Molecular Marker-assisted Breeding in RicePresentation Transcript
Molecular Marker-assisted Breeding in Rice Jian-Long Xu Institute of Crop Sciences, CAAS Email: firstname.lastname@example.org
Expertise & experiencesMolecular rice breeding (including allele mining& marker-assisted breeding)August 2003 ~ presentMolecular Rice Breeder in the Institute of Crop Sciences, CAAS2008 ~ 2012One month per year for Consultant in PBGB Division, IRRI2005 ~ 2007Three months per year for Collaboration Research in PBGB Division, IRRIJanuary 2002 ~ October 2003Postdoctoral Fellow in PBGB Division, IRRIMarch 1999 ~ October 2000PhD thesis research in PBGB Division, IRRIAugust 1990 – July 2003Senior Rice Breeder in Zhejiang Academy of Agricultural Sciences PhD 2001 Zhejiang University, Genetics (minor in China Statistics) MSc 1990 Zhejiang Agricultural Plant Breeding and University, China Genetics BSc 1977 Zhejiang Agricultural Plant Breeding and University, China Genetics
Successful breeding depends on:(1)Variation: Sufficient (novel) genetic variation for target traits in breeding populations(2) Selection efficiency: Effective selection approach to identify desirable alleles or allelic combinations for the target traits in breeding populationsTraditional breeding depends on phenotypic selections.Efficiency of selection is largely influenced by environment,gene interaction, and gene by environment interaction.Genetic markers can improve efficiency of selection. Geneticmarkers include morphological marker (plant height, leafcolor), cytological marker (chr structure and no mutant),biochemical marker (isozyme), and molecular marker (SSR).
Direct DNA selection: Based on phenotypic value Phenotypic indirect selection RNA (based on correlation between Indirect traits) selection Genotypic indirect selection Protein (based on markers associated with a gene or QTL)Phenotype
Marker-assisted selection (MAS) is a method wherebya phenotype is selected on the genotype of the linked marker.Note: marker isn’t the target gene itself, there is just anassociation between them. Resistant donor Recipient Linkage of the target gene with the marker Genotypes of the parents Genotypes of the F1 Three genotypes of the F2 population Selection with 95% confidence based on marker genotypes when recombination rate (r) of 5%
The advantages of MAS:(1) Time saving from the substitution of complex field trials (that need to be conducted at particular times of year or at specific locations, or are technically complicated) with molecular tests;(2) Elimination of unreliable phenotypic evaluation associated with field trials due to environmental effects;(3) Selection of genotypes at seedling stage;(4) Gene ‘pyramiding’ or combining multiple genes simultaneously;(5) Avoid the transfer of undesirable or deleterious genes (‘linkage drag’; this is of particular relevance when the introgression of genes from wild species is involved);(6) Selecting for traits with low heritability;(7) Testing for specific traits where phenotypic evaluation is not feasible (e.g. quarantine restrictions may prevent exotic pathogens to be used for screening).
Procedure of MAS Considering mapping and Population development breeding purposes Gene or QTL mapping Linkage map construction/ phenotypic evaluation for traits/ QTL analysis QTL validation Confirmation of position and effect of QTL/verification of QTL in different populations and genetic backgrounds / fine-mapping Marker validation Testing of marker in important breeding parents Marker-assisted selection
Requirements for large-scale application of MAS ◆ Validation of QTL in breeding materials Multiple markers in vicinity of QTL desirable. ◆ Simple, quick, inexpensive protocols for tissue sampling, DNA extraction, genotyping and data collection ◆ Efficient data tracking, management and intergration with phenotypic data ◆ Decision support tools for breeders optimal design of selection strategies accurate selection of genotypes
Strategies of MAS1 Foreground selection Selection against the target gene.◆ Single marker selectionReliability: depends on linkage between the marker and the target gene. For example, marker locus (M/m) links with the target gene locus (S/s), if the recombination rate between the two loci is r, the probability of selection of genotype S/S based on marker genotype of M/M is P=(1-r)2So, reliability of MAS will sharply decrease with the increase of recombination rate. To ensure reliability of MAS more than 90%, the r should be lower than 5%.
If the probability to select 1 target plant is P, the minimumnumber of plants with marker genotype M/M will becalculated as: N=log(1-P)2/log(1-r)2So, when the recombination rate (r) is high as 30%,selection of 7 plants with M/M genotype will ensure toobtain 1 target plant with probability of 99%, whereas wemust select 16 plants if MAS isn’t applied (namely, there isno linkage between the marker and the target gene).
MAS scheme for early generation selection in a typical breeding program for diseaseresistance. A susceptible (S) parent is crossed with a resistant (R) parent and the F1plant is self-pollinated to produce a F2 population. In this diagram, a robust marker hasbeen developed for a major QTL controlling disease resistance (indicated by the arrow).By using a marker to assist selection, plant breeders may substitute large field trials andeliminate many unwanted genotypes (indicated by crosses) and retain only those plantspossessing the desirable genotypes (indicated by arrows). Note that 75% of plants maybe eliminated after one cycle of MAS.
◆ Bilateral marker selectionBilateral marker selection will greatly improve reliability ofMAS.If marker loci M1 and M2 locate each side of the target genelocus S, and the recombination is r1 and r2 respectively,thus F1 genotype is M1SM2/m1sm2, F1-derived F2population has two genotypes, M1SM2 (harbor the targetgene) and M1sM2 (without the target allele). In view ofprobability of double crossing over is very low, so selectinggenotypes at M1 and M2 loci to track the garget gene S ishigh reliable.
Without interrupt, the probability to obtain genotype S/Sby selection of bilateral marker genotypes M1M2/M1M2 is:P=(1-r1)2 (1-r2)2/[(1-r1)2 (1-r2)2 + r1r2]◆ When r1=r2 (the target gene is located in the middle ofthe two marker loci), P will be minimum.◆ In fact, two single crossing over generally interrupteach other, thus resulting in even small probability ofdouble crossing over, so reliability of bilateral markerselection is higher than expected.
Comparison of target control between single marker and bilateral markerIt is clearly indicated that control of the For the case of bilateral markers, even iftarget gene by a single marker isn’t so the two marker loci are far apart, forsatisfactory in most cases. The marker example 10 cM, efficiency of keeping themust be as close as 1 cM to the target to risk of losing the target is almost same askeep the risk of ‘losing’ the target below that in the case of 1 cM under single5% after five BC generations. Even with marker. Obviously, breaking linkagea single marker at 1 cM, the risk of losing between marker locus and the targetthe target is close to 10% in BC10. For gene in bilateral markers more difficultgreater distance of a single marker, the than in single marker.risk becomes rapidly too high.
2 Background selectionBesides selection of the target gene (foreground selection), backgroundselection will be implemented if to keep original characters of a variety.◆ MAS method: use a set of markers, which are evenly selected fromthe whole genome to identify the genotype of the recurrent parent.Normally screening background will be focused on those plants withtarget gene.◆ Consecutive backcrossing: backcrossing progeny will soon recover itsrecurrent parental genome after several rounds of backcrossing. % of the recurrent parental genome Breeding method BC1F1 BC2F1 BC3F1 BC6F1 Traditional backcrossing 75 87.7 93.3 99 MAS-based backcrossing 85.5 98 100 Young & Tanksley 1989
Comparison of MAS and traditional BC breeding for recovery of genetic background of the recurrent parentTraditionalBC breedingYearMAS BCbreeding Black bar represents donorYear genome Only two BC generations, the target segment can be narrowed down into 2 cM by MAS and completely diminish linkage drag from donor parent.
MAS application in qualitative traitsIn most cases, it is unnecessary to apply MAS forqualitative traits. However, MAS does improve efficiencyof selection of qualitative traits in following cases:◆ Pyramiding different resistance genes;◆Difficulty in or high cost of phenotyping;◆ Hope to select in early growing stage but the traits normally express in late developing stages◆ Screening genetic background besides the target traits
1 Pyramiding of multiple genesPyramid different genes dispersed in various varieties intoone variety by MAS. Different genes for the same target trait: to improve trait value. Multiple genes underlying different traits into the same variety: ensure new variety having more favorable traits
Example of genes for pyramiding in cereals
Three bBlast resistance genes used for pyramiding Chr6 Chr11 Chr12 Zheng et al. 1995
Scheme of thre blast resistance genes pyramidingC101LAC x C101A51 C101LAC x C101PKT Pi-1 Pi-2 Pi-1 Pi-4 F1 F1 F2 150 plants F2 150 plants Bilateral marker selection 10 plants homozygous X 10 plants homozygous at Pi-1 & Pi-2 at Pi-1 & Pi-4 F1 X F2 150 plants MAS Plants with 3 resistance genes
To pyramid different blast resistant genes in Zanhuangzhan2 (3major genes and 1 QTL) and one brown planthopper resistant gene(Bph18(t)) in IR65482 into 3 dominant restorer lines (Chen et al. 2012) Information of resistant genes and their linked markers Linkage Size ofResistance Marker Annealing Chr. distance Primer sequence amplified gene name temperature (cM) fragment (bp) TCGAGCAGTACGTGGATCTG RM6208 3.4 55 90Pi-GD-1(t) CACACGTACATCTGCAAGGG 8 -G1 ACCAAACAAGCCCTAGAATT R8M10 3.4 56 235 TGAGAAAGATGGCAGGACGCPi-GD-2(t) AATTTCTTGGGGAGGAGAGG 9 RM3855 3.2 55 424 –G2 AGTATCCGGTGATCTTCCCC CCCCATTAGTCCACTCCACCACPi-GD-3(t) C 12 RM179 4.8 61 190 –G3 CCAATCAGCCTCATGCCTCCCCGLP8-6(t) ATCCGGCACTACCTTTCCC 8 G8-6ID-1 2.8 55 235 –G8 CTGCTCCCACCGCATCTGT AACAGCAGAGGGTTTGGCTABph18(t) 12 7312.T4A 1.3 50 1078 CAGACTTTTCTTGGGGGTCA
Minghui86, Shuhui527 and x 、 Sanhuangzhan 2、IR65482Zhehui7954 (Recurrent parent, RP) (Donor parent, DP) F1 RP Pyramiding BC1F1 Pyramiding F1 RP MAS MAS BC2F1 F2 RP MAS MAS BC3F1 F3 MAS MAS BC3F2 F4 MAS MAS BC3F3 F5 Test-crosses with II-32A and Huhan11A Evaluation on resistance and agronomic traits for restorer lines and their derived hybrids Scheme of molecular improvement of blast and brown planthopper resistance for restorer lines
Evaluation of resistance of newly bred restorer lines to Pyricularia grisea Sacc. Strain Reaction Resistance Restorer lines S S S S S S S S S S S S S S S S S S S S frequency S R (%) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20CO39 S S S S S S S S S S S S S R R S S S S S 18 2 10Sanhuangzhan2 R S R R R R R R R S R R R R R R R R R R 2 18 90Minghui86 R R R R R R R R R S R R R R R R S R R R 2 18 90Shuhui527 R S R R R S R R R R R S R R R R R R R R 3 17 85Zhehui7954 R S S S S S S S S S R S S R R R S S R R 13 7 35Minghui86-G2 R R R R R R R R R S R R R R R R R R R R 1 19 95Minghui86-G1-G2 R R R R R R R R R S R R R R R R S R R R 2 18 90Shuhui527-G2 R R R R R S R R R R R R R R R R R R R R 1 19 95Shuhui527-G1-G2 R R R R R S R R R R R R R R R R R R R R 1 19 95Zhehui7954-G1-G2 R S R R R R R R R S R R R R R S R R R R 3 17 85Zhehui7954-G1-G2-G8 S R R R R R R R R S R R R R R R R R R R 2 18 90Zhehui7954-G1 -G8- R S S S R S R S S R R R S R R R S S R R 9 11 55 Bph18(t)Zheshu-G2-G8 R S R R R R R R R S R R R R R R R R R R 2 18 90Mingzhe-G2-G8 R S S R R R R R R S R R R R R R R R R S 4 16 80Mingzhe-G1-G2-G8 R R R R R R R R R S R R R R R R R R R R 1 19 95Mingzhe-G1-G2-Bph18(t) S R R R R R S S R R R S R S S R R R R R 6 14 70
Some important issues about MAS improvement of resistance for restorer lines(1) Firstly, the resistance improvement of parental lines of hybrids is much different from that of conventional varieties. In the backcross progenies of restorer parental lines, selections were performed not only for similarity to the recurrent parents (RP), but also for their fertility restoring gene(s) and specific combining ability to the CMS lines. ◆ background recovery of the RP ◆ the qualitatively inherited fertility restoring gene(s) of the RP ◆ the quantitatively inherited specific combining ability. It is gradually recovered through backcrossing in different individuals to a varying extent. It was indicated that a minimum of three backcrosses in conjunction with stringent phenotypic selection for the RP in each BC progenies and combining ability testing on a relatively large scale, guarantees the recovery of recurrent parental characteristics even without MAS against the background of the RP
(2) Secondly, the level of hybrid rice resistance is determined by the restorer line when CMS is susceptible, whereas the resistance level of F1 is controlled by the interaction between CMS and restorer line when CMS is resistant. Expression of many resistance genes such as Xa21, etc., are affected by genetic background. So resistance of hybrids derived from the resistant restorer lines probably compromise and show resistance inferior to our expected. So we should choose highly resistance genes for resistance improvement of hybrid rice.(3) Backcrossing is a very efficient strategy to improve single trait. However, the newly released lines are phenotypically identical to the RP, i.e. there is no break through in traits of the new variety. So composite intercrossing is recommended to pyramid multiple resistance genes as well as to create new variety. In MAS breeding programs, polymorphic markers are the key problem when multiple parents are involved. So it is better to develop linked markers showing polymorphism among all parents, otherwise efficiency of MAS will be degraded.
MAS for quantitative genesMost important agronomic traits are genetically quantitativeand controlled by polygenes. In the past decades, some majorQTLs have been implemented by MAS. Procedures MAS for quantitative traits: ◆ QTL initial mapping ◆ Fine-mapping of major QTL ◆ Verification of gene effect using NILs ◆ Validation of molecular markers ◆ MAS application
Progress of Saltot locus Short arm of chromosome 10.0 • Saturated map of the RM283 Chromosome 127.4 (Saltol segment) is R844 0.0 AP320628.4 CP03970 developed S2139 1.040.0 1.3 RM341260.6 RM23 RM809464.9 1.2 • Closely linked RM140 1.8 RM49366.2 C52903S CP6224 markers linked to 1.971.2 the saltol locus C1733S RM14075.3 identified RM113 77.2 S1715 91.9 98.2 S13994 • MAS is being 99.1 RM9 validated in 3103.1 R2374B119.5 RM5 breeding populations123.5 C1456129.9 RM237 A RM246 (Source: Glenn B. Gregorio)
Chromosome location of associated QTL ofSalinity tolerance trait AP3206 CP010136 RM3412 LOD threshold CP03970 a RM8094 RM493 CP6224 b RM140 2.5 0.0
preprotein chloroplast SAM membrane CBL-interacting translocase, Sec23/Sec24 protein kinase 19 S_Tkc; synthetase protein SecA subunit trunk Ser Thr Kc WD40 WD40 secretory Receptor like cold Peroxidase, shock peroxidase putative kinase protein SALtol Region ( Major QTL K+/Na+) 12.0Mb 0.27 Mb (~40 genes) 12.27 Mb 12.11Mb 12.27Mb 11.9 Mb 12.13 Mb 12.25Mb 12.40Mb11.10Mb OSJNBa0011P19 12.7Mb B1153f04 P0426D06 B1135C02cM 60.6 60.9 62.5 64.9 65.4 65.8 66.2 67.6 67.9 Chromosome 1 of Rice
A major QTL on chrom. 9 forsubmergence tolerance – Sub1 QTL LOD score 0 10 20 30 40 OPQ1600 OPN4 IR40931-26 PI543851 120020 OPAB16 850 C1232 Sub-1(t) RZ69815 OPS14 900 RG553 R1016 50cM RZ20610 OPH7 950 RZ422 5 100cM C985 0 1 2 3 4 5 6 7 8 9 Submergence tolerance score RG570 150cMSegregation in an F3 population RG451 RZ404 Xu and Mackill (1996) Mol Breed 2: 219
Sub1 locus, there are three structurally related genes Sub1A,Sub1B, and Sub1C present in the same QTL region, encodingethylene-responsive factor (ERF) genes. Fukao, et al., Annals of Botany, 2009,103: 143–150
Development of the submergence-tolerant Swarna-Sub1 with details of markers used for foreground, recombinant, and background selection.
Field plot test of submergence tolerance of Sub1 and non-Sub1 varieties. The SUB1 locus fromFR13A was introduced into the rice varieties IR64 and Samba Mahsuri by marker-assistedbackcrossing and into IR49830-7-1-2-2 through conventional breeding. A field trial performedat IRRI in 2007 included Sub1 lines, the progenitors, and IR49830-7-1-2-2 (tolerant, used asSUB1 donor) and IR42 (sensitive) as checks. Fourteen-day-old seedlings were transplantedinto a field with high levees, grown for 14 days and then completely submerged with about 1.25m of water for 17 days. The field was drained, and the plants were allowed to recover undernon-stress conditions. The photograph shows the performance of the lines about 60 days afterde-submergence.
Swarna with Sub1
MAS of Minor-effect QTLsAt present, using limited number of markers and smallmapping populations, only few QTLs with relatively largephenotypic-effect have been identified, which account for asmall portion of QTLs affecting the target traits. Moreover,QTL epistasis has great effect on selection. So, it is difficultto implement MAS for minor-QTLs.Genome selection (GS) will provide a new strategy formionr-QTLs (introduced later).
Genome-wide selection Training population: used for genotyping with high throughput SNP marker and phenotyping in the target environment, setting up genetic predict model to estimate all possible QTL effects affecting a trait Breeding population: used for genotyping and predicting breeding values for selectionIn a training population (both genotypic and phenotypic data available),fit a large number of markers as random effects in a linear model toestimate all genetic effects simultaneously for a quantitative trait. Theaim is to capture all of the additive genetic variance due to alleles withboth large and small effects on the trait.In a breeding population (only genotypic data available), use estimatesof marker effects to predict breeding values and select individuals withthe best GEBVs (genomic estimated breeding values).
GS consists of three steps:(1) Prediction model training and validationA training population (TP) consisting of germplasm having both phenotypic and genome-wide marker data is used to estimate marker effects.(2) Breeding value prediction of single-crossesThe combination of all marker effect estimates and the marker data of the single crosses is used to calculate genomic estimated breeding values (GEBVs).(3) selection based on these predictionsSelection is then imposed on the single crosses using GEBVs as selection criterion. Thus, GS attempts to capture the total additive genetic variance with genome-wide marker coverage and effect estimates, contrasting with MARS strategies that utilize a small number of significant markers for prediction and selection.
Advantages of GS:◆ It is especially important for quantitative traits conferred by alarge number of genes each with a small effect.◆ GS includes all markers in the model so that effect estimates areunbiased and small effect QTL can be accounted for.◆ Reduce the frequency of phenotyping because selection is based ongenotypic data rather than phenotypic data.◆ Reduce cycle time, thereby increasing annual gains from selection.Disadvantages of GS:◆ Traits with lower heritability require larger TPs to maintain highaccuracies.◆ When single crosses are unrelated to the training population (TP),even if sufficient markers and training records are available, markereffects could be inconsistent because of the presence of differentalleles, allele frequencies, and genetic background effects, i.e.epistasis. So genetic model isn’t universal in different populations.
Summary of MAS for quantitative traitsMost agronomic important traits are quantitatively inherited. A widerange of segregating populations derived from bi-parental crosses,including RILs, DHs, F2 and its derived populations, and BC or testcrosspopulations, have been used for QTL mapping. And many majorimportant QTLs have been cloned in rice. Oppositely, slow progresseshave been made so far in MAS-based breeding for complex traits, mainlydue to the following two aspects.(1) Segregation populations derived from bi-parents can’t identifyfavorable alleles for the target traits. So we don’t have information aboutfavorable alleles for the target trait which will be best used in molecularbreeding.(2) QTL mapping is separate from breeding program. Owing to QTLmapping results are seriously dependent on genetic background. So QTLinformation from mapping populations can’t be directly applied in MAS-breeding.
So, integration of QTL mapping with MAS-basedbreeding in the same genetic background has beenstrongly recommended for complex quantitative traits byTanksley and Nelson (1996). So far, AB-QTL method hasbeen widely used in QTL identification from germplasm.However, there are still some defects:(1) Relative high expenses resulting from phenotyping andgenotyping for a large mapping population.(2) Favorable alleles can not be mined using populationsderived from bi-parents.
With the development of sequencing technologies and the sharpdecreased sequencing cost, genome wide association (GWS) hasbeen recently used for QTL mapping and allele mining fromgermplasm resources and made good progresses. However, thereare still some problems with this method.(1) Wide variations in plant height and heading date of a naturalpopulation seriously affect growth and development for someearly and dwarf entries, thus resulting in inaccurate phenotypingfor those parts of entries.(2) There is population structure effect on QTL associationmapping.(3) GWS and MAS-based breeding is still separate.
Germplasm holds a large of genetic variation for improving agriculturalcrops. However, in the past favorable genes from germplasm have notbeen efficiently used in plant breeding due to linkage drag. Althoughbackcross is effective to simple qualitative traits, it has not beensuccessful to improve quantitative traits by backcross breedingprocedure.Here we demonstrate a new breeding strategy of backcross combinedmolecular marker technology to efficiently identify QTL and improvemultiple complex traits based on designed QTL pyramiding (DQP).
Strategy of integration of QTL mining with QTL-designed pyramiding using backcross introgression lines in elite background RP x donors (many) F1s x RP BC1F1s x RP ~25 BC2F1s/donor x RP BC3F1s x RP Selection for target traits Self and bulk Self and bulk x x and backcrossing harvest harvest BC2F3-5 bulk populations BC3F2-3 bulk populations BC4F1s x 1, 2, 3, 4, 5, 6, …… 1, 2, 3, 4, 5, 6, …… BC4F2s Screening for target traits such as tolerances to drought, salinity, high temperature, anaerobic germ., P & Zn def., BPH, etc. Confirmation of the selected traits by replicated phenotyping then genotyping of trait-specific lines (ILs) QTL identification and allele mining Crosses made between sister ILs DQP & MAS for pyramiding desirable having unlinked desirable QTLs and against undesirable donor QTLs for target ecosystem segments for target ecosystem Develop multiple stress tolerant lines for different ecosystems and release NILs for individual genes/QTLs for functional genomic studies
Salt tolerant introgression lines (ILs) and QTL mapping Minghui86/Gayabyeo (37) ST-ILs selected from four Minghui86/Shennong265 (40)introgression populations inMinghui86 background at the Minghui86/Zaoxian14 (33) overall growth stage Minghui86/Y134 (40)
Principle of using selected ILs and molecular markers to identify QTLsQTL detectionTaken allele frequency of the random population as an expected value, asignificant deviation (excess or deficiency) of donor allele frequency atsingle loci in the selected IL population from the expected level implies apositive selection favoring the donor allele (in excess), or negativeselection against the donor allele (in deficiency). Significant deviationloci are considered as QTLs affecting the selected traits.Gene action at putative QTLs● Excess of the donor homozygote additive gene action● Excess of the heterozygote overdominance gene action● Excess of both the donor homozygote and heterozygote partial or complete dominance gene action
ST-QTLs detected in at least the two different ST-IL populations Gayabyeo Shennong265 Zaoxian14 Y134 Bin2.2 √ √ √ √ Bin1.1 √ √ √ Bin6.1 √ √ √ Bin2.6 √ √ Bin4.6 √ √ Bin5.2 √ √ Bin5.4 √ √ Bin5.6 √ √ Bin8.3 √ √ Bin9.1 √ √ Bin10.3 √ √Based on phenotypic value and QTL allele distribution, we can easilyselect ideal ILs to pyramid different alleles from different donors toimprove the target traits.
MAS-based pyramiding of QTLsA case study of high yield (HY), drought and salinity tolerance (DT, ST) using the selected ILs
Development of HY-, DT- and Pyramiding of QTLs ST-ILs for QTL mapping for HY, DT and ST For DT For ST SN89366 Bg94-1 GH122 YJ7 JXSM IL1 × IL2 IL3 × IL4 IL5 × IL6 IL7 × IL8 F1 F1 F1 F1 Feng-Ai-Zhan 1 (FAZ1) Backcross & selfing with HY selection F2 populationsBC3F5 Pop. 1 Pop. 2 Pop. 3 Pop. 4 Pop. 5 60 random ~30 HY ~30 DT ~30 ST plants plants plants plants DT screening ST screening HY & DT ILs HY & ST ILs Confirmed or cross-testing of selected ILs for QTL mapping QTL mapping QTL mapping FAZ1/SN89366 (IL1) FAZ1/SN89366 (IL5) New breeding lines with HY, DT and/or ST HY & FAZ1/Bg94-1 (IL2) FAZ1/Bg94-1 (IL6) HY & DT ILs FAZ1/GH122 (IL3) FAZ1/JXSM (IL7) ST ILs Promising lines for RYT FAZ1/YJ7 (IL4) FAZ1/BG94-1 (IL8)
QTLs affecting high yield (HY), drought tolerance (DT) and salinity tolerance (ST) detected in two pyramiding populations by frequency distortion of genotypes Pop. Locus Ch. Posi. HY DT ST 2 2 2 X P Gene X P Gene X P Gene action action actionIL3/IL4 RM486 1 153.5 18.75 0 OD 27.34 0 OD 25.87 0 OD(DTP2) OSR14 2 6.9 7.76 0.0206 PD F2 RM471 4 53.8 13.46 0.0011 OD RM584 6 26.2 7.74 0.0208 OD RM3 6 74.3 7.67 0.0216 AD 13.66 0.001 OD RM2 7 8.08 0.0175 OD RM547 8 58.1 19.97 0 OD 27.89 0 OD 30.97 0 OD RM21 11 85.7 10.78 0.0045 AD RM4A 12 5.2 11.93 0.0025 ODIL5/IL6 RM297 1 155.9 10.45 0.0053 AD 6.49 0.0389 AD 9.93 0.0069 AD (STP1) RM324 2 66 6.31 0.0426 PD F2 RM55 3 168.2 6.51 0.0385 PD RM3 6 74.3 13.44 0.0012 AD 9.48 0.0087 AD 7.7 0.0212 AD RM444 9 3.3 56.43 0 PD RM434 9 57.7 30.82 0 AD RM4A 12 5.2 6.29 0.043 OD RM519 12 62.6 8.19 0.0166 OD RM235 12 91.3 12.67 0.0017 PD
Promising pyramiding lines selected from intercross or repeated screening for HY and ST from IL1x IL2 population Selected pop. Intercross No. of Line # Yield of introgression line (g) Salt tolerance of introgression line at the seedling stage or selected repeated lines Trait Check ±% No. of survival days Score of salt toxicity of leaves screening value of comp. trait higher with Trait Check of ±% Trait Check of ±% value check value higher comp value higher comp parent parent check parent check HY 1 QP49 43.5 30.1 44.8 10 8.8 13.6 4.5 5.5 18.2 QP47 31.8 30.1 5.5 11 8.8 20.6 4.5 5.5 18.2 QP48 29.8 30.1 -0.9 11 8.8 22.9 4.5 5.5 18.2 QP63 24.3 30.1 -19.3 12 8.8 36.4 4.5 5.5 18.2DT selected (30) ST 10 QP60 26.3 30.1 -12.6 12 8.8 31.8 4 5.5 27.3 QP61 28.8 30.1 -4.3 11 8.8 30.3 4 5.5 27.3 QP36 28 30.1 -7 11 8.8 29.5 4 5.5 27.3 QP37 28.2 30.1 -6.3 11 8.8 29.7 5 5.5 9.1 QP163 38.6 30.1 28.4 9.6 8.8 9.1 5 5.5 9.1 HY 2 QP167 36.6 30.1 21.8 11.4 8.8 29.5 4 5.5 27.3 QP171 35.8 30.1 18.9 10 8.8 17.1 4.5 5.5 18.2 QP169 32.1 30.1 6.7 12 8.8 33 4.5 5.5 18.2HY selected (30) QP168 25.4 30.1 -15.6 13 8.8 51.1 4 5.5 27.3 ST 7 QP166 28.3 30.1 -6 11 8.8 29.1 4 5.5 27.3 QP164 23 30.1 -23.4 11 8.8 25.7 4 5.5 27.3 QP170 17.4 30.1 -42.2 11 8.8 25.1 4.5 5.5 18.2 QP165 24.5 30.1 -18.7 11 8.8 20.6 4 5.5 27.3 QP327 36.6 30.1 21.6 NA NA NA NA NA NAST selected (33) HY 2 QP337 34.9 30.1 15.9 NA NA NA NA NA NA
Based on phenotypic and QTL information of trait-specific ILs, a new line with HY, DT and ST was developed by pyramiding of different target QTLs （ ） Zhong-Guang-Lv 1（HY, DT & ST） RYT in Yunnan province in 2011
Zhong-Guang-You 2RYT in Guangxi province in 2010-11
Molecular recurrent selection systems for improving multiple complex traits based on trait-specific ILs and dominant male sterile (DMS) line
Selection for multiple traitsDevelopments of MAS-based improvement strategies required formultiple traits should include understanding the correlation betweendifferent traits◆ Interaction between components of a very complex trait such asdrought tolerance◆ Genetic dissection of the developmental correlation◆ Understanding of genetic networks◆ Construction of selection indices across multiple traits.The methods for pyramiding genes affecting a specific trait can be usedto accumulate QTL alleles controlling different traits. A distinctdifference in concept is that alleles at different trait loci to beaccumulated may have different favorable directions, i.e. negative allelesare favorable for some traits but positive alleles are favorable for others.Therefore, we may need to combine the positive QTL alleles of sometraits with the negative alleles of others to meet breeding objectives.
Development of a DMS line in HHZ background Jiafuzhan (rr, fertile) Spontaneous mutation Jiafuzhan (Rr, sterile) x Jiafuzhan (rr, fertile)Jiafuzhan (1Rr sterile : 1rr fertile) x HHZ (rr) F1 (1Rr sterile : 1rr fertile) x HHZ (rr), backcross 4-5 times Anthers with different fertility HHZ (1Rr sterile : 1rr fertile) A: full sterile anther B: full fertile anther C,D: partial fertile anther
Composition of the molecular RS (MRS) populations:30-50 ILs/PLs carrying favorable QTL alleles from differentdonors plus the DMS line in the same genetic backgrounds (HHZ) MRS population in HHZ GB Ovals or boxes of Bulk harvest different colors seeds from represent different ILs fertile plants carrying genes/QTLs to be screened for target traits for different target traitsHHZ MS Bulk harvestline seeds from Development of RS sterile plants population is still for next round under the way of RS Each fertile individual has even chance to pollinate with DMS plants, ensuring all possible recombination produced inside the RS population
Combine DMS line-based RS system with whole genome selection RS populations based on trait-specific ILs and a DMS line in the same GB Continued 50% fertile plants 50% DMS plants introgressionTrait screening breeding/DQP Irrigated Abiotic Biotic (YP) stresses stresses RILs New ILs/PLs GS Trait-improved model lines New MRS New lines with multiple population for traits by pyramiding GS next round RYT and NCT under different GS target Es Continuation of MRS Farmers in dif. target Es
Precise and high-throughput phenotypingHigh-throughput and precision phenotyping is critical for geneticanalysis of traits using molecular markers, and for time- and cost-effective implementation of MAS in breeding. To match up withthe capacity and costefficiency of currently available genotypingsystems, a precision phenotyping system needs high-throughputdata generation, collection, processing, analysis, and delivery.High Resolution Plant Phenomics The Plant Accelerator
The High Resolution Plant Phenomics Centre (HRPPC) Phenomics technology in the field
Designed: to straddle a plot and collect measurements of canopy temperature, crop stress indices, crop chemometrics, canopy volume, biomass and crop ground coverPhenomobile From 16 meters above the crop canopy. Phenotower collects infra-red thermography and colour imagery of field plots. This data is used for spatial comparison of canopy temperature, leaf greenness and groundcover between genotypes at a single point in time. Phenotower
Plant scan Tethered blimpMeasurements include:◆ Leaf size The blimp will carry both infrared◆ Number of leaves and digital color cameras operating◆ Shape in a height range of 10 m to 80 m◆ Topology (study of constant properties) above the field.◆ Surface orientation It will identify the relative◆ Leaf color differences in canopy temperature◆ Plant area and volume indicating plant water use.
Remote Sensing techniques
A flowchart for whole-genome strategies in marker-assisted plant breeding. The system starts withnatural and artificial crop populations to develop novel germplasm through four key platforms,genotyping, phenotyping, e-typing (environmental assay), and breeding informatics, which needdecision support system in various steps towards product development.