Of mice and men: Sparse statistical modeling in cardiovascular genomics

TL;DR

This paper applies sparse statistical modeling to high-throughput cardiovascular genomics data, focusing on gene-environment interactions, cross-species extrapolation, and addressing various statistical and computational challenges.

Contribution

It introduces the use of shrinkage models for analyzing complex genomic data and explores cross-species translation of gene expression signatures in cardiovascular research.

Findings

Identification of genes linked to disease and risk factors

Successful cross-species extrapolation of gene expression signatures

Addressed key statistical and computational challenges in genomic data analysis

Abstract

In high-throughput genomics, large-scale designed experiments are becoming common, and analysis approaches based on highly multivariate regression and anova concepts are key tools. Shrinkage models of one form or another can provide comprehensive approaches to the problems of simultaneous inference that involve implicit multiple comparisons over the many, many parameters representing effects of design factors and covariates. We use such approaches here in a study of cardiovascular genomics. The primary experimental context concerns a carefully designed, and rich, gene expression study focused on gene-environment interactions, with the goals of identifying genes implicated in connection with disease states and known risk factors, and in generating expression signatures as proxies for such risk factors. A coupled exploratory analysis investigates cross-species extrapolation of gene expression signatures--how these mouse-model signatures translate to humans. The latter involves exploration of sparse latent factor analysis of human observational data and of how it relates to projected risk signatures derived in the animal models. The study also highlights a range of applied statistical and genomic data analysis issues, including model specification, computational questions and model-based correction of experimental artifacts in DNA microarray data.