LAUSR

Description Giraffe pangenomes Genomes within a species often have a core, conserved component, as well as a variable set of genetic material among individuals or populations that is referred to as a “pangenome.” Inference of the relationships between pangenomes sequenced with short-read technology is often done computationally by mapping the sequences to a reference genome. The computational method affects genome assembly and comparisons, especially in cases of structural variants that are longer than an average sequenced region, for highly polymorphic loci, and for cross-species analyses. Siren et al. present a bioinformatic method called Giraffe, which improves mapping pangenomes in polymorphic regions of the genome containing single nucleotide polymorphisms and structural variants with standard computational resources, making large-scale genomic analyses more accessible. —LMZ A mapping algorithm named Giraffe has been developed to allow mapping of short-read sequences for thousands of 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. Overview of the experiments. Variant calls from long read–based and large-scale sequencing studies were used to construct pangenome reference graphs (top). Giraffe (and competing mappers) mapped reads to the graph or to linear references, and mapping accuracy, allele coverage balance, and speed were evaluated (middle). Then, mapped reads were used for variant calling, and variant call accuracy was evaluated (bottom). Structural variant calls were analyzed alongside expression data to identify eQTLs and population frequency estimates. We introduce Giraffe, a pangenome short-read mapper that can efficiently map to a collection of haplotypes threaded through a sequence graph. Giraffe maps sequencing reads to thousands of human genomes at a speed comparable to that of standard methods mapping to a single reference genome. The increased mapping accuracy enables downstream improvements in genome-wide genotyping pipelines for both small variants and larger structural variants. We used Giraffe to genotype 167,000 structural variants, discovered in long-read studies, in 5202 diverse human genomes that were sequenced using short reads. We conclude that pangenomics facilitates a more comprehensive characterization of variation and, as a result, has the potential to improve many genomic analyses.