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• W2017694711 abstract "The current need for high-throughput genotyping platforms for targeted validation of disease-associated single nucleotide polymorphisms (SNPs) motivated us to evaluate a novel nanofluidics platform for genotyping DNA extracted from peripheral blood and buccal wash samples. SNP genotyping was performed using a Fluidigm 48.48 Dynamic Array biochip on the BioMark polymerase chain reaction platform and results were compared against standard TaqMan assays and DNA sequencing. Pilot runs using these dynamic arrays on 90 samples against 20 SNP assays had an average call rate of 99.7%, with 100% call rates for 16 of the assays. Manual TaqMan genotyping of these samples against three SNPs demonstrated 100% correlation between the two platforms. To understand the influence of DNA template variability, three sources of blood samples (CH-1, n = 20; CH-2, n = 47; KK, n = 47) and buccal washes (n = 37) were genotyped for 24 SNPs. Although both CH-1 and CH-2 batches showed good base calling (≥98.8%), the KK batch and buccal wash samples exhibited lower call rates (82.1% and 94.0%). Importantly, repurification of the KK and buccal wash samples resulted in significant improvements in their call rates (to ≥97.9%). Scale-up for genotyping 1698 cases and controls for 24 SNPs had overall call rates of 97.6% for KK and 99.2% for CH samples. The Dynamic Array approach demonstrated accuracy similar to that of TaqMan genotyping, while offering significant savings in DNA, effort, time, and costs. The current need for high-throughput genotyping platforms for targeted validation of disease-associated single nucleotide polymorphisms (SNPs) motivated us to evaluate a novel nanofluidics platform for genotyping DNA extracted from peripheral blood and buccal wash samples. SNP genotyping was performed using a Fluidigm 48.48 Dynamic Array biochip on the BioMark polymerase chain reaction platform and results were compared against standard TaqMan assays and DNA sequencing. Pilot runs using these dynamic arrays on 90 samples against 20 SNP assays had an average call rate of 99.7%, with 100% call rates for 16 of the assays. Manual TaqMan genotyping of these samples against three SNPs demonstrated 100% correlation between the two platforms. To understand the influence of DNA template variability, three sources of blood samples (CH-1, n = 20; CH-2, n = 47; KK, n = 47) and buccal washes (n = 37) were genotyped for 24 SNPs. Although both CH-1 and CH-2 batches showed good base calling (≥98.8%), the KK batch and buccal wash samples exhibited lower call rates (82.1% and 94.0%). Importantly, repurification of the KK and buccal wash samples resulted in significant improvements in their call rates (to ≥97.9%). Scale-up for genotyping 1698 cases and controls for 24 SNPs had overall call rates of 97.6% for KK and 99.2% for CH samples. The Dynamic Array approach demonstrated accuracy similar to that of TaqMan genotyping, while offering significant savings in DNA, effort, time, and costs. The HapMap and genome-wide association (GWA) projects have led to the identification of hundreds of single nucleotide polymorphisms (SNPs) that are associated with more than 80 disease states and traits.1International HapMap ConsortiumThe International HapMap Project.Nature. 2003; 426: 789-796Crossref PubMed Scopus (4975) Google Scholar, 2Nischwitz S. Cepok S. Kroner A. Wolf C. Knop M. Müller-Sarnowski F. Pfister H. Roeske D. Rieckmann P. Hemmer B. Ising M. Uhr M. Bettecken T. Holsboer F. Müller-Myhsok B. Weber F. Evidence for VAV2 and ZNF433 as susceptibility genes for multiple sclerosis.J Neuroimmunol. 2010; 227: 162-166Abstract Full Text Full Text PDF PubMed Scopus (59) Google Scholar, 3Dick D.M. Aliev F. Krueger R.F. Edwards A. Agrawal A. Lynskey M. Lin P. Schuckit M. Hesselbrock V. Nurnberger Jr, J. Almasy L. Projesz B. Edenberg H.J. Bucholz K. Kramer J. Kuperman S. Bierut L. Genome-wide association study of conduct disorder symptomatology.Mol Psychiatry. 2010; https://doi.org/10.1038/mp.2010.73Crossref PubMed Scopus (75) Google Scholar, 4Hindorff L.A. Sethupathy P. Junkins H.A. Ramos E.M. Mehta J.P. Collins F.S. Manolio T.A. Potential etiologic and functional implications of genome-wide association loci for human diseases and traits.Proc Natl Acad Sci USA. 2009; 106: 9362-9367Crossref PubMed Scopus (3057) Google Scholar, 5Easton D.F. Pooley K.A. Dunning A.M. Pharoah P.D. Thompson D. Ballinger D.G. et al.Genome-wide association study identifies novel breast cancer susceptibility loci.Nature. 2007; 447: 1087-1093Crossref PubMed Scopus (1936) Google Scholar, 6Todd J.A. Walker N.M. Cooper J.D. Smyth D.J. Downes K. Plagnol V. Bailey R. Nejentsev S. Field S.F. Payne F. Lowe ce Szeszko J.S. Hafler J.P. Zeitels L. Yang J.H. Vella A. Nutland S. Stevens H.E. Schuilenburg H. Coleman G. Maisuria M. Meadows W. Smink L.J. Healy B. Burren O.S. Lam A.A. Ovington N.R. Allen J. Adlem E. Leung H.T. Wallace C. Howson J.M. Guja C. Ionescu-Tîrgovişte C. Simmonds M.J. Heward J.M. Gough S.C. Dunger D.B. Wicker L.S. Clayton D.G. Genetics of Type 1 Diabetes in FinlandWellcome Trust Case Control ConsortiumRobust associations of four new chromosome regions from genome-wide analyses of type 1 diabetes.Nat Genet. 2007; 39: 857-864Crossref PubMed Scopus (1187) Google Scholar, 7Thorleifsson G. Walters G.B. Gudbjartsson D.F. Steinthorsdottir V. Sulem P. Helgadottir A. Styrkarsdottir U. Gretarsdottir S. Thorlacius S. Jonsdottir I. Jonsdottir T. Olafsdottir E.J. Olafsdottir G.H. Jonsson T. Jonsson F. Borch-Johnsen K. Hansen T. Andersen G. Jorgensen T. Lauritzen T. Aben K.K. Verbeek A.L. Roeleveld N. Kampman E. Yanek L.R. Becker L.C. Tryggvadottir L. Rafnar T. Becker D.M. Gulcher J. Kiemeney L.A. Pedersen O. Kong A. Thorsteinsdottir U. Stefansson K. Genome-wide association yields new sequence variants at seven loci that associate with measures of obesity.Nat Genet. 2009; 41: 18-24Crossref PubMed Scopus (1065) Google Scholar, 8Voight B.F. Scott L.J. Steinthorsdottir V. Morris A.P. Dina C. Welch R.P. et al.Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis.Nat Genet. 2010; 42: 579-589Crossref PubMed Scopus (1439) Google Scholar Although most of these SNPs have revealed novel risk loci or genes for particular diseases, they are mostly of moderate to low penetrance. Thus, the next stage of research will likely involve the validation and replication of smaller, relevant subsets of SNPs against targeted populations of interest; that is, smaller subsets of interesting SNPs will need to be genotyped against a large set of individuals. Classical genotyping assays using gel electrophoresis-based procedures, restriction fragment length polymorphism (RFLP) analysis, and allele-specific PCR analyses are labor intensive and unsuitable for high-throughput approaches, whereas genotyping platforms such as the GeneChip (Affymetrix, Santa Clara, CA) and Hap300 (Illumina, San Diego, CA) are more appropriate for GWA studies involving hundreds to thousands of SNPs.9Long J. Cai Q. Shu X.O. Qu S. Li C. Zheng Y. Gu K. Wang W. Xiang Y.B. Cheng J. Chen K. Zhang L. Zheng H. Shen C.Y. Huang C.S. Hou M.F. Shen H. Hu Z. Wang F. Deming S.L. Kelley M.C. Shrubsole M.J. Khoo U.S. Chan K.Y. Chan S.Y. Haiman C.A. Henderson B.E. Le Marchand L. Iwasaki M. Kasuga Y. Tsugane S. Matsuo K. Tajima K. Iwata H. Huang B. Shi J. Li G. Wen W. Gao Y.T. Lu W. Zheng W. Identification of a functional genetic variant at 16q12.1 for breast cancer risk: results from the Asia Breast Cancer Consortium.PLoS Genet. 2010; 6: e1001002Crossref PubMed Scopus (98) Google Scholar, 10Helgadottir A. Thorleifsson G. Manolescu A. Gretarsdottir S. Blondal T. Jonasdottir A. Jonasdottir A. Sigurdsson A. Baker A. Palsson A. Masson G. Gudbjartsson D.F. Magnusson K.P. Andersen K. Levey A.I. Backman V.M. Matthiasdottir S. Jonsdottir T. Palsson S. Einarsdottir H. Gunnarsdottir S. Gylfason A. Vaccarino V. Hooper W.C. Reilly M.P. Granger C.B. Austin H. Rader D.J. Shah S.H. Quyyumi A.A. Gulcher J.R. Thorgeirsson G. Thorsteinsdottir U. Kong A. Stefansson K. A common variant on chromosome 9p21 affects the risk of myocardial infarction.Science. 2007; 316: 1491-1493Crossref PubMed Scopus (1323) Google Scholar Clearly, there is an immediate need for scalable and cost-effective platform technologies capable of analyzing flexible sample sizes on a specific set of SNPs and suitable for validation of SNPs identified from disease association studies. Two technologies, the SNPlex platform (Applied Biosystems, Foster City, CA) and the iPLEX platform (Sequenom, San Diego, CA)11Tobler A.R. Short S. Andersen M.R. Paner T.M. Briggs J.C. Lambert S.M. Wu P.P. Wang Y. Spoonde A.Y. Koehler R.T. Peyret N. Chen C. Broomer A.J. Ridzon D.A. Zhou H. Hoo B.S. Hayashibara K.C. Leong L.N. Ma C.N. Rosenblum B.B. Day J.P. Ziegle J.S. De La Vega F.M. Rhodes M.D. Hennessy K.M. Wenz H.M. The SNPlex genotyping system: a flexible and scalable platform for SNP genotyping.J Biomol Tech. 2005; 16: 398-406PubMed Google Scholar, 12Long J. Shu X.O. Cai Q. Gao Y.T. Zheng Y. Li G. Li C. Gu K. Wen W. Xiang Y.B. Lu W. Zheng W. Evaluation of breast cancer susceptibility loci in Chinese women.Cancer Epidemiol Biomarkers Prev. 2010; 19: 2357-2365Crossref PubMed Scopus (98) Google Scholar meet these criteria, but neither is based on the TaqMan assay,13Livak K.J. Marmaro J. Todd J.A. Towards fully automated genome-wide polymorphism screening.Nat Genet. 1995; 9: 341-342Crossref PubMed Scopus (293) Google Scholar which is generally accepted as the gold standard for genotyping. Recent advances in nanofluidics technology have made possible the use of integrated fluidic circuits (IFCs) for high-throughput real-time PCR.14Spurgeon S.L. Jones R.C. Ramakrishnan R. High throughput gene expression measurement with real time PCR in a microfluidic Dynamic Array.PLoS One. 2008; 3: e1662Crossref PubMed Scopus (316) Google Scholar Nanoliter-scale quantities of samples and reagents are channeled into thousands of nanoliter-scale chambers in which distinct real-time PCRs can be run. Nanofluidic arrays have been successfully used in quantifying the absolute amounts of circulating fetal DNA in a background of maternal DNA by exploiting the compartmentalization feature of nanofluidic chips.15Lun F.M. Chiu R.W. Chan K.C. Leung T.Y. Lau T.K. Lo Y.M. Microfluidics digital PCR reveals a higher than expected fraction of fetal DNA in maternal plasma.Clin Chem. 2008; 54: 1664-1672Crossref PubMed Scopus (363) Google Scholar Furthermore, single-molecule detection of epidermal growth receptor mutations in plasma has been achieved by partitioning DNA molecules in the nanofluidic chip,16Yung T.K. Chan K.C. Mok T.S. Tong J. To K.F. Lo Y.M. Single-molecule detection of epidermal growth factor receptor mutations in plasma by microfluidics digital PCR in non-small cell lung cancer patients.Clin Cancer Res. 2009; 15: 2076-2084Crossref PubMed Scopus (358) Google Scholar thereby allowing the fraction of two common epidermal growth receptor mutant alleles in plasma to be accurately quantified. Other applications of nanofluidic arrays include studies on copy number variation17Bhat S. Herrmann J. Armishaw P. Corbisier P. Emslie K.R. Single molecule detection in nanofluidic digital array enables accurate measurement of DNA copy number.Anal Bioanal Chem. 2009; 394: 457-467Crossref PubMed Scopus (180) Google Scholar and accurate calibration of input DNA for next-generation sequencing.18White R.A. Blainey P.C. Fan H.C. Quake S.R. Digital PCR provides sensitive and absolute calibration for high throughput sequencing.BMC Genomics. 2009; 10 ([Erratum appeared in BMC Genomics 2009, 10:541]): 116Crossref PubMed Scopus (158) Google Scholar Recently, Fluidigm (South San Francisco, CA) has introduced a nanofluidics chip, the Dynamic Array chip, that is compatible with existing TaqMan genotyping assays.19Wang J. Lin M. Crenshaw A. Hutchinson A. Hicks B. Yeager M. Berndt S. Huang W. Hayes R.B. Chanock S.J. Jones R.C. Ramakrishnan R. High-throughput single nucleotide polymorphism genotyping using nanofluidic Dynamic Arrays.BMC Genomics. 2009; 10: 561Crossref PubMed Scopus (120) Google Scholar Potentially, this technology allows up to 9216 individual TaqMan reactions to be run in a single experiment, with the promise of considerable reagent and time savings achievable from using nanofluidics arrays, compared with standard TaqMan genotyping. Of note, the Dynamic Array has been successfully used by U.S. federal and Alaskan fishery organizations to genotype salmon samples for the purpose of fisheries management.20Narum S, Campbell N, Yi Y: High-throughput SNP genotyping in salmon & steelhead (abstract). In Proceedings of Plant and Animal Genomes XVII Conference. 2009January 10-14, San Diego, CA. P578Google Scholar, 21Habicht C. Templin W.D. Willette T.M. Fair L.F. Raborn S.W. Seeb L.W. Post-season stock composition analysis of Upper Cook Inlet sockeye salmon harvest, 2005–2007.Fishery Manuscript No. 07–07. 2007Google Scholar Although one group has demonstrated the proof-of-concept of genotyping human samples from the PLCO Screening Trial22Andriole G.L. Reding D. Hayes R.B. Prorok P.C. Gohagan J.K. The prostate, lung, colon, and ovarian (PLCO) cancer screening trial: status and promise.Urol Oncol. 2004; 22: 358-361Abstract Full Text Full Text PDF PubMed Scopus (33) Google Scholar and HapMap samples from cell lines19Wang J. Lin M. Crenshaw A. Hutchinson A. Hicks B. Yeager M. Berndt S. Huang W. Hayes R.B. Chanock S.J. Jones R.C. Ramakrishnan R. High-throughput single nucleotide polymorphism genotyping using nanofluidic Dynamic Arrays.BMC Genomics. 2009; 10: 561Crossref PubMed Scopus (120) Google Scholar using the Dynamic Array, their reports focused mainly on the description of chip design, and details of the DNA extraction method and its effects on data quality were not included. Population genotyping typically uses genomic DNA from different resources, extracted using a variety of methods, including automated platforms, and often from limited amounts of clinical specimens (eg, peripheral blood, saliva, and buccal swabs or washes). Thus, it is imperative to understand the influence of various DNA extraction methods on the quality of the results, and also to establish procedures that allow for small amounts of DNA to be used. In the present study, we evaluated the performance of the Dynamic Array nanofluidics platform for SNP genotyping against standard TaqMan genotyping assays, by comparing overall call rates and concordance. The effect of DNA extraction methods, types of clinical specimens, and the use of archival frozen whole blood were also assessed. Archived frozen peripheral blood samples were obtained from the Singapore SingHealth Tissue Repository. Before DNA extraction, these blood samples had been stored at −80°C for up to 12 years. The CH-1 batch of samples comprised 111 blood samples from the SingHealth Tissue Repository, and the extracted DNA had been archived at −20°C for approximately 7 years. The CH-2 batch of blood samples (n = 179) were also obtained from the SingHealth Tissue Repository, and DNA from these samples was extracted within the last year. In addition, the CH-3 samples (peripheral blood, n = 134; buccal wash, n = 37) were collected from patients attending outpatient clinics at the National Cancer Centre; DNA from these samples was extracted within the last year. Written informed consent was obtained from all contributing volunteers, and ethics approval was obtained from the Centralised Institutional Review Board of SingHealth. The CH-1, CH-2, and CH-3 samples are collectively referred to as CH samples. The KK batch of samples (n = 1237) comprised purified DNA obtained from the DNA Diagnostic and Research Lab, KK Women's and Children's Hospital, Singapore. The CH DNA samples were extracted using an optimized in-house method. Red blood cell lysis was performed by adding 9 volumes of buffer A (0.32 mol/L sucrose, 10 mmol/L Tris-HCl pH 7.5, 5 mmol/L MgCl2, 1% Triton X-100) to 5 mL of frozen or freshly drawn peripheral blood. The mixture was mixed well and centrifuged at 2095 × g for 20 minutes, after which the supernatant was discarded and the lysis step was repeated once. Buccal wash samples were directly centrifuged to pellet the buccal cells. Cell pellets containing lymphocytes or buccal cells were resuspended in 5 mL Buffer B (25 mmol/L EDTA, 75 mmol/L NaCl) and lysed with the addition of 250 μL of 10% SDS. Proteinase treatment was performed using 20 μL proteinase K (20 mg/mL), followed by incubation at 56°C for 24–48 hours with shaking. The DNA was precipitated using ethanol and sodium acetate and was washed with 70% ethanol. The dried DNA was dissolved in a TE buffer with reduced EDTA (10 mmol/L Tris, 1 mmol/L EDTA). Buccal wash samples were subsequently further purified by organic extraction using an equal volume of phenol/chloroform/isoamyl alcohol (25:24:1) (Sigma-Aldrich, St. Louis, MO) and were reprecipitated as described above. The KK DNA samples were extracted from 300 μL of blood using the MagNA Pure compact system (Roche, Basel, Switzerland),23Wittor H. Aschenbrenner A. Thoenes U. Schnittger S. Leying H. Fully automated sample preparation: use for the detection of BCR-ABL fusion transcripts.Biochemica. 2000; 3: 5-8Google Scholar a robotic system based on magnetic-bead technology, and were dissolved in a proprietary elution buffer. These DNA samples were subsequently purified using a column method (QIAamp blood mini kit; Qiagen, Hilden, Germany) according to a modification of the manufacturer's protocol. Briefly, DNA samples were reconstituted to 200 μL with PBS buffer, after which 200 μL of buffer AL (a proprietary buffer from the QIAamp kit) and 100% ethanol were added. The solution was then loaded onto the mini spin columns and was processed according to the manufacturer's protocol. The DNA was eluted with 30 μL of autoclaved reverse osmosis water. Real-time PCR with the TaqMan assay (Applied Biosystems) was performed according to the manufacturer's instructions. Briefly, 5-μL reactions were run, comprising 2.5 μL of TaqMan universal genotyping master mix, 0.25 μL of TaqMan 20× SNP assay, 1.125 μL autoclaved reverse osmosis water, and 1.125 μL DNA (5 ng/μL) per reaction. Each run included non-template controls (NTC). The real-time PCR reactions were run using a 7500 Fast Real-Time PCR system (Applied Biosystems). The SNPs that were genotyped to validate the Dynamic Array results were rs2981582 (SNP1; Assay ID: C_2917302_10), rs3803662 (SNP2; Assay ID: C_25968567_10), and rs3817198 (SNP10; Assay ID: C_27493923_10). The 48.48 Dynamic Array used in the present study is able to analyze 48 samples with 48 assays on the BioMark platform (Fluidigm). The array is mounted on a plastic interface containing 48 sample and 48 assay inlets on the left and right of the array. In the present study, each array was loaded with 47 samples and one non-template control. Twenty-four SNP assays were loaded in duplicate into the 48 assay inlets. The array contains a network of fluid lines (integrated fluidic circuit, IFC) and chambers that are controlled by elastomeric valves. These valves deflect under pressure to create a tight seal, thereby regulating the flow of liquids into the IFC. Before reagents are loaded, the array is primed using the IFC Controller MX, which pressurizes the control lines and closes the interface valves. The same genotyping assay reagents and enzyme master mixes used for conventional genotyping were used for the nanofluidics array. Each assay (5 μL) comprised 2.5 μL DA assay loading reagent (2×) (Fluidigm), 1.25 μL SNP genotyping assay mix (40×) (Applied Biosystems), 0.25 μL ROX (50×) (Invitrogen, Carlsbad, CA), and 1 μL autoclaved reverse osmosis water. Each sample (5 μL) comprised 2.5 μL TaqMan genotyping master mix (2×), 0.05 μL AmpliTaq Gold DNA polymerase, 0.25 μL GT sample loading reagent (20×) (Fluidigm), 0.1 μL autoclaved reverse osmosis water, and 2.1 μL genomic DNA. Of note, during the course of experimentation we found it imperative to set up assays fresh, because freezing and thawing premixed assays appeared to have a detrimental effect on the quality of the results (data not shown). Each of the assays (4 μL) and samples (5 μL) was pipetted into separate inlets on the frame of the chip according to the manufacturer's instructions. The assays and samples were loaded into the reaction chambers and mixed using the IFC Controller MX. Arrays should be run immediately after assays, with samples pipetted into wells. The arrays were processed using the BioMark system (Fluidigm), which performs the thermal cycling and fluorescent image acquisition. The data were analyzed using the BioMark SNP Genotyping Analysis software version 2.1.1, to obtain genotype calls. Briefly, the software calculates the FAM and VIC relative fluorescence intensities (relative to ROX background), and then automatically classifies samples into three genotypes and NTCs using a k-means clustering algorithm. In a typical computer-generated image of the data obtained, each row represents data from one DNA sample loaded from each sample inlet, with data assigned using four color codes (one for each genotype and black for NTC). To confirm the genotyping results from both the standard TaqMan assay and the 48.48 Dynamic Array, PCR followed by DNA sequencing was performed on 10% of the samples used in the pilot run of two chips (90 samples). DNA sequence confirmation was performed on 9 of the 90 samples against 3 SNPs (ie, 27 out of 270 data points). The targeted loci were PCR-amplified from DNA samples and were sequenced using BigDye chemistry (Applied Biosystems) on a 3130xl DNA analyzer (Applied Biosystems) according to manufacturer's instructions, as described previously.24Ong D.C. Ho Y.M. Rudduck C. Chin K. Kuo W.L. Lie D.K. Chua C.L. Tan P.H. Eu K.W. Seow-Choen F. Wong C.Y. Hong G.S. Gray J.W. Lee A.S. LARG at chromosome 11q23 has functional characteristics of a tumor suppressor in human breast and colorectal cancer.Oncogene. 2009; 28: 4189-4200Crossref PubMed Scopus (24) Google Scholar Genotyping of 90 samples (from the CH-2 batch) for 20 SNPs was performed on two 48.48 Dynamic Array chips, with each SNP assay run in duplicate. Thus, a total of 3600 data points were collected (90 × 20 × 2), corresponding to 1800 genotyping calls. These runs resulted in an average call rate of 99.7%, with call rates of 100% in most of the assays (16 of 20), 99.4% in 3 of 20 assays, and 97.2% in 1 of 20 assays (Figure 1A). The same set of 90 samples used on the arrays was also genotyped manually using TaqMan real-time PCR for three SNPs. The three SNPs used were SNPs 1, 2, and 10, which had 100% call rates on the Dynamic Arrays. The results between the two genotyping approaches were 100% consistent, demonstrating a high degree of accuracy for the genotyping approach established for the Dynamic Arrays. Both experimental systems showed similar clustering patterns in allelic discrimination plots generated by the respective customized analysis software package (Figure 1B). To further confirm the accuracy of the genotyping data, PCR followed by DNA sequencing was performed on 9 of the same 90 samples against SNPs 1, 2, and 10. As expected, the DNA sequencing results were also 100% consistent with the TaqMan and Dynamic Array results, confirming the accuracy of the genotyping approaches. We next sought to investigate the influence of different DNA resources on the outcome of the established nanofluidics genotyping approach. SNP genotyping of CH-1 samples (n = 20), comprising DNA samples that had been stored for approximately 7 years, and CH-2 (n = 47) samples, which had been stored for ≤1 year, showed good base calling results (average call rates of 99.8% and 98.8%, respectively, for 24 SNPs), but the success rates for the KK samples (n = 47) and buccal wash samples (n = 37) were significantly lower (average call rates of 82.1% and 94.0%, respectively) (Figure 2), suggesting that the KK and buccal wash samples cannot be used directly, but require further purification. To understand the cause of lower success rates associated with the KK DNA samples, which had been extracted from peripheral blood using the automated MagNA Pure compact system, these samples were purified by column purification and compared against those before column purification, for 24 SNPs run in duplicate. In the resultant call map (Figure 3), the columns correspond to 24 SNP assays loaded in duplicate, and the rows correspond to DNA samples and one NTC. In sample groups A, B, and C, unpurified KK samples tested at 100, 50, and 25 ng/μL demonstrated low call rates (47.5% to 77.5%). In contrast, group E, corresponding to column-purified samples, had average call rates of ≥97.9%, showing significant improvement with column purification. Furthermore, a fourfold dilution of non-column-purified samples to 25 ng/μL (group D) showed significantly higher call rate (97.9%), compared with 68.7% for samples that were originally at 25 ng/μL (group C). This suggests that the low call rate for group C was not due to insufficient template, but more likely was caused by inhibitory compounds present in the unpurified samples. For comparison purposes, CH-1 samples (group F) were assayed on the same chip. Twenty of these samples had a standardized final concentration of 50 ng/μL, and one sample was used at 25 μg/μL, for confirming the minimum usable concentration. As expected, group F showed high overall call rates (≥98.9%). These observations suggested that inhibitory compounds might be present in the KK samples, which could be removed either by column repurification or by dilution. It should be noted that there were several discordant duplicates in Group D, suggesting that column purification should be the method of choice for processing the KK samples. The success rates for buccal wash samples could be improved significantly, from 94.0% to 98.7%, by the inclusion of a phenol-chloroform extraction step, followed by ethanol reprecipitation of DNA. Phenol-chloroform extraction was chosen as the approach for repurification, instead of using spin-columns, because of the presence of particulate impurities that could clog the spin columns. Scale-up genotyping of 1698 subjects was performed with 461 CH samples (CH-1, CH-2, and CH-3 DNA samples) and 1237 samples from the KK batch (Table 1). On scale-up, one SNP for CH samples (SNP 20) and two for KK samples (SNPs 17 and 20) had failure rates of >19% and were excluded from analysis. Exclusion of a small fraction of SNP assays is unavoidable, given that all assays were amplified together, and optimization of thermal cycling conditions for individual SNP assays was not possible. The total sample size of 1698 was derived after the exclusion of 10 samples showing >58% failure across all 24 SNPs. The call rates obtained for both KK samples (97.6 ± 1.93%) and CH samples (99.2 ± 0.60%) were largely similar to those calculated for the validation phase. Although KK samples had been repurified for the Dynamic Array, on scale-up the KK samples still demonstrated slightly lower call rates (97.6 ± 1.93% versus 99.2 ± 0.60%) and slightly lower reproducibility between replicates (96.3 ± 1.90% versus 99.4 ± 0.77%), compared with the CH samples. The call and reproducibility rates indicated are expressed as means ± SD. This difference was statistically significant, with P values of <0.005 for both call and reproducibility rates obtained from standard two-tailed Student's t-tests (paired). Within the CH samples, there was no observable difference in data quality among the CH-1, CH-2 or CH-3 samples, or between buccal wash and peripheral blood samples.Table 1Call Rates and Reproducibility of the Dynamic Array for Scale-up Genotyping of 1698 Samples against 24 SNPsSNPCH samples (n = 461)KK samples (n = 1237)Call rate (%)Reproducibility (%)Call rate (%)Reproducibility (%)Avg99.299.497.696.3110099.899.796.2299.799.199.294.339898.589.595.3499.810096.497.3599.198.799.498.5610010099.395.5710010099.397.6899.998.995.895.6999.799.397.994.71010010099.698.1119999.898.6941299.699.197.991.51398.299.399.2961499.799.699.497.51599.999.895.397.61699.799.699.595.7179498.9(76.7)⁎Values in parentheses were excluded from the overall rate computations as they had failure rates of >19%.(93.8)⁎Values in parentheses were excluded from the overall rate computations as they had failure rates of >19%.1899.799.399.798.41998.999.694.797.720(31.8)⁎Values in parentheses were excluded from the overall rate computations as they had failure rates of >19%.(97.6)⁎Values in parentheses were excluded from the overall rate computations as they had failure rates of >19%.(6.2)⁎Values in parentheses were excluded from the overall rate computations as they had failure rates of >19%.(94.1)⁎Values in parentheses were excluded from the overall rate computations as they had failure rates of >19%.2199.999.899.296.8229899.393.5972399.897.697.396.7249899.696.897.1The performance indicators used to evaluate the genotyping platforms were defined according to the following formulae: i) Call rate (%) = (No. of successful base calls)/(Total no. of assays performed) × 100 and ii) Reproducibility between replicates (%) = {1 − [(No. of assays inconsistent between replicates)/(Total no. of assays)]} × 100. Values in parentheses were excluded from the overall rate computations as they had failure rates of >19%. Open table in a new tab The performance indicators used to evaluate the genotyping platforms were defined according to the following formulae: i) Call rate (%) = (No. of successful base calls)/(Total no. of assays performed) × 100 and ii) Reproducibility between replicates (%) = {1 − [(No. of assays inconsistent between replicates)/(Total no. of assays)]} × 100. To further study the" @default.
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• W2017694711 date "2011-05-01" @default.
• W2017694711 modified "2023-10-16" @default.
• W2017694711 title "Evaluation of Nanofluidics Technology for High-Throughput SNP Genotyping in a Clinical Setting" @default.
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