Genetics Selection Evolution

Social genetic effects can substantially influence phenotypes in animals and plants and the study of them is of interest. Group housing in animals for example, in pigs provide an ideal framework for studying interactions among individuals. In animals, the genetic of social effects has been studied by simplifying the underlying interactions for example, by assuming that all the individuals in the group contribute equally to the social environment. This homogeneous behaviour is not observed in real conditions. Here, we address this issue showing that identification of interacting individuals is feasible in pig behaviour during growth, and we propose a model that leads to more accurate modelling of social genetics. Our approach infers the strength of interaction via time interval between visits of animals to automatic feeders. This measure of strength was then used in a reaction norm model to evaluate the genetic determinism of social effects. We also build a framework to transform variance components estimated for a trait continuously measured, such as feed intake per visit, into variance of total or aggregated trait e.g. during the growth period. Including social effects improves the fitting of the models. Visit-based social interaction was highest at short time intervals, decreasing rapidly and becoming negligible after 80 seconds. Variance components for the aggregated trait the total feed intake support the contribution of the social interactions. Our approach allows more precise understanding of social effects through individual feeding events and a measure of interaction strength, and further translates these into in an aggregated phenotype of interest.

The goat research community benefits from one of the most comprehensive livestock genome assemblies, ARS1. However, the rich diversity of goat breeds suggests that a single individual genome is not enough to represent the species full genomic content. Third-generation sequencing technologies have recently made it possible to generate very high quality assemblies that should enable to elucidate unique and/or complex phenotype determinisms depending on genetic structural variations. In the SeqOccin project, nine goat breeds were selected to represent French goat genetic diversity: Alpine, Boer, Creole, Fosses, Poitevine, Provencale, Pyreneenne, Rove and Saanen. High-molecular-weight DNA was extracted from blood samples and subjected to long read whole-genome sequencing. There reads were used to generate de novo assemblies and were aligned onto the public reference to call structural variants. PacBio HiFi and parental Illumina reads were used to assemble phased haplotypes of the Alpine breed. For the other breeds, PacBio raw (CLR) or ONT reads were generated. Our work demonstrates the feasibility of assembling genomes with a reasonable amount of data and appropriate software packages, delivering one high-quality assembly and 8 standard goat assemblies that pave the way for pangenome construction and structural variant functional studies.

Background: The yak (Bos grunniens) is an essential livestock species in high-altitude alpine ecosystems, serving key roles in meat, milk, and wool production as well as ecological maintenance. However, traditional yak breeding systems face several limitations, including fragmented herd data management, inefficient phenotypic collection, missing pedigree records, and high technical barriers to applying genomic technologies. These factors constrain the widespread application of modern genetic evaluation methods in yak breeding programs. Description: We developed the Yak Genomic Evaluation Platform (YGEP, https://yakbreeding.swun.edu.cn/en/), an integrated digital system for yak genetic selection and herd management. YGEP integrates three core analytical modules: pedigree reconstruction, genotype imputation and genomic breeding value estimation. Benchmarking on 706 yaks showed that GBLUP achieved a prediction accuracy of r = 0.96 for cannon bone circumference (CCB), and KBeagle reached 84.0% imputation accuracy with the combined reference panel. Conclusion: YGEP bridges the gap between advanced genomic analytics and practical yak breeding. By integrating pedigree reconstruction, genotype imputation, and GEBV estimation into a user-friendly cloud platform, YGEP effectively eliminates technical and data bottlenecks. Overall, it provides an accessible, scalable, and standardized solution to advance digital herd management and precision genetic improvement in alpine livestock.

We propose a stochastic epidemic model to describe the spread of avian influenza in a network of poultry farms. We consider three main paths of infection transmission between poultry farms: from nearby farms, mainly through airborne diffusion; from farms belonging to the same company, through shared veterinarians, forage providers and similar; from undetected small farms or wildlife. From the parameter estimation, based on a modified Expectation-Maximization algorithm, we infer that approximately 63% of the farms were infected from nearby ones, with an infection force declining with distance; of these, more than one third belonged to the same company of the estimated infector. About 20% were infected from premises belonging to the same company but farther away than the distance threshold of 2 km; the remaining ones from wildlife or unidentified sources. These estimates have been validated by a comparison with genetic data, available for a subset of the farms: the genetic distance between two farms identified, with high probability, as an infector-infectee pair is much lower than between random pairs. These results may help in the implementation of tailored prevention and control measures for future outbreaks.

Skeletal dysplasias can occur in animals, including humans, and are attributed to both environmental and genetic causes. As our understanding of the distinguishing phenotypes of different skeletal dysplasias improves, alongside elucidation of the mechanisms and genetic networks that contribute to skeletal development, identifying and controlling genetic mutations in livestock populations has become increasingly possible, including approaches beyond selective breeding alone. Within Scotland an 'achondroplasia' syndrome that was first described in the South Country Cheviot breed of sheep (OMIA 000004-9940), has continued to occur periodically within the same geographical locations, despite culling of affected lambs over many decades. Affected lambs exhibit rounded heads, short legs, a loss of hooves, abnormal stance and short twisted tails. As the condition has been observed in flocks and as progeny from specific ram and ewe matings, and cases have been co-twinned to normal siblings, this suggests a syndrome caused by a recessive genetic condition rather than environmental factors. Through homozygosity mapping and whole genome sequence analyses, we identified a 10 bp deletion in the open reading frame of the PRICKLE1 gene that encodes prickle planar cell polarity protein 1 and is essential for normal vertebrate embryonic development. We identified additional phenotypes present in affected lambs, including a loss of phalanges, neural tube defects, vertebral and articular chondrocyte defects, hair orientation differences and polycystic kidneys, consistent with a loss of PRICKLE1 function based on comparisons to mouse mutant lines. These findings enabled the development of a simple DNA test for the identified PRICKLE1 mutation that was made commercially available to facilitate the elimination of this genetic disease from sheep flocks.

The sheep research community benefits from a genome assembly that has been refined over the last 15 years. However, the rich diversity of sheep breeds suggests that a single individual genome is not enough to represent the species full genomic content. Third-generation sequencing technologies have recently made it possible to generate high quality assemblies that should enable to elucidate unique and/or complex phenotype determinisms depending on genetic structural variations. In the SeqOccin project, twelve breeds were selected to represent (mostly French) sheep genetic diversity: Basco-Bearnaise, Black Belly, Blanche du Massif Central, Corse, Ile de France, Lacaune, Manech Tete Noire, Noire du Velay, Ouessant, Rambouillet, Vendeen, Soay. High-molecular-weight DNA was extracted from blood samples and subjected to long read whole-genome sequencing. The reads were used to generate de novo assemblies and were aligned onto the public reference to call structural variants. PacBio HiFi and parental Illumina reads were used to assemble phased haplotypes of the Lacaune breed. For the other breeds, PacBio raw (CLR) or ONT reads were generated. Our work demonstrates the feasibility of assembling genomes with a reasonable amount of data and appropriate software packages, delivering one high-quality assembly and 11 standard sheep assemblies that pave the way for pangenome construction and structural variant functional studies.

Genetic loci influencing multiple complex traits often remain difficult to interpret because the underlying functional variants, target genes and tissue contexts are unresolved. Here, we investigated the regulatory basis of a shared signal for average daily gain (ADG) and backfat thickness (BF) in Yorkshire pigs. By integrating GWAS in 7,972 pigs, targeted capture sequencing, LD-based fine-mapping, porcine epigenomic annotations, three-dimensional chromatin interaction maps and functional assays, we narrowed the shared SSC1 signal to a 1.59-Mb interval and functionally prioritized rs334769214 (T>G) as the leading candidate variant. The T>G substitution produced marked allele-specific differences in enhancer activity and nuclear-protein binding, while transcription-factor co-transfection assays identified FOXL1-responsive, allele-dependent regulatory activity. Chromatin interaction and CRISPRa analyses supported a regulatory connection between the rs334769214-containing enhancer and PMAIP1, and PMAIP1 overexpression promoted apoptosis in porcine intestinal epithelial cells. In the capture-sequenced subset, the G allele was associated with higher ADG and BF, consistent with a relative loss-of-activation model. Together, these findings delineate a candidate FOXL1-enhancer-PMAIP1 regulatory pathway and demonstrate how multi-omics fine-mapping can advance a shared quantitative-trait locus toward a testable molecular mechanism underlying variation in growth and fatness.

Polyandry, multiple mating by females, both benefits females by increases genetic variation within families and provides an opportunity for sexual selection through sperm competition and cryptic female choice. Estimating polyandry in species with large clutches is challenging because genotyping all progeny individually can be impractical. We present a likelihood-based framework for estimating the number of sires and their relative contributions from the maternal genotype and pooled sequencing of entire progeny pools. The method evaluates the probability of observed pooled offspring read counts under alternative paternal genotype mixtures, accounting for Mendelian segregation and sampling variation from finite sequencing depth. We implement this framework for both biallelic SNPs and short-read mini-haplotype markers constructed from closely linked SNPs, which provide multi-allelic information comparable to traditional microsatellite markers. Simulations show that this approach accurately recovers sire number and sire contributions across a range of conditions. We then applied the method to 275 wild-caught Drosophila melanogaster females and their offspring from a Kansas population using 3,053 biallelic SNPs and 239 four-allele mini-haplotype markers. Estimates from the two marker sets were highly concordant, and combined analyses indicate that 57% of families were sired by a single male. These results demonstrate that pooled progeny sequencing provides an efficient genomic approach for estimating polyandry in species with large families.

Forward-in-time population genetic simulations are widely used in evolutionary analyses, but simulating large populations and long genomic regions remains computationally demanding. To reduce this cost, parameter rescaling is widely employed, in which the original evolutionary process is approximated by one with a smaller population size and fewer generations. Recently, several studies using the SLiM simulator have raised concerns about the accuracy of this rescaling approach. In this study, we show that many of the biases reported in these studies can be mitigated by using a different simulation algorithm. These results reveal that the accuracy of parameter rescaling depends on how well the simulation algorithm preserves diffusion-limit properties under rescaling.

The hybrid grouper 'Yushuban' (Cromileptes altivelis [female] x Epinephelus fuscoguttatus [male]) has shown promising performance in aquaculture trials, yet no complete reference genome has been available for this hybrid. We generated two gapless, telomere-to-telomere (T2T) haplotype-resolved genome assemblies of a single individual by integrating short-read, PacBio HiFi, Oxford Nanopore ultralong-read and Hi-C data. The maternal (Yushu_Calti_1.0) and paternal (Yushu_Efusc_1.0) haplotypes are 1,077.33 Mb and 1,083.36 Mb in length, with N50 values of 45.46 Mb and 45.55 Mb, respectively. Across all platforms, over 99.4% of reads mapped to each assembly, and Merqury-based quality values reached 57.7413 and 59.2068 (PacBio). BUSCO recovered 99.78% and 99.75% of complete core genes. We predicted 27,248 and 27,566 protein-coding genes, of which 98.52% and 99.23% were functionally annotated, and annotated telomeric motifs and repetitive elements (46.12% and 47.18% of each genome). These assemblies fill a gap in grouper genomic resources and provide a foundation for genetic dissection, parental genome comparison and molecular breeding of this hybrid.

The emergence of Highly Pathogenic Avian Influenza (HPAI 2.3.4.4b) in dairy herds in 2024 across 19 states in the United States of America has raised concerns regarding the potential national and global zoonotic impact. All recent modeling efforts implemented homogenous cattle-to-cattle (both intra and inter-herd) transmission models which did not capture the real-world heterogeneity in mixing of animals, individual variations in susceptibility and infectiousness and clinical incidences across pens and lactation groups. The aim of this study was to develop a heterogenous transmission model to estimate the epidemiological parameters for intra-herd HPAI transmission on Californian dairies. We developed a validated stochastic agent-based model to estimate the epidemiological parameters for intra-herd HPAI transmission on California dairies. The hierarchical agent-based model also parameterized stochastic cattle movements within-herd to simulate real dairy management practices. A cow-level SEIR transmission approach was assumed during the outbreak. A novel Bayesian Optimizer with Gaussian Process (BO-GP) was fitted to the agent-based model for validation which converged within 25-40 iterations (out of 100 per farm) with minimal loss over two distinct error metrics, namely, Poisson loss function and temporal distance metric. Our optimized simulations estimated an average R0 was around 10.7-10.8 across all farms within the first 15 days of observed outbreak on four dairy farms with a mean effective transmission rate of 4.5% per contact between susceptible and infectious cows within each pen. Our model demonstrated that the movement of cows between pens ensured localized clusters of outbreaks within the sub-herds (pen population) that prolonged the overall outbreak within farms. We estimated the total duration of infection between 14.5 and 28 days, which is higher than the estimates from the homogenous models. With an integrated hierarchical agent-based model combined with Bayesian approximation, we produced actionable insights on the epidemiology of intra-farm spread of HPAI within cow herds, thereby guiding both future model development and applied disease control strategy.

During biogeographical ancestry (BGA) analysis, an unknown individual for whom ancestry informative markers (AIMs) were analyzed is often assigned to one of a few reference populations. For some individuals, such an assignment is not meaningful: the two parents of the individual may come from different populations, or the individual may come from a population not included in the reference database. In this study, we examined classification methods in combination with 104 AIMs from the VISAGE Enhanced Tool to provide one of the following three possible outcomes: (i) in-reference (IR), with an assignment to one of the reference populations; (ii) admixed (AD), with parents from two in-reference populations; and (iii) out-of-reference (OR), where the individual's ancestry is not sufficiently represented by the reference populations. We therefore compared seven classifiers in combination with class-specific acceptance thresholds for the OR decision. The best-performing method according to our selection criterion was a naive Bayes classifier over pairs of populations, which we call the recent naive Bayes (RNB) classifier. Populations held out of the reference panel were correctly rejected in $84.9\%$ of cases. We further tested the method on $800$ individuals from seven additional groups not included in the reference panel. Only $29.4\%$ of individuals from four non-admixed populations were rejected, resembling joint ancestry with reference populations. In addition, we investigated the prediction accuracy in a newly collected dataset of 28 individuals with self-reported ancestry by analyzing AIMs using massively parallel sequencing.

Motivation: Optimum contribution selection (OCS) maximizes genetic gain under a constraint on relatedness. Genomic relationship matrices are dense, so the quadratic term costs O(n^3) operations, and existing OCS software assumes pedigree sparsity. Results: We replace the relationship matrix by its leading eigenpairs, computed by randomized SVD, and solve OCS in factorized form. On Norway spruce population (n=5,525), ranks 10--30 kept the exact objective within 1% of the optimum at 110--330-fold speedups; on the QTL-MAS 2010 (n=3,226), ranks up to 100 were needed (24-211 fold). The required rank rises as the diversity constraint loosens. Gain alone is a misleading accuracy check, since coancestry exceeded the optimum by up to 28%, so we choose the rank on the exact objective, which can be checked without a dense solve. The speedup comes from computing only the leading eigenpairs. We extend the factorization to robust OCS (18-fold). Availability and implementation: Julia implementation, and examples are available at https://github.com/jonhar97/fastocs under the MIT license (archived at Zenodo, DOI 10.5281/zenodo.23214804).

Given unprecedented environmental change, Pacific salmonids are becoming increasingly threatened; however, genomic technologies provide an opportunity to assess Pacific salmonid resilience to on-going environmental change. Also, given increasing conservation and animal welfare concerns, non-lethal tissue sampling is emerging for assessing the physiological status of wild species. Relatively few studies, however, have compared the transcriptomic response of fish sampled non-lethally versus lethally for fish health and stress quantification. Here, we conducted a comparative gene transcriptional profiling study using 28 qPCR assays on the OpenArray platform as an osmoregulatory stress transcriptional profiling chip. We sampled both non-lethal (gill biopsies, skin swabs, and water [eRNA]) and lethal (whole gill and liver) tissue samples from Chinook salmon (Oncorhynchus tshawytscha) smolts exposed for 0 h, 24 h, and 72 h to freshwater (0 PSU), brackish water (15 PSU) or seawater (30 PSU). We observed that most assays showed higher qPCR amplification success in both non-lethal gill biopsies and lethal gill tissues, compared to other tissues. We found more significant differences in transcript abundance in traditional lethal tissues compared to non-lethal tissues. With increasing duration of salinity exposure, gene transcript levels were typically upregulated in seawater samples compared to freshwater samples for both non-lethal and lethal tissues. The targeted genes for both non-lethal and lethal tissues were significantly and positively correlated across time points. Our findings support the use of non-lethal tissue sampling to improve Chinook salmon hatchery rearing, inform hatchery release timing, and facilitate future field application for the conservation of wild stocks.

Amongst measures of linkage disequilibrium, the correlation measure r2, due originally to Sewall Wright, has several advantages. Although basically a haploid statistic, it can readily be estimated using covariance and correlation from diploid data. Its population expectation can also be found from probability methods. We show that the probability of linked identity by descent, LIBD, directly estimates the expectation for r2 for Wright-Fisher models. The simplest such model for two loci is the haploid model, where each haplotype is chosen independently from haplotypes of the previous generation. This gives an equilibrium between recombination (recombination frequency c) and drift (population size N) where the expected value of r2 is approximately 1/(1+4Nc). For diploid models, the expectation is similar for low values of c, but contains a term in c2 and rises to double the haploid value for the limiting value of c = 0.5. An increase in r2 is also expected for the model of monogamous mating, as opposed to random mating for each offspring. The expectation for r2 is unaffected by the use of diploid rather than haploid data, an exception being the case of monogamous mating. Computer simulation shows close agreement of r2 with probability expectations, with minor discrepancies observed for unlinked loci. Using the probabilistic definition, r2 can be intuitively understood as a parameter shaping the effective sample size in genome-wide association studies.

Macrophages, alongside mammary epithelial cells, are major contributors to the innate immune response triggered by bacterial invasion of the mammary gland. To investigate breed-specific immune mechanisms, we stimulated monocyte-derived macrophages from Holstein and Normande cattle with Escherichia coli and Streptococcus uberis, two major mastitis pathogens. We monitored bacterial survival using [methode] and profiled gene expression via RNA-seq. E. coli exhibited significantly higher survival 6 hours post-infection compared to S. uberis (p < 0.05). Moreover, E. coli induced a robust transcriptional response, with 4403 differentially expressed genes (FDR < 0.05, |log2FC| > 1), versus only 813 for S. uberis. Qualitative differences were also observed: genes such as CCL2 and CSF1 were uniquely induced by S. uberis. Additionally, macrophages from Holstein and Normande cattle showed distinct expression profiles: 185, 45, and 174 genes were differentially expressed at steady state or after E. coli or S. uberis stimulation, respectively. These genes were enriched in immune-related pathways. Overall, our results demonstrate a stronger response to E. coli, although S. uberis specifically induces a subset of genes. The observed breed-specific differences may reflect underlying immune mechanisms that could influence disease susceptibility, warranting further functional validation.

As the use of population descriptors such as race, ethnicity, and ancestry have become increasingly common in modern genetics research, there have been growing calls to critically examine their use. Most notably, in 2023, the National Academies of Science, Engineering, and Medicine (NASEM) published a report titled Using Population Descriptors in Genetics and Genomics Research: A New Framework for an Evolving Field, which included eight specific and actionable recommendations for researchers to implement the ethical and accurate use of population descriptors in genetic research. Here, we use the 2023 NASEM report as a benchmark to analyze the use of population descriptors in genome-wide association studies (GWAS). We develop a general toolkit for large language model-based bibliometrics, operationalize the report's recommendations into an evaluation framework, and apply this framework to evaluate all 4,007 papers from the GWAS Catalog published between 2007 and 2025 with full text available on PubMedCentral. We find significant improvements in adherence to NASEM report recommendations over time. However, most improvements predate the publication of the NASEM report itself, suggesting the report functioned primarily as a synthesis of existing best practices rather than a catalyst for change. We conclude by highlighting opportunities for growth in the field of human genetics.

Abstract Napier grass (Cenchrus purpureus, NG) is an important forage in tropical and subtropical livestock systems, but the extent of naturally occurring variation in its methane (CH4) production and its potential for selecting low-CH4 emitting genotypes remains poorly characterised. This study evaluated variation in CH4 yield among NG accessions, examined the effects of season and water stress, validated contrasting CH4 phenotypes across donor cows, and identified elite accessions combining low CH4 yield with favourable digestibility. A total of 750 NG samples representing 84 accessions grown under dry and wet seasons and moderate and severe water-stress were evaluated using a high-throughput in-vitro fermentation system. To further identify forages combining low-CH4 production with acceptable digestibility, a multi-trait selection approach was applied using biologically defined thresholds for CH4 yield and in vitro organic matter digestibility (IVOMD), followed by a weighted index assigning 60% weight to CH4 yield and 40% to IVOMD. Methane yield varied widely among accessions, ranging from 0.02 to 5.07 mL/g DM and was affected by growing conditions. Methane production was generally lower during the wet season, while water stress was associated with changes in forage quality and fermentation characteristics. Low-CH4 producing accessions generally had lower IVOMD, crude protein and metabolizable energy and relatively higher fibre than high-CH4 producing accessions. Accessions initially classified as low and high-CH4 emitters based solely on CH4 yield were subsequently validated using rumen inoculum from three individual donor cows. Although the magnitude of CH4 production varied among donors, the contrast between low- and- high-CH4 accessions was generally retained, supporting the robustness of accession differences in CH4 production. Several accessions also maintained relatively low CH4 production across contrasting season and water-stress conditions, indicating that favourable CH4 phenotypes were not entirely environment dependent. A multi-trait selection identified ten elite accessions that produced 0.019-0.810 mL CH4/g DM and had 55.2-69.0% IVOMD, whereas ten poor-performing accessions produced 2.80-3.89 mL CH4/g DM and had 50.1-54.7% IVOMD. Differences in hexose fermentation and CH4 produced per unit of fermented substrate further indicated contrasting fermentation efficiency between the groups. These results demonstrate substantial variation in NG and identify accessions with low-CH4 production, favourable digestibility and relatively stable performance across growing conditions. In conclusion, incorporating CH4 yield and digestibility into forage selection could support the development of lower-emission forage cultivars for sustainable climate-smart livestock production.

Statistical associations between two variables can arise via various causal mechanisms. For example, one variable may influence the other, or they may share common causes. Surprisingly little attention has been devoted to quantifying how such causal pathways contribute to the overall association between variables. Here, I present two methods for partitioning a covariance between variables X and Z into a causal component arising from the effect of X and Z and a spurious component arising via other pathways. Both methods rely on averaging causal decompositions over the distributions of the variables, but the decomposition and averaging are performed in different ways. The 'gradient' definition decomposes causal effects in the neighbourhood of naturally occurring values, making it relatively straightforward to estimate. It assumes that the cause variable X is normally distributed. In contrast, the 'two step' definition decomposes large ('step-wise') changes in values and makes no distributional assumptions. I describe applications of these methods to analysing natural selection and illustrate them using the covariance between body size and the number of eggs carried by pregnant male dusky pipefish, Syngnathus floridae. I partition this total covariance into a causal component due to the effect of body size on egg number and a spurious component arising from variation in body size and egg number among collection sites.

Bacterial mutation rates are traditionally inferred from phenotypic data using fluctuation assays. Mutation rate computation from these assays relies on a mathematical model describing the emergence of mutants during population growth. However, this standard model relies on restrictive assumptions that are often transgressed in real biological setups. Key assumptions include equal growth rate of the wild-type and the mutant population, no cell death during the growth phase, and no post-growth sampling. Although several refined mathematical models have been proposed over the last decades to circumvent some of these assumptions, none can fully account for the complex ecological scenarios frequently observed in the lab or in the wild, such as the combination of cell death and an unknown fitness effect of the mutation, or non-constant death rates. In this work, we propose to replace these standard mathematical models by a stochastic simulation framework, which can circumvent all these assumptions. We provide a fast and accurate implementation of this stochastic model, and combine it with an Approximate Bayesian Computation scheme to perform simulation-based, likelihood-free parameter inference. Our approach can simultaneously infer mutation rate and fitness effect of the mutation while accounting for death and partial plating, and could be extended to arbitrary complex conditions that can be efficiently simulated.