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In order to identify genetic causes of VACTERL association (V vertebral defects, A anorectal malformations, C cardiac defects, T tracheoesofageal fistula, E esophageal atresia, R renal anomalies, L limb deformities), we have collected DNA samples from 20 patients diagnosed with VACTERL or with a VACTERL-like phenotype as well as samples from 19 aborted fetal cases with VACTERL. To investigate the importance of gene dose alterations in the genetic etiology of VACTERL association we have performed a systematic analysis of this cohort using a 180K array comparative genomic hybridization (array-CGH) platform. In addition, to further clarify the significance of PCSK5, HOXD13 and CHD7 genes in the VACTERL phenotype, mutation screening has been performed. We identified pathogenic gene dose imbalances in two fetal cases; a hemizygous deletion of the FANCB gene and a (9;18)(p24;q12) unbalanced translocation. In addition, one pathogenic mutation in CHD7 was detected, while no apparent disease-causing mutations were found in HOXD13 or PCSK5. Our study shows that although large gene dose alterations do not seem to be a common cause in VACTERL association, array-CGH is still important in clinical diagnostics to identify disease cause in individual cases.
Neurofibromatosis type 1 (NF1) is a genetic disorder where affected individuals develop benign or malignant nervous system tumors. To date, NF1 is caused by mutations in the NF1 tumor suppressor gene located at chromosome band 17q11.2. In this study, we aimed to characterize novel recurrent regional chromosomal imbalances and tumor-related candidate genes in NF1-associated cutaneous neurofibromas. Nine cutaneous neurofibromas from NF1 patients were screened for recurrent chromosomal imbalances using high-resolution 400K oligonucleotide array comparative genomic hybridization (aCGH). All the cases exhibited at least one sub-microscopic abnormality. Regions of recurrent chromosomal imbalances in a least one third of cases were loss of 1q13.2 (33%, FAM19A3), 1q21.1 (44%, RABGAP1L), 2q37.1 (56%, INPP5D), 3p25.1 (67%, CHCHD4), 4p15.32 (56%, FGFBP1), 5q11.2 (56%, ARL15), 6q22.31 (56%, NKAIN2), 6q22.33 (67%, ARHGAP18), 6q25.1 (67%, UST), 7q13 (56%, ADCY1), 12q13.13 (44%, KRT71), 19q13.32 (56%, GRLF1), and 20p11.21 (56%, NLP) and gain of 2p23.3 (76%, C2orf53), 8q22.3 (44%, ODF1) and 8q24.3 (67%, ARC). Several chromosomal imbalances, including loss of 7q11.23, 13q14.1, 14q32.13, 17p12, and 17q11.2 were detected at a lower frequency. We also confirmed that these chromosomal imbalances were not detected in the patient-matched lymphocyte DNAs. Amongst the 6 tumor-related candidate genes (RABGAP1L, ADCY1, SLIT2, GRLF1, UST, and ARC) identified in the regions of recurrent chromosomal imbalances, the gene expression changes of UST (down-regulation) and ARC (up-regulation) were found to be significantly associated with copy number alterations. The novel recurrent chromosomal imbalances and the altered expression levels of the tumor-related candidate genes may be associated with the development of NF1-associated benign cutaneous neurofibromas.
The detection of copy number variants (CNV) by array-based platforms provides valuable insight into understanding human diversity. However, suboptimal study design and data processing negatively affect CNV assessment. We quantitatively evaluate their impact when short-sequence oligonucleotide arrays are applied (Affymetrix Genome-Wide Human SNP Array 6.0) by evaluating 42 HapMap samples for CNV detection. Several processing and segmentation strategies are implemented, and results are compared to CNV assessment obtained using an oligonucleotide array CGH platform designed to query CNVs at high resolution (Agilent). We quantitatively demonstrate that different reference models (e.g. single versus pooled sample reference) used to detect CNVs are a major source of inter-platform discrepancy (up to 30%) and that CNVs residing within segmental duplication regions (higher reference copy number) are significantly harder to detect (P < 0.0001). After adjusting Affymetrix data to mimic the Agilent experimental design (reference sample effect), we applied several common segmentation approaches and evaluated differential sensitivity and specificity for CNV detection, ranging 39-77% and 86-100% for non-segmental duplication regions, respectively, and 18-55% and 39-77% for segmental duplications. Our results are relevant to any array-based CNV study and provide guidelines to optimize performance based on study-specific objectives.
Dose-dependent differential gene expression provides critical information required for regulatory decision-making. The lower costs associated with RNA-Seq have made it the preferred technology for transcriptomic analysis. However, concordance between RNA-Seq and microarray analyses in dose response studies has not been adequately vetted.
Microarray gene-expression profiles are generally validated one gene at a time by real-time RT-PCR. We describe here a different approach based on simultaneous mutual validation of large numbers of genes using two different expression-profiling platforms. The result described here for the NCI-60 cancer cell lines is a consensus set of genes that give similar profiles on spotted cDNA arrays and Affymetrix oligonucleotide chips. Global concordance is parameterized by a 'correlation of correlations' coefficient.
Array comparative genomic hybridisation (aCGH) profiling is currently the gold standard for genetic diagnosis of copy number. Next generation sequencing technologies provide an alternative and adaptable method of detecting copy number by comparing the number of sequence reads in non-overlapping windows between patient and control samples. Detection of copy number using the BlueGnome 8×60k oligonucleotide aCGH platform was compared with low resolution next generation sequencing using the Illumina GAIIx on 39 patients with developmental delay and/or learning difficulties who were referred to the Leeds Clinical Cytogenetics Laboratory. Sensitivity and workflow of the two platforms were compared. Customised copy number algorithms assessed sequence counts and detected changes in copy number. Imbalances detected on both platforms were compared. Of the thirty-nine patients analysed, all eleven imbalances detected by array CGH and confirmed by FISH or Q-PCR were also detected by CNV-seq. In addition, CNV-seq reported one purported pathogenic copy number variant that was not detected by array CGH. Non-pathogenic, unconfirmed copy number calls were detected by both platforms; however few were concordant between the two. CNV-seq offers an alternative to array CGH for copy number analysis with resolution and future costs comparable to conventional array CGH platforms and with less stringent sample requirements.
Thymidine kinase 2 (TK2), encoded by the TK2 gene on chromosome 16q22, is one of the deoxyribonucleoside kinases responsible for the maintenance of mitochondrial deoxyribonucleotide pools. Defects in TK2 mainly cause a myopathic form of the mitochondrial DNA depletion syndrome (MDDS). Currently, only point mutations and small insertions and deletions have been reported in TK2 gene; gross rearrangements of TK2 gene and possible hepatic involvement in patients with TK2 mutations have not been described. We report a non-consanguineous Jordanian family with three deceased siblings due to mtDNA depletion. Sequence analysis of the father detected a heterozygous c.761T>A (p.I254N) mutation in his TK2 gene; however, point mutations in the mother were not detected. Subsequent gene dosage analysis using oligonucleotide array CGH identified an intragenic approximately 5.8-kb deletion encompassing the 5'UTR to intron 2 of her TK2 gene. Sequence analysis confirmed that the deletion spans c.1-495 to c.283-2899 of the TK2 gene (nucleotide 65,136,256-65,142,086 of chromosome 16). Analysis of liver and muscle specimens from one of the deceased infants in this family revealed compound heterozygosity for the paternal point mutation and maternal intragenic deletion. In addition, a significant reduction of the mtDNA content in liver and muscle was detected (10% and 20% of age- and tissue-matched controls, respectively). Prenatal diagnosis was performed in the third pregnancy. The fetus was found to carry both the point mutation and the deletion. This child died 6months after birth due to myopathy. A serum specimen demonstrated elevated liver transaminases in two of the infants from whom results were available. This report expands the mutation spectrum associated with TK2 deficiency. While the myopathic form of MDDS appears to be the main phenotype of TK2 mutations, liver dysfunction may also be a part of the mitochondrial depletion syndrome caused by TK2 gene defects.
The frequency of disease-related large rearrangements (referred to as copy-number mutations, CNMs) varies among genes, and search for these mutations has an important place in diagnostic strategies. In recent years, CGH method using custom-designed high-density oligonucleotide-based arrays allowed the development of a powerful tool for detection of alterations at the level of exons and made it possible to provide flexibility through the possibility of modeling chips. The aim of our study was to test custom-designed oligonucleotide CGH array in a diagnostic laboratory setting that analyses several genes involved in various genetic diseases, and to compare it with conventional strategies. To this end, we designed a 12-plex CGH array (135k; 135 000 probes/subarray) (Roche Nimblegen) with exonic and intronic oligonucleotide probes covering 26 genes routinely analyzed in the laboratory. We tested control samples with known CNMs and patients for whom genetic causes underlying their disorders were unknown. The contribution of this technique is undeniable. Indeed, it appeared reproducible, reliable and sensitive enough to detect heterozygous single-exon deletions or duplications, complex rearrangements and somatic mosaicism. In addition, it improves reliability of CNM detection and allows determination of boundaries precisely enough to direct targeted sequencing of breakpoints. All of these points, associated with the possibility of a simultaneous analysis of several genes and scalability 'homemade' make it a valuable tool as a new diagnostic approach of CNMs.
High coverage whole genome sequencing provides near complete information about genetic variation. However, other technologies can be more efficient in some settings by (a) reducing redundant coverage within samples and (b) exploiting patterns of genetic variation across samples. To characterize as many samples as possible, many genetic studies therefore employ lower coverage sequencing or SNP array genotyping coupled to statistical imputation. To compare these approaches individually and in conjunction, we developed a statistical framework to estimate genotypes jointly from sequence reads, array intensities, and imputation. In European samples, we find similar sensitivity (89%) and specificity (99.6%) from imputation with either 1× sequencing or 1 M SNP arrays. Sensitivity is increased, particularly for low-frequency polymorphisms (MAF < 5%), when low coverage sequence reads are added to dense genome-wide SNP arrays--the converse, however, is not true. At sites where sequence reads and array intensities produce different sample genotypes, joint analysis reduces genotype errors and identifies novel error modes. Our joint framework informs the use of next-generation sequencing in genome wide association studies and supports development of improved methods for genotype calling.
Until recently, few genomic reagents specific for non-human primate research have been available. To address this need, we have constructed a macaque-specific high-density oligonucleotide microarray by using highly fragmented low-pass sequence contigs from the rhesus genome project together with the detailed sequence and exon structure of the human genome. Using this method, we designed oligonucleotide probes to over 17,000 distinct rhesus/human gene orthologs and increased by four-fold the number of available genes relative to our first-generation expressed sequence tag (EST)-derived array.
High-density tiling microarrays are a powerful tool for the characterization of complete genomes. The two major computational challenges associated with custom-made arrays are design and analysis. Firstly, several genome dependent variables, such as the genome's complexity and sequence composition, need to be considered in the design to ensure a high quality microarray. Secondly, since tiling projects today very often exceed the limits of conventional array-experiments, researchers cannot use established computer tools designed for commercial arrays, and instead have to redesign previous methods or create novel tools.
Multiple methods to detect copy number variants (CNV) relying on different types of data have been developed and CNV have been shown to have an impact on phenotypes of numerous traits of economic importance in cattle, such as reproduction and immunity. Further improvements in CNV detection are still needed in regard to the trade-off between high-true and low-false positive variant identification rates. Instead of improving single CNV detection methods, variants can be identified in silico with high confidence when multiple methods and datasets are combined. Here, CNV were identified from whole-genome sequences (WGS) and genotype array (GEN) data on 96 Holstein animals. After CNV detection, two sets of high confidence CNV regions (CNVR) were created that contained variants found in both WGS and GEN data following an animal-based (n = 52) and a population-based (n = 36) pipeline. Furthermore, the change in false positive CNV identification rates using different GEN marker densities was evaluated. The population-based approach characterized CNVR, which were more often shared among animals (average 40% more samples per CNVR) and were more often linked to putative functions (48 vs 56% of CNVR) than CNV identified with the animal-based approach. Moreover, false positive identification rates up to 22% were estimated on GEN information. Further research using larger datasets should use a population-wide approach to identify high confidence CNVR.
Oligonucleotide microarrays measure the relative transcript abundance of thousands of mRNAs in parallel. A large number of procedures for normalization and detection of differentially expressed genes have been proposed. However, the relative impact of these methods on the detection of differentially expressed genes remains to be determined.
Methylation of CpG islands associated with genes can affect the expression of the proximal gene, and methylation of non-associated CpG islands correlates to genomic instability. This epigenetic modification has been shown to be important in many pathologies, from development and disease to cancer. We report the development of a novel high-resolution microarray that detects the methylation status of over 25,000 CpG islands in the human genome. Experiments were performed to demonstrate low system noise in the methodology and that the array probes have a high signal to noise ratio. Methylation measurements between different cell lines were validated demonstrating the accuracy of measurement. We then identified alterations in CpG islands, both those associated with gene promoters, as well as non-promoter-associated islands in a set of breast and ovarian tumors. We demonstrate that this methodology accurately identifies methylation profiles in cancer and in principle it can differentiate any CpG methylation alterations and can be adapted to analyze other species.
The sheep (Ovis aries) plays a major socio-economic role in the world. Copy number variations (CNVs) are increasingly recognized as a key and potent source of genetic variation and phenotypic diversity, but little is known about the extent to which CNVs contribute to genetic variation in Chinese sheep breeds. Analyses of CNVs in the genomes of eight sheep breeds were performed using the sheep SNP50 BeadChip genotyping array. A total of 111 CNV regions (CNVRs) were obtained from 160 Chinese sheep breeds. These CNVRs covered 13.75Mb of the sheep genome sequence. A total of 22 Go terms and 17 candidate genes were obtained from the functional analysis. Ten CNVRs were selected for validation, of which 7 CNVRs were further experimentally confirmed by quantitative PCR. Four candidate genes were selected to confirm the results of the functional analysis. These results provide a resource for furthering understanding of ruminant biology, and for further improving the genetic quality of sheep breeds.
The ability to efficiently and economically generate libraries of defined pieces of DNA would have a myriad of applications, not least in the area of defined or directed sequencing and synthetic biology, but also in applications associated with encoding and tagging. In this manuscript DNA microarrays were used to allow the linear amplification of immobilized DNA sequences from the array followed by PCR amplification. Arrays of increasing sophistication (1, 10, 3,875, 10,000 defined sequences) were used to validate the process, with sequences verified by selective hybridization to a complementary DNA microarray and DNA sequencing, which demonstrated a PCR error rate of 9.7×10(-3)/site/duplication. This technique offers an economical and efficient way of producing specific DNA libraries of hundreds to thousands of members with the DNA-arrays being used as "factories" allowing specific DNA oligonucleotide pools to be generated. We also found substantial variance observed between the sequence frequencies found via Solexa sequencing and microarray analysis, highlighting the care needed in the interpretation of profiling data.
MicroRNAs (miRNAs) have critical functions in various biological processes. MiRNA profiling is an important tool for the identification of differentially expressed miRNAs in normal cellular and disease processes. A technical challenge remains for high-throughput miRNA expression analysis as the number of miRNAs continues to increase with in silico prediction and experimental verification. Our study critically evaluated the performance of a novel miRNA expression profiling approach, quantitative RT-PCR array (qPCR-array), compared to miRNA detection with oligonucleotide microchip (microarray).
The use of microarray technology to assess gene expression levels is now widespread in biology. The validation of microarray results using independent mRNA quantitation techniques remains a desirable element of any microarray experiment. To facilitate the comparison of microarray expression data between laboratories it is essential that validation methodologies be critically examined. We have assessed the correlation between expression scores obtained for 48 human genes using oligonucleotide microarrays and the expression levels for the same genes measured by quantitative real-time RT-PCR (qRT-PCR).
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