Segmental duplications and copy-number variation in the human genome

The human genome contains numerous blocks of highly homologous duplicated sequence. This higher-order architecture provides a substrate for recombination and recurrent chromosomal rearrangement associated with genomic disease. However, an assessment of the role of segmental duplications in normal variation has not yet been made. On the basis of the duplication architecture of the human genome, we defined a set of 130 potential rearrangement hotspots and constructed a targeted bacterial artificial chromosome (BAC) microarray (with 2,194 BACs) to assess copy-number variation in these regions by array comparative genomic hybridization. Using our segmental duplication BAC microarray, we screened a panel of 47 normal individuals, who represented populations from four continents, and we identified 119 regions of copy-number polymorphism (CNP), 73 of which were previously unreported. We observed an equal frequency of duplications and deletions, as well as a 4-fold enrichment of CNPs within hotspot regions, compared with control BACs (P < .000001), which suggests that segmental duplications are a major catalyst of large-scale variation in the human genome. Importantly, segmental duplications themselves were also significantly enriched >4-fold within regions of CNP. Almost without exception, CNPs were not confined to a single population, suggesting that these either are recurrent events, having occurred independently in multiple founders, or were present in early human populations. Our study demonstrates that segmental duplications define hotspots of chromosomal rearrangement, likely acting as mediators of normal variation as well as genomic disease, and it suggests that the consideration of genomic architecture can significantly improve the ascertainment of large-scale rearrangements. Our specialized segmental duplication BAC microarray and associated database of structural polymorphisms will provide an important resource for the future characterization of human genomic disorders.     

SW-ARRAY: a dynamic programming solution for the identification of copy-number changes in genomic DNA using array comparative genome hybridization data

Comparative genome hybridization (CGH) to DNA microarrays (array CGH) is a technique capable of detecting deletions and duplications in genomes at high resolution. However, array CGH studies of the human genome noting false negative and false positive results using large insert clones as probes have raised important concerns regarding the suitability of this approach for clinical diagnostic applications. Here, we adapt the Smith–Waterman dynamic-programming algorithm to provide a sensitive and robust analytic approach (SW-ARRAY) for detecting copy-number changes in array CGH data. In a blind series of hybridizations to arrays consisting of the entire tiling path for the terminal 2 Mb of human chromosome 16p, the method identified all monosomies between 267 and 1567 kb with a high degree of statistical significance and accurately located the boundaries of deletions in the range 267–1052 kb. The approach is unique in offering both a nonparametric segmentation procedure and a nonparametric test of significance. It is scalable and well-suited to high resolution whole genome array CGH studies that use array probes derived from large insert clones as well as PCR products and oligonucleotides.     

Distributions of exons and introns in the human genome

The human genome is revisited using exon and intron distribution profiles. The 26,564 annotated genes in the human genome (build October, 2003) contain 233,785 exons and 207,344 introns. On average, there are 8.8 exons and 7.8 introns per gene. About 80% of the exons on each chromosome are < 200 bp in length. < 0.01% of the introns are < 20 bp in length and < 10% of introns are more than 11,000 bp in length. These results suggest constraints on the splicing machinery to splice out very long or very short introns and provide insight to optimal intron length selection. Interestingly, the total length in introns and intergenic DNA on each chromosome is significantly correlated to the determined chromosome size with a coefficient of correlation r = 0.95 and r = 0.97, respectively. These results suggest their implication in genome design.     

Array rank order regression analysis for the detection of gene copy-number changes in human cancer

cDNA microarray technology has been applied to the detection of DNA copy-number changes in malignant tumors. Test and control genomic DNA samples are differentially labeled and cohybridized to a spotted cDNA microarray. The ratio of test to control fluorescence intensities for each spot reflects relative gene copy number. The low signal-to-noise ratios of this assay and the variable levels of gene amplification and deletion among tumors hamper the detection of deviations from the diploid complement. We describe a regression-based statistical method to test for altered copy number on each gene and apply the technique to copy-number profiles in 10 thyroid tumors. We show that a novel transformation of fluorescence ratios into array rank order efficiently normalizes the heterogeneity among copy-number profiles and improves the reproducibility of the results. Array rank order regression analysis enhances the detection of consistent changes in gene copy number in solid tumors by cDNA microarray-based comparative genome hybridization.     

A comprehensive analysis of common copy-number variations in the human genome

Segmental copy-number variations (CNVs) in the human genome are associated with developmental disorders and susceptibility to diseases. More importantly, CNVs may represent a major genetic component of our phenotypic diversity. In this study, using a whole-genome array comparative genomic hybridization assay, we identified 3,654 autosomal segmental CNVs, 800 of which appeared at a frequency of at least 3%. Of these frequent CNVs, 77% are novel. In the 95 individuals analyzed, the two most diverse genomes differed by at least 9 Mb in size or varied by at least 266 loci in content. Approximately 68% of the 800 polymorphic regions overlap with genes, which may reflect human diversity in senses (smell, hearing, taste, and sight), rhesus phenotype, metabolism, and disease susceptibility. Intriguingly, 14 polymorphic regions harbor 21 of the known human microRNAs, raising the possibility of the contribution of microRNAs to phenotypic diversity in humans. This in-depth survey of CNVs across the human genome provides a valuable baseline for studies involving human genetics.     

Array-MAPH: a methodology for the detection of locus copy-number changes in complex genomes

High-throughput genome-wide screening methods to detect subtle genomic imbalances are extremely important for diagnostic genetics and genomics. Here, we provide a detailed protocol for a microarray-based technique, applying the principle of multiplex amplifiable probe hybridization (MAPH). Methodology and software have been developed for designing unique PCR-amplifiable sequences (400-600 bp) covering any genomic region of interest. These sequences are amplified, cloned and spotted onto arrays (targets). A mixture of the same sequences (probes) is hybridized to genomic DNA immobilized on a membrane. Bound probes are recovered and quantitatively amplified by PCR, labeled and hybridized to the array. The procedure can be completed in 4-5 working days, excluding microarray preparation. Unlike array-comparative genomic hybridization (array-CGH), test DNA of specifically reduced complexity is hybridized to an array of identical small amplifiable target sequences, resulting in increased hybridization specificity and higher potential for increasing resolution. Array-MAPH can be used for detection of small-scale copy-number changes in complex genomes, leading to genotype-phenotype correlations and the discovery of new genes.     

Variations of the length of exons and introns in human genome genes

The exon-intron organization of genes of all human chromosomes was studied in relation to the density of gene distribution on DNA strands and on the number of introns in genes. The lengths of exons, introns and genes have been found to vary correlatively, and this correlation depends on the density of genes in human chromosomes. It has been established that genes with the exon-intron organization have similar tendencies of variation of the lengths of exons and introns with an increase in the number of introns.     

Accurate detection of aneuploidies in array CGH and gene expression microarray data

MOTIVATION: Chromosomal copy number changes (aneuploidies) are common in cell populations that undergo multiple cell divisions including yeast strains, cell lines and tumor cells. Identification of aneuploidies is critical in evolutionary studies, where changes in copy number serve an adaptive purpose, as well as in cancer studies, where amplifications and deletions of chromosomal regions have been identified as a major pathogenetic mechanism. Aneuploidies can be studied on whole-genome level using array CGH (a microarray-based method that measures the DNA content), but their presence also affects gene expression. In gene expression microarray analysis, identification of copy number changes is especially important in preventing aberrant biological conclusions based on spurious gene expression correlation or masked phenotypes that arise due to aneuploidies. Previously suggested approaches for aneuploidy detection from microarray data mostly focus on array CGH, address only whole-chromosome or whole-arm copy number changes, and rely on thresholds or other heuristics, making them unsuitable for fully automated general application to gene expression datasets. There is a need for a general and robust method for identification of aneuploidies of any size from both array CGH and gene expression microarray data. RESULTS: We present ChARM (Chromosomal Aberration Region Miner), a robust and accurate expectation-maximization based method for identification of segmental aneuploidies (partial chromosome changes) from gene expression and array CGH microarray data. Systematic evaluation of the algorithm on synthetic and biological data shows that the method is robust to noise, aneuploidal segment size and P-value cutoff. Using our approach, we identify known chromosomal changes and predict novel potential segmental aneuploidies in commonly used yeast deletion strains and in breast cancer. ChARM can be routinely used to identify aneuploidies in array CGH datasets and to screen gene expression data for aneuploidies or array biases. Our methodology is sensitive enough to detect statistically significant and biologically relevant aneuploidies even when expression or DNA content changes are subtle as in mixed populations of cells. AVAILABILITY: Code available by request from the authors and on Web supplement at http://function.cs.princeton.edu/ChARM/     

Prediction of replication time zones at single nucleotide resolution in the human genome

The human genome is structured at multiple levels: it is organized into a series of replication time zones, and meanwhile it is composed of isochores. Accumulating evidence suggests a match between these two genome features. Based on newly developed software GC-Profile, we obtained a complete coverage of the human genome by 3198 isochores with boundaries at single nucleotide resolution. Interestingly, the experimentally confirmed replication timing sites in the regions of 1p36.1, 6p21.32, 17q11.2 and 22q12.1 nearly all coincide with the determined isochore boundaries. The precise boundaries of the 3198 isochores are available via the website: http://tubic.tju.edu.cn/isomap/.     

Flexible and accurate detection of genomic copy-number changes from aCGH

Genomic DNA copy-number alterations (CNAs) are associated with complex diseases, including cancer: CNAs are indeed related to tumoral grade, metastasis, and patient survival. CNAs discovered from array-based comparative genomic hybridization (aCGH) data have been instrumental in identifying disease-related genes and potential therapeutic targets. To be immediately useful in both clinical and basic research scenarios, aCGH data analysis requires accurate methods that do not impose unrealistic biological assumptions and that provide direct answers to the key question, "What is the probability that this gene/region has CNAs?" Current approaches fail, however, to meet these requirements. Here, we introduce reversible jump aCGH (RJaCGH), a new method for identifying CNAs from aCGH; we use a nonhomogeneous hidden Markov model fitted via reversible jump Markov chain Monte Carlo; and we incorporate model uncertainty through Bayesian model averaging. RJaCGH provides an estimate of the probability that a gene/region has CNAs while incorporating interprobe distance and the capability to analyze data on a chromosome or genome-wide basis. RJaCGH outperforms alternative methods, and the performance difference is even larger with noisy data and highly variable interprobe distance, both commonly found features in aCGH data. Furthermore, our probabilistic method allows us to identify minimal common regions of CNAs among samples and can be extended to incorporate expression data. In summary, we provide a rigorous statistical framework for locating genes and chromosomal regions with CNAs with potential applications to cancer and other complex human diseases.     

FACADE: a fast and sensitive algorithm for the segmentation and calling of high resolution array CGH data

The availability of high resolution array comparative genomic hybridization (CGH) platforms has led to increasing complexities in data analysis. Specifically, defining contiguous regions of alterations or segmentation can be computationally intensive and popular algorithms can take hours to days for the processing of arrays comprised of hundreds of thousands to millions of elements. Additionally, tumors tend to demonstrate subtle copy number alterations due to heterogeneity, ploidy and hybridization effects. Thus, there is a need for fast, sensitive array CGH segmentation and alteration calling algorithms. Here, we describe Fast Algorithm for Calling After Detection of Edges (FACADE), a highly sensitive and easy to use algorithm designed to rapidly segment and call high resolution array data.     

The individual and global impact of copy-number variants on complex human traits

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Linkage disequilibrium and heritability of copy-number polymorphisms within duplicated regions of the human genome

Studies of copy-number variation and linkage disequilibrium (LD) have typically excluded complex regions of the genome that are rich in duplications and prone to rearrangement. In an attempt to assess the heritability and LD of copy-number polymorphisms (CNPs) in duplication-rich regions of the genome, we profiled copy-number variation in 130 putative "rearrangement hotspot regions" among 269 individuals of European, Yoruba, Chinese, and Japanese ancestry analyzed by the International HapMap Consortium. Eighty-four hotspot regions, corresponding to 257 bacterial artificial chromosome (BAC) probes, showed evidence of copy-number differences. Despite a predisposing genetic architecture, no polymorphism was ever observed in the remaining 46 "rearrangement hotspots," and we suggest these represent excellent candidate sites for pathogenic rearrangements. We used a combination of BAC-based and high-density customized oligonucleotide arrays to resolve the molecular basis of structural rearrangements. For common variants (frequency >10%), we observed a distinct bias against copy-number losses, suggesting that deletions are subject to purifying selection. Heritability estimates did not differ significantly from 1.0 among the majority (30 of 34) of loci analyzed, consistent with normal Mendelian inheritance. Some of the CNPs in duplication-rich regions showed strong LD with nearby single-nucleotide polymorphisms (SNPs) and were observed to segregate on ancestral SNP haplotypes. However, LD with the best available SNP markers was weaker than has been reported for deletion polymorphisms in less complex regions of the genome. These observations may be accounted for by a low density of SNP data in duplicated regions, challenges in mapping and typing the CNPs, and the possibility that CNPs in these regions have rearranged on multiple haplotype backgrounds. Our results underscore the need for complete maps of genetic variation in duplication-rich regions of the genome.     

An experimental loop design for the detection of constitutional chromosomal aberrations by array CGH

Background  

Comparative genomic hybridization microarrays for the detection of constitutional chromosomal aberrations is the application of microarray technology coming fastest into routine clinical application. Through genotype-phenotype association, it is also an important technique towards the discovery of disease causing genes and genomewide functional annotation in human. When using a two-channel microarray of genomic DNA probes for array CGH, the basic setup consists in hybridizing a patient against a normal reference sample. Two major disadvantages of this setup are (1) the use of half of the resources to measure a (little informative) reference sample and (2) the possibility that deviating signals are caused by benign copy number variation in the "normal" reference instead of a patient aberration. Instead, we apply an experimental loop design that compares three patients in three hybridizations.     

Exploiting noise in array CGH data to improve detection of DNA copy number change

Developing effective methods for analyzing array-CGH data to detect chromosomal aberrations is very important for the diagnosis of pathogenesis of cancer and other diseases. Current analysis methods, being largely based on smoothing and/or segmentation, are not quite capable of detecting both the aberration regions and the boundary break points very accurately. Furthermore, when evaluating the accuracy of an algorithm for analyzing array-CGH data, it is commonly assumed that noise in the data follows normal distribution. A fundamental question is whether noise in array-CGH is indeed Gaussian, and if not, can one exploit the characteristics of noise to develop novel analysis methods that are capable of detecting accurately the aberration regions as well as the boundary break points simultaneously? By analyzing bacterial artificial chromosomes (BACs) arrays with an average 1 mb resolution, 19 k oligo arrays with the average probe spacing <100 kb and 385 k oligo arrays with the average probe spacing of about 6 kb, we show that when there are aberrations, noise in all three types of arrays is highly non-Gaussian and possesses long-range spatial correlations, and that such noise leads to worse performance of existing methods for detecting aberrations in array-CGH than the Gaussian noise case. We further develop a novel method, which has optimally exploited the character of the noise, and is capable of identifying both aberration regions as well as the boundary break points very accurately. Finally, we propose a new concept, posteriori signal-to-noise ratio (p-SNR), to assign certain confidence level to an aberration region and boundaries detected.     

Detection of single nucleotide variations in expressed exons of the human genome using RNA-Seq

Whole-genome resequencing is still a costly method to detect genetic mutations that lead to altered forms of proteins and may be associated with disease development. Since the majority of disease-related single nucleotide variations (SNVs) are found in protein-coding regions, we propose to identify SNVs in expressed exons of the human genome using the recently developed RNA-Seq technique. We identify 12 176 and 10 621 SNVs, respectively, in Jurkat T cells and CD4⁺ T cells from a healthy donor. Interestingly, our data show that one copy of the TAL-1 proto-oncogene has a point mutation in 3′ UTR and only the mutant allele is expressed in Jurkat cells. We provide a comprehensive dataset for further understanding the cancer biology of Jurkat cells. Our results indicate that this is a cost-effective and efficient strategy to systematically identify SNVs in the expressed regions of the human genome.     

利用生物信息数据库筛选乳腺癌CGH微阵列BAC克隆

利用CCAP数据库和UCSC数据库检索出乳腺癌发生、发展过程有意义的BAC克隆,然后利用CGAP数据库设计更有意义的BAC克隆。结果：获得1286条BAC克隆,可用于打印CCH微阵列,进行乳腺癌的检测。