Back

Genomes of Aegilops umbellulata provide new insights into unique structural variations and genetic diversity in the U-genome for wheat improvement

Singh, J.; Gudi, S.; Maughan, P. J.; Liu, Z.; Kolmer, J.; Wang, M.; Chen, X.; Rouse, M.; Lasserre-Zuber, P.; Rimbert, H.; Sehgal, S.; Fiedler, J.; Choulet, F.; Acevado, M.; Gupta, R.; Gill, U.

2024-01-14 plant biology
10.1101/2024.01.13.575480 bioRxiv
Show abstract

Aegilops spp. serve as an important reservoir for novel sources of resistance or tolerance to biotic and abiotic stresses. To harness this reservoir, we have generated a high-quality chromosome-level genome assembly of an Ae. umbellulata accession PI 554389 using a combination of PacBio HiFi, Oxford nanopore, and chromosome conformation capture (Hi-C) sequencing technologies and resequenced 20 Ae. umbellulata genomes using Illumina sequencing. We assembled a 4.20 Gb genome spanned over seven chromosomes, rich in repetitive elements ([~]84%), achieving a QV of 59.54 with 98.14% completeness. The phylogenetic analysis places the U-genome with D-lineage, but major and distinct rearrangements were revealed in the U-genome. Unique transposon landscape of diploid U-genome and complex chromosomal rearrangements, most prominently in 4U and 6U chromosomes uncovered a distinct evolutionary trajectory of Ae. umbellulata. Additionally, the resequencing of geographically and morphologically diverse Ae. umbellulata accessions revealed three distinctive evolutionary sub-populations. Resequencing also identified six new haplotypes for Lr9, the first leaf rust resistance gene introgressed and cloned from Ae. umbellulata. These genomics resources along with high levels of resistance in the resequenced accessions against five devastating wheat diseases affirmed the genetic potential of Ae. umbellulata for wheat improvement.

Matching journals

The top 6 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.