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

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.

Euphytica ◽  
2009 ◽  
Vol 174 (3) ◽  
pp. 315-324 ◽  
Author(s):  
Nikola Hristov ◽  
Novica Mladenov ◽  
Veselinka Djuric ◽  
Ankica Kondic-Spika ◽  
Ana Marjanovic-Jeromela ◽  
...  

2021 ◽  
Author(s):  
C. Brandon Ogbunugafor

Measuring the fitness effects of mutations and epistasis remain central yet provocative concepts in evolutionary and population genetics. In addition to the baseline complexity that arises from the notion that genetic information can interact in a nonlinear way, recent studies have revealed that interactions can change as a function of environmental context. Here I propose the fusion of measurements of the effect of mutations and physiological epistasis with the reaction norm, a central abstraction used to depict genotype by environment interactions. In doing so, I formalize the notion of a "mutation effect reaction norm" (MERN) as an instrument through which one can analyze or depict the phenotypic consequences of interactions between mutations across environmental contexts. I demonstrate its utility through a discussion of the signature of mutations that undermine reverse evolution of antimicrobial resistance. In closing, I argue that the mutation effect reaction norm may help us resolve the dynamism of evolution across fitness seascapes through specific insight into how mutation effects and interactions are modulated by environmental context.


Author(s):  
Fernando B Rodrigues ◽  
Carlos HM Malhado ◽  
Paulo LS Carneiro ◽  
Diego P Ambrosini ◽  
Marcos PG Rezende ◽  
...  

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rui Shi ◽  
Luiz Fernando Brito ◽  
Aoxing Liu ◽  
Hanpeng Luo ◽  
Ziwei Chen ◽  
...  

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.


Euphytica ◽  
2017 ◽  
Vol 213 (5) ◽  
Author(s):  
Megan M. Mathey ◽  
Sonali Mookerjee ◽  
Lise L. Mahoney ◽  
Kazim Gündüz ◽  
Umesh Rosyara ◽  
...  

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