Biology subjects

Malaspinas, A.-S.

Publications and source records attributed to Malaspinas, A.-S..

10 recordsLinked to original sources

bioRxiv2026

mixKIN: An ancestry-aware kinship estimation method for low coverage genomes

Genetic kinship analysis provides insights into the social and biological organization of past populations. However, kinship estimation from low-coverage genomes, which are common in ancient DNA (aDNA) studies, remains challenging, particularly in genetically heterogeneous or admixed populations. Approaches that assume a homogeneous background relatedness can bias estimates when individuals have distinct ancestry. We developed mixKIN, an ancestry-aware kinship estimation from low-coverage genomes. Rather than using a single mismatch baseline for all pairs, mixKIN estimates pair-specific baseline by weighting within-population diversity and between-population divergence according to ancestry proportions of both individuals in each pair. We compared mixKIN with correctKin and RelateAdmix, which account for population structure and admixture, and with READv2 representing a widely used approach that assumes a homogeneous population background. To benchmark the methods, we ran extensive pedigree simulations across diverse demographic scenarios, including distant-population admixture, intra-population heterogeneity, and genetic outliers, at coverages from 1x to 0.03x. mixKIN achieved higher average classification performance in admixed and heterogeneous scenarios, while maintaining comparable performance in homogeneous populations, with relationships evaluated up to third-degree. In an application to an empirical data from a medieval English cemetery, mixKIN recovered relationships involving an individual with West African and European ancestries without the need for a manual adjustment to account for admixture. These findings support ancestry-aware mismatch normalization as a practical approach to improving kinship estimation and retaining genetically diverse individuals in kinship studies.

bioinformatics↗

bioRxiv2024

Out-of-Anatolia: cultural and genetic interactions during the Neolithic expansion in the Aegean

Western Anatolia has been a crucial yet elusive element in the Neolithic expansion from the Fertile Crescent to Europe. Using 30 new palaeogenomes from Anatolia c.8000-6000 BCE we describe the early Holocene genetic landscape of Western Anatolia, which reveals population continuity since the late Upper Pleistocene. Our findings indicate that the Neolithisation of Western Anatolia in the 7th millennium BCE was a multifaceted process, characterised by the assimilation of Neolithic practices by indigenous groups and the influx of populations from the east, their admixed descendants eventually laying the foundations of Neolithic Southeast Europe. Intriguingly, the observed diversity in material culture among Aegean Early Neolithic communities correlates with their geographical distances but not their genetic differences, signifying a decoupling between cultural developments and genetic admixture processes.

genomics↗

bioRxiv2023

Assessing the impact of post-mortem damage and contamination on imputation performance in ancient DNA

Low-coverage imputation is becoming ever more present in ancient DNA (aDNA) studies. Imputation pipelines commonly used for present-day genomes have been shown to yield accurate results when applied to ancient genomes. However, post-mortem damage (PMD), in the form of C-to-T substitutions at the reads termini, and contamination with DNA from closely related species can potentially affect imputation performance in aDNA. In this study, we evaluated imputation performance i) when using a genotype caller designed for aDNA, ATLAS, compared to bcftools, and ii) when contamination is present. We evaluated imputation performance with principal component analyses (PCA) and by calculating imputation error rates. With a particular focus on differently imputed sites, we found that using ATLAS prior to imputation substantially improved imputed genotypes for a very damaged ancient genome (42% PMD). For the remaining genomes, ATLAS brought limited gains. Finally, to examine the effect of contamination on imputation, we added various amounts of reads from two present-day genomes to a previously downsampled high-coverage ancient genome. We observed that imputation accuracy drastically decreased for contamination rates above 5%. In conclusion, we recommend i) accounting for PMD by using a genotype caller such as ATLAS before imputing highly damaged genomes and ii) only imputing genomes containing up to 5% of contamination.

genomics↗

bioRxiv2023

Pre-processing of paleogenomes: Mitigating reference bias and postmortem damage in ancient genome data

Ancient DNA analysis is subject to various technical challenges, including bias towards the reference allele ("reference bias"), postmortem damage (PMD) that confounds real variants, and limited coverage. Here, we conduct a systematic comparison of alternative approaches against reference bias and against PMD. To reduce reference bias, we either (a) mask variable sites before alignment or (b) align the data to a graph genome representing all variable sites. Compared to alignment to the linear reference genome, both masking and graph alignment effectively remove allelic bias when using simulated or real ancient human genome data, but only if sequencing data is available in FASTQ or unfiltered BAM format. Reference bias remains indelible in quality-filtered BAM files and in 1240K-capture data. We next study three approaches to overcome postmortem damage: (a) trimming, (b) rescaling base qualities, and (c) a new algorithm we present here, bamRefine, which masks only PMD-vulnerable polymorphic sites. We find that bamRefine is optimal in increasing the number of genotyped loci up to 20% compared to trimming and in improving accuracy compared to rescaling. We propose graph alignment coupled with bamRefine to minimise data loss and bias. We also urge the paleogenomics community to publish FASTQ files.

bioinformatics↗

bioRxiv2023

A resampling-based approach to share reference panels

For many genome-wide association studies, imputing genotypes from a haplotype reference panel is a necessary step. Over the past 15 years, reference panels have become larger and more diverse, leading to improvements in imputation accuracy. However, the latest generation of reference panels is subject to restrictions on data sharing due to concerns about privacy, limiting their usefulness for genotype imputation. In this context, we propose RESHAPE, a method that employs a recombination Poisson process on a reference panel to simulate the genomes of hypothetical descendants after multiple generations. This data transformation helps to protect against re-identification threats and preserves important data attributes, such as linkage disequilibrium (LD) patterns and, to some degree, Identity-By-Descent (IBD) sharing, allowing for genotype imputation. Our experiments on gold standard datasets show that simulated descendants up to eight generations can serve as reference panels without significantly reducing genotype imputation accuracy. We suggest that this specific type of data anonymization could be used to generate synthetic reference panels available under less restrictive data sharing policies.

bioinformatics↗

bioRxiv2022

Imputation of ancient genomes

Due to postmortem DNA degradation, most ancient genomes sequenced to date have low depth of coverage, preventing the true underlying genotypes from being recovered. Genotype imputation has been put forward to improve genotyping accuracy for low-coverage genomes. However, it is unknown to what extent imputation of ancient genomes produces accurate genotypes and whether imputation introduces bias to downstream analyses. To address these questions, we downsampled 43 ancient genomes, 42 of which are high-coverage (above 10x) and three constitute a trio (mother, father and son), from different times and continents to simulate data with coverage in the range of 0.1x-2.0x and imputed these using state-of-the-art methods and reference panels. We assessed imputation accuracy across ancestries and depths of coverage. We found that ancient and modern DNA imputation accuracies were comparable. We imputed most of the 42 high-coverage genomes downsampled to 1x with low error rates (below 5%) and estimated higher error rates for African genomes, which are underrepresented in the reference panel. We used the ancient trio data to validate imputation and phasing results using an orthogonal approach based on Mendels rules of inheritance. This resulted in imputation and switch error rates of 1.9% and 2.0%, respectively, for 1x genomes. We further compared the results of downstream analyses between imputed and high-coverage genomes, notably principal component analysis (PCA), genetic clustering, and runs of homozygosity (ROH). For these three approaches, we observed similar results between imputed and high-coverage genomes using depths of coverage of at least 0.5x, except for African genomes, for which the decreased imputation accuracy impacted ROH estimates. Altogether, these results suggest that, for most populations and depths of coverage as low as 0.5x, imputation is a reliable method with potential to expand and improve ancient DNA studies.

genomics↗

bioRxiv2022

Indigenous peoples in eastern Brazil: insights from 19th century genomes and metagenomes

Although Brazil was inhabited by more than 3,000 Indigenous populations prior to European colonization, todays Indigenous peoples represent less than 1% of Brazils census population. Some of the decimated communities belonged to the so-called "Botocudos" from central-eastern Brazil. These peoples are thought to represent a case of long-standing genetic continuity bearing a strong craniometric resemblance to that of the oldest Indigenous Americans ("Paleoamericans"). Yet, little is known about their origins and genetic relationship to other Native Americans, as only two "Botocudo" genomes have been sequenced so far and those were surprisingly of Polynesian ancestry. To deepen our knowledge on the genomic history of pre-contact Indigenous Americans and the pathogens they were exposed to, we carbon-dated and sequenced 24 ancient Brazilians (including 22 "Botocudos") whose remains were hosted at the National Museum of Rio de Janeiro and recovered prior to the tragic 2018 fire. The resulting genomes depth of coverage ranged from 0.001x to 24x. Their genetic ancestry was found to be Indigenous American without gene flow from external populations such as Europeans, Africans or Polynesians. Unlike Mesoamericans, the "Botocudos" and Amazonians do not seem to have experienced a population expansion once in the Americas. Moreover, remarkably, their genomes exhibit amongst the lowest levels of heterozygosity worldwide and long runs of homozygosity, which could be explained by unique social practices or a very small effective size. Finally, whole genomes of likely ancient pathogens were recovered, including lineages of Human parvovirus B19 that were possibly introduced after the European contact. Significance statementTo better understand the genetic relationship among Indigenous populations in Brazil, we sequenced the genomes of 24 ancient individuals (22 of which labelled as "Botocudos", a term used to describe hunter-gatherer tribes) whose remains were hosted at the Museu Nacional of Rio de Janeiro prior to the tragic fire that consumed it in 2018. Unlike two previously published "Botocudo" genomes, the 22 "Botocudos" from this study have Indigenous American-related ancestry without any Polynesian-related ancestry, and they are similarly related to several Native Brazilian populations. Finally, unlike Eurasian hunter-gatherers, the "Botocudos" exhibit among the lowest heterozygosity and longest runs of homozygosity worldwide - compatible with a very small effective size and suggesting a unique social structure among hunter-gatherers in the Americas.

genomics↗

bioRxiv2021

Benchmarking metagenomics classifiers on ancient viral DNA: a simulation study

Owing to technological advances in ancient DNA, it is now possible to sequence viruses from the past to track down their origin and evolution. However, ancient DNA data is considerably more degraded and contaminated than modern data making the identification of ancient viral genomes particularly challenging. Several methods to characterise the modern microbiome (and, within this, the virome) have been developed; in particular, tools that assign sequenced reads to specific taxa in order to characterise the organisms present in a sample of interest. While these existing tools are routinely used in modern data, their performance when applied to ancient microbiome data to screen for ancient viruses remains unknown. In this work, we conducted an extensive simulation study using public viral sequences to establish which tool is the most suitable to screen ancient samples for human DNA viruses. We compared the performance of four widely used classifiers, namely Centrifuge, Kraken2, DIAMOND and MetaPhlAn2, in correctly assigning sequencing reads to the corresponding viruses. To do so, we simulated reads by adding noise typical of ancient DNA to a set of publicly available human DNA viral sequences and to the human genome. We fragmented the DNA into different lengths, added sequencing error and C to T and G to A deamination substitutions at the read termini. Then we measured the resulting sensitivity and precision for all classifiers. Across most simulations, more than 228 out of the 233 simulated viruses are recovered by Centrifuge, Kraken2 and DIAMOND, in contrast to MetaPhlAn2 which recovers only around one third. Overall, Centrifuge and Kraken2 have the best performance with the highest values of sensitivity and precision. We found that deamination damage has little impact on the performance of the classifiers, less than the sequencing error and the length of the reads. Since Centrifuge can handle short reads (in contrast to DIAMOND and Kraken2 with default settings) and since it achieves the highest sensitivity and precision at the species level across all the simulations performed, it is our recommended tool. Regardless of the tool used, our simulations indicate that, for ancient human studies, users should use strict filters to remove all reads of potential human origin. Finally, we recommend to verify which species are present in the database used, as it might happen that default databases lack sequences for viruses of interest.

genomics↗

bioRxiv2019

A likelihood method for estimating present-day human contamination in ancient DNA samples using low-depth haploid chromosome data

1MotivationThe presence of present-day human contaminating DNA fragments is one of the challenges defining ancient DNA (aDNA) research. This is especially relevant to the ancient human DNA field where it is difficult to distinguish endogenous molecules from human contaminants due to their genetic similarity. Recently, with the advent of high-throughput sequencing and new aDNA protocols, hundreds of ancient human genomes have become available. Contamination in those genomes has been measured with computational methods often developed specifically for these empirical studies. Consequently, some of these methods have not been implemented and tested while few are aimed at low-depth data, a common feature in aDNA datasets.\n\nResultsWe develop a new X-chromosome-based maximum likelihood method for estimating present-day human contamination in low-depth sequencing data. We implement our method for general use, assess its performance under conditions typical of ancient human DNA research, and compare it to previous nuclear data-based methods through extensive simulations. For low-depth data, we show that existing methods can produce unusable estimates or substantially underestimate contamination. In contrast, our method provides accurate estimates for a depth of coverage as low as 0.5x on the X-chromosome when contamination is below 25%. Moreover, our method still yields meaningful estimates in very challenging situations, i.e., when the contaminant and the target come from closely related populations or with increased error rates. With a running time below five minutes, our method is applicable to large scale aDNA genomic studies.\n\nAvailability and implementationThe method is implemented in C++ and R and is freely available in https://github.com/sapfo/contaminationX.\n\nContactmorenomayar@gmail.com, annasapfo.malaspinas@unil.ch.

genomics↗

bioRxiv2017

In-solution Y-chromosome capture-enrichment on ancient DNA libraries

BackgroundAs most ancient biological samples have low levels of endogenous DNA, it is advantageous to enrich for specific genomic regions prior to sequencing. One approach - in-solution capture-enrichment - retrieves sequences of interest and reduces the fraction of microbial DNA. In this work, we implement a capture-enrichment approach targeting informative regions of the Y chromosome in six human archaeological remains excavated in the Caribbean and dated between 200 and 3,000 years BP. We compare the recovery rate of Y-chromosome capture (YCC) alone, whole-genome capture followed by YCC (WGC+Y) versus non-enriched (pre-capture) libraries.\n\nResultsWe recovered 17-4,152 times more targeted unique Y-chromosome sequences after capture, where 0.01-6.2% (WGC+Y) and 0.01-23.5% (YCC) of the sequence reads were on-target, compared to 0.0002-0.004% pre-capture. In samples with endogenous DNA content greater than 0.1%, we found that WGC followed by YCC (WGC+Y) yields lower enrichment due to the loss of complexity in consecutive capture experiments, whereas in samples with lower endogenous content, WGC+Y yielded greater enrichment than YCC alone. Finally, increasing recovery of informative sites enabled us to assign Y-chromosome haplogroups to some of the archeological remains and gain insights about their paternal lineages and origins.\n\nConclusionsWe present to our knowledge the first in-solution capture-enrichment method targeting the human Y-chromosome in aDNA sequencing libraries. YCC and WGC+Y enrichments lead to an increase in the amount of Y-DNA sequences, as compared to libraries not enriched for the Y-chromosome. Our probe design effectively recovers regions of the Y-chromosome bearing phylogenetically informative sites, allowing us to identify paternal lineages with less sequencing than needed for pre-capture libraries. Finally we recommend considering the endogenous content in the experimental design and avoiding consecutive rounds of capture for low-complexity libraries, as clonality increases considerably with each round.

genomics↗