Clemens L. Weiß

Genetic diversity within species underpins evolutionary adaptation and has recently been included as a target for protection in the United Nations’ Global Biodiversity Framework (GBF). Yet, we lack mathematical tools to estimate past genetic diversity loss across species—or predict future losses—based on demographic proxies used in conversation policy. To fill this gap, we developed a spatiotemporal framework to predict the dynamics of genetic diversity under realistic habitat change scenarios, calibrated with population-scale genomic data from 29 plant and animal species. To estimate how much genetic diversity has already been lost, we analyzed habitat area and population size losses for 4,611 species from the last five decades, using data from the Living Planet Index, the Red List, and new GBF indicators. We estimate that species have already lost 1 to 13% of π genetic diversity. Furthermore, we predict that genetic diversity losses lag behind population and habitat area declines, such that an average of 6 to 45% of genetic diversity will be lost under different scenarios even if population sizes or habitats do not decline further. Our results highlight that safeguarding existing habitats is insufficient to maintain the genetic health of species, and that genetic monitoring from proxy indicators will only detect major genetic diversity losses after it is already too late.

Extrachromosomal DNA (ecDNA) are circularized genomic elements that reside outside canonical chromosomes. ecDNA amplify oncogene copy number, enhance chromatin accessibility, and act as mobile enhancers through cis- and trans-regulatory interactions, collectively boosting oncogene expression. ecDNA has been implicated in tumor progression, intratumoral heterogeneity, and poor patient prognosis. Despite various lines of evidence that ecDNA promotes aggressive disease, the mechanisms and selective pressures leading to ecDNA formation and propagation remain poorly understood as are their structures. While several computational tools have been developed to infer ecDNA presence or absence from short read sequencing data, accurate identification of large or complex ecDNA structures remains challenging. Here we introduce ecDNAInspector, a novel computational framework to systematically assess the confidence of ecDNA predictions from existing inference tools. Leveraging abundant short-read whole genome sequencing (WGS) data from population-scale cohorts, we demonstrate that ecDNAInspector accurately identifies high-confidence ecDNA calls, improving interpretability and facilitating the association with clinical features. As an illustrative example, applied to a cohort of 250 breast cancers, ecDNAInspector identifies associations between ecDNA structure and molecular subgroups of disease. These findings are supported by orthogonal omic data and experimental characterization of ecDNA captured in representative cell lines. ecDNAInspector provides a scalable, data-driven approach to characterize ecDNA structure, enabling integrative studies of the clinical and biological impact of this non-mendelian mode of oncogene amplification and inheritance.

Natural selection on complex traits is difficult to study in part due to the ascertainment inherent to genome-wide association studies (GWAS). The power to detect a trait-associated variant in GWAS is a function of its frequency and effect size – but for traits under selection, the effect size of a variant determines the strength of selection against it, constraining its frequency. Recognizing the biases inherent to GWAS ascertainment, we propose studying the joint distribution of allele frequencies across populations, conditional on the frequencies in the GWAS cohort. Before considering these conditional frequency spectra, we first characterized the impact of selection and non-equilibrium demography on allele frequency dynamics forwards and backwards in time. We then used these results to understand conditional frequency spectra under realistic human demography. Finally, we investigated empirical conditional frequency spectra for GWAS variants associated with 106 complex traits, finding compelling evidence for either stabilizing or purifying selection. Our results provide insights into polygenic score portability and other properties of variants ascertained with GWAS, highlighting the utility of conditional frequency spectra.

Dissecting plant responses to the environment is key to understanding if and how plants adapt to anthropogenic climate change. Stomata, plants’ pores for gas exchange, are expected to decrease in density following increased CO ₂ concentrations, a trend already observed in multiple plant species. However, it is unclear if such responses are based on genetic changes and evolutionary adaptation. Here we make use of extensive knowledge of 43 genes in the stomatal development pathway and newly generated genome information of 191 A. thaliana historical herbarium specimens collected over the last 193 years to directly link genetic variation with climate change. While we find that the essential transcription factors SPCH, MUTE and FAMA, central to stomatal development, are under strong evolutionary constraints, several regulators of stomatal development show signs of local adaptation in contemporary samples from different geographic regions. We then develop a polygenic score based on known effects of gene knock-out on stomatal development that recovers a classic pattern of stomatal density decrease over the last centuries without requiring direct phenotype observation of historical samples. This approach combining historical genomics with functional experimental knowledge could allow further investigations of how different, even in historical samples unmeasurable, cellular plant phenotypes have already responded to climate change through adaptive evolution. One sentence summary: Using a molecular-knowledge based genetic phenotype proxy, historical whole-genome A. thaliana timelines compared with contemporary data indicate a shift of stomatal density following climate-associated predictions.

Anthropogenic habitat loss and climate change are reducing species’ geographic ranges, increasing extinction risk and losses of species’ genetic diversity. Although preserving genetic diversity is key to maintaining species’ adaptability, we lack predictive tools and global estimates of genetic diversity loss across ecosystems. We introduce a mathematical framework that bridges biodiversity theory and population genetics to understand the loss of naturally occurring DNA mutations with decreasing habitat. By analyzing genomic variation of 10,095 georeferenced individuals from 20 plant and animal species, we show that genome-wide diversity follows a mutations-area relationship power law with geographic area, which can predict genetic diversity loss from local population extinctions. We estimate that more than 10% of genetic diversity may already be lost for many threatened and nonthreatened species, surpassing the United Nations’ post-2020 targets for genetic preservation.

Ancient DNA research in the past decade has revealed that European population structure changed dramatically in the prehistoric period (14,000-3,000 years before present, YBP), reflecting the widespread introduction of Neolithic farmer and Bronze Age Steppe ancestries. However, little is known about how population structure changed from the historical period onward (3,000 YBP - present). To address this, we collected whole genomes from 204 individuals from Europe and the Mediterranean, many of which are the first historical period genomes from their region (e.g. Armenia and France). We found that most regions show remarkable inter-individual heterogeneity. At least 7% of historical individuals carry ancestry uncommon in the region where they were sampled, some indicating cross-Mediterranean contacts. Despite this high level of mobility, overall population structure across western Eurasia is relatively stable through the historical period up to the present, mirroring geography. We show that, under standard population genetics models with local panmixia, the observed level of dispersal would lead to a collapse of population structure. Persistent population structure thus suggests a lower effective migration rate than indicated by the observed dispersal. We hypothesize that this phenomenon can be explained by extensive transient dispersal arising from drastically improved transportation networks and the Roman Empire’s mobilization of people for trade, labor, and military. This work highlights the utility of ancient DNA in elucidating finer scale human population dynamics in recent history.

The Iron Age was a dynamic period in central Mediterranean history, with the expansion of Greek and Phoenician colonies and the growth of Carthage into the dominant maritime power of the Mediterranean. These events were facilitated by the ease of long-distance travel following major advances in seafaring. We know from the archaeological record that trade goods and materials were moving across great distances in unprecedented quantities, but it is unclear how these patterns correlate with human mobility. To investigate population mobility and interactions directly, we sequenced the genomes of 30 ancient individuals from coastal cities around the central Mediterranean, in Tunisia, Sardinia, and central Italy. We observe a meaningful contribution of autochthonous populations, as well as highly heterogeneous ancestry including many individuals with non-local ancestries from other parts of the Mediterranean region. These results highlight both the role of local populations and the extreme interconnectedness of populations in the Iron Age Mediterranean. By studying these trans-Mediterranean neighbors together, we explore the complex interplay between local continuity and mobility that shaped the Iron Age societies of the central Mediterranean.

Background: The identification of bona fide microbial taxa in microbiomes derived from ancient and historical samples is complicated by the unavoidable mixture between DNA from ante- and post-mortem microbial colonizers. One possibility to distinguish between these sources of microbial DNA is querying for the presence of age-associated degradation patterns typical of ancient DNA (aDNA). The presence of uracils, resulting from cytosine deamination, has been detected ubiquitously in aDNA retrieved from diverse sources, and used as an authentication criterion. Here, we employ a library preparation method that separates molecules that carry uracils from those that do not for a set of samples that includes Neandertal remains, herbarium specimens and archaeological plant remains. Results: We show that sequencing DNA libraries enriched in molecules carrying uracils effectively amplifies age associated degradation patterns in microbial mixtures of ancient and historical origin. This facilitates the discovery of authentic ancient microbial taxa in cases where degradation patterns are difficult to detect due to large sequence divergence in microbial mixtures. Additionally, the relative enrichment of taxa in the uracil enriched fraction can help to identify bona fide ancient microbial taxa that could be missed using a more targeted approach. Conclusions: Our experiments show, that in addition to its use in enriching authentic endogenous DNA of organisms of interest, the selective enrichment of damaged DNA molecules can be a valuable tool in the discovery of ancient microbial taxa.

Species’ responses at the genetic level are key to understanding the long‐term consequences of anthropogenic global change. Herbaria document such responses, and, with contemporary sampling, provide high‐resolution time‐series of plant evolutionary change. Characterizing genetic diversity is straightforward for model species with small genomes and a reference sequence. For nonmodel species—with small or large genomes—diversity is traditionally assessed using restriction‐enzyme‐based sequencing. However, age‐related DNA damage and fragmentation preclude the use of this approach for ancient herbarium DNA. Here, we combine reduced‐representation sequencing and hybridization‐capture to overcome this challenge and efficiently compare contemporary and historical specimens. Specifically, we describe how homemade DNA baits can be produced from reduced‐representation libraries of fresh samples, and used to efficiently enrich historical libraries for the same fraction of the genome to produce compatible sets of sequence data from both types of material. Applying this approach to both Arabidopsis thaliana and the nonmodel plant Cardamine bulbifera, we discovered polymorphisms de novo in an unbiased, reference‐free manner. We show that the recovered genetic variation recapitulates known genetic diversity in A. thaliana, and recovers geographical origin in both species and over time, independent of bait diversity. Hence, our method enables fast, cost‐efficient, large‐scale integration of contemporary and historical specimens for assessment of genome‐wide genetic trends over time, independent of genome size and presence of a reference genome.

Background: The identification of bona fide microbial taxa in microbiomes derived from ancient and historical samples is complicated by the unavoidable mixture between DNA from ante- and post-mortem microbial colonizers. One possibility to distinguish between these sources of microbial DNA is querying for the presence of age-associated degradation patterns typical of ancient DNA (aDNA). The presence of uracils, resulting from cytosine deamination, has been detected ubiquitously in aDNA retrieved from diverse sources, and used as an authentication criterion. Here, we employ a library preparation method that separates molecules that carry uracils from those that do not for a set of samples that includes Neandertal remains, herbarium specimens and archaeological plant remains. Results: We show that sequencing DNA libraries enriched in molecules carrying uracils effectively amplifies age associated degradation patterns in microbial mixtures of ancient and historical origin. This facilitates the discovery of authentic ancient microbial taxa in cases where degradation patterns are difficult to detect due to large sequence divergence in microbial mixtures. Additionally, the relative enrichment of taxa in the uracil enriched fraction can help to identify bona fide ancient microbial taxa that could be missed using a more targeted approach. Conclusions: Our experiments show, that in addition to its use in enriching authentic endogenous DNA of organisms of interest, the selective enrichment of damaged DNA molecules can be a valuable tool in the discovery of ancient microbial taxa.