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NOVA PhD training course on pre-breeding, Nordic University Network (2012)
 

NOVA PhD training course on pre-breeding, Nordic University Network (2012)

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Pre-breeding for sustainable plant production. Nova PhD course, January 2012 at Röstånga in Southern Sweden. Nova is a Nordic University Network. ...

Pre-breeding for sustainable plant production. Nova PhD course, January 2012 at Röstånga in Southern Sweden. Nova is a Nordic University Network.

Pre-breeding provides an important element in broadening the genetic diversity and introducing new and useful traits and properties to the food crops. New traits introduced in pre-breeding activities are not least important to meet the new challenges agriculture will face from the on-going climate change. The needed genetic diversity is often available outside of the genepool of cultivars and elite breeding lines. And sources of novel genetic diversity such as the primitive crops and even the wild relatives of the cultivated plants are expected to get increased focus when facing new challenges in agriculture.
The GBIF data portal provides information on in situ occurrences for many of the wild relatives to the cultivated plants that are not (yet) collected and accessioned by the ex situ seed genebank collections. The GBIF data portal will therefore provide a very valuable bridge between these data sources for genebank accessions and occurrence data sources outside of the genebank community. Occurrences from the GBIF data portal will assist in the identification of locations where potentially useful populations of crop wild relatives can be found. Ecological niche modeling provides a widely used approach for predicting species distributions and can be used for this purpose.
Recent work on predictive modeling to identify a link between useful crop traits and eco-geographic data associated with the source locations for germplasm may have particular value for pre-breeding efforts. The Focused Identification of Germplasm Strategy (FIGS) provides and approach for efficient identification of germplasm material with new and useful genetic diversity for a target trait property. Such predictive modeling approaches are of particular interest when performing pre-breeding because of the high costs related to working with this material. Cultivated plants are domesticated for properties and traits such as non-shattering seed behavior and more uniform harvest time that makes conducting agricultural experiments easier and less costly. Non-domesticated germplasm material and also the older cultivars and landraces have many agro-botanical traits that was moderated in modern cultivars to better suit agricultural practices and efficiency. Pre-breeding is largely about removing such undesired traits from the non-cultivated and less intensively domesticated material while maintaining potentially useful traits.

Nova PhD course home page:
http://www2.nova-university.org/chome/cpage.php?cnr=03-110404-412
https://sites.google.com/site/novaplantimprovementnetwork/home/phd-course-in-sweden-january-2012

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  • NOVA University Network, PhD course, 22-29 January 2012, http://www2.nova-university.org/chome/cpage.php?cnr=03-110404-412Course home page: https://sites.google.com/site/novaplantimprovementnetwork/home/phd-course-in-sweden-january-2012Photo: Oat (Avena sativa L.) at Alnarp on 3 Aug 2010, by Dag Endresen.
  • Photo: Wheat, Triticum aestivum L., at Nöbbelöv in Lund Sweden, June 2010 by Dag Endresen. URL: http://www.flickr.com/photos/dag_endresen/4826175873/, https://picasaweb.google.com/dag.endresen/GermplasmCrops#5497796034327520578
  • Genetic resources from the wild relatives of the cultivated plants contributes the raw material required for domesticated forms and the furtherdevelopment of these food crops. Genebanks preserve and provides plant genetic resources for utilization by plant breeders and other bona fide use.
  • Photo from the USDA Photo archive. Slide text by Ken Street, ICARDA FIGS team (2009).
  • Photo: Bread wheat (Triticum aestivum L.) at Nöbbelöv in Lund July 2010 by Dag Endresen. URL: http://www.flickr.com/photos/dag_endresen/4826565058/
  • Photo: Potato (Solanum tuberosum L.) at Polli in Latvia, May 2004, by Dag Endresen, https://picasaweb.google.com/lh/photo/iNfd1IaEMTh6IOL2ptbtytMTjNZETYmyPJy0liipFm0?feat=directlink
  • The assumption for trait mining using the FIGS approach is that there is a link between trait properties for crop landraces and crop wild relatives and the eco-climatic environment at the source location (collecting site). And that this link can be captured and described using a computer modeling approach.
  • Modern agriculture uses advanced plant varieties based on the most productive genetics. The original land races and wild forms produce lower yields, but their greater genetic variation contains a higher diversity in e.g. resistance to disease. High-yielding modern crops are therefore vulnerable when a new disease arises.
  • Illustration traditional cattle farming: http://commons.wikimedia.org/wiki/File:Traditional_farming_Guinea.jpg (USAID, Public Domain).
  • Photo: Wheat in the Hulah valley (Israel), 2007 by AviadBublil. License: Public Domain. http://commons.wikimedia.org/wiki/File:Wheat-haHula-ISRAEL2.JPG
  • Illustration of trait mining with ecoclimatic GIS layers. GIS layers included in the illustration are from the ICARDA ecoclimatic database, average: annual temperature (front), annual precipitation (middle), and winter precipitation (back) (De Pauw, 2003).
  • The WorldClim dataset is described in: Hijmans, R.J., S.E. Cameron, J.L. Parra, P.G. Jones and A. Jarvis, 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25: 1965-1978.NOAA GHCN-Monthly version 2: http://www.ncdc.noaa.gov/oa/climate/ghcn-monthly/index.phpWeather stations, precipitation: 20 590 stations; temperature:7280 stations.
  • Photo: Salix Accessions at Alnarp, 2011 by Dag Endresen, https://picasaweb.google.com/lh/photo/ub14FvkbCURoZp3WMkdd-tMTjNZETYmyPJy0liipFm0?feat=directlink
  • Photo: Bhullar et al (2009). PNAS 106:9519-9524,Figure 2.Bhullar, N.K., K. Street, M. Mackay, N. Yahiaoui, and B. Keller (2009). Unlocking wheat genetic resources for the molecular identification of previously undescribed functional alleles at the Pm3 resistance locus. PNAS 106(23):9519-9524. DOI: 10.1073/pnas.0904152106. http://www.pnas.org/content/106/23/9519.full Wikipedia: Heuristic ( /hjʉˈrɪstɨk/; or heuristics; Greek: "Εὑρίσκω", "find" or "discover") refers to experience-based techniques for problem solving, learning, and discovery. Where an exhaustive search is impractical, heuristic methods are used to speed up the process of finding a satisfactory solution. Examples of this method include using a "rule of thumb", an educated guess, an intuitive judgment, or common sense.In more precise terms, heuristics are strategies using readily accessible, though loosely applicable, information to control problem solving in human beings and machines.
  • * Bouhssini, M., Street, K., Joubi, A., Ibrahim, Z., Rihawi, F. (2009). Sources of wheat resistance to Sunn pest, EurygasterintegricepsPuton, in Syria. Genetic Resources and Crop Evolution. URL http://dx.doi.org/10.1007/s10722-009-9427-1,http://www.springerlink.com/content/587250g7qr073636/(Recent FIGS study at ICARDA, Syria.)
  • The barley trait dataset was developed by KolodinskaBrantestam (2005) and published as part of her doctoral thesis. The field trials were performed in two replications during 2 yr (2002 and 2003), at three locations (Bjørke in southern Norway, Landskrona in southern Sweden, and Priekuļi in Latvia). Six agronomic traits were scored: days to heading (from 1 June), days to maturity (from 1 July), plant height (cm), harvest index (percent), volumetric weight (kg/hl) and thousand-grain weight (gram).--KolodinskaBrantestam, Agnese (2005). A century of breeding - is genetic erosion a reality? [PhD Doctoral thesis]. Sverigeslantbruksuniv. [Swedish Agricultural University] (SLU), Alnarp. ActaUniversitatisagriculturaeSueciae, 1652-6880 ; 2005:30. ISBN 91-576-7029-3. Available at http://pub.epsilon.slu.se/797/--Nordic barley landraces included in this study: NGB27 NGB456 NGB468 NGB469 NGB775 NGB776 NGB792 NGB2072 NGB2565 NGB4641 NGB4701 NGB6300 NGB9529 NGB13458 --Accession no. Locality and country of origin Elevation Latitude LongitudeNGB27 Sarkalahti, Luumäki, Finland 95 61.033 27.333NGB456 Dønna, Nordland, Norway 71 66.117 12.500NGB468 Trysil, Norway 400 61.283 12.283NGB469 Bjørneby, Norway 400 61.283 12.283NGB775 Överkalix, Allsån, Sweden† 45 66.400 22.933NGB776 Överkalix, Sweden 100 66.400 22.767NGB792 Luusua, Kemijärvi, Finland 145 66.483 27.350NGB2072 Finset, Norway 1220 60.600 7.500NGB2565 Öland, Sweden 11 56.733 16.667NGB4641 Støvring, Jylland, Denmark 55 56.883 9.833NGB4701 Faroe Islands 81 62.017 −6.767NGB6300 Faroe Islands 81 62.017 −6.767NGB9529 Lyderupgaard, Denmark 9 56.567 9.350NGB13458 Koskenkylä, Rovaniemi, Finland 91 66.517 25.867--NGB27, http://sesto.nordgen.org/sesto/index.php?scp=ngb&thm=sesto&accnumtxt=27NGB456, http://sesto.nordgen.org/sesto/index.php?scp=ngb&thm=sesto&accnumtxt=456NGB468, http://sesto.nordgen.org/sesto/index.php?scp=ngb&thm=sesto&accnumtxt=468NGB469, http://sesto.nordgen.org/sesto/index.php?scp=ngb&thm=sesto&accnumtxt=469NGB775, http://sesto.nordgen.org/sesto/index.php?scp=ngb&thm=sesto&accnumtxt=775NGB776, http://sesto.nordgen.org/sesto/index.php?scp=ngb&thm=sesto&accnumtxt=776NGB792, http://sesto.nordgen.org/sesto/index.php?scp=ngb&thm=sesto&accnumtxt=792NGB2072, http://sesto.nordgen.org/sesto/index.php?scp=ngb&thm=sesto&accnumtxt=2072NGB2565, http://sesto.nordgen.org/sesto/index.php?scp=ngb&thm=sesto&accnumtxt=2565NGB4641, http://sesto.nordgen.org/sesto/index.php?scp=ngb&thm=sesto&accnumtxt=4641NGB4701, http://sesto.nordgen.org/sesto/index.php?scp=ngb&thm=sesto&accnumtxt=4701NGB6300, http://sesto.nordgen.org/sesto/index.php?scp=ngb&thm=sesto&accnumtxt=6300NGB9529, http://sesto.nordgen.org/sesto/index.php?scp=ngb&thm=sesto&accnumtxt=9529NGB13458, http://sesto.nordgen.org/sesto/index.php?scp=ngb&thm=sesto&accnumtxt=13458--
  • Endresen, D.T.F. (2010). Predictive association between trait data and ecogeographic data for Nordic barley landraces. Crop Science 50: 2418-2430. DOI: 10.2135/cropsci2010.03.0174
  • GRIN database (USDA-ARS, National Plant Germplasm System, Germplasm Resources Information Network, online http://www.ars-grin.gov/npgs).USDA GRIN, trait data online: http://www.ars-grin.gov/cgi-bin/npgs/html/desc.pl?1041.Dr. Harold Bockelman extracted the trait data (C&E).
  • Endresen, D.T.F., K. Street, M. Mackay, A. Bari, E. De Pauw (2011). Predictive association between biotic stress traits and ecogeographic data for wheat and barley landraces. Crop Science 51: 2036-2055. DOI: 10.2135/cropsci2010.12.0717
  • GRIN database (USDA-ARS, National Plant Germplasm System, Germplasm Resources Information Network, online http://www.ars-grin.gov/npgs). USDA GRIN, trait data online: http://www.ars-grin.gov/cgi-bin/npgs/html/desc.pl?65049. Dr. Harold Bockelmanextracted the trait data (C&E).
  • Photo: USDA ARS Image k1192-1, http://www.ars.usda.gov/is/graphics/photos/mar09/k11192-1.htm
  • GRIN database (USDA-ARS, National Plant Germplasm System, Germplasm Resources Information Network, online http://www.ars-grin.gov/npgs). USDA GRIN, trait data online: http://www.ars-grin.gov/cgi-bin/npgs/html/desc.pl?65049. Dr. Harold Bockelmanextracted the trait data (C&E). Photo: USDA ARS Image Archive, http://www.ars.usda.gov/is/graphics/photos/
  • Endresen, D.T.F., K. Street, M. Mackay, A. Bari, E. De Pauw, K. Nazari, and A. Yahyaoui (2012). Sources of Resistance to Stem Rust (Ug99) in Bread Wheat and Durum Wheat Identified Using Focused Identification of Germplasm Strategy (FIGS). Crop Science [online first]. doi: 10.2135/cropsci2011.08.0427; Published online 8 Dec 2011.
  • Photo: Wheat infected by stem rust (Ug99) at the Kenya Agricultural Research Station in Njoro northwest of Nairobi. This study is in press and will soon be available from Crop Science.
  • More than 7.4 million genebank accessions; and more than 1400 genebanks - including approximately 140 large genebanks each holding more than 10.000 accessions: Second Report on the State of the World’s Plant Genetic Resources for Food and Agriculture (2010) Food and Agriculture Organization of the United Nations (FAO).Photo: Genebank Accession at IPK Gatersleben by Dag Endresen, 2010.
  • Photo: South of Tunisia, by Dag Endresen, http://www.flickr.com/photos/dag_endresen/4221301525/For more information on Gap Analysis see: http://gisweb.ciat.cgiar.org/GapAnalysis/.* Ramírez-Villegas J, Khoury C, Jarvis A, Debouck DG, and Guarino L (2010). A Gap Analysis Methodology for Collecting Crop Genepools: a Case Study with PhaseolusBean. PLoS ONE 5(10): e13497. doi:10.1371/journal.pone.0013497* Jarvis et al. 2009. Value of a Coordinate: geographic analysis of agricultural biodiversity. Presentation for Biodiversity Information Standards (TDWG), November 2009.* Jarvis, A., Ferguson, M., Williams, D., Guarino, L., Jones, P., Stalker, H., Valls, J., Pittman, R., Simpson, C. & Bramel, P. 2003. Biogeography of Wild Arachis: AssessingConservation Status and Setting Future Priorities. Crop Science 43, 1100-1108.
  • NordGen study in June 2010, Wormwood (Artemisia absinthiumL.). Species distribution model using the Maxent desktop ecological niche modeling software. Only the niche model study to identify suitable locations for the presence of the species was made. The next step to combine this result with a FIGS study to identify locations where a target trait in these predicted populations would be likely to be found was only planned but not completed for this experiment conducted at NordGen.
  • A data exchange protocol, format, infrastructure and a network for sharing datasets are important elements.
  • GBIF Young Researcher Award (of 4 000 € each): http://www.gbif.org/communications/news-and-events/young-researchers-award/* Suwon, Thursday, 14 October 2010, the first two Young Researcher Awards,Amy McDougall and Andrés Lira-Noriega. http://www.gbif.org/communications/news-and-events/showsingle/article/young-researchers-impress-gbif-science-committee/* Buenos Aires, Argentina on 4-6 October 2011, César Antonio Ríos-Muñoz and Conor Ryan.http://www.gbif.org/communications/news-and-events/showsingle/article/young-scientists-showcase-gbif-data-use/
  • GBIF Young Researcher Award (of 4 000 € each): http://www.gbif.org/communications/news-and-events/young-researchers-award/
  • NOVA University Network, PhD course, 22-29 January 2012, http://www2.nova-university.org/chome/cpage.php?cnr=03-110404-412Course home page: https://sites.google.com/site/novaplantimprovementnetwork/home/phd-course-in-sweden-january-2012Photo: Oat (Avena sativa L.) at Alnarp on 3 Aug 2010, by Dag Endresen.

NOVA PhD training course on pre-breeding, Nordic University Network (2012) NOVA PhD training course on pre-breeding, Nordic University Network (2012) Presentation Transcript

  • Oat (Avena sativa L.) at Alnarp Aug 2010, by Dag Endresen, CC-By.
  • • Focused Identification of Germplasm Strategy (FIGS) – Predictive link between climate data and trait data – Heuristic approach – Trait mining with FIGS• Some FIGS case studies• The FIGS approach for pre- breeding Bread wheat at Alnarp, June 2010 by Dag Endresen , CC-By 2
  • wild tomato tomatoteosinte cultivation corn, maize 3
  • • Scientists and plant breeders want a few hundred germplasm accessions to evaluate for a particular trait.• How does the scientist select a small subset likely to have the useful trait? Photo from the USDA Photo archive 4
  • What is Focused Identification of Germplasm Strategy Mediterranean Sea South AustraliaOrigin of Concept:Boron toxicity for wheat andbarley in Australia, late 1980s Slide made by M.C. Mackay, 1995
  • • Identify new and useful genetic diversity for crop improvement.• Using eco-geographic data for prediction of crop traits a priori BEFORE the field trials.• Subset with a higher density of genetic diversity for a target trait property. Bread wheat at Nöbbelöv in Lund by Dag Endresen (CC-By). 6
  • Illustration by Mackay (1995) based on latitude & longitude Data layers sieve accessions Temperature• Based on heuristic experience Salinity score and expert knowledge. Elevation• Finding upper and lower boundary limits for individual Rainfall environmental parameters. Agro-climatic zone Disease distribution Origin of FIGS: Michael Mackay (1986, 1990, 1995) FOCUSED IDENTIFICATION OF GERMPLASM STRATEGY 7
  • • Based on multivariate and multi-way data analysis.• Eco-geographic data analysis using climate and other environmental data.• Focused Identification of Germplasm Strategy (FIGS). Potato (Solanum tuberosum L.) at Polli in Latvia, May 2004. 8
  • Trait mining usingthe FIGS approach isa new method topredict crop traits ofprimitive cultivatedmaterial fromclimate variables byusing multivariatestatistical methods. 9
  • To build a predictive computer model- explaining the crop trait score- using environmental data. 10
  • Wild relatives are shaped Primitive cultivated crops are Traditional cultivated cropsby the environment shaped by local climate and (landraces) are shaped by climate humans and humans Modern cultivated crops are Perhaps future crops are shaped mostly shaped by humans (plant in the molecular laboratory…? breeders) 11
  • It is possible that thehuman mediatedselection of landracescontributes to the linkbetween ecogeographyand traits.During traditionalcultivation the farmeractively selects for andintroduces germplasmfor improved suitabilityof the landrace to thelocal conditions. 12
  • • Landraces and wild relatives – The link between climate data and the trait data is required for trait mining with FIGS. Modern cultivars are not expected to show this predictive link (complex pedigree).• Georeferenced accessions – Trait mining with FIGS is based on multivariate models using climate data from the source location of the germplasm. To extract climate data the accessions need to be accurately georeferenced. Wheat in the Hulah valley (Israel), 2007 by Aviad Bublil 13
  • Climate layers from the ICARDAeco-climatic database (De Pauw, 2003) 14
  • Layers used for these early FIGS studies:• Precipitation (rainfall)• Maximum temperatures• Minimum temperaturesSome of the other layers available:• Potential evapotranspiration (water-loss)• Agro-climatic Zone (UNESCO classification)• Soil classification (FAO Soil map)• Aridity (dryness) Eddy De Pauw (ICARDA, 2008) (mean values for month and year) 15
  • The climate data can be extractedfrom the WorldClim dataset.http://www.worldclim.org/(Hijmans et al., 2005)Data from weather stationsworldwide are combined to acontinuous surface layer.Climate data for each landrace is Precipitation: 20 590 stationsextracted from this surface layer. Temperature: 7 280 stations 16
  • • Heuristic approach: – Sunn pest – Powdery mildew, Pm3• Multi-way approach – Morphological traits for Nordic Barley landraces• Multivariate approach – Net blotch on barley landraces – Stem rust on wheat landraces – Ug99 stem rust on wheat• Wild relatives – PGR Secure (EU 7th framework) Salix Accessions at Alnarp, 2011 by Dag Endresen, CC-By 17
  • • A FIGS set for Powdery mildew resistance was derived based on the environmental conditions for PM hotspots. • Starting with 16,089 wheat landraces (6159 sites). • FIGS subset of 1320 wheat accessions (420 sites). • 211 accessions were scored as resistant in the field trials.• Allele mining was made using Virus Induced Gene Silencing (VIGS). • Only 7 resistance alleles (Pm3a to Pm3g) were previously known at the Pm3 locus. • This study found 7 new resistance alleles (Pm3h to Pm3n). Bhullar, N.K., K. Street, M. Mackay, N. Yahiaoui, and B. Keller (2009). Unlocking wheat genetic resources for the molecular identification of Powdery mildew on wheat. Bhullar et al previously undescribed functional alleles at the Pm3 resistance locus. (2009) PNAS 106: PNAS 106(23):9519-9524. DOI: 10.1073/pnas.0904152106. 9519-9524, Fig 2. 18
  • • No previous sources of Sunn pest resistance had been found in hexaploid wheat.• 2 000 accessions were screened at ICARDA without result (during 2000 to 2006).• A FIGS set of 534 accessions was developed and screened (during 2007 and 2008). • Starting with 16 000 wheat landraces from VIR, ICARDA and AWCC. • Excluding origin CHN, PAK, IND - were Sunn pest was only recently reported (6 328 accessions). • One 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 accessions). • Reduced to 534 accessions, using PCA clustering.• 10 resistant accessions were found! Bouhssini, M., K. Street, A. Joubi, Z. Ibrahim, and F. Rihawi (2009). Sources of wheat resistance to Sunn pest, Eurygaster integriceps Puton, in Syria. Genetic Resources and Crop Evolution 56:1065-1069. Based on a DOI: 10.1007/s10722-009-9427-1 slide by Ken 19 Street, ICARDA
  • Field observations by Agnese Kolodinska Brantestam (2002- 2003) Multi-way N-PLS data analysis, Dag Endresen (2009- 2010)Google Maps © 2010Tele Atlas 20 Priekuli (LVA) Bjørke (NOR) Landskrona (SWE)
  • Experiment Heading Ripening Length Harvest Volumetric Thousand Site Year days days of plant index weight grain weight LVA 20021 n.s. n.s. n.s. n.s. *** n.s. LVA 2003 *** n.s. ** ** *** n.s. NOR 2002 - * ** *** ** n.s. NOR 2003 ** *** *** * * n.s. SWE 2002 ** *** n.s. ** * n.s. SWE 20032 n.s. ** n.s. n.s. ** n.s. *** Significant at the 0.001 level (p-value) 1 LVA 2002 Germination on spikes (very wet June) ** Significant at the 0.01 level * Significant at the 0.05 level 2 SWE 2003 Incomplete grain filling (very dry June) n.s. Not significant (at the above levels)Endresen, D.T.F. (2010). Predictive association between trait data and ecogeographic data for Nordic barleylandraces. Crop Science 50: 2418-2430. DOI: 10.2135/cropsci2010.03.0174 21
  • Green dots indicate collecting sites for resistant wheat landraces and reddots collecting sites for susceptible landraces. Field experiments made inUSDA GRIN, trait data online: Minnesota, North Dakotahttp://www.ars-grin.gov/cgi-bin/npgs/html/desc.pl?1041 and Georgia in the USA 22
  • Dataset (unit) PPV LR+ Estimated gainNet blotch (accession) 0.54 (0.48-0.60) 1.75 (1.42-2.17) 1.35 (1.19-1.50) Random 0.40 (0.35-0.45) 0.99 (0.84-1.17) 0.99 (0.87-1.12) (40 % resistant samples) PPV = Positive Predictive Value; LR+ = Positive Diagnostic Likelihood RatioEndresen, D.T.F., K. Street, M. Mackay, A. Bari, E. De Pauw (2011). Predictive associationbetween biotic stress traits and ecogeographic data for wheat and barley landraces. CropScience 51: 2036-2055. DOI: 10.2135/cropsci2010.12.0717 23
  • Green dots indicate collecting sites for resistant wheat landraces and reddots collecting sites for susceptible landraces.USDA GRIN, trait data online: Field experiments made inhttp://www.ars-grin.gov/cgi-bin/npgs/html/desc.pl?65049 Minnesota by Don McVey 24
  • Dataset (unit) PPV LR+ Estimated gainStem rust (accession) 0.54 (0.50-0.59) 3.07 (2.66-3.54) 1.95 (1.79-2.09) Random 0.29 (0.26-0.33) 1.04 (0.90-1.20) 1.03 (0.91-1.16) (28 % resistant samples) Stem rust (site) 0.50 (0.40-0.60) 4.00 (2.85-5.66) 2.51 (2.02-2.98) Random 0.19 (0.13-0.26) 0.94 (0.63-1.39) 0.95 (0.66-1.33) (20 % resistant samples) PPV = Positive Predictive Value; LR+ = Positive Diagnostic Likelihood RatioEndresen, D.T.F., K. Street, M. Mackay, A. Bari, E. De Pauw (2011). Predictive associationbetween biotic stress traits and ecogeographic data for wheat and barley landraces. CropScience 51: 2036-2055. DOI: 10.2135/cropsci2010.12.0717 25
  • Classifier method AUC Cohen’s KappaPrincipal Component Regression (PCR) 0.69 (0.68-0.70) 0.40 (0.37-0.42) Partial Least Squares (PLS) 0.69 (0.68-0.70) 0.41 (0.39-0.43) Random Forest (RF) 0.70 (0.69-0.71) 0.42 (0.40-0.44) Support Vector Machines (SVM) 0.71 (0.70-0.72) 0.44 (0.42-0.45) Artificial Neural Networks (ANN) 0.71 (0.70-0.72) 0.44 (0.42-0.46) AUC = Area Under the ROC Curve (ROC, Receiver Operating Curve)Bari, A., K. Street, , M. Mackay, D.T.F. Endresen, E. De Pauw, and A. Amri(2011). Focused Identification of Germplasm Strategy (FIGS) detects wheatstem rust resistance linked to environment variables. Genetic Resources andCrop Evolution [online first]. doi:10.1007/s10722-011-9775-5; Publishedonline 3 Dec 2011. Abdallah Bari (ICARDA) 26
  • Ug99 set with 4563 wheat landraces screened for Ug99 in Yemen 2007, 10.2 % resistant accessions. The truetrait scores for 20% of the accessions (825 samples) were revealed. We used trait mining with SIMCA toselect 500 accessions more likely to be resistant from 3728 accession with true scores hidden (to the personmaking the analysis). The FIGS set was observed to hold 25.8 % resistant samples and thus 2.3 times higherthan expected by chance. 27
  • Classifier method PPV LR+ Estimated gain kNN (pre-study) 0.29 (0.13-0.53) 5.61 (2.21-14.28) 4.14 (1.86 - 7.57) SIMCA 0.28 (0.14-0.48) 5.26 (2.51-11.01) 4.00 (2.00 - 6.86) Ensemble classifier 0.33 (0.12-0.65) 8.09 (2.23-29.42) 6.47 (2.05-11.06) Random 0.06 (0.01-0.27) 0.95 (0.13 - 6.73) 0.97 (0.16 - 4.35) (pre-study, 550 + 275 accessions) Ensemble 0.26 (0.22-0.30) 2.78 (2.34-3.31) 2.32 (2.00-2.68) Random 0.11 (0.09-0.15) 1.02 (0.77-1.36) 0.95 (0.77-1.32) (blind study, 825 + 3738 accessions) PPV = Positive Predictive Value; LR+ = Positive Diagnostic Likelihood RatioEndresen, D.T.F., K. Street, M. Mackay, A. Bari, E. De Pauw, K. Nazari, and A. Yahyaoui (2012).Sources of Resistance to Stem Rust (Ug99) in Bread Wheat and Durum Wheat Identified UsingFocused Identification of Germplasm Strategy (FIGS). Crop Science [online first]. doi:10.2135/cropsci2011.08.0427; Published online 8 Dec 2011. 28
  • Genebank Accession at IPK Gaterslebenby Dag Endresen, 2010, CC-By. 29
  • • Advice the planning of new collecting/gathering expeditions – Identification of relevant areas were the crop species is predicted to be present (using GBIF data and niche models). – Focus on areas least well represented in the genebank collection (maximize diversity). – Focus on areas with a higher likelihood for a desired target trait (FIGS). South of Tunisia, by Dag Endresen, CC-By For more information on Gap Analysis see: http://gisweb.ciat.cgiar.org/GapAnalysis/ 30
  • Speciesdistribution Wormwood (Artemisia absinthium L.)model(7 364 records)Using the Maxentdesktop software. 31
  • Genebank accessions: http://data.gbif.org/datasets/network/2Using GBIF/TDWG technology (and The compatibility of data standardscontributing to its between PGR and biodiversitydevelopment), the PGR community collections made it possible to integratecan more easily establish specific the worldwide germplasm collectionsPGR networks without duplicating into the biodiversity communityGBIFs work. (TDWG, GBIF). 32
  • Genebank datasets Spatial data Threatened species Migratory Crop standards speciesLegislation and Global crop systemregulations etc. Crop collections in Global crop collections Europe 33
  • Suwon, Thursday, 14 October 2010:• Amy McDougall, Environmental Sciences School at the University of East Anglia, United Kingdom, “Bridging the Gap in a Climate Changed World: Is Conservation Planning by Taxa a Realistic Aim?”• Andrés Lira-Noriega, Department of Ecology and Evolutionary Biology and the Biodiversity Institute at the University of Kansas, “A Comparison of Correlative and Mechanistic Ecological Niche Modeling Approaches for Amy McDougall (left) and Andrés Lira-Noriega (right) Understanding Biodiversity Patterns.“Buenos Aires, Argentina on 4-6 October 2011:• César Antonio Ríos-Muñoz (ornithologist from Mexico) will investigate the evolutionary processes leading to the current distribution of species in Mesoamerican lowland tropical forests.• Conor Ryan (marine biologist from Ireland) will study otoliths, calcium carbonate deposits found in the inner ear of fish, whose shape is highly specific to each species. César Antonio Ríos-Muñoz (left) and Conor Ryan (right) 34
  • The 3rd Young Researcher Award(Lillehammer, September 2012):GBIF invites proposals from graduate students forthe 2012 Young Researchers Award. This prizeintends to foster innovative research and discoveryin biodiversity informatics.Two awards of €4,000 will be available to graduatestudents in a master’s or doctoral programme at auniversity in a GBIF Voting Participant or AssociateParticipant country.The deadline to receive nominations from theHeads of Delegation is 15 March 2012. Perhaps one of the students from the Nordichttp://www.gbif.org/communications/news-and- Plant Improvement Network will become theevents/young-researchers-award/ next GBIF Young Researcher Awardee…? 35
  • Thanks for listening!NOVA University NetworkPhD training coursePre-breeding for sustainable plant production22 to 29 January 2012Dag Endresen (GBIF)dag.endresen@gmail.com 36