Genetics Selection Evolution

The measurement of inbreeding has gained significance across diverse fields, including population and conservation genetics, agricultural genetics, breeding programs for animals and plants, and wildlife management. This is due to the fact that inbreeding leads to increased homozygosity and results in lower genetic diversity, rendering populations more vulnerable to environmental changes, diseases, and other stressors. High or mid-coverage whole genome sequencing (WGS) has been widely used for inbreeding estimation, but it is resource-intensive. We aimed to investigate the use of ultra low-coverage whole genome sequencing (ulcWGS) as a cost-effective alternative for inbreeding analysis. Domestic dogs were used for our study as their extensive breeding histories lead to populations with a wide range of inbreeding levels. We constructed a multi-breed reference panel from high-coverage WGS samples. Inbreeding in independent ulcWGS samples was then estimated using runs of homozygosity (RoH) and inbreeding coefficients (F). We modeled the relationship between these measures and sequencing depth using nonlinear regression, to generate inbreeding estimates relative to sequencing depth. Resulting relative RoH and F measurements were significantly correlated, with purebred dogs exhibiting more runs of homozygosity and higher inbreeding coefficients compared to mixed-breed dogs. Our findings demonstrate that ulcWGS can provide reliable and economical estimations of inbreeding, expanding accessibility to genetic monitoring.

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 R/C++. 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.

Epigenetics may play a crucial role in livestock adaptation to environmental challenges like heat stress. In recent years, a growing number of studies have investigated the epigenetic mechanisms underlying dairy cow adaptation to heat stress. However, there is still limited knowledge about the effects of heat stress on immune cells and immune-related phenotypes. Herein we aim to identify heat-stress induced DNA methylation variations on blood methylome potentially affecting regulatory regions and associated phenotypes. Blood samples were collected and peripheral blood mononuclear cell (PBMC) isolated from four cows before (D0) and after (D14) a 14-d heat stress challenge (cyclical THI 72-82) and, from four cows kept in thermoneutral conditions (THI 61-64). Heat-stressed cows had ad libitum access to diets supplemented with adequate levels of vitamin D and Ca (12,000 IU/kg of vitamin D and 0.73% Ca, respectively). To eliminate confounding effects due to differences in nutrient intake, cows maintained under thermoneutral conditions were pair-fed (PF) to their heat-stressed counterparts and received adequate concentrations of vitamin D and Ca as well. Reduced representation bisulphite sequencing (RRBS) was used to profile PBMCs methylome. Differential methylation analysis was performed using methylKit and DSS softwares ({Delta}meth [&ge;] 25%, adjusted p-value < 0.01), retaining only commonly detected differentially methylated cytosines (DMCs). A total of 2,908 DMCs were identified when comparing pre- and post-heat stress samples. After excluding 649 DMCs that were also detected under thermoneutral conditions, as these changes were likely associated with feed restriction inherent to the pair-feeding design rather than with heat stress per se, 2,259 heat stress-specific DMCs remained, predominantly hypomethylated. About half of the DMCs are annotated in intronic and intergenic regions; known to harbor regulatory elements. By intersecting the DMRs with publicly available functional annotation data, we observed hypomethylation on regulatory regions putatively affecting cows immune system. As an example, we identified a loss of methylation within an enhancer region of the MSN gene, which is involved in lymphocyte homeostasis, and a loss of methylation in the promoter region of MECP2, a well-established epigenetic regulator with a central role in chromatin organization and gene expression. These findings highlight the impact of heat stress on dairy cow immunity and provide new insights into its epigenetic regulation under environmental stress. Interpretative summaryThis study examined DNA methylation changes induced by heat stress in dairy cows to elucidate epigenetic mechanisms of thermal adaptation. Using RRBS on PBMCs, 2,259 heat stress-specific differentially methylated cytosines were identified, predominantly hypomethylated and enriched in regulatory regions. Functional annotation highlighted immune-related pathways, including hypomethylated regulatory regions near genes (e.g., MSN, ZBTB33, SLC25A5, GNAS, FAM3A, and MECP2) associated with immune function. These findings indicate that heat stress induces targeted epigenetic modifications potentially affecting immune regulation in dairy cows.

Mycoplasma bovis was first detected in cattle in New Zealand in 2017, prompting an eradication programme that incorporated extensive surveillance and a test-and-cull policy. Genome sequence data and phylodynamic models were used to inform decision making throughout the eradication programme. Isolates from 697 cattle on 126 farms were collected and sequenced between July 2017 and December 2023. Phylodynamic models were used to estimate the time of most recent common ancestor, the effective reproduction number (Reff) and effective population size, and long-range and local between-farm transmission dynamics. The analysis revealed the dramatic impact of movement restrictions and culling up to early 2020, with a sharp reduction in the Reff to less than 1 in 2018/9 and the extinction of two of three major lineages in 2020. This was followed by three-years of residual infection in farms in the South Island, associated with persistent infection of a large feedlot farm and nearby farms. The comprehensive dataset of genomic and epidemiological data provided a unique opportunity to study the dynamics of a country-wide outbreak of a single-host pathogen from first detection to potential eradication, underlining the utility of integrated genomic surveillance during an outbreak response. Author summaryThe economically important cattle pathogen, Mycoplasma bovis, was first detected in New Zealand in 2017. This led to a large-scale, successful control programme aimed at eradication of the pathogen. The decision to undertake an eradication programme was informed by initial analyses of whole genome sequences from isolates collected as part of the surveillance programme. The analysis showed that the bacteria had entered New Zealand relatively recently and was unlikely to be widespread. Over the subsequent years, genome sequencing and modelling of transmission dynamics informed important policy decisions made by the New Zealand Government and the cattle industry, and helped to monitor progress of the eradication programme. The impact of the detection, movement control and culling programme was profound, with sharp reductions in transmission between 2018 and 2020. This was followed by a long tail of localised infection in the South Island, involving transmission from a large feedlot farm. Provisional eradication was achieved after depopulation of this feedlot. This analysis highlights the role of genomic surveillance and modelling to inform decision making during an infectious disease outbreak.

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse biobased products. Increasing biomass yield will increase profitability and environmental benefits, so is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; GxE interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for GxE interaction presented the highest PA and the lowest MSE for CNN (PA: [~]0.77, MSE: [~]0.5) and YDY (PA: [~]0.70, MSE: [~]1.3) while for TCM and AIL these ranged from [~]0.28 to 0.41 and [~]1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

Data contamination--from recording errors to extreme outliers--can compromise statistical models by biasing predictions, inflating prediction errors, and, in severe cases, destabilizing performance in high-dimensional settings. Although contamination can affect responses and covariates, we focus on response contamination and evaluate Random Forests through simulation. Using a synthetic animal-breeding dataset, we assess robust Random Forests across several contamination scenarios and validate them on plant and animal datasets. We thereby clarify the consequences of contamination for prediction, develop a robust Random Forest framework, and evaluate its performance. We examine preprocessing or data-transformation strategies, algorithmic modifications, and hybrid approaches for robustifying Random Forests. Across these approaches, data transformation emerges as the most effective strategy, delivering the strongest performance under contamination. This strategy is simple, general, and transferable to other Machine Learning methods, offering a remedy for robust genomic prediction. In real breeding data, robust Random Forests are useful when substantial contamination, phenotypic corruption, misrecording, or train-deployment mismatch is plausible and the goal is to recover a latent signal for genomic prediction and selection; ranking-based robust Random Forests are the dependable first option, whereas weighting-based Random Forests should be used only when their weighting scheme preserves rank structure and improves prediction. Robustification is not universally necessary, but it becomes important when contamination distorts the link between observed responses and the predictive target; standard Random Forests remain the default for clean data, whereas robust Random Forests should be fitted alongside them whenever contamination is plausible, with the final choice guided by data, trait, and breeding objective. Author summaryMachine learning (ML) methods are widely used for prediction with high-dimensional, complex data, and supervised approaches such as Random Forests (RF) have proved effective for genomic prediction (GP) and selection. Yet their performance can be severely compromised by data contamination if the algorithms rely on classical data-driven procedures that are sensitive to atypical observations. Robustifying ML methods is therefore important both for improving predictive performance under contamination and for guiding their practical use in high-dimensional prediction problems. To address this need, we develop robust preprocessing, algorithm-level, and hybrid strategies for improving RF performance with contaminated data. Using simulated animal data, we show that ranking-and weighting-based robust RF provide the strongest overall compromise for genomic prediction and selection under contamination. Validation on several plant and animal breeding datasets further shows that the benefits of robustification are not universal, but depend on the dataset, trait, and breeding objective. Although motivated by RF, the framework we propose is general, practical, and readily transferable to other ML methods. It also offers a basis for deciding when robustness should complement standard RF rather than replace it outright.

Introducing cognitive enrichment from an early age has the potential to enhance an animals capacity to learn both simple and complex tasks, promote neural plasticity, and support cognitive development. This is applicable for young cattle who are at a critical stage in their development and could benefit from the influence cognitive enrichment has on their behavioral expression. This study aims to explore the effects cognitive enrichment has on weaned dairy calves through analyzing behavioral measures of voluntary participation and short-term behavioral reactions to enrichment exposure. Our study involved a total of five pairs of weaned calves (n=8 treatment; n=2 control). The treatment groups were presented with three variations of a puzzle box, each equipped with unique challenges that offer different solutions (push, slide, pull). These boxes were provided to the calves twice daily over the span of nine days in an isolated corridor located behind their pen. We hypothesized that motivated calves would consistently engage with cognitive enrichment voluntarily over time and express directed natural behaviors, reflecting sustained participation across repeated trials. Results demonstrated that calves consistently visited the cognitive enrichment area across trials, with an average latency of 75.7 {+/-} 47.0s from the pen to the enrichment. Secondly, the calves spent a significant proportion of trial time within the enrichment area at 65% (870.1 {+/-} 21s). Lastly, all calves expressed a broad range of behaviors in line with their natural exploration within the enrichment area, while the puzzle box treatment groups expressed higher durations of behavioral expressions when compared to the control (F=11.7, p<0.0001). Combined, these results indicate the calves motivations to voluntarily participate in a cognitive challenge. While these are promising findings for cognitive enrichment and its applicability to dairy calves, further work is needed to understand broader parameters. Specifically, how can social dynamics influence enrichment interaction in groups, how can this type of enrichment be implemented on farms, and what are the long-term effects to providing cognitive enrichment in the early stages of development.

Environmental factors affect crop growth and development thus their consideration across sites and years become essential for genotypic evaluation. Genomic selection (GS) has been broadly implemented to accelerate breeding cycles by skipping field evaluations thus allowing early identification of outperforming genotypes. In this study, 7,740 phenotypic records corresponding to 516 Miscanthus sacchariflorus genotypes evaluated in five locations across three years were considered for analysis. Additionally, environmental data on six weather covariates was implemented to characterize similarities between locations. Different sets of locations of variable sizes were used for model calibration based on two cross-validations (CV00 and CV0) schemes leaving out one location at a time. Predictive ability across locations of the best model varied between 0.45 and 0.90 for both schemes. These results were compared to associate predictive ability in function of weather patterns between training and testing sets to allow models calibration optimization. We found it is feasible to optimize resource allocation by considering environmentally correlated sets. In most cases, the information from only one and, at most, two locations were enough to deliver better results than using all four locations, reducing training sets by up to 75%. The results obtained shed light on helping breeders make informed decisions considering weather data when designing evaluations.

Restlessness is a symptom of chronic boredom in humans and a behavioural phenomenon anecdotally described as a concern in bulls raised for fattening purposes, but it has so far not been addressed in research. The two studies presented in this paper aimed to gain first insights into restlessness in bulls. We operationally defined restlessness by the number of transitions between behaviours in a given time period, and quantified restlessness in bulls of different weight classes (300, 400, 500 kg) on farms keeping bulls on fully-slatted floors (n=8, Study 1) as well as across three different husbandry systems (fully-slatted floor (FS, n=4), straw-based (SB, n=4) and organic pasture (OP, n=3), Study 2). All farms were visited twice, and the behaviour of different individuals was continuously recorded for 15 minutes each between 9 a.m. and 5 p.m. (Study 1) and for 8 minutes each between 6 a.m. and 10 p.m. (Study 2). The effects of weight class and husbandry system were analysed using generalised linear mixed-effects models, and we ran a sequence analysis to cluster observations by the sequence, frequency, and duration of bulls behaviours in Study 1. Bulls kept in fully-slatted floor systems in Study 1 changed their behaviour on average 48.3 times per 10 minutes, with high variability both within and across farms. Weight class did not have a statistically supported effect on the number of transitions, and the sequence analysis revealed four clusters that differed in sequence and in the number of transitions. In Study 2, OP bulls showed fewer transitions than SB and FS bulls (X22 = 23.6, p < 0.001), while SB and FS bulls did not differ. While SB pens were more structured and offered more space per animal, both SB and FS systems can be characterised by monotony, which may explain the similar level of restlessness in both systems. Alternatively, or in addition, the high feeding intensity in SB and FS systems may have caused the higher number of transitions compared to the OP system, potentially elicited by subacute ruminal acidosis and/or laminitis and the resulting pain. However, these explanations are speculative and require systematic disentanglement in future studies. This study provides initial insights into restlessness in bulls and lays the groundwork for future research to identify the causes underlying restlessness and investigate its association with bull welfare.

Genomic selection holds the potential to serve as a strategic tool to enhance the genetic gain of complex traits in Miscanthus breeding programs. The development of improved cultivars requires their assessment for various traits across diverse environments to ensure suitable overall performance. Hence, the multi-trait multi-environment (MTME) genomic prediction (GP) models offer an opportunity to improve selection accuracy. This study aims to evaluate the potential of five GP models: (1) three MTME models including genotype-by-trait-by-environment interaction (GxExT) and (2) two single-trait multi-environment (STME) models (with and without GxE interaction). A Miscanthus sacchariflorus population comprising 336 genotypes evaluated in three environments and scored for four traits (biomass yield YDY, total culm number TCM, average internode length AIL, and culm node number CNN) was analyzed. The predictive ability of the models was evaluated considering three cross-validation schemes resembling realistic scenarios (CV1: predicting new genotypes, CVP: predicting missing traits in a given environment, and CV2: predicting partially observed genotypes). On average, in all cross-validation schemes compared to the STME the predictive ability of the MTME models was 10% to 70% higher for TCM and AIL. On the other hand, for YDY and CNN, both STME models performed similarly or slightly better (between 5 to 64%) than the MTME models in most environments. While the MTME models were not successful for all traits when compared to their STME counterparts, MTME models improved the prediction of the performance of genotypes that were untested across environments or lacked trait information in a specific environment. Overall, our study suggests that MTME GP models can be implemented in Miscanthus breeding programs to improve the predictive ability of the complex traits, shorten breeding cycles, and accelerate selection decisions.

Deciphering the genetic architecture of complex quantitative phenotypes remains challenging in quantitative genetics. These traits not only depend of multiple genetic factors but are also established over time and environments. Although quantitative genetics has investigated the genetic determinism of phenotypic plasticity in contrasted environmental conditions, the time related phenotypic plasticity has received less attention. Here we proposed a multivariate Bayesian framework, the Bayesian Varying Coefficient Model, designed for analysing the genetic architecture of the time related phenotypic plasticity by a multilocus approach. We applied the BVCM to time series phenotypes measured at various time scales (daily, monthly, yearly) across a diverse set of biological species. We included in this study: yeast (Saccharomyces cerevisiae), fungi (Fusarium graminearum), eucalyptus (Eucalyptus urophylla x E. grandis), and sweet cherry tree (Prunus avium). The BVCM results were compared with those obtained with a known genome-wide association method carried out time by time. For all species and traits, the BVCM was able to detect the major QTL identified by marker-trait association methods and revealed additional genetic regions of weak effect. It also increased the phenotypic variance explained for most of the phenotypes considered. It revealed dynamic QTLs with transitory, increasing or decreasing effects over time. By considering both the temporal and genetic multivariate structures in a single statistical model, we increased our understanding of the genetic architecture of complex traits notably by reducing the issue of missing heritability. More broadly, this work raises the foundation for extended applications in functional genomics, evolutionary ecology, and crop breeding programs, in which time-related phenotypic plasticity remains crucial for predicting and selecting key quantitative complex traits. Key messageBy capturing the genetic factors influencing the time related phenotypic plasticity, our approach contributes to a deeper understanding of the dynamic nature of genotype-phenotype relationships.

As purifying selection becomes less effective and inbreeding increases, small populations frequently develop an increased load of genome-wide deleterious mutations. Reflecting this pattern, deleterious mutations in genes associated with fertility and immunity have previously been identified in the cheetah (Acinonyx jubatus), which has had a low effective population size for at least the last 10,000 years. However, the distribution of deleterious mutations across cheetah populations is currently unknown. Here, we analysed novel whole genome resequencing data from 30 ex situ and 9 wild cheetahs. We investigated variation in genetic diversity, genomic measures of inbreeding, and the distribution of deleterious mutations across cheetah populations. South Sudanese and Tanzanian cheetahs showed higher inbreeding and realized load, while Namibian cheetahs had a higher proportion of population-specific deleterious mutations. Genes containing high- or moderate-impact deleterious mutations were significantly enriched for sperm-related functions, highlighting putative causative loci associated with poor sperm quality in cheetahs. Similar levels of genetic diversity and inbreeding were observed in ex situ cheetahs compared to their wild counterparts, providing empirical evidence of the efficacy of captive breeding programmes in maintaining genetic variation in ex situ populations.

Global networks of crop breeding programs leverage diverse germplasm, but diversity increases the complexity of maintaining stability in their elite genepools. To characterize genetic heterogeneity in breeding metapopulations and develop insights on how to manage it, we simulated the evolution of breeding populations on fitness landscapes. We revealed the geometric decrease in the average effect size of alleles segregating as standing variation that become fixed along an adaptive walk. We also demonstrated how independent adaptive walks of subpopulations are influenced by genetic drift, leading to cryptic genetic heterogeneity among elite genepools. This variation is released when elite lines derived from independent subpopulations are crossed, leading to segregation for 2-4X more major QTL in admixed families as in unadmixed families, and 2-4X more epistatic interactions. The emergent property of fitness epistasis for traits under stabilizing selection is well-understood in evolutionary genetics, but under-appreciated in crop quantitative genetics. To highlight the importance of this phenomenon, we constructed an empirical genotype-to-fitness landscape from the sorghum NAM, a global admixed prebreeding resource, demonstrating the utility of fitness landscapes for inferring genetic compatibilities within metapopulations. Our findings suggest that in breeding networks, strategies for effective germplasm exchange must account for epistasis in the oligogenic component of the genetic architecture of locally-adapted traits. Article summaryModern public sector crop improvement happens in networks of breeding programs that routinely exchange genetic information. Traditional models for understanding quantitative traits have limited predictiveness in situations with such genetic heterogeneity. This study uses breeding simulations and empirical data to show the utility of the fitness landscape framework for characterizing the genetic architecture of complex traits in breeding metapopulations. By simulating the evolution of breeding programs and integration into networks, it demonstrates how epistatic interactions between large-effect alleles are a fundamental property that must be accounted for when exchanging germplasm. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=102 SRC="FIGDIR/small/712732v1_ufig1.gif" ALT="Figure 1"> View larger version (25K): org.highwire.dtl.DTLVardef@1541326org.highwire.dtl.DTLVardef@b553a8org.highwire.dtl.DTLVardef@8758b4org.highwire.dtl.DTLVardef@1d0bdcd_HPS_FORMAT_FIGEXP M_FIG C_FIG

Alleles of ASIP gene (Agouti locus) in dogs determine a wide spectrum of coat colors, from red to black. Gain-of-function Ay allele is the most dominant in the range of known ASIP mutations: when all other genes affecting coat pigmentation are intact, presence of Ay allele results in red coat color. Loss-of-function a allele is the most recessive allele of this gene. When homozygous, it gives black coat color. Usually, dogs with Ay/a genotype have red coat, because a single copy of Ay allele is sufficient to fully compensate for the non-functional allele a, implying the complete dominance in this pair of alleles. However exceptions are known. In the Hungarian Puli breed there is a specific coat pigmentation type called fako. We investigated the genetic composition of fako dogs and found evidence that the dominance of the Ay allele over the a allele may be incomplete in these dogs. Analysis of the MC1R gene that interacts with ASIP in the hair pigmentation genetic cascade allowed us to find the variants that may be responsible for the incomplete dominance of Ay allele over a allele in Hungarian Puli dogs.

This study aimed to investigate the dynamics of gene expression in pigs during heat stress (HS), focusing on both short-term (STHA) and long-term (LTHA) heat acclimation phases. A total of 12 castrated males were exposed to thermoneutral temperatures (24{degrees}C) for 14 days (TN) and then to a constant temperature of 30{degrees}C for 21 days. Rectal temperature measurements indicated a biphasic thermoregulatory response, with an initial peak followed by acclimation. Using whole blood transcriptome analysis at seven time points between day 5 before the initiation of HS challenge and day 13 post HS. A total of 525 genes were differentially expressed during the STHA (day 0-day 2) phase. A switch in the expression of most genes was observed around 20 hours after HS. Functional pathway enrichment analysis identified through shape-based clustering revealed the activation of the immune system, especially mediated through toll-like receptor signaling pathways. The LTHA phase (day 2-day 13) revealed 985 differentially expressed genes, with pathways associated with various metabolisms, including mitochondrial fatty acid beta-oxidation, and electron transport, ATP synthesis, and heat production by uncoupling proteins. Interestingly, oxidative phosphorylation was predicted to be activated during the LTHA, particularly in Complex V, whereas other complexes showed mixed regulation. Comparative pathway analysis indicated distinct metabolic adaptations between STHA and LTHA, with up-regulation of glucose and lipid metabolism in late STHA and down-regulation of lipid metabolism during LTHA. This study contributes to a better understanding of the time course of adaptation mechanisms in pigs to HS, underlying a coordinated regulation during STHA involving several stress-specific mechanisms (via the HSP) and metabolic variation to help pigs achieve homeothermy.

BackgroundPangenomes are a promising new approach to genomics that can reduce reference bias in genotyping, but the reliability of such a data model remains unclear in tracking variation across species. To test the utility of graph-based pangenomes for interspecific breeding, we developed a Minigraph-Cactus super-pangenome representing four Citrus species derived from the founder lines of a citrus breeding program. To benchmark SNP calling accuracy using graph and linear-based approaches, we performed whole genome short read sequencing for two sets of pedigreed progeny: 30 F1 hybrids and 244 advanced hybrids from an F1 crossed with a parent not included in the pangenome. ResultsThe linear approach yielded more SNP calls than the graph-based approach, however, both methods exhibited similar Mendelian Inheritance Error Rates (MIER) in a tool-dependent manner. Reconstruction of parental haplotype blocks in the advanced hybrids revealed a striking improvement in performance in the pangenome graph-based calls, suggesting MIER is vulnerable to error when reference bias influences both parental and progeny genotype calls. Masking of regions diverged from the reference path improved MIER accuracy metrics and haplotype block reconstruction in both the linear and graph-based SNP calls. ConclusionsIn non-model systems, inheritance patterns observed from pedigreed hybrids provide a framework for benchmarking variant-calling accuracy using pangenomes. SNP miscalls originating from diverged regions can falsely satisfy MIER filters, thus we recommend haplotype blocks. The inherent structure of the pangenome graph has promising applications for removing regions of unreliable mapping quality, which cannot otherwise be reliably removed using traditional filtering metrics.

Gene drives are genetic elements that bias their own inheritance to spread desired traits in target populations, enabling population modification or suppression. Although homing-based drives can propagate efficiently, their potential for uncontrolled spread may present a challenge for field deployment. Thus, confined drive systems are needed. Here, we developed a confined modification drive, called Toxin-Antidote Recessive Embryo (TARE) drive, in the globally important malaria vector Anopheles stephensi. This drive works by cleaving and disrupting wild-type alleles in the germline or early embryo from maternally deposited Cas9. Disrupted alleles are recessive lethal, thus increasing the drive in a frequency-dependent manner. Inheritance bias was moderate in crosses between drive heterozygote mosquitoes, possibly due to low gRNA activity and thus moderate germline cleavage rates. Single-release cage trials confirmed the TARE drives ability to spread, although the drive ultimately declined due to fitness costs and resistance alleles associated with repetitive elements. Nonetheless our modelling analysis indicate that this TARE system could achieve population spread if the resistance issue is addressed. These findings demonstrate a functional prototype TARE drive in Anopheles stephensi and highlight key parameters governing its performance. Minor design optimizations could substantially improve efficiency and integrity, enabling rapid but confined population modification.

Conservation interventions are increasingly required for species threatened by population declines and isolation due to anthropogenic pressures. Small, isolated populations are particularly vulnerable to the loss of genetic diversity, increased inbreeding, and the accumulation of deleterious mutations. Translocations or supplementation of allopatric individuals for genetic rescue may be the only way to increase genetic diversity to increase population persistence via increased adaptive potential. Here, we use an experimentally admixed population of sand lizards on a small island in Sweden as a valuable model of genetic rescue. This population was established approximately 20 years ago (5-6 generations) resulting in increased fecundity and hatchling viability. This population was founded from crossings between individuals from an inbred population from the nearby mainland and individuals sourced from populations in southern Sweden. Low-coverage whole-genome sequencing revealed elevated genetic diversity and reduced realized genetic load in this admixed population relative to the source populations. Ancestry analyses indicated a greater contribution of southern Swedish genetic variation, potentially reflecting contribution of beneficial adaptive variation from this region that may underlie the positive population effects. This system provides valuable empirical insights into the long-term genomic consequences of genetic rescue in this model vertebrate population.

Individual phenotypes often depend on the genotypes of other individuals within a group. These phenomena are termed indirect genetic effects (IGEs) and have been distinguished from direct genetic effects (DGEs) using quantitative genetic models. Recent studies have utilized high-resolution polymorphism data to enable genomic prediction (GP) and genome-wide association study (GWAS) of IGEs, but unified methods remain limited. Here we integrate polygenic and oligogenic IGEs using a multi-kernel mixed model incorporating two random effects with a single covariance parameter. Underlying this implementation, the Ising model of ferromagnetics enabled us to simplify locus-wise and background IGEs for GWAS and GP, respectively. Our simulations demonstrated that, while the previous and present models exhibited similar performance, the present model can infer a trade-off between DGEs and IGEs. By applying this method to three species of woody plants, we found evidence for intergenotypic competition in aspen and apple trees, but limited evidence in climbing grapevines. Based on GWAS, we also detected significant variants associated with the competitive IGEs on the apple trunk growth. Our study offers a flexible implementation for GWAS/GP of IGEs, thereby providing an effective tool to dissect the genetic architecture of group performance.

Alleles with opposing effects on fitness characters are said to exhibit selectional antagonistic pleiotropy (broadly construed so that effects are not necessarily confined to the same individual). A number of theoretical investigations considered the case where a pair of alleles at a locus influences two fitness components and derived the conditions giving rise to stable polymorphism under various assumptions about the mode of trait-interaction. Strikingly, many of these analyses concluded that the potential for maintaining polymorphism is strongly constrained by the joint influence of two factors: (1) the prevalence of weak selection coefficients over coefficients of large magnitude, and (2) the absence of beneficial dominance reversals (where the deleterious effects of each allele are partially or completely masked in the heterozygous genotype). Consequently, the conclusion that selective polymorphism is unlikely to be maintained by intralocus mechanisms of antagonistic pleiotropy has achieved widespread acceptance. Here we argue that such conclusions do not apply to any of the following models of antagonism: (i) additive trait-interaction, (ii) multiplicative trait-interaction, (iii) bivoltine selection, (iv) soft selection, (v) hard selection, and (vi) sexual antagonism. We demonstrate that the parameter space giving rise to stable allelic variation is quite large throughout, and moreover, the plenitude of suitable parameters neither depends on the strength of selection nor requires dominance reversal. Dominance coefficients associated with stringent conditions for stable polymorphism are shown to be atypical as compared to all feasible parameters, and best regarded as an outcome of adherence to a special relation: dominance with a constant magnitude and direction, which includes the case of additive allelic effects at a locus. Properties of single-locus equilibria (heterozygosity, allele frequency differentiation) are investigated, as well as the contribution of dominance schemes to the genetic variance in fitness characters in populations at multilocus linkage equilibrium. Author summaryAllelic variants at a locus with opposing effects on multiple fitness components (antagonistic fitness pleiotropy) have long been appreciated as a possible source of balancing selection. The prevalence of polymorphism owing to this form of natural selection, however, has been doubted on theoretical grounds due to the fact that standard assumptions of genetic models (namely, constant magnitudes for the dominance coefficients) are hardly conducive to the maintenance of polymorphism. The major exception to this conclusion lies with schemes that exhibit dominance reversal (where the direction of dominance for antagonistic alleles flips across fitness components). Here we conduct a geometric analysis of the space of polymorphism-promoting dominance parameters and conclude that the conditions for maintaining balanced alleles is unrestrictive, with non-reversals playing an underappreciated role.