Massively Parallel Sequencing and STR Typing Basics and Forensic Applications

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1 Massively Parallel Sequencing and STR Typing Basics and Forensic Applications Bruce Budowle Institute of Applied Genetics Department of Molecular and Medical Genetics, University of North Texas Health Science Center Fort Worth, Texas USA

2 Outline Describe the technology/methodology Focus primarily on STRs and results to date Provide brief discussion on other markers Consider transfer to the laboratory Suggest additional forensic applications

3 DNA Typing Identification Associations Investigative leads Databases Demands High volume Lead to backlogs High throughput sample processing Special attention situations Expedites Technology developments and enhancements to meet demands

4 Current Forensic DNA Workflows CE-based systems Mainstay of DNA typing Limitations Low capacity for marker multiplexing Tend to multiplex one type of marker One sample per reaction Genotyping based on size only Complex mixture analyses Low depth sequencing coverage

5 Current Forensic DNA Workflows Decision trees required for marker set selection for various sample types Autosomal STRs Y STRs X STRs SNPs mtdna Markers provide no additional lead without a suspect or a database hit Need tests that provide additional investigative leads Ex: facial reconstruction; phenotype and ancestry markers; familial searching

6 The Human Genome Project Scale years ~40 Institutions 8-9X Coverage $3.8 Billion 350 = Human Genome Project - 10 years and cost $2.7 billion

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8 Next Generation Sequencing NGS is no longer next generation, consider: Current Generation Sequencing Massively Parallel Sequencing (MPS)

9 Personalized Sequencing Genome Center Capabilities in the Forensic Laboratory

10 MPS Features Massively parallel sequencing Higher throughput Faster Depth of coverage Lower cost per nucleotide Reduction in error

11 MPS and Gold Standard Sanger Sequencing Difference in scale: hundreds of billions of bases vs. hundreds Each sequence is the product of a single molecule (individually or clonally interrogated) No a priori sequence knowledge required unless targeting specific regions Will likely target sequences (by PCR) for sensitivity of detection

12 Length Variations Length & Sequence Variations MPS and Forensic STR Gold Standard Capillary electrophoresis fragments (size differences) Targeted MPS ~13-24 markers 100s of Markers GTGTGATGTAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGGTGTGTG GTGTGATGTAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGGTGTGTG GTGTGATGTAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGGTGTGTG GTGTGATGTAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGGTGTGTG GTGTGATGTAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGGTGTGTG GTGTGATGTAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGGTGTGTG GTGTGATGTAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGGTGTGTG GTGTGATGTAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGGTGTGTG Increasing size (bp) Massively parallel sequencing Fragments can overlap in size Read counts / noise

13 Size No Longer Matters With CE the marker amplicons tagged with the same fluor had to be different sizes and/or use of mobility modifiers Limits the number of loci that can be placed in a CE-based multiplex With MPS size [for detection purposes] is no longer an issue Each molecule is read independently Enables more markers to be analyzed simultaneously

14 Benefits of MPS in Forensics Real estate is no longer a concern Create a panel with all the markers to avoid having to choose a particular set Exploit SNP variation in current markers and flanking area of novel markers Integrate STRs and SNPs in the reference database allowing the needs of the analysis (or case evidence) to dictate the marker set used Increase success in typing results

15 Massively Parallel Sequencing Immediate goals Large battery of genetic markers can be analyzed simultaneously Far exceeding the current capacity of STRs of CE system Autosomal STRs, Y STRs, X STRs, and SNPs (~400 markers) mtdna Barcoding 16 to 384 (in theory) multiple individuals Economies of scale Can rival current costs of typing a modicum of STRs

16 Types of SNPs Individual Identification SNPs: SNPs that collectively give very low probabilities of two individuals having the same multisite genotype; individualization, High heterozygosity, low Fst Ancestry Informative SNPs: SNPs that collectively give a high probability of an individual s ancestry being from one part of the world or being derived from two or more areas of the world Lineage Informative SNPs: Sets of tightly linked SNPs that function as multiallelic markers that can serve to identify relatives with higher probabilities than simple di-allelic SNPs Phenotype Informative SNPs: SNPs that provide high probability that the individual has particular phenotypes, such as a particular skin color, hair color, eye color, etc. Pharmacogenetic SNPs molecular autopsy

17 General MPS Workflow Extract DNA Fragment genomic DNA or targeted amplification Library preparation Cluster generation/clonal amplification Sequence Data analysis Bioinformatics

18 General Library Preparation Fragmentation End repair Adapter ligation

19 Overview Of Ion Torrent PGM Technology and Workflow Personal Genome Machine Ion 314, 316, 318 Chip v2 Up to 2Gb and 5.5M reads hour runs Depending on read length and chip size Multiplex 96 samples

20 Concept To date, DNA sequencing required imaging technology to support detection of electromagnetic intermediates (light) and specialized nucleotides or other reagents An alternative now exists that is based on nonoptical sequencing on integrated circuits

21 Library and Template Preparation Clonal amplification using emulsion PCR Emulsion droplet IS P Primer dntps Polymerase MgCl 2 Isolate templated ISPs Final Templated ISPs ready for sequencing

22 Sequence Detection by ph

23 Chemistry Reduce sequencing errors: Modified bases Fluorescent bases Laser detection Enzymatic amplification cascades Improve read length limitations: Unnatural bases Faulty synthesis Slow cycle time Deliver highly uniform genome coverage

24 Sequencing: Flows

25 Sequencing: Flows

26 Sequencing: Flows 26 2_IonU_PGM Worfkflow_v13_la

27 Sequencing: Flows 27 2_IonU_PGM Worfkflow_v13_la

28 Data Output is an Ionogram An ionogram is the output of the signals in flow space Must be read up-and-down along with left-to-right Height of bar indicates how many nucleotides incorporated during flow Key Sequence TTT AA TCAG Sequence: AATCTTCTG

29 MiSeq Overview of Illumina Technology and Workflow FGx- Forensic Genomics System Up to 15Gb and 50M reads 4-55 hour runs Depending on read length Multiplex 96 samples

30 The Illumina Sequencing Process

31 Tagmentation Preparing Libraries with Nextera XT adapters

32 Cluster Generation Cluster generation occurs on a flow cell A flow cell is a thick glass slide with channels or lanes Each lane is randomly coated with a lawn of oligos that are complementary to the library adapters Clusters and subsequent sequencing are performed in a contained environment (reduces contamination)

33 Illumina Sequencing Technology Overview 3 5 DNA ( μg) Library Preparation Single molecule array Cluster Growth A G T C C G T G C A A G C 5 T G T A C G A T C A C C C G A T C G A A Sequencing T G T A C G A T Image Acquisition Base Calling

34 Illumina Sequencing-by-Synthesis Natural competition reversible terminators for nucleotide incorporation Add 4 Fl- NTP s + Polymerase Incorporated Fl-NTP is imaged Terminator and fluorescent dye are cleaved from the Fl- NTP

35 Bioinformatics A science in itself Many science experiments are carried out with bioinformatics the new field that merges biology, computer science, and information technology to manage and analyze the data, with the ultimate goal of understanding and modeling living systems." Genomics and Its Impact on Medicine and Society - A 2001 Primer U.S. Department of Energy Human Genome Program

36 Bioinformatics Allele calls Alignment Strand bias Coverage.

37 First Term Coverage Number of times a base is sequenced Sanger sequencing of mtdna --- 1X or 2X Human Genome project --- 6X MPS s 1000sX

38 Multiplexing/Demultiplexing Barcoding/Index Sequences Multiplexing samples with index sequences Allows multiple samples to be sequenced together Index sequences allow reads to be sorted Sample 10 Sample 9 Sample 8 Sample 7 Sample 6 Sample 1 Sample 2 Sample 3 Sample 4 Sample 5

39 STRs Current mainstay for identity testing High discrimination power AATG AATG AATG AATG AATG AATG AATG AATG AATG AATG AATG AATG AATG AATG AATG AATG AATG AATG

40 Challenges of Sequencing STRs Read length Sampling Coverage 146 bases Flanking region Repeat Repeat Repeat Repeat Flanking region Flanking region Repeat Repeat Repeat Repeat Flanking Flanking region Repeat Repeat Repeat Repeat Flanking region Repeat Repeat Repeat

41 STR Motifs Simple: Compound: Complex: TA

42 STRait Razor STR Allele Identification Tool Razor Tool for STR allele calling for concordance studies with CE data Linux-based Perl script that identifies alleles based on length Handles repeat motifs ranging from simple to complex Does not require a reference composed of extensive allelic sequence data

43 STRait Razor Original Image

44 STRait Razor Original Image

45 STRait Razor Produces a colon-delimited text file that lists the alleles called at each STR locus, in order of the number of reads in which each allele is detected Analyzes both single-end and paired-end data Accepts either 1 (single-end) or 2 (paired-end) input FASTQ files Recognizes STR loci in both forward and reverse complement forms

46 STRait Razor v2.0 STRait Razor includes the following new features: Expanded marker set (89 STRs*) Autosomal STRs X-STRs Y-STRs Amelogenin *Y-STR duplications considered single markers Ability to analyze all markers, X only, and Y only

47 STRait Razor v2.0 STRait Razor includes the following new features: Sorting and counting of intra-repeat variants (SNPs within STRs) Ability to queue batches of samples for analysis, for ease of use and improved throughput Improved locus configuration tool for creating your own marker list Electropherogram -style data visualization

48 Sequence Data Sorting

49 Excel-based Data Analysis Length-based genotypes RazorGenotyper Sequence-based genotypes RazorAnalysis Mock-electropherograms RazorHistogram Easy data visualization

50 RazorAnalysis Sequence-based analysis of STRs generates Genotype table Top-20 sequences for each locus Multiple contributor mixtures

51 RazorHistogram Converts genotype tables into mockelectropherograms Easy data visualization

52 Prototype NGS STR Multiplex System (Promega ) 17 STRs CODIS 13 core loci Penta D, Penta E loci D2S1338, D19S433 loci Amelogenin locus Amplicon lengths range is bp

53 Amplicon Sizes Locus Smallest Known Allele Largest Known Allele Penta E D18S D21S TH D3S FGA TPOX D8S vwa Penta D CSF1PO D16S D7S D13S D5S D2S D19S

54 PCR Conditions 1 min at 96 C for polymerase activation 30 cycles 10 s at 94 C for denaturation 1 min at 59 C for primer annealing 30 s at 72 C for primer extension 10 min at 60 C for final extension Total time <90 minutes

55 Sample Preparation and Sequencing Amplified products purified using the MinElute PCR Purification Kit (Qiagen) Libraries were prepared using the TruSeq DNA LT Sample Preparation Kit (Illumina) Indexed DNA library (up to 24) Normalized to 2 nm and pooled Pooled libraries diluted to 10 pm MiSeq v2 (2 x 250 bp) chemistry (Illumina) MiSeq re-sequencing protocol for small genome sequencing

56 Studies for Developing Effective Protocol Size selection SPRI beads v MinElute Library input Amplified product range PCR input Analysis of 24 individuals at 62 and 250 pg Concordance with PowerPlex Fusion System (Promega)

57 Solid Phase Reversible Immobilization Purification/Size selection Selectively bind fragments based on ratio of beads to sample Paramagnetic bead technology Adjusting the ratio eliminates smaller or larger fragment sizes STRs have size ranges Balance between differential amplification of alleles and size selection

58 SPRI Results (n=3) Lower Bead Mixture Ratios (BMRs) recovered larger amplicons better, while higher ratios were better for recovering smaller amplicons No one BMR yielded a superior Allele Coverage Ratios (ACR) at all loci Selected the least spread of heterozygote ACRs per BMR Two out of three samples favored BMRs of 0.65 and 0.70

59 MinElute PCR Purification Kit Purification/Size selection Silica-membrane-based purification of PCR products 70 bp 4 kb size range May capture range of allele sizes more effectively

60 Average ACRs Average Allele Coverage Ratios (n=5) BMRs (0.65, 0.70) v MinElute size selection MinElute STR loci

61 Locus Coverage/ Total Coverage Average Locus/Total Coverage Ratios (n=5) BMRs (0.65, 0.70) v MinElute size selection MinElute STR loci

62 DNA ng/ul Size Selection % AMPXP 70% AMPXP MinElute

63 Results of Size Selection Evaluation Average ACRs for 0.65 and 0.70 BMRs and MinElute method were comparable Locus-to-locus balance was substantially higher with the MinElute method Especially notable at Amelogenin locus MinElute method yielded greater amounts of library for sequencing MinElute method yield was ng Largest yield for 0.65 and 0.70 BMR <29.6 ng

64 PCR Product for Library Preparation TruSeq LT protocol input was ng genomic DNA TruSeq LT was developed and optimized for genomic DNA The STR protocol substitutes single copy target with amplified product Thus, amount of library input should be able to be reduced considerably with PCR-based enrichment of the STR loci

65 ACR Allele Coverage Ratios different quantities of PCR product ng 100 ng 50 ng 25 ng 12.5 ng 6 ng 0 STR loci Sample # pg The loci Amelogenin (X,X) and D18S51 (17, 17) were homozygotes

66 Locus coverage/total coverage Comparison of Locus/Total Coverage different quantities of PCR product ng 100 ng 50 ng 25 ng 12.5 ng 6 ng STR loci Sample # pg

67 Results of Library Input Evaluation Six different library inputs 500 pg for initial PCR Only 6 ng input could not meet 2 nm normalization target 1.33, 1.29, and 1.79 nm Equal volumes of these samples (1 µl) were used when pooling libraries All heterozygous loci had an ACR 0.6 Indicates that <500 pg starting DNA could be placed into the PCR

68 STR Profile - 500pg Input DNA 50 ng library input

69 Sensitivity Study DNA samples (n=3) ranging from 16 to 500 pg 6 ng (and 50 ng) for library input 500 pg results consistent library preparation ACRs 0.58 at all loci Heterozygote imbalance increased with decreasing template Similar results with CE-based methods Generally good results to about 62 pg Further testing at 250 pg and 62 pg

70 Allele Coverage Ratios (n=24) with 250 pg Input DNA

71 Allele Coverage Ratios (n=24) with 62 pg Input DNA

72 Distribution of Individual Locus ACRs

73 Average Locus Coverage (n=24) with 250 pg of input DNA 50,000X

74 Average Locus Coverage (n=24) with 62 pg of input DNA

75 Performance Results 250 pg of input DNA generated balanced ACR at all loci Average ACR at 18 loci > pg of input DNA more imbalance Average ACR > 0.5 Except at the Amelogenin locus Majority complete and some partial profiles

76 STR Profile 62 pg Input DNA 50 ng library input

77 Loci with Intra-Repeat Variation (n=24) Loci Repeats Observations D2S D3S D3S D3S D3S D3S D3S D3S D8S D8S D8S D8S D8S D8S D8S VWA 16 3 VWA 16 4 VWA 17 1 VWA VWA 18 1 VWA 18 5 VWA 19 1 VWA 19 1 Loci Repeats Observations D21S D21S D21S D21S D21S D21S D21S D21S D21S D2S D2S D2S D2S D2S D2S D2S D2S D2S

78 Variation Example at D3S1358 Locus Stutter sequence for 15 alleles AGATAGATAGATAGATAGATAGATAGATAGATAGATAGACAGACAGACAGATAGAT AGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGACAGACAGATAGAT Sequence for 15 alleles AGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGACAGACAGACAGATAGAT AGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGACAGACAGATAGAT

79 Allele and Stutter Distribution for D3S1358 Homozygote

80 Variation Example at D21S11 Locus Allele 30 TAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATATGGATAGATAGATGATA GATAGATAGATATAGATAGATAGACAGACAGACAGACAGACAGATAGATAGATAGATAGAT AGATAGA Minus Stutter Allele 31 TAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATATGGATAGATAGATGATA GATAGATAGATATAGATAGATAGACAGACAGACAGACAGACAGACAGATAGATAGATAGAT AGATAGA Allele 31 TAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATATGGATAGATAGAT GATAGATAGATAGATATAGATAGATAGACAGACAGACAGACAGACAGACAGATAGATAGAT AGATAGATAGA Plus Stutter Allele 30 TAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATATGGATAGATAGAT GATAGATAGATAGATATAGATAGATAGACAGACAGACAGACAGACAGATAGATAGATAGAT AGATAGATAGA

81 Allele and Stutter Distribution for D21S11 Heterozygote

82 Depth of Coverage Minor and Major Contributor Alleles AGAT...AGAT D5S818 Mixture of 2 people Both 11, AGAT...AGAT 12 distinguishable indistinguishable AGAT...AGAG 1000 Stutter from allele Nominal Alleles by Repeat Major Shared Minor

83 Value of Sequence Variation Increased discrimination power Use more data for stutter calculations Potentially enhanced mixture deconvolution Stutter v minor contributor at some loci Note that D5S818, D7S820, and D13S317 loci did not display allele sequence variation Detected by mass spectrometry Variants reside in flanking DNA (Oberacher et al, Hum. Mutat. 29 (2008) )

84 Conclusions Prototype NGS STR Multiplex System (18 loci) for MPS MinElute PCR Purification Kit was better for size selection A wide range of library input quantities could be used with no notable differences in the generated STR profile High depth of coverage and balanced ACRs can be obtained with input DNA 250 pg Similar to that typically observed in CE-based systems Input DNA as little as 62 pg could generate complete or nearly complete (i.e., limited allele drop-out) profiles Studies indicate that this STR multiplex and the Illumina MiSeq system can generate reliable DNA profiles at a sensitivity of detection level that is comparable to that of current CE-based approaches Think about selecting STRs that exhibit intra-allele variation!

85 59 STRs Markers Included in ForenSeq DNA Signature Prep Kit 27 autosomal 24 Y 8 X 173 SNPs 95 HID 54 ancestry 24 phenotypic (2 can be used for ancestry)

86 Beta Testing-32 samples Reproducibility- Control 2800M DNA in triplicate at 1ng Sensitivity- Control 2800M DNA prepared at 1ng, 500pg, 250pg, 100pg, and 50pg. CE concordance- Eleven reference samples were sequenced and typed for autosomal STRs and Y-STRs, using multiplex PCR and CE to evaluate concordance. Casework-type samples- Ten difficult/challenged samples (i.e., degraded, inhibited, or low-quantity (<1ng)) were sequenced. Traditional STR typing and CE were performed for comparison. Mixture detection- Four mixture samples were prepared, each consisting of 2 individuals at a total of 1ng DNA. Each mixture was prepared in a 1:10 ratio. The four mixture samples were as follows: 1:10 male/female; 1:10 female/male; 1:10 male/male; and 1:10 female/female.

87 Study Methods Samples: Reference samples and casework-type samples (aged buccal swabs and human bone samples) Quantification: Determined using the Quantifiler Human DNA Quantification Kit (Thermo Fisher Scientific) Library preparation and sequencing: The ForenSeq DNA Signature Prep Kit (Illumina) to barcode and create libraries; Sequencing on the MiSeq FGx Sequencer (Illumina) Data analysis: ForenSeq UAS STR typing for concordance: AmpFLSTR Identifiler Plus PCR Amplification Kit and the AmpFLSTR Yfiler PCR Amplification Kit and CE was performed using the 3130xl Genetic Analyzer Data analysis: STR data analysis was performed using GeneMapper ID v3.2.1

88 Sensitivity - Alleles Observed

89 Average Depth of Coverage (DoC) for STRs (1ng)

90 Allele Coverage Ratio (ACR) for STRs (1ng)

91 MPS and CE Concordance 11 reference and 10 casework-type samples 546 shared loci

92 CE and MPS STR Loci Detection (Sample 13) A B Minimum Coverage Threshold - 30X

93 Total STR and SNP Loci Observed for Casework-Type Samples

94 Detectable STR Alleles with CE and MPS for Casework-Type Samples

95 Mixtures male/male, female/female, female/male, male/ female 1:10 ratio Assessed by presence of alleles consistent with the minor contributor s type within each mixture Alleles lying within two repeats upstream (minus stutter) or one repeat downstream (plus stutter) were not considered for minor contributor assessment

96 FGx Performance Population studies Phase I: Autosomal frequencies Hispanic, African American, Asian, and Caucasian 200 individuals each Phase II: Y-STR Haplotype frequencies ~1000 individuals across four populations Performance of first 29 population samples

97 Personal Genome Machine and Chip Low cost, convenient, single use device Easy, automatic fluid connections Reduced Footprint 2_IonU_PGM Worfkflow_v13_la

98 Green Mountain Study Data from blind study Green Mountain Study 12 samples Used 1 ng template DNA Markers HID SNPs AIMs Y SNPs STRs Whole Genome mtdna

99 SNP Panels Two SNP Panels HID-Ion AmpliSeq Identity Panel 90 autosomal SNPs 34 upper Y-clade SNPs HID-Ion AmpliSeq Ancestry Panel 165 autosomal SNPs Sequenced with HID-Ion PGM TM workflow Analyzed with HID SNP Genotyper Plugin

100 HID-Ion AmpliSeq Identity Panel Sample Gender 1 Male 3 Female 4 Male 5 Female 6 Male 7 Male 10 Female 13 Female 14 Female 15 Female 16 Male 17 Female

101 rs rs rs rs rs rs L298 P256 P202 rs rs rs rs rs20320 rs rs rs rs rs rs rs rs rs M479 rs rs3900 rs3911 rs rs rs rs rs rs rs HID-Ion AmpliSeq Identity Panel Y-SNPs: Sample 1 C C A G C G T G T T A T T G T C C G G G G C A C C G A A T T T A G C 4 C C C G C G T G T T A T T G T T A G G A G C A C C G A A T T T A G C 6 C C C G C C T G T A A T T G T C A G G A G C A C C C A G T T T A G A 7 C C C G A G T G T T A T T G T C A G G A G C A C C G A G T T G G G C 16 C C C A C G T G T T G T T G T C A G G A G C A C T C A G T C T A T A No evidence of paternal relationships among the males

102 HID-Ion AmpliSeq Identity Panel Haplogroup Informative Y-SNPs Sample Y-Clade Region 1 R1b West Asia, Russian Plain or Central Asia 4 Q Central Asia, the Indian Subcontinent, Siberia 6 J Arabian Peninsula 7 O2 Asia 16 E Africa

103 HID-Ion AmpliSeq Ancestry Panel Sample Biogeographic Ancestry 1 European 3 European 4 Asian 5 European 6 European 7 Asian 10 European 13 African Americans 14 African admix 15 African admix 16 African 17 European

104 STR Panel 10-plex STR Panel Amelogenin D16S359 D3S1358 D5S818 CSF1PO Analyzed data STRait Razor D7S820 D8S1179 TH01 TPOX vwa STR Genotyper Plugin Compared data with genotypes generated on 3130xl Genetic Analyzer Also evaluated of sequence variants within alleles STRait Razor: Warshauer et al. 2013

105 STR Panel Sample AMEL CSF1PO D16S539 D3S1358 D5S818 D7S820 D8S1179 TH01 TPOX vwa 1 X,Y 11,11 8,12 16*,17 12,12 8,10 13,13 8,9.3 10,12 17,18 3 X,X 10,11 12,12 15,18 9,11 11,12 12*,13 9.3,9.3 11,11 16,17 4 X,Y 12,15 12,13 16*,17 10,12 10,10 11,15 9,9.3 8,8 16,17 5 X,X 11,11 10,12 15,15 11,12 10,11 12*,13 6,7 8,8 15,19 6 X,Y 11,12 10,11 15,17 11,13 8,11 12*,13 7,9.3 8,11 16*,17 7 X,Y 10,12 9,12 15*,16 9,10 11,11 12*,13 6,9 10,11 14,18 10 X,X 12,12 11,14 16,18 11,11 10,11 11,13 7,9 9,11 14*,15 13 X,X 12,12 11,12 16*,17 12,13 10,11 14*,14* 6,7 9,11 15,18 14 X,X 12,13 9,12 16*,17 12,12 8,8 13*,13* 6,8 8,9 15*,17 15 X,X 12,12 9,13 16,17 11,12 8,9 10,13 6,9 9,9 15,16 16 X,Y 11,13 9,9 15,17 12,13 8,11 12,13 6,8 8,11 15,17 17 X,X 10,12 11,12 15*,16 12,13 8,8 13*,13* 7,8 8,9 17,19 * Indicates the presence of a sequence variant for that allele STR Genotyper Plugin and STRait Razor produced concordant analysis results

106 Resolving Same Size Alleles

107 STR Panel Locus Allele Number of Varying Sequences vwa 14 2 vwa 15 2 vwa 16 2 D3S D3S D8S D8S D8S

108 Allele variants detected by Mass Spectrometry variant does not reside within repeats STR Panel Sample AMEL CSF1PO D16S539 D3S1358 D5S818 D7S820 D8S1179 TH01 TPOX vwa 1 X,Y 11,11 8,12 16*,17 12,12 8,10 13,13 8,9.3 10,12 17,18 3 X,X 10,11 12,12 15,18 9,11 11,12 12*,13 9.3,9.3 11,11 16,17 4 X,Y 12,15 12,13 16*,17 10,12 10,10 11,15 9,9.3 8,8 16,17 5 X,X 11,11 10,12 15,15 11,12 10,11 12*,13 6,7 8,8 15,19 6 X,Y 11,12 10,11 15,17 11,13 8,11 12*,13 7,9.3 8,11 16*,17 7 X,Y 10,12 9,12 15*,16 9,10 11,11 12*,13 6,9 10,11 14,18 10 X,X 12,12 11,14 16,18 11,11 10,11 11,13 7,9 9,11 14*,15 13 X,X 12,12 11,12 16*,17 12,13 10,11 14*,14* 6,7 9,11 15,18 14 X,X 12,13 9,12 16*,17 12,12 8,8 13*,13* 6,8 8,9 15*,17 15 X,X 12,12 9,13 16,17 11,12 8,9 10,13 6,9 9,9 15,16 16 X,Y 11,13 9,9 15,17 12,13 8,11 12,13 6,8 8,11 15,17 17 X,X 10,12 11,12 15*,16 12,13 8,8 13*,13* 7,8 8,9 17,19 * Indicates the presence of a sequence variant for that allele

109 PGM mtgenome Data (initial study, Seo et al BMC Genomics, in press) Long PCR Whole genome

110 mtdna Genome Sample Haplogroup 1 J1c5 3 H3b 4 U7b 5 H6a1b4 6 H33 7 M7b1a1c1 10 H5n 13 L2a1f 14 H1c 15 H1c 16 L3e1a1a 17 H1c

111 mtdna Genome Sample Haplogroup Population 1 J1c5 European 3 H3b European 4 U7b European 5 H6a1b4 European 6 H33 European 7 M7b1a1c1 Asian 10 H5n European 13 L2a1f African 14 H1c European 15 H1c European 16 L3e1a1a African 17 H1c European

112 mtdna Genome Sample Haplogroup 1 J1c5 3 H3b 4 U7b 5 H6a1b4 6 H33 7 M7b1a1c1 10 H5n 13 L2a1f 14 H1c 15 H1c 16 L3e1a1a 17 H1c

113 Identifying Relationships Genotypes from STRs and Identity SNPs allow for expansion and refinement of the partial pedigree identified with the mitochondrial haplotypes

114 Identifying Relationships STRs: Likelihood Ratio Results: Posterior probability = Combined likelihood ratio = 300 million SNPs: Likelihood Ratio Results: Combined likelihood ratio = 3.34 E46 Internal ThermoFisher kinship algorithm used for calculations

115 Family Reconstructions Intra-allele Variation Sample 16 Sample 17 12,13 13,13 [TCTA]2 [TCTG]1 [TCTA]9 [TCTA]2 [TCTG]1 [TCTA]10 12,13 13,13 [TCTA]13 [TCTA]1 [TCTG]1 [TCTA]11 Sample 14 13,13 13,13 [TCTA]2 [TCTG]1 [TCTA]10 [TCTA]1 [TCTG]1 [TCTA]11 10,13 [TCTA]10 [TCTA]1 [TCTG]1 [TCTA]11 10,13 Sample 15 Granddaughter and Grandmother - 13 allele IBD D8S1179

116 Applying This Technology Reference Samples for Databases Comprehensive typing Better for investigative leads Overall cost benefit Validation Data support reliability and robustness Complete studies Population data for statistical analyses

117 Applying This Technology Casework All types of samples Challenged samples Mixtures Seek additional STRs with intra-repeat/near flanking area SNPs Investigative leads

118 Combining and Bringing On Line MPS with Current Workflow Essentially the same concepts as current capabilities QA Pre and post areas Validation Interpretation Better dynamic range*** Work on automation capabilities Develop workflow for barcoding to minimize contamination Data analyses workflows Run side-by-side with current technology for verification Education and Training

119 UNTHSC MPS STUDIES

120 Identical Twins

121 MPS and Forensic Applications Human ID mtdna STRs SNPs Ancestry Phenotype Mixtures Identical twins Animal ID Pharmacogenetics Molecular autopsy Microbial Forensics Biodefense Human ID Cause of death Time since death

122 Molecular Autopsy Pharmacogenetics Codeine Infant died of morphine overdose at 13 days old Mother was prescribed Tylenol #3 (acetaminophen and codeine) Codeine is metabolized into morphine Mother was an ultra rapid metabolizer

123 Microbial Forensics Biodefense Ex: 2001 Amerithrax case Real-time outbreak surveillance (Ex: 2011 Germany E. coli, Ebola) Biocrime investigations Metagenomics Cause of death (ex: drowning); time since death Human ID

124 Human Microbiome Human body - 10 trillion cells Humans carry over 90 trillion microbes Microbiome is unique to every individual We die with more DNA than we are born with Nature 2010

125 Microbiome-Human ID Fierer et al, 2009

126 Whole Genome Sequencing Privacy Concerns

127 ACKNOWLEDGMENTS #1 Promega Corporation Ann MacPhetridge Jaynish Patel Douglas R. Storts Illumina, Inc. Cydne Holt Joe Valaro Kathy Stephens Research Team Thermo Fisher Robert Lagace Wenchi Liao Joseph Chang Narasimhan Rajagopalan Sharon Wootton Chien-Wei Chang Reina Langit Nnamdi Ihuegbu Carolina Dallett Gloria Lam Jianye Ge