Microhomology-mediated mechanisms underlie non-recurrent disease-causing microdeletions of the FOXL2 gene or its regulatory domain

Genomic disorders are often caused by recurrent copy number variations (CNVs), with nonallelic homologous recombination (NAHR) as the underlying mechanism. Recently, several microhomology-mediated repair mechanisms-such as microhomology-mediated end-joining (MMEJ), fork stalling and template switching (FoSTeS), microhomology-mediated break-induced replication (MMBIR), serial replication slippage (SRS), and break-induced SRS (BISRS)-were described in the etiology of non-recurrent CNVs in human disease. In addition, their formation may be stimulated by genomic architectural features. It is, however, largely unexplored to what extent these mechanisms contribute to rare, locus-specific pathogenic CNVs. Here, fine-mapping of 42 microdeletions of the FOXL2 locus, encompassing FOXL2 (32) or its regulatory domain (10), serves as a model for rare, locus-specific CNVs implicated in genetic disease. These deletions lead to blepharophimosis syndrome (BPES), a developmental condition affecting the eyelids and the ovary. For breakpoint mapping we used targeted array-based comparative genomic hybridization (aCGH), quantitative PCR (qPCR), long-range PCR, and Sanger sequencing of the junction products. Microhomology, ranging from 1 bp to 66 bp, was found in 91.7% of 24 characterized breakpoint junctions, being significantly enriched in comparison with a random control sample. Our results show that microhomology-mediated repair mechanisms underlie at least 50% of these microdeletions. Moreover, genomic architectural features, like sequence motifs, non-B DNA conformations, and repetitive elements, were found in all breakpoint regions. In conclusion, the majority of these microdeletions result from microhomology-mediated mechanisms like MMEJ, FoSTeS, MMBIR, SRS, or BISRS. Moreover, we hypothesize that the genomic architecture might drive their formation by increasing the susceptibility for DNA breakage or promote replication fork stalling. Finally, our locus-centered study, elucidating the etiology of a large set of rare microdeletions involved in a monogenic disorder, can serve as a model for other clustered, non-recurrent microdeletions in genetic disease

A backward procedure for change-point detection with applications to copy number variation detection

Change-point detection regains much attention recently for analyzing array or sequencing data for copy number variation (CNV) detection. In such applications, the true signals are typically very short and buried in the long data sequence, which makes it challenging to identify the variations efficiently and accurately. In this article, we propose a new change-point detection method, a backward procedure, which is not only fast and simple enough to exploit high-dimensional data but also performs very well for detecting short signals. Although motivated by CNV detection, the backward procedure is generally applicable to assorted change-point problems that arise in a variety of scientific applications. It is illustrated by both simulated and real CNV data that the backward detection has clear advantages over other competing methods especially when the true signal is short

Penalized weighted low-rank approximation for robust recovery of recurrent copy number variations

2015-2016 UNCG University Libraries Open Access Publishing Fund Grant Winner. BackgroundCopy number variation (CNV) analysis has become one of the most important researchareas for understanding complex disease. With increasing resolution of array-basedcomparative genomic hybridization (aCGH) arrays, more and more raw copy numberdata are collected for multiple arrays. It is natural to realize the co-existence of bothrecurrent and individual-specific CNVs, together with the possible data contaminationduring the data generation process. Therefore, there is a great need for an efficient androbust statistical model for simultaneous recovery of both recurrent and individualspecificCNVs.ResultWe develop a penalized weighted low-rank approximation method (WPLA) for robustrecovery of recurrent CNVs. In particular, we formulate multiple aCGH arrays into arealization of a hidden low-rank matrix with some random noises and let an additionalweight matrix account for those individual-specific effects. Thus, we do not restrict therandom noise to be normally distributed, or even homogeneous. We show itsperformance through three real datasets and twelve synthetic datasets from different typesof recurrent CNV regions associated with either normal random errors or heavilycontaminated errors.ConclusionOur numerical experiments have demonstrated that the WPLA can successfully recoverthe recurrent CNV patterns from raw data under different scenarios. Compared with twoother recent methods, it performs the best regarding its ability to simultaneously detectboth recurrent and individual-specific CNVs under normal random errors. Moreimportantly, the WPLA is the only method which can effectively recover the recurrentCNVs region when the data is heavily contaminated

Genome-Wide Mapping of Copy Number Variation in Humans: Comparative Analysis of High Resolution Array Platforms

Accurate and efficient genome-wide detection of copy number variants (CNVs) is essential for understanding human genomic variation, genome-wide CNV association type studies, cytogenetics research and diagnostics, and independent validation of CNVs identified from sequencing based technologies. Numerous, array-based platforms for CNV detection exist utilizing array Comparative Genome Hybridization (aCGH), Single Nucleotide Polymorphism (SNP) genotyping or both. We have quantitatively assessed the abilities of twelve leading genome-wide CNV detection platforms to accurately detect Gold Standard sets of CNVs in the genome of HapMap CEU sample NA12878, and found significant differences in performance. The technologies analyzed were the NimbleGen 4.2 M, 2.1 M and 3×720 K Whole Genome and CNV focused arrays, the Agilent 1×1 M CGH and High Resolution and 2×400 K CNV and SNP+CGH arrays, the Illumina Human Omni1Quad array and the Affymetrix SNP 6.0 array. The Gold Standards used were a 1000 Genomes Project sequencing-based set of 3997 validated CNVs and an ultra high-resolution aCGH-based set of 756 validated CNVs. We found that sensitivity, total number, size range and breakpoint resolution of CNV calls were highest for CNV focused arrays. Our results are important for cost effective CNV detection and validation for both basic and clinical applications

Copy Number Variation in Chickens: A Review and Future Prospects

DNA sequence variations include nucleotide substitution, deletion, insertion, translocation and inversion. Deletion or insertion of a large DNA segment in the genome, referred to as copy number variation (CNV), has caught the attention of many researchers recently. It is believed that CNVs contribute significantly to genome variability, and thus contribute to phenotypic variability. In chickens, genome-wide surveys with array comparative genome hybridization (aCGH), SNP chip detection or whole genome sequencing have revealed a large number of CNVs. A large portion of chicken CNVs involves protein coding or regulatory sequences. A few CNVs have been demonstrated to be the determinant factors for single gene traits, such as late-feathering, pea-comb and dermal hyperpigmentation. The phenotypic effects of the majority of chicken CNVs are to be delineated

Data analysis methods for copy number discovery and interpretation

Copy number variation (CNV) is an important type of genetic variation that can give rise to a wide variety of phenotypic traits. Differences in copy number are thought to play major roles in processes that involve dosage sensitive genes, providing beneficial, deleterious or neutral modifications to individual phenotypes. Copy number analysis has long been a standard in clinical cytogenetic laboratories. Gene deletions and duplications can often be linked with genetic Syndromes such as: the 7q11.23 deletion of Williams-­‐Bueren Syndrome, the 22q11 deletion of DiGeorge syndrome and the 17q11.2 duplication of Potocki-­‐Lupski syndrome. Interestingly, copy number based genomic disorders often display reciprocal deletion / duplication syndromes, with the latter frequently exhibiting milder symptoms. Moreover, the study of chromosomal imbalances plays a key role in cancer research. The datasets used for the development of analysis methods during this project are generated as part of the cutting-­‐edge translational project, Deciphering Developmental Disorders (DDD). This project, the DDD, is the first of its kind and will directly apply state of the art technologies, in the form of ultra-­‐high resolution microarray and next generation sequencing (NGS), to real-­‐time genetic clinical practice. It is collaboration between the Wellcome Trust Sanger Institute (WTSI) and the National Health Service (NHS) involving the 24 regional genetic services across the UK and Ireland. Although the application of DNA microarrays for the detection of CNVs is well established, individual change point detection algorithms often display variable performances. The definition of an optimal set of parameters for achieving a certain level of performance is rarely straightforward, especially where data qualities vary ... [cont.]