wild tomato tomato teosinte corn, maize

4 Crop Wild Relatives Traditional landraces Modern cultivars Genetic bottlenecks during crop domestication and during modern plant breeding. The circles represent allelic variation. The funnels represents allelic variation of genes found in the crop wild relatives, but gradually lost during domestication, traditional cultivation and modern plant breeding.

Scientists and plant breeders want a few hundred germplasmaccessions to evaluate for a particular trait. How does the scientist select a small subset likely to have the useful trait? Example: More than 560 000 wheat accessions in genebanksworldwide. Slide adopted from a slide by Ken Street, ICARDA (FIGS team) 6

The scientist or the breeder need a smaller subset to cope with the field screening experiments. A common approach is to create a so-called core collection. 7

Given that the trait property you are looking for is relatively rare: Perhaps as rare as a unique allele for one single landrace cultivar... Getting what you want is largely a question of LUCK! Slide adopted from a slide by Ken Street, ICARDA (FIGS team) 8

Objective of this study: Explore climate data as a prediction model for computer pre-screening of crop traits BEFORE full scale field trials. Identification of landraces with a higher probability of holding an interesting trait property. 10

Wild relatives are shaped by the environment Primitive cultivated crops are shaped by local climate and humans Traditional cultivated crops (landraces) are shaped by climate and humans Modern cultivated crops are mostly shaped by humans (plant breeders) Perhaps future crops are shaped in the molecular laboratory? 11

Primitive crops and traditional landraces are an important source for novel traits for improvement of modern crops. Landraces are often not well described for the economically valuable traits. Identification of novel crop traits will often 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 Assumption: the climate at the original source location, where the landrace was developed during long-term traditional cultivation, 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).

14 1) Landrace samples (genebank seed accessions) 2) Trait observations (experimental design) - High cost data 3) Climate data (for the landrace location of origin) -Low cost data The accession identifier (accession number) provides the bridge to the crop trait observations. The longitude, latitude coordinates for the original collecting site of the accessions (landraces) provide the bridge to the environmental data.

15 Alnarp, Sweden Lima, Peru Svalbard Benin

ba bean, Finland Field trials, Gatersleben, Germany Potato Priekuli Latvia rage crops, Dotnuva, Lithuania Radish (S. Jeppson) Linnés äpple owdery Mildew, lumeria graminis Leaf spots Ascochyta sp. Yellow rust Puccinia strilformis Black stem rust Puccinia graminis http://barley.ipk-gatersleben.de 16

The climate data is extracted from the WorldClim dataset. http://www.worldclim.org/ Data from weather stations worldwide are combined to a continuous surface layer. Climate data for each landrace is extracted from this surface layer. Precipitation: 20 590 stations Temperature: 7 280 stations 17

FIGS selection is a new method to predict crop traits of primitive cultivated material from climate variables by using multivariate statistical methods. 18

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

FIGS The FIGS technology takes much of the guess work out of choosing which accessions are most likely to contain the specific characteristics being sought by plant breeders to improve plant productivity across numerous challenging environments. http://www.figstraitmine.org/ FIGS salinity set 20 20

Elevation Rainfall Data layers sieve accessions based on latitude & longitude Temperature Salinity score Agro-climatic zone Disease distribution F OCUSED I DENTIFICATION OF G ERMPLASM S TRATEGY Slide made by Michael Mackay 1995 21

No sources of Sunnpest 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 selection started from 16 000 landraces from VIR, ICARDA and AWCC Exclude origin CHN, PAK, IND were Sunnpest only recently reported (6 328 acc). Only accession per collecting site (2 830 acc). Excluding dry environments below 280 mm/year Excluding sites of low winter temperature below 10 degrees Celsius (1 502 acc) http://dx.doi.org/10.1007/s10722-009-9427-1 Slide adopted from Ken Street, ICARDA (FIGS team) 26

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

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

Michael Mackay FIGS coordinato Barley (Hordeum vulgare ssp. vulgare) collected 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 the fungus Pyrenophora teres. Screened at four USDA research stations: North Dakota (Langdon, Fargo), Minnesota (Stephen), Georgia (Athens). 1-3 are basically resistant group 1 4-6 are intermediate group 2 7-9 are susceptible group 3 Ken Street FIGS project lead Harold Bockelm Net blotch data Eddy De Pauw Climate data Discriminant analysis (DA): Correctly classified groups: 45.9% in the training set and 44.4% in the test set. Work in progress! (SIMCA, D-PLS) Dag Endresen Data analysis 29