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

English version České info

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.

Vyšlo v časopise: Fusion of Large-Scale Genomic Knowledge and Frequency Data Computationally Prioritizes Variants in Epilepsy. PLoS Genet 9(9): e32767. doi:10.1371/journal.pgen.1003797
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003797

Souhrn

Zdroje

1. LupskiJR, BelmontJW, BoerwinkleE, GibbsRA (2011) Clan genomics and the complex architecture of human disease. Cell 147 : 32–43 doi:10.1016/j.cell.2011.09.008

2. AertsS, LambrechtsD, MaityS, Van LooP, CoessensB, et al. (2006) Gene prioritization through genomic data fusion. Nat Biotechnol 24 : 537–544 doi:10.1038/nbt1203

3. FrankeL, van BakelH, FokkensL, de JongED, Egmont-PetersenM, et al. (2006) Reconstruction of a functional human gene network, with an application for prioritizing positional candidate genes. The American Journal of Human Genetics 78 : 1011–1025 doi:10.1086/504300

4. HuangN, LeeI, MarcotteEM, HurlesME (2010) Characterising and predicting haploinsufficiency in the human genome. PLoS Genet 6: e1001154 doi:10.1371/journal.pgen.1001154

5. SakaiY, ShawCA, DawsonBC, DugasDV, Al-MohtasebZ, et al. (2011) Protein interactome reveals converging molecular pathways among autism disorders. Science Translational Medicine 3 : 86ra49 doi:10.1126/scitranslmed.3002166

6. JiaP, EwersJM, ZhaoZ (2011) Prioritization of epilepsy associated candidate genes by convergent analysis. PLoS ONE 6: e17162 doi:10.1371/journal.pone.0017162

7. KahleJJ, GulbahceN, ShawCA, LimJ, HillDE, et al. (2011) Comparison of an expanded ataxia interactome with patient medical records reveals a relationship between macular degeneration and ataxia. Hum Mol Genet 20 : 510–527 doi:10.1093/hmg/ddq496

8. Hehir-KwaJY, WieskampN, WebberC, PfundtR, BrunnerHG, et al. (2010) Accurate distinction of pathogenic from benign CNVs in mental retardation. PLoS Comput Biol 6: e1000752 doi:10.1371/journal.pcbi.1000752

9. BanerjeePN, FilippiD, Allen HauserW (2009) The descriptive epidemiology of epilepsy-a review. Epilepsy Res 85 : 31–45 doi:10.1016/j.eplepsyres.2009.03.003

10. CowanLD (2002) The epidemiology of the epilepsies in children. Ment Retard Dev Disabil Res Rev 8 : 171–181 doi:10.1002/mrdd.10035

11. BergAT, BerkovicSF, BrodieMJ, BuchhalterJ, CrossJH, et al. (2010) Revised terminology and concepts for organization of seizures and epilepsies: report of the ILAE Commission on Classification and Terminology, 2005–2009. Epilepsia 51 : 676–685 doi:10.1111/j.1528-1167.2010.02522.x

12. HelbigI, SchefferIE, MulleyJC, BerkovicSF (2008) Navigating the channels and beyond: unravelling the genetics of the epilepsies. Lancet Neurol 7 : 231–245 doi:10.1016/S1474-4422(08)70039-5

13. de VriesBBA, PfundtR, LeisinkM, KoolenDA, VissersLELM, et al. (2005) Diagnostic genome profiling in mental retardation. Am J Hum Genet 77 : 606–616 doi:10.1086/491719

14. WeissLA, ShenY, KornJM, ArkingDE, MillerDT, et al. (2008) Association between microdeletion and microduplication at 16p11.2 and autism. N Engl J Med 358 : 667–675 doi:10.1056/NEJMoa075974

15. StankiewiczP, LupskiJR (2010) Structural variation in the human genome and its role in disease. Annu Rev Med 61 : 437–455 doi:10.1146/annurev-med-100708-204735

16. International Schizophrenia Consortium (2008) Rare chromosomal deletions and duplications increase risk of schizophrenia. Nature 455 : 237–241 doi:10.1038/nature07239

17. StefanssonH, RujescuD, CichonS, PietiläinenOPH, IngasonA, et al. (2008) Large recurrent microdeletions associated with schizophrenia. Nature 455 : 232–236 doi:10.1038/nature07229

18. LupskiJR (2008) Schizophrenia: Incriminating genomic evidence. Nature 455 : 178–179 doi:10.1038/455178a

19. MeffordHC, ShaferN, AntonacciF, TsaiJM, ParkSS, et al. (2010) Copy number variation analysis in single-suture craniosynostosis: multiple rare variants including RUNX2 duplication in two cousins with metopic craniosynostosis. Am J Med Genet A 152A: 2203–2210 doi:10.1002/ajmg.a.33557

20. HeinzenEL, RadtkeRA, UrbanTJ, CavalleriGL, DepondtC, et al. (2010) Rare deletions at 16p13.11 predispose to a diverse spectrum of sporadic epilepsy syndromes. Am J Hum Genet 86 : 707–718 doi:10.1016/j.ajhg.2010.03.018

21. de KovelCGF, TrucksH, HelbigI, MeffordHC, BakerC, et al. (2010) Recurrent microdeletions at 15q11.2 and 16p13.11 predispose to idiopathic generalized epilepsies. Brain 133 : 23–32 doi:10.1093/brain/awp262

22. HelbigI, MeffordHC, SharpAJ, GuipponiM, FicheraM, et al. (2009) 15q13.3 microdeletions increase risk of idiopathic generalized epilepsy. Nat Genet 41 : 160–162 doi:10.1038/ng.292

23. OttmanR, HiroseS, JainS, LercheH, Lopes-CendesI, et al. (2010) Genetic testing in the epilepsies–report of the ILAE Genetics Commission. Epilepsia 51 : 655–670 doi:10.1111/j.1528-1167.2009.02429.x

24. LemkeJR, RieschE, ScheurenbrandT, SchubachM, WilhelmC, et al. (2012) Targeted next generation sequencing as a diagnostic tool in epileptic disorders. Epilepsia 53 : 1387–1398 doi:10.1111/j.1528-1167.2012.03516.x

25. AdieEA, AdamsRR, EvansKL, PorteousDJ, PickardBS (2005) Speeding disease gene discovery by sequence based candidate prioritization. BMC Bioinformatics 6 : 55 doi:10.1186/1471-2105-6-55

26. ZuffereyF, SherrEH, BeckmannND, HansonE, MaillardAM, et al. (2012) A 600 kb deletion syndrome at 16p11.2 leads to energy imbalance and neuropsychiatric disorders. J Med Genet 49 : 660–668 doi:10.1136/jmedgenet-2012-101203

27. GolzioC, WillerJ, TalkowskiME, OhEC, TaniguchiY, et al. (2012) KCTD13 is a major driver of mirrored neuroanatomical phenotypes of the 16p11.2 copy number variant. Nature 485 : 363–367 doi:10.1038/nature11091

28. CoventryA, Bull-OttersonLM, LiuX, ClarkAG, MaxwellTJ, et al. (2010) Deep resequencing reveals excess rare recent variants consistent with explosive population growth. Nat Commun 1 : 131 doi:10.1038/ncomms1130

29. CrowJF (2008) Maintaining evolvability. J Genet 87 : 349–353.

30. MarthGT, YuF, IndapAR, GarimellaK, GravelS, et al. (2011) The functional spectrum of low-frequency coding variation. Genome Biol 12: R84 doi:10.1186/gb-2011-12-9-r84

31. AntonES, GhashghaeiHT, WeberJL, McCannC, FischerTM, et al. (2004) Receptor tyrosine kinase ErbB4 modulates neuroblast migration and placement in the adult forebrain. Nat Neurosci 7 : 1319–1328 doi:10.1038/nn1345

32. LawAJ, KleinmanJE, WeinbergerDR, WeickertCS (2007) Disease-associated intronic variants in the ErbB4 gene are related to altered ErbB4 splice-variant expression in the brain in schizophrenia. Hum Mol Genet 16 : 129–141 doi:10.1093/hmg/ddl449

33. BremerA, GiacobiniM, ErikssonM, GustavssonP, NordinV, et al. (2010) Copy number variation characteristics in subpopulations of patients with autism spectrum disorders. Am J Med Genet B Neuropsychiatr Genet 156 : 115–24 doi:10.1002/ajmg.b.31142

34. BackxL, CeulemansB, VermeeschJR, DevriendtK, Van EschH (2009) Early myoclonic encephalopathy caused by a disruption of the neuregulin-1 receptor ErbB4. Eur J Hum Genet 17 : 378–382 doi:10.1038/ejhg.2008.180

35. LiK-X, LuY-M, XuZ-H, ZhangJ, ZhuJ-M, et al. (2012) Neuregulin 1 regulates excitability of fast-spiking neurons through Kv1.1 and acts in epilepsy. Nat Neurosci 15 : 267–273 doi:10.1038/nn.3006

36. GirirajanS, RosenfeldJA, CoeBP, ParikhS, FriedmanN, et al. (2012) Phenotypic Heterogeneity of Genomic Disorders and Rare Copy-Number Variants. N Engl J Med 367 : 1321–1331 doi:10.1056/NEJMoa1200395

37. KlassenT, DavisC, GoldmanA, BurgessD, ChenT, et al. (2011) Exome sequencing of ion channel genes reveals complex profiles confounding personal risk assessment in epilepsy. Cell 145 : 1036–1048 doi:10.1016/j.cell.2011.05.025

38. BurkeW, EmeryJ (2002) Genetics education for primary-care providers. Nat Rev Genet 3 : 561–566 doi:10.1038/nrg845

39. Gonzaga-JaureguiC, LupskiJR, GibbsRA (2012) Human genome sequencing in health and disease. Annu Rev Med 63 : 35–61 doi:10.1146/annurev-med-051010-162644

40. AshburnerM, BallCA, BlakeJA, BotsteinD, ButlerH, et al. (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25 : 25–29 doi:10.1038/75556

41. BlakeJA, BultCJ, KadinJA, RichardsonJE, EppigJT, et al. (2011) The Mouse Genome Database (MGD): premier model organism resource for mammalian genomics and genetics. Nucleic Acids Res 39: D842–D848 doi:10.1093/nar/gkq1008

42. LewisBP, BurgeCB, BartelDP (2005) Conserved seed pairing, often flanked by adenosines, indicates that thousands of human genes are microRNA targets. Cell 120 : 15–20 doi:10.1016/j.cell.2004.12.035

43. KanehisaM, GotoS, FurumichiM, TanabeM, HirakawaM (2010) KEGG for representation and analysis of molecular networks involving diseases and drugs. Nucleic Acids Res 38: D355–D360 doi:10.1093/nar/gkp896

44. SuAI, WiltshireT, BatalovS, LappH, ChingKA, et al. (2004) A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Natl Acad Sci USA 101 : 6062–6067 doi:10.1073/pnas.0400782101

45. CowleyMJ, PineseM, KassahnKS, WaddellN, PearsonJV, et al. (2012) PINA v2.0: mining interactome modules. Nucleic Acids Res 40: D862–D865 doi:10.1093/nar/gkr967

46. EstradaE, HatanoN (2008) Communicability in complex networks. Phys Rev E 77 : 036111 doi:10.1103/PhysRevE.77.036111

47. BoonePM, BacinoCA, ShawCA, EngPA, HixsonPM, et al. (2010) Detection of clinically relevant exonic copy-number changes by array CGH. Hum Mutat 31 : 1326–1342 doi:10.1002/humu.21360