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An evaluation of the interpretability and predictive performance of the BayesR model for genomic prediction
[article]
2020
bioRxiv
pre-print
Technological advances and decreasing costs have led to the rise of increasingly dense genotyping data, making feasible the identification of potential causal markers. Custom genotyping chips, which combine medium-density genotypes with a custom genotype panel, can capitalize on these candidates to potentially yield improved accuracy and interpretability in genomic prediction. A particularly promising model to this end is BayesR, which divides markers into four effect size classes. BayesR has
doi:10.1101/2020.10.23.351700
fatcat:jn3r6vj7orearekosnxal44bkq