Linear reaction norm models for genetic merit prediction of Angus cattle under genotype by environment interaction1

2012 ◽  
Vol 90 (7) ◽  
pp. 2130-2141 ◽  
Author(s):  
F. F. Cardoso ◽  
R. J. Tempelman
Keyword(s):  
Reaction Norm ◽  
Genetic Merit ◽  
Genotype By Environment ◽  
Angus Cattle ◽  
Linear Reaction

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.

PSVII-27 Genetic analysis of disease resilience of nursery-to-finish pigs under a natural disease challenge model using reaction norms

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 235-235
Author(s):  
Jian Cheng ◽  
KyuSang Lim ◽  
Austin Putz ◽  
Anna Wolc ◽  
John Harding ◽  
...  
Keyword(s):  
Genetic Model ◽  
Average Daily Gain ◽  
Genetic Correlations ◽  
Reaction Norm ◽  
Cubic Spline ◽  
Clinical Disease ◽  
Random Regression ◽  
Disease Phenotypes ◽  
Linear Reaction ◽  
Better Than

Abstract Disease resilience is the ability of an animal to maintain performance across environments with different disease challenge loads (CL) and can be quantified using random regression reaction norm models that describe phenotype as a function of CL. Objectives of this study were to: 1) develop measures of CL using growth rate and clinical disease phenotypes under a natural disease challenge; 2) evaluate genetic variation in disease resilience. Data used were late nursery and finisher growth rates and clinical disease phenotypes, including medical treatment and mortality rates, and subjective health scores, collected on 50 batches of 60/75 crossbred (LRxY) barrows under a polymicrobial natural disease challenge. All pigs were genotyped using a 650K SNP panel. Different CL were derived from estimates of contemporary group effects and used as environmental covariates in reaction norm analyses of average daily gain (ADG) and treatment rate (TRT). The CL were compared based on model loglikelihoods and estimates of genetic variance, using both linear and cubic spline reaction norm models. Linear reaction norm models fitted the data significantly better than the standard genetic model and the cubic spline models fitted the data significantly better than the linear reaction norm model for most traits. CL based on early finisher ADG provided the best fit for nursery ADG, while CL based on clinical disease phenotypes was best for finisher ADG and TRT. With increasing CL, estimates of heritability for ADG initially decreased and then increased, while estimates of heritability for TRT generally increased with CL. Genetic correlations were low between ADG or TRT at high versus low CL but high for close CLs. Results can be used to select more resilient pigs across different CL levels, or high-performance animals at a given CL level, or a combination of these. Funded by Genome Canada, Genome Alberta, USDA-NIFA, and PigGenCanada.

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 Evaluation of the sensitivity of pulmonary arterial pressure to elevation using a reaction norm model in Angus Cattle

2020 ◽  
Vol 98 (5) ◽  
Author(s):  
Scott E Speidel ◽  
Milton G Thomas ◽  
Timothy N Holt ◽  
R Mark Enns
Keyword(s):  
Arterial Pressure ◽  
High Altitude ◽  
Genetic Relationship ◽  
Pulmonary Arterial Pressure ◽  
Genetic Correlations ◽  
Reaction Norm ◽  
High Elevation ◽  
Random Regression ◽  
Angus Cattle ◽  
Pulmonary Arterial

Abstract Pulmonary arterial pressure (PAP) is a diagnostic measure used to determine an individual’s susceptibility to developing high-altitude disease. The importance of PAP measures collected at elevations lower than the intended breeding elevation of the bulls (i.e., ≥1,520 m) is unknown. Therefore, the objective of this study was to determine the genetic relationship between PAP measures collected in a range of elevations using reaction norm models. A total of 9,177 PAP and elevation observations on purebred Angus cattle, which averaged 43.49 ± 11.32 mmHg and 1,878.6 ± 296.8 m, respectively, were used in the evaluation. The average age of the individuals in the evaluation was 434.04 ± 115.9 d. A random regression model containing the effects of sex, a linear covariate of age, a quadratic fixed covariate of elevation, and random effects consisting of a contemporary group and a linear regression of PAP on elevation was used for the evaluation of PAP. Two forms of PAP were evaluated with this model. First, to address the non-normality of the data, PAP was raised to the power of −2.6 (ptPAP) based on the results of a Box–Cox analysis. Second, raw PAP (rPAP) phenotypes were evaluated to compare the results to those obtained from the transformed data. For ptPAP, heritability ranged from 0.25 to 0.37 corresponding to elevations of 1,900 and 1,215 m, respectively. For rPAP, heritability ranged from 0.22 to 0.41 corresponding to elevations of 1,700 and 2,495 m, respectively. Generally, lower elevations corresponded to decreased heritabilities while higher elevations corresponded to increased heritability estimates. For ptPAP, genetic correlations ranged from 0.18 (elevation: 1,215 and 2,495 m) to 1.00. For rPAP, genetic correlations ranged from 0.08 (elevation: 1,215 and 2,495 m) to 1.00. In general, the closer the elevations in which PAP was measured, the greater the genetic relationship. The greater the difference in elevation between PAP measures resulted in lower genetic correlations. The rank correlation between expected progeny differences (EPD) for 1,215 and 2,495 m was 0.65 and 0.49 for the ptPAP and rPAP, respectively. These results suggested that PAP measures collected in lower elevations may be used as an indicator of high-altitude adaptability. In the estimation of EPD to rank sires for their suitability for use in high-elevation production systems, it is important to account for the relationships among varied altitudes.

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.

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