Joint segregation and linkage analysis of a quantitative trait compared to separate analyses
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Updated: Oct 30, 2026

Large-Scale Multi-Omics Genome-Wide Association Studies (Mo-GWAS): Guidelines for Sample Preparation and Normalization
Published on: July 27, 2021
W J Gauderman1, C L Faucett, J L Morrison
1Department of Preventive Medicine, University of Southern California, Los Angeles 90033, USA.
Abstract:
Our goal was to determine the degree to which joint segregation and linkage analysis leads to increased efficiency for estimating the recombination fraction and to greater power for detecting linkage, compared to separate analyses. We concentrated on the quantitative phenotype Q2 and analyzed linkage with a tightly linked marker, a loosely linked marker, and eight unlinked markers, the latter chosen to evaluate false positive rates. We considered both nuclear-family and extended-pedigree data, using the 200 replicates of each provided to GAW participants. We found joint analysis to be consistently more efficient, with relative efficiencies for the tightly linked marker of 1.16 and 1.06 in extended pedigrees and nuclear families, respectively. These relative efficiencies translated into modest but consistent gains in power to detect linkage. Both methods appear to produce unbiased parameter estimates and similar false positive rates.
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