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Genome Res. 2018 Aug;28(8):1228-1242. doi: 10.1101/gr.229401.117. Epub 2018 Jun 15.

Predicting human genes susceptible to genomic instability associated with Alu/Alu-mediated rearrangements.

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Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas 77030, USA.
Baylor Genetics, Houston, Texas 77021, USA.
Department of Pediatrics, Baylor College of Medicine, Houston, Texas 77030, USA.
Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas 77030, USA.
Texas Children's Hospital, Houston, Texas 77030, USA.


Alu elements, the short interspersed element numbering more than 1 million copies per human genome, can mediate the formation of copy number variants (CNVs) between substrate pairs. These Alu/Alu-mediated rearrangements (AAMRs) can result in pathogenic variants that cause diseases. To investigate the impact of AAMR on gene variation and human health, we first characterized Alus that are involved in mediating CNVs (CNV-Alus) and observed that these Alus tend to be evolutionarily younger. We then computationally generated, with the assistance of a supercomputer, a test data set consisting of 78 million Alu pairs and predicted ∼18% of them are potentially susceptible to AAMR. We further determined the relative risk of AAMR in 12,074 OMIM genes using the count of predicted CNV-Alu pairs and experimentally validated the predictions with 89 samples selected by correlating predicted hotspots with a database of CNVs identified by clinical chromosomal microarrays (CMAs) on the genomes of approximately 54,000 subjects. We fine-mapped 47 duplications, 40 deletions, and two complex rearrangements and examined a total of 52 breakpoint junctions of simple CNVs. Overall, 94% of the candidate breakpoints were at least partially Alu mediated. We successfully predicted all (100%) of Alu pairs that mediated deletions (n = 21) and achieved an 87% positive predictive value overall when including AAMR-generated deletions and duplications. We provided a tool, AluAluCNVpredictor, for assessing AAMR hotspots and their role in human disease. These results demonstrate the utility of our predictive model and provide insights into the genomic features and molecular mechanisms underlying AAMR.

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