Low-Temperature Response to Major Agronomic Traits by Using Recombinant Inbred Line(RIL) Populations Derived from Towada × Kunmingxiaobaigu

Development of the recombinant inbred line populations （RILs） is important basis to detect QTLs for cold tolerance at booting stage in rice. A set of 230 RILs derived from the cross of Towada and Kunmingxiaobaigu were used for evaluation of low-temperature response on major agronomic traits of plant height （PH）, panicle length （PL）, panicle exsertion （PE）, spikelet fertility （SF）, specific spikelet fertility （SSF）, and spikelets per panicle （SPP） under natural low-temperature growing environments in Yunnan Province, China. The results showed PH, PE, and SPP were mainly attributed by genotypes. PL was mainly influenced interactively by the genotypes × environments. SF and SSF were mainly controlled by the environments. Under the five different growth environments, F values of the six agronomic traits mentioned above ranged from 4.019 to 97.284. Significant difference was revealed between the lines. Under every environment, it indicated significantly positive correlation between SF and SSF, with correlation coefficients ranged from 0.826 to 0.885. It indicated significantly positive correlation between PH, PL, and PE. Under five different growing environments, variation coefficients of the six characters ordered in SSF （66.3%） 〉 PE （57.4%） 〉 SP （37.2%） 〉 SPP （16.2%） 〉 PH （9.6%） 〉 PL （6.4%）. SSF, PE and SF were most sensitive to low temperature stress at booting stage, while SPP, PH and PL being least. The RILs of Towada/ Kunmingxiaobaigu can be used as a genetic population to investigate cold tolerance at booting stage. SSF, PE and SF are most sensitive to cold tolerance at booting stage in rice. So far the the variation of PH, PL, and SPP related to cold tolerance are not clear under natural low-temperature environment. More tested environments and years are required to identify and evaluate cold tolerance at booting stage in rice.

Identifying Genomic Regions Conferring Morphological and Yield-related Traits in Soybean Based On A Four-way Recombinant Inbred Line Population

Improvement of seed yield of soybean(Glycine max(L.)Merr.)is generally achieved by combining morphological and yield-related traits,such as plant height(PH),node number on main stem(NN),pod number per plant(NP),seed number per plant(NS),100-seed weight(HSW)and seed weight per plant(SWPP).Identifying quantitative trait loci(QTLs)for morphological and yield-related traits is therefore important for breeding.In this study,a four-way recombinant inbred line population comprising 160 lines derived from the cross(Kenfeng14×Kenfeng15)×(Heinong48×Kenfeng19)was planted in five different environments and morphological and yield-related trait data were used to identify QTLs by the inclusive composite interval mapping method.Totally 38 QTLs for PH,40 QTLs for NN,26 QTLs for NP,10 QTLs for NS,26 QTLs for HSW and 49 QTLs for SWPP were detected in 125 genomic regions.Single QTLs explained 2.17%-14.60%,2.00%-10.04%,2.37%-9.77%,2.62%-8.61%,0.47%-6.51%and 0.14%-12.39%of the phenotypic variation for PH,NN,NP,NS,HSW and SWPP,respectively.Among these 125 genomic regions,120 were newly associated with morphological and yield-related traits.The results would facilitate the molecular breeding of morphological and yield-related traits in soybean.

Mapping Quantitative Trait Loci Associated with Aluminum Toxin Tolerance in NJRIKY Recombinant Inbred Line Population of Soybean (Glycine max)

To investigate the genetic mechanism of AI-tolerance in soybean, a recombinant inbred line population （RIL） with 184 F2：7：11 lines derived from the cross of Kefen9 No.1×Nannong 1138-2 （AI-tolerant×AI-sensitive） were tested in pot experiment with sand culture medium in net room in Nanjing. Four traits, i.e. plant height, number of leaves, shoot dry weight and root dry weight at seedling stage, were evaluated and used to calculate the average membership index （FAi） as the indicator of AI-tolerance. The composite interval mapping （CIM） under WinQTL Cartographer v. 2.5 detected five QTLs （i.e. qFAi-1, qFAi- 2, qFAi-3, qFAi-4 and qFAi-5）, explaining 5.20%-9.07% of the total phenotypic variation individually. While with the multiple interval mapping （MIM） of the same software, five QTLs （qFAi-1, qFAi.5, qFAi.6, qFAi-7, and qFAi-8） explaining 5.7%-24.60% of the total phenotypic variation individually were mapped. Here qFAi-1 and qFAi-5 were detected by both CIM and MIM with the locations in a same flanking marker region, GMKF046-GMKF080 on B1 and satt278-sat_95 on L, respectively. While qFAi-2 under CIM and qFAi-6 under MIM both on Dlb2 were located in neighboring regions with their confidence intervals overlapped and might be the same locus. Segregation analysis under major gene plus polygene inheritance model showed that AI-tolerance was controlled by two major genes （h^2mg=33.05%） plus polygenes （h^2pg=52.73%）. Both QTL mapping and segregation analysis confirmed two QTLs responsible for AI-tolerance with relatively low heritability, and there might be a third QTL, confounded with the polygenes in segregation analysis.

Inheritance Analysis and QTL Mapping of Rice Starch Viscosity (Rapid Visco Analyzer Profile) Characteristics

The rice starch viscosity characteristics, which can be indicated by Rapid Visco Analyzer profile （RVA profile）, have been proved useful for the evaluation of cooking and eating quality in rice breeding program. To study the inheritance of the RVA profile, an F2 population of Wuyujing 3/Aichi 106 was used. The results indicated that the peak viscosity （PKV） was a typical quantitative character, and the hot paste viscosity （HPV）, cool paste viscosity （CPV）, setback viscosity （SBV）, breakdown viscosity （BDV）, and consistence viscosity （CSV） might be controlled by one major gene and several minor genes To elucidate the genetic basis of the paste viscosity characteristics, a recombinant inbred line （RIL） population derived from Nikken 2/Milyang 23 and its genetic linkage map were used to map the QTLs controlling RVA profiles in 2005 and 2006. A total of 34 QTLs distributed on chromosomes 1,2, 3, 4, 6, 7 and 8 were detected, including 19 and 15 QTLs in 2005 and 2006 respectively. Eight QTLs were both detected in the two years, qHPV6, qCPV6, qCSV6, qSBV6, and qBDV6 were located on chromosome 6, while qHPV2, qCSV2, and qCPV2were on chromosome 2.