Genetical Genomics
The abundance of gene transcripts throughout the genome can be explored as QTL (i.e. expression (e) QTL) to identify regulatory loci. By combining data from eQTL and CGH, we observed that copy number influences gene expression; moreover, the impact of trans regulators on co-expressed genes can effectively ‘rewire’ gene expression (see Computational and systems biology). Key manuscripts resulting from this work include: Regulatory Hotspots in the Malaria Parasite Genome Dictate Abundant Transcriptional Variation, PLoS Biol, 2008 and Metabolic QTL analysis links chloroquine resistance in Plasmodium falciparum to impaired hemoglobin catabolism, PLoS Genetics, pending revision. This approach opened a path to new computational partnerships (e.g. Symmetric Epistasis Estimation and its application to dissecting interaction map of Plasmodium falciparum, Mol Biosyst, 2012; A supervised learning approach to the Ensemble clustering of genes. Int J of Data Mining and Bioinf, 2011).
