1. Canadian Bioinformatics Workshops

2. Module #: Title of Module2

3. Module 8 – Variants to Networks
Part 1 – How to annotate variants and prioritize potentially relevant ones
Jüri Reimand
Bioinformatics for Cancer Genomics
May 25-29, 2015
Informatics and Biocomputing
Ontario Institute for Cancer Research

4. Learning Objectives of Module
I have detected somatic variants in a cancer sample.
What information can I use to interpret them?
What variant annotations can I use?
How do impact prediction models work?
How to use an annotation tool: Annovar (LAB)

5. Introduction

6. Variant vs Gene Information
We have to consider information at two levels:
Gene
Is the gene central to processes related to cancer?
(e.g. proliferation, apoptosis, matrix degradation)
Is the gene sensitive to perturbation?
(e.g. haploinsufficiency)
Variant
What is the variant effect on the gene product?

7. Integrating Different Evidences
Variant Recurrence
Gene Product
Function / Pathway
Variant
Gene Product
Effect

8. On Variant Size Small: 1-50 bp Medium: 100-1,000 bp Large: > 5 kbp
SNV (Single Nucleotide Variants): 1 bp substitution, relatively easy to detect
Small In/dels: a bit more challenging to detect
Most available in databases; can be mapped by exact coordinates
Medium: 100-1,000 bp
Insertions, Deletions, Translocations, Complex re-arrangements
Most challenging to detect
More tolerant mapping (e.g. partners of gene fusion)
Large: > 5 kbp
Copy number variants relatively easy to detect using arrays, more challenging using next generation sequencing
More tolerant mapping (e.g. 50% reciprocal overlap, cytoband(s))

9. Variant Annotation Components
Variant database mapping
Allele frequencies from reference data-sets (1000G, NHLBI-ESP, ExAC)
dbSNP (sequence variation database)
COSMIC (somatic variant database)
Gene mapping (coding/splicing, UTR, intergenic)
Gene product effect type (e.g. loss of function, missense)
Coding Missense Effect Scoring
SIFT
PolyPhen2
MutationAssessor
Other Effect Scoring
PhyloP (conservation)
CADD
Splicing-regulatory predictions
Consequtive basepairs

10. Variant databases and allele frequencies

11. 1000 Genomes (Phase 3) Goal: Subjects: 2,504 Platform: Illumina
Identify all variants at > 1% frequency in represented human populations
Subjects: 2,504
Apparently healthy
Ethnicities: caucasian European, admixed Latin Americans, African, South Asians, East Asians
Platform: Illumina
Low coverage (2-4x) whole genome
Exon (50x coverage)

12. NHLBI-ESP Goal: Subjects: 6,503 (ESP 6500 release)
discover heart, lung and blood disorder variants at frequency < 1%
Subjects: 6,503 (ESP 6500 release)
Not necessarily healthy (includes individuals with extreme subclinical traits and diseased)
Ethnicities: 2,203 African-Americans, 4,300 European-Americans
Platform: Illumina, exome sequencing (average 110x)
Consequtive basepairs

13. ExAC (Exome Aggregation Consortium)
Goal:
Compile the largest set of exomes ever
Subjects: 60,706 (unrelated)
Not necessarily healthy: includes cardiovascular, autoimmune, schizophrenia and cancer, but removed individuals with severe pediatric disease
Ethnicities: non-Finnish European, Finnish, Latin Americans, African, South Asians, East Asians, Other
Platform: Illumina, exome
Variant calling:
GATK

14. dbSNP Broad scope repository of “small” genetic variation
(e.g. NCBI counterpart for structural variants: dbVAR)
Submissions before and after NGS era
Includes polymorphisms found in general population
Includes rare germline disease-associated (or suspected to be)
Includes somatic variants (also in COSMIC)
Good to look up variants
If you want to use as filter, make sure you remove “clinically flagged” variants (somatic, germline)
Consequtive basepairs

15. COSMIC “Catalogue of Somatic Mutation In Cancer”
Reference database for somatic variation in cancer
Worth following up variants matching COSMIC entries
How many studies/samples was it found in? 1, many?
Does the variant overlap a hotspot?
Is the gene frequently mutated?
Consequtive basepairs

16. Gene mapping

17. Gene Mapping: Types of Genes
Protein-coding genes
Non-protein-coding RNA genes (e.g. miRNA)
Different functional relevance
Different knowledge of variant effects
Consequtive basepairs

18. Gene Mapping: Parts of Genes
Protein-coding genes have these parts:
UTR (transcribed, not translated)
Coding exons (translated)
Introns (spliced out, not translated)
Splice sites
Also:
Upstream, downstream transcribed gene
Inter-genic
Consequtive basepairs

19. Gene Mapping: Annovar’s priority system
Gene types and parts: what if they overlap..?
Whenever more than one mapping is possible, Annovar will follow this priority system
You can also ask Annovar to report all possible effects
Consequtive basepairs

20. Gene Mapping: Annovar’s priority system
Protein Coding Gene G1
>>>>
>> >>
>>
>>>>
>>>>>>>>>>
TSS of G1
(Transcription Start Site)
Non-coding RNA ncR1 (e.g. miRNA)

21. Gene Mapping: Annovar’s priority system
>>>>
>> >>
>>
>>>>
>>>>>>>>>>
G1 Intronic
G1 Upstream
G1 UTR 5’
G1 Exonic
G1 Exonic
G1 Intronic
G1 Exonic
G1 Intronic
ncR1
G1 Exonic
ncR1
G1 UTR 3’
G1 Downstream
ncR1 Downstream
G1 Splicing **
** Splice sites after the first were omitted to avoid clutter

22. Gene Mapping: Database
Goal:
map our variants to (coding and non-coding) genes
RefSeq is the suggested database for transcribed gene and coding sequence definition
In the lab we will use Annovar with RefSeq database
Other databases available: UCSC known genes, Ensembl
Consequtive basepairs

23. Gene product effect type

24. Gene Product Effect Regulatory / other non-protein-coding sequences:
difficult to establish what a change “means”
(certain cases are easier, e.g. miRNA seed)
Protein-coding sequences:
how is protein sequence affected?
Definitely easier to chase after protein effects
But should don’t forget other gene products exist…
Consequtive basepairs

25. Gene Product Effect: Protein-coding
Stop-gain SNV: adds a STOP codon  truncated protein
Frameshift In/Del: shifts the reading frame
 protein translated incorrectly from that point
Splicing: alters key sites guiding splicing
In-frame In/Del: removes/add one or more amino acids
Stoploss: loss of STOP codon  extra piece in the protein
Missense SNV: modifies one amino acid
Synonymous: no amino acid change
Consequtive basepairs

26. Loss of Function (LOF) Variants
Definition: Stop-gain, Frameshift, Splicing
These are the more disruptive, BUT:
What percentage of the protein is affected?
Are there multiple transcript isoforms?
Splicing effect difficult to predict
Cryptic splice sites
Frameshift can be rescued by another frameshift or bypassed by splicing
Consequtive basepairs

27. Missense Variants: Tell Me More..
How do we tell if a missense alters protein function?
Type of amino acid change (amino acid groups)
Conservation across species
Conserved protein domain
Secondary protein structure
Tertiary (3D) protein structure + simulation
Other functional features (e.g. phosphosite)
Machine learning model tying all of these together
What training set?
Consequtive basepairs

28. Missense Example: Back to BRAF BRAF V600E T>C Somatic Pathogenic
BRAF V600A
T>A
Somatic / germline
Pathogenicity untested

29. Conservation and Missense Variant Scoring Models

30. Conservation Conservation is a powerful and broadly used idea
How conserved is a given nucleotide or genomic interval, comparing different species to human?
How conserved is an amino acid in a protein sequence?
Available from UCSC (nucleotide conservation):
PhyloP score – useful to assess single variants
PhastCons score/element – useful to assess putative regulatory regions and genes not coding proteins
Multi-species alignment – generally useful

31. Look for coding exons, UTRs and third nucleotide within codons

32. PhyloP
PhyloP: test to detect if nucleotide substitution rates are faster or slower than expected under neutral drift
Only where aligned sequence available!
PhyloP score
Positive: conserved (e.g. PhyloP > 2)
Zero: neutral
Negative: more diverged than neutral
Species group:
All vertebrates
Only placental mammals
Only primates

33. Conservation Main caveat:
if you use conservation for a given position, this will not tell you directly what is the effect of your variants, but only if the position is important!

34. Missense Variant Effect: Scoring Models Overview
Criteria to keep in mind:
What features are used?
Nucleotide / amino acid conservation
Amino acid physicochemical properties
Direct scoring versus Machine learning
Machine learning models are heavily dependent on the training-set used
What data-set used for assessment / learning / optimization?
E.g. Activating / gain-of-function versus inactivating / loss-of-function mutations
E.g. Mendelian disorders (prevailingly loss-of-function) versus cancer (some are unique to cancer, e.g. drug resistance)

35. SIFT Broadly used, relatively old (first published: 2001)
Designed for deleterious mutation (i.e. disruptive of protein function)
Based uniquely on protein sequence (amino acid) conservation
Start from query protein sequence
Identify similar protein sequences (PSI-BLAST)
Multiple alignment of protein sequences (orthologs and paralogs)
Amino acid x residue probability matrix (PSSM)
For every residue, amino acid probability reweighted by amino acid diversity at the position (sum of frequency rank * frequency)
 Score: probability of observing amino acid normalized by residue conservation
cut-off: 0.05 (based on case studies)
Consequtive basepairs
Predicting deleterious amino acid substitutions.
Ng PC, Henikoff S. Genome Res May;11(5):

36. PolyPhen2 Integrates multiple features
8 sequence-based, 3 structure-based (nucleotide and amino acid level)
(e.g. side chain volume change, overlap with PFAM domain, multiple alignment metrics)
Machine learning method (Naïve Bayes)  Requires training set
Set 1: HumDiv
Positive: damaging alleles for known Mendelian disorders (Uniprot)
Negative: nondamaging differences between human proteins and related mammalian homologs
Performance 5-fold crossv: (TP ~ 80%, FP ~10%), (TP ~ 90%, FP ~ 20%)
Set 2: HumVar
Positive: all human disease causing mutations (Uniprot)
Negative: non-synonymous SNPs without disease association
Richer model than SIFT
More biased towards training set(s) than SIFT
Consequtive basepairs
A method and server for predicting damaging missense mutations.
Adzhubei IA, Schmidt S, Peshkin L, […], Bork P, Kondrashov AS, Sunyaev SR. Nat Methods Apr;7(4):248-9.

37. MutationAssessor Direct / theoretical model (no machine learning)
Based on amino acid conservation also specifically modeling conservation unique to protein subfamilies (can be regarded as an enhanced SIFT)
Entropy-based score based on protein sequence alignment
Performs well for (recurrent) somatic variants
Consequtive basepairs
Predicting the functional impact of protein mutations: application to cancer genomics.
Reva B, Antipin Y, Sander C. Nucleic Acids Res Sep 1;39(17):e118

38. CADD
Intended as a measure of “deleteriousness” for coding and non-coding sequence, not biased to known disease variation
However does not model gene specific constrain in detail
Machine learning model (Linear SVM)
Negative training set: nearly fixed human alleles, variant if compared to inferred human-chimp ancestral genome
Positive training set: simulated variants based on mutation model aware of sequence context and primate substitution rates
Predictive features (63): VEP (Variant Effect Predictor) output, UCSC tracks, Encode tracks  includes missense predictions and nucleotide-level conservation
Performance assessment: using pathogenic variants from ClinVar performs a bit better PhyloP for all sites and PolyPhen/SIFT for missense coding
A general framework for estimating the relative pathogenicity of human genetic variants.
Kircher M, Witten DM, Jain P, O'Roak BJ, Cooper GM, Shendure J. Nat Genet Mar;46(3):310-5.

39. CADD
Pathogenic ClinVar vs NHLBI-ESP > 5%

40. Splicing Regulatory Predictions
Goal: predict how SNVs affect exon inclusion / exclusion
Strategy:
Learn “Wild Type” splicing code based on reference genome sequence motifs and experimentally-measured splicing patterns in human tissues
“Mutant” code: predicts splicing change when variant alters splicing-guiding sequence motif
Does not learn based on known disease splicing alterations
Science 2015

41. Phosphorylation and other protein modifications
Post-translational modifications (PTMs) extend protein function
Human: >130,000 PTM sites, % of protein sequence
Enriched in inherited disease and somatic cancer mutations
Negatively selected in population
Often not detected with mutation assessment tools
Reimand et al, 2013 Mol Sys Bio; 2015 PLOS Genet

42. Effect Scoring: Conclusive Remarks
Nucleotide-level conservation (PhyloP) is simple yet powerful, and multiple alignments can be additionally inspected
Missense scoring models are powerful, but their strengths and weaknesses need to be understood
Variants should be always reviewed putting all information in context
Consider conservation and effect scores using different models
Review the amino acid change and sequence context
Look for clusters of somatic variants and protein domain
Don’t forget gene-level information!

43. We are on a Coffee Break & Networking Session