Standardized microhaplotype databases and frameworks for assessing and mining crop genetic diversity

Zhao D.; Lin M.; Taniguti C.H.; Sandercock A.M.; Chen S.; Babiker E.; Bassil N.V.; Brummer E.C.; Camacho J.R.; Chatwin W.; Chen S.-Y.; Clare S.J.; da Silva Pereira G.; David M.; Fraher S.P.; Hardigan M.; Hilton A.; Hislop L.M.; Irish B.M.; Kante M.; Kim T.H.; Lee C.-W.; Lindqvist-Kreuze H.; Loarca J.; Lu P.-H.; Medina Culma C.A.; Jiménez Morales J.F.; Polashock J.; Price J.H.; Riday H.; Samac D.A.; Sandhu D.; Ssali R.; Castro Vásquez R.M.; Wadl P.A.; Wang X.; Webster S.A.; Xu Z.; Yencho G.C.; Beil C.T.; Sheehan M.J. · Theoretical and Applied Genetics (2026) · DOI: 10.1007/s00122-026-05340-4

Resumen

Keymessage: Standardized microhaplotype databases for eight diverse crops enable multiallelic analyses, comparative genetics, and breeding decisions. Abstract: Microhaplotypes are short genomic segments that contain multiple tightly linked variants, providing multi-allelic data that can enhance genetic resolution compared to traditional biallelic single nucleotide polymorphism (SNP) markers. Here, we present the creation and utilization of separate microhaplotype databases for eight crop species representing diverse genome sizes, ploidy levels, and breeding systems. We developed a standardized, species-agnostic pipeline for processing, filtering, and databasing microhaplotypes generated using the DArTag targeted genotyping platform. To enhance user accessibility, we developed a no-code, user-friendly application, HapApp, that uses an R Shiny front-end interface to allow breeders and researchers to add unique, standardized microhaplotype identities from raw DArTag reports and iteratively update the existing crop-specific database with the newly discovered microhaplotypes. Selected case studies with these databases highlight the operational advantages of microhaplotypes, especially for challenging, highly heterozygous, or polyploid species. They offer an informative alternative to traditional biallelic SNP analyses for resolving population structures and improving linkage map ordering. This integrated framework provides a reproducible and scalable foundation for managing and exploiting microhaplotype data in plant breeding and genetic research, enabling robust cross-project comparisons and facilitating trait discovery in both simple and complex crop genomes, while enabling comparative genomics and cross-species functional transfer that accelerates genetic gains across all crop species. © The Author(s) 2026.