De novo mutations in psychiatric disorders ; a New Paradigm

1. De novo mutations in psychiatricdisorders; a New Paradigm Simon L. Girard, simon.girard.3@umontreal.ca Université de Montréal

2. Schizophrenia

3. Genetics of Schizophrenia Girard et al. COGEDE 2011

4. Reduced reproductive fitness • Rates of reproduction are significantly reduced in SCZ = negative selection that should reduce the number of mutant alleles in the population. • However, SCZ has been maintained at a constant high prevalence worldwide. Two possible explanations: • There is a strong positive selection • New disease alleles are continuously generated through de novo mutations • The relatively uniform high worldwide incidence of SCZ across a wide range of environments argues against drift or positive selection. De novo mutations, which continually add disease alleles to the population, provides a possible explanation.

5. Our hypothesis • Why don’t we look for small de novo (rare) DNA polymorphism (DNAp)?

7. S2D-Project Overview Pool of available patients 1,370 SCZ 440 ASD 731 MR Databases PubMed 142 ASD 143 SCZ 95 NSMR Selection criteria 1,000 synaptic genes PCR 380 patients (12 fragments/gene) + 4 controls 4,560,000 fragments Direct re-sequencing Variant Detection Genetic Validation Biological (functional) validation Fly Worm Fish Mouse  23 genes Validated Genes

8. De Novo mutations in Schizophrenia

10. Small DNApde novo study • Population design : Family Trios • Rationale : Look for all variantspresent in proband but absent in either of the parents • Case selection : SporadicSchziphrenia • Proband : DSM-IV criteria for schizophrenia (DIGS) • Parents : Clear of any mental disorders (FIGS) • Population : All patients wererecruited in France, through a consortium (MO Krebs) • In total : 14 trios (42 individuals) • Probands : 7 M / 7 F

11. Experimental Design • High throughput sequencing • Exome Capture (Agilent SureSelect 38MB) • Sequencing on GAIIx (one sample by lane) • Bioinformatics analysis • Read mapping and storage: BWA and Samtools • SNP-calling : Varscan • Low stringency for parents • High stringency for probands • Annotation : Annovar • Segregation analysis • Priorization • In total 73 variants were kept for validation (sanger sequencing)

12. Girard et al. Nat Gen (2011)

13. Technical challenge : The high number of false positive De novo mutation are sporadic event seen in only one individual; they are usually mistaken for a False Positve It is very important to set an appropriated threshold in order to restrict the number of candidate de novo to validate

14. Technical challenge : Use of an appropriate control dataset Due to technical error (false negative in parents), it is important to use an external control dataset

15. Systematic challenge : How to distinguish between a benign and a pathogenic de novo mutation • Once true de novo mutations are identified, many challenges remains, notably how to select which mutations are linked to diseases. • Many suggested approach : • Establish a mutation prediction profile using amino acid changes and compare against a neutral database (Vissers et al. Nat Gen 2010) • Comparison of the mutation against a simulated profile made using control exomes (O’Roak et al. Nat Gen 2011) • Comparison of the ratio of protein truncating variants against a neutral database and a pathogenic database (Girard et al. Nat Gen 2011, based on Awadalla et al. AJHG 2010) • Additionnal approach could include : • Systems biology approach : Network of genes harboring de novo mutations • Additionnal screening of each gene harboring de novo mutations in a disease population

16. Girard et al. Nature Genetics 2011

17. Girard et al. Nature Genetics 2011

18. The de novo mutation rate in SCZ

19. The DNM rate amongst SCZ patients • Reason #1 : The DNM rate • To estimateourDNMr: • Cross-referencedregionsfrom the Agilent Probe Sheetwith the CCDS • ~ 31 Mb / individuals • A total of 289 Mb screened in 14 individuals • Using the standard DNMr rate, wewouldexpect ~ 6.87 DNM • SCZ cohortDNMr : 2.42 x 10-8 • Binomial test indicates that the number of DNM observed in our study differs significantly • p-value = 0.007736, • CI 95% = 2.6427 x 10-8 – 8.1103 x 10-8 • Conclusion #1 : The DNM rate issignificantlyhigher in ourcohort of SCZ patients

20. Whythisisinteresting ? • Reason #2 : The number of nonsense variants • 4 nonsense mutation in 14 total DNM • a 4/14 ratio of NS to MS mutation is significantly higher from the expected ratio of 1/20, as calculated by Kryukovet al.(p-value = 0.004173 using a binomial test, CI 95% = 0.0838 – 0.5810) • amongst all mutations reported to cause Mendelian diseases (HGMD), the ratio of NS versus MS mutations is roughly 1/4, which is not significantly different from the 4/14 ratio observed in our study • Conclusion #2 : The high number of NS mutations suggests that at least some of them are causative

21. Validation is The Challenge • Many genes will be identified – need rapid methods to flag those that are causative • Screen more trios to find multiple de novo mutations in the same gene • Genetic validation of the genes by sequencing additional cases – rare variants mean must sequence many cases • Bioinformatic analysis to identify pathways • Biological validation of genes and pathways

22. Epic Quote In the past two years, we have sequenced thousands of human genomes. However, not a single one of those reaches the quality of the only one we did in 2005. E. Eichler, Genome Informatics 2011

23. Acknowledgements • Université de Montréal • Guy Rouleau, • Patrick Dion • Julie Gauthier • Anne Noreau • Lan Xiong • Alexandre Dionne-Laporte • Dan Spiegelman • EdouardHenrion, M.Sc. • Ousmane Diallo • Loubna Jouan • Sirui Zhou • Marie-Pierre Dubé RQCHP (Quebec’s High-Performance Computation group) Jonathan Ferland Suzanne Talon INSERM Marie-Odile Krebs Hong Kong Si Lok