A poster presented at POPGEN47 conference at University of BATH, UK (7-10 Jan. 2014)

1. Genetic differentiation of Ethiopian and
Nigerian village chicken
Raman A. Lawal1, Ayotunde. O. Adebambo2, Takele T. Desta1, David Wragg1, Olivier Hanotte1
1School of Life Sciences, The University of Nottingham, University Park, Nottingham. NG7 2RD, United Kingdom
2Department of Animal Breeding and Genetics, Federal University of Agriculture, P. M. B. 2240, Abeokuta, Nigeria
Corresponding author: Raman Lawal (plxral@nottingham.ac.uk)
Introduction
Archaeological evidences show that chicken, whose economy of millions of subsistence farmers depend on, likely entered
the African continent first through the North of Africa, and subsequently dispersed towards Eastern and Western Africa1,2.
It is assumed that this geographical dispersion pattern may have caused variation in the genetic structure of the village
chicken. This study examines the differentiation that may have taken place between and within Ethiopian and Nigerian
village chicken populations.
Methodology
A total of 52 samples from 14 populations of village chickens across Ethiopia (East Africa) and Nigeria (West Africa) were
genotyped using the high density (580K) Axiom® genome-wide chicken genotyping array3. Quality control include a SNP
call rate of > 0.95, P < 0.001 for HWE, and maf (minor allele frequency) > 0.05. Principal component analysis (PCA4) was
performed in R5 of GenABEL6 and Adegenet7 libraries using custom script, genetic relationships using MEGA 5.28 and
admixture analysis using R5, distruct9 and GSview version 5.010. The Fst and heterozygosities (Ho and He) of the
population were calculated also in R5 using custom scripts.
Sampling area
Sampling location in
blue circle
Results and Discussion
The PCA results are presented in figure 1. 1st and 2nd PC
explains together about 50% of total variance. Both
separate the Nigerian and Ethiopian chicken with more
genetic variations across Ethiopian chicken. A group of 3
Ethiopian and one Nigerian chicken occupied an
intermediate position (C2) and 3 Nigerian chicken were
separated from the others (C1). Relationship tree (figure
3) largely support the PCA results (C1 and C2).
Incomplete genetic differentiation (Fst = 0.053) was
observed between the two countries despite the
presence of large geographical distances. Admixture (k =
2) clearly separate Ethiopian and Nigerian populations
(figure 2). At advance level of k there is more diversity
within Ethiopian population than in Nigerian population.
Table 1 Mean and SD of Heterozygosity across loci within population
Population
Nigeria
Ethiopia
Ho ± SD
0.318 ± 0.035
0.299 ± 0.035
He ± SD
0.353 ± 3.33e-05
0.345 ± 4.20e-05
Inbreeding (Fis)
0.101 ± 0.099
0.133 ± 0.102
Ethiopian chicken
Nigerian chicken
C1
PC2 (5.65)
C1
C2
C2
2.0
Figure 3 Genetic relationship tree (Neighbour Joining) of Ethiopian – Nigerian
chicken population
PC1 (49.52)
Figure 1 PCA of Ethiopian - Nigerian population
K=2
Nigerian village chicken11
K=3
Conclusions
K=4
K=5
K=6
K=7
K=8
Figure 2 Genetic admixture observed in the sampled
of African village chicken population
Jarso chicken (Ethiopia)
with villager
Ethiopia village chicken
1. Despite large geographical distance between the two populations, incomplete
genetic differentiation was observed between countries perhaps a legacy of
common ancestry and recent arrival on chicken on the studied geographic areas
2. Alternatively, common commercial introgression may have happen in some birds
in Nigerian and Ethiopia
3. Higher proportion of molecular variations observed within Ethiopian population
support level of differentiation observed between population within country
4. Further studies with more locations and birds may further clarify these issues
Acknowledgment
This project was supported by The University of Nottingham Vice chancellor’s
Scholarship (International)
References
(1) MacDonald KC .1992. J. of Arch. Sci., 19, 303–318. (2) Blench R, MacDonald KC. 2000. Origins & Development of African Livestock. (3) Kranis A.
et al., 2013 BMC Genomics 14:59 (4) Dray, S. and Dufour, A.B. 2007. J. Stat. Softw. 22(4), 1–20. (5) R Development Core Team, 2012. R Version
2.15.1 (6)Aulchenko, Y. S. et al., 2007. Bioinformatics,1294–1296 (7) Jombart T and Ahmed I (2011). d oi:10.1093/bioinformatics/btr521
Bioinformatics. (8) Saitou N. and Nei M. 1987. Mol. Bio and Evol. 4:406-425. (9) Rosenberg N. A. 2004. Molecular Ecology Notes 4:137-138.
(10) http://pages.cs.wisc.edu (11) www.bellanaija.com