A framework for analyzing both linkage and association: an analysis of Genetic Analysis Workshop 16 simulated data.
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).
Daw
E Warwick
EW
Division of Statistical Genomics, Washington University School of Medicine, 4444 Forest Park Boulevard, Campus Box 8506, St, Louis, Missouri 63108 USA. warwick@wustl.edu.
Plunkett
Jevon
J
Feitosa
Mary
M
Gao
Xiaoyi
X
Van Brunt
Andrew
A
Ma
Duanduan
D
Czajkowski
Jacek
J
Province
Michael A
MA
Borecki
Ingrid
I