Skip to main content
NCBI home page
Plant and Cell Physiology logoLink to Plant and Cell Physiology
. 2020 Oct 12;61(12):2087–2096. doi: 10.1093/pcp/pcaa125

Investigation of the Genetic Diversity of a Rice Core Collection of Japanese Landraces using Whole-Genome Sequencing

Nobuhiro Tanaka 1, Matthew Shenton 1, Yoshihiro Kawahara 1,2, Masahiko Kumagai 2, Hiroaki Sakai 2, Hiroyuki Kanamori 1, Jun-ichi Yonemaru 1, Shinichi Fukuoka 1, Kazuhiko Sugimoto 1, Masao Ishimoto 1, Jianzhong Wu 1, Kaworu Ebana 3,✉
PMCID: PMC7861467  PMID: 33539537

Abstract

The Rice Core Collection of Japanese Landraces (JRC) consisting of 50 accessions was developed by the genebank at the National Agriculture and Food Research Organization (NARO) in 2008. As a Japanese landrace core collection, the JRC has been used for many research projects, including screening for different phenotypes and allele mining for target genes. To understand the genetic diversity of Japanese Landraces, we performed whole-genome resequencing of these 50 accessions and obtained a total of 2,145,095 single nucleotide polymorphism (SNPs) and 317,832 insertion–deletions (indels) by mapping against the Oryza sativa ssp. japonica Nipponbare genome. A JRC phylogenetic tree based on 1,394 representative SNPs showed that JRC accessions were divided into two major groups and one small group. We used the multiple genome browser, TASUKE+, to examine the haplotypes of flowering genes and detected new mutations in these genes. Finally, we performed genome-wide association studies (GWAS) for agronomical traits using the JRC and another core collection, the World Rice Core Collection (WRC), comprising 69 accessions also provided by the NARO genebank. In leaf blade width, a strong peak close to NAL1, a key gene for the regulation of leaf width, and, in heading date, a peak near HESO1 involved in flowering regulation were observed in GWAS using the JRC. They were also detected in GWAS using the combined JRC + WRC. Thus, JRC and JRC + WRC are suitable populations for GWAS of particular traits.

Keywords: Genetic diversity, GWAS, Rice core collection, WGS

Introduction

Exotic materials for breeding can be used to produce advanced rice cultivars, exploiting the variation contained within them to breed for improved performance. To avoid the evaluation of large numbers of samples in large genebank collections of genetic resources, rice core collections have been developed around the world (Kojima et�al. 2005, Ebana et�al. 2008, Agrama et�al. 2009, Zhang et�al. 2011). Among the 2,000 Japanese accessions stored in the NARO genebank, 236 accessions were selected based on their passport data. The Rice Core Collection of Japanese Landraces (JRC) comprising 50 accessions was selected to retain 87.5% of the alleles in these 236 accessions based on 32 genome-wide SSR markers (Ebana et�al. 2008).

The JRC was developed by the NARO genebank as a suitable population for understanding rice adaptation in northern areas, such as Japan (Ebana et�al. 2008).

The JRC and the World Rice Core Collection (WRC), which was also developed by the NARO genebank, have been used for many research projects, in screens for different phenotypes and allele mining for target genes (Suzaki et�al. 2009, Ueno et�al. 2009, Ochiai et�al. 2011, Fujino et�al. 2013, Taguchi-Shiobara et�al. 2013, Iijima et�al. 2019). Because the JRC and WRC include only 50 and 69 accessions, respectively, they are compact and manageable populations for use in these research projects. Although the genetic diversity of JRC seems smaller than that of WRC, significant variations in genes have been identified in the JRC. For example, to identify genes important for the adaptation of rice cultivars to northern latitudes, the JRC accessions were used for the investigation of allelic variations in Hd5 (Fujino et�al. 2013). A 19-bp deletion causing the loss of function of Hd5 was found in JRC accessions, and it was likely selected as the mutation that causes earlier heading, thus adapting rice to cultivation at higher latitudes. JRC accessions were also used for the evaluation of endosperm enzyme activity to improve the texture and eating quality of rice (Iijima et�al. 2019). Evaluation of eating quality and endosperm enzyme activities in a large set of Japanese rice cultivars, including those in the JRC, revealed that low levels of endosperm enzyme activity are associated with high eating quality in rice varieties bred in Japan. BORON EXCESS TOLERANT1 (BET1), responsible for boron-toxicity, was isolated by the observation of cultivar differences among JRC accessions, especially Wataribune (JRC19) (Ochiai et�al. 2011). A major cadmium (Cd) transporter, OsHMA3, was isolated from WRC30 (Anjana Dhan) through measurements of Cd concentration in shoots of the WRC and JRC (Ueno et�al. 2010).

To date, in order to identify causal genes from these core collections, it has been necessary to perform quantitative trait loce (QTL) analyses on F2 populations derived from crosses between divergent cultivars. High-resolution whole-genome sequencing (WGS) data of core collections enables efficient QTL analysis and gene isolation from cultivars in core collections; however, the JRC WGS data have not been published so far.

Recently, genome-wide association studies (GWAS) have become a common tool in rice, especially because of the reduction in the cost of next-generation sequencing (NGS) analysis. Although GWAS enables us to omit the crossing process for the isolation of causal genes (Yano et�al. 2016, Yano et al. 2019), there are a number of factors to consider ineffective GWAS analysis. GWAS populations usually consist of large numbers of landraces and cultivars, e.g. 517 landraces in Huang et�al. (2010), 373 indica accessions in Huang et�al. (2012) and 529 cultivated lines in Yang et�al. (2018). Phenotyping of such large populations is time-consuming. Previously, Tanaka et�al. (2020) reported that several peaks related to seed traits were detected by using the WRC core collection in GWAS and these peaks were close to well-known genes, such as Rc, waxy and GS3. Although the WRC represents a highly structured population, for some traits, this core collection that consists of only 69 rice cultivars could match the performance of a larger GWAS population (Tanaka et�al. 2020).

In this report, we evaluated the genetic diversity of the JRC using WGS data and performed a haplotype analysis of different genes using TASUKE+ to detect new alleles in JRC accessions. We also performed GWAS for normally distributed agronomical traits using the JRC and WRC accessions and detected significant peaks close to well-known genes.

Results

Classification of the JRC based on WGS data

The JRC comprises 50 accessions (Supplementary Table S1) and is provided by the genebank at NARO as a suitable population for understanding rice adaptation in northern areas, such as Japan. The NARO WRC was also developed by the genebank at NARO (Kojima et�al. 2005) and both core collections have been distributed to >300 research groups for different purposes. To promote effective use of the JRC as a genetic and breeding resource, we obtained WGS data of the 50 accessions and mapped the reads to the Oryza sativa ssp. japonica Nipponbare genome (Os-Nipponbare-Reference-IRGSP-1.0) (Kawahara et�al. 1997) using bwa (Li and Durbin 2009). The average depth was 28.8, ranging from 17.4 to 39.9; the average number of SNPs and insertion–deletions (indels) per accession were 523,212 and 103,299, respectively (Supplementary Table S1). When we simultaneously used all 50 JRC accessions, we obtained a total of 2,145,095 SNPs and 317,832 indels by mapping against the O. sativa ssp. japonica Nipponbare genome. Based on 1394 representative SNPs selected to be in approximate linkage equilibrium (using the Snphylo pipeline; Lee et�al. 2014), we constructed a phylogenetic tree to classify the 50 accessions (Fig.�1A). JRC accessions were divided into three major groups: two japonica groups (J-1 and J-2) and one small indica group (I-1). J-2 corresponded to tropical japonica varieties, and J-1 was a group of temperate japonica. The number of SNPs detected in these accessions is J-1 < J-2 < I-1 reflecting their genetic relationship with the temperate japonica reference genome.

Fig. 1.

Fig. 1

Open in a new tab

Phylogenetic tree of JRC (50 accessions) and WRC (69 accessions). (A) Unrooted maximum likelihood tree of the JRC based on 1,394 SNPs called by mapping JRC resequencing data against the O. sativa ssp. japonica Nipponbare genome. Five accessions, JRC21, 41, 42, 43 and 44, were classified into the indica group (red). (B) Unrooted maximum likelihood tree of JRC and WRC based on 2,004 SNPs. JRC and WRC were divided into japonica (blue), indica (red) and aus (magenta). WRC01 (Nipponbare), WRC02 (Kasalath) and WRC05 (Naba) are shown in green color as representative cultivars for each group. JRC accessions are shown in black.

Next, we performed joint genotyping of the JRC and WRC accessions. We used a total of 119 accessions and obtained 2,595,145 SNPs and 321,694 indels by mapping against the O. sativa ssp. japonica Nipponbare genome (Kawahara et�al. 1997). Then, we constructed a phylogenetic tree using both the JRC and the WRC accessions based on 2,004 representative SNPs (Fig.�1B). In a previous report (Ebana et�al. 2008), JRC40, 41, 42, 43 and 44 were classified into the indica group based on both 32 SSR markers and their color reaction with phenol; however, JRC21, 41, 42, 43 and 44 were classified into a distant group based on WGS data in this study, while JRC40 was classified as japonica (Fig.�1A, B).

Haplotype analysis of flowering time genes in the JRC

Heading date is one of the most important agronomical traits, and the diversity of heading date has contributed to the development of rice cultivation in a wider range of latitudes (Khush, 1997). To clarify the functional diversity of the flowering time genes in JRC, we exhibited sequence variation in JRC using a multiple genome browser, TASUKE+ (Kumagai et�al. 2013, Kumagai et�al. 2019), and performed haplotype analysis for flowering genes (Fig.�2, Table�1).

Fig. 2.

Fig. 2

Open in a new tab

Screen shot of Os06g0275000 (Hd1) genotypes in JRC accessions displayed using the TASUKE+ system. Each color represents the effect of sequence changes scored by SnpEff. Blue: modifier. Yellow: low. Orange: moderate. Red: high.

Table 1.

Distribution of functional mutations in the JRC previously identified in the flowering time genes

QTLs Indel/SNPs Substitution Reference JRC accessions
Hd1 4 bp Del Glu230fs JRC7
43bp Del Pro237fs Yano et�al. (2000) JRC20, 49, 50
2 bp Del Phe279fs Yano et�al. (2000) JRC18
G → T Gly24* JRC22
C → A Ser57* JRC38
Hd2 1 bp del Arg82fs JRC21, 42, 43
8 bp Del Lys505fs JRC44
T → C Tyr704His Koo et�al. (2013) JRC41
Hd16 G → A Ala331Thr Hori et�al. (2013) JRC50
Hd6 A → T *146Lys Takahashi et�al. (2001) Except for JRC23, 27, 30, 50
Ghd7 C → A Glu53* Xue et�al. (2008) JRC17
RFT1 G → A Glu105Lys Ogiso-Tanaka et�al. (2013) JRC21, 42, 43
DTH8 1 bp Del Lys108fs JRC21, 43 ,44
Hd17 A → G Leu558Ser Matsubara et�al. (2012) JRC1–17, 21, 26–29, 40–44, 46–48, 50, 54
Open in a new tab

fs, frame shift; *, stop codon. Newly identified mutations are shown in blue text.

We detected three kinds of deletions in Hd1 among JRC accessions. JRC20, 49, and 50 (Hosogara, Rikutou Rikuu 2 and Himenomochi) carried a 43-bp deletion in exon 1 and JRC14 and 18 carried a 2-bp deletion in exon 2 of Hd1 (Yano et�al. 2000). JRC07 (Iruma Nishiki) carried a 4-bp deletion in exon 1 of Hd1, and this deletion has not been previously reported (Table�1). A nonsynonymous substitution at amino acid 24 resulting in a premature stop codon was detected in the Hd1 gene in JRC22 (Mansaku). A nonsynonymous substitution at amino acid 57 resulting in a premature stop codon was also detected in the Hd1 gene in JRC38 (Nagoya Shiro), and these mutations have also not been previously reported.

JRC44 (Karahoushi) has an 8-bp deletion in the 7th exon of Hd2, and JRC41 (Akamai) carries a variant that changes Tyr to His at amino acid 704 in Hd2 CCT domain, resulting in a nonfunctional protein (Koo et�al. 2013) (Table�1). JRC21 (Akamai), 42 (Touboshi) and 43 (Akamai) classified into indica group have a previously unreported 1-bp deletion in Hd2 (Table�1). These results indicate that haplotype analysis of flowering genes in JRC with TASUKE+ is an efficient way to discover new mutations.

Both Hd6 and Hd16, encoding casein kinase proteins, are involved in photoperiod sensitivity, which is known to be the main determinant of heading date (Takahashi et�al. 2001, Hori et�al. 2013). In Hd6, a nonsynonymous substitution at amino acid 146 resulting in a premature stop codon was observed in JRC23, 27, 30 and 50 (Table�1) and the same SNP was also reported in Nipponbare (WRC01) (Takahashi et�al. 2001). Only JRC50 carries a functional substitution from Ala to Thr at amino acid 331 of Hd16 which was identified in a commercial Japanese cultivar, Koshihikari (Hori et�al. 2013) (Table�1). An SNP that changes a Gln to a stop codon at amino acid 53 of Ghd7 (Xue et�al. 2008) was only observed in JRC17 (Table�1). Matsubara et�al. (2012) reported a functional substitution from Leu to Ser at amino acid 558 of Hd17, and this substitution was observed in 27 accessions in the JRC (Table�1). JRC21, 42 and 43 have a functional mutation, which changes Glu to Lys at amino acid 105 in RFT1 (Ogiso-Tanaka et�al. 2013), and JRC21, 43 and 44 carry a 1-bp deletion in DTH8 (Table�1). JRC21, 42, 43 and 44 were categorized as indica in the phylogenetic tree based on WGS data (Fig.�1), and these two mutations were also detected only from indica accessions in the WRC (Tanaka et�al. 2020). We concluded that these mutations occurred after haplogroup differentiation.

GWAS

We performed GWAS for different agronomical traits: amylose content in seed, leaf blade width and heading date, using the JRC and WRC accessions. We compared the utility of the JRC or WRC populations alone, or combined as one JRC + WRC population as a resource for GWAS (Figs.�3–5, Supplementary Figs. S1–S3). Previously, GWAS with WRC only detected high impact QTLs, such as Rc, waxy and GS3 (Tanaka et�al. 2020). First, we used the amylose content of JRC and JRC + WRC seeds for GWAS (Fig.�3, Supplementary Fig. S1). The distribution of seed amylose content was trimodal (Fig.�3A, Supplementary Fig. S1A). As with GWAS using the WRC, a significant peak associated with amylose content was observed close to the waxy (Os06g0133000) gene using the JRC and JRC + WRC populations (Fig.�3B, Supplementary Fig. S1B). As an outlier (as shown in Fig.�3D), two accessions, JRC22 (Mansaku) and JRC50 (Himenomochi), showed significantly lower amylose content, although they do not have a 23-bp insertion in waxy (Fig.�3C, D, Supplementary Table S1). When we investigated the details of the genome sequence of waxy in JRC22 and JRC50, we detected 67-bp deletion in exon 1 of waxy in JRC22 and the insertion of a 7.8-kb retrotransposon-like sequence in the waxy sequence of JRC50 (Hori et�al. 2007). To our knowledge, the 67-bp deletion in the waxy gene seen in JRC22 has not been reported before.

Fig. 3.

Fig. 3

Open in a new tab

GWAS for amylose content using the linear mixed model. (A) Histogram of amylose content (mass %) in JRC. (B) Manhattan plot for amylose content using JRC (top) and WRC (bottom) accessions. Arrows indicate the waxy locus. (C) Gene structure of and DNA polymorphism in the waxy gene. The red arrowhead indicates the position of the 23-bp insertion. The blue arrowhead indicates the position of the 67-bp deletion in JRC22. The black arrowhead indicates the position of the transposon insertion in JRC50. (D) Box plot showing amylose content for JRC accessions bearing haplotype A or haplotype B at the waxy gene.

Next, we performed GWAS for leaf blade width using JRC only, WRC only and JRC + WRC (Fig.�4, Supplementary Fig. S2). No significant peak was observed in the result of GWAS with WRC (Fig.�4B). JRC and WRC accessions exhibited a normal distribution for leaf width (Fig.�4A, Supplementary Fig. S2A), and a high peak was observed on chromosome 4 in GWAS using the JRC (Fig.�4B). When we used JRC + WRC as the GWAS population, the peak on chromosome 4 was higher than a significance threshold (Supplementary Fig. S2B). The detected peaks were close to NARROW LEAF1 (NAL1), Os04g061500, known as a major regulator of leaf width (Fujita et�al. 2013, Jiang et�al. 2015). JRC and WRC accessions carrying haplotype A in NAL1 had wider leaves than those with haplotype B (Fig.�4C, D, Supplementary Fig. S2C, D).

Fig. 4.

Fig. 4

Open in a new tab

GWAS for leaf blade width using the linear mixed model. (A) Histogram of leaf blade width in JRC. (B) Manhattan plot for leaf blade width in JRC (top) and WRC (bottom). The arrow indicates the NAL1 locus. (C) Gene structure of and DNA polymorphism in the NAL1 gene. The red arrowhead indicates the position of the A → G substitution. (D) Box plot showing leaf blade width for JRC accessions bearing haplotype A or haplotype B at the NAL1 gene.

Compared with GWAS using the WRC, we improved the detectability of known genes associated with normally distributed phenotypes, such as leaf width, using the JRC only or JRC + WRC as the GWAS population (Fig.�4B, Supplementary Fig. S2B). Next, we performed GWAS for heading date with JRC only, WRC only and JRC + WRC (Fig.�5, Supplementary Fig. S3). Because the heading date was affected by a large number of genes (Tsuji et�al. 2011), no significant peak was detected by GWAS with WRC only (Fig.�5B). On the other hand, relatively high peaks were detected on chromosomes 1, 3 and 11 in the results of GWAS with JRC only (Fig.�5B). When we used JRC + WRC as a GWAS population for heading date, these peaks were beyond a P-value threshold (Supplementary Fig. S3B). On chromosome 1, a peak was located near to the homolog of Arabidopsis thaliana HEN1 suppressor 1 (HESO1) gene, Os01g0846450, recently reported as a new gene for the regulation of days to heading (Yano et�al. 2016). JRC and WRC accessions carrying haplotype A in HESO1 showed earlier heading dates than those with haplotype B (Fig.�5D, Supplementary Fig. S3D). These results indicate that the JRC and the combined JRC and WRC will be suitable populations for GWAS in particular traits, such as leaf width and heading date.

Fig. 5.

Fig. 5

Open in a new tab

GWAS for days to heading using the linear mixed model. (A) Histogram of days to heading (days) in JRC. (B) Manhattan plot for days to heading in JRC (top) and WRC (bottom). The arrow indicates the HESO1 locus. (C) Gene structure of and DNA polymorphism in the HESO1 gene. The red arrowhead indicates the position of the G → A substitution. (D) Box plot showing days to heading for JRC accessions bearing haplotype A or haplotype B at the HESO1 gene.

Discussion

Genetic diversity of the JRC

In this study, we evaluated the genetic diversity of the JRC comparing with the WGS data of the WRC. In addition, we performed haplotype analysis of flowering genes in the JRC using TASUKE+ system and also developed GWAS pipelines using the JRC and WRC accessions that were able to detect known genes for agronomic traits.

Our genetic study revealed that the JRC was divided into three subgroups, J-1, J-2 and I-1 (Fig.�1). In passport data recorded when the accessions were collected, JRC01 to JRC14 were upland rice varieties and were also categorized as tropical japonica. These 12 accessions in the JRC were included in the J-2 group of our results (Fig.�1, Supplementary Table S1). These results indicated that the classification of the JRC using WGS accurately reflects their passport data.

Four accessions in the JRC, JRC21, JRC40, JRC41 and JRC43, have the same accession name, Akamai. When we used the WGS data of the JRC, the classification of JRC21 and JRC40 was found to be the opposite of that of a previous report where SSR markers were used, i.e. JRC21 was indica, not japonica, and JRC40 was japonica, not indica. One advantage of using WGS data to characterize genetic resources is that whole-genome genotype information allows an understanding of their exact genetic diversity, avoiding errors based on small numbers of markers and correcting any errors of identification that may have occurred during collection or propagation.

Haplotype analysis of the waxy gene in JRC and WRC accessions

Amylose content in the endosperm is known as an important determinant of rice-eating quality because it reflects the functionality of the rice grain’s starch. The waxy (Wx) gene (Os06g0133000) encodes granule-bound starch synthase, which controls amylose synthesis in rice endosperm. The differentiation of glutinous and non-glutinous rice is determined by alleles of the waxy locus. Rice cultivars carrying Wxa have a high (∼25–30%) amylose content and those carrying Wxb, with a G-to-T substitution at the 5′ UTR splicing site, show decreased expression of waxy, associated with intermediate (∼15–20%) amylose concentration (Hirano et�al. 1998) (Supplementary Table S1). Glutinous rice varieties generally result from loss-of-function mutations in the waxy gene, resulting in very low amylose concentrations. In the JRC and WRC, most glutinous rice cultivars containing little seed amylose have a 23-bp insertion or a retrotransposon-like sequence in waxy (Wanchana et�al. 2003, Hori et�al. 2007). Only JRC22 was glutinous rice without a known allele of waxy (Fig.�3, Supplementary Table S1). A new allele of waxy from this JRC accession suggested the usefulness of JRC. This is a small population but containing new genetic information useful for rice breeding.

Oryza sativa was derived from ancestral wild rice, Oryza rufipogon Griff. O. rufipogon carries the Wxa allele and the G-to-T substitution at the 5′ UTR splicing site was selected during the domestication of O. sativa japonica as the Wxb allele (Hirano et�al. 1998), resulting in intermediate amylose concentrations, and sticky, but non-glutinous rice. In the JRC and WRC, all glutinous rice accessions carried the Wxb allele at the 5′ UTR splicing site, and in addition, the 23-bp insertion, the transposon insertion or the 67-bp deletion inactivating the waxy gene and resulting in the glutinous phenotype (Supplementary Table S1). These results suggested that glutinous rice was selected from among japonica cultivars that were domesticated in East Asia, wherein the Wxb allele had become fixed in the population based on the resulting stickiness of the cooked rice. Most Japanese glutinous rice varieties in the JRC obtained the same 23-bp insertion in waxy, except for the 67-bp deletion and transposon insertion (Fig.�3). These results suggest that the desirability of the glutinous rice phenotype led to the wide distribution of the mutations in the local region.

Genetic diversity of NAL genes in the JRC and WRC

In Fig.�4, we detected the NAL1 locus associated with leaf width using the JRC as a GWAS population, or using the combined JRC + WRC, when the peak was higher than a significance threshold. Haplotype B of NAL1 was present in almost half of the JRC and WRC accessions; however, no significant peak was observed in GWAS using the WRC alone (Fig.�4B). To determine the reason for the difference in GWAS results between the JRC and the WRC, we investigated the relationship between the NAL1 genotypes and leaf width in the WRC (Supplementary Fig. S4). In the WRC, accessions carrying haplotype A in NAL1 also showed wider leaves than those with haplotype B (Supplementary Fig. S4A). These results indicate that population structure and/or kinship relatedness in the WRC might cause a lower P-value in NAL1 locus. Until now, four genes, NAL1, NAL2, NAL3 and NAL7, have been reported as major regulators of leaf width (Fujino et�al. 2008, Ishiwata et�al. 2013, Jiang et�al. 2015). In the JRC and WRC accessions, we did not find any SNPs in NAL2 or NAL3. In the NAL7 gene, several moderate-impact nonsynonymous SNPs causing amino acid substitutions were observed in WRC accessions. Conversely, only three accessions categorized into the indica group carried a moderate-impact SNP in the JRC (Supplementary Fig. S4B). These results suggest that the genetic diversity of NAL7 gene in the WRC masked the effect of the NAL1 gene for leaf width. Although the peak was observed in the JRC alone, it became significant using JRC + WRC due to the increased statistical power obtained when using a greater number of accessions.

GWAS for heading date using the JRC and WRC

Although we detected HESO1 for heading date using the JRC and WRC as the GWAS population, peaks close to strong QTLs for heading dates, such as Hd1 and Hd2, were not observed (Fig.�5, Supplementary Fig. S3). In Hd1, except for the 2-bp deletion in exon 2 (Yano et�al. 2000), widely distributed DNA polymorphisms with high impacts, such as indels, were not observed in the JRC using TASUKE+ (Fig.�2, Table�1). In GWAS with JRC + WRC, DNA polymorphisms with minor allele frequency (MAF) <0.05 among the 119 accessions were removed from the variant dataset. These rare DNA polymorphisms in Hd1 were not used in the GWAS procedure, so a significant peak close to Hd1 was not detected in our experiments.

An 8-bp deletion on exon 7 in Hd2 was commonly observed both in the JRC and WRC; however, it was only distributed in accessions categorized into indica group both in JRC and WRC (Table�1) (Tanaka et�al. 2020). Because of the problem of population structure, the effect of 8-bp deletion in Hd2 might be ignored in the calculation of GWAS by the linear mixed model (Korte et�al. 2012).

Using the JRC and WRC as the GWAS population, a significant peak associated with heading date was detected on chromosome 1 close to HESO1. HESO1 was originally isolated by GWAS with japonica cultivars (Yano et�al. 2016), and haplotypes A and B were almost equally distributed in the JRC. On the other hand, only WRC01 (Nipponbare, japonica) and WRC43 (Dianyu 1, japonica) carried the haplotype A allele of the HESO1 gene among the WRC accessions (Fig.�5). In this report, the japonica allelic frequency of HESO1 was sufficient to allow the detection of the HESO1 locus associated with heading date.

Although JRC contains only 50 accessions, the detectability of GWAS with the JRC for particular traits was higher than with WRC. In addition, the combination of JRC and WRC includes 119 accessions, so it is still possible to collect high-quality phenotype data with low cost and also to improve the statistical power of the detection. For many traits, phenotyping is labor intensive, and thus, it is important to select the minimum size of the population adequate for the detection of significant association peaks. The JRC is a small population without a high degree of population structure except for the five indica varieties. In some cases, it could out-perform the WRC in GWAS. However, a better statistical power could be achieved by using the JRC + WRC population if it is possible to acquire phenotype data for all accessions.

Materials and Methods

Plant materials and WGS

We used JRC accessions maintained at the NARO gene bank (Supplementary Table S1). Total DNA was extracted from leaves from one plant of each variety using the DNeasy Plant Mini Kit (Qiagen). The DNA libraries were sequenced using Illumina Hiseq 2000 or Hiseq X instruments (Illumina Co, Ltd.), and paired-end reads were obtained. All reads were mapped against Os-Nipponbare-Reference-IRGSP-1.0 (Kawahara et�al. 1997) pseudomolecules using bwa mem (Li and Durbin 2009), and duplicates were removed using Picard MarkDuplicates (http://broadinstitute.github.io/picard/).

Variant calling

Variants were called essentially following the GATK Best practices for germline SNP/Indel discovery (Auwera et�al. 2013). Variants were first called on a by-sample basis using GATK HaplotypeCaller, and then variants were consolidated in a joint calling step with GenotypeGVCFs (Poplin et�al. 2017). GATK version 4.0.11.0 was employed for all steps. Variants were then filtered using bcftools view (Li 2011) with the parameters: -m2 -M2 -g hom --output-type z --exclude-uncalled -e "MAF <0.05 || N_MISSING > 0 || QD < 5.0 || FS > 50.0 || SOR > 3.0 || MQ < 50.0|| MQRankSum < -2.5 || ReadPosRankSum < -1.0 || ReadPosRankSum > 3.5", resulting in a set of variants where no position had missing data or a minor allele frequence of <0.05. All nucleotide polymorphisms were categorized for their potential effects using SnpEff 4.3t (Cingolani et�al. 2012) with the Oryza_sativa database.

Phylogenetic tree and population structure

The Snphylo pipeline (Lee et�al. 2014) was used to create a maximum likelihood phylogenetic tree based on representative genomic SNPs. The pipeline was employed using default parameters and 100 bootstrap replicates to create the bootstrapped maximum likelihood tree.

GWAS

For GWAS, we used MLM models (Yu et�al. 2006). Association studies were performed using R (R Core Team 2018) using modified scripts from the MVP (https://rdrr.io/github/XiaoleiLiuBio/MVP/), GAPIT (Lipka et�al. 2012) and GENESIS (Gogarten et�al. 2019) packages. Visualization used scripts from MVP, GAPIT and the R package qqman (Turner 2014). We removed variants with MAF < 5% from the relevant dataset in GWAS when using the WRC, JRC or JRC + WRC populations.

Visualization of genotypes using TASUKE+

Variants were genotyped by sample up to the GATK HaplotypeCaller (Poplin et�al. 2017) step in the variant calling section as above and filtered using bcftools (Li 2011) with the condition -e “QD < 5.0 ‖ FS > 50.0 ‖ SOR > 3.0 ‖ MQ < 50.0 ‖ MQRankSum < -2.5 ‖ ReadPosRankSum < -1.0 ‖ ReadPosRankSum > 3.5”. Variants for each accession were displayed using the TASUKE+ genome browser (Kumagai et�al. 2019). TASUKE+ is a web browser-based visualization system for whole-genome variant data. The input files of WRC for TASUKE were created using a custom data analysis pipeline, using the same mapping and variant calls as for the GWAS variant dataset, but without filtering for minor allele frequency or allele number. These datasets are accessible at https://ricegenome-corecollection.dna.affrc.go.jp, and users can choose a specific region and/or gene in which they are interested.

Accession number

The sequence data have been deposited in the DNA Data Bank of Japan Sequence Read Archive (DRA). The accession numbers are from DRR240814 to DRR240859 and DRR095338, DRR095344, DRR095348, and DRR095346 (Supplementary Table S1).

Supplementary Data

Supplementary data are available at PCP online.

Funding

NARO Genebank Project to N.T., M.S. and K.E. and the Ministry of Agriculture, Forestry and Fisheries of Japan (Genomics for Agricultural Innovation, QTL5003) to J.W.

Supplementary Material

pcaa125_Supplementary_Data
Click here for additional data file. (1.2MB, zip)

Acknowledgment

We thank the Advanced Analysis Center of the National Agriculture and Food Research Organization (NARO) for the use of its facilities.

References

  1. Agrama H.A., Yan W., Lee F., Fjellstrom R., Chen M.-H., Jia M., et al. (2009) Genetic assessment of a mini-core subset developed from the USDA rice genebank. Crop Sci. 49: 1336–1346. [Google Scholar]
  2. Auwera GAV D., Carneiro M.O., Hartl C., Poplin R., Angel G. D., Levy‐Moonshine A., et al. (2013) From FastQ data to high-confidence variant calls: the genome analysis toolkit best practices pipeline. Curr. Protoc. Bioinformatics 43: 11.10.1–11.10.33. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Cingolani P., Platts A., Wang L.L., Coon M., Nguyen T., Wang L., et al. (2012) A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly 6: 80–92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Ebana K., Kojima Y., Fukuoka S., Nagamine T., Kawase M. (2008) Development of mini core collection of Japanese rice landrace. Breed. Sci. 58: 281–291. [Google Scholar]
  5. Fujino K., Matsuda Y., Ozawa K., Nishimura T., Koshiba T., Fraaije M.W., et al. (2008) NARROW LEAF 7 controls leaf shape mediated by auxin in rice. Mol. Genet. Genomics 279: 499–507. [DOI] [PubMed] [Google Scholar]
  6. Fujino K., Yamanouchi U., Yano M. (2013) Roles of the Hd5 gene controlling heading date for adaptation to the northern limits of rice cultivation. Theor. Appl. Genet. 126: 611–618. [DOI] [PubMed] [Google Scholar]
  7. Fujita D., Trijatmiko K.R., Tagle A.G., Sapasap M.V., Koide Y., Sasaki K., et al. (2013) NAL1 allele from a rice landrace greatly increases yield in modern indica cultivars. Proc. Natl. Acad. Sci. USA 110: 20431–20436. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Gogarten S.M., Sofer T., Chen H., Yu C., Brody J.A., Thornton T.A., et al. (2019) Genetic association testing using the GENESIS R/Bioconductor package. Bioinformatics 35: 5346–5348. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Hirano H.Y., Eiguchi M., Sano Y. (1998) A single base change altered the regulation of the Waxy gene at the posttranscriptional level during the domestication of rice. Mol. Biol. Evol. 15: 978–987. [DOI] [PubMed] [Google Scholar]
  10. Hori K., Ogiso-Tanaka E., Matsubara K., Yamanouchi U., Ebana K., Yano M. (2013) Hd16, a gene for casein kinase I, is involved in the control of rice flowering time by modulating the day-length response. Plant J. 76: n/a–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Hori Y., Fujimoto R., Sato Y., Nishio T. (2007) A novel wx mutation caused by insertion of a retrotransposon-like sequence in a glutinous cultivar of rice (Oryza sativa). Theor. Appl. Genet. 115: 217–224. [DOI] [PubMed] [Google Scholar]
  12. Huang X., Wei X., Sang T., Zhao Q., Feng Q., Zhao Y., et al. (2010) Genome-wide association studies of 14 agronomic traits in rice landraces. Nat. Genet. 42: 961–967. [DOI] [PubMed] [Google Scholar]
  13. Huang X., Zhao Y., Wei X., Li C., Wang A., Zhao Q., et al. (2012) Genome-wide association study of flowering time and grain yield traits in a worldwide collection of rice germplasm. Nat. Genet. 44: 32–39. [DOI] [PubMed] [Google Scholar]
  14. Iijima K., Suzuki K., Hori K., Ebana K., Kimura K., Tsujii Y., et al. (2019) Endosperm enzyme activity is responsible for texture and eating quality of cooked rice grains in Japanese cultivars. Biosci. Biotechnol. Biochem. 83: 502–510. [DOI] [PubMed] [Google Scholar]
  15. Ishiwata A., Ozawa M., Nagasaki H., Kato M., Noda Y., Yamaguchi T., et al. (2013) Two WUSCHEL-related homeobox genes, narrow leaf2 and narrow leaf3, control leaf width in rice. Plant Cell Physiol. 54: 779–792. [DOI] [PubMed] [Google Scholar]
  16. Jiang D., Fang J., Lou L., Zhao J., Yuan S., Yin L., et al. (2015) Characterization of a null allelic mutant of the rice NAL1 gene reveals its role in regulating cell division. PLoS One 10: e0118169. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Kawahara Y., de la Bastide M., Hamilton J.P., Kanamori H., McCombie W.R. [Google Scholar]
  18. Khush G. S. (1997)�Origin, dispersal, cultivation and variation of rice.�Plant Mol Biol.�35: 25–34. [PubMed] [Google Scholar]
  19. Ouyang, S. et al. (2013) Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data. Rice (N Y). 6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Kojima Y., Ebana K., Fukuoka S., Nagamine T., Kawase M. (2005) Development of an RFLP-based rice diversity research set of germplasm. Breed. Sci. 55: 431–440. [Google Scholar]
  21. Koo B.-H., Yoo S.-C., Park J.-W., Kwon C.-T., Lee B.-D., An G., et al. (2013) Natural variation in OsPRR37 regulates heading date and contributes to rice cultivation at a wide range of latitudes. Mol. Plant 6: 1877–1888. [DOI] [PubMed] [Google Scholar]
  22. Korte A., Vilhj�lmsson B.J., Segura V., Platt A., Long Q., Nordborg M. (2012) A mixed-model approach for genome-wide association studies of correlated traits in structured populations. Nat. Genet. 44: 1066–1071. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Kumagai M., Kim J., Itoh R., Itoh T. (2013) Tasuke: a web-based visualization program for large-scale resequencing data. Bioinformatics 29: 1806–1808. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Kumagai M., Nishikawa D., Kawahara Y., Wakimoto H., Itoh R., Tabei N., et al. (2019) TASUKE+: a web-based platform for exploring GWAS results and large-scale resequencing data. DNA Res. 26: 445–452. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Lee T.-H., Guo H., Wang X., Kim C., Paterson A.H. (2014) SNPhylo: a pipeline to construct a phylogenetic tree from huge SNP data. BMC Genomics 15: 162. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Li H. (2011) A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27: 2987–2993. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Li H., Durbin R. (2009) Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25: 1754–1760. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Lipka A.E., Tian F., Wang Q., Peiffer J., Li M., Bradbury P.J., et al. (2012) GAPIT: genome association and prediction integrated tool. Bioinformatics 28: 2397–2399. [DOI] [PubMed] [Google Scholar]
  29. Matsubara K., Ogiso-Tanaka E., Hori K., Ebana K., Ando T., Yano M. (2012) Natural variation in Hd17, a homolog of Arabidopsis ELF3 that is involved in rice photoperiodic flowering. Plant Cell Physiol. 53: 709–716. [DOI] [PubMed] [Google Scholar]
  30. Ochiai K., Shimizu A., Okumoto Y., Fujiwara T., Matoh T. (2011) Suppression of a NAC-Like transcription factor gene improves boron-toxicity tolerance in rice. Plant Physiol. 156: 1457–1463. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Ogiso-Tanaka E., Matsubara K., Yamamoto S., Nonoue Y., Wu J., Fujisawa H., et al. (2013) Natural variation of the RICE FLOWERING LOCUS T 1 contributes to flowering time divergence in rice. PLoS One 8: e75959. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Poplin R., Ruano-Rubio V., DePristo M.A., Fennell T.J., Carneiro M.O., Van der Auwera G.A., et al. (2017) Scaling accurate genetic variant discovery to tens of thousands of samples. bioRxiv 201178. [Google Scholar]
  33. �R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/. [Google Scholar]
  34. Suzaki T., Ohneda M., Toriba T., Yoshida A., Hirano H.-Y. (2009) FON2 SPARE1 redundantly regulates floral meristem maintenance with FLORAL ORGAN NUMBER2 in rice. PLoS Genet. 5: e1000693. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Taguchi-Shiobara F., Ozaki H., Sato H., Maeda H., Kojima Y., Ebitani T., et al. (2013) Mapping and validation of QTLs for rice sheath blight resistance. Breed. Sci. 63: 301–308. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Takahashi Y., Shomura A., Sasaki T., Yano M. (2001) Hd6, a rice quantitative trait locus involved in photoperiod sensitivity, encodes the α subunit of protein kinase CK2. Proc. Natl. Acad. Sci. USA 98: 7922–7927. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Tanaka N., Shenton M., Kawahara Y., Kumagai M., Sakai H., Kanamori H., et al. (2020) Whole-genome sequencing of the NARO world rice core collection (WRC) as the basis for diversity and association studies. Plant Cell Physiol. 61: 922–932. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Tsuji H., Taoka K., Shimamoto K. (2011) Regulation of flowering in rice: two florigen genes, a complex gene network, and natural variation. Curr. Opin. Plant Biol. 14: 45–52. [DOI] [PubMed] [Google Scholar]
  39. Turner S.D. (2014) qqman: an R package for visualizing GWAS results using Q–Q and manhattan plots, doi: 10.1101/005165. [Google Scholar]
  40. Ueno D., Kono I., Yokosho K., Ando T., Yano M., Ma J.F. (2009) A major quantitative trait locus controlling cadmium translocation in rice (Oryza sativa). New Phytol. 182: 644–653. [DOI] [PubMed] [Google Scholar]
  41. Ueno D., Yamaji N., Kono I., Huang C.F., Ando T., Yano M., et al. (2010) Gene limiting cadmium accumulation in rice. Proc. Natl. Acad. Sci. USA 107: 16500–16505. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Wanchana S., Toojinda T., Tragoonrung S., Vanavichit A. (2003) Duplicated coding sequence in the waxy allele of tropical glutinous rice (Oryza sativa L.). Plant Sci. 165: 1193–1199. [Google Scholar]
  43. Xue W., Xing Y., Weng X., Zhao Y., Tang W., Wang L., et al. (2008) Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice. Nat. Genet. 40: 761–767. [DOI] [PubMed] [Google Scholar]
  44. Yang M., Lu K., Zhao F.-J., Xie W., Ramakrishna P., Wang G., et al. (2018) Genome-wide association studies reveal the genetic basis of ionomic variation in rice. Plant Cell 30: 2720–2740. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Yano K., Yamamoto E., Aya K., Takeuchi H., Lo P., Hu L., et al. (2016) Genome-wide association study using whole-genome sequencing rapidly identifies new genes influencing agronomic traits in rice. Nat. Genet. 48: 927–934. [DOI] [PubMed] [Google Scholar]
  46. Yano K., Morinaka Y., Wang F., Huang P., Takehara S., Hirai T., et al. (2019) GWAS with principal component analysis identifies a gene comprehensively controlling rice architecture. Proc. Natl. Acad. Sci. USA 116: 21262–21267. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Yano M., Katayose Y., Ashikari M., Yamanouchi U., Monna L., Fuse T., et al. (2000) Hd1, a major photoperiod sensitivity quantitative trait locus in rice, is closely related to the Arabidopsis flowering time gene CONSTANS. Plant Cell 12: 2473–2483. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Yu J., Pressoir G., Briggs W.H., Vroh Bi I., Yamasaki M., Doebley J.F., et al. (2006) A unified mixed-model method for association mapping that accounts for multiple levels of relatedness. Nat. Genet. 38: 203–208. [DOI] [PubMed] [Google Scholar]
  49. Zhang P., Li J., Li X., Liu X., Zhao X., Lu Y. (2011) Population structure and genetic diversity in a rice core collection (Oryza sativa L.) investigated with SSR markers. PLoS One 6: e27565. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

pcaa125_Supplementary_Data
Click here for additional data file. (1.2MB, zip)

Articles from Plant and Cell Physiology are provided here courtesy of Oxford University Press

RESOURCES