RT Journal
A1 Markello, Charles
A1 Huang, Charles
A1 Rodriguez, Alex
A1 Carroll, Andrew
A1 Chang, Pi-Chuan
A1 Eizenga, Jordan
A1 Markello, Thomas
A1 Haussler, David
A1 Paten, Benedict
T1 A complete pedigree-based graph workflow for rare candidate variant analysis
JF Genome Research 
JO Genome Research 
YR 2022 
FD May 01 
VO 32 
IS 5 
SP 893 
OP 903 
DO 10.1101/gr.276387.121 
UL http://genome.cshlp.org/content/32/5/893.abstract 
AB Methods that use a linear genome reference for genome sequencing data analysis are reference-biased. In the field of clinical genetics for rare diseases, a resulting reduction in genotyping accuracy in some regions has likely prevented the resolution of some cases. Pangenome graphs embed population variation into a reference structure. Although pangenome graphs have helped to reduce reference mapping bias, further performance improvements are possible. We introduce VG-Pedigree, a pedigree-aware workflow based on the pangenome-mapping tool of Giraffe and the variant calling tool DeepTrio using a specially trained model for Giraffe-based alignments. We demonstrate mapping and variant calling improvements in both single-nucleotide variants (SNVs) and insertion and deletion (indel) variants over those produced by alignments created using BWA-MEM to a linear-reference and Giraffe mapping to a pangenome graph containing data from the 1000 Genomes Project. We have also adapted and upgraded deleterious-variant (DV) detecting methods and programs into a streamlined workflow. We used these workflows in combination to detect small lists of candidate DVs among 15 family quartets and quintets of the Undiagnosed Diseases Program (UDP). All candidate DVs that were previously diagnosed using the Mendelian models covered by the previously published methods were recapitulated by these workflows. The results of these experiments indicate that a slightly greater absolute count of DVs are detected in the proband population than in their matched unaffected siblings.