340 Introduction Spikelet number per panicle (SPP, also known as SNP) is one of the predominant components of rice yield. Improving SPP can increase reproductive sink capacity and promote rice yield. SPP is a typical quantitative trait controlled by quantitative trait loci (QTLs), and the phenotype can be af- fected by epistatic interactions and environmental factors (Xing et al. 2002). Therefore, SPP is difficult to improve efficiently by traditional breeding methods. In the last 10 years, five SPP-related genes, including GN1A, Ghd7, DEP1, WFP/IPA1 and SPIKE/NAL1, have been cloned, and the molecular mechanisms underlying SPP have been described (Ashikari et al. 2005, Fujita et al. 2013, Huang et al. 2009, Jiao et al. 2010, Miura et al. 2010, Xue et al. 2008). In addition, numerous QTLs for SPP have been identified (Balkunde et al. 2013, Fujita et al. 2012, Ikeda et al. 2010, Koide et al. 2013, Liu et al. 2009, Luo et al. 2009, Mei et al. 2006, Terao et al. 2010, Tian et al. 2006, Xing et al. 2008, Zhang et al. 2009). These findings have allowed for early-generation marker-assisted selection of breeding materials, to more efficiently improve rice yield (Lai et al. 2016). SPP is associated with many morphological components of panicle architecture, including secondary branch number (SBN). SBN and SPP are positively correlated (Luo et al. 2009, Mei et al. 2006). A few studies have identified QTLs for SBN (Ando et al. 2008, Ikeda et al. 2010, Luo et al. 2009, Mei et al. 2006). Eleven QTLs were found associated with SBN in a study of two populations of reciprocal intro- gression lines. Among these QTLs, qSBN-9, contributed a large effect on both SBN and SPP (Mei et al. 2006). Anoth- er major QTL for SBN, qSBN1, was detected in a study of 39 chromosome segment substitution lines derived from a cross between the japonica cultivar Sasanishiki and indica cultivar Habataki. The qSBN1 from Habataki increased SPP approximately 30% (Ando et al. 2008). A total of 9 QTLs for SBN were mapped by using 265 introgression lines of common wild rice (Oryza rufipogon Griff.) in the back- ground of indica cultivar Guichao 2 (Luo et al. 2009). Three Breeding Science 67: 340–347 (2017) doi:10.1270/jsbbs.17007 Research Paper Genetic mapping of the qSBN7 locus, a QTL controlling secondary branch number per panicle in rice Sheng-Shan Wang †1) , Rong-Kuen Chen †1) , Kai-Yi Chen 2) , Chu-Yin Liu 2) , Shu-Min Kao 2) and Chia-Lin Chung* 3) 1) Tainan District Agricultural Research and Extension Station, No. 70, Pasture, Xinhua, Tainan 71246, Taiwan 2) Department of Agronomy, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan 3) Department of Plant Pathology and Microbiology, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan Secondary branch number (SBN) is an important component affecting spikelet number per panicle (SPP) and yield in rice. During recurrent backcross breeding, four BC 2 F 4 populations derived from the high-yield donor parent IR65598-112-2 and the recurrent parent Tainan 13 (a local japonica cultivar) showed discontinuous variations of SPP and SBN within populations. Genetic analysis of 92 BC 2 F 4 individuals suggested that both SPP and SBN are controlled by a single recessive allele. Two parents and 37 BC 2 F 4 individuals showing high- and low-SBN type phenotypes were analyzed by restriction-site associated DNA sequencing (RAD-seq). Based on 2,522 reliable SNPs, the qSBN7 was mapped to a distal region of the long arm of chromosome 7. Trait-marker association analysis with an additional 166 high-SBN type BC 2 F 4 individuals and 8 newly devel- oped cleaved amplified polymorphic sequence markers further delimited the qSBN7 locus to a 601.4-kb region between the markers SNP2788 and SNP2849. Phenotype evaluation of two BC 2 F 5 backcross inbred lines re- vealed that qSBN7 increased SPP by 83.2% and SBN by 61.0%. The qSBN7 of IR65598-112-2 could be used for improving reproductive sink capacity in rice. Key Words: Oryza sativa L., spikelet number per panicle (SPP), restriction-site associated DNA sequencing (RAD-seq), substitution mapping, yield trait. Communicated by Motoyuki Ashikari Received February 7, 2017. Accepted April 23, 2017. First Published Online in J-STAGE on July 28, 2017. *Corresponding author (e-mail: [email protected]) † These authors contributed equally to this work
Transcript
340
Introduction
Spikelet number per panicle (SPP, also known as SNP) is one of the predominant components of rice yield. Improving SPP can increase reproductive sink capacity and promote rice yield. SPP is a typical quantitative trait controlled by quantitative trait loci (QTLs), and the phenotype can be af-fected by epistatic interactions and environmental factors (Xing et al. 2002). Therefore, SPP is difficult to improve efficiently by traditional breeding methods.
In the last 10 years, five SPP-related genes, including GN1A, Ghd7, DEP1, WFP/IPA1 and SPIKE/NAL1, have been cloned, and the molecular mechanisms underlying SPP have been described (Ashikari et al. 2005, Fujita et al. 2013, Huang et al. 2009, Jiao et al. 2010, Miura et al. 2010, Xue et al. 2008). In addition, numerous QTLs for SPP have been identified (Balkunde et al. 2013, Fujita et al. 2012, Ikeda et
al. 2010, Koide et al. 2013, Liu et al. 2009, Luo et al. 2009, Mei et al. 2006, Terao et al. 2010, Tian et al. 2006, Xing et al. 2008, Zhang et al. 2009). These findings have allowed for early-generation marker-assisted selection of breeding materials, to more efficiently improve rice yield (Lai et al. 2016).
SPP is associated with many morphological components of panicle architecture, including secondary branch number (SBN). SBN and SPP are positively correlated (Luo et al. 2009, Mei et al. 2006). A few studies have identified QTLs for SBN (Ando et al. 2008, Ikeda et al. 2010, Luo et al. 2009, Mei et al. 2006). Eleven QTLs were found associated with SBN in a study of two populations of reciprocal intro-gression lines. Among these QTLs, qSBN-9, contributed a large effect on both SBN and SPP (Mei et al. 2006). Anoth-er major QTL for SBN, qSBN1, was detected in a study of 39 chromosome segment substitution lines derived from a cross between the japonica cultivar Sasanishiki and indica cultivar Habataki. The qSBN1 from Habataki increased SPP approximately 30% (Ando et al. 2008). A total of 9 QTLs for SBN were mapped by using 265 introgression lines of common wild rice (Oryza rufipogon Griff.) in the back-ground of indica cultivar Guichao 2 (Luo et al. 2009). Three
1) Tainan District Agricultural Research and Extension Station, No. 70, Pasture, Xinhua, Tainan 71246, Taiwan2) Department of Agronomy, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan3) Department of Plant Pathology and Microbiology, National Taiwan University, No. 1, Sec. 4, Roosevelt Rd., Taipei 10617, Taiwan
Secondary branch number (SBN) is an important component affecting spikelet number per panicle (SPP) and yield in rice. During recurrent backcross breeding, four BC2F4 populations derived from the high-yield donor parent IR65598-112-2 and the recurrent parent Tainan 13 (a local japonica cultivar) showed discontinuous variations of SPP and SBN within populations. Genetic analysis of 92 BC2F4 individuals suggested that both SPP and SBN are controlled by a single recessive allele. Two parents and 37 BC2F4 individuals showing high- and low-SBN type phenotypes were analyzed by restriction-site associated DNA sequencing (RAD-seq). Based on 2,522 reliable SNPs, the qSBN7 was mapped to a distal region of the long arm of chromosome 7. Trait-marker association analysis with an additional 166 high-SBN type BC2F4 individuals and 8 newly devel-oped cleaved amplified polymorphic sequence markers further delimited the qSBN7 locus to a 601.4-kb region between the markers SNP2788 and SNP2849. Phenotype evaluation of two BC2F5 backcross inbred lines re-vealed that qSBN7 increased SPP by 83.2% and SBN by 61.0%. The qSBN7 of IR65598-112-2 could be used for improving reproductive sink capacity in rice.
Key Words: Oryza sativa L., spikelet number per panicle (SPP), restriction-site associated DNA sequencing (RAD-seq), substitution mapping, yield trait.
Communicated by Motoyuki AshikariReceived February 7, 2017. Accepted April 23, 2017.First Published Online in J-STAGE on July 28, 2017.*Corresponding author (e-mail: [email protected])† These authors contributed equally to this work
Characterization of QTL for high-SBN in rice BS
341
genetic background of a japonica cultivar TN13 was estab-lished by recurrent backcross breeding, with IR65598-112-2 used as a donor parent. Four BC2F2 individuals with the GN1A homozygous allele were self-pollinated to produce bulk BC2F3 seeds for phenotypic investigation. Six BC2F3 plants showing similar plant height and heading date to TN13 were self-pollinated to create six BC2F4 populations. Variations in SPP and SBN were observed in four of the six populations. One BC2F4 population (n = 800) which showed segregation of SPP, SBN, and spikelet number per primary branch (SPPB) was arbitrarily selected for subsequent analyses (Fig. 1). In this population, 92 individuals were randomly selected for evaluating of SPP, SBN, and SPPB. Totally 37 individuals, including 18 individuals showing high-SBN type (SPP > 210, SBN > 45 and SPPB > 18.5) and 19 individuals showing low-SBN type (SPP < 170, SBN < 35 and SPPB < 14.0), were used for substitution mapping by RAD-seq. An additional 166 high-SBN type individuals (SPP > 210, SBN > 45, and SPPB > 18.5) were used to construct a high-resolution map of qSBN7. Two BC2F5 BILs, BIL-Sbn/Sbn, with a homozygous qSBN7 al-lele of TN13, and BIL-sbn/sbn, with a homozygous qSBN7 allele of IR65598-112-2, were chosen from the same BC2F4 population based on the genotypes of two cleaved amplified polymorphic sequence (CAPS) markers, SNP2750 and SNP2962, on chromosome 7 (Table 1).
Genetic analysisFour panicle traits, SPP, SBN, SPPB, and primary branch
number (PBN), were manually counted in plants. The popu-lation for genetic analysis was grown in the experimental
major QTLs for secondary rachis branch number (SRN; same as SBN) on chromosomes 1 and 8 were detected in an F2 segregated population derived from a cross between a japonica cultivar Koshihikari and a new plant type (NPT) cultivar NP-6 (Ikeda et al. 2010).
Tainan 13 (TN13) is a local japonica cultivar with good characteristics such as early maturing and good taste. To improve its reproductive sink capacity, marker-assisted backcrossing was used to introgress GN1A from a high-yield NPT cultivar IR65598-112-2 into TN13. GN1A is a previously cloned OsCKX2 (cytokinin oxidase/dehydroge-nase) gene affecting grain number per panicle (Ashikari et al. 2005). During the process, SPP and SBN distinctly seg-regated in four BC2F4 populations (homozygous for GN1A).
In the current study, one of the segregating BC2F4 popu-lations was investigated for genetic dissection of SBN. To achieve high-resolution mapping, restriction-site associated DNA sequencing (RAD-seq) was performed for genotyp-ing. Genetic evidence confirmed that a QTL located on the long arm of chromosome 7 (designated qSBN7) was respon-sible for the SPP and SBN variations. In addition, the genet-ic effect of qSBN7 was validated and assessed using two backcross inbred lines (BILs) carrying different alleles. Our results suggest the potential of using the qSBN7 of IR65598-112-2 for SPP improvement and high-yield breeding in rice.
Materials and Methods
Plant materialsThe breeding scheme for developing mapping popula-
tions is illustrated in Fig. 1. A BC2F2 population with the
Fig. 1. Schematic of the generation of the genetic materials used in this study. MAS, marker-assisted selection; BIL, backcross inbred line; SBN, secondary branch number.
Breeding Science Vol. 67 No. 4
Wang, Chen, Chen, Liu, Kao and ChungBS
342
“variant detection” and “comparing variants” functions in CLC Genomics Workbench v.7.05 with the following pa-rameters: mismatch cost 2, insertion cost 3, deletion cost 3, length fraction 0.9, and similarity fraction 0.97. All reads were aligned to the Nipponbare reference genome IRGSP1.0 (Kawahara et al. 2013). A total of 109 SNPs were detected between the physical positions 26.7 and 29.6 Mb on the long arm of chromosome 7. Eight SNPs, evenly distributed around the markers 87830 (29.0 bp) and 87941 (29.2 bp), were designed as CAPS markers. Primers were designed by using Primer3 (Untergrasser et al. 2012; http://bioinfo.ut.ee/primer3-0.4.0/primer3/). The primer sequences for CAPS markers and corresponding annealing temperatures are in Table 1.
Genomic DNA was extracted from 166 individuals by using a simple, fast CTAB method (Murray and Thompson 1980). PCR was performed as described (Wang et al. 2014). The PCR products were separated on 2% Agrose SFR gel (Amresco, Solon, OH, USA) with the TBE buffer system and stained with ethidium bromide.
High-resolution mappingOne BC2F4 individual was genotyped with 8 CAPS
markers (Table 1) to find the borders of the IR65598-112-2 introgression segment. Overall, 26 BC2F4 recombinants with a single crossover event in the interval between the markers SNP2750 and SNP2962 were self-pollinated to produce BC2F5 lines for the progeny test. Field trials for the progeny test involved three replications in the first cropping season of 2015. In each replication, 16 plants per BC2F5 line were transplanted into rows 25 cm apart with 20 cm within- row spacing. Ten individuals per BC2F5 line were visually rated as low-SBN, segregating, or high-SBN types. More-over, six BC2F5 lines containing different recombinant breakpoints were assessed for SBN. The data were analyzed by ANOVA with SAS 9.2 (SAS Institute, Cary, NC). Com-parisons among lines were assessed by Fisher’s least signif-icant difference test at α = 0.05.
Phenotype evaluation of BILsBIL-Sbn/Sbn, BIL-sbn/sbn and TN13 were grown with
three replications in the experimental farm at Chiayi Branch
farm at Chiayi Branch Station, Tainan District Agricultural Research and Extension Station, in the second cropping sea-son of 2014. Three panicles per plant were measured. Nor-mality of the phenotypic distribution was investigated by generating the normal probability plot in R 3.0.2 (R Core Team 2013). The chi-square goodness-of-fit test was used to examine the hypothesis of segregation ratio. Pearson corre-lation coefficients between traits were analyzed by using R 3.0.2 (R Core Team 2013).
RAD library preparation and sequence analysisGenomic DNA was extracted from leaves of mature
plants as described (Murray and Thompson 1980). For sub-stitution mapping, two parents and 37 BC2F4 individuals (18 high-SBN type and 19 low-SBN type) were genotyped by using RAD-seq. The RAD library was constructed as de-scribed (Etter et al. 2011) with some modifications. DNA was digested by using PstI (New England Biolabs, MA, USA), then ligated with the adapters containing a multiplex identifier we previously designed (Hsieh et al. 2014). DNA libraries were sequenced as 100-bp single-end reads on an Illumina HiSeq2500 at the VYM Genome Research Center in National Yang-Ming University (Taipei). Raw sequence data were de-multiplexed by using Stacks software (Catchen et al. 2013), then mapped to the Nipponbare reference ge-nome sequences IRGSP1.0 (Kawahara et al. 2013) by using Bowtie2 software (Langmead and Salzberg 2012). SNP identification and genotype scoring involved use of Stacks package (Catchen et al. 2013). The tags from IR65598-112-2 and TN13 underwent comparative analysis to identify SNPs. To improve the accuracy of data, low-quality SNPs (read depth <6 or >5 missing data per locus) were filtered. After filtering, 2,522 SNPs were retained for the 37 BC2F4 individuals. Linkage analysis involved use of the R/qtl package (Broman et al. 2003). The SNP positions in this study are based on the MSU Rice Genome Annotation Pro-ject Database release 7 (http://rice.plantbiology.msu.edu/) (Kawahara et al. 2013).
CAPS marker developmentRaw RAD-seq reads for two parents were analyzed by
using the “demultiplex reads”, “map reads to reference”,
Table 1. Newly developed cleaved amplified polymorphic sequence markers used for high-resolution linkage mapping of qSBN7
ID SNP position (Mb)a Tab (°C) Forward Primer (5’-3’) Reverse Primer (5’-3’) REc
a Physical map position of the targeted SNP, based on the MSU Rice Genome Annotation Project Database release 7.b Ta, annealing temperature.c RE, restriction enzyme.
Breeding Science Vol. 67 No. 4
Characterization of QTL for high-SBN in rice BS
343
PBN exhibited a normal distribution. The frequency distri-butions of SPP, SBN and SPPB exhibited discontinuous variations (Fig. 2, Supplemental Fig. 1). The 92 BC2F4 in-dividuals were classified into low- and high-SBN type sub-groups, with separation of SPP, SBN and SPPB at 210–230, 45–50, and 18.5–20.0, respectively. The ratio of number of individuals in the low- and high-SBN subgroups was 3:1 (χ2
1,0.05 = 0.93 < 3.84). Furthermore, significant positive correlations between SPP and SBN, SPP and SPPB, and SPPB and SBN were found. PBN was not correlated with SPP and SBN (Table 2). The results suggested that SPP, SBN and SPPB may be controlled by a single locus and the high-SBN phenotype may have a recessive pattern of inher-itance.
Station, Tainan District Agricultural Research and Exten-sion Station, in the first cropping season of 2015. A total of 6 agronomic traits, including plant height (PH), panicle number per plant (PN), SPP, PBN, SBN, and SPPB were evaluated at maturity. For each line, 5 individuals from the middle of a row were chosen for phenotyping. Panicles in the tallest tillers were harvested and measured. The data for each trait were analyzed by ANOVA. Comparisons among lines were assessed by Fisher’s least significant difference test at α = 0.05.
To evaluate the effect of qSBN7 on grain traits, BIL-Sbn/Sbn and BIL-sbn/sbn were grown with three replications, 52 plants (in 2 rows) per line per replication, in the experimen-tal farm at Chiayi Branch Station, Tainan District Agricul-tural Research and Extension Station in the second cropping season of 2016. Traits including 1,000-grain weight (TGW), ripened grain ratio (RGR), and chalky grain ratio (CGR) were evaluated at maturity. For each line, 10 individuals were randomly chosen for measurement. Differences be-tween lines were analyzed by Student’s t test at α = 0.05.
Results
Phenotypic variation and genetic analysis of SBNNinety-two BC2F4 individuals among one segregation
population were evaluated for four panicle traits, including SPP, PBN, SBN and SPPB. The frequency distribution of
Fig. 2. Frequency distribution of (A) spikelet number per panicle (SPP), (B) primary branch number (PBN), (C) secondary branch number (SBN), and (D) spikelet number per primary branch (SPPB) in the BC2F4 population.
Table 2. Pearson correlation coefficients between 4 panicle traits from 92 BC2F4 individuals
a PBN, primary branch number; SBN, secondary branch number; SPP, spikelet number per panicle; SPPB, spikelet number per primary branch.
* 0.01 < P < 0.05; ** 0.001 < P < 0.01; *** P < 0.001.
Breeding Science Vol. 67 No. 4
Wang, Chen, Chen, Liu, Kao and ChungBS
344
Linkage analysis using 18 high-SBN individuals and the markers targeting the three segregating segments (2L, 3S and 7L) revealed only one chromosome interval, 7L (mark-ers 87830–87941; 29.0–29.2 Mb), co-segregating with the SBN phenotype (Fig. 3B). The SBN-associated locus on the long arm of chromosome 7 was designated qSBN7.
High-resolution mapping of qSBN7To confirm the borders of the IR65598-112-2 introgres-
sion segment on chromosome 7, a BC2F4 individual with high-SBN phenotype was genotyped by using 8 additional CAPS markers that locate downstream of the marker 86544 (26.7 Mb) (Table 1). The markers SNP2750 (27,504,237 bp) and SNP2962 (29,627,810 bp) were identified to be the poly-morphic markers closest to either end of the IR65598-112-2 introgression segments (Fig. 4A). Genotyping 166 high-SBN type plants (BC2F4) with SNP2750 and SNP2962
Substitution mapping by RAD-seqTrait-marker association was conducted to examine 17
low-SBN and 18 high-SBN BC2F4 individuals. RAD-seq resulted in ~1.23 × 108 single-end reads, from which 2,522 valid SNP markers were obtained for two parents and 37 BC2F4 individuals. Among these individuals, six chromo-some intervals, including 1L (markers 12382–12807; 39.6–40.3 Mb), 4S (marker 58649; 6.7 Mb), 9S-1 (marker 100074; 1.2 Mb), 9S-2 (markers 105417–105650; 4.3–5.1 Mb), 9L (markers 106710–106787; 9.4–9.7 Mb) and 11L (markers 116181–116251; 18.2–18.4 Mb), exhibited homozygous substituted segments of IR65598-112-2; three chromosome intervals, 2L (markers 26476–26797; 32.0–32.6 Mb), 3S (marker 44580; 5.1 Mb) and 7L (markers 87830–87941; 29.0–29.2 Mp), showed segregating geno-types of IR65598-112-2 and TN13; and the remaining chro-mosome intervals were homozygous for TN13 (Fig. 3A).
Fig. 3. Substitution mapping of secondary branch number (SBN) by restriction-site associated DNA sequencing (RAD-seq) and linkage map-ping. (A) Graphical genotypes of 37 BC2F4 individuals. The 7 bars represent rice chromosomes, numbered at the top. The numbers on the left and right of the bars represent physical map positions (Mb) and the single nucleotide polymorphisms (SNPs) resulting from RAD-seq analysis, re-spectively. White squares: homozygous chromosomal segments of TN13; black squares: homozygous chromosomal segment of IR65598-112-2; grey squares: chromosomal segments still segregating in the BC2F4 population. (B) A rough map from linkage analysis based on 18 high-SBN type plants and the markers targeting three segregating segments (2L, 3S and 7L).
Breeding Science Vol. 67 No. 4
Characterization of QTL for high-SBN in rice BS
345
Phenotype evaluation of BILs for qSBN7To characterize the effect of qSBN7, agronomic traits of
BIL-sbn/sbn and BIL-Sbn/Sbn were compared (Table 3, Supplemental Table 1). For panicle traits, no significant difference in PN or PBN was observed between the two BILs. Nevertheless, values for PH, SPP, SBN and SPPB were significantly higher for BIL-sbn/sbn than BIL-Sbn/Sbn (Table 3). Compared to BIL-Sbn/Sbn, BIL-sbn/sbn showed an 83.2% increase in SPP and a 61.0% increase in SBN (Fig. 5). Moreover, SPP and SBN values were significantly higher for BIL-Sbn/Sbn than TN13 perhaps because of dif-ferent alleles of GN1A in IR65598-112-2 and TN13. For grain traits, BIL-sbn/sbn showed significantly lower TGW and RGR values. No significant difference was observed for CGR (Supplemental Table 1).
identified 26 recombinants, which were further genotyped with 6 additional CAPS markers in the interval. These BC2F4 recombinants were classified into 6 groups (A–F groups) based on the positions of recombination break-points (Fig. 4B). According to progeny test using BC2F5 lines, all groups showed a high-SBN phenotype. The A, B, C and D groups placed the qSBN7 locus to a region up-stream of SNP2962, SNP2906, SNP2855 and SNP2849, re-spectively. The E and F groups located qSBN7 to a region downstream of SNP2788 and SNP2750, respectively. Thus, qSBN7 was located within a region of 601.4 kb between the markers SNP2788 (27,889,190 bp) and SNP2849 (28,490,603 bp) (Fig. 4B).
Fig. 4. Linkage map around the qSBN7 locus on chromosome 7. (A) The qSBN7 was mapped to the distal region of the long arm of chromosome 7 in 37 BC2F4 individuals. (B) High-resolution mapping of qSBN7 in additional 26 recombinants. White bars: homozygous TN13 chromosome segment; black bars: homozygous IR65598-112-2 chromosome segment; grey bars: heterozygous chromosome segment. The numbers in paren-thesis are the mean SBN for each recombinant group. Different letters indicate significant differences based on Fisher’s least significant difference test at α = 0.05.
Table 3. Agronomic traits of TN13 and two backcross inbred lines (BILs) carrying different alleles at qSBN7
Line PH (cm)Panicle traitsa
PN SPP PBN SBN SPPBTN13 78.7 ± 4.6 a 10.4 ± 2.0 a 143.3 ± 5.7 a 11.1 ± 2.3 a 27.2 ± 2.0 a 13.4 ± 3.1 aBIL-Sbn/Sbn 79.5 ± 6.9 a 10.3 ± 1.9 a 167.5 ± 19.0 b 11.8 ± 2.3 a 34.1 ± 2.9 b 14.4 ± 1.8 aBIL-sbn/sbn 85.7 ± 5.2 b 9.7 ± 2.0 a 306.8 ± 35.6 c 12.5 ± 0.8 a 54.9 ± 4.7 c 24.8 ± 4.4 b
a PH, plant height; PN, panicle number per plant; SPP, spikelet number per panicle; PBN, primary branch number; SBN, secondary branch num-ber; SPPB, spikelet number per primary branch. Data are mean ± SE. Different letters indicate significant differences based on Fisher’s least significant difference test at α = 0.05.
Breeding Science Vol. 67 No. 4
Wang, Chen, Chen, Liu, Kao and ChungBS
346
thickness, and spikelet number per panicle (Bai et al. 2010). The qSBN7 allele of IR65598-112-2 increased SBN and SPP and also reduced TGW and RGR. Whether qGL7 and qSBN7 are identical, allelic, or linked remains to be re-solved.
qSBN7 is a pleiotropic QTL affecting multiple panicle traits. The homozygous IR65598-112-2 allele at qSBN7 sig-nificantly improved PH, SPP and SBN in the genetic back-ground of TN13. High SPP potentially enhances the repro-ductive sink capacity of rice. Several SPP-related genes and QTLs have been identified (Ashikari et al. 2005, Fujita et al. 2013, Huang et al. 2009, Jiao et al. 2010, Miura et al. 2010, Xue et al. 2008). Previous studies indicated that favorable alleles at two SPP-related genes, GN1A and SPIKE/NAL1, found widely in modern rice varieties, could improve rice grain yield (Wang et al. 2014, Yan et al. 2009). GN1A is a major gene encoding OsCKX2. The nearly isogenic line (NIL) carrying the Habataki allele at GN1A in the Koshi-hikari genetic background showed a 20.7% increase in grain number per panicle (Ashikari et al. 2005). SPIKE/NAL1 has pleiotropic effects on several components of plant architec-ture. The NIL carrying the IR68522-10-2-2 allele at SPIKE/NAL1 achieved a 13% to 36% increase in yield (Fujita et al. 2013). This study revealed a newly discovered and fine-mapped SPP/SBN-related QTL, qSBN7, for high-sink ca-pacity breeding in rice. Because qSBN7 has a similar effect as GN1A and SPIKE/NAL1 and it negatively affected TGW and RGR, the sink–source balance should be considered when pyramiding qSBN7 with other SPP- and grain-filling- related genes. In addition, the phenotype of SPP is easily affected by genetic and environmental factors (Xing et al. 2002). To facilitate the use of qSBN7 in rice breeding, future work should focus on understanding the effect of qSBN7 in diverse genetic backgrounds and/or environments.
Acknowledgements
The work was supported by the Tainan District Agricultural Research and Extension Station and Bureau of Animal and Plant Inspection and Quarantine (BAPHIQ), Council of Ag-riculture, Taiwan.
Literature Cited
Ando, T., T. Yamamoto, T. Shimizu, X.F. Ma, A. Shomura, Y. Takeuchi, S.Y. Lin and M. Yano (2008) Genetic dissection and pyramiding of quantitative traits for panicle architecture by using chromosomal segment substitution lines in rice. Theor. Appl. Genet. 116: 881–890.
Ashikari, M., H. Sakakibara, S. Lin, T. Yamamoto, T. Takashi, A. Nishimura, E.R. Angeles, Q. Qian, H. Kitano and M. Matsuoka (2005) Cytokinin oxidase regulates rice grain production. Science 309: 741–745.
Bai, X., L. Luo, W. Yan, M.R. Kovi, W. Zhan and Y. Xing (2010) Genet-ic dissection of rice grain shape using a recombinant inbred line population derived from two contrasting parents and fine mapping a pleiotropic quantitative trait locus qGL7. BMC Genet. 11: 16.
Discussion
An increase in productivity has been one of the principal goals in rice breeding. Many major QTLs for yield-related traits have been identified and used for developing high-yield rice (Miura et al. 2011). At the beginning of this study, we aimed to alter SPP in a japonica cultivar, TN13, by using marker-assisted backcrossing. The GN1A allele of IR65598-112-2 was introgressed into TN13. Of note, four BC2F4 populations with a homozygous GN1A allele of IR65598-112-2 showed segregation of SPP and SBN, which suggest-ed the existence of another QTL(s) controlling SPP and SBN. The donor parent, IR65598-112-2, is an NPT rice cul-tivar with low tiller number, high SPP, high SBN, and strong culms (Khush and Peng 1996). The phenotypes of IR65598-112-2 are similar to plants with the IPA1/WFP al-lele at OsSPL14. High expression of OsSPL14 in the repro-ductive stage promotes panicle branching and grain yield in rice (Jiao et al. 2010, Miura et al. 2010). OsSPL14 was not considered a causative QTL in our materials, because among F2 individuals derived from the cross of IR65598-112-2 and TN13, there was no significant association detect-ed between SPP/SBN and an OsSPL14-linked marker (data not shown).
The current study located a novel QTL for SBN (qSBN7) at a region between 27.9–28.5 Mb on chromosome 7, and the SBN trait was completely co-segregated with the mark-ers SNP2830 and SNP2835. Although several QTLs for SBN have been identified (Ando et al. 2008, Ikeda et al. 2010, Luo et al. 2009, Mei et al. 2006), none was localized to the same region of qSBN7. A major QTL, qTSN7.1, for total spikelet number per panicle (TSN) (Koide et al. 2013), located close to qSBN7 and resembled the gene effect of qSBN7. However, its position on chromosome 7 (24.0–26.0 Mb) (Koide et al. 2013) is different from that of qSBN7 (27.9–28.5 Mb). It is worth noting that a pleiotropic QTL, qGL7, was mapped to the long arm of chromosome 7 (28.2–28.5 Mb) (Bai et al. 2010), which shares a small overlap-ping region with qSBN7. As a major QTL for grain size, qGL7 simultaneously affects grain weight, length, width,
Fig. 5. Phenotype characteristics of TN13 and two backcross inbred lines (BILs) carrying different alleles at qSBN7.
Breeding Science Vol. 67 No. 4
Characterization of QTL for high-SBN in rice BS
347
Balkunde, S., H.L. Le, H.S. Lee, D.M. Kim, J.W. Kang and S.N. Ahn (2013) Fine mapping of a QTL for the number of spikelets per pan-icle by using near-isogenic lines derived from an interspecific cross between Oryza sativa and Oryza minuta. Plant Breed. 132: 70–76.
Broman, K.W., H. Wu, S. Sen and G.A. Churchill (2003) R/qtl: QTL mapping in experimental crosses. Bioinformatics 19: 889–890.
Catchen, J., P.A. Hohenlohe, S. Bassham, A. Amores and W.A. Cresko (2013) Stacks: an analysis tool set for population genomics. Mol. Ecol. 22: 3124–3140.
Etter, P.D., S. Bassham, P.A. Hohenlohe, E.A. Johnson and W.A. Cresko (2011) SNP discovery and genotyping for evolutionary genetics using RAD sequencing. Methods Mol. Biol. 772: 157–178.
Fujita, D., A.G. Tagle, L.A. Ebron, Y. Fukuta and N. Kobayashi (2012) Characterization of near-isogenic lines carrying QTL for high spike-let number with the genetic background of an indica rice variety IR64 (Oryza sativa L.). Breed. Sci. 62: 18–26.
Fujita, D., K.R. Trijatmiko, A.G. Tagle, M.V. Sapasap, Y. Koide, K. Sasaki, N. Tsakirpaloglou, R.B Gannaban, T. Nishimura, S. Yanagihara et al. (2013) NAL1 allele from a rice landrace greatly increases yield in modern indica cultivars. Proc. Natl. Acad. Sci. USA 110: 20431–20436.
Hsieh, M.H., D.H. Wu and K.Y. Chen (2014) Mapping of QTLs con-trolling pre-harvest sprouting in the cross between japonica rice cultivars using RAD sequencing. Crop Environ. Bioinform. 7: 1–20.
Huang, X., Q. Qian, Z. Liu, H. Sun, S. He, D. Luo, G. Xia, C. Chu, J. Li and X. Fu (2009) Natural variation at the DEP1 locus enhances grain yield in rice. Nat. Genet. 41: 494–497.
Ikeda, M., Y. Hirose, T. Takashi, Y. Shibata, T. Yamamura, T. Komura, K. Doi, M. Ashikari, M. Matsuoka and H. Kitano (2010) Analysis of rice panicle traits and detection of QTLs using an image analyzing method. Breed. Sci. 60: 55–64.
Jiao, Y., Y. Wang, D. Xue, J. Wang, M. Yan, G. Liu, G. Dong, D. Zeng, Z. Lu, X. Zhu et al. (2010) Regulation of OsSPL14 by OsmiR156 defines ideal plant architecture in rice. Nat. Genet. 42: 541–544.
Kawahara, Y., M. de la Bastide, J.P. Hamilton, H. Kanamori, W.R. McCombie, S. Ouyang, D.C. Schwartz, T. Tanaka, J. Wu, S. Zhou et al. (2013) Improvement of the Oryza sativa Nipponbare refer-ence genome using next generation sequence and optical map data. Rice 6: 4.
Khush, G.S. and S. Peng (1996) Breaking the yield frontier of rice. In: Reynolds, M.P., S. Rajaram and A. McNab (eds.) Increasing Yield Potential in Wheat: Breaking the Barriers, International Maize and Wheat Improvement Center, Mexico, pp. 36–51.
Koide, Y., D. Fujita, A.G. Tagle, K. Sasaki, T. Ishimaru, Y. Fukuta and N. Kobayashi (2013) QTL for spikelet number from a high- yielding rice variety, Hoshiaoba, detected in an introgression line with the genetic background of an indica rice variety, IR64. Euphytica 192: 97–106.
Lai, M.H., C.P. Li, W.S. Jwo, H.M. Yen, K.K. Hwu and D.H. Wu (2016) Study on development of japonica breeding lines introgressed high grain number gene through marker-assisted backcross approach. J. Taiwan Agric. Res. 65: 31–44.
Langmead, B. and S.L. Salzberg (2012) Fast gapped-read alignment with Bowtie 2. Nat. Methods 9: 357–359.
Liu, T., D. Mao, S. Zhang, C. Xu and Y. Xing (2009) Fine mapping SPP1, a QTL controlling the number of spikelets per panicle, to a BAC clone in rice (Oryza sativa). Theor. Appl. Genet. 118: 1509–
1517.Luo, X., F. Tian, Y. Fu, J. Yang and C. Sun (2009) Mapping quantitative
trait loci influencing panicle-related traits from Chinese common wild rice (Oryza rufipogon) using introgression lines. Plant Breed. 128: 559–567.
Mei, H.W., J.L. Xu, Z.K. Li, X.Q. Yu, L.B. Guo, Y.P. Wang, C.S. Ying and L.J. Luo (2006) QTLs influencing panicle size detected in two reciprocal introgressive line (IL) populations in rice (Oryza sativa L.). Theor. Appl. Genet. 112: 648–656.
Miura, K., M. Ikeda, A. Matsubara, X.J. Song, M. Ito, K. Asano, M. Matsuoka, H. Kitano and M. Ashikari (2010) OsSPL14 pro-motes panicle branching and higher grain productivity in rice. Nat. Genet. 42: 545–549.
Miura, K., M. Ashikari and M. Matsuoka (2011) The role of QTLs in the breeding of high-yielding rice. Trends Plant Sci. 16: 319–326.
Murray, M.G. and W.F. Thompson (1980) Rapid isolation of high mo-lecular weight plant DNA. Nucleic Acids Res. 8: 4321–4325.
R Core Team (2013) R: A language and environment for statistical com-puting. R Foundation for Statistical Computing, Vienna, Austria. URL: http://www.R-project.org/.
SAS Institute (2009) The SAS system for Microsoft Windows. Release 9.2 SAS Inst, Cary.
Terao, T., K. Nagata, K. Morino and T. Hirose (2010) A gene con-trolling the number of primary rachis branches also controls the vascular bundle formation and hence is responsible to increase the harvest index and grain yield in rice. Theor. Appl. Genet. 120: 875–893.
Tian, F., Z. Zhu, B. Zhang, L. Tan, Y. Fu, X. Wang and C.Q. Sun (2006) Fine mapping of a quantitative trait locus for grain number per panicle from wild rice (Oryza rufipogon Griff.). Theor. Appl. Genet. 113: 619–629.
Untergrasser, A., I. Cutcutache, T. Koressaar, J. Ye, B.C. Faircloth, M. Remm and S.G. Rozen (2012) Primer3—new capabilities and interfaces. Nucleic Acids Res. 40: e115.
Wang, S.S., R.K. Chen, A.H. Yang and J.C. Lo (2014) Development of functional markers in yield-related genes and analyzation of allelic diversity in rice cultivars. Res. Bull. Tainan Dist. Agric. Improv. Stn. 64: 56–72.
Xing, Y.Z., Y.F. Tan, J.P. Hua, X.L. Sun, C.G. Xu and Q. Zhang (2002) Characterization of the main effects, epistatic effects and their en-vironmental interactions of QTLs on the genetic basis of yield traits in rice. Theor. Appl. Genet. 105: 248–257.
Xing, Y.Z., W.J. Tang, W.Y. Xue, C.G. Xu and Q. Zhang (2008) Fine mapping of a major quantitative trait loci, qSSP7, controlling the number of spikelets per panicle as a single Mendelian factor in rice. Theor. Appl. Genet. 116: 789–796.
Xue, W., Y. Xing, X. Weng, Y. Zhao, W. Tang, L. Wang, H. Zhou, S. Yu, C. Xu, X. Li et al. (2008) Natural variation in Ghd7 is an important regulator of heading date and yield potential in rice. Nat. Genet. 40: 761–767.
Yan, C.J., S. Yan, Y.C. Yang, X.H. Zeng, Y.W. Fang, S.Y. Zeng, C.Y. Tian, Y.W. Sun, S.Z. Tang and M.H. Gu (2009) Development of gene-tagged markers for quantitative trait loci underlying rice yield components. Euphytica 169: 215–226.
Zhang, Y., L. Luo, T. Liu, C. Xu and Y. Xing (2009) Four rice QTL con-trolling number of spikelets per panicle expressed the characteris-tics of single Mendelian gene in near isogenic backgrounds. Theor. Appl. Genet. 118: 1035–1044.
![Quantitative Trait Locus (QTL) Mapping Reveals a Role for …summit.sfu.ca/system/files/iritems1/13352/journal.pone... · 2020. 9. 5. · Yersinia pseudotuberculosis [29]. In addition](https://static.documents.pub/doc/80x56/6068f314f94ad21c506e92d2/quantitative-trait-locus-qtl-mapping-reveals-a-role-for-2020-9-5-yersinia.jpg)
