Biology subjects

DeGiorgio, M.

Publications and source records attributed to DeGiorgio, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Multi-scale haplotype signatures distinguish among evolutionary processes

Natural selection embeds distinct genomic signatures as populations adapt to environmental pressures. Machine learning approaches, particularly deep neural networks, have emerged as powerful tools to detect adaptive signatures from genomic data. While machine learning has dramatically improved our ability to extract these signals from sequence variation, distinguishing among multiple evolutionary processes remains challenging. We introduce SHADES, a deep learning framework that employs information about sequence differences and their frequencies at various spatial resolutions to capture evolutionary signatures at different genomic scales. We found that SHADES has the capacity to distinguish among neutrality, selective sweeps, balancing selection, and adaptive introgression and outperformed several contemporary methods evaluated for both classification accuracy and power. In addition, SHADES remained robust to a number of confounding factors, with only modest changes in classification performance. Furthermore, application of SHADES to empirical haplotypic variation from European humans recovered previously reported signals for selective sweeps, balancing selection, and adaptive introgression, while also identifying novel genes broadly involved in immune defense, inflammatory regulation, and epithelial barrier biology.

genomics↗

bioRxiv2026

Integrative genetic and transcriptomic analysis identifies candidate genes and compounds for Fragile X syndrome and autism spectrum disorder

Fragile X syndrome (FXS) is the most commonly known monogenic cause of autism spectrum disorder (ASD), yet how FXS biology relates to polygenic architecture of idiopathic ASD remains poorly defined. We integrated publicly available genetic data relevant to ASD and FXS, including population case-control risk, symptom discordance within monozygotic twin pairs, contrasts between ASD and other psychiatric disorders, and genetic variation associated with circulating plasma fragile X messenger ribonucleoprotein (FMRP) abundance. These data were used to identify associated genes and pathways across tissues and prioritize small molecules predicted to reverse genetically associated transcriptional signatures. Cross-tissue analysis identified XRN2 as the strongest association for population ASD risk and PPP3CA as the strongest association with FMRP abundance, providing distinct candidate links to ASD and FXS biology. We found no evidence that population ASD risk and symptom discordance within monozygotic twin pairs share common-variant architecture after accounting for linkage disequilibrium, suggesting that these phenotypes capture at least partially distinct sources of genetic signal. Connectivity mapping identified candidate compounds across eight genetic signatures, including several with biologically plausible mechanisms. Together, these findings provide a genetics-anchored framework for connecting FXS and ASD biology and for prioritizing candidate therapeutic mechanisms and compounds, while highlighting methodological safeguards needed for reliable integrative genomic drug repurposing.

bioinformatics↗

bioRxiv2026

Bacterial DNA invasion triggers transposable element proliferation and genome expansion

Genome size variation in eukaryotes is driven largely by transposable elements (TEs), yet the biological mechanisms that initiate their proliferation remain understudied. Here, we identify a recurrent association between bacterial horizontal gene transfer (HGT) and bursts of TE activity that contribute to genome expansion. By leveraging comparative genomics and genus-level pangenome analyses across three species of the nut weevil, Curculio, we detect extensive bacterially derived DNA sequences embedded within structurally dynamic genomic regions. These HGT-associated regions are dominated by a small number of young, proliferating TE families, particularly DNA type II Mavericks, which encapsulate transferred bacterial sequences and comprise a substantial fraction of recent genomic DNA in derived lineages. Analyses of codon usage bias, intron length, and functional enrichment suggest that most transferred genes undergo progressive pseudogenization over evolutionary time, whereas a subset of selectively advantageous HGTs persist. Together, our findings support a model linking foreign DNA invasion with TE proliferation, genome size variation, and molecular innovation.

genomics↗

bioRxiv2025

Genomic insights into ancestry and infectious disease in 17th-century colonial Brazil

During urban redevelopment in the historic district of a Brazilian metropolis, archaeologists uncovered a previously undocumented 17th-century cemetery, containing the closely spaced remains of over two dozen young adult males of military age. Historical records suggest the site once housed a makeshift hospital, raising questions about the origins and causes of death of those interred, particularly given the absence of skeletal evidence for violent or fatal trauma. The current study integrates bioarchaeological, historical, and genomic data to investigate the ancestry and putative disease-related mortality of nine individuals whose remains were recovered and subsequently sequenced. Population-genetic analyses revealed strong affinities with Northern European populations, particularly from Norway, Iceland, Estonia, and Czechoslovia, consistent with their likely roles as soldiers or laborers employed by the Dutch West India Company. To explore potential causes of death, we conducted a metagenomic screening with a novel pipeline optimized for degraded DNA, which revealed widespread presence of Klebsiella pneumoniae and Mycobacterium tuberculosis pathogens across all samples. Authenticity was confirmed through post-mortem damage patterns characteristic of historical samples. These findings, together with the absence of combat trauma and the collective burial context at the site, support the hypothesis of an epidemic-related mortality event. This study contributes to the growing field of historical pathogen genomics and offers a rare genomic perspective on life, mobility, and health during a period of colonial upheaval in South America.

evolutionary biology↗

bioRxiv2025

Semi-supervised detection of natural selection with positive-unlabeled learning

Identifying genomic regions shaped by natural selection is a central goal in evolutionary ge-nomics. Existing machine learning methods for this task are typically trained using simulated genomic data labeled according to specific evolutionary scenarios. While effective in controlled settings, these models are limited by their reliance on explicit class labels. They can only detect the specific processes they were trained to recognize, making it difficult to interpret predictions for regions influenced by other evolutionary forces. This limitation is especially problematic when analyzing empirical genomes shaped by a mixture of adaptive, demographic, and ecolog-ical factors. One-vs.-rest strategies offer a potential alternative, but suffer from the inherent complexity of modeling all other evolutionary and demographic processes as a catch-all "rest" class. Here, we explore positive-unlabeled learning as a more flexible framework for detection of adaptive events. Positive-unlabeled learning is a semi-supervised approach that permits iden-tification of samples of a target class using only positive labels and an unlabeled background, without requiring explicit modeling of negative samples. To assess the utility of this approach, we focus on a binary classification setting for detecting selective sweeps (positive samples) aris-ing from positive natural selection against a mixed background composed of both unlabeled sweeps and neutrally-evolving regions. To accomplish this goal, we introduce PULSe, a method that employs only a set of labeled sweep observations for training while treating all remaining data as unlabeled. By avoiding assumptions about the composition of the background, PULSe enables robust sweep discovery in realistic genomic landscapes. We systematically evaluate its performance across a range of demographic, adaptive, and confounding contexts, including domain shift arising from misspecified demographic models, and find that PULSe delivers high performance and generalizability. To demonstrate a practical application of PULSe, we ana-lyzed European and Bengali in Bangladesh human genomes, treating the empirical genomes as unlabeled sets, and recapitulating several previously-identified sweep candidates. Our results show that PULSe provides a powerful and versatile alternative for detecting adaptive regions, with the potential to generalize across a range of genomic landscapes.

genomics↗

bioRxiv2025

Discriminating models of trait evolution

AO_SCPLOWBSTRACTC_SCPLOWA central challenge in comparative biology is linking present-day trait variation across species with unobserved evolutionary processes that occurred in the past. In this endeavor, phylogenetic comparative methods are invaluable for fitting, comparing, and selecting evolutionary models of varying complexity and biological meaning. Traditionally, evolutionary studies have relied on conventional statistical approaches to assess model fit and identify the one that best explains variation in a given trait. Here we explore an alternative strategy by applying supervised learning to predict evolutionary models via discriminant analysis. We formally introduce Evolutionary Discriminant Analysis (EvoDA) as an addition to the biologists toolkit, offering a suite of new methods for studying trait evolution. We evaluate the performance of EvoDA alongside conventional model selection through a series of fungal phylogeny case studies, each targeting increasingly challenging analytical tasks. These results showcase the strengths of EvoDA, with substantial improvements over conventional approaches when studying traits subject to measurement error, which likely reflect realistic conditions in empirical datasets. To complement our simulation-based benchmarking, we explore the application of EvoDA for tackling a notoriously difficult task: predicting the mode and tempo of gene expression evolution. This empirical analysis suggests that stabilizing selection acts on a majority of genes, with bursts of expression evolution in a handful of genes related to stress, cellular transportation, and transcription regulation. Collectively, our findings illustrate the promise of EvoDA for predicting trait models across a range of evolutionary and experimental contexts, establishing a new methodological framework for the next era of comparative research. SO_SCPLOWIGNIFICANCEC_SCPLOW SO_SCPLOWTATEMENTC_SCPLOWTo make sense of biodiversity, evolutionary studies have historically relied on conventional statistical procedures to evaluate competing hypotheses about the mode and tempo of trait evolution. Here, we introduce new supervised learning methods that substantially outperform traditional techniques for correctly assigning trait models across a range of evolutionary and experimental conditions. We find that these methods are highly robust to measurement noise expected from realistic trait data and offer new insights into a central question in comparative genomics: what are the evolutionary forces shaping variation in gene expression across species?

evolutionary biology↗

bioRxiv2025

Efficient detection and characterization of targets of natural selection using transfer learning

Natural selection leaves detectable patterns of altered spatial diversity within genomes, and identifying affected regions is crucial for understanding species evolution. Recently, machine learning approaches applied to raw population genomic data have been developed to uncover these adaptive signatures. Convolutional neural networks (CNNs) are particularly effective for this task, as they handle large data arrays while maintaining element correlations. However, shallow CNNs may miss complex patterns due to their limited capacity, while deep CNNs can capture these patterns but require extensive data and computational power. Transfer learning addresses these challenges by utilizing a deep CNN pre-trained on a large dataset as a feature extraction tool for downstream classification and evolutionary parameter prediction. This approach reduces extensive training data generation requirements and computational needs while maintaining high performance. In this study, we developed TrIdent, a tool that uses transfer learning to enhance detection of adaptive genomic regions from image representations of multilocus variation. We evaluated TrIdent across various genetic, demographic, and adaptive settings, in addition to unphased data and other confounding factors. TrIdent demonstrated improved detection of adaptive regions compared to recent methods using similar data representations. We further explored model interpretability through class activation maps and adapted TrIdent to infer selection parameters for identified adaptive candidates. Using whole-genome haplotype data from European and African populations, TrIdent effectively recapitulated known sweep candidates and identified novel cancer, and other disease-associated genes as potential sweeps.

evolutionary biology↗

bioRxiv2024

A tale of too many trees: a conundrum for phylogenetic regression

AO_SCPLOWBSTRACTC_SCPLOWJust exactly which tree(s) should we assume when testing evolutionary hypotheses? This question has plagued comparative biologists for decades. Given a perfectly estimated tree (if this is even possible in practice), we seldom know with certainty whether such a tree is truly best (or even adequate) to represent the evolutionary history of our studied traits. Regardless of our certainty, choosing a tree is required for all phylogenetic comparative methods. Yet, phylogenetic conflict and error are ubiquitous in modern comparative biology, and we are still learning about their dangers when testing evolutionary hypotheses. Here we investigated the consequences of gene tree-species tree mismatch for phylogenetic regression in the presence of incomplete lineage sorting. Our simulation experiments reveal excessively high false positive rates for mismatched phylogenetic regression with both small and large trees, simple and complex traits, and known and estimated phylogenies. In some cases, we find evidence of a directionality of error: incorrectly assuming a species tree for traits that evolved according to a gene tree sometimes fares worse than the opposite. To explore difficult yet realistic regression scenarios, we also used estimated rather than known trees to conduct case studies, as well as an expansive gene expression dataset to investigate an arguably best-case scenario in which one may have a better chance to match tree with trait. Though never meant to be a panacea for all that may ail phylogenetic comparative methods, we found promise in the application of a robust estimator as a potential, albeit imperfect, solution to some issues raised by tree mismatch, perhaps offering a path forward. Collectively, our results emphasize the importance of careful study design for comparative methods, highlighting the need to fully appreciate the role of adequate phylogenetic modeling for testing evolutionary hypotheses.

evolutionary biology↗

bioRxiv2023

Genomic analyses correspond with deep persistence of peoples of Blackfoot Confederacy from glacial times

Mutually beneficial partnerships between genomics researchers and North American Indigenous Nations are rare. Here, we present one such partnership that provides insight into the peopling of the Americas and furnishes a new line of evidence that can be used to further treaty and Indigenous rights. We show that the genomics of sampled individuals from the Blackfoot Confederacy belong to a previously undescribed ancient lineage that diverged from other genomic lineages in the Americas in Late Pleistocene times. Using multiple complementary forms of knowledge, we provide a scenario for Blackfoot population history that fits with oral tradition and provides a plausible model for the evolutionary process of the peopling of the Americas. One-Sentence SummaryPreviously unknown genomic lineage in North America revealed in present-day Indigenous community and historic ancestors.

genomics↗

bioRxiv2023

Tensor decomposition based feature extraction and classification to detect natural selection from genomic data

Inferences of adaptive events are important for learning about traits, such as human digestion of lactose after infancy and the rapid spread of viral variants. Early efforts toward identifying footprints of natural selection from genomic data involved development of summary statistic and likelihood methods. However, such techniques are grounded in simple patterns or theoretical models that limit the complexity of settings they can explore. Due to the renaissance in artificial intelligence, machine learning methods have taken center stage in recent efforts to detect natural selection, with strategies such as convolutional neural networks applied to images of haplotypes. Yet, limitations of such techniques include estimation of large numbers of model parameters under non-convex settings and feature identification without regard to location within an image. An alternative approach is to use tensor decomposition to extract features from multidimensional data while preserving the latent structure of the data, and to feed these features to machine learning models. Here, we adopt this framework and present a novel approach termed T-REx, which extracts features from images of haplotypes across sampled individuals using tensor decomposition, and then makes predictions from these features using classical machine learning methods. As a proof of concept, we explore the performance of T-REx on simulated neutral and selective sweep scenarios and find that it has high power and accuracy to discriminate sweeps from neutrality, robustness to common technical hurdles, and easy visualization of feature importance. Therefore, T-REx is a powerful addition to the toolkit for detecting adaptive processes from genomic data.

genomics↗

bioRxiv2022

Uncovering footprints of natural selection through time-frequency analysis of genomic summary statistics

Natural selection leaves a spatial pattern along the genome, with a distortion in the haplotype distribution near the selected locus that becomes less prominent with increasing distance from the locus. Evaluating the spatial signal of a population-genetic summary statistic across the genome allows for patterns of natural selection to be distinguished from neutrality. Different summary statistics highlight diverse components of genetic variation and, therefore, considering the genomic spatial distribution of multiple summary statistics is expected to aid in uncovering subtle signatures of selection. In recent years, numerous methods have been devised that jointly consider genomic spatial distributions across summary statistics, utilizing both classical machine learning and contemporary deep learning architectures. However, better predictions may be attainable by improving the way in which features used as input to machine learning algorithms are extracted from these summary statistics. To achieve this goal, we apply three time-frequency analysis approaches (wavelet transform, multitaper spectral analysis, and S-transform) to summary statistic arrays. Each analysis method converts a one-dimensional summary statistic arrays to a two-dimensional image of spectral density or visual representation of time-frequency analysis, permitting the simultaneous assessment of temporal and spectral information. We use these images as input to convolutional neural networks and consider combining models across different time-frequency representation approaches through the ensemble stacking technique. Application of our modeling framework to data simulated from neutral and selective sweep scenarios reveals that it achieves almost perfect accuracy and power across a diverse set of evolutionary settings, including population size changes and test sets for which sweep strength, softness, and timing parameters were drawn from a wide range. Moreover, a scan of whole-genome sequencing of central European humans recapitulated previous well-established sweep candidates, as well as predicts novel cancer associated genes as sweeps with high support. Given that this modeling framework is also robust to missing data, we believe that it will represent a welcome addition to the population-genomic toolkit for learning about adaptive processes from genomic data.

genomics↗

bioRxiv2022

Robust Phylogenetic Regression

AO_SCPLOWBSTRACTC_SCPLOWModern comparative biology owes much to phylogenetic regression. At its conception, this technique sparked a revolution that armed biologists with phylogenetic comparative methods (PCMs) for combatting evolutionary pseudoreplication, which arises inherently from trait data sampled across related species. Over the past few decades, the phylogenetic regression framework has become a paradigm of modern comparative biology that has been widely embraced as a remedy for evolutionary pseudoreplication. However, recent evidence has sown doubt over the efficacy of phylogenetic regression, and PCMs more generally, with the suggestion that many of these methods fail to provide an adequate defense against unreplicated evolution--the primary justification for using them in the first place. Importantly, some of the most compelling examples of biological innovation in nature result from abrupt, lineage-specific evolutionary shifts, which current regression models are largely ill-equipped to deal with. Here we explore a solution to this problem by applying robust linear regression to comparative trait data. We formally introduce robust phylogenetic regression to the PCM toolkit with linear estimators that are less sensitive to model violations while still retaining high power to detect true trait associations. Our analyses also highlight an ingenuity of the original algorithm for phylogenetic regression based on independent contrasts, whereby robust estimators are particularly effective. Collectively, we find that robust estimators hold promise for improving tests of trait associations and offer a path forward in scenarios where classical approaches may fail. Our study joins recent arguments for increased vigilance against pseudoreplication and a better understanding of evolutionary model performance in challenging-yet biologically important-settings.

evolutionary biology↗

bioRxiv2022

Predicting expression divergence and its evolutionary parameters between single-copy genes in two species

Predicting gene expression divergence and its evolutionary parameters is integral to understanding the emergence of new gene functions and associated traits. Whereas several sophisticated methods have been developed for these tasks, their applications are either limited to duplicate genes or require expression data from more than two species. Thus, here we present PiXi, the first machine learning framework for predicting expression divergence and its evolutionary parameters between single-copy genes in two species. In particular, PiXi models gene expression evolution as an Ornstein-Uhlenbeck process, and overlays this model with multi-layer neural network, random forest, and support vector machine architectures for making predictions. We show that PiXi has high power and accuracy in predicting gene expression divergence and its underlying parameters across a wide range of evolutionary scenarios, with the globally best performance achieved by a multi-layer neural network. Moreover, application of our best performing PiXi predictor to empirical data from single-copy genes residing at different loci in two species of Drosophila reveals that expression divergence occurs in approximately 20% of these positionally relocated genes and is driven by a combination of neutral and selective forces. Further analysis shows that several of these genes are involved in the electron transport chain of the mitochondrial membrane, suggesting that new chromatin environments may impact energy production in Drosophila. Thus, by providing a toolkit for predicting expression divergence and its evolutionary parameters between single-copy genes in two species, PiXi can shed light on the origins of novel phenotypes across diverse biological processes and study systems.

evolutionary biology↗

bioRxiv2022

Genomic evidence for ancient migrations routes along South America's Atlantic coast

An increasing body of archaeological and genomic evidence has hinted to a complex settlement process of the Americas. This is especially true for South America, where unexpected ancestral signals have raised perplexing scenarios for the early migrations into different regions of the continent. Here we present ancient genomes from the archaeologically rich Northeast Brazil and compare them to ancient and present-day genomic data. We find a distinct relationship between ancient genomes from Northeast Brazil, Lagoa Santa, Uruguay and Panama, representing evidence for ancient migration routes along South Americas Atlantic coast. To further add to the existing complexity, we also detect greater Denisovan than Neanderthal ancestry in ancient Uruguay and Panama individuals. Moreover, we find a strong Australasian signal in an ancient genome from Panama. This work sheds light on the deep demographic history of eastern South America and presents a starting point for future fine-scale investigations on the regional level.

genomics↗

bioRxiv2021

The Genomic Prehistory of the Indigenous People of Uruguay

The prehistory of the people of Uruguay is greatly complicated by the dramatic and severe effects of European contact, as with most of the Americas. After the series of military campaigns that exterminated the last remnants of nomadic peoples, Uruguayan official history masked and diluted the former indigenous ethnic diversity into the narrative of a singular people that all but died out. Here we present the first whole genome sequences of the Indigenous people of the region before the arrival of Europeans, from an archaeological site in eastern Uruguay that dates from 2,000 years before present. We find a surprising connection to ancient individuals from Panama and eastern Brazil, but not to modern Amazonians. This result may be indicative of a distinct migration route into South America that may have occurred along the Atlantic coast. We also find a distinct ancestry previously undetected in South America. Though this work begins to piece together some of the demographic nuance of the region, the sequencing of ancient individuals from across Uruguay is needed to better understand the ancient prehistory and genetic diversity that existed before European contact, thereby helping to rebuild the history of the indigenous population of what is now Uruguay.

genomics↗

bioRxiv2021

A spatially aware likelihood test to detect sweeps from haplotype distributions

The inference of positive selection in genomes is a problem of great interest in evolutionary genomics. By identifying putative regions of the genome that contain adaptive mutations, we are able to learn about the biology of organisms and their evolutionary history. Here we introduce a composite likelihood method that identifies recently completed or ongoing positive selection by searching for extreme distortions in the spatial distribution of the haplotype frequency spectrum along the genome relative to the genome-wide expectation taken as neutrality. Furthermore, the method simultaneously infers two parameters of the sweep: the number of sweeping haplotypes and the "width" of the sweep, which is related to the strength and timing of selection. We demonstrate that this method outperforms the leading haplotype-based selection statistics. As a positive control, we apply it to two well-studied human populations from the 1000 Genomes Project and examine haplotype frequency spectrum patterns at the LCT and MHC loci. We also apply it to a data set of brown rats sampled in NYC and identify genes related to olfactory perception. To facilitate use of this method, we have implemented it in user-friendly open source software.

evolutionary biology↗

bioRxiv2020

Properties and unbiased estimation of F- and D-statistics in samples containing related and inbred individuals

The Patterson F- and D-statistics are commonly-used measures for quantifying population relationships and for testing hypotheses about demographic history. These statistics make use of allele frequency information across populations to infer different aspects of population history, such as population structure and introgression events. Inclusion of related or inbred individuals can bias such statistics, which may often lead to the filtering of such individuals. Here we derive statistical properties of the F- and D-statistics, including their biases due to finite sample size or the inclusion of related or inbred individuals, their variances, and their corresponding mean squared errors. Moreover, for those statistics that are biased, we develop unbiased estimators and evaluate the variances of these new quantities. Comparisons of the new unbiased statistics to the originals demonstrates that our newly-derived statistics often have lower error across a wide population parameter space. Furthermore, we apply these unbiased estimators using several global human populations with the inclusion of related individuals to highlight their application on an empirical dataset. Finally, we implement these unbiased estimators in open-source software package funbiased for easy application by the scientific community.

genetics↗

bioRxiv2020

Learning retention mechanisms and evolutionary parameters of duplicate genes from their expression data

Learning about the roles that duplicate genes play in the origins of novel phenotypes requires an understanding of how their functions evolve. To date, only one method--CDROM--has been developed with this goal in mind. In particular, CDROM employs gene expression distances as proxies for functional divergence, and then classifies the evolutionary mechanisms retaining duplicate genes from comparisons of these distances in a decision tree framework. However, CDROM does not account for stochastic shifts in gene expression or leverage advances in contemporary statistical learning for performing classification, nor is it capable of predicting the underlying parameters of duplicate gene evolution. Thus, here we develop CLOUD, a multi-layer neural network built upon a model of gene expression evolution that can both classify duplicate gene retention mechanisms and predict their underlying evolutionary parameters. We show that not only is the CLOUD classifier substantially more powerful and accurate than CDROM, but that it also yields accurate parameter predictions, enabling a better understanding of the specific forces driving the evolution and long-term retention of duplicate genes. Further, application of the CLOUD classifier and predictor to empirical data from Drosophila recapitulates many previous findings about gene duplication in this lineage, showing that new functions often emerge rapidly and asymmetrically in younger duplicate gene copies, and that functional divergence is driven by strong natural selection. Hence, CLOUD represents the best available method for classifying retention mechanisms and predicting evolutionary parameters of duplicate genes, thereby also highlighting the utility of incorporating sophisticated statistical learning techniques to address long-standing questions about evolution after gene duplication.

evolutionary biology↗