Tobias marschall haplotype aware genotyping

1. Cross-Platform Haplotyping and
Haplotype-Aware Genotyping
Tobias Marschall
January 25, 2018
GIAB Workshop

2. Haplotype Phasing
TTTCTTATCCATGGACACCTTCTGCTTC reference
A/A genotypes
C
T
A
C
A
A
haplotypes
CTATGG
CTGCTC
TCTGCT
TTATCC
C
G
A
G
A
T
T
G
A/C C/T G/C A/G T/A G/T
TTTATT CATCTT
TATTCT TGCACA
TTATTA ATGCAC
ACGCCT TTCTC
2

3. Haplotype Phasing
TTTCTTATCCATGGACACCTTCTGCTTC reference
1/1 genotypes
0
1
0
1
1
1
haplotypes
1 0
0
0
0 0
1
0
0
1
1
0
1
0
0/1 0/1 0/1 0/1 0/1 0/1
1 1 1
1 1 1 0
1 0 1
1 0 1
2

4. Haplotype Phasing
TTTCTTATCCATGGACACCTTCTGCTTC reference
genotypes
0
1
0
1
haplotypes
1 0
0 0
1
0
0
1
1
0
1
0
0/1 0/1 0/1 0/1 0/1 0/1
1 1
1 0
1 0
2

5. Haplotype Phasing
TTTCTTATCCATGGACACCTTCTGCTTC reference
1 0
0 0
1 1
1 0
1 0
0 0
1 0
1 0
1 0
1 1
- - - -
- - -
- -
-
----
- - -
--
-
2

6. Haplotype Phasing
0 0
1 0
1 0
1 0
1 1
- - - -
- - -
- -
-
----
- - -
--
-
2

7. What about Sequencing Errors?
Each character in a read comes with a “phred score”:
−10 · log10(pwrong), where pwrong is the probability that a
position has been wrongly sequenced
Given the genotype, this implies we have a (Bernoulli)
distribution for each position in a read
T G C A C20 10 30 15 5 0 1 0 0 120 10 30 15 5
(0.316, 0.684)
(0.968, 0.032)
probability that true character is 0
probability that true character is 1
(0.999, 0.001)
(0.100, 0.900)
(0.990, 0.010)
3

8. Maximum Likelihood Haplotypes
0 0
0 0
1 0
1 0
1 1
1 - - -
- - -
1 -
-
1---
- - -
--
1
10 30
9 18
21 29
19 31
11 13
23Read 1
Read 2
Read 3
Read 4
Read 5
Variant1Variant2Variant3Variant4Variant5Variant6
6
24
14
Joint work with Benedict Paten, Marina Haukness, Trevor Pesout (UCSC) and Jana Ebler (MPII)
4

9. Maximum Likelihood Haplotypes
0 0
0 0
1 0
1 0
1 1
1 - - -
- - -
1 -
-
1---
- - -
--
1
10 30
9 18
21 29
19 31
11 13
23Read 1
Read 2
Read 3
Read 4
Read 5
Variant1Variant2Variant3Variant4Variant5Variant6
6
24
14
0 0
1 0
1 0
1 0
1 1
1 - - -
- - -
1 -
-
0---
- - -
--
1
10 30
9 18
21 29
19 31
11 13
23
6
24
14
0 0
1 0
1 0 1 1
1 0 01
haplotypes:
bipartition:
Goal: cluster reads to obtain a maximum likelihood bipartition
and maximum likelihood haplotypes
Joint work with Benedict Paten, Marina Haukness, Trevor Pesout (UCSC) and Jana Ebler (MPII)
4

10. Maximum Likelihood Haplotypes
0 0
0 0
1 0
1 0
1 1
1 - - -
- - -
1 -
-
1---
- - -
--
1
10 30
9 18
21 29
19 31
11 13
23Read 1
Read 2
Read 3
Read 4
Read 5
Variant1Variant2Variant3Variant4Variant5Variant6
6
24
14
0 0
1 0
1 0
1 0
1 1
1 - - -
- - -
1 -
-
0---
- - -
--
1
10 30
9 18
21 29
19 31
11 13
23
6
24
14
0 0
1 0
1 0 1 1
1 0 01
haplotypes:
bipartition:
Goal: cluster reads to obtain a maximum likelihood bipartition
and maximum likelihood haplotypes
Idea: define Hidden Markov Model (HMM), where states
represent bipartitions:
{1,2}{3}
1|0
HMM state
bipartation of reads
in a given column
allele in haplotype 2
allele in haplotype 1
Emission:
- column of input matrix
Emission probabilities:
- probability of generating
the column, given the
quality values (red)
Joint work with Benedict Paten, Marina Haukness, Trevor Pesout (UCSC) and Jana Ebler (MPII)
4

11. HMM for Maximum Likelihood Haplotypes
start
B1 B2C1SNP1 SNP2 SNP3
1
10
0
0 -
1
- -
1
2
3
B3C2
end
{1,2}{}
1|0
{1,2}{}
0|1
{1}{2}
1|0
{1}{2}
0|1
{1,2}{}
{1,2}{}
1|0
{1,2}{}
0|1
{1}{2}
1|0
{1}{2}
0|1
{2}{}
{2,3}{}
1|0
{2,3}{}
0|1
{2}{3}
1|0
1
1 1
1
1
1
1
1
1
1/4
1/4
1/4
1/4
1/2
1/2
1/4
1/4
1/4
{1}{2}
1
1
1/2
1/2
{2}{3}
0|1
11/4
One “B column” for each column in the input matrix
“C columns” to simplify connectivity between compatible
bipartitions
Each path gives rise to one bipartition
Viterbi algorithm yields maximum likelihood bipartition
Solves Minimum Error Correction (MEC) problem
Implemented in WhatsHap
Joint work with Benedict Paten, Marina Haukness, Trevor Pesout (UCSC) and Jana Ebler (MPII)
5

12. Integrative Haplotyping across
Platforms
D. Porubsky, S. Garg, A. Sanders, J. Korbel, V. Guryev, P. Lansdorp, T. Marschall, Nature Communications, 2017.
6

13. Haplotype Blocks from Individual Technologies
0.06% 30204
1927
199
1
1.25%
4.66%
57.6%
Illumina
PacBio
10X Genomics
SN
Vs
in
largest
segm
entN
o.ofsegm
ents
NA12878 Chromosome 1
0 20 40 60 100
102
104
D. Porubsky, S. Garg, A. Sanders, J. Korbel, V. Guryev, P. Lansdorp, T. Marschall, Nature Communications, 2017.
7

14. Haplotype Blocks from Individual Technologies
0.06% 30204
1927
199
1
1.25%
4.66%
57.6%
Illumina
PacBio
10X Genomics
SN
Vs
in
largest
segm
entN
o.ofsegm
ents
NA12878 Chromosome 1
0 20 40 60 100
102
104
switcherrorrate[%]
0.3%
0.2%
0.1%
0%
PacBio
10X
G
enom
ics
Illum
ina
0.13% 0.025% 0.3%
Ground truth: phasing based
on platinum genomes
pedigree (17 family members)
D. Porubsky, S. Garg, A. Sanders, J. Korbel, V. Guryev, P. Lansdorp, T. Marschall, Nature Communications, 2017.
7

15. Strand-specific single-cell sequencing (Strand-seq)
Homologous
chromosomes
3'
Watson(W)
5'
5'
3'
Crick(C)
[Figure adapted from D Porubsk´y, A Sanders, et al., Genome Research, 2016]
8

16. Strand-specific single-cell sequencing (Strand-seq)
s
Homologous
chromosomes
Sister chromatids
during DNA replication
3'
Watson(W)
5'
5'
3'
Crick(C)
DNA synthesis in
presence of BrdU
[Figure adapted from D Porubsk´y, A Sanders, et al., Genome Research, 2016]
8

17. Strand-specific single-cell sequencing (Strand-seq)
s
Homologous
chromosomes
Sister chromatids
during DNA replication
3'
Watson(W)
5'
5'
3'
Crick(C)
DNA synthesis in
presence of BrdU
DNA segragation,
removal of
BrdU strands
WW
CC
[Figure adapted from D Porubsk´y, A Sanders, et al., Genome Research, 2016]
8

18. Strand-specific single-cell sequencing (Strand-seq)
s
Homologous
chromosomes
Sister chromatids
during DNA replication
3'
Watson(W)
5'
5'
3'
Crick(C)
DNA synthesis in
presence of BrdU
DNA segragation,
removal of
BrdU strands
WW WC
CC CW
[Figure adapted from D Porubsk´y, A Sanders, et al., Genome Research, 2016]
8

19. Strand-specific single-cell sequencing (Strand-seq)
s
Homologous
chromosomes
Sister chromatids
during DNA replication
ACGGTACCA
TCGATAGCC
GATACGCTA
GCTATACGA
3'
Watson(W)
5'
5'
3'
Crick(C)
DNA synthesis in
presence of BrdU
DNA segragation,
removal of
BrdU strands
Haplotype 1
WW WC
CC CW
Haplotype 2
Haplotype 2
Haplotype 1
[Figure adapted from D Porubsk´y, A Sanders, et al., Genome Research, 2016]
8

20. Haplotype blocks from individual technologies
0.06% 30204
1927
199
1
1.25%
4.66%
57.6%
Illumina
PacBio
10X Genomics
SN
Vs
in
largest
segm
entN
o.ofsegm
ents
NA12878 Chromosome 1
0 20 40 60 100
102
104
D. Porubsky, S. Garg, A. Sanders, J. Korbel, V. Guryev, P. Lansdorp, T. Marschall, Nature Communications, 2017.
9

21. Haplotype blocks from individual technologies
0.06% 30204
1927
199
1
1.25%
4.66%
57.6%
Illumina
PacBio
10X Genomics
Strand-seq
(134 cells)
SN
Vs
in
largest
segm
entN
o.ofsegm
ents
0 20 40 60 100
102
104
NA12878 Chromosome 1
D. Porubsky, S. Garg, A. Sanders, J. Korbel, V. Guryev, P. Lansdorp, T. Marschall, Nature Communications, 2017.
9

22. Integrating Strand-seq and PacBio data
Variant1
Variant2
Variantn
1 - - - - - - 0 - 0 - - 0 - - - - 0 - 0
0 - - - - 0 - - - 1 - - - - - - - 1 - -
- 1 - - - 0 - - - - - 0 1 - - - - - 0 -
- - 0 - - - 1 - 0 - 1 - - - - 1 - - - 0
- 0 - - - - - 0 - - 1 - - - 1 - - - 1 -
0 - - - 0 - - - - - - - - 0 - 1 - - - -
23 15
25
2317
15
25 17
15 25
Cell 1 (W)
Cell 1 (C)
Cell 2 (W)
Cell 2 (C)
Cell 3 (W)
Cell 3 (C)
21 30 2 25 29
131425233111
43
19 24 19 5
15282631
1 0 0 1 - - - - - - - - - - - - - - - -
- 0 0 1 1 - - - - - - - - - - - - - - -
- 1 1 1 0 - - - - - - - - - - - - - - -
- - 1 0 0 0 0 - - - - - - - - - - - - -
Read 1
Read 2
PacBio
23 15 7 25
25 17 12 32
Strand-seq
37 18 23 31 22
14 25 4 31Read 3
Read 4
D. Porubsky, S. Garg, A. Sanders, J. Korbel, V. Guryev, P. Lansdorp, T. Marschall, Nature Communications, 2017.
10

23. Integrating Strand-seq and PacBio data
Variant1
Variant2
Variantn
1 0 0 - - - 1 0 0 0 1 - 0 - 1 1 - 0 1 0
0 1 - - 0 0 - - - 1 - 0 1 0 - 1 - 1 0 -
23 15
25
1725 17
15 25
Hap 1
Hap 2 21 30 2 25 29
25142523311143
24 19 5
1528
1 0 0 1 - - - - - - - - - - - - - - - -
- 0 0 1 1 - - - - - - - - - - - - - - -
- 1 1 1 0 - - - - - - - - - - - - - - -
- - 1 0 0 0 0 - - - - - - - - - - - - -
Read 1
Read 2
PacBio
23 15 7 25
25 17 12 32
Strand-seq
consensus
37 18 23 31 22
14 25 4 31Read 3
Read 4
D. Porubsky, S. Garg, A. Sanders, J. Korbel, V. Guryev, P. Lansdorp, T. Marschall, Nature Communications, 2017.
10

24. Haplotype blocks: Strand-seq + PacBio (10-fold)
0
25
50
75
100
0
5
10
20
40
60
80
100
120
134
Strand-seq
cells
SNVs in
largest block
0
25
50
75
100
0
5
10
20
40
60
80
100
120
134
ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/NA12878/analysis/MPG_WhatsHap_phasing_07202017/
D. Porubsky, S. Garg, A. Sanders, J. Korbel, V. Guryev, P. Lansdorp, T. Marschall, Nature Communications, 2017.
11

25. Hamming error rates
Depth of coverage
Hammingerrorrate
Strand-seq
cells
5
10
20
40
60
80
100
120
134
2 3 4 5 10 15 25 30 all
2%
3%
4%
Ground truth: Illumina Platinum genomes, genetic phasing from
17-member pedigree
ftp://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/NA12878/analysis/MPG_WhatsHap_phasing_07202017/
D. Porubsky, S. Garg, A. Sanders, J. Korbel, V. Guryev, P. Lansdorp, T. Marschall, Nature Communications, 2017.
12

26. Comprehensive Comparison in the HGSVC
Trio
Population
Illumina
Moleculo
10X
PacBio
StrandSeq
Hi-C
StrandSeq+10X
StrandSeq+PacBio
StrandSeq+Moleculo
StrandSeq+HiC
HiC+10X
HiC+PacBio
HiC+Moleculo
10X+PacBio
10X+Moleculo
Moleculo+PacBio
M. Chaisson*, A. Sanders*, X. Zhao*, ... (83 authors) ..., T. Marschall†
, J. Korbel†
, E. Eichler†
, C. Lee†
, biorxiv, 2017. 13

27. Comprehensive Comparison in the HGSVC
0 50 100
SNVs in
largest block [%]
Trio
Population
Illumina
Moleculo
10X
PacBio
StrandSeq
Hi-C
StrandSeq+10X
StrandSeq+PacBio
StrandSeq+Moleculo
StrandSeq+HiC
HiC+10X
HiC+PacBio
HiC+Moleculo
10X+PacBio
10X+Moleculo
Moleculo+PacBio
Hamming
error rate
0.1% 1% 10% 50%
N/A
M. Chaisson*, A. Sanders*, X. Zhao*, ... (83 authors) ..., T. Marschall†
, J. Korbel†
, E. Eichler†
, C. Lee†
, biorxiv, 2017. 13

28. Haplotype-Aware Genotyping from
Long Reads
Joint work with Benedict Paten, Marina Haukness, Trevor Pesout (UCSC) and Jana Ebler (MPII)
14

29. Horizontal Genotyping
Tools for genotyping from long noisy reads are rare
Current long read sequencers have high error rates ∼10%,
their power lies in the read length
A
C
A
A
C
C
T
T
G
T
T
G
SNP2SNP1
G
C
G
G
C
C
SNP3
Joint work with Benedict Paten, Marina Haukness, Trevor Pesout (UCSC) and Jana Ebler (MPII)
15

30. Horizontal Genotyping
Tools for genotyping from long noisy reads are rare
Current long read sequencers have high error rates ∼10%,
their power lies in the read length
A
C
A
A
C
C
T
T
G
T
T
G
SNP2SNP1
G
C
G
G
C
C
SNP3
Joint work with Benedict Paten, Marina Haukness, Trevor Pesout (UCSC) and Jana Ebler (MPII)
15

31. Method for Horizontal Genotyping
Extend previous HMM: include states for all genotypes
Use Forward-Backward algorithm to compute genotype
likelihoods
Implemented in MarginPhase and WhatsHap
start
B1 B2
SNP1 SNP2
1
10
01
2
{1,2}{}
1|1
{1,2}{}
1|0
{1,2}{}
0|1
{1}{2}
1|1
{1}{2}
1|0
{1}{2}
0|1
{1,2}{}
1|1
{1,2}{}
1|0
{1,2}{}
0|1
{1}{2}
1|1
{1}{2}
1|0
{1}{2}
0|1
1
1
1
1
1
1
{1,2}{}
0|0
{1}{2}
0|0
{1}{2}
0|0
{1,2}{}
0|0
1
1
end
1
1
1
1
1
1
1
1
Joint work with Benedict Paten, Marina Haukness, Trevor Pesout (UCSC) and Jana Ebler (MPII)
16

32. Results for Horizontal Genotyping
1 variant
2 variants
full length
coverage
errorrate
0%
2%
4%
6%
8%
10 15 20 30 42
Joint work with Benedict Paten, Marina Haukness, Trevor Pesout (UCSC) and Jana Ebler (MPII)
17

33. Do we find novel variants?
Creating a discovery set
Identify candidate SNVs and genotype them
Use PacBio and Nanopore data separately
Take the intersection → converative call set
Compared to high confidence GIAB call set:
Precision: 99.6 %
Recall: 78.6 %
Compared to GIAB GATK/HC call set:
Novel variants found: 51 301 SNVs
Joint work with Benedict Paten, Marina Haukness, Trevor Pesout (UCSC) and Jana Ebler (MPII)
18

34. Chromosome 6
Calls concordant between PacBio and ONT, but not in the GIAB
GATK/HC call set
Joint work with Benedict Paten, Marina Haukness, Trevor Pesout (UCSC) and Jana Ebler (MPII)
19

35. Summary
Minimum Error Correction (MEC) problem and its HMM
formulation
Solving MEC using WhatsHap
Single-cell template strand sequencing Strand-seq
Chromosome-length haplotyping feasible for single
individuals
Extensive benchmark data available from the HGSVC
Haplotype-aware genotyping: MarginPhase / WhatsHap
[We are hiring: looking for postdoc + software engineer]
20