Efficient strategies for leave-one-out cross validation for genomic best linear unbiased prediction

Efficient strategies for leave-one-out cross validation for genomic best linear unbiased prediction

Abstract Background A random multiple-regression model that simultaneously fit all allele substitution effects for additive markers or haplotypes as uncorrelated random effects was proposed for Best Linear Unbiased Prediction, using whole-genome data. Leave-one-out cross validation can be used to qu...

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Bibliographic Details
Main Authors: Hao Cheng, Dorian J. Garrick, Rohan L. Fernando
Format: Article
Language:English
Published: BioMed Central 2017-05-01
Series:Journal of Animal Science and Biotechnology
Subjects:
Leave-one-out cross validation
GBLUP
Online Access:http://link.springer.com/article/10.1186/s40104-017-0164-6

Internet

http://link.springer.com/article/10.1186/s40104-017-0164-6

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