PopDel identifies medium-size deletions simultaneously in tens of thousands of genomes

Sebastian Niehus, Hákon Jónsson, Janina Schönberger, Eythór Björnsson, Doruk Beyter, Hannes P. Eggertsson, Patrick Sulem, Kári Stefánsson, Bjarni V. Halldórsson and Birte Kehr ()
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Sebastian Niehus: Regensburg Center for Interventional Immunology (RCI)
Hákon Jónsson: deCODE genetics/Amgen Inc.
Janina Schönberger: Regensburg Center for Interventional Immunology (RCI)
Eythór Björnsson: deCODE genetics/Amgen Inc.
Doruk Beyter: deCODE genetics/Amgen Inc.
Hannes P. Eggertsson: deCODE genetics/Amgen Inc.
Patrick Sulem: Charité—Universitätsmedizin Berlin
Kári Stefánsson: deCODE genetics/Amgen Inc.
Bjarni V. Halldórsson: deCODE genetics/Amgen Inc.
Birte Kehr: Regensburg Center for Interventional Immunology (RCI)

Nature Communications, 2021, vol. 12, issue 1, 1-10

Abstract: Abstract Thousands of genomic structural variants (SVs) segregate in the human population and can impact phenotypic traits and diseases. Their identification in whole-genome sequence data of large cohorts is a major computational challenge. Most current approaches identify SVs in single genomes and afterwards merge the identified variants into a joint call set across many genomes. We describe the approach PopDel, which directly identifies deletions of about 500 to at least 10,000 bp in length in data of many genomes jointly, eliminating the need for subsequent variant merging. PopDel scales to tens of thousands of genomes as we demonstrate in evaluations on up to 49,962 genomes. We show that PopDel reliably reports common, rare and de novo deletions. On genomes with available high-confidence reference call sets PopDel shows excellent recall and precision. Genotype inheritance patterns in up to 6794 trios indicate that genotypes predicted by PopDel are more reliable than those of previous SV callers. Furthermore, PopDel’s running time is competitive with the fastest tested previous tools. The demonstrated scalability and accuracy of PopDel enables routine scans for deletions in large-scale sequencing studies.

Date: 2021
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DOI: 10.1038/s41467-020-20850-5

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