Identification of Selection Signatures in Commercial Asian Rice Subspecies Deciphered Selection Footprints for Four Overrepresented Biological Processes

doi:10.21203/rs.3.rs-121958/v1

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Identification of Selection Signatures in Commercial Asian Rice Subspecies Deciphered Selection Footprints for Four Overrepresented Biological Processes

https://doi.org/10.21203/rs.3.rs-121958/v1

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Background

Selective breeding pressures have led to gradual genomic changes in Asian commercial rice, which have shaped selection footprints on its genome level. Tracing genomic selection footprints might be illuminative for better understanding of recent selection breeding objectives, and how breeding strategies have formed the Asian commercial rice genome.

Results

In this study, the genotypic information (HDRA 700K) of four Asian commercial rice subspecies including Indica (n=498), Aus (n=187), Temperate japonica (n=241), and Tropical japonica (n=361) were downloaded from Rice Diversity Project database (http://www.ricediversity.org) to detect selection signatures by employing the Z-transformed of fixation index and Tajima’s D test, based on a sliding window approach. Although we could not identify overrepresented genomic regions underlying selection pressure among all aforementioned Asian commercial rice subspecies, interestingly, our findings revealed four overrepresented biological processes underlying selection pressure including proteolysis (GO:0006508), phosphorylation (GO:0016310), protein catabolic process (GO:0030163), and transmembrane transport (GO:0055085) that might be associated with immunity, senescing leaves, transporting, and absorption of ions.

Conclusions

These results can provide knowledge on how breeding efforts shaped the Asian commercial rice subspecies genome, and which genomic regions of these subspecies have been targeted in recent decades.

Epigenetics & Genomics

Asian commercial rice

genome

Oryza sativa

signature of selection

Asian rice (Oryza sativa.) was domesticated in China sometime between 7,500-12,500 BP [1] and have been adapted to different environmental conditions [2] and human needs [3]. Natural selection and intensive selective breeding programs have played a key role in genetically, morphologically, and physiologically evolving of various commercial Asian rice subspecies, such as Indica, Aus, Temperatejaponica, and Tropical japonica [3, 4]. These selection pressures can be associated with selection footprints on the genome level which is known as selection signatures [5, 6]. Tracing selection footprints in the genome can be helpful for better understanding the evolutionary trend [7, 8], genetic background of recent selective breeding [3, 9], and breeding programs, and for optimizing genomic evaluation [10]. Recent swift technical advances of cost-effective genotyping have provided an opportunity to reveal the genomic selection signatures in plant [3, 11] and animal species [12-16]. Genomic signatures of selection studies on rice have been illuminative in identifying genomic regions associated with productivity [3], adaptation [2, 17], and domestication [8]. Xie et al. (2015) detected six candidate genes involving in nitrogen assimilation pathway by employing a cross population signatures of selection approach and a genome-wide association study. He et al. (2017) revealed 107 genes as selective signals associated with biotic stress pathway using whole-genome sequences of Korean weedy rice.

Although a number of literatures are available on reported important selection signatures in rice [2, 3, 8], our knowledge of the genomic changes associated with domestication remains limited. For instance, there is a strong controversy about division of the domestication process into single or independent origins [8]. Evaluating the genetic structure of O. sativa is very important to provide more evidence for the important selected regions and use this knowledge into the breeding of rice for sustainable agriculture.

In this study, our main goals were: 1) investigation into the genetic structure of Indica, Aus, Temperatejaponica, and Tropical japonica populations, 2) identifying and investigating selection signals in these populations. The results of this study can be useful in revealing novel candidate genes underlying recent selection pressures of the populations. Additionally, it may be illuminative for better understanding of the evolutionary trend in Asian rice.

Plant materials and SNP array data

The used data in this study were retrieved from three large density panels including RDP1 (representing 343 accessions), RDP2 (representing 880 accessions) and the National Institute of Agrobiological Sciences (NIAS) (representing 61 accessions) from many landraces which originated from 108 different countries, more details about the data were presented previously [18]. The germplasm collections used in this study are summarized in Supplementary Data 1. Briefly, the subpopulation distribution of lines in our studied population consisted of 14.6% (Aus), 38.7% (Indica), 18.7% (Temperate-japonica), and 28.0% (Tropical-japonica). All data are publicly available in the Rice Diversity Project (http://www.ricediversity.org). Each germplasm was genotyped by using the high-density rice array (HDRA) comprising of 700,000 single-nucleotide polymorphisms (SNPs). The HDRA represents a very high-density genotyping array with one SNP every 0.54 kb across the rice genome [18].

Quality control

Quality control was performed using the PLINK 1.9 software [19]. First, SNPs with call rate less than 80% were removed from each subpopulation, separately, and 598739, 619563, 661469 and 658713 number of SNPs remained for Aus, Indica, Temperate japonica, and Tropical japonica populations were 598739, 619563, 661469 and 658713, respectively. A total number of 513,703 SNPs remained in the merged rice subpopulations. After applying minor allelic frequency < 0.05, 286,183 SNPs remained for the subsequent analyses. Individuals with the sample call rate more than 80% were used in the final data. Finally, the missing genotypes were imputed using the FImpute 2.2 [20].

Population genetic structure

To investigate the genetic diversity pattern in the studied populations, nucleotide diversities () [21] of each 10 Kb per chromosome were calculated and visualized in VCFtools 0.1.15 package (http://vcftools.sourceforge.net/index.html) [22] and Circos 0.69.6 software [23], respectively. In this study, three methods including Neighbor-joining, Bayesian model-based approach exemplified by ADMIXTURE at K=2, K=3 and K=4, and Principal component analysis (PCA), were used to assess the population genetic structure of the studied populations. The neighbor-joining analysis was performed by applying VCF-kit 0.1.6 (https://vcf-kit.readthedocs.io/en/latest) [24]. In order to visualize the results of neighbor-joining analysis, FigTree 1.4.3 software was employed (http://tree.bio.ed.ac.uk/software/figtree). The admixture analysis and PCA were performed using ADMIXTURE 1.3 (http://software.genetics.ucla.edu/admixture) and SNP1101 1.0 [25], respectively. Additionally, a linkage disequilibrium estimation analysis across all chromosomes was performed and visualized for each population using PopLDdecay 1.01 software [26] and a perl script (https://github.com/BGI-shenzhen/PopLDdecay), respectively.

Selection signals and gene ontology

In this study, two methods including Z-transformed fixation index (Z(F_st)) [27] and Tajima’s D test based on a sliding window approach (100 kb with a step size of 25 kb) were implemented using VCFtools 0.1.15 [22] and VCF-kit 0.1.6 [24], respectively. The Z-transformation of F_st values was calculated using “scale” function in R software (https://cran.r-project.org). The windows in the top 1% of Z (F_st) with a negative Tajima’s D value in populations were considered as selection signals in each scenario. The Gene ontology analyses were conducted using Gene Ontology Consortium (http://geneontology.org). The overlapped windows underlying selection pressures and overlapped biological processes among selection signatures scenarios visualized via Venn Painter 1.2.0 [28].

Population genetic structure and nucleotide diversity

The PCA, admixture, and neighbor-joining were performed to visualize a comprehensive investigation into the genetic separation of four commercial Asian rice subspecies. More than 37% of the genetic variation was explained by the top three PCs (Fig. 1). PC1 separates the Indica/Aus and two japonica varietal groups. The Indica and Aus clustered in two separate clusters at PC2. Finally, the PC3 classified the Temperatejaponica and Tropical japonica subpopulations into two separate groups. Similar results were achieved by admixture analysis (Fig. 2). The admixture analysis at K=2 and K=3 showed both Temperatejaponica and Tropical japonica in one cluster; and also, at K=2, it showed Indica and Aus together in one group each of which was grouped into a cluster by admixture at K=4. Similar to PC3, the admixture at K=4 separated Temperatejaponica and Tropical japonica subpopulations. Same interpretation concluded from the neighbor-joining analysis (Fig. 3).

Study of linkage disequilibrium patterns (mean of r²) in populations can be informative for having an overview of population demography background, due to the effective role of linkage disequilibrium patterns in demographic forces and evolutionary patterns [29]. Our linkage disequilibrium results illustrated a rapid drop at approximately 5 Kb in all groups (Fig. 4). The means of r² at 300 Kb for Indica, Aus, Temperatejaponica, and Tropical japonica groups were 0.04, 0.05, 0.10 and 0.07, respectively.

In this study, values (in 10Kb windows) were calculated to evaluate genomic diversities in studied subpopulations (Fig. 5). The values were ranged from 5.35E-7 to 0.0015 across all subpopulations. The mean of values demonstrated higher nucleotide diversities in Aus (0.16E-3) and Indica (0.17E-3) in comparison with Temperate japonica (7.97E-5) and Tropical japonica (1.09E-4) groups.

Selective signals and gene ontology

In this study, based on windows located in the top 1% of Z (Fst) values with Tajima’s D values < 0, six scenarios were considered to detect genomic regions underlying selection pressures including: Tropical japonica vs. Temperate japonica, Temperate japonica vs. Indica, Tropical japonica vs. Indica, Indica vs. Aus, Tropical japonica vs. Aus and Aus vs. Temperate japonica.

Tropical japonica vs. Temperate japonica

A total number of 66 windows were identified in the top 1% of Z (Fst) values (Fig. 6) with Tajima’s D values < 0, in the Tropical japonica vs. Temperate japonica scenario (Supplementary data 2). These windows contain 145 genes related to 40 biological processes (Supplementary data 2 and Supplementary information, Table S1); however, these biological processes were not significantly enriched. Additionally, we detected three important candidate genes including: Os05g0405000, Os05g0402700 and Os03g0318500 related to different economical traits, such as temperature response [30], response to stresses [31], starch metabolism, structure [32], root growth, and development. The Glucose-6-phosphate1-dehydrogenase gene is the ortholog of Os03g0318500; the important role of this gene was shown in response to stresses, such as metal toxicity [33], osmotic stress [34] and pathogenesis [35]. The ortholog of Os05g0405000 is PPDK1, which in rice endosperm can play a key role in starch metabolism and structure by regulating starch synthesis-related enzymes expression [32]. The temperature tolerance has been one of the important traits in rice breeding strategies [36, 37]. The Os05g0405000 gene is also related to temperature response. A gene expression study revealed that high temperature can decrease the PPDK enzyme activity [30]. The ortholog of Os05g0402700 gene is Fructose-bisphosphate aldolase 1; Konishi et al (2005) demonstrated the pivotal role of Fructose-bisphosphate aldolase enzyme activity in root growth of rice [38].

Temperate japonica vs. Indica

In Temperate japonica vs. Indica scenario, a total number of 104 windows including 274 genes related to 87 biological processes were screened in the top 1% of Z(Fst) values (Fig. 6) with Tajima’s D values < 0 (Supplementary data 3 and Supplementary information, Table S2). The detected biological processes were not significant similar to Tropical japonica vs. Temperate japonica scenario. Additionally, there were not overlapped genomic regions underlying selection pressures among Tropical japonica vs. Temperate japonica and Temperate japonica vs. Indica scenarios (Fig. 7). In this scenario, we screened some candidate genes such as, Os01g0894000 (Calcineurin-related phosphoesterase-like), and Os10g0430200 (CAD1) related to drought tolerance [39], and lignification development [40] in rice, respectively. A former study revealed that lignification development can be associated with intensification of drought tolerance in rice [41]. The drought tolerance, as a complex trait, plays a key role in saving water and sustainable agriculture development, which have been the objectives of Indica breeding strategies [42], as they are broadly cultivated in tropical regions with warm climate [43].

Tropical japonica vs. Indica

A genome scan for signatures of selection between Tropical japonica and Indica subpopulations showed a total of 87 windows including 213 genes related to 64 biological processes in the top 1% of Z(Fst) values (Fig. 6) with a negative Tajima’s D value (Supplementary data 4 and Supplementary information, Table S3). This scenario had 61 and one overlapped windows with Temperate japonica vs. Indica and Tropical japonica vs. Temperate japonica scenarios, respectively (Fig. 7). The Os08g0559400 (OsCyp3), Os02g0462900, Os02g0332200 (T-complex protein 1 subunit delta), Os02g0782500 (18.6 kDa class III heat shock protein), and Os05g0533900 genes involve in protein folding (GO:0006457) as a significant biological process (q-value = 0.027) in Tropical japonica vs. Indica scenario. This biological process is related to heat shock proteins [44]. The heat shock proteins have a fundamental role in response to abiotic stressful conditions through helping to refold proteins which were damaged by heat stresses [44, 45]. The higher thermo- tolerability of Indica compared to the japonica subspecies was revealed in a comparative former study [46].

Indica vs. Aus

The number of signatures of selection windows in Indica vs. Aus scenario was 17, which included 55 genes related to 27 biological processes in the top 1% of Z(Fst) values (Fig. 6) with a negative Tajima’s D value (Supplementary data 5 and Supplementary information, Table S4). Among 17 detected windows underlying selection pressures, two overlapped windows (chromosome 4: 34.47- 34.57 Mb and chromosome 4: 34.50- 34.60 Mb) were observed in both Indica vs. Aus and Temperate japonica vs. Indica scenarios (Fig. 7). These genomic regions include seven genes (Os04g0676800, Os04g0675700, Os04g0676000, Os04g0676700, Os04g0676800, Os04g0677100, and Os04g0677200). There is a lack of information about biological processes related to these genes except for Os04g0676700, and Os04g0677100. The Os04g0676700 gene participates in response to salicylic acid (GO:0009751). Salicylic acid is a well-known molecule which involves in protection under biotic and abiotic stress responses [47, 48]. The Os04g0677100 involves in proteolysis (GO:0006508) and protein catabolic process (GO:0030163). These GOs can play important roles, such as senescing leaves [49] and releasing trans-membrane proteins [50].

Tropical japonica vs. Aus

A total number of 91 windows containing 254 genes were detected in the top 1% of Z(Fst) values (Fig. 6) with Tajima’s D values < 0 (Supplementary data 6). Our GO analyses showed that these genes are related to 77 biological processes (Supplementary information, Table S5). Among genes underlying selection pressures in this scenario, we detected two candidate genes including Os01g0266600 (Peroxiredoxin-2F or PRXIIF), Os03g0710500 (Heat shock 70 kDa protein BiP2 or BiP2) which are related to regulating root growth [51], folding and secretion of pathogenesis-related (PR) proteins [52] and stress response [53]. In the presence of salicylhydroxamic acid and cadmium, the PRXIIF gene can contribute to regulating root growth of rice [51]. In Arabidopsis, Wang et al (2005) showed the participation of BiP2 gene in folding and secretion of PR proteins during systemic acquired resistance; the PR proteins are classified as a conserved protein family which plays multiple roles in immune responses [54], plant development, and biotic and abiotic stresses [55]. Here, we also detected 6 genes related to phosphorylation (GO:0016310) including Os03g0755000, Os03g0756200, Os03g0686800, Os02g0241600, Os02g0769700, and Os05g0521300. Although this GO term was not significant in our gene ontology analysis, former studies revealed the effective role of phosphorylation in immunity of plants [56, 57]. For example, Matsui et al (2015) indicated that the phosphorylation of pto-interacting protein 1a can be important for activating rice immunity.

Aus vs. Temperate japonica

In Aus vs. Temperate japonica scenario, we screened a total number of 80 windows including 234 genes in the top 1% of Z(Fst) values (Fig. 6) with Tajima’s D values < 0 (Supplementary data 7 and Supplementary information, Table S6). These genes were related to 61 biological functions. However, the biological functions in this scenario were not significant in our gene ontology analysis. Additionally, our findings showed three genes including Os10g0430200, Os08g0463900, Os03g0810800 that are related to lignin biosynthetic process (GO:0009809), thylakoid membrane organization (GO:0010027) and response to water deprivation (GO:0009414), respectively. Lignin as a natural phenolic polymer is an important component of the cell wall structure in plants. The biosynthesis of lignin can widely participate in different traits such as responses to biotic and abiotic stresses through lignin accumulation which results in the accumulation of reactive oxygen species in plants, under such stresses [58]. The thylakoid membrane has an effective role in photosynthesis and electron transportation [59]. The chloroplast thylakoid membranes are dispersed in the stroma, where photochemical reactions take place in green algae and higher plants. Additionally, rice thylakoid membrane complexes can play a pivotal role in growth and crop production [60].

Overrepresented genes and GOs among the selection signature scenarios

In this study, we could not identify any overrepresented genomic region among all adopted selection signature scenarios. Conversely, gene ontology analyses screened four biological processes including proteolysis (GO:0006508), phosphorylation (GO:0016310), protein catabolic process (GO:0030163), and transmembrane transport (GO:0055085) which are underlying selection pressures (Fig. 7). Although, several involved candidate genes were intriguingly different from aforementioned biological processes among scenarios (Supplementary Table S2), which can be due to the different breeding programs or environmental conditions.

Investigation of the population genetic structure can provide an opportunity to determine the effect of evolutionary processes, such as selection [3, 61, 62], on populations. Former studies suggested that employing different population genetic structure analysis methods and comparing their results can be informative for having a better overview of genetic distances [9, 17, 61]. These results were in concordance with those of former studies [3, 4, 18]. A visual appraisal of the three approaches indicated that the genetic distances of the samples within each Indica and Aus clusters were more than Temperate and Tropicaljaponica subpopulations.

The pattern of nucleotide diversities may explain the results of a high genetic distance within Aus and Indica groups, as well as a low genetic distance within Temperatejaponica and Tropicaljaponica, which were obtained from our PCA, admixture and neighbor-joining assessments. The most diverse window was located on chromosome 9: 6.33-6.33 Mb of Indica, Aus, and across all population groups. This region includes a protein coding gene (Os09g0285900). In Temperate and Tropical japonica groups the most diverse windows were located on chromosome 4:1.36-1.37 Mb and chromosome 11:28.00-28.01 Mb, respectively. The chromosome 11:28.00-28.01 Mb contains OsRH40, Os11g0689500, and Os11g0689650 genes. The chromosome 4:1.36-1.37 Mb region includes Os04g0122200, Os04g0122300, and two non-protein coding genes (Os04g0122750 and Os04g0122750). The serine/threonine kinase activity, and ATP binding proteins encoded by the Os04g0122200 gene; the serine/threonine kinase plays a pivotal role in resistance to rice stripe disease [63]. It can support that the recombination rate and nucleotide diversity in genomic regions related to disease resistance can potentially be higher than other genomic regions, such as MHC and CNL gene groups in mammals [64] and barley [65], respectively.

The previous studies on genomic signatures of selection using different tools revealed a large number of genomic regions underlying selection pressures which are related to productivity [3], domestication [2, 17, 66] and adaptation [8] in various rice subspecies during recent decades. Therefore, identifying the genomic selection signatures in Asian commercial subspecies can reveal novel candidate genomic regions related to economic traits. Furthermore, it can be helpful to understand the recent selection patterns in these subspecies. We found the four overrepresented biological processes, including GO:0006508, GO:0016310, GO:0030163 and GO:0055085 which can contribute in some important traits such as immunity (phosphorylation) [56], senescing leaves (proteolysis and protein catabolic) [49], and root membrane transporting (transmembrane transport) which can be highly important for absorption, and transporting of ions in rice [67]. Additionally, there were overlapped biological processes underlying selection pressures among several scenarios which can have effective roles in economical traits of Asian rice subspecies; For example, developmental process was identified as an overrepresented biological process among three scenarios (Tropical japonica vs. Temperate japonica, Tropical japonica vs. Indica, and Tropical japonica vs. Indica) which can have a fundamental role in growth and development of rice seeds [68]. Moreover, selection for these biological processes can be reasonable considering the effective role of phosphorylation, proteolysis and protein catabolic process in immunity [56], senescing leaves [49] and releasing of transmembrane proteins [50], respectively.

In this study, we detected genomic regions underlying selection pressures in four Asian commercial rice subspecies genome. By employing two selection signatures methods, we could not identify any overrepresented genomic region among all adopted selection signatures scenarios; however, the gene ontology analyses indicated the four overrepresented biological processes underlying selection pressures among these scenarios which can participate in important traits such as immunity (phosphorylation), senescing leaves (proteolysis and protein catabolic), and root membrane transporting (transmembrane transport). These results can be helpful for better understanding of the genetic background of recent selective breeding efforts. Additionally, identified genes as selection signatures can be considered for optimizing future SNP panels in rice and rice breeding.

NIAS: National Institute of Agrobiological Sciences

HDRA: High-density rice array

SNP: Single-nucleotide polymorphism

PCA: Principal component analysis

Z(Fst): Z-transformed fixation index

Conflict of interest

The authors declare that they have no conflict of interest.

Author Contribution Statement:

MAR and SSA conceived and planned the experiments. SSA carried out the experiments with support from MAR, MHB and SFM. MAR and SFM wrote the manuscript. MB and MM contributed to the experimental design of the study and participated in the manuscript writing. All authors read and approved the final draft of the manuscript.

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Identification of Selection Signatures in Commercial Asian Rice Subspecies Deciphered Selection Footprints for Four Overrepresented Biological Processes

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