perspectives on the future of forensic genetics

Perspectives on the Future of Forensic Genetics

Bruce BudowleInstitute of Applied Genetics

Department of Molecular and Medical Genetics,

University of North Texas Health Science Center

Fort Worth, Texas USA

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The Motivation


Goals

• Identification

• Associations

• Exclusions

• Investigative leads

• Databases

• Demands• High volume

• Lead to backlogs

• High throughput sample processing

• Special attention situations

• Expedites

• Technology developments and enhancements to meet demands


• Redefining Forensic Genetics

• Sample Collection

• Low Copy Number Typing

• Statistical Analyses• For LCN and Mixture

• Rapid DNA Typing

• Next Generation Sequencing***• Novel Investigative Leads

Prospects for the Future


• No field has embraced molecular biology as a diagnostic tool more so than forensics

• The “Gold Standard” of the forensic science disciplines

• Such accolades while somewhat deserved can be detrimental to quality and performance• become complacent

• Need to question, critique, and build

• Still much to improve upon

Forensic Genetics


• The application of genetics for the resolution of legal cases (Jobling and Gill 2004)

• The application of genetics to human and nonhuman material (in the sense of a science with the purpose of studying inherited characteristics for the analysis of inter- and intraspecific variations in populations) for the resolution of legal conflicts (FSI Genetics 2007)

• Commonly used descriptions that reflect typical applications – support in resolution of established cases (suspect/victim….)

Definition of Forensic Genetics


• Many applications that do not begin with courtroom type scenario

• Investigative leads• Database searches

• Familial searches

• Phenotype

• Ancestry

• Cause and manner of death

• etc

Expand Definition


Crime Scene Investigation

• Recovery of Sample• Swab

• Adsorption/absorption• Efficiency of sample recovery from scene

• Release from swab matrix• DNA yield from swab

• These two desirable features must be balanced

• Opposing properties


Diversity of SwabsFab Swab

GE EasiCollect

Bode Buccal Collector FTA Paper

Hydraflock Swab with breakoff tab

Cotton-Tip Swab

• Omni Swab


DiomicsSwab Material

• Features that suggest this material may perform better than current swabs• Polymer

• Highly absorptive

• Potentially can dissolve swab

• Better is defined as greater DNA yield; and quality of DNA typing results


DNA Yield - SalivaX Swab vsCopan Swab


Initial Low-Copy Number Work

• Early work on “touch samples”:• van Oorschot, R. A. and Jones, M. K. (1997) Nature.

387(6635): 767

• Findlay, I., et al (1997) Nature. 389(6651): 555-556

• Application to routine limited quantity casework:

• Gill, P., et al (2000) Forensic Sci. Int. 112(1): 17-40

• Whitaker, J. P., et al (2001) Forensic Sci. Int. 123(2-3): 215-223

• Gill, P. (2001) Croatian Medical Journal 42(3): 229-32

• Note that Touch Samples do not necessarily equate to LCN samples


Comparison of STR Results with Different DNA Amounts

1ng Standard Result

Allele Drop In Allele Drop Out

Locus Drop Out

Increased Stutter (43%)

Allele Drop Out

33pg LT-DNA: 2 repsHeterozygote peak imbalance (57%)


Hypothesis Driven Methodologies

• Gold standard limitation is most evident in mixture interpretation

• Substantial subjectivity

• But good sign is substantial discussion• The real strength of the field

• Present and future issues will be in hypothesis formulation, interpretation of results, documentation and communication• mtDNA led the way

• Education


Models

From J Bright Presentation 2013


Allele Drop-out and Drop-in Rates

• D: the probability of drop-out of one allele of a heterozygote ( )• Depends on locus and DNA quantity; from 0.0 to 0.66

have been reported• Can be as high as 100% in a specific case

• D2: the homozygote drop-out probability• D2 ≈ ½ D2;

•

• C: drop in probability • Some include both stutter and contamination together

1D D= −

2 21D D= −

Balding. Interpreting low template DNA profiles. Forensic Sci Int Genet. 2009 Dec;4(1):1-10.


Hd: V + Unknown = E

Victim Unknown Likelihood

AB AA

AB BB

AB CC

AB DD

AB AB

AB AC

AB AD

AB BC

AB BD

AB CD

Pr( | ,Unknown, )pABC AB H

2

2

Pr( ) Pr( | , ) Pr( )

Pr( ) Pr( | , ) Pr( )

Pr( ) Pr( | , ) Pr( )

Pr( ) Pr( | , ) Pr( )

Pr( ) Pr( | , ) Pr( )

Pr( ) Pr( | , ) Pr( )

Pr( ) Pr

AA ABC AB AA AA DDC

BB ABC AB BB BB DDC

CC ABC AB CC CC DDD C

DD ABC AB DD DD DDD C

AB ABC AB AB AB DDC

AC ABC AB AC AC DDDC

AD

× = ×

× = ×

× = ×

× = ×

× = ×

× = ×

× ( | , ) Pr( )

Pr( ) Pr( | , ) Pr( )

Pr( ) Pr( | , ) Pr( )

Pr( ) Pr( | , ) Pr( )

ABC AB AD AD DDDC

BC ABC AB BC BC DDDC

BD ABC AB BD BD DDDC

CD ABC AB CD CD DDDC

= ×

× = ×

× = ×

× = ×

1

2

3

4

5

6

7

8

9

10

w

w

w

w

w

w

w

w

w

w

××××××××××


Statistical Models

• Estimate the drop-out or drop-in rates through the observed peak heights or the quantity of input DNA (Logistic Model)• The higher the observed peak, the

lower the drop-out rate

• The parameters (β0 and β1) depend on the process used to generate DNA profiles• # of cycles, the kind of samples used; the allelic

composition; etc.

0 1

0 1

ˆexp( log( ))ˆ( | )ˆ1 exp( log( ))

HP D H

H

β ββ β

+=+ +


STRMIX

http://strmix.esr.cri.nz/


Issues for LCN Typing

• Do models reflect reality?• Variation of profile with low level DNA

• What constitutes an effective validation for drop out and drop in?

• Multiple amplifications vs one amplification


Timely Response

• DNA database of known offenders and forensic samples

• Timeliness of the match is critical and can reduce the resources required to solve the crime

DNA database


Traditional DNA Typing Process

DNA Extraction

DNA Quantification

DNA Amplification

CEData

analysis

2 hours 2 hours 3 hours 30 min 2-3 hours


Direct Amplification Workflow

Fast with Direct Amplification


Five/Four Lab Processes in One “Field” Device

Human AnalystProfile MatchHours - Weeks

• Disposable microfluidic biochip technology integrates and automates five laboratory processes into one field device operated by non-expert users

XXX


Rapid DNA Single Platform Systems


A “Trainable” Component of the System


Potential Applications

• Law Enforcement—Booking desk/arrestee processing to generate investigative leads

• Forensic DNA Laboratories

• Borders and Ports of Entry

• Immigration/Illegal Adoption/Human Trafficking

• Victim Identification at Mass Disaster Sites

• Military and Military Intelligence


Areas of Interest

• STRs

• Mitochondrial DNA

• HID SNPs

• Ancestry SNPs

• Phenotype SNPs

• Mixtures

• Pharmacogenetics

• Microbial Forensics

• …going to need a bigger boat

Image courtesy: geek.com


• 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

Current Forensic DNA Workflows


Next Generation Sequencing

• NGS is no longer next generation, consider:• Current Generation Sequencing

• Massively Parallel Sequencing (MPS)

• Sequencing on steroids


MPS andForensic STR“GoldStandard”

~13-24 markers

• Capillary electrophoresis fragments (size differences)

Length Variations

Increasing size (bp)

100s of Markers

Massively parallel sequencingFragments can overlap in sizeRead counts / noise

Length & Sequence Variations

Targeted MPS

GTGTGATGTA ATA ATA ATA ATA ATA ATA ATA ATA ATA ATA ATA ATA ATA ATAGGTGTGTG G G G G G G G G G G G G G G









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


AATG AATG AATG AATG AATG



AATG

AATG AATG

STRs

• Current mainstay for identity testing• High discrimination power


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


General MPS Workflow

• Extract DNA

• Fragment genomic DNA or targeted amplification

• Library preparation

• Cluster generation/clonal amplification

• Sequence

• Data analysis• Bioinformatics


Overview Of Ion Torrent PGM™Technology and Workflow

• Personal Genome Machine• Ion 314, 316, 318 Chip v2

• Up to 2Gb and 5.5M reads

• 2.3-7.3 hour runs• Depending on read length and chip size

• Multiplex 96 samples


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 non-optical sequencing on integrated circuits


Chemistry


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

• In principle similar to pyrosequencing• But less complex


• 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

Data Output is an Ionogram

Sequence: AATCTTCTG…

Key Sequence

TTT

TCAG

AA


Overview of IlluminaTechnology and Workflow

• MiSeq• FGx- Forensic Genomics System

• Up to 15Gb and 50M reads

• 4-55 hour runs• Depending on read length

• Multiplex 96 samples


Library Preparation

DNA(0.1-5.0 µg)

1 2 3 7 8 94 5 6T G T A C G A T …

Illumina Sequencing Technology Overview

CC

C

CC

CC

AA

AA

AA

TT

TT

GG

GG

GG

GG

Sequencing

Single molecule array

Cluster Growth

Image Acquisition Base Calling

5’

5’3’

TGTACGATCACCCGATCGAA


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


CACACTTGCATGTGAGAGCTTCTAATATCTAAATTAATGTTGAATCATTATTCAGAAACAGAGAGCTAACTGTTATCCCATCCTGACTTTATTCTTTATG AGAAAAATACAGTGATTCCAAGTTACCAAGTTAGTGCTGCTTGCTTTATAAATGAAGTAATATTTTAAAA GTTGTGCATAAGTTAAAATTCAGAAATAAAACTTCATCCTAAAACTCTGTG TGTTGCTTTAAATAATCAGAGCATCTGC TACTTAATTTTTTGTGTGTGGGTGCACAATAGATGTTTAATGAGATCCTGTCATCTGTCTGCTTTTTTATTGTAAAACAGGAGGGGTTTTAATACTGGAGGAACAACTGATGTACCTCTGAAAAGAGA AGAGATTAGTTATTAATTGAATTGAGGGTTGTCTTGTCTTAGTAGCTTTTATTCTCTAGGTACTATTTGATTATGATTGTGAAAATAGAATTTATCCCTCATTAAATGTAAAATCAACAGGAGAATAGCAAAAACTTATGAGATAGAT GAACGTTGTGTGAGTGGCATGGTTTAATTTGTTTGGAAGAAGCACTTGCCCCAGAAGATACACAATGAAATTCATGTTATTGAGTAGAGTAGTAATACAGTGTGTTCCCTTGTGAAGTTCATAACCAAGAATTTTAGTAGTGGATAGGTAGGCTGAATAACTGACTTCCTATC ATTTTCAGGTTCTGCGTTTGATTTTTTTTACATATTAATTTCTTTGATCCACATTAAGCTCAGTTATGTATTTCCATTTTATAAATGAAAAAAAATAGGCACTTGCAAATGTC AGATCACTTGCCTGTGGTCATTCGGGTAGAGATTTGTGGAGCTAAGTTGGTCTTAATCAAATGTCAAGCTTTTTTTTTTCTTATAAAATATAGGTTTTAATATGAGTTTTAAAATAAAAT TAATTAGAAAAAGGCAAATTACTCAATATATATAAGGTATTGCATTTGTAATAGGTAGGTATTTCATT TTCTAGTTATGGTGGGATATTATTCAGACTATAATTCCCAATGAAAAAACT TTAAAAAATGCTAGTGATTGCACACTTAAAACACCTTTTAAAAAGCATTGAGAGCTTATAAAATTTTA ATGAGTGATAAAACCAAATTTGAAGAGAAAAGAAGAACCCAGAGAGGTAAG GATATAACCTTACCAGTTGCAATTTGCCGATCTCTACAAATATTAATATTTATTTTGACAGTTTCAGGGTGAATGAGAAAGAAACCAAAACCCAAGACTAGCATATGTTGTCTTCTTAAGGAGCCCTCCCCTAAAAGATTGAGATGACCAAATCTTATACTCTCAGCATAAGGTGAACCAGACAGACCTAAAGCAGTGGTAGCTTGGATCCACTACTTGGGTTTGTGTGTGGCGTGACTCAGGTAATCTCAAGAATTGAACATTTTTTTAAGGTGGTCCTACTCATACACTGCCCAGGTATTAGGGAGAAGCAAATCTGAATGCTTTATAAAAATACCCTAAAGCTAAATC TTACAATATTCTCAAGAACACAGTGAA ACAAGGCAAAATAAGTTAAAATCAACAAAAACAACATGA AACATAATTAGACACACAAAGACTTCAAACATTGGAAAATACCAGAGAAAG ATAATAAATATTTTACTCTTTAAAAATTTAGTTAAAAGCTTAAACTAATTGTAGAGAAAA A ACTATGTTAGTATTATATTGTAGATGAAATAAGCAAAACATTTAAAATACA AATGTGATTACTTAAATTAAATATAATAGATAATTTACCACCAGATTAGATACCATTGAAGGAATAAT TAATATACTGAAATACAGGTCAGTAGAATTTTTTTCAATTCAGCATGGAGA TGTAAAAAATGAAAATTAATGCAAAAAATAAGGGCACAAAAAGAAATGAGTAATTTTGATCAGAAA TGTATTAAAATTAATAAACTGGAAATTTGACATTTAAAAAAAGCATTGTCA TCCAAGTAGATGTGTCTATTAAATAGTTGTTCTCATATCCAGTAATGTAATTATTATTCCCTCTCATGCAGTTCAGATTCTGGGGTAATCTTTAGACATCAGTTTTGTCTTTTATATTATTTATTCTGTTTACTACATTTTATTTTGCTAATGATATTTTTAATTTCTGACATTCTGGAGTATTGCTTGTAAAAGGTATTTTTAAAAATACTTTATGGTTATTTTTGTGATTCCTATT CCTCTATGGACACCAAGGCTATTGACATTTTCTTTGGTTTCTTCTGTTACTTCTATTTTCTTAGTGTTTATATCATTTCATAGATAGGATATTCTTTATTTTTTATTTTTATTTAAATATTT GGTGATTCTTGGTTTTCTCAGCCATCTATTGTCAAGTGTTCTTATTAAGCATTATTATTAAATAAAGATTATTT CCTCTAATCACATGAGAATCTTTATTTCCCCCAAGTAATTGAAAATTGCAATGCCATGCTGCCATGTGGTACAGCATGGGTTTGGGCTTGCTTTCTTCTTTTTTTTTTAACTTTTATTTTAGGTTTGGGAGTACCTGTGAAAGTTTGTTATATAGGTAAACTCGTGTCACCAGGGTTTGTTGTACAGATCATTTTGTCACCTAGGTACCAAGTACTCAACAATTATTTTTCCTGCTCCTCTGTCTCCTGTCACCCTCCACTCTCAAGTAGACTCCGGTGTCTGCTGTTCCATTCTTTGTGTCCATGTGTTCTCATAATTTAGTTCCCCACTTGTAAGTGAGAACATGCAGTATTTTCTAGTATTTGGTTTTTTGTTCCTGTGTTAATTTGCCCAGTATAATAGCCTCCAGCTCCATCCATGTTACTGCAAAGAACATGATCTCATTCTTTTTTATAGCTCCATGGTGTCTATATACCACATTTTCTTTATCTAAACTCTTATTGATGAGCATTGAGGTGGATTCTATGTCTTTGCTATTGTGCATATTGCTGCAAGAACATTTGTGTGCATGTGTCTTTATGGTAGAATGATATATTTTCTTCTGGGTATATATGCAGTAATGCGATTGCTGGTTGGAATGGTAGTTCTGCTTTTATCTCTTTGAGGAATTGCCATGCTGCTTTCCACAATAGTTGAACTAACTTACACTCCCACTAACAGTGTGTAAGTGTTTCCTTTTCTCCACAACCTGCCAGCATCTGTTATTTTTTGACATTTTAATAGTAGCCATTTTAACTGGTATGAAATTATATTTCATTGTGGTTTTAATTTGCATTTCTCTAATGATCAGTGATATTGAGTTTGTTTTTTTTCACATGCTTGTTGGCTGCATGTATGTCTTCTTTTAAAAAGTGTCTGTTCATGTACTTTGCCCACATTTTAATGGGGTTGTTTTTCTCTTGTAAATTTGTTTAAATTCCTTATAGGTGCTGGATTTTAGACATTTGTCAGACGCATAGTTTGCAAATAGTTTCTCCCATTCTGTAGGTTGTCTGTTTATTTTGTTAATAGTTTCTTTTGCTATGCAGAAGCTCTTAATAAGTTTAATGAGATCCTGATATGTTAGGCTTTGTGTCCCCACCCAAATCTCATCTTGAATTATATCTCCATAATCACCACATGGAGAGACCAGGTGGAGGTAATTGAATCTGGGGGTGGTTTCACCCATGCTGTTCTTGTGATAGTGAATGAGTTCTCACGAGATCTAATGGTTTTATGAGGGGCTCTTCCCAGCTTTGCCTGGTACTTCTCCTTCCTGCCGCTTTGTGAAAAAGGTGCATTGCGTCCCTTTCACCTTCTTCTATAATTGTAAGTTTCCTGAGGCCTTCCCAGCCATGCTGAACTTCAAGTCAATTAAACCTTTTTCTTTATAAATTACTCAGTCTCTGGTGGTTCTTTATAGCAGTGTGAAAATGGACTAATGAAGTTCCCATTTATGAATTTTTGCTTTTGTTGCAATTGCTTTTGACATCTTAGTCATGAAATCCTTGCCTGTTCTAAGTACAGGACGGTATTGCCTAGGTTGTCTTCCAGGGTTTTTCTAATTTTGTGTTTTGCATTTAAGTGTTTAATCCATCTTGAGTTGATTTTTGTATATTGTGTAAGGAAGGGGTCCAGTTTCAATCTTTTGCATATGGCTAGTTAGTTATCCCAGTACCATTTATTGAAAAGACAGTCTTTTCCCCATCGCTCGTTTTTGTCAGTTTTATTGATGATCAGATAATCATAGCTGTGTGGCTTTATTTCTGGGTTCTTTATTCTGTTCTATTGGTTTATGTCCCTGTTTTTGTGCCAGTACCATGCTGTTTTGGTTAACATAGCCCTGTAGTATAGTTTGAGGTCAGATAGCCTGATGCTTCCAGCTTTGTTCTTTTTCTTAAGATTGCCTTGGCTATTTGGCCTCTTTTTTGGTTCCACATGAATTTTAAAACAGTTGTTTCTAGTTTTTGAAGAATGTCATTGGTAGTTTGATAGAAATAGCATTTAATCTGTAAATTGATTT GTGCAGTATGGCCTTTTAATGATATTGATTCTTCCTATCCATGAGCATGATATGTTTTCCATTTTGTTTGTATCCTCTCTGATTTCTTTGTGCAGTGTTTTGTAATTCTCAT TGTAGAGATTTTTCACCTCCCTGGTTAGTTGTATTTTACCCTAGATATTT TATTCTTTTTGTGAAAATTGTGAATGGGATTGCCTTCCTGATTTGACTGC CAGCTTGGTTACTGTTGGTTTATAGAAATGCTAGTGATTTTTGTACATTG ATTTTCTTTCTAAAACTTTGCTGAAGTTTTTTTTATTAGCAGAAGGAGCTTTGGGGCTGAGACTATGGGGTTTTCTAGATATAGAATCATGTCAGCTTCAAATAGGGATAATTTTACTTCCTCTCTTCCTATTTGGATGCCCTTTATTTCTTTCTCTTGCCTGATTACTCTGGCTGGGATTTCCTATGTTGAATAGGAGT CATGAGAGAGGGCATCAAATCTACACATATCAAATACTAACCTTGAATGTCTAGATATTT TATTCTTTTTGT GAAAATTGTGAATGGGAT

5000 Bases per Page


The magnitude of genomic data in a an analysis!

• 3 pallets with 40 boxes per pallet x 5000 pages per box x 5000 bases per page = 3,000,000,000 bases!

• To get accurate sequence • requires 6-fold coverage

• Now: Shred 18 pallets and reassemble

• Really need Bioinformatics


Bioinformatics

• Allele calls

• Alignment

• Strand bias

• Coverage

• Thresholds

• ….


STR Allele Calling Software

• Compare back to nominal allele length

• Needed better approaches

• More versatile, more flexible


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

Prototype NGS (Promega)


Loci with Intra-Repeat Variation (n=24)

Loci Repeats ObservationsD21S11 29 8D21S11 29 1D21S11 30 3D21S11 30 2D21S11 30 5D21S11 31 5D21S11 31 2D21S11 32.2 4D21S11 32.2 1D2S1338 20 1D2S1338 20 2D2S1338 20 1D2S1338 22 1D2S1338 22 2D2S1338 23 1D2S1338 23 5D2S1338 25 5D2S1338 25 1

Loci Repeats ObservationsD2S1338 25 1D3S1358 15 3D3S1358 15 10D3S1358 15 1D3S1358 16 10D3S1358 16 4D3S1358 17 3D3S1358 17 4D8S1179 13 11D8S1179 13 5D8S1179 14 1D8S1179 14 7D8S1179 14 1D8S1179 15 3D8S1179 15 1

VWA 16 3VWA 16 4VWA 17 1VWA 17 15VWA 18 1VWA 18 5VWA 19 1VWA 19 1

Consistent with MS work


Variation Example at D3S1358 Locus

Stutter sequence for 15 alleles

AGATAGATAGATAGATAGATAGATAGATAGATAGATAGACAGACAGACAGA TAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGACAGACAGA TAGAT

Sequence for 15 alleles

AGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGACAGACAGA CAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGACAGA CAGATAGAT


Allele and Stutter Distributionfor D3S1358 Homozygote


Allele 30

TAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATATGGAT AGATAGATGATAGATAGATAGATATAGATAGATAGACAGACAGACAGACAGACAGATAGATAG ATAGATAGATAGATAGA

Minus Stutter Allele 31

TAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATATGGAT AGATAGATGATAGATAGATAGATATAGATAGATAGACAGACAGACAGACAGACAGACAGATAG ATAGATAGATAGATAGA

Allele 31

TAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAT GGATAGATAGATGATAGATAGATAGATATAGATAGATAGACAGACAGACAGACAGACAGACAG ATAGATAGATAGATAGATAGA

Plus Stutter Allele 30

TAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAGATAT GGATAGATAGATGATAGATAGATAGATATAGATAGATAGACAGACAGACAGACAGACAGATAG ATAGATAGATAGATAGATAGA

Variation Example at D21S11 Locus


Allele and Stutter Distributionfor D21S11 Heterozygote


Mixture Interpretation EnhancementMinor and Major Contributor Alleles

0

1000

2000

3000

4000

5000

6000

9 10 11 12 13

De

pth

of

Co

ve

rag

e

Nominal Alleles by Repeat

D5S818

Major Shared Minor

AGAT...AGAT

AGAT...AGAT

AGAT...AGAG

Mixture of 2 peopleBoth 11,12

11 indistinguishable

Stutter from allele 11

12 distinguishable


Analysis of Difficult Samples

mtDNA is the most successful marker


Advantages of mtDNAAnalysis

• High copy numberlimited samplehair, teeth, bones

• Less prone to degradationstructure, location

• Maternal inheritancematernal relatives source of known sample in missing persons cases

• Highly variable among individuals


Limitations of Sanger Sequencing

• Sequencing is labor intensive

• Analysis of results is time consuming

• Costly (prices range from $1000 to $3000 per mtDNAsample and only HV1 and HV2)

• Variation in intensity of peaks

• Not quantitative– impacts mixture interpretation

• Heteroplasmy difficulties


Length Heteroplasmy

...CCACCAAACCCCCCCCTCCCCCCGCTT… 8 C’s

...CCACCAAACCCCCCCCCTCCCCCCGCTT… 9 C’s

...CCACCAAACCCCCCCCCCTCCCCCCGCTT… 10 C’s


mitoSAVE

IGV

mitoSAVE: King et al. 2014

• Alignment issues

• Tool to provide consistencyin nomenclature

• EMPOP tools


Population Studies Become Easy

• Sequenced 283 whole genomes on MiSeqfollowing protocol

• Three populations – African American, Caucasian, SW Hispanics

• Multiple software

• mitoSAVE

• Haplogrep

• First upload of MPS mtDNAdata to EMPOP


Variation Across the mtGenome(n=283)

• 11,607 variants• defined in relation to the rCRS

• Polymorphism density clustered in HVI/HVII • 2,938 of the variants (25.3%)

• ~75% of variation in coding region

• Increase the value of mtDNA


Length heteroplasmy sequences detected using MPS

Sanger sequencing of homopolymer C stretch (forward and reverse strands)

Length HeteroplasmyTissue Sample 400


Sample number Method6997 Sanger 16192T 16260T

6997 NGS 16192T 16260T

0056-12 Sanger 16093Y 16223T 16239T 16260T 16274A 16325C 16362C

0056-12 NGS 16093Y* 16223T 16239T 16260T 16274A 16325C 16362C

H2 Sanger 16111T 16184T 16223T 16290T 16319A 16362C

H2 NGS 16111T 16184T 16223T 16290T 16319A 16362C

7000 Sanger 16126C 16223T 16325C 16362C

7000 NGS 16126C 16223T 16325C 16362C

Sample number Method6997 Sanger 263G 315.1C

6997 NGS 263G 315.1C

0056-12 Sanger 73G 263G

0056-12 NGS 73G 263G

H2 Sanger 64T 73G 146C 153G 155C 203A 222T 235G 263G 315.1C

H2 NGS 64T 73G 146C 153G 155C 203A 222T 235G 263G 315.1C

7000 Sanger 55C 56G 64T 73G 263G 279C 309.1C 315.1C

7000 NGS 55C 56G 64T 73G 263G 279C 309.1C 315.1C

16093Y* is 75% C and 25% T

HV2

HV1

Sanger vsMiSeqfour bone samples

All samples had threshold set at 25%; position 16093 for sample 0056-12 detected heteroplasmy at 19.1% showing all sites concordant


Short AmpliconMultiplexesWhole Genome

• Degraded sample analyses


History of Deadwood, S.D.

Deadwood Cemeteries

• Ingleside Cemetery • (1876 – 1878) located near the

downtown core business district; approximately 100 burials

• Mt. Moriah Cemetery • Established in 1878

• Final resting place of Western legends, murderers, madams, and pillars of Deadwood’s early economic development


Unidentified Skeletal Remains: Case Objectives

The Washington Times, July 2014http://www.washingtontimes.com/news/2014/jul/6/scientists-unraveling-a-

historic-deadwood-mystery/?page=all

• Who is he?• What did he look

like?

• Where did he come from?

• Genetic Typing• Y-STRs

• AIMS

• Phenotype-informative SNPs

• mtDNA


Y SNPs in the HID-Ion AmpliSeq™ Identity Panel• 34 upper clade SNPsDeadwoodFemur

008.001 E1 EVCV2DeadwoodFemur 008.002 E1 EVCV2

DeadwoodFemur 008.002 E2 EVCV2 Consensus

rs2032636 G G G Grs9341278 G G N Grs2032658 G G G Grs2319818 G G G Grs17269816 C C C Crs17222573 A A A AM479 C C N Crs3848982 C C C Crs3900 G G G Grs3911 A A N Ars2032631 A A A Ars2032673 T T T Trs2032652 T T T Trs16980426 T T T Trs13447443 A A N Ars17842518 G G G Grs2033003 C C C C

R1b


European AncestryAIMs


Phenotype Probabilities

http://hirisplex.erasmusmc.nl/Accessed on 10-07-2014HIrisPlex: Walsh et al. (2013)IrisPlex: Walsh et al. (2011)


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


STR Panel

• 10-plex STR Panel• Amelogenin• D16S359• D3S1358• D5S818• CSF1PO

• Analyzed data• STRait Razor• STR Genotyper Plugin

• Compared data with genotypes generated on 3130xl Genetic Analyzer

• Also evaluated of sequence variants within allelesSTRait Razor: Warshauer et al. 2013

• D7S820• D8S1179• TH01• TPOX• vWA


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


Identifying Relationships

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


Identifying Relationships

Pedigree supported by data from Ancestry SNP panel

Biogeography ≠ Bioancestry


Biogeography = Bioancestry

Phenotype: brown hair, hazel eyes, white fair skin complexion

BioancestryInferences


Identical Twins


• After complete forensic autopsy

• Cause of death unknown 0.3 - 0.6 % (~1/200)

• Manner of death unknown 3 - 6 % (~1/20)

Negative Autopsies


Microbiome-Human ID

• Fierer et al, 2009


Microbial Forensics

• Analysis of evidence from a bioterrorism act, biocrime, or inadvertent microorganism/toxin release for attribution purposes

• Essentially the same as any other forensic discipline


Turn Research to Practice


Some Issues to Address

• Data quality• Q scores

• Filters

• Noise

• Thresholds• Detection

• Stochastic

• Heteroplasmy

• Marker specific criteria

• STRs• Nominal length vs sequence variation

• Bioinformatics• Software

• Reference alignment

• Training/Education

• Typical Validation Studies


We have come a long way!

Yet more is still to come!


• Thermo Fisher Scientific• Illumina• Promega• IntegenX• NetBio• South Dakota Historical Society

Archaeological Research CenterCity of Deadwood

#1ACKNOWLEDGMENTS

• Walther Parson• Antonio Amorim• Research Team


Life Technologies and its affiliates are not endorsing, recommending, or promoting any use or application of Life Technologies products presented by third parties during this seminar. Information and materials presented or provided by third parties are provided as-is and without warranty of any kind, including regarding intellectual property rights and reported results. Parties presenting images, text and material represent they have the rights to do so.


WHEN USED FOR PURPOSES OTHER THAN HUMAN IDENTIFICATION HID-ION PGMTM IS FOR RESEARCH USE ONLY. NOT FOR USE INDIAGNOSTIC PROCEDURES.

HID Ion AmpliSeq Identity Panel and HID Ion AmpliSeqAncestry Panel are for Research, Forensics and Paternity Use Only. Y Filer is for Forensics and Paternity Use Only.

Speaker was provided travel and hotel support by Thermo Fisher Scientific for this presentation, but no remuneration.

perspectives on the future of forensic genetics

Technology

application of genetics

medical genetics

swab matrix dna yield

forensic science

diomics swab material

quality of dna

greater dna yield

mixture rapid dna