Skip to content

Advertisement

You're viewing the new version of our site. Please leave us feedback.

Learn more

BMC Genetics

Open Access

Genetic association meta-analysis: a new classification to assess ethnicity using the association of MCP-1 -2518 polymorphism and tuberculosis susceptibility as a model

  • Tania Vásquez-Loarte1,
  • Milana Trubnykova1 and
  • Heinner Guio1Email author
BMC Genetics201516:128

https://doi.org/10.1186/s12863-015-0280-2

Received: 2 November 2014

Accepted: 12 October 2015

Published: 30 October 2015

Abstract

Background

In meta-analyses of genetic association studies, ancestry and ethnicity are not accurately investigated. Ethnicity is usually classified using conventional race/ethnic categories or continental groupings even though they could introduce bias increasing heterogeneity between and within studies; thus decreasing the external validity of the results. In this study, we performed a meta-analysis using a novel ethnic classification system to test the association between MCP-1 -2518 polymorphism and pulmonary tuberculosis. Our new classification considers genetic distance, migration and linguistic origins, which will increase homogeneity within ethnic groups.

Methods

We included thirteen studies from three continents (Asia, Africa and Latin America) and considered seven ethnic groups (West Africa, South Africa, Saharan Africa, East Asia, South Asia, Persia and Latin America).

Results

The results were compared to the continental group classification. We found a significant association between MCP-1 -2518 polymorphism and TB susceptibility only in the East Asian and Latin American groups (OR 3.47, P = 0.08; OR 2.73, P = 0.02). This association is not observed in other ethnic groups that are usually considered in the Asian group, such as India and Persia, or in the African group.

Conclusions

There is an association between MCP-1 -2518 polymorphism and TB susceptibility only in the East Asian and Latin American groups. We suggest the use of our new ethnic classification in future meta-analysis of genetic association studies when ancestry markers are not available. This new classification increases homogeneity for certain ethnic groups compared to the continental classification. We recommend considering previous data about migration, linguistics and genetic distance when classifying ethnicity in further studies.

Keywords

PolymorphismCCL2MCP-1TuberculosisEthnicity

Background

Tuberculosis disease (TB) is a major public health problem worldwide. To create new strategies that will improve TB control, we need a better understanding of the biological, environmental, social, and ethnic factors [1]. One promising route is the study of polymorphisms involved in pulmonary TB susceptibility [2, 3]. Several human genes have been associated with TB development [46], including the monocyte chemoattractant protein 1 (MCP-1), also called CCL2. MCP-1 belongs to a group of CC chemokines located in chromosome 17q11.2. MCP-1 protein interacts with chemokine C-C motif receptor 2 (CCR2) to activate and recruit monocytes, macrophages, CD4+ T cells and immature dendritic cells to the site of infection [79]. The presence of MCP-1 protein in an adequate concentration is important for granuloma formation and M. tuberculosis clearance [10, 11].

Although there are more than ten genetic polymorphisms in the MCP-1 promoter and coding region, only the MCP-1 -2518 A/G allele (reference sequence 1024611) is functional and affects gene expression [10]. A substitution from A to G in -2518 position of the promoter region increases the levels of MCP-1. This action decreases the concentration of IL-12p40, which recruits and activates memory/effector Th1 cells, thus impairing long-term protection to intracellular pathogens [10]. Observational studies have shown that MCP-1 -2518 A/G polymorphism is associated with the development of pulmonary tuberculosis (pTB) and could be a potential marker for latent TB and disease severity [3, 12]. However, this association is different among countries such as Persia, India, Korea and China, which share continental groups [13, 14].

Geographical distribution by continents is the conventional way to assess ethnicity in meta-analysis of genetic association studies. However, population genetics has demonstrated that ethnic composition is related more with genetic distance, migration and linguistic origins rather than continental groups. In terms of ancestry biomarkers, continental grouping relies on markers such as Y-DNA and mtDNA haplogroups and varies within continents [15, 16]. As a consequence, conventional classification might introduce bias and increase heterogeneity between and within studies, decreasing the external validity of the results. Thus, it is questionable if the conventional classification is an appropriate proxy for ethnicity.

In order to have a better understanding of the relationship between ethnicity and the susceptibility to infectious diseases such as TB, we evaluated the association between the MCP-1 -2518 A/G polymorphism and pTB susceptibility using a new multi-factorial ethnic classification and compared it with the conventional approach of continental groups. This new classification is based on previous research on genetic distance, migration and linguistic origins [1619], which improves the homogeneity of ethnic groups. We believe that our new classification for ethnicity offers a more robust approach to explain susceptibility to disease, and that it can increase the internal validity of genetic studies when ancestry markers are not available.

Methods

Search strategy

A literature search was carried out in NCBI database, Scielo and Lilacs to identify genetic association studies between MCP-1 polymorphism and pTB risk prior to December 2013. We used the following MESH terms: (("Polymorphism, Genetic"[Mesh]) AND "Chemokine CCL2"[Mesh]) AND "Tuberculosis"[Mesh]. Mesh term “MCP-1” gave the result CCL2. “Reference sequence 1024611 A/G” gave zero results. Our selection criteria included: 1) studies evaluating the association between MCP-1-2518 A/G and TB risk, 2) observational studies, 3) pulmonary TB, 4) studies performed in adults and children, 4) patients without HIV or cancer, 5) available allelic and the genotype frequencies to estimate an odds ratio (OR), 6) control groups that met Hardy Weinberg Equilibrium, and 7) articles published until December 2013. Studies that did not meet these criteria were excluded. When original articles included more than one study population, we considered each as an independent study. In case of multiple publications on the same study, we included the study with the larger sample and/or the most recently published. The data search retrieved 23 articles. Ten studies were excluded because they were reviews or meta-analyses, or corresponded to pediatric populations, spinal TB, latent TB, HIV positive individuals or data from controls was inaccessible. At the end, 13 studies (7651 cases and 8056 controls) [3, 10, 2030] (Fig. 1) were considered.
Fig. 1

Flow chart of the selection of studies and specific reasons for exclusion from the meta-analysis. TB = tuberculosis; CCL2 = (C-C motif) ligand 2; MCP-1 = monocyte chemoattractant protein; HIV = human immunodeficiency virus

Data collection

All articles were separately extracted, reviewed and collated by two independent reviewers who checked for any discordance and reached a consensus in all items. Authors were contacted by email when we needed more information about an article. The following information was extracted for each study: author, year of publication, country of origin, ethnicity, sample size, type of study population, TB definition, allele and the genotype frequencies in cases, controls and methods. The information was systematically reviewed using STROBE and STREGA parameters [31, 32].

Ethnic classification

We proposed a new ethnic classification based on previous information about genetic distance, migration and linguistic origins [16, 17, 3335] and compared it to the conventional classification. The new ethnic classification considered previous findings about genetic distance [17]. For this purpose, data such as country of origin was extracted from each study. Finally our new ethnic classification included: Middle East Asia (Persia), East Asia (Korea and China), South Asia (India), Saharan region (Morocco and Tunisia), South Africa (South Africa), West Africa (Guinea-Bissau, Gambia, and Ghana) and Latin America (Peru and Mexico). The conventional classification includes three groups: Africa (37 %), Asia (43.8 %) and Latin America (18.8 %). The characteristics of each study are listed in Table 1. We hypothesized that the new classification creates ethnic groups that have more homogeneity than the groups obtained by the conventional group classification.
Table 1

Characteristics of studies included in the meta-analysis

Author, year, reference

Country

Male cases (%)

Age, mean (SD)

Diagnosis of cases

Control source and characteristics

Methods

Africa

      

 Ben-Selma et al., [23]

Tunisia

75

44(-)/-

Clinical and radiological pTB, BCG+

Healthy individuals, same community and ethnicity, BCG+

RFLP

 Arji et al., [24]

Morocco

56

30(16)/38(17)

Clinical and radiological pTB, AFB+, HIV-, HBV-, HCV-

Healthy blood donors

RFLP

 Möller et al., [22]

South Africa

-

-

Clinical and radiological pTB, AFB+, HIV-

Healthy individuals, same community, HIV-

SNPlex genotyping system

 Thye et al., [20]

Ghana

-

-

Clinical and radiological pTB, AFB+, HIV-

Healthy individuals, TST-

Light type-based genotype

 Velez et al., [21]

Guinea- Bissau

60

37(14)/36(12)

Clinical Pulmonary pTB, AFB+, HIV-

Healthy individuals, same community

Real-time PCR

 Velez et al., [21]

Gambia

69

33(14)/ 29(13)

Clinical Pulmonary pTB, AFB+, HIV-

Neighbors, spouses

Real-time PCR

Asia

      

 Flores-Villanueva et al., [10]

Korea

67

38(-)/34(-)

Clinical and radiological pTB, AFB+, culture+, HIV-

Healthy blood donors

RFLP

 Chu et al., [27]

Hong Kong

66

48(18)/31(9)

Clinical pTB, AFB+, HIV-

Healthy blood donors

RFLP

 Xu et al., [29]

China

51

45(14)/42(13)

Clinical and radiological pTB, AFB+, in treatment

Healthy children

SSP-PCR

 Yang et al., [28]

China

66

-

Clinical and radiological pTB, AFB+, in treatment

Surgery and Gynecology patients, no prior TB

RFLP

 Naderi et al., [30]

Persia

22

50(21)/51(13)

Patients with confirmed pTB

Healthy individuals

Tetra-ARMS PCR

 Mishra et al., [26]

India

69

37(7)/38(6)

AFB+ or patients under treatment

Healthy individuals, same ethnicity, AFB-

RFLP

 Alagarasu et al., [25]

India

66

34(10)/31(9)

Clinical and radiological pTB, AFB+, HIV-

Healthy individuals

RFLP

Latin America

      

 Flores-Villanueva et al., [10]

Mexico

68

37(7)/36(7)

Clinical and radiological pTB, AFB+, culture+

Healthy neighbors, 334 TST+, 176 TST-

RFLP

 Ganachari et al., [3]

Mexico

65

36(6)/37(3)

BCG+, clinical and radiological pTB, AFB+, HIV-

Healthy neighbors, TST+, HIV-

Tetra-ARMS

 Ganachari et al., [3]

Peru

58

30(10)/34(9)

Clinical and radiological pTB, AFB+

Healthy individuals

Tetra-ARMS

pTB = pulmonary TB, AFB = acid fast bacilli, BCG, = Bacillus Calmette-Guérin vaccine, HIV = human immune deficiency virus, HBV = Hepatitis B virus, HCV = Hepatitis C virus, TST = tuberculosis skin test, RFLP = restriction fragment length polymorphism, Tetra-ARMS = amplification refractory mutation system-PCR, PCR = polymerase chain reaction

Statistical analysis

For each study, the Hardy Weinberg Equilibrium (HWE) was calculated for the controls using X 2 statistic. Genotypes deviated from HWE if two-sided p values were <0.05. Begg funnel plot and Egger’s test indicated publication bias if p value was <0.05. Sensitivity analysis was performed by removing one study at a time to assess the stability of the meta-analysis results.

To prove our hypothesis, we assessed heterogeneity and the magnitude of association for each ethnic group. We assessed heterogeneity by using the χ 2 based Q test and I 2 statistic. P values less than 0.01 were considered significant for heterogeneity. To assess the magnitude of association (pooled OR), in the presence of homogeneity, we used a fixed effects model (inverse variance weighted). Otherwise, we used a random effects model (DerSimonian and Laird, D + L). Pooled OR for the association between MCP-1 - 2518 A/G polymorphism and pTB risk was determined in three steps. First, we did an allelic comparison (G vs. A) to determine the pooled OR in the overall data and by ethnic subgroups. Second, using our new ethnic classification, we analyzed four genotype models: a) recessive (GG vs. AG + AA), b) homogenous co-dominant (GG vs. AA), c) heterogeneous co-dominant GA vs. AA) and c) dominant (GG + GA vs. AA). Third, we compared these results to those obtained from the analysis using the conventional classification. Odds ratio estimates were considered significant if P was <0.05, and were expressed using a 95 % confidence interval (CI). When analyzing by ethnicity, we used the groups that had ≥1 degree of freedom. For our analysis, the wild type allele was A, and the risk allele was G. We did not adjust our model for environmental effects. The statistical analysis was performed using STATA 11.0. (STATA Corp, College Station, TX, USA).

Results

The conventional ethnic analysis found heterogeneity in the three continental study groups for both allelic and genotype analysis. Using our new classification, we found homogeneity for South Asia (India) and West Africa, which were further analyzed with a fixed effects model. We could not improve homogeneity within the rest of the ethnic groups and used a random effects model for their analysis. According to the Begg’s funnel plot and Egger’s test, we did not find any bias in the analysis of the entire group (t = 1.76, P = 0.1; Fig. 2). Sensitivity analysis did not find any prominent effect of each individual study when estimating the pooled OR. Characteristics of each study, allele and genotype distributions are shown in Tables 1 and 2 respectively.
Fig. 2

Begg’s funnel plot analysis, which detects publication bias for G allele comparison. We did not find any publication bias in the entire group analysis (t = 1.76, P = 0.1). OR = Odds Ratio

Table 2

MCP-1 allele and genotype distribution in different ethnic groups

Author

Country

Continent

Ethnic group

Cases/Controls

G allele (%) cases/controls

Cases GG

Cases AG

Cases AA

Controls GG

Controls AG

Controls AA

P HWE

Ben-Selma et al., [23]

Tunisia

Africa

Saharian

168/150

33.6/21.7

25

63

80

8

49

93

0.6

Arji et al., [24]

Morocco

Africa

Saharian

337/204

21.7/27.0

9

128

200

15

80

109

0.8

Möller et al., [22]

South Africa

Africa

South Africa

431/482

22.5/26.0

26

142

263

39

173

270

0.2

Thye et al., [20]

Ghana

Africa

West Africa

1964/2312

17.1/20.2

63

546

1355

92

748

1472

0.8

Velez et al., [21]

Guinea- Bissau

Africa

West Africa

314/341

25.0/21.3

17

123

174

21

103

217

0.07

Velez et al., [21]

Gambia

Africa

West Africa

236/252

24.6/24.4

18

80

138

15

93

144

0.9

Flores-Villanueva et al., [10]

Korea

Asia

East Asia

129/162

60.1/36.4

46

63

20

22

74

66

0.5

Chu et al., [27]

China

Asia

East Asia

403/461

52.1/49.8

110

200

93

113

233

115

0.8

Xu et al., [29]

China

Asia

East Asia

100/100

55.5/36.0

29

53

18

13

46

41

0.7

Yang et al., [28]

China

Asia

East Asia

167/167

68.9/50.0

84

62

21

42

83

42

0.9

Naderi et al., [30]

Persia

Asia

Middle East

142/166

29.6/29.5

17

50

75

15

68

83

0.8

Mishra et al., [26]

India

Asia

South Asia

215/294

25.1/25.9

18

72

125

20

112

162

0.9

Alagarasu et al., [25]

India

Asia

South Asia

153/203

31.4/34.2

21

54

78

29

81

93

0.1

Flores-Villanueva et al., [10]

Mexico

South America

Latin America

435/334

72.0/51.3

229

168

38

91

161

82

0.8

Ganachari et al., [3]

Mexico

South America

Latin America

193/243

68.1/54.9

93

77

23

70

127

46

0.4

Ganachari et al., [3]

Peru

South America

Latin America

701/796

70.0/64.4

354

273

74

327

371

98

0.6

G allele frequencies in conventional and new ethnic classification

The conventional classification showed that G allele is frequent in Asia and South America (45 % in cases vs. 39 % in controls and 70 % in cases vs. 59 % in controls, respectively) but not in Africa (22 % in cases vs. 20 % in controls). Our new ethnic classification showed that East Asia has the highest frequency of the G allele (57 % cases and 46 % controls) in the Asian group. In Latin America, this allele has a similar frequency in Mexico and Peru. In contrast, the African ethnic groups (Saharan, South and West Africa) have a low frequency of G allele in a similar proportion (Fig. 3). The pooled OR shows that presence of G allele increases the risk to develop pTB by 30 %. The conventional classification shows that this association is only significant for South America and Asia (OR 1.76, 95 % CI 1.7-2.6, P < 0.01; OR = 1.41,95 % CI 1.02-1.96, P = 0.03, respectively). Interestingly, our new ethnic classification showed that in the Asian continent, the G allele increases risk only in the East Asian ethnic group (OR 1.9, 95 % CI 1.2-3, P <0.01), but not for South Asian and Persia (Fig. 4). We did not find any association for any of the African groups.
Fig. 3

G allele MCP-1 -2518 polymorphism distribution in study populations and incidence of pulmonary TB (2010). a shows the frequency of G allele among ethnic countries. G allele is more frequent in individuals with pulmonary TB from East Asian and Latin American ethnic countries (* = P <0.01) and there is no difference within African subgroups, Persia and South Asia. b shows the ethnic countries considered in our new ethnic classification. c The chart shows the incidence of tuberculosis in the groups studied found at http://data.worldbank.org/indicator/SH.TBS.INCD. (this incidence includes HIV cases)

Fig. 4

New and traditional ethnic classification to assess TB susceptibility in MCP-1 -2518 G allele carriers. We observe that the new classification finds a significant association only for the East Asian and Latin American groups. In South Asia (India), where there is homogeneity between studies, we can rule out that the polymorphism is associated with pulmonary TB

MCP-1 -2518 A/G genotypes and pTB susceptibility

The conventional classification showed that individuals from South America and Asia that carry GG genotype have 2.7 and 2.1 times the risk to develop pTB as compared to the ones with AA genotype (OR 2.72, 95 % CI 1.6–6.3, P = 0.02 and OR 2.09, 95 % CI 1.1–3.8, P = 0.01, respectively). The recessive model also showed increased susceptibility in both continents but to a lesser extent (OR 2.12, 95 % CI 1.5–5.4, P <0.01 in Asia and OR 1.76, 95 % CI 1.1–2.6, P < 0.01 in South America). Our new ethnic classification showed similar results for Latin America but not for Asian ethnic groups. Only the East Asian group that had the MCP-1 -2518 polymorphism in a homozygote co-dominant and recessive model had an increased risk to develop pTB (OR 3.47, 95 % CI 1.4–8.7, P <0.01 and OR 2.34, 95 % CI 1.3-4.3, P <0.01, respectively). The new classification did not find any association for the Persian and South Asian groups. Neither the new nor the continental group classifications found any association in Africa or any of its ethnic groups (Figs. 4, 5).
Fig. 5

New and traditional ethnic classification to assess TB susceptibility in MCP-1 -2518 GG genotype carriers. The homogenous co-dominant model (GG vs AA) shows that people who carry the GG genotype have 3.49 times the risk to develop pulmonary TB compared to people who have the AA genotype. The magnitude of the association in the Asian continent according to the traditional classification appears diluted because it includes South Asia and Persia, which have different ancestry and increase the heterogeneity in this continent. For Latin America, similarly to the traditional ethnic classification, we find that subjects with the GG genotype have 2.72 times the risk to develop pulmonary TB

Ethnic groups and heterogeneity

We found heterogeneity within the ethnic groups from the conventional classification (I 2 73.1 %, P <0.01, for Africa; I 2 83.8 %, P <0.01, for Asia; I 2 90.7 %, P <0.01, for South America). The new ethnic classification showed homogeneity for West Africa and South Asia (I 2 0 %, P = 0.3; I2 0 %, P = 0.5, respectively). We found heterogeneity within Arabia, East Asia and Latin America (I 2 93.5 %, P <0.01; I 2 88.7 %, P <0.01; I 2 91.6 %, P <0.01, respectively). We could not obtain results for South Africa and Persia, since there was only one study in each group.

Discussion

We found an association between the MCP-1 -2518 polymorphism and tuberculosis susceptibility in East Asian and Latin American populations [13, 14, 36]. Previous meta-analyses try to extrapolate this association to the Asian continent. However, since there is ethnic variability within each continent, we cannot generalize this conclusion to every ethnic group. In this way, our meta-analysis groups study populations by using information about migration and linguistics to make ethnic groups more similar. Using this method, we found that an association does not apply to every country in the same continent.

Our new ethnic classification creates ethnic groups (e.g. West Africa and South Asia) with countries sharing similar characteristics. This new classification must be further evaluated with new studies related to genetic susceptibility for infectious and noninfectious diseases.

Regarding TB susceptibility, our new classification, in contrast to the conventional classification, helped to clarify that the association between MCP-1 -2518 A/G polymorphism and pTB is specific for certain populations such as East Asia and Latin America. To our knowledge, this is the first meta-analysis that uses a model of genetic susceptibility for pTB to assess if a new ethnic classification based on previous findings about genetic distance, migration and linguistic origins, improves homogeneity within each ethnic groups [13]. Thus, we propose our new classification as a good proxy when genetic markers are not available [17, 3742].

Previous meta-analyses that use a continental group classification found an association between the MCP-1- 2518 A/G polymorphism and pTB, which is significant for Asia and South America [13, 14]. However, these ethnic groups include countries that are different in terms of ancestry and therefore genetic susceptibility. Our new classification helps to improve homogeneity in South Asia and West Africa. However our new classification does not help us with homogeneity in East Asia and Latin America, where we found association between polymorphism MCP-1 -2518 G allele and pTB. Failure to reach homogeneity could be explained because of gene-gene or gene-environmental interactions. It has been reported that people carrying polymorphism MCP-1 -2518 and MMP-1 -1607 have a higher risk to develop severe TB [12]. The high frequency of G allele observed in cases compared to controls in both East Asia and Latin America might support the hypothesis of a similar ancestry between these two groups [43].

Recent studies from Africa show that the G polymorphism is not common among this population [21, 24]. Human population started in Africa, which means it is the oldest population, and therefore it has had the opportunity to accumulate genetic changes, such as the accumulation of -2518 MCP-1 A allele in its inhabitants that conferred protection and made it possible to adapt to hazardous environmental conditions [44]. We also have to consider other factors influencing TB susceptibility such as malnourishment, socioeconomic, environmental and health factors. The homogeneity found in West Africa cannot be completely explained in our study. We did not assess homogeneity in South Africa, since we only had one study population [22].

To deal with heterogeneity in Asia, we considered three groups: East Asia, South Asia and the Middle East [3739]. The Asian population started from an “out of Africa” migration 50,000 years ago. It originated from two main migratory routes. The first one moved towards South Asia (India), and the second one to East Asia. Later, Central Asia was populated by Eurasian descendants. This is why we grouped Chinese and Korean populations under the East Asia group, India under the South Asia, and considered Persia under Persia. Also, these study populations have social, educational and mating habits that have that are particular to each group [16, 33, 34, 45]. Our classification groups two similar study populations from India under South Asia. Thus in this setting, it is unlikely that pTB susceptibility is due to the presence of MCP-1 -2518 G polymorphism. In contrast to South Asia, we found an association between this polymorphism and pTB in East Asia where we also found heterogeneity. Interestingly, India (South Asia) and China (East Asia) accounted together for more than 40 % of TB cases worldwide in the last decade [46]. However the implementation of TB control strategies in China has helped decrease prevalence by 50 %, mortality rates by almost 80 % and TB incidence rates by 3.4 % per year between 1990 and 2010 [47]. Thus, even though there is a better control of TB in China, genetic factors might be playing an important role in the development of this disease. In contrast, India maintains its high incidence for the last 10 years, which might be due to a lack of social and public health control rather than genetic factors. It is difficult to assess homogeneity within Persia [48, 49] because we only had one study population. In further meta-analyses about genetic susceptibility we recommend to give a special consideration to Central Asian countries since they share European ancestry and therefore different genetic markers compared to East and South Asia [33].

Latin American ethnic groups that originated from a Han Chinese migration to South America 3000 years ago share HLA markers with this population [50]. This similarity might also explain similar frequency of MCP-1 -2518 G allele and other genetic markers between East Asians and Latin Americans. Since Mexico and Peru share migration, common history, language routes and admixture indexes [40, 5153], we decided to maintain them in the same ethnic group as previous meta-analyses. However, we consider that for Latin America, we should consider two groups: one of Andean and another of European origin.

The limitations in our study are very common to meta-analyses about genetic association studies. We could not consider environmental and genetic factors that influence this association because this information was not found in the original articles. Thus, for research in multifactorial diseases such as TB, we strongly recommend future studies to include information about malnourishment, socioeconomic factors, BCG and TST status, which could also help to control for heterogeneity.

We obtained homogeneity within South Asia and West Africa, where we can rule out that MCP-1 -2518 polymorphism is associated to susceptibility. However, despite tuberculosis susceptibility found in Latin America and East Asia due to MCP-1 -2518 polymorphism, the populations within each group are still genetically different.

Genetic association studies in populations from Persia, South Africa, South Asia, East Asia and the Americas, where infectious diseases represent a public health problem, will help assess heterogeneity in order to understand the role of ethnicity in genetic susceptibility to these diseases. In the absence of an adequate classification that groups similar genetic characteristics, suitable for understanding genetic susceptibility, our new classification might be a potential proxy for ethnic classification in meta-analysis of genetic association studies when genetic markers are not available.

Conclusions

In summary, using this novel approach, we found an association between the MCP-1 -2518 polymorphism and pTB susceptibility, specifically in Latin American and East Asian populations not detected by using conventional classification. We encourage the use of our new ethnic classification in further genetic association studies for infectious and non-infectious diseases.

Abbreviations

CCL2: 

Chemokine (C-C motif) ligand 2

CCR2: 

Chemokine C-C motif receptor

HIV: 

Human immunodeficiency virus

HWE: 

Hardy weinberg equilibrium

MCP-1: 

Monocyte chemoattractant protein 1

MMP-1: 

Matrix metalloproteinase-1

mtDNA: 

Mitochondrial DNA

NCBI: 

National Center for Biotechnology Information

OR: 

Odds ratio

pTB: 

Pulmonary tuberculosis

STREGA: 

Strengthening the reporting of genetic association studies

STROBE: 

Strengthening the reporting of observational studies in epidemiology

TB: 

Tuberculosis

Declarations

Acknowledgements

This work was supported by the Peruvian National Institute of Health. We thank Drs. Kim Hoffman and Cesar Sanchez for reviewing this manuscript.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Laboratorio de Biotecnología y Biología Molecular, Instituto Nacional de Salud

References

  1. Lönnroth K, Raviglione M. Global Epidemiology of Tuberculosis: Prospects for Control. Semin Respir Crit Care Med. 2008;29(05):481–91.View ArticlePubMedGoogle Scholar
  2. Qidwai T, Jamal F, Khan MY. DNA sequence variation and regulation of genes involved in pathogenesis of pulmonary tuberculosis. Scand J Immunol. 2012;75(6):568–87.View ArticlePubMedGoogle Scholar
  3. Ganachari M, Ruiz-Morales JA, Gomez de la Torre Pretell JC, Dinh J, Granados J, Flores-Villanueva PO. Joint effect of MCP-1 genotype GG and MMP-1 genotype 2G/2G increases the likelihood of developing pulmonary tuberculosis in BCG-vaccinated individuals. PloS one. 2010;5(1), e8881.PubMed CentralView ArticlePubMedGoogle Scholar
  4. Pacheco AG, Cardoso CC, Moraes MO. IFNG +874 T/A, IL10–1082G/A and TNF -308G/A polymorphisms in association with tuberculosis susceptibility: a meta-analysis study. Hum Genet. 2008;123(5):477–84.View ArticlePubMedGoogle Scholar
  5. Correa PA, Gomez LM, Cadena J, Anaya JM. Autoimmunity and Tuberculosis. Opposite Association with TNF Polymorphism. J Rheumatol. 2005;32(2):219–24.PubMedGoogle Scholar
  6. Tian C, Zhang Y, Zhang J, Deng Y, Li X, Xu D, et al. The +874 T/A polymorphism in the interferon-γ gene and tuberculosis risk: An update by meta-analysis. Hum Immunol. 2011;72(11):1137–42.View ArticlePubMedGoogle Scholar
  7. Hodge DL, Reynolds D, Cerban FM, Correa SG, Baez NS, Young HA, et al. MCP-1/CCR2 interactions direct migration of peripheral B and T lymphocytes to the thymus during acute infectious/inflammatory processes. Eur J Immunol. 2012;42(10):2644–54.PubMed CentralView ArticlePubMedGoogle Scholar
  8. Tanaka T, Terada M, Ariyoshi K, Morimoto K. Monocyte chemoattractant protein-1/CC chemokine ligand 2 enhances apoptotic cell removal by macrophages through Rac1 activation. Biochem Biophys Res Commun. 2010;399(4):677–82.View ArticlePubMedGoogle Scholar
  9. Sodhi A, Biswas SK. Monocyte chemoattractant protein-1-induced activation of p42/44 MAPK and c-Jun in murine peritoneal macrophages: a potential pathway for macrophage activation. J Interferon Cytokine Res. 2002;22(5):517–26.View ArticlePubMedGoogle Scholar
  10. Flores-Villanueva PO, Ruiz-Morales JA, Song CH, Flores LM, Jo EK, Montano M, 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):1649–58.PubMed CentralView ArticlePubMedGoogle Scholar
  11. Hussain R, Ansari A, Talat N, Hasan Z, Dawood G. CCL2/MCP-I genotype-phenotype relationship in latent tuberculosis infection. PloS one. 2011;6(10), e25803.PubMed CentralView ArticlePubMedGoogle Scholar
  12. Ganachari M, Guio H, Zhao N, Flores-Villanueva PO. Host gene-encoded severe lung TB: from genes to the potential pathways. Genes Immun. 2012;13(8):605–20.PubMed CentralView ArticlePubMedGoogle Scholar
  13. Zhang Y, Zhang J, Zeng L, Huang H, Yang M, Fu X, et al. The -2518A/G Polymorphism in the MCP-1 Gene and Tuberculosis Risk: A Meta-Analysis. PloS one. 2012;7(7), e38918.PubMed CentralView ArticlePubMedGoogle Scholar
  14. Feng WX, Flores-Villanueva PO, Mokrousov I, Wu XR, Xiao J, Jiao WW, et al. CCL2-2518 (A/G) polymorphisms and tuberculosis susceptibility: a meta-analysis. Int J Tuberc Lung Dis. 2012;16(2):150–6.View ArticlePubMedGoogle Scholar
  15. Roewer L, Nothnagel M, Gusmao L, Gomes V, Gonzalez M, Corach D, et al. Continent-wide decoupling of chromosomal genetic variation from language and geography in native South Americans. PLoS Genet. 2013;9(4), e1003460.PubMed CentralView ArticlePubMedGoogle Scholar
  16. Foster MW, Sharp RR. Race, ethnicity, and genomics: social classifications as proxies of biological heterogeneity. Genome Res. 2002;12(6):844–50.View ArticlePubMedGoogle Scholar
  17. Tishkoff SA, Reed FA, Friedlaender FR, Ehret C, Ranciaro A, Froment A, et al. The genetic structure and history of Africans and African Americans. Science. 2009;324(5930):1035–44.PubMed CentralView ArticlePubMedGoogle Scholar
  18. Pagani L, Kivisild T, Tarekegn A, Ekong R, Plaster C, Gallego Romero I, et al. Ethiopian genetic diversity reveals linguistic stratification and complex influences on the Ethiopian gene pool. Am J Hum Genet. 2012;91(1):83–96.PubMed CentralView ArticlePubMedGoogle Scholar
  19. Cooke GS, Hill AV. Genetics of susceptibility to human infectious disease. Nat Rev Genet. 2001;2(12):967–77.View ArticlePubMedGoogle Scholar
  20. Thye T, Nejentsev S, Intemann CD, Browne EN, Chinbuah MA, Gyapong J, et al. MCP-1 promoter variant -362C associated with protection from pulmonary tuberculosis in Ghana, West Africa. Hum Mol Genet. 2009;18(2):381–8.PubMed CentralView ArticlePubMedGoogle Scholar
  21. Velez Edwards DR, Tacconelli A, Wejse C, Hill PC, Morris GA, Edwards TL, et al. MCP1 SNPs and pulmonary tuberculosis in cohorts from West Africa, the USA and Argentina: lack of association or epistasis with IL12B polymorphisms. PloS one. 2012;7(2), e32275.PubMed CentralView ArticlePubMedGoogle Scholar
  22. Moller M, Nebel A, Valentonyte R, van Helden PD, Schreiber S, Hoal EG. Investigation of chromosome 17 candidate genes in susceptibility to TB in a South African population. Tuberculosis (Edinb). 2009;89(2):189–94.View ArticleGoogle Scholar
  23. Ben-Selma W, Harizi H, Boukadida J. MCP-1 -2518 A/G functional polymorphism is associated with increased susceptibility to active pulmonary tuberculosis in Tunisian patients. Mol Biol Rep. 2011;38(8):5413–9.View ArticlePubMedGoogle Scholar
  24. Arji N, Busson M, Iraqi G, Bourkadi JE, Benjouad A, Boukouaci W, et al. The MCP-1 (CCL2) -2518 GG genotype is associated with protection against pulmonary tuberculosis in Moroccan patients. J Infect Dev Ctries. 2012;6(1):73–8.PubMedGoogle Scholar
  25. Alagarasu K, Selvaraj P, Swaminathan S, Raghavan S, Narendran G, Narayanan PR. CCR2, MCP-1, SDF-1a & DC-SIGN gene polymorphisms in HIV-1 infected patients with & without tuberculosis. Indian J Med Res. 2009;130(4):444–50.PubMedGoogle Scholar
  26. Mishra G, Poojary SS, Raj P, Tiwari PK. Genetic polymorphisms of CCL2, CCL5, CCR2 and CCR5 genes in Sahariya tribe of North Central India: an association study with pulmonary tuberculosis. Infect Genet Evol. 2012;12(5):1120–7.View ArticlePubMedGoogle Scholar
  27. Chu SF, Tam CM, Wong HS, Kam KM, Lau YL, Chiang AK. Association between RANTES functional polymorphisms and tuberculosis in Hong Kong Chinese. Genes Immun. 2007;8(6):475–9.View ArticlePubMedGoogle Scholar
  28. Yang BF, Zhuang B, Li F, Zhang CZ, Song AQ. The relationship between monocyte chemoattractant protein-1 gene polymorphisms and the susceptibility to pulmonary tuberculosis. Chin J Tuberc Respir Dis. 2009;32(6):454–6.Google Scholar
  29. Xu ZE, Xie YY, Chen JH, Xing LL, Zhang AH, Li BX, et al. Monocyte chemotactic protein-1 gene polymorphism and monocyte chemotactic protein-1 expression in Chongqing Han children with tuberculosis. Chin J Pediatr. 2009;47(3):200–3.Google Scholar
  30. Naderi M, Hashemi M, Karami H, Moazeni-Roodi A, Sharifi-Mood B, Kouhpayeh H, et al. Lack of association between rs1024611 (-2581 A/G) polymorphism in CC-chemokine Ligand 2 and susceptibility to pulmonary Tuberculosis in Zahedan, Southeast Persia. Prague Med Rep. 2011;112(4):272–8.PubMedGoogle Scholar
  31. Gallo V, Egger M, McCormack V, Farmer PB, Ioannidis JP, Kirsch-Volders M, et al. STrengthening the Reporting of observational studies in Epidemiology - Molecular Epidemiology (STROBE-ME): an extension of the STROBE statement. Eur J Clin Investig. 2012;42(1):1–16.View ArticleGoogle Scholar
  32. Little J, Higgins JP, Ioannidis JP, Moher D, Gagnon F, von Elm E, et al. STrengthening the REporting of Genetic Association studies (STREGA)--an extension of the STROBE statement. Eur J Clin Investig. 2009;39(4):247–66.View ArticleGoogle Scholar
  33. Ramsay M. Africa: continent of genome contrasts with implications for biomedical research and health. FEBS Lett. 2012;586(18):2813–9.View ArticlePubMedGoogle Scholar
  34. Heyer E, Balaresque P, Jobling MA, Quintana-Murci L, Chaix R, Segurel L, et al. Genetic diversity and the emergence of ethnic groups in Central Asia. BMC Genet. 2009;10:49.PubMed CentralView ArticlePubMedGoogle Scholar
  35. Muro T, Iida R, Fujihara J, Yasuda T, Watanabe Y, Imamura S, et al. Simultaneous determination of seven informative Y chromosome SNPs to differentiate East Asian, European, and African populations. Legal Med. 2011;13(3):134–41.View ArticlePubMedGoogle Scholar
  36. Gong T, Yang M, Qi L, Shen M, Du Y. Association of MCP-1 -2518A/G and -362G/C variants and tuberculosis susceptibility: a meta-analysis. Infect Genet Evol. 2013;20:1–7.View ArticlePubMedGoogle Scholar
  37. Colonna V, Boattini A, Guardiano C, Dall'ara I, Pettener D, Longobardi G, et al. Long-range comparison between genes and languages based on syntactic distances. Hum Hered. 2010;70(4):245–54.View ArticlePubMedGoogle Scholar
  38. Dulik MC, Zhadanov SI, Osipova LP, Askapuli A, Gau L, Gokcumen O, et al. Mitochondrial DNA and Y chromosome variation provides evidence for a recent common ancestry between Native Americans and Indigenous Altaians. Am J Hum Genet. 2012;90(2):229–46.PubMed CentralView ArticlePubMedGoogle Scholar
  39. Stoneking M, Delfin F. The human genetic history of East Asia: weaving a complex tapestry. Curr Biol. 2010;20(4):R188–93.View ArticlePubMedGoogle Scholar
  40. Majumder PP. The human genetic history of South Asia. Curr Biol. 2010;20(4):R184–7.View ArticlePubMedGoogle Scholar
  41. Abdulla MA, Ahmed I, Assawamakin A, Bhak J, Brahmachari SK, Calacal GC, et al. Mapping human genetic diversity in Asia. Science. 2009;326(5959):1541–5.View ArticlePubMedGoogle Scholar
  42. Bedoya G, Montoya P, Garcia J, Soto I, Bourgeois S, Carvajal L, et al. Admixture dynamics in Hispanics: a shift in the nuclear genetic ancestry of a South American population isolate. Proc Natl Acad Sci U S A. 2006;103(19):7234–9.PubMed CentralView ArticlePubMedGoogle Scholar
  43. Qin H, Zhu X. Power comparison of admixture mapping and direct association analysis in genome-wide association studies. Genet Epidemiol. 2012;36(3):235–43.PubMed CentralView ArticlePubMedGoogle Scholar
  44. Hill AV. Aspects of genetic susceptibility to human infectious diseases. Annu Rev Genet. 2006;40:469–86.View ArticlePubMedGoogle Scholar
  45. Tamang R, Singh L, Thangaraj K. Complex genetic origin of Indian populations and its implications. J Biosci. 2012;37(5):911–9.View ArticlePubMedGoogle Scholar
  46. Watts G. WHO annual report finds world at a crossroad on tuberculosis. BMJ. 2012;345, e7051.View ArticlePubMedGoogle Scholar
  47. de Colombani P, Dadu A, Dravniece G, Hoffner S, Ilyenkova V, Kovac Z, et al. Review of the national tuberculosis programme in Belarus, 10-21 October 2011. Copenhagen: WHO Regional Office for Europe; 2012. p. 76.Google Scholar
  48. Fazeli Z, Vallian S. Phylogenetic relationship analysis of Persiaians and other world populations using allele frequencies at 12 polymorphic markers. Mol Biol Rep. 2012;39(12):11187–99.View ArticlePubMedGoogle Scholar
  49. Fazeli Z, Vallian S. Molecular phylogenetic study of the Persiaians based on polymorphic markers. Gene. 2013;512(1):123–6.View ArticlePubMedGoogle Scholar
  50. Tokunaga K, Ohashi J, Bannai M, Juji T. Genetic link between Asians and native Americans: evidence from HLA genes and haplotypes. Hum Immunol. 2001;62(9):1001–8.View ArticlePubMedGoogle Scholar
  51. Reich D, Patterson N, Campbell D, Tandon A, Mazieres S, Ray N, et al. Reconstructing Native American population history. Nature. 2012;488(7411):370–4.PubMed CentralView ArticlePubMedGoogle Scholar
  52. Galanter JM, Fernandez-Lopez JC, Gignoux CR, Barnholtz-Sloan J, Fernandez-Rozadilla C, Via M, et al. Development of a panel of genome-wide ancestry informative markers to study admixture throughout the Americas. PLoS Genet. 2012;8(3), e1002554.PubMed CentralView ArticlePubMedGoogle Scholar
  53. Johnson NA, Coram MA, Shriver MD, Romieu I, Barsh GS, London SJ, et al. Ancestral components of admixed genomes in a Mexican cohort. PLoS Genet. 2011;7(12), e1002410.PubMed CentralView ArticlePubMedGoogle Scholar

Copyright

© Vásquez-Loarte et al. 2015

Advertisement