The Modular Breeding Program Simulator (MoBPS) allows efficient simulation of complex breeding programs
Torsten Pook
A B , Christian Reimer A B , Alexander Freudenberg C , Lisa Büttgen A B , Johannes Geibel A B , Amudha Ganesan A , Ngoc-Thuy Ha A B , Martin Schlather B C , Lars Friis Mikkelsen D and Henner Simianer A B
A University of Goettingen, Department of Animal Sciences, Animal Breeding and Genetics Group, Albrecht-Thaer Weg 3, 37075 Goettingen, Germany.
B University of Goettingen, Center for Integrated Breeding Research, Albrecht-Thaer Weg 3, 37075 Goettingen, Germany.
C University of Mannheim, Applied Stochastics Group, B6 26, 68159 Mannheim, Germany.
D Ellegaard Göttingen Minipigs, Sorø Landevej 300, 4261 Dalmose, Denmark.
E Corresponding author. Email: torsten.pook@uni-goettingen.de
Animal Production Science - https://doi.org/10.1071/AN21076
Submitted: 11 February 2021 Accepted: 27 August 2021 Published online: 23 September 2021
Journal Compilation © CSIRO 2021 Open Access CC BY-NC-ND
Abstract
Context: Breeding programs aim at improving the genetic characteristics of livestock populations with respect to productivity, fitness and adaptation, while controlling negative effects such as inbreeding or health and welfare issues. As breeding is affected by a variety of interdependent factors, the analysis of the effect of certain breeding actions and the optimisation of a breeding program are highly complex tasks.
Aims: This study was conducted to display the potential of using stochastic simulation to analyse, evaluate and compare breeding programs and to show how the Modular Breeding Program Simulator (MoBPS) simulation framework can further enhance this.
Methods: In this study, a simplified version of the breeding program of Göttingen Minipigs was simulated to analyse the impact of genotyping and optimum contribution selection in regard to both genetic gain and diversity. The software MoBPS was used as the backend simulation software and was extended to allow for a more realistic modelling of pig breeding programs. Among others, extensions include the simulation of phenotypes with discrete observations (e.g. teat count), variable litter sizes, and a breeding value estimation in the associated R-package miraculix that utilises a graphics processing unit.
Key results: Genotyping with the subsequent use of genomic best linear unbiased prediction (GBLUP) led to substantial increases in genetic gain (15.3%) compared with a pedigree-based BLUP, while reducing the increase of inbreeding by 24.8%. The additional use of optimum genetic selection was shown to be favourable compared with the plain selection of top boars. The use of graphics processing unit-based breeding value estimation with known heritability was ~100 times faster than the state-of-the-art R-package rrBLUP.
Conclusions: The results regarding the effect of both genotyping and optimal contribution selection are in line with well established results. Paired with additional new features such as the modelling of discrete phenotypes and adaptable litter sizes, this confirms MoBPS to be a unique tool for the realistic modelling of modern breeding programs.
Implications: The MoBPS framework provides a powerful tool for scientists and breeders to perform stochastic simulations to optimise the practical design of modern breeding programs to secure standardised breeding of high-quality animals and answer associated research questions.
Keywords: resource management, animal breeding, Göttingen Minipigs, optimum contribution selection, OGC, OCS.
References
Aguilar I, Misztal I, Johnson DL, Legarra A, Tsuruta S, Lawlor TJ (2010) Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score. Journal of Dairy Science 93, 743–752.| Hot topic: a unified approach to utilize phenotypic, full pedigree, and genomic information for genetic evaluation of Holstein final score.Crossref | GoogleScholarGoogle Scholar | 20105546PubMed |
Büttgen L, Geibel J, Simianer H, Pook T (2020) Simulation Study on the Integration of Health Traits in Horse Breeding Programs. Animals (Basel) 10, 1153
| Simulation Study on the Integration of Health Traits in Horse Breeding Programs.Crossref | GoogleScholarGoogle Scholar |
Daetwyler HD, Villanueva B, Bijma P, Woolliams JA (2007) Inbreeding in genome‐wide selection. Journal of Animal Breeding and Genetics 124, 369–376.
| Inbreeding in genome‐wide selection.Crossref | GoogleScholarGoogle Scholar | 18076474PubMed |
Donnelly KP (1983) The probability that related individuals share some section of genome identical by descent. Theoretical Population Biology 23, 34–63.
| The probability that related individuals share some section of genome identical by descent.Crossref | GoogleScholarGoogle Scholar | 6857549PubMed |
Endelman JB (2011) Ridge regression and other kernels for genomic selection with R package rrBLUP. The Plant Genome 4, 250–255.
| Ridge regression and other kernels for genomic selection with R package rrBLUP.Crossref | GoogleScholarGoogle Scholar |
Falconer DS, Mackay TF (1996) ‘Introduction to quantitative genetics,’ 4th edn. (Longmans Green: Essex, UK)
Hazel LN, Lush JL (1942) The efficiency of three methods of selection. The Journal of Heredity 33, 393–399.
| The efficiency of three methods of selection.Crossref | GoogleScholarGoogle Scholar |
Henryon M, Berg P, Sørensen AC (2014) Animal-breeding schemes using genomic information need breeding plans designed to maximise long-term genetic gains. Livestock Science 166, 38–47.
| Animal-breeding schemes using genomic information need breeding plans designed to maximise long-term genetic gains.Crossref | GoogleScholarGoogle Scholar |
Köhn F, Sharifi AR, Malovrh S, Simianer H (2007) Estimation of genetic parameters for body weight of the Goettingen minipig with random regression models. Journal of Animal Science 85, 2423
| Estimation of genetic parameters for body weight of the Goettingen minipig with random regression models.Crossref | GoogleScholarGoogle Scholar | 17526674PubMed |
Legarra A, Christensen OF, Aguilar I, Misztal I (2014) Single Step, a general approach for genomic selection. Livestock Science 166, 54–65.
| Single Step, a general approach for genomic selection.Crossref | GoogleScholarGoogle Scholar |
Meuwissen THE (1997) Maximizing the response of selection with a predefined rate of inbreeding. Journal of Animal Science 75, 934–940.
| Maximizing the response of selection with a predefined rate of inbreeding.Crossref | GoogleScholarGoogle Scholar |
Meuwissen THE, Hayes BJ, Goddard ME (2001) Prediction of total genetic value using genome-wide dense marker maps. Genetics 157, 1819–1829.
| Prediction of total genetic value using genome-wide dense marker maps.Crossref | GoogleScholarGoogle Scholar |
Miesenberger J (1997) ‘Zuchtzieldefinition und Indexselektion für die österreichische Rinderzucht.’ (Universität für Bodenkultur Wien (BOKU)). Available at https://forschung.boku.ac.at/fis/suchen.projekt_uebersicht?sprache_in=de&menue_id_in=300&id_in=1265 [Verified 15 July 2021]
Misztal I (2016) Inexpensive computation of the inverse of the genomic relationship matrix in populations with small effective population size. Genetics 202, 401–409.
| Inexpensive computation of the inverse of the genomic relationship matrix in populations with small effective population size.Crossref | GoogleScholarGoogle Scholar | 26584903PubMed |
Pedersen HD, Mikkelsen LF (2019) Göttingen Minipigs as large animal model in toxicology. In ‘Biomarkers in Toxicology’. pp. 75–89. (Elsevier)
Pook T, Schlather M, Simianer H (2020) MoBPS: Modular Breeding Program Simulator. G3: Genes, Genomes. Genetics 10, 1915–1918.
| MoBPS: Modular Breeding Program Simulator.Crossref | GoogleScholarGoogle Scholar |
Pook T, Büttgen L, Ganesan A, Ha N-T, Simianer H (2021) MoBPSweb: a web-based framework to simulate and compare breeding programs. G3: Genes, Genomes. Genetics 11, jkab023
| MoBPSweb: a web-based framework to simulate and compare breeding programs.Crossref | GoogleScholarGoogle Scholar |
Reimer C, Ha N-T, Sharifi AR, Geibel J, Mikkelsen LF, Schlather M, Weigend S, Simianer H (2020) Assessing breed integrity of Göttingen Minipigs. BMC Genomics 21, 308
| Assessing breed integrity of Göttingen Minipigs.Crossref | GoogleScholarGoogle Scholar | 32299342PubMed |
Schaeffer LR (2006) Strategy for applying genome‐wide selection in dairy cattle. Journal of Animal Breeding and Genetics 123, 218–223.
| Strategy for applying genome‐wide selection in dairy cattle.Crossref | GoogleScholarGoogle Scholar | 16882088PubMed |
Schlather M, Erbe M, Skene F, Freudenberg A (2021) ‘miraculix: Algebraic and Statistical Functions for Genetics.’ R-package version 1.0.1. Available at https://github.com/tpook92/MoBPS
Simianer H, Köhn F (2010) Genetic management of the Göttingen Minipig population. Journal of Pharmacological and Toxicological Methods 62, 221–226.
| Genetic management of the Göttingen Minipig population.Crossref | GoogleScholarGoogle Scholar | 20570747PubMed |
Simianer H, Büttgen L, Ganesan A, Ha NT, Pook T (2021) A unifying concept of animal breeding programmes. Journal of Animal Breeding and Genetics 2021, 1–14.
Täubert H, Reinhardt F, Simianer H (2010) ZPLAN+, a new software to evaluate and optimize animal breeding programs. In ‘World Congress on Genetics Applied to Livestock’, 950. Available at https://www.uni-goettingen.de/de/document/download/dc6158ed8a8d81851a97da84dbf9b561.pdf/t%25C3%25A4ubert.pdf
VanRaden PM (2008) Efficient methods to compute genomic predictions. Journal of Dairy Science 91, 4414–4423.
| Efficient methods to compute genomic predictions.Crossref | GoogleScholarGoogle Scholar | 18946147PubMed |
Wellmann R (2017) ‘optiSel: optimum contribution selection and population genetics.’ R Package. version 0.9.1. Available at https://cran.r-project.org/web/packages/optiSel/index.html
Wellmann R (2019) Optimum contribution selection for animal breeding and conservation: the R package optiSel. BMC Bioinformatics 20, 25
| Optimum contribution selection for animal breeding and conservation: the R package optiSel.Crossref | GoogleScholarGoogle Scholar | 30642239PubMed |
