Biology subjects

Caduff, M.

Publications and source records attributed to Caduff, M..

5 recordsLinked to original sources

bioRxiv2026

SweepLink: Joint Inference of Demography and Linked~Selection from Time-series Data

Genome-wide time-series data, i.e. allele frequency trajectories tracked across multiple sampling times, are among the richest sources of information for inferring selection. Beyond a beneficial allele's own rise in frequency, such data capture how it drags nearby loci upward via linkage, an effect known as genetic hitch-hiking. Yet most existing tools are single-locus, treating loci independently: they infer site-specific selection coefficients in isolation, then rely on ad hoc window statistics to account for hitch-hiking. Many existing tools further require a predefined population size, or scale poorly when jointly inferring selection and demography, and their power is highly sensitive to a significance threshold. To address these shortcomings, we here present SweepLink, a two-layer Hidden Markov Model that overcomes these limitations by jointly inferring demography and linked selection genome-wide: a spatial layer captures correlations between neighboring selection coefficients, coupled with a temporal Wright-Fisher diffusion layer. As we show with extensive simulations, this setup pushes drift-driven false signals toward neutrality while reinforcing loci that receive support from neighbouring loci, thereby increasing the sensitivity for weak and moderate selection, while matching the power of existing tools to detect strong selection. These simulations further show that SweepLink yields confident posteriors that remain stable at maximal significance, removing the need for arbitrary thresholds. We applied SweepLink to ancient DNA time-series data from the British population, previously analysed with a single-locus tool. SweepLink recovers four of the previously reported signals (LCT, SLC45A2, DHCR7, HERC2), and partially recovers the MHC/HLA signal. It also identifies additional candidate regions, including DPYD, FADS1/2 and OAS1, missed by the prior scan but supported by independent studies.

evolutionary biology↗

bioRxiv2026

Gene-environment interactions govern early regeneration in fir and beech: evidence from participatory provenance trials across Europe

O_LIForest regeneration is shaped by strong demographic filters during germination and early establishment, yet the relative contributions of provenance and environment remain poorly resolved under natural conditions. C_LIO_LIWe combined climate-chamber trials with a continental-scale, field-based citizen science seed-sowing experiment to quantify germination, early phenological development, and three-year post-germination survival fir (Abies spp.) and beech (Fagus spp.) provenances across Europe. Using mixed-effects models and a state-based Markov framework to reconstruct phenological trajectories, we quantified successive demographic filters from seed to established seedling. C_LIO_LIGermination was structured by genotype-by-environment interactions (G x E) in both genera, but with contrasting expression: fir showed broadly parallel environmental responses among provenances and partial concordance between climate-chamber and field, whereas beech showed strong provenance-by-environment responses and weak predictability from controlled conditions. Seed weight was a key axis in beech (lighter seeds germinated more and developed faster), but confounded with geographic differentiation in fir. C_LIO_LIIn contrast to germination, post-establishment survival was dominated by site-level environmental filtering with limited consistent provenance effects, revealing a life-stage shift from fine-scale G x E during germination and early development to broader environmental control of survival--an important consideration for regeneration--based management and assisted migration. C_LI

evolutionary biology↗

bioRxiv2025

Testing for changes in population trends from low-cost ecological count data

O_LIAccurate and up-to-date knowledge of population trends is essential for effective biodiversity conservation, as is assessing the impact of conservation measures designed to alter these trends. Estimating population trends is challenging, however, either due to altogether insufficient data or due to so-called noisy data that do not readily allow for standard statistical analyses. In addition, many existing methods require monitoring data over long periods of time, which is in contrast to the quick interventions needed by conservation projects, especially when endangered species are involved. C_LIO_LITo address these issues, we here present birp, a novel Bayesian tool that maximizes the power to test for population trends and changes in trends under arbitrary designs, including the canonical before-after (BA), control-intervention (CI) and before-after-control-intervention (BACI) designs often used to assess conservation impact. Our model builds on classic Poisson and negative binomial models for ecological count data and infers changes in population trends jointly from data obtained with multiple survey methods such as track counts, camera trap surveys, or distance sampling, and also from limited and noisy data not necessarily collected in standardized ecological surveys. By focusing on the change itself, our method side-steps common challenges of estimating population trends and does not need to know about absolute population densities or detection probabilities. birp is open-source and available as both a standalone command line tool as well as an R-package for fast and easy use. C_LIO_LIWe illustrate the power of our tool through extensive simulations and show that changes in trends are accurately estimated under various designs, even when data are noisy and sparse, and thereby enables biodiversity research also in regions that are remote and difficult to access. C_LIO_LIUsing birp, we further test for changes in population trends of Tasmanian devils in Australia and of apex predators and their main prey in the Central African Republic. Based on these results we give general guidelines on survey designs that maximize the power to detect trends. C_LI

ecology↗

bioRxiv2023

Inference of Locus-Specific Population Mixtures From Linked Genome-Wide Allele Frequencies

1Admixture between populations and species is common in nature. Since the influx of new genetic material might be either facilitated or hindered by selection, variation in mixture proportions along the genome is expected in organisms undergoing recombination. Various graph-based models have been developed to better understand these evolutionary dynamics of population splits and mixtures. However, current models assume a single mixture rates for the entire genome and do not explicitly account for linkage. Here, we introduce TreeSwirl, a novel method for inferring branch lengths and locus-specific mixture proportions by using genome-wide allele frequency data, assuming that the admixture graph is known or has been inferred. TreeSwirl builds upon TreeMix that uses Gaussian processes to estimate the presence of gene flow between diverged populations. However, in contrast to TreeMix, our model infers locus-specific mixture proportions employing a Hidden Markov Model that accounts for linkage. Through simulated data, we demonstrate that TreeSwirl can accurately estimate locus-specific mixture proportions and handle complex demographic scenarios. It also outperforms related D- and f-statistics in terms of accuracy and sensitivity to detect introgressed loci.

genomics↗

bioRxiv2023

Accurate Bayesian inference of sex chromosome karyotypes and sex-linked scaffolds from low-depth sequencing data

1The identification of sex-linked scaffolds and the genetic sex of individuals, i.e. their sex karyotype, is a fundamental step in population genomic studies. If sex-linked scaffolds are known, single individuals may be sexed based on read counts of next-generation sequencing data. If both sex-linked scaffolds as well as sex karyotypes are unknown, as is often the case for non-model organisms, they have to be jointly inferred. For both cases, current methods rely on arbitrary thresholds, which limits their power for low-depth data. In addition, most current methods are limited to euploid sex karyotypes (XX and XY). Here we develop BeXY, a fully Bayesian method to jointly infer the posterior probabilities for each scaffold to be autosomal, X-or Y-linked and for each individual to be any of the sex karyotypes XX, XY, X0, XXX, XXY, XYY and XXYY. If the sex-linked scaffolds are known, it also identifies autosomal trisomies and estimates the sex karyotype posterior probabilities for single individuals. As we show with downsampling experiments, BeXY has higher power than all existing methods. It accurately infers the sex karyotype of ancient human samples with as few as 20,000 reads and accurately infers sex-linked scaffolds from data sets of just a handful of samples or with highly imbalanced sex ratios, also in the case of low-quality reference assemblies. We illustrate the power of BeXY by applying it to both whole-genome shotgun and target enrichment sequencing data of ancient and modern humans, as well as several non-model organisms.

genomics↗