Pangenomics enables genotyping of known structural variants in 5202 diverse genomes

INTRODUCTION Modern genomics depends on inexpensive short-read sequencing. Sequenced reads up to a few hundred base pairs in length are computationally mapped to estimated source locations in a reference genome. These read mappings are used in myriad sequencing-based assays. For example, through a process called genotyping, mapped reads from a DNA sample can be used to infer the combination of alleles present at each site in the reference genome. RATIONALE A single reference genome cannot capture the diversity within even a single person (who gets a genome copy from each parent), let alone in the whole human population. Genomes differ not only by point variations, where one or a few bases are different, but also by structural variations, where differences can be much larger than an individual read. When a person’s genome differs from the reference by a structural variation, the reference may contain no location to correctly map the corresponding reads. Although newer long-read sequencing allows structural variation to be more directly observed in sequencing reads, short-read sequencing is still less expensive and more widely available. RESULTS We present a short read–mapping tool, Giraffe. Giraffe maps to a pangenome reference that describes many genomes and the differences between them. Giraffe can accurately map reads to thousands of genomes embedded in a pangenome reference as quickly as existing tools map to a single reference genome. Simulations in which the true mapping for each read is known show that Giraffe is as accurate as the most accurate previously published tool. Giraffe achieves this speed and accuracy by using a variety of algorithmic techniques. In particular, and in contrast to previous tools, it focuses on mapping to the paths in the pangenome that are observed in individuals’ genomes: the reference haplotypes. This has two key benefits. First, it prioritizes alignments that are consistent with known sequences, avoiding combinations of alleles that are biologically unlikely. Second, it reduces the size of the problem by limiting the sequence space to which the reads could be aligned. This deals effectively with complex graph regions where most paths represent rare or nonexistent sequences. Using Giraffe in place of a single reference genome reduces mapping bias, which is the tendency to incorrectly map reads that differ from the reference genome. Combining Giraffe with state-of-the-art genotyping algorithms demonstrates that Giraffe mappings produce accurate genotyping results. Using mappings from Giraffe, we genotyped 167,000 recently discovered structural variations in short-read samples for 5202 people at an average computational cost of $1.50 per sample. We present estimates for the frequency of different versions of these structural variations in the human population as a whole and within individual subpopulations. We identify thousands of these structural variations as expression quantitative trait loci (eQTLs), which are associated with gene-expression levels. CONCLUSION Giraffe demonstrates the practicality of a pangenomic approach to short-read mapping. This approach allows short-read data to genotype single-nucleotide variations, short insertions and deletions, and structural variations more accurately. For structural variations, this allowed the estimation of population frequencies across a diverse cohort of 5000 individuals. A single reference genome must choose one version of any variation to represent, leaving the other versions unrepresented. By making more broadly representative pangenome references practical, Giraffe attempts to make genomics more inclusive.

• Health & Bioscience