Agaviezor et al. Journal of Animal Science and Biotechnology 2012, 3:38
http://www.jasbsci.com/content/3/1/38
RESEARCH
JOURNAL OF ANIMAL SCIENCE
AND BIOTECHNOLOGY
Open Access
Morphological and microsatellite DNA diversity of
Nigerian indigenous sheep
Brilliant O Agaviezor1,3,6, Sunday O Peters1,6,7, Mufliat A Adefenwa2,6, Abdulmojeed Yakubu4,6,
Olufunmilayo A Adebambo1, Michael O Ozoje1, Christian ON Ikeobi1, Matthew Wheto1, Oyeyemi O Ajayi1,
Samuel A Amusan1, Oludotun J Ekundayo1, Timothy M Sanni1, Moses Okpeku5, Gbolabo O Onasanya1,
Marcos De Donato6, Babatunde M Ilori1, Kadir Kizilkaya8 and Ikhide G Imumorin6*
Abstract
Background: Sheep is important in the socio-economic lives of people around the world. It is estimated that more
than half of our once common livestock breeds are now endangered. Since genetic characterization of Nigerian
sheep is still lacking, we analyzed ten morphological traits on 402 animals and 15 microsatellite DNA markers in
384 animals of the 4 Nigerian sheep breeds to better understand genetic diversity for breeding management and
germplasm conservation.
Results: Morphological traits of Uda and Balami were significantly (P < 0.05) higher than Yankasa, which were both
higher than West African Dwarf (WAD) sheep. Stepwise discriminant analysis showed tail length, rump height, chest
girth, ear length and chest depth as the most discriminating variables for classification. Mahalanobis distances show
the least differentiation between Uda and Balami and the largest between WAD and Balami sheep. While 93.3% of
WAD sheep were correctly assigned to their source genetic group, 63.9% of Yankasa, 61.2% of Balami and 45.2% of
Uda were classified correctly by nearest neighbour discriminant analysis. The overall high Polymorphism
Information Content (PIC) of all microsatellite markers ranged from 0.751 to 0.927 supporting their use in genetic
characterization. Expected heterozygosity was high for all loci (0.783 to 0.93). Mean heterozygote deficiency across
all populations (0.171 to 0.534) possibly indicate significant inbreeding (P < 0.05). Mean values for FST, FIT and FIS
statistics across all loci were 0.088, 0.394 and 0.336 respectively. Yankasa and Balami are the most closely related
breeds (DA = 0.184) while WAD and Balami are the farthest apart breeds (DA = 0.665), which is coincident with
distance based on morphological analysis and population structure assessed by STRUCTURE.
Conclusions: These results suggest that within-breed genetic variation in Nigerian sheep is higher than
between-breeds and may be a valuable tool for genetic improvement and conservation. The higher genetic
variability in Yankasa suggests the presence of unique ancestral alleles reflecting the presence of certain functional
genes which may result in better adaptability in more agro-ecological zones of Nigeria. These genetic
characteristics are potentially useful in planning improvement and conservation strategies in Nigerian
indigenous sheep.
Keywords: Discriminant analysis, Genetic distance, Microsatellite DNA, Morphological traits, Nigerian sheep
* Correspondence: igi2@cornell.edu
6
Dept of Animal Science, Cornell University, Ithaca, NY 14853, USA
Full list of author information is available at the end of the article
© 2012 Agaviezor et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and
reproduction in any medium, provided the original work is properly cited.
Agaviezor et al. Journal of Animal Science and Biotechnology 2012, 3:38
http://www.jasbsci.com/content/3/1/38
Background
The population of sheep in Nigeria is currently estimated at 33.9 million making up 3.1% of the world’s
total [1]. Sheep is an important livestock species in the
socio-economic lives of people around the world including Nigerians [2]. Increased loss of genetic diversity has
been observed for all agriculturally used species, and it
is estimated that more than half of our once common
livestock breeds are now endangered [1]. According to
FAO [3], it is estimated that the world loses two breeds
of its valuable domestic animal diversity every week.
Already, 740 breeds are recorded as extinct, with 1,335
(32% of the estimated total) being classified at high risk
of loss or under threat of extinction. If the erosion of
animal genetic diversity continues without adequate action, more than 2,000 domestic animal breeds could be
lost within the next two decades [3]. The first step toward an efficient conservation strategy for cattle, sheep,
and goat genetic resources is the proper characterization
of the conservation value of the different breeds and
their wild relatives [4].
Sheep biodiversity have been described using morphological measurements [5,6] or characterized using molecular data [7-13]. The phenotypic variation in a
population arises due to genotypic and environmental
effects, and the magnitude of phenotypic variability differs under different environmental conditions. Morphometric characters are continuous characters describing
aspects of body shape [14,15]. Morphometric variation
between populations can provide a basis for understanding flock structure, and may be more applicable for
studying short-term, environmentally induced variation
and thus more applicable to livestock management.
According to Gizaw et al. [5], morphological description
is an essential component of breed characterization that
can be used to physically identify, describe, and
recognize a breed, and also to classify livestock breeds
into broad categories. Dossa et al. [16] reported that
morphological measurements such as heart girth, height
at withers and body length can be used for rapid selection of large size individuals in the field to enable the establishment of elite flocks. In addition, microsatellites
have been used successfully over the years to
characterize the genetic diversity of sheep populations
in China [12], Ethiopia [5], Europe and Middle East
[7-9,17], India [10,11,18] and Brazil [13].
The Nigerian sheep is still genetically unimproved, and
the pressure of modern genetic improvement has
increased the need to better understand natural genetic
variation in Nigerian sheep breeds, as well as formulate
germplasm conservation policies. The only genetic diversity study of genetic variation among Nigerian sheep
breeds using microsatellite markers covered a limited
geographical area [19]. Therefore, a more detailed study
Page 2 of 16
using a larger sample size from across the entire country
is still required to better understand the genetic structure of Nigerian sheep population. In this study, morphological data on 402 sheep and molecular data on 15
microsatellite DNA markers in 384 sheep sampled
across the entire country were used to evaluate the morphological and genetic diversity of the four major extant
sheep breeds in Nigeria. This study complements our recent molecular characterization of the mitochondrial Dloop region in Nigerian sheep [20]. The information
obtained will be useful for designing appropriate breeding and selection schemes for indigenous sheep improvement and sustainable conservation.
Results
Morphological characterization
The basic descriptive statistics of the morphological
traits of WAD, Yankasa, Uda and Balami sheep are presented in Table 1. The biometric traits of Uda and
Balami sheep were significantly (p < 0.05) higher than
those of WAD and Yankasa, although the latter had superior mean values than the former for all morphometric traits with the exception of EL. The effects of sex and
system of management on the body parameters of the
four sheep breeds are presented in Tables 2 and 3. Male
animals had significantly higher body parameters than
their female counterparts and higher values are observed
in Balami and Uda compared with other breeds. The
sexual dimorphism observed could be attributed to differences in the genetic architecture of the sheep populations. Animals reared intensively also had superior
means than those semi-intensively managed for all morphological traits. While all the sheep breeds are reared
semi intensively, only WAD and Yankasa are reared extensively. However, WAD is not reared intensively. The
effect of age on morphological parameters was significant with slight increases with age (Table 4). Table 5
shows the spread of the four Nigerian sheep breeds
across the country. WAD is only found in the southern
part of the country. Variation was observed in the various morphological parameters studied according to
breed in the different sampling location. The stepwise
discriminant procedure showed that TL, RH, CG, EL
and CD were the most discriminating variables to separate WAD, Yankasa, Uda and Balami sheep based on
their significance and partial R2 values ≥0.01 (Tables 6
and 7, respectively). The canonical variate analysis
(Table 7) clearly showed distinctive differences in the
morphological traits of the four sheep breeds. Table 8
shows the percentage of individual sheep classified into
genetic groups. The highest value is between WAD and
WAD (93.33) and the lowest is between Uda and Uda
(45.16). Error level is lowest in Yankasa (0.361). Results
for kinship coefficient (Dkf ) and proportion of shared
Agaviezor et al. Journal of Animal Science and Biotechnology 2012, 3:38
http://www.jasbsci.com/content/3/1/38
Table 1 Descriptive statistics of the morphological traits
of Nigerian sheep breeds
Trait
WAD sheep
Yankasa sheep Uda sheep Balami sheep
LSM ± SEM
LSM ± SEM
LSM ± SEM
LSM ± SEM
BW
20.12 ± 1.23d
30.87 ± 1.05c
37.86 ± 1.20a
39.00 ± 0.94a
WH
52.35 ± 1.07d
68.52 ± 0.6c
74.53 ± 0.87a
74.31 ± 0.59a
a
73.64 ± 0.57a
a
101.77 ± 1.21a
a
RH
BL
d
52.12 ± 0.87
d
76.87 ± 1.53
67.44 ± 0.63
c
93.56 ± 0.91
99.40 ± 0.99
13.03 ± 0.39
13.19 ± 0.28
15.63 ± 0.54
14.54 ± 0.28b
FCL
13.45 ± 0.21d
14.85 ± 0.22c
16.53 ± 0.31a
15.60 ± 0.19b
d
c
a
39.67 ± 0.50a
a
19.42 ± 0.63
d
c
73.78 ± 0.82
EL
TL
c
c
35.28 ± 0.54
44.13 ± 0.73
CG
65.04 ± 1.60
75.83 ± 0.90
81.30 ± 1.12
82.87 ± 1.19a
CD
25.73 ± 0.78d
33.38 ± 0.45c
35.22 ± 0.58a
33.82 ± 0.32a
RW
d
c
a
15.32 ± 0.22a
11.84 ± 0.29
c
13.77 ± 0.22
15.35 ± 0.30
a,b,c – means with different superscript are significantly (p < 0.05) different.
BW body weight, WH withers height, RH rump height, BL body length, EL ear
length, FCL fore cannon bone length, TL tail length, CG chest girth, CD chest
depth, RW rump width WAD West African Dwarf sheep, LSM least square
means, SEM standard error of mean.
alleles are presented in Table 9. The highest kinship coefficient and number of shared alleles is between WAD
and Balami.
Molecular genetic diversity between populations
Polymorphism Information Content (PIC) and F statistics (FIS, FIT, FST) according to Weir and Cockerham
(1984), GST and Shannon index values for all 15 microsatellite markers analyzed in Nigerian sheep breeds are
shown in Table 10. The l5 microsatellite loci demonstrated high polymorphism in this population with PIC
values ranging from 0.751 to 0.927 (Table 10) lending
strong support to the use of this panel of markers for
assessing genetic diversity in Nigerian sheep.
Differences in the values of global FST, FIT and FIS over
all loci (Table 10) considered in this study shows the
Page 3 of 16
suitability of some microsatellite markers over the others
in the study of genetic diversity in Nigerian sheep
breeds. The mean value of FST is 0.088 while that of FIT
and FIS are 0.394 and 0.335 respectively. The highest
value of FST (0.146) was observed for BM8125 while the
lowest value of 0.022 was seen in DYMS1. Inbreeding
values within and across breeds can also be attributed to
selection. FST and GST are indices of population subdivision. Global FST, GST and GST’ over all loci were all
significantly different (p < 0.001). The population differences examined by global analysis of FST (coefficient of
multilocus genetic differentiation fixation index) for each
of 15 microsatellite loci across the four sheep breeds
revealed that most of the total genetic variation corresponds to differences among individuals within breeds
(91.2%) and 8.8% result from differences among breeds.
Values of GST ranged from 0.033 for OarFCB304 to
0.211 for OarHH47 with a mean of 0.120 (Table 10).
The results of GST in this study reveal that gene variation among the breeds is still low. This differentiation
formed the basis for describing how genetic variation is
partitioned within Nigerian sheep breeds. Among the
loci considered in this study, OarFCB304 had the highest
Shannon information index value of 2.51 and the least
index value of 1.391 was observed for OarHH47.
Number of effective alleles, allelic richness, expected
heterozygosity and observed heterozygosity are presented in Table 11. Effective number of alleles ranged
from 17.330 in Yankasa to 7.200 in WAD. This trend
was also observed for allelic richness with Yankasa having a value of 10.51 and WAD, 6.59. Yankasa had the
highest expected heterozygosity (0.849) while WAD had
the least (0.684). Mean values for observed heterozygosity across the breeds ranged from 0.405 in Uda to 0.563
in Yankasa (Table 11).
The analysis of molecular variance (AMOVA) results
revealed that the greatest variation (60.716%) is within
individuals, 30.545% among individuals within populations
Table 2 Effects of sex on the morphological traits of Nigerian sheep breeds
Male
Female
Balami
Uda
WAD
Yankasa
Balami
Uda
WAD
Yankasa
40.23 ± 1.69
44.28 ± 1.86
27.01 ± 3.46
37.12 ± 1.83
37.49 ± 1.08
33.89 ± 1.53
17.37 ± 2.63
27.99 ± 1.25
BL
91.34 ± 3.92
102.06 ± 4.32
79.63 ± 8.02
89.67 ± 4.26
82.65 ± 2.51
96.01 ± 3.55
75.26 ± 6.10
91.34 ± 2.90
TL
45.00 ± 1.09
46.37 ± 1.20
21.27 ± 2.23
35.39 ± 1.18
44.01 ± 0.69
42.64 ± 0.98
18.34 ± 1.69
34.74 ± 0.80
BW
WH
73.89 ± 1.36
76.66 ± 1.50
53.09 ± 2.78
67.02 ± 1.48
71.16 ± 0.87
70.98 ± 1.23
47.07 ± 2.12
67.01 ± 1.00
RW
74.72 ± 1.07
77.73 ± 1.17
54.95 ± 2.19
68.00 ± 1.16
73.34 ± 0.68
71.68 ± 0.97
50.47 ± 1.66
66.45 ± 0.79
CG
78.39 ± 1.44
83.79 ± 1.59
63.59 ± 2.95
74.64 ± 1.57
78.25 ± 0.92
77.85 ± 1.31
60.21 ± 2.24
73.05 ± 1.07
CD
27.95 ± 1.85
35.70 ± 2.03
25.68 ± 3.78
32.05 ± 2.01
29.58 ± 1.18
30.96 ± 1.67
23.07 ± 2.88
30.35 ± 1.37
RH
14.44 ± 0.35
15.38 ± 0.39
11.36 ± 0.74
12.84 ± 0.39
14.91 ± 0.23
14.45 ± 0.32
11.52 ± 0.55
13.86 ± 0.26
FCL
14.70 ± 0.55
17.73 ± 0.60
14.05 ± 1.12
16.05 ± 0.59
12.78 ± 0.35
14.40 ± 0.49
13.10 ± 0.85
14.59 ± 0.41
EL
16.91 ± 0.52
18.84 ± 0.58
14.63 ± 1.07
15.42 ± 0.57
13.65 ± 0.33
13.93 ± 0.47
12.10 ± 0.82
12.51 ± 0.39
Semi intensive
WAD
Extensive
Yankasa
Balami
Uda
WAD
Intensive
Balami
Uda
Yankasa
Balami
Uda
WAD
Yankasa
BW
34.18 ± 1.14
35.76 ± 1.20
21.54.19.44
30.65 ± 1.03
-
-
19.44 ± 3.77
26.00 ± 11.32
BL
69.86 ± 2.39
95.43 ± 2.68
76.81 ± 5.14
90.23 ± 2.15
-
-
77.00 ± 7.85
110.00 ± 23.56
44.81 ± 1.45
48.58 ± 2.74
-
47.00 ± 8.00
109.54 ± 3.01
112.17 ± 5.71
-
116.00 ± 16.66
TL
41.94 ± 0.72
43.13 ± 0.80
20.11 ± 1.54
34.71 ± 0.64
-
-
17.77 ± 2.36
38.00 ± 7.09
48.05 ± 0.90
48.76 ± 0.90
-
47.50 ± 5.02
WH
71.10 ± 0.95
73.03 ± 1.07
49.00 ± 2.05
66.99 ± 0.85
-
-
49.94 ± 3.13
RW
73.08 ± 0.74
73.03 ± 0.84
52.33 ± 1.61
66.84 ± 0.67
-
-
51.61 ± 2.45
65.00 ± 9.39
73.31 ± 1.20
74.41 ± 2.27
-
69.5 ± 6.64
69.00 ± 7.37
74.80 ± 0.94
78.23 ± 1.78
-
72.00 ± 5.21
CG
76.70 ± 0.98
78.86 ± 1.10
60.79 ± 2.12
73.44 ± 0.88
-
-
63.00 ± 3.24
74.00 ± 9.73
80.82 ± 1.24
86.58 ± 2.36
-
80.00 ± 6.88
CD
25.54 ± 1.24
32.06 ± 1.40
24.02 ± 2.68
30.82 ± 1.12
-
-
24.05 ± 4.09
32.00 ± 12.28
34.78 ± 1.57
36.58 ± 2.97
-
34.50 ± 8.68
RH
14.23 ± 0.23
14.26 ± 0.26
11.26 ± 0.513
13.50 ± 0.21
-
-
11.94 ± 0.78
15.00 ± 2.35
15.13 ± 0.48
17.41 ± 0.57
-
15.00 ± 1.66
FCL
12.21 ± 0.38
15.65 ± 0.43
13.52 ± 0.82
15.06 ± 0.34
-
-
13.27 ± 1.25
15.00 ± 3.77
15.13 ± 0.48
16.17 ± 0.91
-
14.50 ± 2.67
EL
15.03 ± 0.40
16.09 ± 0.45
13.04 ± 0.86
13.47 ± 0.36
-
-
13.00 ± 1.31
12.00 ± 3.94
13.91 ± 0.50
15.14 ± 0.95
-
11.50 ± 2.78
Agaviezor et al. Journal of Animal Science and Biotechnology 2012, 3:38
http://www.jasbsci.com/content/3/1/38
Table 3 Effect of management systems on the morphological traits of Nigerian sheep breeds
Page 4 of 16
< 1 year
1 -2 years
Yankasa
Balami
Uda
2 – 3 years
Balami
Uda
WAD
WAD
Yankasa
Balami
BW
33.66 ± 2.27
42.33 ± 4.64
13.50 ± 8.04
29.14 ± 4.29
30.86 ± 2.27
33.92 ± 2.68
15.54 ± 3.42
27.46 ± 2.14
37.53 ± 1.52
BL
77.68 ± 4.94
88.45 ± 10.09
74.00 ± 17.47
76.10 ± 9.34
67.12 ± 4.94
89.25 ± 5.82
70.45 ± 7.45
83.06 ± 4.67
77.11 ± 3.30
TL
43.47 ± 1.41
38.45 ± 2.89
19.75 ± 5.00
28.71 ± 2.67
42.48 ± 1.41
41.85 ± 1.66
18.09 ± 2.13
35.93 ± 1.33
42.05 ± 0.94
WH
68.08 ± 1.80
66.13 ± 3.68
46.00 ± 6.38
58.14 ± 3.41
67.05 ± 1.80
70.62 ± 2.12
47.50 ± 2.72
66.21 ± 1.70
72.25 ± 1.20
RW
70.45 ± 1.43
71.25 ± 2.92
50.00 ± 5.05
61.28 ± 2.70
70.02 ± 1.43
72.31 ± 1.68
49.04 ± 2.15
65.11 ± 1.35
74.50 ± 0.95
CG
74.16 ± 1.86
77.63 ± 3.79
56.80 ± 6.58
67.21 ± 3.51
71.53 ± 1.86
75.45 ± 2.19
58.22 ± 2.80
72.34 ± 1.75
79.19 ± 1.24
CD
31.69 ± 2.51
35.06 ± 5.12
22.25 ± 8.88
34.77 ± 4.74
22.84 ± 2.51
30.25 ± 2.96
22.36 ± 3.78
28.29 ± 2.37
28.29 ± 1.67
RH
14.04 ± 0.45
12.42 ± 0.92
9.50 ± 1.59
11.31 ± 0.85
13.15 ± 0.45
13.53 ± 0.53
11.00 ± 0.68
12.66 ± 0.42
14.62 ± 0.30
FCL
13.01 ± 0.76
14.76 ± 1.56
13.25 ± 2.71
15.18 ± 1.45
11.16 ± 0.76
14.49 ± 0.90
12.86 ± 1.15
14.60 ± 0.72
13.30 ± 0.51
EL
13.28 ± 0.79
15.61 ± 1.61
12.50 ± 2.80
12.14 ± 1.49
15.04 ± 0.79
14.55 ± 0.93
12.31 ± 1.19
13.53 ± 0.74
15.22 ± 0.52
Agaviezor et al. Journal of Animal Science and Biotechnology 2012, 3:38
http://www.jasbsci.com/content/3/1/38
Table 4 Effects of age on the morphological traits of Nigerian sheep breeds
Page 5 of 16
2 – 3 years
3 – 4 years
Uda
WAD
Yankasa
BW
36.27 ± 1.87
25.92 ± 3.03
32.62 ± 1.59
BL
99.23 ± 4.06
81.07 ± 6.60
92.58 ± 3.46
TL
43.85 ± 1.16
20.67 ± 1.89
35.50 ± 0.99
WH
73.69 ± 1.48
50.89 ± 2.41
67.39 ± 1.26
RW
73.84 ± 1.17
54.50 ± 1.91
CG
79.51 ± 1.53
63.07 ± 2.48
CD
33.36 ± 2.06
RH
FCL
EL
Balami
5 – 6 years
Uda
WAD
Yankasa
Balami
Uda
44.89 ± 1.75
40.51 ± 2.04
22.16 ± 6.56
103.44 ± 4.43
103.23 ± 4.43
82.66 ± 14.27
31.79 ± 2.01
48.11 ± 3.79
58.75 ± 8.04
96.13 ± 4.36
121.00 ± 8.23
47.07 ± 1.09
45.72 ± 1.27
18.16 ± 4.08
34.47 ± 1.25
51.11 ± 2.36
75.32 ± 1.39
75.16 ± 1.62
50.50 ± 5.20
68.33 ± 1.59
880.33 ± 3.00
67.75 ± 1.00
76.62 ± 1.10
75.41 ± 1.28
53.66 ± 4.13
68.24 ± 1.26
73.07 ± 1.30
83.10 ± 1.67
83.10 ± 1.67
68.83 ± 5.37
76.12 ± 1.64
25.64 ± 3.35
31.44 ± 1.75
30.92 ± 1.93
32.85 ± 2.25
23.83 ± 7.25
30.96 ± 2.22
14.70 ± 0.37
11.78 ± 0.60
13.92 ± 0.31
16.36 ± 0.34
15.90 ± 0.40
13.00 ± 1.30
16.38 ± 0.63
13.85 ± 1.02
15.35 ± 0.53
14.56 ± 0.59
15.99 ± 0.69
13.83 ± 2.21
16.33 ± 0.65
13.85 ± 1.05
14.01 ± 0.55
14.38 ± 0.61
16.23 ± 0.71
12.16 ± 2.28
WAD
Yankasa
-
28.80 ± 5.08
-
102.68 ± 11.05
63.00 ± 5.00
-
35.50 ± 3.16
82.00 ± 6.38
-
71.64 ± 4.03
76.88 ± 2.38
84.50 ± 5.05
-
68.54 ± 3.19
101.00 ± 6.58
101.00 ± 6.58
-
77.72 ± 4.16
36.66 ± 4.18
41.50 ± 8.81
-
32.54 ± 5.61
14.16 ± 0.39
15.33 ± 0.75
19.50 ± 1.59
-
13.64 ± 1.01
14.67 ± 0.67
14.66 ± 1.28
14.50 ± 2.71
-
16.88 ± 1.71
12.61 ± 0.70
14.38 ± 1.32
16.50 ± 2.80
-
14.04 ± 1.77
123 ± 17.47
Agaviezor et al. Journal of Animal Science and Biotechnology 2012, 3:38
http://www.jasbsci.com/content/3/1/38
Table 4 Effects of age on the morphological traits of Nigerian sheep breeds (Continued)
Page 6 of 16
South
Middle belt
Yankasa
Balami
Uda
North East
Balami
Uda
WAD
WAD
Yankasa
Balami
BW
45.66 ± 6.37
43.00 ± 3.67
20.91 ± 2.01
44.26 ± 2.53
47.22 ± 3.67
46.99 ± 3.32
-
28.47 ± 1.74
32.79 ± 1.61
BL
102.66 ± 11.77
98.11 ± 6.79
76.86 ± 3.72
92.000 ± 4.67
110.09 ± 6.79
108.89 ± 6.14
-
98.94 ± 3.22
59.74 ± 2.95
TL
44.00 ± 4.03
41.74 ± 2.32
19.41 ± 1.27
36.36 ± 1.60
43.78 ± 2.32
44.50 ± 2.10
-
31.47 ± 1.10
44.06 ± 1.01
WH
74.66 ± 5.19
77.88 ± 3.00
49.28 ± 1.64
65.23 ± 2.06
79.29 ± 3.0
82.01 ± 2.71
-
68.95 ± 1.42
67.62 ± 1.31
RW
77.00 ± 4.17
78.33 ± 2.40
52.11 ± 1.31
68.39 ± 1.65
77.57 ± 2.40
79.60 ± 2.17
-
67.69 ± 1.14
73.25 ± 1.05
CG
76.50 ± 5.58
78.92 ± 3.22
61.45 ± 1.76
72.26 ± 2.21
82.05 ± 3.22
86.60 ± 2.91
-
75.60 ± 1.52
75.57 ± 1.41
CD
33.66 ± 6.71
33.33 ± 3.87
24.03 ± 2.12
30.15 ± 2.66
36.56 ± 3.87
41.26 ± 3.50
-
32.72 ± 1.83
25.31 ± 1.69
RH
15.16 ± 1.35
14.61 ± 0.78
11.46 ± 0.42
12.86 ± 0.53
15.51 ± 0.78
15.65 ± 0.70
-
14.08 ± 0.37
13.84 ± 0.34
FCL
20.00 ± 1.75
20.88 ± 1.01
13.45 ± 0.55
17.21 ± 0.69
18.95 ± 1.01
20.37 ± 0.91
-
16.48 ± 0.48
11.41 ± 0.44
EL
19.83 ± 2.06
20.33 ± 1.19
13.03 ± 0.65
16.26 ± 0.82
18.83 ± 1.19
21.46 ± 1.07
-
13.12 ± 0.56
14.77 ± 0.52
Agaviezor et al. Journal of Animal Science and Biotechnology 2012, 3:38
http://www.jasbsci.com/content/3/1/38
Table 5 Effects of sampling location on the morphological traits of Nigerian sheep breeds
Page 7 of 16
North East
North West
North Central
Uda
WAD
Yankasa
Balami
Uda
WAD
Yankasa
Balami
Uda
WAD
Yankasa
BW
32.10 ± 2.08
-
23.00 ± 7.80
42.32 ± 1.32
40.15 ± 1.89
-
27.08 ± 2.67
34.18 ± 2.01
34.38 ± 3.18
-
29.15 ± 1.64
BL
102.47 ± 3.85
-
70.55 ± 14.42
108.43 ± 2.45
106.11 ± 3.49
-
99.76 ± 4.94
58.09 ± 5.88
58.09 ± 5.88
-
80.60 ± 3.04
TL
43.71 ± 1.32
-
44.50 ± 4.93
47.51 ± 0.84
45.85 ± 1.19
-
37.47 ± 1.69
37.47 ± 1.27
41.84 ± 2.01
-
36.05 ± 1.04
WH
70.92 ± 1.70
-
55.50 ± 6.36
73.56 ± 1.08
72.67 ± 1.54
-
69.52 ± 2.18
72.59 ± 1.64
69.05 ± 2.59
-
65.60 ± 1.34
RW
71.65 ± 1.36
-
62.75 ± 5.11
74.88 ± 0.87
74.20 ± 1.23
-
69.58 ± 1.75
70.42 ± 1.31
71.53 ± 2.08
-
64.85 ± 1.07
CG
78.87 ± 1.82
-
71.00 ± 6.83
80.32 ± 1.16
81.79 ± 1.65
-
78.05 ± 2.34
76.94 ± 1.76
74.31 ± 2.79
-
70.70 ± 1.44
CD
32.65 ± 2.19
-
48.00 ± 8.22
34.37 ± 1.40
35.58 ± 1.99
-
33.94 ± 2.82
20.24 ± 2.12
17.72 ± 3.35
-
27.67 ± 1.73
RH
14.42 ± 0.44
-
12.05 ± 1.65
15.39 ± 0.28
16.02 ± 0.40
-
13.35 ± 0.56
14.55 ± 0.42
11.80 ± 0.67
-
13.47 ± 0.35
FCL
16.62 ± 0.57
-
15.60 ± 2.15
14.78 ± 0.36
14.55 ± 0.52
-
13.94 ± 0.73
10.68 ± 0.55
8.98 ± 0.87
-
13.28 ± 0.45
EL
15.75 ± 0.67
-
12.00 ± 2.52
13.62 ± 0.43
13.89 ± 0.61
-
11.52 ± 0.86
14.48 ± 0.65
13.64 ± 1.03
-
13.30 ± 0.53
Agaviezor et al. Journal of Animal Science and Biotechnology 2012, 3:38
http://www.jasbsci.com/content/3/1/38
Table 5 Effects of sampling location on the morphological traits of Nigerian sheep breeds (Continued)
Page 8 of 16
Agaviezor et al. Journal of Animal Science and Biotechnology 2012, 3:38
http://www.jasbsci.com/content/3/1/38
Page 9 of 16
Table 6 Stepwise selection of traits
Traits entered
Partial R2
F value
Pr > F
Wilk’s lambda
Pr < lambda
ASCC
Pr > ASCC
Tail length
0.4913
128.12
<0.0001
0.508710
<0.0001
0.164
<0.0001
Rump height
0.1056
15.63
<0.0001
0.454978
<0.0001
0.183
<0.0001
Chest girth
0.1055
15.49
<0.0001
0.356692
<0.0001
0.246
<0.0001
Ear length
0.0650
9.18
<0.0001
0.425392
<0.0001
0.201
<0.0001
Chest depth
0.0626
8.79
<0.0001
0.398760
<0.0001
0.212
<0.0001
ASCC Average squared canonical correlation.
and 8.739 among populations which are consistent with
the FST results (Table 12). Figure 1 presents the genetic
distances (DA) between Nigerian sheep breeds.
Population structure assessed by STRUCTURE software
and barplots generated by DISTRUCT are presented in
Figure 2. Classifications with the highest probability under
the model that assumes independent allele frequencies
and inbreeding coefficients among assumed clusters
revealed the presence of ancestral populations (K) which
is consistent with the morphological and diversity analyses
reported earlier in this study. At K = 2, two clusters were
constituted from breeds descended from Balami and Yankasa, both of which are from Northern Nigeria. At K = 3
and K = 4, one more cluster emerged and further analyses
did not reveal any additional strong high level substructure, so separating the entire dataset into 3 major clusters
was chosen as the final configuration. There are however,
several cases of admixtures in the genome of some of the
individuals that constitute the cluster. Yankasa and Balami
breeds had more cases of admixtures followed by Uda
while the WAD breed had the least cases of admixtures.
Discussion
Morphological diversity
Phenotypic characterization has been shown to be an accessible and easy-to-use tool in conservation and breeding
programs [6]. This could be explained in part by the high
heritability of measures of size across ontogeny [21].
Marked differences were observed in the morphological
traits of the sheep breeds in this study. The present values
of BW and linear body measurements of Yankasa, Uda
and Balami sheep are higher than the range of values
reported for Ganjam sheep in India by Arora et al. [10].
Most researchers consider an organism’s phenotype as a
multivariate set of variables and the covariation of traits
an important analytical consideration [22]. Discriminant
analysis of morphometric traits is a mathematical approach that has been widely used in determining the relationships between different breeds of livestock [6,23-25].
The most discriminating variables obtained in this study
are similar to previous reports by Dossa et al. [16] and
Vargas et al. [26]. The present results indicate that there is
significant morphological differentiation among Nigerian
sheep populations. This morphological diversity pattern
could be as a result of inherent genetic potential of each
breed, alongside geographical isolation, ecological variation and community isolation [5,27]. This is noticeable in
the remarkable morphological differences between WAD
(more adaptable to the wet, dense forest and derived
savannah zones of southern Nigeria) and Yankasa, Uda
and Balami sheep (more suited to the dry climatic conditions of Northern Nigeria).
The larger values reported for the conformation traits of
males are in consonance with earlier reports on sheep
[28], goats [26] and cattle [29]. However, Bacchi et al. [30]
found no sexual dimorphism in the morphometric
characters considered in Lama guanicoe guanicoe in
Argentina. The higher values recorded for intensively
managed animals might be as a result of better nutrition and management practices as they tend to gain more
attention than those reared semi-intensively. Riva et al.
[14] reported that husbandry system was a source of variation in the body measurements of Bergamasca sheep;
theirs was a case between transhumance and sedentary
systems, where the former was taller, longer and showed a
wider hock while the latter was wider at the chest and
rump.
Table 8 Percentage of individual sheep classified into
genetic group
Table 7 Total canonical structure of the discriminant
analysis of the four sheep breeds
Breed
WAD
Yankasa
Uda
Traits
CAN1
CAN2
CAN3
WAD
93.33
6.67
0.00
Balami
0.00
Rump height
0.839
0.098
0.038
Yankasa
1.64
63.93
15.57
18.85
Chest girth
0.493
−0.112
−0.159
Uda
0.00
17.20
45.16
37.63
Chest depth
0.485
0.261
−0.012
Balami
0.00
17.83
21.02
61.15
Tail length
0.922
−0.107
0.098
Error level
0.067
0.361
0.548
0.389
Ear length
0.239
−0.242
0.791
Priors
0.250
0.250
0.250
0.250
Agaviezor et al. Journal of Animal Science and Biotechnology 2012, 3:38
http://www.jasbsci.com/content/3/1/38
Table 9 Kinship coefficient (Dkf) below the diagonal and
proportion of shared alleles above the diagonal between
the Nigerian sheep breeds
Balami
Uda
WAD
Yankasa
Balami
0.000
0.757
0.800
0.366
Uda
0.941
0.000
0.481
0.643
WAD
0.946
0.789
0.000
Yankasa
0.861
0.906
0.915
Page 10 of 16
Table 11 Effective number of alleles (Na), Allelic Richness,
Expected Heterozygosity (He) and Observed
Heterozygosity (Ho) in Nigerian sheep breeds
Breed
Effective
No. of
alleles
(Na)
Allelic
richness
Expected
Heterozygosity
Observed
heterozygosity
0.693
Balami
13.730
9.240
0.823
0.558
0.000
Uda
9.000
8.200
0.754
0.405
WAD
7.200
6.590
0.684
0.448
Yankasa
17.330
10.510
0.849
0.563
Mean
11.815
8.635
0.778
0.494
The three canonical functions (linear combinations of
the continuous variables that summarize variation between the four sheep breeds) obtained could be used
as criterion for establishing phenotypic standards for
Nigerian sheep. The closeness between Uda and Balami
sheep compared to their WAD and Yankasa counterparts might be as a result of near biometric convergence,
which may function as a guide to genetic and evolutionary relationships between the two breeds. The longer
distance between WAD and other breeds, especially
Balami and Uda revealed that phenotypic differences are
maintained in part by the reduction of gene flow among
populations separated by large distances as well as
physical-ecological barriers. The WAD sheep predominate in the trypano-endemic humid zones of Southern Nigeria. The intermediate morphology of Yankasa sheep
might be maintained by natural selection on body size of
individuals inhabiting intermediate or a range of different environments (this breed of sheep, which occupies a
central geographical position, is more widely distributed
Table 10 Polymorphism Information Content (PIC), F
statistics (FIS, FIT, FST) according to Weir and Cockerham
(1984) GST and shannon index values for 15 microsatellite
markers analyzed in Nigerian sheep breeds
Locus
PIC
FIS
FIT
FST
GST
DYMS1
0.927
0.442
0.454
0.022
0.125
Shannon index
2.964
OarCP34
0.751
0.373
0.436
0.099
0.106
2.203
OarFCB193
0.845
0.220
0.329
0.140
0.163
2.321
BM8125
0.808
0.316
0.416
0.146
0.130
2.082
OarJMP29
0.904
0.287
0.352
0.092
0.084
2.854
OarJMP58
0.899
0.318
0.361
0.063
0.079
2.325
OarFCB128
0.782
0.534
0.593
0.126
0.187
2.212
OarFCB304
0.901
0.319
0.339
0.029
0.033
2.882
SRCRSP1
0.787
0.251
0.301
0.066
0.046
2.263
OarAE129
0.792
0.325
0.365
0.059
0.130
1.653
OarVH72
0.853
0.171
0.278
0.129
0.142
2.375
SRCRSP5
0.896
0.406
0.472
0.111
0.175
2.914
MCM 140
0.872
0.428
0.459
0.055
0.069
2.332
OarHH 47
0.825
0.387
0.436
0.079
0.211
SRCRSP9
0.857
0.245
0.320
0.099
Mean
0.846
0.335
0.394
0.088
in the country than the other three sheep breeds).
Selective advantage might therefore favor Yankasa sheep
from the biogeographical context, although more heterotic gains might be attained from crosses involving
WAD and Uda or Balami. This is because populations
are dynamic units which adapt physiologically and genetically to their environments and sensitive to, and
within limits, responsive to any change in their environmental conditions. An appreciable percentage of animals
were classified into their distinct breeds. However, some
level of intermingling was observed between Uda and
Balami, which could partly be attributed to indiscriminate
crossbreeding due to geographical proximity.
Molecular genetic diversity between populations
The high number of alleles observed in Yankasa must
have contributed to its adaptability to more agroecological zones in Nigeria compared to other sheep
breeds that may confer selective advantages [19]. Genetic variation is necessary to allow organisms to adapt to
ever-changing environments with some of this variation
stemming from introduction of new alleles by the random and natural process of mutation, since the frequency of occurrence of an allele changes regularly as a
result of mutation, genetic drift, and selection [31]. The
number of alleles identified in this study is slightly
higher than those reported by Adebambo et al. [19] with
observed number of alleles in Yankasa, Balami, Uda and
WAD being 11.5, 3.9, 5.85 and 5.05, respectively, based
on relatively small sample sizes. Higher numbers of
alleles may also be reflective of larger sample sizes used
in this study. However, the values fall within the range
Table 12 AMOVA design and results (average over 15 loci)
Source of variation
Sum of
squares
Variance
components
Percentage
variation
Among populations
172.808
0.585
8.739
Among individuals
within populations
1432.042
2.04
30.545
2.172
0.122
2.268
Within individuals
759.500
4.063
60.716
0.120
2.388
Total
2364.350
6.692
Agaviezor et al. Journal of Animal Science and Biotechnology 2012, 3:38
http://www.jasbsci.com/content/3/1/38
1.300
0.975
0.650
0.325
0.000
Uda
WAD
Balami
Yankasa
Figure 1 Dendogram showing diversity and similarity among
Nigerian breeds of sheep.
reported for other sheep breeds. Arora et al. [10]
reported values for observed number of alleles in the
range of 7 and 25 with a mean value of 13.96. The small
average number of alleles per locus in Uda and WAD
may be the result of a small number of founder animals
since the degree of genetic variability among these animals will be small even in the presence of crossbreeding
and mutation rates [32].
Different indices used in this study demonstrate the
existence of genetic diversity among Nigerian sheep
breeds. Variations in allelic richness were observed
across all loci. Mean values among the various loci ranged from 3.66 in OarHH47 to 14.73 in OarJMP29, with
mean values of 8.63. Dalvit et al. [33] found similar
values of allelic richness with an average of 8.8, when
they analyzed 10 European sheep breeds. Measures of
genetic diversity based on allelic richness are considered
important in conservation genetics because markerassisted methods for maximizing the number of alleles
conserved have been shown to be effective [34]. Allelic
richness may be a useful indicator of a decrease in
Page 11 of 16
population size or of past bottlenecks [35]. It is also relevant in a long-term perspective, as selection limits are
determined by the initial allelic composition rather than
by heterozygosity [36]. As noted by El Mousadik & Petit
[37] the often reported numbers of alleles per locus for
the whole population or averaged over subpopulations
are not comparable because of the much larger sample
size of the whole population.
Differences among populations are commonly quantified by the use of one of several statistics, including
Wright’s inbreeding coefficient (FST) and Nei’s coefficient
of gene variation (GST) [38]. The rather high level of
genetic variability in Yankasa is due to the fact that this
is the most numerous sheep breed in Nigeria [19]. These
values (0.658 to 0.902) are within the range observed in
other sheep breeds in other parts of the world. Arora
et al. [10] observed 0.594 to 0.922 for sheep breeds from
Southern Peninsular and Eastern regions of India. Values
for Italian sheep breeds are 0.761 to 0.805 [39] and
European sheep breeds ranged from 0.538 to 0.807 [40].
The observed heterozygosity values are generally lower
than the expected heterozygosity in all the breeds and
loci considered. The highest value of observed heterozygosity in Yankasa could also be attributed to its large
number in Nigeria [19]. The results of GST analysis in
this study reveal that the proportion of gene variation
among the breeds is still low.
The Shannon index of Nigerian sheep breed in this
study revealed low species richness and evenness since
all the indices were below 3.5, the mark set for high species evenness and richness [41]. This might not be unconnected with the level of heterozygote deficiency
observed among this population, possibly due to the
management system [42]. Data collected within the
EU-ECONOGENE project on sheep and goat diversity
in marginal areas indicate the presence of significant
K=2
K=3
K=4
Figure 2 Population structure assessed by Structure. Bar plot, generated by DISTRUCT, depicts classifications with the highest probability
under the model that assumes independent allele frequencies and inbreeding coefficients among assumed clusters. Each individual is
represented by a vertical bar, often partitioned into colored segments with the length of each segment representing the proportion of the
individual’s genome from K = 2, 3, 4 ancestral populations. Breeds are separated by black lines.
Agaviezor et al. Journal of Animal Science and Biotechnology 2012, 3:38
http://www.jasbsci.com/content/3/1/38
inbreeding in most of the breeds [7,43]. This is likely
due to poor breeding management of frequently small
herds, which leads to partial isolation and fragmentation
both at the local and breed levels [44].
Differences in the values of global FST, FIT and FIS over
all loci considered in this study supports the suitability
of some microsatellite markers over the others in the
study of genetic diversity in Nigerian sheep breeds. Selection based on estimated breeding values rather than
phenotypic merits can lead to the extensive use of a
small number of elite individuals in purebred livestock
populations, and could potentially increase inbreeding
[45]. It has therefore been suggested that breeds with
wide range of genetic diversity are needed in the future
for generating transgressive variation for quantitative
trait loci mapping and developing new genotypes for
particular management systems and market demands
[46]. On the average, within-breed heterozygote deficit
(FIS) was observed to be 33.5% whereas the total population (FIT) exhibited 39.5% deficit with significant values
(p < 0.05). Some investigations have reported relatively
low variability in local non-selected breeds, as is the case
of certain Nigerian [19] and Chinese sheep [47].
The relative higher genetic identity is probably due to
the continuous crossing between populations at least in
the recent past [48]. The AMOVA results revealed that
the greatest variation (60.716%) is within the individual,
30.545% among individuals within populations and 8.739
among populations which are consistent with FST results.
Finally, the trend in genetic relationship between these
Nigerian sheep breeds is the same for delta mu square,
kinship coefficient and proportion of shared alleles.
Indeed, our recent molecular characterization of Nigerian
sheep using mtDNA sequences of the D-loop found 96
haplotypes, but only 5 haplotypes are common to all the
breeds [20], showing relatively divergent haplotypes within
breeds and geographical locations. This suggests that gene
flow has occurred on a regional scale at some time in the
recent past and that the breeds have not been subdivided
by long term biogeographic barriers.
Genetic structure of the populations
The closest genetic distance between Yankasa and
Balami at 0.184 and the farthest distance between
Balami and WAD (0.665) may be reflective of their geographical locations in Nigeria since Yankasa and Balami
are more of northern than southern breeds. This is further confirmed by the greater genetic distance between
WAD and Balami observed in this study. These results
are supported by Adebambo et al. [19] among these
breeds. The pattern of differentiation revealed by the
matrix of Nei’s genetic distances and the tree topology reflected the evolutionary history, geographical distributions and the gene flow among breeds. Genetic
Page 12 of 16
structure of a breed at any time is the result of a balance
between genetic drift (founder effect and selection) and
gene flow [32]. WAD and Balami shared the highest
number of alleles while the least is between Yankasa and
Balami. However, the pattern of genetic distances
deviated from the morphological distances obtained for
the four sheep breeds. This may be related to varying
sensitivity of the two distance estimates, although both
methods were able to separate the southern WAD goats
from their northern counterparts. A similar finding was
reported in Ankole cattle [49] where the results of morphological analyses were not in concordance with the
molecular genetic relationship results. The authors
attributed this to the fact that microsatellites loci are selectively neutral whereas morphological traits are under
selection.
Results from the STRUCTURE analysis revealed that
varying the number of presumed ancestral populations
(K) produces clusters that are consistent with the
observed morphological categorization. The first level of
clustering (K = 2) reflects the presence of two clusters in
the four breeds examined and further evaluation
revealed a third strong cluster. This result suggests that
the four breeds originated from three ancestral populations which diverged as a result of several years of adaptation and domestication. Further evaluation of the
clusters revealed the presence of sub clusters and admixtures which are indicative of substantial gene flow between these breeds. WAD is the only breed with a
minimal case of admixture. This may be due to geographical delineation of the breed and the breeding practices of the owners of these animals predominantly in
the southern part of the country. Yankasa possess alleles
which are shared by the other three breeds and this is
consistent with the results discussed above.
The mitochondrial data showed a different relationship
in the neighbor-joining tree for the four Nigerian sheep
breeds [20]. In that tree, the first divergence was for Yankasa breed, followed by WAD and later by Uda and
Balami. This can be explained by differences in the breeding patterns, by the use of dams and rams in different
management schemes. The higher level of concordance of
morphological and mitochondrial DNA data could mean a
more ancestral relationship among the breeds revealed by
mitochondrial DNA that is maternally inherited. Differences found in the microsatellite data may indicate recent
crossbreeding due to geographical closeness among the
sympatric breeds, especially involving males from one
breed crossing with females of the other breeds.
Conclusion
Morphological and genetic diversity of Nigerian sheep
breeds is eroding gradually. These results suggest that
within-breed genetic variation observed in Nigeria sheep
Agaviezor et al. Journal of Animal Science and Biotechnology 2012, 3:38
http://www.jasbsci.com/content/3/1/38
is more than between-breed and this variation could be
a valuable tool for genetic improvement and conservation. The higher genetic variability in Yankasa may mean
the presence of unique alleles reflecting the presence of
certain functional genes which may possibly be related
to better adaptability of Yankasa in more agro-ecological
zones of Nigeria. The higher level of heterozygosity in
this study provides the basis for further improvement
through selection of primarily Yankasa as well as the
other breeds.
Methods
Study area and population
Samples were collected across Nigeria covering all agroecological zones from the arid North to the dense rainforest bordering the coast in the South. Nigeria is
located in West Africa on the Gulf of Guinea (latitude
10º00’ N, longitude 8º00’ E) with a total area of
923,768 km2 (twice the size of California). Nigeria is
bounded by Niger, Benin and Cameroon Republics on
the North, West and East, respectively (Figure 3). The
protocol for the experiment was approved by the Institutional Animal Use and Care Committee of the University
of Agriculture, Abeokuta (UNAAB), Nigeria. A random
sample of 402 sheep (132 males and 270 females) of the
four Nigerian sheep breeds [Balami – 133, Uda – 94, West
African Dwarf (WAD) – 52, Yankasa – 123] were selected
from cities and villages across the country. There is no relationship between sires and dams of the animals sampled
since they came from different parts of the country. The
animals were reared in semi-intensive (323 animals) and
Figure 3 Map of Nigeria showing sampled locations.
Page 13 of 16
intensive (79 animals) systems of management. They were
at least 15.5 months of age (2-tooth to 8-tooth age) and
where records were missing; age was estimated using dentition as described by Wilson and Durkin [50]. Body
weight (BW) (kg) and nine body linear measurements
(cm), namely height at withers (HW), rump height (RH),
body length (BL), ear length (EL), fore cannon bone length
(FCL), tail length (TL), chest girth (CG), chest depth (CD)
and rump width (RW) respectively were taken on each
animal, following standard procedure and anatomical
reference points earlier reported [24,28]. In addition,
5–7 ml of whole blood were collected from 384 sheep
of the four breeds [Balami – 106, Uda – 94, West
African Dwarf (WAD) – 52, Yankasa – 132] into heparinized tubes from the jugular vein of each animal and
stored on ice before they were transferred to the laboratory for analysis.
DNA extraction, polymerase chain reaction (PCR) and
fragment analysis
DNA was extracted from 50 μl of whole blood using the
ZymoBead™ Genomic DNA Kit (Zymo Research Corp.
Irvine, CA, USA) according to the manufacturer’s
recommendations and DNA yield and quality were
assessed using a Nanodrop ND-100 Spectrophotometer
(Nanodrop Technologies, Inc., DE, USA). The DNA was
amplified by PCR in a MyCycler™ Thermal Cycler
(Biorad, Hercules, CA, USA) using 15 microsatellite
markers selected from the FAO recommended list [51]
described in Table 13. The 20 μl amplification reactions
contained 2 μl containing 30-50 ng template DNA,
Agaviezor et al. Journal of Animal Science and Biotechnology 2012, 3:38
http://www.jasbsci.com/content/3/1/38
Page 14 of 16
Table 13 Microsatellite primer sequences that were used and their base lengths
1
2
Name
Sequence
Base length
Annealing temperature (°C)
Allele range (bp)
Dye
OarFCB193
TTCATCTCAGACTGGGATTCAGAAAGGC
28
54
96-136
6FAM
GCTTGGAAATAACCCTCCTGCATCCC
26
56
96-150
NED
58
145-169
6FAM
56
150-188
6FAM
54
133-159
6FAM
50
110-130
VIC
55
96-130
VIC
50
112-130
VIC
57
121-145
VIC
58
130-152
VIC
59
159-211
NED
54
116-148
NED
56
126-158
NED
55
99-135
6FAM
60
167-193
NED
OarJMP29
GTA TAC ACG TGG ACA CCG CTT TGT AC
26
GAA GTG GCA AGA TTC AGA GGG GAA G
25
3
OarJMP58
GAAGTCATTGAGGGGTCGCTAACC
24
CTTCATGTTCACAGGACTTTCTCTG
25
4
OarFCB304
CCCTAGGAGCTTTCAATAAAGAATCGG
27
CGCTGCTGTCAACTGGGTCAGGG
23
AATCCAGTGTGTGAAAGACTAATCCAG
27
GTAGATCAAGATATAGAATATTTTTCAACACC
32
CTCTATCTGTGGAAAAGGTGGG
22
GGGGGTTAGACTTCAACATACG
22
ATTAAAGCATCTTCTCTTTATTTCCTCGC
29
CAGCTGAGCAACTAAGACATACATGCG
27
5
6
7
OarAE129
BM8125
OarFCB128
8
OarCP34
GCTGAACAATGTGATATGTTCAGG
24
GGGACAATACTGTCTTAGATGCTGC
25
9
OarVH72
GGCCTCTCAAGGGGCAAGAGCAGG
24
CTCTAGAGGATCTGGAATGCAAAGCTC
27
31
10
OarHH47
TTTATTGACAAACTCTCTTCCTAACTCCACC
GTAGTTATTTAAAAAAATATCATACCTCTTAAGG
34
11
DYMS1
AACAACATCAAACAGTAAGAG
21
CATAGTAACAGATCTTCCTACA
22
12
13
14
15
SRCRSP1
SRCRSP5
SRCRSP9
MCM140
TGC AAG AAG TTT TTC CAG AGC
21
ACC CTG GTT TCA CAA AAG G
19
GGA CTC TAC CAA CTG AGC TAC AAG
24
GTT TCT TTG AAA TGA AGC TAA AGC AAT GC
29
AGA GGA TCT GGA AAT GGA ATC
21
GCA CTC TTT TCA GCC CTA ATG
21
GTT CGT ACT TCT GGG TAC TGG TCT C
25
GTC CAT GGA TTT GCA GAG TCA G
22
2.0 μl of each primer, and 16 μl nuclease free water in a
AccuPower TLA PCR Premix containing NTPs, MgCl2
and Taq DNA polymerase (Bioneer Corp., Irvine, CA,
USA) using annealing temperatures shown in Table 13.
PCR protocol was as follows: denaturing at 94°C for
5 minutes, 35 cycles of amplification at 94°C for 30 seconds, annealing at annealing temperature of marker for
30 seconds, extension at 72°C for 1 minute, final extension
at 72°C for 5 minutes and held at 4°C until analysis. PCR
products were separated by electrophoresis in 1.5% agarose gel stained with 0.5 μg/ml ethidium bromide. Electrophoresis was carried out at room temperature for 1 hour
at 100 volts using a Bio-Rad Power Pac™ electrophoresis
apparatus (Biorad, Hercules, CA, USA). The resulting
amplified bands were visualized with UV light and photographed using the Alphalmager™ 2200 gel documentation
and analysis system (Cell Biosciences, CA, USA), and were
scored using GENEMate Quanti-Marker 100 bp DNA ladder (BioExpress, Kaysville, UT, USA).
DNA fragment analysis of microsatellite markers was
carried out using the Applied BioSystems 3730xl DNA
Analyzer (Applied Biosystems, Carlsbad, CA, USA) at
the Cornell University Genomics Core Facility. GeneMapper Software version 3.0 [52] (which combines the
functions of GeneScan and Genotyper software in one
convenient package) was used to generate microsatellite
genotypes.
Data analysis
Means, standard deviations, standard errors and coefficients of variation were computed for all the traits measured using the GLM procedure of SAS statistical
package [53]. Sources of variation in the linear model
were breed, sex and system of management. Multivariate
Agaviezor et al. Journal of Animal Science and Biotechnology 2012, 3:38
http://www.jasbsci.com/content/3/1/38
analysis [discriminant analysis] was employed to investigate morphological structure, and quantify differences
among the sheep populations. Stepwise discriminant
analysis was performed to gain information about the
most important traits in separating the four sheep
breeds using the STEPDISC procedure. These most important variables were then subjected to canonical discriminant analysis using the CANDISC procedure to
derive canonical functions and estimate Mahalanobis
distances necessary for the differentiation of the sheep
populations. The ability of these canonical functions to
allocate individual sheep to its original breed was calculated as percentage correct assignment of each breed
using the DISCRIM procedure (Nearest Neighbour Discriminant Analysis with Mahalanobis Distances) of the
SAS statistical package. Microsatellite Analyzer (MSA)
version 4.05 [54] and GENEPOP [55] were used to generate genetic diversity parameters.
A model-based Bayesian clustering analysis was used
to infer population structure and the level of admixture
in the sheep breeds implemented in STRUCTURE v2.3
[56]. The STRUCTURE algorithm assumes K populations, each of which is in Hardy-Weinberg and linkage
equilibria and characterized by a set of allele frequencies
at each locus. Analysis was performed with a burn in
length of 20,000 followed by 100,000 Markov chain
Monte Carlo iterations for each of K = 1 to 6, with ten
replicate runs for each K using independent allele frequencies and an admixture model. Results across ten
runs at each K were compared based on similarity coefficients (SC) as previously described [57]. The breeds were
assigned to wide clusters based on major ancestry and
submitted to a second round of STRUCTURE analysis
performed within each wide cluster.
Abbreviations
BL: Body length; BW: Body weight; CD: Chest depth; CG: Chest girth; EL: Ear
length; FCL: Fore cannon bone length; HW: Height at withers; RH: Rump
height; RW: Rump width; TL: Tail length.
Competing interests
The authors declare that they have no competing interests.
Authors’ contributions
IGI and BOA conceived the study; BOA, IGI, OAA, MOO, CONI designed the
study; IGI and MO obtained funding for the study; BOA, MAA, AY, MW, TMS,
OOA, BMI, SAA, GOO and JOE collected phenotypic data and contributed to
morphological data analysis; BOA, AY, and OOA carried out morphological
data analysis; BOA, MAA and MD carried out the molecular lab analysis; BOA,
MAA, SOP, MD, KK and IGI carried out the molecular data analysis and
interpretation, BOA, MAA, IGI, MO, AY and SOP wrote the manuscript. All
authors read and approved the manuscript.
Acknowledgements
This study was supported by the College of Agriculture and Life Sciences,
Cornell University, Ithaca, NY. USA. Additional support from the United States
Agency for International Development (USAID) through the award of the
Norman Borlaug Leadership Enhancement in Agriculture Program (LEAP)
Fellowship to Moses Okpeku and as well as a research grant from
International Foundation for Science, Stockholm to Moses Okpeku are
Page 15 of 16
gratefully acknowledged. Special thanks to Dr. W. Ron Butler for the
opportunity given to Brilliant O. Agaviezor, Mufliat A. Adefenwa and
Abdulmojeed Yakubu as visiting graduate students at Cornell University.
Author details
1
Department of Animal Breeding and Genetics, University of Agriculture,
Abeokuta, Nigeria. 2Department of Cell Biology and Genetics, University of
Lagos, Lagos, Nigeria. 3Department of Animal Science and Fisheries,
University of Port Harcourt, Port Harcourt, Nigeria. 4Department of Animal
Science, Nasarawa State University, Keffi, Shabu-Lafia Campus, Lafia, Nigeria.
5
Dept of Livestock Production, Niger Delta University, Amasomma, Bayelsa
State, Nigeria. 6Dept of Animal Science, Cornell University, Ithaca, NY 14853,
USA. 7Dept of Animal Science, Berry College, Mount Berry, GA 30149, USA.
8
Department of Animal Science, Adnan Menderes University, Aydin 09100,
Turkey.
Received: 30 December 2011 Accepted: 1 November 2012
Published: 24 November 2012
References
1. FAOSTAT: Food and Agriculture Organization of the United Nations. FAOSTAT
Database on Agriculture. Rome, Italy: 2011 [http://faostat.fao.org/default.aspx.]
2. Yakubu A, Ibrahim IA: Multivariate analysis of morphostructural
characteristics in Nigerian indigenous sheep. Ital J Anim Sci 2011,
10:17.
3. FAO: The State of Food and Agriculture, Livestock in the Balance. Rome:
2010:176.
4. Taberlet P, Coissac E, Pansu J, Pompanon F: Conservation genetics of
cattle, sheep, and goats. C R Biol 2011, 334:247–254.
5. Gizaw S, van Arendonk JAM, Komen H, Windig JJ, Hanotte O: Population
structure, genetic variation and morphological diversity in indigenous
sheep of Ethiopia. Anim Genet 2007, 38:621–628.
6. Carneiro H, Louvandini H, Paiva SR, Macedo F, Mernies B, McManus C:
Morphological characterization of sheep breeds in Brazil. Uruguay and
Colombia. Small Rum Res 2010, 94:58–65.
7. Peter C, Bruford M, Perez T, Dalamitra S, Hewitt G, Erhardt G: Genetic
diversity and subdivision of 57 European and Middle-Eastern sheep
breeds. Anim Genet 2007, 38:37–44.
8. Cinkulov M, Popovski Z, Porcu K, Tanaskovska B, Hodzic A, Bytyqi H,
Mehmeti H, Margeta V, Djedovic R, Hoda A, Trailovic R, Brka M, Markovic B,
Vazic B, Vegara M, Oslaker I, Kantanen J: Genetic diversity and structure of
the West Balkan pramenka sheep types as revealed by microsatellite
and mitochondrial DNA analysis. J Anim Breed Genet 2008, 125:417–426.
9. D’Angelo CS, Gajecka M, Kim CA, Gentles AJ, Glotzbach CD, Shaffer LG,
Koiffmann CP: Further delineation of nonhomologous-based
recombination and evidence for subtelomeric segmental duplications in
1p36 rearrangements. Hum Genet 2009, 125:551–563.
10. Arora RJ, Bhatia SB, Mishra P, Jain A, Prakash B: Diversity analysis of sheep
breeds from Southern peninsular and Eastern regions of India. Trop Anim
Health Prod 2010, 43:401–408.
11. Arora R, Bhatia S, Mishra BP, Joshi BK: Population structure in Indian sheep
ascertained using microsatellite information. Anim Genet 2011, 42:242–250.
12. Niu BF, Fu LM, Sun SL, Li WZ: Artificial and natural duplicates in
pyrosequencing reads of metagenomic data. BMC Bioinform 2010, 11:187.
13. Paiva SR, Mariante AD, Blackburn HD: Combining US and Brazilian
microsatellite data for a meta-analysis of sheep (Ovis aries) breed
diversity: facilitating the FAO global plan of action for conserving animal
genetic resources. J Hered 2011, 102:697–704.
14. Riva J, Rizzi R, Marelli S, Cavalchini LG: Body measurements in Bergamasca
sheep. Small Rum Res 2004, 55:221–227.
15. Cervantes I, Baumung R, Molina A, Druml T, Gutierrez JP, Solkner J, Valera M:
Size and shape analysis of morphofunctional traits in the Spanish Arab
horse. Livestock Sci 2009, 125:43–49.
16. Dossa LH, Wollny C, Gauly M: Spatial variation in goat populations from
Benin as revealed by multivariate analysis of morphological traits.
Small Rum Res 2007, 73:150–159.
17. Zahedi-Zahra, Esmaeelkhanian S, Torshizi RV: Microsatellite variation of one
breed of Iranian sheep with 12 markers. Pak J Biol Sci 2007, 10:4455–4460.
18. Kumar D, Sharma R, Pandey AK, Gour DS, Malik G, Ahlawat SPS, Jain A:
Genetic diversity and bottleneck analysis of Indian Bellary sheep by
microsatellite markers. Russ J Genet 2006, 43:996–1005.
Agaviezor et al. Journal of Animal Science and Biotechnology 2012, 3:38
http://www.jasbsci.com/content/3/1/38
19. Adebambo OA, Williams JL, Blott S, Urquhart B: Genetic relationships
between native sheep breeds in Nigeria based on microsatellite DNA
polymorphisms. Anim Genet Res Inform Bull 2004, 34:27–39.
20. Agaviezor BO, Adefenwa MA, Peters SO, Yakubu A, Adebambo AO, Ozoje
MO, Ikeobi CON, Ilori BM, Wheto M, Okpeku M, De Donato M, Imumorin IG:
Mitochondrial D-loop genetic diversity of Nigerian indigenous sheep.
Anim Genet Res 2012, 50:13–20.
21. Wilson AJ, Pemberton JM, Pilkington JG, Clutton Brock TH, Coltman DW,
Kruuk LEB: Quantitative genetics of growth and cryptic evolution of body
size in an island population. Evol Ecol 2007, 21:337–356.
22. Collyer ML, Adams DC: Analysis of 2 states multivariate phenotypic
change in ecological studies. Ecology 2007, 88:683–692.
23. Yakubu A, Salako AE, Imumorin IG, Ige AO, Akinyemi MO: Discriminant
analysis of morphometric differentiation in the West African Dwarf and
Red Sokoto goats. South African J Anim Sci 2010, 40:381–387.
24. Yakubu A, Idahor KO, Haruna HS, Wheto M, Amusan S: Multivariate analysis
of phenotypic differentiation in Bunaji and Sokoto Gudali cattle. Acta
Agric Slov 2010, 96:75–80.
25. Ajayi OO, Yakubu A, Jayeola OO, Imumorin IG, Adeleke MA, Takeet MI, Ozoje
MO, Ikeobi CON, Peters SO: Multivariate analysis of sexual size
dimorphism in domestic turkeys (Meleagris gallopavo) in Nigeria.
Trop Anim Hth Prod 2012, 44(5):1089–1095. doi:10.1007/s11250-011-0044-6.
In press.
26. Vargas S, Larbi A, Sanchez M: Analysis of size and conformation of native
Creole goat breeds and crossbreds used in smallholder agrosilvopastoral
systems in Puebla, Mexico. Trop Anim Hth Prod 2007, 39:279–286.
27. Sun W, Chang H, Ren ZJ, Yang ZP, Geng RQ, Tsunoda K: Multivariate
statistical analysis of morphological and ecological characters of some
sheep populations in China. Sci Agric Sin 2004, 37:285–290.
28. Yakubu A, Akinyemi MO: An evaluation of sexual size dimorphism in Uda
sheep using multifactorial discriminant analysis. Acta Agric Scand Sect A
Anim Sci 2010, 60:74–78.
29. Polak J, Frynta D: Patterns of sexual size dimorphism in cattle breeds
support Rensch’s rule. Evol Ecol 2010, 24:1255–1266.
30. Bacchi CS, Lanari MR, von Thungen J: Non-genetic factors affecting
morphometric and fleece traits in guanaco (Lama guanicoe guanicoe).
Small Rum Res 2010, 88:54–61.
31. Felsenstein J: Has natural selection been refuted? The Arguments of
William Dembski. Rep Na Center Sci Educ (RNCSE) 2007, 27:20–26.
32. Farid A, O’Reilly E, Dollard C, Kelsey CR: Genetic analysis of ten sheep
breeds using microsatellites. Canad J Anim Sc 2000, 80:9–17.
33. Dalvit C, Saccà E, Cassandro M, Gervaso M, Pastore E, Piasentier E: Genetic
diversity and variability in Alpine sheep breeds. Small Rum Res 2008,
80:45–51.
34. Bataillon TM, David JL, Schoen DJ: Neutral genetic markers and
conservation genetics: simulated germplasm collections. Genetics 1996,
144:409–417.
35. Nei M, Maruyama T, Chakraborty R: The bottleneck effect and genetic
variability in populations. Evolution 1975, 29:1–10.
36. Petit RJ, El Mousadik A, Pons O: Identifying populations for conservation
on the basis of genetic markers. Conserv Biol 1998, 12:844–855.
37. El Mousadik A, Petit RJ: High level of genetic differentiation for allelic
richness among populations of the argan tree [Argania spinosa (L)
Skeels] endemic to Morocco. Theor Appl Genet 1996, 92:832–839.
38. Ryman N, Leimar O: GST is still a useful measure of genetic differentiation
- a comment on Jost’s D. Molec Ecol 2009, 18:2084–2087.
39. Bozzi R, Degl’Innocenti P, Rivera-Diaz P, Nardi L, Crovetti A, Sargentini C,
Giorgetti A: Genetic characterization and breed assignment in five Italian
sheep breeds using microsatellite markers. Small Rum Res 2009,
85:50–57.
40. Handley LL, Byrne K, Santucci F, Townsend S, Taylor M, Bruford MW, Hewitt
GM: Genetic structure of European sheep breeds. Heredity 2007,
99:620–631.
41. Krebs C: Ecological Methodology. New York: HarperCollins; 1989:1–5.
42. Mukesh M, Sodhi M, Bhatia S: Microsatellite-based diversity analysis and
genetic relationship of three Indian sheep breeds. J Anim Breed Genet
2006, 123:258–264.
43. Cañon J, Garcia D, Garcia-Atance MA, Obexer-Ruff G, Lenstra JA, AjmoneMarsan P, Dunner S, ECONOGENE Consortium: Geographical partitioning
of goat diversity in Europe and the Middle East. Anim Genet 2006,
37:327–334.
Page 16 of 16
44. Taberlet P, Valentini A, Rezaei HR, Naderi S, Pompanon F, Negrini R, AjmoneMarsan P: Are cattle, sheep, and goats endangered species? Mol Ecol
2008, 17:275–284.
45. Wiener G, Lee GJ, Wooliams JA: Consequences of inbreeding for financial
returns from sheep. Anim Prod 1994, 59:245–249.
46. Notter DR: The importance of genetic diversity in livestock populations
of the future. J Anim Sci 1999, 77:61–69.
47. Jia B, Chen J, Zhao RQ, Luo QJ, Yan GQ, Chen J: Microsatellite analysis of
genetic diversity and phylogenetic relationship of eight sheep breeds in
Xinjiang. Yi Chuan Xue Ba 2003, 30:847–854.
48. Pedrosa S, Uzun M, Arranz JJ, Gutiérrez-Gil B, San Primitivo F, Bayón Y:
Evidence of three maternal lineages in Near Eastern sheep supporting
multiple domestication events. Proc R Soc Lond 2007,
272:2211–2217.
49. Ndumu DB, Baumung R, Hanotte O, Wurzinger M, Okeyo MA, Jianlin H,
Kibogo H, Solkner J: Genetic and morphological characterization of the
Ankole Longhorn cattle in the African Great Lakes regions. Genet Sel Evol
2008, 40:467–490.
50. Wilson RT, Durkin JW: Age at permanent incisor eruption in indigenous
goats and sheep in semi-arid Africa. Livest Prod Sci 1984,
11:451–455.
51. FAO: Molecular genetic characterization of animal genetic resources.
FAO animal production and health guidelines. Rome. 2011, 9:65–84.
52. Genemapper Software Version 3.0. Applied Biosystems. Carlsbad, CA, USA:
http://mvz.berkeley.edu/egl/resources/manuals/GeneMapper.
53. SAS: Statistical Analysis System User’s Guide, Statistic. NC 27513, USA: SAS
Institute Inc. Cary; 2010.
54. Dieringer D, Schlotterer C: Microsatellite Analyser (MSA): a platform
independent analysis tool for large microsatellite data sets. Molec Ecol
Notes 2003, 3:167–169.
55. Raymond M, Rousset F: Genepop (Version 1.2): population genetics
software for exact tests and ecumenicism. J Hered 1995, 86:248–249.
56. Pritchard JK, Stephens M, Donnelly P: Inference of population structure
using multilocus genotype data. Genetics 2000, 155:945–959.
57. Rosenberg NA, Pritchard JK, Weber JL, et al: Genetic structure of human
populations. Science 2002, 298:2381–2385.
doi:10.1186/2049-1891-3-38
Cite this article as: Agaviezor et al.: Morphological and microsatellite
DNA diversity of Nigerian indigenous sheep. Journal of Animal Science
and Biotechnology 2012 3:38.
Submit your next manuscript to BioMed Central
and take full advantage of:
• Convenient online submission
• Thorough peer review
• No space constraints or color figure charges
• Immediate publication on acceptance
• Inclusion in PubMed, CAS, Scopus and Google Scholar
• Research which is freely available for redistribution
Submit your manuscript at
www.biomedcentral.com/submit