Biology subjects

Kyriazis, C. C.

Publications and source records attributed to Kyriazis, C. C..

10 recordsLinked to original sources

bioRxiv2026

Long-term isolation and introgression shape the genomic distinctiveness of Rice's Whale

Species complex demographic history, including population size changes, isolation and gene flow, shapes patterns of genetic variation and deleterious load. In small and declining populations, understanding these processes is critical for predicting inbreeding depression and extinction risk. The Rices whale (Balaenoptera ricei) is a newly described baleen whale species resident to the heavily industrialized Gulf of Mexico, with a current abundance estimate of 51 (95 % CI: 20-130) individuals, making it one of the most endangered baleen whales globally. Using whole-genome sequences from 25 individuals, we reconstructed the evolutionary and demographic history of Rices whale and assessed its genomic health. Our analyses reinforce its distinctiveness from Brydes whales, and suggest that Rices whale has persisted as a small and isolated population in the Gulf of Mexico for tens of thousands of years. Despite its long-term small effective population size, genomes show modest impacts of inbreeding, including few long runs of homozygosity. We detected a distinct pulse of introgression [~]350 years ago from a Brydes whale-like lineage that resulted in windows of elevated heterozygosity in Rices whale, though it did not alter the burden of deleterious variation. Forward simulations indicate that a recent population collapse to [~]100 breeding individuals places the species at high risk of future inbreeding and genomic erosion unless population growth occurs. These findings highlight that while gene flow can increase genetic diversity, demographic recovery is essential to mitigate long-term genomic risks, underscoring the importance of management actions that promote sustained population growth.

genomics↗

bioRxiv2026

Elevated recessive lethal frequencies drive hatching failure following near extinction in 'Alala, the Hawaiian crow

Near-extinction events impose severe genomic bottlenecks that can have lasting fitness consequences, yet the specific mechanisms involved remain poorly understood. Alal[a], the last remaining Hawaiian crow, narrowly avoided extinction when a conservation breeding program was initiated from just eight genetic founders. Although the conservation breeding program has since recovered to [~]120 birds, it remains plagued by egg hatching failure rates >50%. To investigate the impacts of this bottleneck on hatching failure and other fitness components, we generated a chromosome-level reference genome and resequenced 175 individuals, including 78 deceased embryos. Although long runs of homozygosity (ROH) >1Mb are abundant in Alal[a] (mean FROH=0.32), associations between FROH and measures of survival and reproduction, including egg failure, were weak or nonexistent. Instead, we identify two recessive lethal haplotypes that together account for nearly all of the excess hatching failure and have persisted in the population at high frequency (15-25%), hinting at impaired purifying selection. Eco-evolutionary simulation models demonstrate that these findings are expected for a species that has endured a severe population bottleneck and exhibits modest levels of pre-bottleneck heterozygosity. Our findings suggest that elevated recessive allele frequencies may be a broadly important consequence of population bottlenecks.

evolutionary biology↗

bioRxiv2025

The distribution of fitness effects varies phylogenetically across animals

1The distribution of fitness effects (DFE) describes the selection coefficients (s) of newly arising mutations and fundamentally influences population genetic processes. However, the extent and mechanisms of DFE variation have not been systematically investigated across species with divergent phylogenetic histories and ecological functions. Here, we inferred the DFE in natural populations of eleven animal (sub)species, including humans, mice, fin whales, vaquitas, wolves, collared flycatchers, pied flycatchers, halictid bees, Drosophila, and mosquitoes. We find that the DFE co-varies with phylogeny, where the expected mutation effects are more similar in closely related species (Pagels{lambda} = 0.84, P = 0.01). Additionally, mammals have a higher proportion of strongly deleterious mutations (22% to 47% in mammals; 0.0% to 5.4% in insects and birds) and a lower proportion of weakly deleterious mutations than insects and birds. Population size is significantly negatively correlated with the expected impact of new deleterious mutations (PGLS{lambda}, P = 0.03), and the proportion of new beneficial mutations ([Formula], P < 0.001). These findings align with Fishers Geometric Model (FGM), which defines organismal complexity as the number of phenotypes under selection. Consistent with the FGMs predictions, we observe that mutations are more deleterious in complex organisms, while beneficial mutations occur more frequently in smaller populations to compensate for the drift load. Our study demonstrates strong phylogenetic constraints in the evolution of a fundamental population genetics parameter, and proposes that, through mechanisms of global epistasis, long-term population size and organismal complexity drive variation in the DFE across animals. 2 Significance StatementUnderstanding how mutations affect fitness is fundamental in evolution, but little is known about how and why the distribution of fitness effects (DFE) varies across species. In this study, we examine the DFE in diverse animal populations and show that closely related species exhibit similar patterns of mutation effects, with new mutations being more deleterious in mammals compared with birds and insects. Our findings corroborate Fishers Geometric Model, which explains the variation in the DFE across species as a function of organismal complexity and long-term population size. By connecting organismal complexity and population size with the DFE, we offer a phylogenetic view into the selective forces shaping species adaptation and evolution.

evolutionary biology↗

bioRxiv2025

Accessible, realistic genome simulation with selection using stdpopsim

Selection is a fundamental evolutionary force that shapes patterns of genetic variation across species. However, simulations incorporating realistic selection along heterogeneous genomes in complex demographic histories are challenging, limiting our ability to benchmark statistical methods aimed at detecting selection and to explore theoretical predictions. stdpopsim is a community-maintained simulation library that already provides an extensive catalog of species-specific population genetic models. Here we present a major extension to the stdpopsim framework that enables simulation of various modes of selection, including background selection, selective sweeps, and arbitrary distributions of fitness effects (DFE) acting on annotated subsets of the genome (for instance, exons). This extension maintains stdpopsims core principles of reproducibility and accessibility while adding support for species-specific genomic annotations and published DFE estimates. We demonstrate the utility of this framework by comparing methods for demographic inference, DFE estimation, and selective sweep detection across several species and scenarios. Our results demonstrate the robustness of demographic inference methods to selection on linked sites, reveal the sensitivity of DFE-inference methods to model assumptions, and show how genomic features, like recombination rate and functional sequence density, influence power to detect selective sweeps. This extension to stdpopsim provides a powerful new resource for the population genetics community to explore the interplay between selection and other evolutionary forces in a reproducible, user-friendly framework.

evolutionary biology↗

bioRxiv2024

Constraining models of dominance for nonsynonymous mutations in the human genome

Dominance is a fundamental parameter in genetics, determining the dynamics of natural selection on deleterious and beneficial mutations, the patterns of genetic variation in natural populations, and the severity of inbreeding depression in a population. Despite this importance, dominance parameters remain poorly known, particularly in humans or other non-model organisms. A key reason for this lack of information about dominance is that it is extremely challenging to disentangle the selection coefficient (s) of a mutation from its dominance coefficient (h). Here, we explore dominance and selection parameters in humans by fitting models to the site frequency spectrum (SFS) for nonsynonymous mutations. When assuming a single dominance coefficient for all nonsynonymous mutations, we find that numerous h values can fit the data, so long as h is greater than [~]0.15. Moreover, we also observe that theoretically-predicted models with a negative relationship between h and s can also fit the data well, including models with h=0.05 for strongly deleterious mutations. Finally, we use our estimated dominance and selection parameters to inform simulations revisiting the question of whether the out-of-Africa bottleneck has led to differences in genetic load between African and non-African human populations. These simulations suggest that the relative burden of genetic load in non-African populations depends on the dominance model assumed, with slight increases for more weakly recessive models and slight decreases shown for more strongly recessive models. Moreover, these results also demonstrate that models of partially recessive nonsynonymous mutations can explain the observed severity of inbreeding depression in humans, bridging the gap between molecular population genetics and direct measures of fitness in humans. Our work represents a comprehensive assessment of dominance and deleterious variation in humans, with implications for parameterizing models of deleterious variation in humans and other mammalian species. Author SummaryThe dominance coefficient (h) of a mutation determines its impact on organismal fitness when heterozygous. For instance, fully recessive mutations (h=0) have no effects on fitness when heterozygous whereas additive mutations (h=0.5) have an effect that is intermediate to the two heterozygous mutations. The extent to which deleterious mutations may be recessive, additive, or dominant is a key area of study in evolutionary genetics. However, dominance parameters remain poorly known in humans and most other organisms due to a variety of technical challenges. In this study, we aim to constrain the possible set of dominance and selection parameters for amino acid changing mutations in humans. We find that a wide range of models are possible, including models with a theoretically-predicted relationship between h and s. We then use a range of plausible selection and dominance models to explore how deleterious variation may have been shaped by the out-of-Africa bottleneck in humans. Our results highlight the subtle influence of dominance on patterns of genetic load in humans and demonstrate that models of partially recessive mutations at amino-acid-changing sites can explain the observed effects of inbreeding on mortality in humans.

genetics↗

bioRxiv2023

The influence of gene flow on population viability in an isolated urban caracal population

Wildlife populations are becoming increasingly fragmented by anthropogenic development. Such small and isolated populations often face an elevated risk of extinction, in part due to inbreeding depression. Here, we examine the genomic consequences of urbanization in a caracal (Caracal caracal) population that has become isolated in the Cape Peninsula region of the city of Cape Town, South Africa and is thought to number [~]50 individuals. We document low levels of migration into the population over the past [~]75 years, with an estimated rate of 1.3 effective migrants per generation. As a consequence of this isolation and small population size, levels of inbreeding are elevated in the contemporary Cape Peninsula population (mean FROH>1Mb=0.20). Inbreeding primarily manifests as long runs of homozygosity >10Mb, consistent with the effects of isolation due to the rapid recent growth of Cape Town. To explore how reduced migration and elevated inbreeding may impact future population dynamics, we parameterized an eco-evolutionary simulation model. We find that if migration rates do not change in the future, the population is expected to decline only slightly, with a low projected risk of extinction. However, if migration rates decline or anthropogenic mortality rates increase, the potential risk of extinction is greatly elevated. To avert a population decline, we suggest that translocating migrants into the Cape Peninsula to initiate a genetic rescue may be warranted in the near future. Our analysis highlights the utility of genomic datasets coupled with computational simulation models for investigating the influence of gene flow on population viability.

evolutionary biology↗

bioRxiv2022

Using computational simulations to quantify genetic load and predict extinction risk

Small and isolated wildlife populations face numerous threats to extinction, among which is the deterioration of fitness due to an accumulation of deleterious genetic variation. Genomic tools are increasingly used to quantify the impacts of deleterious variation in small populations; however, these approaches remain limited by an inability to accurately predict the selective and dominance effects of individual mutations. Computational simulations of deleterious genetic variation offer an alternative and complementary tool that can help overcome these limitations, though such approaches have yet to be widely employed. In this Perspective, we aim to encourage conservation genomics researchers to adopt greater use of computational simulations to aid in quantifying and predicting the threat that deleterious genetic variation poses to extinction. We first provide an overview of the components of a simulation of deleterious genetic variation, describing the key parameters involved in such models. Next, we clarify several misconceptions about an essential simulation parameter, the distribution of fitness effects (DFE) of new mutations, and review recent debates over what the most appropriate DFE parameters are. We conclude by comparing modern simulation tools to those that have long been employed in population viability analysis, weighing the pros and cons of a genomics-informed simulation approach, and discussing key areas for future research. Our aim is that this Perspective will facilitate broader use of computational simulations in conservation genomics, enabling a deeper understanding of the threat that deleterious genetic variation poses to biodiversity.

evolutionary biology↗

bioRxiv2021

Quantifying the scale of genetic diversity extinction in the Anthropocene

More species than ever before are at risk of extinction due to anthropogenic habitat loss and climate change. But even species that are not threatened have seen reductions in their populations and geographic ranges, likely impacting their genetic diversity. Although preserving genetic diversity is key to maintaining adaptability of species, we lack predictive tools and global estimates of genetic diversity loss across ecosystems. By bridging theories of biodiversity and population genetics, we introduce a mathematical framework to understand the loss of naturally occurring DNA mutations within decreasing habitat within a species. Analysing genome-wide variation data of 10,095 geo-referenced individuals from 20 plant and animal species, we show that genome-wide diversity follows a power law with geographic area (the mutations-area relationship), which can predict genetic diversity loss in spatial computer simulations of local population extinctions. Given pre-21st century values of ecosystem transformations, we estimate that over 10% of genetic diversity may already be lost, surpassing the United Nations targets for genetic preservation. These estimated losses could rapidly accelerate with advancing climate change and habitat destruction, highlighting the need for forecasting tools that facilitate implementation of policies to protect genetic resources globally.

ecology↗

bioRxiv2019

A community-maintained standard library of population genetic models

The explosion in population genomic data demands ever more complex modes of analysis, and increasingly these analyses depend on sophisticated simulations. Recent advances in population genetic simulation have made it possible to simulate large and complex models, but specifying such models for a particular simulation engine remains a difficult and error-prone task. Computational genetics researchers currently re-implement simulation models independently, leading to inconsistency and duplication of effort. This situation presents a major barrier to empirical researchers seeking to use simulations for power analyses of upcoming studies or sanity checks on existing genomic data. Population genetics, as a field, also lacks standard benchmarks by which new tools for inference might be measured. Here we describe a new resource, stdpopsim, that attempts to rectify this situation. Stdpopsim is a community-driven open source project, which provides easy access to a growing catalog of published simulation models from a range of organisms and supports multiple simulation engine backends. This resource is available as a well-documented python library with a simple command-line interface. We share some examples demonstrating how stdpopsim can be used to systematically compare demographic inference methods, and we encourage a broader community of developers to contribute to this growing resource.

evolutionary biology↗

bioRxiv2019

High genetic diversity can contribute to extinction in small populations

Human-driven habitat fragmentation and loss have led to a proliferation of small and isolated plant and animal populations with high risk of extinction. One of the main threats to extinction in these populations is inbreeding depression, which is primarily caused by the exposure of recessive deleterious mutations as homozygous by inbreeding. The typical approach for managing these populations is to maintain high genetic diversity, often by translocating individuals from large populations to initiate a genetic rescue. However, the limitations of this approach have recently been highlighted by the demise of the gray wolf population on Isle Royale, which was driven to the brink of extinction soon after the arrival of a migrant from the large mainland wolf population. Here, we use a novel population genetic simulation framework to investigate the role of genetic diversity, deleterious variation, and demographic history in mediating extinction risk due to inbreeding depression in small populations. We show that, under realistic models of dominance, large populations harbor high levels of recessive strongly deleterious variation due to these mutations being hidden from selection in the heterozygous state. As a result, when large populations contract, they experience a substantially elevated risk of extinction after these strongly deleterious mutations are exposed by inbreeding. Moreover, we demonstrate that although translocating individuals to small populations is broadly effective as a means to reduce extinction risk, using small or moderate-sized source populations rather than large source populations can greatly increase the effectiveness of genetic rescue due to greater purging in these smaller populations. Our findings challenge the traditional conservation paradigm that focuses on maximizing genetic diversity to reduce extinction risk in favor of a view that emphasizes minimizing strongly deleterious variation. These insights have important implications for managing small and isolated populations in the increasingly fragmented landscape of the Anthropocene. Impact SummaryNumerous threats to extinction exist for small populations, including the detrimental effects of inbreeding. Although much of the focus in reducing these harmful effects in small populations has been on maintaining high genetic diversity, here we use simulations to demonstrate that emphasis should instead be placed on minimizing strongly deleterious variation. More specifically, we show that historically-large populations with high levels of genetic diversity also harbor elevated levels of recessive strongly deleterious mutations hidden in the heterozygous state. Thus, when these populations contract, inbreeding can expose these strongly deleterious mutations as homozygous and lead to severe inbreeding depression and rapid extinction. Moreover, we demonstrate that, although translocating individuals to these small populations to perform a genetic rescue is broadly beneficial, the effectiveness of this strategy can be greatly increased by targeting historically-smaller source populations where recessive strongly deleterious mutations have been purged. These results challenge long-standing views on how to best conserve small and isolated populations facing the threat of inbreeding depression, and have immediate implications for preserving biodiversity in the increasingly fragmented landscape of the Anthropocene.

evolutionary biology↗