Scalable and rare-variant aware genome inference across the 1kGP cohort

Abstract

Pangenome graphs built from haplotype-resolved de novo assemblies enable accurate analysis of genetic variation. The short-read-based tool PanGenie efficiently genotypes variants discovered in a pangenome across large cohorts and outper-forms linear reference-based methods for structural variants (SVs). However, it cannot detect novel variants absent from the graph, missing many rare SVs (allele frequency < 1%) and was limited to graphs with 254 haplotypes. First, we introduce a haplotype sampling step that reduces the number of haplotypes using sample-specific k-mers before genotyping, decreasing runtime twelvefold and memory usage 1.4-fold at 30x coverage. Second, we present a polishing work-flow that corrects residual errors in haplotypes inferred from PanGenie genotypes and incorporates rare and private mutations. We genotype 3,202 samples from the 1000 Genomes Project and use low-coverage ONT data (967 samples) for polishing. We achieve a median QV of 46 and provide the 1,934 polished haplotype sequences as a community resource.