Getting at Genetic Architecture

1
Getting at Genetic Architecture
WRKY
putative MYB4
Oxalate Oxidase
ankyrin-like protein
cytochrome P450

• Adventures with QTL for disease resistance

heat shock protein 70
DNAJ heat shock protein
glutathione S-transferase
blight-associated protein p12
wound-induced basic protein
AP2 domain protein RAP2.12
CC-NBS-LRR similar to RPM1
plasma membrane intrinsic protein
Rebecca Nelson Cornell University
putative AVR9 elicitor response protein
pathogen-responsive alpha-dioxygenase
2
Quantitative disease resistance

• Questions about dQTL in rice, maize
• How consistent are dQTL across tests, germplasm,
  diseases?
• What genes underlie QTL? Do blast-responsive
  genes tend to co-localize with blast QTL?
• What loci / alleles are selected under recurrent
  selection for disease resistance?
• Do loci and/or genes exist that condition durable
  and/or broad-spectrum resistance (multiple
  diseases?)?
• How can we use knowledge of QDR for improved
  allele discovery and utilization?

3
Disease QTL summary for rice

• 16 QTL studies for rice diseases

• 8 for blast
• 4 for sheath blight
• 2 for RYMV
• One each for sheath rot and bacterial blight

4
R.J. Wisser
5
QTL distribution

• Half the genome (49) is covered by D-QTL
• Overall distribution looks random
• Evidence for clustering
• Based on the coefficient of dispersion
• Clusters have QTL for multiple diseases

6
(Non) Coincidences QTLs, R-genes, RGAs
Telomeric section of chr. 9
Telomeric section of chr. 3
RGAs
Major genes for rice blast
Pericentric section of chr. 12
7
R-genes and RGA associate with dQTL
8
Inspection of annotation under QTL
Caution 20 of genes under dQTL are
recognisable as potentially defense-related
9
Digital Northern Analysis of Magnaporthe
-induced genes
Digital Northern Analysis of Magnaporthe
-induced genes

• Local database of all publicly-available cereal
  ESTs
• Used Magnaporthe-inoculated and non-inoculated
  leaf libraries of rice to identify cDNAs that are
  significantly differentially represented

http//telethon.bio.unipd.it/bioinfo/IDEG6
10
Rice blast-related EST libraries

• Libraries by G. Wang et al.
• Nipponbare compatible, incompatible isolates
• Different times post-inoculation
• Significantly more differentially represented
  genes co-localized with blast QTL (P0.0007)
• With Bonferroni but not False Discovery Rate
  correction for multiple tests

11
QTLs for other traits
Telomeric section of chr. 9
Elongation of plant height Stem space Decreased
chl. content
Ishimaru et al., 2001
JRGP cM 50.7 91.8
No. FL-cDNAs 555 No. genes 1083
12
Conclusions re dQTL in rice

• Half the genome dQTL
• Some clusters - dQTL for multiple diseases
• BSR chromosomal segments exist
• R genes significantly associated with QTL
• Blast-induced genes associated with blast QTL
• We can identify many positional candidates based
  on different criteria but there are very many!
• Genetic dissection of QTL will allow many
  questions to be addressed

13
Near-isogenic lines (maize)

• Genetic dissection
• Phenotypic analysis

• -conventional and novel

14
NCLB QTL P 0.70
GLS QTL P 0.022
Necrotroph QTL P 0.0017
All disease QTL P 6.8 x 10-9
15
Standard mappingpopulation (IBM)
Recurrent selectionpopulations
QTL-preNILs
Other sources of resistance

• Genetic dissection
• Phenotypic analysis

• -conventional and novel

16
Maize QTL Northern corn leaf blight
Chr. 5
Chr. 7
Chr. 3
Chr. 8
Ht2
Htn1
Source of resistance Mo17 Ref. Dingerdissen et
al., 1996 Data above for Kenya Freymark et al.,
1993-4 for Iowa
17
(No Transcript)
18
RILs chosen to cover QTL regions

• Lines chosen from QTL mapping population based on
  SSR data
• Field data to confirm that RILs captured
  resistance
• NCLB data --gtbut similar pattern for GLS

19
MR
MS
Mo17
B73
X
BCn
x
B73
Starting material - RILs
x
B73
x
BC1
BC2
B73
x
B73
x
B73
x
B73
x
BCn
20
BC2 families field phenotypes
2.5
2
1.5
1
0.5
0
B73
Mo17
IBM275_5G
IBM357_5F
IBM054_1D
IBM054_1E
IBM054_3E
IBM054_4B
IBM054_7H
IBM054_8A
IBM054_8B
IBM262_4H
IBM262_7H
IBM275_1H
IBM275_3A
IBM275_7C
IBM275_9A
IBM357_3C
IBM357_4C
IBM357_7D
IBM357_1G
IBM357_9G
IBM262_11E
IBM357_10F
IBM054_11H
IBM275_12D
IBM275_12G
Ying Wei
21
Chr.5 (IBM357)
MR
MS
MS
umc1587
umc2036
umc1365
umc1478
cM
Mo17
B73
Ying Wei
22
Chr.8 (IBM054)
umc1724
umc1997
Htn1
umc1149
umc1263
Ht2
umc1130
umc1316
umc1202
umc1457
umc1984
umc1530
cM
Ying Wei
23
RT-PCR for E. turcicum biomass
Chia-Lin Chung
24
Standard mappingpopulation (IBM)
Recurrent selectionpopulations
QTL-NILs
QT allelediscovery
Other sources of resistance

• Genetic dissection
• Phenotypic analysis

• -conventional and novel

25
Response to selection for quantitative resistance
to NCLB in 8 maize pools (CIMMYT Ceballos et
al., 1991 Crop Sci.)
26
Loci deviating from drift vs. reported dQTLs
(NCLB)
R.J. Wisser
27
Selection for quantitative resistance to
NCLB aligns with major genes and QTLs on maize
chromosome 8
R.J. Wisser
28
In progress

• Analysis of 7 other pools
• Efficiency enhancement
• Universal primer label - reduced costs
• Differences in allele frequency can be detected
  in pooled samples
• Saturate regions showing allele shifts
• SSRs
• Candidate genes
• Functional analysis
• Selection could be due to soil conditions or
  other factors
• Bulked segregant analysis and QTL-NILs ? assess
  phenotypes associated with loci showing shifts in
  allele frequency

29
Quantitative disease resistance

• Much of the genome is covered by reported QTL
• Some dQTL clustering in both maize and rice
• Multiple diseases per cluster broad spectrum
  chromosomal segments
• Triangulation of evidence still leaves a huge
  number of implicated genes
• R-genes remain candidates for quantitative
  resistance
• Blast-responsive genes associated with blast QTL
• Lots of other tantalizing candidates
• Genetic dissection of dQTL will allow many
  remaining questions to be addressed
• Recurrent selection as an excellent source of ()
  alleles

30
Acknowledgements

• Randy Wisser
• Ying Wei
• Chia-Lin Chung
• IGD
• Steve Kresovich
• Sharon Mitchell, gang
• Qi Sun - Bioinformatics
• Margaret Smith
• Owen Hoekenga
• Sherrie White
• Stats consulting
• R. Neilsen, S. Despa

• KSU
• RGA Scot Hulbert
• CIMMYT
• Hernan Ceballos
• David Beck
• Support
• Rockefeller Foundation
• McKnight Foundation