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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Animal Genomics and Improvement Laboratory » Research » Publications at this Location » Publication #423840
Research Project: Accelerating Genetic Improvement of Ruminants Through Enhanced Genome Assembly, Annotation, and Selection

Location: Animal Genomics and Improvement Laboratory

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

Author
item YANG, LIU - University Of Maryland
item GAO, YAHUI - University Of Maryland
item Kuhn, Kristen
item Bhowmik, Nayan
item Li, Wenli
item Zanton, Geoffrey
item FANG, LINGZHAO - Aarhus University
item COLE, JOHN - Council On Dairy Cattle Breeding
item Li, Congjun
item Baldwin, Ransom
item Van Tassell, Curtis
item Rosen, Benjamin
item MA, LI - University Of Maryland
item Smith, Timothy
item Liu, Ge

Submitted to: Nature Communications
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/8/2026
Publication Date: 1/30/2026
Citation: Yang, L., Gao, Y., Kuhn, K.L., Bhowmik, N., Li, W., Zanton, G.I., Fang, L., Cole, J.B., Li, C., Baldwin, R.L., Van Tassell, C.P., Rosen, B.D., Ma, L., Smith, T.P., Liu, G. 2026. Phased-assembly-driven pangenome graphs for structural variant genotyping and complex trait mapping in dairy cattle. Nature Communications. 17. Article 2186. https://doi.org/10.1038/s41467-026-68807-4.
DOI: https://doi.org/10.1038/s41467-026-68807-4

Interpretive Summary: Tradition breeding efforts to introduce needed production traits in dairy cattle would normally take decades to achieve the desired outcome. In approximately the last twenty years scientist have been able to decipher the entire genetic material (genome) of organisms, including dairy cattle. Elucidation of the dairy cattle genome has allowed for the incorporation genetic assisted approaches in breeding regimens to more rapidly select for more economically beneficial production traits. However, to improve upon this approach it is important to have high quality genome information for use as a reference along and a thorough understanding of processes that control genetic variation. Structural variation, a type of genetic variation that includes phenomena such as gene deletions or insertions and rearrangement of genes in the chromosome, is particularly difficult to detect. To address this, we integrated high-quality comparative analyses of the genomes of 20 Holstein and 10 Jersey cattle. Using this approach, we were able to detect and predict structural variations. In addition, analyses of those changes allowed us to uncover key insights into function and biological impact of those changes on dairy cattle phenotype. These findings fill important knowledge gaps and provide a foundation for incorporating structural variation information into future breeding programs, leading to healthier and more productive animals for farmers and the dairy industry in the U.S. and globally.

Technical Abstract: Structural variations (SVs), including insertions, deletions, and complex rearrangements, are crucial components of the genetic diversity driving economically important traits in livestock. Using the Minigraph-Cactus pipeline, we constructed and compared multiple pangenome graphs based on 40 Holstein phased haploid assemblies (H20D) and various breed combinations, assembly counts, and types (diploid or haploid) to capture breed-specific and shared genomic variations. Compared with assembly- and read-based long-read SV calling strategies, the H20D pangenome identified over 10,000 more SVs per sample. Additionally, phased pangenomes improved SV detection and genotyping accuracy over primary haploid assemblies. PanGenie, a mapping-free, pangenome-based SV genotyping strategy, significantly increased SV genotyping from short-read sequencing data, yielding a more comprehensive variant catalog. Leveraging the within-breed H20D pangenome reference, we genotyped common SNVs and SVs across 173 Holstein cattle and performed genome-wide association studies (GWAS). SV- and SNP-based GWAS showed similar significance patterns with overlapping peaks, but a higher proportion of SVs reached genome-wide significance. The SV-based GWAS identified 196 significant SV-trait associations across 29 traits, involving 49 uniquely expressed genes, many of which influence production and other complex phenotypic traits. Our results underscore the biological significance of SVs in cattle genetics and highlight the power of pangenomics in improving genotyping accuracy, breed characterization, and genomic selection. By exploring the optimal settings for pangenome graph construction, this study enhances the ability to identify and utilize genetic diversity for more precise and efficient dairy cattle breeding.