title: A framework for analyzing both linkage and association: an analysis of Genetic Analysis Workshop 16 simulated data.
abstract: We examine a Bayesian Markov-chain Monte Carlo framework for simultaneous segregation and linkage analysis in the simulated single-nucleotide polymorphism data provided for Genetic Analysis Workshop 16. We conducted linkage only, linkage and association, and association only tests under this framework. We also compared these results with variance-component linkage analysis and regression analyses. The results indicate that the method shows some promise, but finding genes that have very small (<0.1%) contributions to trait variance may require additional sources of information. All methods examined fared poorly for the smallest in the simulated "polygene" range (h2 of 0.0015 to 0.0002).
authors.affiliation: Division of Statistical Genomics, Washington University School of Medicine, 4444 Forest Park Boulevard, Campus Box 8506, St, Louis, Missouri 63108 USA. warwick@wustl.edu.
authors.forename: E Warwick
authors.initials: EW
authors.lastname: Daw
authors.forename: Jevon
authors.initials: J
authors.lastname: Plunkett
authors.forename: Mary
authors.initials: M
authors.lastname: Feitosa
authors.forename: Xiaoyi
authors.initials: X
authors.lastname: Gao
authors.forename: Andrew
authors.initials: A
authors.lastname: Van Brunt
authors.forename: Duanduan
authors.initials: D
authors.lastname: Ma
authors.forename: Jacek
authors.initials: J
authors.lastname: Czajkowski
authors.forename: Michael A
authors.initials: MA
authors.lastname: Province
authors.forename: Ingrid
authors.initials: I
authors.lastname: Borecki