Jan2015 ga4 gh variant comparison

1. GA4GH work towards a standardized
variant comparison tool
Kevin Jacobs
1/30/2015

2. Simple Question
Is a variant present within a genome?

3. Simple Question?
Is a variant present within a genome?
• sequence at a location:
– allele
– haplotype
– genotype
• something like a
– VCF genotype
– HGVS string
– dbSNP/ClinVar/HGMD entry

4. Simple Question?
Is a variant present within a genome?
• Collection of variants (and reference) in
– VCF/gVCF/BCF file
– var/MasterVar file
– dbSNP/ClinVar/HGMD/etc.
– your fancy new file format
– your fancy new database

5. Problem Statement
Is a variant present within a genome?
Surely this must be a solved problem?

6. Dr. Seuss
• Sometimes the questions
are complicated and the
answers are simple

7. Is this a simple question?
• It also depends on how we define…
– variant, genome, location, genotype, present
• Can we answer this question?
– Is the location well defined?
– Did we observe reads that location?
– Could we infer a single most-likely genotype at that
location?
– Are we asking about “simple” variation in a “nice”
region of the genome?
• If yes to all of these, then we can almost always
answer our question correctly.

8. Don’t Panic!

9. Why is this so hard?
• Consider c.2_4delCTAinsGC
– REF: ACTAC
– H1: =G-C=
• It can also be spelled
– c.[2C>G; 3del; 4A>C]
– c.[2C>G; 3T>C; 4del]
– c.[2del; 3T>G; 4A>C]
– …

10. Assumptions and notation
• We have an accurate reference genome
sequence
• Queries are relative to well-defined non-
ambiguous regions of the reference sequence
• Simple sequence query / assertion:
– VCF: (chrom, pos, ref, alts, geno)
• E.g. (chrZ, 55, A, G, 1/1)
– Generic: (chrom, start, stop, alleles)
• E.g. (chrZ, 54, 55, G, G)
– These representations are equivalent modulo some
strange encoding rules for VCF relating to null alleles

11. Most basic model
• A “genome” G is a set of sequence assertions
• A “query” is a proposition q∈G where q is a
sequence assertion
• E.g.
– G = { (chrZ, 55, G, G) }
– Q1 : (chrZ, 55, A, G) ∈ G = False
– Q2 : (chrZ, 55, G, G) ∈ G = True

12. Basic model extensions
• Simple extensions
– Indels / MNVs
– Reference calls (like gVCF)
– No calls, partial calls
– Arbitrary ploidy
– Phase, quality, filters, etc. (not show)
G = {(chrZ, 0, 24, =, =), (chrZ, 24, 25, G, G),
(chrZ, 25, 53, =, =), (chrZ, 53, 55, NN, NN),
(chrZ, 55, 88, =, =), (chrZ, 88, 92, ATAT, NNNN),
(chrZ, 92, 96, =, =), (chrZ, 96, 98, A, ☐),
(chrZ, 98, 100, =, =)}

13. Limitations of the basic model
• Sequence assertions do not have unique
representations
– Alignments are not unique
– Alignment models differ
– Nearby variants / phase information
– Missing data and uncertainty
• Sometimes we aren’t asking the right question

14. Alignments are not unique
• Precedence of insertions, deletions and
mismatches:
– REF: ACAC
– H1: =-G= (AGC)
– H2: =G-= (AGC)

15. Limitations of the basic model
• Sequence assertions do not have unique
representations
– REF: TCACACACAG
– H1: T--CACACAG (REF, 1, 3, ☐)
– H2: TC--ACACAG (REF, 2, 4, ☐)
– H3: TCA--CACAG (REF, 3, 5, ☐)
– H4: TCAC--ACAG (REF, 4, 6, ☐)
– H5: TCACA--CAG (REF, 5, 7, ☐)
– H6: TCACAC--AG (REF, 6, 8, ☐)
– H7: TCACACA--G (REF, 7, 9, ☐)

16. Alignments models differ
• Different alignment scoring:
– REF: A--CAC
– H1: =GG--= (REF, 1, 1, ☐, GG)
(REF, 1, 3, CA, ☐)
– H2: =--GG= (REF, 1, 3, CA, GG)
• Base quality aware alignments algorithms are
even more susceptible to non-unique
alignments

17. Ignoring phase or phase uncertainty
introduces ambiguity
– REF: ACGT
– H1: =A== (REF, 1, 2, C, A)
– H2: ==C= (REF, 2, 3, G, C)
• Vs
– REF: ACGT
– H1: =AC= (REF, 1, 2, C, A)
(REF, 2, 3, G, C)
– H2: ====
• Vs
– REF: ACGT
– H1: =AC= (REF, 1, 3, CG, AC)
– H2: ====

18. Missing data
G = {(chrZ, 0, 24, =, =), (chrZ, 24, 25, G, G),
(chrZ, 25, 53, =, =), (chrZ, 53, 55, NN, NN),
(chrZ, 55, 88, =, =), (chrZ, 88, 92, ATAT, NNNN),
(chrZ, 92, 96, =, =), (chrZ, 96, 98, A, ☐),
(chrZ, 98, 100, =, =)}
• Q: (chrZ, 54, 55, A, T) ∈ G  False

19. Multiple alleles/samples
• Remember our friend:
– REF: TCACACACAG
– H1: T--CACACAG (REF, 1, 3, ☐)
– H2: TCACACA--G (REF, 7, 9, ☐)

20. Multiple alleles/samples
• What left-normalizing H2 will look like in VCF?
– REF: TCACACACAG
– H2: TCACACA--G (REF, 7, 9, ☐)
– H3: TCACACACTG (REF, 8, 9, T)
– H4: TCACTCACAG (REF, 4, 5, T)
– H5: TTACACACAG (REF, 1, 2, T)
– H1: T--CACACAG (REF, 1, 3, ☐)

21. Bottom Line
• Is there a canonical form for sequence
assertions?
– If so, then we can normalize our data into that
form and rely on simple set-existential queries
– If not, then we need a better model
– In the mean time, we rely on heuristics to perform
comparisons and understand that they are
imperfect

22. Better models
• Two basic approaches
1. Standardize alignment and representations so
that we can always derive a unique canonical
representation
2. Make the comparison model “spelling agnostic”

23. Reference graph model
• Convert (g)VCF and other file formats into a graph
representation
• Compute whether graph can “generate” the query
haplotype or genotype
– Supporting multiple forms of ambiguity that are inherent
in the biological questions we ask.
Phase constraint

24. Related Problems
• What are all of the differences between two
genomes?
• Collect all alleles observed across multiple
genomes
• Merge genomes into a single coherent
representation
• Efficiently store and query a large number of
genomes

25. Implementation plan
• Build a reference implementation
– Open source, free, and hosted by GA4GH
– Built in Python + Cython
– Include an extensive test suite
• Not inventing any new file formats
• Implementation underway
– VCF processor built on htslib
– Rest of the engine in progress
– Accounting and testing coming soon after

26. Thanks to:
• Justin Zook and the other GIAB organizers
• Geneticists, who have been doing this right all along
• Complete Genomics for their calldiff algorithm
• Great discussions and debates with friends and
colleagues at NCI, NCBI, Invitae, 23andMe, 1000
Genomes, GA4GH, etc.