A framework for analyzing both linkage and association: an analysis of Genetic Analysis Workshop 16 simulated data.
BMC Proc
; 3 Suppl 7: S98, 2009 Dec 15.
Article
in En
| MEDLINE
| ID: mdl-20018095
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).
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Type of study:
Risk_factors_studies
Language:
En
Journal:
BMC Proc
Year:
2009
Document type:
Article