Premium
“Bending” and beyond: Better estimates of quantitative genetic parameters?
Author(s) -
Meyer Karin
Publication year - 2019
Publication title -
journal of animal breeding and genetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.689
H-Index - 51
eISSN - 1439-0388
pISSN - 0931-2668
DOI - 10.1111/jbg.12386
Subject(s) - pooling , restricted maximum likelihood , multivariate statistics , econometrics , penalty method , multivariate normal distribution , statistics , estimation , sampling (signal processing) , maximum likelihood , population , mathematics , computer science , mathematical optimization , economics , artificial intelligence , demography , management , filter (signal processing) , sociology , computer vision
Multivariate estimation of genetic parameters involving more than a handful of traits can be afflicted by problems arising through substantial sampling variation. We present a review of underlying causes and proposals to improve estimates, focusing on linear mixed model‐based estimation via restricted maximum likelihood ( REML ). Both full multivariate analyses and pooling of results from overlapping subsets of traits are considered. It is suggested to impose a penalty on the likelihood designed to reduce sampling variances at the expense of a little additional bias. Simulation results are discussed which demonstrate that this can yield REML estimates that are on average closer to the population values than their unpenalized counterparts. Suitable penalties can be obtained based on assumed prior distributions of selected parameters. Necessary choices of penalty functions and of the stringency of penalization are examined. We argue that scale‐free penalty functions lend themselves to a simple scheme imposing a mild, default penalty which can yield “better” estimates without being likely to incur detrimental effects.