Skip to main content

ORIGINAL RESEARCH article

Front. Plant Sci., 18 November 2021
Sec. Plant Systematics and Evolution
https://doi.org/10.3389/fpls.2021.781793

The History and Diversity of Rice Domestication as Resolved From 1464 Complete Plastid Genomes

  • 1Shenzhen Branch, Guangdong Laboratory of Lingnan Modern Agriculture, Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs, Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, Shenzhen, China
  • 2MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, China
  • 3Institute of Biological and Environmental Sciences, University of Aberdeen, Aberdeen, United Kingdom
  • 4School of Landscape and Architecture, Zhejiang A&F University, Hangzhou, China
  • 5Department of Horticulture, Washington State University, Pullman, WA, United States
  • 6Department of Agricultural Biology, Colorado State University, Fort Collins, CO, United States

The plastid is an essential organelle in autotrophic plant cells, descending from free-living cyanobacteria and acquired by early eukaryotic cells through endosymbiosis roughly one billion years ago. It contained a streamlined genome (plastome) that is uniparentally inherited and non-recombinant, which makes it an ideal tool for resolving the origin and diversity of plant species and populations. In the present study, a large dataset was amassed by de novo assembling plastomes from 295 common wild rice (Oryza rufipogon Griff.) and 1135 Asian cultivated rice (Oryza sativa L.) accessions, supplemented with 34 plastomes from other Oryza species. From this dataset, the phylogenetic relationships and biogeographic history of O. rufipogon and O. sativa were reconstructed. Our results revealed two major maternal lineages across the two species, which further diverged into nine well supported genetic clusters. Among them, the Or-wj-I/II/III and Or-wi-I/II genetic clusters were shared with cultivated (percentage for each cluster ranging 54.9%∼99.3%) and wild rice accessions. Molecular dating, phylogeographic analyses and reconstruction of population historical dynamics indicated an earlier origin of the Or-wj-I/II genetic clusters from East Asian with at least two population expansions, and later origins of other genetic clusters from multiple regions with one or more population expansions. These results supported a single origin of japonica rice (mainly in Or-wj-I/II) and multiple origins of indica rice (in all five clusters) for the history of rice domestication. The massive plastomic data set presented here provides an important resource for understanding the history and evolution of rice domestication as well as a genomic resources for use in future breeding and conservation efforts.

Introduction

The domestication and improvement of wild plant species forms the basis of our current global food system (Hancock, 2004). Among plant lineages that have been domesticated to feed humans directly, Asian cultivated rice (Oryza sativa L.) is one of the most important given that it provides roughly 20% of the calories consumed by humans worldwide (Smith, 1998). Like other core staple food crops, documenting and analyzing wild and cultivated genetic diversity is the basis for increasing yield and the development of insect/drought/disease resistant varieties (Brown, 1987; Valliyodan et al., 2017; Wang H. et al., 2018; Vikas et al., 2020). Currently, the genetic diversity (especially in the wild) of the crop species upon which humans depend for survival are profoundly threatened from forces such as habitat loss, climate change, regional conflict, human population growth, and anthropogenic vectoring of pathogens (Wilkes and Williams, 1983; Smith et al., 2006; Ford-Lloyd et al., 2011; Hoban et al., 2020). Thus, it is increasingly necessary to improve our understanding of rice domestication and genetic diversity.

With the advent of high-throughput DNA sequencing technology many genetic studies of crop origins have focused on allelic variation from whole nuclear genomes (Rojas-Barrera et al., 2019; Razifard et al., 2020) or domestication genes (Scott et al., 2019; Liu et al., 2020; Lu et al., 2020) with limited use of whole plastid genomes for inferring crop origins. However organellar genomes (in particular plastids) possess several advantageous qualities in the inference of crop origin and diversity including high copy number, which is useful in degraded archeological finds (Schlumbaum et al., 2008; Wagner et al., 2018), uniparental inheritance resulting in more recent inferences for coalescence times (Palumbi et al., 2001), lack of recombination reducing problems associated with introgression and incomplete lineage sorting (Hu et al., 2016), and a variable mutation rate which is useful for inferences at multiple taxonomic levels (Pollmann et al., 2005). There are numerous well-known examples using organellar DNA in studies of origin, dispersal, and diversity, like the resolution of our own human history using mitochondrial DNA (Ballinger et al., 1992; Forster et al., 2001; Tanaka et al., 2004) and the use of plastid DNA as a standard phylogenetic marker in the Angiosperm Phylogeny Group (APG) classification of flowering plant orders and families (Byng et al., 2016). Therefore, pangenomic approaches to plastomic DNA analyses are expected to provide useful insights into the origin, and diversity of numerous domesticated crop species.

As a staple food crop which feeds over 3.5 billion people on the planet, domestication of cultivated rice ranks as one of the most important developments in the history of humankind. Accordingly, the origins and demographic history of Asian cultivated rice (O. sativa) have been frequently studied using a wide range of analytical approaches mainly from the fields of genetics and archeology. Yet debates regarding the exact origin and timing of domestication persist (Londo et al., 2006; Caicedo et al., 2007; Fuller et al., 2009; Molina et al., 2011; Huang et al., 2012; Civan et al., 2015; Choi et al., 2017), especially regarding the number and source of origins with some researchers suggesting a single origin from one population while others proposed multiple distinct origins from different populations (Sang and Ge, 2007; Gross and Zhao, 2014; Civan et al., 2019). The single-origin hypotheses primarily focus on domestication genes (such as the non-shattering gene sh4) proposing that a desirable set of alleles occurred once, was selected for and that subsequent introgression with wild gene pools (while maintaining strong selection for the domestication genes) accounts for the current high diversity found in domesticated rice (Li C. B. et al., 2006; Sweeney et al., 2006; Vaughan et al., 2008). In contrast, the multi-origin hypotheses posit that distinct lineages of wild rice (Oryza rufipogon) in multiple locations were the source of different domesticated lineages (e.g., indica and japonica) and that the multi-origin sourcing accounts for most of the current diversity in domesticated rice rather than amalgamative introgression (Gross and Zhao, 2014).

In the present study, we constructed a large plastome data set containing 1445 complete de novo assembled genomes from wild and domesticated accessions in order to address the main outstanding questions in rice domestication: (1) Is the current diversity of domesticated rice derived from a single or multiple independent lineages? and (2) whether a common geographic origin or multiple geographic origins existed for the lineages? We employed numerous analytical approaches from population genetics and phylogeography to test these hypotheses. The results from these analyses were interpreted in light of previous work on rice history such that an updated model based on matrilineal diversity could be presented for this essential food crop. Our results from this large dataset of complete plastid genomes for wild and domesticated rice resolves distinct patterns in the evolution and domestication of different rice lineages. As with similar studies on the history and diversity of crop species the results herein will be directly applicable to crop improvement as well as in situ and ex situ conservation efforts.

Materials and Methods

Raw Dataset

Whole genome sequencing (WGS) data for O. rufipogon and O. sativa accessions were obtained from the EMBL database uploaded from previous studies (Huang et al., 2012; Wang W. S. et al., 2018). A total of 1445 samples, including 1135 O. sativa cultivars, 295 O. rufipogon accessions and 15 accessions from other wild rice species, were selected and collected from multiple regions in Asia, Europe, Africa, America, and Oceania (Supplementary Table 1 and Supplementary Figure 1). The samples were first selected randomly from the original datasets and then adjusted to ensure that they were distributed evenly according to geographic distribution. The accessions of O. sativa were classified into five categories including indica, japonica (including tropical japonica, typical japonica, and temperate japonica), aus, aromatic, and intermediate based on the identification included in the uploaded information (Wang W. S. et al., 2018). Nineteen published plastomes of Oryza species were downloaded from the NCBI database (Supplementary Table 2).

The de novo Assembly and Annotation of Complete Plastid Genomes

The raw sequencing data were processed using FastQC v0.11.5 and NGSQCToolkit v2.3 software to control sequence quality, and were then filtered using BWA and SAMtools (Li and Durbin, 2010) software to extract plastid-origin reads that were properly paired to the reference plastid genomes from Oryza (Supplementary Table 3). The de novo assembly of complete plastid genomes were conducted using SPAdes (Bankevich et al., 2012). Several strategies were applied in the de novo assembly pipeline: (1) The problem of excessive variation introduced by using highly variable reference sets was reduced by using a narrower set of reference plastomes only from Oryza and (2) the F 12 option in SAMtools was used to bait target reads of plastomes from the WGS dataset. The obtained plastid contigs were further visualized and scaffolded to complete circular genomes in Bandage (Wick et al., 2015). The read coverage depth of each contig was used to identify and remove the mis-assembled contigs derived from nuclear or mitochondrial genomes, which were usually lower than one-fifth the depth of plastid contigs. Other optimized options were also applied as reported in our previous study (He et al., 2020).

Summary statistics were calculated to evaluate the quality of the assembled plastid genomes by using QUAST (Gurevich et al., 2013). Gene annotation was performed using Geseq (Tillich et al., 2017) and manually modified if necessary. Gene function annotation of nucleotide variations were performed with ANNOVAR software (Wang et al., 2010).

Haplotype and Genetic Diversity Analyses

Population structure of all rice accessions were inferred based on plastomic single nucleotide variants (SNVs) by using Admixture software (Alexander et al., 2009), with 5 replications for different number of potential sub-populations (K) ranging from 1 to 25. The runs at each K showing the minimum cross-validation (CV) errors were used as optimal results to estimate the number of sub-populations. For the optimal K, the run with the lowest CV error was used to assign accessions with membership probability ≥0.65 to a sub-population (genetic cluster). Accessions with membership probability <0.65 were assigned to an unclassified group. Genealogical relationships of the identified haplotypes were inferred using a median joining method in Popart v1.7 (Leigh and Bryant, 2015) with annotations to the output figure completed in Adobe Illustrator software (Adobe Systems Incorporated, United States). Haplotype diversity, evolutionary distances based on the Tajima-Nei model, and population differentiation (Fst) were calculated for each group of haplotypes based on plastomic SNVs by using DnaSP v6.0 (Rozas et al., 2017) and MEGA7 (Kumar et al., 2016). The variational autoencoder plotting was calculated with popvae software (Battey et al., 2021). The principal coordinates analysis (PCoA) and multidimensional scaling (MDS) were conducted in TASSEL 5 (Bradbury et al., 2007). Nucleotide diversity (π) within and between different genetic clusters was calculated and tested using the pairwise differences method in Arlequin 3.5 (Excoffier and Lischer, 2010).

Phylogenetic Analysis and Molecular Dating

To perform the phylogenetic analysis, complete sequences of plastid genomes were aligned in MAFFT (Katoh and Standley, 2013) and manually adjusted in MEGA7 (Kumar et al., 2016). Phylogenetic analyses were conducted using maximum likelihood methods in IQ-Tree (Nguyen et al., 2014), and the Bayes information criterion was used to determine the best-fit model for nucleotide substitution. Branch support was determined with ultrafast bootstrap of 2000 replicates. The final trees were then plotted using the online tool in the Interactive Tree of Life.1

For molecular dating, a set of representative haplotypes from major clades in the phylogenetic tree (consistent with the genetic clusters) was used for analyses. The smaller data set was used in order to simplify analyses and avoid zero-length branches from including repeat or highly similar haplotypes (Hou et al., 2014). A relaxed lognormal molecular clock with Yule priors was chosen to estimate the divergence time of lineages as implemented in BEAST v1.8.4 (Drummond et al., 2012). Three calibration points, from previous studies2 were employed: the divergence between O. rufipogon and Oryza punctata [5.7 mya 95% highest posterior density (HPD): 2.6–8.9], the crown age of O. rufipogon (2.32 mya HPD: 2–2.93), and the divergence time between O. rufipogon and Oryza glaberrima and its wild relative Oryza barthii (estimated at c. 0.83 mya HPD: 0.51–1.11). Following the suggestion of Ho (2007), we assigned a normally distributed prior for the three calibrations, in which standard deviations of 2, 0.3, and 0.2 were used, respectively.

The Markov Chain Monte Carlo procedure was run for 100 million generations, with sampling every 10,000 generations. Tracer v.1.7.13 was used to assess appropriate burn-in and the adequate effective sample size values (>200). A burn-in of 25% was applied, and the maximum clade credibility (MCC) tree with the mean ages and 95% HPD intervals on nodes was produced in TreeAnnotator v1.8.4 (part of the BEAST package) and visualized in FigTree v.1.4.2.4

Biogeographic Analyses and Historical Population Dynamics

For the ancestral range reconstruction, seven geographical areas based on the regions of endemism of rice were defined (Takhtajan, 1986; Wu and Wu, 1996). R package BioGeoBEARS (Matzke, 2013) was used to compare biogeographical models and estimate ancestral ranges of the Oryza populations using the MCMC tree from BEAST. Three biogeographical models were compared using a ML framework: Dispersal-Extinction-Cladogenesis (DEC) model (Ree and Smith, 2008), dispersal-vicariance analysis (DIVA) (Ronquist, 1997), and BayArea (Landis et al., 2013), with each model run with and without the + J parameter. The fit for the different models was assessed using AIC and AICc scores. The maximum range size was set to four, as no extant species occurs in more than four biogeographical regions. To test the robustness of the ancestral range reconstruction, we also used statistical dispersal-vicariance analysis (S-DIVA) (Yu et al., 2010) and DEC (Ree and Smith, 2008) in RASP v3.2 (Yu et al., 2015). MaxEnt species distribution modeling was used to predict past and current climatically suitable areas for different genetic clusters of wild rice (Phillips et al., 2017). Nineteen bioclimatic variables with the 2.5 arc-minute spatial resolution were downloaded from WorldClim 1.45 (Hijmans et al., 2005). The neutrality test and mismatch distribution for plastomic variations of different groups were conducted using DnaSP v6.0 (Rozas et al., 2017). The reconstruction of population demography was estimated with stairway plot based on folded site frequency spectrum (SFS) as described in Liu and Fu (2015). The assumed mutation rate (μ) of plastomes in Oryza species was defined as 1.5 × 10–10 mutations per site per generation which was calculated in BEAST v1.8.4 (Drummond et al., 2012).

Results

De novo Assembly of Rice Plastomes Based on Whole Genome Sequencing Data

Based on the WGS data derived from the EMBL database (Huang et al., 2012; Wang W. S. et al., 2018), de novo assembly of 1445 plastid genomes (also referred to as plastomes) was completed from 1135 O. sativa and 295 O. rufipogon accessions and 15 in other rice species with AA genomes in Oryza (Table 1 and Supplementary Table 1). A total length of 7.0–224.8 Mb of reads was yielded per plastome when the original WGS data was mapped to the reference genome, with read coverage averaging 222.6×. The de novo assemblies of each plastome yielded from 3 to 67 contigs, with an average of 7.1, for each accession, which were further extended and connected according to Kmer depth and De Bruijn paths among them. Accordingly, a total of 1445 high-quality complete circular genomes were assembled for each of the 1445 rice accessions. The obtained plastomes had an average length of 134,527.3 bp, and varied from 134,518 to 134,695 bp, with an overall average GC content of 39.0%, varying from 39.0 to 39.5%. A typical quadripartite structure was resolved among all of the genomes and included two inverted repeats (IRs) with an average length of 20,802.7 bp and an average GC content of 44.3%, separated by a large single copy region (LSC) with an average length of 80,575.7 bp and an average GC content of 37.1%, and a small single copy region (LSC) with an average length of 12,346.2 bp and an average GC content of 33.3%. The highest GC content was observed in the IRs, followed by the LSC and SSC, suggesting that different functions (e.g., gene regulation; Smarda et al., 2014) and/or evolutionary biases exist between these regions.

TABLE 1
www.frontiersin.org

Table 1. Characteristics of 1445 de novo assembled rice plastid genomes.

Variation Among Plastomes in Oryza Species With AA Genomes

To investigate differences in DNA sequences at the whole plastome level, comparative analyses and gene annotations were conducted for 1464 complete plastomes comprised of 1445 Oryza plastomes assembled in this study (Supplementary Table 1) and 19 plastomes downloaded from NCBI database (Supplementary Table 2) for 8 Oryza species (Supplementary Figure 2 and Supplementary Table 4). A total of 1896 nucleotide variations from 1642 loci were detected among the 1464 plastomes (Supplementary Figure 2). The types of variation across the entire dataset included 1287 SNVs, 431 small insertions or deletions with length of <50 bp (InDels), 74 small fragment substitutions with length of <50 bp (Sub.), 86 fragment length polymorphisms (FLPs), and 18 large structural variations (17 large insertions or deletions, 1 large substitution) >50 bp. Among them, 351 nucleotide variations were observed in coding regions and included 6 frameshift mutations, 5 non-frameshift indels, 10 block substitutions, 215 synonymous SNVs, 113 non-synonymous SNVs, and 2 stop-codon-gain loci. These mutations were located in the coding regions of 52 different genes, including 41 protein-coding genes, 8 transfer RNAs (tRNA), 2 ribosomal RNAs (rRNA), and 1 antisense gene.

Maternal Phylogeny and Population Structure Reveal Distinct Genetic Clusters in Wild and Cultivated Rice

To investigate the plastomic relationships among all rice accessions, phylogenetic and population structure analyses were conducted, that consistently resulted in the resolution of multiple distinct genetic clusters with high support among both wild and cultivated accessions (Figure 1 and Supplementary Table 5). A total of 12 genetic clusters and 1 unclassified genetic cluster for all accessions were obtained from their phylogenetic relationship and the best partition of K = 12 as calculated in ADMIXTURE, although a secondary optimal partition of K = 17 was also observed (Figures 1A,B and Supplementary Figure 3). Two major maternal lineages were observed across all the O. rufipogon and O. sativa accessions, which could be further resolved into 4 O. rufipogon-wild-type genetic clusters (Or-w-I, Or-w-II, Or-w-III, and Or-w-IV), and 2 O. rufipogon-wild-and-cultivated-type clusters (Or-wj and Or-wi). The cultivated accessions were separated into Or-wj and Or-wi with multiple well-supported genetic clusters within each. Three O. rufipogon-wild-to-japonica (Or-wj) genetic clusters were resolved. The genetic clusters Or-wj-I and Or-wj-II mainly consisted of japonica cultivated varieties with some other cultivars, while Or-wj-III contained most of the aromatic rice cultivars that have often been considered as a related ecotype of japonica rice (Figure 1). Two O. rufipogon-wild-to-indica (Or-wi) genetic clusters were resolved and referred to as Or-wi-I and Or-wi-II. The Or-wi-I genetic cluster consisted of mainly indica rice cultivars and the aus rice ecotype, while the Or-wi-II genetic cluster contained indica cultivated rice and a high percentage (45.1%) of wild rice accessions (Figures 1, 2 and Supplementary Table 5). Almost all the wild rice accessions fell into one of four genetic clusters Or-w-I, Or-w-II, Or-w-III, or Or-w-IV, among which the Or-w-I was most closely related to the Or-wj genetic clusters and the Or-w-II was closest to the Or-wi genetic clusters (Figure 1A). Accessions from the other AA genome species (O. barthii, O. glaberrima, Oryza glumipatula, Oryza meridionalis, and Oryza longistaminata) resolved in genetic clusters Oryza-I (O-I), Oryza-II (O-II), and Oryza-unclassified (O-unc) with the BB genome species O. punctata (Op), used as the outgroup (Figure 1). The vast majority of cultivated rice accessions (1114/1137) were clustered in either the Or-wj or Or-wi groups. Notably, 23 domesticated accessions clustered into various wild genetic clusters indicating a more distant maternal relationship between them (Figures 1, 2).

FIGURE 1
www.frontiersin.org

Figure 1. Plastomic relationships in all the wild and cultivated rice accessions. (A) Phylogenetic tree of 1464 plastomes in Oryza species with AA genomes and an outgroup species (Oryza punctata with BB genome). Different genetic clusters were denoted with different colors for the corresponding branches. (B) The two barplots indicate individual assignment posterior probabilities to a given group under the best partition of K = 12 and a secondary optimal partition of K = 17 as calculated in ADMIXTURE. (C) Plot illustrating all the plastome sequence differences across all accessions; A range of 2–20 different alleles, denoted with different colors, were detected at each variant locus in the 1464 accessions. In the diagram, the light blue indicates the ancestral alleles inferred from comparisons with the Oryza punctata plastome. (D) The corresponding species/cultivars were denoted for each accession: TemJ = Temperate japonica; Jap = typical japonica; TroJ = tropical japonica; Aro = aromatic rice; Ind = indica rice; Aus = aus rice; OR = Oryza rufipogon; OB = O. barthii; Ogla = O. glaberrima; Oglu = O. glumipatula; OM = O. meridionalis; OL = O. longistaminata; OP = O. punctata.

FIGURE 2
www.frontiersin.org

Figure 2. Haplotype network of plastomes in cultivated and wild rice species. Different cultivars/species were denoted in the haplotype network, and the haplotypes without a subgroup label belonged to the O-unc genetic cluster. The cultivars/species were designated for each accession as in Figure 1.

Nucleotide Divergence of Plastomes Within and Between Different Genetic Clusters

To further assess the nucleotide divergence of different clusters, a haplotype network of plastomes was constructed for all the accessions of wild and cultivated rice species from this study (Figure 2). In total 266 haplotypes were obtained from 1464 accessions which were resolved into 12 well supported genetic clusters and 1 mixed genetic cluster, which were classified as described above. Haplotype diversity in this study exhibited a similar pattern to other studies in grain crops where a broader diversity is found in wild haplotypes than in cultivated haplotypes. That is, clusters containing wild accessions exhibited a greater number of differences between haplotypes within the cluster, whereas haplotypes in the cultivated genetic clusters were separated only by a single or few steps (Figure 2). The wild rice (O. rufipogon) genetic clusters (Or-w groups) showed higher haplotype diversity (as a function of accessions per haplotype and multi-step branching within each genetic cluster) with 81 haplotypes in total and 1–11 accessions per haplotype. While cultivated rice genetic clusters (Or-wi and Or-wj groups) had 149 haplotypes in total with 1–301 accessions per haplotype. The nine most common haplotypes (>21 accessions) contained 77.3% of all the accessions in the cultivated rice genetic clusters. The cultivated clusters showed very low nucleotide diversity (π) with a range from 0.00047 to 0.0015 which is lower than wild genetic clusters ranging from 0.0035 to 0.014 (Figure 3 and Supplementary Figure 4). Similarly, the pairwise genetic distances within Or-wj (4.0–7.3) and Or-wi (4.2) were much lower than those of Or-w genetic clusters (31.4–41.9). This narrowing of genetic diversity is common among domesticated lineages wherein artificial selection and demographic bottlenecking can induce such patterns.

FIGURE 3
www.frontiersin.org

Figure 3. Genetic distances and population differentiation within and between different genetic clusters. Below diagonal: Pairwise genetic distances (K); diagonal: the nucleotide diversity (π) for each group; above diagonal: The population differentiation coefficient (Fst) between groups.

Group and individual based calculations provided further evidence for a multiple origin with long-term artificial selection scenario for the domestication of rice. On average, each Or-wj accession differed from an Or-wi accession by 31.6 SNVs with a range of 29.4–34.0, which is roughly 8-fold more than detected between accessions within Or-wj (4.0, 6.0, and 7.3) or Or-wi (4.2) genetic clusters (Figure 3). When using Fst (calculated from SNVs) to assess divergence between different clusters, the Or-wj genetic clusters all showed relatively large population differentiation to Or-wi genetic clusters with a range of Fst from 0.946 to 0.979 at a significance level of p < 0.01. These values are larger than the range of Fst between either the Or-wj genetic clusters and Or-w-I (Fst = 0.859–0.914; p < 0.01) or between Or-wi genetic clusters and Or-w-II (Fst = 0.675, 0.863; p < 0.01). Additionally, the Or-wj genetic clusters showed the least genetic distance from Or-w-I (K = 24.3–26.3) while Or-wi genetic clusters were the least distant from Or-w-II (K = 18.2, 20.7). The variational autoencoder estimation (VAE), principal component analysis (PCA), and MDS plot were conducted to further investigate the genetic relationships of all the accessions based on plastomic SNVs. From all multidimensional individual based analyses, the accessions in Or-wj and Or-wi genetic clusters were grouped together in a similar manner to the other analyses and resolved closest to Or-w-I and Or-w-II, respectively (Figure 4 and Supplementary Figure 5). All of these results consistently resolve a pattern wherein the two genetic clusters containing mainly cultivated accessions were selected from two distinct wild lineages, thus supporting a multiple maternal genetic origin scenario for the history of rice domestication.

FIGURE 4
www.frontiersin.org

Figure 4. Variational autoencoder plotting for different genetic clusters in Oryza accessions.

Molecular Dating, Phylogeography, and Historical Demography Reconstructions for the Origin and Timing of Rice Domestication

To infer the historical dynamics of origin and evolution for wild and cultivated rice, a total of 61 representative plastid haplotypes from the cultivated and wild rice genetic clusters were employed in molecular dating and historical biogeographic reconstruction (Figure 5 and Supplementary Figures 6, 7). From molecular dating the clade of O. rufipogon and O. sativa diverged from a clade containing O. barthii and O. glaberrima (an African species of cultivated rice) approximately 1.6 mya. This indicated that the wild progenitors of the two cultivated rice species may have originated from a common ancestral lineage. The O. rufipogon and O. sativa clade diverged into two major lineages 1.31 mya and subsequently split up into multiple genetic clusters before 0.86 mya. The Or-wj genetic clusters have a stem age of 0.87 mya and further diverged into two mainly cultivated genetic clusters Or-wj-I (99.3% were cultivated accessions) and Or-wj-II (95.6%) and a wild-cultivated cluster Or-wj-III (61.7%) 0.43 mya (Supplementary Table 5 and Figures 5B,C). The Or-wi genetic clusters have a stem age of 0.86 mya and further diverged into a mainly cultivated genetic cluster Or-w-I (97.3% were cultivated accessions) and a wild-cultivated cluster Or-w-II (54.9%) 0.26 mya (Supplementary Table 5 and Figures 5B,C). From the phylogeographic analyses the best supported scenario for Or-wj groups is an independent single origin of Or-wj-I/II from East Asia and multiple origins of Or-wj-III (Supplementary Table 6 and Figure 5). For the Or-wi genetic clusters the best supported origin was from East Asia with dispersal and secondary diversification in Southeast and South Asia (Figure 5 and Supplementary Figures 7, 8). These results revealed a pre-domestication landscape of the divergency of different maternal lineages in wild and cultivated rice.

FIGURE 5
www.frontiersin.org

Figure 5. The origin and divergence patterns of cultivated rice and wild progenitors. (A) The delimitations of geographic origins used for the biogeographic reconstruction of wild and cultivated rice lineages. (B) Inferred phylogeographic patterns for the origin and dispersal of wild and cultivated rice genetic clusters. Pie charts represent probability of origin for a given clade. (C) Schematic of the origin and the inferred maternal lineages of cultivated rice and their wild relatives based on the results of the phylogeographic analyses. The white character “s” on the tree denotes when artificial selection may have started among each lineage.

From neutrality (Supplementary Table 7) and mismatch distribution tests (Supplementary Figure 9) all Or-wj and Or-wi genetic clusters were inferred to have undergone relatively recent population expansion which could be related to recent artificial selection associated with domestication compared to natural radiations among the wild progenitor lineages. Reconstruction of past effective population sizes and population historical distribution among the different groups suggests that population expansion occurred in different regions at different times between the genetic clusters (Figure 6). While analysis of all the Or-wj genetic clusters indicated a two-step population expansion pattern, the increases were not synchronous across genetic clusters with the first rapid increase in effective population occurring around 50 kya in Or-wj-II and Or-wj-III whereas in Or-wj-I this step occurs at about 15 kya. Similarly, the second rapid increases in effective population size between the Or-wj genetic clusters were asynchronous in all instances. Between the two Or-wi genetic clusters, inferred rapid expansion events were asynchronous in number, timing, and extent (Figure 6 and Supplementary Figure 8). Nearly all of the recently inferred expansion events appear to have occurred between 5.0 and 10.3 kya, except for Or-wj-I which was estimated to be more recent (2.0–3.0 kya). Modeling for climatically suitable areas of different genetic clusters suggested that all Or-wj and Or-wi clusters underwent geographic expansions both during periods of LGM to mid-Holocene and mid-Holocene to the present except for Or-wi-I (Figure 6C). The Or-wj-I/II were inferred to have a first dramatic expansion in East Asia and secondary expansion mainly in South Asia, while the other clusters appeared to expand in multiple regions in most periods. In total these results from different analytical approaches are consistent with the expectation of a multiple-origin scenario.

FIGURE 6
www.frontiersin.org

Figure 6. Historical dynamics of range and effective population size in different genetic clusters during the last 100,000 years. (A) Changes of the effective population sizes of the cultivated genetic clusters based on stairway plot estimations. (B) Change in Global Average Surface Temperature (GAST) for the last 100,000 years, compared with the average present temperature (0–5 kya), data were collected from Snyder (2016). (C) Potential distribution probability of occurrence for different genetic clusters of wild rice inferred by Maxent modeling. Warmer colors show areas with higher probabilities: red denotes the maximum value (1) and blue the minimum (0). White dots show the presence locations used for training. LGM, Last Glacial Maximum; mid-Holocene, middle Holocene. EA, East Asia; SA, South Asia; SEA, Southeast Asia. Red arrows indicate the expansion of geographic distribution for the population in a region while green arrows indicate the contraction of that population. These regions were defined the same as in Figure 5A.

Discussion

Large Scale de novo Assemblies of Rice Plastid Genomes

Plastid has been a part of plant cells for roughly one billion years and remains the essential compartment for harvesting light energy in the cells of eukaryotic photosynthetic organisms. The plastid genome (plastome) possesses a number of special characteristics such as lack of recombination, conserved gene order and content, and differential rates of mutation (Supplementary Figure 2). These characteristics make plastomes ideally suited in the study of evolution and historical biogeography in plants (Gitzendanner et al., 2018), intergenomic transfer between organelles and nuclear genomes (Ma et al., 2020), plastid genetic engineering (South et al., 2019), and breeding and conservation of important crop germplasm (Tonti-Filippini et al., 2017; Gao et al., 2019). However, among the ∼35,000 cultivated species (Khoshbakht and Hammer, 2008), fewer than 80 complete plastid genomes are available in the NCBI database. Although the One Thousand Plants Transcriptome Project (1KP)6, as well as other recent efforts have contributed over 1,000 complete or nearly complete plastid genomes to global databases, most of these are from plants that are not of economic importance (Gitzendanner et al., 2018; Leebens-Mack et al., 2019; Li H. T. et al., 2019). Additionally, several large-scale datasets analyzing plastomic variations of wild and cultivated rice accessions were recently published, however these studies relied on a mapping-reads-to-a-reference method instead of de novo assembly and complete genome scaffolding (Civan et al., 2019; Moner et al., 2020). As such these analyses may have omitted regions of greater dissimilarity as these methods do not perform for longer genomic regions of this type. To date, there remains a lack of de novo assemblies for complete plastid genomes available in public databases that are sampled at the population level for important crop species.

In the present study, to conduct large scale de novo assemblies of well over 1000 plastid genomes, we developed an accurate and effective pipeline for auto-assembly of rice plastid genomes from high throughout WGS data. Several strategies were employed to obtain the optimal result of complete high-quality plastid genomes. The strategies included: (1) using only 102 plastomes in the Oryza genus as reference, instead of using a collection of dozens of distantly related species for reference which is the common practice; (2) applying the F 12 option when using SAMtools software to bait target reads of plastomes from the WGS dataset which resulted in improved baiting efficiency of plastomic reads from the WGS dataset and reduced non-target reads. Assembly parameters were also optimized according to the methods previously reported in He et al. (2020). Using this pipeline, we were able to assembly a total of 1445 completed plastomes of wild and cultivated rice accessions, with a high average depth of read coverage (222.6×). In order to confirm the accuracy of our assembly pipeline, two previously published plastomes for rice, cultivars Nipponbare (GU592207.1, AY522330.1) and TN1 (NC_031333.1) were selected to compare with the plastomes of the two accessions generated in this study (CX140 and CX162), respectively. Both genome sequences showed identical alignments with no nucleotide changes between them, further confirming the high reliability of the assembled plastome sequences generated by our pipeline. The results of this effort established a large set of 1445 complete plastomes including 1135 plastomes of Asian cultivated rice varieties and 310 plastomes of wild rice accessions. This dataset will be a very useful resource for future rice research on molecular diversity and phylogeny, plastid genetic engineering in rice, marker assisted breeding, and germplasm preservation efforts.

Multiple Primary Origins With Ongoing Selection and Admixture Generate Secondary Origins Resulting in a High Diversity of Cultivated Rice Haplotypes

While much work has been dedicated to understanding the origin and evolution of domesticated rice the issue of multiple versus single origins is a topic of ongoing debate. Most recent studies have concluded that a multiple origin scenario is most likely however the number of domestication events wherein key grain domestication loci were acquired remains unresolved (Huang et al., 2012; Civan et al., 2015, 2019; Choi et al., 2017; Choi and Purugganan, 2018; Civan and Brown, 2018; Wang W. S. et al., 2018; Moner et al., 2020). The two major type of cultivated rice, indica and japonica, originated from different ancestral wild rice populations, with gene flows observed from japonica to indica as well as two relatively small ecotypes, aus and aromatic rice. The plastome data in this study provides more strong evidence for multiple origins of Asian cultivated rice and further resolution of the maternal relationships between its different subspecies/groups (Supplementary Figure 9). The cultivated rice and its wild relatives both derived from two major maternal lineages which could be traced back to a most recent common ancestor before 1.31 mya (Figure 5B). Extensive and directional introgressions were indicated between different cultivated groups according to polyphyletic cultivar designations to different plastid lineages, e.g., a mainly japonica (61.9%) type of genetic cluster Or-wj-II contained a large proportion of indica accessions (26.3%); more than half (58.2%) of aromatic rice accessions shared a common maternal lineage in the genetic cluster Or-wj-III, in which 31.6% accessions were indica or aus rice (Figures 1, 2 and Supplementary Table 5). Those results suggested abundant introgressions from japonica and aromatic rice to indica and aus rice cultivars. Furthermore, when comparing the different cultivated genetic clusters by using demographic and phylogeographic reconstruction the patterns appear to vary among the groups suggesting differences in geographic origin, intensity targets of selection, as well as timing of the initiation of these steps. It should be noted that the process of domestication by relaxing natural selection and increasing effective population sizes might have biased divergence time estimates. From the stairway plots (Figure 6) the inferred early diverging domesticated genetic clusters Or-wj-III and Or-wi-II had secondary rapid increases in effective population size starting around 15 and 7 kya, respectively. These times correspond roughly with dates inferred from other studies for when rice domestication could have reasonably started in China for japonica rice and in India for indica rice (Gross and Zhao, 2014). The older increases inferred in effective population size may have been the result of population responses to climatic changes related to past glacial events. Because the increases in effective population size are inferred to have occurred earlier in japonica genetic clusters, a scenario in which Sh4 and other genes evolved once in East Asian japonica and were introgressed into indica lines is not unreasonable but also not the only explanation for this pattern. Evidence for introgression is present in this plastome dataset in the form of polyphyletic cultivar designations to different plastid lineages.

While plastid genomes are not generally considered to possess genes involved with the domestication syndrome the patterns found in plastomic data nonetheless provide invaluable data for reconstructing the domestication history of a crop species. For instance, the loss of nucleotide diversity is a common feature of grain domestication (Haudry et al., 2007; Gross and Olsen, 2010; Avni et al., 2017) and was evident in our plastid data as well. While nucleotide diversity was low in the domesticated groups compared to the wild groups, haplotype diversity was higher (Hd: Or-wj-I = 0.744, Or-wj-II = 0.887, Or-wj-III = 0.801; Supplementary Table 8). However, the high haplotype diversity in cultivated groups is mainly the result of many single step changes from one or two highly abundant haplotypes possibly reflecting the relaxation of natural selection and correlated increase in effective population sizes among domesticated lineages (Petit and Barbadilla, 2009; Liu et al., 2019). This contrasts with the wild genetic clusters wherein many multiple step branching events typify the diversity of haplotypes (Figure 4 and Supplementary Figure 10). Similar patterns between progenitor and derived domesticated lineages have been described for numerous cultivated and domesticated species (Bjornerfeldt et al., 2006; Miller and Gross, 2011; Tembrock et al., 2017; Alves-Pereira et al., 2018). Because plastids are not thought to be a target of selection during domestication and are mostly uniparentally inherited, they are considered superb markers to avoid the interruption of artificial intervention to the genomic mutation rate in resolving the geographic origins and timing of domestication in crop species. While these and similar characteristics do make plastid data well suited for resolving questions of crop origin such data are also not expected to be entirely neutral during domestication as plastids are important in numerous organismic processes such as environmental adaptation, growth rate, and cytonuclear incompatibility during reproduction (Allen, 2005; Greiner et al., 2011; Roux et al., 2016; Bogdanova, 2019; Flood et al., 2020). By combining plastome data with data from nuclear sequences, phenotype, and crop characteristics the degree to which plastid genes are involved in the process of domestication and improvement can be more effectively assessed.

Cultivar Determination and Plastome Based Population Structure in Rice

Thousands (more than 40,000) of rice cultivars have been described since the beginning of documentation of rice cultivation (Li R. et al., 2019). Distinctive morphological traits and growth characteristics have been the primary means to recognize rice cultivars (Fujita et al., 1984; Li R. et al., 2019). More recently large genetic and genomic data sets have been employed to characterize rice diversity and understand the genetic underpinnings of desired traits (Kovi et al., 2011; Huang et al., 2013; Choi and Purugganan, 2018).

In our analysis of population structure using plastomic data most of the japonica (90.8%) and aromatic (86.6%) cultivars (determined a prior by original authors using cultivar traits) were present in the Or-wj genetic clusters. While the indica (67.0%) and aus (63.5%) cultivars (also determined a priori by original authors using cultivar traits) were mainly present in the Or-wi genetic clusters. From this it appears that japonica and aromatic cultivars are more closely associated with the Or-wj genetic clusters than the indica and aus cultivars are associated with the Or-wi genetic clusters. Such a pattern might be the result of biased maternal introgression from the Or-wj genetic clusters into indicia and aus cultivars. However, the inferred introgression of plastomes from the Or-wj genetic cluster into indica and aus varieties do not appear to have resulted in morphological changes that made them unrecognizable as indicia or aus cultivars. That said the influence of the Or-wj plastomes on the indica and aus traits might be minimal or they could be expressed in traits typically unrelated to cultivar recognition such as changes to photosynthetic rate.

The higher rate of inferred plastomic introgression from the Or-wj into the indica and aus cultivars raise several questions. For instance, is this pattern of asymmetrical introgression evidence of the transfer of the sh4 and other domestication genes from gene pools containing the desired traits to gene pools without the desired traits as proposed in Choi and Purugganan (2018)? Has unidirectional cytoplasmic incompatibility resulted in higher rates of Or-wj plastomes in indica and aus cultivars? Do Or-wj plastomes provide desired traits not found in Or-wi plastomes to indica and aus cultivars? Obviously further work is needed to better understand how plastids are involved in the domestication process generally and in rice specifically. With datasets such as those presented here these questions are now possible to investigate more thoroughly. The outcome of such studies could be directly applicable in the development of new rice cultivars through plastomic marker assisted breeding and editing of key plastid genes. In addition, geographic locations with high plastomic haplotype diversity can be identified and prioritized for conservation of valuable wild rice germplasm such that they can be preserved for use in future rice breeding efforts.

Data Availability Statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ebi.ac.uk/ena, PRJEB43684 and https://ngdc.cncb.ac.cn/, PRJCA006880.

Author Contributions

WH, ZW, DJ, and LT conceived and designed the study. WH, CC, KX, JW, and PZ collected the public data and performed the data analysis. WH, CC, and KX wrote the manuscript. ZW, LT, and DJ revised the manuscript. All authors have read and agreed to the published version of the manuscript and reviewed the manuscript.

Funding

This study was co-supported by the National Natural Science Foundation of China (31970244), the Science, Technology and Innovation Commission of Shenzhen Municipality (RCYX20200714114538196), and the Chinese Academy of Agricultural Sciences Elite Youth Program to ZW.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpls.2021.781793/full#supplementary-material

Footnotes

  1. ^ https://itol.embl.de/
  2. ^ http://www.timetree.org/
  3. ^ http://beast.bio.ed.ac.uk/Tracer/
  4. ^ http://beast.bio.ed.ac.uk/FigTree
  5. ^ http://www.worldclim.org
  6. ^ https://db.cngb.org/onekp/

References

Alexander, D. H., Novembre, J., and Lange, K. (2009). Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664. doi: 10.1101/gr.094052.109

PubMed Abstract | CrossRef Full Text | Google Scholar

Allen, J. O. (2005). Effect of teosinte cytoplasmic genomes on maize phenotype. Genetics 169, 863–880. doi: 10.1534/genetics.104.027300

PubMed Abstract | CrossRef Full Text | Google Scholar

Alves-Pereira, A., Clement, C. R., Picanco-Rodrigues, D., Veasey, E. A., Dequigiovanni, G., Ramos, S. L. F., et al. (2018). Patterns of nuclear and chloroplast genetic diversity and structure of manioc along major Brazilian Amazonian rivers. Ann. Bot. 121, 625–639. doi: 10.1093/aob/mcx190

PubMed Abstract | CrossRef Full Text | Google Scholar

Avni, R., Nave, M., Barad, O., Baruch, K., Twardziok, S. O., Gundlach, H., et al. (2017). Wild emmer genome architecture and diversity elucidate wheat evolution and domestication. Science 357, 93–97. doi: 10.1126/science.aan0032

PubMed Abstract | CrossRef Full Text | Google Scholar

Ballinger, S. W., Schurr, T. G., Torroni, A., Gan, Y. Y., Hodge, J. A., Hassan, K., et al. (1992). Southeast Asian mitochondrial DNA analysis reveals genetic continuity of ancient mongoloid migrations. Genetics 130, 139–152.

Google Scholar

Bankevich, A., Nurk, S., Antipov, D., Gurevich, A. A., Dvorkin, M., Kulikov, A. S., et al. (2012). SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477.

Google Scholar

Battey, C. J., Coffing, G. C., and Kern, A. D. (2021). Visualizing population structure with variational autoencoders. G3 (Bethesda) 11:jkaa036. doi: 10.1093/g3journal/jkaa036

PubMed Abstract | CrossRef Full Text | Google Scholar

Bjornerfeldt, S., Webster, M. T., and Vila, C. (2006). Relaxation of selective constraint on dog mitochondrial DNA following domestication. Genome Res. 16, 990–994. doi: 10.1101/gr.5117706

PubMed Abstract | CrossRef Full Text | Google Scholar

Bogdanova, V. S. (2019). Genetic and molecular genetic basis of nuclear-plastid incompatibilities. Plants (Basel) 9:23. doi: 10.3390/plants9010023

PubMed Abstract | CrossRef Full Text | Google Scholar

Bradbury, P. J., Zhang, Z., Kroon, D. E., Casstevens, T. M., Ramdoss, Y., and Buckler, E. S. (2007). TASSEL: software for association mapping of complex traits in diverse samples. Bioinformatics 23, 2633–2635.

Google Scholar

Brown, W. L. (1987). Global crop resources: gene banks and the world’s food. Science 236, 617–618. doi: 10.1126/science.236.4801.617

PubMed Abstract | CrossRef Full Text | Google Scholar

Byng, J. W., Chase, M. W., Christenhusz, M. J. M., Fay, M. F., Judd, W. S., Mabberley, D. J., et al. (2016). An update of the angiosperm phylogeny group classification for the orders and families of flowering plants: APG IV. Bot. J. Linnean Soc. 181, 1–20.

Google Scholar

Caicedo, A. L., Williamson, S. H., Hernandez, R. D., Boyko, A., Fledel-Alon, A., York, T. L., et al. (2007). Genome-wide patterns of nucleotide polymorphism in domesticated rice. PLoS Genet. 3:1745–1756. doi: 10.1371/journal.pgen.0030163

PubMed Abstract | CrossRef Full Text | Google Scholar

Choi, J. Y., Platts, A. E., Fuller, D. Q., Hsing, Y. I., Wing, R. A., and Purugganan, M. D. (2017). The rice paradox: multiple origins but single domestication in asian rice. Mol. Biol. Evol. 34, 969–979. doi: 10.1093/molbev/msx049

PubMed Abstract | CrossRef Full Text | Google Scholar

Choi, J. Y., and Purugganan, M. D. (2018). Multiple origin but single domestication led to Oryza sativa. G3 Gen. Genom. Genet. 8, 797–803. doi: 10.1534/g3.117.300334

PubMed Abstract | CrossRef Full Text | Google Scholar

Civan, P., Ali, S., Batista-Navarro, R., Drosou, K., Ihejieto, C., Chakraborty, D., et al. (2019). Origin of the aromatic group of cultivated rice (Oryza sativa L.) traced to the Indian subcontinent. Genome Biol. Evolu. 11, 832–843.

Google Scholar

Civan, P., and Brown, T. A. (2018). misconceptions regarding the role of introgression in the origin of Oryza sativa subsp. indica. Front. Plant Sci. 9:1750. doi: 10.3389/fpls.2018.01750

PubMed Abstract | CrossRef Full Text | Google Scholar

Civan, P., Craig, H., Cox, C. J., and Brown, T. A. (2015). Three geographically separate domestications of Asian rice. Nat. Plants 1:15164. doi: 10.1038/nplants.2015.164

PubMed Abstract | CrossRef Full Text | Google Scholar

Drummond, A. J., Suchard, M. A., Xie, D., and Rambaut, A. (2012). Bayesian phylogenetics with BEA Uti and the BEAST 1.7. Mol. Biol. Evolu. 29, 1969–1973.

Google Scholar

Excoffier, L., and Lischer, H. E. L. (2010). Arlequin suite ver 3.5: a new series of programs to perform population genetics analyses under linux and windows. Mol. Ecol. Res. 10, 564–567. doi: 10.1111/j.1755-0998.2010.02847.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Flood, P. J., Theeuwen, T. P. J. M., Schneeberger, K., Keizer, P., Kruijer, W., Severing, E., et al. (2020). Reciprocal cybrids reveal how organellar genomes affect plant phenotypes. Nat. Plants 6, 13–21. doi: 10.1038/s41477-019-0575-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Ford-Lloyd, B. V., Schmidt, M., Armstrong, S. J., Barazani, O., Engels, J., Hadas, R., et al. (2011). Crop wild relatives-undervalued, underutilized and under threat? Bioscience 61, 559–565.

Google Scholar

Forster, P., Torroni, A., Renfrew, C., and Rohl, A. (2001). Phylogenetic star contraction applied to Asian and papuan mtDNA evolution. Mol. Biol. Evol. 18, 1864–1881. doi: 10.1093/oxfordjournals.molbev.a003728

PubMed Abstract | CrossRef Full Text | Google Scholar

Fujita, K., Coronel, V. P., and Yoshida, S. (1984). Grain-filling characteristics of rice varities (Oryza sativa l.) differing in grain size under controlled environmental conditions. Soil Sci. Plant Nut. 30, 445–454. doi: 10.1080/00380768.1984.10434709

CrossRef Full Text | Google Scholar

Fuller, D. Q., Qin, L., Zheng, Y., Zhao, Z., Chen, X., Hosoya, L. A., et al. (2009). The domestication process and domestication rate in rice: spikelet bases from the lower yangtze. Science 323, 1607–1610. doi: 10.1126/science.1166605

PubMed Abstract | CrossRef Full Text | Google Scholar

Gao, L. Z., Liu, Y. L., Zhang, D., Li, W., Gao, J., Liu, Y., et al. (2019). Evolution of oryza chloroplast genomes promoted adaptation to diverse ecological habitats. Commun. Biol. 2:278.

Google Scholar

Gitzendanner, M. A., Soltis, P. S., Wong, G. K. S., Ruhfel, B. R., and Soltis, D. E. (2018). Plastid phylogenomic analysis of green plants: a billion years of evolutionary history. Am. J. Bot. 105, 291–301. doi: 10.1002/ajb2.1048

PubMed Abstract | CrossRef Full Text | Google Scholar

Greiner, S., Rauwolf, U., Meurer, J., and Herrmann, R. G. (2011). The role of plastids in plant speciation. Mol. Ecol. 20, 671–691. doi: 10.1111/j.1365-294X.2010.04984.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Gross, B. L., and Olsen, K. M. (2010). Genetic perspectives on crop domestication. Trends Plant Sci. 15, 529–537. doi: 10.1016/j.tplants.2010.05.008

PubMed Abstract | CrossRef Full Text | Google Scholar

Gross, B. L., and Zhao, Z. (2014). Archaeological and genetic insights into the origins of domesticated rice. Proc. Natl. Acad. Sci. U.S.A. 111, 6190–6197. doi: 10.1073/pnas.1308942110

PubMed Abstract | CrossRef Full Text | Google Scholar

Gurevich, A., Saveliev, V., Vyahhi, N., and Tesler, G. (2013). QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075. doi: 10.1093/bioinformatics/btt086

PubMed Abstract | CrossRef Full Text | Google Scholar

Hancock, J. F. (2004). Plant Evolution and the Origin of Crop Species. Cambridge: CABI pubulishing.

Google Scholar

Haudry, A., Cenci, A., Ravel, C., Bataillon, T., Brunel, D., Poncet, C., et al. (2007). Grinding up wheat: a massive loss of nucleotide diversity since domestication. Mol. Biol. Evol. 24, 1506–1517. doi: 10.1093/molbev/msm077

PubMed Abstract | CrossRef Full Text | Google Scholar

He, W., Chen, C., Adedze, Y. M. N., Dong, X., Xi, K., Sun, Y., et al. (2020). Multicentric origin and diversification of atp6-orf79-like structures reveal mitochondrial gene flows in Oryza rufipogon and Oryza sativa. Evol. Appl. 13, 2284–2299. doi: 10.1111/eva.13022

PubMed Abstract | CrossRef Full Text | Google Scholar

Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G., and Jarvis, A. (2005). Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978.

Google Scholar

Ho, S. Y. W. (2007). Calibrating molecular estimates of substitution rates and divergence times in birds. J. Avian Biol. 38, 409–414.

Google Scholar

Hoban, S., Bruford, M., Jackson, J. D., Lopes-Fernandes, M., Heuertz, M., Hohenlohe, P. A., et al. (2020). Genetic diversity targets and indicators in the CBD post-2020 global biodiversity framework must be improved. Biol. Conserv. 248:108654.

Google Scholar

Hou, Z. G., Sket, B., and Li, S. Q. (2014). Phylogenetic analyses of Gammaridae crustacean reveal different diversification patterns among sister lineages in the Tethyan region. Cladistics 30, 352–365.

Google Scholar

Hu, H., Hu, Q. J., Al-Shehbaz, I. A., Luo, X., Zeng, T. T., Guo, X. Y., et al. (2016). Species delimitation and interspecific relationships of the genus Orychophragmus (Brassicaceae) inferred from whole chloroplast genomes. Frontiers in Plant Science 7:1826.

Google Scholar

Huang, R., Jiang, L., Zheng, J., Wang, T., Wang, H., Huang, Y., et al. (2013). Genetic bases of rice grain shape: so many genes, so little known. Trends Plant Sci. 18, 218–226. doi: 10.1016/j.tplants.2012.11.001

PubMed Abstract | CrossRef Full Text | Google Scholar

Huang, X., Kurata, N., Wei, X., Wang, Z. X., Wang, A., Zhao, Q., et al. (2012). A map of rice genome variation reveals the origin of cultivated rice. Nature 490, 497–501. doi: 10.1038/nature11532

PubMed Abstract | CrossRef Full Text | Google Scholar

Katoh, K., and Standley, D. M. (2013). MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evolu. 30, 772–780. doi: 10.1093/molbev/mst010

PubMed Abstract | CrossRef Full Text | Google Scholar

Khoshbakht, K., and Hammer, K. (2008). How many plant species are cultivated? Gene. Res. Crop Evolu. 55, 925–928. doi: 10.1007/s10722-008-9368-0

CrossRef Full Text | Google Scholar

Kovi, M. R., Zhang, Y., Yu, S., Yang, G., Yan, W., and Xing, Y. (2011). Candidacy of a chitin-inducible gibberellin-responsive gene for a major locus affecting plant height in rice that is closely linked to green revolution gene sd1. Theor. Appl. Genet. 123, 705–714. doi: 10.1007/s00122-011-1620-x

PubMed Abstract | CrossRef Full Text | Google Scholar

Kumar, S., Stecher, G., and Tamura, K. (2016). MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets. Mol. Biol. Evolu. 33, 1870–1874.

Google Scholar

Landis, M. J., Matzke, N. J., Moore, B. R., and Huelsenbeck, J. P. (2013). Bayesian analysis of biogeography when the number of areas is large. Syst. Biol. 62, 789–804.

Google Scholar

Leebens-Mack, J. H., Barker, M. S., Carpenter, E. J., Deyholos, M. K., Gitzendanner, M. A., Graham, S. W., et al. (2019). One thousand plant transcriptomes and the phylogenomics of green plants. Nature 574, 679–685. doi: 10.1038/s41586-019-1693-2

PubMed Abstract | CrossRef Full Text | Google Scholar

Leigh, J. W., and Bryant, D. (2015). POPART: full-feature software for haplotype network construction. Methods Ecol. Evolu. 6, 1110–1116.

Google Scholar

Li, C. B., Zhou, A. L., and Sang, T. (2006). Rice domestication by reducing shattering. Science 311, 1936–1939. doi: 10.1126/science.1123604

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, H., and Durbin, R. (2010). Fast and accurate long-read alignment with burrows–wheeler transform. Bioinformatics 26, 589–595.

Google Scholar

Li, H. T., Yi, T. S., Gao, L. M., Ma, P. F., Zhang, T., Yang, J. B., et al. (2019). Origin of angiosperms and the puzzle of the Jurassic gap. Nat. Plants 5, 461–470. doi: 10.1038/s41477-019-0421-0

PubMed Abstract | CrossRef Full Text | Google Scholar

Li, R., Li, M., Ashraf, U., Liu, S., and Zhang, J. (2019). Exploring the relationships between yield and yield-related traits for rice varieties released in China from 1978 to 2017. Front. Plant Sci. 10:543. doi: 10.3389/fpls.2019.00543

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, H. B., Shi, J. P., Cai, Z. X., Huang, Y. M., Lv, M. L., Du, H. L., et al. (2020). Evolution and domestication footprints uncovered from the genomes of coix. Mol. Plant 13, 295–308. doi: 10.1016/j.molp.2019.11.009

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, L., Wang, Z., Huang, L., Wang, T., and Su, Y. (2019). Chloroplast population genetics reveals low levels of genetic variation and conformation to the central-marginal hypothesis in taxus wallichiana var. mairei, an endangered conifer endemic to China. Ecol. Evol. 9, 11944–11956. doi: 10.1002/ece3.5703

PubMed Abstract | CrossRef Full Text | Google Scholar

Liu, X. M., and Fu, Y. X. (2015). Exploring population size changes using SNP frequency spectra. Nat. Genet. 47, 555–559.

Google Scholar

Londo, J. P., Chiang, Y. C., Hung, K. H., Chiang, T. Y., and Schaal, B. A. (2006). Phylogeography of Asian wild rice, Oryza rufipogon, reveals multiple independent domestications of cultivated rice, Oryza sativa. Proc. Natl. Acad. Sci. U.S.A. 103, 9578–9583. doi: 10.1073/pnas.0603152103

PubMed Abstract | CrossRef Full Text | Google Scholar

Lu, S. J., Dong, L. D., Fang, C., Liu, S. L., Kong, L. P., Cheng, Q., et al. (2020). Stepwise selection on homeologous PRR genes controlling flowering and maturity during soybean domestication. Nat. Genet. 52:428.

Google Scholar

Ma, X., Fan, J., Wu, Y., Zhao, S., Zheng, X., Sun, C., et al. (2020). Whole-genome de novo assemblies reveal extensive structural variations and dynamic organelle-to-nucleus DNA transfers in African and Asian rice. Plant J. 104, 596–612. doi: 10.1111/tpj.14946

PubMed Abstract | CrossRef Full Text | Google Scholar

Matzke, N. J. (2013). Probabilistic historical biogeography: new models for founder event speciation, imperfect detection, and fossils allow improved accuracy and model-testing. Front. Biogeogr. 5:242–248.

Google Scholar

Miller, A. J., and Gross, B. L. (2011). From forest to field: perennial fruit crop domestication. Am. J. Bot. 98, 1389–1414. doi: 10.3732/ajb.1000522

PubMed Abstract | CrossRef Full Text | Google Scholar

Molina, J., Sikora, M., Garud, N., Flowers, J. M., Rubinstein, S., Reynolds, A., et al. (2011). Molecular evidence for a single evolutionary origin of domesticated rice. Proc. Natl. Acad. Sci. U.S.A. 108, 8351–8356. doi: 10.1073/pnas.1104686108

PubMed Abstract | CrossRef Full Text | Google Scholar

Moner, A. M., Furtado, A., and Henry, R. J. (2020). Two divergent chloroplast genome sequence clades captured in the domesticated rice gene pool may have significance for rice production. BMC Plant Biol. 20:472. doi: 10.1186/s12870-020-02689-6

PubMed Abstract | CrossRef Full Text | Google Scholar

Nguyen, L.-T., Schmidt, H. A., von Haeseler, A., and Minh, B. Q. (2014). IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evolu. 32, 268–274.

Google Scholar

Palumbi, S. R., Cipriano, F., and Hare, M. P. (2001). Predicting nuclear gene coalescence from mitochondrial data: the three-times rule. Evolution 55, 859–868.

Google Scholar

Petit, N., and Barbadilla, A. (2009). Selection efficiency and effective population size in Drosophila species. J. Evol. Biol. 22, 515–526. doi: 10.1111/j.1420-9101.2008.01672.x

PubMed Abstract | CrossRef Full Text | Google Scholar

Phillips, S. J., Anderson, R. P., Dudik, M., Schapire, R. E., and Blair, M. E. (2017). Opening the black box: an open-source release of maxent. Ecography 40, 887–893.

Google Scholar

Pollmann, B., Jacomet, S., and Schlumbaum, A. (2005). Morphological and genetic studies of waterlogged Prunus species from the roman vicus tasgetium (Eschenz, Switzerland). J. Archaeol. Sci. 32, 1471–1480.

Google Scholar

Razifard, H., Ramos, A., Della Valle, A. L., Bodary, C., Goetz, E., Manser, E. J., et al. (2020). Genomic evidence for complex domestication history of the cultivated tomato in Latin America. Mol. Biol. Evol. 37, 1118–1132. doi: 10.1093/molbev/msz297

PubMed Abstract | CrossRef Full Text | Google Scholar

Ree, R. H., and Smith, S. A. (2008). Maximum likelihood inference of geographic range evolution by dispersal, local extinction, and cladogenesis. Syst. Biol. 57, 4–14.

Google Scholar

Rojas-Barrera, I. C., Wegier, A., Gonzalez, J. D. S., Owens, G. L., Rieseberg, L. H., and Pinero, D. (2019). Contemporary evolution of maize landraces and their wild relatives influenced by gene flow with modern maize varieties. Proc. Natl. Acad. Sci. U.S.A. 116, 21302–21311. doi: 10.1073/pnas.1817664116

PubMed Abstract | CrossRef Full Text | Google Scholar

Ronquist, F. (1997). Dispersal-vicariance analysis: a new approach to the quantification of historical biogeography. Syst. Biol. 46, 195–203.

Google Scholar

Roux, F., Mary-Huard, T., Barillot, E., Wenes, E., Botran, L., Durand, S., et al. (2016). Cytonuclear interactions affect adaptive traits of the annual plant Arabidopsis thaliana in the field. Proc. Natl. Acad. Sci. U.S.A. 113, 3687–3692. doi: 10.1073/pnas.1520687113

PubMed Abstract | CrossRef Full Text | Google Scholar

Rozas, J., Ferrer-Mata, A., Sánchez-DelBarrio, J. C., Guirao-Rico, S., Librado, P., Ramos-Onsins, S. E., et al. (2017). DnaSP 6: DNA sequence polymorphism analysis of large data sets. Mol. Biol. Evol. 34, 3299–3302.

Google Scholar

Sang, T., and Ge, S. (2007). Genetics and phylogenetics of rice domestication. Curr. Opin. Genet. Dev. 17, 533–538. doi: 10.1016/j.gde.2007.09.005

PubMed Abstract | CrossRef Full Text | Google Scholar

Schlumbaum, A., Tensen, M., and Jaenicke-Despres, V. (2008). Ancient plant DNA in archaeobotany. Vegetation History Archaeobotany 17, 233–244.

Google Scholar

Scott, M. F., Botigue, L. R., Brace, S., Stevens, C. J., Mullin, V. E., Stevenson, A., et al. (2019). A 3,000-year-old egyptian emmer wheat genome reveals dispersal and domestication history. Nat. Plants 5, 1120–1128. doi: 10.1038/s41477-019-0534-5

PubMed Abstract | CrossRef Full Text | Google Scholar

Smarda, P., Bures, P., Horova, L., Leitch, I. J., Mucina, L., Pacini, E., et al. (2014). Ecological and evolutionary significance of genomic GC content diversity in monocots. Proc. Natl. Acad. Sci. U.S.A. 111, E4096–E4102.

Google Scholar

Smith, B. D. (1998). The Emergence of Agriculture. New York: Scientific American Library, A Division of HPHLP.

Google Scholar

Smith, K. F., Sax, D. F., and Lafferty, K. D. (2006). Evidence for the role of infectious disease in species extinction and endangerment. Conserv. Biol. 20, 1349–1357.

Google Scholar

Snyder, C. W. (2016). Evolution of global temperature over the past two million years. Nature 538:226.

Google Scholar

South, P. F., Cavanagh, A. P., Liu, H. W., and Ort, D. R. (2019). Synthetic glycolate metabolism pathways stimulate crop growth and productivity in the field. Science 363:45.

Google Scholar

Sweeney, M. T., Thomson, M. J., Pfeil, B. E., and McCouch, S. (2006). Caught red-handed: RC encodes a basic helix-loop-helix protein conditioning red pericarp in rice. Plant Cell 18, 283–294. doi: 10.1105/tpc.105.038430

PubMed Abstract | CrossRef Full Text | Google Scholar

Takhtajan, A. (1986). Floristic Regions of the World. California: University of California Press.

Google Scholar

Tanaka, M., Cabrera, V. M., Gonzalez, A. M., Larruga, J. M., Takeyasu, T., Fuku, N., et al. (2004). Mitochondrial genome variation in eastern Asia and the peopling of Japan. Genome Res. 14, 1832–1850. doi: 10.1101/gr.2286304

PubMed Abstract | CrossRef Full Text | Google Scholar

Tembrock, L. R., Simmons, M. P., Richards, C. M., Reeves, P. A., Reilley, A., Curto, M. A., et al. (2017). Phylogeography of the wild and cultivated stimulant plant qat (Catha edulis, celastraceae) in areas of historical cultivation. Am. J. Bot. 104, 538–549. doi: 10.3732/ajb.1600437

PubMed Abstract | CrossRef Full Text | Google Scholar

Tillich, M., Lehwark, P., Pellizzer, T., Ulbricht-Jones, E. S., Fischer, A., Bock, R., et al. (2017). GeSeq - versatile and accurate annotation of organelle genomes. Nucleic Acids Res. 45, W6–W11.

Google Scholar

Tonti-Filippini, J., Nevill, P. G., Dixon, K., and Small, I. (2017). What can we do with 1000 plastid genomes? Plant J. 90, 808–818.

Google Scholar

Valliyodan, B., Ye, H., Song, L., Murphy, M., Shannon, J. G., and Nguyen, H. T. (2017). Genetic diversity and genomic strategies for improving drought and waterlogging tolerance in soybeans. J. Exp. Bot. 68, 1835–1849. doi: 10.1093/jxb/erw433

PubMed Abstract | CrossRef Full Text | Google Scholar

Vaughan, D. A., Lu, B. R., and Tomooka, N. (2008). The evolving story of rice evolution. Plant Sci. 174, 394–408. doi: 10.1016/j.plantsci.2008.01.016

CrossRef Full Text | Google Scholar

Vikas, V. K., Kumar, S., Archak, S., Tyagi, R. K., Kumar, J., Jacob, S., et al. (2020). Screening of 19,460 genotypes of wheat species for resistance to powdery mildew and identification of potential candidates using focused identification of germplasm strategy (FIGS). Crop Sci. 60, 2857–2866. doi: 10.1002/csc2.20196

CrossRef Full Text | Google Scholar

Wagner, S., Lagane, F., Seguin-Orlando, A., Schubert, M., Leroy, T., Guichoux, E., et al. (2018). High-throughput DNA sequencing of ancient wood. Mol. Ecol. 27, 1138–1154.

Google Scholar

Wang, H., Takano, T., and Liu, S. K. (2018). Screening and evaluation of saline-alkaline tolerant germplasm of rice (Oryza sativa L.) in soda saline-alkali soil. Agron. Basel 8:205. doi: 10.3390/agronomy8100205

CrossRef Full Text | Google Scholar

Wang, W. S., Mauleon, R., Hu, Z. Q., Chebotarov, D., Tai, S. S., Wu, Z. C., et al. (2018). Genomic variation in 3,010 diverse accessions of Asian cultivated rice. Nature 557:43. doi: 10.1038/s41586-018-0063-9

PubMed Abstract | CrossRef Full Text | Google Scholar

Wang, K., Li, M. Y., and Hakonarson, H. (2010). ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38:e164.

Google Scholar

Wick, R. R., Schultz, M. B., Zobel, J., and Holt, K. E. (2015). Bandage: interactive visualization of de novo genome assemblies. Bioinformatics 31, 3350–3352. doi: 10.1093/bioinformatics/btv383

PubMed Abstract | CrossRef Full Text | Google Scholar

Wilkes, G., and Williams, J. T. (1983). Current status of crop plant germplasm. Crit. Rev. Plant Sci. 1, 133–181. doi: 10.1080/07352688309382175

CrossRef Full Text | Google Scholar

Wu, Z. Y., and Wu, S. G. (1996). “A proposal for a new floristic kingdom (realm) — the E. Asiatic kingdom, its delimitation and characteristics,” in Floristic Characteristics and Diversity of East Asian Plants, eds A. L. Zhang and S. G. Wu (Beijing, Berlin: China Higher Education Press, Springer-Verlag), 3–42.

Google Scholar

Yu, Y., Harris, A. J., Blair, C., and He, X. J. (2015). RASP (reconstruct ancestral state in phylogenies): a tool for historical biogeography. Mol. Phylogenet. Evol. 87, 46–49.

Google Scholar

Yu, Y., Harris, A. J., and He, X. J. (2010). S-DIVA (statistical dispersal-vicariance analysis): a tool for inferring biogeographic histories. Mol. Phylogenet. Evol. 56, 848–850.

Google Scholar

Keywords: plant domestication, crop wild relatives, conservation genetics, phylogeography, germplasm resources

Citation: He W, Chen C, Xiang K, Wang J, Zheng P, Tembrock LR, Jin D and Wu Z (2021) The History and Diversity of Rice Domestication as Resolved From 1464 Complete Plastid Genomes. Front. Plant Sci. 12:781793. doi: 10.3389/fpls.2021.781793

Received: 23 September 2021; Accepted: 27 October 2021;
Published: 18 November 2021.

Edited by:

Romain A. Guyot, IRD UMR 232 Diversité, Adaptation, Développement des Plantes (DIADE), France

Reviewed by:

Rie Shimizu-Inatsugi, University of Zurich, Switzerland
Jie Qiu, Shanghai Normal University, China

Copyright © 2021 He, Chen, Xiang, Wang, Zheng, Tembrock, Jin and Wu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Zhiqiang Wu, wuzhiqiang@caas.cn; Deming Jin, djin@mail.hzau.edu.cn; Luke R. Tembrock, 101020tembrock@gmail.com

Download