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Use of copy number deletion polymorphisms to assess DNA chimerism.

Genomic chimerism describes the coexistence of DNA or cells originating from more than one individual. The most frequently encountered examples are donor-recipient mixtures in individuals who have received an allogeneic organ transplant and fetal-maternal mixtures in the blood of pregnant women. Plasma DNA chimerism, which refers to the situation in which mixtures of "self' and "nonself' circulating cell-free DNA (ccfDNA) [9] are present in an individual's blood plasma, has recently acquired major clinical significance and application in noninvasive detection of fetal chromosome aneuploidy in pregnancy. In a research context, plasma DNA chimerism analysis promises new avenues for monitoring the immunological rejection of organ transplants.

Quantitative assessment of the concentration of nonself plasma ccfDNA relies on distinct genetic differences between recipient and donor or mother and fetus. Copy number variants (CNVs) have not hitherto been used for this purpose. CNVs are losses or gains of segments of the genome, ranging from a few hundred base pairs to several megabases, which vary in copy number between individuals in a population (1). Genome analyses using massively parallel sequencing (MPS) and microarray technologies have revealed that 10%-15% of the genome is subject to copy number variation (2). The ubiquity, size, and allelic distribution of CNVs make them attractive putative genetic markers.

We describe a novel use of ubiquitous CNVs, specifically copy number deletion (CND) polymorphisms, for highly accurate quantification of plasma DNA chimerism. The approach relies on the presence of a biallelic genomic deletion (i.e., "'null" or "0-copy" genotype) in the "self' DNA with the presence of at least one wild-type, nondeleted allele (i.e., 1- or 2-copy genotype) at the corresponding DNA locus in the "nonself" DNA. Our goal was to obtain performance superior to previous approaches using other polymorphic markers (3-5), because targeting of relatively large DNA sequences (i.e., >50 bp) that are absent in the self should provide a very analytically sensitive, background-free measurement of nonself DNA.

Materials and Methods


Renal transplant recipients were recruited through the Nephrology Department, Austin Health, Melbourne, Australia. All participants gave written informed consent to the protocol approved by the Austin Health Human Research Ethics Committee.


Peripheral blood samples from renal transplant recipients and pregnant women were collected in EDTA and cell-free DNA BCT[R] tubes (Streck), respectively. Samples from renal transplant patients and pregnant women were processed within 4 and 48 h of venipuncture, respectively. In brief, blood was transferred into 15-mL falcon tubes and centrifuged at 1600g for 10min at 4[degrees]C to separate the blood cells and plasma. Plasma was then transferred into 1.5-mL microcentrifuge tubes and centrifuged at 16 000g for 10 min at 4[degrees]C to remove residual cells. Finally, plasma was transferred into new microcentrifuge tubes and stored at--80[degrees]C until further processing. The blood cell fraction was stored at--20[degrees]C.


DNA was isolated from plasma using the QIAamp circulating nucleic acid kit (Qiagen, Melbourne, Australia) according to the manufacturer's guidelines, with slight modifications in reagent volumes as described below. All buffers were sourced from the QIAamp circulating nucleic acid kit; formulas are included in the manufacturer's handbook. Briefly, 1-mL aliquots of plasma were lysed with 100 [micro]L of proteinase K and 0.8 mL of ACL buffer containing 5.6 [micro]g of carrier RNA. This solution was mixed thoroughly by vortex mixing and incubated at 60[degrees]C for 30 min. The tubes were centrifuged at 376g for 30 s and 1.8 mL of buffer ACB was added and mixed thoroughly by vortex mixing. This was followed by incubation of the lysate-buffer ACB mixture on ice for 5 min. The QIAvac 24 Plus system (Qiagen) was used for processing of Qiagen mini spin columns. The column was washed with buffer ACW1 (600 [micro]L), buffer ACW2 (750 [micro]L), and 750 [micro]L of ethanol (100%), which were added and drawn through the column by the vacuum pump. The column was removed from the QIAvac 24 Plus system and placed in a clean tube and centrifuged at 16 000g for 3 min. Flow-through was discarded and the column was placed in a new collection tube and incubated in a heat block at 56[degrees]C for 10 min to dry the membrane. The column was placed in a new collection tube and 50-100 [micro]L of RNase-free diethyl bicarbonate (DEPC)-treated [H.sub.2]O was added to the center of the membrane and incubated at room temperature for 5 min. The column was centrifuged at 16 000g for 1 min and the eluted volume was reapplied to the column and incubated at room temperature for a further 5 min. Finally, the column was centrifuged at 16 000g for 1 min to elute the DNA.


Genomic DNA was isolated from leukocytes using the Nucleobond blood extraction kit (Macherey-Nagel) according to the manufacturer's instructions. DNA was quantified with the NanoDrop ND-1000 UV Vis spectrophotometer.


Quantitative PCR (qPCR) assays with TaqMan probes were developed for the panel of 10 CND markers. Each qPCR assay used CND-specific internal PCR primers and a target-specific TaqMan probe (see Table 1 in the Data Supplement that accompanies the online version of this article at vol60/issue8).

qPCRs were performed in duplicate in a reaction volume of 10 [micro]L (as detailed in online Supplemental Table 2) and the reactions were performed on the ViiA 7 real-time PCR system (Applied Biosystems). ViiA 7 software version 1.2 (Applied Biosystems) was used for the data analysis. For all reactions, the input volume of DNA was 2.5 [micro]L. As an example, for the 1000-genomic equivalent (GE) standard curve point, the DNA input volume was 2.5 [micro]L, so the concentration of input DNA was 400 GE/[micro]L. The final concentration of DNA in the reaction volume was 100 GE/[micro]L (1000 GE total). The Cq (quantitative cycle) was based on the intersection between the amplification curve and an empirically adjusted threshold. The concentration of ccfDNA in GE per milliliter of plasma (GE/mL) was calculated using the formula described by Lo et al. (6). PCR efficiencies were standardized across all 10 CND qPCR assays. The standard curve characteristics for all assays are provided in online Supplemental Table 3.

We assessed the sensitivity of the qPCR assays in 2 ways. First, a 1-copy DNA control sample was used to spike a 0-copy DNA sample of known quantities. Second, we performed serial dilutions spanning 0-16 000 GE, with quadruple replicates. Precision and accuracy was determined by using a mixed effects model for the increase in levels over increasing concentrations.


Massively parallel sequencing. Duplicate samples from 12 pregnant women previously used for noninvasive prenatal testing (NIPT) by MPS were provided by Natera using the procedure described by Zimmermann et al. (7). In all cases the genomic analysis report provided the fetal fraction (%) of the total ccfDNA present in the sample and showed the fetus to be male. This measure of fetal fraction was used as the primary comparator for the fetal fraction assays described below, which used ccfDNA extracted from the remaining duplicate samples.

Sex determining region Y (SRY) [10] and hemoglobin, beta (HBB) quantitative PCR assay. qPCR analysis was performed using primers and probes for the SRY gene located on chromosome Y and the HBB, both as previously described (6). The fetal ccfDNA fraction (%) was calculated from the ratio of SRY to HBB DNA concentrations.

CND and HBB quantitative PCR. qPCR analysis as described above was performed using informative CND markers. Seven samples had at least 2 informative CNDs and 5 had 1 informative CND. Total ccfDNA was again measured using the HBB quantitative PCR assay and the fetal fraction (%) was calculated from the ratio of the individual CND (5 samples) or mean of multiple CND measurements (7 samples) as described above.


All statistical analyses were performed using the R statistical programming language (http://www.r-project. org/). Deming regression analysis was carried out using the R package Method Comparison Regression (mcr) from



CNDs with null deletion (i.e., 0-copy) frequencies between 0.3 and 0.7 were identified by in silico analysis of 2 high-resolution data sets. Data set 1 included: genotype data for 5238 CNV loci generated by testing 450 HapMap samples at 500-bp resolution (2). Data set 2 included genotype data for 1319 CNV loci generated by testing 270 HapMap samples at 2-kb resolution (8). Analyses used Centre d'Etude du Polymorphisme Humain (CEPH) data only.

Since the CEPH HapMap data included trio data on proband and parents, calculation of 0-, 1-, and 2-copy genotype frequencies was performed using only parental data to minimize overestimation. No further correction, such as for autozygosity (9, 10), was done. In silico selection used the following criteria: each CNV constitutes a single locus in the human genome that spanned <3 kilobases, with copy number genotypes of 0, 1, or 2. CNVs showing copy number genotypes >2 and CNVs on the X and Y chromosomes were excluded to avoid sex bias. This resulted in 54 candidates; all of these CNV regions are polymorphic and none have any known intrinsic clinical significance.


Of the 54 CNDs identified above, the 10 predicted to have the most informative 0-copy genotype frequency ranking were selected as an initial panel for in vitro assessments (Table 1) (also see online Supplemental Table 5). The null genotype frequencies ranged between 0.410 and 0.508, the mean value being 0.452 with an SD of 0.038. Based on these null genotype frequencies, it was calculated and later confirmed by observation that 99% of individuals are likely to be null for at least 1 CND in the panel (see online Supplemental Fig. 1).


The estimated population frequencies for the 10 CNDs obtained from the in silico selection were compared with those in an unselected sample of 93 healthy individuals. The local population from which this sample was taken was ethnically very diverse and we assumed that our random sampling reflects this. Despite a paucity of information for these CNDs in different ethnic populations, the potential of these CND markers for chimerism testing in diverse populations was highlighted by the in silico and in vitro frequencies being very similar (see online Supplemental Table 4).


Genotypes for all CND markers were determined on DNA extracted from leukocytes using a simple PCR-based assay. PCR primers were designed to locate within (i.e., internal PCR) and flank the deleted region (i.e., external PCR) of each CND marker in the panel. Short amplicons, ranging in size from 58 to 74 bp (mean 65 bp) were chosen for the internal PCRs so that they would be suitable for quantification of plasma ccfDNA, which is highly fragmented (11).

Samples with a null genotype give no internal PCR product. Verification of the null genotypes was made using the external PCR assays, which gave unique PCR products. The identities of the internal and external PCR amplicons were confirmed using Sanger sequencing. All internal PCR amplicon sequences of nondeleted alleles mapped to the expected regions within the CND loci and external PCR amplicons of deleted alleles mapped to the flanking regions of the CND loci. The combination of internal/external PCR results distinguishes the null, 1-copy, and 2-copy genotypes (see examples in online Supplemental Fig. 2).


The distributions of null CND frequencies observed in 3 independent population cohorts are almost superimposable and indicate that, on average, the 10-panel genotype for any individual contains 4-5 null CNDs (see online Supplemental Fig. 1). An informative test result occurs when the transplant recipient or pregnant woman is null for at least 1 of the CNDs in the panel and the corresponding qPCR assay shows a detectable level of ccfDNA in plasma, which infers that the donor sample was 1 or 2 copy. In the situation in which the nonself and self DNA are from unrelated individuals, the expected proportion of test results with at least 3 informative markers is 46% (Fig. 1). This increases to 90% and 99% with the use of expanded panels of 20 and 30 CNDs, respectively. With 30 markers (see online Supplemental Table 5) we expected at least 3 informative markers 99% of the time. Samples involving admixtures of sibling-sibling and parent-offspring DNA had <6% chance of having no informative markers with the use of a panel of 30 CNDs (see online Supplemental Fig. 3).


The PCR assays described above for genotyping cellular genomic DNA from blood samples were used with plasma ccfDNA samples under the same conditions (see online Supplemental Fig. 4A).


A blood sample was collected from a 62-year-old male patient who had received an allogeneic kidney transplant from an unrelated female donor 13 months previously. Internal PCRs for all panel CNDs were performed on cellular DNA to identify null genotypes, and qPCR assays for these CNDs were performed on plasma ccfDNA. The patient was null for CND_02, CND_03, and CND_08; nonself, transplant-derived DNA was detected qualitatively for CND_03 and CND_08 (see online Supplemental Fig. 4B). Quantification of nonself DNA using CND03 and CND08 qPCR assays gave measurements of nonself ccfDNA of 450 GE/mL and 469 GE\mL, respectively.


The quantitative assays for each CND were validated by serial dilution (with quadruple replicates) and spike-in mixture experiments using well-characterized genomic DNA samples. To assess the limit of detection, a previously determined 1-copy genomic DNA sample was spiked into a 0-copy genomic DNA sample, thus simulating chimerism. Multiple combinations of spiked control mixtures were prepared to assess all 10 CND qPCR assays.

These experiments showed each of the assays to be highly specific and sensitive; the lower limit of detection is 4 GE and was defined by the mean of the replicate data (Fig. 2, bold line) for the no-DNA sample (i.e., 0 GE) plus 3 SD. The assay measurements were linear (slope = 1) from this level up to at least 16 000 GE (Fig. 2). The spike-in mix experiments showed that across the range of 4-600 GE, accuracy was very high (estimated slope was 0.99, close to the expected value of 1) as was the imprecision (SE of the slope estimate 0.01). Ninety-five percent of marker slope estimates ranged between 0.96 and 1.01 (Fig. 2). The slope estimates for individual CND assays (grey lines) varied slightly, with an SD of 0.03, indicating only very minor differences in assay performance. This is relevant in clinical applications because each individual informative CND should provide an independent measurement of plasma DNA chimerism.

The analytical specificity of the assays was further assessed by quantitative measurement of amplification in 0-copy genomic DNA samples containing no copies of each respective CND target per diploid cell (see online Supplemental Table 3 and Supplemental Fig. 5). The 0-copy DNA amplification plots were consistent for all CND assays, except for CND10, which showed detectable signal overlapping the standard curve at 1 GE. These data indicated very low-level amplification of nontarget DNA in a 0-copy background at 1000 GE input per reaction and are consistent with a lower limit of quantification of 16 GE for these CND qPCR assays.

The CND qPCR assays exhibited reliability (i.e., reproducibility) upon repeat testing of the 4-fold serial dilution standards over time (8 separate qPCR experimental runs); the CVs ranged from 1.7% to 3.8% (total SDs) for measurements across 4-16 000 GE.


The technical performance characteristics of our approach indicated that the underlying premise of exploiting CNDs to quantify DNA chimerism was sound. As proof of principle, we measured the minor chimeric DNA component in 2 potential clinical applications: allogeneic organ transplantation and NIPT.

We measured transplant-derived ccfDNA levels in serial blood samples collected shortly after and during the weeks following transplantation from 2 patients who had received an allogeneic kidney transplant (cases 1 and 2) (Fig. 3). Both patients showed measurements that increased substantially above 70 GE/mL within the first 2 weeks post-transplantation, which we ascribed to the trauma that the organs had undergone during the transplantation procedure. However, the concentrations stabilized below 70 GE/mL within 3 weeks and remained so for all subsequent time points.

Prior knowledge of the fetal ccfDNA fraction in maternal plasma samples may also have clinical utility. Therefore, we also measured the fetal DNA fraction in 12 maternal plasma samples, for which the singleton fetus was known to be male, using the CND method, and compared results with those derived from an SRY qPCR and also from singlenucleotide polymorphism (SNP)-based whole genome sequencing data (Table 2 and Fig. 4). Better agreement was observed for the fetal fraction values calculated by CND-qPCR and SNP-based MPS methods. Indeed, SRY qPCR-based estimates of the fetal fraction were consistently lower than those obtained by both other methods.


There is considerable interest in ccfDNA because of its potential use in noninvasive, rapid diagnosis and monitoring of many acute and chronic disorders. We describe here a novel application of CNVs, specifically CNDs, for measurement of the very low levels of fragmented DNA that are typically found in plasma. These deletion variants are present in all individuals and their use in this context offers significant advantages over existing allele-specific, hybridization-based techniques that target SNPs or short indels (3, 4, 12, 13). Most importantly, since nonself DNA measurement using CNDs is made against a zero background, allele discrimination and, consequently, analytical sensitivity should be maximized. The fragmented nature of plasma ccfDNA (11) presents additional challenges for PCR-based assessment of chimerism. To address this, we restricted amplicon sizes to fewer than 80 bp and showed that the assay can be used with fragmented ccfDNA. Because each individual has hundreds of CNDs in their genome and genotype differences between individuals are very numerous, large numbers of potential targets are available for assay design (see online Supplemental Table 5). The panel of 10 unique CNDs used here, which provides a mean of 2-3 informative markers in unrelated donor-recipient pairs, was sufficient for proof of principle demonstration of how this novel method can be used. In practice, there would be a need for additional CND markers, and we have shown that incorporating a further 20 CNDs would provide at least 1 informative discriminating marker in a chimeric mixture of DNA from almost any 2 individuals (Fig. 1; also see online Supplemental Fig. 3).

The availability of multiple informative markers allows for identification and statistical handling of out-lier measurements resulting from a DNA sample having a rare duplication or triplication of a CND locus or inflated measurements when the nonself DNA (transplant or fetus) is homozygous wild type (i.e., 2 copy instead of 1 copy) at a given CND locus (e.g., suspected in sample 8, Table 2).

Using qPCR, we showed the measurement of nonself DNA to have high specificity, low imprecision, and high reliability for linear quantification of DNA chimerism down to 16 GE. Our assessment of misamplification of CND targets in 0-copy DNA samples demonstrated the high specificity of this approach (see online Supplemental Fig. 5 and Supplemental Table 3). Further improvements in imprecision and sensitivity would be expected through use of digital PCR or other absolute molecular counting approaches.

We developed this approach to measure nonself, cell-free plasma DNA originating from a transplanted allogeneic kidney. We envisage other applications in NIPT (14-16). In particular, we have compared the estimation of fetal ccfDNA fractions using the CND approach with values derived from MPS and from qPCR of SRY. Our comparison shows superior linear (Deming) relationships between the CND-based and SNP-based MPS estimates of fetal fraction than that obtained using SRY qPCR (Fig. 4). There is currently no established gold standard for measuring fetal fraction in maternal plasma samples. Our data suggests that using SRY qPCR gives an underestimate of fetal fraction relative to either the CND-based or SNP-based MPS methods (Table 2 and Fig. 4), a finding consistent with other reports (17, 18). Evaluation of the fetal ccfDNA fraction in maternal plasma has emerged as a critical factor for clinical assessment of NIPT data (19-23). The simplicity, expandability, and low cost of the CND method make it an attractive alternative to existing approaches for measurement of the fetal ccfDNA fraction in maternal plasma.

For the CND method to be widely adopted, further work is needed to establish the stability of CNDs across generations and within individuals and to accurately define their allelic distributions and frequencies for application in different ethnic groups. Although our assessment used qPCR methodology, the concept of targeting CND polymorphisms for chimerism analysis is translatable to other plat forms, such as digital PCR and next generation sequencing.

Author Contributions: All authors confirmed they have contributed to the intellectualcontentofthispaperand have met thefollowing3 requirements: (a) significant contributions to the conception and design, acquisition of data, or analysis and interpretation of data; (b) drafting or revising the articleforintellectualcontent;and(c)finalapprovalofthepublishedarticle.

Authors' Disclosures or Potential Conflicts ofInterest: Upon manuscript submission, all authors completed the author disclosure form. Disclosures and/or potential conflicts ofinterest:

Employment or Leadership: None declared.

Consultant or Advisory Role: F.L. Ierino, Medical Advisory Board for Novartis and Medical Advisory Board for Pfizer.

Stock Ownership: None declared.

Honoraria: None declared.

Research Funding: Roche Organ Transplant Research Fund; N.P. Thorne, Australian Government National Health and Medical Research Council (NHMRC) IRIISS; M. Bahlo, Australian Research Council Future Fellowship (FT100100764), Australian Government National Health and Medical Research Council (NHMRC) IRIISS, and NHMRC Program grant (490037); H. Slater, grant from the Roche Organ Transplant Research Fund "Improving Organ Transplant Outcomes using a New Noninvasive Monitoring Test" and institutional funding from the Victorian Government's Operational Infrastructure Support to MCRI and WEHI.

Expert Testimony: None declared.

Patents: D.L. Bruno, D. Ganesamoorthy, and H. Slater, patent number WO/2013/049892.

Role of Sponsor: The funding organizations played no role in the design of study, choice of enrolled patients, review and interpretation of data, or preparation or approval of manuscript.

Acknowledgments: We acknowledge Dineika Chandrananda for advice on the manuscript.


(1.) Stankiewicz P, Lupski JR. Structural variation in the human genome and its role in disease. Annu Rev Med 2010;61:437-55.

(2.) Conrad DF, Pinto D, Redon R, Feuk L, Gokcumen O, Zhang Y, et al. Origins and functional impact of copy number variation in the human genome. Nature 2010;464:704-12.

(3.) Gineikiene E, Stoskus M, Griskevicius L. Single nucleotide polymorphism-based system improves the applicability of quantitative PCR for chimerism monitoring. J Mol Diagn 2009;11:66-74.

(4.) Hochberg EP, Miklos DB, Neuberg D, Eichner DA, McLaughlin SF, Mattes-RitzA, et al. A novel rapid single nucleotide polymorphism (SNP)-based method for assessment of hematopoietic chimerism after allogeneic stem cell transplantation. Blood 2003;101:363-9.

(5.) Kletzel M, Huang W, Olszewski M, Khan S. Validation of chimerism in pediatric recipients of allogeneic hematopoietic stem cell transplantation (HSCT) a comparison between two methods: Real-time PCR (qPCR) vs. variable number tandem repeats PCR (VNTR PCR). Chimerism 2013; 4:1-8.

(6.) Lo YM, Tein MS, Lau TK, Haines CJ, Leung TN, Poon PM, et al. Quantitative analysis of fetal DNA in maternal plasma and serum: implications for noninvasive prenatal diagnosis. Am J Hum Genet 1998;62:768-75.

(7.) Zimmermann B, Hill M, Gemelos G, Demko Z, Banjevic M, Baner J, et al. Noninvasive prenatal aneuploidy testing of chromosomes 13,18, 21,X, and Y, using targeted sequencing of polymorphic loci. Prenat Diagn 2012;32:1233-41.

(8.) McCarroll SA, Kuruvilla FG, Korn JM, Cawley S, Nemesh J, Wysoker A, et al. Integrated detection and population-genetic analysis of SNPs and copy number variation. Nat Genet 2008;40:1166-74.

(9.) Stevens EL, Heckenberg G, Baugher JD, Roberson ED, Downey TJ, Pevsner J. Consanguinity in Centre d'Etude du Polymorphisme Humain (CEPH) pedigrees. Eur J Hum Genet 2012;20:657-67.

(10.) Stevens EL, Baugher JD, Shirley MD, Frelin LP, Pevsner J. Unexpected relationships and inbreeding in HapMap phase III populations. PLoS One 2012;7:e49575.

(11.) Zheng YW, Chan KC, Sun H, Jiang P, Su X, Chen EZ, et al. Nonhematopoietically derived DNA is shorter than hematopoietically derived DNA in plasma: a transplantation model. Clin Chem 2011;58:549-58.

(12.) Lee TH, Chafets DM, Reed W, Wen L, Yang Y,

Chen J, et al. Enhanced ascertainment of microchimerism with real-time quantitative polymerase chain reaction amplification of insertion-deletion polymorphisms. Transfusion 2006;46:1870-8.

(13.) Montgomery SB, Goode DL, Kvikstad E, Albers CA, Zhang ZD, Mu XJ, et al. The origin, evolution, and functional impact of short insertion-deletion variants identified in 179 human genomes. Genome Res 2013;23:749-61.

(14.) Langlois S, Brock JA, Genetics C, Wilson RD, Audibert F, Brock JA, et al. Current status in non-invasive prenatal detection of down syndrome, trisomy 18, and trisomy 13 using cell-free DNA in maternal plasma. J Obstet Gynaecol Can 2013;35:177-81.

(15.) Avent ND. RHD genotyping from maternal plasma: guidelines and technical challenges. Methods Mol Biol 2008;444:185-201.

(16.) Wright CF, Wei Y, Higgins JP, Sagoo GS. Noninvasive prenatal diagnostic test accuracy for fetal sex using cell-free DNA a review and metaanalysis. BMC Res Notes 2012;5:476.

(17.) Canick JA, Palomaki GE, Kloza EM, LambertMesserlian GM, Haddow JE. The impact of maternal plasma DNA fetal fraction on next generation sequencing tests for common fetal aneuploidies. Pre nat Diagn 2013;33:667-74.

(18.) Rava RP, Srinivasan A, Sehnert AJ, Bianchi DW. Circulating fetal cell-free DNA fractions differ in autosomal aneuploidies and monosomy X. Clin Chem 2014;60:243-50.

(19.) Wang E, Batey A, Struble C, Musci T, Song K, Oliphant A. Gestational age and maternal weight effects on fetal cell-free DNA in maternal plasma. Prenat Diagn 2013;33:662-6.

(20.) Poon LC, Musci T, Song K, Syngelaki A, Nico laides KH. Maternal plasma cell-free fetal and maternal DNA at 11-13 weeks' gestation: relation to fetal and maternal characteristics and pregnancy outcomes. Fetal Diagn Ther 2013; 33:215-23.

(21.) Ashoor G, Syngelaki A, Poon LC, Rezende JC, Nicolaides KH. Fetal fraction in maternal plasma cell-free DNA at 11-13 weeks' gestation: relation to maternal and fetal characteristics. Ultrasound Obstet Gynecol 2013;41:26-32.

(22.) Canick JA, Palomaki GE, Kloza EM, LambertMesserlian GM, Haddow JE. The impact of maternal plasma DNA fetal fraction on next generation sequencing tests for common fetal aneuploidies. Prenat Diagn 2013:1-8.

(23.) White HE, Dent CL, Hall VJ, Crolla JA, Chitty LS. Evaluation of a novel assay for detection of the fetal marker RASSF1A: facilitating improved diagnostic reliability of noninvasive prenatal diagnosis. PLoS One 2012;7:e45073.

Damien L. Bruno, [1] [[dagger]] Devika Ganesamoorthy, [1,2] [[dagger]] Natalie P. Thorne, [3-4] Ling Ling, [1] Melanie Bahlo, [3,5] Sue Forrest, [6] Marieke Veenendaal, [7] Marina Katerelos, [7] Alison Skene, [8] Frank L. Ierino, [7] David A. Power, [7] and Howard R. Slater [1,2] *

[1] Murdoch Childrens Research Institute, Melbourne, VIC, Australia; [2] Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia; [3] Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, Melbourne, VIC, Australia; [4] Department of Medical Biology, University of Melbourne, Melbourne, VIC, Australia; [5] Department of Mathematics and Statistics, University of Melbourne, Melbourne, VIC, Australia; [6] The Australian Genome Research Facility, Parkville, VIC, Australia; [7] Department of Nephrology, Austin Health, Melbourne, VIC, Australia; [8] Department of Anatomical Pathology, Austin Hospital, Melbourne, VIC, Australia.

([dagger]) Damien L. Bruno and Devika Ganesamoorthy contributed equally to the work, and both should be considered as first authors.

* Address correspondence to this author at: Victorian Clinical Genetics Services, Murdoch Childrens Research Institute, Flemington Rd., Parkville 3052, VIC, Australia. Fax +61-3-8341-6366; e-mail

Received September 11, 2013; accepted May 20, 2014.

Previously published online at DOI: 10.1373/clinchem.2013.216077

[9] Nonstandard abbreviations: ccfDNA, circulating cell-free DNA; CNV, copy number variant; MPS, massively parallel sequencing; CND, copy number deletion; qPCR, quantitative PCR; GE, genomic equivalent; NIPT, noninvasive prenatal testing; CEPH, Centre d'Etude du Polymorphisme Humain; SNP, singlenucleotide polymorphism.

[10] Human genes: SRY, sex determining region Y; HBB, hemoglobin, beta.

Table 1. Genomic details of the panel of 10 selected CNDs.

CND      Chr (a)    Start bp       End bp      Size    0-Copy
marker     (n)       (hg18)        (hg18)      (bp)   frequency

CND_01      1      157 134 152   157 136 674   2522     0.508

CND_02     18      53 097 735    53 099 702    1967     0.508

CND_03      8      112 363 260   112 365 469   2209     0.500

CND_04     14      21 951 540    21 952 070    530      0.451

CND_05     16      25 247 611    25 250 595    2984     0.443

CND_06      7      24 004 755    24 006 584    1829     0.434

CND_07     13      37 955 318    37 958 191    2873     0.426

CND_08      8       4 110 308     4 112 335    2027     0.426

CND_09     18      33 560 073    33 560 645    572      0.418

CND_10     10      108 020 303   108 022 518   2215     0.410

CND       1-Copy      2-Copy      Polymorphism over       Gene
marker   frequency   frequency      primers/probe       content

CND_01     0.418       0.074     No                      Agenic

CND_02     0.393       0.082     Reverse primer only.    Agenic
                                 rs61147755 (9 nt
                                 from 5' start; MAF =
                                 13%) rs57750171 (3
                                 nt from 3' end; MAF
                                 = 8%)

CND_03     0.410       0.082     No                      Agenic

CND_04     0.418       0.107     No                     Intronic

CND_05     0.459       0.074     No                      Agenic

CND_06     0.344       0.139     No                      Agenic

CND_07     0.434       0.139     No                      Agenic

CND_08     0.426       0.131     Forward primer only.   Intronic
                                 rs1354514 (8 nt from
                                 5' start; MAF <5%)

CND_09     0.451       0.123     No                      Agenic

CND_10     0.418       0.172     No                      Agenic

(a) Chr, chromosomes; bp, base pair; hg18, version 18 of the human
genome; nt, nucleotides; MAF, minor-allele frequency.

Table 2. CND, SRY, and total plasma ccfDNA measurements and fetal
fraction estimates for 12 singleton male pregnancy samples.

                   qPCR copy number   CND-based fetal
Maternal                (GE/mL)          fraction %
plasma sample                         (difference (b))
                CND   SRY     HBB

Sample 1        95    52      629         15 (+2)
Sample 2        127   57     1389          9 (+2)
Sample 3        176   78     1175         15 (+2)
Sample 4        62    58      855          7 (0)
Sample 5        60    55      920          6 (-6)
Sample 6        48    49      924          5 (0)
Sample 7        132   75     1746          7 (+1)
Sample 8        82a   52      721         11 (-2)
Sample 9        45    38      587          8 (-1)
Sample 10       225   81     1651         14 (+3)
Sample 11       43    29      531          8 (0)
Sample 12       47    45      803          6 (-1)

                SRY-based fetal    SNP-based MPS (c)
Maternal           fraction %      fetal fraction %
plasma sample   (difference (b))

Sample 1             8 (-5)               13
Sample 2             4 (-3)                7
Sample 3             7 (-6)               13
Sample 4              7(0)                 7
Sample 5             6 (-6)               12
Sample 6             5 (0)                 5
Sample 7             4 (-2)                6
Sample 8             7 (-6)               13
Sample 9             6 (-3)                9
Sample 10            5 (-6)               11
Sample 11            6 (-2)                8
Sample 12            6 (-1)                7

(a) Sample 8 gave 2 informative CND measurements that differed by
1.8-fold; the lower value (82 GE/mL) is listed here. The higher
measurement (152 GE/mL) likely represented an outlier arising
from the fetus being a wild-type homozygote (2 copies) for the
given CND, although this was not confirmed.

(b) The difference between the CND-based and SRY-based estimates
of fetal fraction to that obtained by SNP-based MPS sequencing is
given for each sample.

(c) SNP-based MPS calculation [using the procedure described by
Zimmermann et al. (7)) of fetal fraction as obtained on a
duplicate blood sample.
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Title Annotation:Molecular Diagnostics and Genetics
Author:Bruno, Damien L.; Ganesamoorthy, Devika; Thorne, Natalie P.; Ling, Ling; Bahlo, Melanie; Forrest, Su
Publication:Clinical Chemistry
Article Type:Report
Date:Aug 1, 2014
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