M. Bonierbale, E. Mihovilovich, W. Amoros, J. Landeo and M. Orrillo
Sustaining and Projecting Genetic Diversity for Potatoes Adapted To Changing Environments
14th Australasian Plant Breeding and 11th SABRAO Conference, Cairns, Queensland, Australia, 10-14 August 2009
Research Program Genetic Gains (RPGG) Review Meeting 2021: From Discovery to ...ICRISAT
Chickpea (Cicer arietinum) is the second most widely grown legume crop after soybean, accounting for a substantial proportion of human dietary nitrogen intake and playing a crucial role in food security in developing countries. We report the∼ 738-Mb draft whole genome shotgun sequence of CDC Frontier, a kabuli chickpea variety, which contains an estimated 28,269 genes. Resequencing and analysis of 90 cultivated and wild genotypes from ten countries identifies targets of both breeding-associated genetic sweeps and breeding-associated balancing selection. Candidate genes for disease resistance and agronomic traits are highlighted, including traits that distinguish the two main market classes of cultivated chickpea—desi and kabuli.
Estimating ecosystem functional features from intra-specific trait dataTano Gutiérrez Cánovas
Biodiversity is a multi-facet concept that accounts for the whole variability of life on Earth. The study of biodiversity has been traditionally focused on the taxonomic components, such as diversity indexes. However, nowadays, the functional components have received more attention because they provide complementary information on the community-environment relationship related to evolution and ecosystem functioning. Recent methodological advances allowed for calculating functional components from multiple traits at community level. These approaches require functional data expressed in form of a unique mean value for each trait and taxon, which could produce an important loss of functional information. Here, we present a method for estimating functional components of biodiversity at both taxon and community levels within the same multidimensional space, using intraspecific fuzzy trait information. At taxon level, this method estimates the functional richness of each taxon (i.e. functional niche) and functional similarity between each pair of taxa (i.e. niche overlap). At community level, it estimates the functional richness, functional dispersion and functional redundancy. As an example of use, we show the functional response of aquatic communities to different habitat filters.
Differences in DNA occur within genes, the differences have the potential to affect the function of the
gene and hence the phenotype of the individual. Genetic markers which have been used a lot in the past
include blood groups and polymorphic enzymes. We have relatively few such markers, but this has been
overcome with the advent of new types of markers. However, most molecular markers are not associated
with a visible phenotype.
Research Program Genetic Gains (RPGG) Review Meeting 2021: From Discovery to ...ICRISAT
Chickpea (Cicer arietinum) is the second most widely grown legume crop after soybean, accounting for a substantial proportion of human dietary nitrogen intake and playing a crucial role in food security in developing countries. We report the∼ 738-Mb draft whole genome shotgun sequence of CDC Frontier, a kabuli chickpea variety, which contains an estimated 28,269 genes. Resequencing and analysis of 90 cultivated and wild genotypes from ten countries identifies targets of both breeding-associated genetic sweeps and breeding-associated balancing selection. Candidate genes for disease resistance and agronomic traits are highlighted, including traits that distinguish the two main market classes of cultivated chickpea—desi and kabuli.
Estimating ecosystem functional features from intra-specific trait dataTano Gutiérrez Cánovas
Biodiversity is a multi-facet concept that accounts for the whole variability of life on Earth. The study of biodiversity has been traditionally focused on the taxonomic components, such as diversity indexes. However, nowadays, the functional components have received more attention because they provide complementary information on the community-environment relationship related to evolution and ecosystem functioning. Recent methodological advances allowed for calculating functional components from multiple traits at community level. These approaches require functional data expressed in form of a unique mean value for each trait and taxon, which could produce an important loss of functional information. Here, we present a method for estimating functional components of biodiversity at both taxon and community levels within the same multidimensional space, using intraspecific fuzzy trait information. At taxon level, this method estimates the functional richness of each taxon (i.e. functional niche) and functional similarity between each pair of taxa (i.e. niche overlap). At community level, it estimates the functional richness, functional dispersion and functional redundancy. As an example of use, we show the functional response of aquatic communities to different habitat filters.
Differences in DNA occur within genes, the differences have the potential to affect the function of the
gene and hence the phenotype of the individual. Genetic markers which have been used a lot in the past
include blood groups and polymorphic enzymes. We have relatively few such markers, but this has been
overcome with the advent of new types of markers. However, most molecular markers are not associated
with a visible phenotype.
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Results show that farmers’ perceptions of varieties are quite complex. Farmers do not only value yield, but a broad spectrum of different characteristics. Yield becomes more important to farmers in the end-season, while plant development and stress resistance play a more important role in the mid-season. Drought resistance is crucial to all farmers in both seasons, which is also confirmed by a follow-up survey. All varieties get significantly better scores on-station than on- farm, which corresponds to yield data that was collected at harvest. The difference of generated yield between on-station and on-farm is higher in a year with little rainfall, matching the farmer evaluations. Overall, women seem to score more nuanced than men, who score quite similarly across the varieties. Tego WE1101 is the least liked variety by farmers, but it yielded most in the trials. DH02, the variety that is liked best on-station, had the lowest yield both on-station and on- farm. Overall, farmers’ scores do not reflect yield data well, indicating farmers’ complex perceptions of varieties beyond yields. Calculations show that farmers in Makueni County could increase their income by 70% with the adoption of the improved maize varieties from this trial.
The analysis shows the importance of participatory evaluations for understanding farmers’ perceptions. It is important for breeders to take farmers' perceptions into consideration when developing new varieties. This is crucial to increase the adoption rate of improved maize varieties and contribute to more food security and a better economic situation for smallholders in Kenya.
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Sustaining and projecting genetic diversity: Potatoes adapted to changing needs
1. Sustaining and projecting genetic
diversity: Potatoes adapted to
changing needs
Merideth Bonierbale
International Potato Center
2. Strengths of Potato for Food
Security
Staple source of minerals,
vitamins and high quality
protein
Source of cash from fresh
and processing markets
High harvest index and
high yield per unit time,
land and water
Versatile in cropping
systems
3. Production Dynamics in Developing
Countries
Potato area has grown
because:
Commercially-oriented
farmers are
responding to
increased demand
from growing markets
and urban centers.
Subsistence farmers
are responding to
ever-shrinking
farm sizes.
4. Key role in moderating the impacts of
global price crisis
Availability of a variety
and quantity of locally
produced foods
moderates impact of
global price rises
Root and tuber crops
are not completely
commercialized
providing options at
the household level
7. Breeding Goals
1. Develop durable
resistance to
predominant diseases
2. Reliable yields with less
water and under warmer
temperatures
3. Improve nutritional and
market traits
8. Assets: Raw and Improved Germplasm
Adapted to Tropical Agro-ecologies
LowlandLowland
tropics andtropics and
Virus ResistantVirus Resistant
(LTVR)(LTVR)
PopulationPopulation
0.0
0.2
0.4
0.6
0.8
1.0
1.2
3 5 7
Days after inoculation
Lesionradius(mm)
y = a+bx
b=LGR= 5.7
y = a+bx
b=LGR= 3.6
HighlandHighland
tropicstropics
Late BlightLate Blight
Resistant (B3)Resistant (B3)
PopulationPopulation
acl
Tbr
blb
dmsNeo
tbr
Tbrphu
dms
Adg
phu
Adg
Neo
tbr
9. Proximity to Diverse Topical EnvironmentsProximity to Diverse Topical Environments
LA MOLINA Cool lowland, 300 m; 11° S, winter & spring
NAZCA Hot arid lowland, 300 m, 14° S, spring-summer
TACNA Hot arid lowland, 200 m; 18° S, spring-summer
MAJES Warm arid lowland; 900 m; 16° S spring-summe
HUANCAYO Cool highland; 3300 m; 12° S
OXAPAMPA: warm, humid
highland; 2000 m; 11° S
SAN RAMON Hot,
humid high jungle;
900 m; 11° S sprong
15. EST sequence
GenBank ID
Clone ID and
request form
GenBank IDs for
best hits
Molecular Support for Characterizing
Resistance
III IV VI
IX
III
VII VIII XI XII
V
X
tbr Meyer et al., 1998
ber Ewing et al., 2000
tbr Leonards-Schippers et al., 1994
tbr, ktz, vrn, tar, stn Collins et al., 1999
tbr, chc, ktz, stn, vrn Oberhagemann et al., 1999
tbr Ghislain et al., 2000
phu Ghislain et al., 2000
blb Naess et al., 2000
mcd Sandbrink et al., 2000
16. Building a genetic map in a haploid
progeny from Population B3
303946.7 IVP35
4x 2x
301071.3 X 703308
Resistant haploid (2x) Susceptible 2x cv.
B3C1PH (n = 91)
F1 progeny of heterozygous
male & female parents
18. QTL untapped when major gene is
defeated
0
1
2
3
4
S032fr
S185
STMIR63
S224fr
S047frS006fr
S181
S121r
S224a
0
2
4
6
8
10
12
14
16
18
20
22
24
S084r
STMJK24
S078f
S231
S083r
S089
STMGQ39
S193S026
S007
S001f
S216r
Comas 2005
Comas 2006
Chr XII
Chr IX
1Rpi-vtn1
2Rpi-phu1
Rpi-moc1 1 Smilde et al. (2005)
2 Jliwka · et al, (2006)
3 Foster et al, (2009)
16%
90%
19. -27600
-27500
-27400
-27300
-27200
-27100
-27000
0 1 2 3 4 5 6
LnP(D)
B1C5
45%
B1C5
28% B1C0
Structure of Population B1 for association
mapping
74 bred clones from C5 + 30 founders, 61 SSR
Number of Subpopulations (K)
20. 500
1000
1500
2000
2500
3000
3500
4000
4500
5000
5500
B1C0 B1C0 B1C5-A B1C5-A B1C5-B B1C5-B
Population
AUDPC
Presence
Absence
B1C0 B1C5
STG-0006.174 33% 0%
STG-0006.170 19% 32%
STG-0006.179 48% 67%
Frequency
Marker alleles
Chromosome II
***
R2 =13%
SSR allele STG-0006.170 associated with resístanse
(reduced AUDPC
Marker-trait association in Population B1
21. Solanum
Piurana
5 spp. + 1
‘outgroup’
Species Series Ploidy(EBN)
sensu Spooner
S. cajamarquense
S. acroglosum
S. piurae
S. paucissectum
S. chiquidenum
S. chomatophilum
Species
Broadening
the base of
resistance
22. Identification of new resistance sources in
wild specie germplasm
Evaluation in 2 endemic locations over 2 years
Acc. SCL
5050
OCH 14187
OCH11640
OCHS15210
23. stn x cjm
phu x chq
Endemic late blight pressure
Oxapampa + Monobamba
Introgression to cultivated species
(2x-2x and 4x-24 crosses)
24. Lbr-40 Atzimba
Monserrate
Yungay
Chata Blanca
AUDPC
Assessment of fertility and resistance of
new 2x hybrids
Phu x chq
N’175
0
10
20
30
40
50
60
70
80
90
100
506047.1 506047.2 506047.3 506116.4
CIP Code
Percentage
Colored
Non-colored
2n pollen
2x bred clones x S. piurae
25. A B C D E F
A B C D E F
Confirming hybrisity of resistant
selections from inter-clade crosses and
embryo rescue cv Group Goniocalyx S. chiquidenum
34. Three sources of PLRV resistance identifiedThree sources of PLRV resistance identified
in Solanum tuberosum ssp andigenain Solanum tuberosum ssp andigena
collectioncollection
2.5
-2.5
-2.0
-1.5
-0.5
0.0
0.5
1.0
1.5
2.0
Titia
(
tbr
)A
lpha
(
tbr
)
A
tlantic
(
tbr
)
Granola
(
tbr
)Spunta
(
tbr
)
A
chirana
(
tbr
)
LO
P-868
(
adg
)
H
U
A
-332
(
adg
)
O
CH
-7643
(
adg
)
ZIM
-440
(
adg
)
H
JT
-5535(
adg
)
Exceptionally high
Combining Ability
High level of resistance in 3
Andigena clones
OCH7643
HUA332
LOP868
DW.84.1457(C)
VID11
PPPDGV94
AchiranaINTA
(C)
CUP-199
OCH-8225
UNSAM51
PPP1627
HJA1489
OCH11195
CCC4726
PPPSA2103
HMX1686
FlorBlanca(C)
Perricholi(C)
0
20
40
60
80
100
%infectedplants
25 aphids/plant
50 aphids/plant
HR R S
35. Major gene for high levels of resistance
confirmed and mapped on 2 homologs of Chr
V
Rladg
Rladg
Rladg
Velásquez et al . 2007
Tetraploid Map AFLP; SSR
36. Combining LTVR and B3 populations
Population
B3
Population
LTVR
acl
dem
blb
tbr
tbr
phuadg
Neo-
tbr
Neo-
tbr
demtbr
phu
tbr
adg
Late blight +
Virus Resistance?
Yield gain through
Heterosis ?
Adaptation
to lowland
tropics
Resistance
to viruses
Field resistance
to late blight
Adaptation to
highland tropics
39. Enhancing Adaptation to HotEnhancing Adaptation to Hot
EnvironmentsEnvironments
10
14
18
22
26
30
34
38
Oct Nov Dec Jan Feb Mar
(Midmore, 1992)
AirTemperatureºC
Max Day Tº
Max Night Tº
- Flexibility in cropping
systems
- Demographic &
climate change
40. Early generation selection for heat tolerance
Tuberization
pattern
I II III IV
B3*B3
TBR*B3 TBR*LTVR
LTVR*LTVR
LTVR-B3
SIn = BPn(3)+TSn(2)+TUn(2)+Spn+Knn+Chn+Crn
Normal
tuberization
Physiological
disorders
Warm temperature screening
41. 10
14
18
22
26
30
34
DEC JAN FEB MAR
TemperatureºC
No adapted material Adapted material
Shorter cycle tuber production under
warmer conditions
43. Micronutrients
Vit “C” & “B” complex
Minerals
Fiber
Macronutrients
Water
Carbohydrates
Proteín
Secondary Metabolites
Attraction / Protection
Plant Responses to Environment
Functional Properties in the Diet
Carotenoids PhenolicCompounds
44. Leading cause of maternal
mortality
Impairs mental development
and learning
Limits capacity to perform
physical labor
Stunting
Susceptibility to infections
Iron and Zinc Deficiencies
Implications for human health & development
47. Cromatogram of a variety with high total carotenoids
Cromatogram of a variety with high ß-carotene n
Carotenoid Profile of Native Potatoess
AU
0.00
0.05
0.10
Minutes
0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00 55.00 60.00
ZeaxanthinZeaxanthinAnteraxanthinAnteraxanthin
703566
AU
0.000
0.010
0.020
Minutes
0.00 5.00 10.00 15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00 55.00 60.00
ßß--carotenecaroteneLuteinLuteinViolaxanthinViolaxanthin
705800