Li Zihe, Aamir Riaz, Zhang Yingxin, Galal Bakr Anis, Zhu Aike, Cao Liyong, Cheng Shihua

Quantitative Trait Loci Mapping for Rice Yield-RelatedTraits Using Chromosomal Segment Substitution Lines

Li Zihe1, #, Aamir Riaz1, #, Zhang Yingxin1, Galal Bakr Anis1, 2, Zhu Aike1, Cao Liyong1, Cheng Shihua1

(1Key Laboratory for Zhejiang Super Rice Research/National Center for Rice Improvement/China National Rice Research Institute, Hangzhou 310006, China;2Rice Research and Training Center / Agriculture Research Center, Field Crops Research Institute, Sakha, Kafrelsheikh 33717 Egypt;#These authors contributed equally to this work)

Rice (L.) is an important cereal crop, consumed as a staple by more than 50% of the global population (Gathala et al, 2011). In Asian countries, the rice production increased to 695.5 million tones in the last years, and this increasing was caused by the main source of income and employment in these countries. Breeding high-yielding hybrid rice is a potentially promising solution for addressing food shortages caused by marked increases in the global population. Recent progress in DNA marker technology and the construction of linkage maps has enabled the detection of quantitative trait loci (QTLs) that regulate different agronomic traits applicable to breeding (Parida et al, 2006; Zhang et al, 2017). QTL analyses for yield and yield-related traits have been conducted using different introgression line populations, such as chromosomal substitute segment lines (CSSLs), back-cross inbred lines (BILs) and near-isogenic lines (NILs) (Tian et al, 2006; Tan et al, 2007; Cheema et al, 2008; Kang et al, 2008; Fu et al, 2010; Wang J et al, 2017). However, a major unresolved issue is how to increase rice yield to meet the requirements of a growing world population.Rice grain yield is mainly determined by three major components: number of grains per panicle, number of panicles per plant, and grain weight. Of these all traits, grain weight is the most reliable trait and measured as 1000-grain weight (TGW). Grain length (GL), grain width (GW), grain length-to-width ratio (LWR), and grain thickness collectively determine grain shape in rice and are positively correlated with TGW (Tan et al, 2000; Xu et al, 2002; Wang Y Q et al, 2017). TGW has been heavily researched in rice breeding programs due to its contributions to rice yield (Lin and Wu, 2003).

The donor parent, XieqingzaoB (XQZB) is one of the most widely usedmaintainer line with longer grain and higher grain weight, whereas Zhonghui9308 (ZH9308) an-restorer line with the wider grain (Table 1) In this study, to identity the QTLs affecting TGW, GL, GW and LWR, a CSSL population consisting of 75 lines was developed from a cross between XQZB and ZH9308 (Supplemental Fig. 1). The field experiments were conducted during 2014 and 2015 in the field at the China National Rice Research Institute, Fuyang, Hangzhou, China. The crossing scheme for 75 CSSLs is shown in Supplemental Fig. 1.

The phenotypic data indicated that all traits of XQZB and ZH9308 were higher grown in Lingshui compared to those in Fuyang. The parents showed relatively large differences for TGW and LWR, but not for GL or GW (Table 1).All the four traits showed continuous variation with normal distributions in the CSSL population (Fig. 1). TGW was significantly and positively correlated with GL and GW, but had a negative or non-significant correlation with LWR. GL was significantly and positively correlated with GW and LWR. GW had a significantly positive correlation with LWR (Supplemental Table 1).

One hundred and eight additive QTLs were detected in the CSSL population in both years. These QTLs were identified on chromosomes 1, 2, 3, 4, 5, 6, 7, 9, 10, 11 and 12, but not on chromosome 8. Out of these 108 QTLs, only 16 were detected in both environments.

For GL, a total of 22 QTLs were detected on chromosomes 1, 2, 3, 4, 5, 6, 7, 11 and 12 (Supplemental Table 2). The stable QTLwas detected on chromosome 1 in Lingshui and Fuyang during 2014 and 2015. It was flanked by RM3746–RM8004 and an allele from ZH9308, contributed to increased GL. For GW, a total of 27 QTLs were identified on chromosomes 1, 2, 3, 4, 5, 6, 7, 9, 10, 11 and 12 (Supplemental Table 3). Among those QTLs,,,andwere stable and detected in Lingshui and Fuyang during 2014 and 2015(Table 2)., mapped to RM3746–RM8004, was derived from parent ZH9308, showing negative additive effects. This QTL corresponded to region ofand(Zhang et al, 2009).was flanked by RM3392–RM1324 on chromosome 3, where the alleles contributing to GW were derived from ZH9308 and had negative additive effects, located close to(Fan et al, 2006).was mapped to RM5879–RM5757 on chromosome 4, within the same interval of fined mapped QTL () responsible for GL (Kato et al, 2011).contributing to GW were derived from parent ZH9308 and had negative additive effects of 0.0023 mm and 0.0072 mm in Fuyang, and 0.0691 mm and 0.0070 mm in Lingshui.was linked with RM6841, which had a negative additive effect, and inherited from ZH9308. For LWR, a total of 30 QTLs associated with LWR were identified from 11 chromosomes (Supplemental Table 4). The staple QTLs,and, were detected in Lingshui and Fuyang during 2014 and 2015. The QTLswas mapped on chromosome 1, flanked by RM3746 and RM8004 and derived from parent XQZB.was flanked by RM5436–RM1132, inherited from the parent XQZB. For TGW, a total of 29 QTLs were identified on chromosomes 1, 2, 3, 4, 5, 6, 7, 9, 10, 11 and 12 (Supplemental Table 5). The stable QTLs across the environments were,,,,,,,and.was mapped between RM6424–RM208, an allele inherited by ZH9308 with negatively additive effects of 1.62 g and 0.53 g in Fuyang and 1.57 g and 1.51 g in Lingshui.was flanked by InD36–RM1324,which was derived from parent ZH9308 and showed negatively additive effects of 1.00 g and 0.38 g in Fuyang and 0.49 g and 1.80 g in Lingshui. The allele, which had negative additive effects, was inherited from the parent ZH9308 and mapped between RM3392–RM1324. These QTLs (and)shared same interval with cloned QTL(Mao et al, 2010; Xu et al, 2012).was mapped between RM5979–RM5757, in the same region a major QTL namedregulating GL was cloned (Kato et al, 2011).was associated with InD71. The allelesandwere derived from the donor parent XQZB. Furthermore,was flanked by RM5436–RM5875 with alleles showing positive additive effects inherited from XQZB.was mapped by RM5436–RM11342,andthe allele was derived from the parent XQZB.

Table 1. Grain trait performances of parentsXieqingzao B (XQZB), Zhonghui 9308 (ZH9308) and CSSLs.

CSSL, Chromosome segment substitution line; GL, Grain length; GW, Grain width; LWR, Grain length-to-width ratio; TGW, 1000-grain weight.

In summary, we identified 108 QTLs collectively with 22, 27, 30 and 29 for GL, GW, LWR and TGW respectively. Among these QTLs, 16 were stable in both environments during 2014– 2015. The GL, GW, LWR and TGW traits were significantly correlated in independent manner. The,,,,,andalleles, which are inherited from the parent ZH9308, exhibited a negative additive effect. In contrast, the,,,,,andalleles were derived from XQZB, and showed positive additive effect. This study also provides information for fine mapping, gene cloning and particularly marker-assistant selection.

Acknowledgements

This study was supported by the National Key Transform Program (Grant No. 2016ZX08001-002), the National Natural Science Foundation of China (Grant No. 31501290), the Zhejiang Provincial Natural Science Foundation of China (Grant No. LQ14C130003), Central Research Institutes of Basic Research and Public Service Special Operations (Grant Nos. Y2016PT34 and Y2017CG23), and the Super Rice Breeding Innovation Team and Rice Heterosis Mechanism Research Innovation Team of the Chinese Academy of Agricultural Sciences Innovation Project (Grant No. CAAS-ASTIP-2013-CNRRI).

Fig. 1.Frequency distribution of grain traits in Fuyang (FY) and Lingshui (LS) during 2014 and 2015.

CSSL, Chromosome segment substitution line; GL, Grain length; GW, Grain width; LWR, Grain length-to-width ratio; TGW, 1000-grain weight.

Table 2.Stablequantitative trait loci (QTLs) for grain traits across the environment.

Chr, Chromosome;, Additive effect; GL, Grain length; GW, Grain width; LWR, Grain length-to-width ratio; TGW, 1000-grain weight.

SUPPLEMENTAL DATA

The following materials are available in the online version of this article at http://www.sciencedirect.com/science/journal/ 16726308; http://www.ricescience.org.

Supplemental File 1. Materials and methods used in this study.

Supplemental Table 1. Correlation among the grain traits.

Supplemental Table 2. Quantitative trait loci (QTLs) for grain length across the environment.

Supplemental Table 3. Quantitative trait loci (QTLs) for grain weight across the environment.

Supplemental Table 4. Quantitative trait loci (QTLs) for grain length-to-width ratio across the environments.

Supplemental Table 5. Quantitative trait loci (QTLs) for 1000-grain weight across the environments.

Supplemental Fig. 1. Crossing scheme for 75 chromosome segment substitution lines crossed from Xieqingzao B and Zhonghui 9308.

Amarawathi Y, Singh R, Singh AK, Singh VP, Mohapatra T, Sharma TR, Singh NK. 2008. Mapping of quantitative trait loci for basmati quality traits in rice (L.)., 21(1): 49–65.

Bai X F, Luo L J, Yan W H, Kovi M R, Zhan W, Xing Y Z. 2010. Genetic dissection of rice grain shape using a recombinant inbred line population derived from two contrasting parents and fine mapping a pleiotropic quantitative trait locus., 11: 16.

Cheema K K, Bains N S, Mangat G S, Das A, Vikal Y, Brar D S, Khush G S, Singh K. 2008. Development of high yielding IR64?×(Griff.) introgression lines and identification of introgressed alien chromosome segments using SSR markers., 160(3):401–409.

Fan C C, Xing Y Z, Mao H L, Lu T THan B, Xu C G, Li X H, Zhang Q F. 2006., a major QTL for grain length and weight and minor QTL for grain width and thickness in rice, encodes a putative transmembrane protein., 112: 1164–1171.

Fu Q, Zhang P J, Tan L B, Zhu Z F, Ma D, Fu Y C, Zhan X C, Cai H W, Sun C Q. 2010. Analysis of QTLs for yield-related traits in Yuanjiang common wild rice (Griff.)., 37(2):147–157.

Gathala M K, Ladha J K, KumarV, Saharawat Y S, Kumar V, Sharma P K, Sharma S, Pathak H. 2011. Tillage and crop establishment affects sustainability of South Asian rice-wheat system., 103(4): 961–971.

Kang J W, Suh J P, Kim D M, Oh C S, Oh J M, Ahn S N. 2008. QTL mapping of agronomic traits in an advanced backcross population from a cross betweenL. cv. Milyang 23 and., 40(3):243–249.

Kato T, Segami S, Toriyama M, Kono I, Ando T, Yano M, Kitano H, Miura K, Iwasaki Y. 2011. Detection of QTLs for grain length from large grain rice (L.).,61: 269–274.

Lin LH, Wu WR. 2003. Mapping of QTL underlying grain shape and grain weight in rice., 1: 337–342.

Mao H L, Sun S Y, Yao J L, Wang C R, Yu S B, Xu C G, Li X H, Zhang Q F. 2010. Linking differential domain functions of theprotein to natural variation of grain size in rice.,107: 19579–19584.

Qiao Y L, Lee S I, Piao R H, Jiang W Z, Ham T H, Chin J H, Piao Z Z, Han L Z, Kang S Y, Koh HJ.2010. Fine mapping and candidate gene analysis of the floury endosperm gene,(), in rice., 29: 167–174.

Marathi B, Guleria S, Mohapatra T, Parsad R, Mariappan N, Kurungara V K, Atwal S S, Prabhu V K, Singh N K, Singh A K. 2012. QTL analysis of novel genomic regions associated with yield and yield related traits in new plant type based recombinant inbred lines of rice (L.)., 12: 137.

Parida S K, Kumar AR, Dalal V, Singh N K, Mohapatra T. 2006. Unigene derived microsatellite markers for the cereal genomes., 112(5): 808–817.

Tan L B, Liu F X, Xue W, Wang G J, Ye S, Zhu Z F, Fu Y C, Wang X K, Sun C Q. 2007. Development ofandintrogression lines and assessment for yield-related quantitative trait loci., 49(6):871–884.

Tan Y F, Xing Y Z, Li J X, Yu S B, Xu C G, Zhang Q F. 2000. Genetic bases of appearance quality of rice grains in Shanyou 63, an elite rice hybrid., 101: 823–829.

Tian F, Li D J, Fu Q, Zhu Z F, Fu Y C, Wang X K, Sun C Q. 2006. Construction of introgression lines carrying wild rice (Griff.) segments in cultivated rice (L.) background and characterization of introgressed segments associated with yield-related traits., 112(3):570–580.

Xu J L, Xue Q Z, Luo L J, Li Z K. 2002. Genetic dissection of grain weight and its related traits in rice (L.)., 16(1): 6–10. (in Chinese with English abstract)

Wang J, Zhu J Y, Tao Y J, Zhou Y, Fan F J, Li W Q, WangF Q, Zhong W G, Yang J, Liang G H. 2017. Mapping of QTLs for heading date using whole-genome re-sequenced chromosome segment substitution lines in rice.,31(4): 364–370. (in Chinese with English abstract)

Wang Y Q, Yang Y L, Leng Y J, Huang L C, Chen L, Dai L P, Tu Z J, Gao Y H, Hu J, Zhu L, Zhang G H, Ren D Y, Gao Z Y, Dong G J, Chen G, Guo L B, Ye G Y, Qian Q, Zeng D L. 2017. Identification and fine mapping of small grain genein rice (L.)., 31(6): 580–589. (in Chinese with English abstract)

Yan C J, Liang G H,Chen F,Li X, Tang S Z,Yi C D,Tian S,Lu J F,Gu M H. 2003. Mapping quantitative trait loci associated with rice grain shape based on an/backcross population., 30(8): 711–716. (in Chinese with English abstract)

Yuan P R, Kim H J, Chen Q H, Ju H G, Lee S J, Ji S D, Ahn S N. 2009. QTL dissection of agronomic and domestication traits using introgression lines carrying wild rice (Griff.) segments in cultivated rice (L.) background.,12(4): 245–252.

Zhang Y S, Luo L J, Liu T M, Xu C G, Xing Y Z. 2009. Four rice QTL controlling number of spikelets per panicle expressed the characteristics of single Mendelian gene in near isogenic backgrounds.,118: 1035–1044.

Zhang Y X, Anis G B, Wang R, Wu W M, Yu N, Shen X H, Zhan X D, Cheng S H, Cao L Y. 2017. Genetic dissection of QTL against phosphate deficiency in the hybrid rice ‘Xieyou9308’., 84(1): 123–133.

Cheng Shihua (chengshihua@caas.cn); Cao Liyong (caoliyong@caas.cn)

12 April 2018;

18 February 2019

Copyright ? 2019, China National Rice Research Institute. Hosting by Elsevier B V

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Peer review under responsibility of China National Rice Research Institute

http://dx.doi.org/10.1016/j.rsci.2019.02.001