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Verdu, P.

Publications and source records attributed to Verdu, P..

8 recordsLinked to original sources

Attentive-SPIDNA: Attention-based neural networks for population genetics

Artificial neural networks (ANNs) have recently offered new perspectives to solve inference problems from high dimensional data in numerous scientific fields, but it is yet unclear which architectures are the most suited to genomic data. Here, we present a new ANN architecture integrating attention mechanisms to infer effective population size history from genomic data. Built upon our previous exchangeable architecture SPIDNA, Attentive-SPIDNA adds attention layers that allow computing more expressive and complex features from combinations of haplotypes. The contribution of each haplotype to the features is learned automatically and depends on its content and affinity with the other haplotypes. Likewise, we use this mechanism to automatically perform a voting scheme that aggregates predictions from different genomic regions. This new architecture outperforms approximate Bayesian computation and previously published neural networks while relying directly on raw genetic data and being invariant to haplotype permutation in the input. As a proof-of-concept, we use this architecture to infer the effective population size history of 54 populations from the HGDP dataset (Bergstrom et al, 2020). This application highlights the ability of the network to handle data with a varying number of haplotypes and to quickly perform predictions for datasets including numerous populations. Therefore, the proposed mechanism could be integrated to various neural networks solving population genetics tasks.

evolutionary biology↗

Nested admixture during and after the Trans-Atlantic Slave Trade on the island of Sao Tome

Human admixture history is rarely a simple process in which distinct populations, previously isolated for a long time, come into contact once to form an admixed population. In this study, we aim to reconstruct the complex admixture histories of the population of Sao Tome, an island in the Gulf of Guinea that was the site of the first slave-based plantation economy, and experienced successive waves of forced and deliberate migration from Africa. We examined 2.5 million SNPs newly genotyped in 96 Sao Tomeans and found that geography alone cannot explain the observed patterns of genetic differentiation within the island. We defined five genetic groups in Sao Tome based on the hypothesis that individuals sharing the most haplotypes are more likely to share similar genetic histories. Using Identical-by-Descent and different local ancestry inference methods, we inferred shared ancestries between 70 African and European populations and each Sao Tomean genetic group. We identified admixture events between admixed groups that were previously isolated on the island, showing how recently admixed populations can be themselves the sources of other admixture events. This study demonstrates how complex admixture and isolation histories during and after the Transatlantic Slave-Trade shaped extant individual genetic patterns at a local scale in Africa.

genetics↗

Ancient tree-topologies and gene-flow processes among human lineages in Africa

The deep history of human evolution in Africa remains intensely debated, with increasingly complex models being proposed. To investigate this, we sequenced and analysed 73 novel high-quality whole genomes from 14 Central and Southern African populations with diverse cultural practices. Using extensive simulations and machine-learning Approximate Bayesian Computation (ABC), we jointly reconstructed their demographic history of divergences and migrations. We find extensive genome-wide diversity within and among populations, including substantial local genetic differentiation not fully explained by geography or cultural practices. These patterns highlight the importance of explicitly considering local genomic diversity when reconstructing human evolutionary history. We find that tree-like population histories with long periods of drift separated by short pulses of unidirectional gene-flow better explain the data than continuous gene-flow. Without invoking archaic admixture, our models accurately fit observed genomic variation and identify multiple episodes of gene-flow coinciding with major ecological and cultural changes in Sub-Saharan Africa.

genetics↗

The admixture histories of Cabo Verde

From the 15th to the 19th century, the Trans-Atlantic Slave-Trade (TAST) influenced the genetic and cultural diversity of numerous populations. We explore genomic and linguistic data from the nine islands of Cabo Verde, the earliest European colony of the era in Africa, a major Slave-Trade platform between the 16th and 19th centuries, and a previously uninhabited location ideal for investigating early admixture events between Europeans and Africans. Using local-ancestry inference approaches, we find that genetic admixture in Cabo Verde occurred primarily between Iberian and certain Senegambian populations, although forced and voluntary migrations to the archipelago involved numerous other populations. Inter-individual genetic and linguistic variation recapitulates the geographic distribution of individuals birth-places across Cabo Verdean islands, following an isolation-by-distance model with reduced genetic and linguistic effective dispersals within the archipelago, and suggesting that Kriolu language variants have developed together with genetic divergences at very reduced geographical scales. Furthermore, based on approximate bayesian computation inferences of highly complex admixture histories, we find that admixture occurred early on each island, long before the 18th-century massive TAST deportations triggered by the expansion of the plantation economy in Africa and the Americas, and after this era mostly during the abolition of the TAST and of slavery in European colonial empires. Our results illustrate how shifting socio-cultural relationships between enslaved and non-enslaved communities during and after the TAST, shaped enslaved-African descendants genomic diversity and structure on both sides of the Atlantic.

genetics↗

Complex genetic admixture histories reconstructed with Approximate Bayesian Computations

Admixture is a fundamental evolutionary process that has influenced genetic patterns in numerous species. Maximum-likelihood approaches based on allele frequencies and linkage-disequilibrium have been extensively used to infer admixture processes from dense genome-wide datasets mostly in human populations. Nevertheless, complex admixture histories, beyond one or two pulses of admixture, remain methodologically challenging to reconstruct, especially when large datasets are unavailable. We develop an Approximate Bayesian Computations (ABC) framework to reconstruct complex admixture histories from independent genetic markers. We built the software package MetHis to simulate independent SNPs in a two-way admixed population for scenarios with multiple admixture pulses, or monotonically decreasing or increasing admixture at each generation; drawing model-parameter values from prior distributions set by the user. For each simulated dataset, we calculate 24 summary statistics describing genetic diversity and moments of individual admixture fraction. We coupled MetHis with existing ABC algorithms and investigate the admixture history of an African American and a Barbadian population. Results show that Random-Forest ABC scenario-choice, followed by Neural-Network ABC posterior parameter estimation, can distinguish most complex admixture scenarios and provide accurate model-parameter estimations. For both admixed populations, we find that monotonically decreasing contributions over time, from the European and African sources, explain the observed data more accurately than multiple admixture pulses. Furthermore, we find contrasted trajectories of introgression decay from the European and African sources between the two admixed populations. This approach will allow for reconstructing detailed admixture histories in numerous populations and species, particularly when maximum-likelihood methods are intractable.

genetics↗

Inferring linguistic transmission between generations at the scale of individuals

Historical linguistics strongly benefited from recent methodological advances inspired by phylogenetics. Nevertheless, no available method uses contemporaneous within-population linguistic diversity to reconstruct the history of human populations. Here, we developed an approach inspired from population genetics to perform historical linguistic inferences from linguistic data sampled at the individual scale, within a population. We built four within-population demographic models of linguistic transmission over generations, each differing by the number of teachers involved during the language acquisition and the relative roles of the teachers. We then compared the simulated data obtained with these models with real contemporaneous linguistic data sampled from Tajik speakers from Central Asia, an area known for its large within-population linguistic diversity, using approximate Bayesian computation methods. Under this statistical framework, we were able to select the models that best explained the data, and infer the best-fitting parameters under the selected models. This demonstrates the feasibility of using contemporaneous within-population linguistic diversity to infer historical features of human cultural evolution.

bioinformatics↗

A Genome Scan for Genes Underlying Adult Body Size Differences between Central African Pygmies and their Non-Pygmy Neighbors

BackgroundCentral African hunter-gatherer Pygmy populations have reduced body size compared with their often much larger agricultural non-Pygmy neighbors, potentially reflecting adaptation to the anatomical and physiological constraints of their lifestyle in tropical rainforests. Earlier studies investigating the genetics of the pygmy phenotype have focused on standing height, one aspect of this complex phenotype that is itself a composite of skeletal components with different growth patterns. Here, we extend the investigations of standing height to the variability and genetic architecture of sitting height and subischial leg length as well as body mass index (BMI) in a sample of 406 unrelated West Central African Pygmies and non-Pygmies.\n\nResultsIn addition to their significantly reduced standing height compared with non-Pygmies, we find Pygmies to have significantly shorter sitting heights and subischial leg lengths as well as higher sitting/standing height ratios than non-Pygmies. However, while male Pygmies had significantly lower BMI compared with male non-Pygmies, the BMI of females were instead similar. Consistent with prior observations with standing height, sitting height and subischial leg length were strongly correlated with inferred levels of non-Pygmy genetic admixture while BMI was instead weakly correlated, likely reflecting the greater contribution of non-genetic factors to the determination of body weight compared with height. Using 196,725 SNPs on the Illumina Cardio-MetaboChip with genotypes on 358 Pygmy and 169 non-Pygmy individuals together with single-and multi-marker association approaches, we identified a single genomic region and seven genes associated with Pygmy/non-Pygmy categorization as well as 9, 10, 9, and 10 genes associated with standing and sitting height, sitting/standing height ratio, and subischial leg length, respectively. Many of the genes identified have putative functions consistent with a role in determining their associated trait as well as the complex Central African pygmy phenotype.\n\nConclusionsThese findings highlight the potential of modestly sized datasets of Pygmies and non-Pygmies to detect biologically meaningful associations with traits contributing to the Central African pygmy phenotype. Moreover, they provide new insights into the phenotypic and genetic bases of the complex pygmy phenotype and offer new opportunities to facilitate our understanding of its complex evolutionary origins.

genetics↗

The demographic history and mutational load of African hunter-gatherers and farmers

The distribution of deleterious genetic variation across human populations is a key issue in evolutionary biology and medical genetics. However, the impact of different modes of subsistence on recent changes in population size, patterns of gene flow, and deleterious mutational load remains unclear. Here, we report high-coverage exome sequencing data from various populations of rainforest hunter-gatherers and farmers from central Africa. We find that the recent demographic histories of hunter-gatherers and farmers differed considerably, with population collapses for hunter-gatherers and expansions for farmers, accompanied by increased gene flow. We show that purifying selection against deleterious alleles is of similar efficiency across African populations, in contrast with Europeans where we detect weaker purifying selection. Furthermore, the per-individual mutation load of rainforest hunter-gatherers is similar to that of farmers, under both additive and recessive models. Our results indicate that differences in the cultural practices and demographic regimes of African populations have not resulted in large differences in mutational burden, and highlight the beneficial role of gene flow in reshaping the distribution of deleterious genetic variation across human populations.

evolutionary biology↗