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Trait data mining using FIGS, seminar at Copenhagen University (27 May 2009)
 Overall	
  goal:	
  
  –  User-­‐friendly	
  access	
  to	
  relevant	
  
     informa3on	
  on	
  plant	
  gene3c	
  resources.	
  	
  
  –  Increased	
  u3liza3on	
  of	
  germplasm	
  for	
  
     gene3c	
  diversity	
  in	
  food	
  crops.	
  

    	
  Strategies	
  to	
  improve	
  the	
  u,liza,on	
  of	
  
        germplasm	
  in	
  seedbank	
  collec3ons	
  to	
  
        increase	
  the	
  gene3c	
  diversity	
  of	
  food	
  
        crops	
  for	
  enhanced	
  food	
  security.	
  

                                                                    2	
  
•  Scien3sts	
  and	
  plant	
  breeders	
  want	
  a	
  
   few	
  hundred	
  germplasm	
  accessions	
  
   to	
  evaluate	
  for	
  a	
  par3cular	
  trait.	
  
•  How	
  does	
  the	
  scien3st	
  select	
  a	
  
   small	
  subset	
  likely	
  to	
  have	
  the	
  
   useful	
  trait?	
  
•  More	
  than	
  560	
  000	
  wheat	
  
   accessions	
  in	
  genebanks	
  worldwide.	
  


                                                                                                             3	
  
            Slide	
  adopted	
  from	
  a	
  slide	
  by	
  Ken	
  Street,	
  ICARDA	
  (FIGS	
  team)	
  
    “I am screening for variations in powdery mildew resistance
     genes can you send me 1200 landrace accessions of bread
     wheat”…

    “I am screening for drought – could you send me some
     landraces from Afghanistan and some other dry countries”…

    “I am screening for rust can you send me 9000 bread wheat
     samples”…

    “I am looking for new salt tolerance genes can you send me
     some wild relatives from salty areas”…

    “I want about 500 bread durum acc to screen for RWA”…

    “I am screening for Sunn Pest and can handle about 200 acc –
     can you send me a selection of Triticum species”…


                                                                                                                       4	
  
                      Slide	
  adopted	
  from	
  a	
  slide	
  by	
  Ken	
  Street,	
  ICARDA	
  (FIGS	
  team)	
  
•  The	
  scien3st	
  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	
  OVo	
  H.	
  Frankel	
  (1900-­‐1998)	
  
                  proposed	
  that	
  a	
  limited	
  or	
  "core	
  
                  collec3on"	
  could	
  be	
  established	
  
                  from	
  an	
  exis3ng	
  collec3on.	
  With	
  
                  minimum	
  similarity	
  between	
  its	
  
                  entries	
  the	
  core	
  collec3on	
  is	
  of	
  
                  limited	
  size	
  and	
  chosen	
  to	
  
                  represent	
  the	
  gene,c	
  diversity	
  
                  of	
  a	
  large	
  collec3on,	
  a	
  crop,	
  a	
  
                  wild	
  species	
  or	
  group	
  of	
  species	
       5	
  
                  (1984)	
  .	
  
•  Given	
  that	
  the	
  trait	
  
   property	
  you	
  are	
  
   looking	
  for	
  is	
  rela3vely	
  
   rare:	
  
•  Perhaps	
  as	
  rare	
  as	
  a	
  
   unique	
  allele	
  for	
  one	
  
   single	
  landrace	
  cul3var...	
  
•  Ge_ng	
  what	
  you	
  want	
  
   is	
  largely	
  a	
  ques3on	
  of	
  
   LUCK!	
  
                                                                                                                           6	
  
                          Slide	
  adopted	
  from	
  a	
  slide	
  by	
  Ken	
  Street,	
  ICARDA	
  (FIGS	
  team)	
  
7	
  
 Objec,ve	
  of	
  this	
  study:	
  	
  

  –  Explore	
  climate	
  data	
  as	
  a	
  predic3on	
  
     model	
  for	
  “pre-­‐screening”	
  of	
  crop	
  
     traits	
  BEFORE	
  full	
  scale	
  field	
  trials.	
  

  –  Iden3fica3on	
  of	
  landraces	
  with	
  a	
  
     higher	
  probability	
  of	
  holding	
  an	
  
     interes3ng	
  trait	
  property.	
  

                                                                8	
  
•  Primi,ve	
  crops	
  and	
  tradi,onal	
  landraces	
  are	
  
   the	
  source	
  of	
  exo3c	
  traits,	
  crop	
  proper3es.	
  
•  Traits	
  from	
  landraces	
  are	
  an	
  interes3ng	
  
   source	
  of	
  novel	
  traits	
  for	
  improvement	
  of	
  
   modern	
  crops.	
  
•  Landraces	
  are	
  ogen	
  not	
  described	
  for	
  the	
  
   economically	
  valuable	
  trait	
  in	
  ques3on.	
  

•  Iden3fica3on	
  of	
  crop	
  traits	
  are	
  ogen	
  the	
  
   result	
  of	
  a	
  larger	
  field	
  trial	
  screening	
  project	
  
   (thousands	
  of	
  individual	
  plants).	
  
•  Large	
  scale	
  field	
  trials	
  are	
  very	
  costly	
  (land	
  
   area	
  and	
  human	
  working	
  hours).	
  
                                                                              9	
  
 The	
  underlying	
  assump3on	
  
    is	
  that	
  the	
  climate	
  at	
  the	
  
    original	
  source	
  loca3on,	
  
    where	
  the	
  landrace	
  was	
  
    developed	
  during	
  long-­‐term	
  
    tradi3onal	
  cul3va3on,	
  is	
  
    correlated	
  to	
  trait.	
  	
  

	
  The	
  aim	
  is	
  to	
  build	
  a	
  
    computer	
  model	
  explaining	
  
    the	
  crop	
  trait	
  score	
  
    (dependent	
  variables)	
  from	
  the	
  
    climate	
  data	
  (independent	
  
    variables).	
  



                                                    10	
  
Wild	
  rela3ves	
  are	
             Primi3ve	
  cul3vated	
  crops	
            Tradi3onal	
  cul3vated	
  crops	
  
shaped	
  by	
  climate	
             are	
  shaped	
  by	
  climate	
            (landraces)	
  are	
  shaped	
  by	
  
                                      and	
  humans	
                             climate	
  and	
  humans	
  




         Modern	
  cul3vated	
  crops	
                      Perhaps	
  future	
  crops	
  are	
  
         (cul3vars)	
  are	
  mostly	
  shaped	
             shaped	
  in	
  the	
  molecular	
  
         by	
  humans	
  (plant	
  breeders)	
               laboratory…?	
                                                11	
  
1)  Landrace	
  samples	
  (genebank	
  seed	
  accessions)	
  
   2)  Trait	
  observa3ons	
  (experimental	
  design)	
  
   3)  Climate	
  data	
  (for	
  the	
  landrace	
  origin	
  loca3ons)	
  




• 	
  The	
  accession	
  iden3fier	
  (accession	
  number)	
  provides	
  the	
  bridge	
  to	
  the	
  crop	
  trait	
  observa3ons.	
  
• 	
  The	
  longitude,	
  la,tude	
  coordinates	
  for	
  the	
  original	
  collec3ng	
  site	
  of	
  the	
  accessions	
  (landraces)	
  provide	
  the	
  
bridge	
  to	
  the	
  environmental	
  data.	
  	
  
                                                                                                                                                                   12	
  
More	
  than	
  6	
  million	
  genebank	
  accessions,	
  more	
  than	
  1	
  400	
  genebanks,	
  worldwide.	
     13	
  
Faba	
  bean,	
  Finland	
                           Field	
  trials,	
  Gatersleben,	
  Germany	
     Cauliflower	
  (S.	
  Jeppson)	
  




Forage	
  crops,	
  Dotnuva,	
  Lithuania	
          Radish	
  (S.	
  Jeppson)	
                       Linnés	
  äpple	
  




 Powdery	
  Mildew,	
  	
             Leaf	
  spots	
                   Yellow	
  rust	
               Black	
  stem	
  rust	
                                              14	
  
 Blumeria	
  graminis	
               Ascochyta	
  sp.	
                Puccinia	
  strilformis	
      Puccinia	
  graminis	
              hVp://barley.ipk-­‐gatersleben.de	
  	
  
 The	
  climate	
  data	
  is	
  extracted	
  from	
  
    the	
  WorldClim	
  dataset.	
  
	
  hVp://www.worldclim.org/	
  	
  

	
  Data	
  from	
  weather	
  sta3ons	
  
    worldwide	
  are	
  combined	
  	
  to	
  a	
  
    con3nuous	
  surface	
  layer.	
  
	
  Climate	
  data	
  for	
  each	
  landrace	
  is	
  
    extracted	
  from	
  this	
  surface	
  layer.	
        Precipita3on:	
  20	
  590	
  sta3ons	
  




                                                            Temperature:	
  7	
  280	
  sta3ons	
  
                                                                                                        15	
  
This	
  study	
  is	
  part	
  of	
  
a	
  new	
  method	
  to	
  
predict	
  crop	
  traits	
  
of	
  primi3ve	
  
cul3vated	
  material	
  
from	
  climate	
  
variables	
  by	
  using	
  
mul3variate	
  
sta3s3cal	
  methods.	
  	
  



                                        16	
  
FIGS	
  
	
  The	
  FIGS	
  technology	
  takes	
  much	
  of	
  the	
  guess	
  
    work	
  out	
  of	
  choosing	
  which	
  accessions	
  are	
  most	
  
    likely	
  to	
  contain	
  the	
  specific	
  characteris3cs	
  being	
  
    sought	
  by	
  plant	
  breeders	
  to	
  improve	
  plant	
  
    produc3vity	
  across	
  numerous	
  challenging	
  
    environments.     	
  	
     	
  	
  hVp://www.figstraitmine.org/	
  	
  




                                                                                   17	
   17	
  
What is                           hVp://www.figstraitmine.org/	
  	
  




    Mediterranean	
  region	
  




Origin of Concept (1980s):
Wheat and barley landraces from           Queensland	
  Australia	
  
marine soils in the Mediterranean
region provided genetic variation
                                         Slide made by
for boron toxicity.                      M C Mackay 1995                18	
  
Slide made by
M C Mackay 1995


                  19	
  
•  No	
  sources	
  of	
  Sunn	
  pest	
  resistance	
  
   previously	
  found	
  in	
  hexaploid	
  
   wheat.	
  
•  2000	
  accessions	
  screened	
  at	
  
   ICARDA	
  without	
  result	
  
•  A	
  FIGS	
  set	
  of	
  534	
  accessions	
  was	
  
   developed	
  and	
  screened.	
  	
  
•  10	
  resistant	
  accessions	
  were	
  found!	
  
    •  The	
  FIGS	
  selec3on	
  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	
  collec3ng	
  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)	
     20	
  
•  The	
  fundamental	
  ecological	
  niche	
  of	
  an	
  organism	
  
   was	
  formalized	
  by	
  Hutchinson[1]	
  in	
  1957	
  as	
  a	
  
   mul3dimensional	
  hypercube	
  defining	
  the	
  ecological	
  
   condi3ons	
  that	
  allow	
  a	
  species	
  to	
  exist.	
  
•  Full	
  understanding	
  of	
  all	
  the	
  environmental	
  
   condi3ons	
  for	
  any	
  organism	
  is	
  a	
  monumental	
  task
   [2].	
  	
  

•  A	
  computer	
  model	
  of	
  the	
  occurrence	
  locali3es	
  
   together	
  with	
  associated	
  environmental	
  condi3ons	
  
   such	
  as	
  rainfall,	
  temperature,	
  day	
  length	
  etc.,	
  
   provides	
  an	
  approxima3on	
  of	
  the	
  fundamental	
  
   niche.	
  
•  Popular	
  soCware	
  implementa3ons	
  for	
  modeling	
  
   the	
  ecological	
  niche	
  include	
  openModeller,	
  MaxEnt,	
  
   BioCLIM,	
  DesktopGARP,	
  etc.	
  
                                                                                                                             21	
  
                                                                           George	
  Evelyn	
  Hutchinson	
  (1903	
  –	
  1991)	
  
 
A flexible, user friendly, cross-
platform environment where the entire process of a
fundamental niche modeling experiment can be
carried out.

Input: species occurrence and environmental data.

Output: a fundamental niche model and projection
of the model into an environmental scenario.

hVp://openmodeller.sourceforge.net/	
  




                                                     22	
  
23	
  
–  The	
  ini3al	
  model	
  is	
  developed	
  from	
  the	
  training	
  
   set	
  

–  Fine	
  tuning	
  of	
  model	
  parameters	
  and	
  se_ngs	
  

–  No	
  model	
  can	
  ever	
  be	
  absolutely	
  correct!	
  
–  A	
  simula3on	
  model	
  can	
  only	
  be	
  an	
  approxima3on	
  
–  A	
  model	
  is	
  always	
  created	
  for	
  a	
  specific	
  purpose!	
  

–  The	
  simula3on	
  model	
  is	
  applied	
  to	
  make	
  
   predic3ons	
  based	
  on	
  new	
  fresh	
  data	
  
–  Be	
  aware	
  of	
  extrapola3on	
  
                                                                                  24	
  
–  For	
  the	
  ini3al	
  calibra3on	
  or	
  
   training	
  step.	
  


–  Further	
  calibra3on,	
  tuning	
  step	
  
–  Ogen	
  cross-­‐valida3on	
  on	
  the	
  
   training	
  set	
  is	
  used	
  to	
  reduce	
  the	
  
   consump3on	
  of	
  raw	
  data.	
  


–  For	
  the	
  model	
  valida3on	
  or	
  
   goodness	
  of	
  fit	
  tes3ng.	
  
–  External	
  data,	
  not	
  used	
  in	
  the	
  
   model	
  calibra3on.	
  
                                                              25	
  
26	
  
Name	
  of	
  the	
  sta3s3c	
                                  Symbol	
             Range	
  

                                                                        *	
  Correla3on	
  coefficient	
  	
                                   r	
          -­‐1	
  to	
  1	
  
                                                                        *	
  Coefficient	
  of	
  determina3on	
  	
                          r2	
           0	
  to	
  1	
  


• 	
  A	
  number	
  of	
  different	
  coefficients	
  are	
  
developed	
  to	
  measure	
  correla3on	
  in	
  
different	
  situa3ons.	
  	
  
• 	
  The	
  best	
  known	
  is	
  the	
  Pearson	
  product-­‐
moment	
  correla,on	
  coefficient.	
  
• 	
  The	
                                       indicates	
  
the	
  strength	
  and	
  direc3on	
  of	
  a	
  linear	
  
rela3onship	
  between	
  two	
  random	
  
variables.	
  
• 	
  The	
  
indicates	
  how	
  well	
  future	
  outcomes	
  are	
  
                                                                                The	
  covariance	
  of	
  the	
  two	
  variables	
  is	
  divided	
  by	
  the	
  
likely	
  to	
  be	
  predicted	
  by	
  a	
  sta3s3cal	
  model.	
             product	
  of	
  their	
  standard	
  devia3ons.	
  

                                                                                                                                                                                27	
  
The	
  distance	
  between	
  the	
  model	
  (predic3ons)	
  and	
  
the	
  reference	
  values	
  (valida3on)	
  is	
  the	
  residuals.	
  




                                                                             Example	
  of	
  a	
  bad	
  
                                                                             model	
  calibra3on	
  




                                                                           Cross-­‐valida3on	
  indicates	
  
                                                                           the	
  appropriate	
  model	
  
 Be	
  aware	
  of	
  over-­‐fi_ng!	
  NB!	
  Model	
  valida3on!	
         complexity.	
                        28	
  
29	
  
30	
  
Sta,on	
                              Al,tude	
   La,tude	
   Longitude	
  
Priekuli,	
  Latvia	
                   83	
  m	
     57.3167	
     25.3667	
  
Bjørke	
  forsøksgård,	
  Norway	
   149	
  m	
       60.7667	
     11.2167	
  
Landskrona,	
  Sweden	
                  3	
  m	
     55.8667	
     12.8333	
  

                                                                                  31	
  
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
                                                                                                          32	
  
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	
  
descrip3ons	
  of	
  the	
  original	
  gathering	
  
site	
  included	
  in	
  SESTO	
  and	
  other	
  
sources.	
  	
  

For	
  5	
  accessions	
  there	
  were	
  not	
  
enough	
  informa3on	
  available	
  to	
  
locate	
  the	
  original	
  gathering	
  loca3on.	
  

                                               Right	
  side	
  illustra.on	
  	
  
Example	
  of	
  georeferencing	
  for	
  NGB9529,	
  landrace	
  reported	
  
     as	
  origina@ng	
  from	
  Lyderupgaard	
  using	
  KRAK.dk	
  and	
  
                                                      maps.google.com	
  
                                                                                      33	
  
34	
  
Score	
  plots	
  
The	
  observa3ons	
  made	
  at	
  Priekuli	
  (Latvia)	
  are	
  
separated	
  from	
  the	
  observa3ons	
  made	
  at	
  
Bjørke	
  (Norway)	
  and	
  Landskrona	
  (Sweden)	
  in	
  
PC1	
  and	
  PC2.	
  

The	
  combined	
  observa3ons	
  from	
  each	
  year	
  
(2002	
  and	
  2003)	
  are	
  less	
  separated.	
  

The	
  two	
  replicate	
  series	
  are	
  NOT	
  separated	
  




                                                                      35	
  
The	
  bi-­‐plot	
  shows	
  heading	
  days	
  
and	
  ripening	
  days	
  as	
  the	
  most	
  
influen3al	
  trait	
  variables	
  for	
  the	
  
separa3on	
  of	
  the	
  observa3ons	
  
from	
  the	
  different	
  observa3on	
  
loca3ons.	
  	
  
Length	
  of	
  plant	
  par3cipate	
  in	
  
spreading	
  out	
  the	
  scores	
  (in	
  PC1	
  
and	
  PC2),	
  but	
  is	
  less	
  ac3ve	
  in	
  the	
  
separa3on	
  of	
  the	
  groups.	
  


The	
  influence	
  plot	
  (residuals	
  
against	
  leverage)	
  shows	
  sample	
  
                  observed	
  at	
  Priekuli	
  in	
  
2003	
  (replicate	
  2)	
  with	
  a	
  very	
  high	
  
leverage	
  -­‐	
  well	
  separated	
  from	
  the	
  
“data	
  cloud”.	
  	
  
Ager	
  looking	
  into	
  the	
  raw	
  data	
  (see	
  
next	
  slide),	
  this	
  data	
  point	
  was	
  
removed	
  as	
  outlier	
  (set	
  to	
  NaN).	
  

                                                              36	
  
Sample	
                   (FRO)	
  observed	
  at	
  Priekuli	
  in	
  2003	
  (replicate	
  2)	
  
has	
  the	
  lowest	
  score	
  for	
  harvest	
  index	
  in	
  the	
  en3re	
  dataset.	
  

Ager	
  looking	
  into	
  the	
  raw	
  data	
  (see	
  the	
  table	
  above),	
  this	
  
observa3on	
  point	
  was	
  removed	
  as	
  outlier	
  (set	
  to	
  NaN).	
  
                                                                                                       37	
  
The	
  ini3al	
  PCA	
  analysis	
  of	
  
the	
  climate	
  data	
  showed	
  a	
  
nice	
  spread	
  of	
  the	
  scores.	
  
No	
  surprises.	
  	
  




The	
  influence	
  plot	
  iden3fied	
  
sample	
                    (NOR)	
  as	
  a	
  
mild	
  outlier.	
  I	
  decided	
  to	
  
keep	
  this	
  sample,	
  but	
  to	
  
keep	
  an	
  eye	
  out	
  for	
  it	
  in	
  the	
  
mul3-­‐way	
  analysis.	
  


                                                         38	
  
39	
  
• 	
  Plot	
  of	
  the	
  trait	
  scores	
  (max	
  –	
  min)	
  from	
  each	
  observa3on	
  loca3on	
  and	
  year.	
  
• 	
  The	
  effect	
  from	
  the	
  different	
  experimental	
  condi3ons	
  have	
  a	
  significant	
  effect	
  on	
  
the	
  trait	
  observa3ons.	
  
                                                                                                                               40	
  
41	
  
tmin	
                   tmax	
                     prec	
     Mode	
  3	
  (climate	
  variables)	
  
                                                               have	
  very	
  different	
  range	
  of	
  	
  
                                                               numerical	
  values	
  (tmin,	
  tmax,	
  
                                                               and	
  prec).	
  Scaling	
  across	
  mode	
  
                                                               3	
  is	
  thus	
  applied	
  to	
  the	
  mul3-­‐
                                                               way	
  models.	
  	
  

                                                               Leg	
  is	
  displayed	
  the	
  box-­‐plot	
  
                                                               for	
  the	
  3-­‐way	
  data	
  unfolded	
  as	
  
                                                               to	
  keep	
  the	
  dimensions	
  of	
  
           Scaling	
  across	
  mode	
  3	
  	
                mode	
  3.	
  


                                                               The	
  3-­‐way	
  climate	
  data	
  was	
  
                                                               reasonably	
  well	
  described	
  by	
  a	
  
                                                               PARAFAC	
  model	
  of	
  two	
  
                                                               components.	
  

                                                                                                                     42	
  
43	
  
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	
  


                      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	
  
                                                                                                                                                                                                44	
  
3	
  



                                                                                                                                                                                           	
  
                                                                                                                                                                          14	
                    12	
  
                        (loca3on	
  of	
  origin)	
  




                                                                                                                        Climate	
  data	
  (mode	
  3):	
  
      14	
  landraces	
  




                                                                                                                        • 	
  Minimum	
  temperature	
  
                                                                                                                        • 	
  Maximum	
  temperature	
  
                                                                                                                        • 	
  Precipita3on	
  
                                                                                                                        • 	
  …	
  (many	
  more	
  can	
  be	
  added)	
  
                                                                   12	
  monthly	
  
                                                                          means	
  

                                                        Min.	
  temperature	
           Max.	
  temperature	
                                Precipita3on	
  


                                                        Jan,	
  Feb,	
  Mar,	
  …	
     Jan,	
  Feb,	
  Mar,	
  …	
                       Jan,	
  Feb,	
  Mar,	
  …	
  

14	
  samples	
  
                                                                                                                                                                                                           45	
  
•  	
  The	
  ini3al	
  PARAFAC	
  models	
  calibrated	
  from	
  the	
  4-­‐way	
  trait	
  dataset	
  failed	
  to	
  
   converge	
  to	
  any	
  good	
  models.	
  The	
  core-­‐consistency	
  remained	
  very	
  low.	
  
•  	
  The	
  problem	
  showed	
  to	
  be	
  lack	
  of	
  systema3c	
  independent	
  varia3on	
  between	
  
   instances	
  of	
  mode	
  3	
  (observa3on	
  years)	
  and	
  mode	
  4	
  (observa3on	
  loca3ons)	
  
•  	
  A	
  two	
  component	
  PARAFAC	
  model	
  was	
  chosen	
  for	
  the	
  new	
  3-­‐way	
  trait	
  dataset.	
  



                                                                                                                        (NOR)	
  was	
  
                                                                                                           iden3fied	
  as	
  a	
  mild	
  
                                                                                                           outlier	
  from	
  the	
  
                                                                                                           influence	
  plot.	
  
                                                                                             	
  
                                                                                                           No3ce	
  that	
  both	
  
                                                                                                           replica3ons	
  are	
  
                                                                                                           located	
  in	
  the	
  same	
  
                                                                                                           part	
  of	
  the	
  plot.	
  And	
  
                                                                                                           that	
  they	
  (together)	
  
                                                                                                           are	
  not	
  isolated	
  
                                                                                                           from	
  the	
  “data	
  
                                                                                                           cloud”.	
  
                                                                                                                                           46	
  
PARAFAC	
  split-­‐half	
  
(mode	
  1)	
  analysis:	
  

The	
  two	
  PARAFAC	
  
models	
  each	
  calibrated	
  
from	
  two	
  independent	
  
split-­‐half	
  subsets,	
  both	
  
converge	
  to	
  a	
  very	
  
similar	
  solu3on	
  as	
  the	
  
model	
  calibrated	
  from	
  
the	
  complete	
  dataset.	
  

The	
  PARAFAC	
  model	
  is	
  
thus	
  a	
  general	
  and	
  
stable	
  model	
  for	
  the	
  
scope	
  of	
  	
  Scandinavia.	
  


                                       47	
  
Further	
  search	
  for	
  any	
  
good	
  PARAFAC	
  split-­‐half	
  
for	
  the	
  climate	
  dataset:	
  

A	
  systema3c	
  recording	
  of	
  
results	
  from	
  10	
  different	
  
split-­‐half	
  alterna3ves	
  
resulted	
  in	
  two	
  good	
  
split-­‐half.	
  

The	
  PARAFAC	
  model	
  for	
  
the	
  climate	
  data	
  is	
  thus	
  
reasonable	
  general	
  (for	
  
Scandinavia),	
  but	
  less	
  
stable	
  than	
  the	
  model	
  
for	
  the	
  3-­‐way	
  trait	
  data.	
  

                                              48	
  
49	
  
50	
  
•  Ogen	
  the	
  cri3cal	
  levels	
  (α)	
  for	
  the	
  p-­‐value	
  is	
  set	
  as	
  0.05,	
  
   0.01	
  and	
  0.001.	
  
•  For	
  the	
  modeling	
  of	
  14	
  samples	
  (landraces)	
  gives:	
  
     –  12	
  degrees	
  of	
  freedom	
  for	
  the	
  correla3on	
  tests	
  
     –  One-­‐tailed	
  test	
  (looking	
  only	
  at	
  posi3ve	
  correla3on	
  of	
  
        predic3ons	
  versus	
  the	
  reference	
  values).	
  
     –  A	
  coefficient	
  of	
  determina3on	
  (r2)	
  larger	
  than	
  0.56	
  is	
  
        significant	
  at	
  the	
  0.001	
  (0.1%)	
  level	
  for	
  14	
  values/samples.	
  




      Many	
  introductory	
  text	
  books	
  on	
  sta3s3cs	
  include	
  a	
  table	
  of	
  Cri3cal	
  Values	
  for	
  Pearson’s	
  r.	
     51	
  
52	
  
•  Latvia	
  2002	
  (LY11)	
  
     –  May	
  2002	
  was	
  extreme	
  dry	
  in	
  Priekuli.	
  
     –  June	
  2002	
  was	
  extreme	
  wet	
  in	
  Priekuli.	
  
     –  The	
  wet	
  June	
  caused	
  germina3on	
  on	
  the	
  
        spikes	
  for	
  many	
  of	
  the	
  early	
  varie3es.	
  

•  Landskrona	
  2003	
  (LY32)	
  
     –  June	
  2003	
  was	
  extreme	
  dry	
  in	
  Landskrona.	
  
     –  June	
  was	
  the	
  3me	
  for	
  grain	
  filling	
  here.	
  

•  Too	
  extreme	
  for	
  the	
  genotype	
  to	
  be	
  
   “normally”	
  expressed	
  ?	
  
•  Too	
  large	
  effect	
  from	
  “G	
  by	
  E”	
  
   interac3on	
  ?	
  
                                                                           53	
  
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	
  

                                                                                                                        54	
  
 
       55	
  
 
              	
  




       	
            	
  




                            56	
  
Exploring	
  why	
  some	
  of	
  the	
  subset	
  (LY)	
  
  give	
  very	
  bad	
  N-­‐PLS	
  regressions...	
  



                                                              57	
  
 




       58	
  
All	
  samples	
              RMSECV=3.72	
                Without	
  NGB456	
   RMSECV=3.18	
  	
  
                              Expl.	
  X	
  =	
  96%	
         r2	
  cal	
  =	
  0.64	
     Expl.	
  X	
  =	
  98%	
  
 r2	
  cal	
  =	
  0.54	
  
  r2	
  cv	
  =	
  0.16	
     Expl.	
  y	
  =	
  54%	
         r2	
  cv	
  =	
  0.33	
      Expl.	
  y	
  =	
  64%	
  




                                                                                                                         59	
  
60	
  
61	
  
Trait data mining using FIGS, seminar at Copenhagen University (27 May 2009)
Trait data mining using FIGS, seminar at Copenhagen University (27 May 2009)
Trait data mining using FIGS, seminar at Copenhagen University (27 May 2009)
65	
  
•  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


                                                           66	
  
•  Field	
  loca3ons	
  (USA)	
  
     –    Athens,	
  Georgia	
  (273	
  observa3ons)	
  
     –    Fargo,	
  North	
  Dakota	
  (3381	
  observa3ons)	
  
     –    Langdon,	
  North	
  Dakota	
  (858	
  observa3ons)	
  
     –    Stephen,	
  Minnesota	
  (139	
  observa3ons)	
  


•  Observa3on	
  years	
  (1987	
  –	
  2004)	
  
     –  9	
  dis3nct	
  years	
  


•  Greenhouse	
  versus	
  field	
  trials	
  
     –  Greenhouse	
  (1676	
  observa3ons)	
  
     –  Field	
  trial	
  (2975	
  observa3ons)	
  

                                                                    67	
  
68	
  
Trait data mining using FIGS, seminar at Copenhagen University (27 May 2009)
Individual 95% CIs For Mean Based on	
  
                                                 Pooled StDev	
  
Level          N        Mean         StDev       -----+---------+---------+---------+-	
  
ATHENS       262      2,0840        0,6555                                (---*---)	
  
FARGO        789      1,6793        0,6023               (-*-)	
  
LANGDON     1558      1,6727        0,6466               (-*)	
  
STEPHEN      136      1,6103        0,7810       (-----*----)	
  
                                                 -----+---------+---------+---------+-	
  
                                                    1,60           1,80 2,00      2,20	
  



  •  one-­‐way	
  ANOVA	
  test	
  for	
  difference	
  between	
  the	
  observa3on	
  
     loca3ons.	
  The	
  p-­‐value	
  of	
  0.000	
  rejects	
  the	
  null	
  hypothesis	
  of	
  no	
  
     difference.	
  

  •  The	
  Tukey	
  pair-­‐wise	
  comparison	
  test	
  gave	
  the	
  same	
  result.	
  
                                                                                                            70	
  
71	
  
•    Agro-­‐clima3c	
  Zone	
  (UNESCO	
  classifica3on)	
  
•    Soil	
  classifica3on	
  (FAO	
  Soil	
  map)	
  
•    Aridity	
  (dryness)	
  
•    Precipita3on	
  
•    Poten3al	
  evapotranspira3on	
  (water	
  loss)	
  
•    Temperature	
  	
  
•    Maximum	
  temperatures	
  	
  
•    Minimum	
  temperatures	
  
     	
  (mean	
  values	
  for	
  month	
  and	
  year)	
  




                                                               72	
  
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;                        •  The	
  correctly	
  classified	
  groups	
  
tmin_annual	
  
                                                                 for	
  the	
  training	
  dataset	
  was	
  
Group
Count         1049
                  1              2
                              1190
                                                  3	
  
                                              234	
  
                                                                 45.9%,	
  and	
  we	
  would	
  expect	
  a	
  
                                                                 similar	
  success	
  rate	
  for	
  the	
  
Summary of classification	
                                      predic3on	
  of	
  the	
  “blinded”	
  
Put into Group            1            2          3	
            values.	
  
1                       523          427        48	
  
2                       287          451        25	
          •  Remember	
  that	
  random	
  
3                       238          314      163	
  
                                                                 classifica3on	
  of	
  three	
  groups	
  
Total N                1048      1192         236	
  
N correct               523          451      163	
  
                                                                 are:	
  33.3%	
  
Proportion            0,499     0,378      0,691	
  
                                                              •  A	
  test	
  set	
  of	
  9	
  samples	
  
N = 2476                 N Correct = 1137                        showed	
  a	
  propor3on	
  correct	
  
Proportion Correct = 0,459	
  	
                                 classifica3ons	
  of	
  44.4%	
  

                                                                                                                   73	
  
Michael	
  Mackay	
  
FIGS	
  coordinator	
  




Ken	
  Street	
  
FIGS	
  project	
  leader	
  




Harold	
  Bockelman	
  
Net	
  blotch	
  data	
  




Eddy	
  De	
  Pauw	
  
Climate	
  data	
  




Dag	
  Endresen	
  
Data	
  analysis	
  




                                74	
  
75	
  

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Trait data mining using FIGS, seminar at Copenhagen University (27 May 2009)

  • 2.  Overall  goal:   –  User-­‐friendly  access  to  relevant   informa3on  on  plant  gene3c  resources.     –  Increased  u3liza3on  of  germplasm  for   gene3c  diversity  in  food  crops.    Strategies  to  improve  the  u,liza,on  of   germplasm  in  seedbank  collec3ons  to   increase  the  gene3c  diversity  of  food   crops  for  enhanced  food  security.   2  
  • 3. •  Scien3sts  and  plant  breeders  want  a   few  hundred  germplasm  accessions   to  evaluate  for  a  par3cular  trait.   •  How  does  the  scien3st  select  a   small  subset  likely  to  have  the   useful  trait?   •  More  than  560  000  wheat   accessions  in  genebanks  worldwide.   3   Slide  adopted  from  a  slide  by  Ken  Street,  ICARDA  (FIGS  team)  
  • 4.   “I am screening for variations in powdery mildew resistance genes can you send me 1200 landrace accessions of bread wheat”…   “I am screening for drought – could you send me some landraces from Afghanistan and some other dry countries”…   “I am screening for rust can you send me 9000 bread wheat samples”…   “I am looking for new salt tolerance genes can you send me some wild relatives from salty areas”…   “I want about 500 bread durum acc to screen for RWA”…   “I am screening for Sunn Pest and can handle about 200 acc – can you send me a selection of Triticum species”… 4   Slide  adopted  from  a  slide  by  Ken  Street,  ICARDA  (FIGS  team)  
  • 5. •  The  scien3st  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  OVo  H.  Frankel  (1900-­‐1998)   proposed  that  a  limited  or  "core   collec3on"  could  be  established   from  an  exis3ng  collec3on.  With   minimum  similarity  between  its   entries  the  core  collec3on  is  of   limited  size  and  chosen  to   represent  the  gene,c  diversity   of  a  large  collec3on,  a  crop,  a   wild  species  or  group  of  species   5   (1984)  .  
  • 6. •  Given  that  the  trait   property  you  are   looking  for  is  rela3vely   rare:   •  Perhaps  as  rare  as  a   unique  allele  for  one   single  landrace  cul3var...   •  Ge_ng  what  you  want   is  largely  a  ques3on  of   LUCK!   6   Slide  adopted  from  a  slide  by  Ken  Street,  ICARDA  (FIGS  team)  
  • 8.  Objec,ve  of  this  study:     –  Explore  climate  data  as  a  predic3on   model  for  “pre-­‐screening”  of  crop   traits  BEFORE  full  scale  field  trials.   –  Iden3fica3on  of  landraces  with  a   higher  probability  of  holding  an   interes3ng  trait  property.   8  
  • 9. •  Primi,ve  crops  and  tradi,onal  landraces  are   the  source  of  exo3c  traits,  crop  proper3es.   •  Traits  from  landraces  are  an  interes3ng   source  of  novel  traits  for  improvement  of   modern  crops.   •  Landraces  are  ogen  not  described  for  the   economically  valuable  trait  in  ques3on.   •  Iden3fica3on  of  crop  traits  are  ogen  the   result  of  a  larger  field  trial  screening  project   (thousands  of  individual  plants).   •  Large  scale  field  trials  are  very  costly  (land   area  and  human  working  hours).   9  
  • 10.  The  underlying  assump3on   is  that  the  climate  at  the   original  source  loca3on,   where  the  landrace  was   developed  during  long-­‐term   tradi3onal  cul3va3on,  is   correlated  to  trait.      The  aim  is  to  build  a   computer  model  explaining   the  crop  trait  score   (dependent  variables)  from  the   climate  data  (independent   variables).   10  
  • 11. Wild  rela3ves  are   Primi3ve  cul3vated  crops   Tradi3onal  cul3vated  crops   shaped  by  climate   are  shaped  by  climate   (landraces)  are  shaped  by   and  humans   climate  and  humans   Modern  cul3vated  crops   Perhaps  future  crops  are   (cul3vars)  are  mostly  shaped   shaped  in  the  molecular   by  humans  (plant  breeders)   laboratory…?   11  
  • 12. 1)  Landrace  samples  (genebank  seed  accessions)   2)  Trait  observa3ons  (experimental  design)   3)  Climate  data  (for  the  landrace  origin  loca3ons)   •   The  accession  iden3fier  (accession  number)  provides  the  bridge  to  the  crop  trait  observa3ons.   •   The  longitude,  la,tude  coordinates  for  the  original  collec3ng  site  of  the  accessions  (landraces)  provide  the   bridge  to  the  environmental  data.     12  
  • 13. More  than  6  million  genebank  accessions,  more  than  1  400  genebanks,  worldwide.   13  
  • 14. Faba  bean,  Finland   Field  trials,  Gatersleben,  Germany   Cauliflower  (S.  Jeppson)   Forage  crops,  Dotnuva,  Lithuania   Radish  (S.  Jeppson)   Linnés  äpple   Powdery  Mildew,     Leaf  spots   Yellow  rust   Black  stem  rust   14   Blumeria  graminis   Ascochyta  sp.   Puccinia  strilformis   Puccinia  graminis   hVp://barley.ipk-­‐gatersleben.de    
  • 15.  The  climate  data  is  extracted  from   the  WorldClim  dataset.    hVp://www.worldclim.org/      Data  from  weather  sta3ons   worldwide  are  combined    to  a   con3nuous  surface  layer.    Climate  data  for  each  landrace  is   extracted  from  this  surface  layer.   Precipita3on:  20  590  sta3ons   Temperature:  7  280  sta3ons   15  
  • 16. This  study  is  part  of   a  new  method  to   predict  crop  traits   of  primi3ve   cul3vated  material   from  climate   variables  by  using   mul3variate   sta3s3cal  methods.     16  
  • 17. FIGS    The  FIGS  technology  takes  much  of  the  guess   work  out  of  choosing  which  accessions  are  most   likely  to  contain  the  specific  characteris3cs  being   sought  by  plant  breeders  to  improve  plant   produc3vity  across  numerous  challenging   environments.        hVp://www.figstraitmine.org/     17   17  
  • 18. What is hVp://www.figstraitmine.org/     Mediterranean  region   Origin of Concept (1980s): Wheat and barley landraces from Queensland  Australia   marine soils in the Mediterranean region provided genetic variation Slide made by for boron toxicity. M C Mackay 1995 18  
  • 19. Slide made by M C Mackay 1995 19  
  • 20. •  No  sources  of  Sunn  pest  resistance   previously  found  in  hexaploid   wheat.   •  2000  accessions  screened  at   ICARDA  without  result   •  A  FIGS  set  of  534  accessions  was   developed  and  screened.     •  10  resistant  accessions  were  found!   •  The  FIGS  selec3on  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  collec3ng  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)   20  
  • 21. •  The  fundamental  ecological  niche  of  an  organism   was  formalized  by  Hutchinson[1]  in  1957  as  a   mul3dimensional  hypercube  defining  the  ecological   condi3ons  that  allow  a  species  to  exist.   •  Full  understanding  of  all  the  environmental   condi3ons  for  any  organism  is  a  monumental  task [2].     •  A  computer  model  of  the  occurrence  locali3es   together  with  associated  environmental  condi3ons   such  as  rainfall,  temperature,  day  length  etc.,   provides  an  approxima3on  of  the  fundamental   niche.   •  Popular  soCware  implementa3ons  for  modeling   the  ecological  niche  include  openModeller,  MaxEnt,   BioCLIM,  DesktopGARP,  etc.   21   George  Evelyn  Hutchinson  (1903  –  1991)  
  • 22.   A flexible, user friendly, cross- platform environment where the entire process of a fundamental niche modeling experiment can be carried out. Input: species occurrence and environmental data. Output: a fundamental niche model and projection of the model into an environmental scenario. hVp://openmodeller.sourceforge.net/   22  
  • 23. 23  
  • 24. –  The  ini3al  model  is  developed  from  the  training   set   –  Fine  tuning  of  model  parameters  and  se_ngs   –  No  model  can  ever  be  absolutely  correct!   –  A  simula3on  model  can  only  be  an  approxima3on   –  A  model  is  always  created  for  a  specific  purpose!   –  The  simula3on  model  is  applied  to  make   predic3ons  based  on  new  fresh  data   –  Be  aware  of  extrapola3on   24  
  • 25. –  For  the  ini3al  calibra3on  or   training  step.   –  Further  calibra3on,  tuning  step   –  Ogen  cross-­‐valida3on  on  the   training  set  is  used  to  reduce  the   consump3on  of  raw  data.   –  For  the  model  valida3on  or   goodness  of  fit  tes3ng.   –  External  data,  not  used  in  the   model  calibra3on.   25  
  • 26. 26  
  • 27. Name  of  the  sta3s3c   Symbol   Range   *  Correla3on  coefficient     r   -­‐1  to  1   *  Coefficient  of  determina3on     r2   0  to  1   •   A  number  of  different  coefficients  are   developed  to  measure  correla3on  in   different  situa3ons.     •   The  best  known  is  the  Pearson  product-­‐ moment  correla,on  coefficient.   •   The   indicates   the  strength  and  direc3on  of  a  linear   rela3onship  between  two  random   variables.   •   The   indicates  how  well  future  outcomes  are   The  covariance  of  the  two  variables  is  divided  by  the   likely  to  be  predicted  by  a  sta3s3cal  model.   product  of  their  standard  devia3ons.   27  
  • 28. The  distance  between  the  model  (predic3ons)  and   the  reference  values  (valida3on)  is  the  residuals.   Example  of  a  bad   model  calibra3on   Cross-­‐valida3on  indicates   the  appropriate  model   Be  aware  of  over-­‐fi_ng!  NB!  Model  valida3on!   complexity.   28  
  • 29. 29  
  • 30. 30  
  • 31. Sta,on   Al,tude   La,tude   Longitude   Priekuli,  Latvia   83  m   57.3167   25.3667   Bjørke  forsøksgård,  Norway   149  m   60.7667   11.2167   Landskrona,  Sweden   3  m   55.8667   12.8333   31  
  • 32. 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 32  
  • 33. 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   descrip3ons  of  the  original  gathering   site  included  in  SESTO  and  other   sources.     For  5  accessions  there  were  not   enough  informa3on  available  to   locate  the  original  gathering  loca3on.   Right  side  illustra.on     Example  of  georeferencing  for  NGB9529,  landrace  reported   as  origina@ng  from  Lyderupgaard  using  KRAK.dk  and   maps.google.com   33  
  • 34. 34  
  • 35. Score  plots   The  observa3ons  made  at  Priekuli  (Latvia)  are   separated  from  the  observa3ons  made  at   Bjørke  (Norway)  and  Landskrona  (Sweden)  in   PC1  and  PC2.   The  combined  observa3ons  from  each  year   (2002  and  2003)  are  less  separated.   The  two  replicate  series  are  NOT  separated   35  
  • 36. The  bi-­‐plot  shows  heading  days   and  ripening  days  as  the  most   influen3al  trait  variables  for  the   separa3on  of  the  observa3ons   from  the  different  observa3on   loca3ons.     Length  of  plant  par3cipate  in   spreading  out  the  scores  (in  PC1   and  PC2),  but  is  less  ac3ve  in  the   separa3on  of  the  groups.   The  influence  plot  (residuals   against  leverage)  shows  sample   observed  at  Priekuli  in   2003  (replicate  2)  with  a  very  high   leverage  -­‐  well  separated  from  the   “data  cloud”.     Ager  looking  into  the  raw  data  (see   next  slide),  this  data  point  was   removed  as  outlier  (set  to  NaN).   36  
  • 37. Sample   (FRO)  observed  at  Priekuli  in  2003  (replicate  2)   has  the  lowest  score  for  harvest  index  in  the  en3re  dataset.   Ager  looking  into  the  raw  data  (see  the  table  above),  this   observa3on  point  was  removed  as  outlier  (set  to  NaN).   37  
  • 38. The  ini3al  PCA  analysis  of   the  climate  data  showed  a   nice  spread  of  the  scores.   No  surprises.     The  influence  plot  iden3fied   sample   (NOR)  as  a   mild  outlier.  I  decided  to   keep  this  sample,  but  to   keep  an  eye  out  for  it  in  the   mul3-­‐way  analysis.   38  
  • 39. 39  
  • 40. •   Plot  of  the  trait  scores  (max  –  min)  from  each  observa3on  loca3on  and  year.   •   The  effect  from  the  different  experimental  condi3ons  have  a  significant  effect  on   the  trait  observa3ons.   40  
  • 41. 41  
  • 42. tmin   tmax   prec   Mode  3  (climate  variables)   have  very  different  range  of     numerical  values  (tmin,  tmax,   and  prec).  Scaling  across  mode   3  is  thus  applied  to  the  mul3-­‐ way  models.     Leg  is  displayed  the  box-­‐plot   for  the  3-­‐way  data  unfolded  as   to  keep  the  dimensions  of   Scaling  across  mode  3     mode  3.   The  3-­‐way  climate  data  was   reasonably  well  described  by  a   PARAFAC  model  of  two   components.   42  
  • 43. 43  
  • 44. 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   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   44  
  • 45. 3     14   12   (loca3on  of  origin)   Climate  data  (mode  3):   14  landraces   •   Minimum  temperature   •   Maximum  temperature   •   Precipita3on   •   …  (many  more  can  be  added)   12  monthly   means   Min.  temperature   Max.  temperature   Precipita3on   Jan,  Feb,  Mar,  …   Jan,  Feb,  Mar,  …   Jan,  Feb,  Mar,  …   14  samples   45  
  • 46. •   The  ini3al  PARAFAC  models  calibrated  from  the  4-­‐way  trait  dataset  failed  to   converge  to  any  good  models.  The  core-­‐consistency  remained  very  low.   •   The  problem  showed  to  be  lack  of  systema3c  independent  varia3on  between   instances  of  mode  3  (observa3on  years)  and  mode  4  (observa3on  loca3ons)   •   A  two  component  PARAFAC  model  was  chosen  for  the  new  3-­‐way  trait  dataset.   (NOR)  was   iden3fied  as  a  mild   outlier  from  the   influence  plot.     No3ce  that  both   replica3ons  are   located  in  the  same   part  of  the  plot.  And   that  they  (together)   are  not  isolated   from  the  “data   cloud”.   46  
  • 47. PARAFAC  split-­‐half   (mode  1)  analysis:   The  two  PARAFAC   models  each  calibrated   from  two  independent   split-­‐half  subsets,  both   converge  to  a  very   similar  solu3on  as  the   model  calibrated  from   the  complete  dataset.   The  PARAFAC  model  is   thus  a  general  and   stable  model  for  the   scope  of    Scandinavia.   47  
  • 48. Further  search  for  any   good  PARAFAC  split-­‐half   for  the  climate  dataset:   A  systema3c  recording  of   results  from  10  different   split-­‐half  alterna3ves   resulted  in  two  good   split-­‐half.   The  PARAFAC  model  for   the  climate  data  is  thus   reasonable  general  (for   Scandinavia),  but  less   stable  than  the  model   for  the  3-­‐way  trait  data.   48  
  • 49. 49  
  • 50. 50  
  • 51. •  Ogen  the  cri3cal  levels  (α)  for  the  p-­‐value  is  set  as  0.05,   0.01  and  0.001.   •  For  the  modeling  of  14  samples  (landraces)  gives:   –  12  degrees  of  freedom  for  the  correla3on  tests   –  One-­‐tailed  test  (looking  only  at  posi3ve  correla3on  of   predic3ons  versus  the  reference  values).   –  A  coefficient  of  determina3on  (r2)  larger  than  0.56  is   significant  at  the  0.001  (0.1%)  level  for  14  values/samples.   Many  introductory  text  books  on  sta3s3cs  include  a  table  of  Cri3cal  Values  for  Pearson’s  r.   51  
  • 52. 52  
  • 53. •  Latvia  2002  (LY11)   –  May  2002  was  extreme  dry  in  Priekuli.   –  June  2002  was  extreme  wet  in  Priekuli.   –  The  wet  June  caused  germina3on  on  the   spikes  for  many  of  the  early  varie3es.   •  Landskrona  2003  (LY32)   –  June  2003  was  extreme  dry  in  Landskrona.   –  June  was  the  3me  for  grain  filling  here.   •  Too  extreme  for  the  genotype  to  be   “normally”  expressed  ?   •  Too  large  effect  from  “G  by  E”   interac3on  ?   53  
  • 54. 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   54  
  • 55.   55  
  • 56.         56  
  • 57. Exploring  why  some  of  the  subset  (LY)   give  very  bad  N-­‐PLS  regressions...   57  
  • 58.   58  
  • 59. All  samples   RMSECV=3.72   Without  NGB456   RMSECV=3.18     Expl.  X  =  96%   r2  cal  =  0.64   Expl.  X  =  98%   r2  cal  =  0.54   r2  cv  =  0.16   Expl.  y  =  54%   r2  cv  =  0.33   Expl.  y  =  64%   59  
  • 60. 60  
  • 61. 61  
  • 65. 65  
  • 66. •  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 66  
  • 67. •  Field  loca3ons  (USA)   –  Athens,  Georgia  (273  observa3ons)   –  Fargo,  North  Dakota  (3381  observa3ons)   –  Langdon,  North  Dakota  (858  observa3ons)   –  Stephen,  Minnesota  (139  observa3ons)   •  Observa3on  years  (1987  –  2004)   –  9  dis3nct  years   •  Greenhouse  versus  field  trials   –  Greenhouse  (1676  observa3ons)   –  Field  trial  (2975  observa3ons)   67  
  • 68. 68  
  • 70. Individual 95% CIs For Mean Based on   Pooled StDev   Level N Mean StDev -----+---------+---------+---------+-   ATHENS 262 2,0840 0,6555 (---*---)   FARGO 789 1,6793 0,6023 (-*-)   LANGDON 1558 1,6727 0,6466 (-*)   STEPHEN 136 1,6103 0,7810 (-----*----)   -----+---------+---------+---------+-   1,60 1,80 2,00 2,20   •  one-­‐way  ANOVA  test  for  difference  between  the  observa3on   loca3ons.  The  p-­‐value  of  0.000  rejects  the  null  hypothesis  of  no   difference.   •  The  Tukey  pair-­‐wise  comparison  test  gave  the  same  result.   70  
  • 71. 71  
  • 72. •  Agro-­‐clima3c  Zone  (UNESCO  classifica3on)   •  Soil  classifica3on  (FAO  Soil  map)   •  Aridity  (dryness)   •  Precipita3on   •  Poten3al  evapotranspira3on  (water  loss)   •  Temperature     •  Maximum  temperatures     •  Minimum  temperatures    (mean  values  for  month  and  year)   72  
  • 73. 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; •  The  correctly  classified  groups   tmin_annual   for  the  training  dataset  was   Group Count 1049 1 2 1190 3   234   45.9%,  and  we  would  expect  a   similar  success  rate  for  the   Summary of classification   predic3on  of  the  “blinded”   Put into Group 1 2 3   values.   1 523 427 48   2 287 451 25   •  Remember  that  random   3 238 314 163   classifica3on  of  three  groups   Total N 1048 1192 236   N correct 523 451 163   are:  33.3%   Proportion 0,499 0,378 0,691   •  A  test  set  of  9  samples   N = 2476 N Correct = 1137 showed  a  propor3on  correct   Proportion Correct = 0,459     classifica3ons  of  44.4%   73  
  • 74. Michael  Mackay   FIGS  coordinator   Ken  Street   FIGS  project  leader   Harold  Bockelman   Net  blotch  data   Eddy  De  Pauw   Climate  data   Dag  Endresen   Data  analysis   74  
  • 75. 75