title: The human gene connectome as a map of short cuts for morbid allele discovery.
abstract: High-throughput genomic data reveal thousands of gene variants per patient, and it is often difficult to determine which of these variants underlies disease in a given individual. However, at the population level, there may be some degree of phenotypic homogeneity, with alterations of specific physiological pathways underlying the pathogenesis of a particular disease. We describe here the human gene connectome (HGC) as a unique approach for human mendelian genetic research, facilitating the interpretation of abundant genetic data from patients with the same disease, and guiding subsequent experimental investigations. We first defined the set of the shortest plausible biological distances, routes, and degrees of separation between all pairs of human genes by applying a shortest distance algorithm to the full human gene network. We then designed a hypothesis-driven application of the HGC, in which we generated a Toll-like receptor 3-specific connectome useful for the genetic dissection of inborn errors of Toll-like receptor 3 immunity. In addition, we developed a functional genomic alignment approach from the HGC. In functional genomic alignment, the genes are clustered according to biological distance (rather than the traditional molecular evolutionary genetic distance), as estimated from the HGC. Finally, we compared the HGC with three state-of-the-art methods: String, FunCoup, and HumanNet. We demonstrated that the existing methods are more suitable for polygenic studies, whereas HGC approaches are more suitable for monogenic studies. The HGC and functional genomic alignment data and computer programs are freely available to noncommercial users from http://lab.rockefeller.edu/casanova/HGC and should facilitate the genome-wide selection of disease-causing candidate alleles for experimental validation.
authors.affiliation: St. Giles Laboratory of Human Genetics of Infectious Diseases, Rockefeller Branch, The Rockefeller University, New York, NY 10065, USA. yitan@rockefeller.edu
authors.forename: Yuval
authors.initials: Y
authors.lastname: Itan
authors.forename: Shen-Ying
authors.initials: SY
authors.lastname: Zhang
authors.forename: Guillaume
authors.initials: G
authors.lastname: Vogt
authors.forename: Avinash
authors.initials: A
authors.lastname: Abhyankar
authors.forename: Melina
authors.initials: M
authors.lastname: Herman
authors.forename: Patrick
authors.initials: P
authors.lastname: Nitschke
authors.forename: Dror
authors.initials: D
authors.lastname: Fried
authors.forename: Lluis
authors.initials: L
authors.lastname: Quintana-Murci
authors.forename: Laurent
authors.initials: L
authors.lastname: Abel
authors.forename: Jean-Laurent
authors.initials: JL
authors.lastname: Casanova
mesh_terms: D000483:Alleles; D016000:Cluster Analysis; D005796:Genes / Q000235:genetics* / Q000502:physiology; D023281:Genomics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods; D006801:Humans; D008957:Models, Genetic; D010641:Phenotype; D015398:Signal Transduction / Q000235:genetics*; D012984:Software; D051196:Toll-Like Receptor 3 / Q000235:genetics
chemical_list: C495344:TLR3 protein, human; D051196:Toll-Like Receptor 3