Personalized pangenome references

• Published in: Nature methods Vol. 21; no. 11; pp. 2017 - 2023
• Main Authors: Sirén, Jouni, Eskandar, Parsa, Ungaro, Matteo Tommaso, Hickey, Glenn, Eizenga, Jordan M., Novak, Adam M., Chang, Xian, Chang, Pi-Chuan, Kolmogorov, Mikhail, Carroll, Andrew, Monlong, Jean, Paten, Benedict
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.11.2024; Nature Publishing Group
• Subjects: 631/208; 631/208/212; Accuracy; Algorithms; Bioinformatics; Biological Microscopy; Biological Techniques; Biomedical and Life Sciences; Biomedical Engineering/Biotechnology; Customization; Error reduction; Gene Frequency; Genetic diversity; Genetic Variation; Genome, Human; Genomes; Genomic analysis; Genomics - methods; Genotyping; Graph theory; Graphs; Haplotypes; High-Throughput Nucleotide Sequencing - methods; Humans; Life Sciences; Proteomics; Software; Toolkits
• ISSN: 1548-7091; 1548-7105; 1548-7105
• DOI: 10.1038/s41592-024-02407-2

Pangenomes reduce reference bias by representing genetic diversity better than a single reference sequence. Yet when comparing a sample to a pangenome, variants in the pangenome that are not part of the sample can be misleading, for example, causing false read mappings. These irrelevant variants are generally rarer in terms of allele frequency, and have previously been dealt with by filtering rare variants. However, this blunt heuristic both fails to remove some irrelevant variants and removes many relevant variants. We propose a new approach that imputes a personalized pangenome subgraph by sampling local haplotypes according to k -mer counts in the reads. We implement the approach in the vg toolkit ( https://github.com/vgteam/vg ) for the Giraffe short-read aligner and compare its accuracy to state-of-the-art methods using human pangenome graphs from the Human Pangenome Reference Consortium. This reduces small variant genotyping errors by four times relative to the Genome Analysis Toolkit and makes short-read structural variant genotyping of known variants competitive with long-read variant discovery methods. This work introduces a k -mer-based approach to customizing a pangenome reference, making it more relevant to a new sample of interest. This method enhances the accuracy of genotyping small variants and large structural variants.