Ion Mandoiu Computer Science and Engineering Department

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Linkage Disequilibrium Based SNP Genotype Calling from 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

Ultra-High Throughput Sequencing Recent massively parallel sequencing technologies deliver orders of magnitude higher throughput compared to classic Sanger sequencing -SBS: Sequencing by Synthesis -SBL: Sequencing by Ligation -Challenges in Genome Assembly: The short read lengths and absence of paired ends make it difficult for assembly software to disambiguate repeat regions, therefore resulting in fragmented assemblies. -New Type of sequencing error: in 454 including incorrect estimates of homopolymer lengths, ‘transposition-like’ insertions (a base identical to a nearby homopolymer is inserted in a nearby nonadjacent location) and errors caused by multiple templates attached to the same bead Roche/454 FLX Titanium 400bp reads 400Mb/10h run ABI SOLiD 2.0 25-35bp reads 3-4Gb/6 day run Helicos HeliScope 25-55bp reads >1Gb/day Illumina Genome Analyzer II 35-50bp reads 1.5Gb/2.5 day run 2

Personal Genomes: The Future is Now! C.Venter Sanger@7.5x J. Watson 454@7.4x NA18507 Illumina@36x SOLiD@12x -SBS: Sequencing by Synthesis -SBL: Sequencing by Ligation -Challenges in Genome Assembly: The short read lengths and absence of paired ends make it difficult for assembly software to disambiguate repeat regions, therefore resulting in fragmented assemblies. -New Type of sequencing error: in 454 including incorrect estimates of homopolymer lengths, ‘transposition-like’ insertions (a base identical to a nearby homopolymer is inserted in a nearby nonadjacent location) and errors caused by multiple templates attached to the same bead 3

Challenges for Genomic Medicine at Single-Base Resolution Medical sequencing focuses on genetic variation (SNPs, CNVs, genome rearrangements) Requires accurate determination of both alleles at variable loci This 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] [Wendl&Wilson 08] predict that 21x coverage is required for sequencing of normal tissue samples based on idealized theory that “neglects any heuristic inputs” What heuristic inputs help? How much can we gain from improved data analysis? 4

Linkage Disequilibrium: Sources & Modeling HMM model of haplotype frequencies F1 F2 Fn … H1 H2 Hn Fi = founder haplotype at locus i, Hi = observed allele at locus i P(Fi), P(Fi | Fi-1) and P(Hi | Fi) estimated from reference panel such as Hapmap For given haplotype h with n SNPs, P(H=h|M) can be computed in O(nK2) using forward algorithm, where K=#founders

Pipeline for LD-Based Genotype Calling Read sequences Reference genome sequence GTCGCCCAGGCTGGTGTGCAGTGGTGCAACCTCAGCTCACTGCAACCTCTGCCTCCAGGTTCAAGCAATT TCAGTGAGGGTTTTTGTTTTGTTTTGTTTTGTTTTGTTTTGTTTTGTTTTTGAGACAGAATTTTGCTCTT >gnl|ti|1779718824 name:EI1W3PE02ILQXT >gnl|ti|1779718825 name:EI1W3PE02GTXK0 TAGTAAAGATGGGGTTTCACTACGTTGGCTGAGCTGTTCTCGAACTCCTGACCTCAAATGAC CTCTGCCTCAGCCTCCCAAGTAGCTGGGATTACAGGCGGGCGCCACCACGCCCAGCTAATTTTGTATTGT AGGTACTTTGAGTCTGGGGGAGACAAAGGAGTTAGAAAGAGAGAGAATAAGCACTTAAAAGGCGGGTCCA TAATATGTTTATTTGTTTTGCTGCTGTTGAGTTGTACAATGTTGGGGAAAACAGTCGCACAACACCCGGC TCAGAATACCTGTTGCCCATTTTTATATGTTCCTTGGAGAAATGTCAATTCAGAGCTTTTGCTCAGCTTT GGGGGCCCGAGCATCGGAGGGTTGCTCATGGCCCACAGTTGTCAGGCTCCACCTAATTAAATGGTTTACA Mapped reads GTCGCCCAGGCTGGTGTGCAGTGGTGCAACCTCAGCTCACTGCAACCTCTGCCTCCAGGTTCAAGCAATT TCAGTGAGGGTTTTTGTTTTGTTTTGTTTTGTTTTGTTTTGTTTTGTTTTTGAGACAGAATTTTGCTCTT >gnl|ti|1779718824 name:EI1W3PE02ILQXT >gnl|ti|1779718825 name:EI1W3PE02GTXK0 TAGTAAAGATGGGGTTTCACTACGTTGGCTGAGCTGTTCTCGAACTCCTGACCTCAAATGAC CTCTGCCTCAGCCTCCCAAGTAGCTGGGATTACAGGCGGGCGCCACCACGCCCAGCTAATTTTGTATTGT AGGTACTTTGAGTCTGGGGGAGACAAAGGAGTTAGAAAGAGAGAGAATAAGCACTTAAAAGGCGGGTCCA TAATATGTTTATTTGTTTTGCTGCTGTTGAGTTGTACAATGTTGGGGAAAACAGTCGCACAACACCCGGC TCAGAATACCTGTTGCCCATTTTTATATGTTCCTTGGAGAAATGTCAATTCAGAGCTTTTGCTCAGCTTT GGGGGCCCGAGCATCGGAGGGTTGCTCATGGCCCACAGTTGTCAGGCTCCACCTAATTAAATGGTTTACA GTCGCCCAGGCTGGTGTGCAGTGGTGCAACCTCAGCTCACTGCAACCTCTGCCTCCAGGTTCAAGCAATT TCAGTGAGGGTTTTTGTTTTGTTTTGTTTTGTTTTGTTTTGTTTTGTTTTTGAGACAGAATTTTGCTCTT >gnl|ti|1779718824 name:EI1W3PE02ILQXT >gnl|ti|1779718825 name:EI1W3PE02GTXK0 TAGTAAAGATGGGGTTTCACTACGTTGGCTGAGCTGTTCTCGAACTCCTGACCTCAAATGAC CTCTGCCTCAGCCTCCCAAGTAGCTGGGATTACAGGCGGGCGCCACCACGCCCAGCTAATTTTGTATTGT AGGTACTTTGAGTCTGGGGGAGACAAAGGAGTTAGAAAGAGAGAGAATAAGCACTTAAAAGGCGGGTCCA TAATATGTTTATTTGTTTTGCTGCTGTTGAGTTGTACAATGTTGGGGAAAACAGTCGCACAACACCCGGC TCAGAATACCTGTTGCCCATTTTTATATGTTCCTTGGAGAAATGTCAATTCAGAGCTTTTGCTCAGCTTT GGGGGCCCGAGCATCGGAGGGTTGCTCATGGCCCACAGTTGTCAGGCTCCACCTAATTAAATGGTTTACA >gi|88943037|ref|NT_113796.1|Hs1_111515 Homo sapiens chromosome 1 genomic contig, reference assembly CTTTGAAGTATTCTGAGACTTGTAGGAAGGTGAAGTAAATATCTAATATAATTGTAACAAGTAGTGCTTG GAATTCTGTGAAAGCCTGTAGCTATAAAAAAATGTTGAGCCATAAATACCATCAGAAATAACAAAGGGAG CTCCTAATTCTGGAGTAGGGGCTAGGCTAGAATGGTAGAATGCTCAAAAGAATCCAGCGAAGAGGAATAT AATACAGATGGATTCAGGAGAGGTACTTCCAGGGGGTCAAGGGGAGAAATACCTGTTGGGGGTCAATGCC GATTGTATGTTTTTGATTATTTTTTGTTAGGCTGTGATGGGCTCAAGTAATTGAAATTCCTGATGCAAGT TCCTTACTAAATTGATGAGACTTAAACCCATGAAAACTTAACAGCTAAACTCCCTAGTCAACTGGTTTGA AATGTACTTTCTCAGATACAGAACACCCTTGGTCAATTGAATACAGATCAATCACTTTAAGTAAGCTAAG TTCTGAGATAATAAATAGGACTGTCCCATATTGGAGGCCTTTTTGAACAGTTGTTGTATGGTGACCCTGA ATCTACTTCTCCAGCAGCTGGGGGAAAAAAGGTGAGAGAAGCAGGATTGAAGCTGCTTCTTTGAATTTAC >gi|88943037|ref|NT_113796.1|Hs1_111515 Homo sapiens chromosome 1 genomic contig, reference assembly CTTTGAAGTATTCTGAGACTTGTAGGAAGGTGAAGTAAATATCTAATATAATTGTAACAAGTAGTGCTTG GAATTCTGTGAAAGCCTGTAGCTATAAAAAAATGTTGAGCCATAAATACCATCAGAAATAACAAAGGGAG CTCCTAATTCTGGAGTAGGGGCTAGGCTAGAATGGTAGAATGCTCAAAAGAATCCAGCGAAGAGGAATAT AATACAGATGGATTCAGGAGAGGTACTTCCAGGGGGTCAAGGGGAGAAATACCTGTTGGGGGTCAATGCC GATTGTATGTTTTTGATTATTTTTTGTTAGGCTGTGATGGGCTCAAGTAATTGAAATTCCTGATGCAAGT TCCTTACTAAATTGATGAGACTTAAACCCATGAAAACTTAACAGCTAAACTCCCTAGTCAACTGGTTTGA AATGTACTTTCTCAGATACAGAACACCCTTGGTCAATTGAATACAGATCAATCACTTTAAGTAAGCTAAG TTCTGAGATAATAAATAGGACTGTCCCATATTGGAGGCCTTTTTGAACAGTTGTTGTATGGTGACCCTGA ATCTACTTCTCCAGCAGCTGGGGGAAAAAAGGTGAGAGAAGCAGGATTGAAGCTGCTTCTTTGAATTTAC >gi|88943037|ref|NT_113796.1|Hs1_111515 Homo sapiens chromosome 1 genomic contig, reference assembly CTTTGAAGTATTCTGAGACTTGTAGGAAGGTGAAGTAAATATCTAATATAATTGTAACAAGTAGTGCTTG GAATTCTGTGAAAGCCTGTAGCTATAAAAAAATGTTGAGCCATAAATACCATCAGAAATAACAAAGGGAG CTCCTAATTCTGGAGTAGGGGCTAGGCTAGAATGGTAGAATGCTCAAAAGAATCCAGCGAAGAGGAATAT AATACAGATGGATTCAGGAGAGGTACTTCCAGGGGGTCAAGGGGAGAAATACCTGTTGGGGGTCAATGCC GATTGTATGTTTTTGATTATTTTTTGTTAGGCTGTGATGGGCTCAAGTAATTGAAATTCCTGATGCAAGT TCCTTACTAAATTGATGAGACTTAAACCCATGAAAACTTAACAGCTAAACTCCCTAGTCAACTGGTTTGA AATGTACTTTCTCAGATACAGAACACCCTTGGTCAATTGAATACAGATCAATCACTTTAAGTAAGCTAAG TTCTGAGATAATAAATAGGACTGTCCCATATTGGAGGCCTTTTTGAACAGTTGTTGTATGGTGACCCTGA ATCTACTTCTCCAGCAGCTGGGGGAAAAAAGGTGAGAGAAGCAGGATTGAAGCTGCTTCTTTGAATTTAC Quality scores 27 42 35 21 6 28 43 36 22 10 27 42 35 20 6 28 43 36 22 9 28 28 28 28 26 28 28 40 34 14 44 36 23 13 2 27 42 35 21 7 >gnl|ti|1779718824 name:EI1W3PE02ILQXT 26 26 37 29 28 28 28 28 27 28 28 28 37 28 27 27 28 36 28 37 40 34 18 3 28 28 28 27 33 24 26 28 28 28 40 33 14 28 36 27 28 43 36 22 9 28 44 36 24 14 4 28 28 28 27 28 26 26 35 26 28 28 28 24 28 37 29 28 19 28 26 37 29 26 39 33 13 37 28 28 28 33 23 28 33 23 28 36 27 33 23 28 35 25 28 28 36 27 36 27 28 28 28 27 28 28 28 24 28 28 27 28 28 37 29 36 27 27 28 27 28 21 24 28 27 41 34 15 28 36 27 26 28 24 35 27 28 40 34 15 27 42 35 21 6 28 43 36 22 10 27 42 35 20 6 28 43 36 22 9 28 28 28 28 26 28 28 40 34 14 44 36 23 13 2 27 42 35 21 7 >gnl|ti|1779718824 name:EI1W3PE02ILQXT 26 26 37 29 28 28 28 28 27 28 28 28 37 28 27 27 28 36 28 37 40 34 18 3 28 28 28 27 33 24 26 28 28 28 40 33 14 28 36 27 28 43 36 22 9 28 44 36 24 14 4 28 28 28 27 28 26 26 35 26 28 28 28 24 28 37 29 28 19 28 26 37 29 26 39 33 13 37 28 28 28 33 23 28 33 23 28 36 27 33 23 28 35 25 28 28 36 27 36 27 28 28 28 27 28 28 28 24 28 28 27 28 28 37 29 36 27 27 28 27 28 21 24 28 27 41 34 15 28 36 27 26 28 24 35 27 28 40 34 15 27 42 35 21 6 28 43 36 22 10 27 42 35 20 6 28 43 36 22 9 28 28 28 28 26 28 28 40 34 14 44 36 23 13 2 27 42 35 21 7 >gnl|ti|1779718824 name:EI1W3PE02ILQXT 26 26 37 29 28 28 28 28 27 28 28 28 37 28 27 27 28 36 28 37 40 34 18 3 28 28 28 27 33 24 26 28 28 28 40 33 14 28 36 27 28 43 36 22 9 28 44 36 24 14 4 28 28 28 27 28 26 26 35 26 28 28 28 24 28 37 29 28 19 28 26 37 29 26 39 33 13 37 28 28 28 33 23 28 33 23 28 36 27 33 23 28 35 25 28 28 36 27 36 27 28 28 28 27 28 28 28 24 28 28 27 28 28 37 29 36 27 27 28 27 28 21 24 28 27 41 34 15 28 36 27 26 28 24 35 27 28 40 34 15 SNP genotype calls Hapmap genotypes … rs12095710 T T 9.988139e-01 rs12127179 C T 9.986735e-01 rs11800791 G G 9.977713e-01 rs11578310 G G 9.980062e-01 rs1287622 G G 8.644588e-01 rs11804808 C C 9.977779e-01 rs17471528 A G 5.236099e-01 rs11804835 C C 9.977759e-01 rs11804836 C C 9.977925e-01 rs1287623 G G 9.646510e-01 rs13374307 G G 9.989084e-01 rs12122008 G G 5.121655e-01 rs17431341 A C 5.290652e-01 rs881635 G G 9.978737e-01 rs9700130 A A 9.989940e-01 rs11121600 A A 6.160199e-01 rs12121542 A A 5.555713e-01 rs11121605 T T 8.387705e-01 rs12563779 G G 9.982776e-01 rs11121607 C G 5.639239e-01 rs11121608 G T 5.452936e-01 rs12029742 G G 9.973527e-01 rs562118 C C 9.738776e-01 rs12133533 A C 9.956655e-01 rs11121648 G G 9.077355e-01 rs9662691 C C 9.988648e-01 rs11805141 C C 9.928786e-01 rs1287635 C C 6.113270e-01 NOTE: P(g|r) is NP-Hard… 90 209342 16 F 0 0 21100012010021001001100?100201?10111110111?0212000 18 F 15 16 2110001?0100210010011002122201210211?1221220212000 21120010012001201001120010110101011111011110212000 15 M 0 0 8 F 0 0 2110001001000200122110001111011100111?121210222000 7 M 0 0 011202100120022012211200101101210211122111?0120000 9 M 0 0 21100010010002001221100010110111001112121210220000 12 F 9 10 11 M 7 8 011?001?012002201221120010?10121021112211110120000 21100210010002001221100012110111001112121210222000 90 209342 16 F 0 0 21100012010021001001100?100201?10111110111?0212000 18 F 15 16 2110001?0100210010011002122201210211?1221220212000 21120010012001201001120010110101011111011110212000 15 M 0 0 8 F 0 0 2110001001000200122110001111011100111?121210222000 7 M 0 0 011202100120022012211200101101210211122111?0120000 9 M 0 0 21100010010002001221100010110111001112121210220000 12 F 9 10 11 M 7 8 011?001?012002201221120010?10121021112211110120000 21100210010002001221100012110111001112121210222000 90 209342 16 F 0 0 21100012010021001001100?100201?10111110111?0212000 18 F 15 16 2110001?0100210010011002122201210211?1221220212000 21120010012001201001120010110101011111011110212000 15 M 0 0 8 F 0 0 2110001001000200122110001111011100111?121210222000 7 M 0 0 011202100120022012211200101101210211122111?0120000 9 M 0 0 21100010010002001221100010110111001112121210220000 12 F 9 10 11 M 7 8 011?001?012002201221120010?10121021112211110120000 21100210010002001221100012110111001112121210222000 6

Genotype Calling Accuracy vs. Coverage Watson/454 reads NA18507/Illumina reads

Conclusions & Ongoing Work Exploiting LD information yields significant improvements in genotyping calling accuracy and/or cost reduction Accuracy achieved by previously proposed binomial test is achieved by HMM-based posterior decoding algorithm using less than 1/4 of the reads Ongoing work Modeling ambiguities in read mapping Haplotype inferrence Extension to population sequencing data (removing need for reference panels) ACKNOWLEDGEMENTS This work was supported in part by NSF under 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 under award CCF-0755373.