Research Article | Volume 12, Issue 1, January, 2024

Population and genetic analyses of mitochondrial DNA variation in Gujarat

Mohammed H. M. Alqaisi Molina Madhulika Ekka M. Anushree Harshit A. Ganatra Bhargav C. Patel   

Open Access   

Published:  Dec 26, 2023

DOI: 10.7324/JABB.2024.142600
Abstract

The hypervariable regions (HV1 and HV2) of the mtDNA of 176 individuals from different regions of Gujarat, India were analyzed for population genetic and forensic parameters within the population and compared to the data of three neighboring states (Maharashtra, Rajasthan, and Madhya Pradesh) for inter-population comparison. The haplotype diversity in Gujarat was 0.9970, with a random match probability of 0.0056 and a discrimination power of 0.9944. We observed 146 haplotypes that belonged to 10 haplogroups (M, U, R, N, HV, W, H, T, J, D). The most frequent haplogroup was M (52.27%) with 43 sub-haplogroups. The other haplogroups were as follows: R (13.63%), H (2.27%), HV (3.41%), T (1.71%), J (0.56%), U (18.18%), W (2.84%), and D (0.56%). Analysis of molecular variance showed the majority of genetic variation was found to exist within populations rather than between populations, and the pairwise Fst showed that Gujarat and Rajasthan had the highest genetic distance (Fst 0.02689). We have generated accessible mtDNA dataset references for Gujarat in the worldwide DNA database [EMPOP and NCBI]. This study demonstrates that mtDNA sequence analysis can contribute to the expansion of population databases and provide important details for population genetic and forensic investigations.


Keyword:     mtDNA Population genetics Forensic Haplogroup Gujarat population India


Citation:

Alqaisi MHM, Ekka MM, Anushree M, Ganatra HA, Patel BC. Population and genetic analyses of mitochondrial DNA variation in Gujarat. J App Biol Biotech. 2024;12(1):133-149. http://doi.org/10.7324/JABB.2024.142600

Copyright: Author(s). This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike license.

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1. INTRODUCTION

Analysis of human mitochondrial DNA (mtDNA) is essential for forensic investigations and population genetics research. Understanding human evolution heavily relies on the study of the frequency and pattern of changes in mtDNA sequences, which have a mutation rate that is 10 times higher than that of the nuclear genome [1]. The mtDNA control region, also known as the hypervariable regions, is a crucial mutational hotspot in the entire genome, comprising three hypervariable regions (HV1, HV2, and HV3). This region is unique in forensics as it is inherited solely from the mother and does not undergo recombination, meaning that all maternal relatives will share the same mtDNA haplotype [2-4]. However, this feature limits the power of discrimination, making it challenging to distinguish between closely related individuals or those with the same haplotype. Despite its limitations, mtDNA analysis is still an available choice when biological evidence is damaged or exhibits mixed short tandem repeat profiles. In such situations, mtDNA offers greater precision and reliability when compared to nuclear DNA analysis [4-6].

The putative genetic structure of the population is an essential component to assess mtDNA match comparison with unrelated individuals. Therefore, the study of population and forensic parameters in a given population, such as the number of haplotypes (H), polymorphic sites (S), nucleotide diversity (π), haplotype diversity (Hd), and haplogroup distribution are an important tool in population and forensic genetics [7-11]. Sequencing of either hypervariable regions or the entire mtDNA may be used to study these parameters.

The consistent advancements in sequencing technology, such as Next-Generation Sequencing (NGS), which allows the examination of the entire mtDNA genome, have led to the development of a substantial forensic mtDNA database. For example, MITOMAP and EMPOP databases are used to analyze the vast majority of mtDNA data collected [12-15]. Nonetheless, the reference mitogenomes and/or control region sequences are either unavailable or insufficient for a variety of Indian populations, including Gujarat.

India is known for its diverse population, encompassing differences in social, linguistic, cultural, geographical, ethnic, and genetic aspects. The population of India can be classified based on caste, tribe, religion, region, and language, with four significant linguistic families: Indo-European, Dravidian, Austroasiatic, and Tibeto-Burman. As a geographical region located at the intersection of Africa, Eurasia, and the Pacific, India served as a corridor for the dispersal of modern humans from Africa around 100,000 years ago [16,17]. Several molecular genetic studies conducted in the late 1990s on Indian populations using high-resolution RFLP and sequencing analysis aimed to comprehend complex relationships between different Indian and worldwide sub-populations. These studies reveal that India’s genetic diversity is higher than other comparable global regions, with variations in mtDNA indicating human dispersal throughout the country during the middle Palaeolithic era [18-20]. Moreover, Recent research has uncovered India’s evolutionary history, encompassing ancient settlements and gene flow from West and East Eurasia, achieved through identifying haplogroups and Indian-specific haplogroups. Genetic relationships among castes, tribes, and communities in India have been investigated, although a limited number of studies have included the state of Gujarat [21-25]. For mtDNA analysis to be useful in forensic investigations, it is important to have a large database of mtDNA profiles from different populations. This database can be used as a reference to compare mtDNA samples obtained from crime scenes or from individuals involved in a case. The unavailability of these data for Gujarat and related populations negatively affects mtDNA-based forensic investigations of cases in which people from such populations are involved. Thus, the present study is an effort to create the necessary data set for the Gujarat population.

Gujarat is the fifth-largest state in the Northwest region of India and the ninth-most populous state overall. It is bounded to the west and southwest by the Arabian Sea and to the north by Pakistan. It has a population of sixty million, which represents 4.99% of India’s total population [26-28]. The population is diverse with 11 Major tribes constituting approximately 15% of the total state population with a history dating back to the Harappan Civilization [29]. The numerous migrations and invasions throughout its history have resulted in a complex admixture with high levels of genetic and phenotypic variation, with a variation among the caste population as high as 40%. Several major haplogroups with the following frequency percentages have also been reported from this region: M (44.1%), U7 (12%), N (2.9%), R* (N) (8.8%), and W(N) (5%) [24].

mtDNA analysis has always been used in forensic and population genetic studies. Thus, the purpose of this study was to analyze the HV1 and HV2 mtDNA sequences of the Gujarat population to generate an mtDNA reference dataset. Furthermore, we investigate genetic variation, identify haplogroups, their frequencies, and geographic origins, as well as estimate forensic and population parameters that can be utilized in population genetic studies and forensic mtDNA typing.


2. MATERIALS AND METHODS

2.1. Population Samples

A total of 5–10 mL of whole blood samples from 72 (n1) maternally unrelated consented individuals from north (N), south (S), central (C) and the Saurashtra (T) regions of Gujarat, were selected for sequencing of the entire mtDNA genome. The participants were evenly split between male and female individuals, with half of the samples collected from each gender. The age range of the participants spanned from 20 to 60 years, and the mean age was calculated to be 34 years. All samples were kept at 4°C until further processing. This study was granted ethical approval by the Institutional Ethical Committee. Along with these samples, HV1 and HV2 regions from 104 (n2) unrelated individuals from our earlier work on the Gujarat population (accession numbers; EMPOP EMP00859 and NCBI OM908544-OM908751) were also considered [30]. As a result, a total of 176 (n1 + n2) samples from Gujarat were considered to analyze HV1 and HV2 of mtDNA for this study. Additionally, mtDNA sequence data were collected for the purpose of inter-population comparative analysis. These data were obtained from published sources and were gathered from three different neighboring states of Gujarat. Figure 1 illustrates the overall number of samples that were collected as well as their distribution.

Figure 1: A schematic map of four states in India displays the total number of samples and their geographic distribution. The number inside the circle represents the total number of samples from the entire state, and the underlined numbers represent number of samples from various regions in Gujarat.



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2.2. DNA Extraction and Quantitation

Extraction of mtDNA from the 72 samples was carried out immediately after the samples were collected. They were extracted and purified using DNeasy® Blood and Tissue kit (Qiagen, Hilden, Germany) [31]. DNA extractions were carried out in a biosafety chamber (Class II/A2) to avoid contamination of extraneous DNA. Extracted DNA was stored at −20°C until further processing. The eluted DNA samples were quantified using the Quantifiler® Trio DNA Quantification Kit (Applied Biosystems, USA) as per manufacturer’s protocol and analyzed by HID Real-Time PCR Analysis Software V1.2 (Applied Biosystems, USA).

2.3. DNA Amplification and Sequencing

Amplification and sequencing of mtDNA were carried out using kits and reagents provided by Applied Biosystems, USA. The whole mitogenome was sequenced using the Precision ID mtDNA Whole Genome Panel. The panel comprises two pools containing a total of 162 primers and 283 degenerate primers for amplification and sequencing of the entire mtDNA genome. mtDNA library for all samples was prepared by automated workflow on Ion Chef with Precision ID Library kit. The library was quantified using a TaqMan® Quantitation Kit after purification with AMPure™ XP Reagent. Diluted libraries were loaded onto the semiconductor sequencing chip for amplification and sequencing using HID Ion Chef™ and Ion Gene Studio™ S5. Next Generation Sequencing was performed on the Ion Torrent S5™ System as per the manufacturer’s protocol [32]. The NGS data of all samples were analyzed using the Ion Torrent Converge™ v2.1 software (Applied Biosystems, USA). The whole mtDNA genome sequence variants were submitted to the mtDNA population database EMPOP (www.empop.org) as per the guideline [33], for evaluating variations and double-checking designated haplogroups with EMPOP accession number EMP00864 [34]. The FASTA format sequences were submitted to GenBank (accession number OP004728-OP004801).

2.4. Statistical Analysis to Understand the Population Structure of Gujarat

Geneious Prime® 2019.1.2 (Biomatters, USA) was used to align and extract HV1 and HV2 regions from FASTA format sequences. All sequences were assembled by aligning and comparing them to annotated revised Cambridge Reference Sequence (rCRS) [35].

Furthermore, the occurrence of poly-C tracts sequencing errors has previously been demonstrated, where the exact number of cytosine residues is difficult to determine due to variable numbers of cytosines present in these homopolymeric tracts [36-39]. Thus, the number of cytosine residues in these regions was ignored for comparative or population study purposes in accordance with SWGDAM, ISFG, and FBI’s Interpretation Guidelines for mtDNA Sequencing. It was assumed that the number of cytosines in these homopolymeric regions would be the same (as rCRS) across all comparisons [39-42]. Therefore, we reported the pattern and frequency of these tracts in Table S1 of the supplementary material for all samples but omitted them from our statistical population genetics analysis.

Population genetic parameters such as the nucleotide diversity (π), Hd, and the number of haplotypes were computed with Arlequin v3.5.2.2 [43] and DnaSP v.6 [44]. In Arlequin v3.5.2.2, population structure and genetic differentiation were calculated using the analysis of molecular variance (AMOVA) (estimated using 1000 permutations) and pairwise fixation index (Fst). The forensic parameters, including the random match probability and discrimination power, were calculated manually. The random match probability was calculated using the formula (p=ΣX2), where X is the frequency of each observed haplotype [45], while the discrimination power was calculated using the formula (1-ΣX2), where X is the frequency of each observed haplotype [46]. Haplogroups were identified and assigned using EMPOP [34]. The matrilineal relationships within the population, which were determined based on haplogroups are illustrated by constructing a Neighbour Joining tree using the Tamura-Nei model [47] using Geneious Prime® 2019.1.2 software (Biomatters, USA). We used Brinkmann et al. [48] method to manually calculate the maximum and minimum estimates of the probability ratio of obtaining an mtDNA haplotype match within Gujarat and between Gujarat and its other three neighboring states.


3. RESULTS

The majority of studies in the fields of population genetics and forensic science that involve the analysis of mtDNA depend substantially on haplotype and haplogroup analysis. mtDNA haplotypes are the unique combination of variations when aligned to a reference sequence rCRS. The haplogroups are variations in haplotypes that are typically inherited together. Therefore, haplotypes aid in defining haplogroups. And hence, maternally related individuals have similar haplogroups with minimal to no variation in their haplotypes [48-50]. A precise calculation of the Hd, random match probability, discrimination power, haplogroup frequency, and other population and forensic parameters in a particular population can offer significant knowledge, such as the population’s historical background, migration patterns, genetic variation, and can assist with forensic investigations. For instance, lower Hd indicates shared haplotypes among individuals, meaning the more likely it is that two unrelated individuals would share it by chance, rendering a match with this mtDNA type less convincing [6,9,51].

3.1. Intra-population Analysis: Genetic Diversity, Population, and Forensic Parameters

High-quality sequences of mtDNA control region (HV1 and HV2) of 176 individuals were provided to be used as reference data in Gujarat. The mtDNA haplotypes and haplogroups of all individuals are presented in the supplementary material Table S2. Gujarat had a total of 780 polymorphic sites (S), which define 146 unique haplotypes that belonged to 10 distinct haplogroups (M, U, R, N, HV, W, H, T, J, D). A summary of the population’s genetic diversity and forensic parameters of all samples are listed in Table 1.

Table 1: Forensic and population genetic indices (parameters) based on HV1 and HV2 regions for each sub-population samples from Gujarat.

ParametersRegion

North (N)Central (C)Saurashtra (T)South (S)Gujarat (Total)
Sample size66593021176
Number of polymorphic sites (S)62877060663780
Nucleotide diversity (π)0.02670.08690.04840.00990.0483
Mean pairwise differences26.234085.403847.54259.795247.4828
Number of haplotypes60542921146
Haplotype diversity (Hd)0.99670.99650.99771.00000.9970
Random match probability0.01840.02040.03560.04760.0056
Discrimination power0.98160.97960.96440.95240.9944

The overall nucleotide diversity (π) was 0.0483, indicating a moderate level of genetic diversity throughout the Gujarat region. However, the level of nucleotide diversity differs significantly across the four distinct regions (ranging from 0.0099 to 0.0869), with certain areas exhibiting notably higher levels of diversity compared to others. The Hd was calculated to be 0.99, indicating a high level of genetic variation among the studied subpopulations in Gujarat. In addition, the probability of two randomly selected individuals sharing the same haplotype was assessed and was found to be as low as 0.0184 (N), 0.0204 (C), 0.0356 (T), and 0.0476 (S), while the discrimination power was 0.9816 (N), 0.9796 (C), 0.9644 (T), and 0.9524 (S).

To further evaluate the genetic diversity of the subpopulations, the mean number of pairwise differences (MPD) was calculated. The results indicated that Central Gujarat had the highest MPD (85.403857 ± 37.258705), suggesting that this subpopulation has the highest genetic diversity among all studied subpopulations. In contrast, the southern region of Gujarat exhibited the lowest MPD (9.795238 ± 4.671970), indicating a lower level of genetic diversity compared to the other subpopulations. In addition, demographic parameters such as Fu and Li’s Fs and Tajima’s D were calculated among the four sub-subpopulations in Gujarat. The results indicated a negative value for both Fu and Li’s Fs (−23.9132) and Tajima’s D (−2.1077).

3.2. Haplotypes and Haplogroups Distribution

In the population of Gujarat, the haplogroup with the highest frequency was M (52.27%), followed by U (18.18%) and R (13.64%). The highest number of sub-haplogroups was also found in M, with 43 sub-haplogroups, whereas U contained only 18 sub-haplogroups. The haplogroups D4 and J1b1b were observed only once. Additional information about the frequency of haplogroups and sub-haplogroups in the population is presented in Table 2, while Figure 2 displays a phylogenetic tree (haplogroup tree) depicting matrilineal relationships for the entire population.

Table 2: The detected haplogroups, their frequency, and the geographical origin of the Gujarat population.

Macro/Sub HaplogroupFrequency (%)Macro/SubFrequency (%)Possiblea OriginMacro/Sub HaplogroupFrequency (%)Macro/Sub HaplogroupFrequency (%)Possiblea Origin

Haplogroup
M11.364AsianU1a1a0.568U1a1c1d10.568West Eurasian
M2a1a1.705M2b1a0.568South AsianU2e1b0.568U2e2a1a20.568West Eurasian
M3a1+2042.273M3a1a2.244South AsianU4b1a1a10.568U5a10.568West Eurasian
M3a1b1.136M3a2a0.568South AsianU5a1b0.568U5a1b10.568West Eurasian
M3d1.136M3d11.136South AsianU5a1f11.136U5a2a10.568West Eurasian
M4a1.705M4b1.136South AsianU71.136U7a5.114West Eurasian
M5a0.568M5a1a0.568South AsianU7a3b1.136U7a4a1a0.568West Eurasian
M5a2a0.568M5a2a10.568South AsianU21.136U2a0.568South Asian
M5a2a1a1.136M5a3b0.568South AsianU2a1b0.568U2b21.705South Asian
M5a40.568M5b20.568South AsianTotal Freq18.182
M5b2b0.568M5c10.568South Asian
M60.568M6a1a0.568South AsianR2.273R21.136South Asian
M6a1b1.136M303.409South AsianR50.568R5a1a0.568South Asian
M30+162342.273M30b0.568South AsianR5a21.136R6+161290.568South Asian
M30c10.568M30c1a0.568South AsianR6a10.568R6a20.568South Asian
M30f1.705M33a1b0.568South AsianR6b1.136R8a1a1a10.568South Asian
M33a20.568M33a30.568South AsianR30a1b0.568R30a1b10.568South Asian
M33b0.568M37e20.568South AsianR30b2a2.273R321.122South Asian
M38a0.568M391.136South AsianTotal Freq13.636
M39b1.136M490.568South Asian
M52a0.568M57b0.568South AsianN3.409East Asian
M57b11.705M65b0.568South AsianN1a1b10.568N1a20.568West Eurasian
Total Freq52.273Total Freq4.545
W0.568West Eurasian
W+1940.568W40.568West EurasianHV2.841HV2a0.568West Eurasian
W60.568W6b0.568West EurasianT1a50.568T2b340.568West Eurasian
Total Freq2.841T2d1b0.568West Eurasian
H13a2a10.568H291.136West EurasianJ1b1b0.568West Eurasian
H7b0.568West EurasianD40.568East Asian
Total Freq2.273Total Freq6.249

[a] Kyoung, “mtDNA Haplogroup Specific Control Region Mutation Motifs,” Am J Hum Genet, vol. 75, pp. 752–770, 2004. M. van Oven, “PhyloTree Build 17: Growing the human mitochondrial DNA tree,” Forensic Sci. Int. Genet. Suppl. Ser., vol. 5, pp. e392–e394, 2015, doi: https://doi.org/10.1016/j.fsigss. 2015.09.155.

Figure 2: Phylogenetic relationship of the four geographic regions (Central, North, South and Saurashtra) based on the major mtDNA haplogroups. Different colors represent major haplogroups according to the following: M (red), U (blue), R (green), N (purple), HV (yellow), W (dark violet), H (sky blue), T (cyan), J (violet), D (grey). The second letter of the sample ID at each tip node represent the geographical location in Gujarat: C–Central, N–North, S–South and T–Saurashtra.



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We observed that the majority of mtDNA lineages in the Gujarat population belong to either the South Asian (Indian) haplogroup M (52.27%) and R (13.64%) or the Western-Eurasian haplogroups H (2.27%), HV (3.41%), T (1.70%), J (0.57%), U (18.18%), and W (2.84%). There was only one individual who belonged to D4 (0.57%), an East Asian haplogroup.

3.3. Inter-population Analysis: Genetic Variation and Population Structure

A comparative analysis of the genetic variation and differentiation was conducted between our population samples, and those from Maharashtra, Rajasthan, and Madhya Pradesh. Figure 1 shows the number of sample population data from the three states obtained from published literature [52]. The sequences from selected regions were downloaded from GenBank (accession numbers: FJ 383814 to FJ 383174). The AMOVA as well as F-statistics (Fst) were calculated from the haplotype frequencies using the Arlequin software. Our findings indicate that genetic variation within populations accounted for 97.57%, while only 2.43% of the variation was observed between populations, as illustrated in Table 3. In addition, the pairwise Fst values, as indicated in Table 4, were both statistically significant and comparable. Gujrat was compared with the three neighboring states and the highest variation in population structure was observed between Gujarat and Rajasthan (Fst 0.02689). The least variation was observed between Gujarat and Madhya Pradesh (Fst 0.0145).

Table 3: Analysis of molecular variance (AMOVA) of four different populations in India.

Source of variationDegree of freedomSum of squaresVariance componentsPercentage of variation
Among populations34.0780.01223 Va*2.43
Within populations328160.8350.49035 Vb97.57
Total331164.9130.50258
Fixation Index (Fst) ‡ = 0.02434/P-value=0.000/number of permutations :1023

Variance:

* Va: Variance for population among groups,

Vb: Variance for haplotypes within a population within a group, Fst‡: Permuting haplotypes among populations within groups

Table 4: Analysis of molecular variance; pairwise Fst and probability values for four different populations in India.

StateFst valueGujaratMadhya PradeshMaharashtraRajasthanP-value
Gujarat0.00000±0.00000.00000±0.00000.00000±0.0000
Madhya Pradesh0.01450.00000±0.00000.00000±0.0000
Maharashtra0.019520.032360.00000±0.0000
Rajasthan0.026890.040190.04509

In forensics, it is important to consider matching probability rather than genetic distances [48,53]. Thus, mtDNA sequences from Gujarat were compared to those from its three neighboring states to examine if there were any regional differences that would affect the possibility of finding sequence matches by chance. Table 5 represents the likelihood of finding a match within Gujarat rather than between populations. The maximum probability of finding two distinct haplotypes is 99.97% when sampling from Gujarat and Maharashtra, 99.95% when sampling from Gujarat and Madhya Pradesh, and 99.64% when sampling from Gujarat and Rajasthan. To rephrase, the probability of finding a match within Gujarat is approximately 26.3 times higher than between Gujarat and Maharashtra, 15.8 times higher than between Gujarat and Madhya Pradesh, and 2.2 times higher than between Gujarat and Rajasthan. The lower estimates of mwmin/mbmin for Gujarat- Maharashtra, Gujarat - Madhya Pradesh, and Gujarat - Rajasthan are 7.7 times, 4.6 times, and 0.6 times, respectively.

Table 5: HV1 and HV2 sequence matching probabilities within Gujarat and between Gujarat and neighbouring populations.

Gujarat (G)Maharashtra (M)Madhya Pradesh (MP)Rajasthan (R)
Na176684543
dwminb0.99200.94850.95110.9248
mw maxc0.00790.05150.04890.0752
mwmind0.00230.03730.02730.0532
mbmine-G-M: 0.0003G-MP: 0.0005G-R: 0.0036
mwmax/mbminf-G-M: 26.3G-MP: 15.8G-R: 2.2
mwmin/mbming-G-M: 7.7G-MP: 4.6G-R: 0.6

a Number of samples,

b Minimum diversity within the population (defined as h by Nei 1987)

c Maximum matching probability within the population

d Minimum matching probability within the population

e Minimum matching probability between two populations

f Maximum estimate to find a match within a population than between two populations

g Minimum estimate to find a match within a population than between two populations c-gcalculated as Brinkmann et al. (1999) [b] Nei M, “Molecular evolutionary genetics.” Columbia University Press, New York, P 178, 1987


4. DISCUSSION

Gujarat has a remarkable level of mtDNA diversity, implying that the genetic makeup of the population has been changed over time by a complex interplay of numerous influences. The history of human migration and settlement is thought to be a major driver of genetic variety in the region. Gujarat has been populated for thousands of years and has been a major center of trade and commerce for much of its history, resulting in a mix of cultural and genetic influences from neighboring countries such as West Asia, Central Asia, and East Africa [28,54].

The high Hd observed in the studied subpopulations indicates the presence of relatively few identical or shared haplotypes, with low random match probability and high discrimination power. The limited recent exchange of genes across linguistic and caste boundaries is suggested by the small number of shared haplotypes between the subpopulations [21,55]. Furthermore, this is of significant forensic importance, as it suggests that chance matches may occur in one in a hundred individuals in the North, two in a hundred in the Central, three in a hundred in the Saurashtra, and four in a hundred in the South. Central Gujarat had the highest MPD, which can be attributed to the presence of three major cities: Ahmedabad, Vadodara, and Anand. These cities have been commercial hubs and have attracted immigrants from other states, resulting in higher genetic diversity. Conversely, the southern region of Gujarat had the lowest MPD due to its small size, with the Arabian Sea and the Western Ghats on either side restricting gene flow.

The overall negative values of the demographic parameters (Fu and Li’s Fs and Tajima’s D) observed in all four sub-subpopulations are indicative of recent population expansion or selection. Our study also revealed a high level of Hd and low nucleotide diversity (π). It is possible that a period of fast population growth contributed to the increased stability of rare mutations, as has been suggested in previous studies [56,57].

More than half of Gujarat’s population belongs to the haplogroup M, which accounts for 52.27% of the population. Prior research conducted by Quintana-Murci and colleagues reported that the frequency of this haplogroup in Gujarat was 44.1% [58]. This increase in frequency could be the result of population growth in a larger geographic area.

The haplogroup M, originating from L3, exhibited 14 (13, if M should not be considered) distinct subclades. M30 (motifs;195A, 16223T) and M3 (motif;16126C) superclades were shown to be the most common, accounting for about 33.70% of the M haplogroup. These haplogroups were defined by fast mutations “speedy mutation” at their motif’s sites, and their phylogenetically status has consequently been challenged [58,59]. The M 30 sub-clade has a more recent expansion time at 33,042 YBP [60]. Four samples of M30 with a specific mutation at 16234 branched out, forming M30+16234, previously reported in the Shin population in Pakistan [61]. The second most frequent super-clade M3 was seen more frequently in the North region and the founder age for this haplogroup is less than 25,000 years [52]. M37, M38, M49, and M52 were the least frequent subclades.

The haplogroup U can be considered among the initial maternal founders in Southwest Asia and Europe having subclades older than 30 thousand years [62]. The clade originated from R with the following motifs 11467G, 12308G, and 12372A [63]. Being the second most frequent lineage in India and Europe, it is geographically distributed through North Africa and Central Asia as well [21,58,64]. Similarly, it was also found to be the second most frequent in the population of Gujarat with a frequency of 18.18. The subclade U7 (motifs;152C, 16318T) was found to be the most predominant with a frequency of 7.96 (U7a being the most frequent) which was found previously in Iran, India and Pakistan [24]. This subclade is comparatively recent (16–19 thousand years) with a wide geographical range across Europe, Near East, and South Asia [62]. It is also highly likely to have emanated from Near East [65]. The subclade U2 (motif;16051G) and U5 (motif;16270T) followed behind closely at 5.11 and 3.41 frequency, respectively, with no apparent geographical variation between the four regions. U4 (motifs; 16356C, 195C) subclade was the least frequent in the studied population.

The Western-Eurasian-specific haplogroups H, HV, J, T, N1 and W shows low frequency in the population. These low-frequency haplogroups and their respective lineages are probably quite useful in providing information on the divergence that took place along the route from Eurasia to South Asia [66,67]. The South Asian M and Western-Eurasian U haplogroups account for the vast majority of the population (71.35%), and their distribution is nearly uniform across Gujarat.

The comparative analysis of the genetic variation and differentiation between our population samples and those from Maharashtra, Rajasthan, and Madhya Pradesh, revealed that the genetic variation within populations was higher than between populations. To determine the effect of geographical substructure on forensic investigations, it is desirable to have a cluster with low within-population variation and high between-population variation [52,68]. Our results suggesting that there was no significant genetic divergence among populations. The differences between them are caused by only 2.43% of total variants, indicating substantial gene flow between them. Although the populations exhibited a high degree of genetic similarity (as evidenced by relatively small and similar Fst values), the pairwise Fst values indicated the existence of some genetic differences among the populations. Notably, the highest variation in population structure was observed between Gujarat and Rajasthan, while the least variation was observed between Gujarat and Madhya Pradesh. The substantial genetic differences observed between the populations of Gujarat and Rajasthan can be due to the historical migration patterns into India, which probably occurred through Rajasthan and Gujarat. Considering Rajasthan’s location at the intersection of Africa, Western Eurasia, and Eastern Eurasia, it is probable that the region served as a critical terrestrial pathway for the migration of human populations, leading to substantial genetic diversity [69,70].

The analysis of the forensic parameter, match probability, between Gujarat and the three neighboring states revealed a notable ethnic disparity. The results indicate that it is more likely to find a sequence match within the population of Gujarat than between Gujarat and the other three neighboring populations. This finding underscores the importance of employing micro-geographic sampling in forensic applications to accurately identify individuals based on their DNA profiles. By sampling individuals from smaller geographic regions, the likelihood of finding a match within the same population increases, thereby improving the reliability of DNA evidence in forensic investigations [48,71]. Considering the current status of the mtDNA data on Indian populations and related genetic parameters, the present study provides some advantages and advancements in the current knowledge. One of the major outcomes is the estimation of various population genetics parameters for the mtDNA and to investigate potential relationships between the sub-populations of Gujarat using phylogenetic analyses. Second, we estimated and compared the population genetics structure between Gujarat and the neighboring states for forensic and population genetic analyses. Third, by incorporating population parameters, forensic scientists can ensure that the criminal justice system operates with accuracy and fairness. Finally, our contribution to the global DNA database (EMPOP) provides accessible forensic mtDNA data references for Gujarat, thereby enhancing the accuracy and efficiency of forensic investigations in the region. In addition, this dataset can have implications for other fields like evolutionary biology, anthropology, and medicine. The study was limited in its ability to determine the precise ancestral migration patterns of the haplogroups studied due to a lack of detailed maternal lineage information for the collected samples. The forensic analysis relies on large amounts of high-quality data, thus it is crucial that further research be carried out with rigorous database sample collection and analysis to encompass the other populations of India.


5. CONCLUSION

The results from the current study demonstrated that sequencing hypervariable regions (HV1 and HV2) can reveal a significant amount of information for tracing maternal lineages and distinguishing between unrelated individuals. To the best of our knowledge, few mtDNA data have been released from Gujarat, hence expanding and improving mtDNA sequence databases is crucial for forensic investigation. We have produced a high-quality database, which may be used as a reference for forensic investigations as well as for population genetics research. Our results show a high Hd with a low random match probability which helps in exploring maternal lineage and forensic analysis. The majority of the maternal lineages that we detected in our sample belonged to haplogroup M, which is a haplogroup that is exclusively present in South Asia (India). West Eurasian haplogroups were also observed in the population indicating genetic continuity with the West Eurasian region during the emergence of these haplogroups. The significant negative neutrality test values show that the population had an excess of rare mutations leading to an increase in diversity.


6. ACCESSION NUMBERS

The nucleotide sequences have been submitted to NCBI GenBank® under accession numbers OP004728-OP004801. The dataset generated is accessible in the EMPOP database under accession number EMP00864


7. SUPPORTING INFORMATION

Supplementary data [Tables S1 and S2] associated with this article can be found in the online version.


8. ACKNOWLEDGEMENTS

The authors greatly appreciate the generosity and kind support of Walther Parson. Thank you to our lab mates Blessy Baby, and Kudzanai Joanna Mushavatu.


9. AUTHORS’ CONTRIBUTIONS

All authors made substantial contributions to the conception and design, acquisition of data, or analysis and interpretation of data; took part in drafting the article or revising it critically for important intellectual content; agreed to submit to the current journal; gave final approval of the version to be published; and agreed to be accountable for all aspects of the work. All the authors are eligible to be an author as per the International Committee of Medical Journal Editors (ICMJE) requirements/guidelines.


10. FUNDING

This work was financially supported by the regular academic grant from National Forensic Sciences University, Gujarat, India. Mohammed H. M Alqaisi would like to acknowledge the Indian Council for Cultural Relations (ICCR) for their financial support for this work.


11. DECLARATION OF COMPETING INTEREST

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


12. COMPLIANCE WITH ETHICAL STANDARDS

This study was approved by the Ethical Committee of National Forensic Sciences University wide letter no. NFSU/SDSR/IEC/Certificate/73/21 Date: June 03, 2021. All samples were collected with detailed informed consent.


13. DATA AVAILABILITY

The mtDNA sequences are available on EMPOP database with accession number EMP00864. The GenBank accession number for the submitted sequences are from OP004728-OP004801.


14. PUBLISHER’S NOTE

This journal remains neutral with regard to jurisdictional claims in published institutional affiliation.

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Table S1: Pattern and frequency of poly-C tracts in HV1 and HV2 based on sequencing 72 samples using NGS (Ion Torrent) and 104 samples previously sequenced by the Sanger sequencing method.

LocationPositionPatternNo. of CNGS method (72 samples)Sanger method (104 samples)Both methods (Total 176 samples)



n%n%n%
HV116182, 16183 and 1618916182-(A > C) (A > C) 5C (T > C) 4C A-161941211.390010.57
16183 and 1618916182-A (A > C) 5C (T > C) 4C A-161941122.7810.9631.7
1618916182-AA 5C (T > C) 4C A-161941056.9421.9273.98
Total116.25
HV2309302-A 7C (ins 1C) T-31073244.444442.37643.18
302-A 7C (ins 2C) T-310822.7810.9631.7
Total7944.88
*315302-A 7C T 5C (ins 1C) G-3161372100104100176100

N: number of individuals,

* 315.1C is not included in frequency calculations due to its exceptionally high prevalence in the population

Table S2: Mitochondrial DNA HV1 and HV2 sequence haplotypes and haplogroups of Gujarat population.

Sample IDRegionHaplogroupHaplotype
FC001UCU7a3b73G151T152C153G263G309.1C315.1C16092C16189C
FC002RCR6a118T73G150T152C228A263G315.1C16129A16319A
FC003UCU1a1c1d173G263G285T309.1C309.2C315.1C16182C16183C16189C
FC004RCR5a1a73G93G200G263G309.1C315.1C16145A16304C16519C
FN005HNH2993G263G309.1C315.1C16319A16519C
FC006MCM5a2a73G146C263G309.1C315.1C16129A16223T16519C
FC007MCM6a1a73G146C263G315.1C16189C16209C16223T16231C16311C
FC008MCM65b73G241G263G309.1C315.1C372.1T16223T16311C16519C
FN009MNM73G151T152C263G309.1C315.1C16051G16319A16519C
FN010MNM5b2b73G263G315.1C16048A16129A16223T16519C
FN011RNR6a273G263G315.1C16129A16213A16362C16519C
FN012MNM2a1a73G195C204C263G309.1C315.1C16223T16270T16319A
FC013RCR3273G152C263G315.1C16145A16185T16239T16325C
FN014RNR8a1a1a173G195C243G315.1C16519C
FN015MNM3a1a73G263G315.1C16126C16150T16223T16519C
FN016UNU1a1a73G195C263G285T309.1C315.1C385G16183C16186T
FN017UNU2a1b73G195C215G263G309.1C309.2C315.1C16051G16206C
FC018MCM4b73G263G315.1C16086C16145A16189C16223T16261T16311C
FN019NNN1a1b173G143A199C204C250C263G297G315.1C16223T
FC020MCM3a1+20473G204C263G309.1C315.1C16126C16223T16519C
FN021MNM5a2a173G263G315.1C16170G16192T16223T16301T16519C
FC022UCU5a1f173G195C200G263G315.1C16192T16256T16270T16311C
FC023MCM30c1a73G146C195A263G309.1C315.1C16166del16223T16519C
FC024HCH7b263G315.1C16519C
FC025RCR30a1b73G152C263G309.1C315.1C16126C16181G16209C16362C
FN026RNR30b2a73G152C215G263G309.1C315.1C373G16129A16311C
FN027RNR30b2a73G263G309.1C315.1C373G16292T16497G16519C
FN028RNR3273G152C263G315.1C16145A16185T16239T16325C
FC029UCU2e2a1a273G152C217C263G315.1C16051G16092C16129C16168T
FN030HNH2993G263G309.1C315.1C16319A16519C
FN031MNM5a73G152C189G195C225T315.1C16129A16209C16223T
FN032MNM33a373G146C152C207A263G315.1C16129A16223T16271C
FN033MNM38a73G246C309.1C315.1C16111T16223T16239T16266T16390A
FC034MCM57a73G146C152C263G309.1C315.1C16051G16223T16311C
MC001MCM3a2a73G263G309.1C315.1C16126C16169T16223T16519C
MC002MCM2b1a73G152C182T195C263G309.1C315.1C16169.1C16183C
MC003MCM30f73G195A263G309.1C315.1C16223T16368C16519C
MC004MCM6a1b73G146C263G309.1C315.1C16188T16223T16231C16362C
MN005WNW6b73G143A189G194T195C204C207A263G309.1C
MC007MCM57b173G146C189G263G315.1C16223T16311C16519C
MC008RCR73G153G189G195C263G315.1C16129A16362C16519C
MN009MNM3d173G263G315.1C16126C16223T16344T16519C
MC010MCM33a1b73G152C199C263G315.1C16223T16519C
MN011MNM30f73G195A263G309.1C315.1C16223T16368C
MN012MNM673G152C214G263G315.1C16223T16362C
MN014HNH13a2a1263G309.1C315.1C16519C
MC015DCD473G263G315.1C16223T16362C
MN016MNM3a1a73G194T195C204C263G315.1C16126C16192T16223T
MN017MNM3a1+20473G150T204C217C263G315.1C16126C16223T16519C
MN018MNM3a1b73G204C217C263G309del315.1C16126C16223T16295T
MN019UNU7a4a1a73G151T152C263G309.1C315.1C16309G16318C16519C
MN020RNR73G153G189G195C263G315.1C16129A16362C16519C
MN021TNT2b3441T61T73G263G309.1C315.1C319C16126C16294T
MN022MNM4b73G146C263G315.1C16145A16223T16234T16261T16311C
MC023RCR564T73G263G309.1C315.1C16304C16524G16526A
MN024TNT1a573G200G263G309.1C315.1C16126C16163G16186T16189C
MN025UNU5a1b73G263G309.1C315.1C16192T16256T16270T16399G
MC026MCM30b73G152C195A263G309.1C315.1C16192T16223T16278T
MC027MCM3d73G263G315.1C16126C16223T16311C16344T16519C
MC028MCM3a1a73G204C263G315.1C16126C16223T16519C
MC029MCM57b173G146C189G263G315.1C16209C16223T16311C16519C
MC030MCM3073G195A263G315.1C16223T16519C
MN031MNM3a1b73G204C263G315.1C16126C16223T16311C16519C
MN032MNM5a3b73G194T263G309.1C315.1C16129A16223T16295T16519C
MN033MNM3d73G263G315.1C16126C16223T16344T16519C
MS034MSM73G199C263G315.1C16093C16223T16239T16304C16519C
MC035MCM3a1a73G204C263G315.1C16126C16223T16497G
MN036UNU5a2a173G263G309.1C315.1C16114A16192T16256T16270T16294T
MN037MNM57b173G146C189G263G315.1C16223T16311C16519C
MN038MNM3d173G263G315.1C16126C16223T16344T16519C
MC039MCM3073G195A263G315.1C16223T16519C
MC040MCM37e273G263G309.1C315.1C16093C16111T16189C16223T16224C
OT001RTR6+1612973G263G309.1C315.1C16129A16213A16362C16519C
OC003MCM73G199C263G315.1C16093Y16223T16239T16304C16519C
OT005UTU273G152C263G309.1C315.1C16051G16207G16227G16519C
OS006MSM4a73G152C263G315.1C16145A16176T16223T16261T16311C
ON007MNM73G263G315.1C16093C16129A16223T16362C16519C16527T
OT008MTM33a273G263G315.1C16169T16172C16223T16519C
OC009UCU2e1b73G152C217C263G315.1C315.2C340T16051G16082T
OT010MTM73G263G315.1C16129A16223T16519C
OT012MTM73G146C263G309.1C315.1C16093C16129A16223T16311C
OC013MCM73G263G315.1C16129A16209C16223T16362C16519C
OC015MCM52a73G146C263G309.1C315.1C16126C16218T16223T16275G
OS016RSR6b73G195C246C263G315.1C16145A16179T16227G16245T
OS017MSM5a2a1a73G263G315.1C16129A16223T16265C16519C
OC018MCM4a73G263G309.1C315.1C16111T16145A16176T16223T16261T
OC019MCM39b73G153G263G315.1C16075C16223T16304C55.1T59del
ON020MNM3073G195A263G315.1C16223T16519C
ON021HNHV263G315.1C16356C16519C
OT022MTM3073G195A225A263G309.1C315.1C16223T16362C16519C
OS024HNHV263G315.1C16356C16519C
ON025MNM2a1a73G195C204C263G315.1C16223T16270T16319A16352C
ON026RNR273G152C263G309.1C315.1C16071T16093C16519C
OT027UTU773G152C200G263G309.1C315.1C16093C16209C16309G
OC028MCM5b273G263G315.1C16048A16129A16223T16519C
OC029MCM5a1a73G263G315.1C334C16129A16189C16223T16265G16291T
ON030UNU7a73G151T152C263G309.1C315.1C16318T16519C
OT031MTM73G263G315.1C16129A16209C16223T16519C
OS032TST2d1b73G150T194T200G263G309.1C315.1C16126C16294T
ON033MNN73G152C195C225T263G315.1C16093C16129A16209C
OS034MSM73G146C189G263G309.1C315.1C16148T16223T16242T
OS035MSM73G152C263G279C309.1C315.1C16192T16223T16311C
ON036MNM5a473G146C263G315.1C16129A16223T16224C16519C
OT037MCM73G263G315.1C16126C16169T16183del16223T16519C
OC038HCHV263G315.1C16356C16519C
OT039UTU2b273G146C234G263G315.1C16051G16093C16239T16288C
ON040UNU5a1b173G263G315.1C16192T16256T16270T16291T16399G
OT041MTM3a1+20473G204C217C263G315.1C16126C16223T16311C16519C
OC042MCM30+1623473G195A263G309.1C315.1C16223T16234T16274A16519C
OC043UCU5a173G263G315.1C16129A16192T16256T16270T16399G
OS044USU7a73G151T152C263G315.1C16309G16318T16519C16527T
OC045UCU773G152C263G309.1C315.1C16093C16309G16318T16519C
ON046MNM30c173G146C195A263G309.1C315.1C16093C16166del16223T
OC047WCW473G143A189G194T195C196C204C207A263G
OT048WTW673G189G194T195C204C207A263G309.1C315.1C
ON049HNHV2a72C73G195C263G309.1C315.1C16217C16286G
ON051RNR30b2a73G150T263G309.1C315.1C373G16292T16311C16497G
ON054NNN73G207A263G309.1C315.1C16223T16256T16266T16311C
ON055NNN73G207A263G309.1C315.1C16223T16256T16266T16311C
ON056MNM30f73G195A200G263G309.1C315.1C16126C16223T16368C
ON057MNM3955.1T59del60del65.1T66T73G153G207A263G
ON058MNM73G263G309.1C315.1C16126C16169T16223T16519C
ON059MNM30+1623473G195A263G309.1C309.2C315.1C16223T16234T16519C
ON061MNM5a2a1a73G263G315.1C16129A16223T16265C16519C
OC062MCM73G195A263G309.1C315.1C16145A16223T16271C16519C
OS063MSM73G152C214G263G315.1C16223T16327T16362C
ON064JNJ1b1b73G263G271T295T315.1C16069T16126C16145A16261T
ON065UNU4b1a1a173G195C263G315.1C16356C16362C16519C
OC066MCM30+1623473G152C195A263G315.1C16092C16223T16234T16353A
OC067UCU7a73G151T152C263G309.1C315.1C16069T16274A16318T
ON068MNM3073G195A263G309.1C315.1C16145A16223T16311C16519C
ON069NNN1a273G199C204C263G315.1C16111T16223T16291T16301T
OT070MTM73G194T195C204C263G315.1C16126C16192T16223T
OT071HTHV263G315.1C16354T
OT072MTM2a1a73G195C204C263G315.1C16223T16270T16319A16352C
OT073UTU5a1f173G195C200G263G315.1C16192T16256T16270T16311C
OT074UTU7a3b73G151T152C263G309.1C315.1C16092C16207G16256T
OT075UTU2b273G146C152C234R263G309.1C315.1C16051G16209C
OT076MTM73G152Y246C263G315.1C16111T16223T16368C16519C
OT078MTM3073G195A263G309.1C315.1C16179del16223T16519C
OT079MTM30+1623473G195A263G309.1C315.1C16223T16234T16519C
OT081RTR73G195C263G309.1C315.1C16519C
OT082UTU7a73G151T152C263G315.1C16309G16318T16519C
OT083UTU2a73G150T152C194T263G315.1C16051G16145A16172C
OT084RTR6b73G195C246C263G315.1C16093C16179T16227G16245T
OT085UTU7a73G151T152C263G309.1C315.1C16309G16318T16519C
OT086MTM73G263G309.1C315.1C16223T16234T16295G16311C16519C
OT087NTN73G152Y263G309.1C315.1C16037G16111T16352C16526A
OT089UTU273G146C263G315.1C16051G16086C16129A16353T16519C
OT091RTR30a1b173G263G315.1C16209C16256T
ON092MNM6a1b73G146C263G309.1C315.1C16188T16223T16231C16362C
OC094RCR273G152C195C249G263G279C315.1C16071T16519C
OS095MSM73G146C178G263G315.1C16126C16223T16519C
ON091RNN73G263G309.1C315.1C16223T16327T16398A16519C
OC097MCM39b55.1T59del60del65.1T66T73G153G263G315.1C
ON098MNM5c173G150T263G315.1C16129A16145A16223T16519C
OS100USU7a73G151T152C263G315.1C16176T16309G16318T16519C
OT102UTU7a73G151T152C263G309.1C315.1C16309G16318C16519C
OC103MCM73G263G315.1C16126C16169T16223T16519C
ON104MNM73G263G315.1C16129A16223T16519C
OC105WCW73G189G195C204C207A263G309.1C315.1C16223T
OS106USU7a73G151T152C263G315.1C16309R16318T16319A16519C
OC107RCR5a273G146C152C263G315.1C16266T16304C16311C16356C
OC108MCM33b73G152C263G315.1C16223T16324C16362C16519C
OS110MSM3955.1T59del60del65.1T73G263G315.1C16223T16325C
OS111RSR30b2a73G152C263G309.1C315.1C373G16258C16292T16497G
OS112HSHV263G315.1C16217C16356C16519C
OC113WCW+19473G189G194T195C204C207A263G315.1C16223T
OS114MSM4a73G146C263G315.1C16145A16176T16223T16234T16261T
OS117MSN73G263G315.1C16129A16209C16223T16362C16519C
OC118MCM3a1+20473G204C263G309.1C315.1C16126C16223T16519C
OS121RSR5a273G152C263G309.1C315.1C16266T16304C16325C16356C
OC122MCM4973G195C263G315.1C16223T16234T16519C
OS123USU7a73G151T152C263G315.1C16140C16207R16242T16309G
OS124RSR73G263G309.1C315.1C16519C
OS125USU2b273G146C152C234G263G309.1C315.1C16051G16184T
Sample IDRegionHaplogroupHaplotype
FC001UCU7a3b16207G16309G16318C16519C
FC002RCR6a116320T16362C16393T16519C
FC003UCU1a1c1d116249C16311C16519C16527T
FC004RCR5a1a16524G
FN005HNH29
FC006MCM5a2a
FC007MCM6a1a16356C16362C16519C
FC008MCM65b
FN009MNM
FN010MNM5b2b
FN011RNR6a2
FN012MNM2a1a16352C16519C
FC013RCR32
FN014RNR8a1a1a1
FN015MNM3a1a
FN016UNU1a1a16189C16249C
FN017UNU2a1b16215G16230G16304C16311C16519C
FC018MCM4b16519C
FN019NNN1a1b116311C16391A16519C
FC020MCM3a1+204
FN021MNM5a2a1
FC022UCU5a1f116399G
FC023MCM30c1a
FC024HCH7b
FC025RCR30a1b16519C
FN026RNR30b2a16497G16519C
FN027RNR30b2a
FN028RNR32
FC029UCU2e2a1a216183C16189C16362C16519C
FN030HNH29
FN031MNM5a16261T16319A16355T16519C16527T
FN032MNM33a316399G16519C
FN033MNM38a16519C
FC034MCM57a16519C
MC001MCM3a2a
MC002MCM2b1a16189C16223T16274A16319A16320T16399G16519C
MC003MCM30f
MC004MCM6a1b16519C
MN005WNW6b315.1C16189C16223T16292T16325C16355T16519C
MC007MCM57b1
MC008RCR
MN009MNM3d1
MC010MCM33a1b
MN011MNM30f
MN012MNM6
MN014HNH13a2a1
MC015DCD4
MN016MNM3a1a16312G16519C
MN017MNM3a1+204
MN018MNM3a1b16519C
MN019UNU7a4a1a
MN020RNR
MN021TNT2b3416296T16304C16519C
MN022MNM4b16519C
MC023RCR5
MN024TNT1a516294T16519C
MN025UNU5a1b
MC026MCM30b16519C
MC027MCM3d
MC028MCM3a1a
MC029MCM57b1
MC030MCM30
MN031MNM3a1b
MN032MNM5a3b
MN033MNM3d
MS034MSM
MC035MCM3a1a
MN036UNU5a2a116526A
MN037MNM57b1
MN038MNM3d1
MC039MCM30
MC040MCM37e216295T16519C
OT001RTR6+16129
OC003MCM
OT005UTU2
OS006MSM4a16519C
ON007MNM
OT008MTM33a2
OC009UCU2e1b16126C16129C16183C16189C16256T16298C16362C16519C
OT010MTM
OT012MTM16390A16519C
OC013MCM
OC015MCM52a16291T16356C16390A16391A16519C
OS016RSR6b16266T16278T16362C16519C
OS017MSM5a2a1a
OC018MCM4a16266T16291T16311C16519C
OC019MCM39b60del65.1T66T
ON020MNM30
ON021HNHV
OT022MTM30
OS024HNHV
ON025MNM2a1a16519C
ON026RNR2
OT027UTU716318T16519C
OC028MCM5b2
OC029MCM5a1a16519C
ON030UNU7a
OT031MTM
OS032TST2d1b16519C
ON033MNN16223T16261T16319A16355T16519C
OS034MSM16311C16519C16527T
OS035MSM
ON036MNM5a4
OT037MCM
OC038HCHV
OT039UTU2b216352C16353T
ON040UNU5a1b1
OT041MTM3a1+204
OC042MCM30+16234
OC043UCU5a1
OS044USU7a
OC045UCU7
ON046MNM30c116519C
OC047WCW4309.1C315.1C16145A16189C16223T16292T16320T16519C
OT048WTW616192T16223T16266T16292T16325C16519C
ON049HNHV2a
ON051RNR30b2a16519C
ON054NNN16519C
ON055NNN16519C
ON056MNM30f16519C
ON057MNM39309.1C315.1C16093Y16223T16304C
ON058MNM
ON059MNM30+16234
ON061MNM5a2a1a
OC062MCM
OS063MSM
ON064JNJ1b1b16357C16519C
ON065UNU4b1a1a1
OC066MCM30+1623416362C16519C
OC067UCU7a16519C
ON068MNM30
ON069NNN1a216356C16519C
OT070MTM16312G16519C
OT071HTHV
OT072MTM2a1a
OT073UTU5a1f116399G
OT074UTU7a3b16318T16519C
OT075UTU2b216239T16244A16274A16352C16353T
OT076MTM
OT078MTM30
OT079MTM30+16234
OT081RTR
OT082UTU7a
OT083UTU2a16206C16256T
OT084RTR6b16266T16278T16362C16519C64T
OT085UTU7a
OT086MTM
OT087NTN
OT089UTU2
OT091RTR30a1b1
ON092MNM6a1b16519C
OC094RCR2
OS095MSM
ON091RNN
OC097MCM39b16075C16223T16304C
ON098MNM5c1
OS100USU7a
OT102UTU7a
OC103MCM
ON104MNM
OC105WCW16519C
OS106USU7a
OC107RCR5a216524G
OC108MCM33b
OS110MSM39
OS111RSR30b2a16519C
OS112HSHV
OC113WCW+19416292T16519C
OS114MSM4a16311C16519C
OS117MSN
OC118MCM3a1+204
OS121RSR5a2
OC122MCM49
OS123USU7a16318T16362C16519C
OS124RSR
OS125USU2b216209C16239T16352C16353T

Haplogroup and haplotype of 176 maternally unrelated individuals (104 from our previous study starting with the letter O in sample ID and 72 from the current study) from different regions in Gujarat (N: North Gujarat; T: Saurashtra; C: Central Gujarat; S: South Gujarat)

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