Download Coordination of CNV analysis for Schizophrenia

Call Details
DATE: Friday, November 9, 2012
START: We will begin promptly on the hour
1100 US eastern
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PGC2 CNV analysis
Jonathan Sebat
PGC lab meeting
November 9, 2012
CNV analysis subgroup
•
Goals
– Identify novel risk factors for
mental illness by direct detection
of structural variants, both rare
and common
– Apply a CNV-based approach
within the collaborative
framework of the Psychiatric
GWAS Consortium
•
Initial Objectives
– Evaluate available software tools
for the analysis of Affymetrix and
Illumina Array data
– Develop a pipeline for CNV
analysis that is optimized for
each platform
– Develop novel statistical methods
for the “mega-analysis” of CNVs
in the large PGC dataset
Partek
GenoCN
iPattern
C-Score
Penn-CNV
Birdsuite
Overview
• Goals of the PGC-CNV effort
• Analysis Plan
– Core analyses
– other (disease-specific or exploratory)
analyses
• Publication
• Timeline
Goals of the PGC CNV group
• Develop a pipeline for processing array
data
• Generate a high-quality set of CNV calls
for each disorder
• Develop (in collaboration with
stats/analysis group) rigorous metaanalytical approach for testing association
with disease
Goals of the PGC CNV group
• Develop a pipeline for processing array
data
• Generate a high-quality set of CNV calls
for each disorder
• Develop (in collaboration with
stats/analysis group) rigorous metaanalytical approach for testing association
with disease
Variable results from different CNV
methods
• The default
outputs of different
methods differ
considerably
• Why?
A composite pipeline
BirdSuite Pipeline
BirdSuite
Preprocessing
BirdSuite
Auxiliary Files
Intensity
Files
Ipattern Pipeline
Ipattern
Preprocessing
C_Score Pipeline
Parse
C_Score
Output
C_Score
Auxiliary Files
PennCNV Pipeline
PennCNV
Preprocessing
Parse
Ipattern
Output
Ipattern
Auxiliary Files
Auxiliary
Files
C_Score
Preprocessing
Parse
BirdSuite
Output
PennCNV
Auxiliary Files
Parse
PennCNV
Output
StandardFormat
Call Files
Status of PGC-SCZ dataset
28,000 arrays have been processed (90%)
Study
MGS
Omar Gustafsson
Jennifer Moran
Brien P. Riley
Moran_Sweden
Elvira Bramon
Total (Affy6.0)
Platform
Affy_6.0
Affy_6.0
Affy_6.0
Affy_6.0
Affy_6.0
Affy_6.0
Case
4195
263
1170
3510
1089
1019
11246
Control
3804
369
1762
2048
1529
3124
12636
Processed
Yes
Yes
Yes
Yes
Yes
Yes
Total
7999
632
2932
5558
2618
4143
23882
J.E. Buizer
Rujescu, Dan
Thomas Werge
Total (Illumina)
Illum_550K
Illum_300K
Illum_550K
843
437
623
1903
683
353
498
1534
Yes
Yes
Yes
1526
790
1121
3437
Jennifer Moran
Jennifer Moran
George Kirov
Total (Others)
Affy_5.0
Affy_500
Affy_500
2133
0
494
2627
1624
169
0
1793
No
No
No
3757
169
494
4420
15776
15963
Total (PGC)
31739
Goals of the PGC CNV group
• Develop a pipeline for processing array
data
• Generate a high-quality set of CNV calls
for each disorder
• Develop (in collaboration with
stats/analysis group) rigorous metaanalytical approach for testing association
with disease
Performance of the composite
pipeline
GLM score
Intersection of
multiple methods
Default
output of
Individual
Methods
Notes: BPIC: Birdseye + PennCNV + Ipattern + Cscore; CSC: Cscore ; BDE: Birdseye; UPC: PennCNV ;
IPT: Ipattern .
Merging Calls from multiple methods
(from a single individual) into a “consensus” call
• A consensus call is
defined as a contiguous
region called by >1
method
• The consensus genotype
(“Del” or “Dup”) is decided
by majority vote
• vote is (with some
specific exceptions*)
virtually always
unanimous
*When consensus overlaps
substantially (>50%) with a
canary call. In this case
(only), canary has veto
power
Goals of the PGC CNV group
• Develop a pipeline for processing array
data
• Generate a high-quality set of CNV calls
for each disorder
• Develop (in collaboration with
stats/analysis group) rigorous metaanalytical approach for testing association
with disease
Core Analyses
• It’s advisable to use a consistent methodology across
disorders for the “core” analyses, which would likely
include
– merging and QC of CNV call sets
– Point-wise and Regional association tests
– Mutational burden analysis
• Note: PGC2 PIs must adhere to the 1 year timeline for
selecting targets for replication (for all disorders)
• How best to coordinate this?
A proposal to use model similar to what’s been done for GWAS
– Implementation of core case/control methodologies by analysis
group (members of CNV, stats and disease group)
– Further analysis by disease group
Core Analyses (cont.)
• Analysis of CNV/gene burden (what
proportion of risk can be explained by
CNVs?)
– CNVs will be subclassified by type, size, gene
content
• locus association tests, options for this
include
– regional test of CNV loci (predefined regions)
• Can be defined based on frequency in the
combined sample
• also may carry out separate test of candidate loci
from prior studies
– gene based or exon based
Meta-analytic method issues
• -Burden and association tests will be
carried out (using R or PLINK) controlling
for several covariates (known to influence
observed CNV burden or allele frequency)
– Ancestry as defined by PCA of ancestry
informative SNP markers (these labels should
already be available for most samples)
– Platform
– Study
– Plate
Many other (non-core) analyses
• Pathway-based association
• Cross-disorder/genetic overlap
• Analysis of clinical data
– Correlating clinical metrics (e.g. head
circumference, IQ, others) with CNV genotype
– CNV burden in clinical subgroups
• Other?
Insert preliminary data here
• Proof of principle. The following
preliminary analyes are in progress
– Consensus calls have been generated on all
Affy6 arrays from the SCZ dataset
– Calls will be compiled for select SCZassociated loci at 1q21.1, 15q13.3, 16p11.2,
22q11.2, NRXN1 and VIPR2
– Concordance will be determined with calls
from published studies (ISC and MGS)
– Any (sizable) discordant calls will be tested
experimentally
Next Steps
• Processing of Bipolar dataset (November 2012 –
February 2013)
• Complete Pipeline for Core Analyses of SCZ
(November-December 2013)
• Preliminary SCZ results on genome wide
mutational burden (Jan-Feb 2013)
• Preliminary SCZ results on regional association
(Jan-Feb 2013)
• Inclusion of other disorders in primary analysis
(for psych chip) will depend on whether data
assembly & data processing can be completed
in 1 year
Psych Chip
• In late 2013, the CNV and SNP content of
the “psych chip” will be finalized.
• Genotyping will be carried out in large
replication samples
Summary
• Affy & Illumina pipeline construction is
complete
• Processing of SCZ dataset is 90%
complete
• Consensus SCZ CNV calls assembled
• Beginning now
– SCZ analysis
– BD data processing
Next CNV call
• Thursday Nov 15, 2pm EST
• Phone: 866-740-1260
• Access code: 5342332
Thank You
•
University of Toronto
–
–
–
–
•
Pat Sullivan
Jin Peng Szatkiewicz
Laura Todd
•
Brien Riley
Ken Kendler
Broad/MGH
–
–
–
–
–
Sarah Bergen
Steve McCarroll
Ben Neale
Jennifer Moran
Stephan Ripke
Danielle Posthuma
University of Colorado
–
•
Elliot Gershon
Ailley Rodriguez
VU
–
•
Doug Greer
Wenting Wu
Madhu Gujral
Abhishek Bhandari
Dheeraj Malhotra
University of Chicago
–
–
VCU
–
–
•
•
University of North Carolina
–
–
–
•
Dalila Pinto
Pamela Sklar
UCSD
–
–
–
–
–
Steve Scherer (co-chair)
Christian Marshall
Bhooma Thiruvahindrapuram
Zhuozhi Wang
Mt. Sinai College of Medicine
–
–
•
•
Matthew Keller
And many more…
–
–
–
–
Doug Levinson
Mick Odonovan
Steve Faraone
George Kirov