This Article Full Text (PDF) References Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via HighWire Citing Articles via Google Scholar Citing Articles via Scopus Google Scholar Articles by Amos, C. I Articles by de Andrade, M. Search for Related Content PubMed PubMed Citation Articles by Amos, C. I Articles by de Andrade, M. Social Bookmarking                         What's this?

Genetic linkage methods for quantitative traits

Departments of Epidemiology and Biomathematics, UT MD Anderson Cancer, Houston, Texas, USA, camos{at}notes.mdacc.tmc.edu

Mariza de Andrade

Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA

We discuss methods for detecting genetic linkage for quantitative data. The usual LOD score method uses a pseudolikelihood formulation and has optimal power provided all parameters are correctly specified, but can lead to erroneous estimates of the location for the locus influencing a trait under misspecification of parameters describing the variance of the trait. Alternative methods, in which attention focuses upon modelling covariation among relatives as a function of genetic marker, similarity lead to unbiased estimates of the location and major gene heritability of the trait influencing locus. The Haseman-Elston approach uses a regression method to perform linkage analysis and its properties have been widely studied. This method is generally less powerful than variance components procedures, but the maximum likelihood-based variance components procedures require normality of the trait to ensure robustness of the genetic linkage tests (i.e. a correct false positive rate). When samples are non-randomly selected an ascertainment correction is generally required in order to obtain unbiased parameter estimates when applying variance components methods. For quantitative traits, ascertainment corrections usually condition either on the proband exceeding a threshold, or on the trait value of the proband. We summarize simulations that show that both approaches lead to similar efficiencies for estimating genetic effects. Finally, we discuss methods for analysing diseases that include time-to-onset information. A variety of methods are available for the linkage analysis of quantitative traits. Here, we have reviewed the most commonly used methods.

Statistical Methods in Medical Research, Vol. 10, No. 1, 3-25 (2001)
DOI: 10.1177/096228020101000102


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?

This article has been cited by other articles:

				N. F Butte, G. Cai, S. A Cole, and A. G Comuzzie Viva la Familia Study: genetic and environmental contributions to childhood obesity and its comorbidities in the Hispanic population. Am. J. Clinical Nutrition, September 1, 2006; 84(3): 646 - 654. [Abstract] [Full Text] [PDF]

				J. C. Charlesworth, T. D. Dyer, J. M. Stankovich, J. Blangero, D. A. Mackey, J. E. Craig, C. M. Green, S. J. Foote, P. N. Baird, and M. M. Sale Linkage to 10q22 for Maximum Intraocular Pressure and 1p32 for Maximum Cup-to-Disc Ratio in an Extended Primary Open-Angle Glaucoma Pedigree Invest. Ophthalmol. Vis. Sci., October 1, 2005; 46(10): 3723 - 3729. [Abstract] [Full Text] [PDF]

				W.-D. Li, C. Dong, D. Li, H. Zhao, and R. A. Price An Obesity-Related Locus in Chromosome Region 12q23-24 Diabetes, March 1, 2004; 53(3): 812 - 820. [Abstract] [Full Text] [PDF]