Characterization of the transcriptional divergence between the subspecies of cultivated rice (Oryza sativa)

Authors

Malachy T. Campbell, University of Nebraska-Lincoln &Follow
Qian Du, University of Nebraska-Lincoln
Kan Li, Univeresity of Nebraska-LincolnFollow
Sandeep Sharma, University of Nebraska-Lincoln & CSIR-CSMCRI, Bhavnagar, Gujarat, India
Chi Zhang, University of Nebraska-LincolnFollow
Harkamal Walia, University of Nebraska-LincolnFollow

ORCID IDs

Malachy T. Campbell

Harkamal Walia

Date of this Version

2020

Citation

BMC Genomics (2020) 21:394 https://doi.org/10.1186/s12864-020-06786-6

Comments

© The Author(s). 2020

Abstract

Background: Cultivated rice consists of two subspecies, Indica and Japonica, that exhibit well-characterized differences at the morphological and genetic levels. However, the differences between these subspecies at the transcriptome level remains largely unexamined. Here, we provide a comprehensive characterization of transcriptome divergence and cis-regulatory variation within rice using transcriptome data from 91 accessions from a rice diversity panel (RDP1).

Results: The transcriptomes of the two subspecies of rice are highly divergent. Japonica have significantly lower expression and genetic diversity relative to Indica, which is likely a consequence of a population bottleneck during Japonica domestication. We leveraged high-density genotypic data and transcript levels to identify cis-regulatory variants that may explain the genetic divergence between the subspecies. We identified significantly more eQTL that were specific to the Indica subspecies compared to Japonica, suggesting that the observed differences in expression and genetic variability also extends to cis-regulatory variation.

Conclusions: Using RNA sequencing data for 91diverse rice accessions and high-density genotypic data, we show that the two species are highly divergent with respect to gene expression levels, as well as the genetic regulation of expression. The data generated by this study provide, to date, the largest collection of genome-wide transcriptional levels for rice, and provides a community resource to accelerate functional genomic studies in rice.