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A nested mixture model for genomic prediction using whole-genome SNP genotypes
Genomic prediction exploits single nucleotide polymorphisms (SNPs) across the whole genome for predicting genetic merit of selection candidates. In most models for genomic prediction, e.g. BayesA, B, C, R and GBLUP, independence of SNP effects is assumed. However, SNP effects are expected to be loca...
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| Pubblicato in: | PLoS One |
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| Autori principali: | , , , |
| Natura: | Artigo |
| Lingua: | Inglês |
| Pubblicazione: |
Public Library of Science
2018
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| Soggetti: | |
| Accesso online: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5862491/ https://ncbi.nlm.nih.gov/pubmed/29561877 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1371/journal.pone.0194683 |
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