Breeding for yield
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Breeding for yield

SPICYBreeding for yieldDr Fred van Eeuwijk and Anja Dieleman outline theachievements that have been made in developing a suiteof tools for plant breeders which will aid the prediction ofphenotypic responses of genotypes across environmentsCould you begin by explaining the goalsof your current research project andthe needs you seek to address in plantbreeding?We aim to develop a general methodologyfor predicting complex traits from genetic andenvironmental information. A complex traitis based on many genes, or quantitative traitloci (QTLs), that interact between themselvesand with the environment to producea phenotype. Thus, the complex trait issupposed to show genotype by environmentinteraction (GxE), ie. superiority of genotypeswill change across environmental gradients.GxE slows down progress in breeding as newgenotypes will need to be tested in manyenvironments to assess their performance.In our project, ‘Smart tools for Predictionand Improvement of Crop Yield’ (SPICY),the complex trait ‘yield’ is dissected incomponent traits that are supposed to beenvironment independent. The componenttraits are integrated to produce the complextrait by means of a crop growth model, agenotype-to-phenotype function that mapsthe component traits and environmentalinputs on the space of the complex trait.The ultimate aim of SPICY is to predicta complex trait that shows GxE fromcomponent traits that don’t show GxE andenvironmental inputs. For the componenttraits, SPICY aims at inserting predictedvalues from a quantitative trait loci (QTL)model, ie. the component traits should bepredicted from DNA signatures. So, finally, acomplex trait exhibiting GxE is predicted fromDNA signatures and environmental inputs.To what extent do climate andenvironmental factors affect a phenotype?How did you take these variables intoaccount during your experiments?In our predictive model for a complex trait,we assume explicitly that the phenotypefor the complex trait is the end productof an integration over time of a numberof component traits together with variousenvironmental inputs such as the croppingsystem, temperature, CO 2, and light. Aswe were aiming at detecting GxE for thecomplex trait of yield, we chose contrastingenvironments in The Netherlands and Spainduring two growing seasons.Could you mention a few of the waysyour new methods improve upon existingtechnology and how they are moreefficient?Regarding fluorescence, our new approachis found in the sensor concept. Insteadof developing or using a bulk (bench top)fluorescence tester, the fluorescence toolis a distributed system of a host unit withmultiple sensors that can be deployed easilyin large span greenhouse environments withremote control and centralised fast (quasireal-time) data collection over wired orwireless infrastructure. The system is capableof mass-producing baseline-correctedtemporal fluorescence curves with batchprocessing for prompt extraction of plentyof feature points. The unique feature of eachsensor is a compact, all in one design (with noseparate sample holder, light guide, etc.) anda measurement protocol integrating severalmethods.For image analysis, our new system usesmultiple cameras that are vertically stackedto measure plants that are over 3 m inheight and fi xed to their position by ropes.The SPICY methodology aims to bring theimaging equipment to the plants and doesnot require an expensive infrastructure fortransporting plants. Additionally, it caneasily be used in more than one greenhouse.Our alternative approach to ‘observe’plants in their own growing environmentinstead of moving them around, posed newchallenges that needed to be tackled. Theseincluded overlapping of plants, problemswith less controllable lighting conditions,and limitations and aberrations due to shortviewing distances and angles.The SPICY crop growth model representsa large step forward for the integration of54 INTERNATIONAL INNOVATION

SPICYSmart and SPICYThe EU-funded SPICY project has made major headway indeveloping an innovative methodology for predicting complextraits from genetic and environmental informationTHE PLANT BREEDING industry has contributedconsiderably to increased quality and yield ofplant products over the last decades. Initiallyachieved by making crosses between promisingor complementary parents followed by screeningthe corresponding offspring on improvedphenotypic performance, molecular markershave more recently been added as a tool inbreeding, and this has increased insight into thegenetics behind phenotypic differences as well assped up the selection process.Molecular markers are tags on chromosomesthat may identify stretches of DNA that areassociated with yield and quality traits. Toimprove a trait, phenotypic selection on the traitis replaced by indirect selection on markers, ie.marker assisted selection (MAS). Selection ofmarkers at the genetic level is in most casesmore efficient than selection at the phenotypiclevel, on the trait as measured.MAS works well for simple traits such as diseaseresistances. Simple traits are based on relativelyfew genetic factors known as Quantitative TraitLoci (QTLs) that have individually strong effects,and whose effects are more or less independentof the environmental conditions. For simpletraits with strong environment-independentQTLs it is relatively easy to identify molecularmarkers and to improve the traits via indirectselection on markers.However, for complex traits like yield, currentmolecular breeding using markers still has severelimitations. Complex traits are based on manyQTLs with relatively small effects. Furthermore,for complex traits, the effects of QTLs oftendepend on environmental conditions. This latterphenomenon is called QTL by environmentinteraction (QTLxE). At the phenotypic level,differences in adaptation and stability areexpressions of what is called genotype byenvironment interaction (GxE). At the geneticlevel, QTLxE is underlying GxE.Selecting and breeding the best genotypes fora complex trait implies focusing on adaptationand stability, taking into account GxE. Toidentify the most suitable markers for MASfor such traits, breeders should test offspringpopulations across a range of environmentalconditions. This would require many andexpensive field trials.INTEGRATING TRAITS AND INPUTSCoordinated by Dr Fred van Eeuwijk atWageningen University (The Netherlands), the‘Smart tools for Prediction and Improvementof Crop Yield’ (SPICY) project proposes analternative strategy for improving complextraits. First, the complex trait with QTLxE isdissected in a limited number of componenttraits without QTLxE. Next, QTLs for thecomponent traits together with environmentalinputs are integrated via the crop growth modelto predict yield for all kinds of environmentalconditions. These predictions form the basis forthe selection of genotypes. The SPICY strategy isan enhanced MAS strategy to improve complextraits with GxE.IMAGING SENSOR IN CROPCOMBINING APPROACHESSPICY’s overarching goal is to develop a suite oftools for the molecular breeding of crop plants forsustainable and competitive agriculture. Thesewill help the breeder in predicting the phenotypicresponse of genotypes for complex traits under arange of environmental conditions, and the suiteincludes genetic, genomic, fluorescence, imageanalysis, phenotyping, and statistical as well ascrop growth modelling tools.The SPICY team has been using a crop growthmodel to predict yield of a genotype underdifferent environmental conditions. SpecificQTL-analysis methods have been developedto find the corresponding QTL for the cropgrowth parameters. The team assumed thatsuch parameters are more directly linked toCROP WITH FIRST FRUITS56 INTERNATIONAL INNOVATION

genetic information than yield itself, as thelatter is the final result of complex interactionsbetween genetic and environmental factors.Hence, QTL regions for the model parametersare more specific and stable. Two contrastingenvironments – The Netherlands and Spain– were chosen for the studies during twogrowing seasons.Covering a wide range of expertise, SPICYallows an integrated approach across scientificdisciplines and biological organisation levels.Breeding companies and related industryplayed a decisive role in the project’s choice ofaims, techniques, procedures and approaches.Notably, pepper was selected as a modelcrop. This decision was based on the factthat although the crop is of strong economicinterest around the world, only relatively fewtechnological tools and genomic resources areavailable for it, especially when compared withcrops like tomatoes.KEEPING UP WITH TECHNOLOGYThe SPICY team had to be flexible in its approachto the project. Technologies in genomics havedrastically changed during the last four years,which has had a major impact on the choiceof strategies and technologies at the project’sdisposal. The team’s initial plan included lowthroughput genomics experiments involvingcloning and sequencing individual genes, whichlimited the quantity of expected data. The recentadvances in gene expression technologies haveconsiderably increased SPICY’s productivitywhile decreasing the costs of information gain. Alarge part of the technical work at the labs couldtherefore be subcontracted to high throughputgenomic platforms and the team has movedto in silico work using databases, transferringgenomic knowledge from the model plantspecies Arabidopsis to pepper. “This permits usto increase significantly the set of data produced,and to explore the pepper genome more widelyand efficiently, for instance in our considerationof more than 1,100 candidate genes instead ofthe 150 initially expected,” van Eeuwijk explains.“Our efforts in data production had to movetowards data exploitation, with a significant gainin the broadness of results”.In addition, the original project proposal didnot include the use of a range camera. Thesedevices measure distance or depth for imageanalysis, yet only became available after theSPICY project started. As such, the team hadto develop a new methodology to merge thestereo vision with the range images. The rangecamera is now a key component of SPICY’simaging platform and has improved theaccuracy of their 3D reconstructions.DISSEMINATING RESULTS,USING THE TOOLSAn Industrial Advisory Board was installed at thebeginning of the project to advise on aims andapproaches. This board influenced SPICY and alsofed knowledge, generated in discussions at SPICYmeetings, directly back into the plant breedingindustry. Additional knowledge flow to industrywas organised to take place via lectures, papers,and a website.SPICY is now in its final phase and van Eeuwijkhas high hopes for the long-term use of thetools that have been successfully developed inthe project. For the fluorescence tool, SPICY hascreated a flexible and easy-to-use sensor system,which looks set to become a popular tool formeasuring physiologically relevant information.Although the team’s imaging equipment (theSPYSEE robot) for the image analysis tool stillneeds further development, van Eeuwijk hopestheir methodology for observing plants in theirown growing environments will be used as a newgeneration of phenotyping tools, to aid breedersin the development of better varieties. “It mayalso have spinoff applications in the roboticharvesting of crops,” he enthuses. “Moreover,the 3D technique we developed to merge colourstereo vision with range imaging has broadpotential outside the plant world”.The integration of crop growth and advancedQTL modelling promises to become a powerfultool to predict complex traits from genetic andenvironmental information without a need forelaborate field trials. Such modelling strategieswill then serve as a basis for fast and costefficientbreeding strategies.INTELLIGENCESPICYSMART TOOLS FOR PREDICTION ANDIMPROVEMENT OF CROP YIELDOBJECTIVESSPICY aims at developing tools for predictingenvironment-conditioned complex traits likeyield from DNA signatures and environmentalinputs using a synthesis of crop growthand statistical genetic models. The DNAsignatures are made up of molecular markersrepresenting quantitative trait loci, or genesfor physiological traits, or approximations tothose traits obtained from image analysis orfluorescence measurements.PARTNERSWageningen University, The Netherlands• Institut National de la RéchercheAgronomique Avignon, France • FlandersInstitute for Biotechnology, VIB, Belgium• James Hutton Institute, BioSS, UK •Stichting DLO, The Netherlands • BudapestUniversity of Technology and Economics,Hungary • Estación Experimental de laFundación Cajamar, SpainCONTACTProfessor Dr Fred van EeuwijkProject CoordinatorBiometrisWageningen UniversityDroevendaalsesteeg 1Radix Building6708 PB WageningenThe NetherlandsT +31 317 482902E fred.vaneeuwijk@wur.nlwww.spicyweb.euFRED VAN EEUWIJK is Professor in AppliedStatistics at Wageningen University (TheNetherlands). He leads a group of about 40statisticians who are responsible for statisticaleducation and support of students andresearchers, as well as research on statisticalmethods relevant to life sciences, with afocus on statistical genetics and statisticsfor food science. His research interestsinclude statistical methods for genotype byenvironment interaction, QTL and associationmapping, and integration of statistical geneticand physiological methods to describeadaptation and stability. He has publishedover 100 papers in referereed journals onthese topics.SPICY TEAM AT PROGRESS MEETING IN AVIGNON, 8 APRIL 2011WWW.RESEARCHMEDIA.EU 57

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