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).
CITATION STYLE
Daw, E. W., Plunkett, J., Feitosa, M., Gao, X., Van Brunt, A., Ma, D., … Borecki, I. (2009). A framework for analyzing both linkage and association: an analysis of Genetic Analysis Workshop 16 simulated data. BMC Proceedings, 3(S7). https://doi.org/10.1186/1753-6561-3-s7-s98
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