Adoption of standard reference SNP udentifiers in agricultural genomics to enable interoperability and data reuse

Authors: TELLO-RUIZ, MARCELA - Cold Spring Harbor Laboratory; CEZARD, TIMOTHEE - Embl-Ebi; Andorf, Carson; BALYAN, SONIA - Government Of India; Bassil, Nahla; BEIER, SEBASTIAN - Julich Research Center; Bushakra, Jill; CHANG, TAO-HO - Chung Hsing University; CHOGULE, KAPEEL - Cold Spring Harbor Laboratory; COBO-SIMON, IRENE - Consultant; DYER, SARAH - Embl-Ebi; ELSIK, CHRISTINE - University Of Missouri; Gladman, Nicholas; Harrison, Melanie; HUMANN, JODI - Washington State University; KIM, CATHERINE - Cold Spring Harbor Laboratory; KUMAR, VIVEK - Cold Spring Harbor Laboratory; Nandety, Raja Sekhar; Nelson, Rex; OLSON, ANDREW - Cold Spring Harbor Laboratory; Sen, Taner; SHEHAN, MOIRA - Cornell University; WEI, SHARON - Cold Spring Harbor Laboratory; Ware, Doreen

Submitted to: Scientific Data
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/1/2026
Publication Date: 4/16/2026
Citation: Tello-Ruiz, M.K., Cezard, T., Andorf, C.M., Balyan, S., Bassil, N.V., Beier, S., Bushakra, J., Chang, T., Chogule, K., Cobo-Simon, I., Dyer, S., Elsik, C.G., Gladman, N.P., Harrison, M.L., Humann, J., Kim, C., Kumar, V., Nandety, R., Nelson, R., Olson, A., Sen, T.Z., Shehan, M., Wei, S., Ware, D. 2026. Adoption of Standard Reference SNP Identifiers in Agricultural Genomics to Enable Interoperability and Data Reuse. Scientific Data. 2026. https://doi.org/10.1038/s41597-026-07208-0.
DOI: https://doi.org/10.1038/s41597-026-07208-0

Interpretive Summary: Data sharing in agricultural genomics has historically been fragmented, often relying on informal channels and requiring extensive cleanup before data can be reused. This has hindered collaboration, reproducibility, and the broader impact of research. While other fields—such as biomedicine—have embraced the FAIR principles (Findable, Accessible, Interoperable, and Reusable) to guide data management, agricultural research has lagged behind in adopting these best practices. The AgBioData Standards for Genetic Variation Working Group is working to bridge this gap by promoting community-driven approaches to FAIR data sharing. A key recommendation from the group is the broader adoption of reference SNP identifiers (rsIDs)—globally recognized, stable variant identifiers that enable consistent tracking and integration of genetic variation data across studies, platforms, and genome assemblies. Through surveys and case studies across multiple crop communities—ranging from well-resourced databases to species without reference genomes—the group has identified both barriers and pathways to standardization. Notable achievements include the integration of over 193 million rsIDs into Gramene and associated pan-genome databases, enabling cross-reference of variants across different crop species and genome versions. rsIDs have also been projected across sorghum and maize pan-genomes, providing a framework for comparative and trait-focused analyses. Additionally, the group has developed a practical guide to help agricultural researchers adopt rsIDs and align with FAIR data standards. Ultimately, the work underscores the urgent need for better coordination among data producers, public repositories, and breeding platforms. Standardizing variant identifiers is a foundational step that can unlock interoperability, improve data consistency, and accelerate the discovery of genetic variants linked to key crop traits. This community-driven effort is a pivotal move toward a more connected, reusable, and impactful agricultural genomics ecosystem.

Technical Abstract: Agricultural research has long faced challenges with data sharing, often relying on informal networks and requiring significant effort to clean and harmonize data. This hampers collaboration and limits data reuse. While FAIR (Findable, Accessible, Interoperable, and Reusable) principles are widely adopted in biomedical research, their uptake in agricultural genomics has lagged. The AgBioData Standards for Genetic Variation Working Group aims to close this gap by promoting FAIR data practices. We surveyed current standards for managing agricultural genetic variation and recommend adopting reference SNP identifiers (rsIDs) as a key step. We present examples from crop research communities with varying data maturity, including those without reference assemblies. Milestones include introducing over 193 million rsIDs to Gramene and pan-genome databases, projecting rsIDs from reference to pan-genome varieties in sorghum and maize, and developing an agricultural FAIR guide for rsID adoption. Better coordination among data producers, repositories, and breeding platforms is essential to improve interoperability, consistency, and accelerate genetic variant discovery for crop trait improvement.