Background A large number of microsatellites have been extensively used to comprehend the genetic diversity of different global groups. This paper entails polymorphism at 15 STR in four predominant and endogamous populations representing Karnataka, located on the southwest coast of India. The populations residing in this region are believed to have received gene flow from south Indian populations and world migrants, hence, we carried out a detailed study on populations inhabiting this region to understand their genetic structure, diversity related to geography and linguistic affiliation and relatedness to other Indian and global migrant populations.
Background
A large number of microsatellites have been extensively used to comprehend the genetic diversity of different global groups. This paper entails polymorphism at 15 STR in four predominant and endogamous populations representing Karnataka, located on the southwest coast of India. The populations residing in this region are believed to have received gene flow from south Indian populations and world migrants, hence, we carried out a detailed study on populations inhabiting this region to understand their genetic structure, diversity related to geography and linguistic affiliation and relatedness to other Indian and global migrant populations.Results Various statistical analyses were performed on the microsatellite data to accomplish the objectives of the paper. The heretozygosity was moderately high and similar across the loci, with low average G ST  value. Iyengar and Lyngayat were placed above the regression line in the R-matrix analysis as opposed to the Gowda and Muslim. AMOVA indicated that majority of variation was confined to individuals within a population, with geographic grouping demonstrating lesser genetic differentiation as compared to linguistic clustering. D A  distances show the genetic affinity among the southern populations, with Iyengar, Lyngayat and Vanniyar displaying some affinity with northern Brahmins and global migrant groups from East Asia and Europe.
Results
Various statistical analyses were performed on the microsatellite data to accomplish the objectives of the paper. The heretozygosity was moderately high and similar across the loci, with low average G ST  value. Iyengar and Lyngayat were placed above the regression line in the R-matrix analysis as opposed to the Gowda and Muslim. AMOVA indicated that majority of variation was confined to individuals within a population, with geographic grouping demonstrating lesser genetic differentiation as compared to linguistic clustering. D A  distances show the genetic affinity among the southern populations, with Iyengar, Lyngayat and Vanniyar displaying some affinity with northern Brahmins and global migrant groups from East Asia and Europe.Conclusion The microsatellite study divulges a common ancestry for the four diverse populations of Karnataka, with the overall genetic differentiation among them being largely confined to intra-population variation. The practice of consanguineous marriages might have attributed to the relatively lower gene flow displayed by Gowda and Muslim as compared to Iyengar and Lyngayat. The various statistical analyses strongly suggest that the studied populations could not be differentiated on the basis of caste or spatial location, although, linguistic affinity was reflected among the southern populations, distinguishing them from the northern groups. Our study also indicates a heterogeneous origin for Lyngayat and Iyengar owing to their genetic proximity with southern populations and northern Brahmins. The high-ranking communities, in particular, Iyengar, Lyngayat, Vanniyar and northern Brahmins might have experienced genetic admixture from East Asian and European ethnic groups.
Conclusion
The microsatellite study divulges a common ancestry for the four diverse populations of Karnataka, with the overall genetic differentiation among them being largely confined to intra-population variation. The practice of consanguineous marriages might have attributed to the relatively lower gene flow displayed by Gowda and Muslim as compared to Iyengar and Lyngayat. The various statistical analyses strongly suggest that the studied populations could not be differentiated on the basis of caste or spatial location, although, linguistic affinity was reflected among the southern populations, distinguishing them from the northern groups. Our study also indicates a heterogeneous origin for Lyngayat and Iyengar owing to their genetic proximity with southern populations and northern Brahmins. The high-ranking communities, in particular, Iyengar, Lyngayat, Vanniyar and northern Brahmins might have experienced genetic admixture from East Asian and European ethnic groups.

Background The Indian subcontinent is regarded as a natural genetic laboratory, owing to the co-existence and interaction of socio-culturally, linguistically, ethnically and genetically diversified endogamous populations in a geographical terrain. It is believed that the earliest humans leaving Africa for Eurasia might have taken a coastal route across Saudi Arabia, through Iraq, Iran, to Pakistan and finally entered India along the coastlines [ 1 ]. A second wave of migration (~10,000 years ago) brought in Proto-Dravidian Neolithic farmers from Afghanistan, who were later displaced southwards by a large influx of Indo-European speakers ~3500 years ago in to the subcontinent [ 2 , 3 ]. The origin and settlement of the Indian people still remains intriguing, fascinating scientists to explore the impact of these past and modern migrations on the genetic diversity and structure of contemporary populations [ 4 - 6 ]. Anthropologically, southern and northern populations are distinct and these differences are further substantiated by (i) the presence of Neolithic sites in this region suggests that Neolithic people of southern India came from north by land and the west-coast by sea [ 7 ], (ii) the southern megaliths resemble closely with those of the Mediterranean and western-Europe, while those from northern India are similar to megaliths found in Iran and Baluchisthan [ 8 ], and (iii) the predominance of Dravidian language in this region as opposed to their secluded occurrence in central Asia and other parts of India, suggests that the Dravidian languages might have originated within India [ 9 ]. It is, thus, of considerable genetic interest to understand the genetic structuring and relationships of southern populations. The present study was carried out on one of the largest southern states, Karnataka, positioned on the southwest coast of India, with a dwelling of about 50 million people. This expanse has been a rich source of prehistoric discoveries dating back to the Paleolithic era that are akin to those seen in Europe [ 7 ]. Karnataka has received continuous gene flow from different caste and linguistic groups residing in the adjoining areas of Maharashtra, Andhra Pradesh and Tamil Nadu [ 10 ], resulting in the congregation of a large number of diverse endogamous groups within this region. Its large coastline of about 400 Km also attracted the Portugese, Dutch and French traders, who were seeking more profitable ventures on the southern coast at large [ 2 ]. Southwest India is, thus, one of the most disparate terrains, with extensive colonization in the past and justifies an in-depth genetic study. A few studies utilizing classical markers have been carried out on southern populations [ 5 , 11 , 12 ], including few communities of Karnataka [ 13 , 14 ]. However, sound inferences relating to their genetic structuring and diversity could not be drawn due to low discriminatory power of these markers. Recently, microsatellite markers have gained immense popularity in precisely defining population structure, diversity, affinities, gene flow and other crucial aspects associated with population genetics [ 15 - 21 ] because of the relative expediency, with which a large number of loci and alleles can be typed, facilitating the accumulation of vast data sets that can be readily analyzed with an extensive array of statistical tools [ 22 , 23 ]. These markers also demonstrate high heterozygosity [ 24 ], rendering them highly suitable for carrying out the present study. Among the different caste and tribal groups inhabiting the southwest coast of India, we have selected four predominant Dravidian-speaking communities from Karnataka: Iyengar Brahmin, Lyngayat, Gowda and Muslim, they not only belong to dissimilar groups of the Indian caste hierarchy but also have varied migration histories, conferring them uniqueness and significance from a genetic perspective. The present microsatellite study primarily attempts to understand the genetic structure of the four selected populations and to determine their genetic relationship with other linguistically and ethnically similar groups of southern India and Brahmin groups of northern India. It has been suggested that that despite the linguistic homogeneity in southern India, these populations have remained genetically diversified [ 25 ]. Hence, we sought to determine the role played by geographical location and linguistic affiliation in genetically differentiating Indian populations. Also, as mentioned earlier, the western coast has witnessed colonization from different world populations, we aim to divulge the impact of these past migrations on the gene pool of the present southern populations by discerning their relationship with historically acclaimed and established migrant groups, ethnically represented by European, Hispanic, East Asian and African populations.
Background
The Indian subcontinent is regarded as a natural genetic laboratory, owing to the co-existence and interaction of socio-culturally, linguistically, ethnically and genetically diversified endogamous populations in a geographical terrain. It is believed that the earliest humans leaving Africa for Eurasia might have taken a coastal route across Saudi Arabia, through Iraq, Iran, to Pakistan and finally entered India along the coastlines [ 1 ]. A second wave of migration (~10,000 years ago) brought in Proto-Dravidian Neolithic farmers from Afghanistan, who were later displaced southwards by a large influx of Indo-European speakers ~3500 years ago in to the subcontinent [ 2 , 3 ]. The origin and settlement of the Indian people still remains intriguing, fascinating scientists to explore the impact of these past and modern migrations on the genetic diversity and structure of contemporary populations [ 4 - 6 ].Anthropologically, southern and northern populations are distinct and these differences are further substantiated by (i) the presence of Neolithic sites in this region suggests that Neolithic people of southern India came from north by land and the west-coast by sea [ 7 ], (ii) the southern megaliths resemble closely with those of the Mediterranean and western-Europe, while those from northern India are similar to megaliths found in Iran and Baluchisthan [ 8 ], and (iii) the predominance of Dravidian language in this region as opposed to their secluded occurrence in central Asia and other parts of India, suggests that the Dravidian languages might have originated within India [ 9 ]. It is, thus, of considerable genetic interest to understand the genetic structuring and relationships of southern populations.The present study was carried out on one of the largest southern states, Karnataka, positioned on the southwest coast of India, with a dwelling of about 50 million people. This expanse has been a rich source of prehistoric discoveries dating back to the Paleolithic era that are akin to those seen in Europe [ 7 ]. Karnataka has received continuous gene flow from different caste and linguistic groups residing in the adjoining areas of Maharashtra, Andhra Pradesh and Tamil Nadu [ 10 ], resulting in the congregation of a large number of diverse endogamous groups within this region. Its large coastline of about 400 Km also attracted the Portugese, Dutch and French traders, who were seeking more profitable ventures on the southern coast at large [ 2 ]. Southwest India is, thus, one of the most disparate terrains, with extensive colonization in the past and justifies an in-depth genetic study.A few studies utilizing classical markers have been carried out on southern populations [ 5 , 11 , 12 ], including few communities of Karnataka [ 13 , 14 ]. However, sound inferences relating to their genetic structuring and diversity could not be drawn due to low discriminatory power of these markers. Recently, microsatellite markers have gained immense popularity in precisely defining population structure, diversity, affinities, gene flow and other crucial aspects associated with population genetics [ 15 - 21 ] because of the relative expediency, with which a large number of loci and alleles can be typed, facilitating the accumulation of vast data sets that can be readily analyzed with an extensive array of statistical tools [ 22 , 23 ]. These markers also demonstrate high heterozygosity [ 24 ], rendering them highly suitable for carrying out the present study.Among the different caste and tribal groups inhabiting the southwest coast of India, we have selected four predominant Dravidian-speaking communities from Karnataka: Iyengar Brahmin, Lyngayat, Gowda and Muslim, they not only belong to dissimilar groups of the Indian caste hierarchy but also have varied migration histories, conferring them uniqueness and significance from a genetic perspective. The present microsatellite study primarily attempts to understand the genetic structure of the four selected populations and to determine their genetic relationship with other linguistically and ethnically similar groups of southern India and Brahmin groups of northern India. It has been suggested that that despite the linguistic homogeneity in southern India, these populations have remained genetically diversified [ 25 ]. Hence, we sought to determine the role played by geographical location and linguistic affiliation in genetically differentiating Indian populations. Also, as mentioned earlier, the western coast has witnessed colonization from different world populations, we aim to divulge the impact of these past migrations on the gene pool of the present southern populations by discerning their relationship with historically acclaimed and established migrant groups, ethnically represented by European, Hispanic, East Asian and African populations.Results Allele frequency at 15 STR was used to compute the heterozygosity (observed) for the four studied populations, which varied for each locus, and population but reflected similar values, ranging between 0.724 and 0.797 (Table  1 ). An average G ST  value of 0.009 elucidates the low degree of genetic differentiation in them. However, the G ST  value for the pooled Indian and global populations demonstrated a high value at 2.3% (data not shown). Genetic relationship of studied populations with other similar southern groups; Vanniyar, Gounder, Pallar and Tanjore Kallar [ 26 , 27 ], northern Brahmins belonging to Orissa [ 28 ] and Bihar [ 29 ], and four relevant global ethnic groups: European, Hispanic, African [ 30 ] and East Asian [ 31 ] was divulged by computing DA distances (Table  2 ) and represented using NJ tree (Fig.  1 ). Among the four studied populations, Iyengar, Gowda and Muslim formed a distinct cluster. Although NJ tree clearly depicts the clustering of southern populations, D A  distances indicate that among these groups, Iyengar, Lyngayat and Vanniyar are more similar to the northern Brahmins (0.030). Furthermore, genetic distances emphasize the affinity of Lyngayat with Tanjore Kallar (0.029), Iyengar (0.026) and Vanniyar (0.028). Estimation of relatedness between the southern and global populations shows that all the southern communities formed a separate cluster, nevertheless, genetic distances disclose the affinity of upper caste Indian communities; Iyengar, Lyngayat, Vanniyar, Bihar and Oriya Brahmin with Europeans and East Asians. The Indian populations were most distant to Africans. Table 1 Average heterozygosity and G ST  values for 15 loci in the four studied populations. OBSERVED HETEROZYGOSITY LOCUS G ST BRAHMIN LINGAYAT GOWDA MUSLIM TPOX 0.707 0.581 0.542 0.555 0.010 D3S1358 0.661 0.793 0.559 0.488 0.036 THO1 0.815 0.785 0.678 0.688 0.005 D21S11 0.876 0.857 0.779 0.733 0.005 D18S51 0.907 0.938 0.779 0.888 0.006 PENTA E 0.921 0.876 0.864 0.800 0.014 D5S818 0.692 0.724 0.525 0.733 0.007 D13S317 0.753 0.714 0.745 0.733 0.007 D7S820 0.723 0.734 0.754 0.800 0.005 D16S539 0.861 0.846 0.830 0.777 0.010 CSF1PO 0.723 0.734 0.745 0.733 0.009 PENTA D 0.815 0.755 0.741 0.933 0.006 vWA 0.784 0.734 0.779 0.688 0.002 D8S1179 0.861 0.822 0.745 0.755 0.007 FGA 0.861 0.894 0.803 0.911 0.017 Average 0.797 0.785 0.724 0.747 0.009 Table 2 D A  distance matrix between ten Indian and four global groups based on allele frequency at 15 microsatellites. Pop HS NE MO CA OB IB LY GO MU BB PL VN TK GD HS NE 0.079 MO 0.049 0.123 CA 0.029 0.086 0.07 OB 0.044 0.092 0.052 0.04 IB 0.044 0.091 0.041 0.04 0.03 LY 0.047 0.096 0.047 0.04 0.03 0.026 GO 0.066 0.122 0.072 0.07 0.055 0.036 0.047 MU 0.076 0.118 0.078 0.07 0.066 0.051 0.056 0.054 BB 0.043 0.101 0.052 0.05 0.038 0.031 0.037 0.054 0.068 PL 0.061 0.11 0.067 0.07 0.064 0.05 0.056 0.075 0.077 0.063 VN 0.045 0.105 0.042 0.04 0.034 0.023 0.028 0.039 0.053 0.037 0.049 TK 0.047 0.096 0.053 0.05 0.044 0.028 0.029 0.052 0.062 0.044 0.052 0.032 GD 0.064 0.112 0.064 0.06 0.051 0.036 0.043 0.057 0.073 0.054 0.059 0.032 0.043 Abbreviations used in Table-  Hispanic – HS, African – AF, Asian – AS, European – EU, OriyaBrahmin – OB, Iyengar Brahmin – IB, Lyngayat – LY, Gowda – GO, Muslim – MU, Bihar Brahmin – BB, Pallar – PL, Vanniyar – VN, Tanjore Kallar – TK, Goundar – GD. Figure 1 Neighbor-joining tree depicting the genetic relationship of Karnataka populations with related Indian and global ethnic groups based on 15 STR markers. The regression model (Fig.  2 ), of mean per locus heterozygosity against distance from centroid assumes that when a population experiences same amount of gene flow from a homogenous source, a linear relationship exists between the expected and observed heterozygosity. A change in gene flow directly affects this linear relationship. The R-matrix when applied to the Indian populations assists in understanding the influence of external gene flow and admixture among populations. The higher observed than expected heterozygosity of Iyengar and Lyngayat, placed above the theoretical regression line helps infer that these populations have received more than average external gene flow, which was also observed in Vanniyar, Pallar and Oriya Brahmin. The Gowda and Muslim groups exhibit lower than expected heterozygosity values and fall below the regression line, suggesting lesser admixture in them. Figure 2 Regression plot demonstrating the relatively higher gene flow levels in high-ranking populations of India.  Abbreviations used in figure:  OB-Oriya Brahmin, PL-Pallar, IB-Iyngar Brahmin, VN-Vanniyar, LY-Lyngayat, TK-Tanjorekallar, GD-Goundar, Mu-Muslim, BB-Bihar Brahmin, GO-Gowda. The microsatellite diversity computed using AMOVA revealed that the genetic variation observed in Indian populations was mainly confined to variation amongst individuals (~98%), irrespective of their geographic or linguistic grouping (Table  3 ). The geographical clustering of populations into three regions: north, southwest (Karnataka) and southeast (Tamil Nadu) demonstrated a low variance of 0.29%, p = 0.010 (Table  3a ). As compared to geographical grouping, the linguistic clustering (Indo-Caucasian and Dravidian) exhibited a noticeable increase in the molecular variance between the two groups, 0.65% (p = 0.06, Table  3b ). The genetic diversity among populations within each group remained almost similar at both levels of analysis. Table 3 Genetic differentiation of Indian populations based on AMOVA (a) Geographical grouping Groups in set 1 Source of Variation Percentage Variation Group 1 – North: Bihar and Orissa populations Among groups 0.29 Group 2 – South-west: Karnataka populations Among populations in groups 0.97 Group 3 – South-east: Tamil Nadu Populations Within populations 98.74 (b) Linguistic grouping Groups in set 2 Source of Variation Percentage Variation Group 1 – Indo-European: Orissa and Bihar Among groups 0.69 Group 2 – Dravidian: Southern populations Among populations in groups 0.94 Within populations 98.40
Results
Allele frequency at 15 STR was used to compute the heterozygosity (observed) for the four studied populations, which varied for each locus, and population but reflected similar values, ranging between 0.724 and 0.797 (Table  1 ). An average G ST  value of 0.009 elucidates the low degree of genetic differentiation in them. However, the G ST  value for the pooled Indian and global populations demonstrated a high value at 2.3% (data not shown). Genetic relationship of studied populations with other similar southern groups; Vanniyar, Gounder, Pallar and Tanjore Kallar [ 26 , 27 ], northern Brahmins belonging to Orissa [ 28 ] and Bihar [ 29 ], and four relevant global ethnic groups: European, Hispanic, African [ 30 ] and East Asian [ 31 ] was divulged by computing DA distances (Table  2 ) and represented using NJ tree (Fig.  1 ). Among the four studied populations, Iyengar, Gowda and Muslim formed a distinct cluster. Although NJ tree clearly depicts the clustering of southern populations, D A  distances indicate that among these groups, Iyengar, Lyngayat and Vanniyar are more similar to the northern Brahmins (0.030). Furthermore, genetic distances emphasize the affinity of Lyngayat with Tanjore Kallar (0.029), Iyengar (0.026) and Vanniyar (0.028). Estimation of relatedness between the southern and global populations shows that all the southern communities formed a separate cluster, nevertheless, genetic distances disclose the affinity of upper caste Indian communities; Iyengar, Lyngayat, Vanniyar, Bihar and Oriya Brahmin with Europeans and East Asians. The Indian populations were most distant to Africans.Average heterozygosity and G ST  values for 15 loci in the four studied populations.D A  distance matrix between ten Indian and four global groups based on allele frequency at 15 microsatellites.PopHSNEMOCAOBIBLYGOMUBBPLVNTKGDHSNEMOCAOBIBLYGOMUBBPLVNTKGDAbbreviations used in Table-  Hispanic – HS, African – AF, Asian – AS, European – EU, OriyaBrahmin – OB, Iyengar Brahmin – IB, Lyngayat – LY, Gowda – GO, Muslim – MU, Bihar Brahmin – BB, Pallar – PL, Vanniyar – VN, Tanjore Kallar – TK, Goundar – GD.Abbreviations used in Table-The regression model (Fig.  2 ), of mean per locus heterozygosity against distance from centroid assumes that when a population experiences same amount of gene flow from a homogenous source, a linear relationship exists between the expected and observed heterozygosity. A change in gene flow directly affects this linear relationship. The R-matrix when applied to the Indian populations assists in understanding the influence of external gene flow and admixture among populations. The higher observed than expected heterozygosity of Iyengar and Lyngayat, placed above the theoretical regression line helps infer that these populations have received more than average external gene flow, which was also observed in Vanniyar, Pallar and Oriya Brahmin. The Gowda and Muslim groups exhibit lower than expected heterozygosity values and fall below the regression line, suggesting lesser admixture in them.The microsatellite diversity computed using AMOVA revealed that the genetic variation observed in Indian populations was mainly confined to variation amongst individuals (~98%), irrespective of their geographic or linguistic grouping (Table  3 ). The geographical clustering of populations into three regions: north, southwest (Karnataka) and southeast (Tamil Nadu) demonstrated a low variance of 0.29%, p = 0.010 (Table  3a ). As compared to geographical grouping, the linguistic clustering (Indo-Caucasian and Dravidian) exhibited a noticeable increase in the molecular variance between the two groups, 0.65% (p = 0.06, Table  3b ). The genetic diversity among populations within each group remained almost similar at both levels of analysis.Genetic differentiation of Indian populations based on AMOVA(a) Geographical grouping(b) Linguistic groupingDiscussion In recent years, population genetics has witnessed extensive use of microsatellite markers to understand and evolutionary histories of contemporary human populations [ 17 , 32 - 34 ]. Though, the populations inhabiting south India have played a major role in formation of the Indian gene pool, however, very few genetic studies have been carried out on them. The present study utilizes 15 STRs to provide comprehensive genetic information on four predominant communities inhabiting the southwest coast of India, which may significantly help in understanding the genetic composition of southern populations. Genetic structure of Karnataka populations The most distinctive feature revealed by the fifteen microsatellites was the considerable genetic homogeneity amongst the four diverse caste groups residing in southwest India. The presence of an almost similar allele frequency pattern [ 34 ], suggests that these populations might have a common ancestry or probably experienced very high gene flow during the period of their coexistence. The above finding is further supported by the low genetic differentiation of 1.0% among the studied groups irrespective of their caste and migration histories. The high heterozygosity and rii values in Lyngayat reflect the admixture and stochastic processes experienced by it. The genetic affinity of Lyngayat with other related southern caste populations, like, Iyengar, Vanniyar and Tanjore Kallar reiterates its heterogeneous past. It is noteworthy that although the southern populations exhibited higher affinity amongst each other, the high-ranking populations, like, Iyengar, Lyngayat and Vanniyar also displayed some genetic similarity to Brahmins from Bihar and Orissa, indicating that the gene pool of Iyengar and Lyngayat probably consists of genetic inputs from both southern and northern groups. However, strong conclusions cannot be drawn due to low genetic differentiation among the studied populations. Though the Gowda is known to have moved in to Karnataka from the adjoining area of Tamil Nadu, our study reveals that Gowda cluster with the studied populations and not with Tamil groups. The low hetetozygosity and high rii values of Gowda implies that it might have differentiated as a result of stochastic processes. Furthermore, the relatively lower heterozygosity and admixture levels of Gowda and Muslim might be attributed to the socio-cultural practice of consanguineous marriages in them. The Muslim group was found to be genetically similar to local populations. Regional conversions from diverse castes that occurred during the period of Islamic dominance might elucidate the more or less identical genetic relationship between Muslims and other studied groups. The microsatellite study emphasizes the genetic similarity among the Karnataka populations, with the lack of any strong caste or religious bias in them. Analysis of genetic variance AMOVA test strongly suggests that genetic diversity among the southern populations was mainly confined to intra-population variation, further emphasizing the genetic homogeneity in them. Analysis using different genetic markers corroborate with our finding that the genetic diversity in human populations can be mainly attributed to variation within populations [ 4 , 17 , 19 , 34 , 36 , 37 ]. An exploration of the genetic differentiation based on geographical grouping of populations discloses the genetic similarity among populations residing in a region. Nevertheless, the geographic affinity was comparably lesser to that observed within the two linguistic families, viz., Dravidian and Indo-European. Our finding provides evidence to the strong linguistic affinity prevailing amongst the Dravidian speaking populations and imparts them genetic distinctness from the Indo-European linguistic group. Even though prior studies have indicated that genetic clusters often correspond closely to predefined regional and linguistic groups [ 34 ], AMOVA suggests that caste system along with geographical contiguity are not ideal platforms for differentiating the analyzed Indian populations. It must, however, be acknowledged that use of less number of polymorphisms in this study might plausibly have led to the greater influence of linguistic affiliation on these populations rather than geographical proximity. Genetic affinity with global populations The genetic differentiation of the studied populations with relevant global migrant groups was estimated to be 2.3%, relatively lower than the 9% observed in another similar study [ 16 ], which had used a different set of microsatellite markers. Sampling from a confined area, as well as the use of lesser number of loci might have contributed to this apparent difference in the results. The southern populations formed a separate cluster from the world populations. Molecular studies on Indian populations using diverse markers (nuclear, mtDNA and Y-chromosome) have demonstrated that the upper caste populations have higher semblance with Europeans than Asians [ 26 ]. Intriguingly, in the present study, communities belonging to the upper strata of the Hindu caste hierarchy, i.e., Iyengar, Lyngayat, Vanniyar and northern Brahmins, displayed almost identical genetic affinity with both Europeans and East Asians. Therefore, all though it is believed that south India remained isolated and cushioned from the foreign invasions, the southern populations, especially, the high-ranking groups might have genetically admixed with migrant groups that entered via the west coast and north. Further exploration of their relationship is essential before drawing concrete conclusions. A more comprehensive picture would emerge on analysis of mtDNA and Y chromosome markers.
Discussion
In recent years, population genetics has witnessed extensive use of microsatellite markers to understand and evolutionary histories of contemporary human populations [ 17 , 32 - 34 ]. Though, the populations inhabiting south India have played a major role in formation of the Indian gene pool, however, very few genetic studies have been carried out on them. The present study utilizes 15 STRs to provide comprehensive genetic information on four predominant communities inhabiting the southwest coast of India, which may significantly help in understanding the genetic composition of southern populations.Genetic structure of Karnataka populations The most distinctive feature revealed by the fifteen microsatellites was the considerable genetic homogeneity amongst the four diverse caste groups residing in southwest India. The presence of an almost similar allele frequency pattern [ 34 ], suggests that these populations might have a common ancestry or probably experienced very high gene flow during the period of their coexistence. The above finding is further supported by the low genetic differentiation of 1.0% among the studied groups irrespective of their caste and migration histories. The high heterozygosity and rii values in Lyngayat reflect the admixture and stochastic processes experienced by it. The genetic affinity of Lyngayat with other related southern caste populations, like, Iyengar, Vanniyar and Tanjore Kallar reiterates its heterogeneous past. It is noteworthy that although the southern populations exhibited higher affinity amongst each other, the high-ranking populations, like, Iyengar, Lyngayat and Vanniyar also displayed some genetic similarity to Brahmins from Bihar and Orissa, indicating that the gene pool of Iyengar and Lyngayat probably consists of genetic inputs from both southern and northern groups. However, strong conclusions cannot be drawn due to low genetic differentiation among the studied populations. Though the Gowda is known to have moved in to Karnataka from the adjoining area of Tamil Nadu, our study reveals that Gowda cluster with the studied populations and not with Tamil groups. The low hetetozygosity and high rii values of Gowda implies that it might have differentiated as a result of stochastic processes. Furthermore, the relatively lower heterozygosity and admixture levels of Gowda and Muslim might be attributed to the socio-cultural practice of consanguineous marriages in them. The Muslim group was found to be genetically similar to local populations. Regional conversions from diverse castes that occurred during the period of Islamic dominance might elucidate the more or less identical genetic relationship between Muslims and other studied groups. The microsatellite study emphasizes the genetic similarity among the Karnataka populations, with the lack of any strong caste or religious bias in them.
Genetic structure of Karnataka populations
The most distinctive feature revealed by the fifteen microsatellites was the considerable genetic homogeneity amongst the four diverse caste groups residing in southwest India. The presence of an almost similar allele frequency pattern [ 34 ], suggests that these populations might have a common ancestry or probably experienced very high gene flow during the period of their coexistence. The above finding is further supported by the low genetic differentiation of 1.0% among the studied groups irrespective of their caste and migration histories. The high heterozygosity and rii values in Lyngayat reflect the admixture and stochastic processes experienced by it. The genetic affinity of Lyngayat with other related southern caste populations, like, Iyengar, Vanniyar and Tanjore Kallar reiterates its heterogeneous past. It is noteworthy that although the southern populations exhibited higher affinity amongst each other, the high-ranking populations, like, Iyengar, Lyngayat and Vanniyar also displayed some genetic similarity to Brahmins from Bihar and Orissa, indicating that the gene pool of Iyengar and Lyngayat probably consists of genetic inputs from both southern and northern groups. However, strong conclusions cannot be drawn due to low genetic differentiation among the studied populations. Though the Gowda is known to have moved in to Karnataka from the adjoining area of Tamil Nadu, our study reveals that Gowda cluster with the studied populations and not with Tamil groups. The low hetetozygosity and high rii values of Gowda implies that it might have differentiated as a result of stochastic processes. Furthermore, the relatively lower heterozygosity and admixture levels of Gowda and Muslim might be attributed to the socio-cultural practice of consanguineous marriages in them. The Muslim group was found to be genetically similar to local populations. Regional conversions from diverse castes that occurred during the period of Islamic dominance might elucidate the more or less identical genetic relationship between Muslims and other studied groups. The microsatellite study emphasizes the genetic similarity among the Karnataka populations, with the lack of any strong caste or religious bias in them.Analysis of genetic variance AMOVA test strongly suggests that genetic diversity among the southern populations was mainly confined to intra-population variation, further emphasizing the genetic homogeneity in them. Analysis using different genetic markers corroborate with our finding that the genetic diversity in human populations can be mainly attributed to variation within populations [ 4 , 17 , 19 , 34 , 36 , 37 ]. An exploration of the genetic differentiation based on geographical grouping of populations discloses the genetic similarity among populations residing in a region. Nevertheless, the geographic affinity was comparably lesser to that observed within the two linguistic families, viz., Dravidian and Indo-European. Our finding provides evidence to the strong linguistic affinity prevailing amongst the Dravidian speaking populations and imparts them genetic distinctness from the Indo-European linguistic group. Even though prior studies have indicated that genetic clusters often correspond closely to predefined regional and linguistic groups [ 34 ], AMOVA suggests that caste system along with geographical contiguity are not ideal platforms for differentiating the analyzed Indian populations. It must, however, be acknowledged that use of less number of polymorphisms in this study might plausibly have led to the greater influence of linguistic affiliation on these populations rather than geographical proximity.
Analysis of genetic variance
AMOVA test strongly suggests that genetic diversity among the southern populations was mainly confined to intra-population variation, further emphasizing the genetic homogeneity in them. Analysis using different genetic markers corroborate with our finding that the genetic diversity in human populations can be mainly attributed to variation within populations [ 4 , 17 , 19 , 34 , 36 , 37 ].An exploration of the genetic differentiation based on geographical grouping of populations discloses the genetic similarity among populations residing in a region. Nevertheless, the geographic affinity was comparably lesser to that observed within the two linguistic families, viz., Dravidian and Indo-European. Our finding provides evidence to the strong linguistic affinity prevailing amongst the Dravidian speaking populations and imparts them genetic distinctness from the Indo-European linguistic group. Even though prior studies have indicated that genetic clusters often correspond closely to predefined regional and linguistic groups [ 34 ], AMOVA suggests that caste system along with geographical contiguity are not ideal platforms for differentiating the analyzed Indian populations. It must, however, be acknowledged that use of less number of polymorphisms in this study might plausibly have led to the greater influence of linguistic affiliation on these populations rather than geographical proximity.Genetic affinity with global populations The genetic differentiation of the studied populations with relevant global migrant groups was estimated to be 2.3%, relatively lower than the 9% observed in another similar study [ 16 ], which had used a different set of microsatellite markers. Sampling from a confined area, as well as the use of lesser number of loci might have contributed to this apparent difference in the results. The southern populations formed a separate cluster from the world populations. Molecular studies on Indian populations using diverse markers (nuclear, mtDNA and Y-chromosome) have demonstrated that the upper caste populations have higher semblance with Europeans than Asians [ 26 ]. Intriguingly, in the present study, communities belonging to the upper strata of the Hindu caste hierarchy, i.e., Iyengar, Lyngayat, Vanniyar and northern Brahmins, displayed almost identical genetic affinity with both Europeans and East Asians. Therefore, all though it is believed that south India remained isolated and cushioned from the foreign invasions, the southern populations, especially, the high-ranking groups might have genetically admixed with migrant groups that entered via the west coast and north. Further exploration of their relationship is essential before drawing concrete conclusions. A more comprehensive picture would emerge on analysis of mtDNA and Y chromosome markers.
Genetic affinity with global populations
The genetic differentiation of the studied populations with relevant global migrant groups was estimated to be 2.3%, relatively lower than the 9% observed in another similar study [ 16 ], which had used a different set of microsatellite markers. Sampling from a confined area, as well as the use of lesser number of loci might have contributed to this apparent difference in the results. The southern populations formed a separate cluster from the world populations. Molecular studies on Indian populations using diverse markers (nuclear, mtDNA and Y-chromosome) have demonstrated that the upper caste populations have higher semblance with Europeans than Asians [ 26 ]. Intriguingly, in the present study, communities belonging to the upper strata of the Hindu caste hierarchy, i.e., Iyengar, Lyngayat, Vanniyar and northern Brahmins, displayed almost identical genetic affinity with both Europeans and East Asians. Therefore, all though it is believed that south India remained isolated and cushioned from the foreign invasions, the southern populations, especially, the high-ranking groups might have genetically admixed with migrant groups that entered via the west coast and north. Further exploration of their relationship is essential before drawing concrete conclusions. A more comprehensive picture would emerge on analysis of mtDNA and Y chromosome markers.Methods The populations The populations selected in this study comprise of three major Hindu castes-Iyengar, Lyngayat, Gowda and a Muslim community, inhabiting the southwest coastal terrain of Karnataka (11.3 – 18.45°N latitudes and 74.12 – 78.40°E longitudes). All the populations belong to the Dravidian linguistic family and are speakers of the local dialect, Kannada, but differ in caste hierarchy and socio-religious practices. Consanguineous marriages have been reported in Karnataka, with inbreeding levels of the order 0.020 to 0.033, in general [ 38 ]. Iyengar hold a high position in the Indian caste hierarchy and sporadic accounts on Brahmin, suggests that they primarily migrated from the upper Gangetic plains to southern India. Nonetheless, few bioanthropological studies have revealed that morphologically Brahmins of a geographical region are similar to the local groups. Lyngayat community was initially formed, as a religious cult by the amalgamation of people from different castes and geographical regions but later developed into a distinct community practicing strict marriage endogamy with social sub-divisions such as clans, sub-castes and sects [ 10 ]. Gowda is a low ranking agriculturist caste group that typically exhibits the Dravidian socio-cultural characteristic of consanguineous marriage. It is believed to have moved in from the adjoining area of Tamil Nadu. Muslim is a linguistically heterogeneous, complex religio-ethnic group, [ 10 ]. It is believed that the invasion of Turks, Afghans (A.D 998–1030) and Moghals during the 15 th  century, introduced new genes only in northern India, suggesting that Muslims from Southern India are mainly local converts [ 3 ]. Micosatellite loci studied The 15 STR marker set analyzed in this study consists of thirteen tetra nucleotide repeat loci: D3S1358, THO1, D21S11, D18S51, D5S818, D13S317, D7S820, D16S539, CSF1PO, vWA, D8S179, TPOX, FGA and two penta nucleotide repeat loci: Penta D, Penta E. Their repeat size makes them less prone to slippage of polymerase during enzymatic amplification compared to the dinucleotide repeats, allowing unambiguous typing [ 20 ]. The 15 selected loci are situated on 13 different chromosomes, with D5S818 and CSF1PO being present on chromosome 5 and Penta D and D21S11, located on chromosome 21. The alleles across the loci are substantially unlinked, making them suitable for analyzing inter and intra-population genetic diversity. STR Typing The blood samples were collected from unrelated individuals belonging to – Iyengar (65), Lyngayat (98), Gowda (59) and Muslim (45) communities, residing in different districts of Karnataka. DNA was extracted from blood by the phenol-chloroform method [ 40 ], followed by quantitation using the QuantiBlot™ kit (Perkin-Elmer, Foster City, CA, USA). Two nanogram of the isolated DNA was used as template for the PCR amplification of the 15 STRs using the PowerPlex™16 kit (Promega Corp., Wisconsin Madison, USA). Raw data were collected with the GeneScan™ software, Ver. 3.2.1 (Applied Biosystems, Foster City, CA, USA) and typed using the PowerTyper™ 16 Macro (Promega Corp., Wisconsin Madison, USA). Statistical Analysis Allele frequencies of the 15 STR loci were calculated using the gene counting method [ 40 ]. The genetic diversity (G ST ), observed heterozygosity and pairwise genetic distances (DA) were computed using allele frequencies [ 42 ]. The DA distance is least affected by sample size and can precisely obtain correct phylogenetic trees under various evolutionary conditions [ 43 ]. Neighbor-joining trees were constructed using DA distances [ 44 ], and its robustness was established by bootstrap resampling procedures. Analysis of molecular variance (AMOVA) was performed using the Arlequin Ver. 2.00 package [ 45 ]. Two levels of analysis were performed to explore the microsatellite diversity among the four studied populations along with six other socio-culturally similar groups inhabiting different regions of India. At the first level, three geographical groups were constructed: (1) north (2) southwest: Karnataka and, (3) southeast: Tamil Nadu, to estimate the genetic variance among populations from diverse geographical regions. The second set of analysis was aimed at investigating the genetic diversity between the Dravidian and Indo-European linguistic family. To assess the gene flow experienced by these populations, the rii value, i.e., the genetic distance of a population from the centroid was calculated using the regression model [ 46 ]. This model utilizes the heterozygosity of each population and the distance from the centroid as the arithmetic mean of allele frequencies: where, r ii  is the distance from the centroid, p i  is the frequency of the allele in i th  population and   is the mean allelic frequency.
Methods
The populations The populations selected in this study comprise of three major Hindu castes-Iyengar, Lyngayat, Gowda and a Muslim community, inhabiting the southwest coastal terrain of Karnataka (11.3 – 18.45°N latitudes and 74.12 – 78.40°E longitudes). All the populations belong to the Dravidian linguistic family and are speakers of the local dialect, Kannada, but differ in caste hierarchy and socio-religious practices. Consanguineous marriages have been reported in Karnataka, with inbreeding levels of the order 0.020 to 0.033, in general [ 38 ]. Iyengar hold a high position in the Indian caste hierarchy and sporadic accounts on Brahmin, suggests that they primarily migrated from the upper Gangetic plains to southern India. Nonetheless, few bioanthropological studies have revealed that morphologically Brahmins of a geographical region are similar to the local groups. Lyngayat community was initially formed, as a religious cult by the amalgamation of people from different castes and geographical regions but later developed into a distinct community practicing strict marriage endogamy with social sub-divisions such as clans, sub-castes and sects [ 10 ]. Gowda is a low ranking agriculturist caste group that typically exhibits the Dravidian socio-cultural characteristic of consanguineous marriage. It is believed to have moved in from the adjoining area of Tamil Nadu. Muslim is a linguistically heterogeneous, complex religio-ethnic group, [ 10 ]. It is believed that the invasion of Turks, Afghans (A.D 998–1030) and Moghals during the 15 th  century, introduced new genes only in northern India, suggesting that Muslims from Southern India are mainly local converts [ 3 ].
The populations
The populations selected in this study comprise of three major Hindu castes-Iyengar, Lyngayat, Gowda and a Muslim community, inhabiting the southwest coastal terrain of Karnataka (11.3 – 18.45°N latitudes and 74.12 – 78.40°E longitudes). All the populations belong to the Dravidian linguistic family and are speakers of the local dialect, Kannada, but differ in caste hierarchy and socio-religious practices. Consanguineous marriages have been reported in Karnataka, with inbreeding levels of the order 0.020 to 0.033, in general [ 38 ].Iyengar hold a high position in the Indian caste hierarchy and sporadic accounts on Brahmin, suggests that they primarily migrated from the upper Gangetic plains to southern India. Nonetheless, few bioanthropological studies have revealed that morphologically Brahmins of a geographical region are similar to the local groups.Lyngayat community was initially formed, as a religious cult by the amalgamation of people from different castes and geographical regions but later developed into a distinct community practicing strict marriage endogamy with social sub-divisions such as clans, sub-castes and sects [ 10 ].Gowda is a low ranking agriculturist caste group that typically exhibits the Dravidian socio-cultural characteristic of consanguineous marriage. It is believed to have moved in from the adjoining area of Tamil Nadu.Muslim is a linguistically heterogeneous, complex religio-ethnic group, [ 10 ]. It is believed that the invasion of Turks, Afghans (A.D 998–1030) and Moghals during the 15 th  century, introduced new genes only in northern India, suggesting that Muslims from Southern India are mainly local converts [ 3 ].Micosatellite loci studied The 15 STR marker set analyzed in this study consists of thirteen tetra nucleotide repeat loci: D3S1358, THO1, D21S11, D18S51, D5S818, D13S317, D7S820, D16S539, CSF1PO, vWA, D8S179, TPOX, FGA and two penta nucleotide repeat loci: Penta D, Penta E. Their repeat size makes them less prone to slippage of polymerase during enzymatic amplification compared to the dinucleotide repeats, allowing unambiguous typing [ 20 ]. The 15 selected loci are situated on 13 different chromosomes, with D5S818 and CSF1PO being present on chromosome 5 and Penta D and D21S11, located on chromosome 21. The alleles across the loci are substantially unlinked, making them suitable for analyzing inter and intra-population genetic diversity.
Micosatellite loci studied
The 15 STR marker set analyzed in this study consists of thirteen tetra nucleotide repeat loci: D3S1358, THO1, D21S11, D18S51, D5S818, D13S317, D7S820, D16S539, CSF1PO, vWA, D8S179, TPOX, FGA and two penta nucleotide repeat loci: Penta D, Penta E. Their repeat size makes them less prone to slippage of polymerase during enzymatic amplification compared to the dinucleotide repeats, allowing unambiguous typing [ 20 ]. The 15 selected loci are situated on 13 different chromosomes, with D5S818 and CSF1PO being present on chromosome 5 and Penta D and D21S11, located on chromosome 21. The alleles across the loci are substantially unlinked, making them suitable for analyzing inter and intra-population genetic diversity.STR Typing The blood samples were collected from unrelated individuals belonging to – Iyengar (65), Lyngayat (98), Gowda (59) and Muslim (45) communities, residing in different districts of Karnataka. DNA was extracted from blood by the phenol-chloroform method [ 40 ], followed by quantitation using the QuantiBlot™ kit (Perkin-Elmer, Foster City, CA, USA). Two nanogram of the isolated DNA was used as template for the PCR amplification of the 15 STRs using the PowerPlex™16 kit (Promega Corp., Wisconsin Madison, USA). Raw data were collected with the GeneScan™ software, Ver. 3.2.1 (Applied Biosystems, Foster City, CA, USA) and typed using the PowerTyper™ 16 Macro (Promega Corp., Wisconsin Madison, USA).
STR Typing
The blood samples were collected from unrelated individuals belonging to – Iyengar (65), Lyngayat (98), Gowda (59) and Muslim (45) communities, residing in different districts of Karnataka. DNA was extracted from blood by the phenol-chloroform method [ 40 ], followed by quantitation using the QuantiBlot™ kit (Perkin-Elmer, Foster City, CA, USA). Two nanogram of the isolated DNA was used as template for the PCR amplification of the 15 STRs using the PowerPlex™16 kit (Promega Corp., Wisconsin Madison, USA). Raw data were collected with the GeneScan™ software, Ver. 3.2.1 (Applied Biosystems, Foster City, CA, USA) and typed using the PowerTyper™ 16 Macro (Promega Corp., Wisconsin Madison, USA).Statistical Analysis Allele frequencies of the 15 STR loci were calculated using the gene counting method [ 40 ]. The genetic diversity (G ST ), observed heterozygosity and pairwise genetic distances (DA) were computed using allele frequencies [ 42 ]. The DA distance is least affected by sample size and can precisely obtain correct phylogenetic trees under various evolutionary conditions [ 43 ]. Neighbor-joining trees were constructed using DA distances [ 44 ], and its robustness was established by bootstrap resampling procedures. Analysis of molecular variance (AMOVA) was performed using the Arlequin Ver. 2.00 package [ 45 ]. Two levels of analysis were performed to explore the microsatellite diversity among the four studied populations along with six other socio-culturally similar groups inhabiting different regions of India. At the first level, three geographical groups were constructed: (1) north (2) southwest: Karnataka and, (3) southeast: Tamil Nadu, to estimate the genetic variance among populations from diverse geographical regions. The second set of analysis was aimed at investigating the genetic diversity between the Dravidian and Indo-European linguistic family. To assess the gene flow experienced by these populations, the rii value, i.e., the genetic distance of a population from the centroid was calculated using the regression model [ 46 ]. This model utilizes the heterozygosity of each population and the distance from the centroid as the arithmetic mean of allele frequencies: where, r ii  is the distance from the centroid, p i  is the frequency of the allele in i th  population and   is the mean allelic frequency.
Statistical Analysis
Allele frequencies of the 15 STR loci were calculated using the gene counting method [ 40 ]. The genetic diversity (G ST ), observed heterozygosity and pairwise genetic distances (DA) were computed using allele frequencies [ 42 ]. The DA distance is least affected by sample size and can precisely obtain correct phylogenetic trees under various evolutionary conditions [ 43 ]. Neighbor-joining trees were constructed using DA distances [ 44 ], and its robustness was established by bootstrap resampling procedures.Analysis of molecular variance (AMOVA) was performed using the Arlequin Ver. 2.00 package [ 45 ]. Two levels of analysis were performed to explore the microsatellite diversity among the four studied populations along with six other socio-culturally similar groups inhabiting different regions of India. At the first level, three geographical groups were constructed: (1) north (2) southwest: Karnataka and, (3) southeast: Tamil Nadu, to estimate the genetic variance among populations from diverse geographical regions. The second set of analysis was aimed at investigating the genetic diversity between the Dravidian and Indo-European linguistic family.To assess the gene flow experienced by these populations, the rii value, i.e., the genetic distance of a population from the centroid was calculated using the regression model [ 46 ]. This model utilizes the heterozygosity of each population and the distance from the centroid as the arithmetic mean of allele frequencies:where, r ii  is the distance from the centroid, p i  is the frequency of the allele in i th  population and   is the mean allelic frequency.List of abbreviations STR – Short Tandem Repeat AMOVA – Analysis of Molecular Variance NJ tree – Neighbor-Joining tree
List of abbreviations
STR – Short Tandem RepeatAMOVA – Analysis of Molecular VarianceNJ tree – Neighbor-Joining treeAuthors' contributions RR carried out the molecular studies, analyzed the genetic data and drafted the manuscript. VKK participated in the design, conceiving and preparation of manuscript. Both authors read and approved the final manuscript.
Authors' contributions
RR carried out the molecular studies, analyzed the genetic data and drafted the manuscript. VKK participated in the design, conceiving and preparation of manuscript. Both authors read and approved the final manuscript.


Background Increased hepatic iron is assumed to potentiate progression towards liver fibrosis in chronic hepatitis C virus (HCV) infection. In this study we have evaluated the potentiating effect of marked hepatic iron overload and chronic HCV infection on hepatic fibrosis in thalassemic patients.
Background
Increased hepatic iron is assumed to potentiate progression towards liver fibrosis in chronic hepatitis C virus (HCV) infection. In this study we have evaluated the potentiating effect of marked hepatic iron overload and chronic HCV infection on hepatic fibrosis in thalassemic patients.Methods Liver biopsies of one group of patients with beta thalassemia major and chronic HCV infection (group 1) was compared with two groups of patients (groups 2&3) with either chronic HCV infection or thalassemia major, respectively (20 patients in each group). Necroinflammation, fibrosis, and iron overload were graded and compared.
Methods
Liver biopsies of one group of patients with beta thalassemia major and chronic HCV infection (group 1) was compared with two groups of patients (groups 2&3) with either chronic HCV infection or thalassemia major, respectively (20 patients in each group). Necroinflammation, fibrosis, and iron overload were graded and compared.Results Stage of fibrosis in group 1 patients was significantly higher than the other two groups (p < 0.05). Necroinflammatory grade was significantly lower, but iron score was significantly higher in thalassemic patients (group 3) in comparison to groups 1 and 2 (p < 0.05).
Results
Stage of fibrosis in group 1 patients was significantly higher than the other two groups (p < 0.05). Necroinflammatory grade was significantly lower, but iron score was significantly higher in thalassemic patients (group 3) in comparison to groups 1 and 2 (p < 0.05).Conclusion Our results indicate that marked liver iron overload and HCV infection in thalassemic patients have potentiating effect on hepatic fibrogenesis.
Conclusion
Our results indicate that marked liver iron overload and HCV infection in thalassemic patients have potentiating effect on hepatic fibrogenesis.

Background Thalassemia major is an inherited disorder particularly common in people of Mediterranean, African, and South-east Asian ancestry. It is characterized by decreased production of beta chain of hemoglobin. Clinical features result from anemia, markedly expanded marrow space, and transfusional and absorptive iron overload. In these patients iron overload is often inevitable, especially when iron chelating agents are not used properly [ 1 ]. Hepatic iron overload leads to different degrees of liver fibrosis, the severity of which is closely correlated with the severity of liver iron overload [ 2 ]. The pattern of iron deposition seen in the initial stages of thalassemia major is preferentially sinusoidal with a more or less diffuse distribution within the acinus. With significant loading, hepatocytes, bile duct epithelia, and fibrous tissue of portal tracts or septa will also show iron deposition [ 3 ]. HCV is responsible for 80–90% of post-transfusional cases of hepatitis in patients who have received blood transfusion(s) prior to the introduction of routine blood products screening in 1990 [ 4 ]. More than 75% of HCV infections become chronic and up to 20–30% progress to cirrhosis [ 3 - 5 ]. Increased hepatic iron may potentiate progression towards liver fibrosis in chronic HCV infection, and may contribute to poor response to interferon therapy [ 3 , 6 , 7 ]. Acceleration of hepatic fibrosis in patients with combined hereditary hemochromatosis (HH) and chronic hepatitis C infection has also been shown [ 7 ]. Nevertheless, the pattern of iron deposition in HH is initially hepatocellular and different from that of thalassemia major. In this study we have evaluated the potentiating effect of marked hepatic iron overload and chronic HCV infection on hepatic fibrosis in thalassemic patients. To the best of our knowledge this synergic effect has been studied just in another study on bone marrow transplanted thalassemic patients [ 8 ].
Background
Thalassemia major is an inherited disorder particularly common in people of Mediterranean, African, and South-east Asian ancestry. It is characterized by decreased production of beta chain of hemoglobin. Clinical features result from anemia, markedly expanded marrow space, and transfusional and absorptive iron overload. In these patients iron overload is often inevitable, especially when iron chelating agents are not used properly [ 1 ]. Hepatic iron overload leads to different degrees of liver fibrosis, the severity of which is closely correlated with the severity of liver iron overload [ 2 ]. The pattern of iron deposition seen in the initial stages of thalassemia major is preferentially sinusoidal with a more or less diffuse distribution within the acinus. With significant loading, hepatocytes, bile duct epithelia, and fibrous tissue of portal tracts or septa will also show iron deposition [ 3 ].HCV is responsible for 80–90% of post-transfusional cases of hepatitis in patients who have received blood transfusion(s) prior to the introduction of routine blood products screening in 1990 [ 4 ]. More than 75% of HCV infections become chronic and up to 20–30% progress to cirrhosis [ 3 - 5 ].Increased hepatic iron may potentiate progression towards liver fibrosis in chronic HCV infection, and may contribute to poor response to interferon therapy [ 3 , 6 , 7 ]. Acceleration of hepatic fibrosis in patients with combined hereditary hemochromatosis (HH) and chronic hepatitis C infection has also been shown [ 7 ]. Nevertheless, the pattern of iron deposition in HH is initially hepatocellular and different from that of thalassemia major. In this study we have evaluated the potentiating effect of marked hepatic iron overload and chronic HCV infection on hepatic fibrosis in thalassemic patients. To the best of our knowledge this synergic effect has been studied just in another study on bone marrow transplanted thalassemic patients [ 8 ].Methods A retrospective cross-sectional study was performed on sixty patients in three different groups as outlined below: Group 1: Twenty patients (10 males, 10 females) with the diagnosis of thalassemia major and chronic HCV infection (BTM/CHI). The only risk factor of HCV infection in these patients was blood transfusion before 1990. Group 2: Twenty patients (13 males, 7 females) with chronic HCV infection (CHI). The route of infection and the duration of disease were not known in most of the patients. Group 3: Twenty patients (10 males, 10 females) with thalassemia major. Thalassemia major was diagnosed by appropriate clinical and laboratory findings, and confirmed by hemoglobin electrophoresis. All the thalassemic patients had received multiple blood transfusions since childhood, and only those with at least moderate hepatic iron overload (2/4 based on Marx and Sindram hepatic iron scoring [ 9 ]) were included. CHI was confirmed by positive anti-HCV (enzyme-linked immunosorbant assay), a positive HCV RNA by polymerase chain reaction and appropriate findings on liver biopsy. Patients with history of alcohol intake, smoking, positive HIV serology, positive HBs antigen or HBc antibody, and other liver diseases (e.g., autoimmune hepatitis, drug hepatotoxicity, Wilson's disease, alpha-1 antitrypsin deficiency, hereditary hemochromatosis) were excluded from all the three groups. All of the patients whose liver biopsies were submitted to the Central Pathology Department of Imam Khomeini Hospital (a Terhan University of Medical Sciences affiliated hospital) between April 2001 and January 2004 were retrieved. During this period, there were only 20 thalassemic patients with moderate to marked hepatic iron overload, so all of them were included in our study. Since the sex and age distribution of patients with thalassemia major and BTM/CHI were relatively the same, 20 patients with BTM/CHI and at least moderate hepatic iron overload were randomly selected from all the patients with this diagnosis. On the other hand, patients with CHI had a considerably higher age, so to match the age of the patients, we selected 20 of the youngest patients. For all the sixty patients, we had hematoxylin and eosin, Masson's trichrome and Perls' Prussian blue stains. All the slides were reviewed by a single pathologist (FAA) who was blind to the diagnoses. The necroinflammation, fibrosis, and iron deposition were scored using modified Hepatitis Activity Index (HAI) grading, modified HAI staging, and Marx/Sindram hepatic iron scoring systems, respectively [ 9 , 10 ]. Presence or absence of macrovesicular steatosis was also assessed. Statistical analysis The results are presented as mean value +/- standard deviation (for age), median and range values (for modified HAI grade, stage, and iron score) and percentages (for steatosis). Group comparisons were made using the two-tailed independent Student's t-test for age and two-sided Fisher's exact or Chi 2  tests for other variables with p < 0.05 considered to be significant. For each patient with BTM/CHI (doubly exposed cases), there were two different unexposed cases, i.e. thalassemic cases (unexposed to HCV) and CHI cases (unexposed to iron overload). Considering "disease" as higher stages of fibrosis (modified HAI stage ≥ 3), our guesstimate of the expected frequency of disease in unexposed and exposed patients were about 25% and 75%, respectively. So the sample size was calculated as about 20 patients for each group with confidence level of 95% and power of 80%.
Methods
A retrospective cross-sectional study was performed on sixty patients in three different groups as outlined below:Group 1: Twenty patients (10 males, 10 females) with the diagnosis of thalassemia major and chronic HCV infection (BTM/CHI). The only risk factor of HCV infection in these patients was blood transfusion before 1990.Group 2: Twenty patients (13 males, 7 females) with chronic HCV infection (CHI). The route of infection and the duration of disease were not known in most of the patients.Group 3: Twenty patients (10 males, 10 females) with thalassemia major.Thalassemia major was diagnosed by appropriate clinical and laboratory findings, and confirmed by hemoglobin electrophoresis. All the thalassemic patients had received multiple blood transfusions since childhood, and only those with at least moderate hepatic iron overload (2/4 based on Marx and Sindram hepatic iron scoring [ 9 ]) were included.CHI was confirmed by positive anti-HCV (enzyme-linked immunosorbant assay), a positive HCV RNA by polymerase chain reaction and appropriate findings on liver biopsy.Patients with history of alcohol intake, smoking, positive HIV serology, positive HBs antigen or HBc antibody, and other liver diseases (e.g., autoimmune hepatitis, drug hepatotoxicity, Wilson's disease, alpha-1 antitrypsin deficiency, hereditary hemochromatosis) were excluded from all the three groups.All of the patients whose liver biopsies were submitted to the Central Pathology Department of Imam Khomeini Hospital (a Terhan University of Medical Sciences affiliated hospital) between April 2001 and January 2004 were retrieved. During this period, there were only 20 thalassemic patients with moderate to marked hepatic iron overload, so all of them were included in our study. Since the sex and age distribution of patients with thalassemia major and BTM/CHI were relatively the same, 20 patients with BTM/CHI and at least moderate hepatic iron overload were randomly selected from all the patients with this diagnosis. On the other hand, patients with CHI had a considerably higher age, so to match the age of the patients, we selected 20 of the youngest patients.For all the sixty patients, we had hematoxylin and eosin, Masson's trichrome and Perls' Prussian blue stains. All the slides were reviewed by a single pathologist (FAA) who was blind to the diagnoses. The necroinflammation, fibrosis, and iron deposition were scored using modified Hepatitis Activity Index (HAI) grading, modified HAI staging, and Marx/Sindram hepatic iron scoring systems, respectively [ 9 , 10 ]. Presence or absence of macrovesicular steatosis was also assessed.Statistical analysis The results are presented as mean value +/- standard deviation (for age), median and range values (for modified HAI grade, stage, and iron score) and percentages (for steatosis). Group comparisons were made using the two-tailed independent Student's t-test for age and two-sided Fisher's exact or Chi 2  tests for other variables with p < 0.05 considered to be significant. For each patient with BTM/CHI (doubly exposed cases), there were two different unexposed cases, i.e. thalassemic cases (unexposed to HCV) and CHI cases (unexposed to iron overload). Considering "disease" as higher stages of fibrosis (modified HAI stage ≥ 3), our guesstimate of the expected frequency of disease in unexposed and exposed patients were about 25% and 75%, respectively. So the sample size was calculated as about 20 patients for each group with confidence level of 95% and power of 80%.
Statistical analysis
The results are presented as mean value +/- standard deviation (for age), median and range values (for modified HAI grade, stage, and iron score) and percentages (for steatosis). Group comparisons were made using the two-tailed independent Student's t-test for age and two-sided Fisher's exact or Chi 2  tests for other variables with p < 0.05 considered to be significant.For each patient with BTM/CHI (doubly exposed cases), there were two different unexposed cases, i.e. thalassemic cases (unexposed to HCV) and CHI cases (unexposed to iron overload). Considering "disease" as higher stages of fibrosis (modified HAI stage ≥ 3), our guesstimate of the expected frequency of disease in unexposed and exposed patients were about 25% and 75%, respectively. So the sample size was calculated as about 20 patients for each group with confidence level of 95% and power of 80%.Results and Discussion The results of the study are summarized in table  1 . This study supports the synergic effects of CHI and marked hepatic iron deposition in thalassemic patients on liver fibrosis. The patients with BTM/CHI had higher stages of liver fibrosis in comparison to patients with either CHI or thalassemia major alone. The patients with BTM/CHI had higher stages of fibrosis despite lower scores for iron overload in comparison to thalassemic patients. The potentiating effect of hepatic iron overload and CHI has been shown in other studies [ 7 , 8 , 11 , 12 ]. The cause of iron overload in the majority of studies has been hereditary hemochromatosis or  HFE  mutations. Table 1 Demographic and histopathologic findings in the three groups Group 1 Group 2 Group 3 P value Group 1 vs. 2 Group 1 vs. 3 Age(years) * 21.55+/-4.74 29.7+/-9.16 18.7+/-4.45 0.001 0.057 Sex (M/F) 10/10 13/7 10/10 0.33 1 Modified Stage ‡ 3 (1–6) 2 (0–6) 2 (1–4) 0.02 0.01 Modified HAI grade ‡ 5 (1–8) 6 (2–11) 2 (0–6) 0.36 0.001  Periportal inflammation ‡ 1.5 (0–3) 2 (0–4) 0 (0–2) 0.77 0.004  Confluent Necrosis ‡ 0 (0–1) 0 (0–2) 0 (0–1) 0.13 0.091  Focal Necrosis ‡ 1 (0–2) 2 (1–2) 1 (0–2) 0.10 0.45  Portal Inflammation ‡ 2 (0–3) 2 (1–4) 1 (0–2) 0.87 0.002 Iron Score ‡ 3 (2–4) 0 (0–1) 4 (2–4) 0.000 0.024 Steatosis (%) 10 20 0 0.66 0.48 * mean+/-standard deviation  ‡ Median (minimum-maximum) Group 1: Patients with beta thalassemia major and chronic HCV infection Group 2: Patients with chronic HCV infection Group 3: Patients with beta thalassemia major Angelucci et al showed for the first time the role of iron overload and HCV positivity as independent risk factors for hepatic fibrosis progression in thalassemic patients following successful bone marrow transplantation [ 8 ]. The Angelucci's study has a few advantages over our study: firstly, serial liver biopsies have been studied and the rate of liver fibrosis progression assessed. Secondly, hepatic iron concentration was used instead of scoring of stainable iron. Thirdly, two pathologists reviewed the slides independently. And finally, the sample size was much larger, but that study does not show whether the rate of hepatic fibrosis progression in BTM/CHI patients is greater than patients with only CHI. The potentiating effect of iron overload and HCV infection can be explained by the fact that both these agents produce oxidative stress in the liver [ 7 , 13 ]. In our study the grade of necroinflammation in BTM/CHI patients was not significantly different from CHI patients. Other studies have shown the same results [ 3 , 7 ]. Limitations of our study were that it was a retrospective study with limited number of patients in each group. The duration of HCV infection and genotype of virus were not known in our patients. The routes of infection in most of the CHI patients were not known and where probably different from our thalassemic patients. The age of our CHI group was significantly higher than the other two groups. Since older age at the time of infection is considered to be a risk factor for progression of chronic hepatitis C, the stage of fibrosis should have been higher in our CHI patients than BTM/CHI patients if iron had not had any potentiating effect on liver fibrosis. Our study was performed on thalassemic patients with moderate to severe liver iron deposition. At these stages of iron overload, the pattern of hepatic iron deposition in HH and thalassemic patients are nearly the same. So this study does not reveal the effect of only sinusoidal iron deposition on progression of fibrosis in HCV infected thalassemic patients.
Results and Discussion
The results of the study are summarized in table  1 . This study supports the synergic effects of CHI and marked hepatic iron deposition in thalassemic patients on liver fibrosis. The patients with BTM/CHI had higher stages of liver fibrosis in comparison to patients with either CHI or thalassemia major alone. The patients with BTM/CHI had higher stages of fibrosis despite lower scores for iron overload in comparison to thalassemic patients. The potentiating effect of hepatic iron overload and CHI has been shown in other studies [ 7 , 8 , 11 , 12 ]. The cause of iron overload in the majority of studies has been hereditary hemochromatosis or  HFE  mutations.HFEDemographic and histopathologic findings in the three groupsGroup 1Group 2Group 3P valueGroup 1 vs. 2Group 1 vs. 3Age(years)0.0010.001Sex (M/F)Modified Stage0.020.020.010.01Modified HAI grade0.0010.0010.0040.0040.0020.002Iron Score0.0000.0000.0240.024Steatosis (%)* mean+/-standard deviation  ‡ Median (minimum-maximum) Group 1: Patients with beta thalassemia major and chronic HCV infection Group 2: Patients with chronic HCV infection Group 3: Patients with beta thalassemia majorAngelucci et al showed for the first time the role of iron overload and HCV positivity as independent risk factors for hepatic fibrosis progression in thalassemic patients following successful bone marrow transplantation [ 8 ]. The Angelucci's study has a few advantages over our study: firstly, serial liver biopsies have been studied and the rate of liver fibrosis progression assessed. Secondly, hepatic iron concentration was used instead of scoring of stainable iron. Thirdly, two pathologists reviewed the slides independently. And finally, the sample size was much larger, but that study does not show whether the rate of hepatic fibrosis progression in BTM/CHI patients is greater than patients with only CHI.The potentiating effect of iron overload and HCV infection can be explained by the fact that both these agents produce oxidative stress in the liver [ 7 , 13 ]. In our study the grade of necroinflammation in BTM/CHI patients was not significantly different from CHI patients. Other studies have shown the same results [ 3 , 7 ].Limitations of our study were that it was a retrospective study with limited number of patients in each group. The duration of HCV infection and genotype of virus were not known in our patients. The routes of infection in most of the CHI patients were not known and where probably different from our thalassemic patients. The age of our CHI group was significantly higher than the other two groups. Since older age at the time of infection is considered to be a risk factor for progression of chronic hepatitis C, the stage of fibrosis should have been higher in our CHI patients than BTM/CHI patients if iron had not had any potentiating effect on liver fibrosis. Our study was performed on thalassemic patients with moderate to severe liver iron deposition. At these stages of iron overload, the pattern of hepatic iron deposition in HH and thalassemic patients are nearly the same. So this study does not reveal the effect of only sinusoidal iron deposition on progression of fibrosis in HCV infected thalassemic patients.Conclusions Our results show that moderate to severe liver iron overload and chronic HCV infection in thalassemic patients have potentiating effect on hepatic fibrogenesis. So the proper use of chelating agents in HCV-infected thalassemic patients seems to be of great importance in delaying progression of the liver disease. The presence of liver siderosis has been shown to be related to poor response to interferon alpha (IFN) in non-thalassemic patients [ 3 , 6 , 7 , 12 , 14 , 15 ]; hence one can expect a poor response to IFN therapy because of transfusion related siderosis in thalassemic patients. However studies have shown that in thalassemic subjects, there is a promising response to IFN therapy (as high as 50% sustained response in some series) [ 16 , 17 ]. Perhaps in these patients (BTM/CHI), the therapeutic protocol for chronic HCV infection should differ from those without significant iron overload, but further studies are needed to confirm or refute these suggestions.
Conclusions
Our results show that moderate to severe liver iron overload and chronic HCV infection in thalassemic patients have potentiating effect on hepatic fibrogenesis. So the proper use of chelating agents in HCV-infected thalassemic patients seems to be of great importance in delaying progression of the liver disease. The presence of liver siderosis has been shown to be related to poor response to interferon alpha (IFN) in non-thalassemic patients [ 3 , 6 , 7 , 12 , 14 , 15 ]; hence one can expect a poor response to IFN therapy because of transfusion related siderosis in thalassemic patients. However studies have shown that in thalassemic subjects, there is a promising response to IFN therapy (as high as 50% sustained response in some series) [ 16 , 17 ]. Perhaps in these patients (BTM/CHI), the therapeutic protocol for chronic HCV infection should differ from those without significant iron overload, but further studies are needed to confirm or refute these suggestions.Competing interests None declared.
Competing interests
None declared.Authors' contributions FAA reviewed all of the slides, participated in study design and statistical analysis of results, and prepared the manuscript. MRFO assisted in collection and entering of the data, cooperated in reviewing slides, statistical analysis of results, and preparing the manuscript. MN was the clinical consultant. GI reviewed some of the problematic slides. All authors read and approved the final manuscript.
Authors' contributions
FAA reviewed all of the slides, participated in study design and statistical analysis of results, and prepared the manuscript. MRFO assisted in collection and entering of the data, cooperated in reviewing slides, statistical analysis of results, and preparing the manuscript. MN was the clinical consultant. GI reviewed some of the problematic slides. All authors read and approved the final manuscript.Pre-publication history The pre-publication history for this paper can be accessed here:
Pre-publication history
The pre-publication history for this paper can be accessed here:


Arguments are presented concerning the deposit of Internet-based information into the Internet Archive, a digital library of Internet sites and other digital data

While we applaud PloS' use of Digital Object Identifiers (“The What and Whys of DOIs,” PLoS Biol 1: e57 doi:  10.1371/journal.pbio.0000057 ), we also note the lack of provisions in your instructions for authors for preserving access to electronic information residing at a cited Internet addresses via Uniform Resource Locators (URLs). Medical and scientific literature increasingly cites information only found on the Internet. However, URLs may become inaccessible shortly after article publication.Please consider requiring PLoS' authors to (1) submit all cited URLs to the Internet Archive ( www.archive.org ), a nonprofit organization that has been preserving electronic content since 1996, and (2) maintain a printed copy of the electronic information for future communication until the URL becomes available at the Internet Archive (about a six-month lag time).The Internet Archive, the largest digital library of Internet sites and other digital data, stores cited Internet information at no cost to the author, reader, or publisher. By requiring PLoS' authors to submit all cited Internet-based information to the Internet Archive, PLoS will better preserve the integrity of its content for the future.PLoS' Response Ms. Kelly and colleagues raise an important issue about the ephemeral nature of many information sources on the Internet. In the case of online scholarly literature, information is more likely to be archived and able to be found—indeed, an open-access article is one in which, according to the Bethesda Definition, “A complete version of the work and all supplemental materials, including a copy of the permission…, in a suitable standard electronic format is deposited immediately upon initial publication in at least one online repository that is supported by an academic institution, scholarly society, government agency, or other well-established organization that seeks to enable open access, unrestricted distribution, interoperability, and long-term archiving (for the biomedical sciences, PubMed Central is such a repository).” Other types of Internet-based information are more likely to change, move, or be removed. We agree that wherever possible we must find a way to preserve the relevant information from the sources cited in our articles. PLoS has always encouraged authors to submit supporting information for their research articles, including raw datasets, spreadsheets, multimedia, and snapshots of Web-based interactive tools. PLoS makes this supporting information available to everyone for download and use. PLoS also requires authors to deposit all appropriate datasets, images, and information in public databases and to list the relevant accession and version numbers in the article. The question, then, is how PLoS and its authors can preserve access to other Internet-based information, including organizational Web sites, articles in the popular media, or interactive databases. Although submitting cited URLs to the Internet Archive is worthwhile, it is still (unfortunately) far from ideal. The Internet Archive is best at archiving simple HTML and may be the most appropriate place to archive a Web site an author has cited for its static information content. The Internet Archive does not, however, archive content with password restrictions or “crawling” restrictions, and it allows the removal of already archived content at the request of Web administrators; it would therefore not be an effective archive, for example, for popular press articles that have restricted access. In addition, the Internet Archive cannot preserve functions that interact with the originating server, so it is not an appropriate way to archive a Web site an author has cited, for example, for its useful interactive tools. Finally, there is currently no automated way for publishers to redirect links from the original address to the address on the Internet Archive. For the time being, PLoS plans to review all electronic citations on a case-by-case basis and, when appropriate, request that authors submit the cited Web site URL to the Internet Archive and additionally submit a digital copy of the information to PLoS for internal archiving. We would also like to encourage further input on this issue from the scientific and medical community and urge them to support the Internet Archive and other organizations working to preserve the digital record for future generations.
PLoS' Response
Ms. Kelly and colleagues raise an important issue about the ephemeral nature of many information sources on the Internet. In the case of online scholarly literature, information is more likely to be archived and able to be found—indeed, an open-access article is one in which, according to the Bethesda Definition, “A complete version of the work and all supplemental materials, including a copy of the permission…, in a suitable standard electronic format is deposited immediately upon initial publication in at least one online repository that is supported by an academic institution, scholarly society, government agency, or other well-established organization that seeks to enable open access, unrestricted distribution, interoperability, and long-term archiving (for the biomedical sciences, PubMed Central is such a repository).”Other types of Internet-based information are more likely to change, move, or be removed. We agree that wherever possible we must find a way to preserve the relevant information from the sources cited in our articles.PLoS has always encouraged authors to submit supporting information for their research articles, including raw datasets, spreadsheets, multimedia, and snapshots of Web-based interactive tools. PLoS makes this supporting information available to everyone for download and use. PLoS also requires authors to deposit all appropriate datasets, images, and information in public databases and to list the relevant accession and version numbers in the article.The question, then, is how PLoS and its authors can preserve access to other Internet-based information, including organizational Web sites, articles in the popular media, or interactive databases.Although submitting cited URLs to the Internet Archive is worthwhile, it is still (unfortunately) far from ideal. The Internet Archive is best at archiving simple HTML and may be the most appropriate place to archive a Web site an author has cited for its static information content. The Internet Archive does not, however, archive content with password restrictions or “crawling” restrictions, and it allows the removal of already archived content at the request of Web administrators; it would therefore not be an effective archive, for example, for popular press articles that have restricted access. In addition, the Internet Archive cannot preserve functions that interact with the originating server, so it is not an appropriate way to archive a Web site an author has cited, for example, for its useful interactive tools. Finally, there is currently no automated way for publishers to redirect links from the original address to the address on the Internet Archive.For the time being, PLoS plans to review all electronic citations on a case-by-case basis and, when appropriate, request that authors submit the cited Web site URL to the Internet Archive and additionally submit a digital copy of the information to PLoS for internal archiving.We would also like to encourage further input on this issue from the scientific and medical community and urge them to support the Internet Archive and other organizations working to preserve the digital record for future generations.