Literature DB >> 28464287

Geno-Diver: A combined coalescence and forward-in-time simulator for populations undergoing selection for complex traits.

J T Howard1, F Tiezzi1, J E Pryce2,3, C Maltecca1.   

Abstract

Geno-Diver is a combined coalescence and forward-in-time simulator designed to simulate complex traits with a quantitative and/or fitness component and implement multiple selection and mating strategies utilizing pedigree or genomic information. The simulation is carried out in two steps. The first step generates whole-genome sequence data for founder individuals. A variety of trait architectures can be generated for quantitative and fitness traits along with their covariance. The second step generates new individuals forward-in-time based on a variety of selection and mating scenarios. Genetic values are predicted for individuals utilizing pedigree or genomic information. Relationship matrices and their associated inverses are generated using computationally efficient routines. We benchmarked Geno-Diver with a previous simulation program and described how to simulate a traditional quantitative trait along with a quantitative and fitness trait. A user manual with examples, source code in C++11 and executable versions of Geno-Diver for Linux are freely available at https://github.com/jeremyhoward/Geno-Diver.
© 2017 Blackwell Verlag GmbH.

Entities:  

Keywords:  Animal breeding; fitness; quantitative genetics; simulation

Mesh:

Year:  2017        PMID: 28464287     DOI: 10.1111/jbg.12277

Source DB:  PubMed          Journal:  J Anim Breed Genet        ISSN: 0931-2668            Impact factor:   2.380


  5 in total

1.  The impact of clustering methods for cross-validation, choice of phenotypes, and genotyping strategies on the accuracy of genomic predictions.

Authors:  Johnna L Baller; Jeremy T Howard; Stephen D Kachman; Matthew L Spangler
Journal:  J Anim Sci       Date:  2019-04-03       Impact factor: 3.159

2.  The impact of reducing the frequency of animals genotyped at higher density on imputation and prediction accuracies using ssGBLUP1.

Authors:  Bruna P Sollero; Jeremy T Howard; Matthew L Spangler
Journal:  J Anim Sci       Date:  2019-07-02       Impact factor: 3.159

3.  The impact of selective genotyping on the response to selection using single-step genomic best linear unbiased prediction.

Authors:  Jeremy T Howard; Tom A Rathje; Caitlyn E Bruns; Danielle F Wilson-Wells; Stephen D Kachman; Matthew L Spangler
Journal:  J Anim Sci       Date:  2018-11-21       Impact factor: 3.159

4.  Genomic prediction using pooled data in a single-step genomic best linear unbiased prediction framework.

Authors:  Johnna L Baller; Stephen D Kachman; Larry A Kuehn; Matthew L Spangler
Journal:  J Anim Sci       Date:  2020-06-01       Impact factor: 3.159

5.  Using pooled data for genomic prediction in a bivariate framework with missing data.

Authors:  Johnna L Baller; Stephen D Kachman; Larry A Kuehn; Matthew L Spangler
Journal:  J Anim Breed Genet       Date:  2022-06-14       Impact factor: 3.271

  5 in total

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