Autism exome sequencing:
design, data processing and analysis
Benjamin Neale
Analytic and Translational Genetics Unit, MGH
&
Medical & Population Genetics Program, Broad Institute
Direct Sequencing has
Enormous Potential…
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Ng, Shendure: Miller syndrome, 4 cases
– exome sequenced reveals causal mutations in DHODH
Lifton: Undiagnosed congenital chloride diarrhoea (consanguinous)
– Exome seq reveals homozygous SLC23A chloride ion transporter mutation
– Return diagnosis of CLD (gi) not suspected Bartter syndrome (renal)
Worthey, Dimmock: 4-year old, severe unusual IBD
– exome seq reveals XIAP mutation (at a highly conserved aa)
– proimmune disregulation opt for bone marrow transplant over chemo
Jones, Marra: Secondary lung carcinoma unresponsive to erlotinib
– Genome and transcriptome sequencing reveals defects
– directs alternative sunitinib therapy
Mardis, Wilson: acute myelocytic leukaemia but not classical translocation
– Genome sequencing (1 week + analysis) reveals PML-RARA translocation
– Directs ATRA (all trans retinoic acid) treatment decision
…and tremendous challenges
• Managing and processing vast quantities of
data into variation
• Interpreting millions of variants per individual
– An individual’s genome harbors
• ~80 point nonsense mutations
• ~100-200 frameshift mutations
• Tens of splice mutants, CNV induced gene disruptions
For very few of these do we have any conclusive understanding
of their medical impact in the population
Successes to date rely on factors that may
not apply generally to common endpoints
• Mendelian disorders
– Single family rare autosomal recessive (linkage
may target 1% of genome, 2 ‘hits’ in the same
gene very unlikely)
– Single (or ‘near single’) gene disorders where
nearly all families carry mutations in the same
underlying gene
• Somatic or de novo mutations
– Extremely rare background rate
Autism exome sequencing
• In progress – ARRA supported by NIMH &
NHGRI
• Collaboration between sequencing centers
(Baylor & Broad) and Y2 follow-up in autism
genetics labs (Buxbaum, Daly, Devlin,
Schellenberg, Sutcliffe)
• Targeted production by years end of 1000
cases and 1000 controls (500/500 from each
site)
Exome production plan
• Baylor: 1000 samples (Nimblegen capture, SOLiD
sequencing)
• Broad: 1000 samples (Agilent capture, Illumina
sequencing)
• Predominantly cases and controls pairwise
matched with GWAS data (one batch of 50 trios
currently being run)
• All samples are available from NIMH repository
Broad Exome Production
• ~700 exomes completely sequenced and
recently completed variant calling
• ~300 completed earlier in the Summer and
fully analyzed (basis of later analysis slides)
• Main production conducted with matched
case-control pairs traveling together through
the sequencing lab and computational runs of
variant calling
From unmapped reads to true genetic variation
in next-generation sequencing data
Solexa
Raw short reads
Mapping and alignment
Region 1
Region 2
Human reference
genome
SOLiD
454
A single run of a sequencer generates
~50M ~75bp short reads for analysis
The origin of each read from the
human genome sequence is found
Quality calibration and annotation
Identifying genetic variation
Region 1
Region 2
Region 1
Human reference
genome
Region 2
Human reference
genome
SNP
The quality of each read is calibrated
and additional information annotated
for downstream analyses
SNPs and indels from the reference
are found where the reads collectively
provide evidence of a variant
Partnership: Genome Sequencing and
Analysis (GSA) team @ Broad
• Genome Sequencing and Analysis
(GSA) develops core capabilities for
genetic analysis
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Data processing and analysis methods
Technology development
High-end software engineering
High-throughput data processing for
MPG exome projects with MPGFirehose
• Staffed by full-time research scientists
in MPG
– PhDs and BAs in biochemistry,
engineering, computer science,
mathematics, and genetics
Group Leader
Mark A. DePristo
Analysis Team
Kiran Garimella [Lead]
Chris Hartl
Corin Boyko
Development Team
Eric Banks [Lead]
Guillermo del Angel
Menachem Fromer
Ryan Poplin
Software Engineering
Matthew Hanna
Khalid Shakir
Aaron McKenna
Developing cutting-edge data
processing and analysis methods
Variation discovery and genotyping
Local realignment
Novel SNPs found
Base quality score recalibration
Read-backed
phasing
Adaptive
error
modeling
VariantEval
Challenges
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Mapping/alignment
Quality score recalibration
Calling variants
Evaluating set of variant sites
Estimating genotypes
Finding the true origin of each read is a
computationally demanding and important first step
Region 1
Enormous pile
of short reads
from NGS
Mapping and
alignment
algorithm
Solexa : BWA
• Robust, accurate ‘gold
standard’ aligner for NGS
• Developed by Li and Durbin
• Recently replaced MAQ, also
by Li and Durbin, used for last
2 years
Region 2
Detects correct read
origin and flags them
with high certainty
454 : SSAHA
• Hash-based aligner with
high sensitivity and
specificity with longer
reads
SAM/BAM files
Region 3
Reference
genome
Detects ambiguity in the
origin of reads and flags
them as uncertain
SOLiD : Corona
• ABI-designed tool for
aligning in color-space
The SAM file format
• Data sharing was a major issue with the 1000 genomes
– Each center, technology and analysis tool used its own
idiosyncratic file formats – no one could exchange data
• The Sequence Alignment and Mapping (SAM) file
format was designed to capture all of the critical
information about NGS data in a single indexed and
compressed file
– Becoming a standard and is now used by production
informatics, MPG, and cancer analysis groups at the Broad
• Has enabled sharing of data across centers and the
development of tools that work across platforms
• More info at http://samtools.sourceforge.net/
Local realignment around indels
Base Quality Score
Recalibration
SLX GA
454
SOLiD
second of pair reads
first of pair reads
Complete Genomics
second of pair reads
first of pair reads
HiSeq
second of pair reads
first of pair reads
How do indel realignment and base quality
recalibration affect SNP calling?
6.5% of calls on raw
reads are likely false
positives due to indels
The process doesn’t
remove real SNPs
http://www.broadinstitute.org/gsa/wiki/index.php/Local_realignment_around_indels
http://www.broadinstitute.org/gsa/wiki/index.php/Base_quality_score_recalibration
Sensitivity and specificity improved by simultaneous
variant calling in 50-100 individuals
• Sensitivity
– Greater statistical evidence compiled for true variants
seen in more than one individual
• Specificity
– Deviations in metrics that flag false positive sites
become much more statistically significant
• Allele balance: Departure from 50:50 (r:nr) in heterozygotes
• Strand bias: non-reference allele preferentially seen on one
of the two DNA strands
• Proportion of reads with low mapping quality
The Genome Analysis Toolkit (GATK) enables rapid
development of efficient and robust analysis tools
• Supports any BAMcompatible aligner
Genome Analysis Toolkit
(GATK) infrastructure
• All of these tools
have been developed
in the GATK
Traversal engine
• They are memory
and CPU efficient,
cluster friendly and are
easily parallelized
Analysis
tool
• They are now
publically and are
being used at many
sites around the world
Provided by framework
Implemented by user
M
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D
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s
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o
Initial alignment
MSA realignment
Q-score
recalibration
Multi-sample
genotyping
SNP filtering
More info: http://www.broadinstitute.org/gsa/wiki/
Additional key advance
• Correcting alignment artifacts and machinespecific biases in read base calling and quality
score assignment enables machineindependent variant identification and
genotype calling
• 1000 Genomes data even contains individuals
with data merged from multiple sequencing
platforms!
For our project this is key
• With two centers generating data via distinct
experimental and sequencing procedure,
harmonizing data is integral to downstream
analysis
Stratified analyses
• Because both processes will not afford
equivalent coverage of all targets:
– Critical that case-control balance and individual
pairings are preserved within center
– Final analysis will be stratified by center such that
rare technical differences, lack of coverage on one
or the other platform, etc can be managed
robustly
Secondary data cleaning is critical
• Identify a quality set of individuals
• Identify a quality set of targets
• Identifying a quality set of variants
Primary cleaning
• Identify a quality set of individuals
• Identify a quality set of targets
• Identifying a quality set of variants
Sample composition thus far
Batch
Case
Control
Total
1
25
25
50
2
22
21
43
3
41
12
53
4
25
25
50
5
25
25
50
6
25
25
50
Total
164
132
296
Individual Cleaning
• Mean depth of coverage for all targets
• Concordance rate with ‘super clean’ SNP Chip
– Contamination checks
Mean Coverage per Sample
Exclude this one
Concordance and contamination
checks
• 1/296 samples fails concordance check (genotype
call discrepancy) with SNP chip data (sample
swap)
• 1/295 samples fails contamination check
(proportion of reads calling non-reference alleles
at SNP chip homozygous sites indicates >5% DNA
from another individual)
• Advance a fully validated set of individuals to
downstream analysis
Number of non-reference
genotypes per individual
1500 high frequency sites
Primary cleaning
• Identify a quality set of individuals
• Identify a quality set of targets
• Identifying a quality set of variants
Distribution of mean target coverage
99.86% targets
Depth vs GC Content
>95% of the targeted exons sequenced between 10 and 500x depth –
Defined as successfully evaluated exome
SNP rate as a function of GC Bin
SNP rate
8
7
6
5
4
SNP rate
3
2
1
0
(30,35]
(35,40]
(40,45]
(45,50]
(50,55]
(55,60]
(60,65]
(65,~]
% discovered variants that are singletons
50-250x - half the data, median coverage,
singleton plateau at 34%
------- 95% of targets covered between 10 and 500x ----------
Lowest bin badly deficient in
singletons – but higher rate of
called variants overall…
Primary cleaning
• Identify a quality set of individuals
• Identify a quality set of targets
• Identifying a quality set of variants
Defining the set of variant sites
• Define the technical parameters of true polymorphisms
using a core set of ‘gold-standard’ true positive variant
sites:
– Are sites contained in a reference sample (e.g., dbSNP,
another exome or genome study)
– High quality target depth range (50-250) – no true sites
should be missed and no excess coverage suggesting
mapping concerns
• Define distribution of technical properties (balance of
reference/non-reference alleles; balance of nonreference alleles on +/- strand; read mapping quality)
– Filter non-dbSNP, non-ideal coverage calls based on these
distributions
Allele Balance Example
1%
5%
95%
99%
In testing now: Variant Quality Score Recalibration enables
definition of data set with user defined sensitivity, specificity
...moving towards posterior estimate for each site
http://www.broadinstitute.org/gsa/wiki/index.php/Variant_quality_score_recalibration
On to analysis…
• 204,123 variants pass all filters across 294
samples…number of all variants & singletons
continue to increase as data is added
• How do we assess how this QC process
performed downstream? Is the experiment
working?
Has the matching worked?
• Matched samples based on MDS distance
from combined GWAS data
• Consider the set of doubletons (two copies in
the dataset)
• Overall, we should see that there are
comparable numbers of variants seen in 2
cases or 2 controls versus 1 case and 1 control;
and we should see an excess of 1 case:1
control variants in matched pairs
Do we see appropriate case:control
statistics for rare variants?
Visual Representation
Case
1
Case
2
3
Case
4
Case
… …
118 Case
Control
Case
Control
Case
Control
Control
Case
Control
Case
Control
Case
Control
Case
Control
Control
Case
Control
Case
Control
Control
Case
Control
Case
Control
Control
VS.
or
Expectation: 1/235 for within pair doubles 15,829 doubletons -> ~67.4
Observed: 163 instances observed,
X:0 missense mutations
While one is intrigued by variants seen in 5 or more copies in cases and
not at all in controls – no evidence using permutation that there is a
significant excess of such variants…
Specific nonsense mutations in cases
or controls only
Impossible to pick out which might be relevant
Aggregations of rare nonsense mutations in
single genes, all in cases only
• Encouraging – many
genes where 1-3% of
cases and no controls
harbor nonsense
mutations – best case
scenario?
Genes with multiple nonsense mutations
seen only in cases or controls
Many genes with 2 or more nonsense mutations seen only in cases –
not appreciably different from rate in controls suggesting vast majority is
simply the background rate at which such variants occur…
Challenges of connecting rare variation
to complex phenotype
• Variation must be considered in aggregate per gene (or pathway…) rather than
individually
• In phenotypically relevant genes, many rare variants will be neutral (i.e.,
background rate is high)
• Many documented cases exist where gain and loss of function mutations in same
gene have opposite effects on phenotype
• Polygenicity will not be our friend here…realistically, no reason to think much
smaller samples than in GWAS will be required
• at this point, the best case scenarios of highly penetrant rare mutations (that
would have escaped prior large linkage and GWAS studies), aggregations of very
rare alleles that explain 1% of the overall variance in risk, etc – cannot be
distinguished from the background distribution of test statistics
• Some opportunistic models (.1%-.5% variants with OR~5-10, high penetrance
recessive subtypes) may be able to be detected and confirmed through follow-up
soon…no reason to have assurance these exist however
Parallel analysis tracks will be taken
• High MZ/DZ ratio suggests potential recessive component: search for
excess autozygosity, IBD=2 sharing with affected sibs then homozygosity
for rare allele; compound heterozygosity for rare alleles
• Highly deleterious alleles: Identify all non-synonymous/nonsense/splice
variants unique to the study, not seen in 1000 Genomes or external
control exome data (mostly singletons, very rare and de novo variants –
perhaps ranked by predicted impact/PolyPhen) and compare burden in
cases versus controls (testing a severe Mendelian mutation model)
• Heritable low frequency: Take all standing variation, observed two or more
times in the study and perform sensitive test of gene-wide variation using
C-alpha test of overdispersion (testing for effect of rare and low frequency
variants of modest/intermediate penetrance across the gene)
Tools for analysis of variation data from next-generation sequencing platforms: the
PLINK/Seq library
Flexible, extensible data representation (variants, genotypes and
meta-data)
A number of ways to use the library
command line, R, web, C/C++
Efficient random-access for very large datasets
Key references datasets
http://pngu.mgh.harvard.edu/purcell/pseq/
dbSNP, 1000G, PolyPhen2, gene transcript and sequence information
Large suite of up-to-date methods available
Madsen-Browning (+/- variable threshold), Li-Leal, C-alpha, etc.
Shaun Purcell
PSEQ Features
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Individual statistics
Variant statistics
Single-locus association
Regional association
Incorporation of annotation information
Data Sharing
• Autism exome data made available
– Providing the gold-standard calibration for variant
calling in other contemporaneous Broad exome
studies
– Control variable sites and counts made available
for comparison with other Broad exome studies
– First batch of raw data deposited to dbGAP
exchange area – NIMH controls broadly consented
for general medical use
Data Sharing
• Summary database of sites discovered, nonreference allele counts and target by target
coverage provide invaluable cross-study
information:
– Technical validation of low frequency sites
– Ability to evaluate whether specific sites or categories
of variants in certain genes are commonly, rarely or
never seen
– Greatly enhance selection for follow-up
– No individual level data or phenotype information
need be exchanged
Already in use across autism, schizophrenia, released 1000G studies would love to collaborate with NHLBI & cancer studies at this level
Credits
• ARRA Autism sequencing
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Mark Daly
Christine Stevens
Stacey Gabriel
Broad Sequencing Platform
• Broad GSA team
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Mark DePristo
Eric Banks
Kiran Garimella
Ryan Poplin
• Jen Baldwin, Jane Wilkinson
Joe Buxbaum, Bernie Devlin,
Richard Gibbs, Jerry
Schellenberg, Jim Sutcliffe
NIMH, NHGRI
• Exome analysis
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Mark Daly
Manny Rivas
Jared Maguire
Ben Voight
Shaun Purcell
– Kathryn Roeder & Bernie Devlin