Touming Liu

Natural variation in Ghd7.1 plays an important role in grain yield and adaptation in rice

Natural variation in Ghd7.1 plays an important role in grain yield and adaptation in rice

Fine mapping SPP1, a QTL controlling the number of spikelets per panicle, to a BAC clone in rice (Oryza sativa)

Near isogenic lines (NILs) can be used to efficiently handle a target quantitative trait locus (QTL) by blocking genetic background noise. One QTL, SPP1, which controls the number of spikelets per panicle (SPP), was located on chromosome 1, near Gn1a, a cloned gene for rice production in a recombinant inbred line population. NILs of the SPP1 regions were quickly obtained by self-crossing recombinant inbred line 30 which is heterozygous around SPP1. Using a random NIL-F2 population of 210 individuals, we mapped SPP1 to a 2.2-cM interval between RM1195 and RM490, which explained 51.1% of SPP variation. The difference in SPP between the two homozygotes was 44. F2-1456, one NIL-F2 plant, was heterozygous in the SPP1 region but was fixed in the region of Gn1a gene. This plant F3 family showed a very wide variation in SPP, which suggested that it was SPP1 but Gn1a affected the variation of SPP in this population. In a word, SPP1 is a novel gene distinct from Gn1a. Four newly developed InDel markers were used for high-resolution mapping of SPP1 with a large NIL-F2 population. Finally, it was narrowed down to a bacterial artificial chromosome clone spanning 107xa0kb; 17 open reading frames have been identified in the region. Of them, LOC_Os01g12160, which encodes an IAA synthetase, is the most interesting candidate gene.

Four rice QTL controlling number of spikelets per panicle expressed the characteristics of single Mendelian gene in near isogenic backgrounds

Development of quantitative trait loci (QTL) near isogenic lines is a crucial step to QTL isolation using the strategy of map-based cloning. In this study, a recombinant inbred line (RIL) population derived from two indica rice varieties, Zhenshan 97 and HR5, was employed to map QTL for spikelets per panicle (SPP). One major QTL (qSPP7) and three minor QTL (qSPP1, qSPP2 and qSPP3) were identified on chromosomes 7, 1, 2 and 3, respectively. Four sets of near isogenic lines (NILs) BC4F2 targeted for the four QTL were developed by following a standard procedure of consecutive backcross, respectively. These QTL were not only validated in corresponding NILs, but also explained amounts of phenotypic variation with much larger LOD scores compared with those identified in RILs. SPP in the four QTL-NILs expressed bimodal or discontinuous distributions and followed the expected segregation ratio of single Mendelian factor by progeny test. Finally, qSPP1, qSPP2, qSPP3 and qSPP7 were respectively mapped to a locus, 0.5xa0cM from MRG2746, 0.6xa0cM from MRG2762, 0.8xa0cM from RM49 and 0.7xa0cM from MRG4436, as co-dominant markers on the basis of progeny tests. These results indicate no matter how small effect minor QTL is, QTL may still express the characteristics of single Mendelian factor in NILs and isolation of minor QTL will be possible using high quality NILs. Pyramiding these QTL into a variety will largely enhance rice grain yield.

Mapping and validation of quantitative trait loci for spikelets per panicle and 1,000-grain weight in rice (Oryza sativa L.)

This study identified four and five quantitative trait loci (QTLs) for 1,000-grain weight (TGW) and spikelets per panicle (SPP), respectively, using rice recombinant inbred lines. QTLs for the two traits (SPP3a and TGW3a, TGW3b and SPP3b) were simultaneously identified in the two intervals between RM3400 and RM3646 and RM3436 and RM5995 on chromosome 3. To validate QTLs in the interval between RM3436 and RM5995, a BC3F2 population was obtained, in which TGW3b and SPP3b were simultaneously mapped to a 2.6-cM interval between RM15885 and W3D16. TGW3b explained 50.4% of the phenotypic variance with an additive effect of 1.81xa0g. SPP3b explained 29.1% of the phenotypic variance with an additive effect of 11.89 spikelets. The interval had no effect on grain yield because it increased SPP but decreased TGW and vice versa. Grain shape was strongly associated with TGW and was used for QTL analysis in the BC3F2 population. Grain length, grain width, and grain thickness were also largely controlled by TGW3b. At present, it is not clear whether one pleiotropic QTL or two linked QTLs were located in the interval. However, the conclusion could be made ultimately by isolation of TGW3b. The strategy for TGW3b isolation is discussed.

Validation and Characterization of Ghd7.1, a Major Quantitative Trait Locus with Pleiotropic Effects on Spikelets per Panicle, Plant Height, and Heading Date in Rice (Oryza sativa L.)

A quantitative trait locus (QTL) that affects heading date (HD) and the number of spikelets per panicle (SPP) was previously identified in a small region on chromosome 7 in rice (Oryza sativa L.). In order to further characterize the QTL region, near isogenic lines (NILs) were quickly obtained by self-crossing recombinant inbred line 189, which is heterozygous in the vicinity of the target region. The pleiotropic effects of QTL Ghd7.1 on plant height (PH), SPP, and HD, were validated using an NIL-F2 population. Ghd7.1 explained 50.2%, 45.3%, and 76.9% of phenotypic variation in PH, SPP, and HD, respectively. Ghd7.1 was precisely mapped to a 357-kb region on the basis of analysis of the progeny of the NIL-F2 population. Day-length treatment confirmed that Ghd7.1 is sensitive to photoperiod, with long days delaying heading up to 12.5 d. Identification of panicle initiation and development for the pair of NILs showed that Ghd7.1 elongated the photoperiod-sensitive phase more than 10 d, but did not change the basic vegetative phase and the reproductive growth phase. These findings indicated that Ghd7.1 regulates SPP by controlling the rate of panicle differentiation rather than the duration of panicle development.

Epistasis and complementary gene action adequately account for the genetic bases of transgressive segregation of kilo-grain weight in rice

Transgressive segregation is a common phenomenon in plant species. In this study, transgressive segregation for kilo-grain weight (KGW) was observed in a recombinant inbred line (RIL) population derived from the cross between an indica variety, Teqing, and a wide compatible japonica variety, 02428, in three environments. A genetic linkage map with 154 single sequence repeat markers (SSR) was developed. Effects on KGW of quantitative trait loci (QTLs), digenetic epistasis, and their environmental interaction (QE) were determined using a mixed linear model approach. 13 QTLs with additive effects and 8 digenetic interactions involving 16 loci were identified. Eight QTLs were involved in interactions. Two QTLs and one epistasis showed QE. 30.0 and 14.0% of variation was explained by the additive effects and epistasis, respectively, which were much greater than the 4.4% of variation explained by QE. According to the model used, predicted KGW values of extreme phenotypes in the RIL population were very close to their observed values. This indicated that complementary action of additive QTLs and epistasis can adequately account for the important genetic bases of transgressive segregation for KGW in the rice RIL population.

Comparison of quantitative trait loci for rice yield, panicle length and spikelet density across three connected populations.

1National Key Laboratory of Crop Genetic Improvement and National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan 430070, People’s Republic of China 2College of Biosafety Science and Technology Hunan Agricultural University, Changsha 410128, People’s Republic of China 3Present address: Institute of Bast Fiber Crops, Chinese Academy of Agricultural Sciences, Changsha 410205, People’s Republic of China

Comparison of quantitative trait loci for 1,000-grain weight and spikelets per panicle across three connected rice populations

The ability to detect quantitative trait loci (QTLs) in a bi-allelic population is often limited. The power of QTL detection and identification of the most beneficial allele at each QTL could be greatly improved by comparing QTLs among different populations derived from connecting multi-parents. In this study, three sets of connected recombinant inbred lines (RILs) derived from the crosses between Zhenshan 97 and Minghui 63 (PZM), Zhenshan 97 and Teqing (PZT), and Minghui 63 and Teqing (PMT), respectively, were used. QTL analyses for the number of spikelets per panicle (SPP) and 1,000-grain weight (TGW) were performed in PZT, and five SPP QTLs on chromosomes 1, 6, and 7 and two TGW QTLs on chromosome 1 were detected. QTL for SPP was also identified in PMT, and six QTLs were detected on chromosomes 1, 2, 3, 6, and 7 in this population. In an earlier study, we identified five SPP QTLs and four TGW QTLs in PMT and nine TGW QTLs in PZM. Comparison of the QTL mapping results of these two studies showed that one QTL was common to the three populations, 11 QTLs were detected in two populations, and six QTLs were found in only one population. Comparison of genetic effect and the action direction of the QTLs detected in the three populations showed that additive effects of QTLs estimated in different populations were also expressed additively among three parental alleles. Additive effects of SPP7a estimated in three near-isogenic line F2 populations supported this finding. Based on these results, we suggest that pyramiding the most beneficial alleles among the three parents could efficiently improve rice yield.

Two complementary recessive genes in duplicated segments control etiolation in rice

The main objective of this study was to identify the genes causing etiolation in a rice mutant, the thylakoids of which were scattered. Three populations were employed to map the genes for etiolation using bulked segregant analysis. Genetic analysis confirmed that etiolation was controlled by two recessive genes, et11 and et12, which were fine mapped to an approximately 147-kb region and an approximately 209-kb region on the short arms of chromosomes 11 and 12, respectively. Both regions were within the duplicated segments on chromosomes 11 and 12. They possessed a highly similar sequence of 38xa0kb at the locations of a pair of duplicated genes with protein sequences very similar to that of HCF152 in Arabidopsis that are required for the processing of chloroplast RNA. These genes are likely the candidates for et11 and et12. Expression profiling was used to compare the expression patterns of paralogs in the duplicated segments. Expression profiling indicated that the duplicated segments had been undergone concerted evolution, and a large number of the paralogs within the duplicated segments were functionally redundant like et11 and et12.

Comparative mapping reveals similar linkage of functional genes to QTL of yield-related traits between Brassica napus and Oryza sativa

Oryza sativa and Brassica napus—two important crops for food and oil, respectively—share high seed yield as a common breeding goal. As a model plant, O. sativa genomics have been intensively investigated and its agronomic traits have been advanced. In the present study, we used the available information on O. sativa to conduct comparative mapping between O. sativa and B. napus, with the aim of advancing research on seed-yield and yield-related traits in B. napus. Firstly, functional markers (from 55 differentially expressed genes between a hybrid and its parents) were used to detect B. napus genes that co-localized with yield-related traits in an F2:3 population. Referring to publicly available sequences of 55 B. napus genes, 53 homologous O. sativa genes were subsequently detected by screening, and their chromosomal locations were determined using silico mapping. Comparative location of yield-related QTL between the two species showed that a total of 37 O. sativa and B. napus homologues were located in similar yield-related QTL between species. Our results indicate that homologous genes between O. sativa and B. napus may have consistent function and control similar traits, which may be helpful for agronomic gene characterization in B. napus based on what is known in O. sativa.