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Evaluation of imputed genomic data in discrete traits using Random forest and Bayesian threshold methodsb
Provedor de dados: 2
Autores: Sadeghi, Saadat; Rafat, Seyed Abbas; Alijani, Sadegh.
The objectives of this study were (1) to quantify imputation accuracy and to assess the factors affecting it; and (2) to evaluate the accuracy of threshold BayesA (TBA), Bayesian threshold LASSO (BTL) and random forest (RF) algorithms to analyze discrete traits. Genomic data were simulated to reflect variations in heritability (h2 = 0.30 and 0.10), number of QTL (QTL = 81 and 810), number of SNP (10 K and 50 K) and linkage disequilibrium (LD=low and high) for 27 chromosomes. For real condition simulating, we randomly masked markers with 90% missing rate for each scenario; afterwards, hidden markers were imputed using FImpute software. In imputed genotypes, a wide range of accuracy was observed for RF (0.164-0.512) compared to TBA (0.283-0.469) and BTL...
Tipo: Info:eu-repo/semantics/article
Palavras-chave: PhD candidate of animal breeding accuracy; Genomic architecture; Linkage disequilibrium; Machine learning; Masked genotypes.
Ano: 2018
URL: http://periodicos.uem.br/ojs/index.php/ActaSciAnimSci/article/view/39007