Presented by Abel Teshome (PhD) at the ILRI Forage Diversity Seminar Series, Addis Ababa, 2 December 2021

1. Better lives through livestock
Whole genome sequencing of Napier grass (C. purpureus)
accessions from sub-Saharan Africa
Abel Teshome (PhD), FLAIR research fellow
Feeds and Forages Program, ILRI
December 2021
Addis Ababa

2. 2
Small-scale dairy
farmer milk yield/day
Diary farms in Ethiopia
2% of the total cattle population
Dairy farms in EU/UK Carcass average/head
Ethiopia:110 kg
UK: 346 kg
Ethiopia boasts the largest livestock inventory in Africa. However, below-par productivity in the
dairy sector forces the country to spend hard currency on importing dairy products from abroad
30L
15L
5L

3. 3
Improving livestock productivity requires:
• Access to feed/ fodder/ grazing resources (improved)
• Access to water
• Breeding stock
• Animal health services
• Access to markets
• Access to finance (incl. insurance)
• Access to knowledge
• Security
Health &
management
Breed
Forage &
feed

4. 4
Macro
economy
Peace &
security
Climate
change
Food
Security
Need to create year-round fodder resources
Challenges of livestock farmers in the tropics

5. 5
Why Napier grass?
• High yield per unit area,
• Perennial,
• Can withstand repeated cuttings,
• Energy crop,
• Tolerance to intermittent drought,
• Cut and carry feeding,
• Pests & disease tolerant,
• Salt tolerance,
• Soil and water conservation, &
• Ease of propagation Kingdom: Plantae
Clade: Tracheophytes
Clade: Angiosperms
Clade: Monocots
Clade: Commelinids
Order: Poales
Family: Poaceae
Subfamily: Panicoideae
Genus: Cenchrus
Species: C. purpureum

6. 6
• Morphological traits
• Height
• No of tillers per plant
• Leaf hairiness
• Agronomic traits
• Number of plants surviving
• Plant vigour
• Yield traits
• Total fresh weight (g)
• Leaf dry matter (g)
• stem dry matter (g)
• Leaf/Stem ratio
Drought tolerance trial established in Bishoftu, Ethiopia (2017 - 2021)

7. 7
• MINING GENEBANKS: Quantify genome variation in a large and
representative sample of accessions from Africa (500)
• POPULATION ANALYSIS: Identify features that make specific geographic or
genetic subsets particularly well suited for forward genetics, field experiments
and select
• ASSOCIATION MAPPING: GWAS analysis for traits of interest such as water
use efficiency, nutritional quality, anthocyanin content, and dry matter yield etc.
• FUNCTIONAL GENETICS: Conduct comparative genomic analysis through
the use of genes that are known to contribute to the regulation of traits of interest
in Arabidopsis and identify their homologues in C. purpureus.
Research objectives

8. 8
0.0e+00
5.0e+07
1.0e+08
1.5e+08
2.0e+08
A01 B01 A02 B02 A03 B03 A04 B04 A05 B05 A06 B06 A07 B07
Chromosome
Position
chr
A01
B01
A02
B02
A03
B03
A04
B04
A05
B05
A06
B06
A07
B07
More than a million polymorphic and bi-allelic SNPs identified from 350
Napier grass accessions

9. 9
0.00
0.25
0.50
0.75
1.00
G
1
_
G
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A
N
T
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1
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1
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9
2
5
6
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C
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2
6
6
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9
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C
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3
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1
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C
N
P
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3
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1
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C
N
9
3
0
4
2
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3
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C
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3
0
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3
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8
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C
N
9
3
1
8
2
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C
N
P
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_
C
N
9
3
1
8
2
C
N
9
3
3
2
2
_
C
N
P
G
L
_
C
N
9
3
3
2
2
C
N
9
3
3
7
5
_
C
N
P
G
L
_
C
N
9
3
3
7
5
C
N
9
4
0
7
2
_
C
N
P
G
L
_
C
N
9
4
0
7
2
C
N
9
4
1
3
1
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C
N
P
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_
C
N
9
4
1
3
1
C
N
9
6
2
3
1
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C
N
P
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L
_
C
N
9
6
2
3
1
C
N
9
6
2
4
1
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C
N
P
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_
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N
9
6
2
4
1
C
N
9
6
2
7
3
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C
N
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N
9
6
2
7
3
I
L
1
4
3
5
5
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I
L
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1
4
3
5
5
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1
4
9
8
4
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L
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1
4
9
8
4
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1
5
3
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1
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3
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1
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1
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1
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1
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1
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1
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1
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0
6
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I
L
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1
6
8
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6
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L
1
6
8
1
1
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I
L
R
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L
1
6
8
1
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L
1
6
8
1
9
_
I
L
R
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_
I
L
1
6
8
1
9
I
L
1
6
8
3
6
_
I
L
R
I
_
I
L
1
6
8
3
6
I
L
1
6
8
3
7
_
I
L
R
I
_
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L
1
6
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3
7
I
L
1
6
8
4
0
_
I
L
R
I
_
I
L
1
6
8
4
0
P
I
O
N
_
P
I
O
N
_
P
I
O
N
nssp
Genotype
key
Q1
Q2
STRUCTURE analysis with 105 ILRI and 3 USA origin accessions

10. 10
−20 −15 −10 −5 0 5 10 15
−15
−10
−5
0
5
10
PC 1
PC
2
EMBRAPA
ILRI
USA
−15 −10 −5 0 5 10
−15
−10
−5
0
5
10
PC 1
PC
2
BAGCE
CNPGL
GIANT
ILRI
PION
USA
PCA for 108 accessions PCA for 107 accessions
PCA for two sets of sequenced accessions

11. 11
6.0E-4
IL16800
C
N
9
4
1
3
1
IL16802
I
L
1
6
7
9
6
IL16788
C
N
9
2
7
9
2
C
N
9
2
1
9
8
7
IL1026
IL16835
IL16902
IL16817
I
L
1
6
8
4
0
I
L
1
6
7
9
9
BA63
IL18438
C
N
96231
CN93182
B
A
9
0
CN9266
3
BA81
IL16804
IL14983
PION
C
N
9
1
1
1
2
CN
93
04
2
CN93081
B
A
2
4
C
N
9
2
3
8
2
IL14389
C
N
9
4
0
7
2
IL1
678
6
I
L
1
5
3
5
7
IL
1
6
7
8
4
C
N
9
3
3
2
2
I
L
1
6
8
1
1
IL
1
6
8
0
9
BA100
B
A
2
5
B
A
1
IL1
680
7
N
1
9 IL16794
IL16791
C
N
9
1
2
5
1
IL18662
IL14982
BA17
N
3
6
IL
1
6
8
2
1
BA80
BA97 C
N
9
2
1
9
0
1
IL16819
I
L
1
6
8
1
8
B
A
1
6
G1
CN96273
I
L
1
6
8
3
4
I
L
1
6
8
3
7
I
L
1
6
7
8
7
CN93011
CN96241
I
L
1
6
8
1
0
IL
1
6
8
1
6
I
L
1
6
7
8
5
CN
91
06
2
IL16813
IL16783
IL16792
CN93061
C
N
9
2
5
6
2
N
2
2
8
IL
16
79
3
IL16812
IL14984
IL16801
CN0011
IL
16
80
5
IL16789
IL16790
I
L
1
6
8
0
6
B
A
7
5
IL
1
6
7
9
7
BA343
IL16782
IL16798
I
L
1
4
3
5
5
B
A
3
0
BA7
IL16795
IL18448
IL1
681
5
Add
IL15743
I
L
1
6
8
2
2
BA86
C
N
9
3
3
7
5
IL
1
6
8
0
8
IL16839
CN921333
CN96211
B
A
5
6
B
A
5
3
BA94
IL1
683
6
I
L
1
6
8
3
8
BA22
IL16803
UPGMA clustering of 108 Napier grass accessions (ILRI and USA)

12. 12
Association mapping for agronomic traits
4 of 17 TIBBS CORTES ET AL.
The Plant Genome
F I G U R E 1 Genome-wide association study methods for improving computational speed and statistical power. Different methods are grouped by
general strategy, and the position of each method shows the general trend of improved statistical power (shown on the x axis) and computational speed
(shown on the y axis). CMLM, compressed mixed linear model; ECMLM, enriched compressed mixed linear model; EMMA, efficient mixed-model
Cortes et al 2021
rMVP (R )
GAPIT (R )
Tassel (GUI)

13. 13
Correlation between six agronomic traits
Traits Acronym
Plant height PH
Leaf length LL
Leaf width LW
Tiller number TN
Total fresh weight TFW
Total dry weight TDW

14. 14
Mean plant height for all accessions under under two seasons

15. 15
SNPs significantly associated (FarmCPU method) with
plant height (PH) under dry seasons

16. 16
SNPs significantly associated (FarmCPU method) with
plant height (PH) under wet seasons
0 1 2 3 4
0
2
4
6
8
1
0
FarmCPU.PH_wet_sws_sqrt
Expected −log10(p)
O
b
s
e
r
v
e
d
−
l
o
g
1
0
(
p
)
0 1 2 3 4
0
2
4
6
8
10
FarmCPU.PH_wet_MWS_sqrt
Expected −log10(p)
Observed
−
log
10
(
p
)

17. 17
Mean total fresh weight for all accessions under two seasons

18. 18
0 1 2 3 4
0
1
2
3
4
5
FarmCPU.TFW_dry_MWS_sqrt
Expected −log10(p)
Observed
−
log
10
(
p
)
0 1 2 3 4
0
1
2
3
4
FarmCPU.TFW_dry_SWS_sqrt
Expected −log10(p)
Observed
−
log
10
(
p
)
SNPs significantly associated (FarmCPU method) with
total fresh weight (TFW) under dry seasons

19. 19
0 1 2 3 4
0
2
4
6
FarmCPU.TFW_wet_MWS_sqrt
Expected −log10(p)
Observed
−
log
10
(
p
)
0 1 2 3 4
0
2
4
6
8
10
FarmCPU.TFW_wet_sws_sqrt
Expected −log10(p)
Observed
−
log
10
(
p
)
SNPs significantly associated (FarmCPU method) with
total fresh weight (TFW) under wet seasons

20. 20
4 of 17 TIBBS CORTES ET AL.
The Plant Genome
F I G U R E 1 Genome-wide association study methods for improving computational speed and statistical power. Different methods are grouped by
general strategy, and the position of each method shows the general trend of improved statistical power (shown on the x axis) and computational speed
(shown on the y axis). CMLM, compressed mixed linear model; ECMLM, enriched compressed mixed linear model; EMMA, efficient mixed-model
association; EMMAX, efficient mixed-model association expedited; FarmCPU, fixed and random model circulating probability unification; FaST-
Cortes et al 2021
rMVP (R )
GAPIT (R )
Tassel (GUI)
Association mapping for nutritional traits

21. 21
Traits Acronym
Acid detergent fibre ADF
Acid detergent lignin ADL
neutral detergent fiber NDF
crude protein CP
Organic matter OM
in vitro organic matter digestibility IVOMD
Metabolizable energy ME
Correlation among seven nutritional traits

22. 22
0 1 2 3 4
0
1
2
3
4
5
FarmCPU.CP_dry_MWS_sqrt
Expected −log10(p)
Observed
−
log
10
(
p
)
0 1 2 3 4
0
1
2
3
4
5
FarmCPU.CP_dry_SWS_sqrt
Expected −log10(p)
Observed
−
log
10
(
p
)
SNPs significantly associated (FarmCPU method) with
crude protein (CP) under dry seasons

23. 23
0 1 2 3 4
0
1
2
3
4
FarmCPU.CP_wet_MWS_sqrt
Expected −log10(p)
Observed
−
log
10
(
p
)
0 1 2 3 4
0
1
2
3
4
FarmCPU.CP_wet_sws_sqrt
Expected −log10(p)
Observed
−
log
10
(
p
)
SNPs significantly associated (FarmCPU method) with
crude protein (CP) under wet seasons

24. 24
Upcoming project activities
Repeat SNP calling with more than 450 bam files
• There are additional accessions currently being squenced
Develop molecular markers (SNPs/SSRs) for all Napier grass
genebank accessions
Paternal analysis for progenies
Plan crosses based on the field trial results and genomic tools
developed in this project
• Crosses between hetrotic groups
Comparative genomics
• Correlate identified SNPs with genes in other
forage/food species such as pearl millet
Publish the Linux/R scripts in CGspace
Manuscript development

25. 25