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Human Sequencing
Stefano Lise
Bioinformatics & Statistical Genetics (BSG) Core
The Wellcome Trust Centre for Human Genetics
(WTCHG), Oxford
Email: [email protected]
Outline
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Human genetic variation in health and disease
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How do we identify pathogenic mutations amongst
many genomic variants?
The WGS500 project
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Whole-genome sequencing of 500 genomes of
clinical significance
Human Genome
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The (haploid) reference human genome is about 3 x
109 bases
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Human genome is diploid => ~ 2 x 3 x 3 109 bases
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The exome is ~ 30-60 Mb (1-2% of the genome)
Some more numbers (from GENCODE, Nov 2012)
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20,387 protein-coding genes
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81,626 protein-coding transcripts
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13,220 long non-coding RNA genes
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9,173 small non-coding RNA genes
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13,419 pseudogenes
Sequence Variants
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Single nucleotide variants (SNV)
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Small insertions/deletions (INDEL)
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Structural variants
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Large insertions/deletions
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Inversions
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Copy number variants
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Translocations
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….
Functional Consequences
From Ensembl
Human Genome Variation
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The 1000 Genomes Project (www.1000genomes.org)
provides a catalogue of all (most) types of human
genetic variation
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Population-scale genome sequencing
Phase 1 (October 2012)
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High-throughput sequencing of 1092 human genomes
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Identified up to 98% of all SNPs with a frequency > 1% in
the population
1,500 additional genomes in the next (final) phase
Human Genome Variation
(1000 Genomes Project, Nature 491, 56-65, 2012 )
Allele Frequency
< 0.5 %
0.5 - 5 %
> 5%
Total
All variants
30 -150 K
120 – 680 K
3.6 – 3.9 M
3.7 – 4.7 M
Synonymous
139 - 640
480 - 2470
12 – 13 K
13 – 16 K
Non-synonymous
(at conserved sites)
220 – 800
(130 - 400)
540 -2400
(240 - 910)
10 – 11 K
(2.3 – 2.7 K)
11 – 14 K
(2.7 – 4 K)
LOF
10 - 20
20 - 55
85 - 105
115 - 180
HGMD-DM
(at conserved sites)
4–8
(2.5 -5)
10 – 33
(4.8 - 17)
28 – 43
(11 - 18)
40 – 85
(18 - 40)
LOF=loss-of-function variant (stop-gain, frameshift indel, essential splice site)
Conserved sites = sites with GERP conservation score > 2
Rare and Common Diseases
Only 1 or 2 causal
variants
adapted from TA Manolio et al. Nature 461, 747-753 (2009)
Sequencing Strategies
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Targeted sequencing
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Whole exome sequencing
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E.g. screening of known genes associated with
cardiomyopathies or ataxia
Applications in clinical diagnostic
Protein coding regions
Whole genome sequencing
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Can detect all types of information relevant to pathology in
a single go
Still costly, but decreasing rapidly
Identifying causal variants:
Assumptions and Filters
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After variant calling, filter out low quality (confidence)
calls
Variant is unique in patients or at least very rare in
the general population, e.g. < 1%
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Use of in-house databases too
Variant has complete penetrance: every carrier will
have the phenotype
In general these steps will not identify the pathogenic
variant uniquely but will restrict the list of candidates.
Further analysis required
Ideal Scenario
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Variant is common amongst all affected and absent in all
unaffected
Variant is in a gene with known function and disrupts the
protein
(Almost) Ideal Scenario
Variant Prioritization
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Focus first on protein-coding regions (exome)
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Easier to interpret the consequences of the variant
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Nonsense and missense mutations
Frame-shift indels
Essential splice sites disruptions
E.g. mutation affects catalytic residues in an enzyme
Targeted exome sequencing has been very successful in
disease gene discovery
Cautionary note: on average each “normal, healthy”
individual carries
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10-20 rare LOF variants
2-5 rare, disease-associated variants
Non-coding variants
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Many functional elements lie outside protein-coding regions
(ENCODE)
Variants can disrupt
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Regulatory elements, e.g. transcription factor binding sites
Splicing regulatory elements (branch sites, intronic splicing
enhancers/inhibitors, …)
ncRNA transcripts
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Many non-coding variants in individual genome sequences
lie in ENCODE-annotated functional regions
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At least as many as in protein-coding genes
Disease models
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Diseases can be
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Mendelian
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Sporadic
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De novo mutation
Cancer
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Dominant, recessive or X-linked
Driver mutations
Analysis strategy needs to be adjusted to each
disease category
Autosomal Dominant Disease
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Familiar, inherited disorder
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Search for heterozygous variants
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Present in affected individuals, absent in non-affected ones
Linkage analysis can substantially narrow the genomic search
space
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E.g. SNP array all family members and sequence one or two affected
members
Recessive Disease
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Suspected consanguinity
Search for homozygous
variants
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Heterozygous in parents
Homozygosity mapping by SNP
arrays can substantially reduce
the number of variants for
follow-up
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No indication of consanguinity
Search for compound
heterozygous variants
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Affected individual carries two
separate variants in the same
gene
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Each parent carries one of the
two variants
Sporadic Genetic Disease
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Dominant disorder, parents are unaffected
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Search for de novo mutations
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Expect 50-100 de novo mutations in “normal, healthy” individual
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Present in child and not in parents
Father’s age effect, 2 extra mutations per year (Kong et al, Nature 488,
471–475, 2012)
Sometimes difficult to distinguish from a recessive disease
Cancer
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Matched normal to tumour samples
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Search for somatic variants
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Present in tumour(s), absent in normal sample
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Identify driver mutations
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More on this tomorrow, JB Cazier’s lecture
Predicting Phenotypic Consequences
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Methods based on comparative genomics
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Evolution as a measure of deleteriousness
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Variants at conserved positions more likely to be deleterious
Several conservation scores
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phyloP - single-site score (http://compgen.bscb.cornell.edu/phast/)
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GERP - single-site score
(http://mendel.stanford.edu/sidowlab/downloads/gerp/index.html)
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phastCons – region-based score
(http://compgen.bscb.cornell.edu/phast/)
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Conservation Scores
Benign vs Pathogenic Variants
Gilissen et al, European Journal of Human Genetics (2012) 20, 490–497;
b-haemoglobin locus
From GM Cooper & J Shendure, Nature Reviews Genetics 12, 628-640 (2011)
Protein Sequence Variants
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Most established methods. They exploit
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Amino acid properties, e.g. charge, size, …
Structural information, e.g. local secondary structure,
surface/core amino acid, …
Evolutionary information, e.g. pattern of observed substitutions
Database information, e.g. known binding site
Several methods available
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SIFT (http://sift.bii.a-star.edu.sg/)
Polyphen-2 (http://genetics.bwh.harvard.edu/pph2/)
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PolyPhen-2
(http://genetics.bwh.harvard.edu/pph2/)
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Prediction based on sequence, phylogenetic and structural
information characterizing the substitution
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8 sequence-based properties
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3 structure-based properties
The 11 properties (features) used as input of a probabilistic classifier
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Trained to differentiate benign from pathogenic variants
Non-coding variants
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A substantial fraction of disease causing mutations are
not exonic
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Regulatory variants can have a large effect
More difficult to discover
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Probably under-represented in databases
Non-coding positions less conserved than coding positions
ENCODE has provided a detailed map of regulatory
regions
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Search for variants that disrupt a consensus sequence
motif within a known binding site
Gene Prioritization Methods
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Methods focus on genes rather than on variants
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Identify the genes most likely to cause a given disease
in a list of candidates
Methods combine heterogeneous pieces of
information
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Shared biological pathways with other disease genes
Orthologues genes involved in similar diseases in
model organisms
Localization in affected tissue
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Follow up
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Definite proof of pathogenicity requires
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Validation in independent patient cohort
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In vitro functional experiments
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But many diseases are genetically heterogeneous and
caused by extremely rare variants
Evaluate molecular consequences, e.g. disruption of
expression or protein folding
In vivo experiments in model organisms
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Is the human phenotype reproduced in, e.g., a knock-out
mouse?
Bioinformatics Challenges
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How reliably can we read and annotate an individual’s genome?
How well can we interpret genetic variation in the context of a clinical
presentation?
Community experiment to objectively assess computational methods
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Critical Assessment of Genome Interpretation (CAGI 2012)
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Distinguish between exomes of Crohn’s disease patients and healthy individuals
PGP genomes: predict clinical phenotypes from genome data, and match individuals to
their health records
Whole genomes of a family affected by primary congenital glaucoma: discover the
genetic basis of the disease
...
Critical Assessment of Massive Data Analysis (CAMDA 2013)
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Reliable variant calling
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The WGS500 Project
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Collaboration involving the WTCHG, Oxford BRC, Oxford University
Hospitals and Illumina
Sequence 500 genomes of clinical significance
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Mendelian diseases
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Immunological disorders
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Cancers
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Target coverage: 25x (50x for cancer)
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Diverse set of experimental designs
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Familial: Linkage information
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De novo: trios
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Cancer: Tumour-normal, metastases, multiple-mets, ..
Substantial follow-up (screening and functional) to establish candidacy
Overview of processing
400 genomes
Oxford
Genomics
100 genomes
Illumina
QC
Large-scale CNV
scan
Read alignment
(Stampy)
Homozygosity
scan
Individual/grou
p variant calls
(Platypus)
Union file
Individual
genotypes
Web server
Annotated
genotypes
Read
alignment
and calls
(Eland/
Casava)
Referencecompressed
Archive
• Frequency (1000G, EVS)
• Conservation
• Coding consequence (x2)
• Predicted effect (x3)
• Pathogenicity (HGMD)
• Regulatory annotation
Case Study
PI: Dr A Nemeth
• 3 affected individuals from a highly consanguineous
family
– Childhood developmental ataxia
– Cognitive impairment
Targeted Sequencing
• Targeted sequencing on V3 using a panel of > 100 known
ataxia genes
– Found an homozygous stop codon in SPTBN2
– Mutation present as homozygous in all 3 affected individuals and
as heterozygous in parents of V3, by Sanger sequencing
• Mutations in SPTBN2 cause spinocerebellar ataxia type 5
(SCA5)
– Sometimes referred to as “Lincoln ataxia”
– Autosomal dominant, slowly progressing, adult onset
• Is the cognitive impairment due to the mutation in SPTBN2?
– Could be caused by mutations in a second gene (homozygous
or compound heterozygous)
• Investigated this possibility using a combination of SNP
array and whole genome sequencing
Homozygosity Mapping
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SNP array genotyped V1, V2, V3, IV3 and IV4 (~300K SNPs)
Identified regions of homozygosity (ROH) shared by V1, V2 and V3
and not present in either IV3 or IV4
– Homozygosity mapping with PLINK
– Found 23 regions totalling 28.7 Mb
– Largest segments on chromosome 11
Chromosome 11
Whole Genome Sequencing
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Searched for rare, homozygous variants in shared ROH
– Present in 1000 Genomes with an allele frequency < 1%
– Not observed in other WGS500 samples
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Found 68 candidate variants
Functional class
Exonic
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2
Stop gain
Synonymous
(1)
(1)
ncRNA
1
UTR
3
Intronic
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Number of
variants
40
Upstream
1
Intergenic
21
Based on evolutionary conservation and available information in databases (eg
HGMD) the only likely pathogenic variant is the stop codon in SPTBN2
Excluded also a compound heterozygous model (data not shown)
SPTBN2 variant
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The position is actually not well conserved
– E.g. G->A in gorilla, baboon and mouse
– GERP = -6.71
– PhyloP = -1.28
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TGT and TGC encode for cysteine
TGA is a stop codon
SPTBN2 knock-out mouse
• Investigated a mouse knock-out of SPTBN2
(Mandy Jackson Lab, Edinburgh)
– Ataxia (previously reported)
– Morphological abnormalities in neurons from
prefrontal cortex, an area believed to be
important in human for cognitive tasks
– Deficits in object recognition tasks
• The mouse model supports the hypothesis that
both ataxia and cognitive impairment are
caused by the recessive mutation in SPTBN2
WGS500 overview of findings (as of Dec 2012)
• Project about 75% complete, with 292 samples (195 case
studies) over 38 projects with initial analysis
• 75/195 cases there is at least one candidate viewed by the PI
and analysts as a strong candidate for causing (strongly
contributing to) the phenotype
– 45/82 in Mendelian
– 19/61 in Immune
– 11/52 in Cancer
• Papers in press/submitted to date on
– Ataxia, CMS, CLL, Multiple adenomas