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Biostatistics Advance Access originally published online on March 14, 2008
Biostatistics 2008 9(4):635-657; doi:10.1093/biostatistics/kxm055
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© 2008 The Authors
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

A likelihood-based approach to mixed modeling with ambiguity in cluster identifiers

Andrea S. Foulkes*, Recai Yucel and Xiaohong Li

Division of Biostatistics, School of Public Health and Health Sciences, University of Massachusetts, Amherst, MA, USA foulkes{at}schoolph.umass.edu

* To whom correspondence should be addressed

This manuscript describes a novel, linear mixed-effects model–fitting technique for the setting in which correlated data indicators are not completely observed. Mixed modeling is a useful analytical tool for characterizing genotype–phenotype associations among multiple potentially informative genetic loci. This approach involves grouping individuals into genetic clusters, where individuals in the same cluster have similar or identical multilocus genotypes. In haplotype-based investigations of unrelated individuals, corresponding cluster assignments are unobservable since the alignment of alleles within chromosomal copies is not generally observed. We derive an expectation conditional maximization approach to estimation in the mixed modeling setting, where cluster assignments are ambiguous. The approach has broad relevance to the analysis of data with missing correlated data identifiers. An example is provided based on data arising from a cohort of human immunodeficiency virus type-1–infected individuals at risk for antiretroviral therapy–associated dyslipidemia.

Keywords: Expectation conditional maximization; Genotype; Haplotype; HIV-1; Lipids; Missing identifiers; Mixed-effects models; Phenotype; Population-based genetic association studies

Received July 20, 2007; revised December 6, 2007; accepted for publication December 14, 2007.


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