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Volume 21 Supplement 1

Selected Topics in “Systems Biology and Bioinformatics” - 2019: genetics

The story of the lost twins: decoding the genetic identities of the Kumhar and Kurcha populations from the Indian subcontinent

BMC Genetics volume 21, Article number: 117 (2020) Cite this article

Abstract

Background

The population structure of the Indian subcontinent is a tapestry of extraordinary diversity characterized by the amalgamation of autochthonous and immigrant ancestries and rigid enforcement of sociocultural stratification. Here we investigated the genetic origin and population history of the Kumhars, a group of people who inhabit large parts of northern India. We compared 27 previously published Kumhar SNP genotype data sampled from Uttar Pradesh in north India to various modern day and ancient populations.

Results

Various approaches such as Principal Component Analysis (PCA), Admixture, TreeMix concurred that Kumhars have high ASI ancestry, minimal Steppe component and high genomic proximity to the Kurchas, a small and relatively little-known population found ~ 2500 km away in Kerala, south India. Given the same, biogeographical mapping using Geographic Population Structure (GPS) assigned most Kumhar samples in areas neighboring to those where Kurchas are found in south India.

Conclusions

We hypothesize that the significant genomic similarity between two apparently distinct modern-day Indian populations that inhabit well separated geographical areas with no known overlapping history or links, likely alludes to their common origin during or post the decline of the Indus Valley Civilization (estimated by ALDER). Thereafter, while they dispersed towards opposite ends of the Indian subcontinent, their genomic integrity and likeness remained preserved due to endogamous social practices. Our findings illuminate the genomic history of two Indian populations, allowing a glimpse into one or few of numerous of human migrations that likely occurred across the Indian subcontinent and contributed to shape its varied and vibrant evolutionary past.

Background

The Indian subcontinent and adjoining regions in South Asia have been a cradle for several waves of human migration during Paleolithic, Neolithic Periods, Bronze and Iron Age [1,2,3,4,5,6]. The genetic and ethnolinguistic landscape of the Indian subcontinent is remarkably heterogeneous and sculpted by the confluence of the indigenous people with immigrants that arrived into India following diverse routes [7,8,9,10,11,12,13]. The extant Indian gene pool is composed of largely four ancestral genetic components, namely Ancestral North Indian (ANI), Ancestral South Indian (ASI), Ancestral Tibeto-Burman (ATB), and Ancestral Austro-Asiatic (AAA) [14,15,16]. Recent studies dissecting the complex genetic history of South Asia suggested that a South Asia Hunter Gatherer lineage with close proximity to the present day Andamanese (AASI) admixed with individuals related to Iranian agriculturalists from Zagros mountains, Iran and West_Siberian_HG (West Siberian Hunter Gatherers) forming the Indus_Periphery gene pool, in the larger Indus valley area during the 3rd millennium BCE, and may be a vital ancestral source for the subsequent peopling of South Asia [17]. Consistent with previous evidences [11, 18] the autochthonous Indian ancestral lineages prior to their admixture with West Eurasians, likely split during eastward migration of the anatomically modern humans, out of Africa, later giving rise to AASI groups [17]. The ANI and ASI gene pools arose subsequently around ~ the 2nd millennium BCE, concurrent with the decline of the Indus Valley civilization (IVC) [19] that propelled a massive upheaval in human settlements across northern parts of the Indian subcontinent. The southward dispersal of Steppe_MLBA (later Middle to late Bronze Age Steppe) populations occurred around this time into South Asia [20,21,22]; it is envisioned that the Indus_Periphery related groups admixed with the Steppe_MLBA immigrants to form the ANI, while additional Indus_Periphery people migrated further south and eastward within peninsular India to mingle with AASI and formed the ASI [17]. The distinctive population structure of the Indian subcontinent is a unique amalgamation resulting from the extensive and intricate percolation of people across it for long periods together with the rigorous enforcement of sociocultural practices, such as endogamy in many groups. Interrogation of population structure, relatedness and ancestry of Indian populations provide valuable insight to not only reconstruct their evolutionary past but may also have important implications in medical genetics and understanding relevant disease biology.

Here we have investigated the population history of the Kumhars, a north Indian population that has likely been practicing endogamy over long periods of time, as evidenced by their Identical by descent (IBD) scores that are significantly higher than that of the Ashkenazi Jews and the Finns [23]. Kumhars are found throughout large parts of northern, western, and eastern India, as well as in Pakistan. The name ‘Kumhar’ is derived from the Sanskrit term ‘Kumbhakar’, which literally means earthen-pot makers alluding to their ancestral way of earning a living [24]. Interestingly, the potters from Amritsar, Punjab in north India are also known as Kulal or Kalal, a term phonetically similar to Kulala, a group of people from Kasaragod district of the southern Indian state of Kerala, whose traditional occupation is also pottery. The phonetic similarity between the two terms is potentially due to their common origin from the Yajurveda, an ancient Vedic Sanskrit text where potters were termed as ‘Kulals’ [25]. In this study we aimed to delineate the population history of Kumhars and examined their genomic similarity with other populations from the Indian subcontinent. To this end we assessed 27 previously published Kumhar samples, which were sampled from the north Indian state of Uttar Pradesh and compared to 2013 modern day South Asian populations [16, 23].

Results

Clustering of Kumhars in the context of other south Asian populations

Principal component analysis (PCA) of South Asian samples exhibited previously described [14, 26] ANI –ASI -AAA cline along the horizontal principal component (PC1) with Ashkenazi Jews, Kalash and other Pakistani populations, and Shia Iranians from Hyderabad clustering at one extreme of the cline, and Juang and other AAA populations congregating at the other extreme (Fig. 1 and Additional file 1 Fig. S2). Concurrent with our previous study [26], ASI-AAA-Ancestral Tibeto-Burman (ATB) contrast was observed along the vertical principal component (PC2) with Juangs clustering at one end, while Nyshi and other ATB populations clustered at the other end. Out of 27 Kumhar samples employed in this study, barring three (stockplate_14_C2, stockplate_14_C4, stockplate_14_C6), the remaining clustered with ASI and AAA samples, largely overlapping with tribal populations from Kerala such as the Kurchas. According to PCA, the only North Indian population that revealed genomic proximity to Kumhars, were the Syons from Uttarakhand. Further we note that the three above-mentioned outlier Kumhar samples overlapped with a population cluster that largely comprised of various non-Brahmin backward castes from Uttar Pradesh.

Fig. 1
figure 1

Principal Component Analysis (PCA) of South Asians. PCA plot showing genetic differentiation among South Asians. The X-axis (PC1) explained 18.3% variance while the Y-axis (PC2) explained 16% variance of the data. Notable populations are marked with circles. A more detailed population classification is shown in Additional file 1 Fig. S2. PCA was performed in PLINK v1.9 and the top four principal components (PCs) were extracted. Top two PCs (PC1 and PC2), explaining the highest variance of the data were plotted in R v3.5.1

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Weighted pairwise FST between Kumhars and 63 selected populations across India using Weir and Cockerham approach [27] implemented in PLINK v1.9 revealed the Kumhars to be genetically almost identical to Kurchas from Kerala in southern India (weighted FST = 0.0008) (Additional file 1 Table S1). Among the remaining populations, Kumhars were genetically closest to Kurumbas from Kerala, south India (weighted FST = 0.019) followed by Vishwabrahmins from Andhra Pradesh, south India (weighted FST = 0.0192) and Chakkiliyans from Tamil Nadu, south India (weighted FST = 0.0196), and farthest from largely homogeneous populations from Kerala and Tamil Nadu such as Adiyan (weighted FST = 0.044), Paniyas (weighted FST = 0.054), Narikuruvar (weighted FST = 0.055), Pulliyar (weighted FST = 0.056), Malaikuravar (weighted FST = 0.07) and Ulladan (weighted FST = 0.077). Weighted pairwise FST between Kurchas and the same 63 populations across India revealed similar results, indicating that Kurchas are genetically more similar to Kumhars than any other Indian populations including its neighboring ones (Additional file 1 Table S2).

The genomic ancestry of all 2040 individuals present in the Modern South Asian only dataset was estimated using the model-based clustering algorithm ADMIXTURE v1.3 [28]. The lowest CVE was estimated for K = 11 (Additional file 1 Fig. S1). At K = 11, discernible degree of genetic admixture was observed between ANI and ASI populations (Fig. 2). Onge (k1, yellow), Juang (k2, tan), Ashkenazi Jews and Shia Iranians (k3, blue), Vysya (k4, gold), Kumhar and Kurchas (k5, red), Palliyar (k6, chocolate brown), Pulliyar (k7, orange), Malaikuravar, Narikuruvar and Hakki Pakki (k8, green), Ulladan (k9, magenta), ATB (k10, cyan), and Siddi (k11, purple) populations were assigned to distinct clusters. Congruent with previous studies [14, 16, 26], Fig. 2 revealed that most South Asians have variable fractions of blue (k3, likely derived from Bronze Age Steppe populations), red (k5, likely derived from ASI populations) and tan (k2, likely derived from Ancient Ancestral South Indians: AASI populations). Component k5, which was assigned to Kumhars and Kurchas, was found to be present in discernibly higher proportion among most non-Brahmin south Indian populations, indicating genomic similarity between Kumhars and ASI populations potentially linked to their common origin and admixture history. Further, the Bronze Age Steppe ancestry proportion was found to be the lowest in Kumhars compared to other populations from Uttar Pradesh and Uttarakhand (Fig. 3), indicating their distinct origin. We note that most Kumhar samples were found to have < 1% Bronze Age Steppe ancestry, except stockplate_14_C2, stockplate_14_C4, stockplate_14_C6 (all three have 25% Steppe related ancestry), and were also found to be outliers in principal component analysis. Both PCA and ADMIXTURE analysis suggested that origin of these three Kumhar samples was divergent from the rest.

Fig. 2
figure 2

Admixture analysis of South Asians. Admixture plot showing the ancestry components of South Asian samples employed in the study. Admixture proportions were generated through an unsupervised admixture analysis at K = 11 using ADMIXTURE v1.3 and plotted in R v3.5.1. Each individual is represented by a vertical line partitioned into colored segments whose lengths are proportional to the contributions of the ancestral components to the genome of the individual

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Fig. 3
figure 3

Comparison of Bronze Age Steppe Ancestry proportion among populations from Uttar Pradesh (UP) and Uttarakhand (UK). The ancestry proportions were obtained using ADMIXTURE v1.3. Multiple comparisons were performed using Tukey’s post hoc analysis implemented in GraphPad Prism v7. While Brahmin and Bhumihars contained the highest fraction of the Steppe Ancestry component (k3) Kumhars contained the least (Tukey’s post hoc analysis, p-value < 0.0001)

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We employed TreeMix v.1.12 [29] to investigate the pattern of population splits and mixtures among selected South Asian populations. Similar to PCA, FST and ADMIXTURE analyses, the ML tree generated by TreeMix revealed high degree of genetic relatedness between Kumhars and Kurchas, with Kurumba (Kerala) and Sugali (Andhra Pradesh) populations as their sister groups (Fig. 4). Overall, all clustering approaches employed in this study revealed high proximity between Kumhar and Kurchas samples, and high degree of genetic similarity between Kumhars and several modern-day South Indian populations. On the contrary, barring the three outlier Kumhar samples, the remainder had very little genomic proximity towards other populations from the same geographic location in Uttar Pradesh and Uttarakhand.

Fig. 4
figure 4

A maximum likelihood (ML) tree examining the genetic relatedness between Kumhars and selected South Asian populations. The ML tree was constructed using TreeMix v1.13. The tree was rooted using Onge, a non-African Andamanese population. Some populations are grouped together for aesthetic reason. The horizontal axis depicts the drift parameter. The scale bar shows ten times the average standard error of the entries in the sample covariance matrix. The tree confirmed the high genetic relatedness between Kumhars and Kurchas, and their genetic similarities with several South Indian populations such as Kurumba, Kuruman and Sugali

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We applied ALDER v.1.02 [30] to investigate the approximate time of admixture between Kumhars and other South Asians. 39% of the successful results in terms of LD decay included Dravidian speaking Brahui population from Pakistan, alongside an Austroasiatic speaking (Oraon, Ho) or an AASI related (Juang) population or a backward caste population (Mohali, Kandha, Gond and Khairwar) from north and central India (Additional file 1 Table S3). Our results indicate that the admixture event that potentially gave rise to Kumhars likely occurred 130–200 generations or 3640–5600 years ago, assuming a generation time of 28 years. This timeline (3600–1640 BCE) likely coincides with or after the decline of the IVC [19, 31].

Biogeographical mapping of Kumhar samples employed in the study

Biogeographical mapping of Kumhar samples was performed using the GPS algorithm. Barring stockplate_14_C2, stockplate_14_C4, stockplate_14_C6, and CCMB_PL_9_298, GPS assigned all 23 Kumhar samples to south-western Karnataka across Western Ghats in southern India with 16 being localized at the Karnataka-Kerala border (Fig. 5). Stockplate_14_C6 was positioned ~ 80 km southeast of Hubballi, northern Karnataka. Stockplate_14_C4 was localized ~ 130 km east of Madurai, along coastal Tamil Nadu, in south India. CCMB_PL_9_298 was assigned < 50 km west of Visakhapatnam, Andhra Pradesh, in south India. Only one Kumhar sample, stockplate_14_C2 was assigned by GPS to Uttar Pradesh in north India, from where all Kumhar samples had been actually collected [23]; it was localized ~ 300 km southwest of Kanpur and ~ 70 km south of Jhansi. The assignment of 26 out of 27 Kumhars sampled from Uttar Pradesh, north India to South India further confirms their high genomic proximity with populations of ASI ancestry and genetic distinctness from other populations found in overlapping and neighboring areas of their geographic sampling source.

Fig. 5
figure 5

Maps depicting the GPS predicted locations of the Kumhar samples. GPS predicted coordinates were plotted in Google Maps (Google LLC) as well as using ‘rworldmap’ package implemented in R v3.5.1. The black triangle represents the original sampling location of Kumhars and the red triangles represent GPS predicted locations of Kumhars. To note, GPS localized 23 out of 27 Kumhar samples to south-western Karnataka across Western Ghats in southern India with 16 being localized at the Karnataka-Kerala border

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Determination of ancestry proportions in Kumhars in a global context

We used qpAdm [32] implemented in AdmixTools v5.1 [33] to estimate ancestry proportions in the South Asians. The Ancient-Modern dataset comprising of 4575 ancient and modern-day individuals worldwide was employed for this analysis. All South Asians were modelled as a combination of three source populations namely Andaman Islanders (Onge), Steppe-related (Steppe-MLBA) and Iran-Turan-related (Indus_Periphery) as Left (Test, Onge, Steppe-MLBA, Indus_Periphery) and O8 was used as the ‘Right’ outgroup (see Methods). Congruent with ADMIXTURE analysis, qpAdm analysis revealed that Kumhars (10.5%) are among the three populations from Uttar Pradesh, north India with low Bronze Age Steppe ancestry (Additional file 1 Table S4); the other two populations being Dharikhar (10.2%) and Sahariya tribes (0%). When we repeated the qpAdm analysis for Kumhars excluding the outlier Kumhars (stockplate_14_C2, stockplate_14_C4 and stockplate_14_C6), the Steppe ancestry reduced to 9.5% and Kumhars emerged as the population with the second lowest Steppe ancestry after Sahariyas. It is noteworthy that Onge, Steppe_MLBA and Indus_Periphery ancestral component in Kumhars, after removal of the three outliers (49.2, 9.5 and 41.2% respectively), was found to be very similar to that in Kurchas (51.4, 9.3 and 39.3% respectively) and Kurumans from Kerala (46.2, 9.3 and 44.5% respectively), and Chakkiliyans from Tamil Nadu (47.3, 9.4 and 43.3% respectively), in south Indian reflecting the significant genomic proximity between Kumhars and ASI populations.

The direction of gene flow: genetic similarities between Kumhars and Kurchas

We found positive Z-scores for all combinations of South Asian populations (X) employed in this study, indicating gene flow between Kumhars (W) and Kurchas (Y) (Additional file 1 Table S5). The positive Z-scores obtained from D-statistical analysis indicates that Kumhars are genetically closer to Kurchas than to any other Indian group employed in this study.

Discussion

Populations from the Indian subcontinent are envisaged as a Pleistocene gene pool [10, 34,35,36] and are a mélange of varied indigenous and immigrant ancestries, which together with the extraordinary diversity in geographical niches and sociocultural stratification has resulted in its complex genetic history. Here we investigated the genetic origin and population history of the Kumhars, a group of people who traditionally worked as potters and are found over large parts of north, west and east India.

Pottery is the art of creating objects from non-metallic minerals, such as earthenware, porcelain by molding them when wet and subsequently firing them at high temperatures. It was practiced by potters referred to as Kumhars in northern, western and eastern regions of the Indian subcontinent. The origin of pottery in the Indian subcontinent can be traced back to cord-impressed style of ceramic ware from the Mesolithic period, found at the site of Lahuradewa, dating back to 7000–6000 BCE [37]. Evidences of both handmade and wheel-made forms of pottery dating back to the IVC have been obtained. The Jhukar phase of pottery corresponding to the Jhukar archaeological type-site in Sindh was coincident with urbanization in the late Harappan period [38]. This was followed by the crude handmade pottery of the Jhangar phase [38] likely reflecting a largely nomadic and pastoralist population of West Asian immigrants. The decline of the IVC and the subsequent peopling of the vast Gangetic plains in central Indian subcontinent was marked by handmade and unpainted pottery forms, such as those of the Swat grave culture and ochre colored pottery culture that further likely coincided with West Eurasian migration into the Indian subcontinent. This was followed by black and red ware and subsequently the painted grey ware cultures of pottery that likely concurred with south and eastward migration of people in the peninsular India and the formation of the ASI [39].

In India, Kumhars and their Southern counterparts such as Kulals (in Kerala), Kummara (Andhra Pradesh and Telangana), Kumbara and Kummari (Andhra Pradesh) are synonymous with pottery. We interrogated 27 previously published Kumhar SNP genotype data [23] and compared them to various modern day and ancient populations.

PCA revealed that except three (stockplate_14_C2, stockplate_14_C4, stockplate_14_C6), all Kumhars congregated with those of ASI and AAA ancestries, largely overlapping with tribal populations from Kerala, in southern India, such as the Kurchas (Fig. 1). Similarly weighted pairwise FST using the Weir and Cockerham approach suggested that the Kumhars were genetically almost identical to Kurchas from Kerala, south India (weighted FST = 0.0008), followed by Kurumbas from Kerala (weighted FST = 0.019), Vishwabrahmins from Andhra Pradesh, south India (weighted FST = 0.0192) and Chakkiliyans from Tamil Nadu, south India (weighted FST = 0.0196) (Additional file 1 Table S1). The strong genomic proximity of Kumhars with Kurchas was further corroborated by TreeMix analysis (Fig. 4). Consistent with this, Admixture analysis also reflected a predominant ASI ancestry among the Kumhars that is also shared by Kurchas and other non-Brahmin south Indian populations (Fig. 2). For most Kumhar samples PCA, Admixture and qpAdm concurred on the presence of a minimal Steppe ancestral component (Fig. 3 and Additional file 1 Table S3).

Given the high genetic similarity between Kumhars and Kurchas it is unsurprising that biogeographical mapping of the Kumhars assigned all but one sample to southern India (Fig. 5). Notably 23 Kumhar samples were positioned to south-western Karnataka across Western Ghats in southern India with 16 being localized at the borders of the Indian states of Karnataka and Kerala, and adjoining the geographic region of Wayanad, which is the native abode of the Kurcha population.

Finally using ALDER we estimated that the Kumhar gene pool likely arose 130–200 generations or 3640–5600 years ago coinciding with two important events that potentially occurred during and/or after the decline of IVC [19, 31]: (a) the emergence of the ASI group, which began ~ 3000 BCE during the course of the spread of West Asian domesticates into peninsular India [40] and (b) the formation of Austroasiatic speaking populations through admixture between the eastward migrating branch of out of Africa populations that arrived in South Asia ~ 3000 BCE and ancient indigenous Indian groups (AASI-related) [17, 18].

In brief, we found very little similarity between Kumhars and other ethnic groups from the same geographic regions in Uttar Pradesh and its adjoining state of Uttarakhand, in north India. The Kumhars appeared to have an overwhelming ASI ancestral component; 24 out of 27 Kumhars appeared to be identical (> 98%) to a small population known as the Kurchas from the Wayanad district in the south Indian state of Kerala, which is approximately 2500 km south of the region from where the Kumhar samples were obtained. Similar to the Kumhars little is known regarding the Kurcha population and to the best of our knowledge there is no existing literature that describes any anthropological or historical connection between Kurchas with either the Kumhars or the Kulalas.

Here, we propose that the significant genomic similarity between two apparently distinct modern day Indian populations that correspond to well separated geographical areas separated by ~ 2500 km with no known overlapping history or links likely alludes to their common origin during or after the decline of IVC; subsequently the two populations likely migrated towards opposite ends of the Indian subcontinent but their genomic integrity was preserved owing to stringent enforcement of endogamy. Our findings illuminate the population history of two Indian groups, allowing a glimpse into one or few of numerous of human migrations that likely occurred across the Indian subcontinent and have shaped its varied and vibrant evolutionary past. Overall our findings help to reconstruct the genomic history of two Indian populations, the Kumhars and Kurchas, which despite significant geographic isolation have remained almost identical, genetically, shining light on how largescale population movements that spanned across the Indian subcontinent over extensive periods of time together with imposition of sociocultural hierarchies have contributed to its diverse evolutionary heritage.

Previous reports have indicated that the IBD scores for Kumhars are significantly higher than that of the Ashkenazi Jews and the Finns, consistent with social practices of consanguinity among them [23]. This is medically relevant as it predicts a high propensity for genetic disorders and consistent with this diseases such as acute intermittent porphyria are reported at higher frequencies in the Kumhar population [41].

Limitations of the study

As mentioned earlier, Kumhars are distributed throughout North India and Pakistan. However, since we did not genotype/sequence any Kumhar sample and our study was completely based on previously published SNP genotype data, we were limited in terms of sample size and distribution. The same is applicable for the Kurchas. We could only employ four Kurcha samples in this study due to unavailability of Kurchas in the published datasets. It can be speculated that sampling across various parts of India can significantly improve the robustness of the study.

Conclusions

Overall, our results reflect the high genomic similarity of Kumhars with various south Indian groups, including the so far little known Kurchas, offering some insight into the latter’s genomic history and likely predisposition to genetic disorders. It further underscores the importance of uncovering founder events among Indian populations and prioritizing them for studies dissecting genetic diseases and their underlying etiology.

Methods

Data sets

To generate the Modern South Asian only dataset we merged two previously published datasets [16, 23] using ‘mergeit’ function implemented in EIG v7.2 [42]. This dataset comprised of 2040 modern South Asian SNP genotype data, including 27 Kumhar samples, and corresponding to total 265 Indian ethnic groups, and assessing 91,781 single nucleotide polymorphisms (SNPs). The Modern South Asian only dataset was then merged with two ancient DNA datasets comprising of 294 and 362 ancient individuals, respectively (N = 2696) [17, 40]. Finally, this dataset was merged with 1879 modern samples [43] across the world to generate the Ancient-Modern dataset comprising of 4575 individuals, and assessing 91,768 SNPs. File conversions and manipulations were performed using EIG v7.2 [42] and PLINK v1.9 [44] (https://www.cog-genomics.org/plink2/).

Genome-wide SNP data analyses

Modern South Asian only dataset (N = 2040) was used for all genome-wide SNP analyses to describe fine-scale population structure recapitulating the population history of Kumhars.

We calculated mean and weighted pairwise FST between Kumhars and 63 selected populations across India using the Weir and Cockerham approach [27] implemented in PLINK v1.9 [44]. PLINK estimated the fixation indices separately for all 91,781 SNPs under evaluation using –fst command alongside –family flag that enables it to group the individuals according to their family id (FID). The 63 populations comprised of all 35 available populations from Uttarakhand and Uttar Pradesh, all 10 populations from Kerala, and 18 populations from elsewhere in India.

The fine population structure of the modern South Asians was delineated using Principal Component Analysis (PCA) implemented in PLINK v1.9 [44] using –pca command. The two most informative PCs are discussed and plotted in R v3.5.1.

The genomic ancestry of all 2040 individuals was estimated using the model-based clustering algorithm ADMIXTURE v1.3 [28]. The optimum number of ancestral components (K) was determined by minimizing the cross-validation error (CVE) using a –cv flag to the admixture command line. The lowest CVE was estimated for K = 11 (Additional file 1 Fig. S1).

We constructed a maximum likelihood (ML) tree for 84 selected populations comprising of all 35 populations from Uttar Pradesh and Uttarakhand, all 10 populations from Kerala, and 39 populations across the rest of India by using TreeMix v.1.12 [29] in order to place Kumhars to a global context. Onge were used to root the ML tree.

We applied ALDER v.1.02 [30] to compute a weighted linkage disequilibrium (LD) analysis to infer the likely date of admixture, based on the exponential LD decay. We aimed to investigate the approximate time of admixture between Kumhars and other South Asians considering a generation time of 28 years. Kumhars were included as the ‘admixpop’ (admixed population) and the remaining South Asian populations present in the Modern South Asian only dataset were used as the ‘refpops’ (reference populations).

Biogeographical mapping of Kumhar samples

Biogeographical analysis was performed using the Geographical Population Structure (GPS) algorithm, which has been successfully used to reconstruct history of several populations worldwide [45,46,47,48,49,50,51,52,53]. GPS correlates the admixture patterns of individuals of unknown origins using the admixture fractions (GEN file) and geographical locations or coordinates (GEO file) of reference individuals with known geographical origin. GPS converts the genetic distances between the query and the most proximal reference populations into geographic distances. Comparing the admixture proportions of the query with the reference populations, GPS extrapolates the genomic similarity of the former and infers its geographic origins using the known biogeographical information of the reference. Our test dataset comprised of the admixture fractions of Kumhars. We curated the reference dataset using the rest of south Asians present in the Modern South Asian only dataset except Siddis and Ashkenazi Jews.

Determination of ancestry proportions in Kumhars in a global context

We used qpAdm [32] implemented in AdmixTools v5.1 [33] to estimate ancestry proportions in the South Asians originating from a mixture of ‘reference’ populations by utilizing shared genetic drift with a set of ‘outgroup’ populations. The Ancient-Modern dataset comprising of 4575 ancient and modern-day samples from across the world was employed for this analysis. In accordance with existing literature [17], three ancient samples namely Shahr-i-Soktha_MLBA2, Shahr-i-Soktha_MLBA3 and Gonur2_BA were referred to as ‘Indus_Periphery’ in qpAdm analysis. All South Asians were modelled as a combination of three source populations namely Andaman Islanders (Onge), Steppe-related (Steppe-MLBA) and Iran-Turan-related (Indus_Periphery) as Left (Test, Onge, Steppe-MLBA, Indus_Periphery) as already described [17]. We used a mixture of eight ancient and modern-day populations comprising of Mabuti.DG, SHG, EHG, Ganj_Dareh_N, Anatolia_N, West_Siberia_N, Han and Karitiana our ‘Right’ outgroup populations (O8).

The direction of gene flow

To investigate whether Kumhar is genetically closer to Kurcha than to any other South Asian group used in this study, we employed qpDstat function implemented in AdmixTools v5.1 [33] in order to acquire information about the gene flow among South Asian population(s) in respect to Kumhar and Kurcha. The D-statistic was modelled as:

Pop1 (Kumhar) Pop2 (Modern South Asian populations): Pop3 (Kurcha) Pop4 (Onge).

Onge was used as an outgroup since it has been disconnected from the mainland populations long while ago. Here, while positive Z-scores will indicate gene flow between Kumhar and Kurcha, negative scores will indicate gene flow between other South Asian population(s) and Kurcha.

Availability of data and materials

While most data are publicly available through the lab database of Dr. David Reich, Harvard University (https://reich.hms.harvard.edu/datasets) in Eigenstrat format, some were obtained from Dr. Reich’s lab through personal communication. The authors do not have the mandate to redistribute these data. Kindly contact Dr. Ranajit Das (das.ranajit@gmail.com) for details regarding the data availability.

Abbreviations

IVC:

Indus Valley Civilization

AASI:

Ancient Ancestral South Indian

ANI:

Ancestral North Indian

ASI:

Ancestral South Indian

ATB:

Ancestral Tibeto-Burman

AAA:

Ancestral Austro-Asiatic

Steppe MLBA:

Steppe Middle-Late Bronze Age

IBD:

Identical by Descent

References

  1. Gangal K, Sarson GR, Shukurov A. The near-eastern roots of the Neolithic in South Asia. PLoS One. 2014;9(5):e95714.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  2. Kivisild T. In: Papiha SS, Deka R, Chakraborty R, editors. Genomic Diversity: Applications in Human Population Genetics. New York: Kluwer; 2001. p. 135–52.

    Google Scholar 

  3. Metspalu M, Kivisild T, Metspalu E, Parik J, Hudjashov G, Kaldma K, Serk P, Karmin M, Behar DM, Gilbert MT, et al. Most of the extant mtDNA boundaries in south and Southwest Asia were likely shaped during the initial settlement of Eurasia by anatomically modern humans. BMC Genet. 2004;5:26.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  4. Misra VN. Prehistoric human colonization of India. J Biosci. 2001;26(4 Suppl):491–531.

    Article  CAS  PubMed  Google Scholar 

  5. Singh S, Singh A, Rajkumar R, Sampath Kumar K, Kadarkarai Samy S, Nizamuddin S, Singh A, Ahmed Sheikh S, Peddada V, Khanna V, et al. Dissecting the influence of Neolithic demic diffusion on Indian Y-chromosome pool through J2-M172 haplogroup. Sci Rep. 2016;6:19157.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Thangaraj K, Chaubey G, Singh VK, Vanniarajan A, Thanseem I, Reddy AG, Singh L. In situ origin of deep rooting lineages of mitochondrial Macrohaplogroup ‘M’ in India. BMC Genomics. 2006;7:151.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  7. Mellars P. Going east: new genetic and archaeological perspectives on the modern human colonization of Eurasia. Science. 2006;313(5788):796–800.

    Article  CAS  PubMed  Google Scholar 

  8. Macaulay V, Hill C, Achilli A, Rengo C, Clarke D, Meehan W, Blackburn J, Semino O, Scozzari R, Cruciani F, et al. Single, rapid coastal settlement of Asia revealed by analysis of complete mitochondrial genomes. Science. 2005;308(5724):1034–6.

    Article  CAS  PubMed  Google Scholar 

  9. Quintana-Murci L, Semino O, Bandelt HJ, Passarino G, McElreavey K, Santachiara-Benerecetti AS. Genetic evidence of an early exit of Homo sapiens sapiens from Africa through eastern Africa. Nat Genet. 1999;23(4):437–41.

    Article  CAS  PubMed  Google Scholar 

  10. Sengupta S, Zhivotovsky LA, King R, Mehdi SQ, Edmonds CA, Chow CE, Lin AA, Mitra M, Sil SK, Ramesh A, et al. Polarity and temporality of high-resolution y-chromosome distributions in India identify both indigenous and exogenous expansions and reveal minor genetic influence of central Asian pastoralists. Am J Hum Genet. 2006;78(2):202–21.

    Article  CAS  PubMed  Google Scholar 

  11. Basu A, Mukherjee N, Roy S, Sengupta S, Banerjee S, Chakraborty M, Dey B, Roy M, Roy B, Bhattacharyya NP, et al. Ethnic India: a genomic view, with special reference to peopling and structure. Genome Res. 2003;13(10):2277–90.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Bamshad M, Kivisild T, Watkins WS, Dixon ME, Ricker CE, Rao BB, Naidu JM, Prasad BV, Reddy PG, Rasanayagam A, et al. Genetic evidence on the origins of Indian caste populations. Genome Res. 2001;11(6):994–1004.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Consortium HP-AS, Abdulla MA, Ahmed I, Assawamakin A, Bhak J, Brahmachari SK, Calacal GC, Chaurasia A, Chen CH, Chen J, et al. Mapping human genetic diversity in Asia. Science. 2009;326(5959):1541–5.

    Article  CAS  Google Scholar 

  14. Basu A, Sarkar-Roy N, Majumder PP. Genomic reconstruction of the history of extant populations of India reveals five distinct ancestral components and a complex structure. Proc Natl Acad Sci U S A. 2016;113(6):1594–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Reich D, Thangaraj K, Patterson N, Price AL, Singh L. Reconstructing Indian population history. Nature. 2009;461(7263):489–94.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Moorjani P, Thangaraj K, Patterson N, Lipson M, Loh PR, Govindaraj P, Berger B, Reich D, Singh L. Genetic evidence for recent population mixture in India. Am J Hum Genet. 2013;93(3):422–38.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Narasimhan VM, Patterson NJ, Moorjani P, Lazaridis I, Mark L, Mallick S, Rohland N, Bernardos R, Kim AM, Nakatsuka N, et al. The Genomic Formation of South and Central Asia. bioRxiv. 2018:292581.

  18. Mallick S, Li H, Lipson M, Mathieson I, Gymrek M, Racimo F, Zhao M, Chennagiri N, Nordenfelt S, Tandon A, et al. The Simons genome diversity project: 300 genomes from 142 diverse populations. Nature. 2016;538(7624):201–6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Kathayat G, Cheng H, Sinha A, Yi L, Li X, Zhang H, Li H, Ning Y, Edwards RL. The Indian monsoon variability and civilization changes in the Indian subcontinent. Sci Adv. 2017;3(12):e1701296.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Underhill PA, Poznik GD, Rootsi S, Jarve M, Lin AA, Wang J, Passarelli B, Kanbar J, Myres NM, King RJ, et al. The phylogenetic and geographic structure of Y-chromosome haplogroup R1a. Eur J Hum Genet. 2015;23(1):124–31.

    Article  PubMed  Google Scholar 

  21. Mathieson I, Alpaslan-Roodenberg S, Posth C, Szecsenyi-Nagy A, Rohland N, Mallick S, Olalde I, Broomandkhoshbacht N, Candilio F, Cheronet O, et al. The genomic history of southeastern Europe. Nature. 2018;555(7695):197–203.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Silva M, Oliveira M, Vieira D, Brandao A, Rito T, Pereira JB, Fraser RM, Hudson B, Gandini F, Edwards C, et al. A genetic chronology for the Indian subcontinent points to heavily sex-biased dispersals. BMC Evol Biol. 2017;17(1):88.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Nakatsuka N, Moorjani P, Rai N, Sarkar B, Tandon A, Patterson N, Bhavani GS, Girisha KM, Mustak MS, Srinivasan S, et al. The promise of discovering population-specific disease-associated genes in South Asia. Nat Genet. 2017;49(9):1403–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Mandal SK: Kumhar/Kumbhar. In: People of India: Rajasthan. Edited by Singh SK: Popular Prakashan; 1998: 565–566.

  25. Saraswati B. Pottery-making cultures and Indian civilization: Abhinav publications; 1979.

    Google Scholar 

  26. Das R, Upadhyai P. An ancestry informative marker set which recapitulates the known fine structure of populations in South Asia. Genome Biol Evol. 2018;10(9):2408–16.

    Article  PubMed  PubMed Central  Google Scholar 

  27. Weir BS, Cockerham CC. Estimating F-statistics for the analysis of population structure. Evolution. 1984;38(6):1358–70.

    CAS  PubMed  Google Scholar 

  28. Alexander DH, Novembre J, Lange K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 2009;19(9):1655–64.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Pickrell JK, Pritchard JK. Inference of population splits and mixtures from genome-wide allele frequency data. PLoS Genet. 2012;8(11):e1002967.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Loh PR, Lipson M, Patterson N, Moorjani P, Pickrell JK, Reich D, Berger B. Inferring admixture histories of human populations using linkage disequilibrium. Genetics. 2013;193(4):1233–54.

    Article  PubMed  PubMed Central  Google Scholar 

  31. Brooke JL. Climate change and the course of global history: a rough journey: Cambridge University press; 2014.

    Book  Google Scholar 

  32. Haak W, Lazaridis I, Patterson N, Rohland N, Mallick S, Llamas B, Brandt G, Nordenfelt S, Harney E, Stewardson K, et al. Massive migration from the steppe was a source for indo-European languages in Europe. Nature. 2015;522(7555):207–11.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Patterson N, Moorjani P, Luo Y, Mallick S, Rohland N, Zhan Y, Genschoreck T, Webster T, Reich D. Ancient admixture in human history. Genetics. 2012;192(3):1065–93.

    Article  PubMed  PubMed Central  Google Scholar 

  34. Majumder PP. Ethnic populations of India as seen from an evolutionary perspective. J Biosci. 2001;26(4 Suppl):533–45.

    Article  CAS  PubMed  Google Scholar 

  35. Majumder PP. The human genetic history of South Asia. Curr Biol. 2010;20(4):R184–7.

    Article  CAS  PubMed  Google Scholar 

  36. Sun C, Kong QP, Palanichamy MG, Agrawal S, Bandelt HJ, Yao YG, Khan F, Zhu CL, Chaudhuri TK, Zhang YP. The dazzling array of basal branches in the mtDNA macrohaplogroup M from India as inferred from complete genomes. Mol Biol Evol. 2006;23(3):683–90.

    Article  CAS  PubMed  Google Scholar 

  37. Cahille MA. Paradise rediscovered: the roots of civilisation: interactive publications; 2012.

    Google Scholar 

  38. Langer WL. An encyclopedia of world history. Boston: Houghton Mifflin Company; 1972.

    Google Scholar 

  39. Southworth F. Linguistic archaeology of South Asia Routledge; 2005.

    Google Scholar 

  40. Kenoyer JM. Ancient cities of the Indus Valley civilization. Karachi: Oxford University Press; 1998.

    Google Scholar 

  41. Sachdev R, Haldiya KR, Dixit AK. Acute intermittent Porphyria in a Kumhar community of Western Rajasthan. J Assoc Physicians India. 2005;53:101–4.

    CAS  PubMed  Google Scholar 

  42. Price AL, Patterson NJ, Plenge RM, Weinblatt ME, Shadick NA, Reich D. Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet. 2006;38(8):904–9.

    Article  CAS  PubMed  Google Scholar 

  43. Lazaridis I, Nadel D, Rollefson G, Merrett DC, Rohland N, Mallick S, Fernandes D, Novak M, Gamarra B, Sirak K, et al. Genomic insights into the origin of farming in the ancient near east. Nature. 2016;536(7617):419–24.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 2007;81(3):559–75.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Elhaik E, Tatarinova T, Chebotarev D, Piras IS, Maria Calo C, De Montis A, Atzori M, Marini M, Tofanelli S, Francalacci P, et al. Geographic population structure analysis of worldwide human populations infers their biogeographical origins. Nat Commun. 2014;5:3513.

    Article  CAS  PubMed  Google Scholar 

  46. Marshall S, Das R, Pirooznia M, Elhaik E. Reconstructing Druze population history. Sci Rep. 2016;6:35837.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Das R, Wexler P, Pirooznia M, Elhaik E. The origins of Ashkenaz, Ashkenazic Jews, and Yiddish. Front Genet. 2017;8:87.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Das R, Upadhyai P. Application of geographic population structure (GPS) algorithm for biogeographical analyses of populations with complex ancestries: a case study of south Asians from 1000 genomes project. BMC Genet. 2017;18(Suppl 1):109.

    Article  PubMed  PubMed Central  Google Scholar 

  49. Das R, Upadhyai P. Adaptation of the Geographic Population Structure (GPS) algorithm for biogeographical analyses of wild and captive Gorillas. BMC Bioinformatics (In Press). 2019;20:35.

    Article  Google Scholar 

  50. Das R, Wexler P, Pirooznia M, Elhaik E. Localizing Ashkenazic Jews to primeval villages in the ancient Iranian lands of Ashkenaz. Genome Biol Evol. 2016;8(4):1132–49.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Aberg KA, Chan RF, Shabalin AA, Zhao M, Turecki G, Staunstrup NH, Starnawska A, Mors O, Xie LY, van den Oord EJ. A MBD-seq protocol for large-scale methylome-wide studies with (very) low amounts of DNA. Epigenetics. 2017;12(9):743–50.

    Article  PubMed  PubMed Central  Google Scholar 

  52. Flegontov P, Changmai P, Zidkova A, Logacheva MD, Altinisik NE, Flegontova O, Gelfand MS, Gerasimov ES, Khrameeva EE, Konovalova OP, et al. Genomic study of the Ket: a Paleo-Eskimo-related ethnic group with significant ancient north Eurasian ancestry. Sci Rep. 2016;6:20768.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Triska P, Chekanov N, Stepanov V, Khusnutdinova EK, Kumar GPA, Akhmetova V, Babalyan K, Boulygina E, Kharkov V, Gubina M, et al. Between Lake Baikal and the Baltic Sea: genomic history of the gateway to Europe. BMC Genet. 2017;18(Suppl 1):110.

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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Acknowledgements

The authors are grateful to the SBB-2019 Committee for support of this work.

About this supplement

This article has been published as part of BMC Medical Genetics Volume 21 Supplement 1, 2020: Selected Topics in “Systems Biology and Bioinformatics” - 2019: medical genetics. The full contents of the supplement are available online at https://bmcmedgenet.biomedcentral.com/articles/supplements/volume-21-supplement-1.

Funding

The publication cost was covered by Russian Ministry of Education and Science, Project No. 28.12487.2018/12.1. “Investigation, analysis and complex independent expertise of projects of the National technological initiatives, including the accompanying of projects of «road map» «NeuroNet»”. The funding body had no role in design of the study, analysis, data interpretation and writing the manuscript.

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Authors and Affiliations

  1. Yenepoya Research Centre (YRC), Yenepoya (Deemed to be University), Mangalore, Karnataka, India

    Ranajit Das

  2. Humanitarian Institute, Novosibirsk State University, 630090, Novosibirsk, Russia

    Vladimir A. Ivanisenko

  3. Institute of Cytology and Genetics SB RAS, Novosibirsk, Russia

    Vladimir A. Ivanisenko

  4. The Digital Health Institute, I.M. Sechenov First Moscow State Medical University (Sechenov University), Moscow, Russia

    Anastasia A. Anashkina

  5. Engelhardt Institute of Molecular Biology RAS, Moscow, Russia

    Anastasia A. Anashkina

  6. Department of Medical Genetics, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, India

    Priyanka Upadhyai

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  1. Ranajit Das
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  3. Anastasia A. Anashkina
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  4. Priyanka Upadhyai
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Contributions

RD conceived the idea of the work. RD did the analysis with inputs from PU. VI and AA helped in the computational analyses. PU wrote the manuscript. All the authors finalized the manuscript. The author(s) read and approved the final manuscript.

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Correspondence to Ranajit Das.

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The SNP genotype data for the current study was obtained from previously published datasets. The original authors collected the samples under the supervision of ethical review boards in India with informed consent obtained from all subjects and sample collection was performed in accordance with the ethical standards of the responsible committees on human experimentation.

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Not applicable.

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The authors declare that they have no competing interests.

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Das, R., Ivanisenko, V.A., Anashkina, A.A. et al. The story of the lost twins: decoding the genetic identities of the Kumhar and Kurcha populations from the Indian subcontinent. BMC Genet 21 (Suppl 1), 117 (2020). https://doi.org/10.1186/s12863-020-00919-2

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Keywords

  • Kumhar
  • Kurchas
  • Indus Valley civilization
  • South Asian population history

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