QTL Mapping Associated With The Traits Of Photosynthesis And Yield Components In Rice(Oryza Sativa L.)

A mapping population of 123 F₁₂ lines(recombinant inbred lines,RILs), derived from a cross between India variety Dular and Javanica variety Lemont. Twelve Characters associated with Photosynthesis and six yield traits were observed. With a our own establishing genetic linkage map consisting of 109 SSR marks, the QTL mapping was conducted for the characters associated with photosynthesis and yield traits by composite Interval Mapping(CIM) using WinQTLcart 2.5 . At the same times, correlation analysis was also conducted to further understand the relationship. The results were as follows.1. QTLs controlling the characters associated with photosynthesisTwo QTLs with -2.0406, 3.7302 additive effects and 4.36, 2.24 LOD value were detected on chromosomes 1 and 2 for net photosynthetic rate(Pn), and individually explained 15% and 43% of total phenotypic variation respectly.Forty seven additive effect QTLs associated with the relative characters of photosynthesis were detected, of which three were responsible for flag leaf length(FLL) located on chromosomes 1, 2, 6, and individually explained 8% to 42% of total phenotypic variation, with 2.5734 -5.4685 additive effects and 2.43 -3.39 LOD values; twelve for flag leaf width(FLW) mapped on 1, 2, 3, 5 and 7 chromosomes repectively, collectively accounting for 95% of total phenotypic variation, with additive effect ranging from 0.0661 to 0.2418 and 3%-20% contribution to total phenotypic variation; six for flag leaf area(FLAr), showing additive effects ranging from 3.5697 to 8.0996 with 2.15-5.37 LOD values mapped to chromosomes 1, 2, 3, 4, 5 and 11, and collectively explaining 82% of total phenotypic variation; three for flag leaf angle (FLAn), showing 1.2421-4.3103 additive effects with 3.27-7.31 LOD, collectively accounting for 54% of total phenotypic variation; six for specific leaf weight of flag leaf(SLW) located on chromosomes 1, 5, 7 and 9 respectively, with the additive effects ranging from 0.1663 to 0.7737 and LOD value from 2.05-5.50, which collectively explained 95% of total phenotypic variation; seven for the content of Chlorophyll a(Chla) located on chromosomes 1, 3, 5, 6 and 8, which collectively explained 78% of the total phenotypic variation, with the additive effects ranging from 0.0812 to 0.1424, ans LOD values from 2.13 to 4.69; five QTLs for the content of chlorophyll b(Chlb), showing 0.0483-0.0621 additive effects and 2.35-3.94 LOD values, identified and located on 1, 3 and 5 chromosomes repectively, collectively explaining 53% of the total phenotypic variation; four for chlorophyll(Chl), showing 0.1467-0.2311 additive effects and 2.01-5.00 LOD values, which were mapped to chromosomes 1, 3 and 5, and explained 45% of the total phenotypic variation, and only one for the ratio of chlorophyll a to chlorophyll b(Chl a/b), showing -0.1017 additive effects and 4.05 LOD value, which were located on chromosomes 6, individually explaining 13% of the total phenotypic variation.Eleven additive effects QTLs were detected for the traits associated with tolerance to photooxidation in total. Of those QTLs, six for tolerance index(TI) detected on chromosomes 1, 2, 3, 6 and 8, with 2.6895-7.9991 additive effects and 2.11-5.31 LOD values, which collectively expained 64% of the total phenotypic variation, and five for sensibility index(SI) located on chromosomes 1, 3 and 8, with 3.8324-6.9314 additive effects and 2.42-5.45 LOD values.2. QTLs associated with grain yield and its componentsA total of 27 additive effect QTLs were detected on yield traits. Of those QTLs, four associated with panicle number per plant(PN) were mapped on chromosomes 2 and 9, repectively, with additive effects ranging from 0.7329 to 3.4915 and LOD values from 2.45 to 3.47, which individually explained 9%-31% of the total phenotypic variation; six with major panicle length(MPL) located on 1, 3, 4 ,5 and 6 chromosomes respectively, which performed 0.7029-1.2717 additive effects and collectively explained 67% of the total phenotypic variation; four with filled grain number per plant (FG) located on chromosomes 1, 2 and 3 respectively, showing 7.6327-10.9550 additive effects and 2.02-3.99 LOD values, which collectively explained 48% of the total phenotypic variation; four with the percentage of seed setting (PSS), showing additive effects from 4.9627 to 11.0826 and LOD values from 2.01 to 9.33 were mapped on chromosomes 1, 4 and 7 respectively and collectively accounted for 56% of the total phenotypic variation; five with 1000-grain weight (KW), performing additive effects from 0.8421 to 2.2702 and LOD value from 2.26 to 5.77 were located on chromosomes 1, 2 and 7 respectively and collectively explained 88% of the total phenotypic variation, and four with grain weight per plant (GWPP) exhibiting additive effects from 1.7724 to 3.3287 and LOD values from 2.24 to 4.77 were located on chromosomes 1, 2 and 4 respectively, and collectively explained 78% of the total phenotypic variation.3. Correlation among the characters associated with photosynthesis ans yield traitsThe correlation analysis for twelve characters associated with photosynthesis and six yield traits of RIL population suggested that increasing PN, TI and reducing FLAr, SI were helpful to increase PSS and GWPP. Of yield traits, on the condition that FG, PSS and KW were increased, properly-reduced PN were helpful to increase GWPP.Moreover, By comparing QTLs of characters associated with photosynthesis and yield traits, we found that QTLs of different characters associated with photosynthesis or yield traits located in the same interval of the same chromosome, implying that the genes with correlative functions became clutered distribution, These genes located in a clustered range in a choromosome might explain the adaptation significance of gene function network, which is considered one of higher forms for genetic information organization. It was also found that some QTLs of characters associated with photosynthesis and yield traits located in the same interval of the same chromosome, which show that the QTLs in the same interval might affect the characters associated with photosynthesis and yield traits simultaneously, and performance for pleiotrophism or tight linkage, implying that it would be possible to select a good combination of hight yield components with excellent photosynthesis traits. Finally, the author deeply discussed the correlation between photosynthesis and yield traits as well as its relationships with the QTLs controlling the traits.