The document discusses utilizing genetic diversity from crop wild relatives and landraces. It describes using trait mining selection (FIGS) and computer modeling to identify a small subset of accessions from a large collection that are likely to contain a particular trait. Climate data is used as a predictor to model trait scores and select accessions for field trials to identify novel crop traits in a lower cost manner than screening the entire collection.

2. • U#liza#on
of
gene#c
diversity
• Core
collec#on
subset
• Trait
mining
selec#on
(FIGS)
• Computer
modeling
• Some
examples
(FIGS)
2

3.
wild
tomato
tomato
teosinte
corn,
maize
3

4. B
B
A
C
A
A
A
A
A
Crop
Wild
Rela#ves
Tradi#onal
landraces
Modern
cul#vars
Gene/c
bo1lenecks
during
crop
domes/ca/on
and
during
modern
plant
breeding.
The
circles
represent
allelic
varia#on.
The
funnels
represents
allelic
varia#on
of
genes
found
in
the
crop
wild
rela#ves,
but
gradually
lost
during
domes#ca#on,
tradi#onal
cul#va#on
and
modern
plant
breeding.
4

5. 5

6. • Scien#sts
and
plant
breeders
want
a
few
hundred
germplasm
accessions
to
evaluate
for
a
par#cular
trait.
• How
does
the
scien#st
select
a
small
subset
likely
to
have
the
useful
trait?
• Example:
More
than
560
000
wheat
accessions
in
genebanks
worldwide.
Slide
adopted
from
a
slide
by
Ken
Street,
ICARDA
(FIGS
team)
6

7. • The
scien#st
or
the
breeder
need
a
smaller
subset
to
cope
with
the
field
screening
experiments.
• A
common
approach
is
to
create
a
so-­‐called
core
collec/on.
Sir
OYo
H.
Frankel
(1900-­‐1998)
proposed
a
limited
set
established
from
an
exis#ng
collec#on
with
between
its
entries.
The
core
collec#on
is
of
limited
size
and
chosen
to
of
a
large
7
collec#on
(1984)
.

8. • Given
that
the
trait
property
you
are
looking
for
is
rela#vely
rare:
• Perhaps
as
rare
as
a
unique
allele
for
one
single
landrace
cul#var...
• Geang
what
you
want
is
largely
a
ques#on
of
LUCK!
8
Slide
adopted
from
a
slide
by
Ken
Street,
ICARDA
(FIGS
team)

9. 9

10. Objec/ve
of
this
study:
– Explore
climate
data
as
a
predic#on
model
for
“computer
pre-­‐screening”
of
crop
traits
BEFORE
full
scale
field
trials.
– Iden#fica#on
of
landraces
with
a
higher
probability
of
holding
an
interes#ng
trait
property.
10

11. Wild
rela#ves
are
shaped
Primi#ve
cul#vated
crops
Tradi#onal
cul#vated
crops
by
the
environment
are
shaped
by
local
(landraces)
are
shaped
by
climate
and
humans
climate
and
humans
Modern
cul#vated
crops
are
Perhaps
future
crops
are
mostly
shaped
by
humans
shaped
in
the
molecular
(plant
breeders)
laboratory…?
11

12. • Primi#ve
crops
and
tradi#onal
landraces
are
an
important
source
for
novel
traits
for
improvement
of
modern
crops.
• Landraces
are
ohen
not
well
described
for
the
economically
valuable
traits.
• Iden#fica#on
of
novel
crop
traits
will
ohen
be
the
result
of
a
larger
field
trial
screening
project
(thousands
of
individual
plants).
• Large
scale
field
trials
are
very
costly,
area
and
human
working
hours.
12

13. Assump/on:
the
climate
at
the
original
source
loca#on,
where
the
landrace
was
developed
during
long-­‐term
tradi#onal
cul#va#on,
is
correlated
to
the
trait
score.
Aim:
to
build
a
computer
model
explaining
the
crop
trait
score
(dependent
variables)
from
the
climate
data
(independent
variables).
13

14. 1) Landrace
samples
(genebank
seed
accessions)
2) Trait
observa#ons
(experimental
design)
-­‐
High
cost
data
3) Climate
data
(for
the
landrace
loca#on
of
origin)
-­‐
Low
cost
data
•
The
accession
iden#fier
(accession
number)
provides
the
bridge
to
the
crop
trait
observa#ons.
•
The
longitude,
la/tude
coordinates
for
the
original
collec#ng
site
of
the
accessions
(landraces)
provide
the
bridge
to
the
environmental
data.
14

15. Alnarp,
Sweden
Lima,
Peru
Svalbard
Benin
15

16. Faba
bean,
Finland
Field
trials,
Gatersleben,
Germany
Potato
Priekuli
Latvia
Forage
crops,
Dotnuva,
Lithuania
Radish
(S.
Jeppson)
Linnés
äpple
16
Powdery
Mildew,
Leaf
spots
Yellow
rust
Black
stem
rust
Blumeria
graminis
Ascochyta
sp.
Puccinia
strilformis
Puccinia
graminis
hYp://barley.ipk-­‐gatersleben.de

17. The
climate
data
is
extracted
from
the
WorldClim
dataset.
hYp://www.worldclim.org/
Data
from
weather
sta#ons
worldwide
are
combined
to
a
con#nuous
surface
layer.
Climate
data
for
each
landrace
is
Precipita#on:
20
590
sta#ons
extracted
from
this
surface
layer.
Temperature:
7
280
sta#ons
17

18. FIGS
selec#on
is
a
new
method
to
predict
crop
traits
of
primi#ve
cul#vated
material
from
climate
variables
by
using
mul#variate
sta#s#cal
methods.
18

19. What is hYp://www.figstraitmine.org/
Mediterranean
region
Origin of Concept (1980s):
Wheat and barley landraces from South
Australia
marine soils in the Mediterranean
region provided genetic variation
Slide made by
for boron toxicity. Michael Mackay 1995 19

20. FIGS
The
FIGS
technology
takes
much
of
the
guess
work
out
of
choosing
which
accessions
are
most
likely
to
contain
the
specific
characteris#cs
being
sought
by
plant
breeders
to
improve
plant
produc#vity
across
numerous
challenging
environments.
hYp://www.figstraitmine.org/
20
20

21. Slide made by
Michael Mackay 1995
21

22. 22

23. – For
the
ini#al
calibra#on
or
training
step.
– Further
calibra#on,
tuning
step
– Ohen
cross-­‐valida#on
on
the
training
set
is
used
to
reduce
the
consump#on
of
raw
data.
– For
the
model
valida#on
or
goodness
of
fit
tes#ng.
– New
external
data,
not
used
in
the
model
calibra#on.
23

24. – No
model
can
ever
be
absolutely
correct
– A
simula#on
model
can
only
be
an
approxima#on
– A
model
is
always
created
for
a
specific
purpose
– The
simula#on
model
is
applied
to
make
predic#ons
based
on
new
fresh
data
– Be
aware
to
avoid
extrapola#on
problems
24

25. 25

26. • No
sources
of
Sunn
pest
resistance
previously
found
in
hexaploid
wheat.
• 2
000
accessions
screened
at
ICARDA
without
result
(during
last
7
years).
• A
FIGS
set
of
534
accessions
was
developed
and
screened
(2007,
2008).
• 10
resistant
accessions
were
found!
• The
FIGS
selec#on
started
from
16
000
landraces
from
VIR,
ICARDA
and
AWCC
• Exclude
origin
CHN,
PAK,
IND
were
Sunn
pest
only
recently
reported
(6
328
acc).
• Only
accession
per
collec#ng
site
(2
830
acc).
• Excluding
dry
environments
below
280
mm/year
• Excluding
sites
of
low
winter
temperature
below
10
degrees
Celsius
(1
502
acc)
hYp://dx.doi.org/10.1007/s10722-­‐009-­‐9427-­‐1
Slide
adopted
from
Ken
Street,
ICARDA
(FIGS
team)
26

27. 27
Priekuli
(L)
Bjorke
(N)
Landskrona
(S)

28. Heading
Ripening
Length
H-­‐Index
Vol
wgt
TGW
Priekuli
(L)
Bjorke
(N)
Landskrona
(S)
28

29. Michael
Mackay
FIGS
coordinator
• Barley (Hordeum vulgare ssp. vulgare) collected Ken
Street
FIGS
project
leader
from different countries worldwide screened for
susceptibility of net blotch infection (1676
greenhouse + 2975 field observations).
• Net blotch is a common disease of barley caused by Harold
Bockelman
the fungus Pyrenophora teres.
Net
blotch
data
• Screened at four USDA research stations: North
Dakota (Langdon, Fargo), Minnesota (Stephen),
Georgia (Athens).
Eddy
De
Pauw
Climate
data
• 1-3 are basically resistant  group 1
• 4-6 are intermediate  group 2
• 7-9 are susceptible  group 3
• Discriminant analysis (DA): Dag
Endresen
Data
analysis
• Correctly classified groups: 45.9% in the training set
and 44.4% in the test set.
• Work in progress! (SIMCA, D-PLS)
29

30. 30