Download Presentation Material

De novo mutations in psychiatric
disorders; a New Paradigm
Simon L. Girard,
[email protected]
Université de Montréal
Schizophrenia
2
Genetics of Schizophrenia
Girard et al.
COGEDE 2011
3
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
Common
SNPs
doesn’t
work
De novo
(rare)
CNV
does
work
• Why don’t we look for small de novo (rare) DNA
polymorphism (DNAp)?
5
S2D- Project Overview
Pool of available patients
Databases
PubMed
Selection
criteria
1,000 synaptic genes
(12 fragments/gene)
1,370 SCZ
440 ASD
731 MR
143 SCZ
142 ASD
95 NSMR
PCR
380 patients
+ 4 controls
Direct re-sequencing
4,560,000 fragments
Variant Detection
Genetic Validation
Biological (functional) validation
Worm
Fly
Fish
Validated Genes
Mouse
 23 genes
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
10
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)
11
Girard et al. Nat Gen (2011)
Technical challenge : The high number of false
positive
Fraction of SNVs found in 1K genome project
Fraction of SNVs with a coverage > 4x
% of mutation with a cov> 4x
Fraction of mutation found in 1KGP
De novo mutation are sporadic event seen in only one individual; they are
usually mistaken for a False Positve
100.00%
90.00%
80.00%
70.00%
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
0.00%
90.00%
80.00%
70.00%
60.00%
50.00%
40.00%
30.00%
20.00%
10.00%
0.00%
1
2
3
4
Number of individual carrying the mutation
5
1
2
3
4
5
Number of individual carrying the mutation
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
True DNM
0,0003%
Low Qual
Variant
3%
False Positive
0,002%
Additional
Control
5%
Found in
parent
92%
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
18
The DNM rate amongst SCZ
patients
• Reason #1 : The DNM rate
• 𝐷𝑁𝑀𝑟 = 1.1 × 10−8
• 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
Observed (SCZ)
Expected (dbsnp)
Nonsense
Missense
Nonsense
Missense
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
Acknowledgements
Université de Montréal
Guy Rouleau,
Patrick Dion
Julie Gauthier
Anne Noreau
Lan Xiong
Alexandre Dionne-Laporte
Dan Spiegelman
Edouard Henrion, 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