Phased-assembly-driven pangenome graphs for structural variant genotyping and complex trait mapping in dairy cattle

TL;DR

This study creates a detailed genetic map for Holstein cattle to better detect structural variants and link them to important traits.

Contribution

The novel contribution is a phased, breed-specific pangenome graph that improves structural variant detection and genotyping in dairy cattle.

Findings

The H20D pangenome graph identifies over 10,000 additional structural variants per sample compared to other methods.

Phased pangenome graphs significantly improve variant detection in complex genomic regions.

Structural variants show greater genome-wide significance in GWAS than SNPs, suggesting their role in economically important traits.

Abstract

Structural variants are an underexplored source of genetic diversity. As part of the FarmGTEx Project, here we report a Holstein breed-specific pangenome graph (H20D) using Minigraph-Cactus and 40 phased haploid assemblies from 20 cows. H20D outperforms both assembly- and read-based long-read callers, and far exceeds short-read approaches, identifying over 10,000 additional structural variants per sample. It also significantly improves structural variant detection and genotyping relative to graphs built across breeds or from fewer/unphased assemblies, with particular advantages in complex regions. Using H20D, we genotype variants in 173 cattle and performed a GWAS, where a larger fraction of structural variants than SNPs reach genome-wide significance, implicating them as potential causal variants. Together, these results demonstrate the power of phased, within-breed pangenome graphs for accurate SV genotyping and trait mapping in dairy cattle.

Structural variants are an underexplored source of genetic diversity. Here, the authors use phased pangenome graphs to improve detection of these variants in dairy cattle and link these variants to traits of economic importance.

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