genetic resources - r computing platform -27jun2016 - ppt
TRANSCRIPT
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
-----------------
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//