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bioRxiv2026

Endemic koloa maoli (Hawaiian Duck, Anas wyvilliana) shows preferential social associations, but not based on plumage or genetic relatedness

Island endemic birds are under greater threat than their mainland counterparts. Sedentary living and historically reduced predation may affect island bird sociality and inform their conservation and management. However, detailed studies on their sociality are uncommon. The federally- endangered koloa maoli (Anas wyvilliana, Hawaiian duck, or koloa) is primarily threatened by hybridization with feral mallards and avian botulism outbreaks. We used capture-mark-recapture and genetic data on koloa on the island of Kaua i, a stronghold of remaining koloa, to construct social networks and examine their associations (inferred from co-occurrence in traps) and spatial genetic structure. Information on associations might shed light on preferences for or against mallards and hybrids, and inform planned translocation efforts. Microgeographic spatial genetic structuring where relatedness among individuals scales with geographic distance is a potential liability for maintaining koloa genetic diversity, and would particularly be detrimental during highly localized outbreaks of botulism that could result in the removal of entire lineages. While we found persistent social associations among adult koloa, they were not apparently influenced by plumage traits or body mass, suggesting a lack of social preference for mallard-like individuals. Importantly, we found no spatial patterns of relatedness within the largest refuge. Therefore, botulism outbreaks remain a demographic threat but are unlikely to remove correlated genetic diversity. There were no sex differences in spatial genetic structure and both sexes moved within a refuge. The lack of spatial genetic structure and the presence of many unrelated conspecifics may enable koloa to limit inbreeding and retain genetic diversity without sex-biased dispersal. In the context of future translocations, our results suggest that translocating koloa captured in the same trap together will reduce disruption of preferred associations while also retaining genetic diversity among translocated individuals. LAY SUMMARYO_LIThe koloa maoli (Hawaiian duck, or koloa) is a federally-endangered, island endemic bird. Like other Hawaiian waterbirds, koloa are threatened by introduced predators and habitat loss, but also specifically by hybridization with feral mallards and localized avian botulism outbreaks. Currently, the island of Kaua i has the largest population of non-admixed koloa. We used capture-mark-recapture and genetic data of koloa at two wetland sites on Kaua i to examine their sociality and spatial genetic structure. C_LIO_LIKoloa formed preferential social associations, but they were not based on plumage traits, body mass or genetic relatedness. C_LIO_LIThere was no spatial genetic structure for males and females within a wetland site. Our results suggested that 1) koloa have no preference for mallard-like plumage that might increase hybrid pairing, 2) localized (within-refuge) botulism outbreaks are unlikely to remove close relatives and unique genetic lineages, and 3) translocation of groups could maintain social associations without limiting genetic diversity. C_LI

animal behavior and cognition↗
bioRxiv2020

Spatial modelling improves genetic evaluation in smallholder breeding programs

Breeders and geneticists use statistical models for genetic evaluation of animals to separate genetic and environmental effects on phenotype. A common way to separate these effects is to model a descriptor of an environment, a contemporary group or herd, and account for genetic relationship between animals across the environments. However, separating the genetic and environmental effects in smallholder systems is challenging due to small herd sizes and weak genetic connectedness across herds. Our hypothesis was that accounting for spatial relationships between nearby herds can improve genetic evaluation in smallholder systems. Further, geographically referenced environmental covariates are increasingly available and could be used to model underlying sources of the spatial relationships. The objective of this study was therefore to evaluate the potential of spatial modelling to improve genetic evaluation in smallholder systems. We focus solely on dairy cattle smallholder systems. We performed simulations and real dairy cattle data analysis to test our hypothesis. We used a range of models to account for environmental variation by estimating herd and spatial effects. We compared these models using pedigree or genomic data. The results show that in smallholder systems (i) standard models are not able to separate genetic and environmental effects, (ii) spatial modelling increases accuracy of genetic evaluation for phenotyped and non-phenotyped animals, (iii) environmental covariates do not substantially improve accuracy of genetic evaluation beyond simple distance-driven spatial relationships between herds, (iv) the benefit of spatial modelling was the largest when the genetic and environmental effects were hard to separate and (v) spatial modelling was beneficial when using either pedigree or genomic data. We have demonstrated the potential of spatial modelling to improve genetic evaluation in smallholder systems. This improvement is driven by establishing environmental connectedness between herds that enhances separation of the genetic and environmental effects. We suggest routine spatial modelling in genetic evaluations, particularly for smallholder systems. Spatial modelling could also have major impact in studies of human and wild populations.

genetics↗
bioRxiv2025

Network-based genetic monitoring of landscape fragmentation

Habitat fragmentation is one of the most immediate and substantial threats to biodiversity, generating isolated populations with reduced genetic diversity. Genetic monitoring has become essential for detecting fragmentation and tracking its progress. However, the coherent interpretation of genetic monitoring data and understanding the genetic consequences of fragmentation require frameworks that accurately represent real-world complexity. Existing theoretical frameworks typically rely on simplified spatial structures and do not adequately capture the heterogeneous migration patterns of natural populations. Here, we integrate network theory and mathematical population genetics to develop a framework for studying the genetic consequences of fragmentation processes, explicitly accounting for heterogeneous connectivity and temporal dynamics. We apply this framework to examine how different fragmentation processes affect genetic measures commonly used in genetic monitoring. We find that different fragmentation scenarios produce substantially distinct trajectories in key genetic measures, sometimes exhibiting rapid transitional dynamics, suggesting that the interpretation of genetic monitoring data must be tailored to ecological contexts. Furthermore, fragmentation can cause deviations from classical theoretical expectations, such as the expected correlation between genetic and geographic distance (isolation-by-distance) or between genetic diversity and connectivity. Finally, we propose and demonstrate detectable early warning signals in genetic monitoring data that precede rapid transitional phases. Our framework thus provides a practical interpretation of genetic monitoring data, bridging the gap between idealized theoretical models and real-world connectivity dynamics.

genetics↗
bioRxiv2026

Designing meta-population genetic management for a small, endangered passerine with fragmented range

ContextGenetic diversity is essential for the persistence and future adaptation of species. However, human-driven habitat fragmentation results in population isolation, often leading to rapid loss of genetic diversity and adaptive capacity. Genetic management of focal taxa may be overlooked in many threatened species conservation programs. The Endangered southeastern Australian mallee emu-wren Stipiturus mallee is a species that may benefit from genetic management. Its current range encompasses patchily distributed sub-populations, prone to bottlenecks and genetic drift. Thus, the reintroduction to areas from which the species has been locally extirpated requires careful selection of founders to maximise genetic diversity. AimsWe analyse reduced-representation genomic data from seven sampling areas across the global meta-population to design a translocation strategy that maximises heterozygosity and retention of mallee emu-wren allelic diversity. MethodsWe estimated genetic structure, genetic diversity within, and differentiation between subpopulations, thus testing previous inference based on 12 length-variable loci of low population differentiation with 10,840 genome-wide SNP loci. We also estimated effective population sizes to identify populations in need of genetic augmentation, Finally, we used metapop2 simulations to estimate the relative contributions of each population to global genetic diversity of the species and to estimate the source and number of founders that would maximise heterozygosity and allelic richness in a hypothetical newly established population. Key resultsWe found weak genetic structure across all sampling areas, supporting previous conclusions that the global mallee emu-wren population should be considered a single genetic unit for management purposes. Low but significant Weir and Cockerham pairwise FST among locations indicated differentiation between sampling areas, suggesting that contemporary gene flow is restricted. Effective population sizes for the two regions supporting the largest numbers of mallee emu-wrens were below the threshold associated with reduced adaptive potential. ConclusionsThe genetic health and adaptive potential of sampled mallee emu-wren sub-populations are at risk. Implications The global mallee emu-wren meta-population would likely benefit from genetic augmentation, including reciprocal gene flow between extant sub-populations. To maximise genetic diversity in newly established populations, managers should prioritise gene-pool mixing with founders sourced from all sampled areas.

genetics↗
bioRxiv2022

Genetic drift promotes and recombination hinders speciation on a holey fitness landscape

Dobzhansky and Muller proposed a general mechanism through which microevolution, the substitution of alleles within populations, can cause the evolution of reproductive isolation between populations and, therefore, macroevolution. As allopatric populations diverge, many combinations of alleles differing between them have not been tested by natural selection and may thus be incompatible. Such genetic incompatibilities often cause low fitness in hybrids between species. Furthermore, the number of incompatibilities grows with the genetic distance between diverging populations. However, what determines the rate and pattern of accumulation of incompatibilities remains unclear. We investigate this question by simulating evolution on holey fitness landscapes on which genetic incompatibilities can be identified unambiguously. We find that genetic incompatibilities accumulate more slowly among genetically robust populations and identify two determinants of the accumulation rate: recombination rate and population size. In large populations with abundant genetic variation, recombination selects for increased genetic robustness and, consequently, incompatibilities accumulate more slowly. In small populations, genetic drift interferes with this process and promotes the accumulation of genetic incompatibilities. Our results suggest a novel mechanism by which genetic drift promotes and recombination hinders speciation. Author summaryAs geographically isolated populations evolve, genetic incompatibilities accumulate between them. Eventually, these incompatibilities may cause the populations to become different species. What determines how quickly species are formed in this way remains unclear. We investigate this question using computer simulations and find that genetic incompatibilities accumulate more slowly among populations with individuals that are robust to genetic perturbations. We identify two factors that influence the accumulation rate via their effects on genetic robustness: the size of the populations and how much recombination takes place in them. Small populations with rare recombination accumulate incompatibilities more quickly.

evolutionary biology↗
bioRxiv2024

Advanced median-based genetic similarity analysis in Kazakh Tazy dogs: A novel approach for breed conformity assessment

The breed conformity evaluation is crucial for the preservation of the traits that characterise each dog breed. The use of genetic markers for this purpose provides a precision and objectivity that can surpass the reliability of phenotypic evaluations. In this study, we present a new simple algorithm for assessing breed conformity. The algorithm creates a similarity matrix based on genotypic data and then uses the median to calculate the percentage of genetic similarity that an individual has in relation to the genetic diversity of the breed. To validate the proposed algorithm, we applied it to the genotypic data of 18 microsatellites and 43,691 single nucleotide polymorphisms (SNPs) of the Kazakh Tazy dog, a breed of great cultural and historical importance to Kazakhstan that is now threatened with extinction due to crossbreeding. The algorithm showed a moderate correlation between the microsatellite and SNP genotyping methods, reflecting the different aspects of genetic similarity. In particular, the SNP-based evaluations agreed better with the expert judgements, highlighting their potential for accurate analysis of breed conformity. The proposed algorithm provides easily interpretable results, is flexible, adapts to different genetic markers and may provide an evaluation mechanism for breed conformity in situations where there is no reference population, incomplete pedigrees, unidentified meta-founders and high genetic diversity in the population. Author summaryOur research was initiated by the urgent concern for the possible extinction of the Kazakh Tazy dog, a breed with deep historical roots and cultural significance in Kazakhstan. Information at the DNA level may lead to faster genetic improvement of the breed than relying only on phenotypic data and pedigrees. Genotypic data can be processed to provide valuable insights into genetic diversity, relatedness and ancestry. However, existing methods do not provide a measure of percentage similarity that can be used to assess how closely a particular individual matches the typical genetic composition of the breed. These challenges have led us to propose an approach that overcomes the limitations of existing methods and allows genetic similarity to be assessed based on genotypes. It is based on a median-based approach to analyze genetic data and has been applied to microsatellite and SNP markers but can also be adapted to other genetic markers. This method provides results even in the absence of a defined reference population, complete pedigrees or identified meta-pedigrees and during high genetic diversity within a breed. It can provide breeders and researchers with a tool to maintain the genetic purity of unique breeds.

genomics↗
bioRxiv2024

Genetic diversity loss in the Anthropocene will continue long after habitat destruction ends

Genetic diversity within species is the basis for evolutionary adaptive capacity and has recently been included as a target for protection in the United Nations Global Biodiversity Framework (GBF). However, we lack large-scale mathematical frameworks to quantify how much genetic diversity has already been lost, let alone to predict future losses under 21st century conservation scenarios. To fill this gap, we developed an area-based spatio-temporal predictive framework of genetic diversity calibrated with population-scale genomic data of 29 plant and animal species. To estimate present genetic diversity loss with our framework, we used species habitat area and population sizes losses reported in the Living Planet Index, the Red List, and new GBF indicators across 13,808 species for the last 5 decades. Applying our evolutionary framework across these species, we estimate genetic diversity loss lags behind population and habitat area declines, with an estimated current 13-22% {pi} genetic diversity loss. However, we forecast future genetic diversity losses will reach 41-76% even if populations are not further contracted. These results highlight that safeguarding existing habitats is insufficient to maintain the genetic health of species and relying solely on continuous genetic monitoring underestimates lagging long term impacts. Significance statementGenetic diversity is crucial for both species adaptation and survival. Recently, it has been included as a target for protection in the United Nations Global Biodiversity Framework. However, we lack large-scale predictive methods to quantify current and future losses of genetic diversity across species. Here, we develop an area-based spatio-temporal predictive framework trained with high-quality genome-wide data from 29 plant and animal species to enable quantitative predictions of genetic biodiversity at global scales. We infer global genetic diversity losses are beyond the preliminary UN targets to protect 90% of genetic diversity of species, evolutionary models dramatic genetic losses will occur in the future even with habitat protection if populations across species are not recovered.

genomics↗
bioRxiv2024

A Genetic Toggle Switch in Plants

In synthetic biology, genetic components are assembled to make transcriptional units, and transcriptional units are assembled into circuits to perform specific and predictable functions of a genetic device. Genetic devices have been described in bacteria, mammalian cell cultures and small organoids, yet development of programmable genetic circuits for devices in plants has lagged. Programmable genetic devices require defining the components quantitative functions. Because plants have long life spans, studies often use transient analysis to define quantitative functions while verification in stably engineered plants is often neglected and largely unknown. This raises a question if unique attributes of plants such as environmental sensitivity, developmental plasticity, or alternation of generations, adversely impacts predictability of plant genetic circuits and devices. Alternatively, it is also possible that genetic elements to produce predictable genetic devices for plants require rigorous characterization with detailed mathematical modeling. Here we use plant genetic elements with quantitatively characterized transfer functions and developed in silico models to guide their assembly into a genetic device: a toggle switch or a mutually inhibitory gene-regulatory device. Our approach allows computational selection of plant genetic components and iterative refinement of the circuit if the desired genetic functions are not initially achieved. We show that our computationally selected genetic circuit functions as predicted in stably engineered plants including through tissue and organ differentiation. Developing abilities to produce predictable and programmable plant genetic devices opens the prospect of predictably engineering plants unique abilities in sustainable human and environmental systems.

synthetic biology↗
bioRxiv2025

Predictive models of the genetic bases underlying budding yeast fitness in multiple environments

The ability of organisms to adapt and survive depends on the effects of genes and the environment on fitness. However, the multigenic nature of fitness traits and genotype-by-environment interactions hinder our ability to understand the genetic basis of fitness. Here, we established fitness prediction models for 35 environments using machine learning and existing fitness data and different types of genetic variants for a population of Saccharomyces cerevisiae isolates. Models revealed that the predictive ability of genetic variants varied across environments, with copy number variants explaining the majority of fitness variation in most cases. Model interpretation further showed that different variant types identified distinct sets of genes associated with predictive variants. These gene sets were significantly enriched in experimentally validated genes affecting fitness in only a subset of environments, indicating that many genes influencing fitness remain unexplored. Notably, non-experimentally validated genes were more important than validated ones for fitness predictions. Gene contributions to fitness predictions were both isolate and environment dependent, pointing to gene-by-gene and gene-by-environment interactions. Further interpretation of models uncovered experimentally validated and novel candidate genetic interactions for a well characterized stress, the fungicide benomyl. These findings highlight the feasibility of identifying the genetic basis of fitness by using different types of genetic variants and offer novel targets for future functional analysis. Author SummaryOrganisms adapt to changing environments by acquiring beneficial traits, which are largely determined by genetic variation. However, predicting how genetic variation influences adaptation, and thus survival, remains a challenge. Here, we used machine learning to identify genes, gene-gene interactions, and gene-by-environment interactions underlying fitness. Specifically, we used machine learning to predict how different genetic variants--such as changes in single nucleotides, presence/absence of a sequence, and differences in copy number--affect fitness in yeast across 35 different environmental conditions. Our results show that prediction accuracy and our ability to interpret the underlying biology depend on the genetic variant type. For example, the best predictions were obtained using differences in copy number. We also found that the contributions of genetic variants to yeast fitness depend on the genetic background. Importantly, our models uncovered known and novel genes that were important across multiple and specific environments and revealed genetic interactions for a well characterized stress, offering insights into how organisms cope with environmental stress. These findings advance our understanding of the genetic basis of fitness and provide a framework for future functional studies and the design of stress-resilient yeast strains.

genomics↗
bioRxiv2019

Efficient use of genomic information for sustainable genetic improvement in small cattle populations

This paper compares genetic gain, genetic variation, and the efficiency of converting variation into gain under different genomic selection scenarios with truncation or optimum contribution selection in a small dairy population by simulation. Breeding programs have to maximize genetic gain but also ensure sustainability by maintaining genetic variation. Numerous studies showed that genomic selection increases genetic gain. Although genomic selection is a well-established method, small populations still struggle with choosing the most sustainable strategy to adopt this type of selection. We developed a simulator of a dairy population and simulated a model after the Slovenian Brown Swiss population with ~10,500 cows. We compared different truncation selection scenarios by varying i) the method of sire selection and their use on cows or bull-dams, and ii) selection intensity and the number of years a sire is in use. Furthermore, we compared different optimum contribution selection scenarios with optimization of sire selection and their usage. We compared the scenarios in terms of genetic gain, selection accuracy, generation interval, genetic and genic variance, the rate of coancestry, effective population size, and the conversion efficiency. The results show that early use of genomically tested sires increased genetic gain compared to progeny testing as expected from changes in selection accuracy and generation interval. A faster turnover of sires from year to year and higher intensity increased the genetic gain even further but increased the loss of genetic variation per year. While maximizing intensity gave the lowest conversion efficiency, a faster turn-over of sires gave an intermediate conversion efficiency. The largest conversion efficiency was achieved with the simultaneous use of genomically and progeny tested sires that were used over several years. Compared to truncation selection optimizing sire selection and their usage increased the conversion efficiency by either achieving comparable genetic gain for a smaller loss of genetic variation or achieving higher genetic gain for a comparable loss of genetic variation. Our results will help breeding organizations to implement sustainable genomic selection.

genomics↗
bioRxiv2019

Genetic contributions to variation in human stature in prehistoric Europe

The relative contributions of genetics and environment to temporal and geographic variation in human height remain largely unknown. Ancient DNA has identified changes in genetic ancestry over time, but it is not clear whether those changes in ancestry are associated with changes in height. Here, we directly test whether changes over the past 38,000 years in European height predicted using DNA from 1071 ancient individuals are consistent with changes observed in 1159 skeletal remains from comparable populations. We show that the observed decrease in height between the Early Upper Paleolithic and the Mesolithic is qualitatively predicted by genetics. Similarly, both skeletal and genetic height remained constant between the Mesolithic and Neolithic and increased between the Neolithic and Bronze Age. Sitting height changes much less than standing height-consistent with genetic predictions-although genetics predicts a small Bronze Age increase that is not observed in skeletal remains. Geographic variation in stature is also qualitatively consistent with genetic predictions, particularly with respect to latitude. Finally, we hypothesize that an observed decrease in genetic heel bone mineral density in the Neolithic reflects adaptation to the decreased mobility indicated by decreased femoral bending strength. This study provides a model for interpreting phenotypic changes predicted from ancient DNA and demonstrates how they can be combined with phenotypic measurements to understand the relative contribution of genetic and developmentally plastic responses to environmental change.\n\nSignificanceMeasurements of prehistoric human skeletal remains provide a record of changes in height and other anthropometric traits, over time. Often, these changes are interpreted in terms of plastic developmental response to shifts in diet, climate or other environmental factors. These changes can also be genetic in origin but, until recently, it has been impossible to separate the effects of genetics and environment. Here we use ancient DNA to directly estimate genetic changes in phenotypes and to identify changes driven not by genetics, but by environment. We show that changes over the past 35,000 years are largely predicted by genetics, but also identify specific shifts that are more likely to be environmentally driven.

evolutionary biology↗
bioRxiv2016

Meta-GWAS Accuracy and Power (MetaGAP) calculator shows that hiding heritability is partially due to imperfect genetic correlations across studies

Large-scale genome-wide association results are typically obtained from a fixed-effects meta-analysis of GWAS summary statistics from multiple studies spanning different regions and/or time periods. This approach averages the estimated effects of genetic variants across studies. In case genetic effects are heterogeneous across studies, the statistical power of a GWAS and the predictive accuracy of polygenic scores are attenuated, contributing to the so-called missing heritability. Here, we describe the online Meta-GWAS Accuracy and Power calculator (MetaGAP; available at www.devlaming.eu) which quantifies this attenuation based on a novel multi-study framework. By means of simulation studies, we show that under a wide range of genetic architectures, the statistical power and predictive accuracy provided by this calculator are accurate. We compare the predictions from MetaGAP with actual results obtained in the GWAS literature. Specifically, we use genomic-relatedness-matrix restricted maximum likelihood (GREML) to estimate the SNP heritability and cross-study genetic correlation of height, BMI, years of education, and self-rated health in three large samples. These estimates are used as input parameters for the MetaGAP calculator. Results from the calculator suggest that cross-study heterogeneity has led to attenuation of statistical power and predictive accuracy in recent large-scale GWAS efforts on these traits (e.g., for years of education, we estimate a relative loss of 51-62% in the number of genome-wide significant loci and a relative loss in polygenic score R2 of 36-38%). Hence, cross-study heterogeneity contributes to the missing heritability.\n\nAuthor SummaryLarge-scale genome-wide association studies are uncovering the genetic architecture of traits which are affected by many genetic variants. Such studies typically meta-analyze association results from multiple studies spanning different regions and/or time periods. GWAS results do not yet capture a large share of the total proportion of trait variation attributable to genetic variation. The origins of this so-called missing heritability have been strongly debated. One factor exacerbating the missing heritability is heterogeneity in the effects of genetic variants across studies. Its influence on statistical power to detect associated genetic variants and the accuracy of polygenic predictions is poorly understood. In the current study, we derive the precise effects of heterogeneity in genetic effects across studies on both the statistical power to detect associated genetic variants as well as the accuracy of polygenic predictions. We provide an online calculator, available at www.devlaming.eu, which accounts for these effects. By means of this calculator, we show that imperfect genetic correlations between studies substantially decrease statistical power and predictive accuracy and, thereby, contribute to the missing heritability. The MetaGAP calculator helps researchers to gauge how sensitive their results will be to heterogeneity in genetic effects across studies. If strong heterogeneity is expected, random-instead of fixed-effects meta-analysis methods should be used.

Genetics↗
bioRxiv2016

Shared genetics and couple-associated environment are major contributors to the risk of both clinical and self-declared depression

BackgroundBoth genetic and environmental contributions to risk of depression have been identified, but estimates of their effects are limited. Commonalities between major depressive disorder (MDD) and self-declared depression (SDD) are also unclear. Dissecting the genetic and environmental contributions to these traits and their correlation would inform the design and interpretation of genetic studies.\n\nMethodsUsing data from a large Scottish family-based cohort (GS:SFHS, N=21,387), we estimated the genetic and environmental contributions to MDD and SDD. Genetic effects associated with common genome-wide genetic variants (SNP heritability) and additional pedigree-associated genetic variation and Non-genetic effects associated with common environments were estimated using linear mixed modeling (LMM).\n\nFindingsBoth MDD and SDD had significant contributions from effects of common genetic variants, the additional genetic effect of the pedigree and the common environmental effect shared by couples. The correlation between SDD and MDD was high (r=1*00, se=0*21) for common-variant-associated genetic effects and moderate for both the additional genetic effect of the pedigree (r=0*58, se=0*08) and the couple-shared environmental effect (r=0*53, se=0*22).\n\nInterpretationBoth genetics and couple-shared environmental effects were the major factors influencing liability to depression. SDD may provide a scalable alternative to MDD in studies seeking to identify common risk variants. Rarer variants and environmental effects may however differ substantially according to different definitions of depression.\n\nFundingStudy supported by Wellcome Trust Strategic Award 104036/Z/14/Z. GS:SFHS funded by the Scottish Government Health Department, Chief Scientist Office, number CZD/16/6.

Genetics↗
bioRxiv2020

Age modulates genetic risk for multiple common diseases

Inherited genetic variation contributes to individual risk for many complex diseases and is increasingly being used for predictive patient stratification. Recent work has shown that genetic factors are not equally relevant to human traits across age and other contexts, though the reasons for such variation are not clear. Here, we introduce methods to infer the form of the relationship between genetic risk for disease and age and to test whether all genetic risk factors behave similarly. We use a proportional hazards model within an interval-based censoring methodology to estimate age-varying individual variant contributions to genetic risk for 24 common diseases within the British ancestry subset of UK Biobank, applying a Bayesian clustering approach to group variants by their risk profile over age and permutation tests for age dependency and multiplicity of profiles. We find evidence for age-varying risk profiles in nine diseases, including hypertension, skin cancer, atherosclerotic heart disease, hypothyroidism and calculus of gallbladder, several of which show evidence, albeit weak, for multiple distinct profiles of genetic risk. The predominant pattern shows genetic risk factors having the greatest impact on risk of early disease, with a monotonic decrease over time, at least for the majority of variants although the magnitude and form of the decrease varies among diseases. We show that these patterns cannot be explained by a simple model involving the presence of unobserved covariates such as environmental factors. We discuss possible models that can explain our observations and the implications for genetic risk prediction. Author summaryThe genes we inherit from our parents influence our risk for almost all diseases, from cancer to severe infections. With the explosion of genomic technologies, we are now able to use an individuals genome to make useful predictions about future disease risk. However, recent work has shown that the predictive value of genetic information varies by context, including age, sex and ethnicity. In this paper we introduce, validate and apply new statistical methods for investigating the relationship between age and genetic risk. These methods allow us to ask questions such as whether risk is constant over time, precisely how risk changes over time and whether all genetic risk factors have similar age profiles. By applying the methods to data from the UK Biobank, a prospective study of 500,000 people, we show that there is a tendency for genetic risk to decline with increasing age. We consider a series of possible explanations for the observation and conclude that there must be processes acting that we are currently unaware of, such as distinct phases of life in which genetic risk manifests itself, or interactions between genes and the environment.

genetics↗
bioRxiv2025

Transcription factor occupancy is affected by genetic variation in the white adipose tissue of C57BL6/j and 129S1/SvImJ mice

Most of the disease associated genetic variants identified in genome wide association studies have been mapped to the non-coding regions of the genome. One of the leading mechanisms by which these variants are thought to affect disease susceptibility is by altering transcription factor (TF) binding. Even though inbred mouse strains have been commonly used to investigate polygenic diseases, less is known on how their genetic differences translate to the level of gene regulation and chromatin landscape. Here, we investigated how genetic variation affects chromatin accessibility, a commonly used proxy for TF binding, and how this relates to gene regulation in the epididymal white adipose tissue (eWAT) of C57BL/6J and 129S1/SvImJ mice fed either chow or high-fat diet. We show that differences in chromatin accessibility are almost exclusively strain-specific and driven by genetic variation. In addition, we integrate ATAC-seq (chromatin accessibility) and H3K27ac ChIP-seq (active regulatory regions) data to show that tissue-specific TFs are commonly found in the active regulatory regions hosting TF binding motif altering variants in eWAT. By incorporating footprint analysis, we also show that TF occupancy is consistent with TF binding motif scores at the genetically altered loci. In addition, we validate these findings by extending the footprint analysis to ATAC-seq and H3K27ac ChIP-seq data obtained from the liver. Also, we employ RNA-seq to show that differentially expressed genes are collocated with differentially accessible regions hosting genetic variants. Overall, our findings highlight the connection between differential chromatin accessibility, TF binding and genetic variation and their role in the gene regulation across metabolically central tissues of a mouse model for polygenic obesity. Author summaryMost of the variants associated with human polygenic disease are outside the protein coding regions of the genome. In this study, we took benefit of the known genetic differences of two inbred mouse strains to investigate the impact of the non-coding genetic variation on the chromatin landscape and the regulation of gene expression in the adipose tissue. Using several complementary sequencing and bioinformatic methods, we show that chromatin accessibility at genetically different transcription factor (TF) binding sites is associated with the strength of the altered TF binding motifs. We also identify the key TFs binding to the regions of genetically determined chromatin accessibility. We further show how the genetic effects propagate to the level of gene expression. Together, these findings give valuable insight into the mechanisms by which genetic variation affects gene regulation and provide future targets for research.

genetics↗
bioRxiv2025

Review: Estimates of livestock genetic diversity in the UK

The United Kingdom is home to many local and rare livestock breeds. The local breed populations are highly adapted to specific environments in the United Kingdom and these and other rare breeds provide solutions to niche needs. Rare breeds often contribute more than expected to overall species genetic diversity, which is important because genetic variation is needed for adapting to new challenges. It is therefore very important from both United Kingdom and global perspectives to maintain genetic diversity of rare livestock breeds in the United Kingdom, and to do this, we need to evaluate the monitoring of genetic diversity to identify gaps in our knowledge and prioritise resources reserved for conservation purposes. The objectives of this study were to survey the literature to: (1) summarise genetic/genomic characterisation (effective population size (Ne) and inbreeding) of domestic populations (livestock and equine) in the United Kingdom and Ireland; (2) compare number of populations on the United Kingdoms Rare Breeds Survival Trust watch list and the number of United Kingdom and Irish populations in the peer-reviewed literature with inbreeding, genetic diversity and/or Ne estimates; and (3) compare annually reported (census-based) estimates of Ne with (inbreeding- and DNA-based) estimates from peer-reviewed literature. We found a total of 37 publications with Ne or inbreeding estimates for United Kingdom or Ireland livestock populations, published from 1975 to 2024. While many (42%) of the breeds on the Rare Breeds Survival Trust watchlist have been included in publications, there are still many breeds, and a few species (turkey, duck and geese) with no publicly available pedigree- or DNA-based genetic diversity measures. We found census-based Ne estimates were, on average, higher than DNA-based estimates, likely due to violated assumptions when estimating Ne with census-based data because of the way livestock mating systems are designed. Most peer-reviewed papers estimated genetic diversity measures using pedigree, microsatellite markers, or single nucleotide polymorphisms (SNP) markers. To identify breed-unique variants responsible for adaptive traits in rare breeds, more studies using whole-genome sequencing will be needed. Altogether, we have summarised genetic diversity estimates of United Kingdom livestock populations, identifying gaps in knowledge. ImplicationsMaintaining genetic diversity in livestock helps safeguard against future challenges to our food production systems. We surveyed publicly available measures of genetic diversity for farm animal populations in the United Kingdom (including government, Rare Breeds Survival Trust, and peer-reviewed scientific publications) to identify any gaps. We found census-based estimates of genetic diversity, which are used to inform and prioritise resources for conservation efforts, on average, are likely overestimating available genetic diversity. Additionally, most farm animal breeds had only these census-based estimates. Genetic diversity estimates could be improved using pedigree or DNA information, more accurately identifying endangered breeds.

genetics↗
bioRxiv2020

Selection in males purges the standing genetic load on female fitness

Theory predicts that the ability of selection and recombination to purge mutation load is enhanced if selection against deleterious genetic variants operates more strongly in males than females. However, direct empirical support for this tenet is limited, in part because traditional quantitative genetic approaches allow dominance and intermediate-frequency polymorphisms to obscure the effects of the many rare and partially recessive deleterious alleles that make up the main part of a populations mutation load. Here, we exposed the partially recessive genetic load of a population of Callosobruchus maculatus seed beetles via successive generations of inbreeding, and quantified its effects by measuring heterosis - the increase in fitness experienced when masking the effects of deleterious alleles by heterozygosity - in a fully factorial sex-specific diallel cross among 16 inbred strains. Competitive lifetime reproductive success (i.e. fitness) was measured in male and female outcrossed F1s as well as inbred parental selfs, and we estimated the 4x4 male-female inbred-outbred genetic covariance matrix for fitness using Bayesian Markov chain Monte Carlo simulations of a custom-made general linear mixed effects model. We found that heterosis estimated independently in males and females was highly genetically correlated among strains, and that heterosis was strongly negatively genetically correlated to outbred male, but not female, fitness. This suggests that genetic variation for fitness in males, but not in females, reflects the amount of (partially) recessive deleterious alleles segregating at mutation-selection balance in this population. The populations mutation load therefore has greater potential to be purged via selection in males. These findings contribute to our understanding of the prevalence of sexual reproduction in nature and the maintenance of genetic variation in fitness-related traits. Impact statementWhy do the large majority of eukaryotic species reproduce sexually if it means that females must spend half of their reproductive effort producing males, while males contribute few or no resources to offspring production themselves? In principle, a lineage of a mutant asexual female that simply clones herself into daughters would grow at twice the rate of her sexual competitors (all else equal). What prevents this from being the predominant mode of reproduction throughout eukaryotes? One hypothesis regards the role of males in facilitating the purging of deleterious mutations from the populations genome since very strong selection in males, unlike selection in females, can occur in many species without reductions in population offspring numbers. Due to the inherent difficulties of isolating this source of standing genetic variation for fitness, empirical evidence for this theory is mixed and limited to indirect evidence from manipulative experiments and experimental evolution studies. Here we demonstrate that recessive deleterious alleles in a population of the seed beetle, Callosobruchus maculatus, are selected against strongly in males but not females. Using a fully factorial diallel cross among 16 inbred strains, we measured the degree to which fitness in the outbred offspring of those crosses improved relative to their inbred parents. This measure is known as heterosis and offers an estimate of the relative amount of partially recessive deleterious alleles carried by a genetic strain. We then analyzed the relationship between the strains heterosis values and their additive genetic breeding values for fitness measured in males and females, revealing the extent to which segregating (partially recessive) deleterious alleles are selected against in males and females. We found that a strains heterosis value was strongly genetically correlated with its additive genetic breeding value for male fitness, but not female fitness. This suggests that mutations with deleterious effects on population growth rate due to their effects on females can be selected against (i.e. purged) more efficiently via their male siblings. This process would offer a benefit to sexual reproduction that may partly compensate for its costs, and therefore yields insight to the prevalence of sex in nature.

evolutionary biology↗
bioRxiv2021

Testing concordance and conflict in spatial replication of landscape genetics inferences

The field of landscape genetics relates habitat features and genetic information to infer dispersal and genetic connectivity between populations or individuals distributed across a landscape. Such studies usually focus on a small portion of a species range, and the degree to which these geographically restricted results can be extrapolated to different areas of a species range remains poorly understood. Studies that have focused on spatial replication in landscape genetics processes either evaluate a small number of sites, are informed by a small set of genetic markers, analyze only a small subset of environmental variables, or implement models that do not fully explore parameter space. Here, we used a broadly distributed ectothermic lizard (Sceloporus occidentalis, Western Fence lizard) as a model species to evaluate the full role of topography, climate, vegetation, and roads on dispersal and genetic differentiation. We conducted landscape genetics analyses in five areas within the Sierra Nevada mountain range, using thousands of ddRAD genetic markers distributed across the genome, implemented in the landscape genetics program ResistanceGA. Across study areas, we found a great deal of consistency in the variables impacting genetic connectivity, but also noted site-specific differences in the factors in each study area. High-elevation colder areas were consistently found to be barriers to gene flow, as were areas of high ruggedness and slope. High temperature seasonality and high precipitation during the winter wet season also presented a substantial barrier to gene flow in a majority of study areas. The effect of other landscape variables on genetic differentiation was more idiosyncratic and depended on specific attributes at each site. Vegetation type was found to substantially affect gene flow only in the southernmost Sequoia site, likely due to a higher proportion of desert habitat here, thereby fragmenting habitats that have lower costs to dispersal. The effect of roads also varied between sites and may be related to differences in road usage and amount of traffic in each area. Across study areas, canyons were always substantially implicated as facilitators to dispersal and key features linking populations and maintaining genetic connectivity across landscapes. We emphasize that spatial data layers are complex and multidimensional, and a careful consideration of associations between variables is vital to form sound conclusions about the critical factors affecting dispersal and genetic connectivity across space.

ecology↗