Regression-based sib pair linkage analysis for binary traits

Maurice P.A. Zeegers, John P. Rice, Frühling V. Rijsdijk, Goncalo R. Abecasis, Pak C. Sham

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)125-131
Number of pages7
JournalHuman heredity
Volume55
Issue number2-3
DOIs
StatePublished - 2003

Keywords

  • Binary traits
  • Haseman-Elston
  • Linkage
  • Variance components

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