Two Evolutionary Histories in the Genome of Rice: the Roles of Domestication Genes

State Key Laboratory of Biocontrol and Guangdong Key Laboratory of Plant Resources, Sun Yat-Sen University, Guangzhou, China.
PLoS Genetics (Impact Factor: 7.53). 06/2011; 7(6):e1002100. DOI: 10.1371/journal.pgen.1002100
Source: PubMed


Genealogical patterns in different genomic regions may be different due to the joint influence of gene flow and selection. The existence of two subspecies of cultivated rice provides a unique opportunity for analyzing these effects during domestication. We chose 66 accessions from the three rice taxa (about 22 each from Oryza sativa indica, O. sativa japonica, and O. rufipogon) for whole-genome sequencing. In the search for the signature of selection, we focus on low diversity regions (LDRs) shared by both cultivars. We found that the genealogical histories of these overlapping LDRs are distinct from the genomic background. While indica and japonica genomes generally appear to be of independent origin, many overlapping LDRs may have originated only once, as a result of selection and subsequent introgression. Interestingly, many such LDRs contain only one candidate gene of rice domestication, and several known domestication genes have indeed been "rediscovered" by this approach. In summary, we identified 13 additional candidate genes of domestication.

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Available from: Anthony J Greenberg, Oct 04, 2015
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    • "Xu et al. (2012) directly resequenced 50 accessions of cultivated and wild rice, and identified genome-wide variation patterns including the identification of more than 6.5 million high-quality SNPs, 808,000 InDels, 94,700 SVs (>100 bp) and 1,676 CNVs. In another study, 66 accessions from three taxa (22 each from O. sativa indica, O. sativa japonica and O. rufipogon) were chosen for whole genome sequencing (He et al. 2011). Huang et al. (2012a) also generated genome sequences from 446 geographically diverse accessions of the wild rice species O. rufipogon, the immediate ancestral progenitor of cultivated rice, and from 1,083 cultivated indica and japonica varieties, to construct a comprehensive map of rice genome variation. "
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    ABSTRACT: Rice is a model system used for crop genomics studies. The completion of the rice genome draft sequences in 2002 not only accelerated functional genome studies, but also initiated a new era of resequencing rice genomes. Based on the reference genome in rice, next-generation sequencing (NGS) using the high-throughput sequencing system can efficiently accomplish whole genome resequencing of various genetic populations and diverse germplasm resources. Resequencing technology has been effectively utilized in evolutionary analysis, rice genomics and functional genomics studies. This technique is beneficial for both bridging the knowledge gap between genotype and phenotype and facilitating molecular breeding via gene design in rice. Here, we also discuss the limitation, application and future prospects of rice resequencing.
    Rice 07/2014; 7(1):4. DOI:10.1186/s12284-014-0004-7 · 3.92 Impact Factor
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    • "putative domestication-associated miRNA genes were characterized. In addition, 24, 36, and 50 miRNAs were identified from other studies by searching the sequence regions with extremely low level of polymorphisms in cultivated but not in wild rice (He et al. 2011; Yang et al. 2012; Yonemaru et al. 2012). Taken together, here, if a miRNA was found within three out of the four studies mentioned above, it was considered as a putative domestication-related candidate (DR-miRNA). "
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    ABSTRACT: Background MiRNAs are key regulators in the miRNA-mediated regulatory networks. Single nucleotide polymorphisms (SNPs) that occur at miRNA-related regions may cause serious phenotype changes. To gain new insights into the evolution of miRNAs after SNP variation, we performed a genome-wide scan of miRNA-related SNPs, and analyzed their effects on the stability of miRNAs structure and the alteration of target spectrum in rice. Results We find that the SNP density in pre-miRNAs is significantly higher than that in the flanking regions, owing to the rapid evolution of a large number of species-specific miRNAs in rice. In contrast, it is obvious that deeply conserved miRNAs are under strong purifying selection during evolution. In most cases, the SNPs in stem regions may result in the miRNA hairpin structures changing from stable to unstable status; And SNPs in mature miRNAs have great potential to have either newly created or disrupted the miRNA-target interactions. However, the total number of gained targets is over 2.5 times greater than that are lost after mutation. Notably, 12 putative domestication-related miRNAs have been identified, where the SNP density is significantly lower. Conclusions The present study provides the first outline of SNP variations occurred in rice pre-miRNAs at the whole genome-wide level. These analyses may deepen our understanding on the effects of SNPs on the evolution of miRNAs in the rice genome.
    Rice 11/2013; 6(1). DOI:10.1186/1939-8433-6-10 · 3.92 Impact Factor
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    • "Given the low coverage for each individual, variant frequencies, rather than the genotypes of individuals, are the quantities of interest. Pooling samples for bulk sequencing may be equally informative but at a lower cost and effort [16]. When pooled samples are sequenced, each haploid genome would not present equally in the final data and the coverage would vary from site to site. "
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    ABSTRACT: As the error rate is high and the distribution of errors across sites is non-uniform in next generation sequencing (NGS) data, it has been a challenge to estimate DNA polymorphism (theta) accurately from NGS data. By computer simulations, we compare the two methods of data acquisition - sequencing each diploid individual separately and sequencing the pooled sample. Under the current NGS error rate, sequencing each individual separately offers little advantage unless the coverage per individual is high (>20X). We hence propose a new method for estimating theta from pooled samples that have been subjected to two separate rounds of DNA sequencing. Since errors from the two sequencing applications are usually non-overlapping, it is possible to separate low frequency polymorphisms from sequencing errors. Simulation results show that the dual applications method is reliable even when the error rate is high and theta is low. In studies of natural populations where the sequencing coverage is usually modest (~2X per individual), the dual applications method on pooled samples should be a reasonable choice.
    BMC Genomics 08/2013; 14(1):535. DOI:10.1186/1471-2164-14-535 · 3.99 Impact Factor
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