Bari a 2nd iwsrs conference - izmir - 29 april2014

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  • Landrace samples (genebank seed accessions)Trait observations (experimental design) - High cost dataClimate data (for the landrace location of origin) - Low cost dataThe 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.
  • Bari a 2nd iwsrs conference - izmir - 29 april2014

    1. 1. 2nd International Wheat Stripe Rust Symposium Predicting and Locating Sources of Resistance to Stripe (Yellow) Rust in Durum Wheat Genetic Resources 2nd International Wheat Stripe Rust Symposium Izmir - Turkey, 28 April – 1st May 2014 Grain Research & Development Corporation
    2. 2. 2nd International Wheat Stripe Rust Symposium Outline Challenges and opportunities Sub setting PGR – FIGS approach Stripe rust resistance case Work ahead Partnership (new)
    3. 3. 2nd International Wheat Stripe Rust Symposium • More than 7 million accessions • More than 1400 genebanks • Data/concepts • Search cost1 implications • Time2 lags implications ----------------- Challenges - opportunities Gollin D, Smale M, Skovmand B (2000) Searching an ex situ collection of wheat genetic resources. Am J Agric Econ 82:812–827 Koo B, Wright BD (2000) The optimal timing of evaluation of genebank accessions and the effects of biotechnology. Am J Agric Econ 82:797–811 1 2
    4. 4. 2nd International Wheat Stripe Rust Symposium The utilisation of genebanks has not kept pace with their expansion! Gollin et al. (2000)
    5. 5. 2nd International Wheat Stripe Rust Symposium New trait variation - FIGS  Net blotch (barley)  Powdery mildew  Russian wheat aphid (RWA)  Sunn pest Braidotti, G. (2009) Partners in Research for Development A wheat landrace from Turkey collected in 1948 was discovered to carry genes of resistance to fungal diseases in 1980s. Atalan-Helicke N (2012) Conserving diversity at the dinner table: plants, food security and gene banks. Origins: Current Events in Historical Perspective Accessed 5 April 2014 Challenges - opportunities
    6. 6. 2nd International Wheat Stripe Rust Symposium PGR (Biodiversity) Stratification/ Multl-stage procedure Sub-setting sub set PGR (Biodiversity) Sub setting Filtering/ Relationship FIGS set (Trait) PGR sub-setting: FIGS approach 6 By applying to plant genetic resources/agro-biodiversity the same selection pressure exerted on plants by evolution. Sub setting to overcome the problem of the large size (search cost) of PGR collections
    7. 7. 2nd International Wheat Stripe Rust Symposium Detect presence of patterns (environment x trait) Presence of patterns -----> quantification and prediction MacArthur (1972) Assessing PGR/Agro-Biodiversity for rust resistance Environment (tmin, tmax, prec) Trait (T) (Resistance to stripe Rust) Bayes – Laplace approach (inverse probability) Learning based approach (risk minimization) Cherkassky & Mulier (2007) The Bayes-Laplace inverse theorem focuses on the probability of causes in relation to their effects, in contrast to the probability of effects in relation to their causes. Fisher (1922, 1930) (E)
    8. 8. 2nd International Wheat Stripe Rust Symposium FIGS powdery mildew set Results of screening Accessions infected with 4 powdery mildewisolates which were avirulent or virulent to the known Pm3 alleles Of these 420 sites, 40% yielded accessions that were resistant to the isolates used – 211 accessions Starting with a total pool of 16,000 accessions collected from 6,159 sites, the FIGS process chose 1,320 accessions collected from 420 sites Kaur K; Street K; Mackay M; Yahiaoui N; Keller B (2008). Allele mining and sequence diversity at the wheat powdery mildew resistance locus Pm3. 11th IWGS, 24-29 Aug., Brisbane
    9. 9. 2nd International Wheat Stripe Rust Symposium 9 Distribution of new Pm3 alleles FIGS powdery mildew set
    10. 10. 2nd International Wheat Stripe Rust Symposium Mining natural variation By linking traits (phenotype), environments (and associated selection pressures) with genebank accessions (e.g. landraces and crop relatives) -> ‘focus’ in on those accessions most likely to possess trait specific genetic variation. 0 50 100 150 0102030405060 Longitude Latitude Trait (disease score)Environnement FIGS subset www.icarda.org/
    11. 11. 2nd International Wheat Stripe Rust Symposium Focused Identification of Germplasm Strategy Geo-referencing of collecting sites 11 Evaluation (phenotyping) Environment (E) Accession (G) Trait (T) FIGS approach – summarized
    12. 12. 2nd International Wheat Stripe Rust Symposium FIGS pathways – so far… User defined trait Evaluation (limited) data No evaluation data Use filtering process Identify environmental x trait relationship (model) Use relationship to predict candidate sites Knowledge (Specialised) Use a priori process
    13. 13. 2nd International Wheat Stripe Rust Symposium Accuracy metrics The ROC curve and the resulting pdf’s of trait distribution (trait states) 1 1 1- ROC curve pdf’s of trait distribution High AUC (area) values indication of potential trait-environment relationship Patterns present in data Predictions Frequency Truepositiverate False positive rate Environment
    14. 14. 2nd International Wheat Stripe Rust Symposium Parameters which provide information on the accuracy of the predictions (“trait x agro-climate”) Observed Tolerant Susceptible Predicted Tolerant a b Susceptible c d Confusion matrix (2-by-2 contingency table) Sensitivity = a/ (a + c) Specificity = d/(b + d) • Sensitivity refers to the proportion of accessions with resistance scored as resistant, while • Specificity refers to the proportion of accessions without resistance scored as susceptible Both are indicators of the models ability to correctly classify observations. Accuracy metrics
    15. 15. 2nd International Wheat Stripe Rust Symposium pdf’s of trait distribution Accuracy metrics Randomness (no pattern) 1 1 1- ROC curve Predictions Frequency Truepositiverate False positive rate
    16. 16. 2nd International Wheat Stripe Rust Symposium Stripe rust – search for resistance Aim Predict accessions/areas likely to be resistant /conducive to stripe rust appearance/presence Hypothesis Relationship exists between the geographic distribution of stripe rust resistance and collection site climate descriptors
    17. 17. 2nd International Wheat Stripe Rust Symposium Sub-Setting procedure – a priori ICARDA genebank ~ 20 000 accessions of durum wheat 2915 accs Entire collection Training set Test set ~ 725 accs (before 2011) ~ 2915 accs (2011/12) Training set Validation (actual evaluation) Prediction/ Location (in silico evaluation)
    18. 18. 2nd International Wheat Stripe Rust Symposium 18 Layers used in the studies: • Precipitation (rainfall) • Maximum temperatures • Minimum temperatures + Derived GIS layers such as: • Potential evapotranspiration (water-loss) • Moisture/Aridity index (mean values for month and year) Eco-climate data (X) ICARDA Geo-Informatics Current ICARDA eco-climatic database, average: annual temperature (front), annual precipitation (middle), and winter precipitation (at the back) (De Pauw 2008) Site code prec01 prec02 prec03 prec04 prec05 …. . ari01 ari02 ari03 ari04 ari05 ETH-S893 25 36 72 154.22 148.88 0.167 0.246 0.439 1.098 1.169 NS_339 44 67 130.43 177.96 185.74 0.351 0.552 0.949 1.457 1.751 NS_559 23 40 61.89 129.04 102 0.226 0.397 0.511 1.206 0.998 Climate data (X)
    19. 19. 2nd International Wheat Stripe Rust Symposium Trait data set (Y) . . . . . Trait data (Y as dependent variable) http://www.icarda.org/striperust2014/2nd-international-wheat-stripe-rust-symposium-2014/ Genetic Resources - ICARDA
    20. 20. 2nd International Wheat Stripe Rust Symposium Modeling framework 20 Yi ~ Trait data (Y) Y ~ f(X) Environmental data (X) X is the set of variables that contains explanatory variables or predictors (climate data) where X ∈ Rm, Y ∈ Y that is either a categorical (label) or a numerical response (trait descriptor states). Bari A. et al. (2011) Genetic Resources and Crop Evolution http://www.springerlink.com/content/m7140x68v2065113/fulltext.pdf Conceptual framework at: Bernoulli distribution
    21. 21. 2nd International Wheat Stripe Rust Symposium Sub setting - variables Stripe rust Resistance/trait states (Y) – Response variable (X) – climate variables
    22. 22. 2nd International Wheat Stripe Rust Symposium Geographical Information System (GIS) Arc Gis Environmental data/layers (surfaces) R language (Development of algorithms) > Data transformation ( ) > Model <- model(trait ~ climate) > Measuring accuracy metrics > …. Platform - analysis 22 Modeling purpose Generation of environmental data Algorithms : to search for dependency, if it exists! Climate data to generate surfaces
    23. 23. 2nd International Wheat Stripe Rust Symposium Machine learning classification (models) algorithms Support Vector Machines (SVM) Random Forest (RF) Neural Network (NN) x1 x2 xp F(x ) Bari A, Street K, Mackey M, Endresen DTF, De Pauw E, Amri A (2012) Focused identification of germplasm strategy (FIGS) detects wheat stem rust resistance linked to environmental variables. Genet Resour Crop Evol 59:1465–1481
    24. 24. 2nd International Wheat Stripe Rust Symposium Models used – non linear • Y normally distributed at each value of X • Variance of Y should be constant for each value of xi (homogeneity of variance) • No serial correlation – values of Y independent of one another • A linear or curvi-linear response Time consuming transformations – if assumptions violated Assumptions of the linear model Limits to detecting relationships that have higher dimensions or are more complex
    25. 25. 2nd International Wheat Stripe Rust Symposium Model AUC Sensitivity Specificity Proportion correct Kappa SVM mean 0.72 0.65 0.78 0.74 0.40 lower 0.69 0.61 0.74 0.72 0.35 upper 0.74 0.69 0.82 0.77 0.45 RF mean 0.70 0.64 0.76 0.73 0.37 lower 0.67 0.61 0.71 0.69 0.30 upper 0.73 0.67 0.81 0.76 0.44 NN mean 0.73 0.69 0.77 0.74 0.41 lower 0.70 0.58 0.69 0.70 0.35 upper 0.76 0.79 0.85 0.78 0.48 Results – accuracy metrics values Training/validation set – define dependency “approximation” function
    26. 26. 2nd International Wheat Stripe Rust Symposium Model AUC Sensitivity Specificity Proportion correct Kappa SVM mean 0.72 0.67 0.78 0.75 0.41 lower 0.71 0.64 0.74 0.73 0.36 upper 0.74 0.70 0.81 0.76 0.45 RF mean 0.71 0.63 0.80 0.75 0.40 lower 0.70 0.58 0.77 0.73 0.36 upper 0.73 0.67 0.84 0.77 0.45 NN mean 0.74 0.74 0.74 0.73 0.41 lower 0.72 0.65 0.67 0.69 0.37 upper 0.76 0.83 0.81 0.76 0.46 Test/unknown set – in silico evaluation vs actual evaluation Results – accuracy metrics values (Yr)
    27. 27. 2nd International Wheat Stripe Rust Symposium Classifier method AUC Cohen’s Kappa Principal 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) 0.0 0.4 0.8 0.00.20.40.60.81.0 0.0 0.5 1.0 0.00.51.01.52.02.53.0 Results – accuracy metrics values Stem rust – previous research
    28. 28. 2nd International Wheat Stripe Rust Symposium False positive rate Truepositiverate 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 -0.2900.290.580.871.16 -0.5 0.0 0.5 1.0 1.5 01234 Distribution bytrait state Truepositiverate Frequency Bari et al. (2014). Predicting resistance to stripe (yellow) rust in wheat genetic resources using Focused Identification of Germplasm Strategy (FIGS). Journal of Agricultural Science ROC plots (left) and density plots class prediction (right) False positive rate Predicted probability Results – Graphs -Stripe rust
    29. 29. 2nd International Wheat Stripe Rust Symposium 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 0.0 0.2 0.4 0.6 0.8 1.0 01234 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 0.0 0.2 0.4 0.6 0.8 1.0 0.00.51.01.52.02.53.0 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 0.0 0.2 0.4 0.6 0.8 1.0 012345 Results – Model predictions SVM RF NN Accuracy metrics (ROC) plots for the SMV, RF and NN models applied to the evaluation data not made known to the model. The histograms are about predictions of resistance and susceptibility, where = susceptibility = resistance.
    30. 30. 2nd International Wheat Stripe Rust Symposium Results – spatial patterns Likelihood of an area yielding traits of resistance to stripe rust (yellow colour) Longitude Latitude
    31. 31. 2nd International Wheat Stripe Rust Symposium Sub-Setting procedure – adjustment based on phenology Alignment of data based on phenology To reduce: • The “out phase” differences due to different growing seasons/periods The daily data were derived from models involving the proposed model by Epstein (1991) as a sum of harmonic components.
    32. 32. 2nd International Wheat Stripe Rust Symposium Modelling/predictions Capturing the shift induced by climate Based on the estimation of the duration of the period during the year in which neither moisture nor temperature are limiting to plants. Target specific phase of crop development Bari et al. (in press). Searching for climate change related traits in plant genetic resources collections using Focused Identification of Germplasm Strategy (FIGS). Options Méditerranéennes. Alignment of data based on phenology
    33. 33. 2nd International Wheat Stripe Rust Symposium Accuracy and agreement parameters of aligned data Sub-Setting procedure – adjustment based on phenology - results Data type AUC Omission rate Sensitivity Specificity Correct classification Kappa monthly 0.81 0.28 0.72 0.90 0.86 0.61 daily data 0.82 0.30 0.70 0.93 0.88 0.64 aligned daily data 0.83 0.28 0.72 0.95 0.90 0.70 210 days False positive rate Truepositiverate 0.0 0.2 0.4 0.6 0.8 1.0 0.00.20.40.60.81.0 -0.2900.290.580.871.16
    34. 34. 2nd International Wheat Stripe Rust Symposium Modelling/predictions Capturing the shift induced by climate - verification 0 100 200 300 020406080 x$x x$ysmth Data alignment to growing season Algorithms Separate phase variation from amplitude variation 0 100 200 300 50100150200 x$x x$ysmth Site (i) : Si(xi, yi) Site (j): Sj(xj, yj) day rainfall day http://mpe2013.org/ We are not there yet …
    35. 35. 2nd International Wheat Stripe Rust Symposium Future directions (in summary) Trait data (Y)Environmental data (X) x u y u for yet unknown variables FIGS aims to deal with unobserved inputs, uncertainty, and un-ambiguity (v) CC induced shift (bias) Z to eventually capture the dynamics (complexity) v Climate change FIGS Z(t)
    36. 36. 2nd International Wheat Stripe Rust Symposium “Applied Mathematics and Omics Technologies for Discovering Biodiversity and Genetic Resources for Climate Change Mitigation and Adaptation to Sustain Agriculture in Drylands” http://mpe2013.org/ Future directions Explore the use of a variety of applied mathematics approaches in relation to phenology aspects of both the pathogen and the host. Expect to appear also at MPE host pathogen Summary proceedings
    37. 37. 2nd International Wheat Stripe Rust Symposium Teşekkür Ederim Thank you Abdallah Bari Kumarse Nazari Miloudi Nachit Ahmed Amri Ken Street Chandra Biradar Amor Yahyaoui Dag Endresen

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