Genetic structure and isolation by altitude in rice landraces of Yunnan, China revealed by nucleotide and microsatellite marker polymorphisms

Di Cui; Cuifeng Tang; Jinmei Li; Xinxiang A; Tengqiong Yu; Xiaoding Ma; Enlai Zhang; Yanjie Wang; Guilan Cao; Furong Xu; Luyuan Dai; Longzhi Han; Hee-Jong Koh

doi:10.1371/journal.pone.0175731

Abstract

Rice landraces, a genetic reservoir for varietal improvement, are developed by farmers through artificial selection during the long-term domestication process. To efficiently conserve, manage, and use such germplasm resources, an understanding of the genetic structure and differentiation of local rice landraces is required. In this study, we analyzed 188 accessions of rice landraces collected from localities across an altitudinal gradient from 425 to 2, 274 m above sea level in Yunnan Province, China using ten target genes and 48 SSR markers. We detected clear differentiation of the rice landraces into indica and japonica groups and further separation of the accessions in each group into two subgroups according to altitude, including a lower altitude subgroup and higher altitude subgroup. The AMOVA results showed significant genetic differentiation among altitude zones at SSRs and most genes, except Os1977 and STS22. We further determined that differentiation among landrace populations followed a model of isolation by altitude, in which gene flow was higher among populations at similar altitude levels than across different altitude levels. Our findings demonstrated that both adaptation to altitude and altitude-dependent gene flow played key roles in the genetic differentiation of rice landraces in Yunnan, China.

Citation: Cui D, Tang C, Li J, A X, Yu T, Ma X, et al. (2017) Genetic structure and isolation by altitude in rice landraces of Yunnan, China revealed by nucleotide and microsatellite marker polymorphisms. PLoS ONE 12(4): e0175731. https://doi.org/10.1371/journal.pone.0175731

Editor: Tzen-Yuh Chiang, National Cheng Kung University, TAIWAN

Received: February 2, 2017; Accepted: March 30, 2017; Published: April 19, 2017

Copyright: © 2017 Cui et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: LZH was supported by the National Natural Science Foundation of China (31671664), the National Key Research and Development Program of China (2016YFD0100101 and 2016YFD0100301), the National Key Technology Research and Development Program of China (2013BAD01B02-2, 2013BAD01B0101-02 and 2015BAD01B01-1), CAAS Science and Technology Innovation Program, the National infrastructure for Crop Germplasm Resources, the Protective Program of Crop Germplasm of China (2016NWB036-01 and 2016NWB036-12-2). HJK was supported by the Next-Generation BioGreen21 Program (PJ01102401), RDA, Republic of Korea. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Crop domestication is a complex process mediated by a series of phenotypic changes to improve cultivation, harvesting, and consumption. Rice (Oryza sativa L.) is one of the earliest domesticated crop species, and the genetic diversity of cultivated rice has been reduced by up to 80% from that of the wild ancestor during the domestication and artificial selection processes [1]. The most extreme loss of diversity is found in modern high-yielding rice varieties, and this has serious consequences for disease susceptibility and adaptation to changing environments [2]. By contrast, rice landraces, which originated and evolved in the field over millennia via selective breeding by farmers, have retained genetic variation [3]. This variation has important implications in rice breeding by providing new genes/alleles for crop improvement. However, with the development of modern agriculture, a large number of local landraces have been replaced with modern varieties introduced over the past 40 years [4]. In China, rice landraces are no longer planted in most provinces, with the exception of some ethnic minority regions, such as areas of Yunnan and Guizhou Provinces.

Selection by various ethnic groups inhabiting areas of different altitudes and climatic conditions and with different cultivation methods, cultures, and traditions has contributed to rice crop diversity in Yunnan. Accordingly, Yunnan is one of the largest centers of genetic diversity for rice worldwide [5–7]. Rice landraces in Yunnan are widely distributed in a region from 21°8′ 32′′ N to 29°11′ 18′′ N and 97°31′ 39′′ E to 106°11′ 47′′ E, and are planted at various altitudes and under diverse climatic conditions [7]. Landraces grow from altitudes of approximately 76 m in Hekou county, Honghe prefecture in the southeastern part of Yunnan to 2,700 m in Weixi county, Diqing prefecture [7]. The wide distribution of rice landraces provides an excellent opportunity for studies of genetic structure and differentiation patterns of rice landraces along the altitudinal range as well as the role of altitude in shaping population genetic structure. Several studies have examined genetic differentiation and the distribution of rice landraces along altitudinal gradients in Yunnan based on phenotype traits or insertion/deletion (indel) molecular markers [8–10]. According to a previous study, both indica and japonica rice varieties are cultivated in Yunnan [6], and the distribution of rice landraces can be artificially categorized into three rice cultivation regions as follows: (1) the indica belt at altitudes of below 1,400 m, (2) the mixed indica and japonica belt at altitudes of between 1,400 and 1,600 m, and (3) the japonica belt at altitudes of above 1,600 m [8]. However, little is known about how altitudinal variation affects population genetic structure. Furthermore, it is not clear whether rice landraces in Yunnan exhibit isolation by altitude. These questions need to be explored using DNA data.

Among various molecular marker types, simple sequence repeat (SSR) markers are commonly used to estimate genetic diversity, population structure, and differentiation in numerous plant species [11]. Furthermore, with the development of DNA sequencing technology, multi-locus DNA sequences have been successfully used to estimate genetic diversity and for phylogenetic analyses [12–15]. DNA sequence differences directly reflect genetic differences; accordingly, sequence comparison is an ideal method for revealing genetic diversity and differentiation.

We collected diverse rice landraces from Yunnan, China along a wide range of altitudes and analyzed gene sequences and SSR markers. Our objectives were to examine the genetic structure of rice landraces and to assess the role of isolation by altitude in genetic differentiation.

Materials and methods

Ethics statement

Field work and the collection of leaves were approved by Institute of Crop Science, CAAS (Chinese Academy of Agricultural Sciences). In this study, the land accessed is not privately owned or protected, and any protected or endangered species were not sampled.

Sampling and choice of loci

In total, 188 rice landraces were collected from a wide altitudinal range (425–2,274 m above sea level) in Yunnan Province, China (S1 Table). These rice landraces were artificially divided into eight groups according to altitude at intervals of 200 m, except for the lowest (≤ 800 m) and highest (≧2,000) zones (S2 Table). The geographic localities of the rice landraces sampled in this study are shown in S1 Fig. In addition, two varieties, 93–11 and Nipponbare, were used as references for indica and japonica, respectively.

A set of 48 SSRs evenly distributed throughout the rice genome (S3 Table) and ten unlinked nuclear gene loci were used in this study. Five of the genes, CatA, GBSSII, Os1977, STS22, and STS90, which have been used to estimate nucleotide diversity in rice populations in previous studies, were used [16,17]. Five additional genes, Ehd1, S5, Pid3, GS3, and GS5, which are associated with agronomic traits in rice, were also used [18–22]. Schematic diagrams of all ten genes are shown in S2 Fig. Detailed information about the genomic location and putative functions of the genes as well as the primer sequences for amplification can be found in S4 Table.

DNA extraction, SSR genotyping, and gene sequencing

Total genomic DNA was extracted from fresh seedling leaves using a modified CTAB procedure [23]. A total of 48 SSRs were amplified by polymerase chain reaction (PCR) with fluorescently labeled primers in a 10-μL reaction volume containing 20 ng of genomic DNA, 10× PCR reaction buffer, 10 mM Mixture dNTP, 2 μM primers, and 0.5 units of Taq polymerase. The PCR profile was as follows: pre-denaturation at 94°C for 5 min, 36 cycles of denaturation at 94°C for 30 s, annealing at 55–60°C (dependent on primers) for 30 s, and extension at 72°C for 40 s, and a final extension at 72°C for 10 min. PCR products were size separated on a 3730XL DNA Sequencer equipped with GENESCAN software (ABI, Waltham, MA, USA). Fragment size was recorded using Gene Marker V1.6 (SoftGene, State College, PA, USA) and manually re-checked.

For the detection of genes, PCR was performed in a 25-μL volume consisting of 0.2 μM of each primer, 200 μM of each dNTP, 10 mM Tris—HCl (pH 8.3), 50 mM KCl, 1.5 mM MgCl₂, 0.5 U of HiFi DNA polymerase (Transgen, Beijing, China), and 10–30 ng of genomic DNA. The PCR profile was as follows: pre-denaturation at 94°C for 5 min, 36 cycles of denaturation at 94°C for 30 s, annealing at 55–60°C (dependent on primers) for 30 s, and extension at 72°C for 1.5 min, and a final extension at 72°C for 10 min. The PCR products were electrophoresed on 1.2% agarose gels, and DNA fragments were cut from the gel and purified using a Tiangen Gel Extraction Kit (Tiangen, Beijing, China). Sequencing reactions were performed using an ABI 3730 Automated Sequencer. Initially, all samples were directly sequenced. However, if haplotypes could not be readily inferred owing to heterozygosity, the PCR product was ligated into an EASY vector (Transgen) and at least four clones were sequenced. For heterozygous individuals, one allele sequence was randomly selected. Because Taq errors occurred, when polymorphisms were only found in one accession, this accession was re-sequenced with the cloning step to verify the polymorphisms.

Population genetic structure

To identify population structure, a Bayesian clustering analysis was conducted using STRUCTURE 2.2 [24,25] based on the 48 SSRs and SNP data, respectively. Fifteen independent runs were performed for each k value (from 1 to 12), using a burn-in length of 100,000, a run length of 100,000, and admixture and correlated allele frequency models. The k value was determined based on LnP(D) in the STRUCTURE output and the ad-hoc statistic Δk [26,27]. A principal component analysis (PCA) was performed using NTSYSpc version 2.11 [28] based on SSR data.

Analysis of DNA sequence and SSR data

DNA sequences were aligned using ClustalX 1.83 [29] and edited using BioEdit 7.0.9.0 [30]. Indels were not included in the analysis. For each locus, the number of segregating sites (S), the number of haplotypes (h), haplotype diversity (Hd), and two nucleotide diversity parameters, mean pairwise differences (θ_π) [31] and Watterson’s estimator based on the number of segregating sites (θ_w) [32], were determined using DnaSP version 5.0 [33]. The minimum number of recombination events (Rm) was estimated using the four-gamete test [34]. The statistical analysis of SSR data, including allele number, genotype number, number of private alleles (i.e., alleles that only appear in certain populations), heterozygosity, gene diversity, and polymorphism information content (PIC), was implemented in PowerMarker version 3.25 [35].

Haplotype network analysis

Haplotype networks were constructed based on mutational steps using Network 4.5 [36]. These networks represent the genetic distances among DNA sequences or alleles and are represented by circles of different sizes and colors and lines linking the circles. Because many haplotypes were obtained for the nuclear loci, only major haplotypes, i.e., those observed in more than three individuals, were selected for network construction.

Genetic differentiation and isolation by altitude

The overall distribution of nucleotide diversity was investigated using an analysis of molecular variance (AMOVA) implemented in Arlequin 3.01 [37]. Sequence variation was hierarchically partitioned between the two subpopulations, among altitude zones within subpopulations, and within altitude zones. The significance of all estimated fixation indices was tested using 10,000 permutations, as described by Excoffier et al. [37]. Pairwise F_ST, generally expressed as the proportion of genetic diversity explained by allele frequency differences among populations [38], was used to measure differentiation within and between subpopulations, as implemented in Arlequin 3.01 [37]. Isolation by altitude was evaluated by assessing the correlation matrix between pairwise altitude difference and genetic differentiation between altitude zones was assessed using Mantel’s tests implemented in Arlequin 3.01 [37]. A total of 10,000 random permutations were performed.

Results

Population structure and genetic relationship analyses

To infer the population structure of 188 rice landraces, we performed model-based simulations using 48 SSRs and 89 SNPs from ten genes, respectively. We observed an increase in the mean posterior probability LnP(D) as the number of groups k increased, but we detected a sharp peak of Δk at k = 2 (S3A Fig) for rice landraces using 48 SSRs. These results were also supported by structural analysis based on SNP data (S3B Fig). These results suggest that rice landraces could be grouped into two subpopulations, referred to as P1 and P2 (Fig 1A). Based on the reference cultivars, the 110 P1 accessions were indica or indica-like, and the 78 P2 accessions were japonica or japonica-like. This result demonstrated that rice landraces from Yunnan were clearly differentiated into indica and japonica groups. Fig 1B shows the distribution of rice accessions from the two subpopulations in each altitude zone. We found only indica at altitudes below 800 m, but we observed both indica and japonica in all other altitude zones. We detected a significant negative correlation between the proportion of indica rice landraces in various altitude zones and altitude (r = -0.814, P < 0.05), but a significant positive correlation between the proportion of japonica rice landraces in various altitude zones and altitude (r = 0.814, P < 0.05) (S4 Fig). In other words, the proportion of rice landraces classified as indica in each altitude zone in Yunnan decreased from low to high altitudes. By contrast, the proportion of landraces classified as japonica in each altitude zone increased from low to high altitudes. Based on a PCA (Fig 2), all rice landraces were distinctly divided into two main groups, indica and japonica, supporting the population structure revealed by STRUCTURE. In each group, we detected further separation into two subgroups according to altitude, including a lower altitude subgroup and higher altitude subgroup, with a few exceptions in middle altitude zones. The indica group was more highly dispersed than the japonica group, indicating higher diversity based on SSRs.

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Fig 1. Model-based ancestries and their distribution in altitude zones.

(A) Model-based ancestry of each accession in P1 and P2; (B) distribution of model-based populations in each altitude zone.

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Fig 2. Principal component analysis of rice landraces from eight altitude zones.

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Nucleotide diversity in model-based populations

We sequenced ten unlinked loci from 188 Yunnan rice landraces covering a wide altitudinal gradient. The aligned sequences for each locus ranged from 420 to 627 bp, with a total length of 4,994 bp, including 1120 bp of coding sequence (S4 Table). We excluded 17 indel polymorphisms, ranging from 1 to 5 bp, from the data analyses. Standard sequence polymorphism statistics at each locus are summarized in Table 1. We observed genetic diversity, as estimated by θ_w, ranging from 0.0014 (STS22) to 0.0082 (CatA) for rice accessions from the indica subpopulation and from 0.0010 (S5) to 0.0074 (CatA) for accessions from the japonica subpopulation. We did not observe a significant difference between the average nucleotide diversity at silent sites between indica accessions (θ_π = 0.0034, θ_w = 0.0030) and the japonica subpopulation (θ_π = 0.0032, θ_w = 0.0031; P > 0.05 for both θ_π and θ_w). In general, based on SSR markers, we detected more genetic variation (gene diversity and PIC), but less heterozygosity, in indica rice than in japonica rice (S5 Table).

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Table 1. Summary of nucleotide polymorphisms.

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Phylogenetic and geographic analyses of haplotypes

Fig 3 shows haplotype networks constructed based on the major haplotypes for each gene. For CatA, GBSSII, Os1977, STS22, STS90, S5, Pid3, Ehd1, GS3, and GS5, we observed 6 (5 and 3 for indica and japonica accessions), 2 (2 and 2), 3 (1 and 3), 5 (5 and 5), 6 (6 and 6), 4 (3 and 2), 5 (3 and 5), 7 (7 and 4), 5 (5 and 5), and 5 (2 and 4) haplotypes, respectively. In total, we found ten indica-specific haplotypes in CatA, S5, GS5 and Ehd1, and eight japonica-specific haplotypes in CatA, Os1977, Pid3, S5 and GS5. In addition, we detected distinct differences in haplotype frequency between indica and japonica rice landraces. We detected five haplotypes (H_3 of CatA; H_2 of GS3; H_1 of GS5; H_1 of Os1977; H_3 of STS90) that were common in indica rice, with an average frequency of 52.51% (8.16–99.05%), but rare in japonica rice, with an average frequency of 2.82% (1.43–4.05%). We observed six haplotypes (H_6 of Ehd1; H_3 of Pid3; H_2 of Os1977; H_5 of STS22; H_3 of S5; H_5 of STS90) that were rare in indica rice, with an average frequency of 1.55% (0.95–2.78%), but were more frequent in japonica rice, with an average frequency of 49.22% (8.00–94.81%). These results further indicated that there was obvious genetic differentiation between indica and japonica rice landraces at some loci.

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Fig 3. Haplotype networks for each gene.

The circle size is proportional to the quantity of samples with a given haplotype, and the numbers next to the circles represent the haplotype number. Lines between haplotypes represent mutational steps between alleles. When more than one nucleotide difference existed between linked haplotypes, the number is indicated next to the lines. Colors for rice landraces collected from different subpopulations are as follows: yellow, P1 (indica) subpopulation; blue, P2 (japonica) subpopulation. The haplotype network for GBSSII, which had only two haplotypes, is not shown.

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We examined the geographic distribution of Ehd1 (Early heading date 1) haplotypes along the altitudinal gradient because this gene had the most polymorphisms. As shown in Fig 4, haplotype H_1 was a major haplotype and was widely distributed across all altitude zones, indicating an ability to adapt to a wide range of conditions in different altitude zones. Haplotype H_6 was a japonica-like haplotype; its frequency was 78.08% in japonica rice, but only 0.97% in indica rice. Interestingly, at altitudes above 1,600 m, we detected an increase in the frequency of haplotype H_6 as the altitude increased. This result suggested that the japonica-like haplotype is more adaptable to high-altitude zones than to low-altitude zones.

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Fig 4. The distribution of haplotypes of rice landraces in eight altitude zones.

Phylogenetic relationships among the haplotypes based on a neighbor-joining analysis are indicated below the map. Pie charts show the proportions of haplotypes within each altitude zone. Haplotypes are indicated by different colors.

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Genetic differentiation between and within subpopulations

Hierarchical AMOVA results are presented in Table 2. We found that a significant proportion of the total variance was explained by variance between subpopulations for all gene loci, ranging from 11.33% (STS22) to 85.81% (Os1977). We observed significant genetic differentiation among altitude zones within subpopulations at most loci, except for Os1977 and STS22. The proportion of the total variance that was explained by variance among altitude zones within subpopulations ranged from 1.63% (CatA) to 9.30% (S5). We detected significant genetic differentiation within altitude zones for all ten loci, representing 14.00% (Os1977) to 89.76% (STS22) of the total variance. For all genes, the average genetic variation between subpopulations (46.92%) and within altitude zones (48.98%) was greater than that found among altitude zones within subpopulations (4.10%). These results were further supported by an AMOVA based on SSRs indicating that 61.85% of the total variation was due to variation within altitude zones, 32.53% between subpopulations, and only 5.62% was found among altitude zones within subpopulations.

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Table 2. Hierarchical analysis of molecular variance for eight altitude zones in two subpopulations (AMOVA).

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Based on F_ST, we observed less genetic differentiation among altitude zones within subpopulations (-0.0282–0.1633 for indica rice and -0.0491–0.1178 for japonica rice; Table 3) than between subpopulations (0.0395–0.8559) for the genes. For SSRs, we also observed less genetic differentiation among altitude zones within subpopulations (0.0222–0.2415 for indica rice and 0.0374–0.2419 for japonica rice; Table 3) than between subpopulations (0.3231–0.5335). These results were fairly consistent with the AMOVA results, indicating that there was less genetic differentiation among altitude zones within subpopulations than between subpopulations. The average F_ST between subpopulations for gene loci (0.4922) was higher than that for SSR loci (0.3893), indicating that genetic differentiation between the indica and japonica subpopulations might be more obvious in gene regions.

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Table 3. Pairwise divergence (F_ST) between altitude zones within subpopulations and between subpopulations.

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Isolation by altitude

The structure analysis suggested that genetic variation is altitude-dependent and specifically that there may be an isolation by altitude. We examined the isolation by altitude using a Mantel’s test implemented in Arlequin 3.01 [37] and observed a significant correlation between genetic differentiation and altitude difference (r = 0.806, P < 0.01) in the entire population based on SSR data (Fig 5A). Notably, we observed the lowest average genetic differentiation (F_ST) between rice in the middle altitude zone and those in other altitude zones, but we detected higher genetic diversity in middle altitude rice (S6 and S7 Tables). Similarly, we detected significant positive correlations between genetic differentiation and altitude difference both in the indica subpopulation (r = 0.868, P < 0.01, Fig 5B) and japonica subpopulation (r = 0.824, P < 0.01, Fig 5C). Furthermore, we found a significant correlation between genetic differentiation and altitude difference for most gene loci in the entire population, except for STS90 and Pid3, with correlation coefficients ranging from 0.510 to 0.819 (Table 4).

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Fig 5. Patterns of isolation by altitude in rice landraces based on SSR markers.

(A) Isolation by altitude in the entire population; (B) isolation by altitude in the indica subpopulation; (C) isolation by altitude in the japonica subpopulation. The correlation coefficients are 0.806, 0.868 and 0.824 for the entire population, indica subpopulation and japonica subpopulation, respectively.

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Table 4. Mantel’s tests for the correlation between genetic differentiation and altitude difference.

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Discussion

Genetic structure and differentiation of rice landraces

Using a model-based structure analysis and a PCA, we found that the rice landraces from Yunnan were clearly differentiated into indica and japonica subpopulations. Within each group, the accessions could be further separated into two subgroups according to altitude, i.e., a lower altitude subgroup and higher altitude subgroup, with the exception of a few accessions from middle altitude zones. These results suggested that altitude is an important determinant of population structure. Genetic differentiation between indica and japonica has been detected based on analyses of morphological traits [39], isozymes [40,41], and DNA markers [42], consistent with our results at the DNA level [7]. Based on the observed distributions across altitudes, indica landraces adapt to a wider range of conditions than japonica landraces, although both types were cultivated across a wide altitude range above 800 m in Yunnan. We verified the relationship between altitude and the distribution of subspecies in a correlation analysis (S4 Fig); indica rice landraces were more frequent at lower altitudes, while japonica rice landraces were more frequent at higher altitudes. The observed patterns of distribution were largely attributable to their adaptations to different ecological conditions, especially temperature; the air temperature in the high-altitude zone was significantly lower than that in the low-altitude zone in Yunnan (S5 Fig).

Genetic diversity and haplotype distribution of rice landraces

Yunnan is a center of diversity for rice in China, and several studies have examined diversity in Yunnan rice landraces. However, to date, such studies focused exclusively on morphological traits or SSR markers [6, 7, 43, 44, 45, 46]. In this study, we used both nuclear gene loci and SSRs to analyze the extent and structure of genetic variation in rice landraces across a wide range of altitudes in Yunnan.

We observed higher levels of nucleotide diversity in both indica and japonica subpopulations than those reported in a previous survey [12] of 30 Asian cultivated rice accessions based on ten genes (θ_π = 0.0029, θ_w = 0.0021 for indica; θ_π = 0.0013, θ_w = 0.0011 for japonica), indicating a high level of nucleotide diversity in rice landraces from Yunnan. Based on SSRs, we detected more genetic variation in indica rice than in japonica rice, which was in agreement with the results of a survey employing 24 SSR markers to examine genetic diversity of 113 rice varieties from Yunnan [47]. By contrast, Zhang et al. found higher genetic diversity in japonica rice landraces than in indica rice landraces of Yunnan based on 20 SSRs [6]. The inconsistent results can probably be explained by differences in sample size, number of molecular markers, marker types, and so on.

In a haplotype analysis, we further found obviously genetic differentiation between indica and japonica rice landraces in some gene regions. Remarkably, for Ehd1 (Fig 4), which promotes short-day flowering and controls FT-like gene expression [18], the frequency of the japonica-like haplotype H_6 increased as the altitude increased above 1,600 m. Indeed, we found that the average days to heading for rice landraces containing haplotype H_6 was less than for landraces containing other haplotypes (S8 Table). These results indicated that early flowering is a critical trait for adaptation to high-altitude zones with short growing seasons and low air temperatures and the japonica-like haplotype is more adapted to high-altitude zones [48].

Isolation by altitude in rice landraces

Rice landraces in Yunnan are planted across a wide range of altitudes [7,8]. A series of environmental factors vary along the altitudinal gradient, and climatic differences could cause genetic divergence among populations [49,50]. High-altitude environmental conditions (e.g., short growing seasons and low temperatures) severely constrain the survival and reproduction of plants and populations; thus, divergence might be due to local adaptation [49,51]. In the present study, we observed a strong association between genetic differentiation and altitude difference at different population levels (Fig 5), indicating an altitude-dependent isolation pattern. Thus, the isolation by altitude was a main factor influencing genetic differentiation among populations in different altitude zones. This raises the question of how isolation by altitude occurred in rice landraces of Yunnan.

The isolation by altitude pattern was consistent with higher rates of gene flow among rice landraces at similar altitudes than along an altitudinal gradient [52]. For inbred cultivated rice, where little to no pollen flow occurs, gene flow must occur by seed movement, and specifically by seed exchange among farmers [2]. Altitude can result in reproductive isolation due to phenological shifts (e.g., flowering period), reducing seed exchange between populations at different altitudes; this may be an important mechanism for creating genetic divergence among populations along the altitudinal gradient [53].

Generally, seeds are exchanged more frequently among farmers within an altitude zone, resulting in high genetic diversity within these zones. Differentiation occurs between altitude zones, reflecting more limited seed exchange. Interestingly, we found that the rice landraces located in the middle altitude zone had the lowest average genetic differentiation (F_ST) with landraces of other altitude zones, but showed higher genetic diversity. These results suggested that seed exchange among farmers in neighboring altitude zones reduces rice genetic structure. In addition, the social structure of communities at different altitudes, such as differences in language or customs, can also influence seed exchange preferences. For example, Daizu people dwell in the flatlands or semi-mountainous areas under 1,200 m, while Lahuzu people prefer medium or highly mountainous regions [54]. These groups have different cultures and food preferences; the Daizu people enjoy glutinous rice, but Lahuzu people prefer red rice varieties. Hence, seed exchange might be restricted between populations at different altitudes under these circumstances, i.e., language and custom barriers. Overall, our results highlight the influence of isolation by altitude on the pattern of the gene flow and genetic differentiation of rice landraces from mountainous regions.

Supporting information

S1 Fig. Geographic localities of rice landraces sampled in this study (When a figure is similar but not identical to the original image, and is therefore for illustrative purposes only).

The localities of rice landraces are indicated by solid circles. Detailed information of the materials is provided in S1 Table.

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S2 Fig. Schematic diagrams of ten nuclear loci and locations of the sequenced regions.

Exons are shown as open boxes and exon numbers are labeled with capital roman numbers. Thin lines between open boxes indicate introns. Locations of primers for each fragment are shown above the diagrams.

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S3 Fig. The ΔK statistic for each given k using 48 SSRs (A) and 89 SNPs from ten genes (B).

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S4 Fig. Correlation between the proportion of indica rice and altitude (A) and between the proportion of japonica rice and altitude (B).

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S5 Fig. The average of daily maximum (red), average (green) and minimum (blue) air temperatures (°C) at the low (Hekou, 425 m) (A) and high-altitude sites (Xianggelila, 2,274 m) (B) during the period from April to October of 2012–2016.

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S1 Table. List of samples included in the study, including their origin and subpopulation.

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S2 Table. Descriptions of agronomic traits of rice landraces from each altitude zone in Yunnan.

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S3 Table. Summary of SSR markers and primer sequences.

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S4 Table. Summary of sequenced genes and primer sequences used in this study.

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S5 Table. Genetic diversity of model-based populations based on SSRs.

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S6 Table. Genetic diversity of populations in each altitude zone based on SSRs.

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S7 Table. Genetic distances among populations in different altitude zones based on SSRs.

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S8 Table. Days to heading for rice landraces with different haplotypes of Ehd1.

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S1 Dataset. Final assembled DNA sequences of CatA.

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S2 Dataset. Final assembled DNA sequences of GBSSII.

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S3 Dataset. Final assembled DNA sequences of Os1977.

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S4 Dataset. Final assembled DNA sequences of STS22.

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S5 Dataset. Final assembled DNA sequences of STS90.

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(TXT)

S6 Dataset. Final assembled DNA sequences of S5.

https://doi.org/10.1371/journal.pone.0175731.s019

(TXT)

S7 Dataset. Final assembled DNA sequences of Pid3.

https://doi.org/10.1371/journal.pone.0175731.s020

(TXT)

S8 Dataset. Final assembled DNA sequences of Ehd1.

https://doi.org/10.1371/journal.pone.0175731.s021

(TXT)

S9 Dataset. Final assembled DNA sequences of GS3.

https://doi.org/10.1371/journal.pone.0175731.s022

(TXT)

S10 Dataset. Final assembled DNA sequences of GS5.

https://doi.org/10.1371/journal.pone.0175731.s023

(TXT)

S11 Dataset. Detial information of 48 SSR primer alleles.

https://doi.org/10.1371/journal.pone.0175731.s024

(XLSX)

Acknowledgments

We thank the Chinese National Germplasm Bank for providing the landrace rice seeds.

Author Contributions

Conceptualization: HJK LZH DC.
Data curation: DC JML CFT.
Formal analysis: DC CFT.
Funding acquisition: HJK LZH.
Investigation: DC JML XDM.
Methodology: DC CFT.
Project administration: HJK LZH.
Resources: CFT XXA TQY ELZ YJW GLC FRX.
Software: DC CFT.
Supervision: LYD HJK LZH.
Validation: HJK LZH.
Visualization: DC.
Writing – original draft: DC CFT.
Writing – review & editing: HJK LZH.

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Figures

Abstract

Introduction

Materials and methods

Ethics statement

Sampling and choice of loci

DNA extraction, SSR genotyping, and gene sequencing

Population genetic structure

Analysis of DNA sequence and SSR data

Haplotype network analysis

Genetic differentiation and isolation by altitude

Results

Population structure and genetic relationship analyses

Nucleotide diversity in model-based populations

Phylogenetic and geographic analyses of haplotypes

Genetic differentiation between and within subpopulations

Isolation by altitude

Discussion

Genetic structure and differentiation of rice landraces

Genetic diversity and haplotype distribution of rice landraces

Isolation by altitude in rice landraces

Supporting information

S1 Fig. Geographic localities of rice landraces sampled in this study (When a figure is similar but not identical to the original image, and is therefore for illustrative purposes only).

S2 Fig. Schematic diagrams of ten nuclear loci and locations of the sequenced regions.

S3 Fig. The ΔK statistic for each given k using 48 SSRs (A) and 89 SNPs from ten genes (B).

S4 Fig. Correlation between the proportion of indica rice and altitude (A) and between the proportion of japonica rice and altitude (B).

S5 Fig. The average of daily maximum (red), average (green) and minimum (blue) air temperatures (°C) at the low (Hekou, 425 m) (A) and high-altitude sites (Xianggelila, 2,274 m) (B) during the period from April to October of 2012–2016.

S1 Table. List of samples included in the study, including their origin and subpopulation.

S2 Table. Descriptions of agronomic traits of rice landraces from each altitude zone in Yunnan.

S3 Table. Summary of SSR markers and primer sequences.

S4 Table. Summary of sequenced genes and primer sequences used in this study.

S5 Table. Genetic diversity of model-based populations based on SSRs.

S6 Table. Genetic diversity of populations in each altitude zone based on SSRs.

S7 Table. Genetic distances among populations in different altitude zones based on SSRs.

S8 Table. Days to heading for rice landraces with different haplotypes of Ehd1.

S1 Dataset. Final assembled DNA sequences of CatA.

S2 Dataset. Final assembled DNA sequences of GBSSII.

S3 Dataset. Final assembled DNA sequences of Os1977.

S4 Dataset. Final assembled DNA sequences of STS22.

S5 Dataset. Final assembled DNA sequences of STS90.

S6 Dataset. Final assembled DNA sequences of S5.

S7 Dataset. Final assembled DNA sequences of Pid3.

S8 Dataset. Final assembled DNA sequences of Ehd1.

S9 Dataset. Final assembled DNA sequences of GS3.

S10 Dataset. Final assembled DNA sequences of GS5.

S11 Dataset. Detial information of 48 SSR primer alleles.

Acknowledgments

Author Contributions

References