Pan-genome inversion index reveals evolutionary insights into the subpopulation structure of Asian rice

Abstract

Understanding and exploiting genetic diversity is a key factor for the productive and stable production of rice. Here, we utilize 73 high-quality genomes that encompass the subpopulation structure of Asian rice (Oryza sativa), plus the genomes of two wild relatives (O. rufipogon and O. punctata), to build a pan-genome inversion index of 1769 non-redundant inversions that span an average of ~29% of the O. sativa cv. Nipponbare reference genome sequence. Using this index, we estimate an inversion rate of ~700 inversions per million years in Asian rice, which is 16 to 50 times higher than previously estimated for plants. Detailed analyses of these inversions show evidence of their effects on gene expression, recombination rate, and linkage disequilibrium. Our study uncovers the prevalence and scale of large inversions (≥100 bp) across the pan-genome of Asian rice and hints at their largely unexplored role in functional biology and crop performance.

Unravelling inversions: Technological advances, challenges, and potential impact on crop breeding

“…Recently, Zhou et al . ( 2023 ) estimated an inversion rate of 67.4 inversions per million years using 1769 inversions, which is two to four times higher than previous estimations. The observed differences may arise from the differential capacity to identify inversions of varying sizes between the two studies, likely attributable to the accessibility and quality of the genomes used.…”

“…A potentially more comparable and functional classification of inversions into three groups can be conducted based on their lengths relative to the chromosome size: small (>50 bp and <0.0025%), intermediate (≥0.0025% and <1.25%) and large inversions (≥1.25%). These guideline percentages are evaluated based on commonly used inversion length thresholds (Bansal et al ., 2007 ; Harringmeyer and Hoekstra, 2022 ; Zhang et al ., 2023 ; Zhou et al ., 2023 ), with the hope that further studies will enable a more rigorous functional classification of inversions. Inversions with different lengths tend to have different evolutionary and functional impacts on organisms.…”

Multitrait engineering of Hassawi red rice for sustainable cultivation

“…Structural variation analysis was performed following standard workflows (RPFP, https://yongzhou2019.github.io/Rice-Population-Reference-Panel/software/) for a rice population reference panel [37]. Briefly, using the Hassawi genome as a reference, the presence and absence of SVs was analyzed using NGMLR [38] as an aligner and SVIM [39] as a caller.…”

Technological Development and Advances for Constructing and Analyzing Plant Pangenomes

“…This facilitates the exploration of genome features and the association of genomic variants with plant traits. For example, a rice pangenome study implemented a machine learning–based workflow to identify inversions and produce a pangenome-wide inversion index using high-throughput sequencing data from the 3K-Rice Genome Project ( Zhou et al 2023 ). A separate study employed soybean pangenome data and multiple machine learning–based models for trait prediction ( Gill et al 2022 ).…”