Molecular Marker‐Assisted Dissection of Genotype × Environment Interaction for Plant Type Traits in Rice (<i>Oryza sativa</i> L.)

Yan, Jiahui; Jun, Zhu; He, Chi; Benmoussa, Mebrouk; Wu, Ping

doi:10.2135/cropsci1999.0011183x003900020039x

Cited by 89 publications

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“…G × E reduces the association between phenotypic and genotypic values, and leads to variable levels of the significance of QTL effects across environments (Hayes et al 1993;Romagosa et al 1996). QTL × environment interaction (QTL × E) has been a subject of importance in many QTL mapping studies (Paterson et al 1991;Bubeck et al 1993;Lee et al 1996;Lu et al 1996;Yan et al 1999;Cao et al 2001). QTL × E in these studies resulted in either significant QTL effects being detected only in a subset of all the environments, or in changes in magnitude of the QTL effect (non-crossover) or changes in QTL effects across environments in rank (crossover, i.e.…”

Section: Introductionmentioning

confidence: 99%

Mapping and characterisation of QTL × E interactions for traits determining grain and stover yield in pearl millet

et al. 2003

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A mapping population of 104 F(3) lines of pearl millet, derived from a cross between two inbred lines H 77/833-2 x PRLT 2/89-33, was evaluated, as testcrosses on a common tester, for traits determining grain and stover yield in seven different field trials, distributed over 3 years and two seasons. The total genetic variation was partitioned into effects due to season (S), genotype (G), genotype x season interaction (G x S), and genotype x environment-within-season interaction [G x E(S)]. QTLs were determined for traits for their G, G x S, and G x E(S) effects, to assess the magnitude and the nature (cross over/non-crossover) of environmental interaction effects on individual QTLs. QTLs for some traits were associated with G effects only, while others were associated with the effects of both G and G x S and/or G, G x S and G x E(S) effects. The major G x S QTLs detected were for flowering time (on LG 4 and LG 6), and mapped to the same intervals as G x S QTLs for several other traits (including stover yield, harvest index, biomass yield and panicle number m(-2)). All three QTLs detected for grain yield were unaffected by G x S interaction however. All three QTLs for stover yield (mapping on LG 2, LG 4 and LG 6) and one of the three QTLs for grain yield (mapping on LG 4) were also free of QTL x E(S) interactions. The grain yield QTLs that were affected by QTL x E(S) interactions (mapping on LG 2 and LG 6), appeared to be linked to parallel QTL x E(S) interactions of the QTLs for panicle number m(-2) on (LG 2) and of QTLs for both panicle number m(-2) and harvest index (LG 6). In general, QTL x E(S) interactions were more frequently observed for component traits of grain and stover yield, than for grain or stover yield per se.

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Section: Introductionmentioning

confidence: 99%

Mapping and characterisation of QTL × E interactions for traits determining grain and stover yield in pearl millet

et al. 2003

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“…The response of such an agronomically important quantitative trait locus (QTL) to the growth environments has been demonstrated by Yan et al (1999), Yamamoto et al (2000) and Kobayashi et al (2003) who conducted QTL analyses across different locations. However, compared with those major quantitative traits, flag leaf traits had seldom been examined despite their importance.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al (1999) examined FLA in the Lemont/Teqing F 2 population and located five QTLs on chromosomes 2, 5, 6, 7 and 9. Yan et al (1999) grew the IR64/Azucena doubled haploid (DH) population under subtropical and temperate zones, and detected seven, seven and six QTLs for FLL, FLW and FLA, respectively. Results from the latter QTL analysis showed the presence of a genomic region affecting both FLL and FLW on chromosome 4 and regions affecting the flag leaf trait at only one location on chromosomes 2, 4, 6, 9, 11 and 12.…”

Section: Introductionmentioning

confidence: 99%

“…Results from the latter QTL analysis showed the presence of a genomic region affecting both FLL and FLW on chromosome 4 and regions affecting the flag leaf trait at only one location on chromosomes 2, 4, 6, 9, 11 and 12. These regions, which have multiple effects or are expressed by a particular environment, possibly play important roles in the genetic control of flag leaf development (Li et al 1995, Lin et al 1996, Yan et al 1999. Further studies should be carried out to locate and characterize such regions using different mapping populations.…”

Section: Introductionmentioning

confidence: 99%

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Quantitative Trait Loci Affecting Flag Leaf Development in Rice (Oryza sativa L.)

et al. 2003

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One hundred and ninety-one recombinant inbred lines (RILs) (F 7 ) derived from a cross between Milyang 23 (M23) and Akihikari (AK) were grown in 1997 in Joetsu, Japan (temperate zone), and during the 2000-01 dry and wet seasons (four consecutive seasons) in Los Baños, Philippines (tropical zone) to detect quantitative trait loci (QTLs) for flag leaf length, width and angle (FLL, FLW and FLA) of rice (Oryza sativa L.). The detected QTLs suggested that flag leaf development was influenced by nine genomic regions categorized into three groups. In Group I, three regions (chromosomes 1, 4 and 6) increased both FLL and FLW. The QTL profiles showed that the effects of these three regions were powerful and stable across locations. Especially, the effect of the region on chromosome 4 (the nearest RFLP marker, XNpb235) accounted for 9-17 % and 20-26 % of the FLL and FLW variances, respectively. In Group II, four regions (chromosomes 2, 3, 10 and 11) affected FLL, whereas in Group III, two regions (chromosomes 8 and 12) affected FLW. The detection pattern of QTLs showed that three regions in Groups II and III (chromosomes 3, 10 and 12) were expressed by the growth conditions of Los Baños. No region with a clear effect on FLA was identified, although this trait was segregated largely in the RILs. By characterizing the nine regions in detail, this paper elaborates on the genetic factors and mechanisms controlling flag leaf development.

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“…Comparison of QTL profiles between related traits, growth stages and growth conditions may enable to further characterize the gene effects expressed with plant ontogeny and growth environments (Yan et al 1999. Thus, QTL analysis is a useful tool for analysing the genetic basis of leaf N concentration, which differs significantly with the growth stages and environments (Ying et al 1998, Zhou and Wang 2003, Lin et al 2006, Wang et al 2006.…”

Section: Introductionmentioning

confidence: 99%

Identification and characterization of genomic regions associated with nitrogen dynamics in rice plants (Oryza sativa L.)

et al. 2008

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In rice (Oryza sativa L.) plants, accumulation and remobilization of nitrogen (N) are essential physiological processes that determine grain yield and quality. The objectives of the present study were to identify and characterize the genomic regions associated with this N dynamics by quantitative trait locus (QTL) analysis. In the present study, 191 recombinant inbred lines (F 7 ) derived from a cross between Milyang 23 (Indicatype) and Akihikari (Japonica-type) were repeatedly evaluated for leaf nitrogen (N) concentration throughout four cropping seasons in Joetsu, Japan and Los Baños, Philippines, to perform interval mappings using a 182 RFLP marker-based linkage map. The locations and effects of the QTLs detected showed that the N dynamics was controlled by 15 genomic regions classifed into three groups. Four regions in Group I (chromosomes 1, 2, 7 and 8) affected only the N concentration before heading, i.e., N accumulation during the vegetative phase; whereas eight regions in Group II (chromosomes 2, 3, 4, 6, 7, 9, 10 and 12) affected the N concentration after heading, i.e., N remobilization during the reproductive phase. Only three regions (chromosomes 1, 2 and 10) in Group III exerted effects on both N concentrations in the vegetative and reproductive phases. In 12 of these 15 regions, expression of the QTLs depended on the cropping seasons and sites. Statistical tests enabled to detect significant genotype × stage and genotype × season interaction effects for nine and eight regions, respectively. Characterization of the 15 regions demonstrated that the genetic control of N dynamics involves a number of genes whose effects were enhanced or suppressed by plant ontogeny and the growth environment.

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Molecular Marker‐Assisted Dissection of Genotype × Environment Interaction for Plant Type Traits in Rice (Oryza sativa L.)

Cited by 89 publications

References 0 publications

Mapping and characterisation of QTL × E interactions for traits determining grain and stover yield in pearl millet

Mapping and characterisation of QTL × E interactions for traits determining grain and stover yield in pearl millet

Quantitative Trait Loci Affecting Flag Leaf Development in Rice (Oryza sativa L.)

Identification and characterization of genomic regions associated with nitrogen dynamics in rice plants (Oryza sativa L.)

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