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. 2015 Apr 13;10(4):e0118660. doi: 10.1371/journal.pone.0118660

GST M1-T1 null Allele Frequency Patterns in Geographically Assorted Human Populations: A Phylogenetic Approach

Senthilkumar Pitchalu Kasthurinaidu 1,2, Thirumurugan Ramasamy 3,*, Jayachitra Ayyavoo 2, Dhvani Kirtikumar Dave 1,#, Divya Anantray Adroja 1,#
Editor: Vince Grolmusz4
PMCID: PMC4395045  PMID: 25867025

Abstract

Genetic diversity in drug metabolism and disposition is mainly considered as the outcome of the inter-individual genetic variation in polymorphism of drug-xenobiotic metabolizing enzyme (XME). Among the XMEs, glutathione-S-transferases (GST) gene loci are an important candidate for the investigation of diversity in allele frequency, as the deletion mutations in GST M1 and T1 genotypes are associated with various cancers and genetic disorders of all major Population Affiliations (PAs). Therefore, the present population based phylogenetic study was focused to uncover the frequency distribution pattern in GST M1 and T1 null genotypes among 45 Geographically Assorted Human Populations (GAHPs). The frequency distribution pattern for GST M1 and T1 null alleles have been detected in this study using the data derived from literatures representing 44 populations affiliated to Africa, Asia, Europe, South America and the genome of PA from Gujarat, a region in western India. Allele frequency counting for Gujarat PA and scattered plot analysis for geographical distribution among the PAs were performed in SPSS-21. The GST M1 and GST T1 null allele frequencies patterns of the PAs were computed in Seqboot, Gendist program of Phylip software package (3.69 versions) and Unweighted Pair Group method with Arithmetic Mean in Mega-6 software. Allele frequencies from South African Xhosa tribe, East African Zimbabwe, East African Ethiopia, North African Egypt, Caucasian, South Asian Afghanistan and South Indian Andhra Pradesh have been identified as the probable seven patterns among the 45 GAHPs investigated in this study for GST M1-T1 null genotypes. The patternized null allele frequencies demonstrated in this study for the first time addresses the missing link in GST M1-T1 null allele frequencies among GAHPs.

Introduction

Metabolic activities play an important role in shaping the livelihood of an organism. Drug-xenobiotic compound metabolizing enzyme (XME) systems are the most investigated pathways that are involved in equilibrating the health status of an individual. Among the numerous drug related genes investigated, Glutathione-S-transferases (GST) of Phase II XMEs were found to play an important role in cellular protection and in cellular resistance to drugs by glutathione conjugation reactions. GST classes convert the active endogenous and/or exogenous carcinogenic compounds to their detoxified form. Among the GST classes, GST M1 and GST T1 were found associated to a loss of function with a structural deletion (null mutation); moreover, they were also found to modify the detoxification ability of the individual exposed to tobacco or carcinogenic pollutants in the environment [1]. Genotoxins such as aromatic hydrocarbon epoxides and products of oxidative stress such as DNA hydroperoxides, polycyclic aromatic hydrocarbon diol epoxides are catalyzed and detoxified by GST M1 while, the constituents of cigarette smoke such as alkyl halides, bezo (a) pyrene diol epoxide, acrolein are catalyzed and detoxified by GST T1 [1, 2]. Several factors such as environmental pollution, dietary habits and activity-dependent genetic differences have been reported as modulators of GST expression and susceptibility to xenobiotic compound detoxification [3]. Numerous studies in the recent past have hypothesized the difference in metabolic rate of M1 and T1 classes of GST as the risk factor associated to cancers of bladder, pancreas, upper aero digestive tract, lung, esophageal, head-neck, melanoma and also in Balken endemic nephropathy patients [4–9]. Further, the inter-individual difference in drug disposition and efficacy has been investigated by various authors [10] and the observed frequency distributions of GST M1-T1 genotypes among different populations are reported as ethnic or PAs dependent [10, 11]. Drugs are the major hope of remedy for the people around the globe with various metabolic and genetic disorders but the scenario in the past was found distressed as the effectiveness of the drugs were reported by the influence of the unidentified polymorphic patterns observed in drug metabolism genes among different ethnics or PAs [11–13]. Though researchers from different PAs are interested in analyzing the frequencies of GST M1 and T1 null genotypes and their possible risk association with various disorders, they are not able to report conclusive association in all major PAs [14, 15]. Recent advances in molecular techniques have opened a new era of pharmacogenomics and several researchers are inclined towards investigating the relationship in genetic diversity and allelic frequency of GST classes to insight genetic predisposition or susceptibility among various ethnics or PAs. In this context, probing the genetic variation in GST classes is inevitable for genomic epidemiological studies and to develop new common drugs in future to majority of PAs [9, 16, 17]. The allele frequency pattern in GST M1-T1 null genotypes of different PAs are yet to be explored to unlock several phenomenons related to a risk association with genetic diseases and drug dispositions [10]. A study including statistically valid number of subjects from various major PAs could address the issue of understanding the phenomenon for frequency distribution pattern in geographically assorted human populations (GAHPs); however, it will be tedious and might require huge population size [16]. Therefore, the present study was focused to uncover the genetic distance based ancestral origin or genetic affinity among GAHPs to address the paradigm for GST M1-T1 null allele frequency diversity. We are currently exploring how best to do this for the large number of populations in the present analysis to understand this phenomenon of frequency distribution pattern in GAHPs. GST M1 and GST T1 loci investigated in this study have been derived from literatures representing 44 different populations affiliated from Africa, Asia, Europe, South America and the genome of Gujarat PA, a region in western India. GST M1-T1 null allele frequency of 45 GAHPs were computed for phylogenesis with pair wise genetic distance based unweighted pair group method with arithmetic mean (UPGMA) and the findings of seven patterns for GST M1-T1 null allele frequency in this study have been demonstrated for the first time with highest genetic affinity. The patterns of null allele frequencies reported in this study add insights to determine a conclusive risk association of GST M1-T1 loci with several cancer or genetic disorders.

Materials and Methods

Subjects

The present investigation includes GST M1 and GST T1 null allele frequency of 45 GAHPs from 39 studies. Null allele frequency of Gujarat population was investigated in this study from 504 healthy unrelated volunteers of Gujarati origin with a mean age of 60 years. After signing the informed consent to participate in the study, blood samples of 2 mL were collected from each subject. Data of the remaining 44 populations were collected from different populations investigated by various authors (Table 1). Several studies of same ethnicity were also gathered in the study to fulfill the statistical significance of the study and to minimize the varying frequency of polymorphism among the ethnic groups while, the data gathered from Naveen et al. [18] had allele frequencies of combined Tamilnadu and Pondicherry PAs. The study was approved by the institutional ethics committee of Shrimathi Vasantben Ratilal Desai Cancer Research Centre, Rajkot Cancer Society - India.

Table 1. GST M1-T1 null allele frequency of geographically assorted human populations.

GST M1 Null GST T1 Null
Geographic Region (short form) Frequency Sample Frequency Sample Reference
I. Eastern Asia (eAs) 0.521 8931 0.476 7875 [10]
    1. Japan (eAs_Jap) 0.501 2215 0.496 1518 [10]
    2. Korea (eAs_Kor) 0.527 3704 0.509 3641 [10]
    3. China (eAs_Chi) 0.535 2467 0.443 2355 [10]
    4. Mongolia (eAs_Mon) 0.464 207 0.256 207 [21]
II. South Eastern Asia (seAs) 0.562 1666 0.351 890 [10]
    5. Vietnam (seAs_Vie) 0.420 100 0.300 100 [22]
    6. Philippines (seAs_Phi) 0.517 60 0.333 60 [23]
    7. Indonesia (seAs_Ids) 0.556 162 0.414 162 [24]
    8. Singapore - Malay (seAs_S_M) 0.653 167 0.383 167 [25]
III. Southern Asia (sAs)
    a. India (sAs_Ind) 0.296 4720 0.182 4570 [14, 15, 18, 26–35], Present Study
    9. Uttar Pradesh (Ind_Up) 0.327 1107 0.174 1107 [14, 29–32]
    10. West Bengal (Ind_Wb) 0.270 67 0.130 67 [34]
    11. Gujarat (Ind_Guj) 0.200 504 0.355 504 Present Study
    12. Maharashtra (Ind_Mah) 0.299 2060 0.138 2060 26–28, 31]
    13. Andhra Pradesh (Ind_Ap) 0.359 370 0.254 370 [18, 33]
    14. Karnataka (Ind_Kar) 0.258 260 0.191 110 [15, 18]
    15. Tamilnadu (Ind_Tn) 0.235 200 0.188 200 [18]
    16. Kerala (Ind_Ker) 0.324 182 0.128 182 [18, 35]
    b. Afghanistan (sAs_Afg) 0.460 656 0.186 656 [36]
    c. Iran (sAs_Iran) 0.406 NA 0.382 NA [37]
    d. Pakistan (sAs_Pak) 0.450 111 0.230 111 [38]
IV. Northern Europe (nEu) 0.533 3686 0.165 2291 [10]
    17. Sweden (nEu_Swd) 0.546 747 0.147 626 [5, 39]
    18. Finland (nEu_Fin) 0.469 482 0.130 385 [5]
    19. Denmark (nEu_Dn) 0.536 537 0.129 358 [5]
    20. UK (nEu_Uk) 0.578 1122 0.205 922 [5]
V. Western Europe (wEu) 0.515 6486 0.183 5562 [10]
    21. Netherlands (wEu_Ned) 0.504 419 0.229 419 [5]
    22. Germany (wEu_Ger) 0.516 3054 0.173 3054 [4]
    23. France (wEu_Fra) 0.534 1184 0.168 512 [5]
VI. Southern Europe (sEu) 0.509 3770 0.195 2660 [10]
    24. Italy (sEu_Ita) 0.494 810 0.163 553 [5]
    25. Spain (sEu_Spa) 0.504 1132 0.221 1121 [7]
    26. Slovenia (sEu_Sln) 0.520 102 0.255 102 [5]
    27. Greece (sEu_Gr) 0.520 171 0.099 171 [6]
VII. Eastern Europe (eEu) 0.511 1184 0.188 1169 [10]
    28. Czech Republic (eEu_Cze) 0.567 67 0.224 67 [40]
    29. Bulgaria (eEu_Bul) 0.518 112 0.161 112 [9]
    30. Poland (eEu_Pol) 0.511 321 0.193 321 [41]
    31. Slovakia (eEu_Slk) 0.512 332 0.180 322 [39]
    32. Russia (eEu_Rus) 0.497 352 0.193 352 [42]
VIII. Africa (Af)
    33. North African Egypt (nAf_Egp) 0.555 200 0.295 200 [21]
    34. West African Nigeria (wAf_Nig) 0.300 300 0.370 300 [43]
    35. South African Xhosa (sAf_Xho) 0.211 128 0.406 128 [44]
    36. South African Namibia (sAf_Nam) 0.112 134 0.358 134 [45]
    37. Middle African Cameroon (mAf_Cam) 0.278 126 0.468 126 [46]
    38. East African Ethiopia (eAf_Eth) 0.435 153 0.373 153 [46]
    39. East African Somalia (eAf_Som) 0.400 100 0.440 100 [47]
    40. East African Zimbabwe (eAf_Zim) 0.240 150 0.260 150 [48]
IX. Caucasian (wAs_Cau) 0.529 2714 0.197 1223 [10]
X. South American Brazil (sAm_Brz) 0.397 794 0.267 794 [49, 50]
    Total number of Samples 36009 29092
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DNA isolation and Genotyping

Lahiri and Nurnberger method was used to isolate genomic DNA from whole blood [19]; the Huang et al., method of multiplex polymerase chain reaction was performed to identify GST M1 and T1 polymorphism with albumin gene as internal control [20]. Amplified products of PCR were visualized in 2% agarose gel and the band patterns were analyzed for polymorphism.

Geographically assorted human populations (GAHPs)

On the basis of interest in allele frequency patterns and availability of GST M1-T1 null allele frequency data shared by all populations, we choose 45 representative geographically assorted human populations around the world from 38 investigations reported by various authors from Asia [10, 14, 15, 18, 21–38], Europe [4–7, 9, 10, 39–42], Africa [21, 37, 43–50] and South America [49, 50] as summarized in Table 1. Of these 45 GAHPs, 4 were chosen from Eastern Asia (Japan, Korea, China and Mongolia [10, 21]), 4 from South Eastern Asia (Vietnam, Philippines, Indonesia and Singapore-Malay [10, 22–25]), 8 from Southern Asia India (Tamilnadu, Kerala, Karnataka, Andhra Pradesh, Maharashtra, West Bengal, Uttar Pradesh and Gujarat [14, 15, 18, 26–35, present study]), 3 from Southern Asia (Afghanistan, Iran, Pakistan [36–38]), 4 from Northern Europe (Sweden, Finland, Denmark and UK [5, 10, 39]), 4 from Southern Europe (Italy, Spain, Slovenia and Greece [5–7, 10]), 5 from Eastern Europe (Czech Republic, Bulgaria, Poland, Slovakia and Russia [9, 10, 39–42]), 3 from Western Europe (Netherlands, Germany and France [4, 5, 10]), 8 from Africa (Egypt, Nigeria, Xhosa tribe, Namibia, Cameroon, Ethiopia, Somalia and Zimbabwe [21, 37, 43–50]), 1 from South American Brazil [49, 50] and Caucasian (Americans and Canadians [10]). The “Caucasian” population used in this study was arbitrarily termed as “West Asian Caucasians” (wAs_Cau) to precise the geographical region and allele frequency patterns. All the PAs were grouped into continental regions as per the guidelines of Statistics Division of the United Nations (http://unstats.un.org/unsd/methods/m49/m49regin.htm [51]). Initially, 20 different continental region populations, as summarized in Table 2, were used in phylogenetic analysis for GST M1-T1 null allele frequency to minimize the effect of inbreeding [52]. Of these 20 continental region populations, the null allele frequency of thirteen populations such as Afghanistan, Iran, Pakistan (3 from South Asia), Brazil (1 from South America), Egypt (1 from North Africa), Nigeria (1 from West Africa), Xhosa tribe, Namibia (2 from South Africa), Cameroon (1 from Middle Africa), Ethiopia, Somalia and Zimbabwe (3 from East Africa), Caucasian (West Asia, as described earlier) and the average null allele frequency of seven continental regions such as East Asia (Japan, Korea, China, Mongolia), South East Asia (Vietnam, Philippines, Indonesia, Singapore-Malay), South Asia India (Tamilnadu, Kerala, Karnataka, Andhra Pradesh, Maharashtra, West Bengal, Uttar Pradesh, Gujarat), North Europe (Sweden, Finland, Denmark, UK), West Europe (Netherlands, Germany, France), South Europe (Italy, Spain, Slovenia, Greece) and East Europe (Czech Republic, Bulgaria, Poland, Slovakia, Russia) were used in the phylogenetic analyses, as summarized in Table 1 for GST M1-T1 null allele frequency. Since, some of the populations have considerable gene admixture from other PAs [52] another phylogenetic analysis was performed using all the 45 GAHPs for GST M1-T1 null allele frequency (Table 3).

Table 2. GST M1-T1 null allele frequency based genetic distance between 20 different continental region populations.

S. No. Populations eAf_Zim sAf_Nam sAf_Xho mAf_Cam wAf_Nig sAs_Ind sAm_Brz eAf_Som sAs_Iran eAf_Eth sAs_Pak sAs_Afg sEu eEu wEu eAs wAs_Cau nEu nAf_Egt seAs
1 eAf_Zim
2 sAf_Nam 0.019641
3 sAf_Xho 0.018002 0.007702
4 mAf_Cam 0.036741 0.028132 0.006642
5 wAf_Nig 0.011698 0.025018 0.007576 0.009135
6 sAs_Ind 0.007203 0.050393 0.046757 0.068305 0.027352
7 sAm_Brz 0.019686 0.071517 0.047429 0.050077 0.017980 0.012453
8 eAf_Som 0.047449 0.069856 0.031371 0.014241 0.013198 0.062987 0.027019
9 sAs_Iran 0.033673 0.067958 0.033278 0.022080 0.010096 0.040405 0.011558 0.003247
10 eAf_Eth 0.041147 0.083845 0.045069 0.031565 0.016495 0.043599 0.010910 0.005520 0.000882
11 sAs_Pak 0.037341 0.107807 0.078333 0.079443 0.037756 0.020062 0.003628 0.042121 0.021916 0.017784
12 sAs_Afg 0.044754 0.124665 0.097123 0.102102 0.052050 0.021304 0.008620 0.060075 0.035118 0.029735 0.001511
13 sEu 0.064578 0.155621 0.119309 0.117913 0.066300 0.036737 0.015131 0.064593 0.039712 0.031804 0.003921 0.002064
14 eEu 0.066210 0.158879 0.122818 0.122007 0.069013 0.037348 0.016296 0.067829 0.042182 0.034056 0.004511 0.002175 0.000038
15 wEu 0.068642 0.163151 0.126834 0.126154 0.072004 0.038764 0.017691 0.070734 0.044494 0.036072 0.005254 0.002530 0.000130 0.000031
16 eAs 0.112148 0.157404 0.093422 0.056769 0.057067 0.119908 0.055200 0.015755 0.021559 0.017606 0.060443 0.079037 0.071492 0.074740 0.077050
17 wAs_Cau 0.114150 0.162684 0.097673 0.060689 0.059434 0.120082 0.054782 0.017359 0.022368 0.017846 0.058754 0.076677 0.068473 0.071609 0.073799 0.000099
18 nEu 0.079650 0.181776 0.143916 0.143304 0.084726 0.045716 0.023990 0.082505 0.054067 0.044384 0.008885 0.004708 0.001084 0.000751 0.000480 0.085835 0.082097
19 nAf_Egt 0.087395 0.174577 0.121158 0.102434 0.066203 0.066198 0.023311 0.043045 0.028058 0.019145 0.013337 0.017263 0.009810 0.010731 0.011240 0.032074 0.029501 0.013382
20 seAs 0.098593 0.178065 0.117995 0.091480 0.065434 0.083794 0.031293 0.033343 0.024411 0.015989 0.023986 0.031810 0.022755 0.024282 0.025180 0.016697 0.014669 0.028655 0.002821
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The values represented in the table were computed between the population affiliations by Nei’s (1972) standard genetic distance (DST) method and were used in phylogenetic tree of 20 different continental regions population affiliations for GST M1-T1 null allele frequency (Fig. 1). Abbreviations used were same as those in Table 1.

Table 3. GST M1-T1 null allele frequency based genetic distance between 45 geographically assorted human populations (GAHPs).

S. No. GAHPs eAf_Zim sAf_Nam Ind_Guj sAf_Xho Ind_Tn Ind_Kar Ind_Wb mAf_Cam Ind_Mah wAf_Nig Ind_Ker Ind_Up Ind_Ap sAm_Brz
1 eAf_Zim
2 sAf_Nam 0.019641
3 Ind_Guj 0.008566 0.004982
4 sAf_Xho 0.018002 0.007702 0.002144
5 Ind_Tn 0.003500 0.033450 0.022055 0.037140
6 Ind_Kar 0.003729 0.037771 0.023632 0.038408 0.000355
7 Ind_Wb 0.012420 0.058122 0.041785 0.061245 0.003252 0.002526
8 mAf_Cam 0.036741 0.028132 0.014999 0.006642 0.063507 0.062461 0.090338
9 Ind_Mah 0.013628 0.064129 0.044263 0.063067 0.004934 0.003307 0.000574 0.088816
10 wAf_Nig 0.011698 0.025018 0.007718 0.007576 0.026876 0.025085 0.042346 0.009135 0.040441
11 Ind_Ker 0.018509 0.075817 0.052630 0.072183 0.008654 0.006243 0.002038 0.096709 0.000540 0.045207
12 Ind_Up 0.011897 0.062884 0.039784 0.055683 0.006375 0.003826 0.003100 0.075196 0.001229 0.031146 0.001306
13 Ind_Ap 0.011155 0.056511 0.029903 0.039082 0.013492 0.009762 0.014788 0.047057 0.011066 0.014791 0.011238 0.005068
14 sAm_Brz 0.019686 0.071517 0.039623 0.047429 0.023229 0.018123 0.023228 0.050077 0.017729 0.017980 0.016579 0.009579 0.001318
15 eAf_Som 0.047449 0.069856 0.039132 0.031371 0.071354 0.065680 0.087771 0.014241 0.080505 0.013198 0.082462 0.062745 0.031855 0.027019
16 sAs_Iran 0.033673 0.067958 0.035776 0.033278 0.050368 0.044624 0.060441 0.022080 0.053470 0.010096 0.054000 0.038840 0.015631 0.011558
17 seAs_Vie 0.027183 0.078906 0.043887 0.048612 0.034281 0.028238 0.035694 0.045118 0.028849 0.017602 0.027294 0.018065 0.004738 0.001318
18 eAf_Eth 0.041147 0.083845 0.047123 0.045069 0.056782 0.049923 0.064060 0.031565 0.055751 0.016495 0.054832 0.040296 0.016788 0.010910
19 sAs_Pak 0.037341 0.107807 0.067666 0.078333 0.036617 0.029669 0.029982 0.079443 0.022419 0.037756 0.018481 0.013929 0.007683 0.003628
20 sAs_Afg 0.044754 0.124665 0.082681 0.097123 0.039341 0.032172 0.028470 0.102102 0.020874 0.052050 0.015882 0.014104 0.012496 0.008620
21 eAs_Mon 0.041766 0.111479 0.069331 0.077606 0.043779 0.036213 0.038385 0.074231 0.029918 0.036336 0.025780 0.019540 0.009564 0.004097
22 nEu_Fin 0.056413 0.147166 0.103438 0.122646 0.045284 0.037978 0.029516 0.132931 0.022041 0.072547 0.015927 0.017497 0.021698 0.018273
23 sEu_Ita 0.061485 0.153594 0.106118 0.122436 0.053407 0.045069 0.038414 0.126364 0.029524 0.070064 0.022816 0.022503 0.022062 0.016456
24 eEu_Rus 0.059262 0.147482 0.099679 0.113354 0.054266 0.045694 0.041407 0.113525 0.032070 0.062336 0.025631 0.023486 0.019501 0.013120
25 eAs_Jap 0.110350 0.146520 0.099722 0.084594 0.142570 0.132315 0.158417 0.048140 0.144938 0.053266 0.143253 0.118214 0.072885 0.059379
26 wEu_Ned 0.059823 0.144141 0.095375 0.105798 0.058632 0.049635 0.048197 0.101024 0.038141 0.055834 0.031893 0.027464 0.018923 0.011362
27 sEu_Spa 0.060246 0.145849 0.097044 0.108186 0.058185 0.049224 0.047092 0.104342 0.037126 0.057711 0.030768 0.026857 0.019275 0.011877
28 eEu_Pol 0.065713 0.157598 0.107503 0.121115 0.060599 0.051493 0.046757 0.119726 0.036775 0.067624 0.029742 0.027621 0.023028 0.015739
29 eEu_Slk 0.067544 0.161727 0.111408 0.126171 0.061054 0.051968 0.046119 0.126173 0.036234 0.071687 0.028989 0.027678 0.024546 0.017452
30 wEu_Ger 0.070266 0.166596 0.115586 0.130954 0.063009 0.053801 0.047246 0.131318 0.037254 0.075319 0.029750 0.028878 0.026377 0.019164
31 seAs_Phi 0.070253 0.140613 0.090160 0.090823 0.080995 0.070962 0.078774 0.072213 0.066801 0.045336 0.061492 0.049833 0.027442 0.016743
32 eEu_Bul 0.072759 0.171473 0.120080 0.136502 0.064240 0.055011 0.047413 0.138030 0.037460 0.079764 0.029721 0.029583 0.028364 0.021260
33 sEu_Sln 0.066941 0.151257 0.099936 0.107876 0.068535 0.058783 0.059143 0.098202 0.048016 0.056728 0.041428 0.035289 0.022831 0.013657
34 sEu_Gre 0.084181 0.192766 0.140787 0.162602 0.069434 0.060400 0.047888 0.171015 0.038494 0.101709 0.029929 0.033419 0.038936 0.032905
35 eAs_Kor 0.131072 0.171461 0.120063 0.103009 0.165454 0.153899 0.180971 0.061388 0.165744 0.067925 0.162999 0.136593 0.087477 0.071632
36 wAs_Cau 0.114150 0.162684 0.110741 0.097673 0.142911 0.131528 0.153814 0.060689 0.139138 0.059434 0.135556 0.112713 0.069730 0.054782
37 wEu_Fra 0.079786 0.181720 0.127681 0.143431 0.071836 0.061962 0.054405 0.142357 0.043640 0.084247 0.035309 0.034781 0.032085 0.023807
38 eAs_Chi 0.107068 0.162350 0.109199 0.098764 0.132097 0.120617 0.139605 0.064352 0.125031 0.057523 0.120667 0.100165 0.060995 0.046306
39 nEu_Dnk 0.086463 0.194885 0.140372 0.159569 0.074495 0.064731 0.053988 0.162804 0.043567 0.097621 0.034687 0.036500 0.038115 0.030493
40 nEu_Swd 0.088823 0.197255 0.141108 0.158695 0.078575 0.068343 0.058687 0.158719 0.047608 0.096026 0.038515 0.039288 0.038475 0.029849
41 nAf_Egt 0.087395 0.174577 0.117293 0.121158 0.093461 0.082045 0.085005 0.102434 0.071600 0.066203 0.063981 0.055069 0.035784 0.023311
42 seAs_Ids 0.110068 0.176501 0.119385 0.111506 0.131239 0.118996 0.134139 0.077907 0.118852 0.064351 0.112960 0.094840 0.058800 0.043301
43 eEu_Cze 0.093352 0.195596 0.135981 0.146458 0.091159 0.079672 0.075757 0.134876 0.062724 0.084693 0.053635 0.049608 0.038780 0.027114
44 nEu_Uk 0.100862 0.209262 0.147632 0.159813 0.096473 0.084651 0.078719 0.149197 0.065400 0.094779 0.055589 0.052818 0.043805 0.031824
45 seAs_S_M 0.171196 0.272027 0.195663 0.189285 0.188418 0.171818 0.180613 0.146069 0.160261 0.120939 0.148990 0.133165 0.096023 0.074061
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The values represented in the table were computed between the population affiliations by Nei’s (1972) standard genetic distance (DST) method and were used in phylogenetic tree of 45 geographically assorted human populations for GST M1-T1 null allele frequency (Fig. 2). Abbreviations used were same as those in Table 1.

Statistical analysis

Distributions of GST M1 and GST T1 null alleles in Gujarati population were calculated by frequency counting method in SPSS-21 (4-27AEA) for windows. The standard genetic distance (DST) between different PAs for GST M1-T1 null allele frequencies were calculated by Nei’s (1972) method in Phylip 3.69 version [52, 53]. Least DST values between the PAs were used to compute clades with more than 50% of 1000 bootstrap replicates by Felsenstein (1989) method and then the phylogenetic trees were constructed in Mega-6 software by UPGMA method [54–56]. Finally, the clusters of PAs split found among the geographically assorted human populations in phylogenetic tree were used in the scattered plot to analyze their geographical distribution. The longitude (X-axis) and latitude (Ys-axis) of different continental regions were used to construct the scattered plot in SPSS-21 as summarized in Table 4. The online web source world atlas was used to compute the latitude and longitude of the respective geographic locations (http://www.worldatlas.com/aatlas/latitude_and_longitude_finder.htm [57]).

Table 4. Geographical location of population affiliations from 20 different continental regions used in scattered plot analyses.

Location a Latitude Longitude
sAf_Xho - 30.33 22.56
eAf_Zim - 19.0 29.9
nAf_Egp 26.49 30.48
eAf_Eth 9.8 40.29
wAf_Nig 9.4 8.4
mAf_Cam 7.22 12.21
eAf_Som 5.9 46.11
sAf_Nam - 22.57 18.29
sAs_Iran 32.25 53.41
sAs_Pak 30.22 69.20
sAs_Af 33.56 67.42
sAs_Ind 20.35 78.57
eAs 22.16 114.14
seAs 11.35 121.37
wAs_Cau 43 43.45
nEu 62.16 12.20
wEu 46.12 1.15
sEu 41.16 - 1.12
eEu 59.80 36.29
sAm _Brz -14.14 - 51.55
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a Latitudes in the northern hemisphere were listed with positive values, as were longitudes in the eastern hemisphere; Latitudes in the southern hemisphere were listed with negative values, as in longitudes of western hemisphere. Abbreviations used were same as those in Table 1.

Results

Phylogenetic tree for GST M1-T1 null allele frequency in GAHPs

The frequency of GST M1 and T1 null genotypes in Gujarat populations of India was observed as 0.200 and 0.355 respectively in this present study. The pair wise genetic distance matrix computed for GST M1-T1 null allele frequency between the respective PAs using Nei’s (1972) standard genetic distance method (DST) in gendist program of Phylip-3.69 software for the 20 different continental regions PAs and the 45 GAHPs described elsewhere were summarized in Tables 2 and 3 respectively. The phylogenetic analyses of 20 different continental regions (Fig. 1) and 45 GAHPs (Fig. 2) for GST M1-T1 null allele frequency were performed using the pair wise genetic distance matrix by UPGMA method in MEGA-6 software [52, 56]. Consense program that clustered more than 50% of 1000 bootstrap replicates was used to assess the reliability of the constructed phylogenetic trees [53, 54]. The Nei’s DST value varies from 0.0001 to 0.007 (Table 2) and 0.0006 to 0.008 (Table 3) for GST M1-T1 null allele frequency among the 20 different continental regions PAs and the 45 GAHPs respectively. The phylogenetic trees in Figs. 1 and 2 showed consistent clusters or patterns for GST M1-T1 null allele frequency from populations such as South African Xhosa tribe, South African Zimbabwe, East African Ethiopia, North African Egypt, Caucasian and South Asian Afghanistan. The least DST value of PAs from South Asian Iran (0.000882) and East African Somalia (0.00552) with East African Ethiopian allele frequency grouped them as one of the clusters in the phylogenetic trees for GST M1-T1 null allele frequency. Few of the African PAs such as West African Nigeria, Middle African Cameroon and South African Namibia were clustered with South African Xhosa tribe allele frequency with least DST value that ranged from 0.006642 to 0.007702. In addition, the South Asian Indian Gujarat PAs (0.00214) was observed with highest affinity to South African Xhosa cluster than any other clusters (Fig. 2). The average GST M1-T1 null allele frequency in South Asian Indian PA was observed as a cluster to East African Zimbabwe allele frequency (Fig. 1) with least DST value (0.007203) out of 20 continental regions. Further, the least DST value to East African Zimbabwe allele frequency was observed with South Asian Indian PAs (Table 3) from Tamilnadu (0.0035) and Karnataka (0.003729). Furthermore, PA from Karnataka was observed with least DST value to Maharashtra allele frequency (0.003307), which was clustered with allele frequency from West Bengal (0.000574), Uttar Pradesh (0.003826) and Kerala PAs of South Asian India (0.006243) as shown in Fig. 2. Nevertheless, the South Asian Indian Andhra Pradesh allele frequency was observed with least DST value only to Uttar Pradesh PA (0.005068) than any other Indian PAs for GST M1-T1 null allele frequency and stood as a separate cluster in the phylogenetic tree with highest genetic affinity to South America (Brazil, 0.001318) and South East Asia allele frequency (Vietnam, 0.004738) from geographically distant continental regions.

Fig 1. Phylogenetic tree of 20 different continental regions population affiliations for GST M1-T1 null allele frequency.

Fig 1

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The tree was produced by the UPGMA method from DST values in Table 2 and cluster with more than 50% of 1000 bootstrap replicates were included in the consensus tree obtained by Felsenstein (1989) phylogeny interference package. Major group of GST M1-T1 null allele frequencies were from population of Xhosa tribe, Zimbabwe, Ethiopia, Egypt, Afghanistan and Caucasian. Abbreviations used were same as those in Table 1.

Fig 2. Phylogenetic tree of 45 geographically assorted human populations for GST M1-T1 null allele frequency.

Fig 2

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This tree was based on DST values in Table 3. Other aspects were same as those in Fig. 1. Major group of GST M1-T1 null allele frequencies were from population of Xhosa tribe, Zimbabwe, Ethiopia, Egypt, Afghanistan, Caucasian and Andhra Pradesh. Abbreviations used were same as those in Table 1.

PAs from China (0.000757), Japan (0.001457) and Korea (0.001522) of East Asia and Pakistan (0.001511) of South Asia were observed as another cluster with least DST value to Caucasians [Americans and Canadians (10)] for GST M1-T1 null allele frequency out of 45 combinations (Fig. 2). However, Mongolia of East Asia was observed with least DST value to Pakistan allele frequency (0.000686) than the Caucasians (0.046671). The least DST value between European continental regions and South Asian Afghanistan allele frequency that ranged from 0.002064 to 0.004708 was clustered together for GST M1-T1 null allele frequency in the phylogenetic tree of 20 different continental regions (Fig. 1). Nevertheless, the phylogenetic tree of 45 GAHPs (Fig. 2) clustered only 13 European PAs (Sweden, Finland, Denmark, Netherlands, Germany, France, Italy, Spain, Greece, Bulgaria, Poland, Slovakia and Russia) out of 16 investigated in this study with South Asian Afghanistan allele frequency (least DST value that ranged from 0.001176 to 0.00723) while, the other 3 PAs [Slovenia (0.002462), Czech Republic (0.004116) and UK (0.006718)] were clustered with North African Egypt allele frequency. Singapore-Malay and Indonesia PAs from South East Asia were observed with least DST value to East Asia (China, 0.001246) and Caucasian (0.003788) respectively for GST M1-T1 null allele frequency. Nevertheless, the other counter parts from same continental region were observed as the most diverse PAs with PA admixture from North Africa (Egypt, 0.002571) and East Africa (Ethiopia, 0.007943) for Philippines; South Asia India (Andhra Pradesh, 0.004738) and East Africa (Ethiopia, 0.004922) for Vietnam among the 45 GAHPs investigated in this study for GST M1-T1 null allele frequency as shown in Table 3 and Fig. 2 respectively.

GST M1-T1 null allele frequency patterns among the GAHPs

The effect of isolation by geographical distance in population differentiation [51] was validated in a scattered plot with respect to the phylogenetic clusters of 45 GAHPs for GST M1-T1 null allele frequency that corresponds to the latitudes and longitudes of 20 different continental regions representing PAs from Africa, Asia, Europe and America (Table 4). The scattered plot illustrated in Fig. 3 suggest three major geographical split for the seven GST M1-T1 null allele frequency clusters or patterns observed in the phylogenetic tree of 45 GAHPs (Fig. 2). South African Xhosa allele frequency pattern (I) observed mostly in continental regions of Africa suggest an “Africa” split in the scattered plot with least population differentiation to Nigeria (West Africa), Cameroon (Middle Africa) and Namibia (South Africa). However, the GST M1-T1 null allele frequency patterns of PAs from other African continental region such as East African Zimbabwe (II), East African Ethiopia (III) and North African Egypt (IV) were observed with least population differentiation to PAs from non African continental regions such as South Asia (India and Iran), South East Asia (Philippines and Vietnam), Southern Europe (Slovenia), Eastern Europe (Czech Republic) and Northern Europe (UK) irrespective of the geographical isolation suggest an “out of Africa” split. Finally, the remaining three non-African GST M1-T1 null allele frequency patterns observed from Caucasian (V), South Asian Afghanistan (VI) and South Asian Indian Andhra Pradesh (VII) were geographically distributed in different continental regions such as Asia, Europe and America with the exception of Africa suggest an “other than Africa” split among the 45 GAHPs in the scattered plot.

Fig 3. Geographic Location of GST M1-T1 null allele frequency patterns in Scattered Plot.

Fig 3

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This scenario is largely a speculation based on Nei’s DST based phylogenetic trees in Figs. 1 and 2, which suggest seven patterns for GST M1-T1 null allele frequency among the 45 geographically assorted human populations. The scattered plot shows the genetic affinity and geographical distribution of the seven probable patterns with three major split among the continental regions such as an “African” split with South African Xhosa pattern (I), an “out of Africa” split with East African Zimbabwe (II), East African Ethiopia (III), North African Egypt (IV) patterns and an “other than Africa” split with South Asian Afghanistan (V), West Asian Caucasian (VI), South Asian Indian Andhra Pradesh patterns (VII). Abbreviations used were same as those in Table 1.

Discussion

Understanding the genetic variation and diversity among the geographically assorted human populations (GAHPs) is an interesting topic in population genetics with a wide range of neutral genetic markers and adaptive markers being employed to uncover the patterns of genetic diversity [58]. To address the extent of diversity in allele frequency distribution among populations from different ethnicity, region, country or continent is difficult, nevertheless understanding this phenomenon is inevitable [8, 11, 16, 17] and recent advances in the molecular techniques excel the perspective of inter-individual genetic variations in GAHPs. Allele frequencies of large number of neutral markers or of even few candidate markers that duplicate or decay to favor new environments and lead to rapid adaptations are often used for investigating the patterns [58]. The paradigm of allele frequency among the populations holds the key to unlock the existing problem of inter-individual genetic variation in xenobiotic metabolizing enzymes (XMEs) and in particular the decay or null allele frequency of Glutathione-S-transferase’s classes such as Mu 1 (GST M1) and Theta 1 (GST T1), which are considered as the major risk factor for various diseases including several types of cancers [8–10]. Therefore, the present investigation analyzed the pattern for GST M1-T1 null allele frequency among GAHPs using a phylogenetic approach. A set of 20 different continental region PAs (Table 2) and 45 GAHPs (Table 3) were recruited for GST M1-T1 null allele frequency data from 38 previously reported works and genomic data of Indian Gujarat PA in this study (Table 1) and the respective phylogenetic trees (Figs. 1 and 2) have been constructed by UPGMA method based on Nei’s (1972) standard genetic distance (DST) with clusters more than 50% of 1000 bootstrap replicates obtained by Felsenstein (1989) program [52, 54, 56]. In addition to the ancestral origin or genetic affinity based clusters for GST M1-T1 null allele frequency demonstrated in the phylogenetic trees, a positive correlation between the genetic distance and geographical distance were analyzed for the effect of isolation in population differentiation by distance in a scattered plot (Fig. 3). Indeed, the observations from phylogenetic trees and scattered plot of different PAs constructively reveals the findings of seven probable patterns for GST M1-T1 null allele frequency among the GAHPs in concordance to the reports of archeological signatures, ancient gene flows and sex-specific components [59–62].

The genetic affinity and geographical distribution of 20 different continental regions that included 45 GAHPs investigated in this study (Figs. 1–3) revealed the findings of an allele frequency pattern for GST M1-T1 null genotypes among Namibia (South Africa), Nigeria (West Africa), Cameroon (Middle Africa), Gujarat (South Asian Indian) and Xhosa tribe (South Africa) for the first time. We report here, the findings of Xhosa allele frequency (I) with major genetic affinity towards populations from Africa (Namibia, Nigeria, Cameroon) as an “Africa” split pattern for GST M1-T1 null allele frequency in agreement to the reports of linkage disequilibrium computed for loss of variants in GST classes by Polimanti et al. (2013). The observations in phylogenetic trees (Figs. 1 and 2) and scattered plot analysis (Fig. 3) demonstrated the findings of another three patterns such as East Africa Zimbabwean allele frequency - II in population from India (South Asia), East Africa Ethiopian allele frequency - III in populations from Iran (South Asia) and Somalia (East Africa) and North Africa Egyptian allele frequency - IV in populations from Slovenia (Southern Europe), Czech Republic (Eastern Europe) and UK (Northern Europe) for GST M1-T1 null genotypes. The findings of Ethiopian - III and Egyptian - IV allele frequency pattern for GST M1-T1 null genotypes in this study are in concordance to the earlier reports of genome wide diversity study in the Levant by Haber et al., (2013), who found two major groups with one close to Africans and Middle Easterners and the other closer to modern day Europeans [61]. Further, the findings of these Zimbabwean, Ethiopian and Egyptian patterns from African populations with high genetic affinity towards non-African populations for GST M1-T1 null allele frequency have been reported as an “Out of Africa” split in this study in corroborate to the findings of Templeton (2002), who reported the out-of-Africa theory of migration and the ancestral root of allele frequency admixture [63].

The allele frequency of population from South Asian Afghanistan with high genetic affinity to majority of European PAs investigated in this study (Tables 2 and 3 and Figs. 2 and 3) has been reported as pattern - V for GST M1-T1 null genotypes in accordance to the reports of various authors [60, 62, 64]. Further, population in Pakistan (South Asia) has been reported with Afghanistan (South Asia) pattern for GST M1-T1 null allele frequency (Table 3) in this study, though it was found with genetic affinity to PAs from Mongolia (East Asia), Europe (South, East and West) and Andhra Pradesh (South India) in corroborate to the earlier reports of Templeton (2002), who stated the findings of considerable overlap among East Asians, Europeans and South Indian populations [64]. Moreover, the pattern of allele frequency from Caucasians (Americans and Canadians) found among East Asians (Fig. 1) in this study has been identified as pattern - VI for GST M1-T1 null allele frequency and reported for the first time. Finally, the allele frequency from South Indian Andhra Pradesh PA was found with least genetic distance (Table 3) to populations from Pakistan (South Asia), Vietnam (South East Asia) and Brazil (South America) irrespective of the phenomenon of population differentiation by geographical isolation [52] and has been reported as pattern - VII for GST M1-T1 null genotypes (Figs. 2 and 3) among the 45 GAHPs in this study. These observations of South East Asian and South American PAs with the null allele frequency pattern from South Indian Andhra Pradesh PA are in agreement to the reports of agro-pastoral system in South India that acted as agricultural center and source of dispersion to lineages from different preexisting populations [60]. Furthermore, the reported East Africa patterns from Zimbabwe (II), Ethiopia (III) among India, Iran (South Asia) and South Asian pattern from South Indian Andhra Pradesh (VII) among Vietnam (South East Asia) populations in this study for GST M1-T1 null allele frequency are in concordance to the reports of migration pattern of Homo sapiens from East Africa with the demographic expansions by Field and Lahr (2006), who investigated the geographic information systems during oxygen isotope stage 4 [62]. GST M1-T1 null allele frequency from South East Asian PAs has been reported as the complex admixture of Zimbabwe (II), Ethiopia (III) and Andhra Pradesh (VII) patterns in this study. Finally, the scattered plot analysis (Fig. 3), clearly demonstrates the findings of allele frequency patterns from South Asian Afghanistan - V, Caucasian - VI and South Indian Andhra Pradesh - VII as an “Other than Africa” split among 45 GAHPs for GST M1-T1 null genotypes with respect to their geographical distribution. This observation of other than Africa split in this study has been reported here in agreement to the concepts of later migration of the populations in regions other than Africa [60, 64]. In conclusion, the data of seven patterns for GST M1-T1 null allele frequency from Xhosa tribe (I), Zimbabwe (II), Ethiopia (III), Egypt (IV), Afghanistan (V), Caucasian (VI) and South Indian Andhra Pradesh (VII) reported in this study compare constructively with the earlier studies that suggested the PAs of relatively recent origin show comparatively small genetic differences and high genetic affinity among them [11, 46, 52, 58]. Findings of these seven patterns (I-VII) for GST M1-T1 null allele frequency reported here, would shed some light to address the missing link in most of the genomic epidemiological studies that lacks conclusive risk association [9, 16, 17]. The “Africa” (I), “Out of Africa” (II, III and IV) and “Other than Africa” (V, VI and VII) split among the 45 GAHPs reported in this study have to be explored further to rationalize the GST M1-T1 null allele’s frequency patterns in world populations.

Acknowledgments

The authors wish to thank the participants of this study for their cooperation; Dr. V. K. Gupta, Director Shree Gulabkuvar Talakchand Sheth Cancer hospital, Rajkot for providing technical guidance; Shree Manibhai Virani and Shremathi Navalben Virani Science College, Rajkot, Gujarat, India and Department of Animal Science, Bharathidasan University, Tiruchirappalli, Tamilnadu, India for providing the support in sample collection and facilities respectively. The Authors are grateful to Dr. Akbar Ali Khan Pathan, Assistant Professor, Genome Research Chair, Department of Biochemistry, College of Science, King Saud University, Riyadh-11451, Kingdom of Saudi Arabia for facilitating data analysis and for Dr. K Balakrishnan, Associate Professor, Department of Immunology, Madurai Kamaraj University, Madurai, Tamilnadu - 625 021, India for his critical suggestions.

Data Availability

All relevant data are within the paper.

Funding Statement

The authors have no support or funding to report.

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