Approximate Bayesian computation with deep learning supports a third archaic introgression in Asia and Oceania

Population genomics advances in frontier ethnic minorities in China

China, with its large geographic span, possesses rich genetic diversity across vast frontier regions in addition to the Han Chinese majority. Importantly, demographic events and various natural and cultural environments in Chinese frontier regions have shaped the genomic diversity of ethnic minorities via local adaptations. Thus, insights into the genetic diversity and adaptive evolution of these under-represented ethnic groups are crucial for understanding evolutionary scenarios and biomedical implications in East Asian populations. Here, we focus on ethnic minorities in Chinese frontier regions and review research advances regarding genomic diversity, genetic structure, population history, genetic admixture, and local adaptation. We first provide an overview of the extensive genetic diversity across populations in different Chinese frontier regions. Next, we summarize research progress regarding genetic ancestry, demographic history, the adaptive process, and the archaic identification of multiple ethnic minorities in different Chinese frontier regions. Finally, we discuss the gaps and opportunities in genomic studies of Chinese populations and the need for a more comprehensive understanding of genomic diversity and the evolution of populations of East Asian ancestry in the post-genomic era.

Genomic regions of current low hybridisation mark long-term barriers to gene flow in scarce swallowtail butterflies

Many closely related species continue to hybridise after millions of generations of divergence. However, the extent to which current patterning in hybrid zones connects back to the speciation process remains unclear: does evidence for current multilocus barriers support the hypothesis of speciation due to multilocus divergence? We analyse whole-genome sequencing data to investigate the speciation history of the scarce swallowtails Iphiclidespodalirius and I . feisthamelii, which abut at a narrow ( ∼ 25 km) contact zone north of the Pyrenees. We first quantify the heterogeneity of effective migration rate under a model of isolation with migration, using genomes sampled across the range to identify long-term barriers to gene flow. Secondly, we investigate the recent ancestry of individuals from the hybrid zone using genome polarisation and estimate the coupling coefficient under a model of a multilocus barrier. We infer a low rate of long-term gene flow from I . feisthamelii into I . podalirius - the direction of which matches the admixture across the hybrid zone - and complete reproductive isolation across  ≈  33% of the genome. Our contrast of recent and long-term gene flow shows that regions of low recent hybridisation are indeed enriched for long-term barriers which maintain divergence between these hybridising sister species. This finding paves the way for future analysis of the evolution of reproductive isolation along the speciation continuum.

Computational Genomics and Its Applications to Anthropological Questions

The advent of affordable genome sequencing and the development of new computational tools have established a new era of genomic knowledge. Sequenced human genomes number in the tens of thousands, including thousands of ancient human genomes. The abundance of data has been met with new analysis tools that can be used to understand populations' demographic and evolutionary histories. Thus, a variety of computational methods now exist that can be leveraged to answer anthropological questions. This includes novel likelihood and Bayesian methods, machine learning techniques, and a vast array of population simulators. These computational tools provide powerful insights gained from genomic datasets, although they are generally inaccessible to those with less computational experience. Here, we outline the theoretical workings behind computational genomics methods, limitations and other considerations when applying these computational methods, and examples of how computational methods have already been applied to anthropological questions. We hope this review will empower other anthropologists to utilize these powerful tools in their own research.

Pollinator shifts despite hybridisation in the Cape's hyperdiverse heathers (Erica, Ericaceae)

Interrogating the ecological and geographic factors that influence population divergence dynamics can reveal why some groups of organisms diversify more prolifically than others. One such group is the heathers (Erica, Ericaceae), the largest plant genus in the Cape Floristic Region. We study Erica abietina, a highly variable species complex with four subspecies differing in geographic range, habitat and pollination syndrome. We test for population differentiation, hybridisation, introgression and pollinator-driven divergence using genotyping-by-sequencing on samples across the entire distribution. We find five variably distinct genetic groups, with one subspecies comprising two independent lineages that are geographically isolated and occur on different soil types. Phylogenetic analysis suggests two independent shifts between bird and insect pollination, with accompanying genetic divergence. However, for one pair of populations with different pollinators, we uncover several individuals of hybrid origin at a site of sympatry. These results suggest that floral differentiation driven by divergent selection acts in concert with geographic isolation to maintain reproductive isolation and promote speciation. Our investigations reveal a highly dynamic system whose diversity has been shaped by a variety of interacting forces. We suggest that such a system could be a model for much of the diversification of the Cape flora.

Modelling the demographic history of human North African genomes points to a recent soft split divergence between populations

BACKGROUND

North African human populations present a complex demographic scenario due to the presence of an autochthonous genetic component and population substructure, plus extensive gene flow from the Middle East, Europe, and sub-Saharan Africa.

RESULTS

We conducted a comprehensive analysis of 364 genomes to construct detailed demographic models for the North African region, encompassing its two primary ethnic groups, the Arab and Amazigh populations. This was achieved through an Approximate Bayesian Computation with Deep Learning (ABC-DL) framework and a novel algorithm called Genetic Programming for Population Genetics (GP4PG). This innovative approach enabled us to effectively model intricate demographic scenarios, utilizing a subset of 16 whole genomes at > 30X coverage. The demographic model suggested by GP4PG exhibited a closer alignment with the observed data compared to the ABC-DL model. Both point to a back-to-Africa origin of North African individuals and a close relationship with Eurasian populations. Results support different origins for Amazigh and Arab populations, with Amazigh populations originating back in Epipaleolithic times, while GP4PG supports Arabization as the main source of Middle Eastern ancestry. The GP4PG model includes population substructure in surrounding populations (sub-Saharan Africa and Middle East) with continuous decaying gene flow after population split. Contrary to ABC-DL, the best GP4PG model does not require pulses of admixture from surrounding populations into North Africa pointing to soft splits as drivers of divergence in North Africa.

CONCLUSIONS

We have built a demographic model on North Africa that points to a back-to-Africa expansion and a differential origin between Arab and Amazigh populations.

Denisovan admixture facilitated environmental adaptation in Papua New Guinean populations

Neandertals and Denisovans, having inhabited distinct regions in Eurasia and possibly Oceania for over 200,000 y, experienced ample time to adapt to diverse environmental challenges these regions presented. Among present-day human populations, Papua New Guineans (PNG) stand out as one of the few carrying substantial amounts of both Neandertal and Denisovan DNA, a result of past admixture events with these archaic human groups. This study investigates the distribution of introgressed Denisovan and Neandertal DNA within two distinct PNG populations, residing in the highlands of Mt Wilhelm and the lowlands of Daru Island. These locations exhibit unique environmental features, some of which may parallel the challenges that archaic humans once confronted and adapted to. Our results show that PNG highlanders carry higher levels of Denisovan DNA compared to PNG lowlanders. Among the Denisovan-like haplotypes with higher frequencies in highlander populations, those exhibiting the greatest frequency difference compared to lowlander populations also demonstrate more pronounced differences in population frequencies than frequency-matched nonarchaic variants. Two of the five most highly differentiated of those haplotypes reside in genomic areas linked to brain development genes. Conversely, Denisovan-like haplotypes more frequent in lowlanders overlap with genes associated with immune response processes. Our findings suggest that Denisovan DNA has provided genetic variation associated with brain biology and immune response to PNG genomes, some of which might have facilitated adaptive processes to environmental challenges.

Computationally Efficient Demographic History Inference from Allele Frequencies with Supervised Machine Learning

Inferring past demographic history of natural populations from genomic data is of central concern in many studies across research fields. Previously, our group had developed dadi, a widely used demographic history inference method based on the allele frequency spectrum (AFS) and maximum composite-likelihood optimization. However, dadi's optimization procedure can be computationally expensive. Here, we present donni (demography optimization via neural network inference), a new inference method based on dadi that is more efficient while maintaining comparable inference accuracy. For each dadi-supported demographic model, donni simulates the expected AFS for a range of model parameters then trains a set of Mean Variance Estimation neural networks using the simulated AFS. Trained networks can then be used to instantaneously infer the model parameters from future genomic data summarized by an AFS. We demonstrate that for many demographic models, donni can infer some parameters, such as population size changes, very well and other parameters, such as migration rates and times of demographic events, fairly well. Importantly, donni provides both parameter and confidence interval estimates from input AFS with accuracy comparable to parameters inferred by dadi's likelihood optimization while bypassing its long and computationally intensive evaluation process. donni's performance demonstrates that supervised machine learning algorithms may be a promising avenue for developing more sustainable and computationally efficient demographic history inference methods.

Harnessing deep learning for population genetic inference

In population genetics, the emergence of large-scale genomic data for various species and populations has provided new opportunities to understand the evolutionary forces that drive genetic diversity using statistical inference. However, the era of population genomics presents new challenges in analysing the massive amounts of genomes and variants. Deep learning has demonstrated state-of-the-art performance for numerous applications involving large-scale data. Recently, deep learning approaches have gained popularity in population genetics; facilitated by the advent of massive genomic data sets, powerful computational hardware and complex deep learning architectures, they have been used to identify population structure, infer demographic history and investigate natural selection. Here, we introduce common deep learning architectures and provide comprehensive guidelines for implementing deep learning models for population genetic inference. We also discuss current challenges and future directions for applying deep learning in population genetics, focusing on efficiency, robustness and interpretability.

Temporal Variation in Introgressed Segments' Length Statistics Computed from a Limited Number of Ancient Genomes Sheds Light on Past Admixture Pulses

Hybridization is recognized as an important evolutionary force, but identifying and timing admixture events between divergent lineages remain a major aim of evolutionary biology. While this has traditionally been done using inferential tools on contemporary genomes, the latest advances in paleogenomics have provided a growing wealth of temporally distributed genomic data. Here, we used individual-based simulations to generate chromosome-level genomic data for a 2-population system and described temporal neutral introgression patterns under a single- and 2-pulse admixture model. We computed 6 summary statistics aiming to inform the timing and number of admixture pulses between interbreeding entities: lengths of introgressed sequences and their variance within genomes, as well as genome-wide introgression proportions and related measures. The first 2 statistics could confidently be used to infer interlineage hybridization history, peaking at the beginning and shortly after an admixture pulse. Temporal variation in introgression proportions and related statistics provided more limited insights, particularly when considering their application to ancient genomes still scant in number. Lastly, we computed these statistics on Homo sapiens paleogenomes and successfully inferred the hybridization pulse from Neanderthal that occurred approximately 40 to 60 kya. The scarce number of genomes dating from this period prevented more precise inferences, but the accumulation of paleogenomic data opens promising perspectives as our approach only requires a limited number of ancient genomes.

Inferring Historical Introgression with Deep Learning

Resolving phylogenetic relationships among taxa remains a challenge in the era of big data due to the presence of genetic admixture in a wide range of organisms. Rapidly developing sequencing technologies and statistical tests enable evolutionary relationships to be disentangled at a genome-wide level, yet many of these tests are computationally intensive and rely on phased genotypes, large sample sizes, restricted phylogenetic topologies, or hypothesis testing. To overcome these difficulties, we developed a deep learning-based approach, named ERICA, for inferring genome-wide evolutionary relationships and local introgressed regions from sequence data. ERICA accepts sequence alignments of both population genomic data and multiple genome assemblies, and efficiently identifies discordant genealogy patterns and exchanged regions across genomes when compared with other methods. We further tested ERICA using real population genomic data from Heliconius butterflies that have undergone adaptive radiation and frequent hybridization. Finally, we applied ERICA to characterize hybridization and introgression in wild and cultivated rice, revealing the important role of introgression in rice domestication and adaptation. Taken together, our findings demonstrate that ERICA provides an effective method for teasing apart evolutionary relationships using whole genome data, which can ultimately facilitate evolutionary studies on hybridization and introgression.

Comparative physiological anthropogeny: exploring molecular underpinnings of distinctly human phenotypes

Anthropogeny is a classic term encompassing transdisciplinary investigations of the origins of the human species. Comparative anthropogeny is a systematic comparison of humans and other living nonhuman hominids (so-called "great apes"), aiming to identify distinctly human features in health and disease, with the overall goal of explaining human origins. We begin with a historical perspective, briefly describing how the field progressed from the earliest evolutionary insights to the current emphasis on in-depth molecular and genomic investigations of "human-specific" biology and an increased appreciation for cultural impacts on human biology. While many such genetic differences between humans and other hominids have been revealed over the last two decades, this information remains insufficient to explain the most distinctive phenotypic traits distinguishing humans from other living hominids. Here we undertake a complementary approach of "comparative physiological anthropogeny," along the lines of the preclinical medical curriculum, i.e., beginning with anatomy and considering each physiological system and in each case considering genetic and molecular components that are relevant. What is ultimately needed is a systematic comparative approach at all levels from molecular to physiological to sociocultural, building networks of related information, drawing inferences, and generating testable hypotheses. The concluding section will touch on distinctive considerations in the study of human evolution, including the importance of gene-culture interactions.

Deep Learning in Population Genetics

Population genetics is transitioning into a data-driven discipline thanks to the availability of large-scale genomic data and the need to study increasingly complex evolutionary scenarios. With likelihood and Bayesian approaches becoming either intractable or computationally unfeasible, machine learning, and in particular deep learning, algorithms are emerging as popular techniques for population genetic inferences. These approaches rely on algorithms that learn non-linear relationships between the input data and the model parameters being estimated through representation learning from training data sets. Deep learning algorithms currently employed in the field comprise discriminative and generative models with fully connected, convolutional, or recurrent layers. Additionally, a wide range of powerful simulators to generate training data under complex scenarios are now available. The application of deep learning to empirical data sets mostly replicates previous findings of demography reconstruction and signals of natural selection in model organisms. To showcase the feasibility of deep learning to tackle new challenges, we designed a branched architecture to detect signals of recent balancing selection from temporal haplotypic data, which exhibited good predictive performance on simulated data. Investigations on the interpretability of neural networks, their robustness to uncertain training data, and creative representation of population genetic data, will provide further opportunities for technological advancements in the field.

Human Genetic Research in Wallacea and Sahul: Recent Findings and Future Prospects

Genomic sequence data from worldwide human populations have provided a range of novel insights into our shared ancestry and the historical migrations that have shaped our global genetic diversity. However, a comprehensive understanding of these fundamental questions has been impeded by the lack of inclusion of many Indigenous populations in genomic surveys, including those from the Wallacean archipelago (which comprises islands of present-day Indonesia located east and west of Wallace's and Lydekker's Lines, respectively) and the former continent of Sahul (which once combined New Guinea and Australia during lower sea levels in the Pleistocene). Notably, these regions have been important areas of human evolution throughout the Late Pleistocene, as documented by diverse fossil and archaeological records which attest to the regional presence of multiple hominin species prior to the arrival of anatomically modern human (AMH) migrants. In this review, we collate and discuss key findings from the past decade of population genetic and phylogeographic literature focussed on the hominin history in Wallacea and Sahul. Specifically, we examine the evidence for the timing and direction of the ancient AMH migratory movements and subsequent hominin mixing events, emphasising several novel but consistent results that have important implications for addressing these questions. Finally, we suggest potentially lucrative directions for future genetic research in this key region of human evolution.

Ghost Lineages Highly Influence the Interpretation of Introgression Tests

Most species are extinct, those that are not are often unknown. Sequenced and sampled species are often a minority of known ones. Past evolutionary events involving horizontal gene flow, such as horizontal gene transfer, hybridization, introgression, and admixture, are therefore likely to involve "ghosts," that is extinct, unknown, or unsampled lineages. The existence of these ghost lineages is widely acknowledged, but their possible impact on the detection of gene flow and on the identification of the species involved is largely overlooked. It is generally considered as a possible source of error that, with reasonable approximation, can be ignored. We explore the possible influence of absent species on an evolutionary study by quantifying the effect of ghost lineages on introgression as detected by the popular D-statistic method. We show from simulated data that under certain frequently encountered conditions, the donors and recipients of horizontal gene flow can be wrongly identified if ghost lineages are not taken into account. In particular, having a distant outgroup, which is usually recommended, leads to an increase in the error probability and to false interpretations in most cases. We conclude that introgression from ghost lineages should be systematically considered as an alternative possible, even probable, scenario. [ABBA-BABA; D-statistic; gene flow; ghost lineage; introgression; simulation.].

Congruent evolutionary responses of European steppe biota to late Quaternary climate change

Quaternary climatic oscillations had a large impact on European biogeography. Alternation of cold and warm stages caused recurrent glaciations, massive vegetation shifts, and large-scale range alterations in many species. The Eurasian steppe biome and its grasslands are a noteworthy example; they underwent climate-driven, large-scale contractions during warm stages and expansions during cold stages. Here, we evaluate the impact of these range alterations on the late Quaternary demography of several phylogenetically distant plant and insect species, typical of the Eurasian steppes. We compare three explicit demographic hypotheses by applying an approach combining convolutional neural networks with approximate Bayesian computation. We identified congruent demographic responses of cold stage expansion and warm stage contraction across all species, but also species-specific effects. The demographic history of the Eurasian steppe biota reflects major paleoecological turning points in the late Quaternary and emphasizes the role of climate as a driving force underlying patterns of genetic variance on the biome level.

Comparative morphology and physiology of the vocal production apparatus and the brain in the extant primates

Objective data mainly from the comparative anatomy of various organs related to human speech and language is considered to unearth clues about the mechanisms behind language development. The two organs of the larynx and hyoid bone are considered to have evolved towards suitable positions and forms in preparation for the occurrence of the large repertoire of vocalization necessary for human speech. However, some researchers have asserted that there is no significant difference of these organs between humans and non-human primates. Speech production is dependent on the voluntary control of the respiratory, laryngeal, and vocal tract musculature. Such control is fully present in humans but only partially so in non-human primates, which appear to be able to voluntarily control only supralaryngeal articulators. Both humans and non-human primates have direct cortical innervation of motor neurons controlling the supralaryngeal vocal tract but only human appear to have direct cortical innervation of motor neurons controlling the larynx. In this review, we investigate the comparative morphology and function of the wide range of components involved in vocal production, including the larynx, the hyoid bone, the tongue, and the vocal brain. We would like to emphasize the importance of the tongue in the primary development of human speech and language. It is now time to reconsider the possibility of the tongue playing a definitive role in the emergence of human speech.

Analysis of the Batch Effect Due to Sequencing Center in Population Statistics Quantifying Rare Events in the 1000 Genomes Project

The 1000 Genomes Project (1000G) is one of the most popular whole genome sequencing datasets used in different genomics fields and has boosting our knowledge in medical and population genomics, among other fields. Recent studies have reported the presence of ghost mutation signals in the 1000G. Furthermore, studies have shown that these mutations can influence the outcomes of follow-up studies based on the genetic variation of 1000G, such as single nucleotide variants (SNV) imputation. While the overall effect of these ghost mutations can be considered negligible for common genetic variants in many populations, the potential bias remains unclear when studying low frequency genetic variants in the population. In this study, we analyze the effect of the sequencing center in predicted loss of function (LoF) alleles, the number of singletons, and the patterns of archaic introgression in the 1000G. Our results support previous studies showing that the sequencing center is associated with LoF and singletons independent of the population that is considered. Furthermore, we observed that patterns of archaic introgression were distorted for some populations depending on the sequencing center. When analyzing the frequency of SNPs showing extreme patterns of genotype differentiation among centers for CEU, YRI, CHB, and JPT, we observed that the magnitude of the sequencing batch effect was stronger at MAF < 0.2 and showed different profiles between CHB and the other populations. All these results suggest that data from 1000G must be interpreted with caution when considering statistics using variants at low frequency.

Informing conservation strategies with museum genomics: Long-term effects of past anthropogenic persecution on the elusive European wildcat

Like many carnivore species, European wildcats (Felis silvestris) have suffered severe anthropogenic population declines in the past, resulting in a strong population bottleneck at the beginning of the 20th century. In Germany, the species has managed to survive its near extinction in small isolated areas and is currently recolonizing former habitats owing to legal protection and concerted conservation efforts. Here, we SNP-genotyped and mtDNA-sequenced 56 historical and 650 contemporary samples to assess the impact of massive persecution on genetic diversity, population structure, and hybridization dynamics of wildcats. Spatiotemporal analyses suggest that the presumed postglacial differentiation between two genetically distinct metapopulations in Germany is in fact the result of the anthropogenic bottleneck followed by re-expansion from few secluded refugia. We found that, despite the bottleneck, populations experienced no severe genetic erosion, nor suffered from elevated inbreeding or showed signs of increased hybridization with domestic cats. Our findings have significant implications for current wildcat conservation strategies, as the data analyses show that the two presently recognized wildcat population clusters should be treated as a single conservation unit. Although current populations appear under no imminent threat from genetic factors, fostering connectivity through the implementation of forest corridors will facilitate the preservation of genetic diversity and promote long-term viability. The present study documents how museum collections can be used as essential resource for assessing long-term anthropogenic effects on natural populations, for example, regarding population structure and the delineation of appropriate conservation units, potentially informing todays' species conservation.

Revisiting the out of Africa event with a deep-learning approach

Anatomically modern humans evolved around 300 thousand years ago in Africa. They started to appear in the fossil record outside of Africa as early as 100 thousand years ago, although other hominins existed throughout Eurasia much earlier. Recently, several studies argued in favor of a single out of Africa event for modern humans on the basis of whole-genome sequence analyses. However, the single out of Africa model is in contrast with some of the findings from fossil records, which support two out of Africa events, and uniparental data, which propose a back to Africa movement. Here, we used a deep-learning approach coupled with approximate Bayesian computation and sequential Monte Carlo to revisit these hypotheses from the whole-genome sequence perspective. Our results support the back to Africa model over other alternatives. We estimated that there are two sequential separations between Africa and out of African populations happening around 60-90 thousand years ago and separated by 13-15 thousand years. One of the populations resulting from the more recent split has replaced the older West African population to a large extent, while the other one has founded the out of Africa populations.

Chromosome-scale inference of hybrid speciation and admixture with convolutional neural networks

Inferring the frequency and mode of hybridization among closely related organisms is an important step for understanding the process of speciation and can help to uncover reticulated patterns of phylogeny more generally. Phylogenomic methods to test for the presence of hybridization come in many varieties and typically operate by leveraging expected patterns of genealogical discordance in the absence of hybridization. An important assumption made by these tests is that the data (genes or SNPs) are independent given the species tree. However, when the data are closely linked, it is especially important to consider their nonindependence. Recently, deep learning techniques such as convolutional neural networks (CNNs) have been used to perform population genetic inferences with linked SNPs coded as binary images. Here, we use CNNs for selecting among candidate hybridization scenarios using the tree topology (((P1 , P2 ), P3 ), Out) and a matrix of pairwise nucleotide divergence (dXY ) calculated in windows across the genome. Using coalescent simulations to train and independently test a neural network showed that our method, HyDe-CNN, was able to accurately perform model selection for hybridization scenarios across a wide breath of parameter space. We then used HyDe-CNN to test models of admixture in Heliconius butterflies, as well as comparing it to phylogeny-based introgression statistics. Given the flexibility of our approach, the dropping cost of long-read sequencing and the continued improvement of CNN architectures, we anticipate that inferences of hybridization using deep learning methods like ours will help researchers to better understand patterns of admixture in their study organisms.

Refining models of archaic admixture in Eurasia with ArchaicSeeker 2.0

We developed a method, ArchaicSeeker 2.0, to identify introgressed hominin sequences and model multiple-wave admixture. The new method enabled us to discern two waves of introgression from both Denisovan-like and Neanderthal-like hominins in present-day Eurasian populations and an ancient Siberian individual. We estimated that an early Denisovan-like introgression occurred in Eurasia around 118.8-94.0 thousand years ago (kya). In contrast, we detected only one single episode of Denisovan-like admixture in indigenous peoples eastern to the Wallace-Line. Modeling ancient admixtures suggested an early dispersal of modern humans throughout Asia before the Toba volcanic super-eruption 74 kya, predating the initial peopling of Asia as proposed by the traditional Out-of-Africa model. Survived archaic sequences are involved in various phenotypes including immune and body mass (e.g., ZNF169), cardiovascular and lung function (e.g., HHAT), UV response and carbohydrate metabolism (e.g., HYAL1/HYAL2/HYAL3), while "archaic deserts" are enriched with genes associated with skin development and keratinization.

Deep Learning for Voltammetric Sensing in a Living Animal Brain

Numerous neurochemicals have been implicated in the modulation of brain function, making them appealing analytes for sensors and diagnostics. However, it is a grand challenge to selectively measure multiple neurochemicals simultaneously in vivo because of their great variations in concentrations, dynamic nature, and composition. Herein, we present a deep learning-based voltammetric sensing platform for the highly selective and simultaneous analysis of three neurochemicals in a living animal brain. The system features a carbon fiber electrode capable of capturing the mixed dynamics of a neurotransmitter, neuromodulator, and ions. Then a powerful deep neural network is employed to resolve individual chemical and spatial-temporal information. With this, a single electrochemical measurement reveals an interplaying concentration changes of dopamine, ascorbate, and ions in living rat brain, which is unobtainable with existing analytical methodologies. Our strategy provides a powerful means to expedite research in neuroscience and empower sensing-aided diagnostic applications.

Coalescent-based species delimitation meets deep learning: Insights from a highly fragmented cactus system

Delimiting species boundaries is a major goal in evolutionary biology. An increasing volume of literature has focused on the challenges of investigating cryptic diversity within complex evolutionary scenarios of speciation, including gene flow and demographic fluctuations. New methods based on model selection, such as approximate Bayesian computation, approximate likelihoods, and machine learning are promising tools arising in this field. Here, we introduce a framework for species delimitation using the multispecies coalescent model coupled with a deep learning algorithm based on convolutional neural networks (CNNs). We compared this strategy with a similar ABC approach. We applied both methods to test species boundary hypotheses based on current and previous taxonomic delimitations as well as genetic data (sequences from 41 loci) in Pilosocereus aurisetus, a cactus species complex with a sky-island distribution and taxonomic uncertainty. To validate our method, we also applied the same strategy on data from widely accepted species from the genus Drosophila. The results show that our CNN approach has a high capacity to distinguish among the simulated species delimitation scenarios, with higher accuracy than ABC. For the cactus data set, a splitter hypothesis without gene flow showed the highest probability in both CNN and ABC approaches, a result agreeing with previous taxonomic classifications and in line with the sky-island distribution and low dispersal of P. aurisetus. Our results highlight the cryptic diversity within the P. aurisetus complex and show that CNNs are a promising approach for distinguishing complex evolutionary histories, even outperforming the accuracy of other model-based approaches such as ABC.

Fine-scale population structure in five rural populations from the Spanish Eastern Pyrenees using high-coverage whole-genome sequence data

The area of the Spanish Pyrenees is particularly interesting for studying the demographic dynamics of European rural areas given its orography, the main traditional rural condition of its population and the reported higher patterns of consanguinity of the region. Previous genetic studies suggest a gradient of genetic continuity of the area in the West to East axis. However, it has been shown that micro-population substructure can be detected when considering high-quality NGS data and using spatial explicit methods. In this work, we have analyzed the genome of 30 individuals sequenced at 40× from five different valleys in the Spanish Eastern Pyrenees (SEP) separated by less than 140 km along a west to east axis. Using haplotype-based methods and spatial analyses, we have been able to detect micro-population substructure within SEP not seen in previous studies. Linkage disequilibrium and autozygosity analyses suggest that the SEP populations show diverse demographic histories. In agreement with these results, demographic modeling by means of ABC-DL identify heterogeneity in their effective population sizes despite of their close geographic proximity, and suggests that the population substructure within SEP could have appeared around 2500 years ago. Overall, these results suggest that each rural population of the Pyrenees could represent a unique entity.

An etiology of human modernity

Following the refutation of the replacement hypothesis, which had proposed that a ‘superior’ hominin species arose in Africa and replaced all other humans existing at the time, the auto-domestication hypothesis remains the only viable explanation for the relatively abrupt change from robust to gracile humans in the Late Pleistocene. It invokes the incidental institution of the domestication syndrome in humans, most probably by newly introduced cultural practices. It also postulates that the induction of exograms compensated for the atrophy of the brain caused by domestication. This new explanation of the origins of modernity in humans elucidates practically all its many aspects, in stark contrast to the superseded replacement hypothesis, which explained virtually nothing. The first results of the domestication syndrome’s genetic exploration have become available in recent years, and they endorse the human self-domestication hypothesis.

Skeletal remains of a Pleistocene modern human (Homo sapiens) from Sulawesi

Major gaps remain in our knowledge of the early history of Homo sapiens in Wallacea. By 70-60 thousand years ago (ka), modern humans appear to have entered this distinct biogeographical zone between continental Asia and Australia. Despite this, there are relatively few Late Pleistocene sites attributed to our species in Wallacea. H. sapiens fossil remains are also rare. Previously, only one island in Wallacea (Alor in the southeastern part of the archipelago) had yielded skeletal evidence for pre-Holocene modern humans. Here we report on the first Pleistocene human skeletal remains from the largest Wallacean island, Sulawesi. The recovered elements consist of a nearly complete palate and frontal process of a modern human right maxilla excavated from Leang Bulu Bettue in the southwestern peninsula of the island. Dated by several different methods to between 25 and 16 ka, the maxilla belongs to an elderly individual of unknown age and sex, with small teeth (only M1 to M3 are extant) that exhibit severe occlusal wear and related dental pathologies. The dental wear pattern is unusual. This fragmentary specimen, though largely undiagnostic with regards to morphological affinity, provides the only direct insight we currently have from the fossil record into the identity of the Late Pleistocene people of Sulawesi.

Modeling colorectal cancer evolution

Understanding cancer evolution provides a clue to tackle therapeutic difficulties in colorectal cancer. In this review, together with related works, we will introduce a series of our studies, in which we constructed an evolutionary model of colorectal cancer by combining genomic analysis and mathematical modeling. In our model, multiple subclones were generated by driver mutation acquisition and subsequent clonal expansion in early-stage tumors. Among the subclones, the one obtaining driver copy number alterations is endowed with malignant potentials to constitute a late-stage tumor in which extensive intratumor heterogeneity is generated by the accumulation of neutral mutations. We will also discuss how to translate our understanding of cancer evolution to a solution to the problem related to therapeutic resistance: mathematical modeling suggests that relapse caused by acquired resistance could be suppressed by utilizing clonal competition between sensitive and resistant clones. Considering the current rate of technological development, modeling cancer evolution by combining genomic analysis and mathematical modeling will be an increasingly important approach for understanding and overcoming cancer.

Our Tangled Family Tree: New Genomic Methods Offer Insight into the Legacy of Archaic Admixture

The archaic ancestry present in the human genome has captured the imagination of both scientists and the wider public in recent years. This excitement is the result of new studies pushing the envelope of what we can learn from the archaic genetic information that has survived for over 50,000 years in the human genome. Here, we review the most recent ten years of literature on the topic of archaic introgression, including the current state of knowledge on Neanderthal and Denisovan introgression, as well as introgression from other as-yet unidentified archaic populations. We focus this review on four topics: 1) a reimagining of human demographic history, including evidence for multiple admixture events between modern humans, Neanderthals, Denisovans, and other archaic populations; 2) state-of-the-art methods for detecting archaic ancestry in population-level genomic data; 3) how these novel methods can detect archaic introgression in modern African populations; and 4) the functional consequences of archaic gene variants, including how those variants were co-opted into novel function in modern human populations. The goal of this review is to provide a simple-to-access reference for the relevant methods and novel data, which has changed our understanding of the relationship between our species and its siblings. This body of literature reveals the large degree to which the genetic legacy of these extinct hominins has been integrated into the human populations of today.

fastsimcoal2: demographic inference under complex evolutionary scenarios

Motivation

fastsimcoal2 extends fastsimcoal, a continuous time coalescent-based genetic simulation program, by enabling the estimation of demographic parameters under very complex scenarios from the site frequency spectrum under a maximum-likelihood framework.

Results

Other improvements include multi-threading, handling of population inbreeding, extended input file syntax facilitating the description of complex demographic scenarios, and more efficient simulations of sparsely structured populations and of large chromosomes.

Availability and implementation

fastsimcoal2 is freely available on http://cmpg.unibe.ch/software/fastsimcoal2/. It includes console versions for Linux, Windows and MacOS, additional scripts for the analysis and visualization of simulated and estimated scenarios, as well as a detailed documentation and ready-to-use examples.

Detecting adaptive introgression in human evolution using convolutional neural networks

Studies in a variety of species have shown evidence for positively selected variants introduced into a population via introgression from another, distantly related population—a process known as adaptive introgression. However, there are few explicit frameworks for jointly modelling introgression and positive selection, in order to detect these variants using genomic sequence data. Here, we develop an approach based on convolutional neural networks (CNNs). CNNs do not require the specification of an analytical model of allele frequency dynamics and have outperformed alternative methods for classification and parameter estimation tasks in various areas of population genetics. Thus, they are potentially well suited to the identification of adaptive introgression. Using simulations, we trained CNNs on genotype matrices derived from genomes sampled from the donor population, the recipient population and a related non-introgressed population, in order to distinguish regions of the genome evolving under adaptive introgression from those evolving neutrally or experiencing selective sweeps. Our CNN architecture exhibits 95% accuracy on simulated data, even when the genomes are unphased, and accuracy decreases only moderately in the presence of heterosis. As a proof of concept, we applied our trained CNNs to human genomic datasets—both phased and unphased—to detect candidates for adaptive introgression that shaped our evolutionary history.

The genomic history of the Aegean palatial civilizations

The Cycladic, the Minoan, and the Helladic (Mycenaean) cultures define the Bronze Age (BA) of Greece. Urbanism, complex social structures, craft and agricultural specialization, and the earliest forms of writing characterize this iconic period. We sequenced six Early to Middle BA whole genomes, along with 11 mitochondrial genomes, sampled from the three BA cultures of the Aegean Sea. The Early BA (EBA) genomes are homogeneous and derive most of their ancestry from Neolithic Aegeans, contrary to earlier hypotheses that the Neolithic-EBA cultural transition was due to massive population turnover. EBA Aegeans were shaped by relatively small-scale migration from East of the Aegean, as evidenced by the Caucasus-related ancestry also detected in Anatolians. In contrast, Middle BA (MBA) individuals of northern Greece differ from EBA populations in showing ∼50% Pontic-Caspian Steppe-related ancestry, dated at ca. 2,600-2,000 BCE. Such gene flow events during the MBA contributed toward shaping present-day Greek genomes.

Extending approximate Bayesian computation with supervised machine learning to infer demographic history from genetic polymorphisms using DIYABC Random Forest

Simulation-based methods such as approximate Bayesian computation (ABC) are well-adapted to the analysis of complex scenarios of populations and species genetic history. In this context, supervised machine learning (SML) methods provide attractive statistical solutions to conduct efficient inferences about scenario choice and parameter estimation. The Random Forest methodology (RF) is a powerful ensemble of SML algorithms used for classification or regression problems. Random Forest allows conducting inferences at a low computational cost, without preliminary selection of the relevant components of the ABC summary statistics, and bypassing the derivation of ABC tolerance levels. We have implemented a set of RF algorithms to process inferences using simulated data sets generated from an extended version of the population genetic simulator implemented in DIYABC v2.1.0. The resulting computer package, named DIYABC Random Forest v1.0, integrates two functionalities into a user-friendly interface: the simulation under custom evolutionary scenarios of different types of molecular data (microsatellites, DNA sequences or SNPs) and RF treatments including statistical tools to evaluate the power and accuracy of inferences. We illustrate the functionalities of DIYABC Random Forest v1.0 for both scenario choice and parameter estimation through the analysis of pseudo-observed and real data sets corresponding to pool-sequencing and individual-sequencing SNP data sets. Because of the properties inherent to the implemented RF methods and the large feature vector (including various summary statistics and their linear combinations) available for SNP data, DIYABC Random Forest v1.0 can efficiently contribute to the analysis of large SNP data sets to make inferences about complex population genetic histories.

Inferring archaic introgression from hominin genetic data

Questions surrounding the timing, extent, and evolutionary consequences of archaic admixture into human populations have a long history in evolutionary anthropology. More recently, advances in human genetics, particularly in the field of ancient DNA, have shed new light on the question of whether or not Homo sapiens interbred with other hominin groups. By the late 1990s, published genetic work had largely concluded that archaic groups made no lasting genetic contribution to modern humans; less than a decade later, this conclusion was reversed following the successful DNA sequencing of an ancient Neanderthal. This reversal of consensus is noteworthy, but the reasoning behind it is not widely understood across all academic communities. There remains a communication gap between population geneticists and paleoanthropologists. In this review, we endeavor to bridge this gap by outlining how technological advancements, new statistical methods, and notable controversies ultimately led to the current consensus.

Widespread Denisovan ancestry in Island Southeast Asia but no evidence of substantial super-archaic hominin admixture

The hominin fossil record of Island Southeast Asia (ISEA) indicates that at least two endemic 'super-archaic' species-Homo luzonensis and H. floresiensis-were present around the time anatomically modern humans arrived in the region >50,000 years ago. Intriguingly, contemporary human populations across ISEA carry distinct genomic traces of ancient interbreeding events with Denisovans-a separate hominin lineage that currently lacks a fossil record in ISEA. To query this apparent disparity between fossil and genetic evidence, we performed a comprehensive search for super-archaic introgression in >400 modern human genomes, including >200 from ISEA. Our results corroborate widespread Denisovan ancestry in ISEA populations, but fail to detect any substantial super-archaic admixture signals compatible with the endemic fossil record of ISEA. We discuss the implications of our findings for the understanding of hominin history in ISEA, including future research directions that might help to unlock more details about the prehistory of the enigmatic Denisovans.

A fully integrated machine learning scan of selection in the chimpanzee genome

After diverging, each chimpanzee subspecies has been the target of unique selective pressures. Here, we employ a machine learning approach to classify regions as under positive selection or neutrality genome-wide. The regions determined to be under selection reflect the unique demographic and adaptive history of each subspecies. The results indicate that effective population size is important for determining the proportion of the genome under positive selection. The chimpanzee subspecies share signals of selection in genes associated with immunity and gene regulation. With these results, we have created a selection map for each population that can be displayed in a genome browser (www.hsb.upf.edu/chimp_browser). This study is the first to use a detailed demographic history and machine learning to map selection genome-wide in chimpanzee. The chimpanzee selection map will improve our understanding of the impact of selection on closely related subspecies and will empower future studies of chimpanzee.

Origins of modern human ancestry

New finds in the palaeoanthropological and genomic records have changed our view of the origins of modern human ancestry. Here we review our current understanding of how the ancestry of modern humans around the globe can be traced into the deep past, and which ancestors it passes through during our journey back in time. We identify three key phases that are surrounded by major questions, and which will be at the frontiers of future research. The most recent phase comprises the worldwide expansion of modern humans between 40 and 60 thousand years ago (ka) and their last known contacts with archaic groups such as Neanderthals and Denisovans. The second phase is associated with a broadly construed African origin of modern human diversity between 60 and 300 ka. The oldest phase comprises the complex separation of modern human ancestors from archaic human groups from 0.3 to 1 million years ago. We argue that no specific point in time can currently be identified at which modern human ancestry was confined to a limited birthplace, and that patterns of the first appearance of anatomical or behavioural traits that are used to define Homo sapiens are consistent with a range of evolutionary histories.

Genome of a middle Holocene hunter-gatherer from Wallacea

Much remains unknown about the population history of early modern humans in southeast Asia, where the archaeological record is sparse and the tropical climate is inimical to the preservation of ancient human DNA¹. So far, only two low-coverage pre-Neolithic human genomes have been sequenced from this region. Both are from mainland Hòabìnhian hunter-gatherer sites: Pha Faen in Laos, dated to 7939-7751 calibrated years before present (yr cal BP; present taken as AD 1950), and Gua Cha in Malaysia (4.4-4.2 kyr cal BP)¹. Here we report, to our knowledge, the first ancient human genome from Wallacea, the oceanic island zone between the Sunda Shelf (comprising mainland southeast Asia and the continental islands of western Indonesia) and Pleistocene Sahul (Australia-New Guinea). We extracted DNA from the petrous bone of a young female hunter-gatherer buried 7.3-7.2 kyr cal BP at the limestone cave of Leang Panninge² in South Sulawesi, Indonesia. Genetic analyses show that this pre-Neolithic forager, who is associated with the 'Toalean' technocomplex^3,4, shares most genetic drift and morphological similarities with present-day Papuan and Indigenous Australian groups, yet represents a previously unknown divergent human lineage that branched off around the time of the split between these populations approximately 37,000 years ago⁵. We also describe Denisovan and deep Asian-related ancestries in the Leang Panninge genome, and infer their large-scale displacement from the region today.

Genomic Adaptations and Evolutionary History of the Extinct Scimitar-Toothed Cat, Homotherium latidens

Homotherium was a genus of large-bodied scimitar-toothed cats, morphologically distinct from any extant felid species, that went extinct at the end of the Pleistocene [1-4]. They possessed large, saber-form serrated canine teeth, powerful forelimbs, a sloping back, and an enlarged optic bulb, all of which were key characteristics for predation on Pleistocene megafauna [5]. Previous mitochondrial DNA phylogenies suggested that it was a highly divergent sister lineage to all extant cat species [6-8]. However, mitochondrial phylogenies can be misled by hybridization [9], incomplete lineage sorting (ILS), or sex-biased dispersal patterns [10], which might be especially relevant for Homotherium since widespread mito-nuclear discrepancies have been uncovered in modern cats [10]. To examine the evolutionary history of Homotherium, we generated a ∼7x nuclear genome and a ∼38x exome from H. latidens using shotgun and target-capture sequencing approaches. Phylogenetic analyses reveal Homotherium as highly divergent (∼22.5 Ma) from living cat species, with no detectable signs of gene flow. Comparative genomic analyses found signatures of positive selection in several genes, including those involved in vision, cognitive function, and energy consumption, putatively consistent with diurnal activity, well-developed social behavior, and cursorial hunting [5]. Finally, we uncover relatively high levels of genetic diversity, suggesting that Homotherium may have been more abundant than the limited fossil record suggests [3, 4, 11-14]. Our findings complement and extend previous inferences from both the fossil record and initial molecular studies, enhancing our understanding of the evolution and ecology of this remarkable lineage.

Componentwise approximate Bayesian computation via Gibbs-like steps

Approximate Bayesian computation methods are useful for generative models with intractable likelihoods. These methods are, however, sensitive to the dimension of the parameter space, requiring exponentially increasing resources as this dimension grows. To tackle this difficulty we explore a Gibbs version of the approximate Bayesian computation approach that runs component-wise approximate Bayesian computation steps aimed at the corresponding conditional posterior distributions, and based on summary statistics of reduced dimensions. While lacking the standard justifications for the Gibbs sampler, the resulting Markov chain is shown to converge in distribution under some partial independence conditions. The associated stationary distribution can further be shown to be close to the true posterior distribution, and some hierarchical versions of the proposed mechanism enjoy a closed-form limiting distribution. Experiments also demonstrate the gain in efficiency brought by the Gibbs version over the standard solution.

Hominin diversity in East Asia during the Middle Pleistocene: A premolar endostructural perspective

Following the recent studies of East Asian mid-Middle to early Late Pleistocene hominin material, a large spectrum of morphological diversity has been recognized and the coexistence of archaic ('Homo erectus-like') and derived ('modern-like') dental morphological patterns has been highlighted. In fact, for most of these Chinese fossils, generally categorized as 'archaic Homo sapiens' or 'post-H. erectus Homo', the taxonomic attribution is a matter of contention. With the help of μCT techniques and a deformation-based 3D geometric morphometric approach, we focused on the morphological variation in the enamel-dentine junction (EDJ) of 18 upper and lower premolars from Chinese Middle Pleistocene hominins. We then compared our results with a number of fossil and modern human groups, including Early Pleistocene H. erectus from Sangiran; late Early Pleistocene hominins from Tighenif, Algeria; classic Neanderthals; and modern humans. Our results highlight an evolutionary/chronological trend of crown base reduction, elevation of EDJ topography, and EDJ surface simplification in the hominin groups studied here. Moreover, this study brings insights to the taxonomy/phylogeny of 6 late Middle Pleistocene specimens whose evolutionary placement has been debated for decades. Among these specimens, Changyang premolars show features that can be aligned with the Asian H. erectus hypodigm, whereas Panxian Dadong and Tongzi premolars are more similar to Late Pleistocene Homo. Compared with early to mid-Middle Pleistocene hominins in East Asia, late Middle Pleistocene hominins evince an enlarged morphological variation. A persistence of archaic morphotypes and possible admixture among populations during the late Middle Pleistocene are discussed.

Methods for detecting introgressed archaic sequences

Analysis of genome sequences from archaic and modern humans have revealed multiple episodes of admixture between highly-diverged population groups. Statistical methods that attempt to localize DNA segments introduced by these events offer a powerful tool to investigate recent human evolution. We review recent advances in methods for detecting introgressed sequences.

Analysis of Haplotypic Variation and Deletion Polymorphisms Point to Multiple Archaic Introgression Events, Including from Altai Neanderthal Lineage

The time, extent, and genomic effect of the introgressions from archaic humans into ancestors of extant human populations remain some of the most exciting venues of population genetics research in the past decade. Several studies have shown population-specific signatures of introgression events from Neanderthals, Denisovans, and potentially other unknown hominin populations in different human groups. Moreover, it was shown that these introgression events may have contributed to phenotypic variation in extant humans, with biomedical and evolutionary consequences. In this study, we present a comprehensive analysis of the unusually divergent haplotypes in the Eurasian genomes and show that they can be traced back to multiple introgression events. In parallel, we document hundreds of deletion polymorphisms shared with Neanderthals. A locus-specific analysis of one such shared deletion suggests the existence of a direct introgression event from the Altai Neanderthal lineage into the ancestors of extant East Asian populations. Overall, our study is in agreement with the emergent notion that various Neanderthal populations contributed to extant human genetic variation in a population-specific manner.

Genomic analysis of the natural history of attention-deficit/hyperactivity disorder using Neanderthal and ancient Homo sapiens samples

Attention-deficit/hyperactivity disorder (ADHD) is an impairing neurodevelopmental condition highly prevalent in current populations. Several hypotheses have been proposed to explain this paradox, mainly in the context of the Paleolithic versus Neolithic cultural shift but especially within the framework of the mismatch theory. This theory elaborates on how a particular trait once favoured in an ancient environment might become maladaptive upon environmental changes. However, given the lack of genomic data available for ADHD, these theories have not been empirically tested. We took advantage of the largest GWAS meta-analysis available for this disorder consisting of over 20,000 individuals diagnosed with ADHD and 35,000 controls, to assess the evolution of ADHD-associated alleles in European populations using archaic, ancient and modern human samples. We also included Approximate Bayesian computation coupled with deep learning analyses and singleton density scores to detect human adaptation. Our analyses indicate that ADHD-associated alleles are enriched in loss of function intolerant genes, supporting the role of selective pressures in this early-onset phenotype. Furthermore, we observed that the frequency of variants associated with ADHD has steadily decreased since Paleolithic times, particularly in Paleolithic European populations compared to samples from the Neolithic Fertile Crescent. We demonstrate this trend cannot be explained by African admixture nor Neanderthal introgression, since introgressed Neanderthal alleles are enriched in ADHD risk variants. All analyses performed support the presence of long-standing selective pressures acting against ADHD-associated alleles until recent times. Overall, our results are compatible with the mismatch theory for ADHD but suggest a much older time frame for the evolution of ADHD-associated alleles compared to previous hypotheses.

Inference of natural selection from ancient DNA

Evolutionary processes, including selection, can be indirectly inferred based on patterns of genomic variation among contemporary populations or species. However, this often requires unrealistic assumptions of ancestral demography and selective regimes. Sequencing ancient DNA from temporally spaced samples can inform about past selection processes, as time series data allow direct quantification of population parameters collected before, during, and after genetic changes driven by selection. In this Comment and Opinion, we advocate for the inclusion of temporal sampling and the generation of paleogenomic datasets in evolutionary biology, and highlight some of the recent advances that have yet to be broadly applied by evolutionary biologists. In doing so, we consider the expected signatures of balancing, purifying, and positive selection in time series data, and detail how this can advance our understanding of the chronology and tempo of genomic change driven by selection. However, we also recognize the limitations of such data, which can suffer from postmortem damage, fragmentation, low coverage, and typically low sample size. We therefore highlight the many assumptions and considerations associated with analyzing paleogenomic data and the assumptions associated with analytical methods.

What have the revelations about Neanderthal DNA revealed about Homo sapiens?

Genetic studies have presented increasing indications about the complexity of the interactions between Homo sapiens, Neanderthals and Denisovans, during Pleistocene. The results indicate potential replacement or admixture of the groups of hominins that lived in the same region at different times. Recently, the time of separation among these hominins in relation to the Last Common Ancestor – LCA has been reasonably well established. Events of mixing with emphasis on the Neanderthal gene flow into H. sapiens outside Africa, Denisovans into H. sapiens ancestors in Oceania and continental Asia, Neanderthals into Denisovans, as well as the origin of some phenotypic features in specific populations such as the color of the skin, eyes, hair and predisposition to develop certain kinds of diseases have also been found. The current information supports the existence of both replacement and interbreeding events, and indicates the need to revise the two main explanatory models, the Multiregional and the Out-of-Africa hypotheses, about the origin and evolution of H. sapiens and its co-relatives. There is definitely no longer the possibility of justifying only one model over the other. This paper aims to provide a brief review and update on the debate around this issue, considering the advances brought about by the recent genetic as well as morphological traits analyses.