Epistasis Blog

From the Artificial Intelligence Innovation Lab at Cedars-Sinai Medical Center (www.epistasis.org)

Saturday, April 23, 2005

Two-level Haseman-Elston regression for general pedigree data analysis

A new paper by Wang and Elston published in Genetic Epidemiology reports an extension of the Haseman-Elston linkage analysis method that allows direct modeling of gene-gene and gene-environment interactions in general pedigrees.

Wang T, Elston RC. Two-level Haseman-Elston regression for general pedigree data analysis. Genet Epidemiol. 2005 Apr 18; [PubMed]


The Haseman-Elston (HE) (Haseman and Elston [1972] Behav Genet 2:3-19) method is widely used in genetic linkage studies for quantitative traits. We propose a new version of the HE regression model, a two-level HE regression model (tHE) in which the variance-covariance structure of family data is modeled under the framework of multiple-level regression. An iterative generalized least squares (IGLS) algorithm is adopted to handle the varying variance-covariance structures across families in a simple fashion. In this way, the tHE can compete favorably with any current version of HE in that it can naturally make use of all the trait information available in any general pedigree, simultaneously incorporate individual-level and pedigree-level covariates, marker genotypes for linkage (i.e., the number of allele shared identically by descent [IBD]), and marker alleles for association. Under the assumption of normality, the method is asymptotically equivalent to the usual variance component model for detecting linkage. For the situation where the assumption of normality is critical, a robust globally consistent estimator of the quantitative trait locus (QTL) variance is available. Complex genetic mechanisms, including gene-gene interaction, gene-environmental interaction, and imprinting, can be directly modeled in this version of HE regression.

Other recent paper on the HE method include:

Wang T, Elston RC. A modified revisited Haseman-Elston method to further improve power. Hum Hered. 2004;57(2):109-16. [PubMed]

Chen WM, Broman KW, Liang KY. Quantitative trait linkage analysis by generalized estimating equations: unification of variance components and Haseman-Elston regression. Genet Epidemiol. 2004 May;26(4):265-72. [PubMed]

Barber MJ, Cordell HJ, MacGregor AJ, Andrew T. Gamma regression improves Haseman-Elston and variance components linkage analysis for sib-pairs. Genet Epidemiol. 2004 Feb;26(2):97-107. [PubMed]

Schaid DJ, Olson JM, Gauderman WJ, Elston RC. Regression models for linkage: issues of traits, covariates, heterogeneity, and interaction. Hum Hered. 2003;55(2-3):86-96. [PubMed]


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