The human genome harbors numerous structural variants (SVs) which, due to their repetitive nature, are currently underexplored in short-read whole-genome sequencing approaches. Using single-molecule, real-time (SMRT) long-read sequencing technology in combination with FALCON-Unzip, we generated a de novo assembly of the diploid genome of a 115-year-old Dutch cognitively healthy woman. We combined this assembly with two previously published haploid assemblies (CHM1 and CHM13) and the GRCh38 reference genome to create a compendium of SVs that occur across five independent human haplotypes using the graph-based multi-genome aligner REVEAL. Across these five haplotypes, we detected 31,680 euchromatic SVs (>50 bp). Of these, ~62% were comprised of repetitive sequences with ‘variable number tandem repeats’ (VNTRs), ~10% were mobile elements (Alu, L1, and SVA), while the remaining variants were inversions and indels. We observed that VNTRs with GC-content >60% and repeat patterns longer than 15 bp were 21-fold enriched in the subtelomeric regions (within 5 Mb of the ends of chromosome arms). VNTR lengths can expand to exceed a critical length which is associated with impaired gene transcription. The genes that contained most VNTRs, of which PTPRN2 and DLGAP2 are the most prominent examples, were found to be predominantly expressed in the brain and associated with a wide variety of neurological disorders. Repeat-induced variation represents a sizeable fraction of the genetic variation in human genomes and should be included in investigations of genetic factors associated with phenotypic traits, specifically those associated with neurological disorders. We make available the long and short-read sequence data of the supercentenarian genome, and a compendium of SVs as identified across 5 human haplotypes.

The practical use of graph-based reference genomes depends on the ability to align reads to them. Performing substring queries to paths through these graphs lies at the core of this task. The combination of increasing pattern length and encoded variations inevitably leads to a combinatorial explosion of the search space. Instead of heuristic filtering or pruning steps to reduce the complexity, we propose CHOP, a method that constrains the search space by exploiting haplotype information, bounding the search space to the number of haplotypes so that a combinatorial explosion is prevented. We show that CHOP can be applied to large and complex datasets, by applying it on a graph-based representation of the human genome encoding all 80 million variants reported by the 1000 Genomes Project.