大豆小区产量及其相关性状QTL间的上位性和环境互作效应

以晋豆23栽培大豆(Glycine max)为母本、灰布支黑豆(ZDD2315,半野生大豆)为父本衍生出447个RIL群体,通过构建SSR遗传图谱及利用混合线性模型分析方法,对2年大豆小区产量及主要植物学性状进行QTL定位,并作加性效应、加性×加性上位互作效应及环境互作效应分析。结果显示,共检测到12个与小区产量、单株粒重、单株茎重、单株粒茎比、有效分枝、主茎节数、株高和结荚高度相关的QTL,分别位于A1、A2、H_1、I、J_2和M连锁群上。其中小区产量、株高、单株粒重、有效分枝和主茎节数均表现为遗传正效应,即增加其性状的等位基因来源于母本晋豆23。同时,检测到11对影响小区产量、单株粒重、单株茎重、株高和结荚高度的加性×加性上位互作效应及环境互作效应的QTL,发现22个QTL与环境存在互作。实验结果表明,上位效应和QE互作效应对大豆小区产量及主要农艺性状的遗传影响很大。进行大豆分子标记辅助育种时,既要考虑效应起主要作用的QTL,又要注重上位性QTL,这样有利于性状的稳定表达和遗传。     

Mapping QTL controlling yield and yield components in a spring barley (Hordeum vulgare L.) cross using marker regression

An RFLP map constructed from 99 doubled haploid lines of a cross between two spring barley varieties (Blenheim × Kym) was used to localize quantitative trait loci (QTL) controlling grain yield and yield components by marker regression and single-marker analysis. Trials were conducted over three years. Genotype-by-year interaction was detected for plant grain weight and ear grain weight so they were analysed separately for each year. None was detected for thousand-grain weight and ear grain number so data were pooled over years. A total of eleven QTL were detected for plant grain weight over two years and fourteen for ear grain weight over three years. Seven QTL were detected for plot yield. The locus with the largest effect was on chromosome 2(2H)L and accounted for 19% of the variation in the progeny. Eight QTL were detected for thousand-grain weight and five for ear grain number. Many of the QTL detected were in comparable positions in each year. Yield and yield components were only partly correlated. Comparisons based on common RFLP markers showed that some QTL were found in positions similar to those identified in other studies. For a number of QTL the identification of linked markers provided suitable opportunities for marker-assisted selection and improvement of barley and reference markers with which to analyse the homoeologous chromosome regions of wheat and other cereals.     

Identification of QTL for growth- and grain yield-related traits in rice across nine locations of Asia

Rice double-haploid (DH) lines of an indica and japonica cross were grown at nine different locations across four countries in Asia. Genotype-by-environment (G x E) interaction analysis for 11 growth- and grain yield-related traits in nine locations was estimated by AMMI analysis. Maximum G x E interaction was exhibited for fertility percentage number of spikelets and grain yield. Plant height was least affected by environment, and the AMMI model explained a total of 76.2% of the interaction effect. Mean environment was computed by averaging the nine environments and subsequently analyzed with other environments to map quantitative trait loci (QTL). QTL controlling the 11 traits were detected by interval analysis using mapmaker/qtl. A threshold LOD of >/=3.20 was used to identify significant QTL. A total of 126 QTL were identified for the 11 traits across nine locations. Thirty-four QTL common in more than one environment were identified on ten chromosomes. A maximum of 44 QTL were detected for panicle length, and the maximum number of common QTL were detected for days to heading detected. A single locus for plant height (RZ730-RG810) had QTL common in all ten environments, confirming AMMI results that QTL for plant height were affected the least by environment, indicating the stability of the trait. Two QTL were detected for grain yield and 19 for thousand-grain weight in all DH lines. The number of QTL per trait per location ranged from zero to four. Clustering of the QTL for different traits at the same marker intervals was observed for plant height, panicle number, panicle length and spikelet number suggesting that pleiotropism and or tight linkage of different traits could be the possible reason for the congruence of several QTL. The many QTL detected by the same marker interval across environments indicate that QTL for most traits are stable and not essentially affected by environmental factors.     

Molecular detection of genomic regions associated with grain yield and yield-related components in an elite bread wheat cross evaluated under irrigated and rainfed conditions

Grain yield and grain weight of wheat are often decreased by water-limitation in the north-eastern cropping belt of Australia. Based on knowledge that CIMMYT lines are well-adapted in this region, a recombinant inbred line (RIL) population between two elite CIMMYT bread wheats (Seri M82 and Babax) was evaluated under water-limited environments. Fourteen productivity traits were evaluated in 192 progeny in up to eight trials. For three aggregations of the environments (all, high yield or low yield), multiple quantitative trait loci (QTL) were detected, each explaining <15% of variation. Co-location of multiple trait QTL was greatest on linkage groups 1B-a, 1D-b, 4A-a, 4D-a, 6A-a, 6B-a, 7A-a and an unassigned linkage group. Two putative QTL (LOD > 3) from Seri (6D-b and UA-d) increased grain yield and co-located with a suggestive (2 < LOD < 3) and a putative QTL for increased stem carbohydrate content (WSC), respectively; the latter QTL also co-located with a putative anthesis QTL for earlier flowering. Both QTL were detected only in high yield (>4t ha⁻¹) environments. A third increased grain yield QTL (7A-a) from Babax co-located with QTL for increased grain number. Six putative QTL increased grain weight and co-located with QTL for harvest index, grains per spike and spike number. Three putative QTL for increased grains per spike co-located with strong QTL for earlier flowering, increased grain weight and fewer spikes. A group of progeny that exceeded the mean grain yield and grain weight of commercial checks had an increased frequency of QTL for high WSC, large grain size, increased harvest index and greater height, but fewer stems, when compared to low yielding (20% less), low grain weight progeny. These findings were consistent with agronomic analyses of the germplasm and demonstrate that there should be opportunities to independently manipulate grain number and grain size which is typically difficult due to strong negative correlations.     

Molecular mapping of quantitative trait loci for yield and yield components in spring wheat (Triticum aestivum L.)

An F1 derived doubled haploid (DH) population of 402 lines from the adapted spring wheat cross Superb (high yielding)/BW278 (low yielding) was developed to identify quantitative trait loci (QTL) associated with yield and yield components. A subset of the population (186 lines) was evaluated in replicated field trials in 2001 and 2002 at six locations in Manitoba and Saskatchewan, Canada. Agronomic parameters, grain yield and yield components including 1,000 grain weight, harvest index, average seed weight spike(-1), seed number spike(-1) and spikes number m(-2) were measured. A genetic map was constructed with 268 microsatellite marker loci and included two morphological genes, reduced plant height, Rht-B1b, and the presence/absence of awns, B1. Composite interval mapping was conducted to estimate the location and effect of QTL associated with the evaluated traits. A total of 53 QTL were identified on 12 chromosomes for the 9 evaluated traits with the coefficient of determination ranging from 0.03 to 0.21 of the total variation. The increase in yield and yield components ranged from 4.5 to 17.1% over the population mean. The five grain yield QTL were detected on chromosomes 1A, 2D, 3B, and 5A and showed a combined increase of 34.4%, over the population mean. The alleles from Superb were associated with increased yield for four of the five QTL. This study identified potential chromosome segments for use in marker-assisted selection to improve yield and yield components in spring wheat.     

The effect of semi-dwarf genes on root system size in field-grown barley

Root system size (RSS) was measured in 12 diverse barley genotypes and 157 double-haploid lines (DHs), using electric capacitance. The parents of the DHs, Derkado and B83-12/21/5, carry different semi-dwarfing genes, sdw1 and ari-e.GP, respectively. Estimates of RSS were taken in the field thrice during plant development: stem elongation (RSS1), heading (RSS2) and grain filling (RSS3). The 12 barley genotypes were assessed over 3 years and at two or three locations each year; the DH mapping population was assessed at two locations in 2002. Among the 12 barley genotypes, those with the semi-dwarf genes had greater RSS values in all 3 years (28.9, 24.6 and 15.0% in years 1, 2 and 3, respectively) compared to non-semi-dwarf controls. The DH population showed transgressive segregation on both sides of the parent means, indicating polygenic control of RSS. Quantitative trait loci (QTLs) for RSS were found on five of the seven chromosomes: 1H, 3H, 4H, 5H and 7H and these were compared with previously mapped agronomic traits. The TotalRSS QTL on 3H was associated with sdw1 and QTLs for height, plant yield and plant weight. The RSS3 QTL on 5H was associated with ari-e.GP and QTLs for height, plant yield, plant weight, harvest index and tiller number. The RSS3 QTL on 7H was also associated with a TotalRSS QTL and QTLs for plant weight and harvest index. Other RSS QTLs were not associated with any other trait studied. RSS is considered to be a polygenic trait linked to important traits, in particular to yield. The study highlights the effects of semi-dwarfing genes and discusses the potential for breeding for root traits.     

Identification of a QTL decreasing yield in barley linked to Mlo powdery mildew resistance

A molecular marker map, including Mlo mildew resistance, of the spring barley cross Derkado (Mlo-resistant) × B83-12/21/5 (Mlo-susceptible) was scanned for yield QTLs to determine whether the association of Mlo resistance with reduced yield was due to linkage or pleiotropy. Over the mapped portion of the genome of the cross, the QTL with the greatest effect upon yield was located within a 22 cM region between mlo and the simple sequence repeat HVM67 on chromosome 4(4H). The association of Mlo resistance with lower yield was therefore due to a repulsion linkage. Analysis of yield component characters revealed QTL alleles for reduced grain number and earlier heading date in the same region, also associated with Mlo resistance. Genotyping of a range of cultivars and sources of Mlo resistance with the HVM67 simple sequence repeat showed that the Derkado HVM67 allele was rare as it was found only in one other cultivar and four land-races or sources of disease resistance. Grannenlose Zweizeilige, the source, and Salome, the carrier of Mlo resistance in Derkado, have the same HVM67 genotype, although Salome was a mixture of two genotypes. The entire mlo-HVM67 chromosomal segment from Grannenlose Zweizeilige is therefore thought to have been transmitted to Derkado, possibly through joint selection for Mlo resistance and early heading. L92, synonym EP79, was another source of Mlo resistance with the same HVM67 allele as Derkado but recombination must have occurred during the breeding of Atem as it possesses a different HVM67 allele which is present in all the other Mlo sources and cultivars surveyed. Abbreviations: GN, grains per main stem ear; HD, heading date; MSTGW, thousand grain weight derived from GN and MSY; MSY, yield of grain on the main stem; PY, yield of grain from the whole plot; sCIM, simplified compound interval mapping; SIM, simple interval mapping; SPY, single plant yield; S-SAP, sequence-specific amplification polymorphism; TGW, thousand grain weight derived from bulk of plot seed; TN, number of fertile stems per plant.     

Gains in QTL detection using an ultra-high density SNP map based on population sequencing relative to traditional RFLP/SSR markers

Huge efforts have been invested in the last two decades to dissect the genetic bases of complex traits including yields of many crop plants, through quantitative trait locus (QTL) analyses. However, almost all the studies were based on linkage maps constructed using low-throughput molecular markers, e.g. restriction fragment length polymorphisms (RFLPs) and simple sequence repeats (SSRs), thus are mostly of low density and not able to provide precise and complete information about the numbers and locations of the genes or QTLs controlling the traits. In this study, we constructed an ultra-high density genetic map based on high quality single nucleotide polymorphisms (SNPs) from low-coverage sequences of a recombinant inbred line (RIL) population of rice, generated using new sequencing technology. The quality of the map was assessed by validating the positions of several cloned genes including GS3 and GW5/qSW5, two major QTLs for grain length and grain width respectively, and OsC1, a qualitative trait locus for pigmentation. In all the cases the loci could be precisely resolved to the bins where the genes are located, indicating high quality and accuracy of the map. The SNP map was used to perform QTL analysis for yield and three yield-component traits, number of tillers per plant, number of grains per panicle and grain weight, using data from field trials conducted over years, in comparison to QTL mapping based on RFLPs/SSRs. The SNP map detected more QTLs especially for grain weight, with precise map locations, demonstrating advantages in detecting power and resolution relative to the RFLP/SSR map. Thus this study provided an example for ultra-high density map construction using sequencing technology. Moreover, the results obtained are helpful for understanding the genetic bases of the yield traits and for fine mapping and cloning of QTLs.     

Quantitative trait loci associated with adaptation to Mediterranean dryland conditions in barley

The objective of the present study was to identify quantitative trait loci (QTL) influencing agronomic performance across rain fed Mediterranean environments in a recombinant inbred line (RIL) population derived from the barley cultivars ER/Apm and Tadmor. The population was tested in four locations (two in Syria and two in Lebanon) during four consecutive years. This allowed the analysis of marker main effects as well as of marker by location and marker by year within location interactions. The analysis demonstrated the significance of crossover interactions in environments with large differences between locations and between years within locations. Alleles from the parent with the higher yield potential, ER/Apm, were associated with improved performance at all markers exhibiting main effects for grain yield. The coincidence of main effect QTL for plant height and yield indicated that average yield was mainly determined by plant height, where Tadmor's taller plants, being susceptible to lodging, yielded less. However, a number of crossover interactions were detected, in particular for yield, where the Tadmor allele improved yield in the locations with more severe drought stress. The marker with the highest number of cross-over interactions for yield and yield component traits mapped close to the flowering gene Ppd-H2 and a candidate gene for drought tolerance HVA1 on chromosome 1H. Effects of these candidate genes and QTL may be involved in adaptation to severe drought as frequently occurring in the driest regions in the Mediterranean countries. Identification of QTL and genes affecting field performance of barley under drought stress is a first step towards the understanding of the genetics behind drought tolerance.     

Use of trial clustering to study QTL × environment effects for grain yield and related traits in maize

A population of 300 F_3:4 lines derived from the cross between maize inbred lines F2 and F252 was evaluated for testcross value in a large range of environmental conditions (11 different locations in 2 years: 1995 and 1996) in order to study (1) the magnitude of genotype × environment and (2) the stability of quantitative trait loci (QTL) effects. Several agronomic traits were measured: dry grain yield (DGY), kernel weight, average number of kernels per plant, silking date (SD) and grain moisture at harvest. A large genotype × environment interaction was found, particularly for DGY. A hierarchical classification of trials and an additive main effects and multiplicative interaction (AMMI) model were carried out. Both methods led to the conclusion that trials could be partitioned into three groups consistent with (1) the year of experiment and (2) the water availability (irrigated vs non-irrigated) for the trials sown in 1995. QTL detection was carried out for all the traits in the different groups of trials. Between 9 and 15 QTL were detected for each trait. QTL × group and QTL × trial effects were tested and proved significant for a large proportion of QTL. QTL detection was also performed on coordinates on the first two principal components (PC) of the AMMI model. PC QTL were generally detected in areas where QTL × group and QTL × trial interactions were significant. A region located on chromosome 8 near an SD QTL seemed to play a key role in DGY stability. Our results confirm the key role of water availability and flowering earliness on grain yield stability in maize.     

The genetics of nitrogen use in hexaploid wheat: N utilisation, development and yield

A genetic study is presented for traits relating to nitrogen use in wheat. Quantitative trait loci (QTLs) were established for 21 traits relating to growth, yield and leaf nitrogen (N) assimilation during grain fill in hexaploid wheat (Triticum aestivum L.) using a mapping population from the cross Chinese Spring × SQ1. Glutamine synthetase (GS) isozymes and estimated locations of 126 genes were placed on the genetic map. QTLs for flag leaf GS activity, soluble protein, extract colour and fresh weight were found in similar regions implying shared control of leaf metabolism and leaf size. Flag leaf traits were negatively associated with days to anthesis both phenotypically and genetically, demonstrating the complex interactions of metabolism with development. One QTL cluster for GS activity co-localised with a GS2 gene mapped on chromosome 2A, and another with the mapped GSr gene on 4A. QTLs for GS activity were invariably co-localised with those for grain N, with increased activity associated with higher grain N, but with no or negative correlations with grain yield components. Peduncle N was positively correlated, and QTLs co-localised, with grain N and flag leaf N assimilatory traits, suggesting that stem N can be indicative of grain N status in wheat. A major QTL for ear number per plant was identified on chromosome 6B which was negatively co-localised with leaf fresh weight, peduncle N, grain N and grain yield. This locus is involved in processes defining the control of tiller number and consequently assimilate partitioning and deserves further examination. Electronic Supplementary Material The online version of this article () contains supplementary material, which is available to authorized users.     

Genetic mapping of QTLs for sugar-related traits in a RIL population of Sorghum bicolor L. Moench

The productivity of sorghum is mainly determined by quantitative traits such as grain yield and stem sugar-related characteristics. Substantial crop improvement has been achieved by breeding in the last decades. Today, genetic mapping and characterization of quantitative trait loci (QTLs) is considered a valuable tool for trait enhancement. We have investigated QTL associated with the sugar components (Brix, glucose, sucrose, and total sugar content) and sugar-related agronomic traits (flowering date, plant height, stem diameter, tiller number per plant, fresh panicle weight, and estimated juice weight) in four different environments (two locations) using a population of 188 recombinant inbred lines (RILs) from a cross between grain (M71) and sweet sorghum (SS79). A genetic map with 157 AFLP, SSR, and EST-SSR markers was constructed, and several QTLs were detected using composite interval mapping (CIM). Further, additive × additive interaction and QTL × environmental interaction were estimated. CIM identified more than five additive QTLs in most traits explaining a range of 6.0–26.1% of the phenotypic variation. A total of 24 digenic epistatic locus pairs were identified in seven traits, supporting the hypothesis that QTL analysis without considering epistasis can result in biased estimates. QTLs showing multiple effects were identified, where the major QTL on SBI-06 was significantly associated with most of the traits, i.e., flowering date, plant height, Brix, sucrose, and sugar content. Four out of ten traits studied showed a significant QTL × environmental interaction. Our results are an important step toward marker-assisted selection for sugar-related traits and biofuel yield in sorghum.     

QTL for yield and associated traits in the Seri/Babax population grown across several environments in Mexico, in the West Asia, North Africa, and South Asia regions

Heat and drought adaptive quantitative trait loci (QTL) in a spring bread wheat population resulting from the Seri/Babax cross designed to minimize confounding agronomic traits have been identified previously in trials conducted in Mexico. The same population was grown across a wide range of environments where heat and drought stress are naturally experienced including environments in Mexico, West Asia, North Africa (WANA), and South Asia regions. A molecular genetic linkage map including 475 marker loci associated to 29 linkage groups was used for QTL analysis of yield, days to heading (DH) and to maturity (DM), grain number (GM2), thousand kernel weight (TKW), plant height (PH), canopy temperature at the vegetative and grain filling stages (CTvg and CTgf), and early ground cover. A QTL for yield on chromosome 4A was confirmed across several environments, in subsets of lines with uniform allelic expression of a major phenology QTL, but not independently from PH. With terminal stress, TKW QTL was linked or pleiotropic to DH and DM. The link between phenology and TKW suggested that early maturity would favor the post—anthesis grain growth periods resulting in increased grain size and yields under terminal stress. GM2 and TKW were partially associated with markers at different positions suggesting different genetic regulation and room for improvement of both traits. Prediction accuracy of yield was improved by 5 % when using marker scores of component traits (GM2 and DH) together with yield in multiple regression. This procedure may provide accumulation of more favorable alleles during selection.     

Merino sheep: a further look at quantitative trait loci for wool production

A quantitative trait loci (QTL) analysis of wool traits from experimental half-sib data of Merino sheep is presented. A total of 617 animals distributed in 10 families were genotyped for 36 microsatellite markers on four ovine chromosomes OAR1, OAR3, OAR4 and OAR11. The markers covering OAR3 and OAR11 were densely spaced, at an average distance of 2.8 and 1.2 cM, respectively. Body weight and wool traits were measured at first and second shearing. Analyses were conducted under three hypotheses: (i) a single QTL controlling a single trait (for multimarker regression models); (ii) two linked QTLs controlling a single trait (using maximum likelihood techniques) and (iii) a single QTL controlling more than one trait (also using maximum likelihood techniques). One QTL was identified for several wool traits on OAR1 (average curvature of fibre at first and second shearing, and clean wool yield measured at second shearing) and on OAR11 (weight and staple strength at first shearing, and coefficient of variation of fibre diameter at second shearing). In addition, one QTL was detected on OAR4 affecting weight measured at second shearing. The results of the single trait method and the two-QTL hypotheses showed an additional QTL segregating on OAR11 (for greasy fleece weight at first shearing and clean wool yield trait at second shearing). Pleiotropic QTLs (controlling more than one trait) were found on OAR1 (clean wool yield, average curvature of fibre, clean and greasy fleece weightand staple length, all measured at second shearing).     

Identification of trait-improving quantitative trait loci for grain yield components from a dent corn inbred line in an advanced backcross BC<Subscript>2</Subscript>F<Subscript>2</Subscript> population and comparison with its F<Subscript>2:3</Subscript> population in popcorn

Normal maize germplasm could be used to improve the grain yield of popcorn inbreds. Our first objective was to locate genetic factors associated with trait variation and make first assessment on the efficiency of advanced backcross quantitative trait locus (AB-QTL) analysis for the identification and transfer of favorable QTL alleles for grain yield components from the dent corn inbred. A second objective was to compare the detection of QTL in the BC₂F₂ population with results using F_2:3 lines of the same parents. Two hundred and twenty selected BC₂F₂ families developed from a cross between Dan232 and an elite popcorn inbred N04 were evaluated for six grain yield components under two environments, and genotyped by means of 170 SSR markers. Using composite interval mapping (CIM), a total of 19 significant QTL were detected. Eighteen QTL had favorable alleles contributed by the dent corn parent Dan232. Sixteen of these favorable QTL alleles were not in the same or near marker intervals with QTL for popping characteristics. Six QTL were also detected in the F_2:3 population. Improved N04 could be developed from 210 and 208 families with higher grain weight per plant and/or 100-grain weight, respectively, and 35 families with the same or higher popping expansion volume than N04. In addition, near isogenic lines containing detected QTL (QTL-NILs) for grain weight per plant and/or 100-grain weight could be obtained from 12 families. Our study demonstrated that the AB-QTL method can be applied to identify and manipulate favorable QTL alleles from normal corn inbreds and combine QTL detection and popcorn breeding efficiently.     

Detection of grain protein content QTLs across environments in tetraploid wheats

Grain protein content (GPC) is an important quality factor in both durum and bread wheats. GPC is considered to be a polygenic trait influenced by environmental factors and management practice. The objectives of this study were both to compare the quantitative trait loci (QTL) for GPC in a population of 65 recombinant inbred lines of tetraploid wheats evaluated in three locations for several years (eight data sets), and to investigate the genetic relationship among GPC and grain yield. QTLs were determined based on the Messapia × dicoccoides linkage map which covers 217 linked loci on the 14 chromosomes with 42 additional loci as yet unassigned to linkage groups. The map extends to 1352 cM; the average distance between adjacent markers was 6.3 cM. Seven QTLs for GPC, located on the chromosome arms 4BS, 5AL, 6AS (two loci), 6BS, 7AS and 7BS, were detected that were significant in at least one environment at P<0.001 or in at least two environments at P<0.01. One QTL was significant in all but one environment, two were significant in four or five environments, and four were significant in two out of eight environments. Six out of seven protein content QTLs had pleiotropic effects or were associated to QTLs for grain yield and explained the negative correlation among GPC and yield components. The present results support the concept that studies conducted in a single environment are likely to underestimate the number of QTLs that can influence a trait and that the phenotypic data for a quantitative trait should be collected over a range of locations to identify putative QTLs and determine their phenotypic effects.     

Potential for Marker-Assisted Simultaneous Improvement of Grain and Biomass Yield in Triticale

Triticale is a promising crop for agricultural biomass production but breeding has until now mainly focused on grain yield. Here, we evaluated the potential of marker-assisted simultaneous improvement of grain yield and biomass yield. To this end, we employed a large triticale doubled haploid population with 647 individuals derived from four families that were phenotyped for grain yield and biomass yield, as well as thousand-kernel weight, tiller density, and plant height in multi-environment field trials. Employing an association mapping approach, we identified quantitative trait loci (QTL) for all the five traits. The phenotypic correlation between grain yield and biomass yield was low, and we detected only one overlapping QTL suggesting different genetic architectures underlying both traits. Our results indicate that a marker-based selection for either grain yield or biomass yield does not adversely affect the other traits. Furthermore, an improvement of the multiplicative yield traits can to some extent also be achieved by selection for QTL identified for the component traits. Taken together, our results suggest that marker-assisted breeding can assist the establishment of dual-purpose triticale cultivars with high grain and biomass yield.     

Genomic Selection and Association Mapping in Rice (Oryza sativa): Effect of Trait Genetic Architecture,Training Population Composition,Marker Number and Statistical Model on Accuracy of Rice Genomic Selection in Elite,Tropical Rice Breeding Lines

Genomic Selection (GS) is a new breeding method in which genome-wide markers are used to predict the breeding value of individuals in a breeding population. GS has been shown to improve breeding efficiency in dairy cattle and several crop plant species, and here we evaluate for the first time its efficacy for breeding inbred lines of rice. We performed a genome-wide association study (GWAS) in conjunction with five-fold GS cross-validation on a population of 363 elite breeding lines from the International Rice Research Institute''s (IRRI) irrigated rice breeding program and herein report the GS results. The population was genotyped with 73,147 markers using genotyping-by-sequencing. The training population, statistical method used to build the GS model, number of markers, and trait were varied to determine their effect on prediction accuracy. For all three traits, genomic prediction models outperformed prediction based on pedigree records alone. Prediction accuracies ranged from 0.31 and 0.34 for grain yield and plant height to 0.63 for flowering time. Analyses using subsets of the full marker set suggest that using one marker every 0.2 cM is sufficient for genomic selection in this collection of rice breeding materials. RR-BLUP was the best performing statistical method for grain yield where no large effect QTL were detected by GWAS, while for flowering time, where a single very large effect QTL was detected, the non-GS multiple linear regression method outperformed GS models. For plant height, in which four mid-sized QTL were identified by GWAS, random forest produced the most consistently accurate GS models. Our results suggest that GS, informed by GWAS interpretations of genetic architecture and population structure, could become an effective tool for increasing the efficiency of rice breeding as the costs of genotyping continue to decline.     

Quantitative trait locus analysis of drought tolerance and yield in Maize in China

Drought accounts for significant yield losses in crops. Maize (Zea mays L.) is particularly sensitive to water stress at reproductive stages, and breeding to improve drought tolerance has been a challenge. By use of a linkage map with 121 single sequence repeat (SSR) markers, quantitative trait loci (QTLs) for grain yield and yield components were characterized in the population of the cross X178×B73 under water-stressed and well-watered conditions. Under the well-watered regime, 2, 4, 4, 1, 2, 2, and 3 QTLs were identified for grain yield, 100-kernel weight, kernel number per ear, cob weight per ear, kernel weight per ear, ear weight, and ear number per plant, respectively, whereas under the water-stressed conditions, 1, 5, 2, 6, 1, 3, and 2 QTLs, respectively, were found. The significant phenotypic correlations among yield and yield components to some extent were observed under both water conditions, and some overlaps between the corresponding QTLs were also found. QTLs for grain yield and kernel weight per ear under well-watered conditions and ear weight under both well-watered and water-stressed conditions over-lapped, and all were located on chromosome 1.03 near marker bnlg176. Two other noticeable QTL regions were on chromosome 9.05 and 9.07 near markers umc1657 and bnlg1525; the first corresponded to grain yield, kernel weight per ear, and ear weight under well-watered conditions and kernel number per ear under both water conditions, and the second to grain yield and cob weight per ear under water-stressed conditions and ear number per plant under both water conditions. A comparative analysis of the QTLs herein identified with those described in previous studies for yield and yield components in different maize populations revealed a number of QTLs in common. These QTLs have potential use in molecular marker-assisted selection.     

Identification and characterization of quantitative trait loci for grain yield and its components under different nitrogen fertilization levels in rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.)

A set of rice (Oryza sativa L.) recombinant inbred lines from a cross between Zhenshan 97 (indica) and HR5 (indica) was planted for four different growing seasons in two locations at three nitrogen (N) fertilization levels (N300, 300 kg urea/ha; N150, 150 kg urea/ha; and N0, 0 kg urea/ha). Grain yield and its components were evaluated, including grain yield per plant (GYPP), panicle number per plant (PNPP), grain number per panicle (GNPP), filled grains per panicle (FGPP), spikelet fertility percentage (SFP) and 100-grain weight (HGW). Correlation and path analysis indicated that SFP had the greatest contribution to GYPP at the N300 and N150 levels, but FGPP contributed the most to GYPP at the N0 level. Quantitative trait loci (QTL) were mapped based on a mixed linear model; genetic components (main effects, epistatic effects and QTL-by-environment interactions) were estimated separately. Six to 15 QTL with main effects were detected for each trait except SFP. Clusters of main-effect QTL associated with PNPP, GNPP, SFP and HGW were observed in regions on chromosomes 1, 2, 3, 5, 7 and 10. The main-effect QTL (qGYPP-4b and qGNPP-12) were only detected at the N0 level and explained 10.9 and 10.2% of the total phenotypic variation, respectively. A total of 33 digenic interactions among grain yield and its components were also identified. The identification of genomic regions associated with yield and its components at different nitrogen levels will be useful in marker-assisted selection for improving the nitrogen use efficiency of rice.