Genomic prediction of growth traits for pigs in the presence of genotype by environment interactions using single‐step genomic reaction norm model

2020 ◽  
Vol 137 (6) ◽  
pp. 523-534
Author(s):  
Hailiang Song ◽  
Qin Zhang ◽  
Ignacy Misztal ◽  
Xiangdong Ding
Keyword(s):  
Genomic Prediction ◽  
Growth Traits ◽  
Reaction Norm ◽  
Single Step ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Norm Model

scholarly journals Genotype-by-environment interaction of fertility traits in Danish Holstein cattle using a single-step genomic reaction norm model

Heredity ◽  
2019 ◽  
Vol 123 (2) ◽  
pp. 202-214 ◽  
Author(s):  
Zhe Zhang ◽  
Morten Kargo ◽  
Aoxing Liu ◽  
Jørn Rind Thomasen ◽  
Yuchun Pan ◽  
...  
Keyword(s):  
Reaction Norm ◽  
Genotype By Environment Interaction ◽  
Single Step ◽  
Holstein Cattle ◽  
Environment Interaction ◽  
Genotype By Environment ◽  
Norm Model ◽  
Fertility Traits

scholarly journals Genotype-by-environment interaction in Holstein heifer fertility traits using single-step genomic reaction norm models

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rui Shi ◽  
Luiz Fernando Brito ◽  
Aoxing Liu ◽  
Hanpeng Luo ◽  
Ziwei Chen ◽  
...  
Keyword(s):  
Heat Stress ◽  
Dairy Cattle ◽  
Candidate Genes ◽  
Heat Tolerance ◽  
Reaction Norm ◽  
Single Step ◽  
Conception Rate ◽  
Critical Periods ◽  
Genotype By Environment ◽  
Fertility Traits

Abstract Background The effect of heat stress on livestock production is a worldwide issue. Animal performance is influenced by exposure to harsh environmental conditions potentially causing genotype-by-environment interactions (G × E), especially in highproducing animals. In this context, the main objectives of this study were to (1) detect the time periods in which heifer fertility traits are more sensitive to the exposure to high environmental temperature and/or humidity, (2) investigate G × E due to heat stress in heifer fertility traits, and, (3) identify genomic regions associated with heifer fertility and heat tolerance in Holstein cattle. Results Phenotypic records for three heifer fertility traits (i.e., age at first calving, interval from first to last service, and conception rate at the first service) were collected, from 2005 to 2018, for 56,998 Holstein heifers raised in 15 herds in the Beijing area (China). By integrating environmental data, including hourly air temperature and relative humidity, the critical periods in which the heifers are more sensitive to heat stress were located in more than 30 days before the first service for age at first calving and interval from first to last service, or 10 days before and less than 60 days after the first service for conception rate. Using reaction norm models, significant G × E was detected for all three traits regarding both environmental gradients, proportion of days exceeding heat threshold, and minimum temperature-humidity index. Through single-step genome-wide association studies, PLAG1, AMHR2, SP1, KRT8, KRT18, MLH1, and EOMES were suggested as candidate genes for heifer fertility. The genes HCRTR1, AGRP, PC, and GUCY1B1 are strong candidates for association with heat tolerance. Conclusions The critical periods in which the reproductive performance of heifers is more sensitive to heat stress are trait-dependent. Thus, detailed analysis should be conducted to determine this particular period for other fertility traits. The considerable magnitude of G × E and sire re-ranking indicates the necessity to consider G × E in dairy cattle breeding schemes. This will enable selection of more heat-tolerant animals with high reproductive efficiency under harsh climatic conditions. Lastly, the candidate genes identified to be linked with response to heat stress provide a better understanding of the underlying biological mechanisms of heat tolerance in dairy cattle.

scholarly journals The Importance of Dominance and Genotype-by-Environment Interactions on Grain Yield Variation in a Large-Scale Public Cooperative Maize Experiment

2021 ◽  
Author(s):  
Anna R Rogers ◽  
Jeffrey C Dunne ◽  
Cinta Romay ◽  
Martin Bohn ◽  
Edward S Buckler ◽  
...  
Keyword(s):  
Grain Yield ◽  
High Throughput ◽  
Genomic Prediction ◽  
Environmental Data ◽  
Environment Interaction ◽  
Hybrid Performance ◽  
Interaction Patterns ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Main Effect

Abstract High-dimensional and high throughput genomic, field performance, and environmental data are becoming increasingly available to crop breeding programs, and their integration can facilitate genomic prediction within and across environments and provide insights into the genetic architecture of complex traits and the nature of genotype-by-environment interactions. To partition trait variation into additive and dominance (main effect) genetic and corresponding genetic-by-environment variances, and to identify specific environmental factors that influence genotype-by-environment interactions, we curated and analyzed genotypic and phenotypic data on 1918 maize (Zea mays L.) hybrids and environmental data from 65 testing environments. For grain yield, dominance variance was similar in magnitude to additive variance, and genetic-by-environment variances were more important than genetic main effect variances. Models involving both additive and dominance relationships best fit the data and modeling unique genetic covariances among all environments provided the best characterization of the genotype-by-environment interaction patterns. Similarity of relative hybrid performance among environments was modeled as a function of underlying weather variables, permitting identification of weather covariates driving correlations of genetic effects across environments. The resulting models can be used for genomic prediction of mean hybrid performance across populations of environments tested or for environment-specific predictions. These results can also guide efforts to incorporate high-throughput environmental data into genomic prediction models and predict values in new environments characterized with the same environmental characteristics.

scholarly journals Genotype-by-environment interactions for reproduction, body composition, and growth traits in maternal-line pigs based on single-step genomic reaction norms

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Shi-Yi Chen ◽  
Pedro H. F. Freitas ◽  
Hinayah R. Oliveira ◽  
Sirlene F. Lázaro ◽  
Yi Jian Huang ◽  
...  
Keyword(s):  
Body Composition ◽  
Candidate Genes ◽  
X Chromosome ◽  
Reaction Norm ◽  
Single Step ◽  
Residual Variance ◽  
Large White ◽  
Maternal Line ◽  
Genotype By Environment ◽  
Genomic Regions

Abstract Background There is an increasing need to account for genotype-by-environment (G × E) interactions in livestock breeding programs to improve productivity and animal welfare across environmental and management conditions. This is even more relevant for pigs because selection occurs in high-health nucleus farms, while commercial pigs are raised in more challenging environments. In this study, we used single-step homoscedastic and heteroscedastic genomic reaction norm models (RNM) to evaluate G × E interactions in Large White pigs, including 8686 genotyped animals, for reproduction (total number of piglets born, TNB; total number of piglets born alive, NBA; total number of piglets weaned, NW), growth (weaning weight, WW; off-test weight, OW), and body composition (ultrasound muscle depth, MD; ultrasound backfat thickness, BF) traits. Genetic parameter estimation and single-step genome-wide association studies (ssGWAS) were performed for each trait. Results The average performance of contemporary groups (CG) was estimated and used as environmental gradient in the reaction norm analyses. We found that the need to consider heterogeneous residual variance in RNM models was trait dependent. Based on estimates of variance components of the RNM slope and of genetic correlations across environmental gradients, G × E interactions clearly existed for TNB and NBA, existed for WW but were of smaller magnitude, and were not detected for NW, OW, MD, and BF. Based on estimates of the genetic variance explained by the markers in sliding genomic windows in ssGWAS, several genomic regions were associated with the RNM slope for TNB, NBA, and WW, indicating specific biological mechanisms underlying environmental sensitivity, and dozens of novel candidate genes were identified. Our results also provided strong evidence that the X chromosome contributed to the intercept and slope of RNM for litter size traits in pigs. Conclusions We provide a comprehensive description of G × E interactions in Large White pigs for economically-relevant traits and identified important genomic regions and candidate genes associated with GxE interactions on several autosomes and the X chromosome. Implementation of these findings will contribute to more accurate genomic estimates of breeding values by considering G × E interactions, in order to genetically improve the environmental robustness of maternal-line pigs.

scholarly journals Multiple trait breeding programs with genotype-by-environment interactions based on reaction norms, with application to genetic improvement of disease resilience

2021 ◽  
Vol 53 (1) ◽  
Author(s):  
Jack C. M. Dekkers
Keyword(s):  
Genetic Improvement ◽  
Reaction Norm ◽  
Character State ◽  
Multiple Trait ◽  
Breeding Programs ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Breeding Goal ◽  
State Models ◽  
Target Environment

Abstract Background Genotype-by-environment interactions for a trait can be modeled using multiple-trait, i.e. character-state, models, that consider the phenotype as a different trait in each environment, or using reaction norm models based on a functional relationship, usually linear, between phenotype and a quantitative measure of the quality of the environment. The equivalence between character-state and reaction norm models has been demonstrated for a single trait. The objectives of this study were to extend the equivalence of the reaction norm and character-state models to a multiple-trait setting and to both genetic and environmental effects, and to illustrate the application of this equivalence to the design and optimization of breeding programs for disease resilience. Methods Equivalencies between reaction norm and character-state models for multiple-trait phenotypes were derived at the genetic and environmental levels, which demonstrates how multiple-trait reaction norm parameters can be derived from multiple-trait character state parameters. Methods were applied to optimize selection for a multiple-trait breeding goal in a target environment based on phenotypes collected in a healthy and disease-challenged environment, and to optimize the environment in which disease-challenge phenotypes should be collected. Results and conclusions The equivalence between multiple-trait reaction norm and multiple-trait character-state parameters allow genetic improvement for a multiple-trait breeding goal in a target environment to be optimized without recording phenotypes and estimating parameters for the target environment.

Single‐Step Reaction Norm Models for Genomic Prediction in Multienvironment Recurrent Selection Trials

Crop Science ◽  
2018 ◽  
Vol 58 (2) ◽  
pp. 592-607 ◽  
Author(s):  
Odilon P. Morais Júnior ◽  
João Batista Duarte ◽  
Flávio Breseghello ◽  
Alexandre S. G. Coelho ◽  
Orlando P. Morais ◽  
...  
Keyword(s):  
Genomic Prediction ◽  
Recurrent Selection ◽  
Reaction Norm ◽  
Single Step ◽  
Single Step Reaction

scholarly journals Use of genomic information to exploit genotype-by-environment interactions for body weight of broiler chicken in bio-secure and production environments

2019 ◽  
Vol 51 (1) ◽  
Author(s):  
Thinh T. Chu ◽  
John W. M. Bastiaansen ◽  
Peer Berg ◽  
Hélène Romé ◽  
Danye Marois ◽  
...  
Keyword(s):  
Body Weight ◽  
Genetic Correlations ◽  
Single Step ◽  
Pedigree Information ◽  
Genomic Information ◽  
Genotype By Environment Interactions ◽  
Genotype By Environment ◽  
Increase In Accuracy ◽  
Close Relatives ◽  
Estimate Variance

Abstract Background The increase in accuracy of prediction by using genomic information has been well-documented. However, benefits of the use of genomic information and methodology for genetic evaluations are missing when genotype-by-environment interactions (G × E) exist between bio-secure breeding (B) environments and commercial production (C) environments. In this study, we explored (1) G × E interactions for broiler body weight (BW) at weeks 5 and 6, and (2) the benefits of using genomic information for prediction of BW traits when selection candidates were raised and tested in a B environment and close relatives were tested in a C environment. Methods A pedigree-based best linear unbiased prediction (BLUP) multivariate model was used to estimate variance components and predict breeding values (EBV) of BW traits at weeks 5 and 6 measured in B and C environments. A single-step genomic BLUP (ssGBLUP) model that combined pedigree and genomic information was used to predict EBV. Cross-validations were based on correlation, mean difference and regression slope statistics for EBV that were estimated from full and reduced datasets. These statistics are indicators of population accuracy, bias and dispersion of prediction for EBV of traits measured in B and C environments. Validation animals were genotyped and non-genotyped birds in the B environment only. Results Several indications of G × E interactions due to environmental differences were found for BW traits including significant re-ranking, heterogeneous variances and different heritabilities for BW measured in environments B and C. The genetic correlations between BW traits measured in environments B and C ranged from 0.48 to 0.54. The use of combined pedigree and genomic information increased population accuracy of EBV, and reduced bias of EBV prediction for genotyped birds compared to the use of pedigree information only. A slight increase in accuracy of EBV was also observed for non-genotyped birds, but the bias of EBV prediction increased for non-genotyped birds. Conclusions The G × E interaction was strong for BW traits of broilers measured in environments B and C. The use of combined pedigree and genomic information increased population accuracy of EBV substantially for genotyped birds in the B environment compared to the use of pedigree information only.

scholarly journals Genomic prediction accounting for genotype by environment interaction offers an effective framework for breeding simultaneously for adaptation to an abiotic stress and performance under normal cropping conditions in rice

2018 ◽  
Author(s):  
M Ben Hassen ◽  
J Bartholomé ◽  
G Valè ◽  
TV Cao ◽  
N Ahmadi
Keyword(s):  
Water Management ◽  
Genomic Prediction ◽  
Cross Validation ◽  
Prediction Models ◽  
Management Systems ◽  
Environment Interaction ◽  
Wetting And Drying ◽  
Genotype By Environment Interactions ◽  
Alternate Wetting And Drying ◽  
Genotype By Environment

AbstractDeveloping rice varieties adapted to alternate wetting and drying water management is crucial for the sustainability of irrigated rice cropping systems. Here we report the first study exploring the feasibility of breeding rice for adaptation to alternate wetting and drying using genomic prediction methods that account for genotype by environment interactions. Two breeding populations (a reference panel of 284 accessions and a progeny population of 97 advanced lines) were evaluated under alternate wetting and drying and continuous flooding management systems. The accuracy of genomic prediction for response variables (index of relative performance and the slope of the joint regression) and for multi-environment genomic prediction models were compared. For the three traits considered (days to flowering, panicle weight and nitrogen-balance index), significant genotype by environment interactions were observed in both populations. In cross validation, prediction accuracy for the index was on average lower (0.31) than that of the slope of the joint regression (0.64) whatever the trait considered. Similar results were found for across population validation (progeny validation). Both cross-validation and progeny validation experiments showed that the performance of multi-environment models predicting unobserved phenotypes of untested entrees was similar to the performance of single environment models with differences in accuracy ranging from - 6% to 4% depending on the trait and on the statistical model concerned. The accuracy of multi-environment models predicting unobserved phenotypes of entrees evaluated under both water management systems outperformed single environment models by an average of 30%. Practical implications for breeding rice for adaptation to AWD are discussed.

Sign in / Sign up

Export Citation Format

Share Document