Genetic diversity of Cameroon indigenous goat populations using microsatellites

F Meutchieye, P J Ema-Ngono¹, M Agaba², A Djikeng³ and Y Manjeli

Faculty of Agronomy and Agriculture, University of Dschang, P.O Box: 188 Dschang-Cameroon
fmeutchieye@gmail.com
¹Veterinary School, University of Ngaoundere, Ngaoundere-Cameroon
²International Livestock Research Institute, Nairobi-Kenya
³Biosciences in eastern and central Africa-International Livestock Research Institute, Nairobi-Kenya

Abstract

In this study, the genetic relationships among 179 adult goats from eight Cameroon ecotypes were evaluated. The sampled goats were genotyped using a panel 12 microsatellites markers.

 All markers were polymorphic with a PIC of 0.39. Mean number of alleles was 5.08 (2 to 8), F_ST value overall ecotypes was 0.453, and expected heterozygosity ranged from 0.130 to 0.338. AMOVA confirmed a high variation among (33.24%) and within (47.92%) ecotypes. The PCA and NJ tree classified the goats into four major clusters related to geography and phenotype. The result of this study has shown that Cameroon indigenous goats have interesting variability and the genetic data can aid the rational conservation of these animal genetic resources.

Key words: animal breeding, caprine, ecotypes, genotypes, simple sequence repeats

Introduction

Small ruminant indigenous breeds are crucial for subsistence farmers worldwide (Pollot and Wilson 2009). In the majority of developing countries, little attention was given to small ruminant genetic resources management policies till some years ago (Wilson 1990). Such management policies in many cases resulted in poor performance yields, random mating and loss of diversity (Kosgey et al 2006; Groenevald et al 2010). Due to weak national programs, animal genetic resources diversity documentation is limited in developing countries (Guimarães et al 2007; FAO 2008).

Small ruminants sector in Cameroon provides for about 20% of present meat consumption (MINEPIA 2010). Small ruminants are found in all agricultural systems, mainly made of smallholders in rural areas. They make a valuable contribution, especially to the rural poor. Goats produce a variety of foods, which are very useful for both urban and rural markets; there are no religious taboos against their products (Tchouamo et al 2005).  The naming of Cameroon indigenous goats suggests some variability. In the literature they are invariably called Cameroon Dwarf, West African Dwarf Goat, Djallonke Goat, Nigerian Goat, Pygmy Goat, Dwarf Goat, Fouta Djallon Goat and Kirdi (Epstein 1962; Devendra and Burns 1982; Lauvergne et al 1993). Doutresoulle (1947) made the first description of Cameroonian goats based on physical features, followed by Epstein (1951, 1962), Devendra and Burns (1982). It was Lauvergne et al (1993) who established the primary nature of Cameroon goat populations in the northern part of the country using morphometric indices and coat color patterns.  Meutchieye et al (2008) found similar results in western highlands of Cameroon.

Breeding strategies could be irrelevant as misleading when they are not well correlated with desired genetic traits. Physical features may be useful for conservation issues, but not enough for breeding for performance (Dekkers and van der Werf 2007). Only few sub-Saharan African goats were sampled while designing microsatellite markers which have never been applied to Cameroon goats (Muema et al 2004). The objective of this study was to evaluate the native goat diversity of Cameroon using microsatellite markers (FAO, 2011). The present work is therefore of scientific relevance.  It aims to evaluate the polymorphic information contents of caprine 12 microsatellites markers in Cameroon native goat populations. These findings provide provide useful information required for a better goat populations’ management.

Material and Methods

Ecotypes studied and DNA extraction

Hair samples were plucked directly from 179 live adult goats in the various Cameroon agro ecological zones (Figure 1) and divided into 8 ecotypes (figure 2) as follows: Zone 1 (Sahelian, n =19; Soudanian, n = 23); Zone 2 (High Guinean Savannah, n = 19); Zone 3 (Western Highlands-West, n=27; Western Highlands-North West, n = 18); Zone 4 (Coastal, n = 26) and Zone 5 (Forest-Centre, n = 27; Forest-East, n = 20). Geographical and morphometric patterns described by Meutchieye et al (2008) and Choupamom (2009) have been used to segregate ecotypes. The individuals were sampled as much as possible at distant locations and on a random basis. Genomic DNA was isolated from hair root cells according to the method described by Adhoch (2007).

Figure 1. Hair samples collection sites in Cameroon

Coastal region Dwarf	Highlands goat	Sahelian longlegged
Figure 2. Physical outlines of some Cameroon goat ecotypes

Microsatellite markers list, PCR conditions and genotyping

Twelve microsatellite markers (BM6444, ILSTS087, INRA063, INRA0132, MAF035, MAF065, MAF70, MAF209, SRCRSP3, SRCRSP9, TGLA53 and SPS113), recommended by ISAG-FAO (2011) were used in this study (Table 1).  Microsatellites were PCR amplified with 25ng genomic DNA in a 25ul reaction volume accordingly to their various annealing temperatures. Multiplex PCR typing has been done according to indications precised by Mburu and Hanotte (2005). Genotyping was undertaken using ABI 3130xl Genetic analyzer (Applied Biosystems). The data were scored using GeneMapper V.4.1 version.

Statistical analysis

Allele frequencies: allelic frequency was estimated based on genotypic frequencies and mean heterozygosity for each ecotype (Nei 1968) using GenAlex 6.0 according to Peakall and Smouse (2009) procedure.

Heterozygosity and gene diversity: GenAlex 6.0 program was used to obtain estimates of observed heterozygosity (Hob) and expected heterozygosity (Het). The algorithm used was the one described by Nei (1968). Genetic distances and relationship: Neighbour Joining (NJ) dendogram construction was done under PowerMarker V.3.25 to estimate Nei’s D_A genetic distances between pairs of goat ecotypes on the basis of the 12 microsatellites markers.

Polymorphic Information Content (PIC): Using allele frequencies of non related individual in each ecotype, the following model was used to estimate PIC

Where pi is allele frequency of ith allele within the ecotype; pj, the frequency of jth allele within the ecotype; and n denotes alleles number.

Molecular variance (AMOVA) was estimated in using GenAlex 6.0 and Arlequin 3.5.1.3 software according to Weirand Cockerham (1984) procedure.Principal Component Analysis (PCA): principal components for all ecotypes were calculated using alleles frequencies of 12 microsatellites markers. PC estimates were obtained with PowerMarker V.3.25 procedure.

Results and discussion

All markers were polymorphic at their respective loci. A total of 53 alleles were scored in all the ecotypes as described by table 2 below.

Figure 3. Principal component analysis in Cameroon native goats

Figure 4. Phylogeny relationships among Cameroon native goats using neighbour-joining procedure

Genomic DNA extraction from hair root samples in Cameroon goats was proven effective. FTA cards have been developed giving a high degree of satisfaction in genotyping (Mburu & Hanotte 2005). However, the costs involved and incompetent procurement procedures remain a great challenge within research institutions in Cameroon. Despite the incidence of PCR inhibitors that could interact and interfere with SSR amplification, Say et al (1999) used hair method in DNA analysis of feral cats.

Cameroon goats had a considerable variation (within and between) ecotypes based molecular variance, heterozygosity and number of alleles. The number of alleles ranged from 2 to 8 which is lower than what Tesfaye (2004) reported in Ethiopian goats (4 to 23), Mujibi (2005) in West African Dwarf goats (4 to 21) and Saitbekova et al (1999) in Swiss goat breeds (3 to19).  The relatively low number of heterozygotes could be due to locus under selection, null alleles, inbreeding or presence of population substructure (Wahlund effect).

Cameroon goat ecotypes studied showed significant differentiation and structuring within themselves. The F_ST value overall ecotypes was 0.053. This indicates that about 5% of the total genetic diversity was observed among populations and 95% was observed within populations. The total genetic diversity observed between populations was similar to other studies done on African goat populations namely West African Dwarf goats was 5.4% (Mujibi 2005). But populations outside Africa showed slightly higher between population variation, 17% among Swiss goats (Saitbekova et al 1999), 11% among Italian goats (Ajamone-Marsan et al 2001) and 10.5% among Chinese goats (Li et al 2002). When a population is divided into isolated subpopulations, there is less heterozygosity than there would be if the populations were undivided. Founder effects acting on different schemes generally lead to subpopulation with allele frequencies that are different from the larger population.

The PCA and NJ tree classified the populations into 4 major clusters mainly along the geographical locations. The Sahelian ecotype (long-legged) stood out distinctly different from the rest thus suggesting a different ancestry or breed development. The southern ecotypes showed a tendency of admixture to be confirmed under structure analysis, probably because of random mating (Meutchieye et al 2008) and trading systems linked to urbanization. The information obtained in this study will aid to some extent their rational development, utilization and conservation, provided complementary investigations.

Conclusions

• Genomic DNA from hair root samples was satisfactory enough to generate fragments despite the presence of PCR inhibitors. All the 12 microsatellites applied were polymorphic in all ecotypes studied. Very low number of alleles was detected by markers ILSTS087 and MAF065. Our results suggest that native goat populations are genetically varied. We observed a tendency of admixture in southern ecotypes compared to northern ecotypes. The phylogenetic tree clustered the various groups accordingly to agro climatic and husbandry systems patterns. These results could be strengthened by application of larger number of microsatellites, mitochondrial DNA analysis, SNP analysis and designing of markers targeting specific interesting trait like precocious and twinning in some of local populations for a better breeding program.

Acknowledgements

We are grateful to BecA-ILRI for grant provided through the Africa Biosciences Challenge Fund (ILRI-CSIRO partnerships). BecA-ILRI lab technicians, goat keepers and Small Ruminants Support Program MINEPIA-Cameroon gave us their attention and guidance during the study.

References

Adhoch D 2007 DNA extraction from hair – Method 1.Standard Operation Procedure ILRI, 2p  

Bourzat D 1985 La chèvre naine d’Afrique Occidentale : Monographie. Document du Groupe N° SRC 4. ILCA/Small Ruminant and Camel Group, Addis Ababa, 68 p. 

Choupamom J 2009 Paramètres phénotypiques et génotypiques du poids et du gain moyen quotidien chez la chèvre Kirdi (Capra hircus) dans la zone soudano sahélienne du Cameroun, 64p. MSc Thesis, University of Dschang (unpublished). 

Dargie J D 2007 Marker-assisted selection: policy considerations and options for developing countries. In Guimarães E P, Ruane J, Scherf B D, Sonnino A, Dargie (eds). 2007. Marker-assisted selection: current status and future perspectives in crops, livestock, forestry and fish. FAO, Rome.  

Dekkers  J C M and Van Der Werf J H J 2007 Strategies, limitations and opportunities for marker-assisted selection in livestock. In Guimarães E P, Ruane J, Scherf B D, Sonnino A, Dargie (eds). 2007. Marker-assisted selection: current status and future perspectives in crops, livestock, forestry and fish. FAO, Rome.  

Devendra C and Burns M 2001 Goat production in the tropics. Commonwealth Agricultural Bureaux éd. 176p. 

Doutressoulle G 1947 L’élevage en Afrique occidentale française. Editions Larose, Paris, 288p. 

Epstein H 1971 The origin of the domestic animals of Africa. Africana Publishing Corp. (ed), New York, London, Munich, Tokyo; pp: 196-209; 211-235; 237-261; 297-309. 

Epstein H 1953 The dwarf goats of Africa. The East African Agricultural Journal N° 18: 123-132. 

FAO 2011 Molecular genetic characterization of animal genetic resources. FAO Animal Production and Health Guidelines. No. 9. Rome. 

FAO  2008 L’état des ressources zoogénétiques pour l’alimentation et l’agriculture dans le monde. Barbara Rischkowsky et Dafydd Pilling eds. Rome 

Groeneveld L F, Lenstra J A, Eding H, Toro M A, Scherf  B, Pilling D, Negrini R, Finlay E K, Jianlin H, Groeneveld E, Weigend S, The GLOBALDIV Consortium 2010 Genetic diversity in farm animals – a review. Animal Genetics 41 (Suppl. 1), 1–26.  

Guimarães E P, Ruane J, Scherf B D, Sonnino A, Dargie (eds) 2007 Marker-asisted selection: current status and future perspectives in crops, livestock, forestry and fish. FAO, Rome.  

Kosgey I S, Baker R L, Udo H M J and Van Arendok J A M 2006 Successes and failures of small ruminants  breeding programmes in the tropics: a review. Small Ruminant Research 61:13-28. 

Lauvergne J J, Bourzat D, Souvenir-Zafindrajaona P, Zeuh V, and Ngo Tama A C 1993 Indices de primarité de        chèvres au Nord Cameroun et au Tchad. Revue Elevage et  Médecine véterinaire des Pays tropicaux 46 (4): 651-665. 

Li J Y, Chen H, Lan X Y, Kong X J and Min L J 2008 Genetic diversity of five Chinese goat breeds assessed by microsatellite markers. Czech Journal of Animal Science 53 (8): 315–319 

Mburu D and Hanotte O 2005 A practical approach to microsatellite genotyping with special reference to livestock population genetics. A manual prepared for the IAEA/ILRI training course on molecular characterisation of small ruminant genetic resource of Asia, October-December 2005, ILRI, Nairobi, Kenya. ILRI Biodiversity project.Pp 82. 

Meutchieye F, Manjeli Y, Lauvergne J J et Choupamom J 2008 Profil visible de la chèvre locale de la région soudano-guinéenne d’altitude de l’Ouest Cameroun. 15th Annual Conference of Cameroon Biosciences Society, 4-6 December, 2008, University of Yaoundé I, Cameroon. 

MINEPIA 2010 Rapport Annuel d’Activités 2010 du Projet d’Appui au Développement des Petits ruminants (PADPR). Yaoundé, Cameroun. 

Muema E K,  Wakhungu J W,  Hanotte O and Jianlin H  2009 Genetic diversity and relationship of indigenous goats of Sub-saharan Africa using microsatellite DNA markers. Livestock Research for Rural Development 21 (2) 2009  Volume 21, Article #28  Retrieved July 15 2012, from http://www.lrrd.org/lrrd21/2/muem21028.htm 

Peakall R and Mouse P E 2006 GENALEX 6.0: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6: 288-295. 

Pollott G and Wilson RT 2009 Sheep and goats for diverse products and profits.FAO Diversification Booklet N°9. FAO, 42p. 

Saitbekova N, Gaillard C, Obexerr-Ruff G and Dolf G 1999 Genetic diversity in Swiss goat breeds based on microsatellite analysis. Animal Genetics 30: 36-41.  

Tchouamo I R, Tchoumboué J et Thibault L 2005 Caractéristiques socio-économiques et techniques de l’élevage de petits ruminants dans la province de l’Ouest Cameroun. Tropicultura 23 (4), 201-211. 

Tesfaye A T 2004 Genetic characterization of indigenous goat populations of Ethiopia using microsatellite DNA markers. PhD Thesis, Deemed University, India. 

Weir B S and Cockerham C C 1984 Estimating F-Statistics for the Analysis of Population Structure. Evolution, Vol. 38, No. 6:1358-1370. 

Received 25 March 2014; Accepted 17 June 2014; Published 1 July 2014

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