Prediction of complex traits: Conciliating genetics and statistics.

Autor: Manfredi E; UMR 1388 INRA-INPT GenPhySE, Castanet Tolosan Cedex, France., Tusell L; UMR 1388 INRA-INPT GenPhySE, Castanet Tolosan Cedex, France., Vitezica ZG; UMR 1388 INRA-INPT GenPhySE, Castanet Tolosan Cedex, France.
Jazyk: angličtina
Zdroj: Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie [J Anim Breed Genet] 2017 Jun; Vol. 134 (3), pp. 178-183.
DOI: 10.1111/jbg.12269
Abstrakt: 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.
(© 2017 Blackwell Verlag GmbH.)
Databáze: MEDLINE
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