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

Tao Wang, Robert C. Elston

Research output: Contribution to journalArticle

31 Scopus citations

Abstract

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.

Original languageEnglish (US)
Pages (from-to)12-22
Number of pages11
JournalGenetic Epidemiology
Volume29
Issue number1
DOIs
Publication statusPublished - Jul 1 2005
Externally publishedYes

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Keywords

  • Association analysis
  • Iterative generalized least squares
  • Linkage analysis
  • Quantitative trait locus

ASJC Scopus subject areas

  • Epidemiology
  • Genetics(clinical)

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