Scalable and comprehensive mosaic variant calling using DRAGEN.
| Publication Type | Preprint |
| Authors | Behera S, Rossi M, Wang Y, Izydorczyk M, Tran D, Dalgard C, Kalef-Ezra E, Kottapalli K, Mehta H, Parnaby G, Risse-Adams O, Scholz S, Shen H, Nelson T, Visvanath A, Zheng X, Doddapaneni H, Garcia T, Mason C, Proukakis C, Han J, Mehio R, Catreux S, Sedlazeck F |
| Journal | medRxiv |
| Date Published | 02/04/2026 |
| Abstract | Detecting low variant allele fraction (VAF) mosaic variants without matching controls remains a major challenge in genomics, limited by technical noise, lack of benchmarks, and computational scalability. We present the DRAGEN mosaic caller, a hardware-accelerated approach identifying variants down to ~1-2% VAF with low false-positive rates and hour-scale runtimes for mosaic SNV/indel detection from bulk sequencing. To support evaluation, we introduce a genome-wide low-VAF benchmark for variants between 1-10% VAF. Application to blood, sperm, and brain tissues revealed patterns, including mosaic hotspots and mutational signatures. The first analysis of HG002 blood showed that many "mosaic" variants defined from HG002 cell lines are likely culture-derived and not in vivo mutations. Importantly, DRAGEN also enables personalized assembly pangenome references to improve alignment and mosaic variant detection in complex regions. Together, this development makes routine low-VAF discovery feasible, opening new opportunities to study mosaic mutations in healthy and disease individuals. |
| DOI | 10.64898/2026.02.03.26345450 |
| PubMed ID | 41674597 |
| PubMed Central ID | PMC12889792 |