Regression-based sib pair linkage analysis for binary traits

M.P.A. Zeegers, J.P. Rice, F.V. Rijsdijk, G.R. Abecasis, P.C. Sham*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


Regression-based sib pair linkage analysis for binary traits.

Zeegers MP, Rice JP, Rijsdijk FV, Abecasis GR, Sham PC.

Department of Epidemiology, Faculty of Health Science, Maastricht University, Maastricht, The Netherlands.

The Haseman-Elston (HE) regression method offers a mathematically and computationally simpler alternative to variance-components (VC) models for the linkage analysis of quantitative traits. However, current versions of HE regression and VC models are not optimised for binary traits. Here, we present a modified HE regression and a liability-threshold VC model for binary-traits. The new HE method is based on the regression of a linear combination of the trait squares and the trait cross-product on the proportion of alleles identical by descent (IBD) at the putative locus, for sibling pairs. We have implemented both the new HE regression-based method and have performed analytic and simulation studies to assess its type 1 error rate and power under a range of conditions. These studies showed that the new HE method is well-behaved under the null hypothesis in large samples, is more powerful than both the original and the revisited HE methods, and is approximately equivalent in power to the liability-threshold VC model. Copyright 2003 S. Karger AG, Basel

Original languageEnglish
Pages (from-to)125-131
Number of pages6
JournalHuman Heredity
Issue number2-3
Publication statusPublished - 1 Jan 2003


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