Pangenome-Wide Association Studies with Frequented Regions

Abstract

Connecting genetic variation (genotype) to trait variation (phenotype) is a critical but often difficult step in genetic research. A genome-wide association study (GWAS) is a common approach to connect underlying genetic variation to complex phenotypic traits, allowing for phenotypic prediction. GWAS is important in many disciplines, including identifying genetic risk factors for common, complex diseases, identifying genes underlying important traits and predicting phenotypes from genotypes. GWAS is limited, though, in that the types of variations typically studied are single nucleotide polymorphisms (SNPs) identified relative to a single reference genome. These limitations lead to bias and preclude GWAS from studies across related species. The advent of next-generation sequencing has brought an exponential growth in DNA sequence data. This has led to the more comprehensive pangenomics approach, where the entire sequence content and variation of a population are succinctly represented independent of a reference. In prior work, we developed a method for identifying genomic regions that characterize complex variations within pangenomic data and showed that these regions provide a more general way to study genetic variation than existing approaches. This work describes our initial results to develop new methods for a new branch of genomic analysis called pangenome-wide association studies (PWAS) that generalizes GWAS to pangenomic datasets both within and across species. We make use of recently developed algorithms for fast compressed De Bruijn graph construction and identifying frequented regions in these graphs that can be used as machine-learning features to identify pangenomic regions, overlaid with gene annotations, that relate to complex phenotypic traits. Initial results on a pangenome composed of 100 yeast indicate that frequented region features provide better machine-learning regression models than SNPs for predicting phenotypic traits.

Haplotype-aware pantranscriptome analyses using spliced pangenome graphs

“…As a result, pangenomes made from tens to hundreds of reference-quality genome assemblies have been constructed for several agricultural organisms [53][54][55], and similar efforts are underway for humans by the Human Pangenome Reference Consortium and others [56]. Simultaneously, the bioinformatics tools to do pangenomic analyses have matured to the point of practicality for many applications [11,57,58]. Moving forward, we anticipate that pangenomic methods will continue to expand to inform increasingly many areas of genomics [59].…”

GWAS for genetics of complex quantitative traits: Genome to pangenome and SNPs to SVs and k‐mers

“…The growth in activity involving pangenomics and SVs in plants has been summarized in some recent reviews, including the three reviews that were cited earlier. [6][7][8]116] These reviews also discuss the use of pangenomes for plant breeding. A list of important agronomical traits in crops, associated with SVs, is also available in these reviews.…”

Scalable, ultra-fast, and low-memory construction of compacted de Bruijn graphs with Cuttlefish 2

“…tools (38)(39)(40)(41)(42)(43) frequently make use of the maximal unitigs of the input references as the primary units upon which their core data structures and algorithms are built.…”