Article

Characterizing complex structural variation in germline and somatic genomes

Department of Biochemistry and Molecular Genetics, University of Virginia School of Medicine, Charlottesville, VA 22908, USA.
Trends in Genetics (Impact Factor: 11.6). 11/2011; 28(1):43-53. DOI: 10.1016/j.tig.2011.10.002
Source: PubMed

ABSTRACT Genome structural variation (SV) is a major source of genetic diversity in mammals and a hallmark of cancer. Although SV is typically defined by its canonical forms (duplication, deletion, insertion, inversion and translocation), recent breakpoint mapping studies have revealed a surprising number of 'complex' variants that evade simple classification. Complex SVs are defined by clustered breakpoints that arose through a single mutation but cannot be explained by one simple end-joining or recombination event. Some complex variants exhibit profoundly complicated rearrangements between distinct loci from multiple chromosomes, whereas others involve more subtle alterations at a single locus. These diverse and unpredictable features present a challenge for SV mapping experiments. Here, we review current knowledge of complex SV in mammals, and outline techniques for identifying and characterizing complex variants using next-generation DNA sequencing.

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    • "The development of cytogenomic techniques, such as high-density arrays and next-generation sequencing technologies (Kloosterman et al. 2011; Chiang et al. 2012), has allowed the detection of complex genomic rearrangements (CGR) and cryptic breakpoints. Thus, many CGR detected to date might actually be more complex than initially thought (Zhang et al. 2009; Quinlan and Hall 2012). "
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    MGG Molecular & General Genetics 07/2014; DOI:10.1007/s00438-014-0876-7 · 2.83 Impact Factor
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    ABSTRACT: Accurate catalogs of structural variants (SVs) in mammalian genomes are necessary to elucidate the potential mechanisms that drive SV formation and to assess their functional impact. Next generation sequencing methods for SV detection are an advance on array-based methods, but are almost exclusively limited to four basic types: deletions, insertions, inversions and copy number gains. By visual inspection of 100 Mbp of genome to which next generation sequence data from 17 inbred mouse strains had been aligned, we identify and interpret 21 paired-end mapping patterns, which we validate by PCR. These paired-end mapping patterns reveal a greater diversity and complexity in SVs than previously recognized. In addition, Sanger-based sequence analysis of 4,176 breakpoints at 261 SV sites reveal additional complexity at approximately a quarter of structural variants analyzed. We find micro-deletions and micro-insertions at SV breakpoints, ranging from 1 to 107 bp, and SNPs that extend breakpoint micro-homology and may catalyze SV formation. An integrative approach using experimental analyses to train computational SV calling is essential for the accurate resolution of the architecture of SVs. We find considerable complexity in SV formation; about a quarter of SVs in the mouse are composed of a complex mixture of deletion, insertion, inversion and copy number gain. Computational methods can be adapted to identify most paired-end mapping patterns.
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    ABSTRACT: Over the past decade, the ubiquity of copy number variants (CNVs, the gain or loss of genomic material) in the genomes of healthy humans has become apparent. Although some of these variants are associated with disorders, a handful of studies documented an adaptive advantage conferred by CNVs. In this review, we propose that CNVs are substrates for human evolution and adaptation. We discuss the possible mechanisms and evolutionary processes in which CNVs are selected, outline the current challenges in identifying these loci, and highlight that copy number variable regions allow for the creation of novel genes that may diversify the repertoire of such genes in response to rapidly changing environments. We expect that many more adaptive CNVs will be discovered in the coming years, and we believe that these new findings will contribute to our understanding of human-specific phenotypes.
    Trends in Genetics 04/2012; 28(6):245-57. DOI:10.1016/j.tig.2012.03.002 · 11.60 Impact Factor
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