Bayesian Variable Selection for Gaussian copula regression models.

We develop a novel Bayesian method to select important predictors in regression models with multiple responses of diverse types. A sparse Gaussian copula regression model is used to account for the multivariate dependencies between any combination of discrete and/or continuous responses and their association with a set of predictors. We utilize the parameter expansion for data augmentation strategy to construct a Markov chain Monte Carlo algorithm for the estimation of the parameters and the latent variables of the model. Based on a centered parametrization of the Gaussian latent variables, we design a fixed-dimensional proposal distribution to update jointly the latent binary vectors of important predictors and the corresponding non-zero regression coefficients. For Gaussian responses and for outcomes that can be modeled as a dependent version of a Gaussian response, this proposal leads to a Metropolis-Hastings step that allows an efficient exploration of the predictors' model space. The proposed strategy is tested on simulated data and applied to real data sets in which the responses consist of low-intensity counts, binary, ordinal and continuous variables.

Differential coexpression analysis of obesity-associated networks in human subcutaneous adipose tissue.

OBJECTIVE: To use a unique obesity-discordant sib-pair study design to combine differential expression analysis, expression quantitative trait loci (eQTLs) mapping and a coexpression regulatory network approach in subcutaneous human adipose tissue to identify genes relevant to the obese state. STUDY DESIGN: Genome-wide transcript expression in subcutaneous human adipose tissue was measured using Affymetrix U133 Plus 2.0 microarrays (Affymetrix, Santa Clara, CA, USA), and genome-wide genotyping data was obtained using an Applied Biosystems (Applied Biosystems; Life Technologies, Carlsbad, CA, USA) SNPlex linkage panel. SUBJECTS: A total of 154 Swedish families ascertained through an obese proband (body mass index (BMI) >30 kg m(-2)) with a discordant sibling (BMI>10 kg m(-2) less than proband). RESULTS: Approximately one-third of the transcripts were differentially expressed between lean and obese siblings. The cellular adhesion molecules (CAMs) KEGG grouping contained the largest number of differentially expressed genes under cis-acting genetic control. By using a novel approach to contrast CAMs coexpression networks between lean and obese siblings, a subset of differentially regulated genes was identified, with the previously GWAS obesity-associated neuronal growth regulator 1 (NEGR1) as a central hub. Independent analysis using mouse data demonstrated that this finding of NEGR1 is conserved across species. CONCLUSION: Our data suggest that in addition to its reported role in the brain, NEGR1 is also expressed in subcutaneous adipose tissue and acts as a central 'hub' in an obesity-related transcript network.

The distribution and causes of meiotic recombination in the human genome.

Using the statistical analysis of genetic variation, we have developed a high-resolution genetic map of recombination hotspots and recombination rate variation across the human genome. This map, which has a resolution several orders of magnitude greater than previous studies, identifies over 25,000 recombination hotspots and gives new insights into the distribution and determination of recombination. Wavelet-based analysis demonstrates scale-specific influences of base composition, coding context and DNA repeats on recombination rates, though, in contrast with other species, no association with DNase I hypersensitivity. We have also identified specific DNA motifs that are strongly associated with recombination hotspots and whose activity is influenced by local context. Comparative analysis of recombination rates in humans and chimpanzees demonstrates very high rates of evolution of the fine-scale structure of the recombination landscape. In the light of these observations, we suggest possible resolutions of the hotspot paradox.