Kelley Paskov1, Brianna Chrisman2, Nathaniel Stockham3, Peter Yigitcan Washington2, Kaitlyn Dunlap1,4, Jae-Yoon Jung1,4 and Dennis P. Wall1,4 1Department of Biomedical Data Science, Stanford University, Stanford, California 94305, USA; 2Department of Bioengineering, Stanford University, Stanford, California 94305, USA; 3Department of Neuroscience, Stanford University, Stanford, California 94305, USA; 4Department of Pediatrics, Stanford University, Stanford, California 94305, USA Corresponding author: kpaskovstanford.edu Abstract

Large, whole-genome sequencing (WGS) data sets containing families provide an important opportunity to identify crossovers and shared genetic material in siblings. However, the high variant calling error rates of WGS in some areas of the genome can result in spurious crossover calls, and the special inheritance status of the X Chromosome presents challenges. We have developed a hidden Markov model that addresses these issues by modeling the inheritance of variants in families in the presence of error-prone regions and inherited deletions. We call our method PhasingFamilies. We validate PhasingFamilies using the platinum genome family NA1281 (precision: 0.81; recall: 0.97), as well as simulated genomes with known crossover positions (precision: 0.93; recall: 0.92). Using 1925 quads from the Simons Simplex Collection, we found that PhasingFamilies resolves crossovers to a median resolution of 3527.5 bp. These crossovers recapitulate existing recombination rate maps, including for the X Chromosome; produce sibling pair IBD that matches expected distributions; and are validated by the haplotype estimation tool SHAPEIT. We provide an efficient, open-source implementation of PhasingFamilies that can be used to identify crossovers from family sequencing data.

Received August 2, 2022. Accepted September 12, 2023.