Breeding design in wheat by combining the QTL information in a GWAS panel with a general genetic map and computer simulation

A large amount of genome-wide association study(GWAS)panels together with quantitative-trait locus(QTL)information associated with breeding-targeted traits have been described in wheat(Triticum aestivum L.).However,the application of mapping results from a GWAS panel to conventional wheat breeding remains a challenge.In this study,we first report a general genetic map which was constructed from 44 published linkage maps.It permits the estimation of genetic distances between any two genetic loci with physical map positions,thereby unifying the linkage relationships between QTL,genes,and genomic markers from multiple genetic populations.Second,we describe QTL mapping in a wheat GWAS panel of 688 accessions,identifying 77 QTL associated with 12 yield and grain-quality traits.Because these QTL have known physical map positions,they could be mapped onto the general map.Finally,we present a design approach to wheat breeding by using known QTL information and computer simulation.Potential crosses between parents in the GWAS panel may be evaluated by the relative frequency of the target genotype,trait correlations in simulated progeny populations,and genetic gain of selected progenies.It is possible to simultaneously improve yield and grain quality by suitable parental selection,progeny population size,and progeny selection scheme.Applying the design approach will allow identifying the most promising crosses and selection schemes in advance of the field experiment,increasing predictability and efficiency in wheat breeding.

QTL IciMapping:Integrated software for genetic linkage map construction and quantitative trait locus mapping in biparental populations

QTL Ici Mapping is freely available public software capable of building high-density linkage maps and mapping quantitative trait loci(QTL) in biparental populations. Eight functionalities are integrated in this software package:(1) BIN: binning of redundant markers;(2) MAP: construction of linkage maps in biparental populations;(3) CMP: consensus map construction from multiple linkage maps sharing common markers;(4) SDL: mapping of segregation distortion loci;(5) BIP: mapping of additive, dominant, and digenic epistasis genes;(6) MET: QTL-by-environment interaction analysis;(7) CSL: mapping of additive and digenic epistasis genes with chromosome segment substitution lines; and(8) NAM: QTL mapping in NAM populations. Input files can be arranged in plain text, MS Excel 2003, or MS Excel 2007 formats. Output files have the same prefix name as the input but with different extensions. As examples, there are two output files in BIN, one for summarizing the identified bin groups and deleted markers in each bin, and the other for using the MAP functionality. Eight output files are generated by MAP, including summary of the completed linkage maps, Mendelian ratio test of individual markers, estimates of recombination frequencies, LOD scores, and genetic distances, and the input files for using the BIP, SDL,and MET functionalities. More than 30 output files are generated by BIP, including results at all scanning positions, identified QTL, permutation tests, and detection powers for up to six mapping methods. Three supplementary tools have also been developed to display completed genetic linkage maps, to estimate recombination frequency between two loci,and to perform analysis of variance for multi-environmental trials.

Linkage analysis and integrated software GAPL for pure-line populations derived from four-way and eight-way crosses

Pure lines derived from multiple parents provide abundant variation for genetic study.However,efficient genetic analysis methods and user-friendly software are still lacking.In this study, we developed linkage analysis methods and integrated analysis software for pure-line populations derived from four-way and eight-way crosses.First, polymorphic markers are classified into different categories according to the number of identifiable alleles in the inbred parents.Expected genotypic probability is then derived for each pair of complete markers, and based on them a maximum likelihood estimate(MLE) of recombination frequency is calculated.An EM algorithm is proposed for calculating recombination frequencies in scenarios that at least one marker is incomplete.A linkage map can thus be constructed using estimated recombination frequencies.We describe a software package called GAPL for recombination frequency estimation and linkage map construction in multi-parental pure-line populations.Both simulation studies and results from a reported four-way cross recombinant inbred line population demonstrate that the proposed method and software can build more accurate linkage maps in shorter times than other published software packages.The GAPL software is freely available from www.isbreeding.net and can also be used for QTL mapping in multi-parental populations.

Use of genomic selection and breeding simulation in cross prediction for improvement of yield and quality in wheat (Triticum aestivum L.)

In wheat breeding, it is a difficult task to select the most suitable parents for making crosses aimed at the improvement of both grain yield and grain quality. By quantitative genetics theory,the best cross should have high progeny mean and large genetic variance, and ideally yield and quality should be less negatively or positively correlated. Usefulness is built on population mean and genetic variance, which can be used to select the best crosses or populations to achieve the breeding objective. In this study, we first compared five models(RR-BLUP, Bayes A, Bayes B, Bayes ridge regression, and Bayes LASSO) for genomic selection(GS) with respect to prediction of usefulness of a biparental cross and two criteria for parental selection, using simulation. The two parental selection criteria were usefulness and midparent genomic estimated breeding value(GEBV). Marginal differences were observed among GS models. Parental selection with usefulness resulted in higher genetic gain than midparent GEBV. In a population of 57 wheat fixed lines genotyped with 7588 selected markers, usefulness of each biparental cross was calculated to evaluate the cross performance, a key target of breeding programs aimed at developing pure lines. It was observed that progeny mean was a major determinant of usefulness, but the usefulness ratings of quality traits were more influenced by their genetic variances in the progeny population. Near-zero or positive correlations between yield and major quality traits were found in some crosses, although they were negatively correlated in the population of parents. A selection index incorporating yield, extensibility, and maximum resistance was formed as a new trait and its usefulness for selecting the crosses with the best potential to improve yield and quality simultaneously was calculated. It was shown that applying the selection index improved both yield and quality while retaining more genetic variance in the selected progenies than the individual trait selection. It was concluded that combining genomic selection with simulation allows the prediction of cross performance in simulated progenies and thereby identifies candidate parents before crosses are made in the field for pure-line breeding programs.

Development of image-based wheat spike counter through a Faster R-CNN algorithm and application for genetic studies

Spike number(SN) per unit area is one of the major determinants of grain yield in wheat. Development of high-throughput techniques to count SN from large populations enables rapid and cost-effective selection and facilitates genetic studies. In the present study, we used a deep-learning algorithm, i.e., Faster Region-based Convolutional Neural Networks(Faster R-CNN) on Red-Green-Blue(RGB) images to explore the possibility of image-based detection of SN and its application to identify the loci underlying SN. A doubled haploid population of 101 lines derived from the Yangmai 16/Zhongmai 895 cross was grown at two sites for SN phenotyping and genotyped using the high-density wheat 660 K SNP array.Analysis of manual spike number(MSN) in the field, image-based spike number(ISN), and verification of spike number(VSN) by Faster R-CNN revealed significant variation(P < 0.001) among genotypes, with high heritability ranged from 0.71 to 0.96. The coefficients of determination(R^(2)) between ISN and VSN was 0.83, which was higher than that between ISN and MSN(R^(2)= 0.51), and between VSN and MSN(R^(2)= 0.50). Results showed that VSN data can effectively predict wheat spikes with an average accuracy of 86.7% when validated using MSN data. Three QTL Qsnyz.caas-4 DS, Qsnyz.caas-7 DS, and QSnyz.caas-7 DL were identified based on MSN, ISN and VSN data, while QSnyz.caas-7 DS was detected in all the three data sets. These results indicate that using Faster R-CNN model for image-based identification of SN per unit area is a precise and rapid phenotyping method, which can be used for genetic studies of SN in wheat.

Effect of calcination temperature on structure and performance of Ni/TiO_2-SiO_2 catalyst for CO_2 reforming of methane

The influence of calcination temperature on the structure and catalytic behavior of Ni/TiO2-SiO2 catalyst, for CO2 reforming of methane to synthesis gas under atmospheric pressure, was investigated. The results showed that the Ni/TiO2-SiO2 catalyst calcined at 700 ℃ had high and stable activity while the catalysts calcined at 550 and 850 ℃ had low and unstable activity. Depending on the calcination temperature, one, two, or three of the following Ni-containing species, NiO, Ni2.44Ti0.72Si0.07O4, and NiTiO3 were identified by combining the temperature programmed reduction （TPR） and X-ray diffraction （XRD） results. Their reducibility decreased in the sequence： NiO〉Ni2.44Ti0.72Si0.07O4〉NiTiO3. It suggests that high and stable activities observed over the Ni/TiO2-SiO2 catalyst calcined at 700 ~C were induced by the formation of Ni2.44Ti0.72Si0.07O4 and smaller NiO species crystallite size.

Ordering of high-density markers by the k-Optimal algorithm for the traveling-salesman problem

Construction of accurate and high-density linkage maps is a key research area of genetics.We investigated the efficiency of genetic map construction(MAP)using modifications of the k-Optimal(k-Opt)algorithm for solving the traveling-salesman problem(TSP).For TSP,different initial routes resulted in different optimal solutions.The most optimal solution could be found only by use of as many initial routes as possible.But for MAP,a large number of initial routes resulted in one optimal order.k-Opt using open route length gave a slightly higher proportion of correct orders than the method of adding one virtual marker and using closed route length.Recombination frequency(REC)and logarithm of odds(LOD)score gave similar proportions of correct order,higher than that given by genetic distance.Both missing markers and genotyping error reduced ordering accuracy,but the best order was still achieved with high probability by comparison of the optimal orders from multiple initial routes.Computation time increased rapidly with marker number,and 2-Opt took much less time than 3-Opt.The 2-Opt algorithm was compared with ordering methods used in two other software packages.The best method was 2-Opt using open route length as the criterion to identify the optimal order and using REC or LOD as the measure of distance between markers.We describe a unified software interface for using k-Opt in high-density linkage map construction for a wide range of genetic populations.

Modeling and simulation of recurrent phenotypic and genomic selections in plant breeding under the presence of epistasis

Recurrent selection is an important breeding method for population improvement and selecting elite inbreds or fixed lines from the improved germplasm.Recently,a computer simulation tool called QuMARS has been developed,which allows the simulation and optimization of various recurrent selection strategies.Our major objective in this study was to use the QuMARS tool to compare phenotypic recurrent,marker-assisted recurrent,and genomic selections(abbreviated respectively as PS,MARS and GS)for both short-and long-termbreeding procedures.ForMARS,twomarker selection models were considered,i.e.,stepwise(Rstep)and forward regressions(Forward).For GS,three prediction models were considered,i.e.,genomic best linear unbiased predictors(GBLUP),ridge regression(Ridge),and regression by Moore-Penrose general inverse(InverseMP).To generate genotypes and phenotypes for a given individual during simulation,one additive and two epistasis genetic models were considered with three levels of heritability.Results demonstrated that selection responses from GBLUP-based GS and MARS(Forward)were consistently greater than those from PS under the additive model,particularly in early selection cycles.In contrast,selection response from PS was consistently superior over MARS and GS under epistatic models.For the two epistasis models,total genetic variance and the additive variance component were increased in some cases after selection.Through simulation,we concluded that GS and PS were effective recurrent selection methods for improved breeding of targeted traits controlled by additive and epistatic quantitative trait loci(QTL).QuMARS provides an opportunity for breeders to compare,optimize and integrate new technology into their conventional breeding programs.

Quantitative genetic studies with applications in plant breeding in the omics era

Quantitative genetics is concerned with the inheritance of biological traits showing continuous(or quantitative)phenotypic variation.Quantitative traits are common and have been extensively investigated and studied in evolutionary and genetic studies and in plant and animal breeding.Quantitative traits are normally controlled by multiple genes with various kinds of genetic effect,and their phenotypes are readily modified by environmental variation.

Breeding to Optimize Agriculture in a Changing World

Breeding to Optimize Chinese Agriculture(OPTICHINA) was a three-year EU–China project launched in June of 2011. As designed, the project acted as a new strategic model to reinforce systematic cooperation on agricultural research between Europe and China. The OPTICHINA International Conference "Breeding to Optimize Agriculture in a Changing World" was held in Beijing, May 26–29, 2014. The conference included six thematic areas:(1) defining and protecting the yield potential of traits and genes;(2) high-throughput precision phenotyping in the field;(3) molecular technologies in modern breeding;(4) plant ideotype;(5) data analysis,data management, and bioinformatics; and(6) national challenges and opportunities for China. The 10 articles collected in this special issue represent key contributions and topics of this conference. This editorial provides a brief introduction to the OPTICHINA project, followed by the main scientific points of articles published in this special issue. Finally, outcomes from a brainstorming discussion at the end of the conference are summarized, representing the authors' opinions on trends in breeding for a changing world.

Metal-organic framework-derived Ni doped Co_(3)S_(4) hierarchical nanosheets as a monolithic electrocatalyst for highly efficient hydrogen evolution reaction in alkaline solution

Hierarchical nanostructure construction and electronic structure engineering are commonly employed to increase the electrocatalytic activity of HER electrocatalysts.Herein,Ni doped Co_(3)S_(4) hierarchical nanosheets on Ti mesh(Ni doped Co_(3)S_(4) HNS/TM)were successfully prepared by using metal organic framework(MOF)as precursor which was synthesized under ambient condition.Characterization results confirmed this structure and Ni incorporation into Co_(3)S_(4) lattice as well as the modified electronic structure of Co_(3)S_(4) by Ni doping.Alkaline HER performance showed that Ni doped Co_(3)S_(4) HNS/TM presented outstanding HER activity with 173 m V overpotential at-10 m A·cm^(-2),surpassing most of metal sulfide-based electrocatalysts.The hierarchical structure,superior electrical conductivity and electronic structure modulation contributed to the accelerated water dissociation and enhanced intrinsic activity.This work provides a new avenue for synthesizing hierarchical nanostructure and simultaneously tuning the electronic structure to promote HER performance,which has potential application in designing highly efficient and cost-effective HER nanostructured electrocatalyst.

Influence of coke rate on thermal treatment of waste selective catalytic reduction(SCR)catalyst during iron ore sintering

Waste selective catalytic reduction(SCR)catalyst as a hazardous waste has a significant impact on the environment and human health.In present study,a novel technology for thermal treatment of waste SCR catalyst was proposed by adding it to sinter mix for iron ore sintering.The influences of coke rate on the flame front propagation,sinter microstructure,and sinter quality during sintering co-processing the waste SCR catalyst process were studied.In situ tests results indicated the maximum sintering bed temperature increased at higher coke rate,indicating more liquid phase generated and higher airflow resistance.The sintering time was longer and the calculated flame front speed dropped at higher coke rate.Sinter microstructure results found the coalescence and reshaping of bubbles were more fully with increasing coke rate.The porosity dropped from 35.28%to 25.66%,the pore average diameter of large pores decreased from 383.76μm to 311.43μm.With increasing coke rate,the sinter indexes of tumbler index,productivity,and yield,increased from 33.2%,9.2 t·m^(-2)·d^(-1),28.9%to 58.0%,36.0 t·m^(-2)·d^(-1),68.9%,respectively.Finally,a comprehensive index was introduced to systematically assess the influence of coke rate on sinter quality,which rose from 100 to 200 when coke rate was increased from 3.5%(mass)to 5.5%(mass).

Association Analysis of Quantitative Traits in F_1 Families Derived from Two Maize Landraces

[ Objective] The objective of this study was to evaluate the genetic diversity and characterization of special maize population consisting of 135 Fl fami- lies. [ Method ] In this study, association analysis was conducted in 135 F1 families derived from two maize landraces, and the efficiency of this method was evalua- ted through simulation. [ Result] Association analysis with different kinds of families showed that large population size and robust phenotypic data were required for association mapping. For all the phenotypic traits, the model controlling beth population structure and relative kinship （ Q ＋ K） performed better than the model controlling relative kinship （K）, and similarly to the model controlling population structure （Q）. Across 100 simulation runs in QULINE, the average power of QTL detection for the two models were 88.64% and 83.64% respectively, and the number of false QTL was reduced from 399 with GLM model to 199 with K mod- el. Our simulation results suggested that these F1 families can be used for association analysis, and the power of the QTL detection was related to the maximum al- lele frequency （MAF）and the phenotypic variation （PVE） explained by QTL. [ Conclusion] The results from this study suggest that association analysis using the F1 families is an effective approach to study maize landraces for discovering elite genes which we are interested in from these special populations.

Investigation on the evolution characteristics of bed porous structure during iron ore sintering

To better understand the evolution characteristics of bed porous structure during iron ore sintering,X-ray computed tomography scanning technology was used to analyze the pore parameters in different areas of the sintering bed.A pore skeleton structure model was established to study the characteristics of the airflow channels in different zones.The absolute permeability of different areas was calculated through simulation,and the corresponding streamline and pressure drop distribution were analyzed.The results show that the porosity of raw material zone,high-temperature zone,and sintered zone increases gradually,which are 37.69%,46.41%,and 55.57%,respectively.The absolute permeability calculation results of the raw material zone and sintered zones are 792.49μm^(2) and 20560.80μm^(2),while the tortuosity is 1.77 and 1.45,respectively.Compared with the raw material zone,the flow streamline in the sintered zone is thicker and denser,the airflow resistance and the pressure drop are minor.

Integration of multi-physics and machine learning-based surrogate modelling approaches for multi-objective optimization of deformed GDL of PEM fuel cells

The development of artificial intelligence(AI)greatly boosts scientific and engineering innovation.As one of the promising candidates for transiting the carbon intensive economy to zero emission future,proton exchange membrane(PEM)fuel cells has aroused extensive attentions.The gas diffusion layer(GDL)strongly affects the water and heat management during PEM fuel cells operation,therefore multi-variable optimization,including thickness,porosity,conductivity,channel/rib widths and compression ratio,is essential for the improved cell performance.However,traditional experiment-based optimization is time consuming and economically unaffordable.To break down the obstacles to rapidly optimize GDLs,physics-based simulation and machine-learning-based surrogate modelling are integrated to build a sophisticated M 5 model,in which multi-physics and multi-phase flow simulation,machine-learning-based surrogate modelling,multi-variable and multi-objects optimization are included.Two machine learning methodologies,namely response surface methodol-ogy(RSM)and artificial neural network(ANN)are compared.The M 5 model is proved to be effective and efficient for GDL optimization.After optimization,the current density and standard deviation of oxygen dis-tribution at 0.4 V are improved by 20.8%and 74.6%,respectively.Pareto front is obtained to trade off the cell performance and homogeneity of oxygen distribution,e.g.,20.5%higher current density is achieved when sacrificing the standard deviation of oxygen distribution by 26.0%.

The Statistical Power of Inclusive Composite Interval Mapping in Detecting Digenic Epistasis Showing Common F_2 Segregation Ratios

Epistasis is a commonly observed genetic phenomenon and an important source of variation of complex traits, which could maintain additive variance and therefore assure the long-term genetic gain in breeding. Inclusive composite interval mapping （ICIM） is able to identify epistatic quantitative trait loci （QTLs） no matter whether the two interacting QTLs have any additive effects. In this article, we conducted a simulation study to evaluate detection power and false discovery rate （FDR） of ICIM epistatic mapping, by considering F2 and doubled haploid （DH） populations, different F2 segregation ratios and population sizes. Results indicated that estimations of QTL locations and effects were unbiased, and the detection power of epistatic mapping was largely affected by population size, heritability of epistasis, and the amount and distribution of genetic effects. When the same likelihood of odd （LOD） threshold was used, detection power of QTL was higher in F2 population than power in DH population; meanwhile FDR in F2 was also higher than that in DH. The increase of marker density from 10 cM to 5 cM led to similar detection power but higher FDR. In simulated populations, ICIM achieved better mapping results than multiple interval mapping （MIM） in estimation of QTL positions and effect. At the end, we gave epistatic mapping results of ICIM in one actual population in rice （Oryza sativa L.）.

Quantitative trait locus mapping with background control in genetic populations of clonal F_1 and double cross

In this study, we considered five categories of molecular markers in clonal F1 and double cross populations, based on the number of distinguishable alleles and the number of distinguishable genotypes at the marker locus. Using the completed linkage maps, incomplete and missing markers were imputed as fully informative markers in order to simplify the linkage mapping approaches of quantitative trait genes. Under the condition of fully informative markers, we demonstrated that dominance effect between the female and male parents in clonal F1 and double cross populations can cause the interactions between markers. We then developed an inclusive linear model that includes marker variables and marker interactions so as to completely control additive effects of the female and male parents, as well as the dominance effect between the female and male parents. The linear model was finally used for background control in inclusive composite interval mapping （ICIM） of quantitative trait locus （QTL）. The efficiency of ICIM was demonstrated by extensive simulations and by comparisons with simple interval mapping, multiple-QTLmodels and composite interval mapping. Finally, ICIM was applied in one actual double cross population to identify QTL on days to silking in maize.

Enhancing Genetic Gain through Genomic Selection: From Livestock to Plants

Although long-term genetic gain has been achieved through increasing use of modern breeding methods and technologies,the rate of genetic gain needs to be accelerated to meet humanity’s demand for agricultural products.In this regard,genomic selection(GS)has been considered most promising for genetic improvement of the complex traits controlled by many genes each with minor effects.Livestock scientists pioneered GS application largely due to livestock’s significantly higher individual values and the greater reduction in generation interval that can be achieved in GS.Large-scale application of GS in plants can be achieved by refining field management to improve heritability estimation and prediction accuracy and developing optimum GS models with the consideration of genotype-by-environment interaction and non-additive effects,along with significant cost reduction.Moreover,it would be more effective to integrate GS with other breeding tools and platforms for accelerating the breeding process and thereby further enhancing genetic gain.In addition,establishing an open-source breeding network and developing transdisciplinary approaches would be essential in enhancing breeding efficiency for small-and medium-sized enterprises and agricultural research systems in developing countries.New strategies centered on GS for enhancing genetic gain need to be developed.

Genetic dissection of top three leaf traits in rice using progenies from a japonica × indica cross

The size of the top three leaves of rice plants is strongly associated with yield; thus, it is important to consider quantitative traits representing leaf size （e.g., length and width） when breeding novel rice varieties. It is challenging to measure such traits on a large scale in the field, and little is known about the genetic factors that determine the size of the top three leaves. In the present study, a population of recombinant inbred lines （RILs） and reciprocal single chromosomal segment substitution lines （SSSLs） derived from the progeny of a japonica Asominori x indica IR24 cross were grown under four diverse environmental conditions. Six morphological traits associated with leaf size were measured,namely length and width of the flag, second and third leaves. In the RIL population, 49 QTLs were identified that clustered in 30 genomic region. Twenty-three of these QTLs were confirmed in the SSSL population. A comparison with previously reported genes/QTLs revealed eight novel genomic regions that contained uncharacterized ORFs associated with leaf size. The QTLs identified in this study can be used for marker- assisted breeding and for fine mapping of novel genetic elements controlling leaf size in rice.

Techniques for postmortem tenderisation in meat processing:effectiveness,application and possible mechanisms

Developing efficient and promising tenderising techniques for postmortem meat is a heavily researched topic among meat scientists as consumers are willing to pay more for guaranteed tender meat.However,emerging tenderising techniques are not broadly used in the meat industry and,to some degree,are controversial due to lack of theoretical support.Thus,understanding the mechanisms involved in postmortem tenderisation is essential.This article first provides an overview of the relationship of ageing tenderisation and calpain system,as well as proteomics applied to identify protein biomarkers characterizing tenderness.In general,the ageing tenderisation is mediated by multiple biochemical activities,and it can exhibit better palatability and commercial benefit by combining other interventions.The calpain system plays a key role in ageing tenderisation functions by rupturing myofibrils and regulating proteolysis,glycolysis,apoptosis and metabolic modification.Additionally,tenderising techniques from different aspects including exogenous enzymes,chemistry,physics and the combined methods are discussed in depth.Particularly,innovation of home cooking could be recommended to prepare relatively tender meat due to its convenience and ease of operation by consumers.Furthermore,the combined interventions provide better performance in controlled tenderness.Finally,future trends in developing new tenderising techniques,and applied consideration in the meat processing industry are proposed in order to improve meat quality with higher economical value.