human gene connectome as a map of short cuts for morbid allele discovery

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 physi...

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Published inProceedings of the National Academy of Sciences - PNAS Vol. 110; no. 14; pp. 5558 - 5563
Main Authors Itan, Yuval, Zhang, Shen-Ying, Vogt, Guillaume, Abhyankar, Avinash, Herman, Melina, Nitschke, Patrick, Fried, Dror, Quintana-Murci, Lluis, Abel, Laurent, Casanova, Jean-Laurent
Format Journal Article
LanguageEnglish
Published United States National Academy of Sciences 02.04.2013
National Acad Sciences
Subjects
Algorithms
Alleles
Biological Sciences
Biotechnology
Cluster Analysis
Collagens
computer software
Evolutionary genetics
Genes
Genes - genetics
Genes - physiology
Genetic diseases
genetic distance
Genetic research
Genomics
Genomics - methods
Genotype & phenotype
High-Throughput Nucleotide Sequencing - methods
Human genetics
Humans
Immunity
Medical genetics
Models, Genetic
Nucleic acids
pathogenesis
patients
Phenotype
Phylogenetics
Physiology
Signal Transduction - genetics
Software
Toll-like receptor 3
Toll-Like Receptor 3 - genetics
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Summary: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.
Bibliography:http://dx.doi.org/10.1073/pnas.1218167110
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Author contributions: Y.I., S.-Y.Z., G.V., D.F., L.A., and J.-L.C. designed research; Y.I., S.-Y.Z., and M.H. performed research; Y.I., A.A., P.N., D.F., L.Q.-M., L.A., and J.-L.C. contributed new reagents/analytic tools; Y.I., S.-Y.Z., A.A., and M.H. analyzed data; and Y.I., S.-Y.Z., L.Q.-M., L.A., and J.-L.C. wrote the paper.
Edited* by Bruce Beutler, University of Texas Southwestern Medical Center, Dallas, TX, and approved February 15, 2013 (received for review October 19, 2012)
ISSN:0027-8424
1091-6490
1091-6490
DOI:10.1073/pnas.1218167110