Abstract
Closely-related cultivars generally used for crossing in breeding lack sufficient known DNA polymorphisms with already developed DNA markers even though they exhibit remarkable phenotype difference. However, next-generation sequencing (NGS) enables the identification of massive DNA polymorphisms such as single nucleotide polymorphisms (SNPs) and insertions-deletions (InDels) between highly homologous genomes. This study conducted a whole-genome re-sequencing of two Korean japonica rice varieties, Junam and Nampyeong. The sequencing yielded 16.6 × 109 bps for Junam, and 15.1 × 109 bps for Nampyeong. After quality trimming and read mapping onto the reference genome sequence of Nipponbare, 11.9 × 109 bps from Junam and 10.6 × 109 bps from Nampyeong were mapped onto the reference sequence. The final effective mapping depth was 31.98x for Junam and 28.41x for Nampyeong. This study found 398,123 DNA polymophisms between Junam and Nampyeong. These were classified into 352,478 SNPs (88.5%) and 45,645 InDels (11.5%) by polymorphism types, 338,485 homozygous (85%) and 59,638 (15%) heterozygous by zygosity, and 331,855 intergenic (83.4%) and 66,268 genic (16.6%) by genomic location. To see the availability of these results in DNA marker development, Cleaved Amplified Polymorphic Sequences (CAPS) markers were developed based on 22 SNPs lying in restriction enzyme sites. Among them, 17 CAPS markers showed polymorphisms between Junam and Nampyeong. It is expected that sufficient DNA markers for mapping genes/QTLs with progeny population from a cross between Junam and Nampyeong can be developed based on the results of the study.
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Key words: Rice, Next-generation sequencing (NGS), Re-sequencing, SNP, DNA marker
INTRODUCTION
While classical genetics revolutionized plant breeding at the beginning of the 20
th century, genomics is leading to a new revolution in plant breeding at the beginning of the 21
th century (
Perez-de-Castro et al. 2012). Among the newly developed genomics tools, the next-generation sequencing (NGS) technologies are being widely used for
de novo sequencing, whole genome sequencing (WGS), whole genome re-sequencing, genotyping-by-sequencing (GBS), and transcriptome and epigenetic analysis (
Varshney et al. 2014). NGS technologies have greatly enhanced structural/functional genomics and molecular breeding studies (
Guo et al. 2014). Especially, re-sequencing and GBS of crop germplasm and lines in genetic/breeding population are being actively utilized in germplasm diversity and evolution analysis, construction of high density genetic maps, QTL/gene identification, and genomic selection (
Guo et al. 2014;
Perez-de-Castro et al. 2012).
In brief review of the application of NGS in mapping genes/QTLs in rice, MutMap and MutMap-Gap enabled identification of mutated genes by re-sequencing of DNA-bulks from mutant phenotype plants in F
2 population derived from the cross between mutant and its wild-type variety (
Abe et al. 2012;
Takagi et al. 2013b). By re-sequencing recombinant inbred lines (RILs) from the crosses of PA64s/93-11, 93-11/Nipponbare, Zenshan97/Minhhui63, ultra-high-density linkage maps were constructed and a number of QTLs for yield-associated traits were identified at high resolution (
Gao et al. 2013;
Wang et al. 2011;
Xie et al. 2010). The QTL-seq method, which enables rapid mapping of QTLs by re-sequencing of DNAs from two populations each composed of 20–50 individuals showing extreme opposite trait values for a given phenotype in a segregating progeny, was developed and applied in mapping QTLs for rice blast disease resistance and seedling vigor (
Takagi et al. 2013a). In a while, GBS technology, which includes the digestion of genomic DNA with restriction enzymes followed by the ligation of barcode adapter, PCR amplification and sequencing of the amplified DNA pool by NGS, was developed and applied for discovering and genotyping SNPs in a cost-effective way (
He et al. 2014). Using GBS, 30,984 markers were added to an
indica (IR64) X
japonica (Azucena) mapping population including 176 RILs, and QTLs for leaf width and aluminum tolerance were mapped (
Spindel et al. 2013).
Closely-related cultivars generally used for crossing in breeding lack sufficient known DNA polymorphisms with already developed DNA markers even though they exhibit remarkable phenotype difference in agronomically important traits. However, NGS enables the identification of massive SNPs between highly homologous genomes. Therefore, it became possible to develop enough DNA markers for mapping with population derived from crosses between closely-related cultivars through re-sequencing parental varieties by NGS. Through re-sequencing Koshihikari, which is a high quality
japonica variety, a total of 67,051 SNPs have been identified in comparison with Nipponbare (
Yamamoto et al. 2010). A total of 25,199 SNPs were newly detected from the comparison of genomic sequences between two Japanese
japonica cultivars (Eiko and Rikuu132) and Nipponbare, and the core set of 768 SNPs were developed for diversity analysis and genetic analysis of the biparental populations of Japanese
japonica rice accessions (
Nagasaki et al. 2010). Through re-sequencing of Omachi, which is a Japanese landrace
japonica variety used for sake brewing, 132,462 SNPs and 35,766 InDels were identified in comparison with Nipponbare (
Arai-Kichise et al. 2014). Two Korean
japonica varieties and three anther-derived lines were re-sequenced and 170,961~253,530 DNA polymorphisms were found in comparison with Nipponbare (
Jeong et al. 2013). Re-sequencing of 6 temperate Japanese
japonica cultivars and 1 tropical
japonica cultivar (Moroberekan) identified 109,972~180,402 DNA polymorphisms in the Japanese
japonica varieties relative to Nipponbare and revealed that Moroberekan had 5-fold more SNPs than temperate
japonica cultivars (
Arai-Kichise et al. 2014). California rice cultivars derived from a very small base of a temperate
japonica germplasm were used in Restriction Enzyme Site Comparative Analysis, which is a reduced representation sequencing approach, and over 20,000 putative SNPs were detected relative to the Nipponbare enabling identification of pedigree haplotypes (
Kim and Tai 2013).
Here, we report massive detection of DNA polymorphisms, including SNPs and InDels, between two Korean japonica rice varieties, Junam and Nampyeong, through whole genome re-sequencing by NGS. These two varieties showed remarkable differences in disease susceptibility, grain weight, and grain yield from each other. The findings of the study will facilitate development of enough DNA markers for mapping genes/QTLs with the progeny population derived from a cross between Junam and Nampyeong.
MATERIALS AND METHODS
Sample preparation and sequencing
Genomic DNA was extracted from two Korean japonica rice varieties, Junam and Nampyeong, using DNeasy Plant Maxi kit (QIAGEN), and used for the preparation of sequencing libraries, following the manufacturer’s protocols (Illumina). Fragments of the libraries were paired-end sequenced using Hiseq2000 (Illumina). The length of all sequences generated was 101 nucleotides. The raw reads that were high quality with Phred Quality Values > Q20 were used to analyze genetic variations. The “Q20” value indicates an accuracy of 99% for the base called.
Mapping of reads and SNP detection
Oryza sativa L. cv. Nipponbare sequence was used as the reference sequence (Pseudomolecules IRGSP-1.0,
http://rapdb.dna.affrc.go.jp/dowmload/irgsp1.html, Rice Genome Sequencing Project 2008). The CLC Assembly Cell program (ver. 3.2.2,
http://www.clcbio.com) was mainly used for read mapping and SNP detection. The generated paired-end reads were mapped on to the Nipponbare reference sequence using clc-mapper command with the following parameters: alignment mode, local; similarity, 95%; gap cost, 3; deletion cost, 3; and mismatch cost, 2. SNPs and small InDels of 1~5 bp size difference relative to Nipponbare were detected using clc_find_variation command. To exclude mismatches due to sequencing errors, parameters were set as follows: minimum depth, 10; minimum variant frequency, 35%; and homo/heterozygote fold change, 2. The RAP-DB (
http://rapdb.dna.affrc.go.jp) was used to locate and annotate discovered SNPs and InDels. DNA polymorphisms in genic regions were classified as coding sequence (CDS), untranslated regions (UTRs), and introns. DNA polymorphisms in the coding region were separated into synonymous SNPs and non-synonymous SNPs by amino acid substitutions. Also, SNPs were classified into two types, homozygous and heterozygous SNPs, based on the mismatch frequency if more than two bases shared the identical position.
Based on the detected DNA polymorphisms between Junam and Nipponbare, and between Nampyeong and Nipponbare, DNA polymorphisms between Junam and Nampyeong were analyzed using a Python program developed in-house. The 22 CAPS markers were designed based on SNPs lied in widely used restriction enzyme sites as follows, 1,000 bp long sequences centering SNPs lying in restriction enzyme sites were used for designing PCR primers by Vector NTI 9.0.0 program (Invitrogen). PCR products using these primers were digested by restriction enzymes at 37°C overnight, and electrophoresed on 1.2% agarose gel.
RESULTS
Sequencing and mapping of the reads to the Nipponbare genome
The whole genome re-sequencing of two Korean
japonica rice varieties, Junam and Nampyeong was done in this study. The sequencing results yielded 16.6 × 10
9 bps (corresponding 164 × 10
6 reads) for Junam, and 15.1 × 10
9 bps (corresponding 150 × 10
6 reads) for Nampyeong. After quality trimming with threshold of Phred Quality Value +33 (>Q20), the generated data, 12.5 × 10
9 bps (corresponding 127 × 10
6 reads) for Junam, and 11.3 × 10
9 bps (corresponding 115 × 10
6 reads) for Nampyeong, were used for mapping into the Nipponbare reference genome (
Table 1). 120.7 × 10
6 reads including 11.9 × 10
9 bps were mapped for Junam, and 107.2 × 10
6 reads including 10.6 × 10
9 bps were mapped for Nampyeong. The final effective mapping depth was 31.98x for Junam and 28.41x for Nampyeong. The sites in Nipponbare reference genome sequence where over 3 reads were mapped covered 96.7% for Junam and 96.1% for Nampyeong (
Table 1).
In comparison between Junam and Nipponbare, 297,034 DNA polymorphisms were found. They were classified into 260,710 SNPs (87.8%) and 36,324 InDels (12.2%) by polymorphism types, 252,852 homozygous (85.1%) and 44,182 heterozygous (14.9%) by zygosity, and 247,045 intergenic (83.2%) and 49,989 genic (16.8%) by genomic location. Chromosome 8 and chromosome 11 showed much higher number of polymorphisms than other chromosomes while chromosome 5 showed lowest number of polymorphisms (
Fig. 1A).
In comparison between Nampyeong and Nipponbare, 263,486 DNA polymorphisms were found. They were classified into 230,215 SNPs (87.4%) and 33,271 InDels (12.6%), 226,565 homozygous (86.0%) and 36,921 heterozygous (14.0%), and 216,592 intergenic (82.2%) and 46,894 genic (17.8%). Chromosome 6 and chromosome 11 showed much higher number of polymorphisms than other chromosomes while chromosome 5 showed lowest number of polymorphisms (
Fig. 1B).
In comparison between Junam and Nampyeong, 398,123 DNA polymorphisms were found. They were classified into 352,478 SNPs (88.5%) and 45,645 InDels (11.5%), 338,485 homozygous (85.0%) and 59,638 heterozygous (15.0%), and 329,959 intergenic (82.9%) and 68,164 genic (17.1%). Chromosome 6, chromosome 8 and chromosome 11 showed much higher number of polymorphisms than other chromosomes while chromosome 5 showed lowest number of polymorphisms (
Fig. 1C).
Fig. 2 shows the chromosomal distribution of DNA polymorphisms per 0.1 Mb between Junam and Nampyeong. The distribution of DNA polymorphisms was uneven within chromosomes. All chromosomes, except chromosome 5, were composed of a mixture of dense and sparse DNA polymorphism regions. For example, on chromosome 1, SNPs were dense (> 100 SNPs per 0.1 Mb) in the regions of 2.3–3.4 Mb, 4.7–5.0 Mb, 16.8–17.0 Mb, 18.5–18.7 Mb, 23.2–25.1 Mb, 33.0–33.3 Mb 34.7–35.4 Mb, 38.4–39.2 Mb, 42.3–43.0, but sparse (< 100 SNPs per 0.1 Mb) in the other regions.
On chromosome 5, there were few dense SNPs regions. There were several regions where dense SNPs are widely distributed. They were 12.9–20.5 Mb (7.6 Mb) on chromosome 6, 5.2–20.2 Mb (15 Mb) on chromosome 8, 2.8–5.9 Mb (3.1 Mb), 9.7–15.5 Mb (5.8 Mb), and 18.7–24.4 Mb (5.7 Mb) on chromosome 11.
Annotations of DNA polymorphisms and development of CAPS markers
The RAP-DB was used to locate detected DNA polymorphisms between Junam and Nampyeong. A total of 68,164 DNA polymorphisms (17.1% of the total) out of 398,123 DNA polymorphisms were found in a gene region, but only 14,194 DNA polymorphisms occurred in a coding region (
Fig. 3). Those in coding region were divided into 6,783 (47.8%) synonymous and 7,411 (52. 2%) non-synonymous DNA polymorphisms.
To see the availability of these results in DNA marker development, CAPS markers were developed based on 22 SNPs lying in restriction enzyme sites. Among them, 17 CAPS markers showed polymorphisms between Junam and Nampyeong, and 5 markers were monomorphic. The examples of polymorphic CAPS markers are shown in
Fig. 4. The results show that enough DNA markers can be developed based on the results of this study.
DISCUSSION
Through re-sequencing of Junam and Nampyeong, 398,123 DNA polymorphisms, including 352,478 (88.5%) SNPs and 45,645 (11.5%) InDels between these two rice varieties, were found. In comparison between Junam and Nipponbare, the number of DNA polymorphisms was 297,034, while that was 263,486 in comparison between Nampyeong and Nipponbare.
In a similar study conducted, two other Korean
japonica rice varieties, Dongjin and Hwayeong were re-sequenced. Results of the study show 170,961 DNA polymorphisms in Dongjin relative to Nipponbare, and 222,862 DNA polymorphisms in Hwayeong relative to Nipponbare (
Jeong et al. 2013). Therefore, Juanam and Nampyeong had higher number of DNA polymorphisms than Dongjin and Hwayeong relative to Nipponbare. It indicates that Junam and Nampyeong are probably genetically-farther related with Nipponbare than Dongjin and Hwayeong.
The number of DNA polymorphisms was very much different according to chromosomes. Junam has higher number of polymorphisms relative to Nipponbare on chromosome 8 and chromosome 11, while Nampyeong has higher number of polymorphisms relative to Nipponbare on chromosome 6 and chromosome 11 (
Fig. 1). In the previous study, Dongjin has higher number of polymorphisms on chromosome 11 and chromosome 12, while Hwayeong has higher number of polymorphisms chromosome 8 and chromosome 11. It was repeatedly observed that chromosome 11 has higher number of polymorphisms. It can be speculated that rice chromosome 11 has a lot of diversified genes. Rice genome has about 480 NBS-LRR genes (
Zhou et al. 2004) which often function in disease resistance and evolves rapidly (
McHale et al. 2006). Chromosome 11 has the highest number of NBS-LRR genes (133 NBS-LRR genes) among rice chromosomes (
Zhou et al. 2004). This may partly explain why chromosome 11 has highest number of polymorphisms.
As observed in the studies, there are high number of DNA polymorphisms on chromosome 6, 8, 11 and 12 in Korean
japonica varieties. This indicates that these chromosomes may possess the genes underlying traits in which
japonica varieties showing diverse phenotypic values. However, chromosome 3, 5 and 9 showed very low number of polymorphisms relative to Nipponbare in 4 Korean
Japonica varieties (Junam, Nampyeong, Dongjin, Hwayeong) and 6 Japanese
japonica varieties (
Arai-Kichise et al. 2014;
Arai-Kichise et al. 2011) indicating that the genes on these chromosomes are very much conserved among
japonica rice varieties.
In the classification of found DNA polymorphisms in Junam and Nampyeong relative to Nipponbare, 14~14.9% of polymorphisms were heterozygotes. In the previous study with 2 Korean
japonica rice varieties and 3 anther culture derived lines, about 13% of polymorphisms were heterozygote (
Jeong et al. 2013). It is still difficult to explain why about 13~15% of heterozygote polymorphisms were observed in rice varieties considering that self-pollinating ratio is very high in rice cultivars. Further studies are needed to determine the cause of the heterozygosity.
Twenty-two CAPS markers designed based on SNPs detected in restriction enzyme sites were tested. Of these, 17 markers (77.3%) showed expected polymorphisms, and 5 (22.7%) were monomorphic. This indicates that a large number of DNA markers can be made for mapping with population derived from a cross between Junam and Nampyeong, considering that a total of 398,123 DNA polymorphisms were found between the two varieties. Further study is needed to solve the observed monomorphism of about 22.7% of markers designed.
The results obtained in this study clearly demonstrates that massive identification of DNA polymorphisms through re-sequencing by NGS opened the way to develop large number of DNA markers and conduct gene/QTL mapping with population derived from a cross between the closely related Korean japonica rice varieties while they show remarkable phenotype difference in important agronomic traits.
ACKNOWLEDGEMENTS
This research was supported by grants from the National Academy of Agricultural Science (NAAS), project code PJ01008902, Republic of Korea.
Fig. 1Classification of DNA polymorphisms found through re-sequencing. Y axis indicates number of DNA polymorphisms. (A) DNA polymorphisms between Junam and Nipponbare. (B) DNA polymorphisms between Nampyeong and Nipponbare. (C) DNA polymorphisms between Junam and Nampyeong.
Fig. 2Distribution of DNA polymorphisms between Junam and Nampyeong per 100 kb in the 12 rice chromosomes. The x-axis represents the physical distance along each chromosome. The y-axis indicates the number of DNA polymorphisms.
Fig. 3Annotations of DNA polymorphisms between Junam and Nampyeong. IRGSP 1.0 reference annotation was used for annotation. The number in each class is shown. nc-exon indicates non-coding exon.
Fig. 4Examples of developing CAPS markers based on found SNPs located in restriction enzyme recognition sites.
Table 1Reference assembly of each rice variety onto Nipponbare genome.
Table 1
|
Variety |
# of raw reads |
# of reads after quality trimming |
# of mapped reads |
Mapped nucleotides (bp) |
Mapping Depth (X) |
Covered 3+ sitesz) (bp) |
Coveragey) (%) |
|
Junam |
164,391,782 |
126,861,174 |
120,728,418 |
11,936,509,638 |
31.98 |
360,843,725 |
96.68 |
|
Nampyeong |
149,706,238 |
114,524,713 |
107,244,401 |
10,602,867,351 |
28.41 |
358,501,094 |
96.05 |
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