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ISSN : 1225-8504(Print)
ISSN : 2287-8165(Online)
Journal of the Korean Society of International Agricultue Vol.32 No.3 pp.271-278
DOI : https://doi.org/10.12719/KSIA.2020.32.3.271

Detecting Loci for Upper Leaf Width Using Recombinant Inbred Line and Its Natural Variation in Korean Rice Germplasms

Su Jang, Yoon Kyung Lee, Seung Young Lee, Hee-Jong Koh
Department of Plant Science, Plant Genomics and Breeding Institute, Research Institute for Agriculture and Life Sciences, Seoul National University, Seoul 08826, South Korea
Corresponding author (Phone) +82-2-880-4551 (E-mail) heejkoh@snu.ac.kr
August 26, 2020 September 2, 2020 September 8, 2020

Abstract


Leaves of rice are main source for photosynthesis, determining production of carbohydrate and grain yield. In particular, the shape of upper leaves is a key factor to develop high-yielding rice varieties. In present study, we identified three stable loci, qLW4.1, qLW4.2, and qLW7, explaining 5.2 ~ 44.9% of phenotypic variance in leaf width traits. Sequence analysis of candidate genes revealed that qLW4.2 is identical to NARROW LEAF 1 (NAL1). Upper leaves showed positively indirect effect to grain yield per plant (GY) via positive contribution to grain number per panicle (GN) and negative contribution to panicle number per plant (PN). Leaf width variation in Korean accessions suggests that relatively narrow ranges of leaf width were preferred. Haplotype analysis for NAL1 revealed that most Korean accessions carried japonica type while all Tongil type varieties carried indica type, implying that Tongil type varieties could be further improved by introducing favorable NAL1japonica allele in temperate regions. These findings could provide genetic information to regulate leaf width and strategy for increasing rice productivity.



벼 상위엽폭 조절 유전자좌 탐색과 유전자원의 자연변이

장 수, 이 윤경, 이 승영, 고 희종†
서울대학교 농업생명과학대학

초록


    Rural Development Administration
    PJ013165

    INTRODUCTION

    Leaves are major component of rice plant type and an essential photosynthetic organ to produce carbohydrate in rice. Upper leaves are the primary source of carbohydrates production and key determinant of grain yield (Jiang et al., 2010;Peng et al., 2008). In particular, 50% of the primary sink capacity was attributable to the flag leaf (Li et al., 1998). Therefore, upper leaves shape-related traits are the important factors to achieve high yield in rice.

    Identification of quantitative trait loci (QTLs) for width of upper leaves could provide genetic information to improve photosynthetic efficiency to develop high-yielding varieties. To date, a number of QTLs regulating leaf size have been reported in rice using genetic populations derived from crosses between inter-subspecies parents. A total of 16 QTLs, explaining phenotypic variance ranged from 9% to 25.3% of the third leaf width, were detected in recombinant inbred line (RIL) developed from the cross between japonica and indica parents (Wen et al., 2020). Other 14 QTLs for flag leaf width (FLW), explaining 2~24% of phenotypic variance, were also detected using RILs derived from the cross between an indica variety, 93–11 and japonica variety, Peiai 64s (Zhang et al., 2015).

    Introduction of QTL alleles through inter-subspecies crosses is efficient strategy in rice breeding program to increase in leaf width. NAL1 (NARROW LEAF 1), also known as GREEN FOR PHOTOSYNTHESIS/ SPIKELET NUMBER/FLAG LEAF SHAPE AND CHLOROPHYLL CONTENT (GPS/SPIKE/LSCHL4), is a major QTL regulating leaf width. NAL1 has different functions, according to their allele types from either indica or japonica (Zhang et al., 2014). Koshihikari (japonica) allele of GPS increase in FLW in Takanari (indica) genetic background. However, Takanari allele in Koshihikari genetic background increase in photosynthetic rate (Takai et al., 2013). SPIKE allele from tropical japonica landrace enhances the grain productivity in genetic background of indica variety IR64, increasing in spikelet number, leaf size, root system, and the number of vascular bundles (Fujita et al., 2013). Furthermore, LSCHL4 allele from japonica rice variety Nipponbare improves the traits related to source-sink flow including flag leaf size into 93-11 genetic background (indica) and ultimately enhances the yield (Zhang et al., 2014).

    In this study, we identified stable loci affecting FLW and SLW using RIL population derived from the cross between Tong88-7 (temperate japonica) and Milyang23 (Tongil type), and high-density SNP markers. Then, we assessed the indirect and direct relationships between leaf width and yield-related traits using path coefficient analysis. Lastly, we found leaf width variations and genetic variation of significant QTL in natural population using Korean rice germplasms collection.

    MATERIALS AND METHODS

    Plant materials and phenotype measurement

    A total of 155 RILs were derived from a cross between Milyang23 (M23), Korean Tongil type variety, and Tong88-7 (T887), Chinese japonica variety, for QTL mapping. To investigate natural variation of flag leaf width (FLW) and secondary leaf width (SLW), 181 Korean germplasms, consist of 90 modern varieties, 70 landraces, and seven Tongil type varieties, were used. All the plants were grown in the experimental field, Seoul National University, Suwon, South Korea (Natural long day condition, latitude = 37° N). Seedlings grown 30 days after sowing were transplanted into the paddy field with the following conditions: a plant per hill, 25 plants per row, 15 cm between plants in row, 30 cm between rows. Leaf width was measured from the middle part of flag leaf (FL) and upper secondary leaf (SL) using ruler for two field tests (field test 1 and field test 2; FT1 and FT2). The leaves at uppermost stem were excluded from the measurement due to presence of large variation of leaf size. Grain number per panicle (GN), hundred grain weight (GW), panicle number per plant (PN), and grain yield per plant (GY)of RIL population were measured to estimate the effects of leaf width to grain yield.

    Genotyping-By-Sequencing (GBS) and QTL analysis using recombinant inbred line (RIL) population

    Genomic DNA was extracted from young leaves using CTAB method (Murray and Thompson, 1980) and the quality was checked using PicoGreen (Invitrogen). Preparation of GBS library and genotyping were performed as explained in previous study (Jang et al., 2020). A total of 26,424 SNPs were discovered by GBS genotyping. SNPs were binned using the BIN function of ICIMapping 4.1 (Meng et al., 2015). In total, 1,327 bin markers were obtained and employed for construction of genetic map and QTL mapping using same program. The recombination distance was calculated using the Kosambi mapping function (Kosambi, 1943). An inclusive composite interval mapping (ICIM) method was performed to detect additive QTLs. The logarithm of odds (LOD) values > 3.5 was applied to define significant QTLs. The physical position of markers was determined based on the Nipponbare reference genome, IRGSP 1.0 (Sakai et al., 2013).

    Haplotype analysis in Korean germplasms

    Whole genome sequencing data of Korean rice were produced by the Illumina HiSeq X platform with depth of >10 per sample. Raw reads were trimmed using Trimmomatic 0.38 (Bolger et al., 2014) and aligned to Nipponbare reference genome, IRGSP 1.0, (Sakai et al., 2013) using the BWA 0.7.17 (Li & Durbin, 2009). Aligned reads were sorted using SAMtools 1.9 (Li et al., 2009) and duplicates were removed using Picard 2.20.2 (Broad Institute, 2018). Nucleotide variants were called by HaplotypeCaller function of GATK 4.1.2 (McKenna et al., 2010). The haplotype was constructed using variants in exonic regions. Haplotype network was calculated by TCS method of PopART 1.7 (Leigh & Bryant, 2015). The haplotype found in only one variety was excluded from the multiple comparisons of FLW and SLW.

    Statistical analyses

    All statistical analyses were performed using R studio v1.2.5033 (Allaire, 2012). Pearson's correlation analysis and Student's t-test were performed using the stats package (R Core Team, 2013). One-way ANOVA (analysis of variance) with Scheffe’s post-hoc test (p ≤ 0.05) was performed using the Agricolae package (Mendiburu, 2020). Path coefficient analyses were conducted to estimate direct and indirect effects between leaf width and yield components of RIL using IBM SPSS Amos 20.

    RESULTS

    FLW and SLW variation in RIL population

    FLW and SLW of T887 were in the range of 13.6 ~ 13.7 mm and 11.2 ~ 13.6 mm for two field tests, respectively. In M23, FLW and SLW were in the range of 15.1 ~ 16 mm and 13 ~ 14.2 mm for two field tests, respectively, and showed wider width in FL than SL. (Fig. 1A-B). In RIL population derived from the cross between T887 and M23, large variations in FLW and SLW were observed with ranges of 8.4 ~ 19.9 mm and 8.7 ~ 18.4 mm for two field tests. A number of lines showed leaf width out of range of both parents, representing transgressive segregation in both direction of phenotypic values (Fig. 1A). Strong positive correlations (r >0.8) were consistently detected between FLW and SLW in each field test. In addition, strong positive correlations for FLW (r = 0.85) and SLW (r = 0.85) were observed between two field tests (Fig. 1C).

    QTL mapping of FLW and SLW in RIL

    A total of four and nine significant loci associated with FLW and SLW, respectively, were identified on five chromosomes: 1, 3, 4, 7, and 12 by QTL mapping with 1,327 bin markers (Fig. 2A; Table 1). The loci located at 18.6 ~ 19.1 Mb and 19.2 ~ 19.7 Mb interval on chromosome 4 were significantly detected for SLW in FT1 and FT2, respectively. The two loci for SLW were separated by approximately 100 Kb. Thus, these loci were considered to be same QTLs for SLW and were named as qLW4.1 (Fig. 2). The locus at 31.1 ~ 31.2 Mb interval on chromosome 4 was consistently associated with SLW and FLW in both field tests, thus were named as qLW4.2. The other consistent QTLs for leaf width were detected on chromosome 7. Since these loci showed LOD peak area overlapped at around 8.8 Mb, these two detected loci were designated as one QTL and were named as qLW7 (Fig. 2).

    qLW4.2 showed the highest PVE value, explaining 25.3 ~ 44.9 % and 11.6 ~ 17.8 % of phenotypic variance in FLW and SLW, respectively (Table 1). qLW4.2 showed positive additive effect, meaning that the allele from T887, temperate japonica variety, contributes to wide width in FL and SL. The RILs carrying qLW4.2 allele from T887 showed significantly wider width than that from M23 (Fig. 2B). qLW4.1 could explain 5.2 ~ 5.5 % of phenotypic variance in SLW and showed negative additive effects, indicating that the allele from M23 causes wide SLW (Table 1; Fig. 2B). qLW7 could explain more than 10 % of variance in FLW and SLW. The RILs with M23 allele of qLW7 showed significantly wider width in FL and SL in both field test (Table 1; Fig. 2B).

    Since qLW4.2 showed the highest PVE value among significant QTLs and was detected into narrow intervals (31147150 ~ 31242229 bp), we investigated candidate genes within qLW4.2 region based on the Rice Annotation Project Database (RAP-DB; Sakai et al., 2013). A total of 12 candidate genes were located within QTL region (Fig. 2C) and Os04g0615000, which was previously reported as a NARROW LEAF1 (NAL1) gene affecting leaf width, was detected (Fujita et al., 2013;Qi et al., 2008;Takai et al., 2013;Zhang et al., 2014). Thus, NAL1 was considered as strong candidate gene regulating FLW and SLW. Six SNPs were found in exonic region of NAL1 between T887 and M23. Among them, four and two SNPs were detected in coding regions and 3’ UTR region, respectively (Fig. 2D). Three SNPs located at coding site 3 and 5 were missense variants, causing amino acid changes.

    Contribution of leaf width to rice yield components

    To investigate relationships among leaf width traits and rice yield, path analyses were conducted in RILs (Fig. 3). FLW and SLW showed similar direct effects to three yield components, including GN, GW, and PN. FLW and SLW showed positively direct contribution to GN with 0.36 and 0.4 of standardized path coefficients, respectively. Both leaf width traits showed negatively direct contribution to PN with ranges from - 0.36 to - 0.44. No significance was found between leaf width and GW. In conclusion, both leaf width traits contributed to GY with indirect effect of 0.15 ~ 0.16 via GN and PN.

    Leaf width variation and haplotype of NAL1 in Korean rice germplasms

    Large variations in leaf width were observed among Korean rice germplasms, with ranges of 9.9 ~ 24.4 mm and 9.4 ~ 18.6 mm in FLW and SLW, respectively (Fig. 4A). FLW was dominantly observed within the range of 14 ~ 16 mm, accounting for 73.9% of germplasms. SLW was dominantly observed within the range of 12 ~ 14 mm, accounting for 83.9% of germplasms. Mogyang, Mogwoo, and Nokyang (forage rice varieties) and Hwaseongbatchal (upland landrace) showed the widest leaf width more than 20 mm while Pungwujo, Pyeongbuk 7, and Junganjeunbaengi (landrace) showed the narrowest width about 10 mm.

    Five haplotypes were constructed for NAL1 using seven variants in exonic regions. Among variants, four SNPs in coding regions (31212801, 31214019, 31214045, and 31214072 bp) were missense variants, causing amino acid changes. In Korean germplasms, H1 haplotype (T887 haplotype) of NAL1 was dominantly observed, accounting for 76.5% of germplasms (Fig. 4B-C). All Tongil type varieties, including M23, possessed H5 haplotype and H5 was separated from the other haplotypes by four specific SNPs at 31205430, 31214019, 31214045, and 31214523 bp position (Fig. 4B-C). H3 haplotype, separated by A allele at 31214072 bp, were found in only one modern variety (Fig. 4B-C). Multiple comparison of FLW and SLW among major haplotype showed that H2, which was mainly found in landrace, had significantly narrower width of FL and SL than those of the other haplotypes (Fig. 4D). However, no significant difference was detected among H1, H4, and H5.

    DISCUSSION

    In present study, to detect QTLs controlling width of upper leaves, we investigated FLW and SLW in RIL population originated from the cross between T887 and M23. FLW and SLW in RIL population were distributed in the range of 8 ~ 20 mm, showing either higher or lower values than those of both parents (Fig. 1A). This transgressive segregation in both directions implies that the genetic factors affecting leaf width might be originated from each parent, and progenies possibly appear a wider range of leaf width by introducing alleles from each parent. As expected, three stable QTLs for leaf width, qLW4.1, qLW4.2, and qLW7, showed additive effects with opposite direction (Table 1). qLW4.1 and qLW7 allele from M23 contributed to increase leaf width. In contrast, qLW4.2 (NAL1) allele for wide leaf width was derived from japonica variety, T887 (Table 1). This result is consistent with previous reports that leaf width was increased by introducing NAL1 japonica allele into indica genetic background (Takai et al., 2013;Zhang et al., 2014). In addition, similar result was also reported in previous study using RILs developed from cross between japonica cultivar Rekuangeng and indica cultivar Taizhong1 (Wen et al., 2020). Transgressive variation of leaf width was observed in RILs and among stable four QTLs for leaf width, the alleles of one QTL increasing leaf width was inherited from Taizhong1, while the alleles of the other QTLs increasing leaf width were derived from Rekuangeng. These results suggest that introducing allelic combination from each other subspecies varieties is efficient to regulate leaf width within a diverse range in rice breeding program.

    Both leaf width traits showed positive and negative direct effect to GN and PN, respectively. They did not significantly contribute to GW which is the other yield component. Consequently, leaf width traits had positive contribution to GY with minor indirect effect of 0.15 ~ 0.16 (Fig. 3). It is consistent with a previous report that leaf width have less effect to GY due to negative correlation between leaf width and PN (Wang et al., 2020). Although leaf width traits had higher contribution to PN than to GN, direct effects of GN to GY was higher than that of PN to GY (Fig. 3). These results indicate that the high source capacity by wide leaf width could positively contribute to GY by increasing GN despite the reduction of PN.

    However, excessively wide leaf area could lead to mutual shading of leaves, reducing photosynthetic efficiency. Negative correlation between leaf area and photosynthetic efficiency, consequently, cause inhibition in production (Cui et al., 2003). Therefore, balancing source and sink capacities should be considered to develop highyielding rice varieties. In present study, FLW and SLW in Korean rice germplasms showed mean values of 14.8 and 13 mm and were dominantly distributed within range of 14 ~ 16 mm and 12 ~ 14 mm, respectively (Fig. 4A). These distributions were similarly observed in modern varieties and landrace. In particular, seven Korean high-yielding varieties with yield greater than 700 kg/10 a (Hanmaeum, Namil, Deuraechan, Hanareum 2, Saegyejinmi, and Hanareum) showed relatively higher means of FLW (17.2 mm) and SLW (15 mm) than the other variety groups (Fig. 4A). These results imply that relatively narrow ranges of leaf width are preferred or artificially selected in temperate japonica varieties, and high-yielding varieties required wider leaf width than common temperate japonica varieties to optimize source and sink capacities.

    NAL1, also known as GPS/SPIKE/LSCHL4, controls lateral leaf growth by affecting polar auxin transport and the vascular patterns (Qi et al., 2008). NAL1 from temperate and tropical japonica leads to increase in leaf width by introducing into indica genetic backgrounds (Fujita et al., 2013;Takai et al., 2013;Zhang et al., 2014). Korean germplasms frequently carried H1 haplotypes of NAL1, T887 haplotype, while all Tongil type varieties carried H4 haplotype, M23 haplotype belonged to indica type (Fig. 4B). Although T887 allele of NAL1 increased FLW and SLW in M23 background (Fig. 2B), no significant difference of FLW and SLW were presented between H1 and H4 (Fig 4D). These results suggest that leaf width of Tongil type varieties were complemented by the effect of the other genetic factors increasing in leaf width, rather than by NAL1 effect. The result that the alleles for wider leaf width of the other QTLs, qLW4.1 and qLW7, were derived from M23, could support this suggestion (Table 1). Therefore, leaf width of Tongil type varieties, Korean high-yielding varieties, could be more increased by introducing NAL1 alleles from temperate or tropical japonica varieties into Tongil type varieties in temperate regions.

    적 요

    벼의 잎은 광합성을 위한 주요 기관으로, 동화산물의 생산 을 통해 벼의 수량 결정에 영향을 준다. 따라서, 상위엽의 형 태적 특징은 다수성 벼 품종의 육성을 위한 필수적인 고려요 소이다. 본 연구에서는, 연차간 안정적으로 발현하는 3개의 엽 폭 조절 QTL인 qLW4.1, qLW4.2, qLW7을 탐지하였다. 이들 은 분석집단에서 나타난 엽폭 표현형 변이의 5.2~44.9% 를 설명할 수 있었다. qLW4.2 영역에 대한 후보유전자 분석 결 과, 해당 QTL 영역에서 엽폭 조절 유전자인 NAL1을 발견하 였다. 상위엽은 수량에 대해 정의 간접효과 나타냈으며, 그 효 과의 크기는 수당립수에 대한 상위엽의 정의 직접효과와 수수 에 대한 상위엽의 부의 직접효과에 의해 결정되었다. 한국 벼 유전자원에서 엽폭은 상대적으로 좁은 범위에서 표현형 변이 를 나타내었으며, 이는 특정 상위엽폭이 한국 자원에서 선호 되었음을 시사한다. NAL1의 하플로타입 분석결과는 대다수의 한국 자원들이 자포니카형 하플로타입을 지닌 반면에, 모든 통 일형 품종들은 인디카형을 지니고 있음을 밝혀냈다. 이러한 결 과는 유용 대립유전자형인 자포니카형 NAL1의 도입을 통해 통일형 품종의 다수성 형질을 더욱 향상시킬 수 있음을 의미 한다.

    ACKNOWLEDGMENTS

    This study was supported by a grant from the Next-Generation BioGreen 21 Program (no. PJ013165) of the Rural Development Administration (RDA), South Korea.

    Figure

    KSIA-32-3-271_F1.gif

    FLW and SLW variations in RILs for two field tests.

    (A) Histogram showing distributions of FLW and SLW in RILs. T and M indicate the phenotypic values of each parent, T887 and M23, respectively. FT, field test. (B) FL of both parents. Scale bar, 10 mm. (C) Correlation plot for leaf width traits. Correlation plot was drawn based on Pearson’s correlation coefficients between traits. Blue and red colors represent positive and negative correlations, respectively.

    KSIA-32-3-271_F2.gif

    QTL mapping for FLW and SLW using RILs

    (A) The results of QTL analysis for FLW and SLW. The Red horizontal line indicates threshold LOD score (> 3.5). Black curve denote LOD score and blue arrow and vertical dotted line indicate the stable QTLs for leaf width detected consistently in two field tests (FT1 and FT2). (B) Comparison of leaf width traits in RILs according to allele of each significant QTL. The mean values on left and right side on vertical dotted lines represent the values of FT1 and FT2, respectively. Asterisk represents statistically significant difference by Student’s t-test (ns, non-significant difference; **, p ≤ 0.01; ***, p ≤ 0.001). (C) Candidate genes found within qLW4.2 region. Physical position and candidate genes were determined based on RAP-DB (IRGSP 1.0). (D) Nucleotide variants detected in NAL1 gene between each parent. Grey and black boxes mean UTR and coding regions, respectively. Red asterisk indicate missense variants.

    KSIA-32-3-271_F3.gif

    Direct and indirect contribution of leaf width to grain yield. Blue and red colors denote positive and negative contribution to dependent variable, respectively. Solid and dotted lines indicate direct and indirect effect to dependent variable, respectively. ns, non-significant effect (p > 0.01); FLW, flag leaf width; SLW, secondary leaf width; GN, Grain number per panicle; GW, hundred grain weight; PN, panicle number per plant; GY, grain yield per plant.

    KSIA-32-3-271_F4.gif

    Leaf width variations in Korean germplasms

    (A) Histogram showing distributions of FLW and SLW in 181 germplasms. Red, green, and blue colors represent that germplasms belong to modern variety, Tongil type variety, and landrace, respectively. Varieties marked in italics denote Korean high-yielding varieties. (B) NAL1 haplotypes constructed using nucleotide variants in exonic region. (C) Haplotype network of NAL1. Circle size represents relative haplotype frequency in germplasms. Dashes between circles denote mutational steps between the haplotypes.

    Table

    Summary of significant QTLs for FLW and SLW in RIL population.

    <sup>z)</sup>Physical position was based on Nipponbare reference genome (IRGSP 1.0)
    <sup>y)</sup>LOD, logarithm of odds
    <sup>x)</sup>PVE, phenotypic variance explained by QTL
    <sup>w)</sup>Additive effect. Positive values show that allelic contribution is from Tong88-7 and negative values from Milyang23.

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