Amy Leonardson

TL;DR: It is shown that this approach can predict transcriptional responses to single gene–perturbation experiments using gene-expression data in the context of a segregating mouse population and the utility of this approach is demonstrated by identifying and experimentally validating the involvement of three new genes in susceptibility to obesity.

TL;DR: Application of this method to liver and adipose gene expression data generated from a segregating mouse population results in the identification of a macrophage-enriched network supported as having a causal relationship with disease traits associated with metabolic syndrome.

TL;DR: Using ‘exon’ and ‘tiling’ arrays fabricated by ink-jet oligonucleotide synthesis, an experimental approach is devised to validate and refine computational gene predictions and define full-length transcripts on the basis of co-regulated expression of their exons.

TL;DR: This work presents the largest survey to date, to the authors' knowledge, of the heritability of gene-expression traits in segregating human populations, and views clusters or networks based on genetic correlation measures and shared QTLs as potentially novel insights into the relationship among genes that may underlie complex traits.