Fusion of Large-Scale Genomic Knowledge and Frequency Data Computationally Prioritizes Variants in Epilepsy

Curation and interpretation of copy number variants identified by genome-wide testing is challenged by the large number of events harbored in each personal genome. Conventional determination of phenotypic relevance relies on patterns of higher frequency in affected individuals versus controls; however, an increasing amount of ascertained variation is rare or private to clans. Consequently, frequency data have less utility to resolve pathogenic from benign. One solution is disease-specific algorithms that leverage gene knowledge together with variant frequency to aid prioritization. We used large-scale resources including Gene Ontology, protein-protein interactions and other annotation systems together with a broad set of 83 genes with known associations to epilepsy to construct a pathogenicity score for the phenotype. We evaluated the score for all annotated human genes and applied Bayesian methods to combine the derived pathogenicity score with frequency information from our diagnostic laboratory. Analysis determined Bayes factors and posterior distributions for each gene. We applied our method to subjects with abnormal chromosomal microarray results and confirmed epilepsy diagnoses gathered by electronic medical record review. Genes deleted in our subjects with epilepsy had significantly higher pathogenicity scores and Bayes factors compared to subjects referred for non-neurologic indications. We also applied our scores to identify a recently validated epilepsy gene in a complex genomic region and to reveal candidate genes for epilepsy. We propose a potential use in clinical decision support for our results in the context of genome-wide screening. Our approach demonstrates the utility of integrative data in medical genomics.

[1]  Jing Zhang,et al.  Neuregulin 1 regulates excitability of fast-spiking neurons through Kv1.1 and acts in epilepsy , 2011, Nature Neuroscience.

[2]  Ton Feuth,et al.  Diagnostic genome profiling in mental retardation. , 2005, American journal of human genetics.

[3]  Caleb Davis,et al.  Exome Sequencing of Ion Channel Genes Reveals Complex Profiles Confounding Personal Risk Assessment in Epilepsy , 2011, Cell.

[4]  C. Burge,et al.  Conserved Seed Pairing, Often Flanked by Adenosines, Indicates that Thousands of Human Genes are MicroRNA Targets , 2005, Cell.

[5]  Taylor J. Maxwell,et al.  Deep resequencing reveals excess rare recent variants consistent with explosive population growth , 2010, Nature communications.

[6]  I. Scheffer,et al.  Navigating the channels and beyond: unravelling the genetics of the epilepsies , 2008, The Lancet Neurology.

[7]  J. Lupski,et al.  Clan Genomics and the Complex Architecture of Human Disease , 2011, Cell.

[8]  J. Lupski,et al.  Human genome sequencing in health and disease. , 2012, Annual review of medicine.

[9]  C. Wijmenga,et al.  Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. , 2006, American journal of human genetics.

[10]  A. Barabasi,et al.  Comparison of an expanded ataxia interactome with patient medical records reveals a relationship between macular degeneration and ataxia , 2010, Human molecular genetics.

[11]  P. Stankiewicz,et al.  Structural variation in the human genome and its role in disease. , 2010, Annual review of medicine.

[12]  Donna M. Martin,et al.  Phenotypic heterogeneity of genomic disorders and rare copy-number variants. , 2012, The New England journal of medicine.

[13]  Diana V. Dugas,et al.  Protein Interactome Reveals Converging Molecular Pathways Among Autism Disorders , 2011, Science Translational Medicine.

[14]  Jianmin Wu,et al.  PINA v2.0: mining interactome modules , 2011, Nucleic Acids Res..

[15]  Daniel R Weinberger,et al.  expression in the brain , 2006 .

[16]  Ernesto Estrada,et al.  Communicability in complex networks. , 2007, Physical review. E, Statistical, nonlinear, and soft matter physics.

[17]  P. Visscher,et al.  Rare chromosomal deletions and duplications increase risk of schizophrenia , 2008, Nature.

[18]  Jon Emery,et al.  Genetics education for primary-care providers , 2002, Nature Reviews Genetics.

[19]  Tobias M. Fischer,et al.  Receptor tyrosine kinase ErbB4 modulates neuroblast migration and placement in the adult forebrain , 2004, Nature Neuroscience.

[20]  Francesca Antonacci,et al.  Copy Number Variation Analysis in Single-Suture Craniosynostosis: Multiple Rare Variants Including RUNX2 Duplication in Two Cousins With Metopic Craniosynostosis , 2010, American journal of medical genetics. Part A.

[21]  J. H. Cross,et al.  Revised terminology and concepts for organization of seizures and epilepsies: Report of the ILAE Commission on Classification and Terminology, 2005–2009 , 2010, Epilepsia.

[22]  Insuk Lee,et al.  Characterising and Predicting Haploinsufficiency in the Human Genome , 2010, PLoS genetics.

[23]  Thomas W. Mühleisen,et al.  Large recurrent microdeletions associated with schizophrenia , 2008, Nature.

[24]  Allison G. Dempsey,et al.  A 600 kb deletion syndrome at 16p11.2 leads to energy imbalance and neuropsychiatric disorders , 2012, Journal of Medical Genetics.

[25]  C. Baker,et al.  Recurrent microdeletions at 15q11.2 and 16p13.11 predispose to idiopathic generalized epilepsies. , 2010, Brain : a journal of neurology.

[26]  S. Batalov,et al.  A gene atlas of the mouse and human protein-encoding transcriptomes. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[27]  I. Scheffer,et al.  Genetic testing in the epilepsies—Report of the ILAE Genetics Commission , 2010, Epilepsia.

[28]  Susumu Goto,et al.  KEGG for representation and analysis of molecular networks involving diseases and drugs , 2009, Nucleic Acids Res..

[29]  Michael R. Johnson,et al.  Rare deletions at 16p13.11 predispose to a diverse spectrum of sporadic epilepsy syndromes. , 2010, American journal of human genetics.

[30]  Peilin Jia,et al.  Prioritization of Epilepsy Associated Candidate Genes by Convergent Analysis , 2011, PloS one.

[31]  S. Gallati,et al.  Targeted next generation sequencing as a diagnostic tool in epileptic disorders , 2012, Epilepsia.

[32]  Paul Flicek,et al.  The functional spectrum of low-frequency coding variation , 2011, Genome Biology.

[33]  Bassem A. Hassan,et al.  Gene prioritization through genomic data fusion , 2006, Nature Biotechnology.

[34]  Caleb Webber,et al.  Accurate Distinction of Pathogenic from Benign CNVs in Mental Retardation , 2010, PLoS Comput. Biol..

[35]  Christian E Elger,et al.  15q13.3 microdeletions increase risk of idiopathic generalized epilepsy , 2009, Nature Genetics.

[36]  A. Reymond,et al.  KCTD13 is a major driver of mirrored neuroanatomical phenotypes of the 16p11.2 copy number variant , 2012, Nature.

[37]  C. Gillberg,et al.  Copy number variation characteristics in subpopulations of patients with autism spectrum disorders , 2011, American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics.

[38]  Maintaining evolvability , 2008, Journal of Genetics.

[39]  Judith A. Blake,et al.  The Mouse Genome Database (MGD): premier model organism resource for mammalian genomics and genetics , 2010, Nucleic Acids Res..

[40]  J. Stockman Association between Microdeletion and Microduplication at 16p11.2 and Autism , 2009 .

[41]  L. Cowan The epidemiology of the epilepsies in children. , 2002, Mental retardation and developmental disabilities research reviews.

[42]  David J. Porteous,et al.  Speeding disease gene discovery by sequence based candidate prioritization , 2005, BMC Bioinformatics.

[43]  P. Stankiewicz,et al.  Detection of clinically relevant exonic copy‐number changes by array CGH , 2010, Human mutation.

[44]  K. Devriendt,et al.  Early myoclonic encephalopathy caused by a disruption of the neuregulin-1 receptor ErbB4 , 2009, European Journal of Human Genetics.

[45]  W. Hauser,et al.  The descriptive epidemiology of epilepsy—A review , 2009, Epilepsy Research.

[46]  M. Ashburner,et al.  Gene Ontology: tool for the unification of biology , 2000, Nature Genetics.

[47]  J. Lupski,et al.  Schizophrenia: Incriminating genomic evidence , 2008, Nature.