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Microarray-based methylation profiling has emerged as a valuable tool for refining diagnoses and revealing novel tumor subtypes, particularly in central nervous system tumors. Despite the increasing adoption of this technique in clinical genomic laboratories, no technical standards have been published in establishing minimum criteria for test validation. A working group with experience and expertise in DNA-based methylation profiling tests on central nervous system tumors collaborated to develop practical discussion points and focus on important considerations for validating this test in clinical laboratory settings. The experience in validating this methodology in a clinical setting is summarized. Specifically, the advantages and challenges associated with utilizing an in-house classifier compared with a third-party classifier are highlighted. Additionally, experiences in demonstrating the assay's sensitivity and specificity, establishing minimum sample criteria, and implementing quality control metrics are described. As methylation profiling for tumor classification expands to other tumor types and continues to evolve for various other applications, the critical considerations described here are expected to serve as a guidance for future efforts in establishing professional guidelines for this assay., Competing Interests: Disclosure Statement All authors are affiliated with clinical laboratories that perform clinical microarray-based methylation profiling testing on a fee-for-service basis., (Copyright © 2024 Association for Molecular Pathology and American Society for Investigative Pathology. All rights reserved.)

Array technology to genotype single-nucleotide variants (SNVs) is widely used in genome-wide association studies (GWAS), clinical diagnostics, and linkage studies. Arrays have undergone a tremendous growth in both number and content over recent years making a comprehensive comparison all the more important. We have compared 28 genotyping arrays on their overall content, genome-wide coverage, imputation quality, presence of known GWAS loci, mtDNA variants and clinically relevant genes (i.e., American College of Medical Genetics (ACMG) actionable genes, pharmacogenetic genes, human leukocyte antigen (HLA) genes and SNV density). Our comparison shows that genome-wide coverage is highly correlated with the number of SNVs on the array but does not correlate with imputation quality, which is the main determinant of GWAS usability. Average imputation quality for all tested arrays was similar for European and African populations, indicating that this is not a good criterion for choosing a genotyping array. Rather, the additional content on the array, such as pharmacogenetics or HLA variants, should be the deciding factor. As the research question of a study will in large part determine which class of genes are of interest, there is not just one perfect array for all different research questions. This study can thus help as a guideline to determine which array best suits a study's requirements., (© 2021. The Author(s).)

Background: Mitochondrial haplogroup assignment is an important tool for forensics and evolutionary genetics. African populations are known to display a high diversity of mitochondrial haplogroups. In this research we explored mitochondrial haplogroup assignment in African populations using commonly used genome-wide SNP arrays., Results: We show that, from eight commonly used SNP arrays, two SNP arrays outperform the other arrays when it comes to the correct assignment of African mitochondrial haplogroups. One array enables the recognition of 81% of the African mitochondrial haplogroups from our compiled dataset of full mitochondrial sequences. Other SNP arrays were able to assign 4-62% of the African mitochondrial haplogroups present in our dataset. We also assessed the performance of available software for assigning mitochondrial haplogroups from SNP array data., Conclusions: These results provide the first cross-checked quantification of mitochondrial haplogroup assignment performance from SNP array data. Mitochondrial haplogroup frequencies inferred from most common SNP arrays used for human population analysis should be considered with caution., (© 2021. The Author(s).)

Background: Introduction of cell-free fetal DNA (cff-DNA) testing in maternal blood opened possibilities to improve the performance of combined first-trimester screening (cFTS) in terms of better detection of trisomies and lowering invasive testing rate. The use of new molecular methods, such as chromosomal microarray analysis (CMA) and next-generation sequencing (NGS), has shown benefits in prenatal diagnosis of chromosomal and genetic diseases, which are not detectable with cff-DNA screening, but require an invasive procedure., Methods: The objective of this study was to evaluate prospectively during two years performance of CMA and NGS in high-risk pregnancies. Initially, we investigated 14,566 singleton pregnancies with cFTS. A total of 334 high-risk pregnancies were selected for CMA diagnostic performance evaluation and 28 cases of highly dysmorphic fetuses for NGS analysis. CMA study group was divided into two groups based on the indications for testing; group A patients with high-risk for trisomies after cFTS, but normal ultrasound and group B patients who met criteria for CMA as a first-tier diagnostic test., Results: The diagnostic yield of CMA was overall 3.6% (1.6% in Group A and 6.0% in Group B). In NGS analysis group, we report diagnostic yield of 17.9%., Conclusion: The use of CMA in high-risk pregnancies is justified and provides relevant clinical information in 3.6% of the cases. NGS analysis in fetuses with multiple anomalies shows promising results, but more investigations are needed for a better understanding of practical applications of this molecular diagnosis method in prenatal settings., (© 2021 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals LLC.)

The domestic cat (Felis silvestris catus) is a valued companion animal throughout the world. Over 60 different cat breeds are accepted for competition by the cat fancy registries in different countries. Genetic markers, including short tandem repeats and SNPs, are available to evaluate and manage levels of inbreeding and genetic diversity, population and breed structure relationships, and individual identification for forensic and registration purposes. The International Society of Animal Genetics (ISAG) hosts the Applied Genetics in Companion Animals Workshop, which supports the standardization of genetic marker panels and genotyping for the identification of cats via comparison testing. SNP panels have been in development for many species, including the domestic cat. An ISAG approved core panel of SNPs for use in cat identification and parentage analyses is presented. SNPs (n = 121) were evaluated by different university-based and commercial laboratories using 20 DNA samples as part of the ISAG comparison testing procedures. Different SNP genotyping technologies were examined, including DNA arrays, genotyping-by-sequencing and mass spectroscopy, to select a robust and efficient panel of 101 SNPs as the ISAG core panel for cats. The SNPs are distributed across all chromosomes including two on the X chromosome and an XY pseudo-autosomal sexing marker (zinc-finger XY; ZFXY). A population study demonstrated that the markers have an average polymorphic information content of 0.354 and a power of exclusion greater than 0.9999. The SNP panel should keep testing affordable while also allowing for the development of additional panels to monitor health, phenotypic traits, hybrid cats and highly inbred cats., (© 2021 The Authors. Animal Genetics published by John Wiley & Sons Ltd on behalf of Stichting International Foundation for Animal Genetics.)

Background: China is the birthplace of the deer family and the country with the most abundant deer resources. However, at present, China's deer industry faces the problem that pure sika deer and hybrid deer cannot be easily distinguished. Therefore, the development of a SNP identification chip is urgently required., Results: In this study, 250 sika deer, 206 red deer, 23 first-generation hybrid deer (F1), 20 s-generation hybrid deer (F2), and 20 third-generation hybrid deer (F3) were resequenced. Using the chromosome-level sika deer genome as the reference sequence, mutation detection was performed on all individuals, and a total of 130,306,923 SNP loci were generated. After quality control filtering was performed, the remaining 31,140,900 loci were confirmed. From molecular-level and morphological analyses, the sika deer reference population and the red deer reference population were established. The Fst values of all SNPs in the two reference populations were calculated. According to customized algorithms and strict screening principles, 1000 red deer-specific SNP sites were finally selected for chip design, and 63 hybrid individuals were determined to contain red deer-specific SNP loci. The results showed that the gene content of red deer gradually decreased in subsequent hybrid generations, and this decrease roughly conformed to the law of statistical genetics. Reaction probes were designed according to the screening sites. All candidate sites met the requirements of the Illumina chip scoring system. The average score was 0.99, and the MAF was in the range of 0.3277 to 0.3621. Furthermore, 266 deer (125 sika deer, 39 red deer, 56 F1, 29 F2,17 F3) were randomly selected for 1 K SNP chip verification. The results showed that among the 1000 SNP sites, 995 probes were synthesized, 4 of which could not be typed, while 973 loci were polymorphic. PCA, random forest and ADMIXTURE results showed that the 1 K sika deer SNP chip was able to clearly distinguish sika deer, red deer, and hybrid deer and that this 1 K SNP chip technology may provide technical support for the protection and utilization of pure sika deer species resources., Conclusion: We successfully developed a low-density identification chip that can quickly and accurately distinguish sika deer from their hybrid offspring, thereby providing technical support for the protection and utilization of pure sika deer germplasm resources., (© 2021. The Author(s).)

Selection of optimal control samples is crucial in expression profiling tumor samples. To address this issue, we performed microarray expression profiling of control samples routinely used in head and neck squamous cell carcinoma studies: human bronchial and tracheal epithelial cells, squamous cells obtained by laser uvulopalatoplasty and tumor surgical margins. We compared the results using multidimensional scaling and hierarchical clustering versus tumor samples and laryngeal squamous cell carcinoma cell lines. A general observation from our study is that the analyzed cohorts separated according to two dominant factors: "malignancy", which separated controls from malignant samples and "cell culture-microenvironment" which reflected the differences between cultured and non-cultured samples. In conclusion, we advocate the use of cultured epithelial cells as controls for gene expression profiling of cancer cell lines. In contrast, comparisons of gene expression profiles of cancer cell lines versus surgical margin controls should be treated with caution, whereas fresh frozen surgical margins seem to be appropriate for gene expression profiling of tumor samples.

The laboratory mouse is the most widely used animal model for biomedical research, due in part to its well-annotated genome, wealth of genetic resources, and the ability to precisely manipulate its genome. Despite the importance of genetics for mouse research, genetic quality control (QC) is not standardized, in part due to the lack of cost-effective, informative, and robust platforms. Genotyping arrays are standard tools for mouse research and remain an attractive alternative even in the era of high-throughput whole-genome sequencing. Here, we describe the content and performance of a new iteration of the Mouse Universal Genotyping Array (MUGA), MiniMUGA, an array-based genetic QC platform with over 11,000 probes. In addition to robust discrimination between most classical and wild-derived laboratory strains, MiniMUGA was designed to contain features not available in other platforms: (1) chromosomal sex determination, (2) discrimination between substrains from multiple commercial vendors, (3) diagnostic SNPs for popular laboratory strains, (4) detection of constructs used in genetically engineered mice, and (5) an easy-to-interpret report summarizing these results. In-depth annotation of all probes should facilitate custom analyses by individual researchers. To determine the performance of MiniMUGA, we genotyped 6899 samples from a wide variety of genetic backgrounds. The performance of MiniMUGA compares favorably with three previous iterations of the MUGA family of arrays, both in discrimination capabilities and robustness. We have generated publicly available consensus genotypes for 241 inbred strains including classical, wild-derived, and recombinant inbred lines. Here, we also report the detection of a substantial number of X O and XXY individuals across a variety of sample types, new markers that expand the utility of reduced complexity crosses to genetic backgrounds other than C57BL/6, and the robust detection of 17 genetic constructs. We provide preliminary evidence that the array can be used to identify both partial sex chromosome duplication and mosaicism, and that diagnostic SNPs can be used to determine how long inbred mice have been bred independently from the relevant main stock. We conclude that MiniMUGA is a valuable platform for genetic QC, and an important new tool to increase the rigor and reproducibility of mouse research., (Copyright © 2020 by the Genetics Society of America.)

Background: Epigenome-wide association studies (EWAS) have been widely applied to identify methylation CpG sites associated with human disease. To date, the Infinium MethylationEPIC array (EPIC) is commonly used for high-throughput DNA methylation profiling. However, the EPIC array covers only 30% of the human methylome. Methylation Capture bisulfite sequencing (MC-seq) captures target regions of methylome and has advantages of extensive coverage in the methylome at an affordable price., Methods: Epigenome-wide DNA methylation in four peripheral blood mononuclear cell samples was profiled by using SureSelectXT Methyl-Seq for MC-seq and EPIC platforms separately. CpG site-based reproducibility of MC-seq was assessed with DNA sample inputs ranging in quantity of high (> 1000 ng), medium (300-1000 ng), and low (150 ng-300 ng). To compare the performance of MC-seq and the EPIC arrays, we conducted a Pearson correlation and methylation value difference at each CpG site that was detected by both MC-seq and EPIC. We compared the percentage and counts in each CpG island and gene annotation between MC-seq and the EPIC array., Results: After quality control, an average of 3,708,550 CpG sites per sample were detected by MC-seq with DNA quantity > 1000 ng. Reproducibility of DNA methylation in MC-seq-detected CpG sites was high among samples with high, medium, and low DNA inputs (r > 0.96). The EPIC array captured an average of 846,464 CpG sites per sample. Compared with the EPIC array, MC-seq detected more CpGs in coding regions and CpG islands. Among the 472,540 CpG sites captured by both platforms, methylation of a majority of CpG sites was highly correlated in the same sample (r: 0.98-0.99). However, methylation for a small proportion of CpGs (N = 235) differed significantly between the two platforms, with differences in beta values of greater than 0.5., Conclusions: Our results show that MC-seq is an efficient and reliable platform for methylome profiling with a broader coverage of the methylome than the array-based platform. Although methylation measurements in majority of CpGs are highly correlated, a number of CpG sites show large discrepancy between the two platforms, which warrants further investigation and needs cautious interpretation.

Background: Aortic arch abnormalities (AAA) are abnormal embryologic developments of the aorta and its branches. Their outcomes often depend on their association with other congenital diseases and genetic testing results., Objective: This study aimed to evaluate the yield of chromosomal microarray analysis (CMA) in fetuses with different patterns of AAA and normal karyotype., Methods: Data from 158 pregnancies referred for prenatal CMA testing due to fetal AAA were obtained between April 2016 and April 2019. Fetuses with isolated AAA, AAA accompanied by soft ultrasound markers, and AAA with other ultrasound malformations were classified into groups A, B, and C, respectively. Cases with detectable karyotype aberrations were excluded from the study., Results: Twenty cases (12.7%) of submicroscopic anomalies were detected in 158 cases with normal karyotype, comprising 16 cases (10.1%) of clinically significant variants, two cases (1.3%) of variants of unknown significance, and two variants (1.3%) that were likely benign. Microdeletion of 22q11.2 accounted for 25% (4/16) of the clinically significant variants. The overall incremental yields by CMA in group A, group B, and group C were 1.8%, 2.3%, and 24.1%, respectively. Except for double aortic arch, the incremental yield of clinical significant findings for each type of AAA in group C was much higher than that in group A and group B. In group A, a clinically significant variant was only detected in one fetus with right aortic arch (RAA) (1.8%, 1/57)., Conclusions: In addition to 22q11.2 microdeletion, many other clinically significant submicroscopic variants are present in fetuses with AAA, especially in fetuses with other ultrasound malformations. Although CMA is always recommended in the presence of any malformation in many countries, our results suggest insufficient evidence to recommend CMA in fetuses with isolated AAA, except for isolated RAA.

Human papillomavirus (HPV) detection is used for screening of cervical cancer and genotype-specific persistence has shown to be mandatory for dysplasia development. Aim of this study was to evaluate the clinical performance of HPV DNA Array for cervical intraepithelial neoplasia 2+ (CIN2+)  lesion detection. HPV DNA Array is a polymerase chain reaction-based assay that targets E1 sequences of 29 HPV types (6, 11, 16, 18, 26, 31, 33, 35, 39, 40, 42, 44, 45, 51, 52, 53, 54, 56, 58, 59, 66, 67, 68, 69, 70, 73, 82, 85, and 97). The clinical evaluation was performed against the reference assay, BS-GP5+/6+ multiplex genotyping (MPG)-Luminex, with 600 cervical smear samples of a referral population. HPV DNA Array detected CIN2+  lesions with a sensitivity of 90.2%, identical to that of MPG-Luminex. Detection of CIN3+  lesions was with a sensitivity of 90.3%, as compared with 88.7% of MPG-Luminex. It demonstrated very good agreement for HPV detection, irrespective of type, of 91.5% (κ  =  0.832). HPV DNA Array is a simple and robust assay, with a short protocol of 4 hours hands-on time and automated readout by ELISpot AiDot software. It permits testing of up to 96 samples in one run and may be considered for use in organized screening programs and low resource settings., (© 2019 The Authors. Journal of Medical Virology Published by Wiley Periodicals, Inc.)

Background: Chromosomal microarray analysis (CMA) is nowadays widely used in the diagnostic path of patients with clinical phenotypes. However, there is no ascertained evidence to date on how to assemble single/combined clinical categories of developmental phenotypic findings to improve the array-based detection rate., Methods: The Italian Society of Human Genetics coordinated a retrospective study which included CMA results of 5,110 Italian patients referred to 17 genetics laboratories for variable combined clinical phenotypes., Results: Non-polymorphic copy number variants (CNVs) were identified in 1512 patients (30%) and 615 (32%) present in 552 patients (11%) were classified as pathogenic. CNVs were analysed according to type, size, inheritance pattern, distribution among chromosomes, and association to known syndromes. In addition, the evaluation of the detection rate of clinical subgroups of patients allowed to associate dysmorphisms and/or congenital malformations combined with any other single clinical sign to an increased detection rate, whereas non-syndromic neurodevelopmental signs and non-syndromic congenital malformations to a decreased detection rate., Conclusions: Our retrospective study resulted in confirming the high detection rate of CMA and indicated new clinical markers useful to optimize their inclusion in the diagnostic and rehabilitative path of patients with developmental phenotypes., (© 2019 The Authors. Molecular Genetics & Genomic Medicine published by Wiley Periodicals, Inc.)

We show the use of 5'-Acrydite oligonucleotides to copolymerize single-cell DNA or RNA into balls of acrylamide g el (BAGs). Combining this step with split-and-pool techniques for creating barcodes yields a method with advantages in cost and scalability, depth of coverage, ease of operation, minimal cross-contamination, and efficient use of samples. We perform DNA copy number profiling on mixtures of cell lines, nuclei from frozen prostate tumors, and biopsy washes. As applied to RNA, the method has high capture efficiency of transcripts and sufficient consistency to clearly distinguish the expression patterns of cell lines and individual nuclei from neurons dissected from the mouse brain. By using varietal tags (UMIs) to achieve sequence error correction, we show extremely low levels of cross-contamination by tracking source-specific SNVs. The method is readily modifiable, and we will discuss its adaptability and diverse applications., (© 2020 Li et al.; Published by Cold Spring Harbor Laboratory Press.)

Illumina HumanMethylation450 BeadChip (450K) has been commonly used to investigate DNA methylation in human tissues. Recently, it has been replaced by Illumina HumanMethylationEPIC BeadChip (EPIC) covering over 850,000 CpGs distributed genome-wide. Many consortia have now datasets coming from both arrays and aspire to analyze the two together. The placenta shows a high number of intermediate methylation levels and is often investigated for obstetric/birth outcomes, and potentially for long-term programming in offspring. We performed a systematic comparison between the two arrays using 108 duplicate placental samples from Gen3G birth cohort. We find that placenta shows a high per-sample correlation between the arrays, and higher median correlations at individual CpGs than those reported for blood. We identify 26,340 probes with absolute difference in per cent methylation >10%. We conclude that EPIC and 450K placental data can be combined, and we provide two lists of CpGs that should be excluded to avoid misleading results.

In the prenatal period, the copy number aberrations of chromosomes 13, 18, 21, X and Y account for over 80% of the clinically significant chromosome abnormalities. Classical cytogenetic analysis is the gold standard in invasive prenatal diagnostics but the long test waiting time affects its clinical utility. Several molecular rapid tests have been developed and employed in clinical practice, however all have substantial drawbacks. The aim of the study was to design and evaluate an optimized tool for rapid molecular detection of fetal aneuploidies. We established a novel single-day method using a chip-based platform, the QuantStudio 3D Digital PCR system. In order to assess the clinical usefulness of our screening test, we analyzed 133 prenatal samples. The difference in distributions of euploid and aneuploid samples identified the ploidy of each of the target chromosomes with high precision. The distribution of the chromosome ratio for euploid and aneuploid samples showed a statistically significant result ( p = 0.003 for trisomy 13, p = 0.001 for trisomies 18 and 21, Mann-Whitney U test). Our results suggest that this novel chip-based approach provides a tool for rapid, technically simple, cost-effective screening for common fetal aneuploidies.

Objective: To assess the clinical application of single nucleotide polymorphism microarray (SNP array) in prenatal genetic diagnosis for fetuses with absent nasal bone., Methods: Seventy four fetuses with absent nasal bone detected by prenatal ultrasound scanning were recruited from Women's Hospital, Zhejiang University School of Medicine during June 2015 and October 2018. The chromosome karyotypes analysis and SNP array were performed. The correlation between absent fetal nasal bone and chromosome copy number variants was analyzed., Results: Among 74 fetuses, 19 were detected to have chromosomal abnormalities, including 16 cases of trisomy-21, 1 case of trisomy-18 and two cases of micro-deletion/duplication. Among 46 cases with isolated absence of nasal bone, 3 had trisomy-21, and 1 had a micro-duplication. Absence of nasal bone in association with nuchal translucency thickening had a higher rate of abnormal karyotypes compared with isolated absence of nasal bone ( χ 2 =32.27, P <0.01)., Conclusions: Fetuses with absent nasal bone and nuchal translucency thickening are likely to have chromosome abnormalities, and SNP array testing is recommended to exclude the chromosome abnormalities.

Objective: To assess the clinical application of single nucleotide polymorphism microarray (SNP array) in patients with intellectual disability/developmental delay(ID/DD)., Methods: SNP array was performed to detect genome-wide DNA copy number variants (CNVs) for 145 patients with ID/DD in Women's Hospital, Zhejiang University School of Medicine from January 2013 to June 2018. The CNVs were analyzed by CHAS software and related databases., Results: Among 145 patients, pathogenic chromosomal abnormalities were detected in 32 cases, including 26 cases of pathogenic CNVs and 6 cases of likely pathogenic CNVs. Meanwhile, 18 cases of uncertain clinical significance and 14 cases of likely benign were identified, no significant abnormalities were found in 81 cases (including benign)., Conclusions: SNP array is effective for detecting chromosomal abnormalities in patients with ID/DD with high efficiency and resolution.

For the DNA microarray datasets, tumor classification based on gene expression profiles has drawn great attention, and gene selection plays a significant role in improving the classification performance of microarray data. In this study, an effective hybrid gene selection method based on ReliefF and Ant colony optimization (ACO) algorithm for tumor classification is proposed. First, for the ReliefF algorithm, the average distance among k nearest or k non-nearest neighbor samples are introduced to estimate the difference among samples, based on which the distances between the samples in the same class or the different classes are defined, and then it can more effectively evaluate the weight values of genes for samples. To obtain the stable results in emergencies, a distance coefficient is developed to construct a new formula of updating weight coefficient of genes to further reduce the instability during calculations. When decreasing the distance between the same samples and increasing the distance between the different samples, the weight division is more obvious. Thus, the ReliefF algorithm can be improved to reduce the initial dimensionality of gene expression datasets and obtain a candidate gene subset. Second, a new pruning rule is designed to reduce dimensionality and obtain a new candidate subset with the smaller number of genes. The probability formula of the next point in the path selected by the ants is presented to highlight the closeness of the correlation relationship between the reaction variables. To increase the pheromone concentration of important genes, a new phenotype updating formula of the ACO algorithm is adopted to prevent the pheromone left by the ants that are overwhelmed with time, and then the weight coefficients of the genes are applied here to eliminate the interference of difference data as much as possible. It follows that the improved ACO algorithm has the ability of the strong positive feedback, which quickly converges to an optimal solution through the accumulation and the updating of pheromone. Finally, by combining the improved ReliefF algorithm and the improved ACO method, a hybrid filter-wrapper-based gene selection algorithm called as RFACO-GS is proposed. The experimental results under several public gene expression datasets demonstrate that the proposed method is very effective, which can significantly reduce the dimensionality of gene expression datasets, and select the most relevant genes with high classification accuracy.

Previously, we classified colorectal cancers (CRCs) into five CRCAssigner (CRCA) subtypes with different prognoses and potential treatment responses, later consolidated into four consensus molecular subtypes (CMS). Here we demonstrate the analytical development and validation of a custom NanoString nCounter platform-based biomarker assay (NanoCRCA) to stratify CRCs into subtypes. To reduce costs, we switched from the standard nCounter protocol to a custom modified protocol. The assay included a reduced 38-gene panel that was selected using an in-house machine-learning pipeline. We applied NanoCRCA to 413 samples from 355 CRC patients. From the fresh frozen samples (n = 237), a subset had matched microarray/RNAseq profiles (n = 47) or formalin-fixed paraffin-embedded (FFPE) samples (n = 58). We also analyzed a further 118 FFPE samples. We compared the assay results with the CMS classifier, different platforms (microarrays/RNAseq) and gene-set classifiers (38 and the original 786 genes). The standard and modified protocols showed high correlation (> 0.88) for gene expression. Technical replicates were highly correlated (> 0.96). NanoCRCA classified fresh frozen and FFPE samples into all five CRCA subtypes with consistent classification of selected matched fresh frozen/FFPE samples. We demonstrate high and significant subtype concordance across protocols (100%), gene sets (95%), platforms (87%) and with CMS subtypes (75%) when evaluated across multiple datasets. Overall, our NanoCRCA assay with further validation may facilitate prospective validation of CRC subtypes in clinical trials and beyond.

Background: With the development of new drug combinations and targeted treatments for multiple types of cancer, the ability to stratify categories of patient populations and to develop companion diagnostics has become increasingly important. A panel of 325 RNA biomarkers was selected based on cancer-related biological processes of healthy cells and gene expression changes over time during nonmalignant epithelial cell organization. This "cancer in reverse" approach resulted in a panel of biomarkers relevant for at least 7 cancer types, providing gene expression profiles representing key cellular signaling pathways beyond mutations in "driver genes." Objective . To further investigate this biomarker panel, the objective of the current study is to (1) validate the assay reproducibility for the 325 RNA biomarkers and (2) compare gene expression profiles side by side using two technology platforms., Methods and Results: We have mapped the 325 RNA transcripts and in a custom NanoString nCounter expression panel to be compared to all potential probe sets in the Affymetrix Human Genome U133 Plus 2.0. The experiments were conducted with 10 unique biological formalin-fixed paraffin-embedded (FFPE) breast tumor samples. Each site extracted RNA from four sections of 10-micron thick FFPE tissue over three different days by two different operators using an optimized standard operating procedure and quality control criteria. Samples were analyzed using mas5 in BioConductor and NanoStringNorm in R. Pearson correlation showed reproducibility between sites for all 60 samples with r = 0.995 for Affymetrix and r = 0.999 for NanoString. Correlation in multiple days and multiple users was for Affymetrix r = (0.962 - 0.999) and for NanoString r = (0.982 - 0.991)., Conclusion: The 325 RNA biomarkers showed reproducibility in two technology platforms with moderate to high concordance. Future directions include performing clinical validation studies and generating rationale for patient selection in clinical trials using the technically validated assay.

Background: There is a clear need for assays that can predict the risk of metastatic prostate cancer following curative procedures. Importantly these assays must be analytically robust in order to provide quality data for important clinical decisions. DNA microarray based gene expression assays measure several analytes simultaneously and can present specific challenges to analytical validation. This study describes the analytical validation of one such assay designed to predict metastatic recurrence in prostate cancer using primary formalin fixed paraffin embedded tumour material., Methods: Accuracy was evaluated with a method comparison study between the assay development platform (Prostate Disease Specific Array) and an alternative platform (Xcel™ microarray) using 50 formalin-fixed, paraffin-embedded prostate cancer patient samples. An additional 70 samples were used to establish the assay reportable range. Determination of assay precision and sensitivity was performed on multiple technical replicates of three prostate cancer samples across multiple variables (operators, days, runs, reagent lots, and equipment) and RNA/cDNA inputs respectively using the appropriate linear mixed model., Results: The overall agreement between the development and alternative platform was 94.7% (95% confidence interval, 86.9-98.5%). The reportable range was determined to be 0.150 to 1.107 for core needle biopsy samples and - 0.214 to 0.844 for radical prostatectomy samples. From the precision study, the standard deviations for assay repeatability and reproducibility were 0.032 and 0.040 respectively. The sensitivity study demonstrated that a total RNA input and cDNA input of 50 ng and 3.5 μg respectively was conservative., Conclusion: The Metastatic Assay was found to be highly reproducible and precise. In conclusion the studies demonstrated an acceptable analytical performance for the assay and support its potential use in the clinic.

Background: Robust clinical and analytical validation of human papillomavirus (HPV) tests is a pre-requisite for their use in cervical cancer screening given the transience of most high-risk HPV infections., Objectives: To evaluate the EUROArray HPV test (PCR-based full HPV genotyping test) using the international validation of the VALGENT framework, which offers an opportunity to determine analytical and clinical performance according to internationally accepted performance metrics., Study Design: A total of 1300 consecutive and 300 abnormal cervical samples derived from the Slovenian screening programme were tested with the EUROArray HPV test. Clinical performance for the detection of cervical intraepithelial neoplasia grade 2 and above (CIN2+) was performed and compared to a standard comparator test (Hybrid Capture 2). Intra- and inter-laboratory reproducibility of the assay was performed in a subset of 500 samples., Results: The relative clinical sensitivity and specificity of EUROArray HPV vs HC2 was 0.93 (95% Confidence Interval (CI), 0.88-0.99; P non-inferiority( ni ) = 0.1413) and 1.01 (95% CI, 0.99-1.02; P ni  = 0.0001), respectively. Application of an a-posteriori cut-off for HPV16 led to relative values of 0.98 (95% CI, 0.92-1.03; P ni  = 0.0076) and 1.00 (95% CI, 0.97-1.03; P ni  = 0.007), respectively. The assay showed excellent intra- and inter-laboratory reproducibility (concordance ≥ 94%, Kappas ≥0.85)., Conclusion: At the predefined cut-off, EUROArray HPV was less sensitive than HC2 for the detection of CIN2+. However, when an optimised cut-off was applied, EUROArray HPV fulfilled international criteria for its use in cervical cancer screening., (Copyright © 2018. Published by Elsevier B.V.)

Background: Mislabeled, contaminated or poorly performing samples can threaten power in methylation microarray analyses or even result in spurious associations. We describe a set of quality checks for the popular Illumina 450K and EPIC microarrays to identify problematic samples and demonstrate their application in publicly available datasets., Methods: Quality checks implemented here include 17 control metrics defined by the manufacturer, a sex check to detect mislabeled sex-discordant samples, and both an identity check for fingerprinting sample donors and a measure of sample contamination based on probes querying high-frequency SNPs. These checks were tested on 80 datasets comprising 8327 samples run on the 450K microarray from the GEO repository., Results: Nine hundred forty samples were flagged by at least one control metric and 133 samples from 20 datasets were assigned the wrong sex. In a dataset in which a subset of samples appear contaminated with a single source of DNA, we demonstrate that our measure based on outliers among SNP probes was strongly correlated (> 0.95) with another independent measure of contamination., Conclusions: A more complete examination of samples that may be mislabeled, contaminated, or have poor performance due to technical problems will improve downstream analyses and replication of findings. We demonstrate that quality control problems are prevalent in a public repository of DNA methylation data. We advocate for a more thorough quality control workflow in epigenome-wide association studies and provide a software package to perform the checks described in this work. Reproducible code and supplementary material are available at 10.5281/zenodo.1172730., Competing Interests: Not applicable.The authors declare that they have no competing interests.Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Integrative analyses of multiple gene expression studies are frequently performed. In the setting of two studies, integrative correlation (IGC) can be used to assess the consistency of co-expression of a given gene. For three or more studies, an extension of IGC gives a global score per gene. We propose to extend IGC and use factor analysis to assess the study-specific consistency of co-expression of genes when there are three or more studies, possibly on different platforms. Our method is able to identify studies whose expression patterns are different from others. Filtering genes based on our score is shown to improve the concordance of association with phenotype across studies., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Epigenome-wide association studies (EWAS) have focused primarily on DNA methylation as a chemically stable and functional epigenetic modification. However, the stability and accuracy of the measurement of methylation in different tissues and extraction types is still being actively studied, and the longitudinal stability of DNA methylation in commonly studied peripheral tissues is of great interest. Here, we used data from two studies, three tissue types, and multiple time points to assess the stability of DNA methylation measured with the Illumina Infinium HumanMethylation450 BeadChip array. Redundancy analysis enabled visual assessment of agreement of replicate samples overall and showed good agreement after removing effects of tissue type, age, and sex. At the probe level, analysis of variance contrasts separating technical and biological replicates clearly showed better agreement between technical replicates versus longitudinal samples, and suggested increased stability for buccal cells versus blood or blood spots. Intraclass correlations (ICCs) demonstrated that inter-individual variability is of similar magnitude to within-sample variability at many probes; however, as inter-individual variability increased, so did ICC. Furthermore, we were able to demonstrate decreasing agreement in methylation levels with time, despite a maximal sampling interval of only 576 days. Finally, at 6 popular candidate genes, there was a large range of stability across probes. Our findings highlight important sources of technical and biological variation in DNA methylation across different tissues over time. These data will help to inform longitudinal sampling strategies of future EWAS.

Background: Schizophrenia is a severe psychiatric disorder associated with IQ deficits. Rare copy number variations (CNVs) have been established to play an important role in the etiology of schizophrenia. Several of the large rare CNVs associated with schizophrenia have been shown to negatively affect IQ in population-based controls where no major neuropsychiatric disorder is reported. The aim of this study was to examine the diagnostic yield of microarray testing and the functional impact of genome-wide rare CNVs in a community ascertained cohort of adults with schizophrenia and low (< 85) or average (≥ 85) IQ., Methods: We recruited 546 adults of European ancestry with schizophrenia from six community psychiatric clinics in Canada. Each individual was assigned to the low or average IQ group based on standardized tests and/or educational attainment. We used rigorous methods to detect genome-wide rare CNVs from high-resolution microarray data. We compared the burden of rare CNVs classified as pathogenic or as a variant of unknown significance (VUS) between each of the IQ groups and the genome-wide burden and functional impact of rare CNVs after excluding individuals with a pathogenic CNV., Results: There were 39/546 (7.1%; 95% confidence interval [CI] = 5.2-9.7%) schizophrenia participants with at least one pathogenic CNV detected, significantly more of whom were from the low IQ group (odds ratio [OR] = 5.01 [2.28-11.03], p = 0.0001). Secondary analyses revealed that individuals with schizophrenia and average IQ had the lowest yield of pathogenic CNVs (n = 9/325; 2.8%), followed by those with borderline intellectual functioning (n = 9/130; 6.9%), non-verbal learning disability (n = 6/29; 20.7%), and co-morbid intellectual disability (n = 15/62; 24.2%). There was no significant difference in the burden of rare CNVs classified as a VUS between any of the IQ subgroups. There was a significantly (p=0.002) increased burden of rare genic duplications in individuals with schizophrenia and low IQ that persisted after excluding individuals with a pathogenic CNV., Conclusions: Using high-resolution microarrays we were able to demonstrate for the first time that the burden of pathogenic CNVs in schizophrenia differs significantly between IQ subgroups. The results of this study have implications for clinical practice and may help inform future rare variant studies of schizophrenia using next-generation sequencing technologies.

RNA sequencing (RNA-Seq) and microarray are two of the most commonly used high-throughput technologies for transcriptome profiling; however, they both have their own inherent strengths and limitations. This research aims to analyze the correlation between microarrays and RNA-Seq detection of transcripts in the same tissue sample to explore the reproducibility between the techniques. Using data of RNA-Seq v2 and three different microarrays provided by The Cancer Genome Atlas, 11,120 genes of 111 lung squamous cell carcinoma samples were simultaneously detected by the four methods. Then we analyzed the Pearson correlation between microarrays and RNA-Seq. Finally, in the six comparison results, 9984 (89.8%) genes, irrespective of which two methods were used, simultaneously showed the existence of correlation, whereas only 83 (0.1%) genes proved to have no significant correlation in either comparison. In addition, the comparisons between 3266 (29.3%) genes showed high correlation (R≥0.8) in all six comparisons, only for 1643 (14.8%) genes correlation were not as high in either comparison. Meanwhile, transcripts with extreme high or low expression levels were more highly discrepant across the methods. In conclusion, we found that, for most transcripts, the results obtained by RNA-Seq and microarrays were highly reproducible., (Copyright © 2017 Elsevier B.V. All rights reserved.)

ChIP-exo/nexus experiments rely on innovative modifications of the commonly used ChIP-seq protocol for high resolution mapping of transcription factor binding sites. Although many aspects of the ChIP-exo data analysis are similar to those of ChIP-seq, these high throughput experiments pose a number of unique quality control and analysis challenges. We develop a novel statistical quality control pipeline and accompanying R/Bioconductor package, ChIPexoQual, to enable exploration and analysis of ChIP-exo and related experiments. ChIPexoQual evaluates a number of key issues including strand imbalance, library complexity, and signal enrichment of data. Assessment of these features are facilitated through diagnostic plots and summary statistics computed over regions of the genome with varying levels of coverage. We evaluated our QC pipeline with both large collections of public ChIP-exo/nexus data and multiple, new ChIP-exo datasets from Escherichia coli. ChIPexoQual analysis of these datasets resulted in guidelines for using these QC metrics across a wide range of sequencing depths and provided further insights for modelling ChIP-exo data., (© The Author(s) 2017. Published by Oxford University Press on behalf of Nucleic Acids Research.)

Introduction: Delayed initial antifungal therapy is associated with high mortality rates caused by invasive candida infections, since accurate detection of the opportunistic pathogenic yeast and its identification display a diagnostic challenge. diagnosis of candida infections relies on time-consuming methods such as blood cultures, serologic and histopathologic examination. to allow for fast detection and characterization of invasive candidiasis, there is a need to improve diagnostic tools. trends in diagnostics switch to non-culture-based methods, which allow specified diagnosis within significantly shorter periods of time in order to provide early and appropriate antifungal treatment. Areas covered: within this review comprise novel pathogen- and host-related testing methods, e.g. multiplex-PCR analyses, T2 magnetic resonance, fungus-specific DNA microarrays, microRNA characterization or analyses of IL-17 as biomarker for early detection of invasive candidiasis. Expert commentary: Early recognition and diagnosis of fungal infections is a key issue for improved patient management. As shown in this review, a broad range of novel molecular based tests for the detection and identification of Candida species is available. However, several assays are in-house assays and lack standardization, clinical validation as well as data on sensitivity and specificity. This underscores the need for the development of faster and more accurate diagnostic tests.

Background: To date, genome-scale analyses in the domestic horse have been limited by suboptimal single nucleotide polymorphism (SNP) density and uneven genomic coverage of the current SNP genotyping arrays. The recent availability of whole genome sequences has created the opportunity to develop a next generation, high-density equine SNP array., Results: Using whole genome sequence from 153 individuals representing 24 distinct breeds collated by the equine genomics community, we cataloged over 23 million de novo discovered genetic variants. Leveraging genotype data from individuals with both whole genome sequence, and genotypes from lower-density, legacy SNP arrays, a subset of ~5 million high-quality, high-density array candidate SNPs were selected based on breed representation and uniform spacing across the genome. Considering probe design recommendations from a commercial vendor (Affymetrix, now Thermo Fisher Scientific) a set of ~2 million SNPs were selected for a next-generation high-density SNP chip (MNEc2M). Genotype data were generated using the MNEc2M array from a cohort of 332 horses from 20 breeds and a lower-density array, consisting of ~670 thousand SNPs (MNEc670k), was designed for genotype imputation., Conclusions: Here, we document the steps taken to design both the MNEc2M and MNEc670k arrays, report genomic and technical properties of these genotyping platforms, and demonstrate the imputation capabilities of these tools for the domestic horse.

As an example of optimizing population-specific genotyping assays using a whole-genome sequence reference set, we detail the approach that followed to design the Axiom-NL array which is characterized by an improved imputation backbone based on the Genome of the Netherlands (GoNL) reference sequence and, compared with earlier arrays, a more comprehensive inclusion of SNPs on chromosomes X, Y, and the mitochondria. Common variants on the array were selected to be compatible with the Illumina Psych Array and the Affymetrix UK Biobank Axiom array. About 3.5% of the array (23 977 markers) represents SNPs from the GWAS catalog, including SNPs at FTO, APOE, Ion-channels, killer-cell immunoglobulin-like receptors, and HLA. Around 26 000 markers associated with common psychiatric disorders are included, as well as 6705 markers suggested to be associated with fertility and twinning. The platform can thus be used for risk profiling, detection of new variants, as well as ancestry determination. Results of coverage tests in 249 unrelated subjects with GoNL-based sequence data show that after imputation with 1000G as a reference, the median concordance between original and imputed genotypes is above 98%. The median imputation quality R 2 for MAF thresholds of 0.001, 0.01, 0.05, and >0.05 are 0.05, 0.28, 0.80, 0.99, respectively, for the 1000G imputed SNPs, with a similar quality for the autosomes and X chromosome, showing a good genome-wide coverage for association studies after imputation.

Background: The Illumina Infinium HumanMethylation450 BeadChip and its successor, Infinium MethylationEPIC BeadChip, have been extensively utilized in epigenome-wide association studies. Both arrays use two fluorescent dyes (Cy3-green/Cy5-red) to measure methylation level at CpG sites. However, performance difference between dyes can result in biased estimates of methylation levels., Results: Here we describe a novel method, called REgression on Logarithm of Internal Control probes (RELIC) to correct for dye bias on whole array by utilizing the intensity values of paired internal control probes that monitor the two color channels. We evaluate the method in several datasets against other widely used dye-bias correction methods. Results on data quality improvement showed that RELIC correction statistically significantly outperforms alternative dye-bias correction methods. We incorporated the method into the R package ENmix, which is freely available from the Bioconductor website ( https://www.bioconductor.org/packages/release/bioc/html/ENmix.html )., Conclusions: RELIC is an efficient and robust method to correct for dye-bias in Illumina Methylation BeadChip data. It outperforms other alternative methods and conveniently implemented in R package ENmix to facilitate DNA methylation studies.

To address the limitations in current classic twin/family research on the genetic and/or environmental causes of human methylomic variation, we measured blood DNA methylation for 479 women (mean age 56 years) including 66 monozygotic (MZ), 66 dizygotic (DZ) twin pairs and 215 sisters of twins, and 11 random technical duplicates using the HumanMethylation450 array. For each methylation site, we estimated the correlation for pairs of duplicates, MZ twins, DZ twins, and siblings, fitted variance component models by assuming the variation is explained by genetic factors, by shared and individual environmental factors, and by independent measurement error, and assessed the best fitting model. We found that the average (standard deviation) correlations for duplicate, MZ, DZ, and sibling pairs were 0.10 (0.35), 0.07 (0.21), -0.01 (0.14) and -0.04 (0.07). At the genome-wide significance level of 10 -7 , 93.3% of sites had no familial correlation, and 5.6%, 0.1%, and 0.2% of sites were correlated for MZ, DZ, and sibling pairs. For 86.4%, 6.9%, and 7.1% of sites, the best fitting model included measurement error only, a genetic component, and at least one environmental component. For the 13.6% of sites influenced by genetic and/or environmental factors, the average proportion of variance explained by environmental factors was greater than that explained by genetic factors (0.41 vs. 0.37, P value <10 -15 ). Our results are consistent with, for middle-aged woman, blood methylomic variation measured by the HumanMethylation450 array being largely explained by measurement error, and more influenced by environmental factors than by genetic factors.

The response of human immunodeficiency virus type 1 (HIV-1) quasispecies to antiretroviral therapy is influenced by the ensemble of mutants that composes the evolving population. Low-abundance subpopulations within HIV-1 quasispecies may determine the viral response to the administered drug combinations. However, routine sequencing assays available to clinical laboratories do not recognize HIV-1 minority variants representing less than 25% of the population. Although several alternative and more sensitive genotyping techniques have been developed, including next-generation sequencing (NGS) methods, they are usually very time consuming, expensive and require highly trained personnel, thus becoming unrealistic approaches in daily clinical practice. Here we describe the development and testing of a HIV-1 genotyping DNA microarray that detects and quantifies, in majority and minority viral subpopulations, relevant mutations and amino acid insertions in 42 codons of the pol gene associated with drug- and multidrug-resistance to protease (PR) and reverse transcriptase (RT) inhibitors. A customized bioinformatics protocol has been implemented to analyze the microarray hybridization data by including a new normalization procedure and a stepwise filtering algorithm, which resulted in the highly accurate (96.33%) detection of positive/negative signals. This microarray has been tested with 57 subtype B HIV-1 clinical samples extracted from multi-treated patients, showing an overall identification of 95.53% and 89.24% of the queried PR and RT codons, respectively, and enough sensitivity to detect minority subpopulations representing as low as 5-10% of the total quasispecies. The developed genotyping platform represents an efficient diagnostic and prognostic tool useful to personalize antiviral treatments in clinical practice., Competing Interests: Competing Interests: We have the following interests: This study was supported by Biotherapix, S.L.U. Patricia Garrido, María Pernas and José Luis Torán were employed by Biotherapix, S.L.U. There are no patents, products in development or marketed products to declare. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials, as detailed online in the guide for authors.

Conceptual models suggest that certain microorganisms (e.g., the "red" complex) are indicative of a specific disease state (e.g., periodontitis); however, recent studies have questioned the validity of these models. Here, the abundances of 500+ microbial species were determined in 16 patients with clinical signs of one of the following oral conditions: periodontitis, established caries, edentulism, and oral health. Our goal was to determine if the abundances of certain microorganisms reflect dysbiosis or a specific clinical condition that could be used as a 'signature' for dental research. Microbial abundances were determined by the analysis of 138,718 calibrated probes using Gene Meter methodology. Each 16S rRNA gene was targeted by an average of 194 unique probes (n=25nt). The calibration involved diluting pooled gene target samples, hybridizing each dilution to a DNA microarray, and fitting the probe intensities to adsorption models. The fit of the model to the experimental data was used to assess individual and aggregate probe behavior; good fits (R 2 >0.90) were retained for back-calculating microbial abundances from patient samples. The abundance of a gene was determined from the median of all calibrated individual probes or from the calibrated abundance of all aggregated probes. With the exception of genes with low abundances (<2 arbitrary units), the abundances determined by the different calibrations were highly correlated (r~1.0). Seventeen genera were classified as 'signatures of dysbiosis' because they had significantly higher abundances in patients with periodontitis and edentulism when contrasted with health. Similarly, 13 genera were classified as 'signatures of periodontitis', and 14 genera were classified as 'signatures of edentulism'. The signatures could be used, individually or in combination, to assess the clinical status of a patient (e.g., evaluating treatments such as antibiotic therapies). Comparisons of the same patient samples revealed high false negatives (45%) for next-generation-sequencing results and low false positives (7%) for Gene Meter results., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Purpose Reproducibility of scientific experimentation has become a major concern because of the perception that many published biomedical studies cannot be replicated. In this article, we draw attention to the connection between inflated overoptimistic findings and the use of cross-validation for error estimation in molecular classification studies. We show that, in the absence of careful design to prevent artifacts caused by systematic differences in the processing of specimens, established tools such as cross-validation can lead to a spurious estimate of the error rate in the overoptimistic direction, regardless of the use of data normalization as an effort to remove these artifacts. Methods We demonstrated this important yet overlooked complication of cross-validation using a unique pair of data sets on the same set of tumor samples. One data set was collected with uniform handling to prevent handling effects; the other was collected without uniform handling and exhibited handling effects. The paired data sets were used to estimate the biologic effects of the samples and the handling effects of the arrays in the latter data set, which were then used to simulate data using virtual rehybridization following various array-to-sample assignment schemes. Results Our study showed that (1) cross-validation tended to underestimate the error rate when the data possessed confounding handling effects; (2) depending on the relative amount of handling effects, normalization may further worsen the underestimation of the error rate; and (3) balanced assignment of arrays to comparison groups allowed cross-validation to provide an unbiased error estimate. Conclusion Our study demonstrates the benefits of balanced array assignment for reproducible molecular classification and calls for caution on the routine use of data normalization and cross-validation in such analysis.

Our objective was to identify suitable reference genes in serum miRNA for normalization and screen potential new biomarkers for osteoporosis diagnosis by a systematic study. Two types of osteoporosis models were used like as mechanical unloading and estrogen deficiency. Through a large-scale screening using microarray, qPCR validation and statistical algorithms, we first identified miR-25-3p as a suitable reference gene for both type of osteoporosis, which also showed stability during the differentiation processes of osteoblast and osteoclast. Then 15 serum miRNAs with differential expression in OVX rats were identified by microarray and qPCR validation. We further detected these 15 miRNAs in postmenopausal women and bedrest rhesus monkeys and evaluated their diagnostic value by ROC analysis. Among these miRNAs, miR-30b-5p was significantly down-regulated in postmenopausal women with osteopenia or osteoporosis; miR-103-3p, miR-142-3p, miR-328-3p were only significantly decreased in osteoporosis. They all showed positive correlations with BMD. Except miR328-3p, the other three miRNAs were also declined in the rhesus monkeys after long-duration bedrest. Their AUC values (all >0.75) proved the diagnostic potential. Our results provided a reliable normalization reference gene and verified a group of circulating miRNAs as non-invasive biomarkers in the detection of postmenopausal- and mechanical unloading- osteoporosis.

Accuracy of the microarray technology results is raised by using the multi-stage normalization of results. One of the principal requirements of such normalization is usage of internal standards. The routine Agilent microarray-based gene expression analysis protocol utilizes a Spike-In Kit during preparation of the samples representing a mixture of RNA fragments in different ratios. RNA probes which were synthesized in vitro conditions could be also used to establish how the magnitude of the fluorescent signal reflects the presence of RNA in the sample. A significant disadvantage of this type of standards is a difficulty of their production and the low RNA stability. In accordance with the Agilent protocol, the presence of the T7 promoter is necessary for the synthesis of labeled cRNA during sample preparation procedure. We hypothesized that we can successfully synthesize any RNA sequence having such type of promoter in its start position. Moreover, DNA sequence would serve as a matrix in this case. Using a set of different genes attached downstream of the T7-promoter in the plasmid DNA we have demonstrated in this study that such system can serve as a reliable template for the fluorescent labeled RNA sequence synthesis. In comparison with the routinely used internal RNA based controls, this template is stable, easy to manufacture and can be easily obtained in large quantities.

Fragile X syndrome (FXS) is the most common inherited cause of intellectual disability. It is most frequently caused by an abnormal expansion of the CGG trinucleotide repeat (>200 repeats) located in the promoter of the fragile X mental retardation gene (FMR1), resulting in promoter DNA hypermethylation and gene silencing. Current clinical tests for FXS are technically challenging and labor intensive, and may involve use of hazardous chemicals or radioisotopes. We clinically validated the Illumina Infinium HumanMethylation450 DNA methylation array for FXS screening. We assessed genome-wide and FMR1-specific DNA methylation in 32 males previously diagnosed with FXS, including nine with mosaicism, as well as five females with full mutation, and premutation carrier males (n = 11) and females (n = 11), who were compared to 300 normal control DNA samples. Our findings demonstrate 100% sensitivity and specificity for detection of FXS in male patients, as well as the ability to differentiate patients with mosaic methylation defects. Full mutation and premutation carrier females did not show FMR1 methylation changes. We have clinically validated this genome-wide DNA methylation assay as a cost- and labor-effective alternative for sensitive and specific screening for FXS, while ruling out the most common differential diagnoses of FXS, Prader-Willi syndrome, and Sotos syndrome in the same assay., (Copyright © 2016 American Society for Investigative Pathology and the Association for Molecular Pathology. Published by Elsevier Inc. All rights reserved.)

Transcriptome profiling is a powerful tool for identifying gene networks from whole genome expression analysis in many living species. Here is described the first extensively characterized platform using Agilent microarray technology for transcriptome analysis in the filamentous fungus Aspergillus (Emericella) nidulans. We developed and validated a reliable gene expression microarray in 8 × 15 K format, with predictive and experimental data establishing its specificity and sensitivity. Either one or two 60-mer oligonucleotide probes were selected for each of 10,550 nuclear as well as 20 mitochondrial coding sequences. More than 99 % of probes were predicted to hybridize with 100 % identity to their aimed specific A. nidulans target only. Probe sensitivity was supported by a highly narrow distribution of melting temperatures together with thermodynamic features, which strongly favored probe-target perfect match hybridization, in comparison with predicted secondary structures. Array quality was evaluated through transcriptome comparison of two A. nidulans strains, differing by the presence or not of Escherichia coli LacZ transgene. High signal-to-noise ratios were measured, and signal reproducibility was established at intra-probe and inter-probe levels. Reproducibility of microarray performances was assessed by high correlation between two-color dye signals and between technical replicates. Results were confirmed by RT-qPCR analysis on five genes. Though it covers 100 % of the A. nidulans targeted coding sequences, this low density array allows limited experimental costs and simplified data analysis process, making it suitable for studying gene expression in this model organism through large numbers of experimental conditions, in basic, biomedical or industrial microbiology research fields.

Chromosome microarrays are an essential tool for investigation of copy number changes in children with congenital anomalies and intellectual deficit. Attempts to standardise microarray testing have focused on establishing technical and clinical quality criteria, however external quality assessment programs are still needed. We report on a microarray proficiency testing program for Australasian laboratories. Quality metrics evaluated included analytical accuracy, result interpretation, report completeness, and laboratory performance data: sample numbers, success and abnormality rate and reporting times. Between 2009 and 2014 nine samples were dispatched with variable results for analytical accuracy (30-100%), correct interpretation (32-96%), and report completeness (30-92%). Laboratory performance data (2007-2014) showed an overall mean success rate of 99.2% and abnormality rate of 23.6%. Reporting times decreased from >90 days to <30 days for normal results and from >102 days to <35 days for abnormal results. Data trends showed a positive correlation with improvement for all these quality metrics, however only 'report completeness' and reporting times reached statistical significance. Whether the overall improvement in laboratory performance was due to participation in this program, or from accumulated laboratory experience over time, is not clear. Either way, the outcome is likely to assist referring clinicians and improve patient care., (Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.)

Objective: To evaluate the performance of a laboratory protocol for direct genetic analysis performed on tissues obtained from miscarriages, stillbirth and postnatal death., Methods: Samples were collected between July 1st, 2011 and June 30th, 2014. QF-PCR analysis was the initial test followed by aCGH analysis performed on the normal QF-PCR specimens., Results: Of the 1195 submitted specimens, a total of 1071 samples were confirmed as true fetal. The failure rate was 1.4%. Of those, 30.8% yielded abnormal results. Of the latter, 57.6% had abnormal QF-PCR and 42.4% had abnormal microarray result. Autosomal trisomies were detected in 61.2%, triploidy in 7.6%, monosomy X in 9.1%, sex-chromosome aneuploidy (apart from monosomy X) in 1.5%, molar pregnancies in 5.8% and copy number variants in 14.2% including microdeletions/microduplications and cryptic unbalanced rearrangements. The highest diagnostic yield was observed in the 1st trimester specimens at 67.6%. We confirmed that maternal age correlates with the likelihood of autosomal trisomies but not with triploidy, sex chromosome aneuploidies, molar pregnancy, or CNVs., Conclusion: An efficient laboratory protocol, based on QF-PCR and aCGH of uncultured cells has replaced standard cytogenetic analysis in testing of tissue from all pregnancy losses in our center and resulted in reduced test failure rate and increased diagnostic yield., (Copyright © 2016 Elsevier Masson SAS. All rights reserved.)

Viruses are major pathogens causing foodborne illnesses and are often present at low levels in foods, thus requiring sensitive techniques for their detection in contaminated foods. The lack of efficient culture methods for many foodborne viruses and the potential for multi-species viral contamination have driven investigation toward non-amplification based methods for virus detection and identification. A custom DNA microarray (FDA&#95;EVIR) was assessed for its sensitivity in the detection and identification of low-input virus targets, human hepatitis A virus, norovirus, and coxsackievirus, individually and in combination. Modifications to sample processing were made to accommodate low input levels of unamplified virus targets, which included addition of carrier cDNA, RNase treatment, and optimization of DNase I-mediated target fragmentation. Amplification-free detection and identification of foodborne viruses were achieved in the range of 250-500 copies of virus RNA. Alternative data analysis methods were employed to distinguish the genotypes of the viruses particularly at lower levels of target input and the single probe-based analysis approach made it possible to identify a minority species in a multi-virus complex. The oligonucleotide array is shown to be a promising platform to detect foodborne viruses at low levels close to what are anticipated in food or environmental samples., (Published by Elsevier B.V.)

The reliability of methylation measures from the widely used HumanMethylation450 (HM450K) microarray has not been assessed for DNA from dried blood spots (DBS) or peripheral blood mononuclear cells (PBMC), nor for combined data from different studies. Repeated HM450K methylation measures in DNA from DBS and PBMC samples were available from participants in six case-control studies nested within the Melbourne Collaborative Cohort Study. Reliability was assessed for individual CpGs by calculating the intraclass correlation coefficient (ICC) based on technical replicates (samples repeated in a single study; 126 PBMC, 136 DBS) and study duplicates (samples repeated across studies; 280 PBMC, 769 DBS) using mixed-effects models. Reliability based on technical replicates was moderate for PBMC (median ICC = 0.42), but lower for DBS (median ICC = 0.20). Study duplicates gave lower ICCs than technical replicates. CpGs that were either highly methylated or unmethylated generally had lower ICCs, which appeared to be mostly related to their lower variability. The ICCs for global methylation measures were high, typically greater than 0.70. The reliability of methylation measures determined by the HM450K microarray is wide-ranging and depends primarily on the variability in methylation at individual CpG sites. The power of association studies is low for a substantial proportion of CpGs in the HM450K assay.

Background: Human methylome mapping in health and disease states has largely relied on Illumina Human Methylation 450k array (450k array) technology. Accompanying this has been the necessary evolution of analysis pipelines to facilitate data processing. The majority of these pipelines, however, cater for experimental designs where matched 'controls' or 'normal' samples are available. Experimental designs where no appropriate 'reference' exists remain challenging. Herein, we use data generated from our study of the inheritance of methylome profiles in families to evaluate the performance of eight normalisation pre-processing methods. Fifty individual samples representing four families were interrogated on five 450k array BeadChips. Eight normalisation methods were tested using qualitative and quantitative metrics, to assess efficacy and suitability., Results: Stratified quantile normalisation combined with ComBat were consistently found to be the most appropriate when assessed using density, MDS and cluster plots. This was supported quantitatively by ANOVA on the first principal component where the effect of batch dropped from p < 0.01 to p = 0.97 after stratified QN and ComBat. Median absolute differences between replicated samples were the lowest after stratified QN and ComBat as were the standard error measures on known imprinted regions. Biological information was preserved after normalisation as indicated by the maintenance of a significant association between a known mQTL and methylation (p = 1.05e-05)., Conclusions: A strategy combining stratified QN with ComBat is appropriate for use in the analyses when no reference sample is available but preservation of biological variation is paramount. There is great potential for use of 450k array data to further our understanding of the methylome in a variety of similar settings. Such advances will be reliant on the determination of appropriate methodologies for processing these data such as established here.

Although emerging sequencing technologies can characterize all genetic variants, the cost is still high. Illumina released the HumanOmni5M-4v1 (Omni5) genotype array with ~4.3M assayed SNPs, a much denser array compared with other available arrays. The Omni5 balances both cost and array density. In this article, we illustrate the power of Omni5 to detect genetic associations. The Omni5 includes variants with a wide range of minor allele frequencies down to <1%. The theoretical power calculation examples indicate the increased power of the Omni5 array compared with other arrays with lower density when evaluating associations with some known loci, although there are exceptions. We further evaluate the genetic associations between known loci and several quantitative traits in the Framingham Heart Study: femoral neck bone mineral density, lumbar spine bone mineral density and hippocampal volume. Finally, we search genome wide for novel associations using the Omni5 genotypes. We compare our association results from Affymetrix 500K+MIPS 50K arrays and two imputed data sets: (1) HapMap Phase II and (2) 1000 Genomes reference panel. We observed increased evidence for genotype-phenotype associations with smaller P-values for selected known loci using the Omni5 genotypes. With limited sample sizes, we identify novel variants with genome-wide significant P-values. Our observations support the notion that dense genotyping using the Omni5 can be powerful in detecting novel associated variants. Comparison with imputed data with higher density also suggests that imputation helps but cannot replace genotyping, especially when imputation quality is low.

Background: The implementation of nucleic acid-based tests for blood donor screening has improved the safety of the blood supply; however, the increasing number of emerging pathogen tests is burdensome. Development of multiplex testing platforms that allow simultaneous screening for different pathogens is a potential solution., Study Design and Methods: The TessArray resequencing microarray is a platform that allows multiplex detection and identification of 97 different blood-borne pathogens in one single test. The objective was to evaluate the lowest concentration detected in blood or plasma, species discrimination, and applicability of the TessArray microarray platform for testing blood donors. Human blood or plasma spiked with selected pathogens (10,000, 1000, or 100 cells or copies/mL), including three viral, four bacterial, and four protozoan pathogens were each tested on this platform. The nucleic acids were extracted, amplified using multiplexed sets of pooled specific primers, fragmented, labeled, and hybridized to a microarray. Finally, the detected sequences were identified using an automated genomic database alignment algorithm., Results: The performance of this platform demonstrated detection for spiked bacterial and protozoan pathogens of 100 cells/mL and viral pathogens as low as 100 copies/mL. Coded specimens, including spiked and negative controls, were identified correctly for blood specimens (31/32, 97%) and for plasma specimens (20/22, 91%) demonstrating the effectiveness of the platform., Conclusion: These results indicated that the TessArray microarray platform could be employed for multiplex detection and identification, with a high level of discriminatory power for numerous blood-borne pathogen targets with potential for use in blood safety., (© 2016 AABB.)

Objective: To validate a 24-chromosome aneuploidy preimplantation genetic screening protocol based on multiple annealing and looping-based amplification cycle (MALBAC) and next-generation sequencing (NGS)., Design: Single-nucleotide polymorphism (SNP) array and MALBAC-NGS analysis., Setting: University-affiliated in vitro fertilization (IVF) center., Patient(s): Fifteen women from whom 30 blastocysts were obtained for genotyping., Intervention(s): Not applicable., Main Outcome Measure(s): Chromosomal status comparison of results of array comparative genomic hybridization (aCGH), SNP array, and MALBAC-NGS for 24-chromosome aneuploidy screening., Result(s): Trophectoderm biopsy samples from blastocysts were first analyzed using array comparative genomic hybridization (aCGH); the embryos with detected with chromosomal abnormalities were rebiopsied, and dissociated into two portions, and subjected to SNP array and MALBAC-NGS for 24-chromosome aneuploidy screening. All 30 samples were successfully genotyped by array CGH, SNP array, and MALBAC-NGS. All blastocysts were correctly identified as aneuploid, and there was a 100% concordance in terms of diagnosis provided between the three methods. In the 720 detected chromosomes, the concordance rate between MALBAC-NGS and array CGH was 99.31% (715 of 720), and the concordance rate between MALBAC-NGS and SNP array was 99.58% (717 of 720). When compared with aCGH, MALBAC-NGS specificity for aneuploidy call was 99.85% (674 of 675; 95% CI, 99.17-99.97) with a sensitivity of 91.11% (41 of 45; 95% CI, 79.27-96.49). When compared with SNP array, MALBAC-NGS specificity for aneuploidy call was 99.85% (676 of 677; 95% CI, 99.17-99.97) with a sensitivity of 95.35% (41 of 43; 95% CI, 85.54-98.72)., Conclusion(s): MALBAC-NGS provides concordant chromosomal results when compared with aCGH and SNP array in blastocysts with chromosomal abnormalities., (Copyright © 2016. Published by Elsevier Inc.)