1. International Center for Tropical Agriculture
Advances in breeding and genetics to improve carotenoids content in cassava roots.
International Center for Tropical Agriculture
(CIAT). Cali, Colombia

2. Why Cassava
A basic staple where both poverty and malnutrition are widespread
70 million people rely on cassava for basic sustenance.
Cassava grows in poor marginal soils, where other crops will fail. 2

3. Why CIAT
Located in the center of diversity for the crop: access to in situ germplasm.
Holds the largest ex-situ cassava germplasm collection >6,000 accessions.
Well-established cassava breeding and
genetics programs, and supporting lab
facilities. 3

4.  Sampling and processing protocols Pre-selection based on NIRS
Genetics of carotenoids
Genetic progress
Next steps: the pathway to impact

5. Some key background work on protocols:
Two challenges of root sampling and processing procedures for carotenoids:
Variation among roots from the same plant and/or roots from different plants within the same genotype (Ortiz et al., 2011).
b) In some cases a drastic variation in levels of pigmentation within a root (following slides).
This section describes solutions that have been implemented to overcome some of the problems listed above, and alternative approaches to quantify carotenoids in fresh roots of cassava.

6. Variation of levels of pigmentation within the root

7. Variation of levels of pigmentation within the root
Source: Peter Kulakow (IITA)

8. Addressing the issue of root to root variation
Former standard procedure: only one root per plant was taken
Beginning in 2011: three roots per plant used in carotenoids quantification.
Uniform homogenized subsamples taken as follows:
Two capsules for Near Infrared Spectroscopy (NIRs) screening
Two samples (≈ 80g and 30g) for dry matter content estimation
One sample (≈ 100g) for chromameter reading
One sample (5g) for carotenoid extraction and quantification in
spectrophotometer and HPLC

9. Food processor used to grind the roots rather than chopping them

10. Examples of the texture in ground root samples

11. Minolta Chromameter CR 410
Root sample ground, not chopped

12.  Sampling and processing protocols
Analysis & pre-selection based on NIRS
Genetics of carotenoids
Genetic progress
Next steps: the pathway to impact 

13. A B C Phytoene = 13.69 ug/g DM GM3736-54 Phytofluene= 7.44 ug/g DM12
1 = Violaxanthin (5.5)
2 = Antheraxanthin? (7.0)
3 = Unknown (7.2)
4 = Unknown (7.4)
5 = Lutein (8.4)
6 = Phytoene (12.6)
7 = Phytofluene (13.5)
8 = β-cryptoxanthin? (13.8)
9 = Unknown (14.6)
10 = 15-cis-β-carotene? (15.2)
11 = 13-cis-β-carotene (15.7)
12 = All-trans- β-carotene (17.8)
13 = 9-cis-β-carotene (18.6)
Phytoene = ug/g DM GM 6 11
λ:286 nm 5 13 B 7 12
Phytofluene= 7.44 ug/g DM 11 5 13
λ:348 nm C 12
All-trans-beta carotenes= ug/g DM
λ:450 nm 1 2 4 9 8 11 13 3 5 10 2 4 6 8 10 12
Minutes 14 16 18 20 22 24

14. A B C GM3739-13 Phytoene = 2.15 ug/g DM Phytofluene= 1.39 ug/g DM
1 = Violaxanthin (5.5)
2 = Antheraxanthin? (7.0)
3 = Unknown (7.2)
4 = Unknown (7.4)
5 = Lutein (8.4)
6 = Phytoene (12.6)
7 = Phytofluene (13.5)
8 = β-cryptoxanthin? (13.8)
9 = Unknown (14.6)
10 = 15-cis-β-carotene? (15.2)
11 = 13-cis-β-carotene (15.7)
12 = All-trans- β-carotene (17.8)
13 = 9-cis-β-carotene (18.6) 12 GM
Phytoene = 2.15 ug/g DM 6 11 13 5
λ:286 nm B 11 12
Phytofluene= ug/g DM 7 5 13
λ:348 nm C 12
All-trans-BC= ug/g DM 2 11 1 13 4 5 8 10
λ:450 nm 3 9 2 4 6 8 10 12
Minutes 14 16 18 20 22 24

15. Wet chemistry descriptive statistics (2009-2012 data; N=3419)
Missing values
Min
Max
Mean
Dry matter content (%) 36
9.8 52 34
HCN Total(ppm)
2621
23.3
3927
792
Total carotenoids (ug/g fresh weight)
By spectrophotometer 24
0.24 25 10
By HPLC
271
0.11 26
β-carotene (ug/g FW)
270
0.0 17
6.0
Anteroxanthins (ug/g FW)
279
3.0
0.3
Violaxanthins (ug/g FW)
707
1.6
Luteins (ug/g FW)
277
3.9
0.4
β-Criptoxantihns (ug/g FW)
854
1.1
0.1
15-cis-β carotene (ug/g FW)
705
1.5
13-cis-β carotene (ug/g FW)
3.4
0.9
9-cis- βcarotene (ug/g FW)
Phytoen(ug/g FW)
986
18.6
3.6
Phytofluen(ug/g FW)
1564
8.8
1.8
Parameters from Minolta Chromameter l*
1985 53
104 73 a*
-3.8 b* 44
127 97

16. Calibration curves (2009-2011)
Dry Matter
R2: 0.947
SECV: 1.604
R2 SECV: 0.937
RPD: 4.0
Total HCN
R2: 0.812
SECV: 309.0
R2 SECV: 0.766
RPD: 2.1

17. Total Carotenoids Colorimetry
SECV: 1.191
R2 SECV: 0.906
RPD: 3.3
Total Carotenoids HPLC
R2: 0.897
SECV: 1.500
R2 SECV: 0.887
RPD: 3.0

18. Beta Carotene HPLC Provitamin A R2: 0.928 SECV: 0.837 R2 SECV: 0.922
RPD: 3.6
Provitamin A
R2: 0.910
SECV: 1.099
R2 SECV: 0.901
RPD: 3.2

19. Scatter plot of Dry Matter Content NIRS predicted values (from equations developed using data) vs. laboratory values (from 2012 nursery)

20. Scatter plot of total carotenoids NIRS predicted values vs
Scatter plot of total carotenoids NIRS predicted values vs. laboratory values (HPLC and spectrophotometer).

21. Scatter plot of β-carotene NIRS predicted values (based on equations developed from data) vs. laboratory values (from 2012 nursery)

22.  Sampling and processing protocols
Analysis & pre-selection based on NIRS
Genetics of carotenoids
Genetic progress
Next steps: the pathway to impact 

23. The Molecular Genetics Approach -- Requires:
Understanding of the factors affecting high β-carotene accumulation.
Understanding of the β-carotene biosynthetic pathway.
A good segregating mapping population (F1 vs. Fn).
Large number of molecular markers (e.g. SNPs)
Good field design.
A high-fidelity phenotyping system. 23

24. Carotene biosynthetic pathway
isopentenyl diphosphate 24

25. Segregation for beta carotene and its building blocks in an F2 population25

26. Carotenoid distribution in GM373X
Potential of intermediate products that are being retained in the pathway (not converted to beta carotene)
Carotenoid Potential
Carotenoid Gained 26

27. Carotenoid distribution in GM373X
Potential contributions of carotenoids not being converted to pro-vitamin A (degraded)
Carotenoid Potential
Carotenoid Gained 27

28. Carotenoid distribution in GM373X
Carotenoid Potential
Carotenoid Gained
Unknown Carotenoids 28

29. Identification of unknown carotenoids29

30. Molecular Genetics Generate high density markers (SSRs and SNPs)
Build a consensus genetic map.
Conduct statistical association between trait values and the genotypes of marker loci
Evaluate changes at the DNA sequence level on genes involved in the carotenoid that may explain high β-carotene varietal improvement (MePSY2 SNP-AC)1.
1 Welsch, R. et al. Provitamin A Accumulation in Cassava (Manihot esculenta) Roots Driven by a Single Nucleotide Polymorphism in a Phytoene Synthase Gene. The Plant Cell Online 22, , doi: /tpc (2010). 30

31. Conclusions from genetics studies
Potential both to promote synthesis of beta carotene and stop the degradation
Fully characterizing beta carotene biosynthesis pathway to maximize genetic gains
Explored one mutation associated with the color trait. Yellow color determined by more than one gene.

32.  Sampling and processing protocols
Analysis & pre-selection based on NIRS
Genetics of carotenoids
Genetic progress
Next steps: the pathway to impact 

33. RAPID CYCLING RECURRENT SELECTION (3-year cycle)
Planting of a new crossing nursery
high-carotenoids
progenitors crossed
RAPID CYCLING RECURRENT SELECTION (3-year cycle)
Visual selection in the field. Pre-selection by NIRS Selection based on total carot. content and total beta carotene
Seed germinated, F1 plants evaluated at 11 MAP
Clonal evaluation trial
Preliminary yield trial
Advanced yield trial
Regional trial
TO ACID SOIL
SAVANNAS FOR
SED AND CBB
IN PALMIRA

34. Nutritional traits across years and plant ages
Dry Matter %
Total carot. (spectroph.)
Total carot. (HPLC)
Total β-carotene
Total carot. (DW basis)
Year of original nursery
2004 35
7.8
8.2
5.3 25
2005 38
9.2
9.6
5.5 26
2006 36
7.5
7.3
4.7 21
2007 37
10.3
10.9
7.2 30
2008
10.4
6.2
2009A
11.8
12.4 32
2009B 39
13.0
13.8
8.6
Age of plants sampled (MAP) 8
9.5
10.0
6.5 9
11.1
7.1 29 10
10.6
10.7
6.7 28 11
10.1
10.5
5.8

35. Human consumption:
Results of evaluation nurseries for high-carotenoids roots 45
Evolution of dry matter content 40
Dry matter content (%) 35 30
Total carotenoids content (μg/g FW)
Evolution of total carotenoids content
Year 35

36. Regression for carotenes and carotenoids on year of selection
(Independent variable is year of original nursery)
DM Content
Total carot. (spectro.)
Total carot. (HPLC)
Total β-carotene
Total carot. (DW basis)
Across Locs
0.49
0.82
0.90
0.54
2.03
Uni. Nac.
0.13
0.72
0.94
0.64
2.47
CIAT
0.66
0.84
0.86
0.48
1.76
Results indicate that an indirect effect of selection for high-carotenoids content was an increase in DMC !!!

37. Clonal Evaluation Trial Results, 2011/12
(First step in the selection for agronomic performance; based
on single row plots with 6-8 plants, no replications)
Fresh root yield
Harvest Index
Dry matter content
Dry matter
yield
Plant type score
(t/ha)
(0-1)
(%)
(1-5)
Data from 46 selected genotypes
Max 64
0.74 42 24
4.0
Min 23
0.37 32 9
1.0
Mean 40
0.51 36 14
2.8
Data from 170 genotypes evaluated
5.0
2.2
0.12 21 1 26
0.43 35
3.3

38. Evaluation Trials, Palmira, 2012
Clonal Evaluation Trials
Planted May entries
Planted August entries
Preliminary Yield Trials
Planted August entries
Advanced Yield Trials
Planted August entries

39. (Materials in preliminary and advanced trials, Palmira)
Results of evaluations for disease resistance in the acid soil savannas
(Materials in preliminary and advanced trials, Palmira)
Average Minimum Maximum
Super Elongation Disease (SED) =
Cassava Bacterial Blight (CBB) =
Disease score, 1= Excellent; 5= Very poor

40.  Sampling and processing protocols
Analysis & pre-selection based on NIRS
Genetics of carotenoids
Genetic progress
Next steps: the pathway to impact 

41. Select high performance materials in multi-location yield trials
Tentative Partners: Corpoica (Colombia)
Prepare pathogen-free in-vitro materials for international shipment
CIAT GRU Plant Health Laboratory
Send selected clones to Haiti for recovery from in vitro culture and multiplication, and testing
Tentative partners: Catholic Relief Services
Processing trials and acceptability studies; studies on gender differentiation
Tentative partners: Catholic Relief Services; CRP 3.4 (Roots, Tubers and Bananas)

42. Recent publications:
Ortiz, D., T. Sánchez, N. Morante, H. Ceballos, H. Pachón, M.C. Duque, A.L. Chávez, and A.F. Escobar (2011). Sampling strategies for proper quantification of carotenoids content in cassava breeding. Journal of Plant Breeding and Crop Science 3(1):14-23.
Morillo-C., A. C., Y. Morillo-C., M. Fregene, H. Ramirez, A.L. Chávez, T. Sánchez, N. Morante and H. Ceballos-L. (2011). Diversidad genética y contenido de carotenos totales en accesiones del germoplasma de yuca (Manihot esculenta Crantz). Acta Agronómica 60(2):
Ceballos, H., J. Luna, A.F. Escobar, J.C. Pérez, D. Ortiz, T. Sánchez, H. Pachón and D. Dufour (2012). Spatial distribution of dry matter in yellow fleshed cassava roots and its influence on carotenoids retention upon boiling. Food Research International (45:52-59).
Morillo-C., Y., T. Sánchez, N. Morante, A.L. Chávez, A.C. Morillo-C., A. Bolaños, and H. Ceballos (2012). Estudio preliminar de herencia del contenido de carotenoides en raíces de poblaciones segregantes de yuca (Manihot esculenta Crantz). Acta Agronómica 61(3):

43. Contributors
CIAT’s breeding team (Hernan Ceballos, Fernando Calle and Nelson Morante)
CIAT’s starch quality lab (Dominique Dufour, Teresa Sanchez, Monica Pizarro)
CIAT’s nutritional lab (Darwin Ortiz, Moralba Dominguez)
CIAT’s cassava genetics lab (Luis Augusto Bececerra, Tatiana Ovalle, Adriana Alzate)
CIARAD collaboration (Fabricio Davrieux ) 43