Array CGH for Constitutional Disorders: From Diagnosis to Gene Discovery

1. Array CGH for constitutional disorders: from diagnosis to disease gene discovery Computational Systems Biology

2. CGH microarraysMolecular karyotyping Location of chromosomal imbalances Statistical analysis Databasing Validation Prioritized candidate genes • Map chromosomal abnormalities • Improved diagnosis Discover new disease causing genes and explain their function Array CGH: from diagnosis to gene discovery Patients with congenital & acquired disorders

3. Part I: Array Comparative Genomic Hybridization (array CGH)

4. Array CGH Child with e.g. heart defect and learning disabilities Sample is collected and sent to genetic center

5. Cytogenetic diagnostic • 2-3% of live birth with major congenital anomaly • 15-25% recognized genetic causes • 8-12% environmental factors • 20-25% multifactorial • 40-60% unknown • 15-20% of those resolved by array CGH • Importance of diagnosis • Usually limited therapeutic impact BUT • Reduce family distress • End of “diagnostic odyssey” • Estimate risk of recurrence • De novo aberration vs. familial mutation • Knowledge of disorder evolution (life planning) • Prevent complications • Future therapies (e.g., fragile-X, Rett + gene therapy)

6. Deletion del(22)(q12.2) • Patient • Pulmonary valve stenosis • Cleft uvula • Mild dysmorphism • Mild learning difficulties • High myopia

7. Deletion del(22)(q12.2) • Deletion on Chromosome 22 • ~0.8Mb • Deletion contains NF2 • NF2  acoustic neurinomas • Benign tumor, BUT • Hard to diagnose • Severe complications

8. The challenge: identifying recurrent imbalances and disease genes

9. The imbalances are scattered across the genome

10. Genotype-phenotype correlation

11. Array CGH: from diagnosis to gene discovery • Processing of array CGH data • Databasing and mining of patient descriptions • Genotype-phenotype correlation • Candidate gene prioritization • Experimental validation of candidate genes

12. Part II: Candidate gene prioritization

13. Information sources • Identify key genes and their function • Integration of multiple types of information Candidate prioritization Validation Candidate gene prioritization High-throughputgenomics Data analysis Candidate genes ?

14. Prioritization by text mining ENSG00000000001 ENSG00000000002 ... ENSG00000109685 ... ENSG00000024999 ENSG00000025000 Microcephaly overrepresented in document set for WHSC1 gene

15. Prioritization by example • Several cardiac abnormalities mapped to 3p22-25 • Atrioventricular septal defect • Dilated cardiomyopathy • Brugada syndrome • Candidate genes (“test set”) • 3p22-25, 210 genes • Known genes (“training set”) • 10-15 genes: NKX2.5, GATA4, TBX5, TBX1, JAG1, THRAP, CFC1, ZFPM2, PTPN11, SEMA3E • Congenital heart defects (CHD) • High scoring genes • ACVR2, SHOX2 - linked to heterotaxy and Turner syndrome (often associated with CHD) • Plexin-A1 - reported as essential for chick cardiac morphogenesis • Wnt5A, Wnt7A – neural crest guidance

16. Prioritization by virtual pulldown

17. Endeavour http://www.esat.kuleuven.ac.be/endeavour Aerts et al. Nature Biotechnology. 2006.

18. Prioritization by text mining in DECIPHER

19. Novel DiGeorge candidate • D. Lambrechts, P. Carmeliet, KUL Cardiovascular Biol. • TBX1 critical gene in typical 3Mb aberration • Atypical 2Mb deletion (58 candidates)

20. YPEL1 • YPEL1 is expressed in the pharyngeal arches during arch development • YPEL1KD zebrafish embryos exhibit typical DGS-like features

21. Congenital heart disease genes • B. Thienpont, K. Devriendt, J. Vermeesch, KUL CME • 60 patients without diagnosis • Congenital heart defect • & Chromosomal phenotype • 2nd major congenital anomaly • Or mental retardation/special education • Or > 3 minor anomalies • Array Comparative Genomic Hybridization • 1 Mb resolution • 11 anomalies detected • 5 deletions • 2 duplications • 3 complex rearrangements • 1 mosaic monosomy 7

22. Candidate regions • 4 regions with known critical genes, 6 new regions, 80 candidate genes

23. Protein interactions Protein domains Cis-regulatory module BLAST KEGG pathways Expressiondata Gene prioritization Pubmed textmining BMP4

24. Biological validation • Candidates currently being validated in zebrafish • Screen about 50 candidates for heart expression at different developmental stages • Morpholino knockdowns of candidates expressed in hearts • Screen for heart phenotypes

25. CGH microarraysMolecular karyotyping Location of chromosomal imbalances Statistical analysis Databasing Validation Prioritized candidate genes • Map chromosomal abnormalities • Improved diagnosis Discover new disease causing genes and explain their function Array CGH: from diagnosis to gene discovery Patients with congenital & acquired disorders

26. Some achievements • Publications • Aerts S et al. Gene prioritization through genomic data fusion. Nat Biotechnol. 2006 May;24(5):537-44. • Balikova I et al., Autosomal dominant microtia linked to five tandem copies of copy number variable region at Chromosome 4p16. Am J Hum Genet. 2007. in press. • Lage K et al. A human phenome-interactome network of protein complexes implicated in genetic disorders. Nat Biotechnol. 2007 Mar;25(3):309-16. • Guidelines for array CGH • Vermeesch J et al. Guidelines for molecular karyotyping in constitutional genetic diagnosis. Eur J Med Genet. 2007 Nov;15(11):1105-14. • Strategic Basic Research (SBO) project • Molecular karyotyping • K.U.Leuven, U.Gent, VUB • €2,800,000 (4 years) • Development of new applications of array CGH technology • FP7 proposal on bioinformatics for congenital heart defects • Visibility • European Cytogenetics association – molecular karyotyping workgroup • INSERM workshop array CGH (La Londe les Maures, FR, Sep 07) • Numerous keynote lectures • Contacts with all major array CGH companies

27. Endocrinology Human genetics Modulediscovery Geneprioritization Probabilisticmodels Networkinference Salmonella sys. biology Partners involved • Yves Moreau • gene prioritization • Roland Barriot • knowledge mining • Francesca Martella • array CGH statistics • Sonia Leach • gene networks • Steven Van Vooren • text mining • Bert Coessens • - array CGH data mgt. • Leo Tranchevent • Endeavour • Yu Shi • prioritization algorithms • Daniela Nitsch • - prioritization algorithms • Peter Konings • statistical genetics CNVs • Joris Vermeesch • array CGH technology • Koen Devriendt • congenital heart defects • Hilde Van Esch • mental retardation • Thierry Voet • - array CGH technology • Femke Hannes • genotype-phenotypecorrelation • Bernard Thienpont • CHD disease genes • Jeroen Breckpot • congenital heart defects • Irina Balikova • eye defects • Liesbeth Backx • mental retardation genes • Boyan Dimitrov • skeletal disorders • An Crepel • microcephaly & autism • Caroline Robberechts • fertility • Evelyne Vanneste • - single cell array CGH CME-UZ ESAT-SCD BioStat Legendo ESAT-SCD

28. Sangersequencing Next-gensequencing Challenges ahead • From genes to networks • The $1000 genome • Data big bang • Phenotypic genome annotation by data fusion