Literature DB >> 11858136

Genome-wide linkage analysis in a general population sample using sigma 2A random effects (SSARs) fitted by Gibbs sampling.

L J Palmer1, K B Jacobs, K J Scurrah, X Xu, S Horvath, S T Weiss.   

Abstract

We used variance components analysis to investigate the underlying determinants of the quantitative phenotypes (Q1-Q5) and their interrelationships in replicate 42 of the Genetic Analysis Workshop 12 simulated general population. Variance components models were fitted using Gibbs sampling in WinBUGS v1.3. Sigma-squared-A-random-effects (SSARs) were estimated for each phenotype, and were used as derived phenotypes in subsequent linkage analyses. Whole-genome, multipoint linkage analyses were based upon a new Haseman-Elston identity-by descent sib-pair method that takes a weighted combination of the trait-sum and trait-difference. The five quantitative traits simulated were closely correlated with each other and with affection status. The whole-genome screen of quantitative traits associated with the simulated complex disease suggested that one or more major loci regulating Q1 localizes to chromosome 2p and that one or more major loci regulating Q5 may localize to chromosome 1p.

Mesh:

Year:  2001        PMID: 11858136     DOI: 10.1002/gepi.2001.21.s1.s674

Source DB:  PubMed          Journal:  Genet Epidemiol        ISSN: 0741-0395            Impact factor:   2.135


  2 in total

1.  Multilevel modeling for the analysis of longitudinal blood pressure data in the Framingham Heart Study pedigrees.

Authors:  Laurent Briollais; Anjela Tzontcheva; Shelley Bull
Journal:  BMC Genet       Date:  2003-12-31       Impact factor: 2.797

2.  Genome-wide linkage analysis of longitudinal phenotypes using sigma2A random effects (SSARs) fitted by Gibbs sampling.

Authors:  Lyle J Palmer; Katrina J Scurrah; Martin Tobin; Sanjay R Patel; Juan C Celedon; Paul R Burton; Scott T Weiss
Journal:  BMC Genet       Date:  2003-12-31       Impact factor: 2.797

  2 in total

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