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G L Snow

Publications and source records attributed to G L Snow.

3 recordsLinked to original sources

Monte Carlo Markov chain methods for genome screening.

We used Monte Carlo Markov chain (MCMC) methods to analyze a quantitative trait, MAO level, and a discrete trait, Collaborative Study on the Genetics of Alcoholism (COGA) alcoholism. Segregation, linkage, and haplotype sharing were analyzed and effects of marker map features were examined. For MAO, modest signals were found on chromosomes 1 and 17 for raw data, and 15 for covariate-adjusted data. For alcoholism, a strong signal was found on chromosome 1 with modest signals on chromosomes 4 and 10.

Alcoholism↗

Pedigree analysis package (PAP) vs. MORGAN: model selection and hypothesis testing on a large pedigree.

The MORGAN package of programs is compared to a commonly used package, PAP, with respect to model selection in segregation analysis of a quantitative trait. MORGAN uses Monte Carlo Markov chain (MCMC) methods to estimate the likelihood, whereas both versions of PAP used employ an approximation to the likelihood for the mixed model. Comparisons are done by using results obtained from simulated data. All simulations were done on the same 232-member pedigree using data generated under each of several variations of models, which included different combinations of environmental, polygenic, and major gene components. PAP, version 4.0, and MORGAN gave similar results with respect to model selection for the majority of situations, suggesting that MCMC methods provide a computationally tractable approach for analysis of more complex models that cannot be analyzed by more direct computational methods. PAP, version 3.0, gave somewhat more disparate results compared with either PAP version 4.0 or MORGAN. Both MORGAN and the two versions of PAP confirmed that the major gene component is much easier to detect in the presence of some dominance. All three packages frequently falsely accepted the polygenic model when there was high residual heritability.

Computer Simulation↗

MCMC segregation and linkage analysis.

Our objective was to infer the genetic model for the quantitative traits using a variety of methods developed in our group. Only a single data set was analyzed in any one analysis, although some comparison between data sets was made. In addition, the simulated model was not known during the course of the analysis. Basic modeling and segregation analyses for the five quantitative traits was followed by several simple genome scans to indicate areas of interest. A Markov chain Monte Carlo (MCMC) multipoint quantitative trait locus (QTL) mapping approach was then used to estimate the posterior probabilities of linkage of QTL to each chromosome simultaneously with trait model parameters, and to further localize the genes. Comparisons between the nuclear family and pedigree data sets indicated a greater power for QTL detection and mapping with the pedigree data sets. Even with the pedigree data, however, precise localization of the QTL did not appear to be possible using single replicate data sets. Two of the three genes with effects on trait Q1 were detected by the MCMC method.

Chromosome Mapping↗