Recurrent evolution of cross-resistance in response to selection for improved post-infection survival in Drosophila melanogaster

The host susceptibility to one pathogen can decrease, increase, or remain unaffected by virtue of the host evolving resistance towards a second pathogen. Negative correlations between a host susceptibility to different pathogens is an often-cited explanation for maintenance of genetic variation in immune function determining traits in a host population. In this study, we investigated the change in susceptibility of Drosophila melanogaster flies to various novel bacterial pathogens after being experimentally selected for increased resistance to one particular bacterial pathogen. We independently selected flies to become more resistant towards Enterococcus faecalis and Pseudomonas entomophila, and baring a few exceptions the evolved populations exhibited cross-resistance against the range of pathogens tested in the study. Neither the identity of the native pathogen nor the host sex was major determining factors in predicting the pattern of cross-resistance exhibited by the selected populations. We therefore report that a generalized cross-resistance to novel pathogens can repeatedly evolve in response to selection for resistance against a single pathogen.

Experimental evolution reveals the synergistic genomic mechanisms of adaptation to ocean warming and acidification in a marine copepod

Metazoan adaptation to global change will rely on selection of standing genetic variation. Determining the extent to which this variation exists in natural populations, particularly for responses to simultaneous stressors, is therefore essential to make accurate predictions for persistence in future conditions. Here, we identify the genetic variation enabling the copepod Acartia tonsa to adapt to experimental ocean warming, acidification, and combined ocean warming and acidification (OWA) conditions over 25 generations. Replicate populations showed a strong and consistent polygenic response to each condition, targeting an array of adaptive mechanisms including cellular homeostasis, development, and stress response. We used a genome-wide covariance approach to partition the genomic changes into selection, drift, and lab adaptation and found that the majority of allele frequency change in warming (56%) and OWA (63%) was driven by selection but acidification was dominated by drift (66%). OWA and warming shared 37% of their response to selection but OWA and acidification shared just 1%. Accounting for lab adaptation was essential for not inflating a shared response to selection between all treatments. Finally, the mechanisms of adaptation in the multiple-stressor OWA conditions were not an additive product of warming and acidification, but rather a synergistic response where 47% of the allelic responses to selection were unique. These results are among the first to disentangle how the genomic targets of selection differ between single and multiple stressors and to demonstrate the complexity that non-additive multiple stressors will contribute to attempts to predict adaptive responses to complex environments.

Predicting Fusarium Head Blight Resistance for Advanced Trials in a Soft Red Winter Wheat Breeding Program With Genomic Selection

Many studies have evaluated the effectiveness of genomic selection (GS) using cross-validation within training populations; however, few have looked at its performance for forward prediction within a breeding program. The objectives for this study were to compare the performance of naïve GS (NGS) models without covariates and multi-trait GS (MTGS) models by predicting two years of F4:7 advanced breeding lines for three Fusarium head blight (FHB) resistance traits, deoxynivalenol (DON) accumulation, Fusarium damaged kernels (FDK), and severity (SEV) in soft red winter wheat and comparing predictions with phenotypic performance over two years of selection based on selection accuracy and response to selection. On average, for DON, the NGS model correctly selected 69.2% of elite genotypes, while the MTGS model correctly selected 70.1% of elite genotypes compared with 33.0% based on phenotypic selection from the advanced generation. During the 2018 breeding cycle, GS models had the greatest response to selection for DON, FDK, and SEV compared with phenotypic selection. The MTGS model performed better than NGS during the 2019 breeding cycle for all three traits, whereas NGS outperformed MTGS during the 2018 breeding cycle for all traits except for SEV. Overall, GS models were comparable, if not better than phenotypic selection for FHB resistance traits. This is particularly helpful when adverse environmental conditions prohibit accurate phenotyping. This study also shows that MTGS models can be effective for forward prediction when there are strong correlations between traits of interest and covariates in both training and validation populations.

Improving Selection Efficiency of Crop Breeding With Genomic Prediction Aided Sparse Phenotyping

Increasing the number of environments for phenotyping of crop lines in earlier stages of breeding programs can improve selection accuracy. However, this is often not feasible due to cost. In our study, we investigated a sparse phenotyping method that does not test all entries in all environments, but instead capitalizes on genomic prediction to predict missing phenotypes in additional environments without extra phenotyping expenditure. The breeders’ main interest – response to selection – was directly simulated to evaluate the effectiveness of the sparse genomic phenotyping method in a wheat and a rice data set. Whether sparse phenotyping resulted in more selection response depended on the correlations of phenotypes between environments. The sparse phenotyping method consistently showed statistically significant higher responses to selection, compared to complete phenotyping, when the majority of completely phenotyped environments were negatively (wheat) or lowly positively (rice) correlated and any extension environment was highly positively correlated with any of the completely phenotyped environments. When all environments were positively correlated (wheat) or any highly positively correlated environments existed (wheat and rice), sparse phenotyping did not improved response. Our results indicate that genomics-based sparse phenotyping can improve selection response in the middle stages of crop breeding programs.

The quantitative genetics of the prevalence of infectious diseases: hidden genetic variation due to Indirect Genetic Effects dominates heritable variation and response to selection

Infectious diseases have profound effects on life, both in nature and agriculture. However, a quantitative genetic theory of the host population for the endemic prevalence of infectious diseases is almost entirely lacking. While several studies have demonstrated the relevance of transmission of infections for heritable variation and response to selection, current quantitative genetics ignores transmission. Thus, we lack concepts of breeding value and heritable variation for endemic prevalence, and poorly understand response of endemic prevalence to selection. Here we integrate quantitative genetics and epidemiology, and propose a quantitative genetic theory for the basic reproduction number R0 and for the endemic prevalence of an infection. We first identify the genetic factors that determine the prevalence. Subsequently we investigate the population level consequences of individual genetic variation, for both R0 and the endemic prevalence. Next, we present expressions for the breeding value and heritable variation, for endemic prevalence and individual binary disease status, and show that these depend strongly on the prevalence. Results show that heritable variation for endemic prevalence is substantially greater than currently believed, and increases strongly when prevalence decreases, while heritability of disease status approaches zero. As a consequence, response of the endemic prevalence to selection for lower disease status accelerates considerably when prevalence decreases, in contrast to classical predictions. Finally, we show that most heritable variation for the endemic prevalence is hidden in indirect genetic effects, suggesting a key role for kin-group selection in the evolutionary history of current populations and for genetic improvement in animals and plants.

The microbiome extends host evolutionary potential

The microbiome shapes many host traits, yet the biology of microbiomes challenges traditional evolutionary models. Here, we illustrate how integrating the microbiome into quantitative genetics can help untangle complexities of host-microbiome evolution. We describe two general ways in which the microbiome may affect host evolutionary potential: by shifting the mean host phenotype and by changing the variance in host phenotype in the population. We synthesize the literature across diverse taxa and discuss how these scenarios could shape the host response to selection. We conclude by outlining key avenues of research to improve our understanding of the complex interplay between hosts and microbiomes.

Effect of Clonal Testing on the Efficiency of Genomic Evaluation in Forest Tree Breeding

Through stochastic simulations, accuracies of breeding values and response to selection were assessed under traditional pedigree- (BLUP) and genomic-based evaluation methods (GBLUP) in forest tree breeding. The latter provides a methodological foundation for genomic selection. We evaluated the impact of clonal replication in progeny testing on the response to selection realized in seed orchards under variable marker density and target effective population sizes. We found that clonal replication in progeny trials boosted selection accuracy, thus provided additional genetic gains under BLUP. While a similar trend was observed for GBLUP, however, the added gains did not surpass those under BLUP. Therefore, breeding programs deploying extensive progeny testing with clonal propagation might not benefit from the deployment of genomic information. These findings could be helpful in the context of operational breeding programs.

Optimizing self-pollinated crop breeding employing genomic selection: from schemes to updating training sets

Long-term breeding schemes employing genomic selection (GS) can boost the response to selection per year. Although several studies show that GS delivers a higher response to selection, only a few analyze the best strategy to employ it, specifically how often and in what manner the training set (TS) should be updated. Therefore, we used stochastic simulation to compare in a long-term breeding program of a hypothetical self-pollinated crop five different strategies to implement GS in the line fixation stage and four methods and sizes to update the TS. Moreover, among breeding schemes, we proposed a new approach for using GS to select the best individuals in each F2 progeny based on genomic estimated breeding and divergence and crossed them to generate a new recombination event. Finally, we compared these schemes to the traditional phenotypic selection and drift. Our results showed that using GS in F2 followed by the phenotypic selection of new parentals in F4 was the best scenario. Furthermore, adding a new set of training data every cycle (over 800) to update the TS maintains the accuracy at satisfactory levels for many more generations, showing that more data is better than optimizing the genetic relationship between TS and the targeted population in a closed system. Hence, we believe that these results may help breeders optimize GS in their programs and improve genetic gain in long-term schemes.

Host–parasite co-evolution

Macroevolutionary patterns concern phylogenies of hosts and their parasites. From those, co-speciation occurs; but host switching is a common evolutionary process and more likely when hosts are close phylogenetically and geographical ranges overlap. Microevolutionary processes refer to allele frequency changes within population. In arms races, traits of hosts and parasites evolve in one direction in response to selection by the other party. With selective sweeps, advantageous alleles rapidly spread in host or parasite population and can become fixed. With antagonistic negative frequency-dependent fluctuations (Red Queen dynamics) genetic polymorphism in populations can be maintained, even through speciation events. A Red Queen co-evolutionary process can favour sexual over asexual reproduction and maintain meiotic recombination despite its other disadvantages (two-fold cost of sex). Local adaptation of host and parasites exist in various combinations; the relative migration rates of the two parties, embedded in a geographical mosaic, are important for this process.