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.
Highlights
Thousands of genomic structural variants (SVs) segregate in the human population and can impact phenotypic traits and diseases
A joint SV detection approach simplifies the calling process, is computationally more efficient if accessing the large amounts of input data only once, eliminates the need for an error-prone variant merging step, and may reveal weakly supported variants if carried by several individuals as the support can be accumulated across individuals
To enable the joint analysis of the increasingly large cohorts that are being sequenced, we developed a deletion calling approach implemented in the tool PopDel
Summary
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. 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. For single-nucleotide variants (SNVs) and small insertions/ deletions (indels), joint calling has become the state of the art with tools that scale to tens of thousands of individuals[21,22]. We designed the approach to scale to large cohorts and demonstrate that it jointly discovers SVs across tens of thousands of individuals, thereby directly creating joint call sets. Our results demonstrate that the joint deletion detection approach can yield reliable call sets across very many genomes
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