Gene prioritization through genomic data fusion.
The identification of genes involved in health and disease remains a challenge. We describe a bioinformatics approach, together with a freely accessible, interactive and flexible software termed Endeavour, to prioritize candidate genes underlying biological processes or diseases, based on their similarity to known genes involved in these phenomena. Unlike previous approaches, ours generates distinct prioritizations for multiple heterogeneous data sources, which are then integrated, or fused, into a global ranking using order statistics. In addition, it offers the flexibility of including additional data sources. Validation of our approach revealed it was able to efficiently prioritize 627 genes in disease data sets and 76 genes in biological pathway sets, identify candidates of 16 mono- or polygenic diseases, and discover regulatory genes of myeloid differentiation. Furthermore, the approach identified a novel gene involved in craniofacial development from a 2-Mb chromosomal region, deleted in some patients with DiGeorge-like birth defects. The approach described here offers an alternative integrative method for gene discovery.
Aerts
Stein
S
Laboratory of Neurogenetics, Department of Human Genetics, Flanders Interuniversity Institute for Biotechnology (VIB), University of Leuven, Herestraat 49, bus 602, 3000 Leuven, Belgium. stein.aerts@med.kuleuven.be
Lambrechts
Diether
D
Maity
Sunit
S
Van Loo
Peter
P
Coessens
Bert
B
De Smet
Frederik
F
Tranchevent
Leon-Charles
LC
De Moor
Bart
B
Marynen
Peter
P
Hassan
Bassem
B
Carmeliet
Peter
P
Moreau
Yves
Y