Genetic Analyses of Longitudinal Phenotype Data: A Comparison of Univariate Methods and a Multivariate Approach


Show simple item record Yang, Qiong en_US Chazaro, Irmarie en_US Cui, Jing en_US Guo, Chao-Yu en_US Demissie, Serkalem en_US Larson, Martin en_US Atwood, Larry D en_US Cupples, L Adrienne en_US DeStefano, Anita L en_US 2012-01-09T20:53:14Z 2012-01-09T20:53:14Z 2003 en_US 2003-12-31 en_US
dc.identifier.citation Yang, Qiong, Irmarie Chazaro, Jing Cui, Chao-Yu Guo, Serkalem Demissie, Martin Larson, Larry D Atwood, L Adrienne Cupples, Anita L DeStefano. "Genetic analyses of longitudinal phenotype data: a comparison of univariate methods and a multivariate approach" BMC Genetics 4(Suppl 1):S29. (2003) en_US
dc.identifier.issn 1471-2156 en_US
dc.description.abstract BACKGROUND. We explored three approaches to heritability and linkage analyses of longitudinal total cholesterol levels (CHOL) in the Genetic Analysis Workshop 13 simulated data without knowing the answers. The first two were univariate approaches and used 1) baseline measure at exam one or 2) summary measures such as mean and slope from multiple exams. The third method was a multivariate approach that directly models multiple measurements on a subject. A variance components model (SOLAR) was employed in the univariate approaches. A mixed regression model with polynomials was employed in the multivariate approach and implemented in SAS/IML. RESULTS. Using the baseline measure at exam 1, we detected all baseline or slope genes contributing a substantial amount (0.08) of variance (LOD > 3). Compared to the baseline measure, the mean measures yielded slightly higher LOD at the slope genes, and a lower LOD at the baseline genes. The slope measure produced a somewhat lower LOD for the slope gene than did the mean measure. Descriptive information on the pattern of changes in gene effects with age was estimated for three linked loci by the third approach. CONCLUSION. We found simple univariate methods may be effective to detect genes affecting longitudinal phenotypes but may not fully reveal temporal trends in gene effects. The relative efficiency of the univariate methods to detect genes depends heavily on the underlying model. Compared with the univariate approaches, the multivariate approach provided more information on temporal trends in gene effects at the cost of more complicated modelling and more intense computations. en_US
dc.description.sponsorship National Institutes of Health (P50-HL55001) en_US
dc.language.iso en en_US
dc.publisher BioMed Central en_US
dc.rights Copyright 2003 Yang et al; licensee BioMed Central Ltd This is an open access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. en_US
dc.rights.uri en_US
dc.title Genetic Analyses of Longitudinal Phenotype Data: A Comparison of Univariate Methods and a Multivariate Approach en_US
dc.type article en_US
dc.identifier.doi 10.1186/1471-2156-4-S1-S29 en_US
dc.identifier.pubmedid 14975097 en_US
dc.identifier.pmcid 1866464 en_US

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