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bioRxiv2025

United States stakeholder insights on genetic testing for equine health and breeding

The S1094 United States Department of Agriculture (USDA) Multistate Research Project is a community of scientists working together to utilize equine genetics and genomics to improve the health and well-being of horses while also providing educational and genetic testing resources to those in the equine industry. To assess the knowledge and priorities of stakeholders regarding genetic testing, we conducted an online survey targeting horse owners and enthusiasts in the United States. Data were collected from March to October 2024 with 412 respondents from 44 states completing the survey. Most of the survey participants were horse owners, and the two most common age ranges for respondents were 31-50 (33.6%) and 51-70 (33.3%) years old. The two most common use categories for horses reported by respondents were sport and stock horses. The survey revealed that a majority of respondents were confident in their knowledge of genetics and that they relied primarily on breed and discipline organizations, as well as universities for their genetic information. Secondarily they relied upon social media and their veterinarians. Respondents expressed significant interest in genetic testing for diversity and performance traits. Across all horse use categories, arthritis, colic and intestinal disease, laminitis, metabolic disorders, and tendon and ligament issues had the greatest level of concern - all of which are complex genetic traits and will require collaborative efforts to discern levels of genetic involvement. Results from the survey underscore the need for continued outreach and education of horse owners, as well as further development of genetic testing tools to support informed decision-making among stakeholders in the equine industry. Lay SummaryGenetic testing helps horse owners, breeders, and breed registries and associations verify parentage, evaluate health, and improve performance. The S1094 USDA Multistate Research Project has a mission of improving genomics and genetics tools, as well as providing educational resources for those in the equine industry. To better understand the knowledge and priorities of stakeholders in the equine industry, we conducted an online survey netting responses from 412 individuals in the United States. Respondents were primarily horse owners who self-identified as confident in their knowledge of horse genetics. Furthermore, those responding indicated that their primary resources for information about genetics were breed and discipline association websites and universities. They also expressed interest in genetic testing for diversity and performance traits. Across all horse use categories, arthritis, colic and intestinal disease, laminitis, metabolic disorders, and tendon and ligament issues had the greatest level of concern. Given the complex nature of these conditions, collaborative efforts will be required to discern levels of genetic involvement. Respondents felt that genetic testing and education efforts have significant impacts on the equine industry because they aid owners in making breeding decisions and in the management of horse health and well-being. Teaser TextThis nationwide survey of U.S. horse owners discerned strong stakeholder interest in equine genetic testing, determined sources for knowledge acquisition, and highlighted key priorities for the development of genetic tools and educational materials.

genetics↗
bioRxiv2018

The ability of transcription factors to differentially regulate gene expression is a crucial component of the mechanism underlying inversion, a frequently observed genetic interaction pattern

Genetic interactions, a phenomenon whereby combinations of mutations lead to unexpected effects, reflect how cellular processes are wired and play an important role in complex genetic diseases. Understanding the molecular basis of genetic interactions is crucial for deciphering pathway organization as well as understanding the relationship between genetic variation and disease. Several putative molecular mechanisms have been linked to different genetic interaction types. However, differences in genetic interaction patterns and their underlying mechanisms have not yet been compared systematically between different functional gene classes. Here, differences in the occurrence and types of genetic interactions are compared for two classes, gene-specific transcription factors (GSTFs) and signaling genes (kinases and phosphatases). Genome-wide gene expression data for 63 single and double deletion mutants in bakers yeast reveals that the two most common genetic interaction patterns are buffering and inversion. Buffering is typically associated with redundancy and is well understood. In inversion, genes show opposite behavior in the double mutant compared to the corresponding single mutants. The underlying mechanism is poorly understood. Although both classes show buffering and inversion patterns, the prevalence of inversion is much stronger in GSTFs. To decipher potential mechanisms, a Petri Net modeling approach was employed, where genes are represented as nodes and relationships between genes as edges. This allowed over 9 million possible three and four node models to be exhaustively enumerated. The models show that a quantitative difference in interaction strength is a strict requirement for obtaining inversion. In addition, this difference is frequently accompanied with a second gene that shows buffering. Taken together, these results provide a mechanistic explanation for inversion. Furthermore, the ability of transcription factors to differentially regulate expression of their targets provides a likely explanation why inversion is more prevalent for GSTFs compared to kinases and phosphatases.\n\nAuthor SummaryThe relationship between genotype and phenotype is one of the major challenges in biology. While many previous studies have identified genes involved in complex genetic diseases, there is still a gap between genotype and phenotype. One of the difficulties in filling this gap has been attributed to genetic interactions. Large-scale studies have revealed that genetic interactions are widespread in model organisms such as bakers yeast. Several molecular mechanisms have been proposed for different genetic interaction types. However, differences in occurrence and underlying molecular mechanism of genetic interactions have not yet been compared between gene classes of different function. Here, we compared genetic interaction patterns identified using gene expression profiling for two classes of genes: gene specific transcription factors and signaling related genes. We modelled all possible molecular networks to unravel putative molecular differences underlying different genetic interaction patterns. Our study proposes a new mechanistic explanation for a certain genetic interaction pattern that is more strongly associated with transcription factors compared to signaling related genes. Overall, our findings and the computational methodologies implemented here can be valuable for understanding the molecular mechanisms underlying genetic interactions.

genetics↗
bioRxiv2017

A genome resequencing-based genetic map reveals the recombination landscape of an outbred parasitic nematode in the presence of polyploidy and polyandry

The parasitic nematode Haemonchus contortus is an economically and clinically important pathogen of small ruminants, and a model system for understanding the mechanisms and evolution of traits such as anthelmintic resistance. Anthelmintic resistance is widespread and is a major threat to the sustainability of livestock agriculture globally; however, little is known about the genome architecture and parameters such as recombination that will ultimately influence the rate at which resistance may evolve and spread. Here we performed a genetic cross between two divergent strains of H. contortus, and subsequently used whole-genome re-sequencing of a female worm and her brood to identify the distribution of genome-wide variation that characterises these strains. Using a novel bioinformatic approach to identify variants that segregate as expected in a pseudo-testcross, we characterised linkage groups and estimated genetic distances between markers to generate a chromosome-scale F1 genetic map composed of 1,618 SNPs. We exploited this map to reveal the recombination landscape, the first for any parasitic helminth species, demonstrating extensive variation in recombination rate within and between chromosomes. Analyses of these data also revealed the extent of polyandry, whereby at least eight males were found to have contributed to the genetic variation of the progeny analysed. Triploid offspring were also identified, which we hypothesise are the result of nondisjunction during female meiosis or polyspermy. These results expand our knowledge of the genetics of parasitic helminths and the unusual life-history of H. contortus, and will enable more precise characterisation of the evolution and inheritance of genetic traits such as anthelmintic resistance. This study also demonstrates the feasibility of whole-genome resequencing data to directly construct a genetic map in a single generation cross from a non-inbred non-model organism with a complex lifecycle.\n\nAuthor summaryRecombination is a key genetic process, responsible for the generation of novel genotypes and subsequent phenotypic variation as a result of crossing over between homologous chromosomes. Populations of strongylid nematodes, such as the gastrointestinal parasites that infect livestock and humans, are genetically very diverse, but little is known about patterns of recombination across the genome and how this may contribute to the genetics and evolution of these pathogens. In this study, we performed a genetic cross to quantify recombination in the barbers pole worm, Haemonchus contortus, an important parasite of sheep and goats. The reproductive traits of this worm make standard genetic crosses challenging, but by generating whole-genome sequence data from a female worm and her offspring, we identified genetic variants that act as though they come from a single mating cross, allowing the use of standard statistical approaches to build a genetic map and explore the distribution and rates of recombination throughout the genome. A number of genetic signatures associated with H. contortus life history traits were revealed in this analysis: we extend our understanding of multiple paternity (polyandry) in this species, and provide evidence and explanation for sporadic increases in chromosome complements (polyploidy) among the progeny. The resulting genetic map will aid in population genomic studies in general and enhance ongoing efforts to understand the genetic basis of resistance to the drugs used to control these worms, as well as for related species that infect humans throughout the world.

genomics↗
bioRxiv2021

The genetic code is very close to a global optimum in a model of its origin taking into account both the partition energy of amino acids and their biosynthetic relationships

We used the Morans I index of global spatial autocorrelation with the aim of studying the distribution of the physicochemical or biological properties of amino acids within the genetic code table. First, using this index we are able to identify the amino acid property - among the 530 analyzed - that best correlates with the organization of the genetic code in the set of amino acid permutation codes. Considering, then, a model suggested by the coevolution theory of the genetic code origin - which in addition to the biosynthetic relationships between amino acids took into account also their physicochemical properties - we investigated the level of optimization achieved by these properties either on the entire genetic code table, or only on its columns or only on its rows. Specifically, we estimated the optimization achieved in the restricted set of amino acid permutation codes subject to the constraints derived from the biosynthetic classes of amino acids, in which we identify the most optimized amino acid property among all those present in the database. Unlike what has been claimed in the literature, it would appear that it was not the polarity of amino acids that structured the genetic code, but that it could have been their partition energy instead. In actual fact, it would seem to reach an optimization level of about 96% on the whole table of the genetic code and 98% on its columns. Given that this result has been obtained for amino acid permutation codes subject to biosynthetic constraints, that is to say, for a model of the genetic code consistent with the coevolution theory, we should consider the following conclusions reasonable. (i) The coevolution theory might be corroborated by these observations because the model used referred to the biosynthetic relationships between amino acids, which are suggested by this theory as having been fundamental in structuring the genetic code. (ii) The very high optimization on the columns of the genetic code would not only be compatible but would further corroborate the coevolution theory because this suggests that, as the genetic code was structured along its rows by the biosynthetic relationships of amino acids, on its columns strong selective pressure might have been put in place to minimize, for example, the deleterious effects of translation errors. (iii) The finding that partition energy could be the most optimized property of amino acids in the genetic code would in turn be consistent with one of the main predictions of the coevolution theory. In other words, since the partition energy is reflective of the protein structure and therefore of the enzymatic catalysis, the latter might really have been the main selective pressure that would have promoted the origin of the genetic code. Indeed, we observe that the {beta}-strands show an optimization percentage of 94.45%, so it is possible to hypothesize that they might have become the object of selection during the origin of the genetic code, conditioning the choice of biosynthetic relationships between amino acids. (iv) The finding that the polarity of amino acids is less optimized than their partition energy in the genetic code table might be interpreted against the physicochemical theories of the origin of the genetic code because these would suggest, for example, that a very high optimization of the polarity of amino acids in the code could be an expression of interactions between amino acids and codons or anticodons, which would have promoted their origin. This might now become less sustainable, given the very high optimization that is instead observed in favor of partition energy but not polarity. Finally, (v) the very high optimization of the partition energy of amino acids would seem to make a neutral origin of the ability of the genetic code to buffer, for example, the deleterious effects of translation errors very unlikely. Indeed, an optimization of about 100% would seem that it might not have been achieved by a simple neutral process, but this ability should probably have been generated instead by the intervention of natural selection. In actual fact, we show that the neutral hypothesis of the origin of error minimization has been falsified for the model analyzed here. Therefore, we will discuss our observations within the theories proposed to explain the origin of the organization of the genetic code, reaching the conclusion that the coevolution theory is the most strongly corroborated theory.

evolutionary biology↗
bioRxiv2019

FINE-SCALE POPULATION GENETIC STRUCTURE OF DENGUE MOSQUITO VECTOR, Aedes aegypti AND ITS ASSOCIATION TO LOCAL DENGUE INCIDENCE

Dengue fever is an important arthropod-borne disease which is transmitted by the mosquito vector, Aedes aegypti. Vector control programs rely heavily on targeting the mosquito vector in order to stop the disease transmission cycle. Hence, the present study conducted a fine-scale population genetics of Ae. aegypti in a highly urbanized and dengue endemic region in the Philippines. Furthermore, the study also explored the correlation of population genetic indices to the local dengue incidence of the region. The genetic diversity and population structure of Ae. aegypti populations were analyzed by genotyping 11 microsatellite loci from 526 adult mosquitoes sampled in 21 study areas in Metropolitan Manila. Five genetic indices and its dengue incidence were then correlated using Pearsons correlation. Results showed low genetic differentiation among mosquito populations indicating high gene flow activity in the region. However, the study also revealed a considerable number of inferred genetic clusters (K=5). The constructed UPGMA dendrogram exhibited close proximity of genetically-similar Ae. aegypti mosquito populations that extends in long distances suggesting passive dispersal ability of the mosquito vector. Moreover, a positive and significant correlation was observed between dengue incidence and inbreeding coefficient (Fis) (r = 0.52, p = 0.02). Overall, the study showed that population genetic structuring can occur in a fine-scale area which consisted notable clustering and extending patterns of genetically-similar mosquito populations. This infers the potential migration ability of Ae. aegypti in different locations of the region where specific vector control zones could be carried out to disrupt its dispersal ability. Also, this is the first study that attempted to correlate genetic indices to dengue incidence that could serve as a supplementary index in identifying high dengue risk areas in the future. AUTHOR SUMMARYDengue disease puts billions of people worldwide at risk. To mitigate this risk, population genetic studies of its vector, Aedes aegypti, are being conducted. The information established from these studies can be utilized to reduce mosquito population and thereby, reduce the opportunity for dengue transmission. In this study, we used microsatellite markers to determine genetic structure and diversity followed by correlation analyses between genetic indices and dengue incidence. Results show a low genetic differentiation among mosquito populations in Metro Manila; it also indicates population genetic structuring in a fine-scale area. This suggest a pattern of migration activity of Ae. aegpyti which can be used to mitigate dengue transmission. Moreover, the study also explored in correlating genetic indices and local dengue incidence where it demonstrated significant correlation with the inbreeding coefficient (Fis). Further investigation is needed on how these genetic indices may be utilized in predicting and identifying high dengue risk areas in endemic areas.

zoology↗
bioRxiv2017

Genomic Relatedness Strengthens Genetic Connectedness Across Management Units

Genetic connectedness refers to a measure of genetic relatedness across management units (e.g., herds and flocks). With the presence of high genetic connectedness in management units, best linear unbiased prediction (BLUP) is known to provide reliable comparisons between genetic values. Genetic connectedness has been studied for pedigree-based BLUP; however, relatively little attention has been paid to using genomic information to measure connectedness. In this study, we assessed genome-based connectedness across management units by applying prediction error variance of difference (PEVD), coefficient of determination (CD), and prediction error correlation (r) to a combination of computer simulation and real data (mice and cattle). We found that genomic information (G) increased the estimate of connectedness among individuals from different management units compared to that based on pedigree (A). A disconnected design benefited the most. In both datasets, PEVD and CD statistics inferred increased connectedness across units when using G- rather than A-based relatedness suggesting stronger connectedness. With r once using allele frequencies equal to one-half or scaling G to values between 0 and 2, which is intrinsic to A, connectedness also increased with genomic information. However, PEVD occasionally increased, and r decreased when obtained using the alternative form of G, instead suggesting less connectedness. Such inconsistencies were not found with CD. We contend that genomic relatedness strengthens measures of genetic connectedness across units and has the potential to aid genomic evaluation of livestock species.\n\nThe problem of connectedness or disconnectedness is particularly important in genetic evaluation of managed populations such as domesticated livestock. When selecting among animals from different management units (e.g., herds and flocks), caution is needed; choosing one animal over others across management units may be associated with greater uncertainty than selection within management units. Such uncertainty is reduced if individuals from different management units are genetically linked or connected. In such a case, best linear unbiased prediction (BLUP) offers meaningful comparison of the breeding values across management units for genetic evaluation (e.g., Kuehn et al., 2007).\n\nStructures of breeding programs have a direct influence on levels of connectedness. Wide use of artificial insemination (AI) programs generally increases genetic connectedness across management units. For example, dairy cattle populations are considered highly connected due to dissemination of genetic material from a small number of highly selected sires. The situation may be different for species with less use of AI and more use of natural service mating such as for beef cattle or sheep populations. Under these scenarios, the magnitude of connectedness across management units is reduced and genetic links are largely confined within management units.\n\nPedigree-based genetic connectedness has been evaluated and applied in practice (e.g., Kuehn et al., 2009; Eikje and Lewis, 2015). However, there is a relative paucity of use of genomic information such as single nucletide polymorphisms (SNPs) to ascertain connectedness. It still remains elusive in what scenarios genomics can strengthen connectedness and how much gain can be expected relative to use of pedigree information alone. Connectedness statistics have been used to optimize selective genotyping and phenotyping in simulated livestock (Pszczola et al., 2012) and plant populations (Maenhout et al., 2010), and in real maize (Rincent et al., 2012; Isidro et al., 2015), and real rice data (Isidro et al., 2015). These studies concluded that the greater the connectedness between the reference and validation populations, the greater the predictive performance. However, 1) connectedness among different management units and 2) differences in connectedness measures between pedigree and genomic relatedness were not explored in those studies. For better understanding of genome-based connectedness, it is critical to examine how the presence of management units comes into play. For instance, genomic relatedness provides relationships between distant individuals that appear disconnected according to the pedigree information. In addition, it captures Mendelian sampling that is not present in pedigree relationships (Hill and Weir, 2011). Thus, genomic information is expected to strengthen measures of connectedness, which in turn refines comparisons of genetic values across different management units. The objective of this study was to assess measures of genetic connectedness across management units with use of genomic information. We leveraged the combination of real data and computer simulation to compare gains in measures of connectedness when moving from pedigree to genomic relationships. First, we studied a heterogenous mice dataset stratified by cage. Then we investigated approaches to measure connectedness using real cattle data coupled with simulated management units to have greater control over the degree of confounding between fixed management groups and genetic relationships.

genetics↗
bioRxiv2021

Genetic nurture effects on education: a systematic review and meta-analysis

Child educational development is associated with major psychological, social, economic and health milestones throughout the life course. Understanding the early origins of educational inequalities and their reproduction across generations is therefore crucial. Recent genomic studies provide novel insights in this regard, uncovering "genetic nurture" effects, whereby parental genotypes influence offsprings educational development via environmental pathways rather than genetic transmission. These findings have yet to be systematically appraised. We conducted the first systematic review and meta-analysis to quantify genetic nurture effects on educational outcomes and investigate key moderators. Twelve studies comprising 38,654 distinct parent(s)-offspring pairs or trios from eight cohorts were included, from which we derived 22 estimates of genetic nurture effects. Multilevel random effects models showed that the effect of genetic nurture on offsprings educational outcomes ({beta}genetic nurture = 0.08, 95% CI [0.07, 0.09]) was about half the size of direct genetic effects ({beta}direct genetic = 0.17, 95% CI [0.13, 0.20]). Maternal and paternal genetic nurture effects were similar in magnitude, suggesting comparable roles of mothers and fathers in determining their childrens educational outcomes. Genetic nurture effects were largely explained by parental educational level and family socioeconomic status, suggesting that genetically influenced environments play an important role in shaping child educational outcomes. Even after accounting for genetic transmission, we provide evidence that environmentally mediated parental genetic influences contribute to the intergenerational transmission of educational outcomes. Further exploring these downstream environmental pathways may inform educational policies aiming to break the intergenerational cycle of educational underachievement and foster social mobility. Public Significance StatementThis meta-analysis demonstrates that parents genetics influence their childrens educational outcomes through the rearing environments that parents provide. This "genetic nurture" effect is largely explained by family socioeconomic status and parental education level, is similar for mothers and fathers (suggesting that both parents equally shape their childrens educational outcomes) and is about half the size of direct genetic effects on childrens educational outcomes. Interventions targeting such environmental pathways could help to break the intergenerational cycle of educational underachievement and foster social mobility.

genetics↗
bioRxiv2025

Trajectories of genetic correlations in populations under selection: from theory to a case-study

BackgroundBreeding programs select for multiple commercial traits, aiming to achieve genetic progress for all. Often, selection is based on a selection index, i.e. a linear combination of traits with weights defined by, among other information, the genetic correlation between traits. These correlations are typically estimated as a static parameter, and assumed equal to all individuals and generations. While research on the consequences of selection to genetic variances (Bulmer effect) is widely available, only a few studies focused on the consequences of selection to genetic correlations. Our study extended the already existing inferences about how selection affects genetic variances, to how multi-trait selection affects genetic correlations. In order to further our understanding of genetic correlations, we also proposed an alternative method to calculate genetic correlations between traits at the individual level, called by us as individualized sire genetic correlation (iSGC), obtained through the estimated breeding values (EBV) from evaluated daughters. Lastly, a case-study was performed on thirty years of data from the French Holstein dairy cattle population, for five traits studied pairwise: milk and protein yield, milking speed, somatic cell score, and cow conception rate. ResultsTheory revealed that multi-trait selection leads to an attenuation (decrease) of positive genetic correlations, with potential to revert them to negative values, if initially low. Uncorrelated traits will become negatively correlated, and negative genetic correlations will be either intensified or attenuated (decrease or increase, respectively), depending on selection intensity, weights applied to the selection index, and the initial genetic correlation. ConclusionBoth theory and empirical results on real data confirm that selection does change the genetic correlation between traits in a population under selection. Moreover, empirical trajectories of the iSGC were in better agreement with the theory, than trajectories of populational genetic correlations. The iSGC searches for individual-specific patterns of correlations, and since it is measured on sires through the EBV of their daughters, it also considers the recombination of the genetic background. Along with the fact that trajectories of iSGC were in better agreement with theory, we believe it to be a potentially less biased measure of genetic correlations between traits.

genetics↗
bioRxiv2026

A disjunct distribution and population fragmentation shape rangewide genetic diversity and structure of the endangered Physaria globosa (Brassicaceae)

Population genetic analysis of species of conservation concern provides information to devise management plans to effectively conserve the genetic variation of endangered species. One such endangered plant, Physaria globosa is a federally endangered species in the mustard family with a geographically restricted range that occurs in four disjunct locations in Indiana, Kentucky, and Tennessee (i.e., Highland Rim and Nashville Basin regions) and along the Wabash, Kentucky, and Cumberland Rivers. In this study, we sampled populations from throughout the range of P. globosa, genotyped them using 20 microsatellite loci, and assessed genetic diversity and structure within and among populations. The goals of the study were to understand: 1) levels of genetic diversity in P. globosa and whether populations show evidence of having experienced reductions in genetic diversity as the result of genetic bottlenecks, genetic drift, or inbreeding, 2) rangewide genetic diversity and structure in P. globosa and how genetic structure is affected by the disjunctions in the species range, and 3) implications for prioritization of in-situ and ex-situ conservation efforts. On average, P. globosa showed comparable levels of genetic diversity to other species of Physaria. However, some populations showed evidence of inbreeding, genetic bottlenecks, or decreases in genetic diversity, possibly due to anthropogenic or climate-related pressures and decreases in population size due to competition with invasive bush honeysuckle. Genetic variation was strongly structured into two main geographic groups, one in the northern part of the species range (KY and IN), and the other in the southern part of the species range (TN), but some populations likely originated via long-distance dispersal. We also found significant isolation by distance, likely due to both life history characteristics and physical barriers associated with the complex topological structure of the landscape occupied by P. globosa, limiting population connectivity. Given the strong genetic structure found in P. globosa, several populations should be protected and managed within each geographic region to conserve genetic variation. Ex situ conservation will also be important to protect genetic diversity, particularly for populations that are difficult to access and manage.

genetics↗
bioRxiv2020

Life tables shape genetic diversity in marine fishes

Genetic diversity varies among species due to a range of eco-evolutionary processes that are not fully understood. The neutral theory predicts that the amount of variation in the genome sequence between different individuals of the same species should increase with its effective population size (Ne). In real populations, multiple factors that modulate the variance in reproductive success among individuals cause Ne to differ from the total number of individuals (N). Among these, age-specific mortality and fecundity rates are known to have a direct impact on the [Formula] ratio. However, the extent to which vital rates account for differences in genetic diversity among species remains unknown. Here, we addressed this question by comparing genome-wide genetic diversity across 16 marine fish species with similar geographic distributions but contrasted lifespan and age-specific survivorship and fecundity curves. We sequenced the whole genome of 300 individuals to high coverage and assessed their genome-wide heterozygosity with a reference-free approach. Genetic diversity varied from 0.2 to 1.4% among species, and showed a negative correlation with adult lifespan, with a large negative effect (slope = - 0.089 per additional year of lifespan) that was further increased when brooding species providing intense parental care were removed from the dataset (slope = -0.129 per additional year of lifespan). Using published vital rates for each species, we showed that the [Formula] ratio resulting simply from life tables parameters can predict the observed differences in genetic diversity among species. Using simulations, we further found that the extent of reduction in [Formula] with increasing adult lifespan is particularly strong under Type III survivorship curves (high juvenile and low adult mortality) and increasing fecundity with age, a typical characteristic of marine fishes. Our study highlights the importance of vital rates as key determinants of species genetic diversity levels in nature. Author SummaryUnderstanding how and why genetic diversity varies across species has important implications for evolutionary and conservation biology. Although genomics has vastly improved our ability to document intraspecific DNA sequence variation at the genome level, the range and determinants of genetic diversity remain partially understood. At a broad taxonomic scale in eukaryotes, the main determinants of diversity are reproductive strategies distributed along a trade-off between the quantity and the size of offspring, which likely affect the long-term effective population size. Long-lived species also tend to show lower genetic diversity, a result which has however not been reported by comparative studies of genetic diversity at lower taxonomic scales. Here, we compared genetic diversity across 16 European marine fish species showing marked differences in longevity. Adult lifespan was the best predictor of genetic diversity, with genome-wide average heterozygosity ranging from 0.2% in the black anglerfish (L. budegassa) to 1.4% in the European pilchard (S. pilchardus). Using life tables summarizing age-specific mortality and fecundity rates for each species, we showed that the variance in lifetime reproductive success resulting from age structure, iteroparity and overlapping generations can predict the range of observed differences in genetic diversity among marine fish species. We then used computer simulations to explore how combinations of vital rates characterizing different life histories affect the relationship between adult lifespan and genetic diversity. We found that marine fishes that display high juvenile but low adult mortality, and increasing fecundity with age, are typically expected to show reduced genetic diversity with increased adult lifespan. However, the impact of adult lifespan vanished using bird and mammal-like vital rates. Our study shows that variance in lifetime reproductive success can have a major impact on species genetic diversity and explains why this effect varies widely across taxonomic groups.

evolutionary biology↗
bioRxiv2019

Invaders weather the weather: rapid adaptation to a novel environment occurs despite reduced genetic diversity

Invasive species often exhibit rapid evolution in their introduced ranges despite the genetic bottlenecks that are thought to accompany the translocation of small numbers of founders; however, some invasions may not fit this "genetic paradox." The invasive cane toad (Rhinella marina) displays high phenotypic variation across its environmentally heterogeneous introduced Australian range. Here, we used three genome-wide datasets to characterize population structure and genetic diversity in invasive toads: RNA-Seq data generated from spleens sampled from the toads native range in French Guiana, the introduced population in Hawaii that was the source of Australian founders, and Australia; RNA-Seq data generated from brains sampled more extensively in Hawaii and Australia; and previously published RADSeq data from transects across Australia. We found that toads form three genetic clusters: (1) native range toads, (2) toads from the source population in Hawaii and long-established areas near introduction sites in Australia, and (3) toads from more recently established northern Australian sites. In addition to strong divergence between native and invasive populations, we find evidence for a reduction in genetic diversity after introduction. However, we do not see this reduction in loci putatively under selection, suggesting that genetic diversity may have been maintained at ecologically relevant traits, or that mutation rates were high enough to maintain adaptive potential. Nonetheless, cane toads encounter novel environmental challenges in Australia and appear to respond to selection across environmental breaks; the transition between genetic clusters occurs at a point along the invasion transect where temperature rises and rainfall decreases. We identify loci known to be involved in resistance to heat and dehydration that show evidence of selection in Australian toads. Despite well-known predictions regarding genetic drift and spatial sorting during invasion, this study highlights that natural selection occurs rapidly and plays a vital role in shaping the structure of invasive populations. Author SummaryDespite longstanding evidence for the link between genetic diversity and population viability, the "genetic paradox" concept reflects the observation that invasive populations are successful in novel environments despite a putative reduction in genetic diversity. However, some recent studies have suggested that successful invasions may often occur due to an absence of obstacles such as genetic diversity loss or novel adaptive challenges. The recent emergence of genome-wide technologies provides us with the tools to study this question comprehensively by assessing both overall genetic diversity, and diversity of loci that underlie ecologically relevant traits. The invasive cane toad is a useful model because there is abundant phenotypic evidence of rapid adaptation during invasion. Our results suggest strong genetic divergence between native and invasive populations, and a reduction in overall genetic diversity; however, we do not see this reduction when solely assessing ecologically relevant loci. This could be for reasons that support or refute the genetic paradox. Further studies may provide perspectives from other systems, allowing us to explore how variables such as propagule size affect the fit of an invasion to the model of the paradox. Studying invasive species remains important due to their largely negative impacts on the environment and economy.

evolutionary biology↗
bioRxiv2024

Optimal implementation of genomic selection in clone breeding programs - exemplified in potato: II. Effect of selection strategy and cross-selection method on long-term genetic gain

Different cross-selection (CS) methods incorporating genomic selection (GS) have been used in diploid species to improve long-term genetic gain and preserve diversity. However, their application to heterozygous and autotetraploid crops such as potato is lacking so far. The objectives of our study were to (i) assess how different CS methods incorporating GS with or without consideration of genetic variability affect both short- and long-term genetic gains compared to strategies using phenotypic selection (PS); (ii) evaluate the changes in genetic variability and the efficiency of converting diversity into genetic gain across different CS methods; and (iii) investigate the interaction effects between different genetic architectures and CS methods on long-term genetic gain. In our simulation results, implementing GS with optimal selection intensities had a higher short- and long-term genetic gain compared to any PS strategy. The CS method considering additive and dominance effects to predict progeny mean based on simulated progenies (MEGV-O) reached the highest long-term genetic gain among the assessed mean-based CS methods. Compared to MEGV-O and usefulness criteria (UC), the linear combination of UC and genome-wide diversity (called EUCD) kept the same level of genetic gain but resulted in a higher diversity and a lower number of fixed QTL. Moreover, EUCD had a relatively high efficiency in converting diversity into genetic gain. However, choosing the most appropriate weight to account for diversity in EUCD depends on the genetic architecture of the target trait and the breeders objectives. Our results provide breeders with concrete methods to improve their potato breeding programs. Key messageCross-selection method considering progeny mean performance and genetic variability can improve long-term genetic gain and keep genetic diversity in clone breeding.

genetics↗
bioRxiv2026

Joint modeling of social genetic effects in mono- and pluri-specific groups: case study in intercrops

The genetics of interspecific groups remains largely unexplored, despite the central role of social (or indirect) genetic effects in shaping phenotypic expression within communities. Intercropping, i.e. the simultaneous cultivation of multiple crop species in the same field, offers a powerful model to harness these interspecific social effects. Such species mixtures provide well-documented agricultural benefits, yet few breeding frameworks have integrated the genetics of social interactions. Here, we address this gap by extending quantitative genetic theory to interspecific groups, with intercropping as a concrete and applied model case. We propose a quantitative genetic model that jointly analyzes intra and interspecific interactions within a unifying framework. Breeding values are decomposed into a direct component, shared in mono and mixed-crops, an interspecific social component corresponding to the effect of one species on another, and an intraspecific component that captures the social effects within a mono-genotypic stand of cloned plants. Statistically, this consists in simultaneously fitting several linear mixed models, one per stand type, all having direct breeding values in common. As no open-source software can fit such a complex mixed model, we provide such an implementation in C++ available in the R package plantmix. Simulations across various genetic (co)variance structures and sparse experimental designs showed accurate estimation of all genetic (co)variances and breeding values. With an incomplete, yet balanced design combining sole crops and intercrops, genetic gains in both systems were achievable simultaneously, enabling breeding strategies that progressively integrate intercropping into existing, sole-crop-only schemes. More broadly, this framework allows dissecting direct and social genetic effects when genotypes are observed in mono- and mixed-species situations, cultivated or not. Article summaryThe genetics of interspecific groups remains largely unexplored, despite the central role of social genetic effects in shaping phenotypic expression within communities. We address this gap by extending quantitative genetic theory to interspecific groups, with intercropping as an example. We propose a quantitative genetic model that jointly analyzes intra- and interspecific interactions within a unifying framework. Simulations showed accurate estimation of genetic (co)variances and breeding values. This framework supports breeding strategies where breeders want to gradually integrate pluri-specific trials in their mono-specific platforms, and allows dissecting direct and social genetic effects when genotypes are observed in mono- and mixed-species situations.

genetics↗
bioRxiv2014

A Genome-Wide Association Analysis Reveals Epistatic Cancellation of Additive Genetic Variance for Root Length in Arabidopsis thaliana

Efforts to identify loci underlying complex traits generally assume that most genetic variance is additive. Here, we examined the genetics of Arabidopsis thaliana root length and found that the narrow-sense heritability for this trait was statistically zero. This low additive genetic variance likely explains why no associations to root length could be found using standard additive-model-based genome-wide association (GWA) approaches. However, the broad-sense heritability for root length was significantly larger, and we therefore also performed an epistatic GWA analysis to map loci contributing to the epistatic genetic variance. This analysis revealed four interacting pairs involving seven chromosomal loci that passed a standard multiple-testing corrected significance threshold. Explorations of the genotype-phenotype maps for these pairs revealed that the detected epistasis cancelled out the additive genetic variance, explaining why these loci were not detected in the additive GWA analysis. Small population sizes, such as in our experiment, increase the risk of identifying false epistatic interactions due to testing for associations with very large numbers of multi-marker genotypes in few phenotyped individuals. Therefore, we estimated the false-positive risk using a new statistical approach that suggested half of the associated pairs to be true positive associations. Our experimental evaluation of candidate genes within the seven associated loci suggests that this estimate is conservative; we identified functional candidate genes that affected root development in four loci that were part of three of the pairs. In summary, statistical epistatic analyses were found to be indispensable for confirming known, and identifying several new, functional candidate genes for root length using a population of wild-collected A. thaliana accessions. We also illustrated how epistatic cancellation of the additive genetic variance resulted in an insignificant narrow-sense, but significant broad-sense heritability that could be dissected into the contributions of several individual loci using a combination of careful statistical epistatic analyses and functional genetic experiments.\n\nAuthor summaryComplex traits, such as many human diseases or climate adaptation and production traits in crops, arise through the action and interaction of many genes and environmental factors. Classic approaches to identify contributing genes generally assume that these factors contribute mainly additive genetic variance. Recent methods, such as genome-wide association studies, often adhere to this additive genetics paradigm. However, additive models of complex traits do not reflect that genes can also contribute with non-additive genetic variance. In this study, we use Arabidopsis thaliana to determine the additive and non-additive genetic contributions to the phenotypic variation in root length. Surprisingly, much of the observed phenotypic variation in root length across genetically divergent strains was explained by epistasis. We mapped seven loci contributing to the epistatic genetic variance and validated four genes in these loci with mutant analysis. For three of these genes, this is their first implication in root development. Together, our results emphasize the importance of considering both non-additive and additive genetic variance when dissecting complex trait variation, in order not to lose sensitivity in genetic analyses.

Genomics↗
bioRxiv2016

Genetic Allee effects and their interaction with ecological Allee effects

SummaryO_LIIt is now widely accepted that genetic processes such as inbreeding depression and loss of genetic variation can increase the extinction risk of small populations. However, it is generally unclear whether extinction risk from genetic causes gradually increases with decreasing population size or whether there is a sharp transition around a specific threshold population size. In the ecological literature, such threshold phenomena are called \"strong Allee effects\" and they can arise for example from mate limitation in small populations.\nC_LIO_LIIn this study, we aim to a) develop a meaningful notion of a \"strong genetic Allee effect\", b) explore whether and under what conditions such an effect can arise from inbreeding depression due to recessive deleterious mutations, and c) quantify the interaction of potential genetic Allee effects with the well-known mate-finding Allee effect.\nC_LIO_LIWe define a strong genetic Allee effect as a genetic process that causes a populations survival probability to be a sigmoid function of its initial size. The inflection point of this function defines the critical population size. To characterize survival-probability curves, we develop and analyze simple stochastic models for the ecology and genetics of small populations.\nC_LIO_LIOur results indicate that inbreeding depression can indeed cause a strong genetic Allee effect, but only if individuals carry sufficiently many deleterious mutations (lethal equivalents) on average and if these mutations are spread across sufficiently many loci. Populations suffering from a genetic Allee effect often first grow, then decline as inbreeding depression sets in, and then potentially recover as deleterious mutations are purged. Critical population sizes of ecological and genetic Allee effects appear to be often additive, but even superadditive interactions are possible.\nC_LIO_LIMany published estimates for the number of lethal equivalents in birds and mammals fall in the parameter range where strong genetic Allee effects are expected. Unfortunately, extinction risk due to genetic Allee effects can easily be underestimated as populations with genetic problems often grow initially, but then crash later. Also interactions between ecological and genetic Allee effects can be strong and should not be neglected when assessing the viability of endangered or introduced populations.\nC_LI

Ecology↗
bioRxiv2022

Genomic Structural Equation Modeling Reveals Latent Phenotypes in the Human Cortex with Distinct Genetic Architecture

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from anatomical, functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to model the genetic architecture of cortical surface area (SA) and cortical thickness (CT) of 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance. Genomic SEM can fit a multivariate GWAS from summary statistics, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA was explained by 6 latent factors and CT was explained by 4 latent factors. The multivariate GWAS of these latent factors identified 74 genome-wide significant (GWS) loci (p<5x10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of latent factor GWAS results found that SA-derived factors had a positive genetic correlation with bipolar disorder (BPD), and major depressive disorder (MDD), and a negative genetic correlation with attention deficit hyperactivity disorder (ADHD), MDD, and insomnia, while CT factors displayed a negative genetic correlation with alcohol dependence. Jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across phenotypes offers a new vantage point for mapping genetically informed cortical networks. HIGHLIGHTSO_LIGenomic SEM can examine genetic correlation across cortical regions. C_LIO_LIWe inferred regional genetic networks of cortical thickness and surface area. C_LIO_LINetwork-associated variants have been implicated in multiple traits. C_LIO_LIThese networks are genetically correlated with several psychiatric disorders including MDD, bipolar, ADHD, and alcohol dependence. C_LI

genomics↗
bioRxiv2023

Parental care drives the evolution of molecular genetic variation

Cooperative social behaviours, such as parental care, have long been hypothesized to relax selection leading to the accumulation of genetic variation in populations. Although the idea has been discussed for decades, there has been relatively little experimental work to investigate how social behaviour contributes to genetic variation in populations. Here, we investigate how parental care can shape molecular genetic variation in the subsocial insect Nicrophorus vespilloides. Using whole genome sequencing of populations that had evolved in the presence or absence of parental care for 30 generations, we show that parental care maintains levels of standing genetic variation. In contrast, under a harsh environment without care, strong directional selection caused a reduction in genetic variation. Furthermore, we show that adaptation to the loss of care is associated with genetic divergence between populations at loci related to stress, morphological development and transcriptional regulation. These data reveal how social behaviour is linked to the genetic processes that shape and maintain genetic diversity within populations, and provides rare empirical evidence for an old hypothesis. Lay SummarySocial behaviours, such as parental care, have long been hypothesized to result in the accumulation of genetic variation in populations. Here, we investigate how parental care can shape molecular genetic variation in a species that perform biparental care, Nicrophorus vespilloides. Using genome sequencing of populations that had evolved in the presence or absence of parental care for 30 generations, we show that parental care maintains levels of standing genetic variation. In contrast, under a harsh environment without care, populations lost genetic variation. Furthermore, we show that adaptation to the loss of care is associated with genetic divergence between populations at genes related to stress, morphological development and transcriptional regulation. These data reveal how social behaviour is linked to the genetic processes that shape and maintain genetic diversity within populations.

evolutionary biology↗
bioRxiv2025

Jointly representing long-range genetic similarity and spatially heterogeneous isolation-by-distance

Isolation-by-distance patterns in genetic variation are a widespread feature of the geo-graphic structure of genetic variation in many species, and many methods have been developed to illuminate such patterns in genetic data. However, long-range genetic similarities also exist, often as a result of rare or episodic long-range gene flow. Jointly characterizing patterns of isolation-by-distance and long-range genetic similarity in genetic data is an open data analysis challenge that, if resolved, could help produce more complete representations of the geographic structure of genetic data in any given species. Here, we present a computationally tractable method that identifies long-range genetic similarities in a background of spatially heterogeneous isolation-by-distance variation. The method uses a coalescent-based framework, and models long-range genetic similarity in terms of directional events with source fractions describing the fraction of ancestry at a location tracing back to a remote source. The method produces geographic maps annotated with inferred long-range edges, as well as maps of uncertainty in the geographic location of each source of long-range gene flow. We have implemented the method in a package called FEEMSmix (an extension to FEEMS from Marcus et al., 2021), and validated its implementation using simulations representative of typical data applications. We also apply this method to two empirical data sets. In a data set of over 4,000 humans (Homo sapiens) across Afro-Eurasia, we recover many known signals of long-distance dispersal from recent centuries. Similarly, in a data set of over 100 gray wolves (Canis lupus) across North America, we identify several previously unknown long-range connections, some of which were attributable to recording errors in sampling locations. Therefore, beyond identifying genuine long-range dispersals, our approach also serves as a useful tool for quality control in spatial genetic studies. Author SummaryThe movement of individuals across landscapes shapes genetic diversity and has significant implications for both evolutionary studies and conservation efforts. Advances in sequencing now allow researchers to analyze thousands of samples from broad geographic areas, helping to estimate local gene flow. However, long-range genetic flow can occur due to a host of reasons (e.g. natural weather patterns, migration for resources, etc.), and existing methods struggle to represent these patterns. In this study, we developed a method to identify and model these long-range genetic similarities as dispersals from a source to a destination over a landscape. In applying this method to over 4,000 human samples from Afro-Eurasia, we detected signatures of known long-distance dispersals from recent centuries. In applying this method to 100 gray wolf samples from North America, we found many unexpected long-range genetic connections, some of which turned out to be recording errors in sample locations. Thus, beyond detecting real long-range dispersal, our approach also serves as a useful tool for quality control in spatial genetic studies.

evolutionary biology↗