Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/19529
Title: | Simple Penalties on Maximum-Likelihood Estimates of Genetic Parameters to Reduce Sampling Variation | Contributor(s): | Meyer, Karin (author) | Publication Date: | 2016 | Open Access: | Yes | DOI: | 10.1534/genetics.115.186114 | Handle Link: | https://hdl.handle.net/1959.11/19529 | Open Access Link: | http://dx.doi.org/10.1101/034447 | Abstract: | Multivariate estimates of genetic parameters are subject to substantial sampling variation, especially for smaller data sets and more than a few traits. A simple modification of standard, maximum-likelihood procedures for multivariate analyses to estimate genetic covariances is described, which can improve estimates by substantially reducing their sampling variances. This is achieved by maximizing the likelihood subject to a penalty. Borrowing from Bayesian principles, we propose a mild, default penalty-derived assuming a Beta distribution of scale-free functions of the covariance components to be estimated-rather than laboriously attempting to determine the stringency of penalization from the data. An extensive simulation study is presented, demonstrating that such penalties can yield very worthwhile reductions in loss, i.e., the difference from population values, for a wide range of scenarios and without distorting estimates of phenotypic covariances. Moreover, mild default penalties tend not to increase loss in difficult cases and, on average, achieve reductions in loss of similar magnitude to computationally demanding schemes to optimize the degree of penalization. Pertinent details required for the adaptation of standard algorithms to locate the maximum of the likelihood function are outlined. | Publication Type: | Journal Article | Source of Publication: | Genetics, 203(4), p. 1885-1900 | Publisher: | Genetics Society of America | Place of Publication: | United States of America | ISSN: | 1943-2631 0016-6731 |
Fields of Research (FoR) 2008: | 070201 Animal Breeding | Fields of Research (FoR) 2020: | 300305 Animal reproduction and breeding | Socio-Economic Objective (SEO) 2008: | 830301 Beef Cattle | Socio-Economic Objective (SEO) 2020: | 100401 Beef cattle | Peer Reviewed: | Yes | HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
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Appears in Collections: | Animal Genetics and Breeding Unit (AGBU) Journal Article |
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