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- Title
Efficient Estimation of Marker Effects in Plant Breeding.
- Authors
Xavier, Alencar
- Abstract
The evaluation of prediction machines is an important step for a successful implementation of genomic-enabled selection in plant breeding. Computation time and predictive ability constitute key metrics to determine the methodology utilized for the consolidation of genomic prediction pipeline. This study introduces two methods designed to couple high prediction accuracy with efficient computational performance: 1) a non-MCMC method to estimate marker effects with a Laplace prior; and 2) an iterative framework that allows solving whole-genome regression within mixed models with replicated observations in a single-stage. The investigation provides insights on predictive ability and marker effect estimates. Various genomic prediction techniques are compared based on cross-validation, assessing predictions across and within family. Properties of quantitative trait loci detection and single-stage method were evaluated on simulated plot-level data from unbalanced data structures. Estimation of marker effects by the new model is compared to a genome-wide association analysis and whole-genome regression methods. The single-stage approach is compared to a GBLUP fitted via restricted maximum likelihood, and a twostages approaches where genetic values fit a whole-genome regression. The proposed framework provided high computational efficiency, robust prediction across datasets, and accurate estimation of marker effects.
- Subjects
SELECTION (Plant breeding); LAPLACE distribution; REGRESSION analysis; WHEAT breeding; PLANT breeding
- Publication
G3: Genes | Genomes | Genetics, 2019, Vol 9, Issue 11, p3855
- ISSN
2160-1836
- Publication type
Article
- DOI
10.1534/g3.119.400728