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The construction of a high-quality wheat genome variation map is important to wheat genetic studies and breeding. In this study, we developed the second-generation whole-genome genetic variation map of wheat (VMap 2.0) by integrating whole-genome sequencing data of 1,062 diverse wheat accessions from 20 species/subspecies. VMap 2.0 contains 195.96 million single nucleotide polymorphisms (SNPs), 2.22 million insertions, and 4.72 million deletions, achieving a high density of variation map in which one variant exists in every 73 base pairs on average. By systematically analyzing the phylogenetic relationships and genetic diversity of tetraploid wheat, hexaploid wheat, and diploid goatgrass (Aegilops tauschii), we found that the genetic diversity of wild emmer wheat was 2.4 times higher than that of common wheat. In contrast, the genetic diversity of diploid goatgrass is 7.8 times higher than the D subgenome of hexaploid wheat. With the high-density genetic variations, VMap 2.0 is anticipated to facilitate high-resolution trait dissection and expedite prediction-based breeding of wheat.

Deep knowledge of crop biodiversity is essential to improve global food security. Despite bread wheat serving as a keystone crop worldwide, the population history of bread wheat and its wild relatives (a.k.a. wheats) remains elusive. By analyzing whole-genome sequences of 795 wheats, we found that bread wheat originated southwest of the Caspian Sea ∼11,700 years ago and underwent a slow speciation process, lasting ∼3,300 years due to persistent gene flow from wild relatives. Soon after, bread wheat spread across Eurasia and reached Europe, South Asia, and East Asia ∼7,000 to ∼5,000 years ago, shaping a diversified but occasionally convergent adaptive landscape of bread wheat in novel environments. Opposite to cultivated wheat, wild wheat populations have declined by ∼82% in the past ∼2,000 years due to the food choice shift of humans, and likely continue to drop because of the changing climate. These findings will guide future efforts in protecting and utilizing wheat biodiversity to improve global food security.

BackgroundStructural variations (SVs) pervade the genome and contribute substantially to the phenotypic diversity of species. However, most SVs were ineffectively assayed because of the complexity of plant genomes and the limitations of sequencing technologies. Recent advancement of third-generation sequencing technologies, particularly the PacBio high-fidelity (HiFi) sequencing, which generates both long and highly accurate reads, offers an unprecedented opportunity to characterize SVs and reveal their functionality. Since HiFi sequencing is new, it is crucial to evaluate HiFi reads in SV detection before applying the technology at scale.ResultsWe sequenced wheat genomes using HiFi, then conducted a comprehensive evaluation of SV detection using mainstream long-read aligners and SV callers. The results showed the accuracy of SV discovery depends more on aligners rather than callers. For aligners, pbmm2 and NGMLR provided the most accurate results while detecting deletion and insertion, respectively. Likewise, cuteSV and SVIM achieved the best performance across all SV callers. We demonstrated that the combination of the aligners and callers mentioned above is optimal for SV detection. Furthermore, we evaluated the impact of sequencing depth on the accuracy of SV detection. The results showed that low-coverage HiFi sequencing is capable of generating high-quality SV genotyping.ConclusionsThis study provides a robust benchmark of SV discovery with HiFi reads, showing the remarkable potential of long-read sequencing to investigate structural variations in plant genomes. The high accuracy SV discovery from low-coverage HiFi sequencing indicates that skim HiFi sequencing is an ideal approach to study structural variations at the population level.