BACKGROUND: Genetic diversity is a necessary condition for populations to evolve under natural adaptation, artificial selection, or both. However, genetic diversity is often threatened, in particular in domestic animal populations where artificial selection, genetic drift and inbreeding are strong. In this context, cryopreserved genetic resources are a promising option to reintroduce lost variants and to limit inbreeding. However, while the use of ancient genetic resources is more common in plant breeding, it is less documented in animals due to a longer generation interval, making it difficult to fill the gap in performance due to continuous selection. This study investigates one of the only concrete cases available in animals, for which cryopreserved semen from a bull born in 1977 in a lost lineage was introduced into the breeding scheme of a French local dairy cattle breed, the Abondance breed, more than 20 years later.
RESULTS: We found that this re-introduced bull was genetically distinct with respect to the current population and thus allowed part of the genetic diversity lost over time to be restored. The expected negative gap in milk production due to continuous selection was absorbed in a few years by preferential mating with elite cows. Moreover, the re-use of this bull more than two decades later did not increase the level of inbreeding, and even tended to reduce it by avoiding mating with relatives. Finally, the reintroduction of a bull from a lost lineage in the breeding scheme allowed for improved performance for reproductive abilities, a trait that was less subject to selection in the past.
CONCLUSIONS: The use of cryopreserved material is an efficient way to manage the genetic diversity of an animal population, by mitigating the effects of both inbreeding and strong selection. However, attention should be paid to mating of animals to limit the disadvantages associated with incorporating original genetic material, notably a discrepancy in the breeding values for selected traits or an increase in inbreeding. Therefore, careful characterization of the genetic resources available in cryobanks could help to ensure the sustainable management of populations, in particular local or small populations. These results could also be transferred to the conservation of wild threatened populations.

In this issue of Molecular Ecology, Mora-Carrera et al. (2022) revisit a case of the loss of an outcrossing system in primroses, which has been studied as an example of balancing selection in the wild since the 1940s. Molecular variants in the gene involved in the mutant self-fertile phenotype, which is now known, help towards understanding this textbook example of breakdown of an outcrossing system. However, as often happens, new information also raises further questions.

This chapter provides an overview of the genomic selection progress in long-lived forest tree species. Factors affecting the prediction accuracy in genomic prediction are assessed with examples from empirical studies. Infrastructure and resources required for the implementation of genomic selection are evaluated. Some general guidelines are provided for the successful application of genomic selection in forest tree breeding programs.

In accordance with the infinitesimal model for quantitative traits, a very large number of genes affect nearly all economic traits. In only two cases has the causative polymorphism been determined for genes affecting economic traits in dairy cattle. Most current methods for genomic evaluation are based on the \"two-step\" method. Genetic evaluations are computed by the individual animal model, and functions of the evaluations of progeny-tested sires are the dependent variable for estimation of marker effects. With the adoption of genomic evaluation in 2008, annual rates of genetic gain in the US increased from ∼50-100% for yield traits and from threefold to fourfold for lowly heritable traits, including female fertility, herd-life and somatic cell concentration. Gradual elimination of the progeny test scheme has led to a reduction in the number of sires with daughter records and less genetic ties between years. As genotyping costs decrease, the number of cows genotyped will continue to increase, and these records will become the basic data used to compute genomic evaluations, most likely via application of \"single-step\" methodologies. Less emphasis in selection goals will be placed on milk production traits, and more on health, reproduction, and efficiency traits and \"environmentally friendly\" production. Genetic variance for economic traits is maintained by increase in frequency of rare alleles, new mutations, and changes in selection goals and management.

It is worth investigating the existence of cooperation, which is costly for the actor but beneficial to the recipient (precisely because it is costly for the former). If players, when they approach defectors, stop their relationship with them, cooperation can pay off and favorably emerge in the course of evolutionary dynamics. The present study examines the situation in which animals, even when they want to cooperate, sometimes lack the necessary resources, and are thereby prevented from cooperating with others. In addition, it is also considered that the underlying information about the presence or absence of these resources can be conveyed to the opponent player. Here, the opponent who defects-has no resources for cooperation-may be a cooperator or a defector. Therefore, it is not clear which behavior is more likely to evolve, if it is keeping the interaction with such an opponent (i.e., being trustful) or stopping the interaction with such an opponent (i.e., being not trustful). By using evolutionary game theory, it is revealed that those who want to keep the interaction with those without the resources to cooperate are favored by natural selection. This study sheds new light on the role of keeping and stopping interaction in the evolution of cooperation under variable availability of resources.

Genetic adaptation to future environmental conditions is crucial to help species persist as the climate changes. Genome scans are powerful tools to understand adaptive landscapes, enabling us to correlate genetic diversity with environmental gradients while disentangling neutral from adaptive variation. However, low gene flow can lead to both local adaptation and highly structured populations, and is a major confounding factor for genome scans, resulting in an inflated number of candidate loci. Here, we compared candidate locus detection in a marine mollusc (Onithochiton neglectus), taking advantage of a natural geographical contrast in the levels of genetic structure between its populations. O. neglectus is endemic to New Zealand and distributed throughout an environmental gradient from the subtropical north to the subantarctic south. Due to a brooding developmental mode, populations tend to be locally isolated. However, adult hitchhiking on rafting kelp increases connectivity among southern populations. We applied two genome scans for outliers (Bayescan and PCAdapt) and two genotype-environment association (GEA) tests (BayeScEnv and RDA). To limit issues with false positives, we combined results using the geometric mean of q-values and performed association tests with random environmental variables. This novel approach is a compromise between stringent and relaxed approaches widely used before, and allowed us to classify candidate loci as low confidence or high confidence. Genome scans for outliers detected a large number of significant outliers in strong and moderately structured populations. No high-confidence GEA loci were detected in the context of strong population structure. However, 86 high-confidence loci were associated predominantly with latitudinally varying abiotic factors in the less structured southern populations. This suggests that the degree of connectivity driven by kelp rafting over the southern scale may be insufficient to counteract local adaptation in this species. Our study supports the expectation that genome scans may be prone to errors in highly structured populations. Nonetheless, it also empirically demonstrates that careful statistical controls enable the identification of candidate loci that invite more detailed investigations. Ultimately, genome scans are valuable tools to help guide further research aiming to determine the potential of non-model species to adapt to future environments.

Genomic selection (GS) is a method of marker-assisted selection revolutionizing crop improvement, but it can still be optimized. For hybrid breeding between heterozygote parents of different populations or species, specific aspects can be considered to increase GS accuracy: (1) training population genotyping, i.e., only genotyping the hybrid parents or also a sample of hybrid individuals, and (2) marker effects modeling, i.e., using population-specific effects of single nucleotide polymorphism alleles model (PSAM) or across-population SNP genotype model (ASGM). Here, this was investigated empirically for the prediction of the performances of oil palm hybrids for yield traits. The GS model was trained on 352 hybrid crosses and validated on 213 independent hybrid crosses. The training and validation hybrid parents and 399 training hybrid individuals were genotyping by sequencing. Despite the small proportion of hybrid individuals genotyped and low parental heterozygosity, GS prediction accuracy increased on average by 5% (range 1.4-31.3%, depending on trait and model) when training was done using genomic data on hybrids and parents compared with only parental genomic data. With ASGM, GS prediction accuracy increased on average by 3% (- 10.2 to 40%, depending on trait and genotyping strategy) compared with PSAM. We conclude that the best GS strategy for oil palm is to aggregate genomic data of parents and hybrid individuals and to ignore the parental origin of marker alleles (ASGM). To gain a better insight into these results, future studies should examine the respective effect of capturing genetic variability within crosses and taking segregation distortion into account when genotyping hybrid individuals, and investigate the factors controlling the relative performances of ASGM and PSAM in hybrid crops.

We explore the interaction between two genetic incompatibilities (underdominant loci in diploid organisms) in a population occupying a one-dimensional space. We derive a system of partial differential equations describing the dynamics of allele frequencies and linkage disequilibrium between the two loci, and use a quasi-linkage equilibrium approximation in order to reduce the number of variables. We investigate the solutions of this system and demonstrate the existence of a solution in which the two clines in allele frequency remain stacked together. In the case of asymmetric incompatibilities (i.e. when one homozygote is favored over the other at each locus), these stacked clines propagate in the form of a traveling wave. We obtain an approximation for the speed of this wave which, in particular, is decreased by recombination between the two loci but is always larger than the speed of \"one cline alone\".

We here propose an analysis pipeline for inferring the distribution of fitness effects (DFE) from either patient-sampled or experimentally-evolved viral populations, that explicitly accounts for non-Wright-Fisher and non-equilibrium population dynamics inherent to pathogens. We examine the performance of this approach via extensive power and performance analyses, and highlight two illustrative applications - one from an experimentally-passaged RNA virus, and the other from a clinically-sampled DNA virus. Finally, we discuss how such DFE inference may shed light on major research questions in virus evolution, ranging from a quantification of the population genetic processes governing genome size, to the role of Hill-Robertson interference in dictating adaptive outcomes, to the potential design of novel therapeutic approaches to eradicate within-patient viral populations via induced mutational meltdown.

In evolutionary quantitative genetics, the genetic variance-covariance matrix, G, and the vector of directional selection gradients, β, are key parameters for predicting multivariate selection responses and genetic constraints. Historically, investigations of G and β have not overlapped with those dissecting the genetic basis of quantitative traits. Thus, it remains unknown whether these parameters reflect pleiotropic effects at individual loci. Here, we integrate multivariate genome-wide association study (GWAS) with G and β estimation in a well-studied system of multivariate constraint: sexual selection on male cuticular hydrocarbons (CHCs) in Drosophila serrata. In a panel of wild-derived re-sequenced lines, we augment genome-based restricted maximum likelihood to estimate G alongside multivariate single nucleotide polymorphism (SNP) effects, detecting 532 significant associations from 1 652 276 SNPs. Constraint was evident, with β lying in a direction of G with low evolvability. Interestingly, minor frequency alleles typically increased male CHC-attractiveness suggesting opposing natural selection on β. SNP effects were significantly misaligned with the major eigenvector of G, gmax, but well aligned to the second and third eigenvectors g2 and g3. We discuss potential factors leading to these varied results including multivariate stabilizing selection and mutational bias. Our framework may be useful as researchers increasingly access genomic methods to study multivariate selection responses in wild populations.