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Genetic aspects of alcoholism have been modeled using rats selectively bred for extremes of alcohol preference and voluntary alcohol intake. These lines show similar alcohol drinking phenotypes but have different genetic and environmental backgrounds and may therefore display diverse behavioral traits as seen in human alcoholics. The multivariate concentric square field™ (MCSF) test is designed to provoke exploration and behaviors associated with risk assessment, risk taking and shelter seeking in a novel environment. The aim was to use the MCSF to characterize behavioral profiles in rat lines from selective breeding programs in the United States (P/NP, HAD1/LAD1, HAD2/LAD2), Italy (sP/sNP) and Finland (AA/ANA). The open field and elevated plus maze tests were used as reference tests. There were substantial differences within some of the pairs of selectively bred rat lines as well as between all alcohol-preferring rats. The most pronounced differences within the pairs of lines were between AA and ANA rats and between sP and sNP rats followed by intermediate differences between P and NP rats and minor differences comparing HAD and LAD rats. Among all preferring lines, P, HAD1 and HAD2 rats shared similar behavioral profiles, while AA and sP rats were quite different from each other and the others. No single trait appeared to form a common 'pathway' associated with a high alcohol drinking phenotype among all of the alcohol-preferring lines of rats. The marked behavioral differences found in the different alcohol-preferring lines may mimic the heterogeneity observed among human alcoholic subtypes.
Pubmed ID: 21521426
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Software package to test for differentially expressed genes with microarray data. It can be used with both cDNA microarrays or Affymetrix chip. The packge fits a robust Bayesian hierarchical model for testing for differential expression. Outliers are modeled explicitly using a $t$-distribution. The model includes an exchangeable prior for the variances which allow different variances for the genes but still shrink extreme empirical variances. The model can be used for testing for differentially expressed genes among multiple samples, and can distinguish between the different possible patterns of differential expression when there are three or more samples. Parameter estimation is carried out using a novel version of Markov Chain Monte Carlo that is appropriate when the model puts mass on subspaces of the full parameter space.
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