Biology subjects

Caliendo, C.

Publications and source records attributed to Caliendo, C..

3 recordsLinked to original sources

bioRxiv2025

Influence of geography, seasonality and experimental selection on Chironomus riparius recombination rates

BackgroundUnderstanding recombination rates is crucial in evolutionary biology, as recombination shapes genetic diversity, natural selection, and adaptation. We investigated recombination rate variation in Chironomus riparius across different latitudes, seasons, and experimental treatments using Pool-seq data from five studies and the ReLERNN neural network-based method. We examined its relationship with genetic diversity, GC content, and FST, assessing causality through structural equation modeling. ResultsIn natural populations, recombination rates showed no clear latitudinal pattern, likely due to interactions between climate-driven selection and regional environmental heterogeneity. However, seasonal variation was evident, with higher recombination rates in autumn than winter, possibly due to temperature-induced plasticity or seasonal bottlenecks. A cold snap in March 2018 triggered a sharp recombination increase, potentially suggesting a stress-induced adaptive response. In experimental populations, thermal regimes had no direct effect on recombination, but adaptation to lab conditions was significant. Environmental stressors produced distinct responses: microplastic exposure reduced recombination genome-wide, likely due to stress-induced DNA repair prioritizing genome integrity, while cadmium exposure generally suppressed recombination. ConclusionsOur findings reveal recombination as a highly dynamic process influenced by environment, selection, and genetic background, underscoring the importance of the context in shaping genomic architecture under both natural and experimental conditions.

evolutionary biology↗

bioRxiv2024

Enhancing Detection of Polygenic Adaptation: A Comparative Study of Machine Learning and Statistical Approaches Using Simulated Evolve-and-Resequence Data

Detecting signals of polygenic adaptation remains a significant challenge in population genomics, as traditional methods often struggle to identify the associated subtle, multi-locus allele-frequency shifts. Here, we introduced and tested several novel approaches combining machine learning techniques with traditional statistical tests to detect polygenic adaptation patterns in time-series of allele frequency changes from whole genome data. We implemented a Naive Bayesian Classifier (NBC) and One-Class Support Vector Machines (OCSVM), and compared their performance against the classical Fishers Exact Test (FET). Furthermore, we combined machine learning and statistical models (OCSVM-FET and NBC-FET), resulting in 5 competing approaches. Using a simulated data set based on empirical evolve-and-resequencing Chironomus riparius genomic data, we evaluated methods across evolutionary scenarios, varying in generations, selection strength and numbers of loci under selection. Our results demonstrate that the combined OCSVM-FET approach consistently outperformed competing methods, achieving the lowest false positive rate, highest area under the curve, and high accuracy. The performance peak aligned with what we term the late dynamic phase of adaptation--the period after initial selection has occurred but before fixation--highlighting the methods sensitivity to ongoing selective processes and thus its value for experimental approaches. Furthermore, we emphasize the critical role of parameter tuning, balancing biological assumptions with methodological rigor. Our approach offers a powerful tool for detecting polygenic adaptation from time series, e.g. pool sequencing data from evolve-and-resequence experiments.

genetics↗

bioRxiv2020

Genomic basis of drought resistance in Fagus sylvatica

In the course of global climate change, central Europe is experiencing more frequent and prolonged periods of drought. The drought years 2018 and 2019 affected European beeches (Fagus sylvatica L.) differently: even in the same stand, drought damaged trees neighboured healthy trees, suggesting that the genotype rather than the environment was responsible for this conspicuous pattern. We used this natural experiment to study the genomic basis of drought resistance with Pool-GWAS. Contrasting the extreme phenotypes identified 106 significantly associated SNPs throughout the genome. Most annotated genes with associated SNPs (>70%) were previously implicated in the drought reaction of plants. Non-synonymous substitutions led either to a functional amino acid exchange or premature termination. A SNP-assay with 70 loci allowed predicting drought phenotype in 98.6% of a validation sample of 92 trees. Drought resistance in European beech is a moderately polygenic trait that should respond well to natural selection, selective management, and breeding. Impact StatementEuropean beech harbours substantial genetic variation at genomic loci associated with drought resistance and the loci identified in this study can help to accelerate and monitor adaptation to climate change.

plant biology↗