Whole-chromosome aneuploidy analysis in human oocytes: focus on comparative genomic hybridization

The study of aneuploidy in human oocytes, discarded from IVF cycles, has provided a better understanding of the incidence of aneuploidy of female origin and the responsible mechanisms. Comparative genomic hybridization (CGH) is an established technique that allows for the detection of aneuploidy in all chromosomes avoiding artifactual chromosome losses. In this review, results obtained using CGH in single cells (1PB and/or MII oocytes) are included. The results of oocyte aneuploidy rates obtained by CGH from discarded oocytes of IVF patients and of oocyte donors are summarized. Moreover, the mechanisms involved in the aneuploid events, e.g. whether alterations occurred due to first meiotic errors or germ-line mitotic errors are also discussed. Finally, the incidence of aneuploid oocyte production due to first meiotic errors and germ-line mitotic errors observed in oocytes coming from IVF patients and IVF oocyte donors was assessed.     

Aneuploidy detection in porcine embryos using fluorescence in situ hybridization

In contrast to human embryos, there are very few studies published on the frequency of chromosomal aneuploidy in farm animals. The objectives of this study were to apply a three-color fluorescent in situ hybridization (FISH) method for evaluating aneuploidy in porcine embryos using chromosome-specific DNA probes, establish baseline frequencies of aneuploidy in embryos and compare the results with our previous findings of aneuploidy in spermatozoa and oocytes. The embryos were collected from superovulated gilts, which were slaughtered 48 h after insemination. FISH was performed using probes specific for the centromeric regions of porcine chromosomes 1, 10 and Y. Altogether 403 blastomeres from 114 porcine embryos were successfully investigated. Diploidy was observed in 101 (88.6%) embryos, triploidy in 2 (1.8%) embryos, mosaicism/mixoploidy in 9 (7.9%) embryos, and trisomy for chromosomes 1 or 10 in 2 (1.8%) embryos. No blastomere showed aneuploidy for chromosome Y. These findings correspond with the frequencies of aneuploidy we have found previously in porcine germ cells.     

Aneuploidy detection in human sperm nuclei using fluorescence in situ hybridization

Fluorescence in situ hybridization (FISH) was performed on human interphase sperm nuclei to determine the utility of this technique for aneuploidy detection. Repetitive DNA sequences specific for chromosomes 1, 12 and X were biotinylated and hybridized with mature sperm, which had been treated with cetyltrimethylammonium bromide and dithiothreitol to render them accessible to the probes. Detection of bound probe was accomplished with fluoresceinated avidin and antiavidin. For each of the chromosomes studied, chromosome number was determined by counting the fluorescent signals, representing hybridized regions, within the sperm nuclei. The frequencies for disomy, that is for nuclei containing two signals, for chromosomes 1, 12 and X were 0.06%, 0.04% and 0.03%, respectively. The congruence of these results with those determined by the cross-species hamster oocyte-human sperm assay, and the high efficiency of hybridization indicate that FISH is a sensitive and reliable tool for aneuploidy detection in human sperm.     

Breaking the waves: improved detection of copy number variation from microarray-based comparative genomic hybridization

Background

Large-scale high throughput studies using microarray technology have established that copy number variation (CNV) throughout the genome is more frequent than previously thought. Such variation is known to play an important role in the presence and development of phenotypes such as HIV-1 infection and Alzheimer's disease. However, methods for analyzing the complex data produced and identifying regions of CNV are still being refined.

Results

We describe the presence of a genome-wide technical artifact, spatial autocorrelation or 'wave', which occurs in a large dataset used to determine the location of CNV across the genome. By removing this artifact we are able to obtain both a more biologically meaningful clustering of the data and an increase in the number of CNVs identified by current calling methods without a major increase in the number of false positives detected. Moreover, removing this artifact is critical for the development of a novel model-based CNV calling algorithm - CNVmix - that uses cross-sample information to identify regions of the genome where CNVs occur. For regions of CNV that are identified by both CNVmix and current methods, we demonstrate that CNVmix is better able to categorize samples into groups that represent copy number gains or losses.

Conclusion

Removing artifactual 'waves' (which appear to be a general feature of array comparative genomic hybridization (aCGH) datasets) and using cross-sample information when identifying CNVs enables more biological information to be extracted from aCGH experiments designed to investigate copy number variation in normal individuals.     

Denoising array-based comparative genomic hybridization data using wavelets

Array-based comparative genomic hybridization (array-CGH) provides a high-throughput, high-resolution method to measure relative changes in DNA copy number simultaneously at thousands of genomic loci. Typically, these measurements are reported and displayed linearly on chromosome maps, and gains and losses are detected as deviations from normal diploid cells. We propose that one may consider denoising the data to uncover the true copy number changes before drawing inferences on the patterns of aberrations in the samples. Nonparametric techniques are particularly suitable for data denoising as they do not impose a parametric model in finding structures in the data. In this paper, we employ wavelets to denoise the data as wavelets have sound theoretical properties and a fast computational algorithm, and are particularly well suited for handling the abrupt changes seen in array-CGH data. A simulation study shows that denoising data prior to testing can achieve greater power in detecting the aberrant spot than using the raw data without denoising. Finally, we illustrate the method on two array-CGH data sets.     

Chromosome analysis of blastomeres from human embryos by using comparative genomic hybridization

Karyotypic studies of aborted fetuses have been used to draw the inference that the proportion of conceptuses with chromosome abnormalities is very high. Fluorescent in situ hybridization (FISH) studies of blastomeres from early cleavage embryos have provided some support for this inference but they are limited to the study of a few chromosomes. We describe the novel application of comparative genomic hybridization (CGH) to the study of numerical and structural abnormalities of single blastomeres from disaggregated 3-day-old human embryos. CGH results were obtained for 63 blastomeres from 12 embryos. Identification of all chromosomes with the exception of chromosomes 17, 19, 20 and 22 was possible. The embryos divided into four groups: (1) embryos with a normal CGH karyotype seen in all blastomeres; (2) embryos with consistent aneuploidy suggesting meiotic non-disjunction had occurred; (3) embryos that were mosaic generally with one or more cells showing aneuploidy for one or two chromosomes but some with cells showing extensive aneuploidy; and (4) one embryo with extensive aneuploidy in all blastomeres. The extensive aneuploidy in group 4 is interpreted as corresponding to the random aneuploidy seen in "chaotic" embryos reported by using interphase FISH. Partial chromosome loss and gain following chromosome breakage was observed in one embryo. Our analysis provides basic biological information on the occurrence of constitutional and post-zygotic chromosome abnormalities in early human embryos. Used in conjunction with embryo biopsy, diagnostic CGH should allow the exclusion of a proportion of embryos that appear normal but that have a poor probability of survival and, therefore, may improve the implantation rate after in vitro fertilization.     

Cytogenetic analysis from DNA by comparative genomic hybridization

Comparative genomic hybridization (CGH) is a modified in situ hybridization technique which allows detection and mapping of DNA sequence copy differences between two genomes in a single experiment. In CGH analysis, two differentially labelled genomic DNA (study and reference) are co-hybridized to normal metaphase spreads. Chromosomal locations of copy number changes in the DNA segments of the study genome are revealed by a variable fluorescence intensity ratio along each target chromosome. Since its development, CGH has been applied mostly as a research tool in the field of cancer cytogenetics to identify genetic changes in many previously unknown regions. CGH may also have a role in clinical cytogenetics for detection and identification of unbalanced chromosomal abnormalities.     

Array-based comparative genomic hybridization for the genomewide detection of submicroscopic chromosomal abnormalities

Microdeletions and microduplications, not visible by routine chromosome analysis, are a major cause of human malformation and mental retardation. Novel high-resolution, whole-genome technologies can improve the diagnostic detection rate of these small chromosomal abnormalities. Array-based comparative genomic hybridization allows such a high-resolution screening by hybridizing differentially labeled test and reference DNAs to arrays consisting of thousands of genomic clones. In this study, we tested the diagnostic capacity of this technology using approximately 3,500 flourescent in situ hybridization-verified clones selected to cover the genome with an average of 1 clone per megabase (Mb). The sensitivity and specificity of the technology were tested in normal-versus-normal control experiments and through the screening of patients with known microdeletion syndromes. Subsequently, a series of 20 cytogenetically normal patients with mental retardation and dysmorphisms suggestive of a chromosomal abnormality were analyzed. In this series, three microdeletions and two microduplications were identified and validated. Two of these genomic changes were identified also in one of the parents, indicating that these are large-scale genomic polymorphisms. Deletions and duplications as small as 1 Mb could be reliably detected by our approach. The percentage of false-positive results was reduced to a minimum by use of a dye-swap-replicate analysis, all but eliminating the need for laborious validation experiments and facilitating implementation in a routine diagnostic setting. This high-resolution assay will facilitate the identification of novel genes involved in human mental retardation and/or malformation syndromes and will provide insight into the flexibility and plasticity of the human genome.     

Detecting copy number variation in the human genome using comparative genomic hybridization

Among human beings, it was once estimated that our genomes were 99.9% genetically identical. While this high level of genetic similarity helps to define us as a species, it is our genetic variation that contributes to our phenotypic diversity. As genomic technologies evolve to provide genome-wide analyses at higher resolution, we are beginning to appreciate that the human genome has a lot more variation than was once thought. Array-based comparative genomic hybridization (CGH) is one of these technologies that has recently revealed a newly appreciated type of genetic variation: copy number variation, in which thousands of regions of the human genome are now known to be variable in number between individuals. Some of these copy number variable regions have already been shown to predispose to certain common diseases, and others may ultimately have a significant impact on how each of us reacts to certain foods (e.g., allergic reactions), medications (e.g., pharmacogenomics), microscopic infections (i.e., immunity), and other aspects of our ever-changing environment.     

Analysis of genetic aberrations in uterine adenomyosis using comparative genomic hybridization

OBJECTIVE: To use comparative genomic hybridization (CGH) to analyzefrozen tissuesfrom adenomyosis cases to discover and map genomic regions for chromosomal gains and losses. STUDY DESIGN: In a retrospective study, upper and lower threshold values of 1.20 and 0.80, respectively, were used to define positive findings. RESULTS: No positive recurrent gene copy number alterations were detected in the 25 cases of pathologically proven adenomyosis. CONCLUSION: Although CGH is extremely useful in investigating candidate genes in the development of adenomyosis, CGH was not useful in this study. Genetic changes might be indeed extremely rare in adenomyosis, or CGH was not sensitive enough to detect candidate genes.     

Fuzzy methods for the detection of copy number variations in comparative genomic hybridization arrays

Genomic copy number variations (CNVs) are considered as a significant source of genetic diversity and widely involved in gene expression and regulatory mechanism, genetic disorders and disease risk, susceptibility to certain diseases and conditions, and resistance to medical drugs. Many studies have targeted the identification, profiling, analysis, and associations of genetic CNVs. We propose herein two new fuzzy methods, taht is, one based on the fuzzy inference from the pre-processed input, and another based on fuzzy C-means clustering. Our solutions present a higher true positive rate and a lower false negative with no false positive, efficient performance and consumption of least resources.     

hybriddetective: A workflow and package to facilitate the detection of hybridization using genomic data in r

The ability to detect and characterize hybridization in nature has long been of interest to many fields of biology and often has direct implications for wildlife management and conservation. The capacity to identify the presence of hybridization, and quantify the numbers of individuals belonging to different hybrid classes, permits inference on the magnitude of, and timescale over which, hybridization has been or is occurring. Here, we present an r package and associated workflow developed for the detection, with estimates of efficiency and accuracy, of multigenerational hybrid individuals using genetic or genomic data in conjunction with the program newhybrids . This package includes functions for the identification and testing of diagnostic panels of markers, the simulation of multigenerational hybrids, and the quantification and visualization of the efficiency and accuracy with which hybrids can be detected. Overall, this package delivers a streamlined hybrid analysis platform, providing improvements in speed, ease of use and repeatability over current ad hoc approaches. The latest version of the package and associated documentation are available on GitHub ( https://github.com/bwringe/hybriddetective ).     

Analysis of wilms tumors using SNP mapping array-based comparative genomic hybridization

Wilms tumor (WT) has been a model to study kidney embryogenesis and tumorigenesis and, although associated with hereditary, cancer predisposition syndromes, the majority of tumors occur sporadically. To analyze genetic changes in WT we have defined copy number changes and loss of heterozygosity in 56 Wilms tumors using high resolution oligonucleotide arrays at a average resolution of ~12 Kb. Consistent deletions were seen on chromosomes 1p, 4q, 7p, 9q, 11p, 11q, 14q, 16q, and 21q. High frequency gains were seen for 1q and lower frequency gains were seen on 7q and chromosomes 8, 12 and 18. The high resolution provided by the SNP mapping arrays has defined minimal regions of deletion for many of these LOH events. Analysis of CNAs by tumor stage show relatively stable karyotypes in stage 1 tumors and more complex aCGH profiles in tumors from stages 3-5.     

Karyotyping of comparative genomic hybridization human metaphases using kernel nearest-neighbor algorithm

BACKGROUND: Comparative genomic hybridization (CGH) is a relatively new molecular cytogenetic method that detects chromosomal imbalances. Automatic karyotyping is an important step in CGH analysis because the precise position of the chromosome abnormality must be located and manual karyotyping is tedious and time-consuming. In the past, computer-aided karyotyping was done by using the 4',6-diamidino-2-phenylindole, dihydrochloride (DAPI)-inverse images, which required complex image enhancement procedures. METHODS: An innovative method, kernel nearest-neighbor (K-NN) algorithm, is proposed to accomplish automatic karyotyping. The algorithm is an application of the "kernel approach," which offers an alternative solution to linear learning machines by mapping data into a high dimensional feature space. By implicitly calculating Euclidean or Mahalanobis distance in a high dimensional image feature space, two kinds of K-NN algorithms are obtained. New feature extraction methods concerning multicolor information in CGH images are used for the first time. RESULTS: Experiment results show that the feature extraction method of using multicolor information in CGH images improves greatly the classification success rate. A high success rate of about 91.5% has been achieved, which shows that the K-NN classifier efficiently accomplishes automatic chromosome classification from relatively few samples. CONCLUSIONS: The feature extraction method proposed here and K-NN classifiers offer a promising computerized intelligent system for automatic karyotyping of CGH human chromosomes.     

Increased susceptibility to maternal aneuploidy demonstrated by comparative genomic hybridization analysis of human MII oocytes and first polar bodies

Single cell comparative genomic hybridization (CGH) was employed to extensively investigate 24 unfertilized or in vitromatured meiosis II oocytes and their corresponding first polar bodies (PBs), to determine how and whether all 23 chromosomes participate in female meiosis I errors and to accurately estimate the aneuploidy rate in the examined cells. Results were obtained for 15 oocytes and 16 PBs, representing 23 eggs (MII oocyte-PB complexes) donated from 15 patients (average age 32.2 years). Abnormalities were detected in ten eggs, giving an overall aneuploidy rate of 43.5%. In all, fourteen anomalies were scored, with the fertilized oocyte being at risk of monosomy in eight cases and at risk of trisomy in six; chromosomes of various sizes participated. CGH was able to give a comprehensive aneuploidy rate, as both absence of chromosomal material and the presence of extra copies were accurately scored. The aneuploidy mechanisms determined were: classical whole univalent non-disjunction; chromatid predivision prior to anaphase I, leading to metaphase II imbalance. There was also evidence of germinal mosaicism for a trisomic cell line. Three patients appeared to be predisposed to meiosis I errors, based on the presence of either multiple abnormalities in one or more of their examined cells, or of the same type of abnormality in all of their cells. Exclusion of these susceptible patients reduces the aneuploidy rate to 20%. Various hypotheses are put forward to explain these observations in order to stimulate research into the complex nature of female meiotic regulation.     

CGHMultiArray: exact P-values for multi-array comparative genomic hybridization data

We compute P-values, based on the Wilcoxon test with ties, to compare two conditions with array comparative genomic hybridization data, and we provide a simple interface to export and plot these P-values.     

Optimizing comparative genomic hybridization probes for genotyping and SNP detection in Plasmodium falciparum

Microarray-based comparative genomic hybridizations (CGH) interrogate genomic DNA to identify structural differences such as amplifications and deletions that are easily detected as large signal aberrations. Subtle signal deviations caused by single nucleotide polymorphisms (SNPs) can also be detected but is challenged by a high AT content (81%) in P. falciparum. We compared genome-wide CGH signal to sequence polymorphisms between parasite strains 3D7, HB3, and Dd2 using NimbleGen microarrays. From 23,191 SNPs (excluding var/rif/stevor genes), our CGH probe set detected SNPs with > 99.9% specificity but low (< 10%) sensitivity. Probe length, melting temperature, GC content, SNP location in the probe, mutation type, and hairpin structures affected SNP sensitivity. Previously unrecognized variable number tandem repeats (VNTRs) also were detected by this method. These findings will guide the redesign of a probe set to optimize an openly available CGH microarray platform for high-resolution genotyping suitable for population genomics studies.     

Copy number variation in Fayoumi and Leghorn chickens analyzed using array comparative genomic hybridization

Copy number variation refers to regions along chromosomes that harbor a type of structural variation, such as duplications or deletions. Copy number variants (CNVs) play a role in many important traits as well as in genetic diversity. Previous analyses of chickens using array comparative genomic hybridizations or single‐nucleotide polymorphism chip assays have been performed on various breeds and genetic lines to discover CNVs. In this study, we assessed individuals from two highly inbred (inbreeding coefficiency > 99.99%) lines, Leghorn G‐B2 and Fayoumi M15.2, to discover novel CNVs in chickens. These lines have been previously studied for disease resistance, and to our knowledge, this represents the first global assessment of CNVs in the Fayoumi breed. Genomic DNA from individuals was examined using the Agilent chicken 244 K comparative genomic hybridization array and quantitative PCR. We identified a total of 273 CNVs overall, with 112 CNVs being novel and not previously reported. Quantitative PCR using the standard curve method validated a subset of our array data. Through enrichment analysis of genes within CNV regions, we observed multiple chromosomes, terms and pathways that were significantly enriched, largely dealing with the major histocompatibility complex and immune responsiveness. Using an additional round of computational and statistical analysis with a different bioinformatic pipeline, we identified 43 CNVs among these as high‐confidence regions, 14 of which were found to be novel. We further compared and contrasted individuals of the two inbred lines to discover regions that have a significant difference in copy number between lines. A total of 40 regions had significant deletions or duplications between the lines. Gene Ontology analysis of genomic regions containing CNVs between lines also was performed. This between‐line candidate CNV list will be useful in studies with these two unique genetic lines, which may harbor variations that underlie quantitative trait loci for disease resistance and other important traits. Through the global discovery of novel CNVs in chicken, these data also provide resources for further genetic and functional genomics studies.     

Copy number variants (CNVs) in primate species using array-based comparative genomic hybridization

A substantial amount of genomic variation is now known to exist in humans and other primate species. Single nucleotide polymorphisms (SNPs) are thought to represent the vast majority of genomic differences among individuals in a given primate species and comprise about 0.1% of the genomes of two humans. However, recent studies have now shown that structural variation msay account for as much as 0.7% of the genomic differences in humans, of which copy number variants (CNVs) are the largest component. CNVs are segments of DNA that can range in size from hundreds of bases to millions of base pairs in length and have different number of copies between individuals. Recent technological advancements in array technologies led to genome-wide identification of CNVs and consequently revealed thousands of variable loci in humans, comprising as much as 12% of the human genome [A.J. Iafrate, L. Feuk, M.N. Rivera, M.L. Listewnik, P.K. Donahoe, Y. Qi, S.W. Scherer, C. Lee, Nat. Genet. 36 (2004) 949–951, [3]]. CNVs in humans have already been associated with susceptibility to certain complex diseases, dietary adaptation, and several neurological conditions. In addition, recent studies have shown that CNVs can be successfully implemented in population genetics research, providing important insights into human genetic variation. Nevertheless, the important role of CNVs in primate evolution and genetic diversity is still largely unknown. This article aims to outline the strengths and weaknesses of current comparative genomic hybridization array technologies that have been employed to detect CNV variation and the applications of these techniques to primate genetic research.     

Karyotyping of comparative genomic hybridization human metaphases by using support vector machines.

BACKGROUND: Comparative genomic hybridization (CGH) is a relatively new molecular cytogenetic method for detecting chromosomal imbalance. Karyotyping of human metaphases is an important step to assign each chromosome to one of 23 or 24 classes (22 autosomes and two sex chromosomes). Automatic karyotyping in CGH analysis is needed. However, conventional karyotyping approaches based on DAPI images require complex image enhancement procedures. METHODS: This paper proposes a simple feature extraction method, one that generates density profiles from original true color CGH images and uses normalized profiles as feature vectors without quantization. A classifier is developed by using support vector machine (SVM). It has good generalization ability and needs only limited training samples. RESULTS: Experiment results show that the feature extraction method of using color information in CGH images can improve greatly the classification success rate. The SVM classifier is able to acquire knowledge about human chromosomes from relatively few samples and has good generalization ability. A success rate of moe than 90% has been achieved and the time for training and testing is very short. CONCLUSIONS: The feature extraction method proposed here and the SVM-based classifier offer a promising computerized intelligent system for automatic karyotyping of CGH human chromosomes.