Biology subjects

Gaggiotti, O. E.

Publications and source records attributed to Gaggiotti, O. E..

12 recordsLinked to original sources

bioRxiv2026

LAVA: a method for identifying local and global adaptation in structured populations

Demonstrating that local adaptation drives phenotypic divergence in quantitative traits requires distinguishing selection from neutral differentiation. Existing methods for detecting selection on quantitative traits, particularly QST - FST comparisons, rely on simplified assumptions about population structure. These methods assume equal relatedness among all subpopulations, which is not necessarily true in a real metapopulation. When assumptions are violated, it has been shown that QST - FST leads to elevated false positive rates. Here we present MONET, a model for the neutral evolution of traits that creates a robust baseline against which hypotheses on trait evolution can be tested. We show how the null model provided by MONET can be the basis of two tests: one based on the log-ratio of ancestral variances (logAV) statistics we previously introduced, and another, based on testing the influence of environmental variables from the populations of origin on the phenotypic traits measured in a common garden. MONET is implemented in an R-package as a Bayesian linear mixed-effect framework modeling population structure through relatedness matrices. We show through extensive simulations across different population structures and selective scenarios how our two hypothesis tests based on MONET maintain proper calibration while maintaining or exceeding the power of alternative methods.

evolutionary biology↗

bioRxiv2026

Breath to genome: Whole genome sequencing of large whales from blow sampling

Preserving biodiversity requires the genetic monitoring of wild populations, but traditional invasive sampling techniques can impact animal welfare. For cetaceans, a promising non-invasive approach is the collection of exhaled respiratory vapour, or blow, to generate individual genetic profiles. Here, we demonstrate the feasibility of generating whole genomes from the blow of humpback whales with data of sufficient quality for population genomics. A total of 58 blow samples from 26 Northeast Pacific humpback whales (Megaptera novaeangliae) were collected in Gitgaat First Nation territory using a commercially available drone. The high endogenous content at 84% on average allowed us to generate low-coverage nuclear genomes (mean 2.3x, range 0.2x-3.5x) and high coverage mitochondrial genomes (mean 94x, range 7.7x-151.3x) for inference of population structure, diversity and phylogenomics. The reliability of the blow-derived genomes was demonstrated by direct comparison between replicate blow samples, as well as paired tissue samples from a subset of individuals.

genomics↗

bioRxiv2025

Convergent molecular evolution of viviparity across squamate reptiles

The molecular mechanisms underlying convergent phenotypic innovations across the tree of life are largely unknown. Viviparity (live-bearing) is a complex reproductive mode transition having more than 100 independent origins in squamate reptiles, making it an ideal model to understand the molecular basis of evolutionary innovations. Here we used 141 squamate genomes, including 42 viviparous species and representing more than 18 independent evolutionary transitions to viviparity, to search for convergent molecular signals in 14,096 protein coding genes. We show that convergent positive selection associated with viviparity at the gene-level and amino acid substitutions at the site-level are widespread in squamates, but there is no universal convergence across all viviparous clades. The chance of molecular convergence associated with viviparity is negatively correlated with genetic distance between species. Nevertheless, convergent genes are shared across independent origins of viviparity even spanning deep divergence times, and these genes are enriched in viviparity-related biological processes, including placental development, nutrient transport, and immune response to external organisms. Our result demonstrates that molecular convergence in the evolution of complex traits decreases in likelihood with evolutionary time, but when it occurs is more likely through changes in different genes of similar functional pathway rather than changes in the same genes.

evolutionary biology↗

bioRxiv2025

Genomic signatures of local adaptation across parasitised cricket populations

Novel species interactions provide an opportunity to assess the earliest stages of genetic adaptation in wild populations. We explored the genomic and geographic landscape of adaptation in small, fragmented Hawaiian cricket populations, which are parasitised by larvae of an introduced fly that targets singing males. Multiple protective male-silencing cricket morphs have recently spread despite songs role in mate attraction. Combining population genomics and field selection experiments, we found sharp declines in the crickets effective population size after the flys introduction. Nevertheless, geographical variation in selection influences the distribution of male-silencing alleles, consistent with local adaptation. Populations showing the strongest evidence of recovery retain large numbers of singing-capable males, suggesting that initially adaptive responses to the fly created an evolutionary trap via loss of sexual signalling.

evolutionary biology↗

bioRxiv2025

A method for identifying spatially divergent selection in structured populations.

Species occupy diverse, heterogeneous environments, which expose populations to spatially varied selective pressures. Populations in different environments can diverge due to local adaptation. However, neutral evolution can also drive population divergence. Thus, testing for local adaptation requires a neutral baseline for population differentiation. The classical QST -FST comparison was developed for this purpose. Yet, QST -FST frequently fails to account for the complexities of population structure because the theory underlying this comparison assumes that all subpopulations are equally related, resulting in inflated false positive rates in metapopulations that deviate from the island model. To address this limitation we use estimates of between- and within-population relatedness to model population structure. Using those relatedness matrices, we infer the between- and within-population ancestral additive genetic variances under a mixed-effects model. Under neutrality, these inferred variances are expected to be equal. We propose here a test to detect selection based on the comparison of these two estimates of the ancestral variance and we compare its performance with earlier solutions. We find our method is well calibrated across various population structures and has high power to detect adaptive divergence. Author summaryPopulations of the same species often face different environmental pressures, driving them to adapt locally. However, even in the absence of adaptation, subpopulations can diverge due to random genetic drift and limited migration. Distinguishing between adaptive evolution and random divergence is a central challenge in evolutionary biology. Traditional methods, such as QST -FST comparison, assume equal relatedness among subpopulations--a simplification that rarely holds in complex real-world scenarios, leading to flawed conclusions. To overcome this limitation, we developed a novel method that incorporates genetic relatedness among subpopulations, leveraging quantitative genetic theory to estimate ancestral additive genetic variances. Our approach provides a powerful tool for testing local adaptation, reliably distinguishing adaptive divergence from drift across a variety of population structures.

evolutionary biology↗

bioRxiv2024

The diffusion of cooperative and solo bubble net feeding in Canadian Pacific humpback whales

Cooperative foraging, a mutualistic resource acquisition behaviour observed across diverse taxa, can drive social network structure, as it typically involves high rates of interactions between individuals. Understanding the mechanisms and distribution of such behaviours is important to elucidate the drivers of social organisation. Bubble net feeding ( bubble netting) is a specialised foraging technique practised by certain humpback whale (Megaptera novaeangliae) populations globally. Over 20 years of study in the northern Canadian Pacific, we observed the diffusion of two morphs of this behaviour: social cooperative and independent, or solo, bubble netting. Network-based diffusion analyses - a tool developed to test for social learning - find strong evidence for the social learning of bubble netting when a static social network is used, even after accounting for individual-level traits such as site fidelity and sex (10.6 x103 to 35.4x103 times more support for social versus asocial learning; p < 0.0001). However, we could not completely exclude homophily due to the inherent sociality of this cooperative foraging behaviour. Nonetheless, it is clear that the rapid diffusion of bubble netting throughout this population has significant consequences for its social structure, which should be information considered by those planning the conservation of this threatened population.

animal behavior and cognition↗

bioRxiv2023

Competing adaptations maintain non-adaptive variation in a wild cricket population

How emerging adaptive variants interact is an important factor in the evolution of wild populations. However, the opportunity to empirically study this interaction is rare. We recently documented the emergence of an adaptive phenotype curly-wing in Hawaiian populations of field crickets (Teleogryllus oceanicus). Curly-wing inhibits males ability to sing, protecting them from eavesdropping parasitoid flies (Ormia ochracea). Surprisingly, curly-wing co-occurs with similarly protective silent flatwing phenotypes in multiple populations, in which neither phenotype has spread to fixation. These two phenotypes are frequently co-expressed, but since either phenotype sufficiently reduces song amplitude to evade the fly, co-expression confers no additional fitness benefit. Numerous negative fitness consequences are known to accompany flatwing, and we find that curly-wing, too, incurs fitness costs via reduced male courtship success and reduced female longevity. We show through crosses, genomic and mRNA sequencing that curly-wing expression is associated with variation on a single autosome. In parallel analyses of flatwing, our results reinforce previous findings of X-linked single-locus inheritance, with the phenotype likely arising through down-regulation of doublesex. By combining insights about the genetic architecture of these alternative phenotypes with simulations and field observations, we show that the co-occurrence of these two adaptations impedes either from fixing, despite extreme fitness benefits. Interestingly, both flatwing and curly-wing are statistically associated with nearby inversions, which are also retained as polymorphisms. This co-occurrence of similar adaptive forms in the same populations might be more common than generally considered, and could be an important force inhibiting adaptive evolution in wild populations.

evolutionary biology↗

bioRxiv2022

Ancient dolphin genomes reveal rapid repeated adaptation to coastal waters

Parallel evolution provides among the strongest evidence of the role of natural selection in shaping adaptation to the local environment. Yet, the chronology, mode and tempo of the process of parallel evolution remains broadly debated and discussed in the field of evolutionary biology. In this study, we harness the temporal resolution of paleogenomics to understand the tempo and independence of parallel coastal ecotype adaptation in common bottlenose dolphins (Tursiops truncatus). For this, we generated whole genome resequencing data from subfossil dolphins (8,610-5,626 years BP) originating from around the formation time of new coastal habitat and compared them with data from contemporary populations. Genomic data revealed a shift in genetic affinity, with the oldest ancient sample being closer to the pelagic populations, while the younger samples had intermediate ancestry that showed greater affinity with the local contemporary coastal populations. We found coastal-associated genotypes in the genome of our highest coverage ancient sample, SP1060, providing rare evidence of rapid adaptation from standing genetic variation. Lastly, using admixture graph analyses, we found a reticulate evolutionary history between pelagic and coastal populations. Ancestral gene flow from coastal populations was the probable source of standing genetic variation present in the pelagic populations that enabled rapid adaptation to newly emerged coastal habitat. The genetic response to past climatic warming provides an understanding of how bottlenose dolphins will respond to ongoing directional climate change and shifting coastlines.

evolutionary biology↗

bioRxiv2021

Information-based summary statistics for spatial genetic structure inference

O_LIInference of spatial patterns of genetic structure often relies on parameter estimation and model evaluation using a set of summary statistics (SS) that summarise the information present in the data. An important subset of these SS is best described as diversity indices, which are based on information theory principles that can be classified as belonging to three different families encompassing a spectrum of information measures, qH. These include the richness family of order q = 0, ArSS; the Shannon family of order q = 1, HSS; and the heterozygosity family of order q = 2, HeSS. Although commonly used by ecologists, the Shannon family has been rather neglected by population geneticists and evolutionary biologists. However, recent population genetic studies have advocated their use, yet the power of these SS for spatial structure discrimination has not been systematically assessed. C_LIO_LIIn this study, we performed a comprehensive assessment of the three families of SS, as well as a fourth family consisting of SS belonging to the Shannon family but expressed in terms of Hill numbers [Formula], for spatial structure inference using simulated microsatellites data under typical spatial scenarios. To give an unbiased evaluation, we used three machine learning methods, Kernel Local Fisher discriminant analysis (KLFDA), random forest classification (RFC), and deep neural network (DL), to test the performance of different SS to discriminate between spatial scenarios, and then identified the most informative metrics for discriminatory power. C_LIO_LIResults showed that the SS family of order q = 1 expressed in terms of Hill numbers, [Formula], outperformed the other two families (Ar SS, He SS) as well as the untransformed Shannon entropy (H SS) family. Jaccard dissimilarity (J) and its Mantels r showed the highest discriminatory power to discriminate all spatial scenarios, followed by Shannon differentiation {Delta}D and its Mantels r. C_LIO_LIInformation-based summary statistics, especially the diversity of order q = 1 and Shannon differentiation measures, can increase the power of spatial structure inference. In addition, different sets of SS provide complementary power for discriminating between spatial scenarios. C_LI

genetics↗

bioRxiv2021

Deciphering signatures of natural selection via deep learning

Identifying genomic regions influenced by natural selection provides fundamental insights into the genetic basis of local adaptation. We propose a deep learning-based framework, DeepGenomeScan, that can detect signatures of local adaptation. We demonstrate that DeepGenomeScan outperformed PCA and RDA-based genome scans in identifying loci underlying quantitative traits subject to complex spatial patterns of selection. Noticeably, DeepGenomeScan increases statistical power by up to 47.25% under non-linear environmental selection patterns. We applied DeepGenomeScan to a European human genetic dataset and identified some well-known genes under selection and a substantial number of clinically important genes that were not identified using existing methods.

genomics↗

bioRxiv2021

Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC) significantly improves the accuracy of predicting geographic origin of individuals

Geographic patterns of human genetic variation provide important insights into human evolution and disease. A commonly used tool to detect geographic patterns from genetic data is principal components analysis (PCA) or the supervised linear discriminant analysis of principal components (DAPC). However, genetic features produced from both approaches could fail to correctly characterize population structure for complex scenarios involving admixture. In this study, we introduce Kernel Local Fisher Discriminant Analysis of Principal Components (KLFDAPC), a supervised nonlinear approach for inferring individual geographic genetic structure that could rectify the limitations of these approaches by preserving the multimodal space of samples. We tested the power of KLFDAPC to infer population structure and to predict individual geographic origin using neural networks. Simulation results showed that KLFDAPC significantly improved the population separability compared with PCA and DAPC. The application to POPRES and CONVERGE datasets indicated that the first two reduced features of KLFDAPC correctly recapitulated the geography of individuals, and significantly improved the accuracy of predicting individual geographic origin when compared to PCA and DAPC. Therefore, KLFDAPC can be useful for geographic ancestry inference, design of genome scans and correction for spatial stratification in GWAS that link genes to adaptation or disease susceptibility.

genomics↗

bioRxiv2020

Selection on ancestral genetic variation fuels parallel ecotype formation in bottlenose dolphins

What are the mechanisms that allow species to extend their ranges and adapt to the novel environmental conditions they find in the newly available habitat? The study of parallel adaptation of pairs of populations to similar environments can provide great insights into this question. Here, we test for parallel evolution driven by niche specialization in a highly social marine mammal, the common bottlenose dolphin, Tursiops truncatus, and investigate the origins of the genetic variation driving local adaptation. Coastal ecotypes of common bottlenose dolphins have recurrently emerged in multiple regions of the world from pelagic ecotype populations, when novel habitat became available. Analyzing the whole genomes of 57 individuals using comparative population genomics approaches, we found that coastal ecotype evolution was relatively independent between the Atlantic and Pacific, but related between different regions within the Atlantic. We show that parallel adaptation to coastal habitat was facilitated by repeated selection on ancient alleles present as standing genetic variation in the pelagic populations. Genes under parallel adaptation to coastal habitats have roles in cognitive abilities and feeding. Therefore, parallel adaptation in long-lived social species may be driven by a combination of ecological opportunities, selection acting on ancient variants, and stable behavioural transmission of ecological specialisations. Tried and tested genetic variation that has been subject to repeated bouts of selection, may promote linked adaptive variants with minimal pleiotropic effects, thereby facilitating their persistence at low frequency in source populations and enabling parallel evolution.

evolutionary biology↗