title: Regression-based sib pair linkage analysis for binary traits.
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.
authors.affiliation: Department of Epidemiology, Faculty of Health Science, Maastricht University, Maastricht, The Netherlands.
authors.forename: Maurice P A
authors.initials: MP
authors.lastname: Zeegers
authors.forename: John P
authors.initials: JP
authors.lastname: Rice
authors.forename: Frühling V
authors.initials: FV
authors.lastname: Rijsdijk
authors.forename: Goncalo R
authors.initials: GR
authors.lastname: Abecasis
authors.forename: Pak C
authors.initials: PC
authors.lastname: Sham
mesh_terms: D003627:Data Interpretation, Statistical; D008040:Genetic Linkage; D006801:Humans; D012044:Regression Analysis; D035781:Siblings