FAST-SeqS: A Simple and Efficient Method for the
`Detection of Aneuploidy by Massively Parallel
`Sequencing
`
`Isaac Kinde*, Nickolas Papadopoulos, Kenneth W. Kinzler*, Bert Vogelstein
`
`The Ludwig Center for Cancer Genetics and Therapeutics and The Howard Hughes Medical Institute, Johns Hopkins Kimmel Cancer Center, Baltimore, Maryland, United
`States of America
`
`Abstract
`
`Massively parallel sequencing of cell-free, maternal plasma DNA was recently demonstrated to be a safe and effective
`screening method for fetal chromosomal aneuploidies. Here, we report an improved sequencing method achieving
`significantly increased throughput and decreased cost by replacing laborious sequencing library preparation steps with PCR
`employing a single primer pair designed to amplify a discrete subset of repeated regions. Using this approach, samples
`containing as little as 4% trisomy 21 DNA could be readily distinguished from euploid samples.
`
`Citation: Kinde I, Papadopoulos N, Kinzler KW, Vogelstein B (2012) FAST-SeqS: A Simple and Efficient Method for the Detection of Aneuploidy by Massively
`Parallel Sequencing. PLoS ONE 7(7): e41162. doi:10.1371/journal.pone.0041162
`
`Editor: Reiner Albert Veitia, Institut Jacques Monod, France
`
`Received April 17, 2012; Accepted June 18, 2012; Published July 18, 2012
`Copyright: ß 2012 Kinde et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits
`unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
`
`Funding: This work was supported by The Virginia and D.K. Ludwig Fund for Cancer Research, the National Institutes of Health (CA62924, CA43460, and
`CA57345), and by a UNCF-Merck Graduate Fellowship to I.K. The funders had no role in study design, data collection and analysis, decision to publish, or
`preparation of the manuscript.
`
`Competing Interests: The authors declare the following competing interests. This study was partially funded by a UNCF-Merck Graduate Fellowship (to I.K.).
`N.P., K.W.K, and B.V. are co-founders of Inostics and Personal Genome Diagnostics and are members of their Scientific Advisory Boards. N.P., K.W.K, and B.V. own
`Inostics and Personal Genome Diagnostics stock, which is subject to certain restrictions under The Johns Hopkins University policy. I.K., N.P., K.W.K, and B.V. have
`submitted a patent application entitled ‘‘Prenatal Aneuploidy Testing through Compressed Genome Sequencing of Maternal Plasma’’ covering the work
`presented in this manuscript. The terms of these arrangements are managed in accordance with University conflict-of-interest policies. There are no further
`patents, products in development or marketed products to declare. This does not alter the authors’ adherence to all the PLoS ONE policies on sharing data and
`materials, as detailed online in the guide for authors.
`
`* E-mail: ik@jhmi.edu (IK); kinzlke@jhmi.edu (KWK)
`
`Introduction
`
`A major chromosomal abnormality is detected in approximately
`1 of 140 live births [1] and in a much higher fraction of fetuses that
`do not reach term or are still-born [2]. The most common
`aneuploidy is trisomy 21 (Down syndrome), which currently occurs
`in 1 of 730 births [1]. Though less common than trisomy 21,
`trisomy 18 (Edwards Syndrome) and trisomy 13 (Patau syndrome)
`occur in 1 in 5,500 and 1 in 17,200 live births, respectively [1]. A
`large variety of congenital defects, growth deficiencies, and
`intellectual disabilities are found in children with chromosomal
`aneuploidies, and these present life-long challenges to families and
`societies [3]. For these reasons, much effort has been devoted to
`detecting chromosome abnormalities during early fetal
`life, at
`a time when therapeutic abortions can be offered as an option to
`prospective parents.
`There are a variety of prenatal tests that can indicate increased
`risk for fetal aneuploidy, although invasive diagnostic tests such as
`amniocentesis or chorionic villus sampling are the current gold
`standard [4] and are associated with a non-negligible risk of fetal
`loss. More reliable, non-invasive tests for fetal aneuploidy have
`therefore long been sought. The most promising of these are based
`on the detection of fetal DNA in maternal plasma, as pioneered by
`Lo’s group [5]. It has recently been demonstrated that massively
`parallel sequencing of libraries generated from maternal plasma
`can reliably detect chromosome 21 abnormalities [6,7]. In the
`most comprehensive study to date [8], 98.6% of fetuses with
`
`trisomy 21 were detected in maternal plasma, with a false positive
`rate of 0.2 percent. In an additional 0.8 percent of samples, the test
`failed to give a result. These exciting studies promise a new era of
`non-invasive prenatal testing.
`Currently, almost half of trisomy 21 babies are born to mothers
`less than 35 years of age, as more than 80% of pregnant women
`are under 35 [9,10]. Though the risk of invasive procedures is
`thought to outweigh the benefit of invasive testing for eligible
`young mothers, it is clear that the vast majority of births associated
`with chromosomal aneuploidies could be safely identified with
`reliable non-invasive tests that could be administered to all
`pregnant women. Prenatal testing is an extraordinarily stressful
`exercise for pregnant mothers and their families, and the more
`rapid the process, the better.
`To achieve this goal with circulating fetal DNA testing,
`decreases in cost and increases in throughput will be necessary.
`There are three major components of plasma DNA testing:
`preparation of DNA libraries for loading on the sequencing
`instrument, the sequencing of these libraries, and their analysis.
`The second component
`is being addressed by instrument
`manufacturers, who have made remarkable progress over the last
`few years. Potential
`improvements
`in the first and third
`components are the subject of the current study.
`The only commercially available tests for circulating fetal DNA
`aneuploidy [8,11] involve the preparation of whole genome
`libraries and the analysis of a sufficient number of sequences on
`the relevant chromosomes to reliably detect small differences in
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`copy number. The preparation of whole genome libraries involves
`several sequential steps - including end-repair, 59-phosphorlyation,
`addition of a terminal dA nucleotide to the 39 ends of
`the
`fragments,
`ligation of
`the fragments
`to adapters, and PCR
`amplification of the ligated products - many of which require
`intervening purifications. The PCR products are then quantified
`and loaded on the sequencing instrument. Following the
`sequencing run, the tags are aligned to the human genome and
`assessed with Digital Karyotyping [12], i.e., the number of tags per
`genomic locus is used to construct a virtual karyotype. Another
`recently described test involves fewer, but still a large number of,
`steps to prepare libraries for sequencing [13,14].
`We reasoned that this process could be simplified if a defined
`number of
`fragments from throughout
`the genome could be
`amplified using a single primer pair, obviating the need for end-
`repair, terminal 39-dA addition, or ligation to adapters. Further-
`more, the smaller number of fragments to be assessed (compared
`to the whole genome) would streamline the genome matching and
`analysis processes. Here we detail our approach, which we have
`named ‘‘Fast Aneuploidy Screening Test-Sequencing System
`(henceforth FAST-SeqS).
`
`Materials and Methods
`
`Templates
`Control DNA was obtained from normal spleen, peripheral
`blood white blood cells (WBCs), or plasma from patients (Table
`S1) giving written informed consent after approval by the
`institutional review board of The Johns Hopkins University.
`Fibroblast DNA from five individuals with trisomy 21 (NA02767,
`NA04616, NG05120, NG05397, and NG07438), two with trisomy
`18 (NA03623 and NG12614), and one with trisomy 13
`(NA03330), all with karyotype-confirmed aneuploidies, were
`purchased from the Coriell
`Institute for Medical Research
`(Camden, New Jersey). A total of 33 ng of DNA was used for
`each experiment. All templates were quantified by OD, except for
`the mixing experiments in which the templates were quantified by
`Digital PCR [15]
`to achieve a more accurate estimate of
`concentration.
`
`Sequencing Library Preparation
`The most significant time savings in FAST-SeqS is afforded by
`the replacement of currently used, laborious library preparation
`steps with an amplification using a single primer pair designed to
`amplify a discrete subset of repeated regions (see ‘Results and
`Discussion’ section). Templates were amplified as described by
`Kinde et al. [16] in which individual template molecules are tagged
`with a unique identifier DNA sequence. Though the unique
`identifier sequences turned out to be not necessary for FAST-SeqS
`(see ‘Results and Discussion’ section), we included them in the
`initial experimental design and continued to use them once they
`were observed to provide robust PCR products in our initial
`experiments. Briefly, FAST-1 amplification primers each con-
`tained a different 59 universal primer sequence (UPS) followed by
`sequences allowing amplification of well-dispersed,
`repeated
`elements (see ‘Results and Discussion’ section and Table S2).
`Additionally, the forward primer contained either a stretch of 16
`or 20 degenerate bases immediately 39 to its UPS (Table S2). PCR
`was performed using Phusion Hot Start II Polymerase (Thermo
`Scientific, cat. no. F-549 L) in a total of 50 mL of 16 Phusion HF
`buffer containing 0.5 mM each primer and two units of poly-
`merase under the following cycling conditions: 98uC for 120 s,
`followed by two cycles of 98uC for 10 s, 57uC for 120 s, and 72uC
`for 120 s. The initial amplification primers were removed with
`
`A Simple and Efficient Aneuploidy Detection Method
`
`AMPure XP beads (Beckman Coulter Genomics, cat. no. A63880)
`according to the manufacturer with the exception that the beads
`were added at only 1.46 the PCR volume and the elution volume
`was reduced to 10 mL of TE. The elution was used directly for
`a second round of amplification using primers that annealed to the
`UPS site introduced by the first
`round primers and that
`additionally contained the 59 grafting sequences necessary for
`hybridization to the Illumina flow cell (Table S2). Further, we
`introduced one of five indexes (‘‘barcodes’’) (Table S2) to each
`sample in the reverse primer to later allow multiplexed sequenc-
`ing. The second round of PCR was performed using Phusion Hot
`Start II Polymerase in a total of 50 mL of 16 Phusion HF buffer
`containing 0.5 mM each primer and two units of polymerase under
`the following cycling conditions: 98uC for 120 s, followed by 13
`cycles of 98uC for 10 s, 65uC for 15 s, and 72uC for 15 s.
`Amplification products were again purified with AMPure XP
`beads and were quantified by spectrophotometry, real time PCR
`or on an Agilent 2100 Bioanalyzer; all methods of quantification
`yielded similar results. All oligonucleotides were purchased from
`IDT (Coralville, Iowa).
`
`Sequence Tag Filtering and Alignment
`Thirty-seven base sequence tags passing the Illumina chastity
`filter and containing at least three correct terminal bases of the
`amplification primer were filtered for quality by masking any base
`with a quality score ,20 with an N using a custom script. Thus,
`tags with low quality bases were given the opportunity to align by
`considering only their most reliable bases. After quality masking,
`only the distinct sequences were aligned to the human genome
`(hg19 [17]) using Bowtie 0.12.7 [18]. When building the reference
`index for Bowtie, we included unresolved or unplaced contigs [19]
`to ensure the most accurate alignments. Sequences that aligned
`uniquely with up to one mismatch (using the flags –m 1 and –v 1,
`respectively) were retained and their alignments were matched
`back to the original data. Because tag alignment to a discrete set of
`chromosomal positions is simpler than alignment to the entire
`genome, the post-sequencing analysis process was very rapid. In
`fact, this mapping plus subsequent statistical analysis could be
`completed in less than 30 min per sample with a single computer
`housing two six-core CPUs (Intel Xeon X5680).
`
`Normalization
`Massively parallel sequencing will generate a different number
`of sequence tags from each sample, as well as from different
`sequencing runs of
`the same sample, due to stochastic and
`experimental variations. Thus, it is essential to normalize the data
`to make meaningful comparisons of the type used here. Although
`it would be most straightforward to simply express tag counts as
`a fraction of the total number of tags sequenced in an experiment,
`this normalization is too simplistic and is highly susceptible to
`systemic biases that frequently plague next generation sequencing
`of both DNA and RNA templates. For example, normalization for
`local GC content is routinely used in Digital Karyotyping [12]
`analyses such as that used for the diagnosis of trisomy 21 [8,20].
`Because of
`the bimodal size distribution of
`the amplicons
`obtained with the FAST-1 primer pair
`(see ‘Results and
`Discussion’ section), we predicted that the majority of bias in
`FAST-1 amplifications would be due to the potential over-
`representation of the smaller-sized fragments. This bias could
`either be introduced during library preparation or during solid-
`phase bridge PCR on the Illumina flow cell. We found that an
`appropriate normalization for this distribution could be obtained
`using the quantile method [21], used extensively within the
`microarray community. By organizing our data into a list of
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`A Simple and Efficient Aneuploidy Detection Method
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`Figure 1. Comparison of observed and predicted distributions of FAST-SeqS amplification products. (A) A density plot of the expected
`distribution of fragment lengths, with peaks at 124 and 142 bp. (B) A density plot of the actual tag counts obtained in eight normal plasma DNAs. The
`124 bp fragments are preferentially amplified compared to the 142 bp fragments, likely due to an amplification bias towards smaller fragments. Inset:
`polyacrylamide gel of a representative FAST-SeqS sequencing library. Note: the amplification products contain an additional ,120 bp of flanking
`sequence to facilitate sequencing (Table S2). (C) The average representation of the most frequently observed L1 retrotransposon subfamilies in eight
`normal plasma samples. Roughly 97% of uniquely aligning tags arise from positions representing only seven L1 retrotransposon subfamilies. (D) A
`detailed examination of the average number of observed positions per chromosome from eight normal plasma DNAs compared with the number
`predicted by RepeatMasker for each of the seven L1 retrotransposon subfamilies noted in (C). Error bars in each panel depict the range.
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`positions (equivalent to probes in microarray data), each associated
`with a tag count (equivalent to intensities in microarray data), we
`were able to apply standard quantile normalization to FAST-SeqS
`data. To best approximate the microarray data format, we chose
`to only analyze positions that were shared within each experi-
`mental group (e.g., the data from eight normal plasma samples).
`As the FAST-1 primers amplified a highly reproducible set of
`positions, this generally only eliminated ,1% of the data. To
`maximize reproducibility, we excluded positions aligning to
`unresolved or unplaced contigs and those aligning to sex
`chromosomes, although inclusion of
`these chromosomes only
`marginally increased variability between experiments (e.g., in eight
`normal plasma samples, the maximum z-score from any chromo-
`some rose from 1.9 to 2.3). The inclusion of sex chromosomes
`could be useful
`for other applications,
`such as detecting
`aneuploidies involving chromosome X or determining the gender
`of a sample (i.e., by the presence or absence of sequences aligning
`to chromosome Y).
`We implemented the quantile normalization [21] for each
`experimental group (each of which contained multiple samples;
`Table S3) by performing the following steps:
`
`1)
`
`2)
`
`3)
`
`4)
`
`5)
`
`generating a table of tag counts for each sample where each
`row represents a unique position (note that all tables will be
`of equal
`length as only the shared positions
`in each
`experiment were analyzed);
`
`sorting the rows in each table based on tag counts, resulting
`in each table having a different order of positions;
`
`determining the mean tag count for each row across all
`samples;
`
`replacing an individual sample’s tag count with the mean tag
`count for all samples at each row; and
`
`sorting the tag counts for each sample’s table back to their
`original order based on position.
`
`The raw distribution of our data was always negatively skewed
`(see ‘Results and Discussion’ section). We excluded the positions
`falling within the left tail of each experiment’s distribution (the
`positions containing the smallest number of tags) from our analysis
`by:
`
`1)
`
`2)
`
`3)
`
`estimating the distribution of normalized values (see ‘Results
`and Discussion’ section);
`
`determining the inflection point between the two peaks of the
`bimodal distribution; and
`
`considering the positions that had a relative density below the
`inflection point as the left tail.
`
`tail was determined and positions within it
`Once the left
`discarded, the quantile normalization was repeated. Through this
`process, each sample within an experimental group had the same
`sum total of tags and an identical distribution of counts, so direct
`comparisons could be made. We automated the quantile
`normalization in R [22]. The entire normalization procedure
`routinely took less than a few minutes to complete.
`
`Quantitative Determination of Aneuploidy Status
`A common method of determining the aneuploidy status of
`a particular sample in Digital Karyotyping-based [12] assays is by
`comparison of z-scores [6,11,23,24]. Through this method, one
`determines the mean and standard deviation of tag counts lying
`within a chromosome of interest in a group of reference samples
`(e.g., samples with known euploid content), and then creates
`a standardized score (i.e., z-score) for a chromosome of interest for
`
`A Simple and Efficient Aneuploidy Detection Method
`
`Figure 2. Demonstration of FAST-SeqS reproducibility among
`different samples, sequencing instruments, and sequencing
`depth. FAST-SeqS was performed on eight normal plasma DNA
`samples, their corresponding matched peripheral blood white blood
`cell (WBC) DNA, and on the splenic or WBC DNA of an additional eight
`unrelated individuals. The eight samples within each experiment
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`A Simple and Efficient Aneuploidy Detection Method
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`constituted the reference group (see ‘Materials and Methods’ section)
`from which the plotted z-scores were calculated. No autosome in any
`sample had a z-score outside the range of 23.0 and 3.0 (dotted lines).
`Despite 3-fold less sequencing of the splenic or WBC samples, the z-
`scores (range: 22.2 to 2.1) were similar to those obtained from the
`plasma (range: 22.1 to 1.9) and matched WBC DNA samples (range:
`22.2 to 1.9).
`doi:10.1371/journal.pone.0041162.g002
`
`each sample as follows:
`
`z-scorei, chrN~(chrNi{mchrN)=sdchrN,
`
`where i represents the sample to be standardized, chrN represents
`the normalized tag count of the sample’s chromosome, and mchrN
`and sdchrN represent the mean and standard deviation of the
`normalized tag counts, respectively, of chrN in the reference
`group. When all samples are standardized in this way, outliers are
`easily detected because they have a z-score .3.0. This indicates
`that the normalized tag count of the outlier exceeds the mean of
`the reference group by at least three standard deviations.
`
`Results and Discussion
`
`Primer Selection and in silico Analysis
`The key innovation behind FAST-SeqS, which increases
`throughput and lowers cost compared to traditional whole-
`genome sequencing fetal aneuploidy screening tests, is the use of
`specific primers that anneal
`to a subset of repeated regions
`dispersed throughout the genome. For maximum utility, we sought
`to target regions with enough similarity so that they could be
`amplified with a single pair of primers, but sufficiently unique to
`allow most of the amplified loci to be distinguished.
`We began by searching a ,6.8 Mb region of chromosome 21
`(hg19 [17] coordinates 35,888,786 to 42,648,523), containing the
`Down syndrome critical region [25], for sequence blocks of ,150
`bp that were similar but not identical to those present on all
`chromosomes. To identify such blocks, we queried sequences
`obtained from 150 bp sliding windows incremented by 50 bp
`(135,186 sequences of 150 bp in length) with the BLAST-Like
`Alignment Tool (BLAT) algorithm [26]. We also required that the
`queried sequence be similar to at least three other blocks on
`chromosome 21, in addition to the one within the ,6.8 Mb region
`described above.
`Out of the 135,186 queried blocks, we found only 56 that met
`the following criteria:
`
`1)
`
`2)
`
`3)
`
`contained at least 11 variant bases from the query sequence,
`to aid in distinguishing amplified loci;
`
`contained no more than 30 variant bases from the query
`sequence, to increase the chance of uniform amplification;
`and
`
`spanned no more than a total of 180 bases, to be compatible
`with the analysis of degraded DNA [8].
`
`We then manually scanned the BLAT alignments of these 56
`blocks to search for those that had highly similar 59 and 39 ends. At
`least three of the 56 sequences met our criteria and we designed
`primers for them. In Silico PCR [27] predicted that each primer
`pair would amplify many distinct sequences from every nuclear
`chromosome.
`Sequences that were too similar could pose a problem during
`alignment because of
`the inevitable errors introduced during
`library preparation or sequencing. We therefore wrote a custom
`script to assess how many distinct sequences would remain after
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`Figure 3. Accurate discrimination of euploid DNA samples
`from those containing trisomic DNA. (A) Comparison of z-scores
`from patients with trisomy 21 (n = 4), trisomy 18 (n = 2), and trisomy 13
`(n = 1) with eight normal spleen or peripheral blood white blood cell
`(WBC) DNAs. The z-scores displayed represent the relevant chromo-
`some for the comparison. The maximum z-score observed for any of the
`compared normal chromosomes was 1.9 (chr13). (B) Control WBC DNA
`was analyzed alone (n = 2) or when mixed with DNA from a patient with
`trisomy 21 at 5% (n = 2), 10% (n = 1), or 25% (n = 1) levels. A tight
`correlation existed between the expected and observed fractions of
`extra chromosome 21 (r = 0.997 by Pearson correlation test, n = 6). (C)
`Control WBC DNA was analyzed alone (z-score range: 20.8 to 1.3) or
`when mixed with DNA from a patient with trisomy 21 at 4% (z-score
`range: 4.5 to 7.2) or 8% (z-score range: 8.9 to 10.)
`levels. Each
`experiment in (C) was performed in quadruplicate.
`doi:10.1371/journal.pone.0041162.g003
`
`allowing one, two, or three errors in each ,150 bp sequence. The
`theoretical amplification products of one primer pair (FAST-1)
`greatly outperformed the other two, and the superiority of FAST-1
`was confirmed in pilot sequencing experiments.
`The FAST-1 primer pair was predicted to amplify subfamilies of
`long interspersed nucleotide element-1 (L1 retrotransposons) in
`a primarily bimodal distribution of amplicon sizes, with the
`majority of amplicons having an average size of 124 or 142 bp
`(Fig. 1A). L1 retrotransposons, like other human repeats, have
`spread throughout the genome via retrotransposition, particularly
`in AT-rich regions [28]. As it
`is generally more difficult
`to
`uniformly amplify and sequence regions that vary widely in their
`GC content [8,20], we expected that this differential localization
`would work in our favor.
`
`FAST-SeqS Yields a Highly Reproducible Subset of
`Sequences
`An average of 38% of tags across all samples could be uniquely
`assigned to a genomic position (range: 31% to 45%; Table S3). As
`opposed to traditional whole genome amplification libraries,
`where the vast majority of tags align to the genome in unique
`positions and thus requiring that each tag be independently
`aligned, FAST-SeqS yielded sequences that aligned to an average
`of only 21,676 positions (Table S3). The number of positions to
`which the sequences aligned varied little compared to the range of
`sequence data obtained across all experiments. Though the
`number of uniquely aligned tags per experiment spanned a 12-
`fold range (1,343,382 to 16,015,347) the number of positions
`varied only by 0.25-fold (range: 18,484 to 24,562 positions; Table
`S3). Not only was the number of aligned positions similar among
`samples, but the identities of the positions were also remarkably
`similar: among samples within an experimental group, ,1% of
`aligned tags were eliminated when limiting the analysis to positions
`shared among each sample.
`
`The Distribution of Sequenced Fragments Agrees with
`in silico Predictions
`Though we only sequenced 37 bases, we could estimate the
`relative size of the original PCR fragment and its unique position
`in the genome after alignment. This exercise provided additional
`evidence that the actual amplification products matched those that
`were predicted and alerted us to a preferential amplification bias
`towards sequences arising from smaller fragments.
`We transformed the tag counts per uniquely aligned position to
`a log scale – a transformation frequently performed to this class of
`data to induce symmetry [29] – for each group of experiments
`(Table S3). Next, we used a nonparametric method to estimate
`a smoothened distribution (a kernel density estimator, implemen-
`
`A Simple and Efficient Aneuploidy Detection Method
`
`ted in R [22] using the density function), which made it
`straightforward to visualize the modality of our data. After
`plotting the distribution using ggplot2 [30] (an R [22] package),
`we observed that each group of experiments showed a similar
`clustering of tag counts per position, consistent with a primarily
`bimodal distribution with a negative skew. Tags originating from
`smaller PCR fragments were observed to have higher average tag
`counts, likely due to amplification biases. A representative plot is
`displayed in Figure 1B.
`
`Autosomal Representation after Performing FAST-SeqS is
`Highly Reproducible
`the performance of FAST-SeqS, we
`As an initial
`test of
`examined the representation of each autosome from different
`biologic sources (Table S1) using different sequencing instruments
`and depth (Table S3).
`We first examined the representation of each autosome in the
`plasma DNA of seven normal females,
`including one biologic
`replicate (a total of eight samples), using only 37 cycles of
`sequencing in one-quarter of a lane on an Illumina HiSeq 2000
`instrument. We recovered an average of 31,547,988 high quality
`tags per individual (range: 27,179,424 to 36,048,017 tags; Table
`S3). An average of 35% of these tags (range: 31 to 37%) could be
`uniquely mapped to one of an average of 23,681 unique
`chromosomal positions (range: 22,589 to 24,562 positions) when
`allowing up to one mismatch during alignment to hg19 [17] using
`Bowtie [18]. Of the uniquely aligned tags, 99.1% aligned to
`positions predicted to be repetitive DNA by RepeatMasker
`(http://www.repeatmasker.org), 97.5% of which fell
`into just
`seven L1 retrotransposon subfamilies (Fig. 1C). Additionally, as
`depicted in Fig. 1D, the distribution of each subfamily was not
`statistically distinguishable from that predicted by RepeatMasker
`(p = 1 for each of the seven L1 retrotransposon subfamilies when
`comparing the observed mean percentage of positions per
`chromosome with the predicted number; correlated two-tailed t-
`test).
`Most importantly, the relative fraction of tags mapping to each
`chromosome was
`remarkably similar among the individual
`samples after normalizing [21] to compare chromosome tag
`counts among different samples (see ‘Materials and Methods’
`section). In particular, the fraction of tags that matched to any of
`the autosomes in any of the eight samples studied never deviated
`from the average by a z-score .3.0 (Fig. 2). Of particular note, the
`maximum z-scores observed among the eight
`samples
`for
`chromosomes 21, 18, and 13 were 1.3, 1.4, and 1.0, respectively.
`In the next experiment, we analyzed DNA from peripheral
`blood white blood cells (WBCs) from the same seven individuals
`who contributed plasma, including the biologic replicate (eight
`total samples). Four samples were sequenced on a single lane of an
`Illumina HiSeq 2000, yielding a mean of 10,835,559 uniquely
`aligned tags per sample (range: 4,905,067 to 16,015,347 tags). The
`maximum z-scores for any of the samples were 1.0, 1.2, and 1.6 for
`chromosomes 21, 18, and 13, respectively (Fig. 2).
`Finally, we analyzed splenic or WBC DNA from an additional
`eight individuals using one-half of a lane of an Illumina GA IIx
`instrument, designed to yield fewer tags per sample than achieved
`above. Despite almost 3-fold less sequencing (average of 4,013,951
`uniquely aligned tags per sample), the maximum z-scores among
`the samples were still only 1.3, 1.5, and 1.9 for chromosomes 21,
`18, and 13, respectively, well below the widely used cutoff of 3.0
`(Fig. 2).
`
`PLoS ONE | www.plosone.org
`
`6
`
`July 2012 | Volume 7 |
`
`Issue 7 | e41162
`
`FOUNDATION EXHIBIT 1049
`IPR2019-00634
`
`Page 6
`
`

`

`FAST-SeqS Readily Identifies Samples Containing
`Trisomic DNA, Even when Present in Low Proportions
`Given the tight distributions of tags evident in Figure 2, we
`expected it would be straightforward to distinguish the DNA of
`patients with trisomies from those of normal
`individuals with
`euploid chromosome constitutions. The data depicted in Figure 3A
`demonstrate that this expectation was realized in each of four
`patients with trisomy 21. The z-scores among these trisomy 21
`patients ranged from 32 to 36, while the maximum z-score among
`eight normal individuals was 1.3. Similarly, the z-scores of DNA
`from two patients with trisomy 18 and one from trisomy 13 were
`51, 56, and 36, respectively, far exceeding the maximum z-scores
`for these chromosomes in normal individuals (Fig. 3A).
`Fetal DNA accounts for a geometric mean of 13.4% of maternal
`DNA, depending largely on maternal weight
`rather
`than
`gestational age [8]. To investigate whether FAST-SeqS could
`distinguish samples
`that contained mixtures of disomic and
`trisomic DNA, we performed mixing experiments using DNA
`from patients with trisomy 21 and normal individuals. In a first
`experiment of this type, we mixed 5% (n = 2), 10% (n = 1), and
`25% (n = 1) trisomy 21 DNA into normal WBC DNA alongside
`two controls (Fig. 3B), and found a tight correlation between the
`expected and observed fractions of extra chromosome 21
`(r = 0.997 by Pearson correlation test, n = 6).
`In a second
`experiment, we evaluated mixtures that contained 4% or 8%
`trisomy 21 DNA. As shown in Figure 3C, there was a clear
`distinction between the samples containing 4% trisomy 21 DNA
`vs. those from normal individuals (p = 261024 as determined by
`uncorrelated two-tailed t-test, n = 4 in each group). The samples
`containing 8% trisomy 21 DNA were of course even more easily
`distinguishable (p = 461026 when compared to the euploid group
`and p = 161023 when compared to the 4% trisomy 21 samples,
`both by uncorrelated two-tailed t-test with n = 4 for each group).
`
`A Simple and Efficient Aneuploidy Detection Method
`
`is possible to uniquely identify each template
`it
`(Table S2),
`molecule giving rise to a PCR product [16]. This could potentially
`increase accuracy by minimizing the chance that
`the same
`template molecule was counted more than once in the final tally
`for each chromosome. In contrast, we found that the maximum z-
`score for any chromosome was subtly increased from 1.9 to 2.0
`when using precise counting. By performing an uncorrelated two-
`tailed t-test on the absolute values of the z-scores for all autosomes,
`we found that the difference between the two methods was not
`statistically significant (p = 0.759, n = 2268 for each group).
`
`Conclusions
`FAST-SeqS was capable of detecting aneuploidies in a re-
`producible fashion in our pilot experiments. It has advantages over
`unbiased whole genome sequencing in ease of imple