Literature DB >> 27902803

AlphaSim: Software for Breeding Program Simulation.

Anne-Michelle Faux, Gregor Gorjanc, R Chris Gaynor, Mara Battagin, Stefan M Edwards, David L Wilson, Sarah J Hearne, Serap Gonen, John M Hickey.   

Abstract

This paper describes AlphaSim, a software package for simulating plant and animal breeding programs. AlphaSim enables the simulation of multiple aspects of breeding programs with a high degree of flexibility. AlphaSim simulates breeding programs in a series of steps: (i) simulate haplotype sequences and pedigree; (ii) drop haplotypes into the base generation of the pedigree and select single-nucleotide polymorphism (SNP) and quantitative trait nucleotide (QTN); (iii) assign QTN effects, calculate genetic values, and simulate phenotypes; (iv) drop haplotypes into the burn-in generations; and (v) perform selection and simulate new generations. The program is flexible in terms of historical population structure and diversity, recent pedigree structure, trait architecture, and selection strategy. It integrates biotechnologies such as doubled-haploids (DHs) and gene editing and allows the user to simulate multiple traits and multiple environments, specify recombination hot spots and cold spots, specify gene jungles and deserts, perform genomic predictions, and apply optimal contribution selection. AlphaSim also includes restart functionalities, which increase its flexibility by allowing the simulation process to be paused so that the parameters can be changed or to import an externally created pedigree, trial design, or results of an analysis of previously simulated data. By combining the options, a user can simulate simple or complex breeding programs with several generations, variable population structures and variable breeding decisions over time. In conclusion, AlphaSim is a flexible and computationally efficient software package to simulate biotechnology enhanced breeding programs with the aim of performing rapid, low-cost, and objective in silico comparison of breeding technologies.
Copyright © 2016 Crop Science Society of America.

Mesh:

Year:  2016        PMID: 27902803     DOI: 10.3835/plantgenome2016.02.0013

Source DB:  PubMed          Journal:  Plant Genome        ISSN: 1940-3372            Impact factor:   4.089


  28 in total

1.  Genomic Prediction Using Individual-Level Data and Summary Statistics from Multiple Populations.

Authors:  Jeremie Vandenplas; Mario P L Calus; Gregor Gorjanc
Journal:  Genetics       Date:  2018-07-18       Impact factor: 4.562

2.  Potential of gene drives with genome editing to increase genetic gain in livestock breeding programs.

Authors:  Serap Gonen; Janez Jenko; Gregor Gorjanc; Alan J Mileham; C Bruce A Whitelaw; John M Hickey
Journal:  Genet Sel Evol       Date:  2017-01-04       Impact factor: 4.297

3.  A method for the allocation of sequencing resources in genotyped livestock populations.

Authors:  Serap Gonen; Roger Ros-Freixedes; Mara Battagin; Gregor Gorjanc; John M Hickey
Journal:  Genet Sel Evol       Date:  2017-05-18       Impact factor: 4.297

4.  A method for allocating low-coverage sequencing resources by targeting haplotypes rather than individuals.

Authors:  Roger Ros-Freixedes; Serap Gonen; Gregor Gorjanc; John M Hickey
Journal:  Genet Sel Evol       Date:  2017-10-25       Impact factor: 4.297

5.  Extending long-range phasing and haplotype library imputation algorithms to large and heterogeneous datasets.

Authors:  Daniel Money; David Wilson; Janez Jenko; Andrew Whalen; Steve Thorn; Gregor Gorjanc; John M Hickey
Journal:  Genet Sel Evol       Date:  2020-07-08       Impact factor: 4.297

6.  A variance component estimation approach to infer associations between Mendelian polledness and quantitative production and female fertility traits in German Simmental cattle.

Authors:  Carsten Scheper; Reiner Emmerling; Kay-Uwe Götz; Sven König
Journal:  Genet Sel Evol       Date:  2021-07-14       Impact factor: 4.297

7.  Powerful detection of polygenic selection and evidence of environmental adaptation in US beef cattle.

Authors:  Troy N Rowan; Harly J Durbin; Christopher M Seabury; Robert D Schnabel; Jared E Decker
Journal:  PLoS Genet       Date:  2021-07-22       Impact factor: 5.917

8.  Genomic Prediction of Yield Traits in Single-Cross Hybrid Rice (Oryza sativa L.).

Authors:  Marlee R Labroo; Jauhar Ali; M Umair Aslam; Erik Jon de Asis; Madonna A Dela Paz; M Anna Sevilla; Alexander E Lipka; Anthony J Studer; Jessica E Rutkoski
Journal:  Front Genet       Date:  2021-06-30       Impact factor: 4.599

9.  The potential of shifting recombination hotspots to increase genetic gain in livestock breeding.

Authors:  Serap Gonen; Mara Battagin; Susan E Johnston; Gregor Gorjanc; John M Hickey
Journal:  Genet Sel Evol       Date:  2017-07-04       Impact factor: 4.297

10.  Origin Specific Genomic Selection: A Simple Process To Optimize the Favorable Contribution of Parents to Progeny.

Authors:  Chin Jian Yang; Rajiv Sharma; Gregor Gorjanc; Sarah Hearne; Wayne Powell; Ian Mackay
Journal:  G3 (Bethesda)       Date:  2020-07-07       Impact factor: 3.154

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