Scientific seminar at the Carlsberg Research Institute (CRI) in Copenhagen, Denmark on trait data mining using the Focused Identification of Germplasm Strategy (FIGS), 4th November 2009. Endresen, D.T.F. (2010). Predictive association between trait data and ecogeographic data for Nordic barley landraces. Crop Sci. 50(6):2418-2430. doi: 10.2135/cropsci2010.03.0174

2. • Domes'ca'on
bo-leneck
• U'liza'on
of
gene'c
diversity
• Core
collec'on
subset
selec'on
• Trait
mining
selec'on
• Computer
modeling
• Example
1:
• Nordic
Barley
Landraces
(2005)
• N-­‐PLS
regression
(in
MATLAB)
• Example
2:
• Net
blotch
in
barley
(ICARDA,
USDA)
• Discriminant
analysis
(DA)
2

3.
wild
tomato
tomato
teosinte
corn,
maize

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
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.

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.
6
Slide
adopted
from
a
slide
by
Ken
Street,
ICARDA
(FIGS
team)

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
O-o
H.
Frankel
(1900-­‐1998)
proposed
a
limited
set
or
"core
collec'on”
established
from
an
exis'ng
collec'on
with
minimum
similarity
between
its
entries.
The
core
collec'on
is
of
limited
size
and
chosen
to
represent
the
gene/c
diversity
of
a
large
collec'on,
a
crop,
a
wild
species
or
group
of
species
(1984)
.
7

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...
• Geeng
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. 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…?
10

11. Objec/ve
of
this
study:
– Explore
climate
data
as
a
predic'on
model
for
“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.
11

12. • Primi/ve
crops
and
tradi/onal
landraces
are
an
important
source
for
novel
traits
for
improvement
of
modern
crops.
• Landraces
are
oien
not
well
described
for
the
economically
valuable
traits.
• Iden'fica'on
of
novel
crop
traits
will
oien
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. The
underlying
assump'on
of
FIGS
selec'on
is
that
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.
The
aim
is
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)
3) Climate
data
(for
the
landrace
loca'on
of
origin)
•
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
Powdery
Mildew,
Leaf
spots
Yellow
rust
Black
stem
rust
16
Blumeria
graminis
Ascochyta
sp.
Puccinia
strilformis
Puccinia
graminis
h-p://barley.ipk-­‐gatersleben.de

17. The
climate
data
is
extracted
from
the
WorldClim
dataset.
h-p://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 h-p://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.
h-p://www.figstraitmine.org/
20
20

21. Slide made by
Michael Mackay 1995
21

22. • 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)
Slide
adopted
from
Ken
Street,
ICARDA
(FIGS
team)
22

23. 23

24. – The
ini'al
model
is
developed
from
the
training
set
– Fine
tuning
of
model
parameters
and
seengs
– 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. – For
the
ini'al
calibra'on
or
training
step.
– Further
calibra'on,
tuning
step
– Oien
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.
25

26. 26

27. 27

28. 28

29. Sta/on
Al/tude
La/tude
Longitude
Priekuli,
Latvia
83
m
57.3167
25.3667
Two
years:
•
2002
Bjørke
forsøksgård,
Norway
149
m
60.7667
11.2167
•
2003
Landskrona,
Sweden
3
m
55.8667
12.8333
29

30. accide AccNum Country Locality Eleva/on La/tude Longitude Coordinate
7436 NGB27 Finland Sarkalahti, Luumäki 95 m 61.0333 27.3333 SESTO
9717 NGB456 Norway Dønna, Nordland 71 m 66.1167 12.5 Georeferenced
9601 NGB468 Norway Trysil 400 m 61.2833 12.2833 Georeferenced
9600 NGB469 Norway BJØRNEBY 400 m 61.2833 12.2833 Georeferenced
7966 NGB775 Sweden Överkalix, Allsån 45 m 66.4 22.9333 SESTO
8510 NGB776 Sweden Överkalix 100 m 66.4 22.7667 SESTO
7810 NGB792 Finland Luusua, Kemijärvi 145 m 66.4833 27.35 SESTO
9538 NGB2072 Norway Finset 1220 m 60.6 7.5 Georeferenced
8482 NGB2565 Sweden Öland 11 m 56.7333 16.6667 Georeferenced
9102 NGB4641 Denmark Støvring, Jylland 55 m 56.8833 9.8333 Georeferenced
9015 NGB4701 Faroe Islands Faroe Islands 81 m 62.0167 -6.7667 Georeferenced
9039 NGB6300 Faroe Islands Faroe Islands 81 m 62.0167 -6.7667 Georeferenced
8531 NGB9529 Denmark Lyderupgaard 9m 56.5667 9.35 Georeferenced
7344 NGB13458 Finland Koskenkylä, Rovaniemi 91 m 66.5167 25.8667 Georeferenced
30

31. From
a
total
of
19
landrace
accessions
included
in
the
dataset,
only
4
of
the
landrace
accessions
included
geo-­‐referenced
coordinates
in
the
NordGen
SESTO
database.
10
accessions
were
geo-­‐referenced
from
the
reported
place
name
and
descrip'ons
of
the
original
gathering
site
included
in
SESTO
and
other
sources.
For
5
accessions
there
were
not
enough
informa'on
available
to
locate
the
original
gathering
loca'on.
Right
side
illustra.on
Example
of
georeferencing
for
NGB9529,
landrace
reported
as
originaGng
from
Lyderupgaard
using
KRAK.dk
and
maps.google.com
31

32. 32

33. 3
14
12
(loca'on
of
origin)
Climate
data
(mode
3):
14
landraces
•
Minimum
temperature
•
Maximum
temperature
•
Precipita'on
•
…
(many
more
layers
can
be
added)
12
monthly
means
Min.
temperature
Max.
temperature
Precipita'on
Jan,
Feb,
Mar,
…
Jan,
Feb,
Mar,
…
Jan,
Feb,
Mar,
…
14
samples
33

34. 6
Mode
3
*
LVA
2002
*
LVA
2003
*
NOR
2002
28
6
*
NOR
2003
*
SWE
2002
14
landraces
(x2)
Mode
2
(Traits)
*
SWE2003
*
Heading
days
*
Ripening
days
*
Length
of
plant
*
Harvest
index
*
Volumetric
weight
6
traits
*
Grain
weight
(tgw)
Bjørke
(N)
Bjørke
(N)
Landskrona
(S)
Landskrona
(S)
Priekuli
(Lv)
Priekuli
(Lv)
2002
2003
2002
2003
2002
2003
6
traits
6
traits
6
traits
6
traits
6
traits
6
traits
28
records
34

35. 35

36. 36

37. tmin
tmax
prec
Mode
3
(climate
variables)
Box
plot,
raw
data
have
very
different
range
of
numerical
values
(tmin,
tmax,
and
prec).
Scaling
across
mode
3
is
thus
applied
to
the
mul'-­‐
way
models.
Lei
is
displayed
the
box-­‐plot
for
the
3-­‐way
data
unfolded
as
tmin
tmax
prec
to
keep
the
dimensions
of
mode
3.
The
3-­‐way
climate
data
was
reasonably
well
described
by
a
PARAFAC
model
of
two
Scaling
across
mode
3
components.
37

38. PARAFAC
split-­‐half
(mode
1)
analysis:
The
two
PARAFAC
models
each
calibrated
from
two
independent
split-­‐half
subsets,
both
converge
to
a
very
similar
solu'on
as
the
model
calibrated
from
the
complete
dataset.
The
PARAFAC
model
is
thus
a
general
and
stable
model
for
the
scope
of
Scandinavia.
38

39. 39

40. • Oien
the
cri'cal
levels
(α)
for
the
p-­‐value
significance
is
set
as
0.05,
0.01
and
0.001.
• For
the
modeling
of
14
samples
(landraces)
gives:
– 12
degrees
of
freedom
for
the
correla'on
tests
(mean
x,
y)
– One-­‐tailed
test
(looking
only
at
posi've
correla'on
of
predic'ons
versus
the
reference
values).
– A
coefficient
of
determina'on
(r2)
larger
than
0.56
is
significant
at
the
0.001
(0.1%)
level
for
14
values/samples.
Many
introductory
text
books
on
sta's'cs
include
a
table
of
Cri'cal
Values
for
Pearson’s
r.
40

41. Heading
Ripening
Length
H-­‐Index
Vol
wgt
TGW
Priekuli
(L)
Bjorke
(N)
Landskrona
(S)
41

42. LVA
(2002)
LVA
(2003)
NOR
(2002)
NOR
(2003)
SWE
(2002)
SWE
(2003)
42

43. • Latvia
2002
(LY11)
– May
2002
was
extreme
dry
in
Priekuli.
– June
2002
was
extreme
wet
in
Priekuli.
– The
wet
June
caused
germina'on
on
the
spikes
for
many
of
the
early
varie'es.
• Landskrona
2003
(LY32)
– June
2003
was
extreme
dry
in
Landskrona.
– June
was
the
'me
for
grain
filling
here.
• Too
extreme
for
the
genotype
to
be
“normally”
expressed
?
• Too
large
effect
from
“G
by
E”
interac'on
?
43

44. Sowing
Rainfall
(mm)
Sta/on
Year
week
May
June
July
August
Bjørke
forsøksgård,
Norway
2002
17
82.9
67.4
128.5
136.5
2003
21
75.1
85.7
67.1
53.2
Landskrona,
Sweden
2002
13
53.5
75.3
76.4
68.9
2003
15
70.7
40.4
76.0
45.7
Priekuli,
Latvia
2002
17
38.2
111.1
67.0
11.3
2003
19
88.0
59.2
87.8
175.8
44

45.
45

46. 46

47. 47

48. • The first dataset I started to work with is a “FIGS”
dataset with genebank accessions of Barley
(Hordeum vulgare ssp. vulgare) collected from
different countries worldwide and tested for
susceptibility of net blotch infection. Net blotch is
a common disease of barley caused by the fungus
Pyrenophora teres.
• The barley plants were inoculated with the fungus
and the percentage of the leaves infected with the
disease was normalized to an interval scale (1 to 9).
• 1-3 are basically resistant  group 1
• 4-6 are intermediate  group 2
• 7-9 are susceptible  group 3
48

49. • Agro-­‐clima'c
Zone
(UNESCO
classifica'on)
• Soil
classifica'on
(FAO
Soil
map)
• Aridity
(dryness)
• Precipita'on
• Poten'al
evapotranspira'on
(water
loss)
• Temperature
• Maximum
temperatures
• Minimum
temperatures
(mean
values
for
month
and
year)
49

50. Discriminant Analysis: obs_nb versus acz_moisture; ...
Quadratic Method for Response: obs_nb
Predictors: acz_moisture; acz_winter_temp;
acz_summer_temp; arid_annual;
pet_annual;
prec_annual; temp_annual; tmax_annual;
tmin_annual
• The
correctly
classified
groups
Group 1 2 3
for
the
training
dataset
was
Count 1049 1190 234
45.9%,
and
we
would
expect
a
Summary of classification
similar
success
rate
for
the
Put into Group 1 2 3
predic'on
of
the
“blinded”
1 523 427 48
values.
2 287 451 25
3 238 314 163
• Remember
that
random
Total N 1048 1192 236
classifica'on
of
three
groups
N correct 523 451 163
Proportion 0,499 0,378 0,691
are:
33.3%
N = 2476 N Correct = 1137 • A
test
set
of
9
samples
showed
a
propor'on
correct
Proportion Correct = 0,459
classifica'ons
of
44.4%
50

51. Michael
Mackay
FIGS
coordinator
Ken
Street
FIGS
project
leader
Harold
Bockelman
Net
blotch
data
Eddy
De
Pauw
Climate
data
Dag
Endresen
Data
analysis
51

52. 52