High-resolution SNP microarray investigation of copy number variations on chromosome 18 in a control cohort

Copy number variations (CNVs) as described in the healthy population are purported to contribute significantly to genetic heterogeneity. Recent studies have described CNVs using lymphoblastoid cell lines or by application of specifically developed algorithms to interrogate previously described data. However, the full extent of CNVs remains unclear. Using high-density SNP array, we have undertaken a comprehensive investigation of chromosome 18 for CNV discovery and characterisation of distribution and association with chromosome architecture. We identified 399 CNVs, of which loss represents 98%, 58% are less than 2.5 kb in size and 71% are intergenic. Intronic deletions account for the majority of copy number changes with gene involvement. Furthermore, one-third of CNVs do not have putative breakpoints within repetitive sequences. We conclude that replicative processes, mediated either by repetitive elements or microhomology, account for the majority of CNVs in the healthy population. Genomic instability involving the formation of a non-B structure is demonstrated in one region.

Detection of cryptic pathogenic copy number variations and constitutional loss of heterozygosity using high resolution SNP microarray analysis in 117 patients referred for cytogenetic analysis and impact on clinical practice

BACKGROUND: Microarray genome analysis is realising its promise for improving detection of genetic abnormalities in individuals with mental retardation and congenital abnormality. Copy number variations (CNVs) are now readily detectable using a variety of platforms and a major challenge is the distinction of pathogenic from ubiquitous, benign polymorphic CNVs. The aim of this study was to investigate replacement of time consuming, locus specific testing for specific microdeletion and microduplication syndromes with microarray analysis, which theoretically should detect all known syndromes with CNV aetiologies as well as new ones. METHODS: Genome wide copy number analysis was performed on 117 patients using Affymetrix 250K microarrays. RESULTS: 434 CNVs (195 losses and 239 gains) were found, including 18 pathogenic CNVs and 9 identified as "potentially pathogenic". Almost all pathogenic CNVs were larger than 500 kb, significantly larger than the median size of all CNVs detected. Segmental regions of loss of heterozygosity larger than 5 Mb were found in 5 patients. CONCLUSIONS: Genome microarray analysis has improved diagnostic success in this group of patients. Several examples of recently discovered "new syndromes" were found suggesting they are more common than previously suspected and collectively are likely to be a major cause of mental retardation. The findings have several implications for clinical practice. The study revealed the potential to make genetic diagnoses that were not evident in the clinical presentation, with implications for pretest counselling and the consent process. The importance of contributing novel CNVs to high quality databases for genotype-phenotype analysis and review of guidelines for selection of individuals for microarray analysis is emphasised.

A new method to detect loss of heterozygosity using cohort heterozygosity comparisons

Background Loss of heterozygosity (LOH) is an important marker for one of the 'two-hits' required for tumor suppressor gene inactivation. Traditional methods for mapping LOH regions require the comparison of both tumor and patient-matched normal DNA samples. However, for many archival samples, patient-matched normal DNA is not available leading to the under-utilization of this important resource in LOH studies. Here we describe a new method for LOH analysis that relies on the genome-wide comparison of heterozygosity of single nucleotide polymorphisms (SNPs) between cohorts of cases and un-matched healthy control samples. Regions of LOH are defined by consistent decreases in heterozygosity across a genetic region in the case cohort compared to the control cohort. Methods DNA was collected from 20 Follicular Lymphoma (FL) tumor samples, 20 Diffuse Large B-cell Lymphoma (DLBCL) tumor samples, neoplastic B-cells of 10 B-cell Chronic Lymphocytic Leukemia (B-CLL) patients and Buccal cell samples matched to 4 of these B-CLL patients. The cohort heterozygosity comparison method was developed and validated using LOH derived in a small cohort of B-CLL by traditional comparisons of tumor and normal DNA samples, and compared to the only alternative method for LOH analysis without patient matched controls. LOH candidate regions were then generated for enlarged cohorts of B-CLL, FL and DLBCL samples using our cohort heterozygosity comparison method in order to evaluate potential LOH candidate regions in these non-Hodgkin's lymphoma tumor subtypes. Results Using a small cohort of B-CLL samples with patient-matched normal DNA we have validated the utility of this method and shown that it displays more accuracy and sensitivity in detecting LOH candidate regions compared to the only alternative method, the Hidden Markov Model (HMM) method. Subsequently, using B-CLL, FL and DLBCL tumor samples we have utilised cohort heterozygosity comparisons to localise LOH candidate regions in these subtypes of non-Hodgkin's lymphoma. Detected LOH regions included both previously described regions of LOH as well as novel genomic candidate regions. Conclusions We have proven the efficacy of the use of cohort heterozygosity comparisons for genome-wide mapping of LOH and shown it to be in many ways superior to the HMM method. Additionally, the use of this method to analyse SNP microarray data from 3 common forms of non-Hodgkin's lymphoma yielded interesting tumor suppressor gene candidates, including the ETV3 gene that was highlighted in both B-CLL and FL.

Genetic susceptibility to complex traits : moving towards informed analysis of whole-genome screens

Susceptibility to complex traits, by definition, involves aetiological polymorphisms at multiple genetic loci combined with variable contributions by environmental factors. However, the approaches taken to identifying genetic loci implicated in susceptibility to complex traits frequently overlooks the compounding contribution of multiple loci in favour of highlighting a single gene solely responsible for predisposition. It is only in a small minority of cases that this has resulted in clear disease heritability associated with polymorphisms in a single gene. More often, this approach has led to an accumulation of single-gene associations with minor contributions to disease susceptibility. As the genomic era advances and genome-wide screens become higher in resolution and throughput, the need for simultaneous consideration of multiple loci is becoming more important. With special reference to non-Hodgkin’s lymphoma (NHL), this chapter will overview the current progress made in elucidating genetic polymorphisms associated with disease susceptibility. We also present novel data from a high-resolution single nucleotide polymorphism (SNP) microarray screen for susceptibility loci that are involved in NHL. Using an ‘informed approach’, the findings are highlighted within the context of cellular pathways, and provide insight and new ideas for methods of analysis for genome-wide screens for susceptibility.

Genomic microarrays in chromosomal analysis of leukemia

Chromosomal alterations in leukemia have been shown to have prognostic and predictive significance and are also important minimal residual disease (MRD) markers in the follow-up of leukemia patients. Although specific oncogenes and tumor suppressors have been discovered in some of the chromosomal alterations, the role and target genes of many alterations in leukemia remain unknown. In addition, a number of leukemia patients have a normal karyotype by standard cytogenetics, but have variability in clinical course and are often molecularly heterogeneous. Cytogenetic methods traditionally used in leukemia analysis and diagnostics; G-banding, various fluorescence in situ hybridization (FISH) techniques, and chromosomal comparative genomic hybridization (cCGH), have enormously increased knowledge about the leukemia genome, but have limitations in resolution or in genomic coverage. In the last decade, the development of microarray comparative genomic hybridization (array-CGH, aCGH) for DNA copy number analysis and the SNP microarray (SNP-array) method for simultaneous copy number and loss of heterozygosity (LOH) analysis has enabled investigation of chromosomal and gene alterations genome-wide with high resolution and high throughput. In these studies, genetic alterations were analyzed in acute myeloid leukemia (AML) and chronic lymphocytic leukemia (CLL). The aim was to screen and characterize genomic alterations that could play role in leukemia pathogenesis by using aCGH and SNP-arrays. One of the most important goals was to screen cryptic alterations in karyotypically normal leukemia patients. In addition, chromosomal changes were evaluated to narrow the target regions, to find new markers, and to obtain tumor suppressor and oncogene candidates. The work presented here shows the capability of aCGH to detect submicroscopic copy number alterations in leukemia, with information about breakpoints and genes involved in the alterations, and that genome-wide microarray analyses with aCGH and SNP-array are advantageous methods in the research and diagnosis of leukemia. The most important findings were the cryptic changes detected with aCGH in karyotypically normal AML and CLL, characterization of amplified genes in 11q marker chromosomes, detection of deletion-based mechanisms of MLL-ARHGEF12 fusion gene formation, and detection of LOH without copy number alteration in karyotypically normal AML. These alterations harbor candidate oncogenes and tumor suppressors for further studies.

Forensisch relevante SNP-Genotypisierung mittels elektronischer Microarray-Technologie

Die vorliegende Dissertation entstand im Rahmen eines multizentrischen EU-geförderten Projektes, das die Anwendungsmöglichkeiten von Einzelnukleotid-Polymorphismen (SNPs) zur Individualisierung von Personen im Kontext der Zuordnung von biologischen Tatortspuren oder auch bei der Identifizierung unbekannter Toter behandelt. Die übergeordnete Zielsetzung des Projektes bestand darin, hochauflösende Genotypisierungsmethoden zu etablieren und zu validieren, die mit hoher Genauigkeit aber geringen Aufwand SNPs im Multiplexformat simultan analysieren können. Zunächst wurden 29 Y-chromosomale und 52 autosomale SNPs unter der Anforderung ausgewählt, dass sie als Multiplex eine möglichst hohe Individualisierungschance aufweisen. Anschließend folgten die Validierungen beider Multiplex-Systeme und der SNaPshot™-Minisequenzierungsmethode in systematischen Studien unter Beteiligung aller Arbeitsgruppen des Projektes. Die validierte Referenzmethode auf der Basis einer Minisequenzierung diente einerseits für die kontrollierte Zusammenarbeit unterschiedlicher Laboratorien und andererseits als Grundlage für die Entwicklung eines Assays zur SNP-Genotypisierung mittels der elektronischen Microarray-Technologie in dieser Arbeit. Der eigenständige Hauptteil dieser Dissertation beschreibt unter Verwendung der zuvor validierten autosomalen SNPs die Neuentwicklung und Validierung eines Hybridisierungsassays für die elektronische Microarray-Plattform der Firma Nanogen Dazu wurden im Vorfeld drei verschiedene Assays etabliert, die sich im Funktionsprinzip auf dem Microarray unterscheiden. Davon wurde leistungsorientiert das Capture down-Assay zur Weiterentwicklung ausgewählt. Nach zahlreichen Optimierungsmaßnahmen hinsichtlich PCR-Produktbehandlung, gerätespezifischer Abläufe und analysespezifischer Oligonukleotiddesigns stand das Capture down-Assay zur simultanen Typisierung von drei Individuen mit je 32 SNPs auf einem Microarray bereit. Anschließend wurde dieses Verfahren anhand von 40 DNA-Proben mit bekannten Genotypen für die 32 SNPs validiert und durch parallele SNaPshot™-Typisierung die Genauigkeit bestimmt. Das Ergebnis beweist nicht nur die Eignung des validierten Analyseassays und der elektronischen Microarray-Technologie für bestimmte Fragestellungen, sondern zeigt auch deren Vorteile in Bezug auf Schnelligkeit, Flexibilität und Effizienz. Die Automatisierung, welche die räumliche Anordnung der zu untersuchenden Fragmente unmittelbar vor der Analyse ermöglicht, reduziert unnötige Arbeitsschritte und damit die Fehlerhäufigkeit und Kontaminationsgefahr bei verbesserter Zeiteffizienz. Mit einer maximal erreichten Genauigkeit von 94% kann die Zuverlässigkeit der in der forensischen Genetik aktuell eingesetzten STR-Systeme jedoch noch nicht erreicht werden. Die Rolle des neuen Verfahrens wird damit nicht in einer Ablösung der etablierten Methoden, sondern in einer Ergänzung zur Lösung spezieller Probleme wie z.B. der Untersuchung stark degradierter DNA-Spuren zu finden sein.

Saliva-derived DNA performs well in large-scale, high-density single-nucleotide polymorphism microarray studies

As of June 2009, 361 genome-wide association studies (GWAS) had been referenced by the HuGE database. GWAS require DNA from many thousands of individuals, relying on suitable DNA collections. We recently performed a multiple sclerosis (MS) GWAS where a substantial component of the cases (24%) had DNA derived from saliva. Genotyping was done on the Illumina genotyping platform using the Infinium Hap370CNV DUO microarray. Additionally, we genotyped 10 individuals in duplicate using both saliva- and blood-derived DNA. The performance of blood- versus saliva-derived DNA was compared using genotyping call rate, which reflects both the quantity and quality of genotyping per sample and the “GCScore,” an Illumina genotyping quality score, which is a measure of DNA quality. We also compared genotype calls and GCScores for the 10 sample pairs. Call rates were assessed for each sample individually. For the GWAS samples, we compared data according to source of DNA and center of origin. We observed high concordance in genotyping quality and quantity between the paired samples and minimal loss of quality and quantity of DNA in the saliva samples in the large GWAS sample, with the blood samples showing greater variation between centers of origin. This large data set highlights the usefulness of saliva DNA for genotyping, especially in high-density single-nucleotide polymorphism microarray studies such as GWAS.

SNP discovery using Next Generation Transcriptomic Sequencing in Atlantic herring (Clupea harengus)

The introduction of Next Generation Sequencing (NGS) has revolutionised population genetics, providing studies of non-model species with unprecedented genomic coverage, allowing evolutionary biologists to address questions previously far beyond the reach of available resources. Furthermore, the simple mutation model of Single Nucleotide Polymorphisms (SNPs) permits cost-effective high-throughput genotyping in thousands of individuals simultaneously. Genomic resources are scarce for the Atlantic herring (Clupea harengus), a small pelagic species that sustains high revenue fisheries. This paper details the development of 578 SNPs using a combined NGS and high-throughput genotyping approach. Eight individuals covering the species distribution in the eastern Atlantic were bar-coded and multiplexed into a single cDNA library and sequenced using the 454 GS FLX platform. SNP discovery was performed by de novo sequence clustering and contig assembly, followed by the mapping of reads against consensus contig sequences. Selection of candidate SNPs for genotyping was conducted using an in silico approach. SNP validation and genotyping were performed simultaneously using an Illumina 1,536 GoldenGate assay. Although the conversion rate of candidate SNPs in the genotyping assay cannot be predicted in advance, this approach has the potential to maximise cost and time efficiencies by avoiding expensive and time-consuming laboratory stages of SNP validation. Additionally, the in silico approach leads to lower ascertainment bias in the resulting SNP panel as marker selection is based only on the ability to design primers and the predicted presence of intron-exon boundaries. Consequently SNPs with a wider spectrum of minor allele frequencies (MAFs) will be genotyped in the final panel. The genomic resources presented here represent a valuable multi-purpose resource for developing informative marker panels for population discrimination, microarray development and for population genomic studies in the wild.

EXPLORATION, NORMALIZATION, AND GENOTYPE CALLS OF HIGH DENSITY OLIGONUCLEOTIDE SNP ARRAY DATA

In most microarray technologies, a number of critical steps are required to convert raw intensity measurements into the data relied upon by data analysts, biologists and clinicians. These data manipulations, referred to as preprocessing, can influence the quality of the ultimate measurements. In the last few years, the high-throughput measurement of gene expression is the most popular application of microarray technology. For this application, various groups have demonstrated that the use of modern statistical methodology can substantially improve accuracy and precision of gene expression measurements, relative to ad-hoc procedures introduced by designers and manufacturers of the technology. Currently, other applications of microarrays are becoming more and more popular. In this paper we describe a preprocessing methodology for a technology designed for the identification of DNA sequence variants in specific genes or regions of the human genome that are associated with phenotypes of interest such as disease. In particular we describe methodology useful for preprocessing Affymetrix SNP chips and obtaining genotype calls with the preprocessed data. We demonstrate how our procedure improves existing approaches using data from three relatively large studies including one in which large number independent calls are available. Software implementing these ideas are avialble from the Bioconductor oligo package.

SNP technologies for drug discovery : a current review

Single nucleotide polymorphisms (SNPs) are unique genetic differences between individuals that contribute in significant ways to the determination of human variation including physical characteristics like height and appearance as well as less obvious traits such as personality, behaviour and disease susceptibility. SNPs can also significantly influence responses to pharmacotherapy and whether drugs will produce adverse reactions. The development of new drugs can be made far cheaper and more rapid by selecting participants in drug trials based on their genetically determined response to drugs. Technology that can rapidly and inexpensively genotype thousands of samples for thousands of SNPs at a time is therefore in high demand. With the completion of the human genome project, about 12 million true SNPs have been identified to date. However, most have not yet been associated with disease susceptibility or drug response. Testing for the appropriate drug response SNPs in a patient requiring treatment would enable individualised therapy with the right drug and dose administered correctly the first time. Many pharmaceutical companies are also interested in identifying SNPs associated with polygenic traits so novel therapeutic targets can be discovered. This review focuses on technologies that can be used for genotyping known SNPs as well as for the discovery of novel SNPs associated with drug response.