NDLI: Likelihood Ratio Tests in Behavioral Genetics: Problems and Solutions

Content Provider	Springer Nature Link
Author	Dominicus, Annica Skrondal, Anders Gjessing, Håkon K. Pedersen, Nancy L. Palmgren, Juni
Copyright Year	2005
Abstract	The likelihood ratio test of nested models for family data plays an important role in the assessment of genetic and environmental influences on the variation in traits. The test is routinely based on the assumption that the test statistic follows a chi-square distribution under the null, with the number of restricted parameters as degrees of freedom. However, tests of variance components constrained to be non-negative correspond to tests of parameters on the boundary of the parameter space. In this situation the standard test procedure provides too large p-values and the use of the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC) for model selection is problematic. Focusing on the classical ACE twin model for univariate traits, we adapt existing theory to show that the asymptotic distribution for the likelihood ratio statistic is a mixture of chi-square distributions, and we derive the mixing probabilities. We conclude that when testing the AE or the CE model against the ACE model, the p-values obtained from using the χ2(1 df) as the reference distribution should be halved. When the E model is tested against the ACE model, a mixture of χ2(0 df), χ2(1 df) and χ2(2 df) should be used as the reference distribution, and we provide a simple formula to compute the mixing probabilities. Similar results for tests of the AE, DE and E models against the ADE model are also derived. Failing to use the appropriate reference distribution can lead to invalid conclusions.
Starting Page	331
Ending Page	340
Page Count	10
File Format	PDF
ISSN	00018244
Journal	Behavior Genetics
Volume Number	36
Issue Number	2
e-ISSN	15733297
Language	English
Publisher	Springer US
Publisher Date	2006-02-11
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Boundary parameter chi-square distribution likelihood ratio test twin model variance component Evolutionary Biology Neurosciences Human Genetics Public Health/Gesundheitswesen
Content Type	Text
Resource Type	Article
Subject	Genetics Ecology, Evolution, Behavior and Systematics Genetics (clinical)

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