Integration of precision phenotyping and genomics for cereal breeding. Tobias Würschum State Plant Breeding Institute,University of Hohenheim

Transcription

1 Integration of precision phenotyping and genomics for cereal breeding Tobias Würschum State Plant Breeding Institute,University of Hohenheim

2 Outline Introduction BreedVision precision phenotyping platform BreedVision : biomass prediction Precision phenotyping and genomics Summary

3 Introduction A short introduction to plant breeding

4 Introduction Phenotypic data are key to selection in plant breeding and also for genomic approaches Genotyping and sequencing technology have strongly improved in recent years BUT field-based phenotyping has not changed much in the last decades Phenotype This phenotyping bottleneck is a major limitation to crop improvement Furbank and Tester 2011

5 The phenotyping bottleneck Introduction

6 Introduction The phenotyping bottleneck Traditional field phenotyping is often - labour- and time consuming - expensive - not totally objective - sometimes destructive no repeated measurements possible Need to establish approaches that enable precision phenotyping of crops under field conditions

7 Outline Introduction BreedVision precision phenotyping platform BreedVision : biomass prediction Precision phenotyping and genomics Summary

8 Precision phenotyping platform 2013 Busemeyer et al. 2013a

9 Precision phenotyping platform 2015

10 Precision phenotyping platform 1,50m 1,25m 1,25m sensor module Carrier vehicle for the sensor module offers maximum flexibility

11 Sensor technology Lichtgitter Rapidoscan, Die Entwickler Auflösung: 2,5mm Länge: 1800mm Datenrate: 300Hz 3D Kamera IFM O3D201 Auflösung: 64 x 80 Pixel Framerate: 10Hz 2 Lichtfeldkameras Raytrix R5 (RGB und NIR) Auflösung: 5 Megapixel Framerate: 10 bzw. 20Hz 3 Triangulationssensoren Baumer OADM20U Baumer OADM13U Baumer OADM20S Datenrate: 1kHz Spectral-Imaging System EVK Helios NIR G2-320 Core Auflösung: 320 Pixel Wellenlänge: nm Framerate: 100Hz 2 Multireflexions- Ultraschallsensoren Fa. iotec Datenrate: max. 120kHz Digitale Spiegelreflex Canon EOS 1100D Auflösung: 12 Megapixel Objektiv: 17-85mm/4,0-5,6 Framerate: max. 1Hz 2 Rotationsencoder Baumer HDmag Flex Auflösung:??? Datenrate: 1kHz GPS Navilock Datenrate: 1Hz Gyro-Sensor Sparkfun Razor IMU 9 Degrees of Freedom Datenrate: 25Hz Geplant Hochauflösendes Lichtgitter iotec OEOS Opto Electronic Object Scanner Auflösung: 64μm Länge: 100cm Framerate: max. 4kHz Multispektralkamera Condor Quest 3 3 CCD Sensoren (RGB nm, nm, nm) Auflösung: je 1360x1024 Pixel Framerate: 15Hz RTK-GPS z.b. Trimble, Topcon Datenrate ca. 20Hz Sensors with morphological or spectral selectivity are integrated

12 System architecture PC Datenaufnahme Slave Panel-PC zur Steuerung des Sensormoduls 10 am Fahrzeug verteilte Sensor- Schnittstellen (Use Cases) PC Datenaufnahme Master Beckhoff Klemmen mit verschiedene Schnittstellen (analog, digital, seriell) Servo Höhenverstellung System architecture allows sensor fusion

13 Sensor module PPP enables a non-invasive high-dimensional high-throughput phenotyping

14 Sensors Light curtain Estimate plant height and coverage density

15 Light curtain laser-based Sensors

16 Sensors Hyperspectral imaging Estimate dry matter content (moisture content of the plants)

17 PPP in action

18 PPP in action

19 PPP in action

20 Outline Introduction BreedVision precision phenotyping platform BreedVision : biomass prediction Precision phenotyping and genomics Summary

21 Biomass yield: Calibration Biomass yield: Usually not assessed in cereals Destructive analysis (field chopper) Complex dynamic trait Calibration experiment: 25 diverse triticale genotypes (AABBRR) 2 planting densities 2 N levels 2 replications per combination 200 plots for each of 3 time points 1 location, 2 years Busemeyer et al. 2013b

22 Biomass yield: Prediction Prediction based on sensor fusion, i.e. information from all sensors Promising prediction accuracy Busemeyer et al. 2013

23 Biomass yield: Prediction Sensor fusion greatly improves prediction accuracy Busemeyer et al. 2013

24 Biomass yield: Prediction Calibration models transferable across environments Busemeyer et al. 2013

25 Outline Introduction BreedVision precision phenotyping platform BreedVision : biomass prediction Precision phenotyping and genomics Summary

26 Biomass yield: Genomics Genomics experiment 4 triticale DH families N = 647 p-rep design 2 locations 2 years Biomass assessed at BM1-3 by PPP Heritability DArT genotyping Busemeyer et al. 2013, Liu et al. 2014

27 Biomass yield: Genomics PPP captures the phenotypic development of biomass Busemeyer et al. 2013, Liu et al. 2014

28 Biomass yield: Genomics Multiple-line cross QTL mapping BM1 BM2 BM3 QTL DS p G-DS QTL ES p G-ES p G-TS Relative bias Liu et al. 2014

29 Biomass yield: Genomics Multiple-line cross QTL mapping Liu et al. 2014

30 Biomass yield: Genomics QTL plasticity: change with time and development of the plants Liu et al. 2014

31 Biomass yield: Genomics The genetic architecture of BM is under dynamic temporal control Liu et al. 2014

32 Ongoing research: Genomics of biomass ~1600 triticale lines 2015 p-rep design at 5 locations Biomass assessed by PPP Genotyping by GBS Aim: GWAS and genomic selection

33 Ongoing research: Genomics of biomass PPP plus genomic prediction well suited for complex traits

34 Future potential Dynamic traits PPP well suited to assess dynamic traits Busemeyer et al. 2013

35 Summary BreedVision precision phenotyping platform (PPP) for fieldbased phenotyping incorporates different sensors Sensor fusion improves prediction accuracy Promising prediction accuracy for biomass yield of triticale enables phenotyping of >1000 plots per day PPP can be used in combination with genomic approaches Temporal changes of genetic architecture need for a temporal assessment of dynamic traits Precision phenotyping can become a valuable tool for plant breeding

36 Outlook Hey Joe, you look better today than last week. Moreover you have grown more than 2 cm. See you next week! Joe

37 Thanks to: Hans Peter Maurer Arno Ruckelshausen The Predbreed Team Thank you for your attention!