Pangenome Graph Node-Phenotype Association shows GWAS-like quality results with only few individuals

Abstract

Purpose: We introduce GraNPA (Graph Node-Phenotype Association), a method that performs a phenotype-sequence association analysis on a pangenome vari- ation graph (PVG). This PVG is built using a small number of individual genome sequences and does not require additional population materials or kinship information for qualitative phenotypes. This approach minimizes the number of individuals required for association studies and mitigates the impact of reference bias in variant calling within these types of analyses. Background: A PVG is a representation of the multiple alignment of a set of complete genomes. It contains all variations, ranging from single nucleotide poly- morphisms (SNPs) to large structural variations (SVs), and these are represented as nodes in the graph. By integrating phenotype information within these nodes, we can assign a Score (S ) to each one and identify regions related to the phe- notype directly within the PVG. These regions represent statistically significant shifts in S distribution, indicating their involvement in the phenotype. Finally, GraNPA provides their positions and scores for further analysis. Results: This method was tested using different simulated contexts and two publicly available datasets: the Sub1A gene locus for Asian rice Oryza sativa in a 13 individuals PVG, and the insertion responsible for the white-headed in cattle (Bos taurus) with a PVG of 24 individuals. Source code of GraNPA is available here https://forge.ird.fr/diade/graphgwas/granpa under GNU GPLv3. Conclusion: GraNPA was able to identify the expected area in two simulated datasets, as well as the loci responsible for these two known traits, using only a few dozen complete genomes from these PVGs. Although this method is currently limited to qualitative phenotypes, it paves the way for more efficient methods that rely on PVGs and a small number of individuals.