doj2016: erlich presentation

Yaniv Erlich@erlichya2/23/16

Advanced Strategies for DNA

Identification

@erlichyaYaniv Erlich

Supported by NIJ 2014-DN-BX-K089


Outline1. Surname inference from DNA

2. The power of genome-wide STR profiles

3. Rapid identification with handheld deice

Surname inference HipSTR Y-STRs MinION Summary

Advanced Strategies for DNA Identification


Correlation between Y-chr and surnames

www.ysearch.org:Y

Y

Smith

Smith

Y

Smith

Erlich


ACGCACGC…



Databases of interest

www.smgf.org www.ysearch.org140,000 publicly accessible surname-Ychr records




How to find surnames?Estimating the time to most recent common ancestor

Target

i-th record in db

surname




Empirical test to determine the probability of recovering a US surname

Y-chr of a real person

Querying Ysearch and SMGF

Inferring surname

x900

For US Caucasian males:12% Successful recoveries 5% Wrong recoveries83% Unknown

Comparing the predicted surname to the true one

Surname inference algorithm




Distribution of inferred surnames

Most of the inferred surnames are relatively rare

Intro. Methodology The Venter case Anonymous datasets Summary

Inferred surname =~ zipcode



Putting it all together: the Venter case

www.ysearch.org:

lobSTRDYS458: 17 repeats

Try it yourself: bit.ly/find_craig

We got a surname from whole genome sequencing data




AftermathOur study




HipSTR: a new STR calling algorithm

• Haplotype-based imputation, phasing, and genotyping of STRs

• Haplotype– Robustness to stutter noise

• Imputation– Recover STR dropouts from nearby SNPs.

• Phasing– Resolve homoplasy between alleles




STR benchmarking results

HipSTR is the most accurate STR caller AND does this ~10x faster than the next best method

GATK

Platyp

us

Freeba

ys

lobSTR

Repea

tSeq

HipSTR

50

55

60

65

70

75

80

85

90

95

100A

ccur

acy




HipSTR: solving homoplasy• Can now correctly detect STRs with identical lengths

but different sequences (homoplasy)• Real example:

– Length based genotype: -4/-4– HipSTR genotype: (AGAT)8(ACAT)9 / (AGAT)10(ACAT)7

• HipSTR available at https://github.com/tfwillems/HipSTR



https://github.com/tfwillems/HipSTR

https://github.com/tfwillems/HipSTR


Chromosome wide scan of Y-STRs• Goal: scan every possible STR on the

Y chromosome and assess mutation rates

• >200,000 transmissions = high accuracy.

• Leverage 1500 whole genome world-wide samples.




Benchmarking mutating ratesOur study




Deliveries

http://bit.ly/DOJERLICH

Scanning 4500 Y-STRs

Estimating mutation rates of 700 polymorphic Y-STRs

Finding additional fast mutating Y-STRs

Imputation of Y-STRs with high accuracy




Oxford Nanopore MinIONFeatures:• USB stick• Portable• Low throughput• High error rate

(10%)Can we identify samples within minutes?




CODIS STR are challenging1. MinION has too many indels

2. Many reads required to correct for error

3. CODIS needs a PCR machine Proposal:Shotgun sequencing + Bayesian approach




ApproachReal-time Sequencing data

(reads)

Filter SNPs

Bayesian algorithm Based on prior knowledge • Frequency alleles in

population • Error rate

Alignment to human genome15% error in base calling

Genome db




Real experiment: me versus Venter

Prob

abilit

y of

a m

atch

6m30s

• Assuming a database of 107

• Retrospect analysis




Real experiment: Venter vs. me

50 150

250

350

450

550

650

750

850

950

0%

1%

10%

100%

P(Erlich)P(Venter)

# of Reads Returned from MinION

• Bad flowcell: 50min for detection

• My genome: 23andMe array




Summary

Advanced capabilities:1. Surnames2. Homoplasy detection3. More Y-STRs4. Imputation of missing markers5. Rapid identification of DNA




Acknowledgements*Thomas Willems (MIT) *Melissa Gymrek (HST – Harvard/MIT)*Sophie Zaajier (Columbia University*Robert Piccone (Columbia University)

Chris Taylor-Smith (Sanger Institute)David Poznik (Stanford University)1000Y analysis group

* Supported by: NIJ 2014-DN-BX-K089

doj2016: erlich presentation

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