Post on 12-Feb-2020
Forensic SNP typing with QIAGEN’s QIAseq NGS chemistry
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Forensic Genetics Unit, University of Santiago de Compostela, Spain
Chris Phillips
• 140-SNP Identification panel
Qiagen SNP panel developments and some key issues: adapting to MPS and new multiple-allele-short-sequence loci
• Unforeseen complications with alignment when porting SNPs to MPS
• Considering linkage as forensic marker sets expand
• The ICMP ‘identification in cases of missing persons’ panel
• Genomic assessments of multiple-allele SNP-based polymorphisms
• Tailoring microhaplotype data compilation to forensic needs
• The EUROFORGEN Global AIMs+ and ‘NAME’+ ancestry panels
• Obtaining SNP data for regions with poor sampling - checkerboards
• Adapting ancestry analysis to multiple autosomal marker types
• USC assessed the panel with Ion PGM and did not use QIAseq then
• Evaluation of the markers
Evaluation of new forensic DNA tests in the MPS era
• Are forensic loci detected and genotyped by MPS as expected ?
• Evaluation of the test’s performance
• Genotype patterns of new tri-allelic SNPs or MH loci - sequence data
• Evaluation and re-development of data analysis regimes
• Setting the extent of polymorphism in each MH; automated phasing
• Obtaining reference data for relevant populations - expensive
• Experimental validation: dilution series / mixtures / bones / controls
• Genotype patterns of binary SNPs - universal controls (used globally)
• Adapting ancestry analysis for inclusion of multiple-allele data
New
MASS loci
Evolving QIAseq
methods
Adapting data analysis/population data for M
Hs
The Qiagen 140-SNP identification panel
0"
0.005"
0.01"
0.015"
0.02"
0.025"
0.03"
0.035"
0.04"
rs10768550.rs10500617"
rs2175957.rs8070085"
rs2255301.rs2269355"
rs8070085.rs1004357"
rs6955448.rs917118"
rs729172.rs2342747"
rs10500617.rs1498553"
rs1478829.rs1358856"
rs2040411.rs1028528"
rs4288409.rs2056277"
Kosambi(adjusted.Rc.value
.
679.nt.
55,162.nt.
36,472.nt.
349,542.nt.
Combining two independent panels into one set can lead to very close linkage - but this allows us to anticipate 1000+ SNP panels
D6S1042-SE33 Rc of 4%
Kiddlab pairs
SNPforID pair
x SNPforID-Kiddlab
Alignment challenges can be anticipated: polymeric tracts, or unanticipated: untracked flanking deletions (often population-specific)
The ICMP ‘identification in cases of missing persons’ panel
ICMP2
The first forensic panel with no binary SNPs
0"
100"
200"
300"
400"
500"
PentaE" D12S391" PentaD" FGA" D21S11" D22S1045" D6S1043" D18S51" D19S433" D1S1656" D2S441" D3S1358" D13S317" D7S820" D16S539" D10S1248" D2S1338" D20S482"" D17S1301" D5S818" D9S1122"" TH01" TPOX" vWA" D8S1179" CSF1PO" D4S2408""0"
50"
100"
150"
200"
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100
200
100
100
100
300
400
500
100
Pent
aE
D12S
391
Pent
aD
FGA
D21S
11
D22S
1045
D6S1
043
D18S
51
D19S
433
D1S1
656
D2S4
41
D3S1
358
D13S
317
D7S8
20
D16S
539
D10S
1248
D2S1
338
D20S
482
D17S
1301
D5S8
18
D9S1
122
TH01
TPO
X
VWA
D8S1
179
CSF
1PO
D4S2
408
≥200
bp
143-173 bp <126 bp
Forensic MPS tests aim to sequence the shortest possible DNA fragments, but SNPs usually have two alleles - so lack power of STRs
FP1!FP2!
• 1457 markers were incorporated after linkage screening - only 6 sites were eliminated based on primer extension disqualification
• 1377 autosomal tri-alleleic SNPs
• 34 tri-allelic X chromosome SNPs
• 46 microhaplotypes with 2, 3, 4, and 5 SNP combinations
• 2832 target enrichment extension primers - 80% of sites with redundant targeting
• 1195 markers currently under further evaluation
• 1123 autosomal tri-alleleic SNPs
• 28 tri-allelic X chromosome SNPs
• 44 microhaplotypes with 2, 3, 4, and 5 SNP combinations
Tri-allelic SNP and microhaplotype components
Tri-allelic SNPs with less frequent third alleles have very similar power to loci with three common alleles - the number of alleles is the important factor
STR sequence variants and MicrohaplotypesHaplotype spans (between the bounding SNPs) are often longer than 200 nucleotides - so need to balance length with power
S ASN
EUR
AFR
0
50
100
150
200
250
300
4 12 20 22 43 33 2 24 40 28 34 26 13 36 31 46 39 32 7 45 35 10 14
Kiddlab
USCKiddlab microhaplotype sizes
ICMP panel microhaplotype sizes
46 Microhaplotypes ranked by descending size - as originally described in Kiddlab list of 130
Average size 128-NT
Average size 60.5-NT
16 microhaplotypes had identical sizes, 14 of these were at the extreme size range with an average 55-nucleotide size
65% of microhaplotypes adopted for the ICMP panel had their haplotype spans reduced by an average of 67 nucleotides
Mic
roha
plot
ype
span
in n
ucle
otid
es
MH-21 73 nt
KHV JPT CHS CHB CDXTSI IBS GBR FIN CEU STU PJL ITU GIH BEB
YRI MSL LWK GWD ESNPEL
GGCTGGCC
CGTTCGTCCGCTCGCCCACT
AGTTAGTCAGCT
GGTC
GGTT
AGCC
Many microhaplotypes satisfy the need for short fragments and maximum levels of polymorphism from the sequenced strand
MH-3 39 nt
TCC
ATT
ATC
ACT
ACC
KHV JPT CHS CHB CDXTSI IBS GBR FIN CEU STU PJL ITU GIH BEB
YRI MSL LWK GWD ESNPEL
TTC
TCT
TTT
Many microhaplotypes satisfy the need for short fragments and maximum levels of polymorphism from the sequenced strand
MH-9 51 nt
GAT
GAC
AGC
AAC
KHV JPT CHS CHB CDXTSI IBS GBR FIN CEU STU PJL ITU GIH BEB
YRI MSL LWK GWD ESNPEL
GGC
GGT
However a large proportion are either polymorphic in some populations only, or at levels lower than good tri-allelic SNPs
MH-15 99 nt
KHV JPT CHS CHB CDXTSI IBS GBR FIN CEU STU PJL ITU GIH BEB
YRI MSL LWK GWD ESNPEL
TAT
TAG
GGG
GAG
However a large proportion are either polymorphic in some populations only, or at levels lower than good tri-allelic SNPs
MH-19 72 nt
TGT
TGG
TAG
KHV JPT CHS CHB CDXTSI IBS GBR FIN CEU STU PJL ITU GIH BEB
YRI MSL LWK GWD ESNPEL
GGG
However a large proportion are either polymorphic in some populations only, or at levels lower than good tri-allelic SNPs
KHV JPT CHS CHB CDXTSI IBS GBR FIN CEU STU PJL ITU GIH BEB
YRI MSL LWK GWD ESNPEL
MH-8 35 nt
TG
TA
CG
CA
However a large proportion are either polymorphic in some populations only, or at levels lower than good tri-allelic SNPs
A A C
G C T
A A T
G C C
A C C
G A T
A C T
G A C
G A C
A C T
G A T
A C C
G C C
A A T
G C T
A A C
AG, AC, CT
33 = 27 genotype combinations
82 = 64/2 32 haplotype combinations
Haplotype combinations offer potentially extensive levels of polymorphism compared to single-site SNPs
AG, AC, CT A A C
G C T
Most Microhaplotypes represent a novel base change on an established allelic background that rises in frequency
A A T
G C C
A C C
G A T
A C T
G A C
G A C
A C T
G A T
A C C
G C C
A A T
G C T
A A C
33 = 27 genotype combinations
Here, the GCT haplotype predominates in the observed variation
82 = 64/2 32 haplotype combinations
FP1!FP2!
• 1457 markers were incorporated after linkage screening - only 6 sites were eliminated based on primer extension disqualification
• 1377 autosomal tri-alleleic SNPs
• 34 tri-allelic X chromosome SNPs
• 46 microhaplotypes with 2, 3, 4, and 5 SNP combinations
• 2832 target enrichment extension primers - 80% of sites with redundant targeting
• 1195 markers currently under further evaluation
• 1123 autosomal tri-alleleic SNPs
• 28 tri-allelic X chromosome SNPs
• 44 microhaplotypes with 2, 3, 4, and 5 SNP combinations
Tri-allelic SNP and microhaplotype genotyping specificity is proving to be a major issue with the ICMP panel
Automated phasing - adapting the TFS prototype plugin
Automated phasing
Automated phasing
• Many 2-SNP microhaplotypes are less polymorphic than the best tri-allelic SNPs (which have a better chance to be very short)
Microhaplotypes bring new aspects to forensic analyses
• Post MPS genotype data compilation steps need to automatically establish phase of component SNP alleles on each sequence strand
• During microhaplotype selection for the ICMP panel, low frequency SNPs observed within the microhaplotype bounds were simply ignored, but these could provide useful data
• Deletions within or close to the target microhaplotype are common, and may create difficulties for reliable alignment or create frameshift effects
• A wide range of haplotypes in the most polymorphic loci will need very large sample sizes to obtain reliable population frequencies (with the possible problem of estimating frequencies of novel, single observations)
• Some established single-site SNPs ‘adventitious’ haplotypes from flanking SNP
The Qiagen Global AIMs+ and Middle East
informative AIMs panels
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1000 Genomes continental population data informs all USC’s AIM SNP choice and analysis
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Rosenberg�s
In
Chr 12
Pairw
ise p
opul
atio
n diffe
rent
iatio
n m
etric
Top 5%Fst In DIV
Automate population data comparisons in 650,000 SNPs
Used the CEPH panel to find Eurasian-informative SNPs
128 61The EUROFORGEN Global AIMs Panel
Qiagen Global AIMs+ 189 SNPs
EUROFORGEN Global ancestry informative SNPs, and QIAGEN’s extended set
extra AIMs
46 64 100
The EUROFORGEN “NAME” Panel110 SNPs
Qiagen Middle East Panel 164 SNPs
31 EUR-ME SNPs
41 EUR-N AFR SNPs
21 EUR-N AFR SNPs
7 Eurasiaplex SNPs
Middle East informative SNPs in two sets under development
AFR
E ASN
EUR
S ASN
ME
AFR
E ASN
EUR
S ASN
ME
AFR
E ASN
EUR
S ASN
ME
AFR
E ASN
EUR
S ASN
ME
AFR
E ASN
EUR
S ASN
ME
AFR
E ASN
EUR
S ASNAFR
E ASN
EUR
S ASNAFR
E ASN
EUR
S ASN
AFR
E ASN
EUR
S ASN
ME
AFR
E ASN
EUR
S ASNAFR
E ASN
EUR
S ASNAFR
E ASN
EUR
S ASN
ME ME ME
AFR
E ASN
S ASN
AFR
E ASN
EUR
S ASN
AFR
E ASN
EUR
S ASN
ME
ME
ME
Britain Denmark Slovenia Albania Greece
Algeria Libya IraqAzerbaijan Morocco
Pakistan IndiaArabia AfghanistanKuwait
GreenlandTurkey Somalia
AFR
E ASN
EUR
S ASN
ME
Simons Foundation provides 200 free complete genomes
Unlike 1000 Genomes, Simons Foundation samples 2-3 individuals per location with widest possible geographic spread
Illumina ancestry panel of 55 (Kiddlab)
Ion PGM ancestry panel of 169 AIMs (Kiddlab + Seldin/Kosoy)
Euroforgen ancestry panel of 128
Snipper
http://mathgene.usc.es/snipper/index.php
Snipper PCA analysis - MPS ancestry markers
PCA analysis can only work with binary SNP data - multiple allele (short sequence) data such as MHs needs genetic cluster analysis e.g. STRUCTURE - and we get much more population detail
Adapting exiting forensic ancestry analysis regimes to new marker sets
Snipper PCA analysis - MPS ancestry markers
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101011111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111121212121212121212121212121212121212121212121212121313131313131313131313141414141414141414141414141414141414141414141414141414141414141414141414141414141414141414141414141414141414151515151515151515151515151515151515151515151515151515151515151515151515151515151515151515151515151616161616161616161616161616161616161616161616161616161616161616161616161616161616161616161617171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717181818181819191919191919191919191920202020202121212121212222222222222222222222222222222222232323232323232323232323232323232323232323232323232323232323232323232323232323232323232323232323242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242525252525252525252525252525252525252525252525252525252525252525252525252525252525252525252525252525252525252526262626262626262626262626262626262626262626262626262626262626262626262626262626262626262626262627272727272727272727272727272727272727272727272727272727272727272727272727272727272727272727272727272727272727272728282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828292929293030303030303030303030303030303030303030303030303031313131313232323232323232
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
121-17-M1 121-17-M2 121-17-M3 121-17-M4 121-17-M5 121-17-M6 121-17-M7 121-17-M8 121-17-M9 121-17-M10
SASN
AFR
OCE
EASN
AME
EUR
ME
K:7
Sub-Saharan Africa
S W E North
Afri
caM
iddle
East
Cauc
asus
-Far
East
Euro
peEa
stern
Eur
ope
Europe
SE Central W N Cent
ral A
sia-W
Midd
le Ea
stGu
jarat
i
Punja
bi
Beng
li
Sout
h-Ce
ntra
l
India
SE E
ast A
siaOc
eania
Mala
yaEast Asian
China Dai SE M
ainlan
d As
ia
NE Asia Amer
ica
South Asia Non-European Eurasian (NEE)
NEE Siberia- America
Japan
PCA analysis can only work with binary SNP data - multiple allele (short sequence) data such as MHs needs genetic cluster analysis e.g. STRUCTURE - and we get much more population detail
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 101010101010101010101010101010101010101010101010101010101010101010101010101010101010101010101011111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111111121212121212121212121212121212121212121212121212121313131313131313131313141414141414141414141414141414141414141414141414141414141414141414141414141414141414141414141414141414141414151515151515151515151515151515151515151515151515151515151515151515151515151515151515151515151515151616161616161616161616161616161616161616161616161616161616161616161616161616161616161616161617171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717171717181818181819191919191919191919191920202020202121212121212222222222222222222222222222222222232323232323232323232323232323232323232323232323232323232323232323232323232323232323232323232323242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242424242525252525252525252525252525252525252525252525252525252525252525252525252525252525252525252525252525252525252526262626262626262626262626262626262626262626262626262626262626262626262626262626262626262626262627272727272727272727272727272727272727272727272727272727272727272727272727272727272727272727272727272727272727272728282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828282828292929293030303030303030303030303030303030303030303030303031313131313232323232323232
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
121-17-M1 121-17-M2 121-17-M3 121-17-M4 121-17-M5 121-17-M6 121-17-M7 121-17-M8 121-17-M9 121-17-M10
SASN
AFR
OCE
EASN
AME
EUR
ME
K:7
Sub-Saharan Africa
S W E North Africa
Middle East
Caucasus-Far
East Europe
Eastern Europe
Europe
SE Central W N Central Asia-W
Middle East
Gujarati
Punjabi
Bengli
South-Central
India
SE East Asia
Oceania
Malaya
East Asian
China Dai SE Mainland Asia
NE Asia America
South Asia Non-European Eurasian (NEE)
NEE Siberia- America
Japan
Concluding remarks - evolving systems of forensic MPS analysis
Not all markers will work well - alignment, close linkage and lack of specificity all affect the usefulness of MPS genotype data
As MPS is costly and work-intensive, generating population data is going to be slow and restricted - microhaplotype variation will need much bigger data scales than SNPs
STRUCTURE handles all types of genetic variant data so should be the system of choice for forensic ancestry analysis to maximise the informativeness of all genotypes
The development of an MPS test for forensic purposes needs time and care as well as detailed scrutiny of genotyping performance - best made in collaborative frameworks
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Thanksc.phillips@mac.com
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