Automated Workflow for the Generation of Mitogenome Reference Data

Joseph Ring, Kimberly Sturk-Andreaggi, Charla MarshallQIAGEN Investigator Forum

May 3, 2018

Disclaimers

2

• The opinions or assertions presented hereafter are the private views of the speaker(s) and should not be construed as official or as reflecting the views of the Department of Defense, its branches, the Defense Health Agency, the U.S. Army Medical Research and Materiel Command or the Armed Forces Medical Examiner System.

• Mention of commercial products does not constitute a recommendation or endorsement by the speakers and/or their associated organization/institute. Commercial equipment, instruments and materials are identified in order to specify experimental procedures as completely as possible, and does not imply that any of the commercial products identified are necessarily the best available for the purpose.

INTRODUCTION

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Mitogenome Databasing

• From 2011-2014, the AFDIL undertook an NIJ grant-funded project for Sanger full mitogenome databasing

• 588 samples were sequenced and analyzed over that timespan– Lab processing performed on a Hamilton

STARplus and STARlet– Redundant review involving at least 2 individual

scientists

4

84% 87% 91% 98%

0%

20%

40%

60%

80%

100%

HV1 HV1/HV2 CR mtG

Unique Haplotypes Shared Haplotypes

Mitogenome Benefits

• Increased power of discrimination– Less common types

5

Impact of the Region

• 283 mitogenomesgenerated with NGS– U.S. populations

(Caucasians, Hispanics, African Americans)

– Compared HV1/HV2 and mitogenome haplogroups

• assigned with HaploGrep2

6

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

HV1/HV2Identical Less Precise

Same Clade Different Clade

Ancestry(HV-mtG)

Less Precise

Same Clade

Different Clade

European-African 0 0 2Asian-African 0 0 2Asian-European 0 0 1Asian-NA 11 3 2

Incorrect Maternal Ancestry

• 21 samples predicted a different maternal ancestry with HV1/HV2 than with the mitogenome

7

Next Generation Sequencing

• Increases mitogenome sequencing feasibility– High throughput

• Increased sensitivity– Mixtures– PHPs

• More quantitative than Sanger sequencing– Heteroplasmy

• Automated bioinformatic analysis– Eases analysis burden

8

Illumina MiSeq

Thermo Fisher Ion S5

Reference Database

• Implementation of mitogenome sequencing requires more reference data for statistics– Around 1000 “forensic-quality” mitogenomes will

be available to search in EMPOP

• AFDIL was awarded an NIJ grant to sequence 5,000 high-quality mitogenomes– Nearly all were previously processed in NIJ-

funded control region (CR) databasing project • Can be used as QC check

– Proposed processing and analysis time of 3 years9

PROCESSING OVERVIEW

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Pre-Amplification

Extraction

• QIAampDNA Blood Mini

Amplification

• Two, ~8.5 kb overlapping amplicons

11Fendt et al. 2009https://www.qiagen.com/us/shop/sample-technologies/dna/genomic-dna/qiaamp-dna-blood-mini-kit/#orderinginformation

Amplicon Quantitation

• Fragment Analyzer• Combine amps in

equal concentration

Pooled Amplicon

Purification

• AMPure XP• Quant pooled

product with Varioskan LUX

Library Preparation

• Roche KAPA HyperPlus

• Equal sample input

Post-Amplification

12

Fragment Analyzer

Varioskan LUX

Roche’s KAPA HyperPlus Library Prep

• HyperPlus utilizes three incubation steps with an optional library PCR step

• Accommodates a wider range of DNA input than Nextera XT– Nextera validated at

AFDIL for HQ samples– Produces more

uniform sequencing coverage as well

• Using half-volume reactions 13DNA input range of 1 - 1000 ng

Fragmentation

End Repair

Adapter Ligation

A-Tailing

Library PCR (optional)

Post-Amplification

Library Prep

• Optional library amplification

Library Purification

• AMPure XP• Quant with

Fragment Analyzer

Library Pooling

• Quant pool with Roche KAPA qPCR

Sequencing • MiSeq 1x150 cycle* (~17 hr run)

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*May switch to 2x300 cycle (~65 hr run)Longer run times, but longer reads and higher read coverage

Illumina MiSeq

0 2 4 6 8 10 12 14Total Processing Time (hours)

Hands Off Time

Manual Processing Time

Amplicon Quantitation

Pooled Amp Purification

Library Preparation

Library Purification

Library Pooling

Sequencing Setup

All post-amplification steps are now fully automated on a Hamilton STARplus liquid handler robot

15

Post-Amplification: 96 Samples

Hands On Time – 9 hrsHands Off Time – 5.5 hrsTotal Time – 14.5 hrs

AUTOMATED METHOD

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Hamilton STARplus

17

Deck Layout

18

On Deck Thermal Cycler (ODTC)

Comfort Lid

Deck Layout

19

Plate Shaker

Chilled Plate and Tube Carriers

Magnet Plate

Additional Features• Barcoding and

worklisting– Method can read plate

and tube barcodes for sample tracking

– Automated worklist handling for easy cherry picking of samples

• All pipetting information is logged in the run report

20https://www.hamiltoncompany.com/~/media/Images/Robotics/Products/Accessories/Barcode-reader.ashx

Additional Features

• Total Aspiration and Dispense Monitoring (TADM)– Allows for complete

monitoring of all pipetting steps

– TADM export file can help determine if failure was due to pipetting error

21

-1600

-1400

-1200

-1000

-800

-600

-400

-200

0

200

0 100 200 300 400 500 600 700

AFDIL_Kapa_50uLF96_CleanSample_SurfaceEmpty 25.0 uL1 - 96

Dispensed some air rather than all sample

Library Prep Run Parameters• HyperPlus or Hyper Prep

– Lower quality samples

• Can choose to process 1-96 samples

• Adapters can be pipetted from a plate or tubes

• Fragmentation time, AMPure ratios, and library amplification cycles are customizable– Optimization work or

different sample types22

Side Methods

• All post-amplification steps are now fully automated

• Saves considerable hands on processing time

23

Automated Processing Time

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0 2 4 6 8 10 12 14

Total Processing Time (hours)

Hands Off Time

Amplicon Quantitation

Pooled Amp Purification

Library Preparation

Library Purification

Library Pooling

Sequencing Setup

0 2 4 6 8 10 12 14Total Processing Time (hours)

Hands Off Time

MANUAL

AUTOMATED

Hands On Time – 9 hrsHands Off Time – 5.5 hrsTotal Time – 14.5 hrs

Hands On Time – 2 hrsHands Off Time – 11 hrsTotal Time – 13 hrs

INITIAL TESTING

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Poor Fragmentation

• Initial testing showed poor fragmentation

• Conditions based on full-volume method that Hamilton had already developed

• Due to insufficient mixing of the fragmentation master mix

Representative Sample Library - Manual

Representative Sample Library - Automated

Increased Mixing Volume

Full Plate Testing

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Representative Sample Library - Manual

Representative Sample Library - Automated

0

10

20

30

40

50

60

70

80

90

100

Manual Automated

Ave

rage

Lib

rary

Con

cent

rati

on (n

M)

Fragment Analyzer Smear Analysis(125-700 bp)

Data Comparison

Run Cluster Density (K/mm2)

Clusters Passing Filter (%) % Reads ≥ Q30 Reads Passing

Filter (M)

Manual 1272 90.38 90.33 27.42

Hamilton 1034 93.09 82.13 23.21

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MiSeq Run Metrics

Sample Mapping Metrics – Average of All SamplesCondition Mapped Reads Mean Coverage Minimum Coverage

Manual 205,637 1178 412

Hamilton 169,053 978 339

Data Comparison

Run Cluster Density (K/mm2)

Clusters Passing Filter (%) % Reads ≥ Q30 Reads Passing

Filter (M)

Manual 1272 90.38 90.33 27.42

Hamilton 1034 93.09 82.13 23.21

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• All sample variant calls were concordant between processing conditions– One sample appears to have been mixed at the extract level– Five samples completely failed or had partial sequencing

coverage• Consistent with amplification results and between conditions

MiSeq Run Metrics

Sample Mapping Metrics – Average of All SamplesCondition Mapped Reads Mean Coverage Minimum Coverage

Manual 205,637 1178 412

Hamilton 169,053 978 339

Manual (Normalized) 174,064 997 349

DATA ANALYSIS

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QIAGEN CLC Genomics Workbench

• Tentative Workflow:– Trim sequences– Map to rCRS– Realign indels– Variant calling

• 100X min. read coverage • 5X min. variant count• 5% min. variant frequency

• Utilizes custom AQME tool for forensic profile generation and haplogrouping

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Workflow takes ~1 hour to analyze 96 samples

AQME Toolbox(AFDIL-QIAGEN mtDNA Expert)

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Realign Variants Create Mitochondrial Variant Table

Mitochondrial Haplogrouper

Variant Track

mtDNA Table

Realigned Variant

Track

Profile Report

Coverage Table

Haplogroup Report

History

Excel spreadsheet PDF documentText file

XML file

Read MappingCLC Input

AQME Tool

AQME Output

Export

mtDNA Table

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Original Data Forensic Profile

Haplogroup Assignment

34

Hg I4a

Phylogenetic Nomenclature

35

Hg M1a1d

Phylogenetic Nomenclature

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Hg M1a1d

Artificial Recombination

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Sample 1 (H7a) = LR Amp A7 J1c3 SNPs in LR Amp A• 2 found• 5 missing• 1 private mutation

Sample 2 (J1c3) = LR Amp B20 J1c3 SNPs in LR Amp B• 19 found• 1 “uncertain” missing• 2 private mutations (1

PHP)

Hg J1c3 Missing SNP Found SNP Private/Ignored SNP

Hg H7

Hg J1c3

A

B

B

SAMPLE REPROCESSING

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Sample Reprocessing• Many of these samples are 10+ years old

– May not be high enough quality for LR amplification• Use an alternate amp strategy amenable to lower quality

samples– Also used on a subset of the samples for QC purposes

• Including heteroplasmy confirmation

Thermo Fisher’s Precision ID mtDNA Whole Genome Panel

QIAGEN’s QIAseq Targeted DNA Human Mitochondrial Panel

Precision ID

• Utilizes 162 primer pairs and 283 degenerate primers across two multiplex reactions

• Average amplicon size of ~160 bp

• Not designed for Illumina sequencing– Roche’s KAPA Hyper

Prep kit – QIAseq 1-Step

Amplicon Library Prep kit

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Fragmentation

End Repair

Adapter Ligation

A-Tailing

Library PCR (optional)

Hyper PrepQIAseq 1-Step

QIAseq Targeted mtDNA Panel

• Adapter ligation– UMI (MB)– P7 adapter with N7 index

P7

P5

i7

i5

Seq Read

P7i7

Seq ReadUMI

Traditional QIAseq

41https://www.qiagen.com/us/shop/sample-technologies/dna/genomic-dna/qiaseq-targeted-dna-panels/#productdetails

QIAseq Molecular Indices

• Unique Molecular Indices (UMIs) are 12 bp of randomized bases

• Attaches a unique (412

possibilities) sequence to each DNA fragment

• Can distinguish false variants due to PCR and/or sequencing error

42QIAGEN, QIAseq Targeted DNA Panel Handbook (R2), 2017.

Sequencing ReadsAGTCTTTCCCA-GTCAGTCGAGTCTTTTCCA-GTCAGTCGAGTCTTTCCCA-GTCAGTCGAGTCTTTCCCAAGTCAGTCGAGTCTTTCCCA-GTCAGTCG

Bioinformatic “Super Read”AGTCTTTCCCAGTCAGTCG

UMI Benefit• Each UMI represents a

single molecule– Multiple sequences

generated on the MiSeq– May contain PCR and

sequencing errors

• Bioinformatically combine all UMI sequences– Represented by consensus

sequence– Map the “super reads”– Eliminate all PCR and

sequencing errors• Damage or mutation

authentic to the sample DNA

QIAseq Procedure

• Target enrichment by Single Primer Extension– 222 mtDNA-specific

primers with tail

44https://www.qiagen.com/us/shop/sample-technologies/dna/genomic-dna/qiaseq-targeted-dna-panels/#productdetails

Sample Testing

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• Tested Precision ID and QIAseq on 15 whole blood samples– Ring et al. In Review.

Electrophoresis• High profile concordance

between kits – Except low-level variants

• Many NUMT-associated variants (NAVs)

• Amount of NAVs due to differences in kit chemistries

• Most NAVs were removed bioinformatically– Consensus sequence

mapping and NAV filtering

0

828

62

0

200

400

600

800

1000

LR PrecisionID

QIAseq

Num

ber o

f NAV

s*

*Above 5% variant frequency

TIME & COST

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0 10 20 30 40 50 60 70 80 90 100

NGS Mitogenome

Sanger Mitogenome

Sanger Control Region

Days

Sample Prep Instrument Analysis*

Estimated Time

47* First and second analyses required; analyses performed by 2 different analysts

Extract In Profile out For 96 Samples

10 days

88 days

25 days

Estimated Cost

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Extract ProfileFor 96 Samples

$- $100,00 $200,00 $300,00 $400,00 $500,00 $600,00

NGSmtGenome

SangermtGenome

SangerControl Region

Cost per Sample

Reagents/Consumables Analysis (Analyst Time)*

*Analyst salary assumed to be $40/hour; estimate for illustration purposes only

$67.29

$524.58

$90.63

FUTURE PLANS

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Library Prep Method

• Further optimization needed– Determine optimal amplicon input and adapter

concentration for the method• Still have adapter dimer left in libraries

– Contamination assessment

• Validation of the method for casework use– Family reference samples

Pre-Amplification Automation

• Modify current CR automated amp setup for LR amp– Hamilton STARlet

• Development of an automated extraction method on the STARlet– Hamilton [MPE]2

positive pressure module

51[MPE]2 Positive Pressure Extraction & Evaporation Module Brochure. Lit. No. F0032 v1.3. 2015.

Acknowledgments

• QIAGEN for the invitation to speak• AFMES-AFDIL Emerging Technology Section

– Cassie Taylor– Erin Gorden– Jennifer Higginbotham

• Hamilton Company– Brandon Bare for method development

• AFMES-DoD DNA Operations– Dr. Timothy McMahon and Lt Col Laura Garner

• Armed Forces Medical Examiner System– Col Finelli and Lt Col Alice Briones

Questions?