Combined analyses of molecular data, such as DNA copy-number alteration, mRNA and
protein expression, point to biological functions and molecular pathways being deregulated …

We introduce a simple new approach to variable selection in linear regression, with a
particular focus on quantifying uncertainty in which variables should be selected. The …

Statistical methods in integrative genomics aim to answer important biology questions by
jointly analyzing multiple types of genomic data (vertical integration) or aggregating the …

Gene-regulatory network analysis is a powerful approach to elucidate the molecular
processes and pathways underlying complex disease. Here we employ systems genetics …

Recent technological advances in molecular biology have given rise to numerous large‐
scale datasets whose analysis imposes serious methodological challenges mainly relating …

We describe a Bayesian technique to (a) perform a sparse joint selection of significant
predictor variables and significant inverse covariance matrix elements of the response …

Recently, large scale genome‐wide association study (GWAS) meta‐analyses have boosted
the number of known signals for some traits into the tens and hundreds. Typically, however …

Cardiac fibrosis is a final common pathology in inherited and acquired heart diseases that
causes cardiac electrical and pump failure. Here, we use systems genetics to identify a pro …

Identification of candidate causal variants in regions associated with risk of common
diseases is complicated by linkage disequilibrium (LD) and multiple association signals …

In high-dimensional regression, grouping pursuit and feature selection have their own merits
while complementing each other in battling the curse of dimensionality. To seek a …