Grain yield is one of the most important indexes in rice breeding,which is governed by quantitative trait loci (QTLs).Different map-ping populations have been used to explore the QTLs controlling yield related traits.Primary populations such as F 2 and recombi-nant inbred line populations have been widely used to discover QTLs in rice genome-wide,with hundreds of yield-related QTLs detected.Advanced populations such as near isogenic lines (NILs) are efficient to further fine-map and clone target QTLs.NILs for primarily identified QTLs have been proposed and confirmed to be the ideal population for map-based cloning.To date,20 QTLs directly affecting rice grain yield and its components have been cloned with NIL-F 2 populations,and 14 new grain yield QTLs have been validated in the NILs.The molecular mechanisms of a continuously increasing number of genes are being unveiled,which aids in the understanding of the formation of grain yield.Favorable alleles for rice breeding have been 'mined' from natural cultivars and wild rice by association analysis of known functional genes with target trait performance.Reasonable combination of favorable alleles has the potential to increase grain yield via use of functional marker assisted selection.
A quantitative trait locus(QTL)that affects heading date(HD)and the number of spikelets per panicle(SPP)was previously identifed 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 confrmed that Ghd7.1 is sensitive to photoperiod,with long days delaying heading up to 12.5 d.Identifcation 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 fndings indicated that Ghd7.1 regulates SPP by controlling the rate of panicle differentiation rather than the duration of panicle development.
For many tller crops,the plant archit ecture(PA),including the plant fresh weight,plant height,number of tllrs,tller angle and stem diameter,sigificantly afects the grain yield.In this study,we propose a method based on volumetric reconstruction for high-throughput three-dimensional(3D)wheat PA studies.The proposed methodology involves plant volumetric reconst ruction from multiple images,plant model processing and phenotypic parameter estimation and analysis.This study was performed on 80 Triticum aestium plants,and the results were analyzed.Comparing the automated measurements with manual measurements,the mean absolute per-centage error(MAPE)in the plant height and the plant fresh weight was 2.71%(1.08cm with an average plant height of 40.07cm)and 10.06%(1.41g with an average plant fresh weight of 14.06 g),respectively.The root mean square error(RMSE)was 137 cm and 1.79g for the plant height and plant fresh weight,respectively.The correlation cofficients were 0.95 and 0.96 for the plant height and plant fresh weight,respectively.Additionally,the proposed methodology,in-cluding plant reconstruction,model processing and trait ext raction,required only approximately 20s on average per plant using parallel computing on a graphics processing unit(GPU),dem-onstrating that the methodology would be valuable for a high-throughput phenotyping platform.
Wei FangHui FengWanneng YangLingfeng DuanGuoxing ChenLizhong XiongQian Liu
Breeding rice with high water use efficiency (WUE) can ameliorate water shortage through water-saving irrigation.However,WUE is a complex quantitative trait and very few studies have been conducted to measure WUE directly.In this study,a recombined inbred line population derived from a cross between an indica lowland rice and upland japonica rice was used to dissect the genetic control of WUE by fine-monitored water supply experiments.Quantitative trait loci (QTL) were scanned for 10 traits including heading date (HD),water-consumption per day (water/d),shoot weight gain per day (shootw/d),root weight gain per day (rootw/d),kernel weight gain per day (kernelw/d),average WUE at whole plant level (WUEwhole/d),average WUE for up-ground biomass (WUEup/d),average WUE for grain yield (WUEyield/d),average economic index (econindex/d),and average root/shoot ratio per day (ratio/d).The results show that most of the traits were significantly correlated to each other.Twenty-four QTL (LOD ≥ 2.0) were detected for econindex,econindex/d,WUEyield,WUEyield/d,WUEup,WUEup/d,WUEwhole,WUEwhole/d,kernelw,kernelw/d,rootw,and water/d by composite interval mapping.These QTLs are located on chromosomes 1,2,4,6,7,8,and 12.Individual QTLs accounted for 4.97%-10.78% of the phenotypic variation explained.Some of these QTLs overlapped with previously reported drought resistance QTLs detected in this population.These results provide useful information for further dissection of the genetic basis and marker-assisted selection of WUE in rice.
Total green leaf area(GLA)is an important trait for agronomic studies.However,existing methods for estimating the GLA of individual rice plants are destructive and labor-intensive.A nondestructive method for estimating the total GLA of individual rice plants based on multi-angle color images is presented.Using projected areas of the plant in images,linear,quadratic,exponential and power regression models for estimating total GLA were evaluated.Tests demonstrated that the side-view projected area had a stronger relationship with the actual total leaf area than the top-projected area.And power models fit better than other models.In addition,the use of multiple side-view images was an efficient method for reducing the estimation error.The inclusion of the top-view projected area as a seoond predictor provided only a slight improvement of the total leaf area est imation.When the projected areas from multi angle images were used,the estimated leaf area(ELA)using the power model and the actual leaf area had a high correlation cofficient(R2>0.98),and the mean absolute percentage error(MAPE)was about 6%.The method was capable of estimating the total leaf area in a nondestructive,accurate and eficient manner,and it may be used for monitoring rice plant growth.
Ni JiangWanneng YangLingfeng DuanGuoxing ChenWei FangLizhong XiongQian Liu
