Biology subjects

Planche, L.

Publications and source records attributed to Planche, L..

3 recordsLinked to original sources

bioRxiv2025

Archaic ancestry inference in imputed ancient human genomes

When modern humans expanded from Africa into Eurasia, they interbred with archaic hominins such as Neanderthals and Denisovans. This groundbreaking discovery, made in part possible through the genomic analysis of archaic remains, reshaped our understanding of human origins, and opened new research avenues to study the effects and consequences of archaic introgression. While significant progress has been made in identifying and quantifying archaic introgression, most studies have focused on contemporary individual genomic data. As a result, relatively little is known about the evolution of archaic variants within modern human populations shortly after interbreeding occurred. While analysis of ancient DNA from modern humans offers the potential to address this scientific gap, its poor quality has hindered its exploration. However, recent studies have shown that imputation using contemporary reference panels can accurately infer missing genotypes in ancient genomes. Here, we investigate the feasibility of using imputation to improve both global and local archaic ancestry inference in ancient genomes, by downsampling to different low-coverage values and imputing 20 high-coverage (>10X) ancient genomes, representing individuals from diverse temporal and geographical contexts. We tested the reliability of detecting and quantifying archaic introgression using D-statistics and the f4-ratio. We identified consistent results from the imputed and the original ancient genomes, suggesting that genomic estimates to detect and quantify introgression work well in imputed genomes. Regarding Local Ancestry Inference we find that we can identify more introgressed segments in imputed genomes than in non-imputed genomes. Surprisingly, we find that imputation accuracy is even higher in regions of archaic ancestry compared to other regions of the genome, facilitating the detection of introgressed segments in imputed genomes. Imputation also allows detection of Denisovan segments in Siberian and Alaskan individuals. We show that segments identified even at 0.0625X coverage can be used to reconstruct the history of introgressed haplotypes. For example, comparisons with archaic segments in contemporary humans reveal that the oldest individual analyzed, UstIshim, carried segments that are now found exclusively in either European or East Eurasian populations, indicating that these segments co-occurred in a shared ancestral population. By comparing only ancient individuals, we demonstrate that clustering based solely on the identified archaic segments effectively groups individuals into genetic clusters that correspond to populations defined by geography and time. This analysis shows a clear distinction between individuals with Denisovan introgression and those without, as well as a more detailed separation between Mesolithic and Neolithic Europeans, with the latter closely resembling Central Eurasian individuals. Furthermore, despite the small sample size in this study, we are able to reconstruct the origins of genes identified as candidates for adaptive introgression in contemporary populations, such as the BCN2 gene, which is an adaptively introgressed gene identified in contemporary West Eurasians. Additionally, we identify new candidates for adaptive introgression including LEMD2 and MLN in Europe. In this gene-region, imputation helps resolve the introgressed haplotype, which is closest to the Vindija Neanderthal haplotype.

genomics↗

bioRxiv2025

An archaic reference-free method to jointly infer Neanderthal and Denisovan introgressed segments in modern human genomes

Admixture between populations is a common feature of human history. Admixture events introduce new genetic variation that can fuel evolution. Characterizing the significance of admixture events on the evolution of a population across various species is of great interest to evolutionary geneticists. Local Ancestry Inference (LAI) methods infer genetic ancestry of an individual at a particular chromosomal location. Certain methods specialize in detecting archaic introgression, which consists of interbreeding between modern and archaic humans like Neanderthals and Denisovans. Most current LAI methods allow the detection of a single archaic ancestry, and post-processing may distinguish between multiple waves of introgression. These methods vary in how they choose archaic or modern reference genomes for the inference. Here, we present a new HMM-based method (DAIseg), which has the advantage of simultaneously distinguishing between multiple waves of ancient and recent admixture, using only modern human reference genomes. Simulations demonstrate that DAIseg achieves higher overall performance than state-of-the-art methods. We also apply DAIseg to Papuan populations to jointly detect Denisovan and Neanderthal introgressed segments, and identify a higher number of archaic segments than previous methods. Analysis of inferred introgressed segments, shows that we can identify evidence for two Denisovan introgression events in Papuans without having any post-processing and filtering. Overall, on top of being able to deal with both Archaic and recent admixture, DAIseg provides a more principled approach for detecting and classifying Denisovan and Neanderthal segments which will improve downstream analysis of introgressed segments to infer the impact of archaic introgression in humans.

bioinformatics↗

bioRxiv2024

Diffusion-based artificial genomes and their usefulness for local ancestry inference

The creation of synthetic data through generative modeling has emerged as a significant area of research in genomics, offering versatile applications from tailoring functional sequences with specific attributes to generating high-quality, privacy-preserving in silico genomes. Notwithstanding these advancements, a key challenge remains: while some methods exist to evaluate artificially generated genomic data, comprehensive tools to assess its usefulness are still limited. To tackle this issue and present a promising use case, we test artificial genomes within the framework of population genetics and local ancestry inference (LAI). Building on previous work in deep generative modeling for genomics, we introduce a novel, frugal diffusion model and show that it produces high-quality genomic data. We then assess the performance of a downstream machine learning LAI model trained on composite datasets comprising both real and/or synthetic data. Our findings reveal that the LAI model achieves comparable performance when trained exclusively on real data versus high-quality synthetic data. Moreover, we highlight how data augmentation using high-quality artificial genomes significantly benefits the LAI model, particularly when real data is limited. Finally, we compare the conventional use of a single synthetic dataset to a robust ensemble approach, wherein multiple LAI models are trained on diverse synthetic datasets, and their predictions are aggregated. Our study highlights the potential of frugal diffusion-based generative models and synthetic data integration in genomics. This approach could improve fair representation across populations by overcoming data accessibility challenges, while ensuring the reliability of genomic analyses conducted on artificial data.

bioinformatics↗