Multi-tissue Expression Data Research Articles

Out of the testis, into the ovary: biased outcomes of gene duplication and deletion in Drosophila.

Gene turnover is a key source of adaptive variation. Yet most evolutionary studies have focused on gene duplication, dismissing gene deletion as a mechanism that simply eradicates redundancy. Here, I use genome-scale sequence and multi-tissue expression data from Drosophila melanogaster and Drosophila pseudoobscura to simultaneously assess the evolutionary outcomes of gene duplication and deletion in Drosophila. I find that gene duplication is more frequent than gene deletion in both species, indicating that it may play a more important role in Drosophila evolution. However, examination of several genic properties reveals that genes likely possess distinct functions after duplication that diverge further before deletion, suggesting that loss of redundancy cannot explain a majority of gene deletion events in Drosophila. Moreover, in addition to providing support for the well-known "out of the testis" origin of young duplicate genes, analyses of gene expression profiles uncover a preferential bias against deletion of old ovary-expressed genes. Therefore, I propose a novel "into the ovary" hypothesis for gene deletion in Drosophila, in which gene deletion may promote adaptation by salvaging genes that contribute to the evolution of female reproductive phenotypes. Under this combined "out of the testis, into the ovary" evolutionary model, gene duplication and deletion work in concert to generate and maintain a balanced repertoire of genes that promote sex-specific adaptation in Drosophila.

Identifying cis-mediators for trans-eQTLs across many human tissues using genomic mediation analysis.

The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is “mediation” by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are “cis-mediators” of trans-eQTLs, including those “cis-hubs” involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.