A Meta-analysis of the Genetics of Fusarium Head Blight Resistance in Barley

Transcription

1 A Meta-analysis of the Genetics of Fusarium Head Blight Resistance in Barley Brian Steffenson Department of Plant Pathology University of Minnesota St. Paul

2 Search for Resistance Sourcing Germplasm USDA-ARS NSGC: USA N.I. Vavilov Institute: Russia NorGen: Sweden ICARDA: Syria ICCI: Israel Plant Gene Resources: Canada In total, over 30,000 accessions evaluated, including wild (H. vulgare ssp. spontaneum) and bulbous (H. bulbosum) barley 2

3 Search for Resistance US: Screening Nurseries 3

4 Search for Resistance China: Screening Nurseries Cooperator: Professor Bingxin Zhang Zhejiang University 4

5 Search for Resistance Standard Controls Chevron Stander CI4196 ICB Six-rowed Two-rowed 5

6 Search for Resistance FHB Screening From >30,000 Hordeum accessions evaluated, <1% had resistance comparable to the 6- and 2-rowed controls Variation for FHB reaction in China 6

7 Type I and Type II Resistance Type I: resistance to initial infection Assayed by spray inoculation of spikes Type II: resistance to spread after initial infection Assayed by single floret inoculation of spikes

8 Infection Patterns In the Field

9 Infection Patterns Barley often shows horizontal spread across a triplet and sometimes vertical 9

10 Infection Patterns Barley often shows horizontal spread across a triplet and sometimes vertical 10

11 Infection Patterns But this may be due to external mycelial spread or nesting and not spread through the rachis Inoculation point 11

12 Infection Patterns But this may be due to external mycelial spread or nesting and not spread through the rachis Inoculation point 12

13 Factors Affecting FHB Severity Spike traits Nodding spikes 2- vs. 6-row Lax vs. dense Hulled vs. Hulless 13

14 Factors Affecting FHB Severity June 26 June 28 July 1 July 8 Heading Date and Height 14

15 QTL Mapping Studies for FHB Resistance Bi/Tri-Parental Mapping Populations Chevron/M69 (): de la Peña et al Chevron/Stander (C/S): Ma et al CIhooster (CIh/F): Horsley et al Fredrickson/Stander (F/S): Mesfin et al Gobernadora/CMB643 (G/CMB): Zhu et al Chevron/M69 (): Canci et al MNS93/Stander (MNS93/S): Canci et al M92-299/M81 (M92/M81): Canci et al Zhedar 2/ND912/Foster (Z/ND/F): Dahleen et al HOR211/Lacey (H/L): Sallam and Smith, 2005 Atahualpa/M81 (AT/M81): Beaubien et al., 2005 Quest/W-365 (Quest/W-365): Haas and Steffenson, 201 Harbin RILs: Sato et al.,

16 Meta-analyses of FHB Resistance Loci It is difficult to effectively utilize the many data collected from FHB resistance mapping populations without integration Meta-analysis: integration of previously identified QTLs from multiple linkage maps Integration of multiple QTLs on the same consensus map provides a comprehensive picture about the genetic control of FHB resistance and DON accumulation 16

17 Objectives Construct a consensus map based on previous mapping studies for FHB resistance Conduct a meta-analysis of QTL for FHB resistance and DON accumulation along with important agro-morphological traits Provide recommendations for the way forward in breeding for FHB resistance 1

18 Consensus Map Construction Difficulties In some previous mapping populations: Different markers used (i.e. SSR), limited allelic diversity, poor marker coverage, and small population sizes Need to integrate all markers types into one consensus map 18

19 Creating Consensus Maps LPMerge Method Constructed using linear programing implemented in LPmerge package in R (Endelman and Plomion, 2014) Reduces mean error between linkage maps and the consensus map to ensure that marker order is preserved Advantages: removes marker constraints to resolve any conflicts in marker order Produces a graphical representation of marker order Eight maps were used to generate the consensus map 19

20 Summary Statistics for Consensus Map Chromosome Population 1H 2H 3H 4H 5H 6H H Total Muñoz et al. consensus map Varshney et al. consensus map Zhedar2/ND912/Foster (Z/ND/F) Chevron/M69 () Chevron/Stander (C/S) Gobernadora/CMB643 (G/CMB) Fredrickson/Stander (F/S) CIhooster () Average (cm) ,083.3 Consensus map distance ,299.0 Number of Markers ,88 20

21 ABC KSUD BCD MWG Hor Act8A 14.6 Hor1 MWG GBM Bmac Chromosome 1H # QTL Trait 6 FHB 2 DON 1 HD M4E4-3.0 BCD M5E GBM ABG ABG452 ABG500A 54.8 CDO ABG Bmag GBMS Bmac0090 GBM HVM GBM MWG Glb1 0.0 GBM GBM Bmag0105 GBM Bmac M5E BCD PTO ABG Bmac GBM GBM GBM Bmag cmwg06 GBM ABC CMWG BCD ABC261 ABC322B 13.9 GBM ABG GBM MWG GBM GBM EBmac GBM ME ABG G/CMB F/S Z/ND/F Z/ND/F Z/ND/F 5-11 G/CMB C/S F/S Bin 4-5 Bin 8-9 Bin 12 Bin 1 21

22 ABG03B 8.6 ABG Bmac MWG CDO ABG ABC ABC156A BCD351F HVM ABG _ MWG _ _ GBM ABG459 GBM MWG520A 56.0 M5E1-5.6 BCD339C 58.3 M1E EBC Adh MWG MWG GBM GMS003 BE BF EBmac055 mac0623 EBmac0521 ABC306 EBmac ABG14 BCD108B BF261363A Bmac _1094 BF Bmag0140 M4E3-9 M8E RZ BF M6E BG BG Vrs Bmag BCD140 KSUF MWG503 MWG MWG882B 92.5 GBM ABG49A 93.8 GBM ABG02 M5E BCD30B 10. M3E KSUD EBmac GBMS ABC252 HVM54 ABG313A BF ABC _ GBM _ ABC153 ABC G/CMB C/S C/S Quest/W-365 F/S F/S F/S F/S F/S F/S 9-1 F/S Z/ND/F Z/ND/F Z/ND/F MNS93/S Jap M92/M AT/M81 AT/M81 MNS93/S MNS93/S AT/M81 MNS93/S Chromosome 2H # QTL Trait G/CMB C/S C/S Quest/W F/S F/S Z/ND/F Z/ND/F MNS93/S M92/M81 MNS93/S C/S Quest/W-365 C/S F/S F/S F/S Z/ND/F Z/ND/F Z/ND/F C/S Z/ND/F G/CMB 43 FHB DON HD HT Spike-density Spike-angle #Rachis-nodes Kernels/spike 22

23 ABG03B 8.6 ABG Bmac MWG CDO ABG ABC ABC156A BCD351F HVM ABG _ MWG _ _ GBM ABG459 GBM MWG520A 56.0 M5E1-5.6 BCD339C 58.3 M1E EBC Adh MWG MWG GBM GMS003 BE BF EBmac055 mac0623 EBmac0521 ABC306 EBmac ABG14 BCD108B BF261363A Bmac _1094 BF Bmag0140 M4E3-9 M8E RZ BF M6E BG BG Vrs Bmag BCD140 KSUF MWG503 MWG MWG882B 92.5 GBM ABG49A 93.8 GBM ABG02 M5E BCD30B 10. M3E KSUD EBmac GBMS ABC252 HVM54 ABG313A BF ABC _ GBM _ ABC153 ABC G/CMB C/S C/S Quest/W-365 F/S F/S F/S F/S F/S F/S 9-1 F/S Z/ND/F Z/ND/F Z/ND/F MNS93/S M92/M AT/M81 AT/M81 MNS93/S MNS93/S AT/M81 MNS93/S Chromosome 2H G/CMB C/S C/S Quest/W F/S F/S Z/ND/F Z/ND/F Chevron allele (QTL mapping and validation) 3 11 Jap MNS93/S M92/M81 MNS93/S C/S Quest/W-365 C/S F/S F/S F/S Z/ND/F Z/ND/F Z/ND/F C/S Z/ND/F 20 3 # QTL Trait G/CMB 43 FHB DON HD HT Spike-density Spike-angle #Rachis-nodes Kernels/spike 23 Bin -8

24 ABG03B 8.6 ABG Bmac MWG CDO ABG ABC ABC156A BCD351F HVM ABG _ MWG _ _ GBM ABG459 GBM MWG520A 56.0 M5E1-5.6 BCD339C 58.3 M1E EBC Adh MWG MWG GBM GMS003 BE BF EBmac055 mac0623 EBmac0521 ABC306 EBmac ABG14 BCD108B BF261363A Bmac _1094 BF Bmag0140 M4E3-9 M8E RZ BF M6E BG BG Vrs Bmag BCD140 KSUF MWG503 MWG MWG882B 92.5 GBM ABG49A 93.8 GBM ABG02 M5E BCD30B 10. M3E KSUD EBmac GBMS ABC252 HVM54 ABG313A BF ABC _ GBM _ ABC153 ABC G/CMB C/S C/S Quest/W-365 F/S F/S F/S F/S F/S F/S 9-1 F/S Z/ND/F Z/ND/F Z/ND/F MNS93/S Jap M92/M AT/M81 AT/M81 MNS93/S MNS93/S AT/M81 MNS93/S Chromosome 2H G/CMB C/S C/S Quest/W F/S F/S Z/ND/F Z/ND/F MNS93/S M92/M81 MNS93/S Vrs1 C/S Quest/W-365 C/S F/S F/S F/S Z/ND/F Z/ND/F Z/ND/F C/S Z/ND/F 20 3 # QTL Trait G/CMB 43 FHB DON HD HT Spike-density Spike-angle #Rachis-nodes Kernels/spike 24 Bin 8

25 ABG03B 8.6 ABG Bmac MWG CDO ABG ABC ABC156A BCD351F HVM ABG _ MWG _ _ GBM ABG459 GBM MWG520A 56.0 M5E1-5.6 BCD339C 58.3 M1E EBC Adh MWG MWG GBM GMS003 BE BF EBmac055 mac0623 EBmac0521 ABC306 EBmac ABG14 BCD108B BF261363A Bmac _1094 BF Bmag0140 M4E3-9 M8E RZ BF M6E BG BG Vrs Bmag BCD140 KSUF MWG503 MWG MWG882B 92.5 GBM ABG49A 93.8 GBM ABG02 M5E BCD30B 10. M3E KSUD EBmac GBMS ABC252 HVM54 ABG313A BF ABC _ GBM _ ABC153 ABC G/CMB C/S C/S Quest/W-365 F/S F/S F/S F/S F/S F/S 9-1 F/S Z/ND/F Z/ND/F Z/ND/F MNS93/S Jap M92/M AT/M81 AT/M81 MNS93/S MNS93/S AT/M81 MNS93/S Chromosome 2H G/CMB C/S C/S Quest/W F/S F/S Z/ND/F Z/ND/F MNS93/S Ppd-H1 M92/M81 MNS93/S C/S Quest/W-365 C/S F/S F/S F/S Z/ND/F Z/ND/F Z/ND/F C/S Z/ND/F 20 3 # QTL Trait G/CMB 43 FHB DON HD HT Spike-density Spike-angle #Rachis-nodes Kernels/spike Bin 4 25

26 Wg M6E CDO Bmac MWG _ _ Bmag cmwg652a 25.0 M4E6-9 M4E _ GBM GBM1021 GBM CDO85d 42.0 MWG MWG ABG Bmag0009 Bmag013 Bmag080 GMS M5E-5 MWG222A Bmac HVM CDO Bmag Bmac0218C Bmag ABC ABC10B Bmac0251 Bmag0613 MWG820 M4E8-5 ABG EBmac EBmac ABC165D 4.5 Amy1 9.2 M3E GBM MWG Bmac0040 GBM _ MWG GBM GBM GBM GBM AS3-S cmwg684a MWG844C G/CMB C/S F/S 11 F/S 6 Z/ND/F Z/ND/F 13 M92/M81 MNS93/S MNS93/S C/S Chromosome 6H Z/ND/F Z/ND/F Z/ND/F 14 Z/ND/F G/CMB C/S Bin 1-6 GPC1 Bin 6-8 # QTL Trait 10 FHB 5 DON 1 HD 2 HT 26 MWG98A 15.0

27 BCD195 MWG MWG36B.6 MWG851A 8.0 Rpg1 8.2 Wg89A 20.1 Bmag BCD EBmag094 Wx 25.2 MWG ABG WG89B 31.0 CDO45 MWG ABC151A 35.0 ABC16A 35.9 ABG380 EBmac MWG ABC KSUA1A 49.4 Bmag ABC154A 51. MWG ABG156B 5. MWG51D 61.5 ABC156D ABC Bmac Bmag050 ABC455 ABG46A Amy2.8 MWG808 nud BCD14 8. Bmac GBMS GBM GBM ABC GBM CDO85E 9.3 Bmag BCD98B G/CMB AT/M G/CMB G/CMB C/S F/S H/L AT/M G/CMB 9-23 AT/M81 C/S H/L Chromosome H G/CMB nud C/S F/S F/S 22 G/CMB G/CMB C/S C/S H/L Bin 1-6 Bin 6-8 Bin 8-12 # QTL Trait 11 FHB 6 DON 6 HD 6 HT 1 Spike-angle ABC310B ABC ABG49B 11. PSR ABC RZ ABG461A EBmac055 ABG WG GBM Bmac ABG HVM MWG635B HVM Cat

28 Summary of Identified FHB and DON QTL in Barley Chrom. Trait No. QTL No. detected in >one site No. QTL explaining >10% No. independent of agronomic traits No. detected >1X, R 2 >10%, & independent of other traits 1H FHB DON H FHB DON H FHB DON H FHB DON H FHB DON H FHB DON H FHB DON Total FHB 8 4 DON 42 3

29 Meta-analysis of QTL Mapping Studies Association Mapping Panels Midwest barley breeding lines: Massman et al Wild barley introgression population (): Tandukar et al. unpublished 29

30 Association Mapping in Midwest Barley Breeding Programs QTL Chr. Pos. Bin Associated traits FHB2H H FHB4H H HT FHB6H H FHB6H H DON1H.88 1H DON2H.4-8 2H HD DON2H H HT DON3H H HT DON4H.03 4H 3 1 DON4H H HT DON4H H DON5H H DON6H H HD - HT Massman et al., 2011

31 Association Mapping in Wild Barley Introgression Population QTL Chrom Position cm Bin Position Associated traits FHB2H.12_ H HD - HT FHB2H.11_ H HD - HT DON2H.12_ H HD - HT DON2H.11_ H HD - HT DON2H.11_1094 2H Tandukar et al., In Progress

32 QTL Mapping Genetics of FHB severity and DON accumulation is complex: many QTL of small effect across genome Many QTL are not robust: detected in one environment only Number of agro-morphological traits co-localizing with QTL for FHB severity & DON accumulation strongly suggests pleiotropy Still, resistance QTL from various sources have been integrated into the breeding programs with some success 32

33 Breeding Lines with Reduced DON Minnesota North Dakota Variety / Line Yield Bu/A DON ppm Variety / Line Yield Bu/A DON ppm Lacey Lacey Tradition Tradition Quest Quest S6M ND32889 (6R) S6M ND32898 (6R) ND32920 (6R) Conlon (2R) Pinnacle (2R) ND Genesis (2R) ND28065 (2R) ND32529 (2R) ND32829 (2R)

34 Genomic Selection Genomic selection aims to predict the genetic value of selection candidates based on the Genomic Estimated Breeding Value (GEBV) Predicted from high-density markers across the genome In contrast to MAS, GEBV is based on all markers across the genome, including those with minor effects as well as those major effects Thus, GEBV may capture more genetic variation for the target under selection 34

35 Genomic Selection for FHB Resistance The Way Forward The greater uniformity of materials in breeding nurseries will parse out the contributions of agro-morphological traits on FHB and DON Evaluations done at multiple locations will provide greater confidence and validation of QTL effects Other traits important in breeding can be selected at the same time 35

36 Year Crossing Block Genomic Selection: DON & Yield Inbreeding Line Evaluation Industry Testing Release Spring Six-row Barley Parents (greenhouse) F1 (greenhouse) F2 (field) F3 (greenhouse) Gen Sel* Winter Nursery Prelim. Yield Trials Adv. Yield Trials Pilot Malting Plant-scale Brewing Percent of Robust Percent of Robust DON Yield M160 Quest Lacey

37 On Using Genomic Selection Genomic selection works--but not all the time Accuracy is greater when the training population is more closely related to the prediction population Technical/logistical issues are not trivial Greatest impacts: Cost of genotyping < less than phenotyping GS can shorten breeding cycle Morale!

38 Genomic Selection has Reduced Field Resources for FHB Screening Number of Plots in FHB Nurseries Morris Crookston St. Paul

39 Summary Biparental and association mapping revealed many QTL for low FHB severity and DON across the genome Although many QTL co-locate with agro-morphological traits, some have contributed to cultivars with low FHB and DON, e.g. Quest et al. Genomic selection is a promising method to increase the the accuracy and shorten the breeding cycle for selection of FHB resistance, thereby hastening the release of new, improved varieties

40 Positive Changes on the Horizon? Two-rowed barley is now the preferred type for malting in the Upper Midwest Autumn-sown facultative barleys are now being bred for the Upper Midwest Two-rowed barleys generally suffer less FHB and DON than six-rowed types Autumn-sown barleys may often escape the most conducive conditions for FHB in the summer

41 Personnel B. Steffenson Project Stephanie Dahl Tamas Szinyei Matthew Martin Matthew Haas Bullo Mamo Ahmad Sallam R. Dill-Macky Project K. Smith Project G. Muehlbauer Project Yanhong Dong Acknowledgements US Wheat Barley Scab Initiative American Malting Barley Association University of Minnesota Lieberman-Okinow Endowment