S4.1 Genomics-assisted breeding for maize improvement
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The document discusses genomics-assisted breeding (GAB) for maize improvement, highlighting the importance of utilizing quantitative trait loci (QTL) and various mapping techniques such as biparental and association mapping. It outlines the methodologies involved in GAB, including marker-assisted selection and genomic selection, along with their applications in breeding for traits like drought tolerance. Additionally, it emphasizes the critical factors for successful implementation of GAB and future opportunities for enhancing maize breeding through collaborative efforts among various scientific disciplines.
S4.1 Genomics-assisted breeding for maize improvement
- 1. Genomics-assisted breeding for maize improvement Roberto Tuberosa Dept. of Agroenvironmental Sciences & Technology University of Bologna, Italy 11th Asian Maize Conference, 8-12 November 2011, Nanning, China
- 2. Outline Setting the stage Implementing genomics-assisted breeding (GAB) Chasing genes and QTLs Biparental (linkage) mapping Association mapping Nested associated mapping Breeding applications MAS, MABC and MARS Genomewide selection Conclusions and perspectives
- 3. Genomics-assisted breeding in maize Setting the stage
- 4. Corn yield in the USA
- 5. © Chappatte - www.globecartoon.com
- 6. …more bad news… Stocks of staples are low Reduced funding for plant breeding and training for several decades Increase and sharp fluctuations in food prices Decline in arable land Higher protein consumption in China and India 7 billion people now and 9 billion people by 2050 Higher energy and water prices Decrease in water available for irrigation
- 7. Los Angeles Times April 13, 2008 Q T Ls It follows that a given QTL can have positive, null, or negative effects depending on the drought scenario. This complication has slowed considerably the utilization of QTL data for breeding. Collins et al. (2008). Plant Physiol. 147: 469-486.
- 8. Genomics-assisted breeding of quantitative traits QTL characterization QTL - QTL x E x M discovery - Validation in different backgrounds - Isogenization QTL Genomics-assisted breeding cloning - Cost-effectiveness - High-throughput profiling Perfect marker
- 9. Genomics-assisted breeding in maize Implementing GAB – Chasing genes and QTLs
- 10. QTL mapping and cloning strategies Biparental Association mapping linkage mapping Genetic (> 200 unrelated (RIL, DH, BC, IL) resolution accessions) QTL coarse 10-20 cM mapping genome-wide (high LD panel) Near isogenic lines (NIL) Positional candidate gene cloning (low LD panel) 1-100 kb Candidate gene validation
- 11. To clone or not to clone QTLs? QTL cloning as an essential step to: • understand the functional basis of quantitatve traits • unlock the allelic richness of germplasm by direct haplotyping and sequencing of target loci • identify the perfect marker for selection • genetically engineer quantitative traits. Salvi & Tuberosa (2005). Trends in Plant Science
- 12. QTL cloning: A very tough nut to crack! QTL
- 13. Mapping and cloning QTLs for drought tolerance at UNIBO • flowering time (escape) • root architecture (avoidance)
- 14. Vegetative to generative transition 1 (Vgt1) Gaspé Flint N28E N28 Salvi et al., 2007. Proc. Nat. Acad. Sci. 104: 11376 N28E N28
- 15. Physical mapping and cloning of Vgt1 Vgt1 M12 M8 AFLP13 AFLP14 Genetic .38 .42 .08 .08 .34 cM map BAC ca. 70 kb clone Vgt1 M8 M12 N28 * ** ** ** ** * * ** ** * ** * * ** C22-4 Rec 144-bp transposon Rec (mite) insertion * = SNP ca. 2.7 kb = INDEL Salvi et al., 2007. PNAS 104: 11376-11381.
- 16. Flowering time in B73 and Gaspé Flint 20-day difference 13 days: loci? 7 days: Vgt1 B73 F1 Gaspé Flint
- 17. IL (BC5 B73 x Gaspe’) graphycal genotype Maize chromosomes 1 2 3 4 5 6 7 8 9 10 Introgression line
- 18. Root phenotypic difference between B73 and Gaspé Flint 2 1 1 2 3 3 abscence of seminal roots
- 19. Root analysis Root analysis in paper rolls in pots
- 20. Chromosomes Seminal roots-roll 1 2 3 4 5 6 7 8 9 10 - + NA IL lines (Salvi et al., unpublished)
- 21. Chromosomes Seminal Seminal Crown roots-roll roots- roots- 1 2 3 4 5 6 7 8 9 10 - + -pots + -pots + (vs. B73) NA IL lines NA NA (Salvi et al., unpublished)
- 22. Chromosomes Seminal Seminal Crown roots-roll roots- roots- 1 2 3 4 5 6 7 8 9 10 - + -pots + -pots + (vs. B73) NA qSR1, bin 1.02 aroll = -1.30 (-45%) apots = -1.11 (-39%) qSR2, bin 3.05-7 aroll = -0.45 (-16%) apots = -0.31 (-14%) IL lines qSR3, bin 7.01-2 aroll = -0.75 (-16%) apots = -0.32 (-14%) NA NA qSR4, bin 8.04-5 aroll = -0.85 (-30%) apots = -1.10 (-40%) (Salvi et al., unpublished)
- 23. Lower yield Higher yield +/+ -/- ABA ABA Root-ABA1 (bin 2.04) (Landi et al. 2007, J. Exp. Bot. 58: 319)
- 24. Higher yield Lower yield Root-yield-1.06 (bin 1.06) (Landi et al, 2010, J. Exp. Bot. 61: 3553)
- 25. QTL mapping and cloning strategies Biparental Association mapping linkage mapping Genetic (> 200 unrelated (RIL, DH, BC, IL) resolution accessions) QTL coarse 10-20 cM mapping genome-wide (high LD panel) Near isogenic lines (NIL) Positional candidate gene cloning (low LD panel) 1-100 kb Candidate gene validation
- 26. QTL mapping/cloning by GWA (Genome-Wide Association) • 8,590 SNPs • 553 maize inbreds • Phenotyped for embryo oleic acid content Fad2 (Fatty acid desaturase 2) Belò et al. (2008) MGG
- 27. Science (2008), 319: 330-333
- 28. QTL mapping and cloning via linkage mapping and GWAS Krill et al. (2010). PLoS ONE 5, (4) e9958. QTLs and candidate genes for Aluminum tolerance Three F2s and a panel of 282 inbreds Lu et al. (2010). PNAS 107: 19585–19590. •QTLs and candidate genes for ASI and drought tolerance •Three RIL populations + one panel of 305 inbreds Li et al. (2011). Plos ONE 9, (6) e24699. •QTL for palmitic acid (unsaturated/saturated ratio and oil content) •One RIL + one BC population + one panel of 155 inbreds
- 29. CSA News, October 2011, 4-11.
- 30. What is NAM? NAM is most powerful genetic resource for dissection of the genetic bases of quantitative traits for any species. Courtesy of Mike McMullen
- 31. Linkage Mapping Association Mapping Recent recombination Historic recombination High power Low power Low resolution High resolution Analysis of 2 alleles Analysis of many alleles Moderate marker density High marker density Genome scan Candidate gene testing Nested Association Mapping Recent and ancient recombination High power High resolution Analysis of many alleles Moderate genetic marker density High projected marker density Courtesy of Mike McMullen
- 32. Nested Association Analysis 25 DL B97 CML52 Hp301 Il14H Ky21 Oh7B P39 Tx303 CML103 CML228 CML247 CML277 CML322 CML333 Ki11 M162W MS71 NC350 NC358 Tzi8 CML69 Ki3 Oh43 Mo18W M37W × B73 F1s SSD 1 2 NAM 200 Courtesy of Mike McMullen Yu et al. (2008) Genetics 178: 539
- 33. Maize Phenomics: Massively Parallel Phenotyping of the Nested Association Mapping Population THE MAIZE DIVERSITY PROJECT Courtesy of Jim Holland
- 34. Genomics-assisted breeding in maize Implementing GAB – MAS, MABC and MARS
- 35. Selection for mapped loci MAS: MARKER-ASSISTED SELECTION Plants are selected for one or more (up to 8-10) alleles MABC: MARKER-ASSISTED BACKCROSS One or more (up to 6-8) donor alleles are transferred to an elite line MARS: MARKER-ASSISTED RECURRENT SELECTION Selection for several (up to 20-30) mapped QTLs relies on index (genetic) values computed for each individual based on its haplotype at target QTLs.
- 36. Development of markers for MAS • Markers should be tightly-linked (< 5 cM) to target loci and preferably within the sequences of interest • Markers must be validated in different genetic backgrounds • Markers should preferably be codominant • Original mapping markers should be converted to markers more suitable for high-throughput profiling at the single locus • Success stories: QPM and pro-vitamin A, disease resistance
- 37. Marker-assisted backcrossing (MABC) a) Select donor alleles at markers flanking target gene b) Select recurrent parent alleles at other linked markers (to reduce linkage drag around target gene) c) Select for recurrent parent alleles in rest of genome (optional) a b c 1 2 3 4 1 2 3 4 1 2 3 4 Target locus ‘TARGET ‘RECOMBINANT’ BACKGROUND’ „ GENE/QTL’ SELECTION SELECTION SELECTION from: Collard and Mackill, 2006
- 38. Under severe WS (ca. 60-80% yield reduction), the best five MABC-derived hybrids outyielded by 50% the controls. Under intermediate WS (< 50% yield reduction), no difference was observed between MABC- derived hybrids and the controls. No yield penalty of the MABC- hybrids under WW conditions. Ribaut and Ragot (2007). J. Exp. Bot. 58: 351-360.
- 39. Outcome of MABC depends on: • Number of genes/QTLs to transfer • Genetic distance between genes and markers • Nature of markers used • Number of genotypes selected at each generation • Genetic background
- 40. Marker-assisted recurrent selection (MARS) When much of the variation is controlled by minor QTLs, MABC has limited applicability because estimates of QTL effects are inconsistent and pyramiding becomes increasingly difficult as the number of QTLs increases. A more effective strategy is to deploy MARS to increase the frequency of favorable marker alleles in the population. MARS involves (i) defining a selection index for F2 or F2-derived progenies with desirable alleles at target QTLs, (ii) recombining selfed progenies of the selected individuals and (iii) repeating the procedure for a number of cycles.
- 41. Marker-assisted recurrent selection (MARS) Although the private sector has reported significant gains through MARS in maize (Johnson, 2004; Eathington, 2005; Crosbie et al., 2006), fewer efforts have been undertaken in the public sector. Moreau et al. (2004) reported no advantage of MARS over phenotypic selection for a multitrait performance index, probably due to the general high heritability of traits and the limited (ca. 50%) σ2P accounted for by QTLs. One shortcoming of MARS is caused by the inconsistency of QTL effects as the genetic background changes during subsequent cycles of selection, a problem which can be partially solved with the “Map as you go” (MAYGO) approach suggested by Podlich et al. (2004).
- 42. Genomics-assisted breeding in maize Implementing GAB – Genomic selection
- 43. Genomic selection • Requires low-cost, high-density molecular markers (LD level) • Unlike in MARS, GS considers the effects of all markers together and captures most of the additive variation • Marker effects are first estimated based on a so-called “training population” that needs to be sufficiently large (> 300) • Breeding value is then predicted for each genotype in the “testing population” using the estimated marker effects
- 44. Genomic selection • GS focuses on the genetic improvement of quantitative traits rather than on understanding their genetic basis • Simulation studies have shown that across different numbers of QTLs (20, 40 and 100) and levels of H, responses to GS were 18 to 43% larger than MARS (Bernardo and Yu, 2007) • GS more effective with complex traits, low H and haplotypes rather than single markers • GS and QTL discovery are not mutually exclusive • Application of GS as a function of objectives, resources of breeding programs and the genetic architecture of traits • Yield per se: difficult to identify major QTLs, particularly in elite x elite
- 45. Genomic selection for introgression of exotic germplasm • Current maize inbreds have very little exotic germplasm • Prebreeding via recurrent selection is usually required • 10 cycles of testcross phenotypic selection require 20 years vs. 4 for GS • The outcome of long-term (5-10 cycles) GS is unknown Response to 15 cycles of GS for F2 is preferable to BC1 and BC2 introgression of exotic germplasm 6-7 cycles of GS appear to be sufficient Bernardo, 2009 After 7th cycle, reestimate of marker- Crop Sci., 49: 419 based selection index
- 46. Drought-tolerant corn by MAB; marketed by Pioneer in 2011 2009, 19, 10 Accelerated Yield Technology (AYT™)
- 47. Genomics-assisted breeding in maize Perspectives and conclusions
- 50. Plant Accelerator, ACPFG, Adelaide, Australia DROPS EU-funded Euro 8.7 M 15 Partners 5 companies
- 51. Critical factors for the success of GAB Existence of a breeding program Breeders familiar with molecular procedures, potential and shortcomings Capacity to run 2-3 generations/year and produce DH Capacity to automate DNA extraction Access to high-throughput genotyping Maintain a healthy pipeline between gene/QTL discovery and MAS Access to an informatics platform to handle samples and data Accurate and relevant phenotyping
- 52. Future opportunities for GAB • Comparative genomics and other “omics” data will accelerate the identification of candidate genes • “Omics” platforms should be used in a very focused way • Sequencing and novel bionformatic tools will facilitate collecting and exploiting “omics” data • Resequencing of target loci in mini-core collections for allele mining and haplotype definition • Crop modeling will increasingly allow us to: • Dissect complex traits into simpler components • Help resolving G x E x M • Support MAB with a breeding-by-design approach
- 53. Tying it all together • On a case-by-case basis, develop appropriate breeding strategies for the improvement of multiple traits and/or complex traits. • Delivering new cultivars via GAB will require a close collaboration among molecular geneticists, breeders, physiologists, pathologists, agronomists and other relevant stakeholders. • Only an appropriate multi-disciplinary effort engagement will allow us to effectively harness the potential of GAB while advancing our quest to dissect the genetic make-up of agronomic traits.
- 54. Many thanks to: • Marco Maccaferri • Silvio Salvi • Maria C. Sanguineti • Pierangelo Landi • Silvia Giuliani • Simona Corneti • Sandra Stefanelli • Marta Graziani G. Taramino et al., Pioneer Dupont, USA M. Ouzunova et al., KWS, Germany Funds: European Union, Pioneer-DuPont, KWS
- 55. INTERDROUGHT-IV 6-9 September 2013 Burswood Entertainment Complex Perth, Western Australia Congress Chair: Roberto Tuberosa, Italy Program Committee Chair: Graeme Hammer, Australia Local Organizing Committee Chair: Mehmet Cakir, Australia www.interdrought4.com
- 56. Questionnaire on marker-assisted breeding (sent to 5 seed companies) What % of financial resources will be devoted to MAB in next 5 years? Company A: 10-15% Company B: MAB will be exploited in all our corn breeding projects As to the resources devoted to MAB, what % is devoted to: - MAS for simple traits to a large extent - MARS for complex traits to a low extent - GS for complex traits moderate with increasing importance Selection for complex traits is increasing, as is selection for both simple and complex traits within the same breeding project
- 57. Questionnaire on marker-assisted breeding Is GS fulfilling the potential expected from published simulations? To a large extent Moderately To what extent has AM allowed you to dissect complex traits? Moderately Moderately What are the 3 main factors limiting a more widespread use of MAB? 1: Cost; 2: Reluctance to change well-established breeding programs 3: Standardization 1: Experience; 2: Logistics; 3: Standardization To what extent has GBS changed your perspective on MAB? Moderately Moderately