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Development of a RAD-Seq Based DNA Polymorphism Identification Software, AgroMarker Finder, and Its Application in Rice Marker-Assisted Breeding

  • Wei Fan ,

    Contributed equally to this work with: Wei Fan, Jie Zong

    Affiliation State Key Laboratory of Hybrid Rice, Shanghai Jiao Tong University–University of Adelaide Joint Centre for Agriculture and Health, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China

  • Jie Zong ,

    Contributed equally to this work with: Wei Fan, Jie Zong

    Affiliation Novel Bioinformatics Company, Shanghai, China

  • Zhijing Luo,

    Affiliation State Key Laboratory of Hybrid Rice, Shanghai Jiao Tong University–University of Adelaide Joint Centre for Agriculture and Health, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China

  • Mingjiao Chen,

    Affiliation State Key Laboratory of Hybrid Rice, Shanghai Jiao Tong University–University of Adelaide Joint Centre for Agriculture and Health, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China

  • Xiangxiang Zhao,

    Affiliation Key Laboratory of Crop Marker-Assisted Breeding of Huaian Municipality, Jiangsu Collaborative Innovation Center of Regional Modern Agriculture and Environmental Protection, Huaiyin Normal University, Jiangsu, China

  • Dabing Zhang,

    Affiliations State Key Laboratory of Hybrid Rice, Shanghai Jiao Tong University–University of Adelaide Joint Centre for Agriculture and Health, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China, Key Laboratory of Crop Marker-Assisted Breeding of Huaian Municipality, Jiangsu Collaborative Innovation Center of Regional Modern Agriculture and Environmental Protection, Huaiyin Normal University, Jiangsu, China, Plant Genomics Center, School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, Urrbrae, South Australia, Australia

  • Yiping Qi,

    Affiliation Department of Biology, East Carolina University, Greenville, North Carolina, United States of America

  • Zheng Yuan

    zyuan@sjtu.edu.cn

    Affiliations State Key Laboratory of Hybrid Rice, Shanghai Jiao Tong University–University of Adelaide Joint Centre for Agriculture and Health, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China, Key Laboratory of Crop Marker-Assisted Breeding of Huaian Municipality, Jiangsu Collaborative Innovation Center of Regional Modern Agriculture and Environmental Protection, Huaiyin Normal University, Jiangsu, China

Development of a RAD-Seq Based DNA Polymorphism Identification Software, AgroMarker Finder, and Its Application in Rice Marker-Assisted Breeding

  • Wei Fan, 
  • Jie Zong, 
  • Zhijing Luo, 
  • Mingjiao Chen, 
  • Xiangxiang Zhao, 
  • Dabing Zhang, 
  • Yiping Qi, 
  • Zheng Yuan
PLOS
x

Abstract

Rapid and accurate genome-wide marker detection is essential to the marker-assisted breeding and functional genomics studies. In this work, we developed an integrated software, AgroMarker Finder (AMF: http://erp.novelbio.com/AMF), for providing graphical user interface (GUI) to facilitate the recently developed restriction-site associated DNA (RAD) sequencing data analysis in rice. By application of AMF, a total of 90,743 high-quality markers (82,878 SNPs and 7,865 InDels) were detected between rice varieties JP69 and Jiaoyuan5A. The density of the identified markers is 0.2 per Kb for SNP markers, and 0.02 per Kb for InDel markers. Sequencing validation revealed that the accuracy of genome-wide marker detection by AMF is 93%. In addition, a validated subset of 82 SNPs and 31 InDels were found to be closely linked to 117 important agronomic trait genes, providing a basis for subsequent marker-assisted selection (MAS) and variety identification. Furthermore, we selected 12 markers from 31 validated InDel markers to identify seed authenticity of variety Jiaoyuanyou69, and we also identified 10 markers closely linked to the fragrant gene BADH2 to minimize linkage drag for Wuxiang075 (BADH2 donor)/Jiachang1 recombinants selection. Therefore, this software provides an efficient approach for marker identification from RAD-seq data, and it would be a valuable tool for plant MAS and variety protection.

Introduction

Rice (Oryza sativa L.) is one of the most important crops, which feeds more than half of the world’s population [1, 2]. To meet the demand for feeding the growing population, rice production has to be greatly increased [2, 3]. Furthermore, due to better living standards, rice varieties with high eating and cooking quality are heavily emphasized during breeding selection processes, as they are key factors in attracting consumers and determining grain prices [4, 5]. In facing these challenges, new plant breeding technologies have to be developed [6, 7]. Conventional rice breeding methods mainly depend on phenotypic selection, a process strongly impacted by environmental factors, genotype factors, and interactions among them [1]. It might require 10–15 years to complete a variety breeding program from initiation to varietal release [1, 8], which is tedious and time consuming. Compared to conventional breeding, DNA polymorphism based marker-assisted selection (MAS) is more reliable and efficient, providing better selection strategies [9, 10] which can accurately select target genes in early generations and avoid the transfer of undesirable or deleterious genes. Recently, many rice varieties have been selected based on the MAS strategy [1114], which opens up a new prospect for genetic improvement in rice.

Molecular marker-based technologies have been developed from hybridization-based, such as restriction fragment length polymorphism (RFLP) [15, 16], to PCR-based procedures, such as random amplified polymorphic DNA (RAPD) [17], simple sequence repeat (SSR) [18, 19] and amplified fragment length polymorphism (AFLP) [20, 21]. All of these markers have been widely used in species identification, phylogenetic analysis, genetic mapping, MAS, and so on [22]. But it takes a long time to determine the genetic or physical distance between markers and target loci before its application. Therefore, with the reduction of sequencing cost, more and more attention has been paid to the detection of genome-wide, high throughput sequencing-based molecular markers [23]. Several studies have reported the development of genome-wide markers in rice. For example, array-based resequencing technology has been used to discover genome-wide SNPs across a 100 Mb fraction of Nipponbare genome for 20 diverse varieties [24]. However, arrays are not able to detect unknown mutations or uncover structural DNA changes, such as translocations and inversions [25, 26]. Next-generation sequencing-based approaches can overcome these shortcomings. For example, through complete sequencing of 150 rice recombinant inbred lines (RILs), a total of 1,493,461 SNPs have been detected for recombination breakpoint determination and quantitative trait loci (QTL) analysis [27]. Later, based on whole genome resequencing technology, 2,819,086 DNA polymorphisms have been discovered between six elite indica rice inbreds and Nipponbare [28] and 1,154,063 DNA polymorphisms have been detected between a Korean rice accession and Nipponbare to estimate sequence diversity [29]. In addition, large amount of SNPs and InDels of three rice cultivars with contrasting drought and salinity stress responses have been identified to understand the genetic basis of phenotypic differences [30]. More recently, the 3,000 Rice Genomes Project provides new opportunities for rice research, which has completed sequencing of 3,000 rice genomes for revealing the genomic diversity across the world’s rice germplasm collections [31]. However, even though next-generation sequencing (NGS) platform can produce billions of DNA sequence data with greater resolution and accuracy [32], costs restrict its practice for some applications, especially in those wild germplasms and locally adapted varieties.

Recently, the restriction-site associated DNA (RAD) sequencing (RAD-seq) method was developed based on the NGS platform, further reducing the research costs. It only acquires the sequences adjacent to a set of particular restriction enzyme recognition sites so as to reduce the representation of a genome [33, 34]. Thus, compared to whole genome sequencing, the RAD-seq seems to be more flexible (with the choice of different restriction enzymes to control the marker density) and cost-effective. Besides, RAD-Seq technology can apply to species with no or limited genome sequence information [35]. The application of the RAD-seq technology facilitates the discovery of large volumes of polymorphism data across the genome, genetic mapping [34, 36, 37], genetic map construction [3841], evolutionary studies [40, 42] and MAS [43]. However, drawbacks of the RAD-seq technology also exist. For example, the high probability of sequencing errors resulted from the NGS technology [44] and the presence of polymorphisms within the restriction site make it difficult to detect allelic polymorphisms [45]. In addition, sequencing data analysis is also a big challenge [46]. Although there are many softwares developed for marker identification [47], most of them are not easy to use because they require bioinformatics background. In addition, these softwares are often independent of each other so that compatibilities among them must to be considered to accomplish the whole analysis.

To simplify the RAD-seq data analysis, we developed an integrated software named AgroMarker Finder (AMF), which combines external tools with self-developed programs and provides a user-friendly graphical interface, making it more accessible to users. To demonstrate the application of AMF in rice breeding and functional studies, a set of 90,743 genome-wide markers had been detected between rice Indica variety JP69 and japonica variety Jiaoyuan5A using this software. Both rice varieties are parents for a new authorized hybrid rice variety, Jiaoyuanyou69, which shows heterosis. In addition to analyzing the distribution and functional relevance of these markers, a subset of 82 SNPs and 31 InDels were validated for their close linkages to 117 important agronomic trait genes, and 10 of the 31 InDels have been used to identify seed authenticity of the Jiaoyuanyou69 variety. Finally, by running AMF, 10 markers closely linked to the fragrance gene BADH2 were selected to minimize linkage drag for MAS. In the F7 generations of Wuxiang075 (donor)/Jiachang1 cross, both linkage analysis and phenotypic analysis on fragrance confirmed that we obtained 8 lines in Jiachang1 background carrying BADH2 chromosomal segment introgressed from Wuxiang075. Refinement of the intervals carrying the BADH2 gene in 8 lines revealed that the shortest donor segment for the introgression of BADH2 was approximately 20.75 cM. Therefore, this study developed and validated a powerful software, AMF, for RAD-seq based genome-wide marker identification. Its user-friendly interface has been demonstrated in rice. It could be broadly used for MAS in other plants as well.

Materials and Methods

Plant material

All plant materials including JP69, Jiaoyuan5A, Jiaoyuanyou69, Wuyungeng7, Jiachang1, Wuxiang075, and the F7 population from a cross between Jiachang1 and Wuxiang075 were grown in the paddy field or greenhouse of Shanghai Jiao Tong University at Shanghai in China.

DNA extraction, library preparation and sequencing

Genomic DNA was extracted from 100 mg fresh young leaves (from pooled samples for each variety) using the CTAB method [48]. One microgram DNA was digested for 10 min at 65°C in a 30 μl reaction with 20 units (U) of TaqαI (New England Biolabs). T4 ligase was used to ligate the adapters to DNA fragments at 22°C for 60 min, then the enzyme was heat-inactivated at 65°C for 30 min. TaqαI was inactivated by adding trichloromethane into the reaction system before selection of DNA fragments. Paired-end sequencing was performed on an Illumina HiSeq2000 after the selection of 400–600 bps adaptors-ligated and PCR-amplified DNA fragments on gel. All these experiments were performed by the Beijing Genomics Institute (BGI). Sequencing data have been deposited at the Sequence Read Archive (SRA) under the accession number SRP052892.

AgroMarker Finder analysis pipeline

The procedure of the analysis is presented in Fig 1B. Filtering of Illumina raw sequencing reads was preformed based on a Java program. Firstly, 84 bp raw sequencing reads were trimmed from both 3’ and 5’ ends (bases with Q<20 [36, 38, 49]). Only trimmed reads up to 50 bp were retained. Then, low-quality reads were discarded with the relax standard. The retained reads were aligned to the reference genome (Nipponbare, Rice Genome Annotation Project) using Burrows-Wheeler Aligner (BWA) with parameters mismatch = 4, gap length = 20 [50]. To avoid the influence of false mapped sequences on the accuracy of the final results, only those mapping to a unique position in the genome were reserved from the BWA results. After sorting and indexing based on SAMtools [51], the realignment analysis was performed based on GATK [52]. Then, SAMtools was used to summarize the base calls of aligned reads to the reference genome. The output files can be directly used to detect variants based on our self-developed SNP InDel Detection and Annotation module and Somatic Detection module (see AgroMarker Finder Manual). In this study, HeteroSNPPropLevel was set to 0.3 to capture more candidate sites. Finally, in order to improve the validity of variants, we filtered variant data generated by Somatic Detection application in accordance with the following conditions: (1) Only positions covered by at least eight reads in one sample and none in the other were reserved (Figure A in S1 File); (2) All variants with intervals less than 10 bp were eliminated.

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Fig 1. Development of AgroMarker Finder (AMF).

(A) Graphical User Interface of AgroMarker Finder. (1) Filter and Mapping module for quality assessment, filtering and mapping of data. (2) Bam Convert module for manipulating alignments in BAM or SAM format, including merging, filtering multiple mapped reads, sorting, indexing, removing duplicates, realigning and recalibrating. (3) SNP InDel Detection and Annotation module for SNP/InDel calling and annotation. (4) Somatic Detection module for variant discovery between two samples. (5) Variant Location module for locating the region of mutation. (B) Analysis pipeline showing procedures for RAD-seq data processing based on AMF. Data processing (shown in dark grey) trims and filters raw reads to remove low-quality bases and reads. Quality of raw data and filtered data are evaluated by FastQC. Filtered data are aligned against a reference genome using BWA. Uniquely mapped reads are sorted and indexed by SAMtools. Afterwards data are realigned by GATK. Then, SAMtools is used to generate pileup format files for each sample (in yellow). These pileup files are input for variant detection and filtering (in orange and grey) using the Java program with optional parameters. High-quality markers are annotated and located to linked genes (in blue) based on the Java program.

https://doi.org/10.1371/journal.pone.0147187.g001

Gene annotation of variants was also accomplished by AMF. Rice genome annotation file in GFF3 format was retrieved from Rice Genome Annotation Project. All 90,743 polymorphic sites were assigned to specific chromosome regions surrounding these sites for predicting their structures and functions. The output file described the SNP location (exons, introns, intergenic regions, 5’UTRs, 3’UTRs or intergenic regions) and gene functional relevance (synonymous or nonsynonymous).

Sequencing validation of SNPs and InDels

By application of AMF analysis, a subset of 121 markers (Table A in S1 File), which are tightly linked to 117 important agronomic trait genes with genetic distance less than 5 cM, was identified by Sanger sequencing to evaluate the accuracy. Flanking primers were designed by Primer 3.0 software ([53]). Each PCR reaction had 1 × PCR buffer, 0.2 mM dNTP, 0.4 μM each of forward and reverse primers (Table B and Table C in S1 File), 20 ng of each genomic DNA, and 1.25 unit of Taq DNA polymerase, in a final volume of 25μl. The cycling conditions were as follows: denaturing of DNA at 94°C for 5 min, 35 cycles of 30 s at 94°C,annealing at 54°C for 30 s, 40s at 72°C, and a final extension at 72°C for 5 min. The PCR products were resolved by electrophoresis with 2% agarose gels in 0.5 × TBE and stained by GelRed. Then, verified fragments were analyzed using the Sanger sequencing method (Beijing Genomics Institute).

Rice variety identification

The validated 121 markers (Table A in S1 File) were used for variety identification. Twelve markers were selected and 9 samples were collected for rice variety identification, including JP69, Jiaoyuan5A, Jiaoyuanyou69, Wuyungeng7 and 5 blind samples. After simultaneous amplification of these 9 samples, PCR products were resolved using the MultiNA microchip electrophoresis system (Shimadzu Corporation, Japan). Similarity was confirmed by comparing the polymorphism of amplified bands from 5 blind samples to that of Jiaoyuanyou69.

MAS on the BADH2 gene

For performing MAS on the BADH2 gene, genomic DNA of the donor variety Wuxiang075 and the recipient variety Jiachang1 were extracted for RAD sequencing as described above. In the F1 generation of Wuxiang075/Jiachang1 cross, plants with fragrance were retained. By application of AMF, 10 markers that are adjacent to BADH2 were selected from 7922 markers in the library to minimize linkage drag (Table C in S1 File). The selected 5 lines containing the desired BADH2 allele were self-crossed and further tested in the next generations to confirm the recombination.

Results

Development of AMF

Currently, many tools are available for NGS data analysis [47]. However, most of them only possess individual functions and provide command line interfaces, which require bioinformatics expertise. Moreover, concerted interoperation of different softwares for a complete analysis is arduous. For rapid and accurate marker identification based on RAD-seq data, we developed a software package, AMF (http://erp.novelbio.com/AMF), which has a graphical user interface (GUI) that visualizes the data analysis process and makes it accessible to general users (Fig 1A). It provides a versatile computational pipeline for quality assessment, filtering, mapping, variant detection, annotation and variant location through five modules: Filter and Mapping, Bam Convert, SNP InDel Detection and Annotation, Somatic Detection and Variant Location (Fig 1).

In Filter and Mapping, FastQ formatted RAD data or whole genome sequencing data are allowed to import for filtering to remove low quality reads or trim adaptor sequences of low quality bases. It provides four different filtering criteria for sequencing reads: (I) Strict standard. Reads showing 7 percent bases with Q<10 or 7 percent with Q<13 or 15 percent with Q<20 are discarded. (II) Moderate standard. Low-quality reads showing 10 percent bases with Q<10 or 14 percent with Q<13 or 20 percent with Q<20 are removed. (III) Relax standard. Low-quality reads showing 15 percent bases with Q<10 or 20 percent bases with Q<13 are discarded. (IV) NotFilter. All reads will be kept without filtering. These filtering criteria can be freely combined with TrimEndQuality (Q score) to gain a broad range of filtering criteria. For quality checking, FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) was employed before and after filtering. Then, filtered reads are mapped to the reference genome based on Burrows-Wheeler Alignment (BWA), which is a fast and accurate short read alignment tool [50]. Besides, Bowtie 2, a memory-efficient short read aligner, is also integrated in the AMF application package to meet different needs [54].

In Bam Convert, both Sequence Alignment/Map files and Binary Alignment/Map (BAM) files can be used. Files with the same prefix will be merged firstly by MergeByPrefix and SAM formatted files will be converted to BAM files directly. Then, uniquely mapped reads can be reserved by FilterMultipleMappedReads. Next, BAM files are sorted and indexed based on Sequence Alignment/Map tools (SAMtools) [51]. After that, ‘samtools rmdup’ command line can be used to remove potential PCR duplicates, and The Genome Analysis Toolkit (GATK) is employed to perform realignment analysis and recalibration analysis, which could reduce mapping errors and eliminate some false positive SNPs [52]. We then use SAMtools to summarize the base calls of aligned reads to the reference genome.

The generated Pileup files can be directly used to detect SNPs/InDels in SNP InDel Detection and Annotation module. The strategy is as follow: mutant site (different from the reference genome) should be covered by at least 3 reads containing more than 2 mutant reads, and mutant ratio should exceed 20%. Based on different requests, mutant ratio can be defined autonomously to determine a mutant site. The output SnpInfo file records the coordinate and coverage information in a straightforward TXT format. The detected polymorphisms in SnpInfo files can then be located to the reference genome for investigating their distribution and functional relevance based on GTF (Gene Transfer Format) or GFF (General Feature Format) files by SNP/InDel annotation function.

In Somatic Detection, users should simultaneously input SnpInfo files and Pileup files to detect polymorphisms between two samples. One sample is regarded as Control group and the other is regarded as Treat group. For control group, site covered by at least 10 reads including less than 2 mutant reads (different from the reference genome) and containing less than 4% mutant ratio will be kept for further comparison. For Treat group, the filtering strategy for mutant sites is the same as in SNP InDel Detection and Annotation module. Then, the software will record polymorphisms by comparing mutant sites kept in Treat group to Control group. To obtain complete SNPs and InDels information between two samples, users should exchange the positions of two samples for comparison.

In Variant Location, based on GFF or GTF files, the region of mutations can be accurately located according to the chromosome and coordinate information of SNPs and InDels, and the output file provides users with detailed position information of polymorphisms. The packages and manual are publicly available at http://erp.novelbio.com/AMF/ and https://github.com/NovelBio-Bioinformatics-Company/NBCsoftware.

Application of AMF in SNP/InDel detection

Jiaoyuanyou69 is a newly authorized superior hybrid rice in Shanghai, which is the F1 generation from a cross of indica variety JP69 and japonica variety Jiaoyuan5A. Jiaoyuanyou69 shows high heterosis and its grain yield reaches 843.16kg/667m2. For the subsequent construction of near isogenic lines (NILs), functional genomic research and MAS, JP69 and Jiaoyuan5A were used to mine genome-wide polymorphic information by the RAD-seq technology. The RAD tags were generated on an Illumina HiSeq2000 sequencing machine. In order to ensure that sufficiently digested fragments were within the desired size range (400–600 bp), we performed in-silico prediction of restriction enzymes on Nipponbare genome (TIGR 7, http://rice.plantbiology.msu.edu/). The distribution of restriction enzyme sites and the length of digested fragments were calculated using a Java program (see AgroMarker Finder Manual). Among all the restriction enzymes, the digestion sites of TaqαI (T/CGA) are evenly distributed in rice genome, producing about 130,940 digested fragments between 400–600 bp in size (Fig 2), which met the required number of RAD tags for subsequent genome sequencing analysis (100,000–150,000) [38].

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Fig 2. In-silico prediction of digested fragments of the rice genome.

(A) In-silico digestion with TaqαI of rice genome, showing 130,940 digested fragments between 400–600 bp in size. The X axis indicates the length of digested fragments; The Y axis indicates the number of digested fragments. (B) Density distribution of TaqαI recognition sites in the rice genome. The X axis indicates twelve chromosomes; The Y axis indicates the number of restriction enzyme sites. The result showed the restriction digestion sites for TaqαI were evenly distributed in the rice genome.

https://doi.org/10.1371/journal.pone.0147187.g002

After sequencing, a total of 40,521,314 paired-end raw reads were collected, with 16,970,386 reads for JP69 and 23,550,928 reads for Jiaoyuan5A. To avoid sequencing errors, reads were then trimmed with Q<20 and filtered with the relax standard option. These retained reads were mapped to Nipponbare genome for screening uniquely mapped reads (Fig 1B). Finally, 22,517,142 high-quality reads were retained from the BWA results and used for further analysis. Among them, 8,893,480 reads were from JP69, and 13,623,662 reads were from Jiaoyuan5A (Table D in S1 File). After filtering (for detailed method, please see Experimental procedures), a total of 90,743 high-quality makers (82,878 SNPs and 7,865 InDels) were obtained between JP69 and Jiaoyuan5A. The SNP frequency detected by AFM is 0.2 per kb, and the InDel frequency is 0.02 per kb (Table 1).

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Table 1. SNP/InDel frequency detected by AMF between JP69 and Jiaoyuan5A.

https://doi.org/10.1371/journal.pone.0147187.t001

To verify the accuracy of these markers identified by AMF, a subset of 121 markers, which was closely linked to 117 important agronomic trait genes with genetic distance less than 5 cM, was verified by Sanger sequencing (Fig 3; Table A in S1 File) [47, 55]. The heterozygous and false calling sites were all included for error rate calculation. Finally, 113 markers consisting of 82 SNPs and 31 InDels were validated, suggesting a high accuracy of 93% (Figure B and Table B in S1 File).

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Fig 3. Identification of markers associated with rice agronomic genes between JP69 and Jiaoyuan5A.

Distribution of rice agronomic genes and linked markers. Red represents disease resistance related genes; blue represents yield related genes; green represents quality related genes. Genes are labeled on the left of chromosomes; linked markers of each gene are labeled on the right. Those incorrect markers have been replaced, and all listed markers have been validated.

https://doi.org/10.1371/journal.pone.0147187.g003

Characterization and annotation of SNPs and InDels

The 90,743 high-quality markers were then located to the Nipponbare genome for investigation of their distribution and functional relevance. It was not surprising that all polymorphic sites were unevenly distributed on each chromosome (Fig 4A). Polymorphism-poor regions (<98 per Mb) were found in all chromosomes except chromosome 10, while polymorphism-rich regions (>491 per Mb) were observed in chromosomes 2, 3, 7, and 11. Those regions with low polymorphic density might be conserved that share a common ancestral origin between JP69 and Jiaoyuan5A in Chromsome 5, 6, and 12 (Fig 4A) [56]. Further analysis of the relevance between density of polymorphisms and chromosomal regions revealed that polymorphism-rich region on chromosome 7 and 11 contained both putative intergenic and transcribed regions, whereas those detected in chromosomes 2, 3, and 11 were in centromeric regions. This uneven distribution pattern of polymorphism sets has been reported in many rice cultivars [28, 57, 58], and there seems to be a selective mechanism in the process of domestication [58, 59]. We defined that transcription start site (TSS) regions and transcription terminate site (TTS) regions are located at position -1000 to +1000 from TSS and -500 to +500 from TTS, respectively. We found that more polymorphisms were present in intergenic regions (59.7%), which was similar in other rice varieties and plants [30, 60, 61]. The remaining polymorphisms (40.3%) were detected in genic region, of which 61.7% were observed in exonic regions and 52.4% in coding regions (CDS) (Fig 4B).

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Fig 4. Marker distribution and annotation.

(A) Distribution of the number of overlapping reads and SNPs between Jiaoyuan5A and JP69 along each chromosome in 500 kb windows. (B) Distribution of 90,743 markers in different genomic regions. The X axis indicates different genic regions; The Y axis indicates the number of markers. (C) Functional relevance of polymorphisms located in the coding regions. (D) Length of insertions and deletions. The X axis shows the length of insertions (shown in red) and deletions (shown in blue). The Y axis shows the number of insertions and deletions at each length.

https://doi.org/10.1371/journal.pone.0147187.g004

Further analysis of polymorphisms located in the coding regions revealed that 58% of these polymorphisms would introduce non-synonymous mutations. Among them, 3.9% polymorphisms would result in the introduction of a stop codon, producing premature proteins. And 0.07% of the polymorphisms would change the stop codon into non-stop codon, which causes the abnormal peptide synthesis (Fig 4C). Previous studies have demonstrated the importance of these genetic polymorphisms in regulation of gene expression and phenotypic traits of plant [62].

Then, we analyzed the length distribution of InDels between JP69 and Jiaoyuan5A. Among 7865 InDels, 4202 deletions were detected, the length of which ranged from 1 to 29 bp,and the number of insertions was 3663, the length of which was up to 30 bp (Fig 4D). It showed that most of the InDels (52.8%) were mononucleotide and 31.2% of which were 2 to 5 bp InDels. Only a few InDels (0.27%) were longer than 22 bp (6 insertion and 15 deletions), two thirds of which were located in centromere-specific retrotransposons and genes involved in processes such as sterol transport, flowering, polygalacturonase inhibition. Further studies on these large InDels would provide valuable insights into the genetic basis of phenotypic differences between these two rice varieties.

Application of markers in rice variety identification

To identify seed authenticity of Jiaoyuanyou69, we selected 12 InDel markers (Table C in S1 File) from 31 validated InDels described above. Among 5 blind rice samples with similar morphological characteristics that cannot be distinguished by straightforward phenotypic examination, blind sample 1 always showed similar amplified bands with Jiaoyuanyou69 positive control (Figure C in S1 File), thus confirming the similarity between them. This result demonstrates that the markers identified by AMF could also be applied in variety registration and protection.

Application of AMF in targeted breeding of the BADH2 gene

Fragrant rice is gaining popularity among consumers worldwide. Studies have indicated that 2-acetyl-1-pyrroline (2AP) is a potential flavor component giving rice distinctive fragrance, which is controlled by BADH2, a betaine aldehyde dehydrogenase that is responsible for aroma metabolism in fragrant rice varieties [63]. The donor parent Wuxiang075 produces rice with tempting fragrance while Jiachang1 has high yield. To perform MAS on the BADH2 gene in the progeny of Wuxiang075/Jiachang1 backcross, RAD-seq data of Wuxiang075 and Jiachang1 were analyzed by AMF. In total, 7922 markers were obtained, and two markers, (Os20105487 and Os20557750) adjacent to BADH2 (less than 1.5 cM) were selected to perform target locus selection (Table C in S1 File). Other 8 markers flanking BADH2 in an interval of 59 cM were used for minimizing linkage drag. In the 210 lines of F7 generation, we obtained 8 lines which show similar agronomic phenotype to that of Jiachang1 while carrying the donor segment introgression of the BADH2 gene. Among them, line 1446 was identified to be homozygous for the BADH2 gene introgression, including an approximately 20.75 cM fragment between marker Os17800476 and Os22293835 (Fig 5).

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Fig 5. Analysis of genome introgression containing BADH2 fragment in the F7 generations of Wuxiang075/Jiachang1 cross.

The X axis shows the 8 lines selected based on two markers Os20105487 and Os20557750; The Y axis indicates the distance distribution of 8 markers for further analysis.

https://doi.org/10.1371/journal.pone.0147187.g005

Discussion

The MAS strategy greatly accelerates the process of crop breeding by integrating molecular genetics with artificial selection [9]. However, a major limitation for applying this method is that recombination between markers and targeted genes would reduce the linkage disequilibrium, which could diminish the effectiveness of selection [64]. The development of new sequencing technologies narrows this gap because these technologies provide genome-wide sequence data for reliable marker identification with relatively low cost [65, 66]. With the increasing data generated from high-throughput sequencing platforms, effective data analysis is essential to further applications. A series of tools have been developed, which however could only support specific parts of a complete NGS data analysis [47, 67]. Therefore, it requires a combination of different technical resources with the additional bioinformatics expertise to finish the complex data analysis, which is cumbersome, especially for researchers with limited bioinformatics skill.

In this article, we integrated softwares including BWA, SAMtools, GATK and HeteroSNPPropLevel, to develop a software named AMF for RAD-seq data analysis. This software provides a graphical user interface and streamlined framework which covers quality assessment, filtering, mapping, variant detection, annotation and variant location. Each function is independent for a flexible application and can be manipulated easily with optional parameters to meet various needs. In addition to the integration of external tools, AMF provides three self-developed modules: SNP InDel Detection and Annotation, Somatic Detection and Variant Location, all of which are customizable for researchers to complete data mining. The output files of these three modules are either in TXT format or Excel spreadsheet format that are easy to understand and manipulate especially for those with minimal bioinformatics expertise. Further, unlike other integrated softwares, such as MAQ that provides command line interfaces [68], AMF provides users a visual and straightforward way to perform data analysis through a user-friendly graphical display, which greatly simplifies the analysis procedure. Additionally, AMF allows users to integrate other genomic information with high flexibility.

Based on this analysis platform, high-density rice markers were successfully identified from RAD tags with 93% accuracy, which could have been underestimated since PCR bias [69, 70], PCR errors, and sequencing errors [71] could all affect the final accuracy. To increase the accuracy, users could apply stricter parameters according to different NGS technologies and genomic features. In addition, repeated trials, and increased sequencing depth, and number of pooled samples may help reduce the error rate.

In addition to the identification of genome-wide markers, this study also demonstrated an application of AMF in MAS. In Variant Location module, the positions or regions of DNA makers could be easily located, which would assist users to determine the tightly linked markers for minimizing linkage drag, as exampled in the targeted breeding of BADH2 (Fig 5). As more and more important genes or quantitative trait loci (QTLs) affecting growth and development of cereal crops are being cloned [72], genomics-assisted breeding will gain significant improvements for precise prediction of phenotypes from genotypes.

Development of AMF is an ongoing process. In current version, AMF only supports a Linux-based application. Efforts will be paid to extend it to support additional platforms for a wider use. Furthermore, because most of the polymorphic markers detected are SNPs, we have to further consider how to apply these markers at a lower cost. AMF will be continuously updated and extended according to the feedback provided by users. In conclusion, AMF provides an efficient strategy for large-scale and accurate marker discovery that can be widely used for basic research and MAS.

Supporting Information

S1 File.

Statistics of the sequenced sites in JP69 and Jiaoyuan5A (Figure A). Marker validation (Figure B). Rice variety identification (Figure C). List of 117 important rice agronomic trait genes (Table A). Primer sequences used for accuracy verification and variety identification (Table B). Primer sequences used for target breeding of BADH2 (Table C). Summary of sequence data (Table D).

https://doi.org/10.1371/journal.pone.0147187.s001

(DOCX)

Acknowledgments

We appreciate Zhibo Chen and Changjian Zhang for rice cultivation. This work was supported by the Funds from the EU FP7 project (DECATHLON, 613908), Project on Breeding from Agriculture Commission of Shanghai (2013–13, 2014-1-3), National Natural Science Foundation of China (31470397, 31270222, 31230051 and 31110103915), Key Project on Basic Research from Science and Technology Commission of Shanghai (14JC1403900, 14391917100), China Innovative Research Team, Ministry of Education and the Programme of Introducing Talents of Discipline to Universities (111 Project, B14016). Novel Bioinformatics Company provided support in the form of salaries for authors [JZ], but did not have any additional role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Author Contributions

Conceived and designed the experiments: WF DZ ZY. Performed the experiments: WF JZ ZL MC. Analyzed the data: WF JZ ZY. Contributed reagents/materials/analysis tools: JZ XZ DZ YQ ZY. Wrote the paper: WF YQ ZY. Rice cultivation: ZL MC.

References

  1. 1. Miah G, Rafii MY, Ismail MR, Puteh AB, Rahim HA, Asfaliza R, et al. Blast resistance in rice: a review of conventional breeding to molecular approaches. Mol. Biol. Rep. 2013;40(3):2369–88. ISI:000314535600035. pmid:23184051
  2. 2. Khush GS. What it will take to Feed 5.0 Billion Rice consumers in 2030. Plant Mol. Biol. 2005;59(1):1–6. ISI:000232498000001. pmid:16217597
  3. 3. Ravn K. Agriculture: The next frontier. Nature. 2014;514(7524):S64–5. Epub 2014/11/05. pmid:25368892.
  4. 4. Fitzgerald MA, McCouch SR, Hall RD. Not just a grain of rice: the quest for quality. Trends Plant Sci. 2009;14(3):133–9. Epub 2009/02/24. pmid:19230745.
  5. 5. Phing Lau WC, Latif MA, Rafii MY, Ismail MR, Puteh A. Advances to improve the eating and cooking qualities of rice by marker-assisted breeding. Crit. Rev. Biotechnol. 2014:1–12. Epub 2014/06/18. pmid:24937109.
  6. 6. Huang JK, Pray C, Rozelle S. Enhancing the crops to feed the poor. Nature. 2002;418(6898):678–84. ISI:000177305600054. pmid:12167874
  7. 7. Cheung F. Yield: The search for the rice of the future. Nature. 2014;514(7524):S60–1. Epub 2014/11/05. pmid:25368890.
  8. 8. Slater A, Cogan NI, Hayes B, Schultz L, Dale MF, Bryan G, et al. Improving breeding efficiency in potato using molecular and quantitative genetics. Theor. Appl. Genet. 2014;127(11):2279–92. pmid:25186170
  9. 9. Collard BCY, Jahufer MZZ, Brouwer JB, Pang ECK. An introduction to markers, quantitative trait loci (QTL) mapping and marker-assisted selection for crop improvement: The basic concepts. Euphytica. 2005;142(1–2):169–96.
  10. 10. Mohan M, Nair S, Bhagwat A, Krishna TG, Yano M, Bhatia CR, et al. Genome mapping, molecular markers and marker-assisted selection in crop plants. Mol. Breeding. 1997;3(2):87–103. ISI:A1997WV54000001.
  11. 11. Dokku P, Das KM, Rao GJN. Pyramiding of four resistance genes of bacterial blight in Tapaswini, an elite rice cultivar, through marker-assisted selection. Euphytica. 2013;192(1):87–96.
  12. 12. Kumar A, Dixit S, Ram T, Yadaw RB, Mishra KK, Mandal NP. Breeding high-yielding drought-tolerant rice: genetic variations and conventional and molecular approaches. J. Exp. Bot. 2014;65(21):6265–78. Epub 2014/09/11. pmid:25205576; PubMed Central PMCID: PMC4223988.
  13. 13. Zhou L, Chen Z, Lang X, Du B, Liu K, Yang G, et al. Development and validation of a PCR-based functional marker system for the brown planthopper resistance gene Bph14 in rice. Breed Sci. 2013;63(3):347–52. Epub 2013/11/26. pmid:24273431; PubMed Central PMCID: PMC3770563.
  14. 14. Li G, Kwon SW, Park YJ. Updates and perspectives on the utilization of molecular makers of complex traits in rice. Genet. Mol. Res. 2012;11(4):4157–68. ISI:000313960500060. pmid:23079968
  15. 15. Tanksley SD, Young ND, Paterson AH, Bonierbale MW. RFLP Mapping in Plant Breeding: New Tools for an Old Science. Nat. Biotech. 1989;7(3):257–64.
  16. 16. Lander ES, Botstein D. Mapping mendelian factors underlying quantitative traits using RFLP linkage maps. Genetics. 1989;121(1):185–99. pmid:2563713
  17. 17. Lynch M, Milligan BG. Analysis of population genetic structure with RAPD markers. Mol. Ecol. 1994;3(2):91–9. pmid:8019690
  18. 18. Lu H, Redus MA, Coburn JR, Rutger JN, McCouch SR, Tai TH. Population structure and breeding patterns of 145 US rice cultivars based on SSR marker analysis. Crop Sci. 2005;45(1):66–76. ISI:000226435300009.
  19. 19. Chakravarthi BK, Naravaneni R. SSR marker based DNA fingerprinting and diversity study in rice (Oryza sativa. L). Afr. J. Biotechnol. 2006;5(9):684–8. ISI:000237358500002.
  20. 20. Vos P, Hogers R, Bleeker M, Reijans M, v d Lee T, Hornes M, et al. AFLP: a new technique for DNA fingerprinting. Nucleic Acids Res. 1995;23(21):4407–14. pmid:7501463
  21. 21. Cervera MT, Gusmao J, Steenackers M, Peleman J, Storme V, Vanden Broeck A, et al. Identification of AFLP molecular markers for resistance against Melampsora larici-populina in Populus. Theor. Appl. Genet. 1996;93(5–6):733–7. pmid:24162401
  22. 22. McCouch SR, Chen XL, Panaud O, Temnykh S, Xu YB, Cho YG, et al. Microsatellite marker development, mapping and applications in rice genetics and breeding. Plant Mol. Biol. 1997;35(1–2):89–99. ISI:A1997XU45800010. pmid:9291963
  23. 23. Henry RJ. Evolution of DNA Marker Technology in Plants. Molecular Markers in Plants: Blackwell Publishing Ltd.; 2012. p. 1–19.
  24. 24. McNally KL, Childs KL, Bohnert R, Davidson RM, Zhao K, Ulat VJ, et al. Genomewide SNP variation reveals relationships among landraces and modern varieties of rice. Proc. Natl. Acad. Sci. U S A. 2009;106(30):12273–8. ISI:000268440200015. pmid:19597147
  25. 25. LaFramboise T. Single nucleotide polymorphism arrays: a decade of biological, computational and technological advances. Nucleic Acids Res. 2009;37(13):4181–93. ISI:000268331800006. pmid:19570852
  26. 26. Waldmuller S, Muller M, Rackebrandt K, Binner P, Poths S, Bonin M, et al. Array-based resequencing assay for mutations causing hypertrophic cardiomyopathy. Clin. Chem. 2008;54(4):682–7. Epub 2008/02/09. pmid:18258667.
  27. 27. Huang X, Feng Q, Qian Q, Zhao Q, Wang L, Wang A, et al. High-throughput genotyping by whole-genome resequencing. Genome Res. 2009;19(6):1068–76. Epub 2009/05/08. pmid:19420380; PubMed Central PMCID: PMC2694477.
  28. 28. Subbaiyan GK, Waters DLE, Katiyar SK, Sadananda AR, Vaddadi S, Henry RJ. Genome-wide DNA polymorphisms in elite indica rice inbreds discovered by whole-genome sequencing. Plant Biotechnol. J. 2012;10(6):623–34. ISI:000306131400002. pmid:22222031
  29. 29. Jeong IS, Yoon UH, Lee GS, Ji HS, Lee HJ, Han CD, et al. SNP-based analysis of genetic diversity in anther-derived rice by whole genome sequencing. Rice. 2013;6. Artn 6 ISI:000323778200002.
  30. 30. Jain M, Moharana KC, Shankar R, Kumari R, Garg R. Genomewide discovery of DNA polymorphisms in rice cultivars with contrasting drought and salinity stress response and their functional relevance. Plant Biotechnol. J. 2014;12(2):253–64. Epub 2014/01/28. pmid:24460890.
  31. 31. Li JY, Wang J, Zeigler RS. The 3,000 rice genomes project: new opportunities and challenges for future rice research. Gigascience. 2014;3:8. Epub 2014/05/30. pmid:24872878; PubMed Central PMCID: PMC4035671.
  32. 32. Wang J, Wang W, Li RQ, Li YR, Tian G, Goodman L, et al. The diploid genome sequence of an Asian individual. Nature. 2008;456(7218):60–U1. ISI:000260674000040. pmid:18987735
  33. 33. Miller MR, Dunham JP, Amores A, Cresko WA, Johnson EA. Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers. Genome Res. 2007;17(2):240–8. Epub 2006/12/26. pmid:17189378; PubMed Central PMCID: PMC1781356.
  34. 34. Baird NA, Etter PD, Atwood TS, Currey MC, Shiver AL, Lewis ZA, et al. Rapid SNP Discovery and Genetic Mapping Using Sequenced RAD Markers. PLoS One. 2008;3(10). ARTN e3376 ISI:000265121600002. pmid:18852878
  35. 35. Davey JW, Blaxter ML. RADSeq: next-generation population genetics. Brief. Funct. Genomics. 2010;9(5–6):416–23. Epub 2011/01/27. pmid:21266344; PubMed Central PMCID: PMC3080771.
  36. 36. Pfender WF, Saha MC, Johnson EA, Slabaugh MB. Mapping with RAD (restriction-site associated DNA) markers to rapidly identify QTL for stem rust resistance in Lolium perenne. Theor Appl Genet. 2011;122(8):1467–80. Epub 2011/02/24. pmid:21344184.
  37. 37. Hegarty M, Yadav R, Lee M, Armstead I, Sanderson R, Scollan N, et al. Genotyping by RAD sequencing enables mapping of fatty acid composition traits in perennial ryegrass (Lolium perenne (L.)). Plant Biotechnol. J. 2013;11(5):572–81. ISI:000319151000006. pmid:23331642
  38. 38. Wang N, Fang LC, Xin HP, Wang LJ, Li SH. Construction of a high-density genetic map for grape using next generation restriction-site associated DNA sequencing. BMC Plant Biol. 2012;12. Artn 148 ISI:000312659800001.
  39. 39. Chutimanitsakun Y, Nipper RW, Cuesta-Marcos A, Cistue L, Corey A, Filichkina T, et al. Construction and application for QTL analysis of a Restriction Site Associated DNA (RAD) linkage map in barley. BMC Genomics. 2011;12. Artn 4 ISI:000286425200001.
  40. 40. Hiremath PJ, Kumar A, Penmetsa RV, Farmer A, Schlueter JA, Chamarthi SK, et al. Large-scale development of cost-effective SNP marker assays for diversity assessment and genetic mapping in chickpea and comparative mapping in legumes. Plant Biotechnol. J. 2012;10(6):716–32. ISI:000306131400011. pmid:22703242
  41. 41. Raman H, Dalton-Morgan J, Diffey S, Raman R, Alamery S, Edwards D, et al. SNP markers-based map construction and genome-wide linkage analysis in Brassica napus. Plant Biotechnol J. 2014;12(7):851–60. ISI:000340528000004. pmid:24698362
  42. 42. Emerson KJ, Merz CR, Catchen JM, Hohenlohe PA, Cresko WA, Bradshaw WE, et al. Resolving postglacial phylogeography using high-throughput sequencing. Proc. Natl. Acad. Sci. U S A. 2010;107(37):16196–200. ISI:000281799000041. pmid:20798348
  43. 43. Yang H, Tao Y, Zheng Z, Li C, Sweetingham MW, Howieson JG. Application of next-generation sequencing for rapid marker development in molecular plant breeding: a case study on anthracnose disease resistance in Lupinus angustifolius L. BMC Genomics. 2012;13:318. Epub 2012/07/19. pmid:22805587; PubMed Central PMCID: PMC3430595.
  44. 44. Mardis ER. The impact of next-generation sequencing technology on genetics. Trends Genet. 2008;24(3):133–41. ISI:000254058200006. pmid:18262675
  45. 45. Gautier M, Gharbi K, Cezard T, Foucaud J, Kerdelhue C, Pudlo P, et al. The effect of RAD allele dropout on the estimation of genetic variation within and between populations. Mol Ecol. 2013;22(11):3165–78. Epub 2012/11/01. pmid:23110526.
  46. 46. Edwards M. Whole-genome sequencing for marker discovery. Molecular Markers in Plants: Blackwell Publishing Ltd.; 2012. p. 21–34.
  47. 47. Ophir R. Bioinformatics tools for marker discovery in plant breeding. Israel Journal of Chemistry. 2013;53(3–4):173–9.
  48. 48. Stewart CN Jr, Via LE. A rapid CTAB DNA isolation technique useful for RAPD fingerprinting and other PCR applications. Biotechniques. 1993;14(5):748–50. Epub 1993/05/01. pmid:8512694.
  49. 49. Kofler R, Orozco-terWengel P, De Maio N, Pandey RV, Nolte V, Futschik A, et al. PoPoolation: A toolbox for population genetic analysis of next generation sequencing data from pooled individuals. PLoS One. 2011;6(1). ARTN e15925 ISI:000286511900025. pmid:21253599
  50. 50. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60. ISI:000267665900006. pmid:19451168
  51. 51. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078–9. ISI:000268808600014. pmid:19505943
  52. 52. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303. ISI:000281520400015. pmid:20644199
  53. 53. Rozen S, Skaletsky H. Primer3. 1998. Code available at http://www-genome wi mit edu/genome_software/other/primer3 html. 2011.
  54. 54. Langmead B, Salzberg SL. Fast gapped-read alignment with Bowtie 2. Nat. Methods. 2012;9(4):357–9. Epub 2012/03/06. pmid:22388286; PubMed Central PMCID: PMC3322381.
  55. 55. Collard BCY, Mackill DJ. Marker-assisted selection: an approach for precision plant breeding in the twenty-first century. Philos T R Soc B. 2008;363(1491):557–72. ISI:000252663100009.
  56. 56. Yamamoto T, Nagasaki H, Yonemaru J, Ebana K, Nakajima M, Shibaya T, et al. Fine definition of the pedigree haplotypes of closely related rice cultivars by means of genome-wide discovery of single-nucleotide polymorphisms. BMC Genomics. 2010;11:267. Epub 2010/04/29. pmid:20423466; PubMed Central PMCID: PMC2874813.
  57. 57. Arai-Kichise Y, Shiwa Y, Nagasaki H, Ebana K, Yoshikawa H, Yano M, et al. Discovery of genome-wide DNA polymorphisms in a landrace cultivar of japonica rice by whole-genome sequencing. Plant Cell Physiol. 2011;52(2):274–82. ISI:000287254000009. pmid:21258067
  58. 58. Wang L, Hao L, Li X, Hu S, Ge S, Yu J. SNP deserts of Asian cultivated rice: genomic regions under domestication. J Evolution Biol. 2009;22(4):751–61. ISI:000264186000009.
  59. 59. Caicedo AL, Williamson SH, Hernandez RD, Boyko A, Fledel-Alon A, York TL, et al. Genome-wide patterns of nucleotide polymorphism in domesticated rice. Plos Genet. 2007;3(9):1745–56. ARTN e163 ISI:000249767800017. pmid:17907810
  60. 60. Sonah H, Bastien M, Iquira E, Tardivel A, Legare G, Boyle B, et al. An improved genotyping by sequencing (GBS) approach offering increased versatility and efficiency of SNP discovery and genotyping. PLoS One. 2013;8(1):e54603. Epub 2013/02/02. pmid:23372741; PubMed Central PMCID: PMC3553054.
  61. 61. Qu J, Liu J. A genome-wide analysis of simple sequence repeats in maize and the development of polymorphism markers from next-generation sequence data. BMC Res. Notes. 2013;6:403. Epub 2013/10/09. pmid:24099602; PubMed Central PMCID: PMC3828028.
  62. 62. Takano-Kai N, Doi K, Yoshimura A. GS3 participates in stigma exsertion as well as seed length in rice. Breed Sci. 2011;61(3):244–50. ISI:000296683100004.
  63. 63. Chen SH, Yang Y, Shi WW, Ji Q, He F, Zhang ZD, et al. Badh2, encoding betaine aldehyde dehydrogenase, inhibits the biosynthesis of 2-acetyl-1-pyrroline, a major component in rice fragrance. Plant Cell. 2008;20(7):1850–61. ISI:000258725600015. pmid:18599581
  64. 64. Xie CQ, Xu SZ. Efficiency of multistage marker-assisted selection in the improvement of multiple quantitative traits. Heredity. 1998;80:489–98. ISI:000073392900012. pmid:9618913
  65. 65. Edwards D, Batley J. Plant genome sequencing: applications for crop improvement. Plant Biotechnol J. 2010;8(1):2–9. ISI:000274366400001. pmid:19906089
  66. 66. Rickert AM, Kim JH, Meyer S, Nagel A, Ballvora A, Oefner PJ, et al. First-generation SNP/InDel markers tagging loci for pathogen resistance in the potato genome. Plant Biotechnol. J. 2003;1(6):399–410. ISI:000188440200002. pmid:17134399
  67. 67. Pabinger S, Dander A, Fischer M, Snajder R, Sperk M, Efremova M, et al. A survey of tools for variant analysis of next-generation genome sequencing data. Brief Bioinform. 2014;15(2):256–78. ISI:000333249500010. pmid:23341494
  68. 68. Li H, Ruan J, Durbin R. Mapping short DNA sequencing reads and calling variants using mapping quality scores. Genome Res. 2008;18(11):1851–8. ISI:000260536100017. pmid:18714091
  69. 69. Oyola SO, Otto TD, Gu Y, Maslen G, Manske M, Campino S, et al. Optimizing illumina next-generation sequencing library preparation for extremely at-biased genomes. BMC Genomics. 2012;13. Artn 1 ISI:000301926300001.
  70. 70. Shokralla S, Spall JL, Gibson JF, Hajibabaei M. Next-generation sequencing technologies for environmental DNA research. Mol. Ecol. 2012;21(8):1794–805. pmid:22486820
  71. 71. Shendure J, Ji HL. Next-generation DNA sequencing. Nat. Biotechnol. 2008;26(10):1135–45. ISI:000259926000028. pmid:18846087
  72. 72. Dwivedi S, Perotti E, Ortiz R. Towards molecular breeding of reproductive traits in cereal crops. Plant Biotechnol. J. 2008;6(6):529–59. ISI:000257571000001. pmid:18507792