THEME – 1 Mining CIMMYT germplasm data to inform breeding targets for CC adaptation
1. Mining CIMMYT germplasm data to inform breeding
targets for CC adaptation
Zakaria KEHEL, Jose CROSSA, Thomas PAYNE and Matthew REYNOLDS
Rabat-Morocco. 24-27 June 2014
2. Collection Wild
Land-
race
Breeding
materials
Genetic
stocks
Cultivars
Unknown
or Other
TOTAL
Bread Wheat 213 32,428 41,995 8,150 6,278 331 89,395
Durum Wheat 25 5,578 14,262 1,089 1,156 58 22,356
Triticale 0 0 16,964 3,402 345 9 20,720
Barley 0 669 13,898 200 1,755 11 16,533
Species &
other
6,541 1,658 155 820 30 15 9,219
Rye 36 109 132 168 219 13 677
Total 6,816 40,442 91,057 13,829 9,783 437 158,713
TOTAL (excl.
barley)
142,180
CIMMYT Wheat Germplasm Bank
3. WGB: Opportunities, Challenges and Gaps
● Pedigrees, for GWAS or GS precision
● Phenotypes, so expensive (Curation)
● Core reference sets (SeeD, GCP, WGB, FIGS)
● GRIN Global and GeneSys
● Actions as a “global system”
● Little overlap with USDA and ICARDA
The phenotypic values, representing over 11.2M data points, are
held by CIMMYT’s IWIS database.
The value of these phenotypic values exceed USD100M, if the
trials resulting in the assembled data were to be repeated today.
4. WGB: Opportunities, Challenges and Gaps
● Species accessions
Too many!
Yet, extent of in situ diversity?
Generate new diversity with existing accessions?
● Frustration of limited access to new, improved
germplasm (this might also extend to collecting
landraces).
● Most exchange is bank-to-bank
● “my institution/government owns the germplasm”
5. Data quality control (single field analysis)
Identification of out layers
Verification (field books)
Data storage
Database with meta data available
Data control of wheat nurseries
7. LOC_N
O COUNTRY LOCDESCRIP INSTITUTEN
10601MAURITIUS REDUIT
Agricultural Research and
Exte
19011ALGERIA ITGC-DAHMOWNE ITGC
19012ALGERIA EL HARRACH ITGC
19121EGYPT SERS EL-LIYAN Agr. Res. Center
20701LEBANON BEKA'A VALLEY Agric. Res. Inst.
21221TURKEY AGRICULTURE FACULTY University of Trakya
22243INDIA NAGAON EXP. STA. DWR
24059CHINA AN DA ALKALI SALINE SOIL INST. Heilongjiang Academy
27121THAILAND NONGKAI RICE EXP. STN. Rice Research Inst.
41303
UNITEDSTAT
ES ALABAMA AMU Alabama A & M Univ.
42109MEXICO MEXICALI CIMMYT
42138MEXICO CIANO - FULL IRRIGATION CIMMYT
65001GREECE KENTZIKO THERMI NA
65004GREECE CEREAL INSTITUTE (EPANOMI) NAGREF-DW Dept.
65009GREECE SCHOOL OF AGRICULTURE YPSILON SA
LOC_NO Point:COUNTRY
Polyg:COUNTR
Y LOCDESCRIP INSTITUTE
12308KENYA Ethiopia ENDEBESS Kenya Seed Company Ltd.
19013ALGERIA Morocco AIN EL HADJAR ITGC
19126EGYPT India KHATTARA Agr. Res. Center
20011AFGHANISTAN Kazakhstan TAKHAR-TALOQAN CIMMYT
20330IRAN Russia BIRJAND AGRIC. RES. STN. SPII
21115SYRIA Turkey AL RQA Ministry of Agriculture
21117SYRIA Turkey HRAN Ministry of Agriculture
21121SYRIA Iraq HIMO Ministry of Agriculture
FIGS without roots, or
imbalanced passport,
characterization &
evaluation data
8. Data control a continuing process
• The same location with different management system has only
one planting and harvest date
• Full irrigation or irrigated locations with “NO” irrigation in the
corresponding field value
• Same location, IRR YLD less than RF YLD
• 13 Ton/Ha in RF location (Mexico Obregon) as an example
other control methods with time
• Outliers across locations and years
• Validating dates using earlier years or neighboring locations
• RF versus IRR
• …
9. MET Analysis
MET data
GxE analysis
Variance components, G corr,
BLUPS, Stability
GxE with
covariables
Patterns of GxE
(spatially changing
relationships)
Identification of
co-variables
(Factors, variates)
Meta data stored
in the DB
All, RF,
IRR
10. y = 0.0176x + 4.3539
R² = 0.1952
y = 0.113x + 8.0045
R² = 0.5792
y = 0.0013x + 1.0054
R² = 0.0005
0.00
2.00
4.00
6.00
8.00
10.00
12.00
14.00
16.00
mean
max
min
Linear (mean)
Linear (max)
Linear (min)
y = -0.3232x + 15.653
R² = 0.4153
y = 0.308x + 82.476
R² = 0.3959
0.00
10.00
20.00
30.00
40.00
50.00
60.00
70.00
80.00
90.00
100.00
Vg
Vgxe
Linear (Vg)
Linear (Vgxe)
Change in yield variability in Wheat Nursery
24. Cycle
SOW_julia
n
Emergence
_Julian
HARVEST_julia
n
FOLIAR_DISEASE_DEVELOPM
ENT
IRRIGATE
D
LODGIN
G
2005 11/19/2005 4/21/2006 TRACES YES SLIGHT
Environmental
data
Cycle SOW_julian Emergence _Julian HARVEST_julian FOLIAR_DISEASE_DEVELOPMENT IRRIGATED LODGING
2005 11/19/2005 4/21/2006 TRACES YES SLIGHT
Traits Varieties tested
PBW343
CHAM 6
KLEIN CHAMACO
HIDDAB
CHAKWAL 86
DHARWAR DRY
MILAN/KAUZ//PASTOR
FLORKWA-1/DHARWAR DRY
PASTOR/BAV92
CNDO/R143//ENTE/MEXI_2/3/AEGILOPS
SQUARROSA (TAUS)/4/WEAVER/5/PASTOR
Grain yield
Days to heading
Plant heightCan feed the phenology
table presented earlier
The Wheat Atlas Website
25. Table of genotype by
location values +
mean, max, min and
SD of genotypes and
locations
Install a win-win
relationships with
collaborators: They send
data, we provide analysis
and reports
The CIMMYT IWIS web-page
http://apps.cimmyt.org/wpgd/index.htm
27. ● Curation is important
● Vey helpful to complete info at the genebank and
creation of stress populations accelerate
germplasm exchange
● Pipelines for prediction and genomic selection:
Pedigrees and markers
● Data management and sharing; analytical and
visualization tools
● Collaborations
Conclusions