Capturing and understanding patterns in plant genetic resources data to help

develop “climate-proof” crops R platform

 

The R User Conference 2016June 27 - June 30 2016Stanford University, Stanford, California 

Source: Millennium Ecosystem Assessment (2005)http://oceanworld.tamu.edu/resources/environment-book/Images/drylandmap.jpgVisited October 21, 2013

GCMs all converge with regard to projections of:

Increased frequency of drought, and high temperatures

In

central North America, northern Africa, central Asia, and western Australia

(Girvetz et al. 2009, Elert & Lemonick 2011)

Climate ChangeGlobal Climate Models’ projections

Climate Change - shift GHG emissions -> heating up of low atmosphere (Mendelsohn & Dinar 2009)

Heat stress will increase vulnerability of crops ..more than drought.(Semenov & Shewry 2011)

sShift

This will require to aim for yields /environmental adaptation in unprecedented/different circumstances!

CIAT

CIMMYT CI

P

I CAR

DA

I CR A

F

I CR ISAT

I I TA

I LR I

I PGR

II RR I

WAR

DA0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

Number of accessions

Source: The CGIAR Genebanks - Seeds for Life (2006)

Background Genetic Resources / Biodiversity

• More than 7 million genetic resources accessions (seed plants)  

• More than 1400 gene banks world wide• Cost/search implications1,2 

-----------------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

Gollin D, Smale M, Skovmand B (2000) Searching an ex situ collection of wheat genetic resources. Am J Agric Econ 82:812–827

1

2

Presence of patterns ----->  quantification and predictions 

Dependency between Environment and the trait (Envt, Trait) -> prediction (unknown)

Assessing genetic resources /Agro-Biodiversity for CC traits Exploring patterns - Modelling/predictions

Bayes – Laplace approach (inverse probability)Learning based approach (risk minimization)

Environment (tmin, tmax, prec)

Trait -grain filling period (gfp)(probability of occurrence)

Bari et al. (2016). In silico evaluation of plant genetic resources to search for traits for adaptation to climate change. Climatic Change 134(4) 667-680. http://dx.doi.org/10.1007/s10584-015-1541-9

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Genetic Resources

(data)

PlatformConceptual frameworksMathematics  

along with farmers’ insights factored in the process

Mitigation Adaptation

Tolerance to heat, drought, salinity and low inputs

Merge and integrate data for a more comprehensive procedure

GHGs elimination, namely carbon dioxide (CO2 sequestration and methane (CH4)

R platform – practical CC solutions

Large data sets including Canadian climate centre data and UN FAO data

FAO

Geographical Information System

(GIS)

Environmental data/layers(surfaces)

R language(Development of algorithms)

> Data transformation ()> Model <- model(trait ~ climate)> Measuring accuracy metrics> ….

R Platform – data integration and analysis

7

Modeling purpose Generation of environmental data

Algorithms : to search for dependency,  if it exists! 

Climate datato generate surface (CC)

R Platform – data integration and analysis

UN Food and Agriculture OrganisationCanadian Centre for Climate

Climatic data extracted from current and future climate scenarios

FAO Database (30 arc-second raster database)

Searching for climate change related traits in plant genetic resources collectionshttp://om.ciheam.org/om/pdf/a110/00007061.pdf 

Data extraction, integration and preparation (transformation) under R

Organisation, community or company names or trademarks are referred to for

identification purpose!.

Support Vector Machines (SVM) 

Random Forest (RF)

Neural Network (NN)

                 

      

  

    

  

         

  

   

  

   

  

         

  

   

x1

x2

xp

F(x)

R Platform – data analysis and predictions

AUC curve

0.00

0.25

0.50

0.75

1.00

0.00 0.25 0.50 0.75 1.00False Positive Rate

True

Pos

itive

Rat

e

A. Bari, A.B. Damania, M. Mackay and S. Dayanandan (Eds.). Applied Mathematics and Omics to Assess Crop Genetic Resources for Climate Change Adaptive Traits. CRC Press, Taylor & Francis Group, Boca Raton, FL, USA. ISBN 9781498730136. . https://www.routledge.com/products/9781498730136

Modelling/predictions Capturing the shift induced by climate - verification

0 100 200 300

020

4060

80

x$x

x$ys

mth

Data alignment to growing season

Algorithms

Separate phase variation from amplitude variation

0 100 200 300

5010

015

020

0

x$x

x$ys

mth

Site (i) : Si(xi, yi) Site (j): Sj(xj, yj)

day

rain

fall

day

The ROC curve and the resulting and trait distribution (trait states)

1

1

1-

ROC curve  trait distribution 

Parameters that provide information on the specificity (“trait agro-climate”) .. 

High AUC (area) values indication of potential trait-environment relationship

Presence of patterns – Accuracy metrics

0.0 0.2 0.4 0.6 0.8 1.0

01

23

4

Probability predictions of resistance to Stripe rust 

in wheat

Predicted probability 

Dens

ity

Barley plants grown to confirm math predictions vis a vis tolerance to heat (plant canopy temperature lower than air temperature)

-8     -6     -4       -2      0        2       4      6

-8     -6     -4       -2      0        2       4      6

15

10

 5

 0

15

10

 5

 0

Plants predicted (in silico) to sustain heat

Plants selected at random (purposive sampling)

Temperature (TPlant – TAir)

Temperature (TPlant – TAir)

Num

ber o

f plants

Jilal

/INRA

Mor

occo

Modelling/predictions Applied to assess barley genetic resources for heat traits traits)

Long-sought-for and different traits of tolerance to heat have been found !

Salt-tolerant varieties/genotypesare also sought-for as sea level rises.

Screening durum wheat for salt tolerance using imaging techniques - Tunisia 

over

the

past

30

year

s

R based Imaging techniques have been used to capture root architectural traits vis-à-vis tolerance to salinity.

Durum wheat

Modelling/predictions Applied to assess wheat genetic resources for salinity traits (root traits)

Faba bean is a valuable source of protein grown mostly prone to climate change effects.

Its diversity is limited as it has no wild or close relatives to help broaden its genetic base.

Recent assessment of accessions held in gene banks by University of Helsinki yielded promising results in terms of tolerance to drought.  

Screening for drought tolerance in faba bean (earliness in right found among accessions) -

Helsinki Stod

dard

/Uni

vers

ity o

f Hel

sinki

pbs.

org

Modelling/predictions Applied to assess faba bean genetic resources for drought tolerance traits (root traits)

Global Platform launched to assess genetic resources for Climate Change related genes/traits

A. Bari, Y.P. Chaubey, M.J. Sillanpää, F.L. Stoddard, H. khazaei, S. Dayanandan, A.B. Damania, , S.B. Alaoui, H. Ouabbou, A. Jilal, M. Maatougui, M. Nachit, R. Chaabane, Z. Kehel and M. Mackay

http://www.dataorigin.net//