Phenotypic Selection of Dairy Cattle Infected with Bovine Leukemia Virus Demonstrates Immunogenetic Resilience through NGS-Based Genotyping of BoLA MHC Class II Genes

Characterization of the bovine leukocyte antigen (BoLA) DRB3 gene has shown that specific alleles associate with susceptibility or resilience to the progression of bovine leukemia virus (BLV), measured by proviral load (PVL). Through surveillance of multi-farm BLV eradication field trials, we observed differential phenotypes within seropositive cows that persist from months to years. We sought to develop a multiplex next-generation sequencing workflow (NGS-SBT) capable of genotyping 384 samples per run to assess the relationship between BLV phenotype and two BoLA genes. We utilized longitudinal results from milk ELISA screening and subsequent blood collections on seropositive cows for PVL determination using a novel BLV proviral load multiplex qPCR assay to phenotype the cows. Repeated diagnostic observations defined two distinct phenotypes in our study population, ELISA-positive cows that do not harbor detectable levels of provirus and those who do have persistent proviral loads. In total, 565 cows from nine Midwest dairy farms were selected for NGS-SBT, with 558 cows: 168 BLV susceptible (ELISA-positive/PVL-positive) and 390 BLV resilient (ELISA-positive/PVL-negative) successfully genotyped. Three BoLA-DRB3 alleles, including one novel allele, were shown to associate with disease resilience, *009:02, *044:01, and *048:02 were found at rates of 97.5%, 86.5%, and 90.3%, respectively, within the phenotypically resilient population. Alternatively, DRB3*015:01 and *027:03, both known to associate with disease progression, were found at rates of 81.1% and 92.3%, respectively, within the susceptible population. This study helps solidify the immunogenetic relationship between BoLA-DRB3 alleles and BLV infection status of these two phenotypic groupings of US dairy cattle.

Genomic Selection for Wheat Blast in a Diversity Panel, Breeding Panel and Full-Sibs Panel

Wheat blast is an emerging threat to wheat production, due to its recent migration to South Asia and Sub-Saharan Africa. Because genomic selection (GS) has emerged as a promising breeding strategy, the key objective of this study was to evaluate it for wheat blast phenotyped at precision phenotyping platforms in Quirusillas (Bolivia), Okinawa (Bolivia) and Jashore (Bangladesh) using three panels: (i) a diversity panel comprising 172 diverse spring wheat genotypes, (ii) a breeding panel comprising 248 elite breeding lines, and (iii) a full-sibs panel comprising 298 full-sibs. We evaluated two genomic prediction models (the genomic best linear unbiased prediction or GBLUP model and the Bayes B model) and compared the genomic prediction accuracies with accuracies from a fixed effects model (with selected blast-associated markers as fixed effects), a GBLUP + fixed effects model and a pedigree relationships-based model (ABLUP). On average, across all the panels and environments analyzed, the GBLUP + fixed effects model (0.63 ± 0.13) and the fixed effects model (0.62 ± 0.13) gave the highest prediction accuracies, followed by the Bayes B (0.59 ± 0.11), GBLUP (0.55 ± 0.1), and ABLUP (0.48 ± 0.06) models. The high prediction accuracies from the fixed effects model resulted from the markers tagging the 2NS translocation that had a large effect on blast in all the panels. This implies that in environments where the 2NS translocation-based blast resistance is effective, genotyping one to few markers tagging the translocation is sufficient to predict the blast response and genome-wide markers may not be needed. We also observed that marker-assisted selection (MAS) based on a few blast-associated markers outperformed GS as it selected the highest mean percentage (88.5%) of lines also selected by phenotypic selection and discarded the highest mean percentage of lines (91.8%) also discarded by phenotypic selection, across all panels. In conclusion, while this study demonstrates that MAS might be a powerful strategy to select for the 2NS translocation-based blast resistance, we emphasize that further efforts to use genomic tools to identify non-2NS translocation-based blast resistance are critical.

Genomic basis of fishing-associated selection varies with population density

Fisheries induce one of the strongest anthropogenic selective pressures on natural populations, but the genetic effects of fishing remain unclear. Crucially, we lack knowledge of how capture-associated selection and its interaction with reductions in population density caused by fishing can potentially shift which genes are under selection. Using experimental fish reared at two densities and repeatedly harvested by simulated trawling, we show consistent phenotypic selection on growth, metabolism, and social behavior regardless of density. However, the specific genes under selection—mainly related to brain function and neurogenesis—varied with the population density. This interaction between direct fishing selection and density could fundamentally alter the genomic responses to harvest. The evolutionary consequences of fishing are therefore likely context dependent, possibly varying as exploited populations decline. These results highlight the need to consider environmental factors when predicting effects of human-induced selection and evolution.

Influence of Intrinsic Aerobic Exercise Capacity and Sex on Cardiac Injury Following Acute Myocardial Ischemia and Reperfusion

Purpose: Previous reports have suggested that active exercise aside, intrinsic aerobic running capacity (Low = LCR, high = HCR) in otherwise sedentary animals may influence several cardiovascular health-related indicators. Relative to the HCR phenotype, the LCR phenotype is characterized by decreased endothelial reactivity, increased susceptibility to reperfusion-induced arrhythmias following short, non-infarction ischemia, and increased diet-induced insulin resistance. More broadly, the LCR phenotype has come to be characterized as a “disease prone” model, with the HCRs as “disease resistant.” Whether these effects extend to injury outcomes in an overt infarction or whether the effects are gender specific is not known. This study was designed to determine whether HCR/LCR phenotypic differences would be evident in injury responses to acute myocardial ischemia-reperfusion injury (AIR), measured as infarct size and to determine whether sex differences in infarction size were preserved with phenotypic selection.Methods: Regional myocardial AIR was induced in vivo by either 15 or 30 min ligation of the left anterior descending coronary artery, followed by 2 h of reperfusion. Global ischemia was induced in isolated hearts ex vivo using a Langendorff perfusion system and cessation of perfusion for either 15 or 30 min followed by 2 h of reperfusion. Infarct size was determined using 2, 3, 5–triphenyltetrazolium chloride (TTC) staining, and normalized to area at risk in the regional model, or whole heart in the global model. Portions of the tissue were paraffin embedded for H&E staining and histology analysis.Results: Phenotype dependent differences in infarct size were seen with 15 min occlusion/2 h reperfusion (LCR > HCR, p < 0.05) in both regional and global models. In both models, longer occlusion times (30 min/2 h) produced significantly larger infarctions in both phenotypes, but phenotypic differences were no longer present (LCR vs. HCR, p = n.s.). Sex differences in infarct size were present in each phenotype (LCR male > LCR female, p < 0.05; HCR male > HCR female, p < 0.05 regardless of length of occlusion, or ischemia model.Conclusions: There is cardioprotection afforded by high intrinsic aerobic capacity, but it is not infinite/continuous, and may be overcome with sufficient injury burden. Phenotypic selection based on endurance running capacity preserved sex differences in response to both short and longer term coronary occlusive challenges. Outcomes could not be associated with differences in system characteristics such as circulating inflammatory mediators or autonomic nervous system influences, as similar phenotypic injury patterns were seen in vivo, and in isolated crystalloid perfused heart ex vivo.

Maintenance of Complex Trait Variation: Classic Theory and Modern Data

Numerous studies have found evidence that GWAS loci experience negative selection, which increases in intensity with the effect size of identified variants. However, there is also accumulating evidence that this selection is not entirely mediated by the focal trait and contains a substantial pleiotropic component. Understanding how selective constraint shapes phenotypic variation requires advancing models capable of balancing these and other components of selection, as well as empirical analyses capable of inferring this balance and how it is generated by the underlying biology. We first review the classic theory connecting phenotypic selection to selection at individual loci as well as approaches and findings from recent analyses of negative selection in GWAS data. We then discuss geometric theories of pleiotropic selection with the potential to guide future modeling efforts. Recent findings revealing the nature of pleiotropic genetic variation provide clues to which genetic relationships are important and should be incorporated into analyses of selection, while findings that effect sizes vary between populations indicate that GWAS measurements could be misleading if effect sizes have also changed throughout human history.

Strategies of Preserving Genetic Diversity While Maximizing Genetic Response From Implementing Genomic Selection in Pulse Breeding Programs

Using a stochastic computer simulation, we investigated the benefit of optimization strategies in the context of genomic selection (GS) for pulse breeding programs. We simulated GS for moderately complex to highly complex traits such as disease resistance, grain weight and grain yield in multiple environments with a high level of genotype-by-environment interaction for grain yield. GS led to higher genetic gain per unit of time and higher genetic diversity loss than phenotypic selection by shortening the breeding cycle time. The genetic gain obtained from selecting the segregating parents early in the breeding cycle (at F1 or F2 stages) was substantially higher than selecting at later stages even though prediction accuracy was moderate. Increasing the number of F1 intercross (F1i) families and keeping the total number of progeny of F1i families constant, we observed a decrease in genetic gain and increase in genetic diversity. Whereas increasing the number of progeny per F1i family while keeping a constant number of F1i families increased rate of genetic gain and had higher genetic diversity loss per unit of time. Adding 50 F2 family phenotypes to the training population increased the accuracy of GEBVs and genetic gain per year and decreased the rate of genetic diversity loss. Genetic diversity could be preserved by applying a strategy that restricted both the percentage of alleles fixed and the average relationship of the group of selected parents to preserve long-term genetic improvement in the pulse breeding program.

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.

Advances in genetic improvement of durable resistance to Fusarium head blight in wheat

Wheat Fusarium head blight (FHB) is a destructive disease in wheat worldwide. Wheat resistance to FHB is a complex with five types. Each type of resistance is controlled by multiple quantitative trait loci (QTLs) with most having minor effects and being affected by environments. This chapter describes methodologies used for evaluating different types of resistance, consolidates the QTLs for type II and Type III resistance into 26 repeatable QTLs, discusses progresses made in genetics and breeding of wheat FHB resistance, and discusses possible new breeding strategies for FHB resistance improvement. The 26 repeatable QTL were located in ~100 Mb intervals based on IWGSC reference sequence map, which will be critical QTLs for functional marker development and for improvement of FHB resistance in breeding. Genomic selection (GS) together with marker-assisted selection (MAS) coupling with phenotypic selection will facilitate accumulation of multiple QTLs from different sources to create highly resistant cultivars.

New cycle, same old mistakes? Overlapping vs. discrete generations in long-term recurrent selection

Background: Recurrent selection is a foundational breeding method for quantitative trait improvement. It typically features rapid breeding cycles that can lead to high rates of genetic gain. In recurrent phenotypic selection, generations do not overlap, which means that breeding candidates are evaluated and considered for selection for only one cycle. With recurrent genomic selection, candidates can be evaluated based on genomic estimated breeding values indefinitely, therefore facilitating overlapping generations. Candidates with true high breeding values that were discarded in one cycle due to underestimation of breeding value could be identified and selected in subsequent cycles. The consequences of allowing generations to overlap in recurrent selection are unknown. We assessed whether maintaining overlapping and discrete generations led to differences in genetic gain for phenotypic, genomic truncation, and genomic optimum contribution recurrent selection by simulation of traits with various heritabilities and genetic architectures across fifty breeding cycles. We also assessed differences of overlapping and discrete generations in a conventional breeding scheme with multiple stages and cohorts. Results: With phenotypic selection, overlapping generations led to decreased genetic gain compared to discrete generations due to increased selection error bias. Selected individuals, which were in the upper tail of the distribution of phenotypic values, tended to also have high absolute error relative to their true breeding value compared to the overall population. Without repeated phenotyping, these erroneously outlying individuals were repeatedly selected across cycles, leading to decreased genetic gain. With genomic truncation selection, overlapping and discrete generations performed similarly as updating breeding values precluded repeatedly selecting individuals with inaccurately high estimates of breeding values in subsequent cycles. Overlapping generations did not outperform discrete generations in the presence of a positive genetic trend with genomic truncation selection, as past generations had lower mean genetic values than the current generation of selection candidates. With genomic optimum contribution selection, overlapping and discrete generations performed similarly, but overlapping generations slightly outperformed discrete generations in the long term if the targeted inbreeding rate was extremely low. Conclusions: Maintaining discrete generations in recurrent phenotypic mass selection leads to increased genetic gain, especially at low heritabilities, by preventing selection error bias. With genomic truncation selection and genomic optimum contribution selection, genetic gain does not differ between discrete and overlapping generations assuming non-genetic effects are not present. Overlapping generations may increase genetic gain in the long term with very low targeted rates of inbreeding in genomic optimum contribution selection.

Optimum breeding strategies using genomic and phenotypic selection for the simultaneous improvement of two traits

Key message A breeding strategy combining genomic with one-stage phenotypic selection maximizes annual selection gain for net merit. Choice of the selection index strongly affects the selection gain expected in individual traits. Abstract Selection indices using genomic information have been proposed in crop-specific scenarios. Routine use of genomic selection (GS) for simultaneous improvement of multiple traits requires information about the impact of the available economic and logistic resources and genetic properties (variances, trait correlations, and prediction accuracies) of the breeding population on the expected selection gain. We extended the R package “selectiongain” from single trait to index selection to optimize and compare breeding strategies for simultaneous improvement of two traits. We focused on the expected annual selection gain (ΔGa) for traits differing in their genetic correlation, economic weights, variance components, and prediction accuracies of GS. For all scenarios considered, breeding strategy GSrapid (one-stage GS followed by one-stage phenotypic selection) achieved higher ΔGa than classical two-stage phenotypic selection, regardless of the index chosen to combine the two traits and the prediction accuracy of GS. The Smith–Hazel or base index delivered higher ΔGa for net merit and individual traits compared to selection by independent culling levels, whereas the restricted index led to lower ΔGa in net merit and divergent results for selection gain of individual traits. The differences among the indices depended strongly on the correlation of traits, their variance components, and economic weights, underpinning the importance of choosing the selection indices according to the goal of the breeding program. We demonstrate our theoretical derivations and extensions of the R package “selectiongain” with an example from hybrid wheat by designing indices to simultaneously improve grain yield and grain protein content or sedimentation volume.