scholarly journals Assessing the impact of natural service bulls and genotype by environment interactions on genetic gain and inbreeding in organic dairy cattle genomic breeding programs

animal ◽  
2014 ◽  
Vol 8 (6) ◽  
pp. 877-886 ◽  
Author(s):  
T. Yin ◽  
M. Wensch-Dorendorf ◽  
H. Simianer ◽  
H.H. Swalve ◽  
S. König
2011 ◽  
Vol 94 (8) ◽  
pp. 4109-4118 ◽  
Author(s):  
N. Mc Hugh ◽  
T.H.E. Meuwissen ◽  
A.R. Cromie ◽  
A.K. Sonesson

2012 ◽  
Vol 52 (3) ◽  
pp. 107 ◽  
Author(s):  
J. E. Pryce ◽  
H. D. Daetwyler

High rates of genetic gain can be achieved through (1) accurate predictions of breeding values (2) high intensities of selection and (3) shorter generation intervals. Reliabilities of ~60% are currently achievable using genomic selection in dairy cattle. This breakthrough means that selection of animals can happen at a very early age (i.e. as soon as a DNA sample is available) and has opened opportunities to radically redesign breeding schemes. Most research over the past decade has focussed on the feasibility of genomic selection, especially how to increase the accuracy of genomic breeding values. More recently, how to apply genomic technology to breeding schemes has generated a lot of interest. Some of this research remains the intellectual property of breeding companies, but there are examples in the public domain. Here we review published research into breeding scheme design using genomic selection and evaluate which designs appear to be promising (in terms of rates of genetic gain) and those that may have unfavourable side-effects (i.e. increasing the rate of inbreeding). The schemes range from fairly conservative designs where bulls are screened genomically to reduce numbers entering progeny testing, to schemes where very large numbers of bull calves are screened and used as sires as soon as they reach sexual maturity. More radical schemes that incorporate the use of reproductive technologies (in juveniles) and genomic selection in nucleus herds are also described. The models used are either deterministic and more recently tend to be stochastic, simulating populations of cattle. A key driver of the rate of genetic gain is the generation interval, which could range from being similar to that in conventional testing (~5 years), down to as little as 1.5 years. Generally, the rate of genetic gain is between 12% and 100% more than in conventional progeny testing, while the rate of inbreeding tends to be lower per generation than in progeny testing because Mendelian sampling terms can be estimated more accurately. However, short generation intervals can lead to higher rates of inbreeding per year in genomic breeding programs.


animal ◽  
2018 ◽  
Vol 12 (7) ◽  
pp. 1475-1483 ◽  
Author(s):  
J.E. Duval ◽  
N. Bareille ◽  
A. Madouasse ◽  
M. de Joybert ◽  
K. Sjöström ◽  
...  

2002 ◽  
Vol 45 (5) ◽  
pp. 433-441
Author(s):  
B. Fuerst-Waltl ◽  
A. Willam ◽  
J. Sölkner

Abstract. A complex deterministic approach (ZPLAN) was used to optimize the breeding programs for beef breeds. For the model population 1,000 beef cows and 60,000 dual purpose Simmental cows for crossbreeding were assumed. The percentage of AI was 25% within the beef breed and 93% within the Simmental cows. Domestic AI beef bulls were used for crossbreeding only. The total merit index included beef traits (birth weight, 200-day-weight direct and maternal, 365-day-weight, daily gain, dressing percentage, EUROP grading score) and functional traits (calving ease, stillbirth, fertility and functional longevity). The proportion of foreign proven and domestic AI bulls was varied as well as the number of bulls tested on stations and on contract farms. Annual monetary genetic gain and discounted profit were used to evaluate alternative breeding strategies. Extending the number of bulls tested on stations and establishing performance testing of natural service bulls on contract farms increased the annual monetary genetic gain and the discounted profit, especially when domestic AI bulls were also used in the beef cattle breeding population.


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.


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