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Post by Admin on Dec 16, 2020 22:38:49 GMT
Table 2: Posterior range for parameters of model B. Parameters Mean CI Events (kya) N_A 13,500 13,495 - 13,501 N_AF 21,689 21,277 - 22,496 N_EU 119,988 111,986 - 134,779 N_AS 122,321 114,667 - 135,974 N_F 20,137 17,149 - 24,731 N_EU0 1,813 1,766 - 1,871 N_AS0 730 719 - 752 N_BC 20,347 13,024 - 25,099 N_B 2,123 2,100 - 2,164 N_AF0 28,491 27,967 - 28,758 T_FM (ky) 3.4 2.1 - 4.9 3.4 (2.1 - 4.9) T_FS (ky) 4.9 0.2 - 9.7 8.3 (3.4 - 13.2) T_DM (ky) 15 14.4 - 15.8 15 (14.4 - 15.8) T_EU_AS (ky) 17.7 17.2 - 18.3 32.7 (31.8 - 33.6) T_NM (ky) 7.2 6.7 - 7.4 39.9 (38.9 - 40.9) T_XM (ky) 14.7 13.7 - 15.7 47.5 (46.1 - 48.8) T_Mix (ky) 15 14.1 - 16.1 47.7 (46.4 - 49.1) T_Sep (ky) 11.2 10.8 - 12.3 59 (57.4 - 60.5) T_B (ky) 13.2 12.7 - 13.5 72.2 (70.6 - 73.7) T_AF (ky) 208.9 196.8 - 218.3 281.1 (270.2 - 291.9) T_N_D (ky) 447.2 444.3 - 448.9 447.2 (444.3 - 448.9) T_H_A (ky) 249.4 247.2 - 250.5 696.6 (693.8 - 699.5) T_H_X (ky) 695.4 686.1 - 700.3 695.4 (686.1 - 700.3) Mix (%) 91.98 89.66 - 93.08 NMix (%) 3.01 2.98 - 3.02 DMix (%) 0.63 0.58 - 0.68 XMix (%) 5.04 4.85 - 5.14 FMix (%) 2.37 2.13 - 2.49 CI is the confidence interval of 2.5%-97.5% of respective parameters. Ky means kilo years and kya means kilo or thousand years ago from now.
138 Parameter Estimation 139 After demonstrating that model B best explains the real SS data, we used the three methods 140 described above (RF, DL and DLS) to estimate the model’s parameters. The confidence intervals 141 returned by DLS are much narrower than those of the alternative approaches (Table 2, 142 Supplementary Tables 9 and 10) and comparable with other methods37,39,49 thus showing good 143 performance of our new method. Hence, all the results discussed below are the ones obtained with 144 DLS. 145 Our inference suggests that there was first a separation between the Ancient African population 146 (AA) and a population ancestral to both Back-to-Africa and the actual Out-of-Africa populations 147 (OOAʹ) around 72.2 (CI 70.6 - 73.7) kya followed by a split between back to Africa (B2A) and 148 OOA 59 (CI 57.4 - 60.5) kya and an admixture between AA and B2A 47.7 (CI 46.4 - 49.1) kya. 149 The Neanderthal introgression to OOA happened much later, 39.9 (CI 38.9 - 40.9) kya, suggesting 150 that this Back to Africa migration cannot explain the Neanderthal ancestry found in modern 151 African populations55. Our method predicted the admixture proportion from B2A to be as high as 152 92% (CI 89.66 - 93.08) suggesting a massive replacement of the AA population. 153 Our results also comply with Y-chromosomal phylogeny and support back to Africa as proposed 154 . However, our estimation time of separation between populations is much younger than 155 what is reported in Y-chromosomes. One explanation might be that we used a slightly higher 156 mutation rate (1.45×10-8 per bp per generation)57 instead of a slightly slower alternative 157 (1.25×10-8)58,59. When we used the slower mutation rate, our estimation for most of 158 the events time increased (Supplementary Table 11). Indeed, the separation time between B2A and 159 OOA populations corresponds to 67 (CI 66.3 - 67.6) kya, which is close to the estimate of TMRCA 160 between Haplogroup D and E (72 kya11).
161 To independently validate our results, we compared effective population size (Ne) trajectories and 162 cross-coalescent rates obtained by applying Relate32 to real data as well as to data simulated under 163 each of the three models using the mean posterior parameters (Table 2 and Supplementary Table 164 2 and 3) predicted by DLS (following a flowchart represented in Supplementary Figure 3a)34. We 165 observe a close match between the estimates for the real data and our best predicted model (Figure 166 2) which suggests our parameter estimation to be accurate. This similarity is particularly 167 interesting, given that we have not used any LD-based SS to optimize those parameters. On the 168 other hand, neither the Ne trajectory nor the cross-coalescent rate over time is informative to 169 differentiate between the three models (data not shown). Specifically, the gradual separation 170 between African and OOA populations, which was observed before with Relate and similar 171 ,34, cannot be directly explained by the back to Africa or two out of Africa migration as 172 this separation is also matched in our model S (Supplementary Figure 4).
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Post by Admin on Dec 17, 2020 22:26:04 GMT
Discussion 174 We presented here that the ABC analysis can be substantially improved by using NN coupled with 175 the SMC approach. Our methodology is robust to test any hypotheses which can be simulated, 176 which cannot be extensively tested by other methods (especially for scenarios of admixture from 177 ghost populations where the ancient genomes are unavailable) and can accommodate any kind of 178 SS. In this study, we used SFS as SS because it is effortless to calculate and have sufficient 179 ,60. Our results might be further improved by using some LD-based SS53,61 but we 180 opted out as they are computationally demanding to produce and the improvement in the result is 181 minimal (at least for the tested scenario). Although our approach (DLS) is fast enough, the main 182 bottleneck currently is the production of the simulated SS data.
183 In our models, we have not adopted any migration rates between populations, although our 184 approach can use it. This is because we found out that our approach (Parameter Estimation using 185 DLS) predicted non-zero migration rates when we used a mock observed SS data coming from a 186 pulse model with no migration (mean values from Table 1) and a NN trained on an island model 187 with migrations (Supplementary Table 4 and 5). This suggests that models including migration 188 rates may lead to equifinality as suggested by others62 and/or our approach is incapable of 189 estimating them.
190 Although in our scenarios model B is preferred over model S, considering no introgression as an 191 option (NI) supported model M over other models (Supplementary Table 7). This result might be 192 a side effect of the Neanderthal introgression in OOA. Under certain conditions (i.e., older 193 separation time between Africa and OOA [T_B]), model M with no introgression and model S 194 with Neanderthal introgression are comparable (Neanderthal population behaves like the first 195 OOA population in this scenario). This result suggests a possible drawback of our method as 196 different demographic histories can give similar SFS patterns, which can bias our interpretation if 197 not incorporated in the model correctly63 and also advocates for the importance of parameter 198 estimation as it can give insight for the choice of model selected.
199 Although the estimated proportions of introgression from Archaic populations have values 200 consistent with those previously reported28,53, the separation time between Homo sapiens and 201 archaic populations are more recent than those previously inferred50,64 if we used a loose prior of 202 400-1,100 kya. These deviations were not reproduced when we used simulated SS generated under 203 known parameters from Table 2. This may be specific to real sequence data and might be a side 204 effect of some of our assumptions (for example some unknown interactions between these 205 populations which was not modelled here) or systematic biases due to the use of European 206 reference genome65 or recent changes of generation time or mutation rate per generation66,67. Thus, 207 the admixture with archaic populations may be seen as a way of introducing noise in the 208 simulations for model selection rather than an attempt to obtain true parameter estimates. Most 209 probably in the future, we can improve this estimate by directly using the available ancient 210 genomes together with modern datasets.
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Post by Admin on Dec 18, 2020 22:08:23 GMT
211 We cannot also reject a simpler model of no Neolithic migration55. Even if we assume the Neolithic 212 migration affected Yoruba, the predicted total length of Neanderthal sequence in an average 213 Yoruba genome would be less than 5 Mb compared to the 17 Mb identified by Chen et al55. This 214 discrepancy also cannot be explained by the back to Africa model as introgression happened much 215 later after the separation. This suggests that most of the Neanderthal signal in Yoruba should be 216 explained by some other migration (for example from Human to Neaderthal28).
217 Our results suggested a back to Africa model (model B) is more likely than a simple out of Africa 218 event (model S). Although this model is better in explaining the real data, it might not be the final 219 one. An even more complicated migration or admixture model which was not tested here might 220 still better explain the real data. We have not tested two out of Africa events directly, although our 221 model M is similar to two out of Africa model under certain conditions (assuming that European 222 and East Asian do not have differential admixture with first OOA population). It will be interesting 223 to revisit this hypothesis with Papuan populations in the future.
224 We would like to caution that although we are naming the model “Back to Africa”, the OOA population did not need to be geographically out of Africa68 225 . Our estimates, particularly the 226 effective population size of B2A (N_BC) and the time of Neanderthal introgression (T_NIntro), 227 advocate that the split might have happened within Africa itself before the actual out of Africa 228 event. In such a case, our results can be explained by the separation of West and East African 229 population 80 kya (T_B) and then later the primary separation of OOA and East African population 230 67 kya (T_Sep) (assuming mutation rate of 1.25×10-8 per bp per generation58,59 and generation 231 time of 29 years69). In this regard, our model is more akin to Lipson et al. 2020 36 model rather than 232 what is suggested by Cole et al. 2020 3 . If we assume model from Lipson et al. to be true, the most 233 parsimonious explanation would be that our B2A population represents Basal West African 234 population which separated from OOA populations 67 kya (T_Sep). Our AA represents Ghost 235 which contributed to modern West African population around 10% which admixed 236 around 60 kya from our prediction. On the other hand, if we assume true back to Africa, then most 237 likely the OOA event took place less than 80 kya (T_B). This suggests that most of the older fossils 238 (>80 kya) found outside Africa2–4 are unlikely to have contributed to OOA populations (assuming 239 the ancestor of all modern human originated in Africa and never left Africa before OOA event). 240 Geographical location where B2A separated from OOA is immensely important for this hypothesis 241 but cannot be estimated from our approach. It will be especially fascinating to test this hypothesis 242 using ancient genomes from those areas from that time point when they will be available.
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Post by Admin on Jan 16, 2021 5:24:22 GMT
Pleistocene North African genomes link Near Eastern and sub-Saharan African human populations Science 04 May 2018: Vol. 360, Issue 6388, pp. 548-552 DOI: 10.1126/science.aar8380 Relationships among North Africans The general view is that Eurasians mostly descend from a single group of humans that dispersed outside of sub-Saharan Africa around 50,000 to 100,000 years ago. Present-day North Africans share a majority of their ancestry with present-day Near Easterners, but not with sub-Saharan Africans. To investigate this conundrum, Van de Loosdrecht et al. sequenced high-quality DNA obtained from bone samples of seven individuals from Taforalt in eastern Morocco dating from the Later Stone Age, about 15,000 years ago. The Taforalt individuals were found to be most closely related to populations from the Near East (Natufians), with a third of their ancestry from sub-Saharan Africa. No evidence was found for introgression with western Europeans, despite attribution to the Iberomaurusian culture. None of the present-day or ancient Holocene African groups are a good proxy for the sub-Saharan genetic component. Science, this issue p. 548 Abstract North Africa is a key region for understanding human history, but the genetic history of its people is largely unknown. We present genomic data from seven 15,000-year-old modern humans, attributed to the Iberomaurusian culture, from Morocco. We find a genetic affinity with early Holocene Near Easterners, best represented by Levantine Natufians, suggesting a pre-agricultural connection between Africa and the Near East. We do not find evidence for gene flow from Paleolithic Europeans to Late Pleistocene North Africans. The Taforalt individuals derive one-third of their ancestry from sub-Saharan Africans, best approximated by a mixture of genetic components preserved in present-day West and East Africans. Thus, we provide direct evidence for genetic interactions between modern humans across Africa and Eurasia in the Pleistocene. Under typical conditions (i.e., aside from intermittent greening periods), the Sahara desert poses an ecogeographic barrier for human migration between North and sub-Saharan Africa (1). Sub-Saharan Africa is home to the most deeply divergent genetic lineages among present-day humans (2), and the general view is that all Eurasians mostly descend from a single group of humans that dispersed outside of sub-Saharan Africa around 50,000 to 100,000 years before the present (yr B.P.) (3). This group likely represented only a small fraction of the genetic diversity within Africa, most closely related to a Holocene East African group (4). Present-day North Africans share a majority of their ancestry with present-day Near Easterners but not with sub-Saharan Africans (5). Thus, from a genetic perspective, present-day North Africa is largely a part of Eurasia. However, the temporal depth of this genetic connection between the Near East and North Africa is poorly understood and has been estimated only indirectly from present-day mitochondrial DNA (mtDNA) variation (6, 7). Owing to challenging conditions for DNA preservation, relatively few ancient genomes have been recovered from Africa. Genome-wide data from 23 individuals have been reported from South and East Africa, with the oldest dating back to 8100 yr B.P. (4, 8, 9). In North Africa, a genomic study of Egyptian mummies from the first millennium BCE showed that the genetic connection between the Near East and North Africa was established by that time (5). However, the genetic affinity of North African populations at a greater time depth has remained unknown. Here we present genome-wide data from seven individuals, directly dated between 15,100 and 13,900 calibrated years before present (cal. yr B.P.) (table S1), from Grotte des Pigeons near Taforalt in eastern Morocco (10). These genomic data provide a critical reference point to help explain the deep genetic history of North Africa and the broader Middle East (Fig. 1). The Taforalt individuals are associated with the Later Stone Age Iberomaurusian culture, whose origin is debated. These individuals may have descended either directly from the manufacturers of the preceding Middle Stone Age technologies (Aterian or local West African bladelet technologies) or from an exogenous population with ties to the Upper Paleolithic technocomplexes of the Near East or Southern Europe (10, 11). Fig. 1 Spatiotemporal locations of the Taforalt and other ancient genomes. (A and B) Geographic locations of representative ancient genomes from West Eurasia and Africa included in our analysis. The Pleistocene Taforalt site is denoted by a red circle. (C) The date range of each ancient group is marked by black bars, representing the range of 95% confidence intervals of radiocarbon dates across all dated individuals (cal. yr B.P. on the x axis). Group labels are taken from previous studies reporting each ancient genome (4, 16, 27). N, Neolithic; WHG, Western European hunter-gatherers; EHG, Eastern European hunter-gatherers; CHG, Caucasus hunter-gatherers. For nine Taforalt individuals (table S2), we created double-indexed single-stranded DNA libraries (12) for next-generation sequencing of DNA isolated from petrous bones. We then used in-solution capture probes (13) to enrich libraries for the whole mitochondrial genome and ~1,240,000 single-nucleotide polymorphisms (SNPs) in the nuclear genome (14). The DNA fragments obtained from seven individuals, six genetic males and one female, had postmortem degradation characteristics typical of ancient DNA (tables S3 to S5 and fig. S6). We reconstructed the mitochondrial genomes of all seven individuals (102× to 1701× coverage, unmerged libraries; table S4) while maintaining a low level of contamination from the DNA of modern humans (1 to 8%; table S4). For the nuclear data analysis, in which ancient DNA is more susceptible to contamination than in mitochondrial analyses, we analyzed five individuals (four males and one female) on the basis of coverage (table S3, merged libraries) and negligible modern human contamination for males (1.7 to 2.5%; table S5). For each individual, we randomly chose a single base per site as a haploid genotype. We intersected our new data with data from a panel of worldwide present-day populations, genotyped on the Affymetrix Human Origins array for ~600,000 markers, as well as ancient genomic data covering Europe, the Near East, and sub-Saharan Africa (4, 8, 15–17). The final data set includes 593,124 intersecting autosomal SNPs with 183,041 to 544,232 SNP positions covered for each of the five individuals (table S3). For group-based analyses involving other ancient individuals, we adopted the population labels from the original studies (4, 16). We found an overall high genetic relatedness between the Taforalt individuals, suggesting a strong population bottleneck (fig. S26).
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Post by Admin on Jan 16, 2021 21:03:07 GMT
We analyzed the genetic affinities of the Taforalt individuals by performing principal components analysis and model-based clustering of worldwide data (Fig. 2). When projected onto the top principal components of African and west Eurasian populations, the Taforalt individuals form a distinct cluster in an intermediate position between present-day North Africans [e.g., Amazighes (Berbers), Mozabites, and Saharawis] and East Africans (e.g., Afars, Oromos, and Somalis) (Fig. 2A). Consistently, we find that all males with sufficient nuclear DNA preservation carry Y haplogroup E1b1b1a1 (M-78; table S16). This haplogroup occurs most frequently in present-day North and East African populations (18). The closely related E1b1b1b (M-123) haplogroup has been reported for Epipaleolithic Natufians and Pre-Pottery Neolithic Levantines (Levant_N) (16). Unsupervised genetic clustering also suggests a connection of Taforalt to the Near East. The three major components that make up the Taforalt genomes are maximized in early Holocene Levantines, East African hunter-gatherer Hadza from north-central Tanzania, and West Africans (number of genetic clusters K = 10; Fig. 2B). In contrast, present-day North Africans have smaller sub-Saharan African components with minimal Hadza-related contribution (Fig. 2B). Fig. 2 Summary of the genetic profile of the Taforalt individuals. (A) The top two principal components (PCs) calculated from present-day African, Near Eastern, and Southern European individuals from 72 populations. The Taforalt individuals are projected thereon (red inverted triangles), and selected present-day populations are denoted by various colored symbols. Labels for other populations (denoted by small gray squares) are provided in fig. S8. (B) ADMIXTURE analysis results of chosen African and Middle Eastern populations (K = 10). Ancient individuals are labeled in red. Major ancestry components in Taforalt individuals are maximized in early Holocene Levantines (green), West Africans (purple), and East African Hadza (brown). The ancestry component prevalent in pre-Neolithic Europeans (beige) is absent in Taforalt. We calculated outgroup f3 statistics of the form f3(Taforalt, X; Mbuti) across worldwide ancient and present-day test populations. Consistent with previous analyses, we find that ancient Near Eastern populations, especially Epipaleolithic Natufians and early Neolithic Levantines, show the highest outgroup f3 values with Taforalt (Fig. 3A). This is confirmed by f4 symmetry statistics of the form f4(Chimpanzee, Taforalt; NE1, NE2) that measure a relative affinity of a pair of Near Eastern (NE) groups to Taforalt. A positive value indicates that NE2 is closer than NE1 to Taforalt. We consistently find positive f4 values when the NE2 group is Natufian or Levant_N and the NE1 group is representative of other populations [z score = 2.2 to 11.0 standard error (SE); table S6]. Congruent to the outgtoup-f3 results, the Natufian population shows higher affinity to Taforalt than does the Levant_N group (z score = 2.2 SE; table S6). This indicates that the early Holocene Levantine populations, overlapping with or postdating our Taforalt individuals by up to 6000 years (16), are most closely related to the Taforalt group, among Near Eastern populations. Next, we evaluated whether the Taforalt individuals have sub-Saharan African ancestry by calculating f4(Chimpanzee, X; Natufian, Taforalt). We observe significant positive f4 values for all sub-Saharan African groups and significant negative values for all Eurasian populations, supporting a substantial contribution from sub-Saharan Africa (Fig. 3B). West Africans, such as Mende and Yoruba, most strongly pull out the sub-Saharan African ancestry in Taforalt (Fig. 3B and figs. S15 and S16). Fig. 3 Geographic distribution of the genetic affinity of the Taforalt group with worldwide populations. (A) Mean shared genetic drift with the Taforalt group, as measured by outgroup f3 statistics in the form f3(Taforalt, X; Mbuti). Warm colors denote populations genetically close to Taforalt. Large diamonds and squares represent the 10 highest and lowest f3 values, respectively. Early Holocene Levantine groups (Natufians and Neolithic Levantines) show the highest affinity with Taforalt. The statistics and their associated SEs for the top 30 signals are presented in fig. S14. (B) Extra genetic affinity with the Taforalt group in comparison to Natufians, as measured by f4 statistics in the form f4(Chimpanzee, X; Natufian, Taforalt). Large diamonds and squares represent the 10 most positive and negative f4 values, respectively. Sub-Saharan Africans show high positive values, with West African Yoruba and Mende having the highest values, supporting the presence of sub-Saharan African ancestry in Taforalt individuals. In contrast, all Eurasian populations are genetically closer to Natufians than to the Taforalt group. The statistics and their associated SEs for the top 30 signals are presented in fig. S16. We investigated whether two first-hand proxies, Natufians and West Africans, are sufficient to explain the Taforalt gene pool or whether a more complex admixture model is required. We thus tested whether Natufians could be a sufficient proxy for the Eurasian ancestry in Taforalt without explicit modeling of its African ancestry (fig. S18). This line of investigation was inspired by proposed archaeological connections between the Iberomaurusian and Upper Paleolithic cultures in Southern Europe, either via the Strait of Gibraltar (19) or Sicily (20). If this connection is true, both the Upper Paleolithic European and Natufian ancestries will be required to explain the Taforalt gene pool. For our admixture modeling with the program qpAdm (16), we chose outgroups that can distinguish sub-Saharan African, Natufian, and Paleolithic European ancestries but are blind to differences between sub-Saharan African lineages (11). A two-way admixture model, comprising Natufian and sub-Saharan African populations, does not significantly deviate from our data (χ2 P ≥ 0.128), with 63.5% Natufian and 36.5% sub-Saharan African ancestry, on average (table S8). Adding Paleolithic European lineages as a third source only marginally increased the model fit (χ2 P = 0.019 to 0.128; table S9). Consistently, by using the qpGraph package (21), we find that a mixture of Natufian and Yoruba reasonably fits the Taforalt gene pool (|z| ≤ 3.7; fig. S19 and table S10). Adding gene flow from Paleolithic Europeans does not improve the model fit and provides an ancestry contribution estimate of 0% (fig. S19). We thus find no evidence of gene flow from Paleolithic Europeans into Taforalt within the resolution of our data.
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