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Semi-parametric estimates of population accuracy and bias of predictions of breeding values and future phenotypes using the LR method.

TitleSemi-parametric estimates of population accuracy and bias of predictions of breeding values and future phenotypes using the LR method.
Publication TypeJournal Article
Year of Publication2018
AuthorsLegarra, A, Reverter, A
JournalGenet Sel Evol
Volume50
Issue1
Pagination53
Date Published2018 Nov 06
ISSN1297-9686
Abstract

BACKGROUND: Cross-validation tools are used increasingly to validate and compare genetic evaluation methods but analytical properties of cross-validation methods are rarely described. There is also a lack of cross-validation tools for complex problems such as prediction of indirect effects (e.g. maternal effects) or for breeding schemes with small progeny group sizes.RESULTS: We derive the expected value of several quadratic forms by comparing genetic evaluations including "partial" and "whole" data. We propose statistics that compare genetic evaluations including "partial" and "whole" data based on differences in means, covariance, and correlation, and term the use of these statistics "method LR" (from linear regression). Contrary to common belief, the regression of true on estimated breeding values is (on expectation) lower than 1 for small or related validation sets, due to family structures. For validation sets that are sufficiently large, we show that these statistics yield estimators of bias, slope or dispersion, and population accuracy for estimated breeding values. Similar results hold for prediction of future phenotypes although we show that estimates of bias, slope or dispersion using prediction of future phenotypes are sensitive to incorrect heritabilities or precorrection for fixed effects. We present an example for a set of 2111 Brahman beef cattle for which, in repeated partitioning of the data into training and validation sets, there is very good agreement of statistics of method LR with prediction of future phenotypes.CONCLUSIONS: Analytical properties of cross-validation measures are presented. We present a new method named LR for cross-validation that is automatic, easy to use, and which yields the quantities of interest. The method compares predictions based on partial and whole data, which results in estimates of accuracy and biases. Prediction of observed records may yield biased results due to precorrection or use of incorrect heritabilities.

DOI10.1186/s12711-018-0426-6
Alternate JournalGenet. Sel. Evol.
PubMed ID30400768
PubMed Central IDPMC6219059
Grant ListExchange program / / INRA-CSIRO /
Exchange program / / INRA-CSIRO /
ARDI / / Poctefa/FEDER /
metaprogram SelGen: EpiSel SelHet OptiMagics / / Institut National de la Recherche Agronomique /
Genopyr / / FEDER /