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Literature DB >> 9326339

Markov chain Monte Carlo segregation and linkage analysis for oligogenic models.

Abstract

A new method for segregation and linkage analysis, with pedigree data, is described. Reversible jump Markov chain Monte Carlo methods are used to implement a sampling scheme in which the Markov chain can jump between parameter subspaces corresponding to models with different numbers of quantitative-trait loci (QTL's). Joint estimation of QTL number, position, and effects is possible, avoiding the problems that can arise from misspecification of the number of QTL's in a linkage analysis. The method is illustrated by use of a data set simulated for the 9th Genetic Analysis Workshop; this data set had several oligogenic traits, generated by use of a 1,497-member pedigree. The mixing characteristics of the method appear to be good, and the method correctly recovers the simulated model from the test data set. The approach appears to have great potential both for robust linkage analysis and for the answering of more general questions regarding the genetic control of complex traits.

Mesh：

Year: 1997 PMID： 9326339 PMCID： PMC1715966 DOI： 10.1086/515506

Source DB: PubMed Journal: Am J Hum Genet ISSN： 0002-9297 Impact factor: 11.025

14 in total

7. Performance of Markov chain-Monte Carlo approaches for mapping genes in oligogenic models with an unknown number of loci.

Authors: J K Lee; D C Thomas
Journal: Am J Hum Genet Date: 2000-10-13 Impact factor: 11.025

Markov chain Monte Carlo segregation and linkage analysis for oligogenic models.

1. A bayesian approach to detect quantitative trait loci using Markov chain Monte Carlo.

2. Simulation of a common oligogenic disease with quantitative risk factors. GAW9 problem 2: the answers.

3. Genetic analysis of a common oligogenic trait with quantitative correlates: summary of GAW9 results.

4. Approximate computation of probability functions for pedigree analysis.

5. A general model for the genetic analysis of pedigree data.

6. Achieving irreducibility of the Markov chain Monte Carlo method applied to pedigree data.

7. Conclusions of segregation analysis for family data generated under two-locus models.

8. Rapid multipoint linkage analysis of recessive traits in nuclear families, including homozygosity mapping.

9. Bayesian inference in multipoint gene mapping.

10. Construction of multilocus genetic linkage maps in humans.

1. A random model approach to mapping quantitative trait loci for complex binary traits in outbred populations.

2. Searching for alcoholism susceptibility genes using Markov chain Monte Carlo methods.

3. Bayesian mapping of quantitative trait loci under complicated mating designs.

4. Efficient multipoint linkage analysis through reduction of inheritance space.

5. Quantitative trait loci mapping in F(2) crosses between outbred lines.

6. Mixed model analysis of quantitative trait loci.

7. Performance of Markov chain-Monte Carlo approaches for mapping genes in oligogenic models with an unknown number of loci.

8. A note on algorithms for genotype and allele elimination in complex pedigrees with incomplete genotype data.

9. Mapping quantitative trait loci in complex pedigrees: a two-step variance component approach.

10. Statistical approaches to gene mapping.