genotype and haplotype reconstruction from low- coverage short sequencing reads ion mandoiu computer...

Genotype and Haplotype

Reconstruction from Low-

Coverage Short Sequencing

Reads

Ion Mandoiu

Computer Science and Engineering Department

University of Connecticut

Joint work with S. Dinakar, J. Duitama, Y. Hernández, J. Kennedy, and Y. Wu

Outline

Introduction

Single SNP Genotype Calling

Multilocus Genotyping Problem

Experimental Results

Conclusion

Illumina Genome Analyzer II35-75bp reads2-3Gb/2 day run

Roche/454 FLX Titanium400bp reads400-600Mb/10h run

ABI SOLiD 335-50bp reads5-7.5Gb/3.5-7 day run

Recent massively parallel sequencing technologies deliver orders of magnitude higher throughput compared to classic Sanger sequencing

Ultra-high throughput DNA sequencing

Helicos HeliScope25-55bp reads~2.5Gb/day

UHTS enables personal genomics

$100

$1,000

$10,000

$100,000

$1,000,000

$10,000,000

$100,000,000

days weeks months years

Sequencing Time

Co

st

[email protected]

J. [email protected]

Illumina@36xSOLiD@12x

Sequencing can potentially provide all genetic variations (SNPs, CNVs, genome rearrangements) at single-base resolution…

However, medical use requires determination of both alleles (genotype) at variable loci

Accurate genotype calling is limited by coverage depth due to random nature of shotgun sequencing

For the Venter and Watson genomes (both sequenced at ~7.5x average coverage), comparison with SNP genotyping chips has shown only ~75% accuracy for sequencing based calls of heterozygous SNPs [Levy et al 07, Wheeler et al 08]

Challenges for medical applications of sequencing

Allele coverage for heterozygous SNPs (Watson 454 @ 5.85x avg. coverage)

-1

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-1 0 1 2 3 4 5 6

Reference allele coverage

Var

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-1 0 1 2 3 4 5 6


Var

ian

t al

lele

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vera

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Most prior genotype calling methods are based on allele coverage

[Levy et al 07] and [Wheeler et al 08] require that each allele be covered by at least 2 reads in order to be called

Combined with hypothesis testing based on the binomial distribution when calling hets

Binomial probability for the observed number of alleles must be at least 0.01

[Wendl&Wilson 08] generalize coverage methods to allow an arbitrary minimum allele coverage k

Prior work

MAQ [Li,Ruan&Durbin 08] Widely used read mapping program Single SNP genotype calling incorporating read

mapping confidence and quality scores Mostly tuned for de novo SNP discovery…

Prior work (contd.)

[Wendl&Wilson 08] estimate that 21x coverage will be required for sequencing of normal tissue samples based on idealized theory that “neglects any heuristic inputs”

What coverage is required?

We propose methods incorporating additional sources of information extracted from a reference panel such as Hapmap:

Allele/genotype frequencies

Linkage disequilibrium Experimental results show significantly improved

genotyping accuracy

Do heuristic inputs help?

Outline

Introduction




Conclusion

Known SNP positions

Biallelic SNPs 0 = major allele, 1 = minor allele

SNP genotypes: 0/2 = homozygous major/minor,

1=heterozygous

Basic assumptions

ri = set of mapped reads covering SNP locus i

For each read r in ri r(i) = the allele observed at locus i

= probability that r(i) is incorrect, where qr(i) is the phred quality score of r(i)

mr = mapping confidence of r

10/)(

)(10 irqir

Incorporating base call and read mapping uncertainty

Mapped reads with allele 0

Mapped reads with allele 1

Sequencing errors

Inferred genotypes012100120

ri = set of mapped reads covering SNP locus i

For each read r in ri r(i) = the allele observed at locus i

= probability that r(i) is incorrect, where qr(i) is the phred quality score of r(i)

mr = mapping confidence of r

10/)(

)(10 irqir

Incorporating base call and read mapping uncertainty

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irr

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Applying Bayes’ formula:

Where are genotype frequencies inferred from a representative panel

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)|r()()r|(

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)( ii gGP

Single SNP genotype calling

Outline

Introduction




Conclusion

Haplotype structure in human populations

Similar models proposed in [Schwartz 04, Rastas et al. 05, Kennedy et al. 07, Kimmel&Shamir 05, Scheet&Stephens 06]

HMM model of haplotype frequencies

Random variables Fi = founder haplotype at locus i Hi = observed allele at locus i

For fully specified model and given haplotype h, P(H=h|M) can be computed in O(nK2) using forward algorithm, where n=#SNPs, K=#founders

Graphical Model Representation

F1 F2 Fn…

H1 H2 Hn

F1 F2 Fn…

H1 H2 Hn

G1 G2 Gn

…R1,1 R2,1

F'1 F'2 F'n…

H'1 H'2 H'n

R1,c … R2,c …Rn,1 Rn,c1 2 n

HF-HMM for multilocus genotype inference

P(f1), P(f’1), P(fi+1|fi), P(f’i+1|f’i), P(hi|fi), P(h’i|f’i) trained using Baum-Welch algorithm on haplotypes inferred from the populations of origin for mother/father

F1 F2 Fn…

H1 H2 Hn

G1 G2 Gn

…R1,1 R2,1

F'1 F'2 F'n…

H'1 H'2 H'n

R1,c … R2,c …Rn,1 Rn,c1 2 n


P(gi|hi,h’i) set to 1 if h+h’i=gi and to 0 otherwise

F1 F2 Fn…

H1 H2 Hn

G1 G2 Gn

…R1,1 R2,1

F'1 F'2 F'n…

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case SNP singlein as defined )|( , iiji gGrRP

GIVEN:

• Shotgun read sets r=(r1, r2, … , rn)

• Trained HMM models representing LD in populations of origin for mother/father

• Quality scores & read mapping confidence values

FIND:

• Multilocus genotype g*=(g*1,g*2,…,g*n) with maximum posterior probability, i.e., g*=argmaxg P(g | r)

Multilocus genotyping problem

Theorem: maxgP(g | r) cannot be approximated within unless ZPP=NP

Computational complexity

)( 1 nO

Idea: reduction from the clique problem

Posterior decoding algorithm

1. For each i = 1..n, compute

2. Return *)*,...,(* 1 nggg

)r,(maxarg)r|(maxarg* igigi gPgPgii

)()|r()r,( '' ''1 ,1 ,, i

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Forward-backward computation

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Runtime Direct recurrences for computing forward

probabilities:

Runtime reduced to O(nK3) by reusing common terms:

where

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Outline

Introduction




Conclusion

>gi|88943037|ref|NT_113796.1|Hs1_111515 Homo sapiens chromosome 1 genomic contig, reference assemblyGAATTCTGTGAAAGCCTGTAGCTATAAAAAAATGTTGAGCCATAAATACCATCAGAAATAACAAAGGGAGCTTTGAAGTATTCTGAGACTTGTAGGAAGGTGAAGTAAATATCTAATATAATTGTAACAAGTAGTGCTTGGATTGTATGTTTTTGATTATTTTTTGTTAGGCTGTGATGGGCTCAAGTAATTGAAATTCCTGATGCAAGTAATACAGATGGATTCAGGAGAGGTACTTCCAGGGGGTCAAGGGGAGAAATACCTGTTGGGGGTCAATGCCCTCCTAATTCTGGAGTAGGGGCTAGGCTAGAATGGTAGAATGCTCAAAAGAATCCAGCGAAGAGGAATATTTCTGAGATAATAAATAGGACTGTCCCATATTGGAGGCCTTTTTGAACAGTTGTTGTATGGTGACCCTGAAATGTACTTTCTCAGATACAGAACACCCTTGGTCAATTGAATACAGATCAATCACTTTAAGTAAGCTAAGTCCTTACTAAATTGATGAGACTTAAACCCATGAAAACTTAACAGCTAAACTCCCTAGTCAACTGGTTTGAATCTACTTCTCCAGCAGCTGGGGGAAAAAAGGTGAGAGAAGCAGGATTGAAGCTGCTTCTTTGAATTTAC


>gnl|ti|1779718824 name:EI1W3PE02ILQXT28 28 28 28 26 28 28 40 34 14 44 36 23 13 2 27 42 35 21 727 42 35 21 6 28 43 36 22 10 27 42 35 20 6 28 43 36 22 928 43 36 22 9 28 44 36 24 14 4 28 28 28 27 28 26 26 35 2640 34 18 3 28 28 28 27 33 24 26 28 28 28 40 33 14 28 36 2726 26 37 29 28 28 28 28 27 28 28 28 37 28 27 27 28 36 28 3728 28 28 27 28 28 28 24 28 28 27 28 28 37 29 36 27 27 28 2728 33 23 28 33 23 28 36 27 33 23 28 35 25 28 28 36 27 36 2728 28 28 24 28 37 29 28 19 28 26 37 29 26 39 33 13 37 28 2828 21 24 28 27 41 34 15 28 36 27 26 28 24 35 27 28 40 34 15


>gnl|ti|1779718824 name:EI1W3PE02ILQXTTCAGTGAGGGTTTTTGTTTTGTTTTGTTTTGTTTTGTTTTGTTTTGTTTTTGAGACAGAATTTTGCTCTTGTCGCCCAGGCTGGTGTGCAGTGGTGCAACCTCAGCTCACTGCAACCTCTGCCTCCAGGTTCAAGCAATTCTCTGCCTCAGCCTCCCAAGTAGCTGGGATTACAGGCGGGCGCCACCACGCCCAGCTAATTTTGTATTGTTAGTAAAGATGGGGTTTCACTACGTTGGCTGAGCTGTTCTCGAACTCCTGACCTCAAATGAC>gnl|ti|1779718825 name:EI1W3PE02GTXK0TCAGAATACCTGTTGCCCATTTTTATATGTTCCTTGGAGAAATGTCAATTCAGAGCTTTTGCTCAGCTTTTAATATGTTTATTTGTTTTGCTGCTGTTGAGTTGTACAATGTTGGGGAAAACAGTCGCACAACACCCGGCAGGTACTTTGAGTCTGGGGGAGACAAAGGAGTTAGAAAGAGAGAGAATAAGCACTTAAAAGGCGGGTCCAGGGGGCCCGAGCATCGGAGGGTTGCTCATGGCCCACAGTTGTCAGGCTCCACCTAATTAAATGGTTTACA


Mapped reads & confidence values

Hapmap haplotypes

90 20934216 F 0 02110001?0100210010011002122201210211?122122021200018 F 15 1621100012010021001001100?100201?10111110111?021200015 M 0 0211200100120012010011200101101010111110111102120007 M 0 02110001001000200122110001111011100111?1212102220008 F 0 0011202100120022012211200101101210211122111?012000012 F 9 10211000100100020012211000101101110011121212102200009 M 0 0011?001?012002201221120010?1012102111221111012000011 M 7 821100210010002001221100012110111001112121210222000

90 20934216 F 0 02110001?0100210010011002122201210211?122122021200018 F 15 1621100012010021001001100?100201?10111110111?021200015 M 0 0211200100120012010011200101101010111110111102120007 M 0 02110001001000200122110001111011100111?1212102220008 F 0 0011202100120022012211200101101210211122111?012000012 F 9 10211000100100020012211000101101110011121212102200009 M 0 0011?001?012002201221120010?1012102111221111012000011 M 7 821100210010002001221100012110111001112121210222000

90 20934216 F 0 02110001?0100210010011002122201210211?122122021200018 F 15 1621100012010021001001100?100201?10111110111?021200015 M 0 0211200100120012010011200101101010111110111102120007 M 0 02110001001000200122110001111011100111?1212102220008 F 0 0011202100120022012211200101101210211122111?012000012 F 9 10211000100100020012211000101101110011121212102200009 M 0 0011?001?012002201221120010?1012102111221111012000011 M 7 821100210010002001221100012110111001112121210222000

Reference genome sequence


…

…

…

… …

…

…



Read sequences

Quality scores

SNP genotype calls

rs12095710 T T 9.988139e-01rs12127179 C T 9.986735e-01rs11800791 G G 9.977713e-01rs11578310 G G 9.980062e-01rs1287622 G G 8.644588e-01 rs11804808 C C 9.977779e-01rs17471528 A G 5.236099e-01rs11804835 C C 9.977759e-01rs11804836 C C 9.977925e-01rs1287623 G G 9.646510e-01 rs13374307 G G 9.989084e-01rs12122008 G G 5.121655e-01rs17431341 A C 5.290652e-01rs881635 G G 9.978737e-01 rs9700130 A A 9.989940e-01 rs11121600 A A 6.160199e-01rs12121542 A A 5.555713e-01rs11121605 T T 8.387705e-01rs12563779 G G 9.982776e-01rs11121607 C G 5.639239e-01rs11121608 G T 5.452936e-01rs12029742 G G 9.973527e-01rs562118 C C 9.738776e-01 rs12133533 A C 9.956655e-01rs11121648 G G 9.077355e-01rs9662691 C C 9.988648e-01 rs11805141 C C 9.928786e-01rs1287635 C C 6.113270e-01

Pipeline for LD-Based Genotype Calling

Datasets

Watson Sequencing data: 74.4 million 454 reads (of 106.5

million reads used in [Wheeler et al 08]) Reference panel: CEU genotypes from Hapmap r23a

phased using the ENT algorithm [Gusev et al. 08] Ground truth: duplicate Affymetrix 500k SNP

genotypes Read length distribution

0

500000

1000000

1500000

2000000

0 100 200 300 400 500 600 700 800 900 1000 1100 1200

Datasets (contd.) NA18507 (Illumina & SOLiD)

Sequencing data: 525 million Illumina reads (36bp, paired) and 764 million SOLiD reads (24 - 44bp, unpaired)

Reference panel: YRI haplotypes from Hapmap r22 excluding NA18507 haplotypes

Ground truth: Hapmap r22 genotypes

Mapping Procedure

454 reads mapped on human genome build 36.3 using the NUCMER tool of the MUMmer package [Kurtz et al 04] with default parameters

Additional filtering: at least 90% of the read length matched to the genome, no more than 10 errors (mismatches or indels)

Reads meeting above conditions at multiple genome positions (likely coming from genomic repeats) were discarded

Illumina and SOLiD reads mapped using MAQ [Li,Ruan&Durbin 08] with default parameters

For reads mapped at multiple positions MAQ returns best position (breaking ties arbitrarily) together with mapping confidence

We filtered bad alignments and discarded paired end reads that are not mapped in pairs using the “submap -p” command

Mapping statistics

DatasetRaw

reads

Raw sequenc

e

Mapped reads

Test SNPs

Avg. mapped SNP cov.

Watson 74.2M 19.7Gb49.8M(67%)

443K 5.85x

NA18507Illumina

525M 18.9Gb397M(78%)

2.85M 6.10x

NA18507SOLiD

764M 21.15Gb324M(42%)

2.85M 3.21x

Concordance vs. avg. coverage(Watson 454 reads)

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Avg. Coverage

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Binomial (Homo)

HMM-Posterior (Homo)

Binomial (Het)

HMM-Posterior (Het)

Tradeoff with call rate (5.85x Watson 454 reads, homo SNPs)

97

97.5

98

98.5

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100

0 10 20 30 40 50

% uncalled

% c

on

cord

ance

1SNP-Posterior Binomial0.01 HMM-Posterior

Tradeoff with call rate (5.85x Watson 454 reads, het SNPs)

80

82

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0 5 10 15 20 25 30 35 40 45 50

% uncalled

% c

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co

rda

nc

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1SNP-Posterior Binomial0.01 HMM-Posterior

Concordance vs. avg. coverage for NA18507 (Illumina & SOLiD reads)

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0 1 2 3 4 5 6

Avg. Coverage

% C

on

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an

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Binomial (Homo) Illumina

HMM-Posterior (Homo) Illumina

Binomial (Het) Illumina

HMM-Posterior (Het) Illumina

Binomial (Homo) SOLiD

HMM-Posterior (Homo) SOLiD

Binomial (Het) SOLiD

HMM-Posterior (Het) SOLiD

Effect of local recombination rate (NA18507 Illumina)

91%

92%

93%

94%

95%

96%

97%

98%

99%

100%

-4.5 -4 -3.5 -3 -2.5 -2 -1.5 -1 -0.5 0 0.5 1 1.5

log(cM/Mb)

% C

on

cord

ance

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

% H

apm

ap S

NP

s

Concordance (homo) Concordance (het)

% of homo % of het

Effect of SNP coverage (NA18507 Illumina)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30

SNP coverage

% C

on

cord

ance

0%

2%

4%

6%

8%

10%

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% H

apm

ap S

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s

Concordance (homo) Concordance (het)% of homo % of het

Posterior decoding algorithm has scalable running time and yields significant improvements in genotyping calling accuracy

Improvement depends on the coverage depth (higher at lower coverage), e.g., accuracy achieved by previously proposed binomial test at 5-6x average coverage is achieved by HMM-based posterior decoding algorithm using less than 1/4 of the reads

Open source code available at http://dna.engr.uconn.edu/software/GeneSeq/

LD-based genotype calling increasingly attractive as reference panels improve (denser, more samples, more populations)

Allows sequencing larger populations for the same cost

Conclusions

Haplotype reconstruction Promising preliminary results using Viterbi-like algorithm

based on HF-HMM

Extension to population sequencing data Removes need for reference panels!

Integrated read mapping, SNP identification, and haplotype reconstruction

EM algorithm that iteratively refines two full haplotype sequences and read mapping probabilities

Integrates read data with LD info available for known SNPs Takes advantage of reads overlapping multiple SNP loci Allows reconstruction of complete sequences for CNVs

Reconstruction of complex haplotype spectra mRNA isoforms, quasispecies

Ongoing work

Acknowledgments

Work supported in part by NSF awards IIS-0546457 and DBI-0543365 to IM and IIS-0803440 to YW. SD and YH performed this research as part of the Summer REU program “Bio-Grid Initiatives for Interdisciplinary Research and Education" funded by NSF award CCF-0755373.

genotype and haplotype reconstruction from low- coverage short sequencing reads ion mandoiu computer...

Documents

x slide

coverage methods

coverage depth

average coverage

allele coverage levy

wu slide

gbday slide

prior work slide