Literature DB >> 31991144

On the approximation of interaction effect models by Hadamard powers of the additive genomic relationship.

Johannes W R Martini1, Fernando H Toledo2, José Crossa2.   

Abstract

Whole genome epistasis models with interactions between different loci can be approximated by genomic relationship models based on Hadamard powers of the additive genomic relationship. We illustrate that the quality of this approximation reduces when the degree of interaction d increases. Moreover, considering relationship models defined as weighted sum of interactions of different degree, we investigate the impact of this decreasing quality of approximation of the summands on the approximation of the weighted sum. Our results indicate that these approximations remain on a reliable level, but their quality reduces when the weights of interactions of higher degrees do not decrease quickly.
Copyright © 2020 The Authors. Published by Elsevier Inc. All rights reserved.

Keywords:  Epistasis; Genetic interaction; Hadamard power

Mesh:

Year:  2020        PMID: 31991144     DOI: 10.1016/j.tpb.2020.01.004

Source DB:  PubMed          Journal:  Theor Popul Biol        ISSN: 0040-5809            Impact factor:   1.570


  8 in total

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Journal:  Genetics       Date:  2020-09-24       Impact factor: 4.562

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3.  Incorporating Omics Data in Genomic Prediction.

Authors:  Johannes W R Martini; Ning Gao; José Crossa
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4.  Approximate Genome-Based Kernel Models for Large Data Sets Including Main Effects and Interactions.

Authors:  Jaime Cuevas; Osval A Montesinos-López; J W R Martini; Paulino Pérez-Rodríguez; Morten Lillemo; Jose Crossa
Journal:  Front Genet       Date:  2020-10-15       Impact factor: 4.599

5.  Robust modeling of additive and nonadditive variation with intuitive inclusion of expert knowledge.

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6.  Genome-Based Genotype × Environment Prediction Enhances Potato (Solanum tuberosum L.) Improvement Using Pseudo-Diploid and Polysomic Tetraploid Modeling.

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Journal:  Front Plant Sci       Date:  2022-02-07       Impact factor: 5.753

7.  Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments.

Authors:  Elaheh Vojgani; Torsten Pook; Johannes W R Martini; Armin C Hölker; Manfred Mayer; Chris-Carolin Schön; Henner Simianer
Journal:  Theor Appl Genet       Date:  2021-06-11       Impact factor: 5.699

8.  Opportunities and limits of combining microbiome and genome data for complex trait prediction.

Authors:  Miguel Pérez-Enciso; Laura M Zingaretti; Yuliaxis Ramayo-Caldas; Gustavo de Los Campos
Journal:  Genet Sel Evol       Date:  2021-08-06       Impact factor: 4.297

  8 in total

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