Abstract
BackgroundCopy number variations (CNVs) are an important type of structural variations in the genome that usually affect gene expression levels by gene dosage effect. Understanding CNVs as part of genome evolution may provide insights into the genetic basis of important agricultural traits and contribute to the crop breeding in the future. While available methods to detect CNVs utilizing next-generation sequencing technology have helped shed light on prevalence and effects of CNVs, the complexity of crop genomes poses a major challenge and requires development of additional tools.ResultsHere, we generated genomic and transcriptomic data of 93 rice (Oryza sativa L.) accessions and developed a comprehensive pipeline to call CNVs in this large-scale dataset. We analyzed the correlation between CNVs and gene expression levels and found that approximately 13% of the identified genes showed a significant correlation between their expression levels and copy numbers. Further analysis showed that about 36% of duplicate pairs were involved in pseudogenetic events while only 5% of them showed functional differentiation. Moreover, the offspring copy mainly contributed to the expression levels and seemed more likely to become a pseudogene, whereas the parent copy tended to maintain the function of ancestral gene.ConclusionWe provide a high-accuracy CNV dataset that will contribute to functional genomics studies and molecular breeding in rice. We also showed that gene dosage effect of CNVs in rice is not exponential or linear. Our work demonstrates that the evolution of duplicated genes is asymmetric in both expression levels and gene fates, shedding a new insight into the evolution of duplicated genes.
Highlights
Copy number variations (CNVs) are an important type of structural variations in the genome that usually affect gene expression levels by gene dosage effect
The data volume of each sample was above 5 Gb and 576 Gb raw RNA-seq data were generated from the 93 accessions in total
We firstly assembled the genome of each accession by CtgRef-CNV and mapped the next-generation sequencing (NGS) reads from each accession to its own assembled genome to obtain the depth data
Summary
Copy number variations (CNVs) are an important type of structural variations in the genome that usually affect gene expression levels by gene dosage effect. Many methods have been developed to detect CNV, such as fluorescence in situ hybridization (FISH), quantitative polymerase chain reaction (qPCR), and microarray These methods are not suitable to detect CNVs in natural population, due to the low throughput or the low resolution and sensitivity. With the advantages of next-generation sequencing (NGS) technologies, new approaches and algorithms have been developed to detect novel CNVs in recent years [5, 6]. These methods are mainly based on the individual or combination of the following strategies: read-pair (RP), split read (SR), read depth (RD), de novo assembly (AS) [7,8,9]. The complexity of crop genomes and the structure and distribution of CNVs, make it a challenge to comprehensively and accurately detect CNVs among different germplasms of crop
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