Prediction of complex traits: Conciliating genetics and statistics. (June 2017)

• Record Type: Journal Article
• Authors: Manfredi, E.; Tusell, L.; Vitezica, Z.G.
• Is Part Of: Journal of animal breeding and genetics. Volume 134:Number 3(2017)
• Page Start: 178
• Page End: 183
• Publication Date: 2017-06
• Subjects: Bayesian methods -- Complex traits -- Genomics -- Genetic prediction; Livestock -- Breeding -- Periodicals; Livestock -- Genetics -- Periodicals; 636.0820
• DOI: 10.1111/jbg.12269
• Languages: English
• ISSNs: 0931-2668

Abstract

Summary: This review focuses on methods used to predict complex traits. Main characteristics of prediction approaches are given: the deterministic or stochastic nature of prediction, the objects of prediction, the sources of information and the main statistical methods. Sources of information discussed are the traditional genealogies and phenotypes, nucleotide sequences, expression data and epigenetics marks. Statistical methods are presented as successive degrees of generalization from the definition of the conditional expectation as the prediction rule, to best linear unbiased prediction, then Bayesian and, recently, machine learning methods, including meta‐methods. We highlight the contributions of Daniel Gianola to this methodological evolution.