Strategies for the detection of copy number and other structural variants in the human genome

OTSUCNV: an adaptive segmentation and OTSU-based anomaly classification method for CNV detection using NGS data

Copy-number variations (CNVs), which refer to deletions and duplications of chromosomal segments, represent a significant source of variation among individuals, contributing to human evolution and being implicated in various diseases ranging from mental illness and developmental disorders to cancer. Despite the development of several methods for detecting copy number variations based on next-generation sequencing (NGS) data, achieving robust detection performance for CNVs with arbitrary coverage and amplitude remains challenging due to the inherent complexity of sequencing samples. In this paper, we propose an alternative method called OTSUCNV for CNV detection on whole genome sequencing (WGS) data. This method utilizes a newly designed adaptive sequence segmentation algorithm and an OTSU-based CNV prediction algorithm, which does not rely on any distribution assumptions or involve complex outlier factor calculations. As a result, the effective detection of CNVs is achieved with lower computational complexity. The experimental results indicate that the proposed method demonstrates outstanding performance, and hence it may be used as an effective tool for CNV detection.

Evaluation of the Oncomine Comprehensive Assay v3 panel for the detection of 1p/19q codeletion in oligodendroglial tumours

AIMS

Accurate assessment of 1p/19q codeletion status in diffuse gliomas is of paramount importance for diagnostic, prognostic and predictive purposes. While targeted next generation sequencing (NGS) has been widely implemented for glioma molecular profiling, its role in detecting structural chromosomal variants is less well established, requiring supplementary informatic tools for robust detection. Herein, we evaluated a commercially available amplicon-based targeted NGS panel (Oncomine Comprehensive Assay v3) for the detection of 1p/19q losses in glioma tissues using an Ion Torrent platform and the standard built-in NGS data analysis pipeline solely.

METHODS

Using as little as 20 ng of DNA from formalin-fixed paraffin-embedded tissues, we analysed 25 previously characterised gliomas for multi-locus copy number losses (CNLs) on 1p and 19q, including 11 oligodendrogliomas (ODG) and 14 non-oligodendroglial (non-ODG) controls. Fluorescence in-situ hybridisation (FISH) was used as a reference standard.

RESULTS

The software confidently detected combined contiguous 1p/19q CNLs in 11/11 ODGs (100% sensitivity), using a copy number cut-off of ≤1.5 and a minimum of 10 amplicons covering the regions. Only partial non-specific losses were identified in non-ODGs (100% specificity). Copy number averages of ODG and non-ODG groups were significantly different (p<0.001). NGS was concordant with FISH and was superior to it in distinguishing partial from contiguous losses indicative of whole-arm chromosomal deletion.

CONCLUSIONS

This commercial NGS panel, along with the standard Ion Torrent algorithm, accurately detected 1p/19q losses in ODG samples, obviating the need for specialised custom-made informatic analyses. This can easily be incorporated into routine glioma workflow as an alternative to FISH.

Identification of immune-related ferroptosis prognostic marker and in-depth bioinformatics exploration of multi-omics mechanisms in thyroid cancer

Background: Factors such as variations in thyroid carcinoma (THCA) gene characteristics could influence the clinical outcome. Ferroptosis and immunity have been verified to play an essential role in various cancers, and could affect the cancer patients’ prognosis. However, their relationship to the progression and prognosis of many types of THCA remains unclear.

Methods: First, we extracted prognosis-related immune-related genes and ferroptosis-related genes from 2 databases for co-expression analysis to obtain prognosis-related differentially expressed immune-related ferroptosis genes (PR-DE-IRFeGs), and screened BID and CDKN2A for building a prognostic model. Subsequently, multiple validation methods were used to test the model’s performance and compare its performance with other 4 external models. Then, we explored the mechanism of immunity and ferroptosis in the occurrence, development and prognosis of THCA from the perspectives of anti-tumor immunity, CDKN2A-related competitive endogenous RNA regulatory, copy number variations and high frequency gene mutation. Finally, we evaluated this model’s clinical practice value.

Results: BID and CDKN2A were identified as prognostic risk and protective factors, respectively. External data and qRT-PCR experiment also validated their differential expression. The model’s excellent performance has been repeatedly verified and outperformed other models. Risk scores were significantly associated with most immune cells/functions. Risk score/2 PR-DE-IRFeGs expression was strongly associated with BRAF/NRAS/HRAS mutation. Single copy number deletion of CDKN2A is associated with upregulation of CDKN2A expression and worse prognosis. The predicted regulatory network consisting of CYTOR, hsa-miRNA-873-5p and CDKN2A was shown to significantly affect prognosis. The model and corresponding nomogram have been shown to have excellent clinical practice value.

Conclusion: The model can effectively predict the THCA patients’ prognosis and guide clinical treatment. Ferroptosis and immunity may be involved in the THCA’s progression through antitumor immunity and BRAF/NRAS/HRAS mutation. CYTOR-hsa-miRNA-873-5p-CDKN2A regulatory networks and single copy number deletion of CDKN2A may also affect THCA′ progression and prognosis.

CNVs with adaptive potential in Rangifer tarandus: genome architecture and new annotated assembly

Rangifer tarandus has experienced recent drastic population size reductions throughout its circumpolar distribution and preserving the species implies genetic diversity conservation. To facilitate genomic studies of the species populations, we improved the genome assembly by combining long read and linked read and obtained a new highly accurate and contiguous genome assembly made of 13,994 scaffolds (L90 = 131 scaffolds). Using de novo transcriptome assembly of RNA-sequencing reads and similarity with annotated human gene sequences, 17,394 robust gene models were identified. As copy number variations (CNVs) likely play a role in adaptation, we additionally investigated these variations among 20 genomes representing three caribou ecotypes (migratory, boreal and mountain). A total of 1,698 large CNVs (length > 1 kb) showing a genome distribution including hotspots were identified. 43 large CNVs were particularly distinctive of the migratory and sedentary ecotypes and included genes annotated for functions likely related to the expected adaptations. This work includes the first publicly available annotation of the caribou genome and the first assembly allowing genome architecture analyses, including the likely adaptive CNVs reported here.

DeepCNV: a deep learning approach for authenticating copy number variations

Copy number variations (CNVs) are an important class of variations contributing to the pathogenesis of many disease phenotypes. Detecting CNVs from genomic data remains difficult, and the most currently applied methods suffer from an unacceptably high false positive rate. A common practice is to have human experts manually review original CNV calls for filtering false positives before further downstream analysis or experimental validation. Here, we propose DeepCNV, a deep learning-based tool, intended to replace human experts when validating CNV calls, focusing on the calls made by one of the most accurate CNV callers, PennCNV. The sophistication of the deep neural network algorithm is enriched with over 10 000 expert-scored samples that are split into training and testing sets. Variant confidence, especially for CNVs, is a main roadblock impeding the progress of linking CNVs with the disease. We show that DeepCNV adds to the confidence of the CNV calls with an optimal area under the receiver operating characteristic curve of 0.909, exceeding other machine learning methods. The superiority of DeepCNV was also benchmarked and confirmed using an experimental wet-lab validation dataset. We conclude that the improvement obtained by DeepCNV results in significantly fewer false positive results and failures to replicate the CNV association results.

Comprehensively benchmarking applications for detecting copy number variation

Motivation: Recently, copy number variation (CNV) has gained considerable interest as a type of genomic variation that plays an important role in complex phenotypes and disease susceptibility. Since a number of CNV detection methods have recently been developed, it is necessary to help investigators choose suitable methods for CNV detection depending on their objectives. For this reason, this study compared ten commonly used CNV detection applications, including CNVnator, ReadDepth, RDXplorer, LUMPY and Control-FREEC, benchmarking the applications by sensitivity, specificity and computational demands. Taking the DGV gold standard variants as a standard dataset, we evaluated the ten applications with real sequencing data at sequencing depths from 5X to 50X. Among the ten methods benchmarked, LUMPY performs the best for both high sensitivity and specificity at each sequencing depth. For the purpose of high specificity, Canvas is also a good choice. If high sensitivity is preferred, CNVnator and RDXplorer are better choices. Additionally, CNVnator and GROM-RD perform well for low-depth sequencing data. Our results provide a comprehensive performance evaluation for these selected CNV detection methods and facilitate future development and improvement in CNV prediction methods.

As an important type of genomic structural variation, CNVs are associated with complex phenotypes because they change the number of copies of genes in cells, affecting coding sequences and playing an important role in the susceptibility or resistance to human diseases. To identify CNVs, several experimental methods have been developed, but their resolution is very low, and the detection of short CNVs presents a bottleneck. In recent years, the advancement of high-throughput sequencing techniques has made it possible to precisely detect CNVs, especially short ones. Many CNV detection applications were developed based on the availability of high-throughput sequencing data. Due to different CNV detection algorithms, the CNVs identified by different applications vary greatly. Therefore, it is necessary to help investigators choose suitable applications for CNV detection depending upon their objectives. For this reason, we not only compared ten commonly used CNV detection applications but also benchmarked the applications by sensitivity, specificity and computational demands. Our results show that the sequencing depth can strongly affect CNV detection. Among the ten applications benchmarked, LUMPY performs best for both high sensitivity and specificity for each sequencing depth. We also give recommended applications for specific purposes, for example, CNVnator and RDXplorer for high sensitivity and CNVnator and GROM-RD for low-depth sequencing data.

The clinical impact of copy number variants in inherited bone marrow failure syndromes

Inherited bone marrow failure syndromes comprise a genetically heterogeneous group of diseases with hematopoietic failure and a wide array of physical malformations. Copy number variants were reported in some inherited bone marrow failure syndromes. It is unclear what impact copy number variants play in patients evaluated for a suspected diagnosis of inherited bone marrow failure syndromes. Clinical and genetic data of 323 patients from the Canadian Inherited Marrow Failure Registry from 2001 to 2014, who had a documented genetic work-up, were analyzed. Cases with pathogenic copy number variants (at least 1 kilobasepairs) were compared to cases with other mutations. Genotype-phenotype correlations were performed to assess the impact of copy number variants. Pathogenic nucleotide-level mutations were found in 157 of 303 tested patients (51.8%). Genome-wide copy number variant analysis by single-nucleotide polymorphism arrays or comparative genomic hybridization arrays revealed pathogenic copy number variants in 11 of 67 patients tested (16.4%). In four of these patients, identification of copy number variant was crucial for establishing the correct diagnosis as their clinical presentation was ambiguous. Eight additional patients were identified to harbor pathogenic copy number variants by other methods. Of the 19 patients with pathogenic copy number variants, four had compound-heterozygosity of a copy number variant with a nucleotide-level mutation. Pathogenic copy number variants were associated with more extensive non-hematological organ system involvement (p = 0.0006), developmental delay (p = 0.006) and short stature (p = 0.04) compared to nucleotide-level mutations. In conclusion, a significant proportion of patients with inherited bone marrow failure syndromes harbor pathogenic copy number variants which were associated with a more extensive non-hematological phenotype in this cohort. Patients with a phenotype suggestive of inherited bone marrow failure syndromes but without identification of pathogenic nucleotide-level mutations should undergo specific testing for copy number variants.

Copy number variation in patients with inherited bone marrow failure syndromes (IBMFSs) is associated with more severe clinical symptoms. In addition to persistently low levels of red blood cells, white blood cells and/ or platelets, patients with IBMFSs also present varying degrees of physical malformations. Most cases are associated with single base-pair mutations in the DNA sequence, but Canadian researchers led by Yigal Dror at The Hospital for Sick Children in Toronto, have found that whole sections of the genome are deleted or repeated in an important proportion of patients. Those carrying copy number variants (CNV) presented more commonly with developmental delay, short stature and defects in more organ systems, than patients with point mutations. CNV analysis of patients with suspected IBMFSs could aid early disease evaluation and management.

Validation of quantitative PCR-based assays for detection of gene copy number aberrations in formalin-fixed, paraffin embedded solid tumor samples

Gene copy number changes are important somatic alterations in cancers. A number of high throughput methods, such as next generation sequencing, are capable of detecting copy number aberrations, but their use can be challenging and cost prohibitive for screening a small number of markers. Furthermore, detection of CNAs by high throughput platforms needs confirmation by an orthogonal technique, especially in cases with low level CNAs. Here, we have validated TaqMan based quantitative PCR (qPCR) assays to detect CNAs in genes of high clinical importance in formalin-fixed, paraffin-embedded (FFPE) samples. A cohort of 22 tumors of various types that harbor 67 CNAs in 13 genes was assessed. The abnormalities in these tumors were detected by using a NGS-based 50 gene hotspot panel on Ion Torrent PGM and molecular inversion probe (MIP) array. The CNAs included ERBB2 (n = 6), PDGFRA (n = 6), KIT (n = 7), NRAS (n = 3), PIK3CA (n = 6), MYC (n = 7), MET (n = 4), FLT3 (n = 6), FGFR3 (n = 3), FGFR2 (n = 3), EGFR (n = 7), KRAS (n = 6) and FGFR1 (n = 5). Different amounts of input DNA were tested and 5 ng FFPE DNA was found to be adequate without limiting detection sensitivity. All 22 (100%) positive tumor samples revealed by MIP array were confirmed by real time qPCR and 17 of 22 (77.2%) samples tested by NGS were confirmed. The limit of detection of the qPCR assay was determined by serial dilution of SKBR3 cell line DNA (with amplified ERBB2) and showed an ability to detect 3 copies consistently up to 0.75% dilution. The ability to use low input of FFPE DNA, high sensitivity, and short turnaround time makes qPCR a valuable and economically viable platform for detecting single gene CNAs as well as for confirmation of CNAs detected by high throughput screening assays.

Copy Number Variants in Alzheimer's Disease

Alzheimer's disease (AD) is a devastating disease mainly afflicting elderly people, characterized by decreased cognition, loss of memory, and eventually death. Although risk and deterministic genes are known, major genetics research programs are underway to gain further insights into the inheritance of AD. In the last years, in particular, new developments in genome-wide scanning methodologies have enabled the association of a number of previously uncharacterized copy number variants (CNVs, gain or loss of DNA) in AD. Because of the exceedingly large number of studies performed, it has become difficult for geneticists as well as clinicians to systematically follow, evaluate, and interpret the growing number of (sometime conflicting) CNVs implicated in AD. In this review, after a brief introduction of this type of structural variation, and a description of available databases, computational analyses, and technologies involved, we provide a systematic review of all published data showing statistical and scientific significance of pathogenic CNVs and discuss the role they might play in AD.

PCR-Based Detection of DNA Copy Number Variation

Copy number variations are important polymorphisms that can influence gene expression within and close to the rearranged region, and results in phenotypic variation. Techniques that detect abnormalities in DNA copy number are therefore useful for studying the associations between DNA aberrations and disease phenotype and for locating critical genes. PCR-based detection of copy number of target gene using TaqMan copy number assay offers a reliable method to measure copy number variation in human genome.

Copy number variations in the genome of the Qatari population

Background

The populations of the Arabian Peninsula remain the least represented in public genetic databases, both in terms of single nucleotide variants and of larger genomic mutations. We present the first high-resolution copy number variation (CNV) map for a Gulf Arab population, using a hybrid approach that integrates array genotyping intensity data and next-generation sequencing reads to call CNVs in the Qatari population.

Methods

CNVs were detected in 97 unrelated Qatari individuals by running two calling algorithms on each of two primary datasets: high-resolution genotyping (Illumina Omni 2.5M) and high depth whole-genome sequencing (Illumina PE 100bp). The four call-sets were integrated to identify high confidence CNV regions, which were subsequently annotated for putative functional effect and compared to public databases of CNVs in other populations. The availability of genome sequence was leveraged to identify tagging SNPs in high LD with common deletions in this population, enabling their imputation from genotyping experiments in the future.

Results

Genotyping intensities and genome sequencing data from 97 Qataris were analyzed with four different algorithms and integrated to discover 16,660 high confidence CNV regions (CNVRs) in the total population, affecting ~28 Mb in the median Qatari genome. Up to 40 % of all CNVs affected genes, including novel CNVs affecting Mendelian disease genes, segregating at different frequencies in the 3 major Qatari subpopulations, including those with Bedouin, Persian/South Asian, and African ancestry. Consistent with high consanguinity levels in the Bedouin subpopulation, we found an increased burden for homozygous deletions in this group. In comparison to known CNVs in the comprehensive Database of Genomic Variants, we found that 5 % of all CNVRs in Qataris were completely novel, with an enrichment of CNVs affecting several known chromosomal disorder loci and genes known to regulate sugar metabolism and type 2 diabetes in the Qatari cohort. Finally, we leveraged the availability of genome sequence to find suitable tagging SNPs for common deletions in this population.

Conclusion

We combine four independently generated datasets from 97 individuals to study CNVs for the first time at high-resolution in a Gulf Arab population.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1991-5) contains supplementary material, which is available to authorized users.

Microarray resources for genetic and genomic studies in chicken: a review

Advent of microarray technologies revolutionized the nature and scope of genetic and genomic research in human and other species by allowing massively parallel analysis of thousands of genomic sites. They have been used for diverse purposes such as for transcriptome analysis, CNV detection, SNP and CNV genotyping, studying DNA-protein interaction, and detection of genome methylation. Microarrays have also made invaluable contributions to research in chicken which is an important model organism for studying embryology, immunology, oncology, virology, evolution, genetics, and genomics and also for other avian species. Despite their huge contributions in life science research, the future of microarrays is now being questioned with the advent of massively parallel next generation sequencing (NGS) technologies, which promise to overcome some of the limitations of microarray platforms. In this article we review the various microarray resources developed for chicken and their past and potential future applications. We also discuss about the future of microarrays in the NGS era particularly in the context of livestock genetics. We argue that even though NGS promises some major advantages-in particular, offers the opportunity to discover novel elements in the genome-microarrays will continue to be major tools for research and practice in the field of livestock genetics/genomics due to their affordability, high throughput nature, mature established technologies and ease of application. Moreover, with advent of new microarray technologies like capture arrays, the NGS and microarrays are expected to complement each other in future research in life science.

Saturation of the human phenome

The phenome is the complete set of phenotypes resulting from genetic variation in populations of an organism. Saturation of a phenome implies the identification and phenotypic description of mutations in all genes in an organism, potentially constrained to those encoding proteins. The human genome is believed to contain 20-25,000 protein coding genes, but only a small fraction of these have documented mutant phenotypes, thus the human phenome is far from complete. In model organisms, genetic saturation entails the identification of multiple mutant alleles of a gene or locus, allowing a consistent description of mutational phenotypes for that gene. Saturation of several model organisms has been attempted, usually by targeting annotated coding genes with insertional transposons (Drosophila melanogaster, Mus musculus) or by sequence directed deletion (Saccharomyces cerevisiae) or using libraries of antisense oligonucleotide probes injected directly into animals (Caenorhabditis elegans, Danio rerio). This paper reviews the general state of the human phenome, and discusses theoretical and practical considerations toward a saturation analysis in humans. Throughout, emphasis is placed on high penetrance genetic variation, of the kind typically asociated with monogenic versus complex traits.

Comprehensive assessment of array-based platforms and calling algorithms for detection of copy number variants

We have systematically compared copy number variant (CNV) detection on eleven microarrays to evaluate data quality and CNV calling, reproducibility, concordance across array platforms and laboratory sites, breakpoint accuracy and analysis tool variability. Different analytic tools applied to the same raw data typically yield CNV calls with <50% concordance. Moreover, reproducibility in replicate experiments is <70% for most platforms. Nevertheless, these findings should not preclude detection of large CNVs for clinical diagnostic purposes because large CNVs with poor reproducibility are found primarily in complex genomic regions and would typically be removed by standard clinical data curation. The striking differences between CNV calls from different platforms and analytic tools highlight the importance of careful assessment of experimental design in discovery and association studies and of strict data curation and filtering in diagnostics. The CNV resource presented here allows independent data evaluation and provides a means to benchmark new algorithms.

Regions of homozygosity and their impact on complex diseases and traits

Regions of homozygosity (ROHs) are more abundant in the human genome than previously thought. These regions are without heterozygosity, i.e. all the genetic variations within the regions have two identical alleles. At present there are no standardized criteria for defining the ROHs resulting in the different studies using their own criteria in the analysis of homozygosity. Compared to the era of genotyping microsatellite markers, the advent of high-density single nucleotide polymorphism genotyping arrays has provided an unparalleled opportunity to comprehensively detect these regions in the whole genome in different populations. Several studies have identified ROHs which were associated with complex phenotypes such as schizophrenia, late-onset of Alzheimer's disease and height. Collectively, these studies have conclusively shown the abundance of ROHs larger than 1 Mb in outbred populations. The homozygosity association approach holds great promise in identifying genetic susceptibility loci harboring recessive variants for complex diseases and traits.

Copy-number variation genotyping of GSTT1 and GSTM1 gene deletions by real-time PCR

BACKGROUND

Structural variation in the human genome is increasingly recognized as being highly prevalent and having relevance to common human diseases. Array-based comparative genome-hybridization technology can be used to determine copy-number variation (CNV) across entire genomes, and quantitative PCR (qPCR) can be used to validate de novo variation or assays of common CNV in disease-association studies. Analysis of large qPCR data sets can be complicated and time-consuming, however.

METHODS

We describe qPCR assays for GSTM1 (glutathione S-transferase mu 1) and GSTT1 (glutathione S-transferase theta 1) gene deletions that can genotype up to 192 samples in duplicate 5-microL reaction volumes in <2 h on the ABI Prism 7900HT Sequence Detection System. To streamline data handling and analysis of these CNVs by qPCR, we developed a novel interactive, macro-driven Microsoft Excel(R) spreadsheet. As proof of principle, we used our software to analyze CNV data for 1478 DNA samples from a family-based cohort.

RESULTS

With only 8 ng of DNA template, we assigned CNV genotypes (i.e., 2, 1, or 0 copies) to either 96% (GSTM1) or 91% (GSTT1) of all DNA samples in a single round of PCR amplification. Genotyping accuracy, as ascertained by familial inheritance, was >99.5%, and independent genotype assignments with replicate real-time PCR runs were 100% concordant.

CONCLUSIONS

The genotyping assay for GSTM1 and GSTT1 gene deletion is suitable for large genetic epidemiologic studies and is a highly effective analysis system that is readily adaptable to analysis of other CNVs. .

Systematic identification of balanced transposition polymorphisms in Saccharomyces cerevisiae

High-throughput techniques for detecting DNA polymorphisms generally do not identify changes in which the genomic position of a sequence, but not its copy number, varies among individuals. To explore such balanced structural polymorphisms, we used array-based Comparative Genomic Hybridization (aCGH) to conduct a genome-wide screen for single-copy genomic segments that occupy different genomic positions in the standard laboratory strain of Saccharomyces cerevisiae (S90) and a polymorphic wild isolate (Y101) through analysis of six tetrads from a cross of these two strains. Paired-end high-throughput sequencing of Y101 validated four of the predicted rearrangements. The transposed segments contained one to four annotated genes each, yet crosses between S90 and Y101 yielded mostly viable tetrads. The longest segment comprised 13.5 kb near the telomere of chromosome XV in the S288C reference strain and Southern blotting confirmed its predicted location on chromosome IX in Y101. Interestingly, inter-locus crossover events between copies of this segment occurred at a detectable rate. The presence of low-copy repetitive sequences at the junctions of this segment suggests that it may have arisen through ectopic recombination. Our methodology and findings provide a starting point for exploring the origins, phenotypic consequences, and evolutionary fate of this largely unexplored form of genomic polymorphism.

Balanced structural polymorphisms are differences in the relative arrangement of genomic features within species that do not affect DNA copy number. Little is known about their prevalence or importance because they are difficult to observe. Here, we present a novel methodology for systematically identifying such polymorphisms based on the idea that single-copy DNA that occupies different genomic locations in two parents will segregate independently during meiosis and will therefore reveal itself as a copy number difference among a fraction of progeny. Comparative hybridization reveals multiple balanced structural polymorphisms that involve changes to gene order in two strains of yeast; the results are independently validated using paired-end whole genome shotgun sequencing. The longest transposed segment we identify comprises 13.5 kb near the telomere of chromosome XV in the S288C reference strain and contains several annotated genes. We map the location of this polymorphism in the non-reference strain using genome-wide genotypic data, which also reveals an appreciable frequency of ectopic recombination among transposed segment pairs. The breakpoints of the remaining polymorphisms are localized by the paired-end sequence data. Our work provides proof-of-principle for a very general approach to systematically identify all balanced genomic polymorphisms in two different genotypes and is a starting point for understanding the frequency, evolutionary origins, and functional consequences of this seldom-studied class of genomic structural variation in eukaryotes.

Detection of Hemizygous Chromosomal Copy Number Variants in Williams-Beuren Syndrome (WBS) by Duplex Quantitative PCR Array: An Unusual Type of WBS Genetic Defect

We have developed a dual probe quantitative PCR (qPCR) mini array enabling a more accurate analysis of the relationship between copy number variants (CNVs) and other genomic features in specific areas. We used it to map hemizygous microdeletion on human chromosome 7 around the elastin gene (ELN), which is the molecular basis of the Williams-Beuren syndrome (WBS). In two WBS patients, the haploid content of the elastin gene was ascertained previously by the fluorescence in-situ hybridization (FISH). Our dual-color qPCR assay used this information to normalize for DNA content in all tests. We mapped the extent of the deleted area using 10 loci spanning over 4 Mb. A border region containing the GTF2I gene, usually deleted in most cases, was found about 10 times amplified in both patients, suggesting an unusual type of the WBS genetic defect. This 10-WBS-loci-specific qPCR assay could be an affirmative diagnostic tool alternative to FISH. Due to low cost, it could be used as a screening test that would not only facilitate research on CNVs, but also allow early diagnosis of the disease, as well-timed diagnosis would benefit WBS children with earlier proper health-care measures.

Improved detection of global copy number variation using high density, non-polymorphic oligonucleotide probes

Background

DNA sequence diversity within the human genome may be more greatly affected by copy number variations (CNVs) than single nucleotide polymorphisms (SNPs). Although the importance of CNVs in genome wide association studies (GWAS) is becoming widely accepted, the optimal methods for identifying these variants are still under evaluation. We have previously reported a comprehensive view of CNVs in the HapMap DNA collection using high density 500 K EA (Early Access) SNP genotyping arrays which revealed greater than 1,000 CNVs ranging in size from 1 kb to over 3 Mb. Although the arrays used most commonly for GWAS predominantly interrogate SNPs, CNV identification and detection does not necessarily require the use of DNA probes centered on polymorphic nucleotides and may even be hindered by the dependence on a successful SNP genotyping assay.

Results

In this study, we have designed and evaluated a high density array predicated on the use of non-polymorphic oligonucleotide probes for CNV detection. This approach effectively uncouples copy number detection from SNP genotyping and thus has the potential to significantly improve probe coverage for genome-wide CNV identification. This array, in conjunction with PCR-based, complexity-reduced DNA target, queries over 1.3 M independent NspI restriction enzyme fragments in the 200 bp to 1100 bp size range, which is a several fold increase in marker density as compared to the 500 K EA array. In addition, a novel algorithm was developed and validated to extract CNV regions and boundaries.

Conclusion

Using a well-characterized pair of DNA samples, close to 200 CNVs were identified, of which nearly 50% appear novel yet were independently validated using quantitative PCR. The results indicate that non-polymorphic probes provide a robust approach for CNV identification, and the increasing precision of CNV boundary delineation should allow a more complete analysis of their genomic organization.

The diploid genome sequence of an individual human

Presented here is a genome sequence of an individual human. It was produced from approximately 32 million random DNA fragments, sequenced by Sanger dideoxy technology and assembled into 4,528 scaffolds, comprising 2,810 million bases (Mb) of contiguous sequence with approximately 7.5-fold coverage for any given region. We developed a modified version of the Celera assembler to facilitate the identification and comparison of alternate alleles within this individual diploid genome. Comparison of this genome and the National Center for Biotechnology Information human reference assembly revealed more than 4.1 million DNA variants, encompassing 12.3 Mb. These variants (of which 1,288,319 were novel) included 3,213,401 single nucleotide polymorphisms (SNPs), 53,823 block substitutions (2-206 bp), 292,102 heterozygous insertion/deletion events (indels)(1-571 bp), 559,473 homozygous indels (1-82,711 bp), 90 inversions, as well as numerous segmental duplications and copy number variation regions. Non-SNP DNA variation accounts for 22% of all events identified in the donor, however they involve 74% of all variant bases. This suggests an important role for non-SNP genetic alterations in defining the diploid genome structure. Moreover, 44% of genes were heterozygous for one or more variants. Using a novel haplotype assembly strategy, we were able to span 1.5 Gb of genome sequence in segments >200 kb, providing further precision to the diploid nature of the genome. These data depict a definitive molecular portrait of a diploid human genome that provides a starting point for future genome comparisons and enables an era of individualized genomic information.

Challenges and standards in integrating surveys of structural variation

There has been an explosion of data describing newly recognized structural variants in the human genome. In the flurry of reporting, there has been no standard approach to collecting the data, assessing its quality or describing identified features. This risks becoming a rampant problem, in particular with respect to surveys of copy number variation and their application to disease studies. Here, we consider the challenges in characterizing and documenting genomic structural variants. From this, we derive recommendations for standards to be adopted, with the aim of ensuring the accurate presentation of this form of genetic variation to facilitate ongoing research.

The application of the HapMap to diabetic nephropathy and other causes of chronic renal failure

The human nuclear genome consists of approximately 3 billion nucleotides. Human beings are 99% similar in DNA sequence to each other, but natural genetic variation in approximately 1% of the DNA sequence is responsible for interindividual differences, including determining who will develop disease and who will remain healthy. The pace and timing of disease initiation also is regulated by exposure to individual-level environmental factors and other random causes. Therefore, an examination of the DNA sequences of individuals with and without diabetic nephropathy, or, more broadly, chronic renal failure, can predict which sequence differences vary with disease (or health). The technology is not yet economical enough to analyze large numbers of individuals down to each nucleotide, but standardized dense genotyping sets for interrogating 1 marker for every 5,000, 10,000, or 15,000 nucleotides now are affordable even in large samples. The swiftness with which disease-gene associations can be mined has improved radically as a result of the availability of discovery human genetic variation data from large-scale public and private initiatives, such as those provided by the International Haplotype Map Consortium and Perlegen Sciences, Inc. (Mountain View, CA). These projects have captured many of the common genetic variants (>1%) in the genome. This information has been buttressed with improvements in large-scale genotyping technologies and statistical methods for data analysis. In summary, the renal community is now poised for discovery of genes for chronic renal failure using these resources.