Application of biotechnology tools for rapid cultivar development and deployment: The case of cassava and maize

1. Application of Biotechnology
Tools For Rapid Cultivar
Development And Deployment:
The Case Of Cassava And Maize
Melaku Gedil
Molecular Geneticist/Breeder
International Institute of Tropical Agriculture
Ibadan, Nigeria
FAO Regional Meeting on Agricultural Biotechnologies
in Sustainable Food Systems and Nutrition in Sub-Saharan Africa
Addis Abeba, Ethiopia November 20-24, 2017

2. Factors for Low Productivity
• Productivity of African staple crops is much lower
than the yield potential è Yield gap
• Several factors contribute to the reduction of yield
Ø Seed, management, postharvest loss, diseases
and pests
• Genetic improvement is acclaimed as the major
contributor of crop productivity, particularly in
developing countries where numerous stresses are
impacting yield in the backdrop of limited resources
by subsistence farmers to mitigate these scourges.

3. Mainstreaming Marker-Assisted Selection
for Accelerated Cultivar Development
• Plant breeding is an ongoing, cyclical process in which
new crop varieties are continuously being developed to
improve yield, quality, resistance to diseases and pests,
and other desirable traits.
• Involves manipulating naturally occurring genetic
variations to identify plants with desirable characteristics
– yield, quality, resilience to adverse environment.
• Conventional selection practices entail evaluation of
thousands of candidate lines in multiple environments
(locations x years)

4. Genetic Gain
The success of breeding program is measured by the
magnitude of genetic gain, the extent of change that can
be achieved through breeding per unit of time and cost.
Target traits include:
• Yield potential – productivity
• Nutritional characteristics, quality traits,
• Stress tolerance – diseases, drought….

5. Increasing the size of the breeding program -
greater selection intensity
Faster breeding cycles
More accurate selection (higher heritability)
Adequate genetic variation
Data Management & Decision support tools
Enhancing Genetic Gain and Breeding
Efficiency

6. Modernizing Plant Breeding
Molecular breeding complements conventional breeding programs by
enhancing selection efficiency, precision of trait measurement, shortening
breeding cycle, and reducing cost.
The most common Molecular Breeding approaches are:
1. Marker-assisted back-crossing (MABC) is used to to introgress genes or
desired traits from un-adapted genotypes to adapted/elite cultivars
2. Marker-assisted recurrent selection (MARS) is used to accumulate
superior alleles in selected genetic background.
3. Genome selection (GS) uses genomic prediction (GP) models which
combines genome-wide marker data with phenotypic data in a training
population to predict the genomic estimated breeding values (GEBV) of
untested individuals
4. Forward breeding is early generation selection for a desired trait or gene
using validated predictive marker in contrast to background selection,
which focuses on eliminating unwanted contribution of genetic materials
(aka. linkage drag), typically from a donor parent during trait introgression
into a recurrent elite genotype.

7. Biotech Intervention in Plant BreedingGermplasm
Genebank
Parental
selection
Crossing
Backcross
A x B
C x D
AxB x CxD
Progeny
selection
Field
evaluation
Multi-
location trial
On-farm triial
Demonstration
Variety release
Seed
multiplication
Scale-up
X
Fingerprinting
Profiling
•Diversity
analysis
•Allele mining
Molecular
Breeding
•MABC
•MARS
•GS
Quality Control
DNA based
•Homogeneity
•Purity
•True-to-type
Adoption
tracking
•Improved
varieties
•Farmers
varieties
Pre-Breeding Field Breeding Seed System

8. Enablers/Motivation
• Affordable, rapid turn-around time, and high
throughput genotyping
• Discovery of validated predictive markers for
key traits
• Efficient workflow for sample processing and
data management
• CG-wide complementary initiatives (Bi-lateral
projects, GOBII, EiB, BigData, CRP)
• National programs can also benefit from
these developments

9. Data management and Decision
making tools
Breeders toolboxes and Comprehensive databases
• Crop specific or general
Genomic Open-Source Breeding Informatics Initiative (GOBII)
• GOBII will serve as the repository for all genotyping data from
HTPG project
• Enhance user interface for better decision making
Integrated Breeding Platform (IBP) – Breeding Management System
(BMS)
• Will be used to create genotyping experiments
• Streamline breeding workflow for better efficiency
Other Initiatives
• Excellence in Breeding (EiB) platform
• Big Data platform
• CG-wide Open Access resources

10. • Advanced electronic data collection through smartphone apps
• Uses novel algorithms for feature extraction and modeling plant phenotypes
• Comprehensive breeding database (www.cassavabase.org) created for
community
Data collection and storage modernized
Field Book 1KK
WWW.CASSAVABASE.ORG

12. Frequency of favorable
alleles increased with MARS
Marker-assisted Recurrent Selection for Drought
Tolerance
Genetic gain for grain yield
as response to MARS

13. MLN
No MLN
Improved MLN tolerance of elite stress
tolerant lines
1.8 t/ha testcross yield
increase under severe MLN
pressure
Yield parity in absence of
MLN

14. Seed germination
and transplanting
(>10,000)
Extract DNA
(2500 samples)
Genomic
Selection of 150
parents
Recombination
of selected
parents
Harvest
seeds;
Phenotype
Genotype by GBS
at GDF
(Cornell Univ.)
Cassavabase
GEBV calculation
and Selection
One Year GS
breeding
cycle of
cassava
Cassavabase
Data repository
and Analysis tools
Advance to
Yield Trials
(PYT & AYT)
DataforRe-tratrainingofGSmodel
Jun
Jan
Sep Apr
One year Genome Selection Breeding Cycle
Genomic selection shortens cassava
breeding cycle duration from 5 to 1-2 years
Conventional phenotype-
based selection (> 5 years)

15. Assessment of genetic gains from
Genomic Selection
Resistance
to CMD
Dry matter
content
Number of
roots/plot
Root
weight/plot
●
●
●
●
●●
●●
●
●
●
●
●●
●
● ●●●●●
●
●●●●
C0 C1 C2
−0.72−0.6
●
C0 C1 C2
0.51.0
C0 C1 C2
46
●
C0 C1 C2
0.40.60.81.0
logFYLD
blups
●
●
●
●
C0 C1 C2
0.40.60.81.0
logRTNO
blups
●
C0 C1 C2
0.40.60.81.0
logRTWT
blups
●
●
●
●
●●
●●
●
●
●
●
●
●
●
●●
●
●●●
●
●●
●
●●
●
● ●●●●●
●
●●●●
C0 C1 C2
−0.72−0.68−0.64−0.60
MCMDS
blups
●
●
●
●
●
●
C0 C1 C2
0.51.01.52.02.5
TC
blups
●
●
●
C0 C1 C2
46810
DM
blups
●
C0 C1 C2
0.40.60.81.0
logFYLD
blups
●
●
●
●
C0 C1 C2
0.40.60.81.0
logRTNO
blups
●
C0 C1 C2
0.40.60.81.0
logRTWT
blups
●●●●●
●
●●●●
1 C2
MDS
●
●
●
●
●
●
C0 C1 C2
0.51.01.52.02.5
TC
blups
●
●
●
C0 C1 C2
46810
DM
blups
1 C2
YLD
●
●
●
●
C0 C1 C2
0.40.60.81.0
logRTNO
blups
●
C0 C1 C2
0.40.60.81.0
logRTWT
blups
MCMDS DM RTNO RTWT
Gene$c variance
• Boxplots showing the distribution average genotype effect of three two successive
cycles of genomic selection (C1 and C2) and founding population (C0).
• Note the decrease in CMD, increase in dry matter content, root number and root
weight after two cycles of Genomic Selection.
• We expect to release varieties with excellent resistance, high yield and starch
content developed in Phase I of NextGen project in the next few years.

16. Other Success Stories
• Banana Xanthomonas wilt
• Cassava Brown Streak disease
• Maize Striga tolerance
• Aflatoxin resistance in maize
• Variety adoption tracking
• Quality control for seed companies
• Biocontrol for cassava mealy bug
– Fall Army Worm control in progress
Adoption and Deployment:
v Scaling up, Scaling out

17. Summary and Opportunities
• Sufficient Genomic resources are available for all of our crops
• Cost-effective genotyping services
• Repertoire of tools and methods
• Data management and analysis
• Potential for collaboration/partnership – Synergy
• Adoption and integration of biotech requires action in terms
of conducive policy environment, and sustainable investment
into research and education
– Infrastructure – power, internet
– Procurement – lab supplies, reagents, equipment
– Repair and maintenance