2017
DOI: 10.1186/s12711-017-0340-3
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Influence of epistasis on response to genomic selection using complete sequence data

Abstract: BackgroundThe effect of epistasis on response to selection is a highly debated topic. Here, we investigated the impact of epistasis on response to sequence-based selection via genomic best linear prediction (GBLUP) in a regime of strong non-symmetrical epistasis under divergent selection, using real Drosophila sequence data. We also explored the possible advantage of including epistasis in the evaluation model and/or of knowing the causal mutations.ResultsResponse to selection was almost exclusively due to cha… Show more

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Cited by 26 publications
(26 citation statements)
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“…Bayesian GBLUP using BRR prior and eigenvalue decomposition of G and D was employed to estimate genomic heritabilities with the BGLR package [see Forneris et al (2017) for an application of this model]. Genomic matrices were computed with a Fortran program that employs Basic Linear Algebra Subroutines (BLAS) (Dongarra et al 1990, www. netlib.org/blas/) for efficient parallelization, available at https://github.com/miguelperezenciso/dogrm.…”
Section: Softwarementioning
confidence: 99%
“…Bayesian GBLUP using BRR prior and eigenvalue decomposition of G and D was employed to estimate genomic heritabilities with the BGLR package [see Forneris et al (2017) for an application of this model]. Genomic matrices were computed with a Fortran program that employs Basic Linear Algebra Subroutines (BLAS) (Dongarra et al 1990, www. netlib.org/blas/) for efficient parallelization, available at https://github.com/miguelperezenciso/dogrm.…”
Section: Softwarementioning
confidence: 99%
“…There is significant evidence from this study that the transgene alters the effect size of key loci in the coho salmon genome. While response to selection is often ascribed primarily to additive genetic variation (Hill, Goddard, & Visscher, 2008), epistatic variation can act to release additive variance by influencing effect sizes QTL (Forneris, Vitezica, Legarra, & Pérez-Enciso, 2017), thus accelerating the rate of evolution (Paixão & Barton, 2016). Selection may direct phenotypes to faster or slower growth, depending on whether T fish have enhanced or diminished fitness relative to wild type.…”
Section: Discussionmentioning
confidence: 99%
“…We illustrate the software with sequence data from the Drosophila genome reference panel (DGRP, [21]), parsed and filtered as explained in [22], and genotype data from tetraploid potato [23], parsed as described in [7]. Data and scripts are in https ://githu b.com/migue lpere zenci so/ # 10 QTNs are simulated, h2 of the trait is 0.7 qtn = gg.QTNs(h2=[0.7], genome=gfeatures, nqtn=10) # environmental variances are computed qtn.get_var(gfeatures, gbase) Selection is implemented in cycles, the number of generations, the numbers of males and females selected, and family size must be specified.…”
Section: Usage and Examplesmentioning
confidence: 99%