QTL Analysis For Eating And Nutrient Quality Of Rice

Improving rice quality is one of the most important domains for genetic breeding.Quantitative trait loci (QTLs) analyses are the theory basis of gene fine- mapping and further improving rice quality. A recombinant inbredpopulation of the indica rice cross"Zhenshan97B"/"Miyang46" was grown in 2015 and 2016, respectively. QTLs for eating and nutrient quality were determined with a linkage map consisting of 247 markers. The results were listed as below.1. Near-infrared reflectance spectroscopy (NIRS) has been used to measure the cooking rice quality according to parameters such as the protein (PC) and amylose content(AC). Using flours from 519 rice samples representing a wide range of grain qualities,the current study compared the calibration models generated by different mathematical,preprocessing, and combinations of regression algorithm. A modified partial least squares model (MPLS) with the mathematic treatment "2, 8, 8, 2" (2nd order derivative computed based on 8 data points, and 8 and 2 data points in the 1st and 2nd smoothing,respectively) and inverse multiplicative scattering correction preprocessing was identified as the best model for simultaneously measuring PC and AC in brown flours. A modified partial least squares model (MPLS) with the mathematic treatment "2, 8, 8, 2" and detrend preprocessing was identified as the best model for simultaneously measuring onset temperature (To), peak temperature (Tp) and conclusion temperature (Tc) of diff'erential scanning calorimeter (DSC). MPLS/"2, 8, 8, 2"/ weighted multiplicative scattering correction preprocessing was identified as the best model for rapid viscosity analyzer(RVA) properties (including trough, cool, breakdown, setback viscosity. These calibrations can be used to predict rice quality parameter for genetic information.2. One QTL (qAC-6), one QTL (qGC-6), one QTL (qASV-6), one QTL (qENT-6), both two QTLs (qON-6, qON-8), (qPEA-6, qPEA-8), (qEND-6, qEND-8), (qPC-6, (qPC-12),showing significant additive effects for amylose content(AC),Gel consistency(GC), alkali spreading value(ASV), gelatinization enthalpy(GEN), onset temperature(ONT), peak temperature(PET), endset temperature (ENT)and protein content(PC) were detected ,respectively. In 2015, the one QTL for AC, GC, ASV and GEN explained 59.9%, 48.8%,79.8% and 20.9%, respectively, of the phenotypic variance; the major QTL for ONT(qON-6), PET (qPEA-6), and ENT(qEND-6) explained 70.2%, 74.4% and 69.9% of the phenotypic variance, respectively; two QTLs(qPC-6, qPC-12) for PC collectively explained 14.65% of the phenotypic variance, with the variance explained by a single QTL from 5.0% to 9.65%. QTL located in the vicinity of Wx gene on chromosome 6 displayed a major effect for AC, GC, ASV, ONT, PET and ENT, and a considerable effect for PC.3. In 2015, 41 individual QTL were identified for the 17 components of amini acid content (AAC) in brown rice flour, ranging from one to five QTL for each component .QTLs for AAC components were distributed in chromosome 1, 2,4,6,7,9,11 and 12, respectively, and mainly were in chromosome 4, 6, and 7, which were located 5, 8,and 12 QTLs, respectively, explaining 4.32% to 15.4% of the variation. Five amino acids components of Ala, Cys, Met, Ile and Leu all were identified one QTL. QTL labeled by RM3325-RM1243 rejions in Chromosome 7 were clusted by Asp,Thr,Gly,Val,Phe,Arg,Leu and Ala eight amino acid components, which contributed 8.69%,5.80%,7.91%,10.9%,6.62%,6.98%,9.86% and9.35% to the ariation, respectively.4. In 2016, 18 individual QTL were identified for the 17 components of amini acid content (AAC) in brown rice flour, ranging from one to three QTL for each component.QTLs for AAC components were mainly distributed in chromosome 6,explaining 4.92% to 9.48% of the variation. QTL of five amino acids components for Ala,Cys,Val?Ile and Lys was not detected. QTL labeled by RM225-RM6917 rejions in Chromosome 6 were clusted byAsp,Thr,Ser,Leu,His,Arg,Gly,and Phe eight amino acid components,which contributed 7.29%?8.26%?6.05%?5.34%?7.44%?6.56%?5.63%and 6.28% to the variation, respectively.5. The identification of QTLs for 17 amino aids components concentration using Zhenshan97B/Miyang46 NIR population was measured by QTLMAPPER 2.0, the results showed that 18 major QTLs were detected for 16 amino acids components(Asp,Thr,Ser,Glu,Gly,Ala,Cys,Val,Met,Ile,Leu,Tyr,Phe,Lys,His and Arg),among then,only 5 amino acids compoents,such as Thr?Glu?Val?Met and His, were significantly interacted with environment.