The Addition of Epistatic Genetic Effects Increases Genomic Prediction Accuracy for Reproduction and Production Traits in Duroc Pigs Using Genomic Models

Background: Genomic selection has been implemented in livestock genetic evaluations for years. However, currently most genomic selection models only consider the additive effects associated with SNP markers and nonadditive genetic effects have been for the most part ignored. Methods: Production traits for 26,735 to 27,647 Duroc pigs and reproductive traits for 5,338 sows were used, including off-test body weight (WT), off-test back fat (BF), off-test loin muscle depth (MS), number born alive (NBA), number born dead (NBD), and number weaned (NW). All animals were genotyped with the PorcineSNP60K Bead Chip. Variance components were estimated using a linear mixed model that includes inbreeding coefficient, additive, dominance, additive-by-additive, additive-by-dominance, dominance-by-dominance effect, and common litter environmental effect. Genomic prediction performance, including all nonadditive genetic effects, was compared with a reduced model that included only additive genetic effect. Results: Significant estimates of additive-by-additive effect variance were observed for NBA, BF, and WT (31%, 9%, and 10%, respectively). Production traits showed significant large estimates of additive-by-dominance variance (9%-23%). MS also showed large estimate of dominance-by-dominance variance (10%). Dominance effect variance estimates were low for all traits (0%-2%). Compared to the reduced model, prediction accuracies using the full model, including nonadditive effects, increased significantly by 12%, 12%, and 1% for NBA, WT, and MS, respectively. A strong dominance association signal with BF was identified near AK5.Conclusions: Sizable estimates of epistatic effects were found for the reproduction and production traits, while the dominance effect was relatively small for all traits yet significant for all production traits. Including nonadditive effects, especially epistatic effects in the genomic prediction model, significantly improved prediction accuracy for NBA, WT, and MS.

Nonadditive Effects on Decomposition of a Mixture of Rice Straw and Groundnut Stover Applied to a Sandy Soil

Rice straw is an abundant resource, but its use as a sandy soil amendment does not increase soil organic matter (SOM) accumulation. Our study aimed to determine the altered decomposition processes that result from mixing rice straw (RS) (low N, high cellulose) with groundnut stover (GN) (high N) relative to applying these residues singly to a sandy soil to identify the mechanisms underlying decomposition of the mixed residues. A microcosm experiment using the litter bag technique showed synergistic, nonadditive effects (observed < predicted values) of residue mass remaining (31.1% < 40.3% initial) that were concomitant with chemical constituent loss, including C (cellulose, lignin) and N. The nonadditive effects of soil microbiological parameters in response to the applied residues were synergistic (observed > predicted values) for microbial biomass C (MBC) (92.1 > 58.4 mg C kg−1 soil) and antagonistic (observed < predicted values) for microbial metabolic quotient (i.e., the inverse of microbial C use efficiency (CUE)) (0.03 < 0.06 mmol CO2-C • mmol MBC−1 • hr−1) and N mineralization (14.8 < 16.0 mg N kg−1 soil). In the early stage of decomposition (0–14 days), mixed residues increased MBC relative to the single residues, while they decreased N mineralization relative to single GN (p ≤ 0.05). These results indicate an increase in microbial substrate CUE and N use efficiency (NUE) in the mixed residues relative to the single residues. This increased efficiency provides a basis for the synthesis of microbial products that contribute to the formation of the stable SOM pool. The SOM stabilization could bring about the SOM accumulation that is lacking under the single-RS application.

Microbial Community Resilience across Ecosystems and Multiple Disturbances

SUMMARY The ability of ecosystems to withstand disturbances and maintain their functions is being increasingly tested as rates of change intensify due to climate change and other human activities. Microorganisms are crucial players underpinning ecosystem functions, and the recovery of microbial communities from disturbances is therefore a key part of the complex processes determining the fate of ecosystem functioning. However, despite global environmental change consisting of numerous pressures, it is unclear and controversial how multiple disturbances affect microbial community stability and what consequences this has for ecosystem functions. This is particularly the case for those multiple or compounded disturbances that occur more frequently than the normal recovery time. The aim of this review is to provide an overview of the mechanisms that can govern the responses of microbes to multiple disturbances across aquatic and terrestrial ecosystems. We first summarize and discuss properties and mechanisms that influence resilience in aquatic and soil biomes to determine whether there are generally applicable principles. Following, we focus on interactions resulting from inherent characteristics of compounded disturbances, such as the nature of the disturbance, timing, and chronology that can lead to complex and nonadditive effects that are modulating the response of microorganisms.

Genomic Studies Reveal Substantial Dominant Effects and Improved Genomic Predictions in an Open-Pollinated Breeding Population of Eucalyptus pellita

Most of the genomic studies in plants and animals have used additive models for studying genetic parameters and prediction accuracies. In this study, we used genomic models with additive and nonadditive effects to analyze the genetic architecture of growth and wood traits in an open-pollinated (OP) population of Eucalyptus pellita. We used two progeny trials consisting of 5742 trees from 244 OP families to estimate genetic parameters and to test genomic prediction accuracies of three growth traits (diameter at breast height - DBH, total height - Ht and tree volume - Vol) and kraft pulp yield (KPY). From 5742 trees, 468 trees from 28 families were genotyped with 2023 pre-selected markers from candidate genes. We used the pedigree-based additive best linear unbiased prediction (ABLUP) model and two marker-based models (single-step genomic BLUP – ssGBLUP and genomic BLUP – GBLUP) to estimate the genetic parameters and compare the prediction accuracies. Analyses with the two genomic models revealed large dominant effects influencing the growth traits but not KPY. Theoretical breeding value accuracies were higher with the dominance effect in ssGBLUP model for the three growth traits. Accuracies of cross-validation with random folding in the genotyped trees have ranged from 0.60 to 0.82 in different models. Accuracies of ABLUP were lower than the genomic models. Accuracies ranging from 0.50 to 0.76 were observed for within family cross-validation predictions with low relationships between training and validation populations indicating part of the functional variation is captured by the markers through short-range linkage disequilibrium (LD). Within-family phenotype predictive abilities and prediction accuracies of genetic values with dominance effects are higher than the additive models for growth traits indicating the importance of dominance effects in predicting phenotypes and genetic values. This study demonstrates the importance of genomic approaches in OP families to study nonadditive effects. To capture the LD between markers and the quantitative trait loci (QTL) it may be important to use informative markers from candidate genes.

Forage potential of S3 corn progenies in topcrosses and selection of testers of different genetic bases

The objective of this work was to identify corn (Zea mays) genotypes with forage potential and to evaluate the efficiency of testers to discriminate forage traits in topcrosses, considering the contribution of additive and nonadditive genes. The experiment was carried out in the 2015/2016 and 2016/2017 crop seasons, in a randomized complete block design with three replicates. Thirty S3 corn progenies were evaluated in topcrosses with the AG8025, P30B39, MLP102, 60.H23.1, and 70.H26.1 testers. The following traits were assessed: forage dry mass yield, neutral detergent fiber, acid detergent fiber, and forage dry mass degradability. Progenies 205.2, 159.6, and 199.2, in this order, presented the best performance for forage potential. Testers 60.H23.1 and 70.H26.1 better expressed the genetic variability between progenies. For all traits in both crop seasons, there is a predominance of the action of genes of nonadditive effects.

Estimating dominance genetic variances for growth traits in American Angus males using genomic models

Estimates of dominance variance for growth traits in beef cattle based on pedigree data vary considerably across studies, and the proportion of genetic variance explained by dominance deviations remains largely unknown. The potential benefits of including nonadditive genetic effects in the genomic model combined with the increasing availability of large genomic data sets have recently renewed the interest in including nonadditive genetic effects in genomic evaluation models. The availability of genomic information enables the computation of covariance matrices of dominant genomic relationships among animals, similar to matrices of additive genomic relationships, and in a more straightforward manner than the pedigree-based dominance relationship matrix. Data from 19,357 genotyped American Angus males were used to estimate additive and dominant variance components for 3 growth traits: birth weight, weaning weight, and postweaning gain, and to evaluate the benefit of including dominance effects in beef cattle genomic evaluations. Variance components were estimated using 2 models: the first one included only additive effects (MG) and the second one included both additive and dominance effects (MGD). The dominance deviation variance ranged from 3% to 8% of the additive variance for all 3 traits. Gibbs sampling and REML estimates showed good concordance. Goodness of fit of the models was assessed by a likelihood ratio test. For all traits, MG fitted the data as well as MGD as assessed either by the likelihood ratio test or by the Akaike information criterion. Predictive ability of both models was assessed by cross-validation and did not improve when including dominance effects in the model. There was little evidence of nonadditive genetic variation for growth traits in the American Angus male population as only a small proportion of genetic variation was explained by nonadditive effects. A genomic model including the dominance effect did not improve the model fit. Consequently, including nonadditive effects in the genomic evaluation model is not beneficial for growth traits in the American Angus male population.