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Nat Rev Genet. 2020 Mar;21(3):171-189. doi: 10.1038/s41576-019-0180-9. Epub 2019 Nov 15.

Structural variation in the sequencing era.

Author information

1
Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA.
2
Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, USA.
3
Department of Genetics, Stanford University School of Medicine, Stanford, CA, USA.
4
Department of Human Genetics, University of Michigan, Ann Arbor, MI, USA. remills@umich.edu.
5
Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA. remills@umich.edu.

Abstract

Identifying structural variation (SV) is essential for genome interpretation but has been historically difficult due to limitations inherent to available genome technologies. Detection methods that use ensemble algorithms and emerging sequencing technologies have enabled the discovery of thousands of SVs, uncovering information about their ubiquity, relationship to disease and possible effects on biological mechanisms. Given the variability in SV type and size, along with unique detection biases of emerging genomic platforms, multiplatform discovery is necessary to resolve the full spectrum of variation. Here, we review modern approaches for investigating SVs and proffer that, moving forwards, studies integrating biological information with detection will be necessary to comprehensively understand the impact of SV in the human genome.

PMID:
31729472
DOI:
10.1038/s41576-019-0180-9

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