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Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
Genomic Selection & Precision Phenotyping
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Genomic Selection & Precision Phenotyping

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Presentation delivered by Dr. Jesse Poland (Kansas State University, USA) at Borlaug Summit on Wheat for Food Security. March 25 - 28, 2014, Ciudad Obregon, Mexico. …

Presentation delivered by Dr. Jesse Poland (Kansas State University, USA) at Borlaug Summit on Wheat for Food Security. March 25 - 28, 2014, Ciudad Obregon, Mexico.
http://www.borlaug100.org

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  • 1. KANSAS STATE U N I V E R S I T Y Jesse Poland Wheat Genetics Resource Center Applied Wheat Genomics Innovation Lab Kansas State University, USA Genomic Selection & Precision Phenotyping March 27, 2014 1 Borlaug Global Summit, Cd. Obregon, Mexico Borlaug Global Summit, Cd. Obregon, Mexico March 27, 2014
  • 2. KANSAS STATE U N I V E R S I T Y Trends in Population, Production & Acres 0 1 2 3 4 5 6 7 8 9 10 0 100 200 300 400 500 600 700 800 900 1,000 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 Population(bil) MH,MMT Acreage Production Demand Population Source – USDA and UN Population Database Slide: Dalton Henry March 27, 2014 2 Borlaug Global Summit, Cd. Obregon, Mexico Mark Tester and Peter Langridge (2010) Breeding Technologies to Increase Crop Production in a Changing World, Science 12:327 pp. 818-822
  • 3. KANSAS STATE U N I V E R S I T Y (Accelerating) The Breeding Cycle March 27, 2014 3 Crossing Evaluation Selection Borlaug Global Summit, Cd. Obregon, Mexico
  • 4. KANSAS STATE U N I V E R S I T Y The breeder’s (favorite) equation: March 27, 2014 4 Rt = irsA y genetic gain over time years per cycle selection intensity selection accuracy genetic variance Crossing Evaluation Selection Borlaug Global Summit, Cd. Obregon, Mexico Selection Intensity  Increase (to a limit)  Need bigger populations Selection Accuracy  Increase  More precise measurements  Reduce Errors  Correct for environment Genetic Variance (Diversity)  Increase  Mixed bag (not all good)  A must have Years per Cycle  Decrease!  Constant ‘rate’ of return
  • 5. KANSAS STATE U N I V E R S I T Y Genomic Selection & Precision Phenotyping Dec 2, 2013 5 NOT NEW CONCEPTS! Phenotype is what we eat! Phenotype results from complex process of genetics and environment. We can improve the environment (i.e. Agronomy) and we can improve the genetics (i.e. Breeding) Use field testing to ‘observe’ the underlying genetics - (GBS) ‘genotyping-by-seeing’
  • 6. KANSAS STATE U N I V E R S I T Y Dr. Borlaug’s favorite equation… March 27, 2014 6 Rt = irsA y genetic gain over time years per cycle selection intensity selection accuracy genetic variance Borlaug Global Summit, Cd. Obregon, Mexico
  • 7. KANSAS STATE U N I V E R S I T Y Dr. Borlaug’s favorite equation… March 27, 2014 7 Rt = irsA y genetic gain over time years per cycle selection intensity selection accuracy genetic variance Borlaug Global Summit, Cd. Obregon, Mexico Selection Intensity  Large F2 populations  Big screening nurseries  Many crosses / populations
  • 8. KANSAS STATE U N I V E R S I T Y Dr. Borlaug’s favorite equation… March 27, 2014 8 Rt = irsA y genetic gain over time years per cycle selection intensity selection accuracy genetic variance Borlaug Global Summit, Cd. Obregon, Mexico Selection Accuracy  Replicated testing  International trials Separate genetics from noise
  • 9. KANSAS STATE U N I V E R S I T Y Dr. Borlaug’s favorite equation… March 27, 2014 9 Rt = irsA y genetic gain over time years per cycle selection intensity selection accuracy genetic variance Borlaug Global Summit, Cd. Obregon, Mexico Genetic Variance  Bring in new genes not present in current program
  • 10. KANSAS STATE U N I V E R S I T Y Dr. Borlaug’s favorite equation… March 27, 2014 10 Rt = irsA y genetic gain over time years per cycle selection intensity selection accuracy genetic variance Borlaug Global Summit, Cd. Obregon, Mexico Shuttle program effectively cut the breeding cycle time in half
  • 11. KANSAS STATE U N I V E R S I T Y Genomic Selection & Precision Phenotyping Dec 2, 2013 11 NOT NEW CONCEPTS….just new tools!
  • 12. KANSAS STATE U N I V E R S I T Y Genomic Selection 1) Training Population (genotypes + phenotypes) 2) Selection Candidates (genotypes) Dec 2, 2013 12 Heffner, E.L., M.E. Sorrells, J.-L. Jannink. 2009. Genomic selection for crop improvement. Crop Sci. 49:1-12. DOI: 10.2135/cropsci2008.08.0512 Inexpensive, high-density genotypes Accurate phenotypes Prediction of total genetic value using dense genome-wide markers
  • 13. KANSAS STATE U N I V E R S I T Y Why use sequencing for genotyping? + Amazing developments in sequencing output + Very good for wheat where polyploidy and duplications cause problems with hybridization/PCR assays + Polymorphism discovery simultaneous with genotyping + No ascertainment bias + Low per sample cost - Complex bioinformatics - Requires paradigm shift in molecular markers Dec 2, 2013 13 Genotyping-by-sequencing (GBS)
  • 14. KANSAS STATE U N I V E R S I T Y Genotyping-by-sequencing (GBS) “massively parallel sequencing” - next-gen sequencing (Illumina) “multiplex” = using DNA barcode - unique DNA sequence synthesized on the adapter - pool 48-384 samples together “reduced-representation” - capture only the portion of the genome flanking restriction sites - methylation-sensitive restriction enzymes - Target rare, low-copy sites in genome - PstI (CTGCAG), MspI (CCGG) 14 “…massively parallel sequencing of multiplexed reduced-representation genomic libraries.” Elshire, R. J., J. C. Glaubitz, Q. Sun, J. A. Poland, K. Kawamoto, E. S. Buckler and S. E. Mitchell (2011). "A Robust, Simple Genotyping-by-Sequencing (GBS) Approach for High Diversity Species." PloS one 6(5): e19379. Dec 2, 2013
  • 15. KANSAS STATE U N I V E R S I T Y Dec 2, 2013 15 Population Sequencing (POPSEQ) Anchoring and ordering of whole-genome assemblies Ordered whole genome assembly for wheat  De novo assembly of Synthetic W7984  Anchoring with SynOpDH  Reference anchoring of GBS markers Size 9.144 Gbp N50 (scaffold) 120,643 / 21.2kbp
  • 16. KANSAS STATE U N I V E R S I T Y GS: Prediction of wheat quality Dec 7, 2013 16 CIMMYT elite breeding lines (n=1,138) Cycle 45 & 46 International Bread Wheat Screening Nursery (C45IBWSN) Replicated yield tests  2009 & 2010  6 environments One replication for quality testing  milling  dough rheology  baking tests Best Linear Unbiased Estimate (BLUE) Genotyping-by-sequencing 15,330 SNPs (imputed with MVN-EM)(rrBLUP) Cross-validation (x100)  Training sets of n=134  Validation sets of n=30 - thousand kernel weight - mix time - pup loaf volume Sarah Battenfield, KSU
  • 17. KANSAS STATE U N I V E R S I T Y Dec 7, 2013 17 GS: Prediction of wheat quality Sarah Battenfield, KSU TRAIT PREDICTION ACCURACY (r) Test Weight 0.725*** Grain Hardness 0.513*** Grain Protein 0.630*** Flour Protein 0.604*** Flour SDS 0.666*** Mixograph Mix Time 0.718*** Alveograph W 0.697*** Alveograph P/L 0.476*** Loaf Volume 0.638***
  • 18. KANSAS STATE U N I V E R S I T Y Feed the Future Innovation Lab for Applied Wheat Genomics Dec 2, 2013 18 www.wheatgenetics.org/research/innovation-lab
  • 19. KANSAS STATE U N I V E R S I T Y Genomic Selection A tool to enable:  Selection on single plant or seed  Selection in unobserved environments  Maintenance of genetic diversity  Evaluation of larger populations March 27, 2014Borlaug Global Summit, Cd. Obregon, Mexico 19 Rt = irsA y genetic gain over time years per cycle selection intensity selection accuracy genetic variance
  • 20. KANSAS STATE U N I V E R S I T Y High-throughput phenotyping • Automated or semi-automated platforms for rapid, precision assessment • High-throughput analysis pipelines • Decrease the phenotyping burden and increase efficiency of the breeding program Dec 2, 2013 20
  • 21. KANSAS STATE U N I V E R S I T Y HTP: A multi-disciplinary approach Dec 2, 2013 21 Plant Breeding & Genetics Physiology Engineering Bioinformatics HTP
  • 22. KANSAS STATE U N I V E R S I T Y HTP: “Geo-referenced proximal sensing” Dec 2, 2013 22 GPS Data logger Sensors Sensors - GreenSeeker = NDVI - IRT = canopy temperature - SONAR = plant height Physiologically define proximal measurements RTK-GPS (cm level accuracy)
  • 23. KANSAS STATE U N I V E R S I T Y HTP: Platform configuration Dec 2, 2013 23 GreenSeeker CropCircle SONAR IRT GPSGPS sensors computer LabView program  10 Hz sampling  Real-time feedback  Flat file output
  • 24. KANSAS STATE U N I V E R S I T Y HTP: Multiple sensor orientation Dec 2, 2013 24 -3908.040 -3908.035 -3908.030 -3908.025 9637.1339637.1349637.1359637.1369637.1379637.1389637.1399637.140 -data$Right_Longitude data$Right_Latitude Right GPS Left GPS NDVI 0.1 0.3 0.5 0.7 0.9
  • 25. KANSAS STATE U N I V E R S I T Y NDVI – raw data Dec 2, 2013 25 -96.6135 -96.6130 -96.6125 39.128439.128639.128839.1290 NDVI - 2012.05.10 Longitude (DD.dddd) Latitude(DD.dddd)
  • 26. KANSAS STATE U N I V E R S I T Y Assigning data to field entries Dec 2, 2013 26 -9636.82 -9636.80 -9636.78 -9636.76 -9636.74 3907.703907.723907.74 NDVI - 2012.05.10 Longitude Latitude -9636.800 -9636.804 -9636.808 3907.7203907.7223907.724 NDVI - 2012.05.10 Longitude Latitude -9636.800 -9636.804 -9636.808 3907.7203907.7223907.724 NDVI - 2012.05.10 -data.2$long[!is.na(data.2$pass)] data.2$lat[!is.na(data.2$pass)] -9636.800 -9636.804 -9636.808 3907.7203907.7223907.724 NDVI - 2012.05.10 Longitude Latitude Raw data Define plot boundaries Trim data Assign to plots
  • 27. KANSAS STATE U N I V E R S I T Y HTP: Plant Height Dec 2, 2013 27 38.85605 38.85610 38.85615 -100 -90 -80 -70 -60 -50 -40 SONAR MEASUREMENT - PLANT HEIGHT Latitude (DD.ddddd) SONAR(cm) Single pass down one column Centimeter level precision in plant height measurements
  • 28. KANSAS STATE U N I V E R S I T Y Dec 2, 2013 28 X2013.05.31 −10 −5 0 5 10 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● 75 80 85 90 95 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● −10−50510 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● −10−50510 0.84*** (0.78,0.89) X2013.06.06 ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● 0.53*** (0.65,0.4) 0.55*** (0.67,0.42) Height_Jesse ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ●● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● 707580859095100 ●● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● 7580859095 0.49*** (0.61,0.35) 0.43*** (0.56,0.29) 0.55*** (0.42,0.66) Height_Jon ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ●● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● −10 −5 0 5 10 0.47*** (0.60,0.34) 0.41*** (0.54,0.27) 70 75 80 85 90 95 100 0.36*** (0.22,0.5) 0.56*** (0.43,0.67) 80 85 90 95 100 80859095100 Height_Jacob Phenotyper: Increased accuracy Plant Height
  • 29. KANSAS STATE U N I V E R S I T Y Dec 2, 2013 29 -9636.82 -9636.80 -9636.78 -9636.76 -9636.74 3907.703907.713907.723907.733907.74 NDVI - 2012.05.03 -data.1$long -9636.82 -9636.80 -9636.78 -9636.76 -9636.74 3907.703907.713907.723907.733907.74 NDVI - 2012.05.10 -data.2$long -9636.82 -9636.80 -9636.78 -9636.76 -9636.74 3907.703907.713907.723907.733907.74 NDVI - 2012.05.15 NDVI: Multi-temporal measurements Rapid assessment enables repeated measurements over time
  • 30. KANSAS STATE U N I V E R S I T Y NDVI: Multi-temporal measurements Dec 2, 2013 30 DATE NDVI 0.0 0.2 0.4 0.6 0.8 5/3/12 5/10/12 5/15/12 5/21/12 Advanced Yield Nursery Identify dynamic differences among genotypes
  • 31. KANSAS STATE U N I V E R S I T Y Phenocorn:  Global Deployment  Low(er) cost Dec 2, 2013 31 GPS IRT GreenSeeker Bipedal Mobile Unit
  • 32. KANSAS STATE U N I V E R S I T Y HTP Platform: Unmanned Aerial Vehicles Dec 2, 2013 32 + Not too expensive + flexible deployment + Image whole field
  • 33. KANSAS STATE U N I V E R S I T Y High-throughput phenotyping A tool to enable:  More precise assessment  Evaluation of larger populations  Multi-temporal assessment  Assessment of intractable traits March 27, 2014Borlaug Global Summit, Cd. Obregon, Mexico 33 Rt = irsA y genetic gain over time years per cycle selection intensity selection accuracy genetic variance
  • 34. KANSAS STATE U N I V E R S I T Y Precision also comes from reducing errors March 27, 2014 34 Rt = irsA y genetic gain over time years per cycle selection intensity selection accuracy genetic variance Crossing Evaluation Selection Borlaug Global Summit, Cd. Obregon, Mexico Seed Increase Harvest / Cleaning Package Seed Planting Field Notes Harvest / Weigh Record Data Analysis / Selection 1% errors / mix ups 8% random data
  • 35. KANSAS STATE U N I V E R S I T Y Apps for managing materials & data  Reduce errors  Increase speed  Reduce fatigue Dec 2, 2013 35 http://wheatgenetics.org/research/technology
  • 36. KANSAS STATE U N I V E R S I T Y Dec 2, 2013 36 The rate of genetic gain [in plant breeding programs] can be increased through adoption of simple but innovative tools for data collection and management.
  • 37. KANSAS STATE U N I V E R S I T Y Turbo-charging the breeding program… Dec 2, 2013 37 To implement Genomic Selection and High-throughput precision phenotyping:  Fundamental mechanics of program must be functioning well  Data management is robust and efficient Implications for:  Funders of crops research  Students in crops research  Research Scientists  Breeders
  • 38. KANSAS STATE U N I V E R S I T Y Ravi Singh David Bonnett Matthew Reynolds Yann Manes Susanne Dreisigacker Jose Crossa Hector Sanchez Shuangye Wu Josh Sharon Ryan Steeves Jared Crain Sandra Dunckel Trevor Rife Traci Viinanen Narinder Singh Daljit Singh Xu “Kevin” Wang Erena Edae Bikram Gill Bernd Friebe Sunish Seghal Jon Raupp Duane Wilson Eric Olson Ed Buckler Rob Elshire Jeff Glaubitz Jean-Luc Jannink Mark Sorrells Jeffrey Endelman Julie Dawson Jessica Rutkoski Rebecca Nelson Mike Gore Robbie Waugh Hui Liu Pedro Andrade-Sanchez John Heun Jeffery White Kelly Thorp Andrew French Mike Salvucci Nils Stein Martin Mascher Burkhard Steuernagel Thomas Nussbaumer Kevin Price Nan An Niaqian Zhang Jed Barker Allan Fritz Sarah Battenfield Chris Gaynor Lee DeHaan

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