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Supervisors:
Dr. Tobias O. Okeno (Dr. rer. agr.)
Dr. Kiplangat Ngeno, PhD
Richard Habimana
KD11/13018/17
Department of Animal Science
GENETIC DIVERSITY, GROWTH
PERFORMANCE, DISEASE RESISTANCE
AND RESPONSE TO SELECTION OF
INDIGENOUS CHICKEN IN RWANDA
Introduction
 Small (26,338 Km2)
 Landlocked (Burundi, DRC, Uganda, Tanzania)
 Densely populated (525 inhab/Km2)
 Population growth : over 2.6%
 Rural country : rural population (82.4%)
1
4/27/2021 2
Minimal
arable land
(56%)
Few natural
resources
Minimal
industry
Subsistence
agriculture
(90%)
Poor population :
<1000$/annum
Access to nutritious
food : 51%
2
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Rwanda
++++++ Livestock production
(cattle, Pig, small ruminant, Poultry,..)
Food security Poverty
Socio-economic development
Improved Livestock
Production
3
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Economy
Income
Protein
Poultry (>40% of households): Indigenous chicken(IC) (80%)
Poverty
Socio-
economic
importance
Comparative
advantage
Cost of production
Adaptability to harsh E.
Scavenging ability
4
4/27/2021 5
Problem statement
IC in Rwanda
productivity
(egg and meat)
Rural livelihood
( improved nutrition, income
generation and job creation)
Unsustainable
Unreliable supply and high cost (acquiring&
maintenaning of exotic cocks)
Reduction of broodiness in crossbred birds
Resultant genotype could not survive  EPS
Crossbreeding
programs
(IC X exotic)
Genetic erosion and dilution
Genetic improvement
IC in Rwanda
5
A holistic approach that
increases IC productivity
without loss of
biodiversity not available
A need for an alternative
approach for genetic
improvement and
conservation of IC in
Rwanda
6
Within-IC breed
selection could be
an option
Within-IC breed
selection
Conservation
Knowledge on IC GnRs
Performance
Genetic improvement
7
4/27/2021 8
8
Information
on IC GnRs
Not available
Overall objective
To contribute to the genetic improvement and
conservation of indigenous chicken in
Rwanda by evaluating their genetic diversity,
productive and functional performances and
response to selection
9
Specific objectives
i. To determine the morphological characteristics of IC ecotypes in
Rwanda
ii. To evaluate the genetic diversity and population structure of IC in
Rwanda
iii. To evaluate the growth performance and disease resistance to ND of
IC in Rwanda
iv. To identify genomic regions associated with growth performance and
disease resistance to ND traits of IC in Rwanda
v. To determine response to selection of IC in Rwanda
10
Research Questions
11
i. What are morphological characteristics of IC in Rwanda ?
ii. What are the genetic diversity and population structure of IC in
Rwanda?
iii. What are the growth performance and disease resistance to ND of IC in
Rwanda?
iv. What are the genomic regions associated with growth performance
and disease resistance to ND traits of IC in Rwanda?
v. What is the response to selection of IC in Rwanda?
4/27/2021 12
Justification
12
 Genetic diversity and population structure
 Provide the genetic variation within and among populations  guide selection decision
 Design the appropriate mating strategies
 Maintain genetic variation and reduce inbreeding within the population  increase the response to selection
 Production and functional traits
 Basis for choosing a suitable IC gene pool for any particular production system
 Genomic regions associated with the production and functional traits
 Manipulation of the genome
 Marker assisted selection (MAS) : indirect selection
 Genetic improvement of IC  increase productivity of IC  food security
and poverty alleviation improve rural livelihood
 Conservation of IC GnRs  Maintain IC unique attributes which are preferred
by producers and consumers
4/27/2021 13
13
MORPHOLOGICAL CHARACTERISTICS OF IC
ECOTYPES IN RWANDA
Materials & Methods
 Study area: 5 agro-ecological zones (E, NW, SW, CN, CS) in Rwanda
14
 Exploratory field survey; structured questionnaire, observation and
body measurements on 1670 mature IC  FAO (2011) guidelines
 8 Qualitative traits (morphology, distribution and colour of feathers, shank colour,
comb type, head shape, ear lobe colour and shank feather)
 13 Quantitative traits (body weight, body length, tarsus length, chest
circumference, shank length, head length, comb length and height, wattle length, beak
length, neck length, wing length, and wingspan)
 Data analysis
 Descriptive statistics: Cross-tabulation and frequency procedures
 Statistical tests:
 Qualitative traits: Non parametric test (Kruskal–Wallis and
Mann–Whitney U)
 Quantitative variables: ANOVA
15
Data collection
 Fixed-effects of ecotype, sex and interaction between sex and ecotype
model:
 where,
 Yij: Body weight and linear body measurement of the chicken,
 µ: Overall mean,
 Ai: Fixed effect of ith ecotype,
 Dj: Effect of jth sex ( male or female),
eij: Random residual error.
16
𝐘𝐢𝐣 = 𝛍 + 𝐀𝐢 + 𝐃𝐣 + 𝐀𝐃 𝐢𝐣 + 𝐞𝐢𝐣
 Post hoc test: Tukey’s test
 Feather morphology, feather distribution and feather colour of
IC populations in Rwanda
17
Significantly different (p < 0.001) between ecotypes
 Feather morphology: normal feather morphology: 98.30%
 Feather distribution: normal feather distribution : 84.40%
 Feather colour: multi-coloured feathered : 38.10%
Results & Discussion
 Comb type and ear lobe colour of IC populations ecotypes in Rwanda
18
 Significantly different (p < 0.001) between ecotypes
 Ear lobe: red ear lobe colour : 49.20%
 Comb type: single comb : 71.70%
 Shank colour of IC populations ecotypes in Rwanda
19
 Significantly different (p < 0.001) between ecotypes
 Yellow shank: 53.80%
 Biometrical characteristics of IC ecotype populations in Rwanda
20
 Significantly different (p < 0.001) between ecotypes
 body weight, body length, tarsus length, shank length, comb
length, comb height, wattle length, chest circumference, beak
length, head length, neck length, wing length and wingspan
 Sex-associated high differences (p < 0.001) in the most traits, with
high values recorded for male IC
 Ecotype by sex interaction was highly significant (p < 0.001)
 body weight, body length, shank length, comb length, comb
height, wattle length, chest circumference, neck length and
wingspan
 Take a home message
21
In Rwanda, IC ecotypes are diverse populations
with huge variation in both qualitative and
quantitative traits
22
GENETIC DIVERSITY AND POPULATION
STRUCTURE OF IC POPULATIONS IN RWANDA
DNA Extraction
DNA Quality and
quantity check
Genotyping
Sampling
Data Analysis
DNA
amplification(PCR)
23
Materials & Methods
 Blood samples collection and DNA extraction
 Blood from wing vein (on FTA cards)  325 birds
 DNA isolation: Boiling method
 DNA quantification (concentration and purity): Nanodrop
 DNA quality control: Gel electrophoresis
24
 DNA amplification (PCR): Applied Biosystems 9700 Thermal
Cycler Gene Amp®
 Microsatellite markers (28) recommended by FAO (2011)
 PCR reaction : 10 µl (30ng target DNA, 5uL of OneTaq® 2X
Master Mix with a standard buffer and 0.2uL of each forward
and reverse primer)
 PCR program:
25
94°C 3min
94°C 30sec
60°C 1min
72°C 2min
72°C 10min
15°C ∞
30 cycles
 Genotyping
 ABI PRISM 377 DNA Sequence: GeneScanTM-500 LIZ®
 Allele scoring : GeneMapper
 Data analysis
 Genetic diversity analysis (PowerMarker ,GeneAIEx ,Fstat, Popgene )
 Intrapopulation
 Interpopulation
 Genetic relationship (PowerMarker, Darwin, Fstat, Genepop)
 Genetic differentiation
 Genetic Distance
 Population structure: Model-based (Structure) gene pools
26
General diversity parameters per locus
(intra population)
LOCUS MAF GNo NA NE NPA He Ho PIC I
ADL0112 0.4985 27 16 2.7204 6 0.6324 0.5938 0.5717 1.318
ADL0268 0.2446 39 14 6.2408 3 0.8398 0.5815 0.8202 2.0217
ADL0278 0.3 39 12 5.3488 4 0.813 0.5477 0.7892 1.8845
LEI0094 0.3923 45 17 4.3604 3 0.7707 0.7138 0.7441 1.8665
LEI0192 0.3169 66 22 5.6987 4 0.8245 0.7754 0.8058 2.1488
LEI0234 0.1769 77 17 8.9015 2 0.8877 0.5692 0.8775 2.3925
MCW0014 0.5123 29 10 3.1071 1 0.6782 0.4862 0.6452 1.4928
MCW0016 0.3169 39 15 4.6988 4 0.7872 0.7723 0.7592 1.8406
MCW0020 0.3046 29 8 4.6607 0 0.7854 0.72 0.753 1.6755
MCW0034 0.3508 46 14 5.2106 5 0.8081 0.7754 0.7875 1.9272
MCW0037 0.5 1 2 2 0 0.5 1 0.375 0.6931
MCW0067 0.3954 31 11 3.5732 1 0.7201 0.68 0.6789 1.6218
MCW0069 0.3385 26 10 3.6706 0 0.7276 0.7385 0.6803 1.5025
MCW0078 0.7662 11 5 1.6504 0 0.3941 0.3692 0.3717 0.8202
MCW0081 0.4938 42 11 3.0007 1 0.6667 0.56 0.6219 1.4829
MCW0098 0.4646 27 9 2.571 1 0.611 0.5231 0.5348 1.1762
MCW0103 0.7077 9 6 1.7362 2 0.424 0.3754 0.3488 0.6929
MCW0104 0.4892 43 18 3.2705 4 0.6942 0.6492 0.6615 1.7013
MCW0111 0.5954 21 8 2.4404 0 0.5902 0.4831 0.55 1.2262
MCW0123 0.5231 38 14 3.1028 3 0.6777 0.64 0.6496 1.5676
MCW0165 0.6354 7 4 1.9239 0 0.4802 0.3015 0.3864 0.7554
MCW0183 0.2923 34 11 5.5162 3 0.8187 0.6585 0.7958 1.8734
MCW0206 0.3938 24 9 3.9919 2 0.7495 0.6985 0.7143 1.5832
MCW0222 0.4 11 6 2.9721 2 0.6635 0.6462 0.5996 1.2097
MCW0248 0.6785 6 4 1.8158 1 0.4493 0.4923 0.366 0.7126
MCW0284 0.3677 29 8 3.9004 0 0.7436 0.6892 0.7061 1.6202
MCW0295 0.4646 34 13 3.4817 3 0.7128 0.5785 0.6802 1.6324
MCW0330 0.3015 26 11 5.3764 5 0.814 0.6154 0.7899 1.8272
Mean 0.4365 30.5714
10.892
9 3.8194 60 0.688 0.6155 0.6451 1.5095
Total 305
Results & Discussion
27
NA : 305 , 2 to 22 with an overall mean of 10.89
NPA : 60 20% of the total alleles
I : 0.692- 2.392 (1.509)
He : 0.394-0.888 (0.688)
Ho :0.301-1.00 (0.616)
General diversity parameters per locus (intra population)
Loci Fis Fit Fst Nm HWE pV
ADL0112 0.097 0.128 0.034 7.006 0.000
ADL0268 0.176 0.306 0.158 1.332 0.000
ADL0278 0.252 0.283 0.041 5.869 0.000
LEI0094 0.017 0.034 0.017 14.344 0.000
LEI0192 -0.005 0.036 0.041 5.829 0.000
LEI0234 0.338 0.354 0.024 10.202 0.000
MCW0014 0.142 0.263 0.142 1.517 0.000
MCW0016 0.002 0.023 0.021 11.392 0.000
MCW0020 0.050 0.095 0.047 5.027 0.000
MCW0034 -0.003 0.032 0.035 6.965 0.191
MCW0037 -1.000 -1.000 0.000 0.000
MCW0067 0.038 0.137 0.103 2.181 0.000
MCW0069 -0.011 0.028 0.038 6.309 0.104
MCW0078 -0.006 0.006 0.011 21.491 0.015
MCW0081 0.126 0.156 0.034 7.140 0.000
MCW0098 0.105 0.170 0.072 3.212 0.000
MCW0103 0.131 0.160 0.033 7.343 0.000
MCW0104 0.066 0.096 0.033 7.385 0.000
MCW0111 0.110 0.141 0.035 6.800 0.000
MCW0123 0.015 0.031 0.016 15.002 0.000
MCW0165 0.325 0.341 0.024 10.050 0.000
MCW0183 0.119 0.189 0.080 2.885 0.000
MCW0206 -0.004 0.044 0.048 5.000 0.000
MCW0222 -0.030 0.023 0.051 4.641 0.000
MCW0248 -0.236 -0.185 0.041 5.864 0.344
MCW0284 0.050 0.117 0.070 3.321 0.000
MCW0295 0.131 0.214 0.096 2.341 0.000
MCW0330 0.147 0.281 0.157 1.339 0.000
Mean 0.041 0.089 0.054 6.060
28
Inbreeding coeff. : -1.00-0.338 (0.041)
Nm : 1.332-21.491(6.060)
10% of loci in HWE(p>0.5)
General diversity parameters per population
Populations N %PL NA PA Ho He F I
Central North 51 100 6.929 6 0.623 0.644 0.021 1.322
Central
South
55 100 7.286 15 0.598 0.661 0.077 1.372
Exotic
chicken
12 100 5.143 4 0.667 0.665 -0.019 1.305
East 102 100 8.250 21 0.611 0.654 0.056 1.358
North West 52 100 6.500 0 0.613 0.645 0.042 1.306
South West 53 100 7.964 14 0.626 0.680 0.063 1.458
Total 325 100 7.011 60 0.623 0.658 0.040 1.353
29
Pairwise Population Matrix of Nei Unbiased
Genetic Distance
North
West
Central
North
Central
South ControlEastern
South
West
North West 0.000
Central North 0.029 0.000
Central South 0.094 0.077 0.000
Control 0.199 0.213 0.231 0.000
Eastern 0.112 0.097 0.117 0.196 0.000
South West 0.104 0.092 0.048 0.118 0.125 0.000
30
Degree of gene differentiation in terms of
allele frequencies(FST)
Central
North
Central
South Control Eastern
North
West
South
West
Central North 0.000
Central South 0.022 0.000
Control 0.052 0.058 0.000
Eastern 0.025 0.027 0.050 0.000
North West 0.012 0.026 0.053 0.028 0.000
South West 0.026 0.014 0.036 0.028 0.027 0.000
31
Analysis of molecular Variance
Among
Populations
8%
Within
Populations
92%
32
Degree of gene differentiation in
terms of gene Flow (Nm)
Central
North
Central
South Control Eastern
North
West
South
West
Central North 0.000
Central South 2.304 0.000
Control 1.412 0.925 0.000
Eastern 2.051 1.471 3.432 0.000
North West 6.274 1.533 1.188 1.783 0.000
South West 2.040 3.847 2.791 1.560 1.471 0.000
33
Phylogenetic relationship
North West
Central North
South West
Central South
Control and
South West
Eastern
34
B
C
A
D
Population structure
The Evanno table output
Gene pool A : CN & NW
Gene pool D : Eastern
Gene pool C : CS & SW
Gene pool B : C & SW
35
 Take a home message
IC populations in Rwanda have a high degree of
substantial genetic variability and are clustered in four
gene pools
36
GROWTH PERFORMANCE AND NEWCASTLE
DISEASE ANTIBODY TITRES IN FOUR GENE
POOLS OF INDIGENOUS CHICKEN IN RWANDA
37
 Sampling
38
Gene pools Collected fertile eggs Incubated eggs Hatched eggs Brooded chicks
A 180 179 72 72
B 180 177 34 34
C 180 175 36 36
D 180 176 47 47
Total 720 707 189 189
Materials & Methods
 Phenotyping: Production trait & Functional trait
 Production trait
 Growth performance  Body weight from day 1 to week 20
 Functional trait
 Disease resistance (ND)  Antibody titers
 Vaccination schedule with a commercial NDV live vaccine:
 At 2 days of age : AVI ND HB1 in drinking water
 At 28 days of age : AVI ND Lasota by eye drop
 At 7 days after the second vaccination (35 days of age):
 Serum samples from the chicken blood
 Antibody response to NDV  Indirect ELISA
 IDSoft™ programme Computation of Ab titres
39
 Data analysis
ANOVA : Fixed-effects of ecotype, sex and interaction between sex and
ecotype model :
 where:
 Yijkl : Record of lth individual from ith gene poo with jth sex
 Μ : Overall mean;
 Gi : Fixed effect of ith gene pool;
 Sj : Fixed effect of kth sex;
 (GS)ij : Interaction between gene pool and sex;
 eijkl : random effect peculiar to each individual
 For the analysis of body weight
 At hatch: egg weight was fitted in the model as a covariate
 Other ages: body weight at hatch was fitted as a covariate
40
𝒀𝒊𝒋𝒌 = µ + 𝑮𝒊 + +𝑺𝒋 + 𝑮𝑺 𝒊𝒋 + 𝒆 𝒊𝒋𝒌
 Non-linear regression analysis of longitudinal growth data
 Logistic regression model using PROC NLIN of SAS 
growth curve parameters (A, b and k)
where:
 Yt : live weight at age t
 A : asymptotic or mature weight
 b : scaling parameter
 k : maturity index
 t : age in n weeks
 Plot least-square means of the body weight in all gene pools
against age  growth curve patterns
41
𝒚𝒕 = 𝑨(𝟏 − 𝒃 ൯
𝒆−𝒌𝒕 −𝟏
Mean body weight within and between IC gene pools in Rwanda
42
Results & Discussion
0
200
400
600
800
1000
1200
1400
1600
1800
0 5 10 15 20 25
Body
weight
Weeks
Gene pool A Gene pool B Gene pool C Gene pool D
a
b,c
c
b
Growth parameters of IC gene pools in Rwanda
43
Predicted growth curves for IC gene pools in Rwanda
Parameters Gene pools Overall P-
value
A B C D
A 1446.57±62.42a 1286.31±58.88ab 1086.38±66.64b 1350.13±69.16a 1309.46±59.82
0.000
b 15.41±5.19a 16.33±4.13a 13.65±3.93b 15.15±4.87a 15.02±4.41
k 0.35±0.05a 0.30±0.04a 0.29±0.04b 0.32±0.05a 0.321±0.04
R2 0.96 0.97 0.96 0.96 0.96
Least square means (±SE) of growth parameters of IC gene pools in Rwanda from logistic function
A : Asymptotic weights; b: scaling parameter; k: maturity index
0
200
400
600
800
1000
1200
1400
1600
0 5 10 15 20 25
Body
weight
(g
)
Age (weeks)
Gene pool A Gene pool B Gene pool C Gene pool D
SI
ExP
FS
Antibody titres for Newcastle disease of IC gene pools
44
a
a
c
b
Gp A Gp D
Gp C
Gp B
 Take a home message
45
Between the four IC gene pools, there is a significant
difference in body weight and antibody response to the
ND
 Gene pool A was the heaviest
 Gene pool C expressed the strongest immune response for ND
46
GENOMIC REGIONS FOR GROWTH
PERFORMANCE AND IMMUNE RESPONSE TO
NEWCASTLE DISEASE OF INDIGENOUS
CHICKEN IN RWANDA
Phenotyping
47
Materials & Methods
• AbR to ND after 7 days from the second immunization
• BW20
48
Blood Sampling
(Wing Vein)
Reads alignment
to Galgal6
(BWA)
 Removal of duplicate Reads
(Picard package)
 Trimming of raw reads
(Sickle)
Genotyping
(GBS)
DNA extraction
(Promega genomic DNA
extraction kit)
SNPs calling
(SAMtools)
SNPs Quality
control
(PLINK)
Genotype
imputation
CF > 95%
HWE (P<10-6)
MAF > 0.03
He > 0.4
Genotype
LD KNNi
65,945 SNPs
49
Genotype
(SNPs)
Phenotype
(Traits)
Indigenous
Chicken
Genotyping
Disease
resistance
Reads alignment
SNPs identification
GWAS
Body weight
Phenotyping
where:
 y: vector of quantitative traits
 𝛃 : vectors containing fixed effects (sex, gene pool, SNP) and covariates
 u: vector of random additive genetic effects of animal
 X and Z: design matrices
 e: vector of random residuals
MLM (Tassel v5.2.6)
y= 𝐗𝛃 + 𝐙𝐮 + 𝐞
DATA ANALYSIS
 FWER was controlled by using:
 A Bonferroni genome-wide significance threshold of 5%
 A suggestive genome-wide threshold of 10%
 Manhattan plots:
 A comprehensive view of P-values (-log10 p-values) for all SNP
markers of each trait  qqman package of R software
50
 ANNOTATION
 Significant SNPs: BioMart data mining tool  ww.ensembl.org/
biomart/ mart-view
 Genes ϵ [Significant SNPs ± 100kb]: Variant effect predictor tool
(www.ensembl.org/tools/VEP)
 List of gene in the vicinity of all significant SNPs
51
Significant Genomic regions associated with BW in IC
52
rs740980181
rs13792572
rs314702374
Rs14123335
Manhattan plot displaying significant and suggestive SNPs associated with IC body
weight (the red line designates a Bonferroni-adjusted genome-wide threshold [-log10 (p-value)
≥ 6.1] and the blue line indicates a suggestive genome-wide threshold [-log10 (p-value) ≥ 4.8)
Results & Discussion
(PBX1)
(GPATCH1)
(MPHOSPH6)
(MRM1)
Significant Genomic regions associated with ND resistance
53
rs314787954
rs13623466
rs13910430
rs737507850
Manhattan plot displaying significant and suggestive SNPs associated with IC body
weight (the red line designates a Bonferroni-adjusted genome-wide threshold [-log10 (p-value)
≥ 6.1] and the blue line indicates a suggestive genome-wide threshold [-log10 (p-value) ≥ 4.8)
(CDC16)
(ZBED1)
(MX1)
(GRAP2 )
 Take a home message
54
Genomic regions that are thought to regulate body
weight and antibody response to ND in IC in Rwanda are
now available
 Overlapped with those previously reported
 Uncovered for the first time in this study
54
RESPONSE TO SELECTION OF INDIGENOUS
CHICKEN IN RWANDA USING WITHIN-BREED
SELECTION STRATEGY
 Deterministic simulation (using SelAction software) was used to model two
different breeding schemes in a closed single-tier
 Conventional breeding scheme (CBS)
 Genomic breeding scheme (GBS)
 Breeding goal
 a dual-purpose IC for both eggs and meat production
 Breeding objective traits
 Egg number (EN)
 Egg weight (EW)
 Body weight (BW)
 Resistance to Newcastle disease (AbR)
 Input parameters  from specific objective 3 and from Meta-Analysis study
55
Materials & Methods
Breeding structure for the simulated IC breeding programme
57
Smallholder indigenous chicken farmers
Nucleus
1000 fertilised eggs
850 day old chicks
510 growers
200 pullets
2000 fertilised eggs
1700 day old chicks
40 cockerels
1020 growers
510 cockerels 510 pullets
85% hatchability
60% Survival rate
Selection on body weight at 16 weeks
10 eggs/hen/clutch
85% hatchability
60% Survival rate
50% Sex ratio
Selection pressure 4%
Selection pressure 20%
96% unselected cockerels 80% unselected pullets
Base pop.
Prediction of response to selection
 Response to selection was computed for all the traits in the breeding
objective (H):
𝑯 = 𝑨𝟏𝑽𝟏 + 𝑨𝟐𝑽𝟐 + ⋯
where
o 𝑨𝒊 : true breeding values
o 𝑽𝒊 ∶ weighting factors for each trait
58
where N : the number of parents, and E (r)2 , the square of the expected
contributions
Prediction of inbreeding rate
∆𝑭 = ൗ
𝟏
𝟐 𝑵𝑬(𝒓)𝟐
Optimum nucleus size
59
 Effect of the nucleus size and different mating ratios
 Rate of genetic gain and inbreeding
• Number of breeding parent flock in the base population
from 240 to 24,000 hens
• Mating ratio from 1:5 to 1:100
60
Scheme Response
(US$)
Rate of inbreeding
(%)
Accuracy of
index
CBS Nucleus 340.41 1.45 0.55
Commercial 301.17 1.91 0.47
GBS Nucleus 1,024.45 0.46 0.97
Commercial 1,024.44 0.46 0.97
Rates of genetic gain (US$), inbreeding (%) and accuracy of selection in the
conventional (CBS) and genomic (GBS) breeding schemes
Results & Discussion
61
Effect of nucleus size on rate of genetic gain and inbreeding
0.0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
240 960 1680 2400 3120 3840 4560
Change
in
inbreeding
rate
(%)
Change
in
genetic
gain
(US$)
Nucleus Size (with sex ratio 1: 5)
∆G-CBS ∆G-GBS ∆F-CBS ∆F-GBS
0
1
2
3
4
5
6
7
8
9
10
0
200
400
600
800
1,000
1,200
1,400
101 303 505 707 909
Nucleus size (with sex ratio 1: 100)
Change
in
inbreeding
rate
(%)
Change
ingenetic
gain
(USD)
∆G-GBS ∆G-CBS ∆F-GBS ∆F-CBS
62
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
0.450
0.500
200.00
400.00
600.00
800.00
1,000.00
1,200.00
1,400.00
5 15 25 35 45 55 65 75 85 95
Change
in
inbreeding
rate
(%)
Change
in
genetic
gain
(US$)
Number of hens per cock
∆G-GBS ∆G-CBS ∆F-CBS ∆F-GBS
Effect of mating ratio on rate of genetic gain and inbreeding
 Take a home message
63
Both CBS and GBS simulated in this study had a
considerable positive response to selection
 It is possible to improve IC in Rwanda for dual-purpose
(both meat and egg) through within-breed selection strategy
using either CBS or GBS
CONCLUSIONS
1. There is a huge morphological variation in both qualitative
and quantitative traits in IC ecotypes
2. There is a significant genetic variability in IC and are
clustered into four gene pools in Rwanda
3. There is a significant difference for growth performance and
antibody response to ND virus among four IC gene pools
4. Genomic regions putatively regulating body weight and
antibody response to ND in IC in Rwanda are now available
5. There is a possibility to improve IC through within-breed
selection strategy using either conventional (CBS) or
genomic (GBS) breeding schemes
64
4/27/2021
RECOMMENDATION
1. Morphological variation in both qualitative and quantitative traits of IC ecotypes
in Rwanda should be taken into account in breeding programme for genetic
improvement and conservation
2. IC gene pools in Rwanda should be considered in breeding programme for
genetic improvement and conservation
3. Gene pools A and C could be bred for growth and ND resistance
performances, respectively. In order to get chicken with a better growth
performance and higher AbR against ND, a crossbreeding programme could
take into consideration IC gene pools A and C
4. Existing genomic regions associated with BW and AbR could be utilised in the
development of marker-assisted and gene-based selection in IC selection for
BW and AbR
5. An IC breeding programme in Rwanda could be implemented using within-
breed strategy to generate genetic progress, but at the moment better to start
with CBS while developing infrastructure for implementation of GBS
65
4/27/2021 66
AREAS FOR FURTHER STUDIES
1. Identification of genomic regions controlling IC morphological
characteristics in Rwanda
2. Quantification of G x E among IC ecotypes in Rwanda
3. Evaluation of egg production performance of IC gene pools in
Rwanda
4. Validation of the role of the identified novel genomic regions for
BW and AbR in IC in Rwanda
5. Estimation of cost and profit for both CBS and GBS in IC in
Rwanda
66
4/27/2021 67
Publications
One scientific publications in conference
Three scientific publications in peer review
journals
67
4/27/2021 68
Acknowledgement
Supervisors
• Dr. Tobias O.Okeno (Dr. er. agr)
• Dr. Kiplangat Ngeno, PhD
68
4/27/2021
Dr. Claire d’ Andre HIRWA (RAB)
Dr Christian T. Keambou (CTLGH)
Dr Yao Nasser (ILRI)
IC Farmers
GENETIC DIVERSITY, GROWTH PERFORMANCE, DISEASE RESISTANCE AND RESPONSE TO SELECTION OF INDIGENOUS CHICKEN IN RWANDA

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GENETIC DIVERSITY, GROWTH PERFORMANCE, DISEASE RESISTANCE AND RESPONSE TO SELECTION OF INDIGENOUS CHICKEN IN RWANDA

  • 1. Supervisors: Dr. Tobias O. Okeno (Dr. rer. agr.) Dr. Kiplangat Ngeno, PhD Richard Habimana KD11/13018/17 Department of Animal Science GENETIC DIVERSITY, GROWTH PERFORMANCE, DISEASE RESISTANCE AND RESPONSE TO SELECTION OF INDIGENOUS CHICKEN IN RWANDA
  • 2. Introduction  Small (26,338 Km2)  Landlocked (Burundi, DRC, Uganda, Tanzania)  Densely populated (525 inhab/Km2)  Population growth : over 2.6%  Rural country : rural population (82.4%) 1 4/27/2021 2
  • 3. Minimal arable land (56%) Few natural resources Minimal industry Subsistence agriculture (90%) Poor population : <1000$/annum Access to nutritious food : 51% 2 4/27/2021 3
  • 4. Rwanda ++++++ Livestock production (cattle, Pig, small ruminant, Poultry,..) Food security Poverty Socio-economic development Improved Livestock Production 3 4/27/2021 4
  • 5. Economy Income Protein Poultry (>40% of households): Indigenous chicken(IC) (80%) Poverty Socio- economic importance Comparative advantage Cost of production Adaptability to harsh E. Scavenging ability 4 4/27/2021 5
  • 6. Problem statement IC in Rwanda productivity (egg and meat) Rural livelihood ( improved nutrition, income generation and job creation) Unsustainable Unreliable supply and high cost (acquiring& maintenaning of exotic cocks) Reduction of broodiness in crossbred birds Resultant genotype could not survive  EPS Crossbreeding programs (IC X exotic) Genetic erosion and dilution Genetic improvement IC in Rwanda 5
  • 7. A holistic approach that increases IC productivity without loss of biodiversity not available A need for an alternative approach for genetic improvement and conservation of IC in Rwanda 6 Within-IC breed selection could be an option
  • 8. Within-IC breed selection Conservation Knowledge on IC GnRs Performance Genetic improvement 7 4/27/2021 8
  • 10. Overall objective To contribute to the genetic improvement and conservation of indigenous chicken in Rwanda by evaluating their genetic diversity, productive and functional performances and response to selection 9
  • 11. Specific objectives i. To determine the morphological characteristics of IC ecotypes in Rwanda ii. To evaluate the genetic diversity and population structure of IC in Rwanda iii. To evaluate the growth performance and disease resistance to ND of IC in Rwanda iv. To identify genomic regions associated with growth performance and disease resistance to ND traits of IC in Rwanda v. To determine response to selection of IC in Rwanda 10
  • 12. Research Questions 11 i. What are morphological characteristics of IC in Rwanda ? ii. What are the genetic diversity and population structure of IC in Rwanda? iii. What are the growth performance and disease resistance to ND of IC in Rwanda? iv. What are the genomic regions associated with growth performance and disease resistance to ND traits of IC in Rwanda? v. What is the response to selection of IC in Rwanda? 4/27/2021 12
  • 13. Justification 12  Genetic diversity and population structure  Provide the genetic variation within and among populations  guide selection decision  Design the appropriate mating strategies  Maintain genetic variation and reduce inbreeding within the population  increase the response to selection  Production and functional traits  Basis for choosing a suitable IC gene pool for any particular production system  Genomic regions associated with the production and functional traits  Manipulation of the genome  Marker assisted selection (MAS) : indirect selection  Genetic improvement of IC  increase productivity of IC  food security and poverty alleviation improve rural livelihood  Conservation of IC GnRs  Maintain IC unique attributes which are preferred by producers and consumers 4/27/2021 13
  • 14. 13 MORPHOLOGICAL CHARACTERISTICS OF IC ECOTYPES IN RWANDA
  • 15. Materials & Methods  Study area: 5 agro-ecological zones (E, NW, SW, CN, CS) in Rwanda 14
  • 16.  Exploratory field survey; structured questionnaire, observation and body measurements on 1670 mature IC  FAO (2011) guidelines  8 Qualitative traits (morphology, distribution and colour of feathers, shank colour, comb type, head shape, ear lobe colour and shank feather)  13 Quantitative traits (body weight, body length, tarsus length, chest circumference, shank length, head length, comb length and height, wattle length, beak length, neck length, wing length, and wingspan)  Data analysis  Descriptive statistics: Cross-tabulation and frequency procedures  Statistical tests:  Qualitative traits: Non parametric test (Kruskal–Wallis and Mann–Whitney U)  Quantitative variables: ANOVA 15 Data collection
  • 17.  Fixed-effects of ecotype, sex and interaction between sex and ecotype model:  where,  Yij: Body weight and linear body measurement of the chicken,  µ: Overall mean,  Ai: Fixed effect of ith ecotype,  Dj: Effect of jth sex ( male or female), eij: Random residual error. 16 𝐘𝐢𝐣 = 𝛍 + 𝐀𝐢 + 𝐃𝐣 + 𝐀𝐃 𝐢𝐣 + 𝐞𝐢𝐣  Post hoc test: Tukey’s test
  • 18.  Feather morphology, feather distribution and feather colour of IC populations in Rwanda 17 Significantly different (p < 0.001) between ecotypes  Feather morphology: normal feather morphology: 98.30%  Feather distribution: normal feather distribution : 84.40%  Feather colour: multi-coloured feathered : 38.10% Results & Discussion
  • 19.  Comb type and ear lobe colour of IC populations ecotypes in Rwanda 18  Significantly different (p < 0.001) between ecotypes  Ear lobe: red ear lobe colour : 49.20%  Comb type: single comb : 71.70%
  • 20.  Shank colour of IC populations ecotypes in Rwanda 19  Significantly different (p < 0.001) between ecotypes  Yellow shank: 53.80%
  • 21.  Biometrical characteristics of IC ecotype populations in Rwanda 20  Significantly different (p < 0.001) between ecotypes  body weight, body length, tarsus length, shank length, comb length, comb height, wattle length, chest circumference, beak length, head length, neck length, wing length and wingspan  Sex-associated high differences (p < 0.001) in the most traits, with high values recorded for male IC  Ecotype by sex interaction was highly significant (p < 0.001)  body weight, body length, shank length, comb length, comb height, wattle length, chest circumference, neck length and wingspan
  • 22.  Take a home message 21 In Rwanda, IC ecotypes are diverse populations with huge variation in both qualitative and quantitative traits
  • 23. 22 GENETIC DIVERSITY AND POPULATION STRUCTURE OF IC POPULATIONS IN RWANDA
  • 24. DNA Extraction DNA Quality and quantity check Genotyping Sampling Data Analysis DNA amplification(PCR) 23 Materials & Methods
  • 25.  Blood samples collection and DNA extraction  Blood from wing vein (on FTA cards)  325 birds  DNA isolation: Boiling method  DNA quantification (concentration and purity): Nanodrop  DNA quality control: Gel electrophoresis 24
  • 26.  DNA amplification (PCR): Applied Biosystems 9700 Thermal Cycler Gene Amp®  Microsatellite markers (28) recommended by FAO (2011)  PCR reaction : 10 µl (30ng target DNA, 5uL of OneTaq® 2X Master Mix with a standard buffer and 0.2uL of each forward and reverse primer)  PCR program: 25 94°C 3min 94°C 30sec 60°C 1min 72°C 2min 72°C 10min 15°C ∞ 30 cycles
  • 27.  Genotyping  ABI PRISM 377 DNA Sequence: GeneScanTM-500 LIZ®  Allele scoring : GeneMapper  Data analysis  Genetic diversity analysis (PowerMarker ,GeneAIEx ,Fstat, Popgene )  Intrapopulation  Interpopulation  Genetic relationship (PowerMarker, Darwin, Fstat, Genepop)  Genetic differentiation  Genetic Distance  Population structure: Model-based (Structure) gene pools 26
  • 28. General diversity parameters per locus (intra population) LOCUS MAF GNo NA NE NPA He Ho PIC I ADL0112 0.4985 27 16 2.7204 6 0.6324 0.5938 0.5717 1.318 ADL0268 0.2446 39 14 6.2408 3 0.8398 0.5815 0.8202 2.0217 ADL0278 0.3 39 12 5.3488 4 0.813 0.5477 0.7892 1.8845 LEI0094 0.3923 45 17 4.3604 3 0.7707 0.7138 0.7441 1.8665 LEI0192 0.3169 66 22 5.6987 4 0.8245 0.7754 0.8058 2.1488 LEI0234 0.1769 77 17 8.9015 2 0.8877 0.5692 0.8775 2.3925 MCW0014 0.5123 29 10 3.1071 1 0.6782 0.4862 0.6452 1.4928 MCW0016 0.3169 39 15 4.6988 4 0.7872 0.7723 0.7592 1.8406 MCW0020 0.3046 29 8 4.6607 0 0.7854 0.72 0.753 1.6755 MCW0034 0.3508 46 14 5.2106 5 0.8081 0.7754 0.7875 1.9272 MCW0037 0.5 1 2 2 0 0.5 1 0.375 0.6931 MCW0067 0.3954 31 11 3.5732 1 0.7201 0.68 0.6789 1.6218 MCW0069 0.3385 26 10 3.6706 0 0.7276 0.7385 0.6803 1.5025 MCW0078 0.7662 11 5 1.6504 0 0.3941 0.3692 0.3717 0.8202 MCW0081 0.4938 42 11 3.0007 1 0.6667 0.56 0.6219 1.4829 MCW0098 0.4646 27 9 2.571 1 0.611 0.5231 0.5348 1.1762 MCW0103 0.7077 9 6 1.7362 2 0.424 0.3754 0.3488 0.6929 MCW0104 0.4892 43 18 3.2705 4 0.6942 0.6492 0.6615 1.7013 MCW0111 0.5954 21 8 2.4404 0 0.5902 0.4831 0.55 1.2262 MCW0123 0.5231 38 14 3.1028 3 0.6777 0.64 0.6496 1.5676 MCW0165 0.6354 7 4 1.9239 0 0.4802 0.3015 0.3864 0.7554 MCW0183 0.2923 34 11 5.5162 3 0.8187 0.6585 0.7958 1.8734 MCW0206 0.3938 24 9 3.9919 2 0.7495 0.6985 0.7143 1.5832 MCW0222 0.4 11 6 2.9721 2 0.6635 0.6462 0.5996 1.2097 MCW0248 0.6785 6 4 1.8158 1 0.4493 0.4923 0.366 0.7126 MCW0284 0.3677 29 8 3.9004 0 0.7436 0.6892 0.7061 1.6202 MCW0295 0.4646 34 13 3.4817 3 0.7128 0.5785 0.6802 1.6324 MCW0330 0.3015 26 11 5.3764 5 0.814 0.6154 0.7899 1.8272 Mean 0.4365 30.5714 10.892 9 3.8194 60 0.688 0.6155 0.6451 1.5095 Total 305 Results & Discussion 27 NA : 305 , 2 to 22 with an overall mean of 10.89 NPA : 60 20% of the total alleles I : 0.692- 2.392 (1.509) He : 0.394-0.888 (0.688) Ho :0.301-1.00 (0.616)
  • 29. General diversity parameters per locus (intra population) Loci Fis Fit Fst Nm HWE pV ADL0112 0.097 0.128 0.034 7.006 0.000 ADL0268 0.176 0.306 0.158 1.332 0.000 ADL0278 0.252 0.283 0.041 5.869 0.000 LEI0094 0.017 0.034 0.017 14.344 0.000 LEI0192 -0.005 0.036 0.041 5.829 0.000 LEI0234 0.338 0.354 0.024 10.202 0.000 MCW0014 0.142 0.263 0.142 1.517 0.000 MCW0016 0.002 0.023 0.021 11.392 0.000 MCW0020 0.050 0.095 0.047 5.027 0.000 MCW0034 -0.003 0.032 0.035 6.965 0.191 MCW0037 -1.000 -1.000 0.000 0.000 MCW0067 0.038 0.137 0.103 2.181 0.000 MCW0069 -0.011 0.028 0.038 6.309 0.104 MCW0078 -0.006 0.006 0.011 21.491 0.015 MCW0081 0.126 0.156 0.034 7.140 0.000 MCW0098 0.105 0.170 0.072 3.212 0.000 MCW0103 0.131 0.160 0.033 7.343 0.000 MCW0104 0.066 0.096 0.033 7.385 0.000 MCW0111 0.110 0.141 0.035 6.800 0.000 MCW0123 0.015 0.031 0.016 15.002 0.000 MCW0165 0.325 0.341 0.024 10.050 0.000 MCW0183 0.119 0.189 0.080 2.885 0.000 MCW0206 -0.004 0.044 0.048 5.000 0.000 MCW0222 -0.030 0.023 0.051 4.641 0.000 MCW0248 -0.236 -0.185 0.041 5.864 0.344 MCW0284 0.050 0.117 0.070 3.321 0.000 MCW0295 0.131 0.214 0.096 2.341 0.000 MCW0330 0.147 0.281 0.157 1.339 0.000 Mean 0.041 0.089 0.054 6.060 28 Inbreeding coeff. : -1.00-0.338 (0.041) Nm : 1.332-21.491(6.060) 10% of loci in HWE(p>0.5)
  • 30. General diversity parameters per population Populations N %PL NA PA Ho He F I Central North 51 100 6.929 6 0.623 0.644 0.021 1.322 Central South 55 100 7.286 15 0.598 0.661 0.077 1.372 Exotic chicken 12 100 5.143 4 0.667 0.665 -0.019 1.305 East 102 100 8.250 21 0.611 0.654 0.056 1.358 North West 52 100 6.500 0 0.613 0.645 0.042 1.306 South West 53 100 7.964 14 0.626 0.680 0.063 1.458 Total 325 100 7.011 60 0.623 0.658 0.040 1.353 29
  • 31. Pairwise Population Matrix of Nei Unbiased Genetic Distance North West Central North Central South ControlEastern South West North West 0.000 Central North 0.029 0.000 Central South 0.094 0.077 0.000 Control 0.199 0.213 0.231 0.000 Eastern 0.112 0.097 0.117 0.196 0.000 South West 0.104 0.092 0.048 0.118 0.125 0.000 30
  • 32. Degree of gene differentiation in terms of allele frequencies(FST) Central North Central South Control Eastern North West South West Central North 0.000 Central South 0.022 0.000 Control 0.052 0.058 0.000 Eastern 0.025 0.027 0.050 0.000 North West 0.012 0.026 0.053 0.028 0.000 South West 0.026 0.014 0.036 0.028 0.027 0.000 31
  • 33. Analysis of molecular Variance Among Populations 8% Within Populations 92% 32
  • 34. Degree of gene differentiation in terms of gene Flow (Nm) Central North Central South Control Eastern North West South West Central North 0.000 Central South 2.304 0.000 Control 1.412 0.925 0.000 Eastern 2.051 1.471 3.432 0.000 North West 6.274 1.533 1.188 1.783 0.000 South West 2.040 3.847 2.791 1.560 1.471 0.000 33
  • 35. Phylogenetic relationship North West Central North South West Central South Control and South West Eastern 34 B C A D
  • 36. Population structure The Evanno table output Gene pool A : CN & NW Gene pool D : Eastern Gene pool C : CS & SW Gene pool B : C & SW 35
  • 37.  Take a home message IC populations in Rwanda have a high degree of substantial genetic variability and are clustered in four gene pools 36
  • 38. GROWTH PERFORMANCE AND NEWCASTLE DISEASE ANTIBODY TITRES IN FOUR GENE POOLS OF INDIGENOUS CHICKEN IN RWANDA 37
  • 39.  Sampling 38 Gene pools Collected fertile eggs Incubated eggs Hatched eggs Brooded chicks A 180 179 72 72 B 180 177 34 34 C 180 175 36 36 D 180 176 47 47 Total 720 707 189 189 Materials & Methods
  • 40.  Phenotyping: Production trait & Functional trait  Production trait  Growth performance  Body weight from day 1 to week 20  Functional trait  Disease resistance (ND)  Antibody titers  Vaccination schedule with a commercial NDV live vaccine:  At 2 days of age : AVI ND HB1 in drinking water  At 28 days of age : AVI ND Lasota by eye drop  At 7 days after the second vaccination (35 days of age):  Serum samples from the chicken blood  Antibody response to NDV  Indirect ELISA  IDSoft™ programme Computation of Ab titres 39
  • 41.  Data analysis ANOVA : Fixed-effects of ecotype, sex and interaction between sex and ecotype model :  where:  Yijkl : Record of lth individual from ith gene poo with jth sex  Μ : Overall mean;  Gi : Fixed effect of ith gene pool;  Sj : Fixed effect of kth sex;  (GS)ij : Interaction between gene pool and sex;  eijkl : random effect peculiar to each individual  For the analysis of body weight  At hatch: egg weight was fitted in the model as a covariate  Other ages: body weight at hatch was fitted as a covariate 40 𝒀𝒊𝒋𝒌 = µ + 𝑮𝒊 + +𝑺𝒋 + 𝑮𝑺 𝒊𝒋 + 𝒆 𝒊𝒋𝒌
  • 42.  Non-linear regression analysis of longitudinal growth data  Logistic regression model using PROC NLIN of SAS  growth curve parameters (A, b and k) where:  Yt : live weight at age t  A : asymptotic or mature weight  b : scaling parameter  k : maturity index  t : age in n weeks  Plot least-square means of the body weight in all gene pools against age  growth curve patterns 41 𝒚𝒕 = 𝑨(𝟏 − 𝒃 ൯ 𝒆−𝒌𝒕 −𝟏
  • 43. Mean body weight within and between IC gene pools in Rwanda 42 Results & Discussion 0 200 400 600 800 1000 1200 1400 1600 1800 0 5 10 15 20 25 Body weight Weeks Gene pool A Gene pool B Gene pool C Gene pool D a b,c c b
  • 44. Growth parameters of IC gene pools in Rwanda 43 Predicted growth curves for IC gene pools in Rwanda Parameters Gene pools Overall P- value A B C D A 1446.57±62.42a 1286.31±58.88ab 1086.38±66.64b 1350.13±69.16a 1309.46±59.82 0.000 b 15.41±5.19a 16.33±4.13a 13.65±3.93b 15.15±4.87a 15.02±4.41 k 0.35±0.05a 0.30±0.04a 0.29±0.04b 0.32±0.05a 0.321±0.04 R2 0.96 0.97 0.96 0.96 0.96 Least square means (±SE) of growth parameters of IC gene pools in Rwanda from logistic function A : Asymptotic weights; b: scaling parameter; k: maturity index 0 200 400 600 800 1000 1200 1400 1600 0 5 10 15 20 25 Body weight (g ) Age (weeks) Gene pool A Gene pool B Gene pool C Gene pool D SI ExP FS
  • 45. Antibody titres for Newcastle disease of IC gene pools 44 a a c b Gp A Gp D Gp C Gp B
  • 46.  Take a home message 45 Between the four IC gene pools, there is a significant difference in body weight and antibody response to the ND  Gene pool A was the heaviest  Gene pool C expressed the strongest immune response for ND
  • 47. 46 GENOMIC REGIONS FOR GROWTH PERFORMANCE AND IMMUNE RESPONSE TO NEWCASTLE DISEASE OF INDIGENOUS CHICKEN IN RWANDA
  • 48. Phenotyping 47 Materials & Methods • AbR to ND after 7 days from the second immunization • BW20
  • 49. 48 Blood Sampling (Wing Vein) Reads alignment to Galgal6 (BWA)  Removal of duplicate Reads (Picard package)  Trimming of raw reads (Sickle) Genotyping (GBS) DNA extraction (Promega genomic DNA extraction kit) SNPs calling (SAMtools) SNPs Quality control (PLINK) Genotype imputation CF > 95% HWE (P<10-6) MAF > 0.03 He > 0.4 Genotype LD KNNi 65,945 SNPs
  • 50. 49 Genotype (SNPs) Phenotype (Traits) Indigenous Chicken Genotyping Disease resistance Reads alignment SNPs identification GWAS Body weight Phenotyping where:  y: vector of quantitative traits  𝛃 : vectors containing fixed effects (sex, gene pool, SNP) and covariates  u: vector of random additive genetic effects of animal  X and Z: design matrices  e: vector of random residuals MLM (Tassel v5.2.6) y= 𝐗𝛃 + 𝐙𝐮 + 𝐞 DATA ANALYSIS
  • 51.  FWER was controlled by using:  A Bonferroni genome-wide significance threshold of 5%  A suggestive genome-wide threshold of 10%  Manhattan plots:  A comprehensive view of P-values (-log10 p-values) for all SNP markers of each trait  qqman package of R software 50
  • 52.  ANNOTATION  Significant SNPs: BioMart data mining tool  ww.ensembl.org/ biomart/ mart-view  Genes ϵ [Significant SNPs ± 100kb]: Variant effect predictor tool (www.ensembl.org/tools/VEP)  List of gene in the vicinity of all significant SNPs 51
  • 53. Significant Genomic regions associated with BW in IC 52 rs740980181 rs13792572 rs314702374 Rs14123335 Manhattan plot displaying significant and suggestive SNPs associated with IC body weight (the red line designates a Bonferroni-adjusted genome-wide threshold [-log10 (p-value) ≥ 6.1] and the blue line indicates a suggestive genome-wide threshold [-log10 (p-value) ≥ 4.8) Results & Discussion (PBX1) (GPATCH1) (MPHOSPH6) (MRM1)
  • 54. Significant Genomic regions associated with ND resistance 53 rs314787954 rs13623466 rs13910430 rs737507850 Manhattan plot displaying significant and suggestive SNPs associated with IC body weight (the red line designates a Bonferroni-adjusted genome-wide threshold [-log10 (p-value) ≥ 6.1] and the blue line indicates a suggestive genome-wide threshold [-log10 (p-value) ≥ 4.8) (CDC16) (ZBED1) (MX1) (GRAP2 )
  • 55.  Take a home message 54 Genomic regions that are thought to regulate body weight and antibody response to ND in IC in Rwanda are now available  Overlapped with those previously reported  Uncovered for the first time in this study
  • 56. 54 RESPONSE TO SELECTION OF INDIGENOUS CHICKEN IN RWANDA USING WITHIN-BREED SELECTION STRATEGY
  • 57.  Deterministic simulation (using SelAction software) was used to model two different breeding schemes in a closed single-tier  Conventional breeding scheme (CBS)  Genomic breeding scheme (GBS)  Breeding goal  a dual-purpose IC for both eggs and meat production  Breeding objective traits  Egg number (EN)  Egg weight (EW)  Body weight (BW)  Resistance to Newcastle disease (AbR)  Input parameters  from specific objective 3 and from Meta-Analysis study 55 Materials & Methods
  • 58. Breeding structure for the simulated IC breeding programme 57 Smallholder indigenous chicken farmers Nucleus 1000 fertilised eggs 850 day old chicks 510 growers 200 pullets 2000 fertilised eggs 1700 day old chicks 40 cockerels 1020 growers 510 cockerels 510 pullets 85% hatchability 60% Survival rate Selection on body weight at 16 weeks 10 eggs/hen/clutch 85% hatchability 60% Survival rate 50% Sex ratio Selection pressure 4% Selection pressure 20% 96% unselected cockerels 80% unselected pullets Base pop.
  • 59. Prediction of response to selection  Response to selection was computed for all the traits in the breeding objective (H): 𝑯 = 𝑨𝟏𝑽𝟏 + 𝑨𝟐𝑽𝟐 + ⋯ where o 𝑨𝒊 : true breeding values o 𝑽𝒊 ∶ weighting factors for each trait 58 where N : the number of parents, and E (r)2 , the square of the expected contributions Prediction of inbreeding rate ∆𝑭 = ൗ 𝟏 𝟐 𝑵𝑬(𝒓)𝟐
  • 60. Optimum nucleus size 59  Effect of the nucleus size and different mating ratios  Rate of genetic gain and inbreeding • Number of breeding parent flock in the base population from 240 to 24,000 hens • Mating ratio from 1:5 to 1:100
  • 61. 60 Scheme Response (US$) Rate of inbreeding (%) Accuracy of index CBS Nucleus 340.41 1.45 0.55 Commercial 301.17 1.91 0.47 GBS Nucleus 1,024.45 0.46 0.97 Commercial 1,024.44 0.46 0.97 Rates of genetic gain (US$), inbreeding (%) and accuracy of selection in the conventional (CBS) and genomic (GBS) breeding schemes Results & Discussion
  • 62. 61 Effect of nucleus size on rate of genetic gain and inbreeding 0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 1.6 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 240 960 1680 2400 3120 3840 4560 Change in inbreeding rate (%) Change in genetic gain (US$) Nucleus Size (with sex ratio 1: 5) ∆G-CBS ∆G-GBS ∆F-CBS ∆F-GBS 0 1 2 3 4 5 6 7 8 9 10 0 200 400 600 800 1,000 1,200 1,400 101 303 505 707 909 Nucleus size (with sex ratio 1: 100) Change in inbreeding rate (%) Change ingenetic gain (USD) ∆G-GBS ∆G-CBS ∆F-GBS ∆F-CBS
  • 63. 62 0.050 0.100 0.150 0.200 0.250 0.300 0.350 0.400 0.450 0.500 200.00 400.00 600.00 800.00 1,000.00 1,200.00 1,400.00 5 15 25 35 45 55 65 75 85 95 Change in inbreeding rate (%) Change in genetic gain (US$) Number of hens per cock ∆G-GBS ∆G-CBS ∆F-CBS ∆F-GBS Effect of mating ratio on rate of genetic gain and inbreeding
  • 64.  Take a home message 63 Both CBS and GBS simulated in this study had a considerable positive response to selection  It is possible to improve IC in Rwanda for dual-purpose (both meat and egg) through within-breed selection strategy using either CBS or GBS
  • 65. CONCLUSIONS 1. There is a huge morphological variation in both qualitative and quantitative traits in IC ecotypes 2. There is a significant genetic variability in IC and are clustered into four gene pools in Rwanda 3. There is a significant difference for growth performance and antibody response to ND virus among four IC gene pools 4. Genomic regions putatively regulating body weight and antibody response to ND in IC in Rwanda are now available 5. There is a possibility to improve IC through within-breed selection strategy using either conventional (CBS) or genomic (GBS) breeding schemes 64 4/27/2021
  • 66. RECOMMENDATION 1. Morphological variation in both qualitative and quantitative traits of IC ecotypes in Rwanda should be taken into account in breeding programme for genetic improvement and conservation 2. IC gene pools in Rwanda should be considered in breeding programme for genetic improvement and conservation 3. Gene pools A and C could be bred for growth and ND resistance performances, respectively. In order to get chicken with a better growth performance and higher AbR against ND, a crossbreeding programme could take into consideration IC gene pools A and C 4. Existing genomic regions associated with BW and AbR could be utilised in the development of marker-assisted and gene-based selection in IC selection for BW and AbR 5. An IC breeding programme in Rwanda could be implemented using within- breed strategy to generate genetic progress, but at the moment better to start with CBS while developing infrastructure for implementation of GBS 65 4/27/2021 66
  • 67. AREAS FOR FURTHER STUDIES 1. Identification of genomic regions controlling IC morphological characteristics in Rwanda 2. Quantification of G x E among IC ecotypes in Rwanda 3. Evaluation of egg production performance of IC gene pools in Rwanda 4. Validation of the role of the identified novel genomic regions for BW and AbR in IC in Rwanda 5. Estimation of cost and profit for both CBS and GBS in IC in Rwanda 66 4/27/2021 67
  • 68. Publications One scientific publications in conference Three scientific publications in peer review journals 67 4/27/2021 68
  • 69. Acknowledgement Supervisors • Dr. Tobias O.Okeno (Dr. er. agr) • Dr. Kiplangat Ngeno, PhD 68 4/27/2021 Dr. Claire d’ Andre HIRWA (RAB) Dr Christian T. Keambou (CTLGH) Dr Yao Nasser (ILRI) IC Farmers