30 Schaid et al. Genetic Epidemiology 19:30–51 (2000) GEPI 9837 © 2000 Wiley-Liss, Inc. Model-Free Sib-Pair Linkage Analysis: Combining Full-Sib and Half-Sib Pairs Daniel J. Schaid, 1 * Robert C. Elston, 2 Lieu Tran, 3 and Alexander F. Wilson 4 1 Section of Biostatistics, Mayo Clinic/Foundation, Rochester, Minnesota 2 Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio 3 McDermott, Inc., New Orleans, Louisiana 4 Genometrics, National Human Genome Research Institute, National Institutes of Health, Baltimore, Maryland When sampling full-sibs for linkage studies, half-sibs are often available. Not only are half-sibs convenient to sample, but they can sometimes offer greater power than full-sibs. We propose a method to combine the information from full-sibs and half-sibs into a single test for linkage. This method is based on the Haseman and Elston [1972] method of regressing the squared trait-difference for a pair of sibs (either full- or half-sibs) on the estimated proportion of alleles shared identical by descent. To approximate the distribution of the test statistic, we propose a correction factor that considers the correlation among sibs, and demonstrate by simulations that this approximation works well in many situa- tions, although there are some conditions for which the statistic can have an inflated Type-I error rate. The main appeal of our proposed method is the speed at which it can be computed, offering a rapid way to perform genome-wide link- age screens. Genet. Epidemiol. 19:30–51, 2000. © 2000 Wiley-Liss, Inc. Key words: allele sharing; complex traits; identical by descent; linkage; regression; robust Contract grant sponsor: MacArthur Research Network I; Contract grant sponsor: National Heart, Lung and Blood Institute; Contract grant number: HL 07567; Contract grant sponsor: National Institute of General Medical Sciences; Contract grant number: GM 28356; Contract grant sponsor: National Cen- ter for Research Resources; Contract grant number: RR-03655. *Correspondence to: Daniel J. Schaid, Ph.D., Section of Biostatistics, Harwick 7, Mayo Clinic, 200 First Street S.W., Rochester, MN 55905. E-mail: schaid@mayo.edu Received 11 June 1998; Revision accepted 3 August 1999