Simultaneously mapping loci related to two plant architecture traits by Phenotypic Recombination BSA/BSR in peanut (Arachis hypogaea L.)

doi:10.21203/rs.3.rs-1656778/v1

Download PDF

Research Article

Simultaneously mapping loci related to two plant architecture traits by Phenotypic Recombination BSA/BSR in peanut (Arachis hypogaea L.)

https://doi.org/10.21203/rs.3.rs-1656778/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 30 May, 2023

Read the published version in Theoretical and Applied Genetics →

You are reading this latest preprint version

BSA-seq has been widely used for identifying the genomic regions affecting a certain trait. In this study, we developed a modified BSA/BSR-seq method, which we named Phenotypic Recombination BSA/BSR (PR-BSA/BSR), to simultaneously identify candidate genomic regions associated with two traits in a segregating population. Lateral branch angle (LBA) and flower-branch pattern (FBP) are two important traits associated with the peanut plant architecture because they affect the planting density and light use efficiency. We generated an F₆ population (with two segregating traits) derived from a cross between the inbred lines Pingdu9616 (erect and sequential; ES-type) and Florunner (spreading and alternating; SA-type). The selection of bulks with extreme phenotypes was a key step in this study. Specifically, 30 individuals with recombinant phenotypes [i.e., spreading and sequential (SS-type) and erect and alternating (EA-type)] were selected to generate two bulks. The transcriptomes of individuals were sequenced and then the loci related to LBA and FBP were simultaneously detected via a ΔSNP-index strategy, which involved the direction of positive and negative peaks in the ∆SNP-index plot. The LBA-related locus was mapped to a 6.82 Mb region (101,743,223–108,564,267 bp) on chromosome B05, whereas the FBP-related locus was mapped to a 2.16 Mb region (117,682,534–119,846,824 bp) on chromosome B02. Furthermore, the marker-based classical QTL mapping method was used to analyze the PF–F₆ population, which confirmed our PR-BSA/BSR results. Therefore, the PR-BSA/BSR method produces accurate and reliable data.

Peanut

Bulked segregant analysis

Lateral branch angle

Flower branch pattern

Phenotypic recombination sequencing

Bulked segregant analysis (BSA), which was developed to analyze markers linked to phenotypes, has been widely used in studies on diverse plant species (Aoun et al. 2017; Davidl et al. 2009; Michelmore and Kesseli 1991; Shoba et al. 2012). Because of the development and application of sequencing technologies, whole-genome sequences of numerous plant species are now publicly available (Initiative and Copenhaver 2000; Kawahara et al. 2013; Schnable et al. 2009). Additionally, the use of the BSA/BSR-seq method has resulted in major breakthroughs in QTL/gene mapping (Takagi et al. 2013). In previous studies, BSA-seq or QTL-seq methods were used to analyze a segregating population derived from a cross between two phenotypically distinct parental lines (Huang et al. 2017; Workie et al. 2018). Briefly, two segregating groups in a population are bulked according to their phenotypes and then the DNA/RNA extracted from the bulks are sequenced and mapped to a reference genome to compare marker [mainly single nucleotide polymorphism (SNP)] allele frequencies between bulks. Moreover, the BSR-seq gene mapping strategy combines BSA and RNA-seq techniques to simplify gene sequencing and generate genotype data, even with relatively little sequencing (Liu et al. 2012).

Two pools with extreme traits in the segregating population are obtained for BSA-seq, but only two extreme phenotypes of a single target trait are selected to ensure the genetic background for the other traits is not selected (Zhang et al. 2018). Theoretically, the differences between two gene pools are mainly in the target region, which can eliminate the influence of environmental and artificial factors, thereby increasing the accuracy and reliability of the results. However, the limitations of this method include the fact that only one trait-related locus can be mapped in a single analysis (Wang et al. 2019; Yang et al. 2019). In contrast, conventional linkage mapping can be used for the simultaneous localization of QTLs/genes governing multiple traits, but this requires labor-intensive, time-consuming, and expensive processes. Phenotypic Recombination BSA/BSR (PR-BSA/BSR) is a new method for gene mapping involving BSA-seq or BSR-seq, which can simultaneously locate the genomic loci associated with two target traits. Individuals with recombinant phenotypes in the segregating population are selected for the construction of two DNA bulks. After sequencing the whole genome or transcriptome of the bulks and then mapping to a reference genome, many molecular markers can be developed and the SNP-index in each bulk can be calculated to detect QTLs. This new method facilitates the simultaneous mapping of more than one locus, making it a cost-effective approach for the rapid mapping of QTLs.

Cultivated peanut (Arachis hypogaea L. 2n = 40, AABB), which is a leguminous species that originated in South America, is one of the most economically important crops and a good source of edible oil and protein. The peanut plant comprises a main stem, two symmetrical cotyledonary lateral branches and a few other lateral branches (Galya et al. 2017). The lateral branch angle (LBA) and the flower-branch pattern (FBP) are two important peanut plant architecture-related traits that affect the planting density and light use efficiency, which ultimately influences peanut yield and quality. The LBA (i.e., growth habit) is generally divided into the following four types: prostrate, spreading, bunch, and erect (Galya et al. 2017). The FBP, which refers to the distribution of flowers/inflorescences and branches on the nodes or leaf axils (Antonio and Gregory 2007), is the main trait used for classifying cultivated peanut into the following two subspecies: A.hypogaea subsp. fastigiata (sequential-type) and A. hypogaea subsp. hypogaea (alternating-type). In the sequential flowering subspecies, leaf axils on the lateral branch exhibit successive flowering, which is accompanied by flowering on the main stem. In the alternating flowering subspecies, only the leaf axils on the main stem form branches. Additionally, flowers/inflorescences and branches alternately occur at all branch nodes or leaf axils, usually with two branches after two inflorescences.

The genes related to LBA and FBP have been identified in Arabidopsis thaliana as well as in many crop species, including rice and corn (Li et al. 2003; Li et al. 2007; Muller et al. 2006; Roychoudhry and Kepinski 2015; Yoshihara et al. 2013), but little is known about the corresponding peanut genes. In the earliest related report, which mainly described cultivated crosses, the runner-type peanut was more dominant than the bunch-type peanut; this trait was controlled by a single gene (Dong et al. 2013; Patel et al. 1936). The subsequent research indicated that two genes with complementary–duplicate actions govern the growth habit (Dalal 1962; Patel et al. 1936; Pattanashetti and Gowda 2008). More recently, the branching habit-related locus Bunch1 was fine-mapped by combining BSA and high-throughput sequencing. The generated data revealed that the spreading/bunching trait is controlled by one gene in a 1.1 Mb segment on chromosome B05, with no cytoplasmic effect (Galya et al. 2017). Another study (Li et al. 2019) mapped the LBA-associated locus in peanut within a 1.7 Mb segment (159,819,755–159,987,803 bp) on chromosome B05. Inheritance studies involving TMV 2 and its mutant (TMV 2-NLM) demonstrated that two duplicate dominant genes govern the branching pattern. Moreover, sequential branching is mediated by duplicate recessive alleles. On the basis of a mapping approach, the gene marker AhTE0357 was revealed to be strongly associated (R² = 32.8%) with the branching pattern (sequential/alternating) of TMV 2 and TMV 2-NLM (Hake et al. 2017; Pattanashetti and Gowda 2008).

In this study, we developed a new gene-mapping method (PR-BSA/BSR), which can identify two loci and simultaneously determine the confidence interval of two trait-related loci. We also mapped the loci related to LBA and FBP to the end of chromosomes B05 and B02, respectively. Therefore, our method may be useful for identifying and localizing genes in many crops.

Plant materials and phenotypes

The RIL segregating offspring of 445 families were studied. The female parent was Pingdu9616 (erect growth habit and a sequential flowering pattern), whereas the male parent was Florunner (spreading growth habit and alternating flowering pattern). From May to September, 2018, the PF–F₆ population was grown under field conditions, with 10 seeds sown per family. Specifically, seeding (ridging and covering) involved one ridge and two rows, with a single seed added to each hole. Plants were separated by 25 cm, whereas families were separated by 50 cm. At 60 days after sowing, the plant-type and flowering/branching pattern of each family were recorded. There were generally three plant-types (erect, spreading, and U-shaped). Spreading was further divided into three types: complete spreading; overall spreading, but only the tips of the lateral branches were upturned; and curved upward from the middle of the lateral branch. The three main types of flowering patterns were as follows: sequential-type, alternating-type, and intermediate-type. The sequential-type plants usually had sequential flowering on the lateral branches, which was accompanied by the flowering of the main stem. The alternating-type plants usually had two branches and two flowers on the lateral branches, with a lack of flowers on the main stem. The intermediate-type plants had sequential flowering and occasional branching.

Selection Of Bulks With Extreme Traits

The selection of bulks was a critical step in this study. Before extracting RNA, samples from the PF–F₆ population were grouped into two bulks, one comprising 30 erect-type and alternating flowering individuals (EA bulk) and the other comprising 30 spreading-type and sequential flowering individuals (SS bulk). The phenotypes of the two bulk samples were derived from both parents (i.e., one trait from each parent). In addition to the two bulks comprising 60 individuals, RNA was extracted from the two parents (Pingdu9616 and Florunner) at the flowering stage. The two bulks were not mixed and they were sequenced separately. The RNA extraction and RNA-seq analysis were performed by Biomarker Technologies Co., Ltd. (Beijing, China).

Transcriptome Sequencing And Evaluation Of Sequencing Quality

The RNA-seq analysis was conducted using the Illumina HiSeq 2500 platform (Illumina, San Diego, CA) according to the manufacturer’s protocol. The raw RNA-seq reads in the fastq format were filtered to produce clean reads, which were subsequently aligned to the Tifrunner cultivated peanut genome (https://www.peanutbase.org/) using the TopHat2 software. The mapped reads were then assembled and expression was calculated in terms of the fragments per kilobase of transcript per million mapped reads (FPKM) value using the Cufflinks software (2012). The sequencing reads of each individual were aligned to the reference genome using STAR (2.3.0e; https://github.com/alexdobin/STAR/releases). Additionally, GATK (v3.1-1; https://www.broadinstitute.org/gatk/index.php) was used for identifying SNPs. Before the association analysis, the SNP information for each PF–F₆ individual was merged and pooled with the SNP information for the EA and SS bulks. We removed the SNPs between the parents if they were not in the two bulks. To map the loci for two traits using one population, we finally selected the high-quality SNPs that were homozygous, but inconsistent between the two parents, for the association analysis.

Calculation Of Snp-index And Δsnp-index

The sequencing data were compared with the reference genome to select SNPs that differed between the parents to calculate the SNP-index. There were two types of SNPs: SNP^M and SNP^F. The SNP genotype of one parent was used as a reference to calculate each SNP genotype frequency in the two pools. Specifically, the proportion of the reference parent SNP among all reads was calculated (i.e., SNP^M-index or SNP^F-index). The D-value between SNP^M-index and SNP^F-index for each SNP in the two pools was then calculated (i.e., Δ SNP^M/F-index).

Molecular Marker Analysis And Qtl Mapping

The candidate genomic regions for loci related to LBA and FBP identified by the BSA-seq analysis were validated by performing an InDel and SNP marker-based traditional QTL analysis. Briefly, InDel and SNP loci were identified by aligning the two parental sequences to the Tifrunner reference genome using the Bioedit software. We selected homozygous SNPs with 200-bp flanking regions (i.e., 100 bp + SNP + 100 bp) and InDels with 200- to 500-bp flanking regions. Primers for the InDel and SNP markers were designed using the Primer Premier 5.0 software. The linkage and QTL analyses were performed using IciMapping (v4.0) according to the multiple QTL model mapping procedure (Ooien et al. 2002).

Principle of the PR-BSA/BSR method

The principle of the PR-BSA/BSR method is presented in Fig. 1, in which peanut plant architecture traits are used as an example. A segregating population, including F₂ generation-derived recombinant inbred lines (RILs), was obtained from a cross between two inbred varieties or lines with substantial genetic polymorphisms. We selected independent target traits A and B, which differed significantly between the two parental lines (Fig. 1A). The segregating progenies were divided into the following two types according to the two traits: parent-type and recombinant-type. Specifically, in the parent-type progenies, both traits were derived from one parent, with the female and male parental traits designated as AfBf and AmBm, respectively. In contrast, in the recombinant-type progenies, one trait was derived from each parent. For example, trait A was from the male parent and trait B was from the female parent (AmBf). Alternatively, trait A was from the female parent and trait B was from the male parent (AfBm) (Fig. 1B).

We selected individuals with extreme traits from among the progenies with recombinant phenotypes to construct AmBf and AfBm bulked pools. The parents and the two bulks were analyzed by genome resequencing or transcriptome sequencing. We subsequently further analyzed the transcriptome sequencing data. Clean reads obtained from the parental sequencing data were compared with the reference genome to detect SNP sites. All SNP sites were compared and the ones that differed between the parents were selected to calculate the SNP-index. Therefore, there were two types of SNPs for genotyping: SNP^M (same as the male parent) and SNP^F (same as the female parent).

The parental SNP genotypes were determined and the proportion of each SNP among all reads in the two pools was calculated (i.e., SNP^M-index and SNP^F-index). Next, the ΔSNP-index was calculated by subtracting the SNPs between the two pools, which can also reveal the direction of the two traits in the graph and further identify the trait-related loci. For trait A, the D-value of the two pools was calculated as AmBf − AfBm (SNP^F-index − SNP^M-index), which resulted in a positive peak for the ΔSNP-index associated with trait A. In contrast, for trait B, the SNP^M-index was lower in the AmBf pool than in the AfBm pool. Accordingly, when SNP^M-index was subtracted from SNP^F-index, the ΔSNP-index associated with trait B was detected as a negative peak (Fig. 1C).

Genetic Analysis Of Lba And Fbp

The LBA and FBP phenotypic segregation data for PF–F₆ are provided in Tables 1. For LBA, 115 RILs exhibited a prostrating phenotype, with only the tips of the lateral branches curved upward (TLB), whereas 133 RILs exhibited a spreading phenotype, but the middle of the lateral branches curved upward (MLB), TLB and MLB both belong to the spreading growth type. A total of 202 RILs exhibited an erect phenotype. Hence, the spreading-type: erect-type segregation ratio was 248:202, which is approximately 1:1. For FBP, 256 and 211 RILs were designated as alternating-type and sequential-type, respectively. Thus, the alternating-type: sequential-type segregation ratio was approximately 1:1 (Table 1). Therefore, LBA and FBP are likely regulated by a major gene.

Mapping Of Loci Related To Lba And Fbp By Pr-bsa/bsr

We used the PR-BSA/BSR method to detect loci related to LBA and FBP in peanut. The F₆ population derived from a cross between Pingdu9616 (Female parent) and Florunner (male parent) was used for gene mapping. Individuals with phenotypes that were similar to the parental phenotypes were considered as extreme-phenotype individuals. Moreover, the extreme-phenotype individuals of the F₆ population were divided into four types (Fig. 1B). Specifically, there were two parent-types: Pingdu9616 was LBA-erect and FBP-sequential (LeFs) and Florunner was LBA-spreading and FBP-alternating (LsFa). Additionally, there were two recombinant phenotypes (PR pool), which were LBA-erect and FBP-alternating (LeFa) and LBA-spreading and FBP-sequential (LsFs). Two bulks were constructed from 30 LeFa individuals (EA bulk) and 30 LsFs individuals (SS bulk). Bulks were not prepared for the parent-types because the allelic variants in their genomes were from the same parents, making it impossible to directly distinguish the trait-related loci.

To eliminate false-positive sites, the SNP-index or ΔSNP-index of the markers on the same chromosome were fitted on the basis of the position of the marker on the genome according to the SNPNUM method (Liu et al. 2012). To clarify the relationship between the traits and the direction of the peaks, the SNP-index of the two pools was calculated, which reflected the proportion of the allelic variations from Pingdu9616 (SNP^F) among all reads. For LBA, there were more SNP^Fs from the LeFa pool than from the LsFs pool. For FBP, there were fewer SNP^Fs from the LeFa pool than from the LrFs pool. When the SNP^F-index (LsFs) was subtracted from the SNP^F-index (LeFa), we detected a positive peak associated with LBA in the 6.82 Mb region (101,743,223–108,564,267 bp) on chromosome 15 as well as a negative peak associated with FBP in the 2.16 Mb region (117,682,534–119,846,824 bp) on chromosome 12 (Fig. 2).

Validation According To Individual Snp-indices

Transcriptome sequencing data for individuals and not mixed samples were analyzed in this study to determine the SNP genotypes of individuals. Therefore, the SNP-index of each mixed pool and the ΔSNP-index between the two pools were accurately calculated by confirming the SNP genotypes of a single individual. For example (Fig. 1C), assuming we determined the SNP genotypes of the female parent and there were five individuals in the SS/EA pool as well as two and three individuals with SNP^F and SNP^M, respectively, then SNP^M-index = 2/5 and SNP^F-index = 3/5. We recalculated the SNP-index and ΔSNP-index according to this method for analyzing individual SNP-indices and prepared a scatter diagram of chromosomes B05 and B02 to verify the PR-BSA/BSR results (Fig. 3). When the SNP-index of the EA pool was subtracted from the SNP-index of the SS pool, the resulting negative peak corresponded to the end of chromosome 15, which is consistent with the peak direction and chromosomal position revealed by the PR-BSA/BSR method. The positive peak corresponding to the end of chromosome 12 was also in accordance with the PR-BSA/BSR results. Thus, consistent results were obtained by the two algorithms, reflecting the accuracy and reliability of the PR-BSA/BSR method.

Validation By Linkage Mapping

We developed 31 and 20 pairs of InDel markers in the candidate regions of chromosome 15 (101,743,223–108,564,267 bp) and chromosome 12 (117,682,534–119,846,824 bp), respectively (Supplementary Table 1). A total of 445 families in the RIL–PF–F₆ populations were used for genotyping and analyses. We constructed genetic linkage maps for chromosomes 15 and 12 to identify and verify the loci related to LBA and FBP, respectively.

Among the InDel markers on chromosome 15, 12 were polymorphic between the parents. A genetic linkage map was constructed, with a total length of 192.19 cM. a major QTL for LBA flanked by InDel markers J15-12 and J15-11 on chromosome 15 was identified (Fig. 4A). The QTL explained 44.59% of the phenotypic variance and had an LOD value of 27.05 (Table 2). This QTL mapping result was consistent with the PR-BSA/BSR result, suggestive of a major QTL associated with LBA at the end of chromosome 15.

Of the InDel markers on chromosome 12, seven co-dominant markers were polymorphic between the parents. A local genetic linkage map was constructed, with a total length of 26.81 cM. On the basis of linkage mapping, the QTL for FBP was located between markers GPR2-21 and PB2-29 at the end of chromosome B02 (Fig. 4B). The QTL explained 85.75% of the phenotypic variance and had an LOD value of 467.30 (Table 2).This result was consistent with the PR-BSA/BSR result.

In this study, combining BSA with next-generation sequencing enabled the rapid mapping of the loci for qualitative and quantitative traits. For BSA, the genotypic data for two bulks with contrasting phenotypes must be tightly linked with the phenotype. Several effective methods, including MutMap, QTL-seq, MutMap+, BSR-seq, QTG-seq, and extreme-phenotype GWAS for BSA, were recently developed. Briefly, MutMap was developed to identify genomic regions using a segregating population derived from a cross between a mutant and the original wild-type line. The F₂ progenies with the mutant phenotype are bulked and sequenced. Moreover, SNPs and InDels in the mutant and wild-type are detected. In a previous study, the causal SNP for the mutant phenotype was identified as the SNP with a SNP-index of 1 that was exclusive to mutant sequences (Abe et al. 2012). For the QTL-seq method, BSA is combined with MutMap to map major QTLs using the NGS platform. Multiple progenies with the opposite phenotypes are selected to prepare two bulked DNA samples (‘Highest’ and ‘Lowest’ bulks). The two DNA bulks are used for a deep-sequencing analysis. The generated reads are aligned to the reference genome and then ΔSNP-index is calculated by subtracting the SNP-index of the ‘Lowest’ bulk from the SNP-index of the ‘Highest’ bulk. By default, the SNP-index plot for each QTL contains peaks or valleys (Takagi et al. 2013). The MutMap + method, which is an extension of MutMap, eliminates the need for genetic hybridization. Specifically, genomic DNA samples from 20–40 M₃ progenies are used to generate wild-type and mutant bulks for sequencing. The resulting reads are aligned to the reference sequence of the cultivar used for calculating the SNP-index. The SNP-index of the wild-type bulk is subtracted from the SNP-index of the mutant bulk to calculate ΔSNP-index. A positive ΔSNP-index indicates the genomic region contains the causal mutation (Fekih et al. 2013). Individuals with extreme phenotypes in natural populations are bulked according to their phenotype for sequencing and then analyzed to determine the differences in allele frequencies. This method, which combines BSA with GWAS, has been used to rapidly reveal the associations between genetic variants and traits of interest in maize (Yang et al. 2015). Compared with a traditional QTL analysis, the biggest advantage of BSA is that it does not require the genotyping and phenotyping of all individuals to map the genomic region for the trait of interest. Instead, by grouping samples according to extreme phenotypes, genotyping may be performed on the basis of high-throughput sequencing at all levels (i.e., DNA, RNA, or protein). The BSA strategy, which is particularly useful for examining polyploid species (e.g., peanut) with large genomes, has been used to identify genomic regions or precisely map genes related to disease resistance (Pandey et al. 2016; Wang et al. 2022), testa color (Chen et al. 2021; Zhao et al. 2020), and seed weight (Wang et al. 2022)and so on. These previous studies confirm that BSA can efficiently identify and map genes associated with specific traits in peanut. In the current study, we modified the BSA-seq method by selecting individuals with extreme phenotypes to simultaneously map two loci related to different target traits according to the transcriptome sequencing data for two extreme-phenotype bulks. In doing so, we substantially improved the BSA method by increasing and decreasing its efficiency and cost, respectively. This strategy is applicable mainly for analyses of segregating populations (e.g., F₂, RIL, and doubled-haploid populations). For qualitative traits, such as FBP, selecting individuals with the opposite phenotypes to prepare the bulks is relatively simple. Many target traits in crops are quantitative traits, which necessitates the selection of extreme phenotypes to build the bulks.

For the genetic mapping of LBA in peanut, the genomic interval revealed in our study overlaps the corresponding genomic interval detected in an earlier study (Galya et al. 2017). We also developed molecular markers for this previously detected genomic interval and verified them in the PF–F₆ population. Unfortunately, an analysis of their predicted genes revealed multiple individuals in our population that had an inconsistent relationship between the phenotypes and markers. Therefore, we speculate that there may be a novel gene regulating LBA in this population. In recently, A major flowering pattern-related locus was mapped to a region on chromosome B02, wherein a 1,492-bp deletion alters the transcription of a candidate gene and results in the production of a truncated TFL1-like protein (Kunta et al. 2022). We detected the major QTL region corresponding to this region and the fine mapping work is being carried out.

The current cultivated peanut is an allotetraploid species, which resulted from the hybridization between two diploid wild species after natural outcrossing and chromosome doubling events (Cuc et al. 2008; Kochert et al. 1991). The genomic sequences of Arachis duranensis and Arachis ipaensis, which are respectively the sources of the A and B subgenomes in cultivated peanut, are more than 80% homologous (Yin et al. 2020). The low genetic diversity among cultivated peanut varieties has hindered the development of molecular markers and research on the peanut genome. The publication of the diploid ancestral genome sequence and the completion of the sequencing of the cultivated peanut genome has accelerated the mapping of peanut genes (Bertioli et al. 2019; Chen et al. 2016).

Acknowledgments

This work was supported by grants from the National Science Foundation of China (No. 31771828), the Seed Project of Shandong Province (No. 2020LZGC001).

Authors' contribution:

Zhang XJ designed the research. Yu XN and Li YY performed most of the experiments; Yan FZ; Zhao RH; Guo R; Zhang SJ; Song DL; Si T; Zou XX; Wang YF and Wang ML provided technical assistance and some analysis; Guo R and Zhang SJ generated the population. Zhang XJ and Yu XN wrote the manuscript.

Funding:

This work was supported by grants from the National Science Foundation of China (No. 31771828), the Seed Project of Shandong Province (No. 2020LZGC001).

Availability of data and materials All information is specified in the manuscript or included as Additional Files

Conflict of interest The authors declare that they have not confict of interest.

Ethical approval The authors declare that the work was original research that has not been published previously, and is not under consideration for publication elsewhere.

Abe A, Kosugi S, Yoshida K, Natsume S, Takagi H, Kanzaki H, Matsumura H, Yoshida K, Mitsuoka C, Tamiru M, Innan H, Cano L, Kamoun S, Terauchi R (2012) Genome sequencing reveals agronomically important loci in rice using MutMap. Nat Biotechnol 30:174–178
Antonio K, Gregory WC (2007) Taxonomy of the genus Arachis (Leguminosae) by Antonio Krapovickas and Walton C. Gregory.Bonplandia16
Aoun M, Kolmer JA, Rouse MN, Chao S, Acevedo M (2017) Inheritance and bulked segregant analysis of leaf rust and stem rust resistance in durum wheat genotypes. Phytopathology 107
Bertioli DJ, Jenkins J, Clevenger J, Dudchenko O, Schmutz J (2019) The genome sequence of segmental allotetraploid peanut Arachis hypogaea. Nat Genet 51:1–8
Chen H, Chen X, Xu R, Liu W, Liu N, Huang L, Luo H, Huai D, Lan X, Zhang Y, Hu R, Chen J, Tang Z, Lin G, Jiang H (2021) Fine-mapping and gene candidate analysis for AhRt1, a major dominant locus responsible for testa color in cultivated peanut. Theor Appl Genet 134:3721–3730
Chen X, Li H, Pandey MK, Yang Q, Yu S (2016) Draft genome of the peanut A-genome progenitor (Arachis duranensis) provides insights into geocarpy, oil biosynthesis, and allergens. Proceedings of the National Academy of Sciences 113:6785
Cuc LM, Mace ES, Crouch JH, Quang VD, Long TD, Varshney RK (2008) Isolation and characterization of novel microsatellite markers and their application for diversity assessment in cultivated groundnut (Arachis hypogaea). BMC Plant Biology 8
Dalal (1962) Inheritance studies in groundnut crop. Indian Oilseeds 6(4):288–292
Davidl H, Jamesr S, Reidd F, Markl T, Song Q, Perryb C (2009) Bulked Segregant Analysis Using the GoldenGate Assay to Locate the Rpp3 Locus that Confers Resistance to Soybean Rust in Soybean. Crop Sci 49:265–271
Dong Z, Jiang C, Chen X, Zhang T, Ding L, Song W, Luo H, Lai J, Chen H, Liu R (2013) Maize LAZY1 Mediates Shoot Gravitropism and Inflorescence Development through Regulating Auxin Transport, Auxin Signaling, and Light Response. Plant Physiol 163:1306–1322
Fekih R, Takagi H, Tamiru M, Abe A, Natsume S, Yaegashi H, Sharma S, Sharma S, Kanzaki H, Matsumura H, Saitoh H, Mitsuoka C, Utsushi H, Uemura A, Kanzaki E, Kosugi S, Yoshida K, Cano L, Kamoun S, Terauchi R (2013) MutMap+: genetic mapping and mutant identification without crossing in rice. PLoS ONE 8:e68529
Galya K, Yael B, Adi FD, Abhinandan P, Ilan H, Ran H (2017) Fine-Mapping the Branching Habit Trait in Cultivated Peanut by Combining Bulked Segregant Analysis and High-Throughput Sequencing. Front Plant Sci 8:1–11
Hake AA, Kenta S, Arati Y, Sukruth M, Malagouda P, Nayak SN, Lingaraju S, Patil PV, Nadaf HL, Gowda M (2017) Mapping of important taxonomic and productivity traits using genic and non-genic transposable element markers in peanut (Arachis hypogaea L.). PLoS ONE 12:e0186113
Huang H, Jiang, Chuanmei, Zhu, Kerrie, Barry J, Jenkins L (2017) Sparse panicle1 is required for inflorescence development in Setaria viridis and maize. Nature plants
Initiative T, Copenhaver GP (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana. The Arabidopsis Genome Initiative
Kawahara Y, Bastide M, Hamilton JP, Kanamori H (2013) Improvement of the Oryza sativa Nipponbare reference genome using next generation sequence and optical map data, vol 6. Rice, New York, NY, p 4
Kochert G, Halward T, Branch WD, Simpson CE (1991) RFLP variability in peanut (Arachis hypogaea L.) cultivars and wild species. Theoretical & Applied Genetics 81:565–570
Kunta S, Chu Y, Levy Y, Harel A, Abbo S, Ozias-Akins P, Hovav R (2022) Identification of a major locus for flowering pattern sheds light on plant architecture diversification in cultivated peanut. Theoretical and applied genetics
Li L, Yang X, Cui S, Meng X, Mu G, Hou M, He M, Zhang H, Liu L, Chen CY (2019) Construction of High-Density Genetic Map and Mapping Quantitative Trait Loci for Growth Habit-Related Traits of Peanut (Arachis hypogaea L.). fronties in Plant Science 10
Li P, Wang Y, Qian Q, Fu Z, Wang M, Zeng D, Li B, Wang X, Li J (2007) LAZY1 controls rice shoot gravitropism through regulating polar auxin transport. Cell reserch 17:9
Liu S, Yeh CT, Tang HM, Nettleton D, Schnable PS (2012) Gene Mapping via Bulked Segregant RNA-Seq (BSR-Seq). PLoS ONE 7:e36406
Michelmore RW, Kesseli IPV (1991) Identification of markers linked to disease-resistance genes by bulked segregant analysis: a rapid method to detect markers in specific genomic regions by using segregating populations. Proc Natl Acad Sci U S A
Muller D, Schmitz G, Theres K (2006) Blind Homologous R2R3 Myb Genes Control the Pattern of Lateral Meristem Initiation in Arabidopsis. THE PLANT CELL ONLINE 18:586–597
Ooien JV, Boer MP, Jansen R, Maliepaard C (2002) MapQTL 4.0,software for the calculation of QTL position on gentic maps. Plant Reserch International, Wageningen,the Netherlands
Pandey MK, Khan AW, Singh VK, Vishwakarma MK, Shasidhar Y, Kumar V, Garg V, Bhat RS, Chitikineni A, Janila P (2016) QTL-seq approach identified genomic regions and diagnostic markers for rust and late leaf spot resistance in groundnut (Arachis hypogaea L.). Plant Biotechnol J 15:927–941
Patel JS, John CM, Seshadri CR (1936) The inheritance of characters in the groundnut Arachis hypogaea. Proceedings of the Indian Academy of Sciences 3:214–233
Pattanashetti SK, Gowda MVC (2008) Inheritance of morphological traits and pod features in groundnut (Arachis hypogaea L.). Indian J Genet Plant Breed 68:157–162
Roychoudhry S, Kepinski S (2015) Shoot and root branch growth angle control—the wonderfulness of lateralness. Curr Opin Plant Biol 23C:124–131
Schnable PS, Ware D, Fulton RS, Stein JC, Wei F, Pasternak S, Liang C, Zhang J, Fulton L, Graves TA (2009) The B73 Maize Genome: Complexity, Diversity, and Dynamics. Science 326:1112–1115
Shoba D, Manivannan N, Vindhiyavarman P, Nigam SN (2012) SSR markers associated for late leaf spot disease resistance by bulked segregant analysis in groundnut (Arachis hypogaea L.). Euphytica 188:265–272
Takagi H, Abe A, Yoshida K, Kosugi S, Natsume S, Mitsuoka C, Uemura A, Utsushi H, Tamiru M, Takuno S, Innan H, Cano LM, Kamoun S, Terauchi R (2013) QTL-seq: rapid mapping of quantitative trait loci in rice by whole genome resequencing of DNA from two bulked populations. Plant J 74:174–183
Wang C, Tang S, Zhan Q, Hou Q, Han B (2019) Dissecting a heterotic gene through GradedPool-Seq mapping informs a rice-improvement strategy.Nature Communications10
Wang Z, Yan L, Chen Y, Wang X, Huai D, Kang Y, Jiang H, Liu K, Lei Y, Liao B (2022) Detection of a major QTL and development of KASP markers for seed weight by combining QTL-seq, QTL-mapping and RNA-seq in peanut. Theoretical and applied genetics
Workie A, Zegeye Y, Zhang, Liyong, Cao, Shihua C (2018) Whole Genome Resequencing from Bulked Populations as a Rapid QTL and Gene Identification Method in Rice. International Journal of Molecular Sciences
Xue Y, Qian ZM, Wang YH, Xiong GS, Zeng DL, Xiao Q (2003) Control of tillering in rice. Nature
Yang J, Jiang H, Yeh CT, Yu J, Jeddeloh JA, Nettleton D, Schnable PS (2015) Extreme-phenotype genome-wide association study (XP-GWAS): a method for identifying trait-associated variants by sequencing pools of individuals selected from a diversity panel. Plant J 84:587–596
Yang X, Xia X, Zhang Z, Nong B, Li D (2019) Identification of anthocyanin biosynthesis genes in rice pericarp using PCAMP.Plant Biotechnology Journal17
Yin D, Ji C, Song Q, Zhang W, Zhang X, Zhao K, Chen CY, Wang C, He G, Liang Z (2020) Peanut Evolution: Comparison of Arachis monticola with Diploid and Cultivated Tetraploid Genomes Reveals Asymmetric Subgenome Evolution and Improvement of Peanut.Advanced Science7
Yoshihara T, Spalding EP, Iino M (2013) AtLAZY1 is a signaling component required for gravitropism of the Arabidopsis thaliana inflorescence. Plant J 74:267–279
Zhang H, Wang X, Pan Q, Li P, Liu Y, Lu X, Zhong W, Li M, Han L, Li J (2018) QTG-Seq Accelerates QTL Fine Mapping through QTL Partitioning and Whole-Genome Sequencing of Bulked Segregant Samples. Molecular Plant
Zhao Y, Ma J, Li M, Deng L, Li G, Xia H, Zhao S, Hou L, Li P, Ma C, Yuan M, Ren L, Gu J, Guo B, Zhao C, Wang X (2020) Whole-genome resequencing-based QTL-seq identified AhTc1 gene encoding a R2R3-MYB transcription factor controlling peanut purple testa colour. Plant Biotechnol J 18:96–105

Table 1 and 2 are available in the Supplementary Files section.

Download PDF

Journal Publication

published 30 May, 2023

Read the published version in Theoretical and Applied Genetics →

Editorial decision: Major revisions
15 Aug, 2022
Reviewers agreed at journal
12 Jun, 2022
Reviewers invited by journal
12 Jun, 2022
Editor assigned by journal
16 May, 2022
First submitted to journal
14 May, 2022

You are reading this latest preprint version

Simultaneously mapping loci related to two plant architecture traits by Phenotypic Recombination BSA/BSR in peanut (Arachis hypogaea L.)

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Materials And Methods

Plant materials and phenotypes

Selection Of Bulks With Extreme Traits

Transcriptome Sequencing And Evaluation Of Sequencing Quality

Calculation Of Snp-index And Δsnp-index

Molecular Marker Analysis And Qtl Mapping

Results

Principle of the PR-BSA/BSR method

Genetic Analysis Of Lba And Fbp

Mapping Of Loci Related To Lba And Fbp By Pr-bsa/bsr

Validation According To Individual Snp-indices

Validation By Linkage Mapping

Discussion

Declarations

References

Tables

Supplementary Files

Status:

Journal Publication

Version 1