Genomic Data Reveal a Complex Making of Humans

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Genomic Data Reveal a Complex Making of Humans Feb 8, 2014 23:20:13 GMT

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Post by Admin on Feb 8, 2014 23:20:13 GMT

Consensus Neighbor-Joining Tree of Populations The thickest edges have at least 95% bootstrap support, and the edges of intermediate thickness have at least 75% support. If all of the groups subtended by an edge have majority membership in the same cluster in Figure 2A (or only plurality membership in the cases of Hazara , Makrani, and Uygur), the edge is drawn in the same color as was used for the cluster.

A study (Rosenberg et al. 2006) showed that the Kalash people are a distinct or aboriginal population with only minor contributions from outside peoples and the Kalash formed one cluster in a cluster analysts. The cluster tree below shows that modern Europeans have descended from an ancestral group closely associated with the Kalash. Therefore, it can be reasonably concluded that the A111T mutation originally occurred in an ancestral group of the Kalash population and it was subsequently passed on to other Middle Eastern populations such as the Druze and Palestinians and then to various modern European populations located at the end of the blue-coloured Kalash branch of the cluster tree (e.g. Italian, French), which is consistent with the aforementioned migration patterns. Moreover, another genetic study (Firasat et al. 2007) analysed haplogroup frequencies among Kalash individuals and it found that there was a mixture of west and east Eurasian haplogroups. Haplogroup L (25%) and H (20.5%) have originated from prehistoric South Asia, while R1a (18.2%), R* (6.8%), R1* (6.8%), G (18.2%) and J2 (9.1%) can be commonly found in Europe and the Middle East. The Kalash people hold a special place in human evolution as the first group of people of Asian descent with the A111T mutation that gave rise to light skinned modern Europeans and they are the "lost white tribe" in Afghanistan from whom all modern European populations originated.

A Kalash girl in Afghanistan

To estimate the age of the A111T mutation, we used a molecular clock approach. We first determined the rate of mutation in the combined C and D regions from number of differences between human and chimpanzee reference sequences. In this alignment, nt changes within 4 nt of gaps were excluded to remove potential biases caused by misalignment. For calibration, we used 6 million years, the midpoint of the range of estimates (5−7 million years) for the divergence time between human and chimpanzee as identified by Kumar et al. (2005) and assumed equal mutation rates (per year) in the human and chimpanzee lineages. We then counted the number of single-nucleotide differences from the modal haplotype for each C11-D4−containing chromosome in the 1000 Genomes dataset by using all reported variants. Each chromosome provides an imprecise estimate of the time since the origin of the haplotype; values were averaged over individual populations, or the entire sample. Because accumulation of mutations in a single lineage is independent of population size, this procedure does not require demographic assumptions or data. Although A111T is subject to selection, we assume only that subsequent mutations are neutral. Our approach to dating selective sweeps differs from that used by Rozas et al. (2001) and Meiklejohn et al. (2004), which counts the number of affected sites and underestimates the coalescence time unless each sampled lineage is independent. In contrast, our estimate is unbiased in the presence of nonindependent samples. Because the most frequent C11 + D4 variant carrying an additional mutation occurs 36 times in 1013 chromosomes, the independence condition is clearly not met.

Diminished variation in the genomic region around SLC24A5 in the HapMap CEU (European ancestry) sample led us to ask what the haplotypes associated with the A111T allele looked like, and how, when, and where they might have arisen. We therefore investigated haplotypes spanning this genomic region (Figure 2). The haplotypes are described in the context of four contiguous subregions defined by blocks of linkage disequilibrium, here designated A (49 kb), B (20 kb), C (78 kb), and D (49 kb) (Figure 2). Blocks B, C, and D together encompass the region of diminished variation in CEU. Analysis of the core subregion C, which includes SLC24A5, yielded 46 haplotypes in HapMap Phase 3 populations (Table S3 and Table S4). The 11 haplotypes with individual abundances >0.5%, which we designate C1 through C11, collectively comprise 93–98% of the total in each population (Figure 3, Table 1, and Table 2). A single haplotype, C11, accounts for 97% of all instances of the A111T variant of SLC24A5. Most of the haplotypes with frequencies <0.5% appear to be products of recombination between more frequent haplotypes. Analysis of common haplotypes found in HGDP and other samples (Li et al. 2008; Behar et al. 2010) yielded results matching those derived from HapMap samples (Table S5 and Table S6), including the equivalence between haplotype C11 and the derived allele of rs1426654. This finding is consistent with a common origin for A111T worldwide. Analysis of data from the 1000 Genomes Project indicated that haplotypes defined on the basis of 16 SNPs corresponded to sets of closely related haplotypes (Figure 4 and File S1).

Haplotype C3 and C11 share the derived allele of SNP c1 (Table 1), suggesting the possibility of recombination. To test this possibility, we examined SNPs not genotyped in HapMap Phase 3. Haplotype C11 carries ancestral alleles of SNPs rs12441154 and rs57108441, whereas C10 carries the derived alleles, a pattern readily explained by a single crossover between C3 and C10 (Figure 6). In support of this notion, the B-region haplotype found associated with C11, B6, is also the one most commonly associated with C3; conversely 96% of C10 haplotypes are associated with B region haplotypes other than B6 (68% with B2; File S2).

Can we determine whether recombination involving C3 preceded or followed the mutation that created A111T? Models in which the recombination or mutation occurred first produce the same end product but proceed through different intermediates, corresponding to C26 or C22, respectively (Table S3). Rare haplotypes matching both potential C11 precursors were found. Because either could have been produced by recombination subsequent to the origin of C11, their occasional occurrence is not informative. However, an evolutionary argument strongly suggests an order of events (Figure S4). If the crossover predated the mutation, the predicted intermediate (C26) would not have experienced positive selection on the basis of lighter pigmentation (Figure S4A). Selection for decreased skin pigmentation would cause the predominant haplotype containing A111T to be C11, as is observed. On the other hand, if the A111T mutation preceded the crossover, the intermediate haplotype (C22) would be predicted to experience the same selective pressure as C11 (Figure S4B). Because C11 is derived by recombination between C3 and C22 in this model, C22 would be expected to predominate over C11, unless C11 had a selective advantage over C22. This outcome is not what is observed. Rather, the frequency of C22 is only approximately 1% that of C11. Furthermore, association with diverse B-region haplotypes rather than one makes it most likely that the existing instances of C22 are the result of recombination after the formation of C11 rather than relicts of a precursor to C11. We conclude that the crossover most likely preceded the A111T mutation.

The preceding analysis is consistent with a wide range of possible dates for the origin of A111T, including the period before the initial colonization of Europe by anatomically modern humans >40 thousand years ago (kya) (Mellars 2006). An estimate for the date of origin of A111T based on microsatellites (Beleza et al. 2012) places the origin at 19 kya (95% confidence interval 6−38 kya), for a dominant model, or 11 kya (95% confidence interval 1−56 kya), for a more plausible additive model. To create an independent estimate, we applied a molecular clock approach to 1000 Genomes data by using the combined C and D subregions. Because proportions of different classes of nucleotide substitutions in the C11 + D4 variants and in the human-chimpanzee alignment are not significantly different (χ2 = 4.42, df = 5, P = 0.49; Table S15), we combined these classes for analysis. For the combined population samples, before making corrections for undercounts in the source data, we obtained an estimate of 7.8 kya for the most recent common ancestor of the C11 + D4 haplotype combination (Table 3). Corresponding 95% confidence limits are 4.8−12.2 kya, whereas uncorrected estimates derived from individual European samples or the combined New World samples (also of European origin) ranged from 5.2 to 10.4 kya (Table 3).

Divergent natural selection caused by differences in solar exposure has resulted in distinctive variations in skin color between human populations. The derived light skin color allele of the SLC24A5 gene, A111T, predominates in populations of Western Eurasian ancestry. To gain insight into when and where this mutation arose, we defined common haplotypes in the genomic region around SLC24A5 across diverse human populations and deduced phylogenetic relationships between them. Virtually all chromosomes carrying the A111T allele share a single 78-kb haplotype that we call C11, indicating that all instances of this mutation in human populations share a common origin. The C11 haplotype was most likely created by a crossover between two haplotypes, followed by the A111T mutation. The two parental precursor haplotypes are found from East Asia to the Americas but are nearly absent in Africa. The distributions of C11 and its parental haplotypes make it most likely that these two last steps occurred between the Middle East and the Indian subcontinent, with the A111T mutation occurring after the split between Europeans and East Asians.

To estimate the age of the A111T mutation, we used a molecular clock approach. We first determined the rate of mutation in the combined C and D regions from number of differences between human and chimpanzee reference sequences. In this alignment, nt changes within 4 nt of gaps were excluded to remove potential biases caused by misalignment. For calibration, we used 6 million years, the midpoint of the range of estimates (5−7 million years) for the divergence time between human and chimpanzee as identified by Kumar et al. (2005) and assumed equal mutation rates (per year) in the human and chimpanzee lineages. We then counted the number of single-nucleotide differences from the modal haplotype for each C11-D4−containing chromosome in the 1000 Genomes dataset by using all reported variants. Each chromosome provides an imprecise estimate of the time since the origin of the haplotype; values were averaged over individual populations, or the entire sample. Because accumulation of mutations in a single lineage is independent of population size, this procedure does not require demographic assumptions or data. Although A111T is subject to selection, we assume only that subsequent mutations are neutral. Our approach to dating selective sweeps differs from that used by Rozas et al. (2001) and Meiklejohn et al. (2004), which counts the number of affected sites and underestimates the coalescence time unless each sampled lineage is independent. In contrast, our estimate is unbiased in the presence of nonindependent samples. Because the most frequent C11 + D4 variant carrying an additional mutation occurs 36 times in 1013 chromosomes, the independence condition is clearly not met.

Diminished variation in the genomic region around SLC24A5 in the HapMap CEU (European ancestry) sample led us to ask what the haplotypes associated with the A111T allele looked like, and how, when, and where they might have arisen. We therefore investigated haplotypes spanning this genomic region (Figure 2). The haplotypes are described in the context of four contiguous subregions defined by blocks of linkage disequilibrium, here designated A (49 kb), B (20 kb), C (78 kb), and D (49 kb) (Figure 2). Blocks B, C, and D together encompass the region of diminished variation in CEU. Analysis of the core subregion C, which includes SLC24A5, yielded 46 haplotypes in HapMap Phase 3 populations (Table S3 and Table S4). The 11 haplotypes with individual abundances >0.5%, which we designate C1 through C11, collectively comprise 93–98% of the total in each population (Figure 3, Table 1, and Table 2). A single haplotype, C11, accounts for 97% of all instances of the A111T variant of SLC24A5. Most of the haplotypes with frequencies <0.5% appear to be products of recombination between more frequent haplotypes. Analysis of common haplotypes found in HGDP and other samples (Li et al. 2008; Behar et al. 2010) yielded results matching those derived from HapMap samples (Table S5 and Table S6), including the equivalence between haplotype C11 and the derived allele of rs1426654. This finding is consistent with a common origin for A111T worldwide. Analysis of data from the 1000 Genomes Project indicated that haplotypes defined on the basis of 16 SNPs corresponded to sets of closely related haplotypes (Figure 4 and File S1). Common haplotypes defined using 1000 Genomes data differed from the ancestral state at 27−48 positions. The number of haplotypes detected depends on the number of polymorphisms used to define them; with inclusion of lower frequency variants, an increasing fraction of chromosomes corresponds to rare haplotypes (Table S7).

Phylogenetic relationships between haplotypes determined using 1000 Genomes data (Figure 4) were equivalent to those deduced from HapMap phase 2 data. The branches comprising C1, C2, C3, C4, and C5-C11 are early diverging clades. The most abundant and extensive lineage includes three branches: C5, C6-C7, and C9-C11. Within the C9-C11 branch, the C9 cluster is ancestral to C10, but nearly all instances of C9 include additional polymorphisms that distinguish them from common ancestors with C10. In contrast, the most commonly observed C10 variant is ancestral to C11.

Haplotype C3 and C11 share the derived allele of SNP c1 (Table 1), suggesting the possibility of recombination. To test this possibility, we examined SNPs not genotyped in HapMap Phase 3. Haplotype C11 carries ancestral alleles of SNPs rs12441154 and rs57108441, whereas C10 carries the derived alleles, a pattern readily explained by a single crossover between C3 and C10 (Figure 6). In support of this notion, the B-region haplotype found associated with C11, B6, is also the one most commonly associated with C3; conversely 96% of C10 haplotypes are associated with B region haplotypes other than B6 (68% with B2; File S2).

Can we determine whether recombination involving C3 preceded or followed the mutation that created A111T? Models in which the recombination or mutation occurred first produce the same end product but proceed through different intermediates, corresponding to C26 or C22, respectively (Table S3). Rare haplotypes matching both potential C11 precursors were found. Because either could have been produced by recombination subsequent to the origin of C11, their occasional occurrence is not informative. However, an evolutionary argument strongly suggests an order of events (Figure S4). If the crossover predated the mutation, the predicted intermediate (C26) would not have experienced positive selection on the basis of lighter pigmentation (Figure S4A). Selection for decreased skin pigmentation would cause the predominant haplotype containing A111T to be C11, as is observed. On the other hand, if the A111T mutation preceded the crossover, the intermediate haplotype (C22) would be predicted to experience the same selective pressure as C11 (Figure S4B). Because C11 is derived by recombination between C3 and C22 in this model, C22 would be expected to predominate over C11, unless C11 had a selective advantage over C22. This outcome is not what is observed. Rather, the frequency of C22 is only approximately 1% that of C11. Furthermore, association with diverse B-region haplotypes rather than one makes it most likely that the existing instances of C22 are the result of recombination after the formation of C11 rather than relicts of a precursor to C11. We conclude that the crossover most likely preceded the A111T mutation.

The world distribution of core region haplotypes, together with their phylogenetic relationships, suggests which haplotypes likely originated in Africa and which most likely arose outside of Africa. As expected from the near fixation of A111T in Europe, the C11 clade predominates there, and all other haplotypes are rare. Of the remaining 10 common core haplotype groups, all ancestral at rs1426654, eight clearly have their origins in Africa (Figure 3B, Figure 4, and Table S4). Three early diverging haplotypes, C1, C2, and C4, are rare outside of Africa and clearly originated there. In the lineage containing the majority of haplotypes, each of the three branches, containing C5, C6-C7, and C8-C11, give strong evidence of having originated in Africa. C5 reaches its greatest abundance in West Africa and is rare outside of Africa. Within the other two branches, C6 and C9, which are the most common haplotypes in Africa, are also common worldwide, whereas C7 is abundant in East Asia and much less common but widespread in Africa. Consideration of the relationships among haplotype variants (Figure 4) indicates that C6, C7, and C9 (but not C8) dispersed out of Africa and have diverse descendants present and originating in East Asia. Among these descendants is C10, which is abundant in East Asia (and the New World) but extremely rare in Africa (0.5% in LWK). Haplotype C3 represents the final early diverging lineage (Figure 4). Although the lineage containing this haplotype must have originated in Africa, C3 is rare in Africa (1.0% in MKK) but widely distributed in East Asia, the New World, and Oceania. The distributions of C3 and C10 are most consistent with origin outside of Africa and subsequent introduction into Africa by migrations such as those documented by uniparental markers (Richards et al. 2006).

The preceding analysis is consistent with a wide range of possible dates for the origin of A111T, including the period before the initial colonization of Europe by anatomically modern humans >40 thousand years ago (kya) (Mellars 2006). An estimate for the date of origin of A111T based on microsatellites (Beleza et al. 2012) places the origin at 19 kya (95% confidence interval 6−38 kya), for a dominant model, or 11 kya (95% confidence interval 1−56 kya), for a more plausible additive model. To create an independent estimate, we applied a molecular clock approach to 1000 Genomes data by using the combined C and D subregions. Because proportions of different classes of nucleotide substitutions in the C11 + D4 variants and in the human-chimpanzee alignment are not significantly different (χ2 = 4.42, df = 5, P = 0.49; Table S15), we combined these classes for analysis. For the combined population samples, before making corrections for undercounts in the source data, we obtained an estimate of 7.8 kya for the most recent common ancestor of the C11 + D4 haplotype combination (Table 3). Corresponding 95% confidence limits are 4.8−12.2 kya, whereas uncorrected estimates derived from individual European samples or the combined New World samples (also of European origin) ranged from 5.2 to 10.4 kya (Table 3). These values are clearly underestimates as a result of low sequence depth (1000 Genomes Project Consortium 2012). Adjustment for undercounting is substantial, increasing the estimated age for the combined samples to 12.4 (95% confidence interval 7.6−19.2) kya. If mutation rates in recent humans are lower than predicted from the human-chimpanzee divergence (Scally and Durbin 2012), true ages will be even older. Our adjusted dates overlap those previously reported (Beleza et al. 2012) and are also consistent with the lower limit for the origin of A111T set by the finding that the Alpine “iceman” dated to 5.3 kya was homozygous for this variant (Keller et al. 2012). This date range implies an origin clearly preceding the Neolithic transition in Europe. These dates are later than the initial colonization of Europe but are consistent with an A111T origin before or after post-glacial population expansions.

The precursors to C11, haplotypes C3 and C10, are common in East Asia and the New World (Figure S5), but the distribution of C11 indicates that these locations are not likely sites for the origin of C11 or its immediate precursor. Similarly, B6 not associated with C11 is distributed widely in East Asia and the New World (data not shown). The paucity of C3 and C10 among existing African haplotypes suggests that both events leading to the origin of C11 took place outside this continent. Our dating for this haplotype is consistent with a non-African origin. The most likely location for the origin of C11 is, therefore, within the region in which it is fixed or nearly so. As both models for the origin of C11 imply that C3 and C10 were present in ancestors of Europeans, the observed and inferred distributions of these autosomal haplotypes are consistent with the single-out-of-Africa hypothesis derived using uniparental markers. Although a non-African origin for C11 is clear, near fixation of this haplotype over a wide geographical region prevents strong inferences regarding a precise location of origin. Existing data are consistent with a model in which the C11 precursor did not extend outside the geographical region in which C11 is now nearly fixed, a conclusion subject to limited haplotype sampling in some neighboring regions, such as India. With sufficiently strong positive selection for C11, it is possible that this haplotype could have originated anywhere within its current range and spread via local migration. However, selection acting in concert with major population migrations would have facilitated a much more rapid dispersal. Archeological, mitochondrial, and Y-chromosomal data suggest involvement of multiple dispersals in shaping the current populations of Europe and the Middle East (Soares et al. 2010).

Canfield, Victor A., et al. "Molecular Phylogeography of a Human Autosomal Skin Color Locus Under Natural Selection." G3: Genes| Genomes| Genetics 3.11 (2013): 2059-2067.

Last Edit: Feb 8, 2014 23:23:14 GMT by Admin

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Genomic Data Reveal a Complex Making of Humans Feb 25, 2014 2:09:06 GMT

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Post by Admin on Feb 25, 2014 2:09:06 GMT

Western Europe has long been held to be the "cradle" of Neanderthal evolution, and anthropologists have theorized that climatic factors or competition from modern humans were the likely causes when Neanderthals started disappearing around 30,000 years ago. But new research suggests that Western European Neanderthals were on the verge of extinction long before modern humans showed up.

This perspective comes from a study of ancient DNA carried out by an international research team. Rolf Quam, a Binghamton University anthropologist, was a co-author of the study led by Anders Götherström at Uppsala University and Love Dalén at the Swedish Museum of Natural History, and published in the journal Molecular Biology and Evolution.

"The Neanderthals are our closest fossil relatives and abundant evidence of their lifeways and skeletal remains has been found at many sites across Europe and western Asia," said Quam, assistant professor of anthropology. "Until modern humans arrived on the scene, it was widely thought that Europe had been populated by a relatively stable Neanderthal population for hundreds of thousands of years. Our research suggests otherwise and, in light of these new results, this long-held theory now faces scrutiny."

Focusing on mitochondrial DNA sequences from 13 Neanderthal individuals, including a new sequence from the site of Valdegoba cave in northern Spain, the research team found some surprising results. When they started looking at the DNA, a clear pattern emerged. Neanderthal individuals from Western Europe that were older than 50,000 years and individuals from sites in western Asia and the Middle East showed a high degree of genetic variation, on par with what might be expected from a species that had been abundant in an area for a long period of time. In fact, the amount of genetic variation was similar to what characterizes modern humans as a species. In contrast, Neanderthal individuals from Western Europe that were younger than 50,000 years show an extremely reduced amount of genetic variation, less even than the present-day population of remote Iceland.

These results suggest that Western European Neanderthals went through a demographic crisis, a population bottleneck that severely reduced their numbers, leaving Western Europe largely empty of humans for a period of time. The demographic crisis seems to coincide with a period of extreme cold in Western Europe. Subsequently, this region was repopulated by a small group of individuals from a surrounding area. The geographic origin of this source population is not clear, but it may be possible to pinpoint it further with additional study.

"The fact that Neanderthals in Western Europe were nearly extinct, but then recovered long before they came into contact with modern humans came as a complete surprise to us," said Dalén, associate professor at the Swedish Museum of Natural History in Stockholm. "This indicates that the Neanderthals may have been more sensitive to the dramatic climate changes that took place in the last Ice Age than was previously thought." Quam concurs and suggests that this discovery calls for a major rethinking of the idea of cold adaptation in Neanderthals.

"At the very least, this tells us that without the aid of material culture or technology, there is a limit to our biological adaptation," Quam said. "It may very well have been the case that the European Neanderthal populations were already demographically stressed when modern humans showed up on the scene." The results presented in the study are based entirely on degraded ancient DNA, and the analyses have therefore required advanced laboratory and computational methods. The research team includes statisticians, experts on modern DNA sequencing and paleoanthropologists from Sweden, Denmark, Spain and the United States.

"This is just the latest example of how studies of ancient DNA are providing new insights into an important and previously unknown part of Neanderthal history," Quam said. "Ancient DNA is complementary to anthropological studies focusing on the bony anatomy of the skeleton, and these kinds of results are only possible with ancient DNA studies. It's exciting to think about what will turn up next."

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Genomic Data Reveal a Complex Making of Humans Mar 2, 2014 22:35:39 GMT

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Post by Admin on Mar 2, 2014 22:35:39 GMT

Although the potential of the Nile River Valley to serve as a corridor for human migration seems obvious, some archeological evidence suggests, instead, that there was a significant and long-standing frontier zone between the northern and southern states in Lower Nubia (Alexander 1988). Moreover, the human populations distributed along the length of the Nile River Valley do exhibit cultural and linguistic differences (Grimes 1996). Egypt, at the northern end of the Nile, is one of the oldest known centralized states in the world; both ancient Egyptian and Arabic (spoken today in Egypt) are Afro-Asiatic languages. Nubia comprises the southern part of Egypt and the northern part of the Sudan and, historically, has consisted of kingdoms that were only occasionally united politically; Nubian languages constitute one branch of the Nilo-Saharan language family. The populations along the southernmost part of the Nile River in the southern Sudan are pastoral nomads with a tribal organization, and they speak languages within the Nilotic branch of the Nilo-Saharan family. Thus, this evidence suggests that there was a barrier between the northern and southern portions of the Nile River Valley and that the latter was a cul-de-sac, rather than a corridor, for human migration.

We report here our analysis of mtDNA variation in contemporary Nile River Valley populations in Egypt, Nubia, and the southern Sudan. By virtue of its strictly maternal and haploid mode of inheritance, mtDNA has already proved to be useful in the tracing of the migrations leading to the colonization of Polynesia (Melton et al. 1995; Redd et al. 1995; Sykes et al. 1995) and the New World (Wallace and Torroni 1992; Merriwether et al. 1996). In addition, comparison with the large amount of existing data on human mtDNA variation enables us to confidently assign mtDNA types in Nile River Valley populations to northern (Eurasian) or southern (sub-Saharan African) affiliation and to use this information to infer migrations. Given the cultural and linguistic differences that exist between Egyptian, Nubian, and southern Sudanese populations, the question that we address here is whether the Nile River Valley has been a corridor or a cul-de-sac for human migrations.

To identify the probable geographic origin of mtDNA types from the Nile River Valley, the published literature on mtDNA-sequence and restriction-site polymorphisms was scanned for polymorphisms that differ significantly, in frequency, between Eurasian (northern) and sub-Saharan African (southern) populations. Three sites (fig. 2) that exhibit significant frequency differences were selected for analysis in Nile River Valley mtDNA types; two of these sites (16223 and 16311) are in HV1 and hence were determined on the basis of sequence analysis of HV1, whereas the third is a polymorphic HpaI site, at position 3592, that was typed by HpaI digestion of a PCR product containing this site.

Figure 2. Frequencies of three mtDNA polymorphisms in Eurasian (blackened bars) and African (hatched bars) mtDNA types. The frequencies of T at 16223 and C at 16311 are based on 1,873 Eurasian and 624 African sequences (Handt et al. 1998). The frequency of the HpaI site at 3592 is based on 1,355 Eurasian and 754 African mtDNA types (Denaro et al. 1981; Johnson et al. 1983; Bonné-Tamir et al. 1986; Brega et al. 1986; Cann et al. 1987; Harihara et al. 1988; Scozzari et al. 1988, 1994; Vilkki et al. 1988; Semino et al. 1989, 1991; Tikochinski et al. 1991; Ballinger et al. 1992; Soodyall and Jenkins 1992; Ritte et al. 1993; Chen et al. 1995; Graven et al. 1995).

Northern mtDNA types are associated with a C at 16223, a T at 16311, and the absence of the HpaI site, whereas southern mtDNA types are associated with a T at 16223, a C at 16311, and the presence of the HpaI site ( fig. 2). Therefore, Nile River Valley mtDNA types with the northern character at two or three of the sites were classified as originating from the north, whereas those with the southern character at two or three of the sites were classified as originating from the south. Utilizing three sites in this manner should minimize incorrect classification of mtDNA types; however, because the two sites in HV1 are subject to repeated mutations (Hasegawa et al. 1993), we were concerned that some incorrect classification might nevertheless occur. We therefore compared the entire HV1 sequences of the Nile River Valley mtDNA types versus a worldwide database of 4,079 HV1 sequences (Handt et al. 1998) and, on the basis of whether it was either identical to or differed by one substitution from database mtDNA types with exclusively northern or southern affiliations, independently classified the origin of each unique Nile River Valley mtDNA sequence. Approximately one-third of the Nile River Valley mtDNA types could be unambiguously classified on the basis of this database comparison; the results were nearly completely concordant with the classification based on the three sites, with the single discrepancy involving an Egyptian mtDNA that, on the basis of the three sites, was classified as northern but, on the basis of the database comparison, was classified as southern because it was identical to sequences found in two Songhai from Mali and two Kikuyu from Kenya (Watson et al. 1996). Because alteration of the classification of this one sequence does not significantly change any of the results that follow, this Egyptian mtDNA was still classified as northern, in accordance with the results from use of the three sites.

Clear trends in diversity were shown when only slowly evolving sites were considered (fig. 3). The diversity in the southern Sudanese, with respect to northern mtDNA types, is dramatically reduced when only slowly evolving sites (i.e., sites that, according to Hasegawa et al. [1993], have mutated only once) are considered; the 9 different types among 11 northern mtDNA types in the southern Sudan are reduced to just 1 type when rapidly evolving sites are excluded. Even though there are only 11 southern Sudanese with northern mtDNA types, this reduction to 1 type for slowly evolving sites is still significantly less (P=.03) than would be expected if 11 individuals were chosen at random from the 57 Egyptians with northern mtDNA types. The diversity in northern mtDNA types in Egypt and Nubia are both reduced to a similar extent, but not as greatly as is observed for northern mtDNA types from the southern Sudan, when only slowly evolving sites are considered.

Figure 3. Diversity, in Egyptians, Nubians, and southern Sudanese, of northern mtDNA types (top) and southern mtDNA types (bottom), for various classes of nucleotide sites. The number at the top of each bar denotes the number of different mtDNA types. The term “all” refers to all nucleotide sites, whereas “m(1),” “m(1+2),” and “m(3+)” refer to sites that mutated once, once or twice, and three or more times, respectively, according to the phylogenetic analysis by Hasegawa et al. ( 1993); the m(1+2) class thus includes the m(1) class. Note that, for northern mtDNA types, there was just one type observed in the southern Sudan, when only the m(1) sites are considered, and hence the diversity is 0.

These analyses show that the diversity of northern mtDNA types is highest in Egypt and lowest in the southern Sudan and that the diversity of southern mtDNA types is highest in the southern Sudan and lowest in Egypt, suggesting that gene flow has occurred in both directions along the Nile River Valley. To further investigate the Nile River Valley populations' spatial structuring with respect to mtDNA variation, we performed a spatial autocorrelation analysis (Bertorelle and Barbujani 1995). Various ways of partitioning the data with respect to geographic distance (equal geographic distances per class, equal numbers of pairwise comparisons per distance class, or arbitrary distance divisions) all yielded quite similar results, with significantly positive II values observed for small distance classes and significantly negative values observed for large distance classes; a representative result is shown in figure 4. The smooth decrease, from small to large geographic distance classes observed in the II values is indicative of a gradient or cline in the distribution of mtDNA types along the Nile River Valley.

Figure 4. Correlogram of the AIDA II for 189 mtDNA haplotypes (HV1 sequences plus the HpaI restriction site) from the Nile River Valley. The value of II is the Y-axis, whereas the X-axis is the width of arbitrarily chosen distance classes. The double asterisks (**) denote that the value of II for all distance classes except 800–1,200 km differs significantly (P<.01) from 0.

The smooth, decreasing gradient in the AIDA II values that is observed in the spatial autocorrelation analysis also supports the conclusion of past migrations along the Nile River Valley. Moreover, the lack of any significant fluctuations in the correlograms suggests that there have not been any significant local barriers to migration or significant local drift effects. However, although the mtDNA results indicate that migrations have occurred in both directions along the Nile River Valley, these migrations have not been extensive enough to genetically homogenize the mtDNA gene pools of Nile River Valley populations. Significant differences exist among Egyptian, Nubian, and southern Sudanese mtDNA types, with Nubians appearing to be more similar to Egyptians than to the southern Sudanese and with Egyptians and the southern Sudanese exhibiting the greatest differences. This is perhaps to be expected, given that, during the second millenium b.c., Pharaonic Egypt colonized part or most of Nubia and that Nubian kings conquered Egypt during the 8th and 7th centuries b.c. (Shaw and Nicholson 1995). Thus, there is historical evidence documenting much direct interaction between Egypt and Nubia.

We can infer that the migration of northern mtDNA types to the south is older than the migration of southern mtDNA types to the north (or that there has been less gene flow from north to south than from south to north along the Nile River Valley) and that Egypt and Nubia have had more genetic contact than either has had with the southern Sudan. Moreover, we can tentatively infer that these migrations occurred recently enough to fall within the period of the documented historical record of human populations in the Nile River Valley. Thus, it is tempting to try to relate these migrations to specific historical events (Shaw and Nicholson 1995). For example, the migration from north to south may coincide with the Pharaonic colonization of Nubia, which occurred initially during the Middle Kingdom (12th Dynasty, 1991–1785 b.c.) and more permanently during the New Kingdom, from the reign of Thotmosis III (1490–1437 b.c.). The migration from south to north may coincide with the 25th Dynasty (730–655 b.c.), when kings from Napata in Nubia conquered Egypt. Of course, additional migrations documented during the Ptolemeic, Roman, and Arabic times are also likely to have contributed to the current distribution of mtDNA types along the Nile River Valley.

The migrations inferred to have occurred along the Nile River Valley are based on an analysis of a single genetic locus that is informative for maternal ancestry only; similar analyses of autosomal and Y-chromosomal loci, which are in progress, are needed to confirm and extend the conclusions of the present study. Indeed, the Nile River Valley may provide an excellent test of the hypothesis that female migration has been greater than male migration (Seielstad et al. 1998). Another potential way of testing the hypothesized correlations between migrations inferred from patterns of mtDNA variation and the historical record would be via ancient-DNA analysis; that is, the temporal variation of migration patterns along the Nile River Valley could be directly assessed, if authentic ancient DNA could be retrieved from a suitable number of specimens. Indeed, a recent study did report success in obtaining DNA from the remains of 15 of 29 ancient Nubians that are ∼2,000 years old (Fox 1997). These individuals were screened for the HpaI site at position 3592; the frequency of this site in the ancient Nubians (26.7%) is not significantly different from that in contemporary Nubians in the present study (32.5%). Thus, it may prove feasible to use ancient-DNA analysis to investigate the temporal stability of migration patterns along the Nile River Valley. However, in a study of 132 mummies and skeletons of late pre-Dynastic and early Pharaonic age, only 2 samples yielded reproducible sequences when cloned amplification products derived from independent extracts were compared (M. Krings, unpublished data). Thus, the prospects for a comprehensive survey of ancient-DNA variation along the Nile River Valley do not look promising (Handt et al. 1996), and further technical advances in ancient DNA analysis will doubtless be required.

Krings, Matthias, et al. "www.sciencedirect.com/science/article/pii/S0002929707631826"]mtDNA analysis of Nile River Valley populations: A genetic corridor or a barrier to migration?." The American Journal of Human Genetics 64.4 (1999): 1166-1176.

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Genomic Data Reveal a Complex Making of Humans Mar 4, 2014 0:01:09 GMT

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Post by Admin on Mar 4, 2014 0:01:09 GMT

The Kebara 2 Neanderthal dates from approximately 60 ka and is part of a near-complete adult male skeleton unearthed in 1983 [1]. Subsequent discoveries of additional fossil hominin hyoids have generated renewed interest in the bone’s potential to inform on the evolution of speech and complex language. These include: a partial Neanderthal hyoid (SDR-034) from El Sidròn Cave (Asturias, Spain) dated to ~43 ka [2]; two Middle Pleistocene hyoids (AT-1500 and AT-2000) assigned to Homo heidelbergensis from Sierra de Atapuerca (Spain) dated at ~530 ka [3]; and a “chimpanzee-like” hyoid assigned to Australopithecus afarensis from Dikika (Ethiopia, ~3.3 Ma) [4].

Figure 3. Computed tomography of Homo sapiens (N. S36-Sulmona Fonte d′Amore T64, University Museum Chieti - Italy).

Examination of internal microscopic anatomy reveals that the medial sagittal microCT section from the Kebara 2 hyoid body (Figure 2) shows a marked arcuate shape, corresponding with deep fossae for the insertion of the geniohyoid muscles [1]. Its histomorphology (Figure 2) is characterized by cortical bone with vascular channels, well-developed intertrabecular spaces and dorsoventrally oriented bony lamellae. In each of these respects its microarchitecture is comparable to that of modern human hyoids (Figure 3).

Figure 4. Computational biomechanical analyses of hyoid models.

Visual plots of von Mises (VM) stress distributions are given in Figure 4. VM stress is a good indicator of material failure in relatively ductile materials such as bone [22]. Mean values for VM stresses are given for each Finite Element Model (FEM) as a whole and for subgroups containing only surface elements in Table 1. Using the Graph Tool in Strand7 (2.4) a straight-line was drawn between the dorso-lateral-most extremes of each model to plot a graph of VM stress for elements intersecting the line (Figure 4).

MicroCT analysis reveals that the hyoid bodies of both Kebara 2 and modern humans are characterized by two thick cortical layers, well-defined vascular channels and well-developed spongy structures. The detail of histological structure in all specimens, including Kebara 2, is typical of bone involved in intense and continuous metabolic activity. Our analysis shows that the similarity in gross surface morphology between the Kebara 2 hyoid and those of modern humans also extends internally to microscopic architecture and the orientations of the bony trabeculae comprising the spongy bone of the hyoid body.

The results of FEA-based comparisons of our high-resolution models further show that the Kebara 2 hyoid presents very similar micro-biomechanical performance to that of modern humans under identical loadings. Minor histomorphological differences are present in that the Kebara 2 hyoid appears more dorsoventrally flattened in the distal regions, and the bony trabeculae appear thicker than in our modern human sample. However, given the considerable variation among the modern human specimens we consider it likely that these differences are a manifestation of individual histological variability and/or size differences.

The hyoid undoubtedly plays an active role in speech and is indicative of the state of the vocal tract. As the vocal tract’s only ossified element, it is the only part likely to be preserved in the fossil record. It is not directly attached to any other bone in the skeleton, being held by ligaments and muscles that attach it to the mandible, temporal bone, thyroid cartilage and sternum. It provides support for the larynx and anchorage for the tongue and other muscles required for speaking. However, other muscles not attached to the hyoid are important in human speech, which is ultimately under neurological control.

In sound production, the tongue assumes configurations that influence the morphology of the vocal tract largely in response to contractions of its intrinsic muscles [23]. However, changes in overall tongue position relative to the hard palate are the result of hyoid movements controlled by differential activity in the hyoid and extrinsic tongue muscles [24], [25].

Although the hyoid moves continuously during speech its movements are not linked to jaw movement. A clear dichotomy has been identified in hyoid movement patterns generated during feeding and those generated during speech. These different behaviours of the hyoid are marked by a shift in the operating length of the anterior and posterior suprahyoid muscles, such that the anterior group (especially the geniohyoid) are functionally ‘shorter’ and the posterior group functionally ‘longer’ in speech than in feeding [26], [27]. This confirms earlier work suggesting that activation patterns in the mandibular muscles during speech are not related to the rhythmic patterns of chewing [28].

Modern-human-like gross anatomy in the hyoid body of a fossil specimen is not, in itself, clear demonstration that the individual was capable of speech [6]. However, our analyses demonstrate that previously observed gross similarities between the Kebara 2 hyoid and those of modern humans are not superficial.

www.plosone.org/article/info:doi/10.1371/journal.pone.0082261

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Genomic Data Reveal a Complex Making of Humans Mar 13, 2014 6:46:01 GMT

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We have identified a Y-chromosomal lineage with several unusual features. It was found in 16 populations throughout a large region of Asia, stretching from the Pacific to the Caspian Sea, and was present at high frequency: ∼8% of the men in this region carry it, and it thus makes up ∼0.5% of the world total. The pattern of variation within the lineage suggested that it originated in Mongolia ∼1,000 years ago. Such a rapid spread cannot have occurred by chance; it must have been a result of selection. The lineage is carried by likely male-line descendants of Genghis Khan, and we therefore propose that it has spread by a novel form of social selection resulting from their behavior.

Figure 1.

In surveys of DNA variation in Asia, we typed 2,123 men with ⩾32 markers to produce a Y haplotype for each man; these included 1,126 individuals described elsewhere (Qamar et al. 2002; Zerjal et al. 2002). Over 90% of the haplotypes showed the usual pattern (Mohyuddin et al. 2001): most males had a unique code; and the few haplotypes present in more than one individual were generally found within the same population. However, we also saw one pattern that was novel in two respects. First, there was a high frequency of a cluster of closely related lineages, collectively called the “star cluster” (fig. 1, shaded area). Second, star-cluster chromosomes were found in 16 populations throughout a large geographical area extending from Central Asia to the Pacific ( fig. 2); thus, they do not result from an event specific to any single population. We can deduce the most likely time to the most recent common ancestor (TMRCA) and place of origin of this unusual lineage from the observed genetic variation. To do this, it is first necessary to distinguish star-cluster chromosomes from the remainder. For this, we used the criterion that haplotypes linked to the central one in the shaded area of the network without gaps would be included ( fig. 1). We then used two approaches to calculate a TMRCA for the star-cluster chromosomes. The program BATWING (Wilson and Balding 1998) uses models of both mutation and population processes, which were specified as described elsewhere (Qamar et al. 2002). With this program, we estimated ∼1,000 years for the TMRCA (95% confidence interval limits ∼700–1,300 years). The use of alternative demographic models with constant or exponentially increasing population size changed the estimate by <10%. A method that does not consider population structure (Morral et al. 1994), ρ, suggested ∼860 (∼590–1,300) years. In both calculations, we assumed a generation time of 30 years. The origin was most likely in Mongolia, where the largest number of different star-cluster haplotypes is found ( fig. 1). Thus, a single male line, probably originating in Mongolia, has spread in the last ∼1,000 years to represent ∼8% of the males in a region stretching from northeast China to Uzbekistan. If this spread were due to a general population expansion, we would expect to find multiple lineages with the same characteristics of high frequency and presence in multiple populations, but we do not (Zerjal et al. 2002). The star-cluster pattern is unique.

Figure 2. Geographical distribution of star-cluster chromosomes. Populations are shown as circles with an area proportional to sample size; star-cluster chromosomes are indicated by green sectors. The shaded area represents the extent of Genghis Khan's empire at the time of his death (Morgan 1986).

This rise in frequency, if spread evenly over ∼34 generations, would require an average increase by a factor of ∼1.36 per generation and is thus comparable to the most extreme selective events observed in natural populations, such as the spread of melanic moths in 19th-century England in response to industrial pollution (Edleston 1865). We evaluated whether it could have occurred by chance. If the population growth rate is known, it is possible to test whether the observed frequency of a lineage is consistent with its level of variation, assuming neutrality (Slatkin and Bertorelle 2001). Using this method, we estimated the chance of finding the low degree of variation observed in the star cluster, with a current frequency of ∼8%, under neutral conditions. Even with the demographic model most likely to lead to rapid increase of the lineage, double exponential growth, the probability was <10−237; if the mutation rate were 10 times lower, the probability would still be <10−10. Thus, chance can be excluded: selection must have acted on this haplotype.

Could biological selection be responsible? Although this possibility cannot be entirely ruled out, the small number of genes on the Y chromosome and their specialized functions provide few opportunities for selection (Jobling and Tyler-Smith 2000). It is therefore necessary to look for alternative explanations. Increased reproductive fitness, transmitted socially from generation to generation, of males carrying the same Y chromosome would lead to the increase in frequency of their Y lineage, and this effect would be enhanced by the elimination of unrelated males. Within the last 1,000 years in this part of the world, these conditions are met by Genghis (Chingis) Khan (c. 1162–1227) and his male relatives. He established the largest land empire in history and often slaughtered the conquered populations, and he and his close male relatives had many children. Although the Mongol empire soon disintegrated as a political unit, his male-line descendants ruled large areas of Asia for many generations. These included China, where the Yüan Dynasty emperors remained in power until 1368, after which the Mongols continued to dominate the country north of the Great Wall for several more centuries, and the region west to the Aral Sea, where the Chaghatai Khans ruled. Although their power diminished over time, they remained at Kashghar near the Kyrgyzstan/China border until the middle of the 17th century (Morgan 1986).

It is striking that the boundary of the Mongol empire when Genghis Khan died (fig. 2), which also corresponds to the boundaries of the regions controlled by later Khans, matches the distribution of star-cluster chromosomes closely, with one exception: the Hazaras. We, therefore, wished to compare Genghis Khan’s Y profile with the star cluster. It is not possible to examine his remains directly, but history provides an alternative. The Hazaras of Pakistan have a Mongol origin (Qamar et al. 2002), and many consider themselves to be direct male-line descendants of Genghis Khan. A genealogy documenting these links has been constructed from their oral history (Mousavi 1998). A large proportion of the Hazara profiles do indeed lie in the star cluster, which is not otherwise seen in Pakistan (fig. 2), thus supporting their oral tradition and suggesting that Genghis Khan carried the star-cluster haplotype.

The Y chromosome of a single individual has spread rapidly and is now found in ∼8% of the males throughout a large part of Asia. Indeed, if our sample is representative, this chromosome will be present in about 16 million men, ∼0.5% of the world's total. The available evidence suggests that it was carried by Genghis Khan. His Y chromosome would obviously have had ancestors, and our best estimate of the TMRCA of star-cluster chromosomes lies several generations before his birth. Several scenarios, which are not mutually exclusive, could explain its rapid spread: (1) all populations carrying star-cluster chromosomes could have descended from a common ancestral population in which it was present at high frequency; (2) many or most Mongols at the time of the Mongol empire could have carried these chromosomes; (3) it could have been restricted to Genghis Khan and his close male-line relatives, and this specific lineage could have spread as a result of their activities. Explanation 1 is unlikely because these populations do not share other Y haplotypes, and explanation 2 is difficult to reconcile with the high Y-haplotype diversity of modern Mongolians (Zerjal et al. 2002). The historically documented events accompanying the establishment of the Mongol empire would have contributed directly to the spread of this lineage by Genghis Khan and his relatives, but perhaps as important was the establishment of a long-lasting male dynasty. This scenario shows selection acting on a group of related men; group selection has been much discussed (Wilson and Sober 1994) and is distinguished by the property that the increased fitness of the group is not reducible to the increased fitness of the individuals. It is unclear whether this is the case here. Our findings nevertheless demonstrate a novel form of selection in human populations on the basis of social prestige. A founder effect of this magnitude will have influenced allele frequencies elsewhere in the genome: mitochondrial DNA lineages will not be affected, since males do not transmit their mitochondrial DNA, but, in the simplest models, the founder male will have been the ancestor of each autosomal sequence in ∼4% of the population and X-chromosomal sequence in ∼2.7%, with implications for the medical genetics of the region. Large-scale changes to patterns of human genetic variation can occur very quickly. Although local influences of this kind may have been common in human populations, it is, perhaps, fortunate that events of this magnitude have been rare.

Zerjal, Tatiana, et al. "The genetic legacy of the Mongols." The American Journal of Human Genetics 72.3 (2003): 717-721.