Supplemental material
Detection of Fetal Subchromosomal Abnormalities by
Sequencing Circulating Cell-Free DNA from Maternal Plasma
Chen Zhao1, John Tynan1, Mathias Ehrich2, Gregory Hannum1, Ron McCullough1, JuanSebastian Saldivar1, Paul Oeth1, Dirk van den Boom2, Cosmin Deciu1*
1
Sequenom Laboratories, San Diego, CA, USA
2
Sequenom Inc., San Diego, CA, USA
*Corresponding author – [email protected]
Genomic and plasma DNA sample preparation and sequencing
45 Genomic DNA samples (130 uL of 30 ng/uL) were processed for library preparation by
sonication on a Covaris M220 Focused-ultrasonicator (Covaris, Inc., Woburn, MA) and
fractionated on a Caliper LabChip XT (PerkinElmer Inc., Waltham, MA) using a DNA 750 kit to
extract 120-180 bp DNA fragments. This fraction was collected into 20 uL, and quantified on a
Bioanalyzer (Agilent Technologies) or Caliper LabChip GX (PerkinElmer Inc., Waltham, MA)
instrument using high sensitivity kits. Samples yielding less than 20 ng of purified fraction were
reprocessed by concentration of the sonicated DNA on a DNA MinElute Column (Qiagen,
Germantown, MD) and repeat of the fractionation step above. 20-30 ng of fragment purified
DNA was diluted to 40 uL and used a template for library preparation.
Sequencing libraries were created as previously described by Jensen et al [1]. Briefly, 40 uL of
DNA was used as template for TruSeq library preparation using AMPure XP bead clean up after
end-repair, ligation and PCR steps. Libraries were quantified via Caliper LabChip GX,
normalized to 1.6 nM, multiplexed, clustered to Illumina v3 flowcells and sequenced on the
HiSeq2000 for 36+7 cycles in a 12-plex sample format.
Genomic DNA mixture models were created by normalizing genomic DNA and non-pregnant
female plasma (NPP) DNA libraries to 1.6 nM. A starting mix of 20% genomic DNA library was
created in a background of NPP library and subsequently used to create 17.5, 15, 12.5, 10, 7.5,
5% mixtures by serial dilution. These were then multiplexed, clustered, and sequenced as
above.
183 Clinical plasma samples were collected using Investigational Review Board (IRB) approved
clinical protocols (protocol numbers: EE101-A-C, “Open Ended Collection of Human Biological
Specimens” - approved by “Independent Investigational Review Board, Inc.”; SQNM-T21-202,
“Noninvasive Screening for Fetal Aneuploidy: A New Maternal Plasma Marker” - approved by
“Western Investigational Review Board”; WIRB SQNM-T21-107, “Collection of Whole Blood
Specimens from Pregnant Women at Increased Risk for Fetal Chromosomal Aneuploidy for use
in Development of the SEQureDx Trisomy 21 Test in the Detection of the Relative Quantity of
Chromosome 21 Circulating Cell-Free DNA Extracted from Maternal Plasma” - approved by
“Compass IRB”; SQNM-T21-302, “RNA Study: An LDT test for the detection of Down syndrome
in early pregnancy” - approved by “Women and Infants Hospital Rhode Island IRB”; SQNMRND-103, “Collection of Biological Specimens from Normal Healthy Volunteers to Support
Research and Development” - approved by “Aspire IRB”.). All subjects provided written
informed consent prior to undergoing any study related procedures.
For all plasma samples, blood was collected in either EDTA-K2 Vacutainers (Becton Dickinson,
Franklin Lakes, NJ) or Cell-Free DNA BCT 10 mL tubes (Streck, Omaha, Nebraska). Plasma
was fractionated via centrifugation of whole blood and plasma DNA was extracted using Qiagen
DSP Nucleic Acid Kits (Qiagen, Germantown, MD) according to previously published methods
[1-3]. Library preparation was conducted as described above.
Log Odds Ratio (LOR)
LOR quantifies the likelihood of the MD event being true at the measured fetal fraction f . It can
be calculated as
log
P( affected | Z , f )
P( Z | affected , f )P( affected , f )
 log
P(euploid | Z , f )
P( Z | euploid, f )P( euploid , f )
(1)
P( Z | affected , f )P( affected )
 log
P( Z | euploid, f )P( euploid )
Where f is the measured fetal fraction and P(affected | Z, f) and P( euploid | Z, f) are the
respective posterior probabilities of the sample being affected or unaffected (i.e., euploid) given
Z and f , and P(affected) and P( euploid ) are the prior probabilities, and P( Z | affected , f )
and P( Z | euploid, f ) are the conditional probabilities, which will be derived subsequently.
For an unaffected euploid sample, let X represent the summed bin count for the event region.
Due to the inherent randomness in sequencing, X is a random variable with X ~ g(  X , X ) ,
where  X and  X are the mean and standard deviation and g(  ) is some distribution function.
For an affected sample with heterogeneous fetal duplication (3 copies for the fetus and 2 copies
for the mother), the bin count for the affected region is linearly increased by fetal fraction due to
the extra fetal copy. Therefore the bin count for the affected region is Y ~ g( Y , Y ) , where
Y   X ( 1  f / 2 ) . Assuming  Y   X , the z-score distribution can be written as:
 f 
Z ~ Normal  X ,1
X 2 
(2)
where  X and  X can be empirically evaluated from a large pool of euploid samples. For a
euploid sample, its z-score is independent of fetal fraction, and P( Z | euploid, f ) follows the
standard normal distribution.
MD limit of detection framework
For subchromosomal abnormality detection, it is of fundamental importance to understand the
limit of detection (LoD) for any given MD. Four factors affect the LoD: fetal fraction ( f ), size of
the event, coverage, and the biological and technical variability of the event region. The first
three factors are easy to understand: it is easier to detect an event with higher fetal fraction,
larger size and higher sequencing coverage. The fourth factor is influenced by the nature of
sequencing, namely certain regions are more variable than others due to various factors (GC
bias, repetitive elements, mapping ability, etc.), and therefore are harder to detect.
The LoD problem has been studied by other researchers. For example, Fan and Quake [4]
applied a weighted GC normalization method and approximated the resulting sequencing
counts with a Poisson distribution. They concluded that the sensitivity of aneuploidy detection
can be raised to arbitrary accuracy with sufficient coverage. In our experience, this claim might
be overly ambitious for two reasons. First, a simple GC correction is usually not adequate to
remove all sequencing biases in a sample, and the normalized data often experiences larger
variability than the Poisson distribution [5]. Second, the model neglects region specific
variability. Yu et al [6] proposed a simulation model to study the minimum counts needed to
detect a 3Mb microdeletion with >99% sensitivity. However, their simulation represented an
ideal situation when all analytical biases were removed, and therefore the results were overly
optimistic.
Here, we propose a different simulation strategy using empirically-derived estimates of technical
and biological variability.
Using historical data from a set of 1000 previously processed euploid samples, we employed
the aforementioned normalization method and estimated the median fetal fraction to be 9.9%.
The resulting distribution of fetal fraction was modeled with a beta distribution. We then sampled
a value f from this distribution and adjusted the normalized bin count profile of a randomly
selected sample from the above listed set such that the bin count of duplication specific region
was increased by a factor of 1  f / 2 (or 1  f / 2 for deletions). This corresponds to creating
in-silico (or “spiking in”) a microduplication or microdeletion in the given region, for the selected
sample. This process was repeated 10,000 times and the sensitivity for a particular MD was
calculated in two ways. First, the z-score for the “spiked in” region was calculated and if the
value exceeded the z-score cutoff C , we counted it as a successful detection. Second, we
applied the decision tree method and counted the proportion of success. Note that the first
approach represented the theoretical sensitivity limit (since the MD location was given) and the
second approach represented the practical sensitivity by our decision tree method.
As fetal fraction plays an important role in the decision tree algorithm, we also assess its
measurement error impact on the sensitivity performance through the simulation study. To
illustrate this, the simulation procedure described above is repeated for different levels of fetal
fraction measurement errors. Assuming the measured fetal fraction f̂ is normally distributed
around the true fetal fraction f , i.e., ˆf ~ N  f ,  , the log odds ratio can then be calculated
with respect to f̂ . In this simulation,   0,1.3%,8%,12% have been chosen to represent the
errors, where   1.3% is the observed measurement error in the plasma sample study (by
comparison with the estimate based on chromosome Y).
As shown on the supplemental Figure 1, detection sensitivity increases rapidly with the MD size.
The sensitivity difference between the decision tree method at   1.3% (black cross) and the
theoretical limit (red circle) becomes less than 5% for MDs >= 9Mb. Furthermore, we expect the
fetal fraction measurement error to negatively impact the decision tree sensitivity, and it is
indeed confirmed by observing the sensitivity drop with increased  . It is also interesting to
note that the fetal fraction measurement error plays a less significant role at the small size range
(1-2Mb) than the large size range (10-24Mb). In fact, the decision tree sensitivity converges at
1Mb on supplemental Figure 1. That is because at the small sizes, only events with high enough
fetal fractions (15%-25%) can be detected, and the log odd ratio is less sensitive to fetal fraction
errors at the high fetal fraction range.
Supplemental Figure 1. Theoretical sensitivity (assuming event location known) and decision
tree sensitivity for MD detection at 0.2X coverage. For the decision tree sensitivity, the
performance is plotted with respect to the fetal fraction measurement errors  .
Analytical validation using genomic DNA mixtures
A blinded genomic DNA study was conducted to test the performance of the proposed decision
tree method on several selected syndromes with high clinical relevance. Ideally, the test would
be done on affected plasma samples; however, due to the rarity of these syndromes, it is very
hard to accumulate enough affected cases for a well powered study. Therefore, we created a
genomic DNA (gDNA) mixture model system. Briefly, gDNA samples from individuals with
DiGeorge, Cri-du-chat, Prader-Willi, Angelman, or 1p36 deletion syndromes were obtained from
Coriell Cell Repositories (Camden, NJ) or from CombiMatrix Diagnostics (Irvine, CA).
CombiMatrix genomic DNA was obtained from peripheral blood mononuclear cells from affected
individuals. Coriell genomic DNA was obtained from either cultured fibroblast derived cell lines
or peripheral blood mononuclear cell derived cell lines originally collected from affected
individuals.
45 genomic DNAs were selected based on chromosomal microarray analysis. The source,
disease and karyotype results of the gDNAs are provided in Table 1. In total, 17 samples were
expected to be called positive for Prader-Willi or Angelman syndrome, 14 samples were
expected to be called positive for Cri-du-chat, 13 samples were expected to be called positive
for DiGeorge, and 1 sample was expected to be called positive for 1p36. All the 45 gDNA
samples were sequenced once from 20% to 7.5%, and they were sequenced twice at 5%.
Therefore, a total of 360 gDNA mixtures were sequenced.
Fetal fraction plays a crucial role in the detectability of any given MD. We measured the
observed fetal fraction by chromosome Y on male samples to confirm the planned titration
levels. Supplemental Figure 2 shows that the measured values agreed well with the planned
titrations, with slight over-dilution. The median measured titration concentrations were 18.3%,
16.0%, 14.0%, 11.6%, 9.3%, 7.1% and 4.7% respectively.
Supplemental Figure 2. Estimates of gDNA mixture by Chromosome Y fraction for male (red)
and female (black) samples. We see a slight over-dilution for the targeted titration levels.
Sensitivity at each titration level
The sensitivity values for each condition and each fetal concentration are plotted in Figure 3 in
the main manuscript (black crosses). The detailed numbers at each titration values (4.7%, 7.1%,
9.3%, 11.6%, 14.0%, 16.0%, 18.3%) are respectively: Cri-du-Chat: 12/14, 14/14, 14/14, 14/14,
14/14, 14/14, 14/14; Prader-Willi / Angelman: 1/17, 10/17, 16/17, 17/17, 17/17, 17/17, 17/17
and DiGeorge: 0/13, 2/13, 2/13, 7/13, 11/13, 11/13, 13/13.
For the 1p36 sample, we were able to detect at 9.3% titration and above. An example is shown
on supplemental Figure 3. We note that this 1p36 sample has a size of only ~3Mb, which is
much smaller than its database definition of 12.83Mb [7]. In real life applications, we expect the
overall sensitivity to be higher.
Supplemental Figure 3. Detection of 1p36 syndrome at 18.3% titration. The plots follow the
same convention as Figure 1 in the main manuscript.
Selection of  in decision tree
In the decision tree,  controls the tradeoff between MD calls and aneuploidy calls. By
definition, trisomy means that the entire chromosome must be elevated, i.e. |Zchr| = |Zcbs|;
however, in practice, sequencing noise could result in unevenness in the normalized 50kb
profiles, and the circular binary segmentation (CBS) method could partition a trisomic
chromosome into 2 or more smaller segments. In such scenario, |Zchr| ≠ |Zcbs|. To determine  ,
MDs at a given size were spiked in to euploid samples, with fetal fractions randomly drawn from
a beta distribution with a median fetal fraction of 9.9%. We then ran the CBS algorithm and
computed the percentage of misclassification of MD to trisomy. Supplemental Figure 4 shows
that the mis-classification percentage starts to approach 0 for  >= 0.6. In fact, there is very
little difference for  = 0.8, 0.9 and 1. Therefore, we selected  = 0.8 in this paper prior to
carrying out the blinded clinical study.
We also performed a post-hoc analysis with respect to  in the plasma study. There were a
total of 17 detected positive samples with 21 MD events and 1 trisomy event (supplemental
table 2). We then varied  from 0.1 to 1 and computed the misclassification percentage of MD
to Trisomy on this dataset. Supplemental Figure 5 shows similar results as in Supplemental
Figure 4, where  = 0.7, 0.8, 0.9 and 1 all achieved 0 percent of misclassification.
gDNA and plasma sample truth table
Supplemental Table 1. Microarray karyotype for the 45 gDNA samples. CDC: cri-du-chat, DG:
Digeorge, PW: Prader-Willi / Angelman. The size column reports aCGH detected size (Mb) for
the respective syndrome.
Source
Coriell
Diagnosi
s
1p36
CombiMatrix
CDC
CombiMatrix
CDC
CombiMatrix
CDC
CombiMatrix
CDC
CombiMatrix
CDC
Coriell
CDC
Coriell
CDC
Coriell
CDC
Coriell
CDC
Coriell
CDC
Coriell
CDC
Coriell
CDC
Coriell
CDC
Coriell
CDC
CombiMatrix
DG
Karyotype.Array.Result
size
46,XX.ish del(1)(p36.32)(CEB108/T7-,SKI-,D1S3739+).arr
1p36.32(742429-5215341)x1
ish del(5)(p15.1)(RP11-135M13-)dn.arr 5p15.33p15.1(016,469,199)x1
ish del(5)(p15.1)(RP11-135M13-)[15/20],del(5)(p15.1)(RP11572D16-).arr 5p15.33p15.1(0-15,314,966)x1,5p15.1(15,314,96617,560,136)x1~2
ish del(5)(p15.31p15.2)(RP11-77B3-).arr 5p15.31p15.2(8,450,06313,265,130)x1
ish der(5?15)t(5?15)(p15.1?p1?3)dup(5)(p13.3p15.1)(RP11135M13-,RP11-195G17++;RP11-80H14+) dn.arr 5p15.33p15.1(017,502,828) x1,5p15.1p13.3(17,657,594-29,411,938)x3
ish del(5)(p15.1)(RP11-135M13-).arr 5p15.33p15.1(015,908,568)x1
46,XY,del(5)(p14.3).ish del(5)(p15.33p14.3)(D5S23-).arr
5p15.33p14.3(68519-22367289)x1
46,XY,del(5)(p13.3).ish del(5)(p15.33p13.3)(D5S23-).arr
5p15.33p13.3(68519-29439248)x1
46,XX,del(5)(p13.3).ish del(5)(p15.33p13.3)(D5S23-).arr
5p15.33p13.3(68519-33959668)x1,5p13.3p13.2(3396185835306549)x3
46,XY,del(5)(p15.2p13.2).ish
del(5)(p15.2p13.2)(C84C11T7+,D5S721+,D5S23+,EGR1+).arr
3q12.2(101822745-101925168)x3,5p15.2p13.2(1060836135281220)x1
46,XX,del(5)(p13.3).ish del(5)(p15.33p13.3)(D5S23-).arr
1q23.3(159790161-159905125)x3,5p15.33p13.3(6851929670921)x1,5p13.3(29674742-33982328)x3
46,XY,del(5)(p15.1).ish del(5)(p15.33p15.1)(D5S23-).arr
5p15.33p15.1(68519-17851538)x1
46,XX,del(5)(p15.3p14).ish del(5)(p15.3p14)(C84C11T7+,D5S721,D5S23-,EGR1+).arr 5p15.31p14.3(760726422321719)x1,14q24.3q31.1(77971893-80669264)x1
46,XX,del(5)(p15.1).ish del(5)(p15.33p15.1)(D5S23-).arr
5p15.33p15.1(68519-18230445)x1
46,XX,del(5)(p15.2p14).ish del(5)(p15.2p14)(C84C11T7+,D5S721,D5S23-,EGR1+).arr 5p15.2p14.2(8686804-24072399)x1
ish del(22)(q11.21q11.21)(RP11-316L10-).arr
22q11.21(18,869,130-20,328,826)x1,22q11.21(20,739,305-
4.47
16.47
15.31
4.82
17.5
15.91
22.3
29.37
33.89
24.67
29.6
17.78
14.71
18.16
15.39
2.18
CombiMatrix
DG
CombiMatrix
DG
CombiMatrix
DG
CombiMatrix
DG
CombiMatrix
DG
Coriell
DG
Coriell
DG
Coriell
DG
Coriell
DG
Coriell
DG
Coriell
DG
Coriell
DG
CombiMatrix
PWA
CombiMatrix
PWA
CombiMatrix
PWA
CombiMatrix
PWA
CombiMatrix
PWA
CombiMatrix
PWA
Coriell
PWA
Coriell
PWA
Coriell
PWA
Coriell
PWA
21,466,662)x1
ish del(22)(q11.21q11.21)(RP11-316L10-).arr
22q11.21(18,890,444-21,803,945)x1
ish del(22)(q11.21q11.21)(RP11-316L10-).arr
22q11.21(18,869,130-21,466,662)x1
ish del(22)(q11.21q11.21)(RP11-316L10-).arr
22q11.21(18,789,535-20,328,826)x1
ish del(22)(q11.21q11.21)(RP11-316L10-).arr
22q11.21(18,869,130-20,328,826)x1,22q11.21(20,714,02321,466,662)x1
ish del(22)(q11.21q11.21)(RP11-316L10-) mat.arr
22q11.21(18,892,575-20,359,379)x1
45,XY,der(3)t(3;22)(q29;q11.2),-22.arr 22q11.1q11.21(1503728818710744)x1
46,XX.ish del(22)(q11.2q11.2)(D22S75-).arr 22q11.21(1725641519795660)x1
46,XY.ish del(22)(q11.2q11.2)(D22S75-).arr 22q11.21(1725641518691905)x1
46,XY,+der(20)t(20;22)(q11.1;q11.2),-22.arr 20p13q11.1(929228264860)x3,22q11.1q11.21(15244885-18853334)x1
46,XY,del(22)(q11.21q11.22).ish del(22)(q11.21q11.22)(TUPLE1,N85A3+).arr 22q11.21(17030682-19792611)x1
45,XY,der(4)t(4;22)(q35;q11.2)mat,-22.arr 4q35.2(191074864191254119)x1,22q11.1q11.21(15037288-18401938)x1
46,XX.arr 3q12.2(101822745-101924406)x4,22q11.21(1702029919897219)x1
ish del(15)(q11.2q13.1)(RP11-373J1-).arr
15q11.2q13.1(23,289,365-28,542,401)x1
ish del(3)(p26.3p26.3)(G248P87273E1-)
pat,del(15)(q11.2q13.1)(RP11-390P7-) dn.arr 3p26.3(2,345,0472,402,125)x1,15q11.2q13.1(23,691,284-28,602,810)x1
ish del(15)(q11.2q11.2)(RP11-80H14-).arr
15q11.2q13.1(21,903,815-28,943,268)x1
ish del(15)(q11.2q13.1)(RP11-80H14-).arr
15q11.2q13.1(22,617,694-28,958,116)x1
ish del(15)(q11.2q13.1)(G248P81477B10-).arr
15q11.2q13.1(23,729,389-28,943,268)x1
ish del(15)(q11.2q11.2)(RP11-80H14-).arr
15q11.2q13.1(22,617,694-28,958,116)x1
46,XY,del(15)(q11.2q13).ish del(15)(q11.2q13)(D15Z1+,SNRPN,PML+).arr 15q11.2q13.1(20224750-26234399)x1
46,XX.ish del(15)(q11.2q13)(D15Z1+,D15S10-,PML+).arr
15q11.2q13.1(19803357-26872582)x1
46,XY,del(15)(q11.2q13).ish del(15)(q11.2q13)(D15Z1+,SNRPN,PML+).arr 15q11.2q13.1(21192942-26234399)x1
46,XY,del(15)(q11.2q13).ish
del(15)(q11.2q13)(D15Z1+,D15S10/UBE-,PML+).arr
2.91
2.6
1.54
2.2
1.47
3.67
2.54
1.44
3.61
2.76
3.36
2.88
5.25
4.91
7.04
6.34
5.21
6.34
6.01
7.07
5.04
5.52
Coriell
PWA
Coriell
PWA
Coriell
PWA
Coriell
PWA
Coriell
PWA
Coriell
PWA
Coriell
PWA
15q11.2q13.1(21192942-26710799)x1,19q13.42(5996002760071456)x1
46,XX,del(15)(q11.2q13).ish
del(15)(q11.2q13)(D15Z1+,D15S10/UBE-,PML+).arr
15q11.2q13.1(20224750-26718813)x1
46,XY,del(15)(q11.2q13).ish del(15)(q11.2q13)(SNRPN,154P1+).arr 15q11.2q13.1(21205647-26500067)x1
46,XX,inv(9),del(15)(q11.2q13).ish del(15)(q11.2q13)(D15S63,ICBD3-,SNRPN-,PAR-5-,154P1+).arr 15q11.2q13.1(2124459726500067)x1
46,XY,del(15)(q11.2q13).ish del(15)(q11.2q13)(D15S11-,GABRB3).arr 15q11.2q13.1(21192942-26234399)x1
46,XY,del(15)(q11.2q13).ish del(15)(q11.2q13)(D15S11-,GABRB3).arr 4p16.3p16.2(2876250-3166409)x3,15q11.2q13.1(2022475026752944)x1
Top of Form46,XX,del(15)(q11.2q13).ish
del(15)(q11.2q13)(D15Z1+,D15S10/UBE-,PML+).arr
15q11.2q13.1(21192942-26234399)x1
46,XX,del(15)(q11q13).ish del(15)(q11q13)(D15Z1+,SNRPN,[D15S10/UBE]-,GABRB3-,PML+).arr 15q11.2q13.1(2022475126500067)x1
6.49
5.29
5.26
5.04
6.53
5.04
6.28
Supplemental Table 2. Karyotype outcomes for the affected samples as well as the algorithmpositive samples. For the samples with euploid karyotype but positive outcome at twelve-plex,
they were further sequenced at uniplex to confirm the detection result. TP: true positive, FP:
false positive, FN: false negative.
Karyotype Outcome
47, XX, +mar.ish
idic(15;15)(q13;q13)(D15Z1++,
SNRPN++)
46 XX,
der(12)t(12,19)(p13.1,q13.1)
mat
46,XX, del(8)(p23.1p23.2)
ish del (5) (p15.2p15.2)
(D5S23-)
45,XX,der(15;18)(q10;q10)
46,XY,del(4)(p15.3)
Unbalanced karyotype showing
a chromosome 11 derived from
a paternal insertion of the
chromosomal region
11p12p13p of short arm of one
chromosome in the long arm of
Expected
Result
elevated
15cen to
15q13
depletion
12p13.1 to
12pter,
elevated
19q13.1 to
19qter
depletion
8p23.1 to
8p23.2
depletion
5p15.2
depletion 18p
to 18pter
depletion
4p15.3
elevated
11p12-13
twelveplex results
Result
Detected size (Mb)
duplication_chr15_q11.2_q13.3
TP
9.05
negative
FN
NA
deletion_chr8_p23.1_p23.1
TP
3.55
deletion_chr5_p15.33_p13.3
TP
31.5
deletion_chr18_p11.32_p11.21
TP
14.8
deletion_chr4_p16.3_p15.31
TP
18.1
deletion_chr11_p14.3_p13;
deletion_chr15_q11.2_q13.1
TP (extra chr15
deletion confirmed
by uniplex
sequencing)
10.2;6.3
chromosome 4
47, XY+22
Trisomy 22
Trisomy 22
TP
33.9
46, X del(X)(q22q26)
depletion
Xq22 to Xq26
depletion
11q23
depletion
18p10
depletion
2p11.2 to
2p12
depletion
6p23 to 6pter,
elevated
8q22 to 8qter
depletion
11q23
deletion_chrX_q26.2_q28
TP
18.65
deletion_chr11_q24.2_q25
TP
10.5
deletion_chr18_p11.32_q11.1
TP
18.55
deletion_chr2_p12_p11.2
TP
9.05
deletion_chr6_p25.3_p25.1;
duplication_chr8_q23.1_q24.3
TP
4.5; 38.7
duplication_chr4_p16.3_p16.2;
deletion_chr11_q23.3_q25
5.9;14.05
46, XY del(4)(q34)mat.ish
del(4)(36P21+,wcp4+,DJ963k6)
euploid
depletion
4q34
negative (maternal event
detected)
no result
euploid
no result
deletion_chr1_p36.33_p35.1
1p36;
deletion_chr1_q42.12_q44;
deletion_chr14_q22.1_q24.3;
deletion_chr3_q26.32_q26.33
duplication_chr13_q21.33_q34
TP (extra chr4
deletion confirmed
by uniplex
sequencing)
excluded from
analysis, maternal
event
TP (chr1 and chr14
deletions confirmed
by uniplex
sequencing)
42.3
euploid
no result
deletion_chr5_p14.1_p13.3
euploid
no result
deletion_chr6_q23.3_q24.1
euploid
no result
deletion_chr10_q25.2_q26.3
TP (confirmed by
uniplex sequencing)
FP (uniplex result
negative)
TP (confirmed by
uniplex sequencing)
TP (confirmed by
uniplex sequencing)
46, XY del(11)(q23)
46, XX del(18)(p10)
46, XX, del(2) (p11.2p12)dn
46, XY
der(6)t(6:8)(p23;q22)(wcp8+)
46, XY del(11)(q23)dn
12.0
32.45;23.55;25;2.85;
NA
NA
3.25
22.15
References:
1. Jensen TJ, Zwiefelhofer T, Tim RC, Dzakula Z, Kim SK, et al. (2013) High-throughput massively parallel
sequencing for fetal aneuploidy detection from maternal plasma. PLoS One 8: e57381.
2. Ehrich M, Deciu C, Zwiefelhofer T, Tynan JA, Cagasan L, et al. (2011) Noninvasive detection of fetal
trisomy 21 by sequencing of DNA in maternal blood: a study in a clinical setting. Am J Obstet
Gynecol 204: 205 e201-211.
3. Palomaki GE, Kloza EM, Lambert-Messerlian GM, Haddow JE, Neveux LM, et al. (2011) DNA
sequencing of maternal plasma to detect Down syndrome: an international clinical validation
study. Genet Med 13: 913-920.
4. Fan HC, Quake SR (2010) Sensitivity of noninvasive prenatal detection of fetal aneuploidy from
maternal plasma using shotgun sequencing is limited only by counting statistics. PLoS One 5:
e10439.
5. Miller CA, Hampton O, Coarfa C, Milosavljevic A (2011) ReadDepth: a parallel R package for detecting
copy number alterations from short sequencing reads. PLoS One 6: e16327.
6. Yu SC, Jiang P, Choy KW, Chan KC, Won HS, et al. (2013) Noninvasive prenatal molecular karyotyping
from maternal plasma. PLoS One 8: e60968.
7. Firth HV, Richards SM, Bevan AP, Clayton S, Corpas M, et al. (2009) DECIPHER: Database of
Chromosomal Imbalance and Phenotype in Humans Using Ensembl Resources. Am J Hum Genet
84: 524-533.