Genetic variation and population structure of Sudanese populations as indicated by 15 Identifiler sequence-tagged repeat (STR) loci
https://doi.org/10.1186/2041-2223-2-12
© A Babiker et al; licensee BioMed Central Ltd. 2011
Received: 2 January 2011
Accepted: 4 May 2011
Published: 4 May 2011
Abstract
Background
There is substantial ethnic, cultural and linguistic diversity among the people living in east Africa, Sudan and the Nile Valley. The region around the Nile Valley has a long history of succession of different groups, coupled with demographic and migration events, potentially leading to genetic structure among humans in the region.
Result
We report the genotypes of the 15 Identifiler microsatellite markers for 498 individuals from 18 Sudanese populations representing different ethnic and linguistic groups. The combined power of exclusion (PE) was 0.9999981, and the combined match probability was 1 in 7.4 × 1017. The genotype data from the Sudanese populations was combined with previously published genotype data from Egypt, Somalia and the Karamoja population from Uganda. The Somali population was found to be genetically distinct from the other northeast African populations. Individuals from northern Sudan clustered together with those from Egypt, and individuals from southern Sudan clustered with those from the Karamoja population. The similarity of the Nubian and Egyptian populations suggest that migration, potentially bidirectional, occurred along the Nile river Valley, which is consistent with the historical evidence for long-term interactions between Egypt and Nubia.
Conclusion
We show that despite the levels of population structure in Sudan, standard forensic summary statistics are robust tools for personal identification and parentage analysis in Sudan. Although some patterns of population structure can be revealed with 15 microsatellites, a much larger set of genetic markers is needed to detect fine-scale population structure in east Africa and the Nile Valley.
Keywords
Background
Sudan is located in northeastern Africa, with a total of 133 living languages listed by Ethnologue [1]. Local languages belong to three of the major African linguistic families proposed by Greenberg [2]: the Niger-Congo, Nilo-Saharan and Afro-Asiatic language families. The considerable ethnic and cultural diversity within Sudan make the study of existing genetic diversity of human populations an attractive effort. The Nile Valley has a long history of succession of different groups, coupled with demographic and migration events, which remain to be fully examined on a genetic level. These groups include people with an established history in the area, (for example, Nuba and Nilotic) and groups that migrated to the area in relatively recent times (for example, Hausa, Copt and Arab). Furthermore, population samples from Sudan are important for studies concerning human migration and the exodus from Africa 60,000 to 80,000 years ago, because the Nile Valley runs through Sudan, which is part of the traditionally favored model of the migratory route out of Africa for anatomically modern humans [3]. Previous genetic studies in Sudan have mainly focused on mitochondrial (mt)DNA, the Y chromosome [4–8], and a small number of autosomal markers [9–12]. Recently, Tishkoff et al.[13], conducted a large survey of 121 African populations using more than 800 microsatellites that included six populations from Sudan. Three of these populations were Nilotic populations, and one was a Nuba population, and these four populations speak Nilo-Saharan languages. The remaining two populations are Beja, who speak Afro-Asiatic languages. The study by Tishkoff et al.[13] showed that eastern Africa harbors substantial amounts of genetic diversity, only superseded by the amount of genetic diversity in southern Africa, but it is difficult to rank these regions, because of the very different sample density across Africa.
Since the introduction of a standardized set of forensic microsatellite markers [14], over 1000 populations across the world have been studied using these genetic markers [15]. The main results from these studies have been used to generate relevant reference data for a large number of populations, and to demonstrate that the set of microsatellite markers were sufficiently diverse to result in high PE (or low match probability) for particular populations. One of the more common commercial sequence-tagged repeat (STR) kits available for human identity testing is the AmpFlSTR® Identifiler™ PCR Amplification Kit (Applied Biosystems, Foster City, CA, USA), which includes the 13 core STR loci from the FBI Combined DNA Index System (CODIS), and two additional markers commonly used for forensic investigations in Europe. The marker panel has also been used in population structure and admixture studies of humans [16–21], and knowledge about population structure has contributed to our understanding of human origins [22]. Admixture or ancestry analysis is also important in forensics; for instance, to pinpoint an appropriate reference population for a particular case from which to compute match and exclusion probabilities, or to potentially get an indication of the perpetrator's ethnicity [23]. However, recent studies investigating the informativeness for ancestry inference of the CODIS markers have suggested that these markers are less informative about ancestry than are many other marker sets of similar size [16, 23, 24].
In this study, we report the genotypes of 15 autosomal STR markers (the markers in the AmpFlSTR Identifiler PCR Amplification Kit) for different populations in Sudan, and compute commonly used forensic summary statistics. We infer population structure for the Sudanese population and for an expanded set of populations (compiled from previous studies) from Uganda [25], Egypt [26] and Somalia [27]. Lastly, we characterize the informativeness of the 15 forensic STR markers for assignment for the populations from northeast Africa and compare the result to another set of microsatellites.
Results and discussion
Map of the Sudan representing the geographic locations of the 18 populations in the present study. Geographic classification (used in the partitioning of variation analysis) of each population is also indicated on the map: northern Sudan (triangle), central Sudan (open square), eastern Sudan (filled circle), western Sudan (open circle) and southern Sudan (filled square). The dotted circle represents the suggested region occupied by the Messiria population.
Sample sizes (n) and linguistic affiliations of the populations in the study
Ethnic group | Population | n | Linguistic family | Linguistic subgroup | Geographic group |
---|---|---|---|---|---|
Arab | Bataheen | 29 | Afro-Asiatic | Semetic | Central |
Arab | Gaalien | 57 | Afro-Asiatic | Semetic | Central |
Arab | Shaigia | 17 | Afro-Asiatic | Semetic | Northern |
Arab | Messiria | 8 | Afro-Asiatic | Semetic | Western |
Copt | Copt | 31 | Afro-Asiatic | Ancient Egyptian | Central |
Hausa | Hausa | 10 | Afro-Asiatic | Chadic | Eastern |
Beja | Beni-Amer | 35 | Afro-Asiatic | Cushitic | Eastern |
Beja | Hadendowa | 29 | Afro-Asiatic | Cushitic | Eastern |
Nubian | Danagla | 40 | Nilo-Saharan | Eastern Sudanic | Northern |
Nubian | Mahas | 31 | Nilo-Saharan | Eastern Sudanic | Northern |
Nubian | Halfawieen | 24 | Nilo-Saharan | Eastern Sudanic | Northern |
Nilotic | Dinka | 30 | Nilo-Saharan | Eastern Sudanic | Southern |
Nilotic | Nuer | 19 | Nilo-Saharan | Eastern Sudanic | Southern |
Nilotic | Shilluk | 19 | Nilo-Saharan | Eastern Sudanic | Southern |
Nilotic | Baria | 6 | Nilo-Saharan | Eastern Sudanic | Southern |
Nuba | Nuba | 34 | Nilo-Saharan and Niger-Congo | Eastern Sudanic and Kordofanian | Western |
Zagawa | Zagawa | 29 | Nilo-Saharan | Saharan | Western |
Gemar | Gemar | 6 | Nilo-Saharan | Eastern Sudanic | Western |
Allele frequencies for each of the 15 Identifiler STR loci found in the Sudanese sample.
Allele | CSF1PO | D13S317 | D16S539 | D18S51 | D19S433 | D21S11 | D2S1338 | D3S1358 | D5S818 | D7S820 | D8S1179 | FGA | TH01 | TPOX | vWA |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
4 | 0.001 | ||||||||||||||
6 | 0.228 | 0.006 | |||||||||||||
7 | 0.007 | 0.001 | 0.004 | 0.001 | 0.397 | 0.005 | |||||||||
8 | 0.048 | 0.069 | 0.039 | 0.002 | 0.084 | 0.228 | 0.001 | 0.091 | 0.390 | ||||||
9 | 0.037 | 0.048 | 0.189 | 0.001 | 0.002 | 0.037 | 0.106 | 0.003 | 0.167 | 0.292 | |||||
9.3 | 0.083 | ||||||||||||||
10 | 0.316 | 0.027 | 0.069 | 0.004 | 0.014 | 0.098 | 0.343 | 0.027 | 0.034 | 0.100 | |||||
10.2 | 0.001 | ||||||||||||||
11 | 0.242 | 0.311 | 0.320 | 0.010 | 0.019 | 0.202 | 0.212 | 0.053 | 0.189 | ||||||
11.2 | 0.001 | 0.004 | |||||||||||||
12 | 0.292 | 0.384 | 0.205 | 0.101 | 0.075 | 0.001 | 0.366 | 0.091 | 0.113 | 0.017 | 0.002 | ||||
12.2 | 0.010 | ||||||||||||||
13 | 0.048 | 0.128 | 0.147 | 0.087 | 0.225 | 0.003 | 0.203 | 0.015 | 0.241 | 0.001 | 0.003 | ||||
13.2 | 0.002 | 0.055 | |||||||||||||
14 | 0.008 | 0.031 | 0.031 | 0.096 | 0.261 | 0.065 | 0.008 | 0.298 | 0.077 | ||||||
14.2 | 0.001 | 0.078 | |||||||||||||
15 | 0.003 | 0.001 | 0.111 | 0.092 | 0.001 | 0.305 | 0.001 | 0.188 | 0.156 | ||||||
15.2 | 0.082 | 0.001 | |||||||||||||
16 | 0.136 | 0.033 | 0.051 | 0.263 | 0.060 | 0.259 | |||||||||
16.2 | 0.001 | 0.033 | 0.001 | ||||||||||||
17 | 0.140 | 0.008 | 0.139 | 0.271 | 0.010 | 0.008 | 0.245 | ||||||||
17.2 | 0.002 | 0.007 | |||||||||||||
18 | 0.114 | 0.001 | 0.091 | 0.082 | 0.003 | 0.009 | 0.155 | ||||||||
18.2 | 0.001 | 0.001 | |||||||||||||
19 | 0.078 | 0.209 | 0.005 | 0.001 | 0.047 | 0.075 | |||||||||
19.2 | 0.001 | 0.001 | |||||||||||||
20 | 0.059 | 0.128 | 0.002 | 0.065 | 0.024 | ||||||||||
21 | 0.034 | 0.060 | 0.108 | 0.003 | |||||||||||
21.2 | 0.005 | ||||||||||||||
22 | 0.013 | 0.128 | 0.183 | 0.001 | |||||||||||
22.2 | 0.002 | ||||||||||||||
23 | 0.003 | 0.091 | 0.166 | ||||||||||||
24 | 0.056 | 0.143 | |||||||||||||
24.2 | 0.003 | 0.002 | |||||||||||||
25 | 0.035 | 0.091 | |||||||||||||
25.2 | 0.001 | ||||||||||||||
26 | 0.002 | 0.009 | 0.039 | ||||||||||||
27 | 0.030 | 0.001 | 0.026 | ||||||||||||
28 | 0.123 | 0.001 | 0.063 | ||||||||||||
29 | 0.286 | 0.035 | |||||||||||||
30 | 0.226 | ||||||||||||||
30.2 | 0.005 | 0.006 | |||||||||||||
31 | 0.082 | ||||||||||||||
31.2 | 0.039 | ||||||||||||||
32 | 0.017 | ||||||||||||||
32.2 | 0.080 | ||||||||||||||
33 | 0.007 | ||||||||||||||
33.2 | 0.034 | ||||||||||||||
34 | 0.009 | ||||||||||||||
34.2 | 0.005 | ||||||||||||||
35 | 0.026 | ||||||||||||||
36 | 0.017 | ||||||||||||||
37 | 0.005 | ||||||||||||||
38 | 0.002 |
Forensic summary statistics, observed and expected heterozygosity, deviation from Hardy-Weinberg-equilibrium (HWE), minimum allele frequency cut-off and sample size for each locus (n)
Locus | CSF1PO | D13S317 | D16S539 | D18S51 | D19S433 | D21S11 | D2S1338 | D3S1358 | D5S818 | D7S820 | D8S1179 | FGA | TH01 | TPOX | vWA |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
n | 485 | 489 | 491 | 485 | 491 | 491 | 489 | 490 | 490 | 485 | 491 | 486 | 490 | 491 | 491 |
MPa | 0.103 | 0.114 | 0.073 | 0.021 | 0.043 | 0.047 | 0.028 | 0.106 | 0.090 | 0.093 | 0.069 | 0.026 | 0.100 | 0.126 | 0.066 |
PDb | 0.897 | 0.886 | 0.927 | 0.979 | 0.957 | 0.953 | 0.972 | 0.894 | 0.91 | 0.907 | 0.931 | 0.974 | 0.9 | 0.874 | 0.934 |
PEc | 0.504 | 0.409 | 0.552 | 0.785 | 0.665 | 0.611 | 0.68 | 0.533 | 0.519 | 0.469 | 0.503 | 0.748 | 0.431 | 0.406 | 0.681 |
PICd | 0.708 | 0.689 | 0.762 | 0.889 | 0.833 | 0.816 | 0.865 | 0.711 | 0.732 | 0.729 | 0.769 | 0.874 | 0.711 | 0.67 | 0.79 |
Hobse | 0.746 | 0.687 | 0.774 | 0.895 | 0.833 | 0.804 | 0.843 | 0.763 | 0.755 | 0.726 | 0.745 | 0.877 | 0.702 | 0.684 | 0.841 |
Hexpf | 0.751 | 0.731 | 0.792 | 0.899 | 0.850 | 0.834 | 0.878 | 0.754 | 0.767 | 0.766 | 0.798 | 0.886 | 0.747 | 0.718 | 0.813 |
Pg | 0.955 | 0.021 | 0.335 | 0.357 | 0.195 | 0.211 | 0.022 | 0.335 | 0.258 | 0.015 | 0.005 | 0.749 | 0.311 | 0.400 | 0.018 |
MAFh | 0.0052 | 0.0051 | 0.005 | 0.0052 | 0.005 | 0.005 | 0.0051 | 0.0051 | 0.0051 | 0.0052 | 0.005 | 0.0052 | 0.0051 | 0.005 | 0.005 |
Informativeness for assignment
Rosenberg et al.[29] developed a statistic, the informativeness for assignment (Ιn), which describes the information content of a particular genetic marker for ancestry inference (it ranges from zero (no information) to the natural logarithm of the number of populations (maximum information)). We computed Ιn values for the 15 Identifiler STRs (all tetranucleotide microsatellites) from the present study. The mean, across markers, (and standard deviation) Ιn was 0.167 (0.070) for the Sudanese populations, and 0.154 (0.066) for the Sudanese populations and the populations from Egypt, Somalia and Uganda (the Karamoja). To determine the relative informativeness of the 15 Identifier microsatellites, we computed In for 377 microsatellites (of which 274 were tetranucleotide microsatellites) for two groups of African populations from Rosenberg et al.[29]; six sub-Saharan African populations (Kenyan Bantu speakers, Mandenka, Yoruba, San, Mbuti Pygmy, Biaka Pygmy) from the Human Genome Diversity Panel (HGDP); the 'HGDP sub-Saharan group'; and a subgroup of three populations from the HGDP, who all speak Niger-Congo languages (Kenyan Bantu speakers, Mandenka, Yoruba), termed the 'HGDP Niger-Congo group'. Based on the 377 microsatellites from Rosenberg et al.[29], the mean In (across markers) was 0.234 (0.098) for the HGDP sub-Saharan group and 0.106 (0.054) for the HGDP Niger-Congo group, which was a significant difference (P < 0.001, Wilcoxon signed-rank test). The In statistic depends on both marker information about ancestry and the level of differentiation between the investigated populations. Because the same markers were used for both the HGDP sub-Saharan group and the HGDP Niger-Congo group, the difference in In between the two groups was due to the greater level of differentiation between populations in the HGDP sub-Saharan group. The In values for the Sudanese populations and the larger group of northeast African populations (from Sudan, Egypt, Somalia and Uganda) were found to lie between the values for the HGDP Niger-Congo group and the HGDP sub-Saharan group. This result is not surprising, considering that the sampled populations from Sudan belonged to different linguistic groups, although the groups are not as differentiated as the Pygmy, the San and the Niger-Congo-speaking populations in the HGDP sub-Saharan group. We concluded that the CODIS STRs contain information on population structure in the same range as many other microsatellites. However, it is possible to select more informative STR loci for population structure inference; for example, the top 15 most informative loci of the 377 microsatellites [29] (for the HGDP sub-Saharan group and for the HGDP Niger-Congo group) had mean In values of 0.489 (0.062) and 0.249 (0.041) respectively. This observation can be explained by the fact that the Identifiler STRs have been selected to have high levels of variation, because of the potentially high mutation rates of these STRs, in order to be powerful tools for identifying individuals, but the high level of variation also makes it challenging to separate alleles that are identical by state from those identical by descent [15].
Population diversity and sub-structure in Sudanese and neighboring populations
The mean number of (A) distinct and (B) private alleles as functions of the sample size g (in chromosomes) for Sudanese populations and neighboring populations.
Mean number of alleles private to a pair of Sudanese populations as a function of the sample size g (in chromosomes).
Mean number of alleles private to a pair of populations as a function of the sample size g (in chromosomes). Comparisons between each Sudanese population and the Egyptian population is shown in blue; between each Sudanese population and the Somali population in orange; and between each Sudanese population and the Karamoja population from Uganda is shown in green.
Clustering of 454 people from 18 Sudanese populations, 218 people from the Karamoja population (Uganda), 265 people from five Egyptian populations and 230 people from one Somali population assuming two or three clusters ( K ).The mean (across replicate runs) log likelihood was -64611 for K = 2 and -64853 for K = 3. Each individual is represented by a column divided into K colors with each color representing a cluster. Different populations are separated by a black line and are labeled below the figure by self-reported ethnicity and above the figure by geographic region.
Principal components based on genetic distance of the Sudanese populations and the Egyptian, the Somali and the Karamoja populations. The populations that have been genotyped in this study are indicated by filled circles. A) The first and the second principal components. B) The first and the third principal components.
The number of unique alleles (Figure 2B) was greatest in the Somali population, and and in the population structure analyses (Figure 5), the Somali population grouped separately from other populations. Because the Somali population is separated both geographically and linguistically from the other populations included in our study, it is not surprising that it is also genetically distinct. It is possible that the Bantu expansion from West Africa had a stronger effect on the region of the Horn of Africa, where Somalia is located, compared with the region where Sudan is located. For example, the languages in Somalia belong to two major linguistic families, the Afro-Asiatic and Niger-Congo, whereas Nilo-Saharan is absent and the Bantu Swahili language is one of the major languages in Somalia (Ethnologue [1]). Another explanation could be that the Somali population is of both Eurasian and sub-Saharan origin, as suggested by a recent study [33], potentially explaining the differentiation of this population from some east African groups, although many of the Sudanese populations, such as Arabs and the Beja, may also have mixed Eurasian and sub-Saharan origin.
The patterns of population structure we found in northeast Africa, in particular the similarity of Nubian (a northern Sudanese group that speak Nilo-Saharan languages) and the Egyptian population. is consistent with the historical evidence for long-term interactions between Egypt and Nubia, probably resulting in genetic flow between the two regions. However, the Nubian group and the Karamoja group from Uganda share a relatively large number of private alleles (Figure 4), potentially reflecting the shared ancestry of the Nubians with populations from southern Sudan and Uganda. Our results, in addition to mtDNA [7] and Y-chromosome [6, 34, 35] data, suggest that migration, potentially bidirectional, occurred along the Nile between Egypt and Nubia.
Partition of genetic variation between the Sudanese populations (using analysis of molecular variance) based on linguistic or geographic classification.
Grouping criteria (n) | Groups | Variance (%) | ||
---|---|---|---|---|
Within populations | Between populations, within groups | Between groups | ||
Linguistic groups,a (2) | Afro-Asiatic, Nilo-Saharan | 99.07 | 0.73 | 0.21 |
Geographic groups, (5) | Central, Northern, Eastern, Western and Southernb | 98.99 | 0.49 | 0.52 |
Conclusion
Even though the 15 Identifiler microsatellites are not the most informative markers for inference of ancestry, these markers contain sufficient information to differentiate (to some degree) between distinct geographic and linguistic groups within Sudan and within a larger collection of northeast African populations. We conclude that geography, language and culture have played an important role in shaping patterns and structure of genetic variation in Sudan, and that these patterns may have been shaped by long-term occupation by Nilo-Saharan groups and more recent migration from North Africa and Eurasia to the Nile Valley, but a larger number of markers would be needed for fine-scale population structure inference. This study confirms that the set of 15 Identifiler microsatellites is a suitable tool for personal identification and parentage analysis in Sudan, despite the levels of population structure.
Methods
The study was described to each participant before sampling, and informed consent was obtained from each participant.
Samples
In total, 498 unrelated subjects (366 men, 132 women) were recruited from different geographic regions of Sudan. For each individual, we collected blood and (self-reported) information about the gender and ethnic background of the participant and their parents and grandparents. For the calculation of forensic summary statistics, we excluded seven people because of a potential first-degree relationship, which was inferred using Relpair v2.0.1 [36, 37], leaving 491 subjects. For the population-genetics analyses, we excluded (i) the same 7 subjects because of a potential first-degree relationship, (ii) 29 for whom the sample size of the particular population was <6, and (iii) eight who failed genotyping for at least one marker, resulting in a final subject group size of 454 from 18 Sudanese populations. The geographic locations of the sampled populations are indicated on a map of Sudan (Figure 1), and sample sizes for the populations are given in Table 1. For the population-genetics analyses, we also combined the genotype data from our Sudanese sample (n = 454) with previously published genotype data from Uganda [25], Egypt [26] and Somalia [27].
Genetic markers
Genomic DNA was extracted from DNA storage cards (FTA Classic Cards; Whatman, Maidstone, Kent, UK), following the manufacturer's instructions. Fifteen STR loci (D3S1358, vWA, FGA, D8S1179, D21S11, D18S51, D5S818, D13S317, D16S539, TH01, TPOX, CSF1PO, D7S820, D2S1338 and D19S433) were co-amplified for each of the 498 subjects (AmpFl STR® Identifiler™ PCR Amplification Kit; Applied Biosystems) following the manufacturer's recommendations [38]. The amplified PCR products were genotyped using an automatic analyzer (ABI PRISM 3730 XL Genetic Analyzer; Applied Biosystems). For each run on the analyzer, an allelic ladder, positive and negative controls, and an internal lane standard (GeneScan-600 LIZ; Applied Biosystems) were included. Allele calling was performed (GeneMapper ID software, version 4.0; Applied Biosystems). We followed the International Society for Forensic Genetics (ISFG) recommendations on the analysis of DNA polymorphisms. The recommended nomenclature was used, and we followed the guidelines on quality control [39].
Analysis of genotype data
where N is the number of alleles for the locus, K is the number of populations, p ij is the (parametric) frequency of allele j in population i, p j is the mean frequency of allele j across populations and 'log' denotes the natural logarithm. The minimal In value is 0 (when all alleles have equal frequencies in all populations), and the maximal value is log K (which occurs when no allele is found in more than one population). In was computed for the combined dataset of northeast African populations from Sudan, Egypt, Somalia and Uganda. For comparison of the level of informativeness for the 15 Identifiler microsatellites, we computed In values for 377 microsatellites genotyped in the HGDP [29]. Average In values were computed for two groups of populations from the HGDP: six African populations (Kenyan Bantu speakers, Mandenka, Yoruba, San, Mbuti Pygmy, Biaka Pygmy), referred to as the 'HGDP sub-Saharan group', and a subgroup of three populations from the HGDP who all speak Niger-Congo languages (Kenyan Bantu speakers, Mandenka, Yoruba), referred to as the 'HGDP Niger-Congo group'.
Based on the genotype data from the 15 microsatellites, we inferred population structure for northeast African populations using the clustering software Structure [32, 40]. We used the admixture model, using the F model of correlated allele frequencies across clusters. Each replicate Structure run used a burn-in period of 100,000 iterations, followed by 10,000 iterations from which estimates were obtained. We replicated the Structure analysis 10 times for each choice, based on the number of assumed clusters (K), from K = 2 to K = 10. The 10 replicates for each choice of K was summarized (CLUMPP software; http://rosenberglab.bioinformatics.med.umich.edu/clumpp.html[41]), with the Large K Greedy algorithm (10,000 random permutations) to identify common modes of replicates, and to combine the clustering-results across replicates. The combined clustering result was visualized (distruct package; http://rosenberglab.bioinformatics.med.umich.edu/distruct.html[42]).Population structure was also assessed through calculating the mean squared distance for microsatellite loci [43] between pairs of populations (Populations version 1.2.30;http://bioinformatics.org/project/?group_id=84). The distance matrix was visualized using PCA.
We computed the mean number of distinct alleles, the mean number of private alleles and the mean number of private alleles for pairs of populations (ADZE; http://rosenberglab.bioinformatics.med.umich.edu/adze.html[44]) for the Sudanese populations (Table 1). For this analysis, we merged populations into larger 'ethnic groups' to avoid small sample sizes, and we also excluded the Gemar because of the small sample size (n = 6), and because they could not be grouped with any of the other populations. This analysis was repeated for the Sudanese ethnic groups, including the populations from Egypt, Somalia and Uganda (the Karamoja). The sample from Egypt was collected from five populations, but because no structure could be detected in these five populations [26], we treated them as one population. Analysis of molecular variance (AMOVA) [45] was used to partition genetic variation for predefined linguistic and geographic groups (Arlequin version 3.11 [28]) and the Nuba population was excluded because the group encompassed individuals speaking different languages belonging to the Niger-Congo and Nilo-Saharan linguistic divisions.
Declarations
Acknowledgements
We thank all the participants who donated blood and everyone who assisted in the blood collection. This project was financially supported by grants to MJ from the Erik Philip Sörensen foundation, the Nilsson-Ehle foundation and the Swedish Research Council.
Authors’ Affiliations
References
- Gordon RG, (Ed): Ethnologue: Languages of the World. 2005, Dallas, Texas: SIL International, FifteenthGoogle Scholar
- Greenberg JH: The languages of Africa. 1963, Bloomington, Indiana: Indiana University PressGoogle Scholar
- Mellars P: Why did modern human populations disperse from Africa ca. 60,000 years ago? A new model. Proc Natl Acad Sci USA. 2006, 103: 9381-9386. 10.1073/pnas.0510792103.PubMed CentralView ArticlePubMedGoogle Scholar
- Fox CL: mtDNA analysis in ancient Nubians supports the existence of gene flow between sub-Sahara and North Africa in the Nile Valley. Ann Hum Biol. 1997, 24: 217-227. 10.1080/03014469700004952.View ArticlePubMedGoogle Scholar
- Seielstad M, Bekele E, Ibrahim M, Toure A, Traore M: A view of modern human origins from Y chromosome microsatellite variation. Genome Res. 1999, 9: 558-567.PubMed CentralPubMedGoogle Scholar
- Hassan HY, Underhill PA, Cavalli-Sforza LL, Ibrahim ME: Y-chromosome variation among Sudanese: restricted gene flow, concordance with language, geography, and history. Am J Phys Anthropol. 2008, 137: 316-323. 10.1002/ajpa.20876.View ArticlePubMedGoogle Scholar
- Krings M, Salem AE, Bauer K, Geisert H, Malek AK, Chaix L, Simon C, Welsby D, Di Rienzo A, Utermann G, Sajantila A, Paabo S, Stoneking M: mtDNA analysis of Nile River Valley populations: A genetic corridor or a barrier to migration?. Am J Hum Genet. 1999, 64: 1166-1176. 10.1086/302314.PubMed CentralView ArticlePubMedGoogle Scholar
- Salas A, Richards M, De la Fe T, Lareu MV, Sobrino B, Sanchez-Diz P, Macaulay V, Carracedo A: The making of the African mtDNA landscape. Am J Hum Genet. 2002, 71: 1082-1111. 10.1086/344348.PubMed CentralView ArticlePubMedGoogle Scholar
- Bayoumi RA, Taha TS, Saha N: A study of some genetic characteristics of the Fur and Baggara tribes of the Sudan. Am J Phys Anthropol. 1985, 67: 363-370. 10.1002/ajpa.1330670408.View ArticlePubMedGoogle Scholar
- Bereir RE, Mohamed HS, Seielstad M, El Hassani AM, Khalil EA, Peacock CS, Blackwell JM, Ibrahim ME: Allele frequency and genotype distribution of polymorphisms within disease-related genes is influenced by ethnic population substructuring in Sudan. Genetica. 2003, 119: 57-63. 10.1023/A:1024486716497.View ArticlePubMedGoogle Scholar
- Mohammed AO, Attalla B, Bashir FM, Ahmed FE, El Hassan AM, Ibnauf G, Jiang W, Cavalli-Sforza LL, Karrar ZA, Ibrahim ME: Relationship of the sickle cell gene to the ethnic and geographic groups populating the Sudan. Community Genet. 2006, 9: 113-120. 10.1159/000091489.View ArticlePubMedGoogle Scholar
- Tay JS, Saha N: Genetic heterogeneity among the Negroid and Arab tribes of the Sudan. Am J Phys Anthropol. 1988, 76: 211-215. 10.1002/ajpa.1330760209.View ArticlePubMedGoogle Scholar
- Tishkoff SA, Reed FA, Friedlaender FR, Ehret C, Ranciaro A, Froment A, Hirbo JB, Awomoyi AA, Bodo JM, Doumbo O, Ibrahim M, Juma AT, Kotze MJ, Lema G, Moore JH, Mortensen H, Nyambo TB, Omar SA, Powell K, Pretorius GS, Smith MW, Thera MA, Wambebe C, Weber JL, Williams SM: The genetic structure and history of Africans and African Americans. Science. 2009, 324: 1035-1044. 10.1126/science.1172257.PubMed CentralView ArticlePubMedGoogle Scholar
- Budowle B, Moretti TR, Niezgoda SJ, Brown BL: CODIS and PCR-based short tandem repeat loci: law enforcement tools. Procedings of the Second European Symposium on Human Identification; Innsbruck, Austria, June 1998. 1998, Madison, WI: Promega Corporation, 73-88.Google Scholar
- Butler JM: Genetics and genomics of core short tandem repeat loci used in human identity testing. J Forensic Sci. 2006, 51: 253-265. 10.1111/j.1556-4029.2006.00046.x.View ArticlePubMedGoogle Scholar
- Phillips C, Fernandez-Formoso L, Garcia-Magarinos M, Porras L, Tvedebrink T, Amigo J, Fondevila M, Gomez-Tato A, Alvarez-Dios J, Freire-Aradas A, Gomez-Carballa A, Mosquera-Miguel A, Carracedo A, Lareu MV: Analysis of global variability in 15 established and 5 new European Standard Set (ESS) STRs using the CEPH human genome diversity panel. Forensic Sci Int Genet. 2010Google Scholar
- El Ossmani H, Bouchrif B, Aboukhalid R, Bouabdillah M, Gazzaz B, Zaoui D, Chafik A, Talbi J: Assessment of phylogenetic structure of Berber-speaking population of Azrou using 15 STRs of Identifiler kit. Leg Med (Tokyo). 2010, 12: 52-56.View ArticleGoogle Scholar
- Rangel-Villalobos H, Rubi-Castellanos R, Morales-Vallejo ME, Molina-Araujo V, Licea-Cadena RA, Rizzo-Juarez S, Muniz-Lozano FE, Nuno-Arana I, Munoz-Valle JF: Admixture estimates and statistical parameters of forensic importance based on PowerPlex 16 system in Mexican-Mestizos from the States of Guanajuato (Center) and Veracruz (East). Forensic Sci Int Genet. 2010, 4: 271-272. 10.1016/j.fsigen.2009.09.001.View ArticlePubMedGoogle Scholar
- Simms TM, Rodriguez CE, Rodriguez R, Herrera RJ: The genetic structure of populations from Haiti and Jamaica reflect divergent demographic histories. Am J Phys Anthropol. 2010, 142: 49-66.PubMedGoogle Scholar
- Stepanov VA, Melnikov AV, Lash-Zavada AY, Kharkov VN, Borinskaya SA, Tyazhelova TV, Zhukova OV, Schneider YV, Shil'nikova IN, Puzyrev VP, Rybakova AA, Yankovsky NK: Genetic variability of 15 autosomal STR loci in Russian populations. Leg Med (Tokyo). 2010, 12: 256-258.View ArticleGoogle Scholar
- Toscanini U, Gusmao L, Berardi G, Amorim A, Carracedo A, Salas A, Raimondi E: Testing for genetic structure in different urban Argentinian populations. Forensic Sci Int. 2007, 165: 35-40. 10.1016/j.forsciint.2006.02.042.View ArticlePubMedGoogle Scholar
- Rosenberg NA, Pritchard JK, Weber JL, Cann HM, Kidd KK, Zhivotovsky LA, Feldman MW: Genetic structure of human populations. Science. 2002, 298: 2381-2385. 10.1126/science.1078311.View ArticlePubMedGoogle Scholar
- Barnholtz-Sloan JS, Pfaff CL, Chakraborty R, Long JC: Informativeness of the CODIS STR loci for admixture analysis. J Forensic Sci. 2005, 50: 1322-1326.View ArticlePubMedGoogle Scholar
- Halder I, Yang BZ, Kranzler HR, Stein MB, Shriver MD, Gelernter J: Measurement of admixture proportions and description of admixture structure in different U.S. populations. Hum Mutat. 2009, 30: 1299-1309. 10.1002/humu.21045.PubMed CentralView ArticlePubMedGoogle Scholar
- Gomes V, Sanchez-Diz P, Alves C, Gomes I, Amorim A, Carracedo A, Gusmao L: Population data defined by 15 autosomal STR loci in Karamoja population (Uganda) using AmpF/STR Identifiler kit. Forensic Sci Int Genet. 2009, 3: e55-58. 10.1016/j.fsigen.2008.06.005.View ArticlePubMedGoogle Scholar
- Omran GA, Rutty GN, Jobling MA: Genetic variation of 15 autosomal STR loci in Upper (Southern) Egyptians. Forensic Sci Int Genet. 2009, 3: e39-44. 10.1016/j.fsigen.2008.05.007.View ArticlePubMedGoogle Scholar
- Tillmar AO, Backstrom G, Montelius K: Genetic variation of 15 autosomal STR loci in a Somali population. Forensic Sci Int Genet. 2009, 4: e19-20. 10.1016/j.fsigen.2009.01.004.View ArticlePubMedGoogle Scholar
- Excoffier L, Laval G, Schneider S: Arlequin ver.3.0: An integrated software package for population genetics data analysis. Evol Bioinfor Online. 2005, 1: 47-50.Google Scholar
- Rosenberg NA, Li LM, Ward R, Pritchard JK: Informativeness of genetic markers for inference of ancestry. Am J Hum Genet. 2003, 73: 1402-1422. 10.1086/380416.PubMed CentralView ArticlePubMedGoogle Scholar
- Kalinowski ST: Counting alleles with rarefaction: private alleles and hierarchical sampling designs. Conservation Genetics. 2004, 5: 539-543.View ArticleGoogle Scholar
- Munro-Hay S: Aksum: An African Civilisation of Late Antiquity. 1991, Edinburgh: Edinburgh University PressGoogle Scholar
- Pritchard JK, Stephens M, Donnelly P: Inference of population structure using multilocus genotype data. Genetics. 2000, 155: 945-959.PubMed CentralPubMedGoogle Scholar
- Sanchez JJ, Hallenberg C, Borsting C, Hernandez A, Morling N: High frequencies of Y chromosome lineages characterized by E3b1, DYS19-11, DYS392-12 in Somali males. Eur J Hum Genet. 2005, 13: 856-866. 10.1038/sj.ejhg.5201390.View ArticlePubMedGoogle Scholar
- Keita SO: History in the interpretation of the pattern of p49a,fTaqI RFLP Y-chromosome variation in Egypt: a consideration of multiple lines of evidence. Am J Hum Biol. 2005, 17: 559-567. 10.1002/ajhb.20428.View ArticlePubMedGoogle Scholar
- Lucotte G, Mercier G: Brief communication: Y-chromosome haplotypes in Egypt. Am J Phys Anthropol. 2003, 121: 63-66. 10.1002/ajpa.10190.View ArticlePubMedGoogle Scholar
- Boehnke M, Cox NJ: Accurate inference of relationships in sib-pair linkage studies. Am J Hum Genet. 1997, 61: 423-429. 10.1086/514862.PubMed CentralView ArticlePubMedGoogle Scholar
- Epstein MP, Duren WL, Boehnke M: Improved inference of relationship for pairs of individuals. Am J Hum Genet. 2000, 67: 1219-1231.PubMed CentralView ArticlePubMedGoogle Scholar
- Applied Biosystems. AmpFlSTRIdentifiler™ PCR Amplification Kit User's Manual. [http://www3.appliedbiosystems.com/cms/groups/applied_markets_support/documents/generaldocuments/cms_041201.pdf]
- Schneider PM: Scientific standards for studies in forensic genetics. Forensic Sci Int. 2007, 165: 238-243. 10.1016/j.forsciint.2006.06.067.View ArticlePubMedGoogle Scholar
- Falush D, Stephens M, Pritchard JK: Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics. 2003, 164: 1567-1587.PubMed CentralPubMedGoogle Scholar
- Jakobsson M, Rosenberg NA: CLUMPP: a cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics. 2007, 23: 1801-1806. 10.1093/bioinformatics/btm233.View ArticlePubMedGoogle Scholar
- Rosenberg NA: DISTRUCT: a program for the graphical display of population structure. Mol Ecol Notes. 2004, 4: 137-138.View ArticleGoogle Scholar
- Goldstein DB, Ruiz Linares A, Cavalli-Sforza LL, Feldman MW: An evaluation of genetic distances for use with microsatellite loci. Genetics. 1995, 139: 463-471.PubMed CentralPubMedGoogle Scholar
- Szpiech ZA, Jakobsson M, Rosenberg NA: ADZE: a rarefaction approach for counting alleles private to combinations of populations. Bioinformatics. 2008, 24: 2498-2504. 10.1093/bioinformatics/btn478.PubMed CentralView ArticlePubMedGoogle Scholar
- Excoffier L, Smouse PE, Quattro JM: Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics. 1992, 131: 479-491.PubMed CentralPubMedGoogle Scholar
Copyright
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.