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

Area of Science:

• Genomics
• Animal Breeding

Background:

• Structural variants (SVs) represent a significant source of genetic diversity but are often underexplored.
• Accurate detection and genotyping of SVs are crucial for understanding genetic variation and its impact on traits.

Purpose of the Study:

• To develop a Holstein breed-specific pangenome graph (H20D) for enhanced structural variant (SV) detection and genotyping in dairy cattle.
• To evaluate the performance of H20D compared to existing methods and assess the utility of SVs in genome-wide association studies (GWAS).

Main Methods:

• Construction of the H20D pangenome graph using Minigraph-Cactus and 40 phased haploid assemblies from 20 Holstein cows.
• Comparative analysis of SV detection and genotyping performance against assembly- and read-based callers, including short-read approaches.
• Genotyping of SVs in 173 cattle and conducting a genome-wide association study (GWAS).

Main Results:

• H20D significantly outperforms existing long-read and short-read callers, identifying over 10,000 additional SVs per sample.
• The breed-specific, phased pangenome graph shows particular advantages in complex genomic regions.
• A higher proportion of SVs compared to single nucleotide polymorphisms (SNPs) reached genome-wide significance in the GWAS, suggesting their role as causal variants.

Conclusions:

• Phased, within-breed pangenome graphs are powerful tools for accurate SV genotyping in dairy cattle.
• H20D facilitates improved trait mapping by revealing the significance of structural variants in cattle genetics.
• This approach advances the understanding of genetic diversity and its contribution to economically important traits in livestock.