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Prunusspecies include many important perennial fruit crops, such as peach, plum, apricot, and related wild species. Here, we report de novogenome assemblies for five species, including the cultivated species peach (Prunus persica), plum (Prunus salicina), and apricot (Prunus armeniaca), and the wild peach species Tibetan peach (Prunus mira) and Chinese wild peach (Prunus davidiana). The genomes ranged from 240 to 276 Mb in size, with contig N50 values of 2.27−8.30 Mb and 25,333−27,826 protein-coding gene models. As the phylogenetic tree shows, plum diverged from its common ancestor with peach, wild peach species, and apricot ~7 million years ago (MYA). We analyzed whole-genome resequencing data of 417 peach accessions, called 3,749,618 high-quality SNPs, 577,154 small indels, 31,800 deletions, duplications, and inversions, and 32,338 insertions, and performed a structural variant-based genome-wide association study (GWAS) of key agricultural traits. From our GWAS data, we identified a locus associated with a fruit shape corresponding to the OVATE transcription factor, where a large inversion event correlates with higher OVATEexpression in flat-shaped accessions. Furthermore, a GWAS revealed a NAC transcription factor associated with fruit developmental timing that is linked to a tandem repeat variant and elevated NACexpression in early-ripening accessions. We also identified a locus encoding microRNA172d, where insertion of a transposable element into its promoter was found in double-flower accessions. Thus, our efforts have suggested roles for OVATE, a NAC transcription factor, and microRNA172d in fruit shape, fruit development period, and floral morphology, respectively, that can be connected to traits in other crops, thereby demonstrating the importance of parallel evolution in the diversification of several commercially important domesticated species. In general, these genomic resources will facilitate functional genomics, evolutionary research, and agronomic improvement of these five and other Prunusspecies. We believe that structural variant-based GWASs can also be used in other plants, animal species, and humans and be combined with deep sequencing GWASs to precisely identify candidate genes and genetic architecture components.