:215–220. doi: 10.1053/jinf.2002.1064. [DOI] [PubMed] [Google Scholar]
  2. Akahoshi M, Ishihara M, Remus N, Uno K, Miyake K, et al. Association between IFNA genotype and the risk of sarcoidosis. Hum Genet. 2004;114(5):503–509. doi: 10.1007/s00439-004-1099-5. [DOI] [PubMed] [Google Scholar]
  3. Ardlie KG, Kruglyak L, Seielstad M. Patterns of linkage disequilibrium in the human genome. Nat Rev Genet. 2002;3(4):299–309. doi: 10.1038/nrg777. [DOI] [PubMed] [Google Scholar]
  4. ATS/CDC. Diagnostic Standards and Classification of Tuberculosis in Adults and Children. This official statement of the American Thoracic Society and the Centers for Disease Control and Prevention was adopted by the ATS Board of Directors, July 1999. This statement was endorsed by the Council of the Infectious Disease Society of America, September 1999. Am J Respir Crit Care Med. 2000;161(4 Pt 1):1376–1395. doi: 10.1164/ajrccm.161.4.16141. [DOI] [PubMed] [Google Scholar]
  5. Awomoyi A, Marchant A, Howson JM, McAdam KP, Blackwell JM, et al. Interleukin-10, polymorphism in SLC11A1 (formerly NRAMP1), and susceptibility to tuberculosis. J Infect Dis. 2002;186:1808–1814. doi: 10.1086/345920. [DOI] [PubMed] [Google Scholar]
  6. Baker AR, Randhawa AK, Shey MS, de Kock M, Kaplan G, et al. Comparison of genotype frequencies in Toll-like receptor genes in Ugandans, South Africans, and African HapMap populations. Presented at the American Society of Human Genetics annual meeting; Honolulu, HI. 22 October 2009.2009. [Google Scholar]
  7. Barrett JC, Hansoul S, Nicolae DL, Cho JH, Duerr RH, et al. Genome-wide association defines more than 30 distinct susceptibility loci for Crohn’s disease. Nat Genet. 2008;40(8):955–962. doi: 10.1038/NG.175. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Bellamy R. Susceptibility to mycobacterial infections: the importance of host genetics. Genes Immun. 2003;4:4–11. doi: 10.1038/sj.gene.6363915. [DOI] [PubMed] [Google Scholar]
  9. Bellamy R, Ruwende C, Corrah T, McAdam KP, Whittle HC, et al. Variations in the NRAMP1 gene and susceptibility to tuberculosis in West Africans. N Engl J Med. 1998;338:640–644. doi: 10.1056/NEJM199803053381002. [DOI] [PubMed] [Google Scholar]
  10. Bellamy R, Beyers N, McAdam KP, Ruwende C, Gie R, et al. Genetic susceptibility to tuberculosis in Africans: a genomewide scan. Proc Nat Acad Sci USA. 2000;97:8005–8009. doi: 10.1073/pnas.140201897. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Blackwell J, Barton CH, White JK, Searle S, Baker AM, et al. Genomic organizaton and sequence of the human NRAMP gene: identification and mapping of a promotor region polymorphism. Mol Med. 1995;1:194–205. [PMC free article] [PubMed] [Google Scholar]
  12. Bodmer W, Bonilla C. Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet. 2008;40(6):695–701. doi: 10.1038/ng.f.136. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Caws M, Thwaites G, Dunstan S, Hawn TR, Lan NT, et al. The influence of host and bacterial genotype on the development of disseminated disease with Mycobacterium tuberculosis. PLoS Pathog. 2008;4(3):e1000034. doi: 10.1371/journal.ppat.1000034. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Cervino A, Lakiss S, Sow O, Hill AV. Allelic association between the NRAMP1 gene and susceptibility to tuberculosis in Guinea-Conakry. Ann Hum Genet. 2000;64:507–512. doi: 10.1046/j.1469-1809.2000.6460507.x. [DOI] [PubMed] [Google Scholar]
  15. Cobat A, Gallant CJ, Simkin L, Black GF, Stanley K, et al. Two loci control tuberculin skin test reactivity in an area hyperendemic for tuberculosis. J Exp Med. 2009;206(12):2583–2591. doi: 10.1084/jem.20090892. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Comstock G. Tuberculosis in twins: a re-analysis of the Prophit Study. Am Rev Respir Dis. 1978;117:621–624. doi: 10.1164/arrd.1978.117.4.621. [DOI] [PubMed] [Google Scholar]
  17. Comstock G. Epidemiology of tuberculosis. Am Rev Respir Dis. 1982;125:8–15. doi: 10.1164/arrd.1982.125.3P2.8. [DOI] [PubMed] [Google Scholar]
  18. Crowle A, Elkins N. Relative permissiveness of macrophages from black and white people for virulent tubercle bacilli. Infect Immun. 1990;58:632–638. doi: 10.1128/iai.58.3.632-638.1990. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. de Jong BC, Hill PC, Aiken A, Awine T, Antonio M, et al. Progression to active tuberculosis, but not transmission, varies by Mycobacterium tuberculosis lineage in The Gambia. J Infect Dis. 2008;198(7):1037–1043. doi: 10.1086/591504. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Delgado J, Baena A, Thim S, Goldfeld AE. Ethnic-specific genetic associations with pulmonary tuberculosis. J Infect Dis. 2002;186:1463–1468. doi: 10.1086/344891. [DOI] [PubMed] [Google Scholar]
  21. Deng HW. Population admixture may appear to mask, change or reverse genetic effects of genes underlying complex traits. Genetics. 2001;159(3):1319–1323. doi: 10.1093/genetics/159.3.1319. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Dubaniewicz A, Jamieson SE, Dubaniewicz-Wybieralska M, Fakiola M, Nancy Miller E, et al. Association between SLC11A1 (formerly NRAMP1) and the risk of sarcoidosis in Poland. Eur J Hum Genet. 2005;13(7):829–834. doi: 10.1038/sj.ejhg.5201370. [DOI] [PubMed] [Google Scholar]
  23. Edwards BJ, Haynes C, Levenstien MA, Finch SJ, Gordon D. Power and sample size calculations in the presence of phenotype errors for case/control genetic association studies. BMC Genet. 2005;6(1):18. doi: 10.1186/1471-2156-6-18. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. El Baghdadi J, Remus N, Benslimane A, El Annaz H, Chentoufi M, et al. Variants of the human NRAMP1 gene and susceptibility to tuberculosis in Morocco. Int J Tuberc Lung Dis. 2003;7:599–602. [PubMed] [Google Scholar]
  25. Fisher R. Statistical methods for research workers. 11. V. Liver & Boyd; Edinburgh, Scotland: 1950. [Google Scholar]
  26. Fitness J, Floyd S, Warndorff DK, Sichali L, Malema S, et al. Large-scale candidate gene study of tuberculosis susceptibility in the Karonga district of Nothern Malawi. Am J Trop Med Hyg. 2004;71:341–349. [PubMed] [Google Scholar]
  27. Flores-Villanueva PO, Ruiz-Morales JA, Song CH, Flores LM, Jo EK, et al. A functional promoter polymorphism in monocyte chemoattractant protein-1 is associated with increased susceptibility to pulmonary tuberculosis. J Exp Med. 2005;202(12):1649–1658. doi: 10.1084/jem.20050126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Flynn J, Goldstein MM, Chan J, Triebold KJ, Pfeffer K, et al. Tumor necrosis factor-alpha is required in the protective immune response against Mycobacterium tuberculosis in mice. Immunity. 1995;2:561–572. doi: 10.1016/1074-7613(95)90001-2. [DOI] [PubMed] [Google Scholar]
  29. Gagneux S, Small PM. Global phylogeography of Mycobacterium tuberculosis and implications for tuberculosis product development. Lancet Infect Dis. 2007;7(5):328–337. doi: 10.1016/S1473-3099(07)70108-1. [DOI] [PubMed] [Google Scholar]
  30. Gagneux S, DeRiemer K, Van T, Kato-Maeda M, de Jong BC, et al. Variable host-pathogen compatibility in Mycobacterium tuberculosis. Proc Nat Acad Sci USA. 2006;103(8):2869–2873. doi: 10.1073/pnas.0511240103. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Gao PS, Fujishima S, Mao XQ, Remus N, Kanda M, et al. Genetic variants of NRAMP1 and active tuberculosis in Japanese populations. Clin Genet. 2000;58:74–76. doi: 10.1034/j.1399-0004.2000.580113.x. [DOI] [PubMed] [Google Scholar]
  32. Garay S. Pulmonary tuberculosis. In: Rom WN, Garay SM, editors. Tuberculosis. Lippincott Williams & Wilkins; Philadelphia: 2004. pp. 345–394. [Google Scholar]
  33. Gustafson P, Gomes VF, Vieira CS, Jensen H, Seng R, et al. Tuberculosis mortality during a civil war in Guinea-Bissau. JAMA. 2001;286(5):599–603. doi: 10.1001/jama.286.5.599. [DOI] [PubMed] [Google Scholar]
  34. Hawn TR, Misch EA, Dunstan SJ, Thwaites GE, Lan NT, et al. A common human TLR1 polymorphism regulates the innate immune response to lipopeptides. Eur J Immunol. 2007;37(8):2280–2289. doi: 10.1002/eji.200737034. [DOI] [PubMed] [Google Scholar]
  35. Heid IM, Huth C, Loos RJ, Kronenberg F, Adamkova V, et al. Meta-analysis of the INSIG2 association with obesity including 74,345 individuals: does heterogeneity of estimates relate to study design? PLoS Genet. 2009;5(10):e1000694. doi: 10.1371/journal.pgen.1000694. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Hoal EG, Lewis LA, Jamieson SE, Tanzer F, Rossouw M, et al. SLC11A1 (NRAMP1) but not SLC11A2 (NRAMP2) polymorphisms are associated with susceptibility to tuberculosis in a high-incidence community in South Africa. Int J Tuberc Lung Dis. 2004;8(12):1464–1471. [PubMed] [Google Scholar]
  37. Iriso R, Mudido PM, Karamagi C, Whalen C. The diagnosis of childhood tuberculosis in an HIV-endemic setting and the use of induced sputum. Int J Tuberc Lung Dis. 2005;9(7):716–726. [PubMed] [Google Scholar]
  38. Jakobsson M, Scholz SW, Scheet P, Gibbs JR, VanLiere JM, et al. Genotype, haplotype and copy-number variation in worldwide human populations. Nature. 2008;451(7181):998–1003. doi: 10.1038/nature06742. [DOI] [PubMed] [Google Scholar]
  39. Kallmann F, Reisner D. Twin studies on the significance of genetic factors in tuberculosis. Am Rev Tuberc. 1943;47:549–574. [Google Scholar]
  40. Kramnik I, Dietrich WF, Demant P, Bloom BR. Genetic control of resistance to experimental infection with virulent Mycobacterium tuberculosis. Proc Nat Acad Sci USA. 2000;97:8560–8565. doi: 10.1073/pnas.150227197. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Kusuhara K, Yamamoto K, Okada K, Mizuno Y, Hara T. Association of IL12RB1 polymorphisms with susceptibility to and severity of tuberculosis in Japanese: a gene-based association analysis of 21 candidate genes. Int J Immunogenet. 2007;34(1):35–44. doi: 10.1111/j.1744-313X.2007.00653.x. [DOI] [PubMed] [Google Scholar]
  42. Leung KH, Yip SP, Wong WS, Yiu LS, Chan KK, et al. Sex- and age-dependent association of SLC11A1 polymorphisms with tuberculosis in Chinese: a case control study. BMC Infect Dis. 2007;7:19. doi: 10.1186/1471-2334-7-19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Lewinsohn DA, Gennaro ML, Scholvinck L, Lewinsohn DM. Tuberculosis immunology in children: diagnostic and therapeutic challenges and opportunities. Int J Tuberc Lung Dis. 2004;8(5):658–674. [PubMed] [Google Scholar]
  44. Lewinsohn DA, Zalwango S, Stein CM, Mayanja-Kizza H, Okwera A, et al. Whole blood interferon-γ responses to Mycobacterium tuberculosis antigens in young household contacts of persons with tuberculosis in Uganda. PLoS One. 2008;3(10):e3407. doi: 10.1371/journal.pone.0003407. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Li HT, Zhang TT, Zhou YQ, Huang QH, Huang J. SLC11A1 (formerly NRAMP1) gene polymorphisms and tuberculosis susceptibility: a meta-analysis. Int J Tuberc Lung Dis. 2006;10(1):3–12. [PubMed] [Google Scholar]
  46. Liaw YS, Tsai-Wu JJ, Wu CH, Hung CC, Lee CN, et al. Variations in the NRAMP1 gene and susceptibility of tuberculosis in Taiwanese. Int J Tuberc Lung Dis. 2002;6:454–460. [PubMed] [Google Scholar]
  47. Liu W, Cao WC, Zhang CY, Tian L, Wu XM, et al. VDR and NRAMP1 gene polymorphisms in susceptibility to pulmonary tuberculosis among the Chinese Han population: a case-control study. Int J Tuberc Lung Dis. 2004;8:428–434. [PubMed] [Google Scholar]
  48. Lurie M. Heredity, constitution and tuberculosis: an experimental study. Am Rev Tuberc. 1941;44(suppl):1–125. [Google Scholar]
  49. Ma X, Dou S, Wright JA, Reich RA, Teeter LD, et al. 5′ dinucleotide repeat polymorphism of NRAMP1 and susceptibility to tuberculosis among Caucasian patients in Houston, Texas. Int J Tuberc Lung Dis. 2002;6:818–823. [PubMed] [Google Scholar]
  50. Ma X, Reich RA, Wright JA, Tooker HR, Teeter LD, et al. Association between interleukin-8 gene alleles and human susceptbility to tuberculosis disease. J Infect Dis. 2003;188:349–355. doi: 10.1086/376559. [DOI] [PubMed] [Google Scholar]
  51. Ma X, Liu Y, Gowen BB, Graviss EA, Clark AG, et al. Full-exon resequencing reveals toll-like receptor variants contribute to human susceptibility to tuberculosis disease. PLoS One. 2007;2(12):e1318. doi: 10.1371/journal.pone.0001318. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Malik S, Abel L, Tooker H, Poon A, Simkin L, et al. Alleles of the NRAMP1 gene are risk factors for pediatric tuberculosis disease. Proc Nat Acad Sci USA. 2005;102(34):12183–12188. doi: 10.1073/pnas.0503368102. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Manolio TA, Collins FS, Cox NJ, Goldstein DB, Hindorff LA, et al. Finding the missing heritability of complex diseases. Nature. 2009;461(7265):747–753. doi: 10.1038/nature08494. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. McCarroll SA, Altshuler DM. Copy-number variation and association studies of human disease. Nat Genet. 2007;39(7 Suppl):S37–S42. doi: 10.1038/ng2080. [DOI] [PubMed] [Google Scholar]
  55. McCarthy MI, Abecasis GR, Cardon LR, Goldstein DB, Little J, et al. Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet. 2008;9(5):356–369. doi: 10.1038/nrg2344. [DOI] [PubMed] [Google Scholar]
  56. Möller M, Hoal EG. Current findings, challenges and novel approaches in human genetic susceptibility to tuberculosis. Tuberculosis (Edinb) 2010;90(2):71–83. doi: 10.1016/j.tube.2010.02.002. [DOI] [PubMed] [Google Scholar]
  57. Möller M, de Wit E, Hoal EG. Past, present and future directions in human genetic susceptibility to tuberculosis. FEMS Immunol Med Microbiol. 2010;58(1):3–26. doi: 10.1111/j.1574-695X.2009.00600.x. [DOI] [PubMed] [Google Scholar]
  58. Motsinger-Reif AA, Antas PR, Oki NO, Levy S, Holland SM, et al. Polymorphisms in IL-1beta, vitamin D receptor Fok1, and Toll-like receptor 2 are associated with extrapulmonary tuberculosis. BMC Med Genet. 2010;11:37. doi: 10.1186/1471-2350-11-37. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Nadeau JH, Topol EJ. The genetics of health. Nat Genet. 2006;38(10):1095–1098. doi: 10.1038/ng1006-1095. [DOI] [PubMed] [Google Scholar]
  60. Nyendak M, Lewinsohn DA, Lewinsohn D. The use of interferon-gamma release assays in clinical practice. In: Davies P, Barnes PF, Gordon SB, editors. Clinical Tuberculosis. Hodder Arnold; London: 2008. [Google Scholar]
  61. Risch N, Merikangas K. The future of genetic studies of complex human diseases. Science. 1996;273(5281):1516–1517. doi: 10.1126/science.273.5281.1516. [DOI] [PubMed] [Google Scholar]
  62. Rossouw M, Nel HJ, Cooke GS, van Helden PD, Hoal EG. Association between tuberculosis and a polymorphic NFκB binding site in the interferon γ gene. Lancet. 2003;361:1871–1872. doi: 10.1016/S0140-6736(03)13491-5. [DOI] [PubMed] [Google Scholar]
  63. Ryu S, Park YK, Bai GH, Kim SJ, Park SN, et al. 3′UTR polymorphisms in the NRAMP1 gene are associated with susceptibility to tuberculosis in Koreans. Int J Tuberc Lung Dis. 2000;4:577–580. [PubMed] [Google Scholar]
  64. Shaw MA, Collins A, Peacock CS, Miller EN, Black GF, et al. Evidence that genetic susceptibility to Mycobacterium tuberculosis in a Brazilian population is under oligogenic control: linkage study of the candidate genes NRAMP1 and TNFA. Tuberc Lung Dis. 1997;78:35–45. doi: 10.1016/s0962-8479(97)90014-9. [DOI] [PubMed] [Google Scholar]
  65. Shey MS, Randhawa AK, Bowmaker M, Smith E, Scriba TJ, et al. Single nucleotide polymorphisms in toll-like receptor 6 are associated with altered lipopeptide- and mycobacteria-induced interleukin-6 secretion. Genes Immun. 2010;11(7):561–572. doi: 10.1038/gene.2010.14. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Sirugo G, Hennig BJ, Adeyemo AA, Matimba A, Newport MJ, et al. Genetic studies of African populations: an overview on disease susceptibility and response to vaccines and therapeutics. Hum Genet. 2008;123(6):557–598. doi: 10.1007/s00439-008-0511-y. [DOI] [PubMed] [Google Scholar]
  67. Søborg C, Andersen AB, Range N, Malenganisho W, Friis H, et al. Influence of candidate susceptibility genes on tuberculosis in a high endemic region. Mol Immunol. 2007;44(9):2213–2220. doi: 10.1016/j.molimm.2006.11.002. [DOI] [PubMed] [Google Scholar]
  68. Stein C, Guwatudde D, Nakakeeto M, Peters P, Elston RC, et al. Heritability analysis of cytokines as intermediate phenotypes of tuberculosis. J Infect Dis. 2003;187:1679–1685. doi: 10.1086/375249. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Stein CM, Zalwango S, Chiunda AB, Millard C, Leontiev DV, et al. Linkage and association analysis of candidate genes for TB and TNFalpha cytokine expression: evidence for association with IFNGR1, IL-10, and TNF receptor 1 genes. Hum Genet. 2007;121(6):663–673. doi: 10.1007/s00439-007-0357-8. [DOI] [PubMed] [Google Scholar]
  70. Stein CM, Zalwango S, Malone LL, Won S, Mayanja-Kizza H, et al. Genome scan of M. tuberculosis infection and disease in Ugandans. PLoS One. 2008;3(12):e4094. doi: 10.1371/journal.pone.0004094. [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Taype CA, Castro JC, Accinelli RA, Herrera-Velit P, Shaw MA, et al. Association between SLC11A1 polymorphisms and susceptibility to different clinical forms of tuberculosis in the Peruvian population. Infect Genet Evol. 2006;6(5):361–367. doi: 10.1016/j.meegid.2006.01.002. [DOI] [PubMed] [Google Scholar]
  72. Thwaites G, Caws M, Chau TT, D’Sa A, Lan NT, et al. The relationship between Mycobacterium tuberculosis genotype and the clinical phenotype of pulmonary and meningeal tuberculosis. J Clin Microbiol. 2008;46(4):1363–1368. doi: 10.1128/JCM.02180-07. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Thye T, Browne EN, Chinbuah MA, Gyapong J, Osei I, et al. IL10 haplotype associated with tuberculin skin test response but not with pulmonary TB. PLoS One. 2009;4(5):e5420. doi: 10.1371/journal.pone.0005420. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Thye T, Vannberg FO, Wong SH, Owusu-Dabo E, Osei I, et al. Genome-wide association analyses identifies a susceptibility locus for tuberculosis on chromosome 18q11.2. Nat Genet. 2010;42(9):739–741. doi: 10.1038/ng.639. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. van der Spuy GD, Kremer K, Ndabambi SL, Beyers N, Dunbar R, et al. Changing Mycobacterium tuberculosis population highlights clade-specific pathogenic characteristics. Tuberculosis (Edinb) 2009;89(2):120–125. doi: 10.1016/j.tube.2008.09.003. [DOI] [PubMed] [Google Scholar]
  76. Vejbaesya S, Chierakul N, Luangtrakool P, Sermduangprateep C. NRAMP1 and TNF-alpha polymorphisms and susceptibility to tuberculosis in Thais. Respirology. 2007;12(2):202–206. doi: 10.1111/j.1440-1843.2006.01037.x. [DOI] [PubMed] [Google Scholar]
  77. Velez DR, Hulme WF, Myers JL, Stryjewski ME, Abbate E, et al. Association of SLC11A1 with tuberculosis and interactions with NOS2A and TLR2 in African-Americans and Caucasians. Int J Tuberc Lung Dis. 2009a;13(9):1068–1076. [PMC free article] [PubMed] [Google Scholar]
  78. Velez DR, Hulme WF, Myers JL, Weinberg JB, Levesque MC, et al. NOS2A, TLR4, and IFNGR1 interactions influence pulmonary tuberculosis susceptibility in African-Americans. Hum Genet. 2009b;126(5):643–653. doi: 10.1007/s00439-009-0713-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Wheeler E, Miller EN, Peacock CS, Donaldson IJ, Shaw MA, et al. Genome-wide scan for loci influencing quantitative immune response traits in the Belem family study: comparison of methods and summary of results. Ann Hum Genet. 2006;70(Pt 1):78–97. doi: 10.1111/j.1529-8817.2005.00223.x. [DOI] [PubMed] [Google Scholar]
  80. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, et al. Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet. 2008;40(5):638–645. doi: 10.1038/ng.120. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES