Abstract
We performed multipoint linkage analyses with multiple programs and models for several gene expression traits in the Centre d'Etude du Polymorphisme Humain families. All analyses provided consistent results for both peak location and shape. Variance-components (VC) analysis gave wider peaks and Bayes factors gave fewer peaks. Among programs from the MORGAN package, lm_multiple performed better than lm_markers, resulting in less Markov-chain Monte Carlo (MCMC) variability between runs, and the program lm_twoqtl provided higher LOD scores by also including either a polygenic component or an additional quantitative trait locus.
Highlights
Our aims were 1) to compare results from several multipoint linkage analysis programs that are available for quantitative traits and 2) to investigate the performance of Markov-chain Monte Carlo (MCMC)-based programs on the GAW15 expression data in 14 three-generation CEPH families genotyped for clustered SNP markers [1]
The jittered and nonjittered maps yielded virtually identical VC LOD scores, except for VAMP8 on chr 2, where the largest peak was slightly narrower with the nonjittered map
The actual locations of these genes were at the maximum VC LOD scores (CHI3L2, GSTM1, PSPH), 10 cM away (VAMP8), or 25 cM away (PPAT)
Summary
Our aims were 1) to compare results from several multipoint linkage analysis programs that are available for quantitative traits and 2) to investigate the performance of MCMC-based programs on the GAW15 expression data in 14 three-generation CEPH families genotyped for clustered SNP markers [1]. We used three recently developed programs in the MORGAN package [2]: lm_markers, lm_multiple, and lm_twoqtl. These programs provide MCMC-based parametric LOD score analysis, the first two with a one-QTL (1Q) model and the last with more complex models, including a second linked (2Q) or unlinked (UQ) QTL and/or a polygenic component (P). We used Loki [3] for Bayesian oligogenic analysis and Merlin [4] for VC analysis. These analyses cover most approaches that fully use quantitative trait data from three-generation pedigrees
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