Dongyan Zhao, Meng Lin, Cristiane H Taniguti, Alexander M Sandercock, Shufen Chen, Ebrahiem Babiker, Nahla V Bassil, E Charles Brummer, Jose Roberto Camacho, Warren Chatwin, Shu-Yun Chen, Shaun J Clare, Guilherme da Silva Pereira, Maria David, Simon Phillip Fraher, Michael Hardigan, Angelyn Hilton, Lillian M Hislop, Brian M Irish, Moctar Kante, Tae Hwa Kim, Chong-Wei Lee, Hannele Lindqvist-Kreuze, Jenyne Loarca, Po-Hsien Lu, Cesar Agusto Medina Culma, Jose Fabián Jiménez Morales, James Polashock, John H Price, Heathcliffe Riday, Deborah A Samac, Devinder Sandhu, Reuben Ssali, Ruth Mayela Castro Vásquez, Phillip A Wadl, Xinwang Wang, Seymour A Webster, Zhanyou Xu, G Craig Yencho, Craig T Beil, Moira J Sheehan
Standardized microhaplotype databases for eight diverse crops enable multiallelic analyses, comparative genetics, and breeding decisions. 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.