» Articles » PMID: 9814896

Maximizing the Response of Selection with a Predefined Rate of Inbreeding: Overlapping Generations

Overview
Journal J Anim Sci
Date 1998 Nov 14
PMID 9814896
Citations 22
Authors
Affiliations
Soon will be listed here.
Abstract

In a breeding scheme, the aim is high rates of genetic gain with limited inbreeding. A dynamic selection rule is developed that maximizes selection response in populations with overlapping generations. The rule maximizes the genetic merit of selected animals while limiting the average relationship of the population after the current round of selection. The latter is shown to limit the contribution of the current population to the future inbreeding. The rule accounts for the selection of some candidates during previous selection rounds and for the expected future contributions of the selection candidates. Inputs for the rule are the BLUP breeding values and ages of selection candidates, the relationship matrix of all animals, and contributions of animals during previous selection rounds. Output is the optimal number of offspring for each candidate. Computer simulations of dairy cattle nucleus schemes showed that predefined rates of inbreeding were actually achieved, without compromising long-term selection response, at least up to 20 yr of selection. At the same rates of inbreeding, the dynamic selection rule obtained up to 44% more genetic gain than direct selection for BLUP breeding values. The advantage of the dynamic rule over BLUP selection decreased with increasing population sizes and with greater predefined rates of inbreeding. Consequently, the dynamic rule should be especially useful in small selection schemes in which relatively low rates of inbreeding are desired.

Citing Articles

SimpleMating: R-package for prediction and optimization of breeding crosses using genomic selection.

Peixoto M, Amadeu R, Bhering L, Ferrao L, Munoz P, Resende Jr M Plant Genome. 2024; 18(1):e20533.

PMID: 39604031 PMC: 11726409. DOI: 10.1002/tpg2.20533.


Evaluation of genomic mating approach based on genetic algorithms for long-term selection in Huaxi cattle.

Wang Y, Zhu B, Wang J, Zhang L, Xu L, Chen Y BMC Genomics. 2024; 25(1):1140.

PMID: 39587475 PMC: 11590262. DOI: 10.1186/s12864-024-11057-9.


Use of simulation to optimize a sweet corn breeding program: implementing genomic selection and doubled haploid technology.

Peixoto M, Coelho I, Leach K, Lubberstedt T, Bhering L, Resende Jr M G3 (Bethesda). 2024; 14(8).

PMID: 38869242 PMC: 11304600. DOI: 10.1093/g3journal/jkae128.


Evaluation of Linear Programming and Optimal Contribution Selection Approaches for Long-Term Selection on Beef Cattle Breeding.

Zheng X, Wang T, Niu Q, Wu J, Zhao Z, Gao H Biology (Basel). 2023; 12(9).

PMID: 37759557 PMC: 10525978. DOI: 10.3390/biology12091157.


Genetic gain and inbreeding from simulation of different genomic mating schemes for pig improvement.

Zhao F, Zhang P, Wang X, Akdemir D, Garrick D, He J J Anim Sci Biotechnol. 2023; 14(1):87.

PMID: 37309010 PMC: 10262571. DOI: 10.1186/s40104-023-00872-x.