De novo mutations in psychiatric disorders; a New ParadigmSimon L. Girard,

simon.girard.3@umontreal.ca

Université de Montréal

Schizophrenia

2

Genetics of Schizophrenia

3

Girard et al. COGEDE 2011

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.

Our hypothesis

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

Common SNPs

doesn’t work

De novo (rare) CNV does work

S2D- Project Overview

1,000 synaptic genes 380 patients

Direct re-sequencing

Biological (functional) validation

Genetic Validation

Validated Genes

PCR

Variant Detection

+ 4 controls(12 fragments/gene)

Worm Fly Fish Mouse

DatabasesPubMed

Selection criteria

4,560,000 fragments

23 genes

143 SCZ 142 ASD

1,370 SCZ 440 ASD

Pool of available patients

95 NSMR

731 MR

De Novo mutations in Schizophrenia

 

GENE Mutation Type Mutation Location AA change

NRXN1 INDEL CODING G140DfsX29

MAP2K1 INTRONIC INTRONIC Within intron

SHANK3 NONSENSE CODING R1117X

SHANK3 MISSENSE CODING R536W

KIF17 NONSENSE CODING Y575X

BSN SILENT CODING V1665V

ATP2B4 SILENT CODING N195N

Small DNAp de novo study

• Population design : Family Trios• Rationale : Look for all variants present in proband

but absent in either of the parents

• Case selection : Sporadic Schziphrenia• Proband : DSM-IV criteria for schizophrenia (DIGS)• Parents : Clear of any mental disorders (FIGS)

• Population : All patients were recruited in France, through a consortium (MO Krebs)

• In total : 14 trios (42 individuals)• Probands : 7 M / 7 F

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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)

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Girard et al. Nat Gen (2011)

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

1 2 3 4 50.00%

10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%

100.00%

Fraction of SNVs found in 1K genome project

Number of individual carrying the mutation

Frac

tion

of m

utati

on fo

und

in 1

KGP

1 2 3 4 50.00%

10.00%20.00%30.00%40.00%50.00%60.00%70.00%80.00%90.00%

Fraction of SNVs with a coverage > 4x

Number of individual carrying the mutation%

of m

utati

on w

ith a

cov>

4x

It is very important to set an appropriated threshold in order to restrict the number of candidate de novo to validate

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

Found in parent

92%

Additional Control5%

Low Qual Variant3%

False Positive0,002%

True DNM0,0003%

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

Girard et al. Nature Genetics 2011

Girard et al. Nature Genetics 2011

The de novo mutation rate in SCZ

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The DNM rate amongst SCZ patients

• Reason #1 : The DNM rate• To estimate our DNMr :

• Cross-referenced regions from the Agilent Probe Sheet with the CCDS• ~ 31 Mb / individuals• A total of 289 Mb screened in 14 individuals

• Using the standard DNMr rate, we would expect ~ 6.87 DNM• SCZ cohort DNMr : 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 is significantly higher in our cohort of SCZ patients

Why this is interesting ?• 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 Kryukov et 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

Expected (dbsnp)

NonsenseMissense

Observed (SCZ)

NonsenseMissense

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

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

Université de MontréalGuy Rouleau,

Patrick DionJulie Gauthier Anne Noreau Lan XiongAlexandre Dionne-LaporteDan SpiegelmanEdouard Henrion, M.Sc.Ousmane DialloLoubna JouanSirui Zhou

Marie-Pierre Dubé

RQCHP (Quebec’s High-Performance Computation group)Jonathan FerlandSuzanne Talon

INSERMMarie-Odile Krebs

Hong KongSi Lok

Acknowledgements