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).

• Publication year

  2009

• Venue

  BMC Proceedings

• Publication date

  2009-12-15

• Fields of study

  Biology, Medicine, Computer Science

• Identifiers
  DOI 10.1186/1753-6561-3-S7-S98PMID 20018095PMCID 2796002
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

• Genetic and Genomic Discovery Using Family Studies

  2008cited by this paper
• Single-nucleotide polymorphism versus microsatellite markers in a combined linkage and segregation analysis of a quantitative trait

  2005cited by this paper
• Markov chain Monte Carlo segregation and linkage analysis for oligogenic models.

  1997cited by this paper

• Genetic influences on preterm birth

  2010cites this paper
• Combining information from linkage and association methods

  2009influential citation