Document Detail


Unraveling additive from nonadditive effects using genomic relationship matrices.
MedLine Citation:
PMID:  25324160     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
The application of quantitative genetics in plant and animal breeding has largely focused on additive models, which may also capture dominance and epistatic effects. Partitioning genetic variance into its additive and nonadditive components using pedigree-based models (P-genomic best linear unbiased predictor) (P-BLUP) is difficult with most commonly available family structures. However, the availability of dense panels of molecular markers makes possible the use of additive- and dominance-realized genomic relationships for the estimation of variance components and the prediction of genetic values (G-BLUP). We evaluated height data from a multifamily population of the tree species Pinus taeda with a systematic series of models accounting for additive, dominance, and first-order epistatic interactions (additive by additive, dominance by dominance, and additive by dominance), using either pedigree- or marker-based information. We show that, compared with the pedigree, use of realized genomic relationships in marker-based models yields a substantially more precise separation of additive and nonadditive components of genetic variance. We conclude that the marker-based relationship matrices in a model including additive and nonadditive effects performed better, improving breeding value prediction. Moreover, our results suggest that, for tree height in this population, the additive and nonadditive components of genetic variance are similar in magnitude. This novel result improves our current understanding of the genetic control and architecture of a quantitative trait and should be considered when developing breeding strategies.
Authors:
Patricio R Muñoz; Marcio F R Resende; Salvador A Gezan; Marcos Deon Vilela Resende; Gustavo de Los Campos; Matias Kirst; Dudley Huber; Gary F Peter
Publication Detail:
Type:  Journal Article     Date:  2014-10-15
Journal Detail:
Title:  Genetics     Volume:  198     ISSN:  1943-2631     ISO Abbreviation:  Genetics     Publication Date:  2014 Dec 
Date Detail:
Created Date:  2014-12-06     Completed Date:  -     Revised Date:  2014-12-09    
Medline Journal Info:
Nlm Unique ID:  0374636     Medline TA:  Genetics     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1759-68     Citation Subset:  IM    
Copyright Information:
Copyright © 2014 by the Genetics Society of America.
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