DEVELOPMENT AND CHARACTERISATION OF MICROSATELLITE MARKERS FOR HELMETED GUINEA FOWL (NUMIDA MELEAGRIS) IN GHANA BY PRINCESS KORKOR BOTCHWAY THIS THESIS IS SUBMITTED TO THE UNIVERSITY OF GHANA, LEGON, IN PARTIAL FULFILMENT OF THE REQUIREMENT FOR THE AWARD OF MPHIL ANIMAL SCIENCE DEGREE JULY, 2013 University of Ghana http://ugspace.ug.edu.gh i DECLARATION I, Princess Korkor Botchway, author of this thesis entitled “Development and Characterisation of Microsatellite Markers for Helmeted Guinea Fowl (Numida meleagris) in Ghana” hereby solemnly declare that except for references to the work of other researchers, the work presented in this thesis is entirely based on work undertaken by me in the Department of Animal Science from August, 2012 to July, 2013. This work has not been submitted in whole or in part for any degree of this university or elsewhere. Other persons’ views and ideas I have quoted and referred to have been fully acknowledged. ..................................... Princess Korkor Botchway This work has been submitted for examination with our approval as supervisors: ..................................... Dr. B.B. Kayang (PRINCIPAL SUPPERVISOR) .................................. Dr. F.Y. Obese (CO-SUPPERVISOR) University of Ghana http://ugspace.ug.edu.gh ii DEDICATION With all the love I possess in my being I dedicate this dissertation to my family who devoted their love, time and other resources to educate me and also to my teacher, the Late Mr. Ampadu who inspired and motivated me to make it this far. University of Ghana http://ugspace.ug.edu.gh iii ACKNOWLEDGEMENT I am entirely grateful to the almighty God for his divine favour of life and also for making this study a success. I acknowledge the support of my family who stood by me with alacrity throughout the period of the project. I am also very grateful to my supervisors, Dr. B.B. Kayang and Dr. F.Y. Obese, of the Department of Animal Science, University of Ghana, Legon, for their expertise, patience, advice, constant encouragement, criticism and contribution towards this study. I also thank all the Senior members of the Department of Animal Science for their criticism and contribution towards making this work a success. To Dr. Erasmus H. Owusu of the Department of Animal Biology and Conservation Science, University of Ghana, Mr. Tiitoe Boon and staff of the Livestock and Poultry Research Center, University of Ghana (Dr. A. Naazie, Mr. H. Manu, Mr. M. Bashiru and Mr. Alex Potir), I appreciate your tremendous assistance during sample collection. This work was made possible through the collaboration between the College of Agriculture & Consumer Sciences of the University of Ghana and the Wildlife Research Center (WRC) of Kyoto University. In light of this, I acknowledge the financial support of the Asian and African Science Platform Program under the Japan Society for the Promotion of Science (JSPS) through which I received vital technical training in Japan and carried out important laboratory analyses. I particularly wish to express special thanks to Prof. M. Murayama, WRC of Kyoto University, who was instrumental in securing funding from JSPS and who showed so much interest in this study. I am also indebted to other members of WRC especially Dr. Azuza Hyano and Mr. Christopher Adenyo (a PhD student), as well as Dr. Eiji Inoue of the Graduate School of Science, Kyoto University for providing needed technical assistance while I was in Japan. Finally, I wish to gratefully acknowledge the financial support of the A.G. Leventis University of Ghana http://ugspace.ug.edu.gh iv Foundation Fellowship Scheme 2012/2013, University of Ghana, Legon that facilitated the final write-up of this thesis. University of Ghana http://ugspace.ug.edu.gh v TABLE OF CONTENTS DECLARATION ...................................................................................................................... i DEDICATION ........................................................................................................................ ii ACKNOWLEDGEMENT ...................................................................................................... iii TABLE OF CONTENTS ......................................................................................................... v LIST OF TABLES ................................................................................................................ vii LIST OF FIGURES .............................................................................................................. viii LIST OF PLATES .................................................................................................................. ix ABSTRACT ............................................................................................................................ x LIST OF ABBREVIATIONS ................................................................................................ xii CHAPTER ONE...................................................................................................................... 1 1.0 INTRODUCTION ..................................................................................................... 1 CHAPTER TWO ..................................................................................................................... 6 2.0 LITERATURE REVIEW ............................................................................................... 6 2.1 Guinea Fowl ............................................................................................................... 6 2.2 Genetic Markers ....................................................................................................... 12 2.3 Microsatellite Markers .............................................................................................. 17 CHAPTER THREE ............................................................................................................... 34 3.0 MATERIALS AND METHODS ............................................................................. 34 3.1 Sampling .................................................................................................................. 34 3.2 DNA Extraction and quality assessment .................................................................... 34 3.3 Marker Development ................................................................................................ 35 3.3.4 Library Screening .................................................................................................. 44 3.3.5 Primer Design ........................................................................................................ 44 3.3.6 Primer Optimisation ............................................................................................... 45 3.3.7 Genotyping ............................................................................................................ 46 3.3.8 Data Analysis......................................................................................................... 48 CHAPTER FOUR ................................................................................................................. 49 4.0 RESULTS ............................................................................................................... 49 4.1 Library development and screening........................................................................... 49 4.2 Primer Design ........................................................................................................... 50 4.3 Primer Testing .......................................................................................................... 51 4.4 Genotyping ............................................................................................................... 63 University of Ghana http://ugspace.ug.edu.gh vi 4.5 Data Analysis ........................................................................................................... 66 CHAPTER FIVE ................................................................................................................... 73 5.0 DISCUSSION .............................................................................................................. 73 5.1 Efficiency of 454 sequencing for microsatellite development in guinea fowl ............. 73 5.2 Characteristics of guinea fowl microsatellite markers ................................................ 74 5.3 Allelic diversity of microsatellites in guinea fowl ..................................................... 78 CHAPTER SIX ..................................................................................................................... 82 6.0 GENERAL CONCLUSION AND RECOMMENDATIONS........................................ 82 6.1 Conclusion ................................................................................................................ 82 6.2 Recommendations .................................................................................................... 82 REFERENCES ...................................................................................................................... 84 APPENDICES ..................................................................................................................... 105 University of Ghana http://ugspace.ug.edu.gh vii LIST OF TABLES Table 1: Characteristics of the main types of molecular markers. ........................................ 15 Table 2: Common classes of microsatellites ........................................................................ 18 Table 3: Categories of microsatellite repeats ....................................................................... 19 Table 4: Multiplex PCR and primer information for nine primers ....................................... 47 Table 5: Number of primers designed ................................................................................. 50 Table 6: PCR optimization at 55°C and 60°C ...................................................................... 52 Table 7: Primer sequence and amplification information for 154 designed microsatellite primers ............................................................................................................................... 54 Table 8: Profile of 31 polymorphic loci ............................................................................... 68 Table 9: Informativeness of 31 guinea fowl polymorphic microsatellite loci ....................... 72 University of Ghana http://ugspace.ug.edu.gh viii LIST OF FIGURES Figure 1: Schematic representation of traditional marker development ................................ 24 Figure 2: Schematic diagram of emulsion PCR process ....................................................... 26 Figure 3: Schematic diagram of bead deposition into PicoTiter Plate .................................. 27 Figure 4: Bench top Next-Generation Sequencer (Roche Genome sequencer junior) ........... 28 Figure 5: Primer design with bioinformatics........................................................................ 29 Figure 6: High sensitivity chip profile of sample before ligation ......................................... 38 Figure 7: High sensitivity chip profile of sample after ligation ............................................ 38 Figure 8: Rapid Library (RL) standard curve ....................................................................... 40 Figure 9: Distribution of microsatellites in five classes of repeats ....................................... 49 Figure 10: Characteristics of microsatellite repeats ............................................................. 51 Figure 11: Gel image showing PCR optimization results of four primers with DNA from four individuals.. ................................................................................................................. 53 Figure 12: Electropherogram of locus GF75 showing three different genotypes for three individuals... ....................................................................................................................... 64 Figure 13: Electropherogram of locus GF46 showing three similar genotypes for three individuals.. ........................................................................................................................ 65 Figure 14: Characteristics of 31 polymorphic loci. .............................................................. 71 University of Ghana http://ugspace.ug.edu.gh ix LIST OF PLATES Plate 1: Species of guinea fowl within the four genera……………………….………..……7 University of Ghana http://ugspace.ug.edu.gh x ABSTRACT The indigenous guinea fowl plays a vital role in the agricultural industry as both its meat and eggs are healthy, tasty and serve as an important protein source for consumers. However, genetic progress on this bird has been partially hindered by the absence of polymorphic markers, especially microsatellites. Therefore, this study developed for the first time original microsatellite markers for this economically important species. The 454 sequencing technique (next-generation sequencing), which is known to eliminate the time consuming cloning step in the traditional microsatellite marker development method, was used in this study. A genomic library was constructed from DNA extracted from the blood of a female guinea fowl, using the next-generation sequencer. A total of 105,015 reads with an average read length of 393 bp containing 1,234 possible microsatellite sites were obtained. One hundred and fifty four primers were designed from the flanking regions of the microsatellites and tested at 55 °C and 60 °C in a polymerase chain reaction using DNA from four unrelated guinea fowls. One hundred and twenty two of these primers showed clear amplification patterns. Polymorphism of 38 of the optimized markers was tested with DNA samples from 32 unrelated individuals and 31 of them were polymorphic. For the 31 polymorphic loci, the observed number of alleles ranged from 2 to 9 (mean 3.39) with allele sizes ranging from 94 bp to 286 bp, while the effective number of alleles ranged from 1.03 to 4.97 (mean 2.04). The observed (HO) and expected heterozygosities (He) ranged from 0.033 to 1.000 (mean 0.396) and 0.033 to 0.799 (mean 0.419), respectively. Nine loci significantly deviated from Hardy-Weinberg Equilibrium (p < 0.05) after Bonferroni correction. The mean fixation index (F) for all 31 loci was 0.052 (-1 to 0.71) while the average probability of identity (PI) was 0.43. Shannon’s Index ranged from 0.085 to 1.821 (mean 0.750). The polymorphism information content (PIC) of the 31 markers averaged 0.3689 (0.0329 to 0.7735) with 29% of them being highly informative (PIC > 0.50), 35.5% being reasonably informative (0.50 > PIC > 0.25), and 35.5% being slightly University of Ghana http://ugspace.ug.edu.gh xi informative (PIC < 0.25). The results of this study would serve as baseline information for genetic diversity studies, genetic linkage mapping, quantitative trait loci analysis as well as inform breeding strategies for the improvement and conservation in both domestic and wild populations of the species. University of Ghana http://ugspace.ug.edu.gh xii LIST OF ABBREVIATIONS AFLP Amplified fragment length polymorphism DNA Deoxyribonucleic acid dNTP Deoxy nucleotide triophosphate F Fixation index He Expected heterozygosity Ho Observed heterozygosity HWE Hardy-Weinberg equilibrium I Shannon’s Index MPC Magnetic Particle Concentrator mtDNA Mitochondrial DNA Na Number of alleles NAF Null allele frequency Ne Effective number of alleles PCR Polymerase chain reaction PI Probability of identity PIC Polymorphism information content RAPD Random amplified polymorphic DNA RFLP Restriction fragment length polymorphism RFU Relative fluorescent unit RL Rapid library SNP Single nucleotide polymorphism SSR Simple sequence repeat University of Ghana http://ugspace.ug.edu.gh 1 CHAPTER ONE 1.0 INTRODUCTION The successful domestication of animals has led to the development and testing of several methods in the pursuit of improving animal productivity. Due to the fact that animal genetic improvement is a continuous and complex process, the face of animal breeding has been very dynamic over the past decades. Traditionally, the genetic improvement of livestock breeds has been based on phenotypic selection. The 20th century was therefore defined by the development of the quantitative theory and methodology towards accurate selection and the prediction of genetic response (Walsh, 2000; Van Marle-Köster et al., 2003). This resulted in the selection of some economically important genetic traits in cattle, sheep, pigs and poultry (Van Marle- Köster et al., 2003). In recent years, the demonstration of genetic polymorphism at the DNA sequence level has listed several marker techniques with a range of applications. This has resulted in the increased regard for the potential value of these markers in animal breeding (Hines, 1999). These markers have been useful in studies including kinship and population studies, gene duplication or deletion, construction of detailed genetic maps of several organisms and the study of genetic variation within populations of the same species (Santana et al., 2009). The utilization of marker- based information for genetic improvement is based on the choice of marker system for a given application. Several factors such as the degree of polymorphism, dominance, technical know-how, possibility of automation, reproducibility of the technique, and finally the expense involved have influenced the selection of markers for different applications (Van Marle-Köster et al., 2003). Autosomal markers are the most efficient markers for genetic diversity studies because they are easily reproducible, co-dominant and highly polymorphic (FAO, 2007; FAO, 2011; Lenstra University of Ghana http://ugspace.ug.edu.gh 2 et al., 2012). These markers have however been used in conjunction with mitochondrial and Y-chromosomal DNA to establish breed relationships (FAO, 2007; FAO, 2011). Current global research efforts on animal genetic variation are geared towards characterising the genetic structure of local populations to serve as the basis for identifying unique populations or genotypes for conservation against future needs (Kayang et al., 2010). This is particularly critical in an era of unnerving global challenges such as population growth, climate change, emerging diseases and rising consumer demands, which would likely require new genotypes in the future (Kayang et al., 2010). Furthermore, it is easier to manage and improve genetic diversity of a population or breed of farm animals but once diversity is lost, it is expensive and difficult to make changes (Lenstra et al., 2012). Existing Animal genetic resources thus represent a massive past investment which, if managed appropriately, can provide insurance against an unknowable global future (FAO, 2007; FAO, 2011). Microsatellite markers are among the key tools for the study of genetic structure of populations (FAO, 2007; Kayang et al. 2010) and have been successfully isolated and used for genetic studies in several valuable poultry and livestock species including Japanese quail (Kayang et al., 2002), ducks (Abdelkrim et al., 2009), chicken (Groenen et al., 2000; Osei-Amponsah et al., 2010), grasscutter (Adenyo et al., 2012) and pigs (Rohrer et al., 1996). However, even though the guinea fowl also plays a major role as an important protein source in food and income security, genetic studies have been hampered by the absence of original microsatellite markers for the bird. The guinea fowl is believed to have originated from the Guinea coast of Africa but is common in the Western, Southern and Central parts of Africa, Europe and Asia. In University of Ghana http://ugspace.ug.edu.gh 3 these areas, the commonest type of guinea fowl is the helmeted type which is the only type that has been successfully domesticated (Ikani and Dafwang, 2004). Wild populations of helmeted guinea fowls can still be found in certain areas but the population of these birds continues to dwindle as a result of hunting and habitat fragmentation (Church and Taylor, 1992). In Ghana, especially in the Guinea savannah areas, helmeted guinea fowls can be found in many households where they are raised for meat and eggs and therefore play an indispensable role in food security for the people. All over the world, consumers are increasingly becoming health conscious and tend to settle for food products that are low in calories. In this context, the guinea fowl is becoming a substitute for other poultry meat because it is lean and low in calories (Moreki, 2009). The tenderness and unique flavour of the meat of young birds can substitute wild game birds including quails and partridges. Currently, the guinea fowl is becoming popular not only because of its high nutritional qualities and unique ornamental value but also because of its peculiar characteristics. The loud and harsh cry, which though makes the bird irritably noisy, enables it to be used as an “intruder alarm” or watch bird to alert owners to a vast array of issues including strange people, animals and events (Ikani and Dafwang, 2004). Furthermore, guinea fowls are natural insect eaters and can be used as biological control agents to eradicate vast levels of insect infestations, especially in gardens or farms. In fact, Duffy et al. (1992) have reported them as an effective control to reduce the deer tick (arachnid) population, which is the vector of Lyme disease. Guinea fowls are also noted for their resistance to most of the common poultry diseases and some toxins (Moreki, 2009) and are therefore valuable models for disease research (Singh et al., 2010). In light of the numerous values of the guinea fowl, there is the need to develop genetic markers that will serve as a tool for the improvement of this valuable species. University of Ghana http://ugspace.ug.edu.gh 4 1.1 Justification A lot of progress has been made in the genetic analysis of several animal species especially among birds in the Galliformes order. However, not much has been done in the guinea fowl. Presently, genetic analysis of guinea fowl includes work done by Sharma et al. (1998) who used Random Amplified Polymorphic DNA (RAPD) markers to differentiate between three varieties of the species; Nahashon et al. (2008) who cross-amplified chicken microsatellites in guinea fowl; and Kayang et al. (2010) who studied genetic diversity of guinea fowls with autosomal microsatellite markers developed from the Japanese quail. Most importantly, not much has been done regarding the development of original microsatellite markers for guinea fowl. Attempts to improvise by using markers from other birds in the same order as the guinea fowl have not been entirely successful (Kayang et al., 2002; Nahashon et al., 2008). Also attempts to develop these markers using the traditional method have yielded limited success (B.B. Kayang, personal communication, June 9, 2013). Although Kayang et al. (2010) were able to study the genetic diversity of guinea fowls using autosomal microsatellites from the Japanese quail, it is relevant to develop original microsatellite markers for the first time since this will help widen the scope of study in guinea fowl. Furthermore, these markers are usually species-specific and hence need to be developed for the first time when analysis of a new species is started. With the advent of the next-generation sequencing technology, the time- consuming traditional method of developing microsatellite markers is becoming less attractive. This new technology has recently been successfully employed in the development of microsatellite markers for grasscutter (Adenyo et al., 2012) and there is every reason to believe that it will work well if applied to guinea fowl. University of Ghana http://ugspace.ug.edu.gh 5 Guinea fowls are indispensable economically important species and therefore some genetic analysis is required, to enable researchers discover the genetic capabilities of this bird and also attribute some of their characteristics to the presence or absence of certain genes in their genome. Also the data from this study will serve as a useful resource base for animal breeders and conservationists interested in genetic improvement and conservation of this valuable species. 1.2 Objective The objective of this study was to develop and characterise original microsatellite markers for guinea fowl using the next-generation sequencing technique. University of Ghana http://ugspace.ug.edu.gh 6 CHAPTER TWO 2.0 LITERATURE REVIEW 2.1 Guinea Fowl Guinea fowls are carinate birds (capable of flight), but are terrestrial and most likely to run rather than fly when startled (Ayorinde, 2004). They are however very agile flyers and can also hover (Adjetey, 2006). They belong to the family Phasianidae, sub-family Numididae and order Galliformes. Other agriculturally important birds in the order include turkeys, chickens and pheasants. Guinea fowls are native to West Africa but are now kept in many parts of the world. Generally, the male guinea fowl (cock) and female (hen) are not distinctly sexually dimorphic until they are about two months of age (Moreki, 2009). 2.1.1 Types of guinea fowl The guinea fowl comprises four genera, namely, Agelastes, Numida, Guttera and Acryllium (Ayorinde, 2004). The Agelastes consists of the White-breasted guinea fowl (meleagrides) and the Black guinea fowl (niger), whilst the Numida consists of the Helmeted type (meleagris). On the other hand, the Guttera consists of the Plumed (plumifera) and the Crested (pucherani) types, with the Vulturine type (vulturinum) being found under the last genera Acryllium (Ayorinde, 2004). The White-breasted guinea fowl, Agelastes meleagrides (Plate 1a), is a medium-sized terrestrial bird, up to 45 cm long with black plumage, a small featherless red head, white breast, long black tail, greenish brown beak and greyish feet (BirdLife International, 2013). It is distributed in the subtropical West African forests of Côte d'Ivoire, Ghana, Guinea, Liberia and Sierra Leone (Francis et al., 1992). The White- breasted guinea fowl however, has been identified by the International Union for Conservation of Nature and Natural Resources (IUCN) as Vulnerable (IUCN, 2007). University of Ghana http://ugspace.ug.edu.gh 7 This type of guinea fowl is heavily poached whilst its habitat is rapidly declining owing to logging, forest clearance for agriculture and human settlement (BirdLife International, 2013). In Ghana the white breasted guinea fowl has been rumored to inhabit Krokosua, Boi Tano and the Draw River forest reserves in the Western region of Ghana (Kierulff et al., 2008). Plate 1: Species of guinea fowl within the four genera (a) Agelastes meleagrides (White-breasted guinea fowl), (b) Agelastes Niger (Black guinea fowl), (c) Acryllium vulturinum (Vulturine guinea fowl), (d) Guttera pucherani (Crested guinea fowl), (e) Guttera plumifera (Plumed guinea fowl), (f) Numida meleagris (Helmeted guinea fowl) Source: BirdLife International (2008); Moreki (2009); Wikipedia (2008). University of Ghana http://ugspace.ug.edu.gh 8 Black guinea fowls, Agelastes Niger (Plate 1b), are 40 cm to 43 cm in length, and have a featherless head, short crests of black down feathers and black plumage (BirdLife International, 2013). These birds are usually found in the humid forests of Central Africa. They possess large toes to enable them grasp the ground, but tiny feet to aid in flight (BirdLife International, 2008). The vulturine guinea fowl, Acryllium vulturinum (Plate 1c), is the largest (61-71 cm in length) and most ornate, with a long, glossy-blue cape, white hackles extending from the neck and cobalt blue breast, with looks similar to the vulture (Jacob and Pescatore, 2011). It has black body plumage finely spangled white spots, with short rounded wings, and a tail longer than those of other members in the family Numididae (BirdLife International, 2008). In contrast to the other guinea fowls the vulturine guinea fowl has the ability to survive for longer periods without water and is mostly found in East Africa (Martinez, 1994). The crested guinea fowl, Guttera pucherani (Plate 1d), is found in open forest, woodland and forest-savanna medley in sub-Saharan Africa with a body length of approximately 50 cm and blackish plumage with dense white spots (Clements, 2010). It has a noticeable black crest on top of its head, which varies from small curly feathers to down feathers depending on the subspecies (BirdLife International, 2008). The plumed guinea fowl, Guttera plumifera (Plate 1e), have a naked head and neck with a small fold of skin at the back of its head, wattles, long straight black crest and black plumage with white spots (BirdLife International, 2008). They are 45 cm to 51 cm in length and can be found in the humid primary forest of Central Africa (BirdLife International, 2008). University of Ghana http://ugspace.ug.edu.gh 9 The helmeted guinea fowl, Numida meleagris (Plate 1f), which is the focus of this study, is naturally distributed in West Africa along with the other species except the vulturine type (Awotwi, 1987; Ikani and Dafwang, 2004; Kayang et al., 2010). In spite of its African origin, it is able to thrive in various climatic conditions and is reared commercially in Europe, America and Asia (Dei and Karbo, 2004; Moreki, 2009). Helmeted guinea fowls are 53 cm to 63 cm in length and are characterized by a bony helmet, naked grey neck and wattles on either side of the beak (Kumssa and Bekelele, 2013). The helmeted type is the most common guinea fowl with varieties including the white, pearl, royal purple, lavender, coral blue and dundotte (Dei and Karbo, 2004; Moreki, 2009). There are nine subspecies of the helmeted guinea fowl. This type of guinea fowl is the most wide spread and though it can still be found in the wild, it has been domesticated and can even be kept as pets in certain areas. The preferred habitat for the helmeted guinea fowl is the savannah, where they thrive best. In Ghana, the Northern Savannah zone is the niche that accommodates the largest population of helmeted guinea fowls. In Northern Ghana, helmeted guinea fowls have a high cultural value and make up about 25% of the poultry population in the zone (Kayang et al., 2010). In Nigeria, 25% of the entire poultry population is made up helmeted guinea fowl (Ikani and Dafwang, 2004). 2.1.2 Important characteristics of helmeted guinea fowl and its production in Ghana Guinea fowl production is a substantial income generating venture especially if proper management practices are observed (Teye and Gyawu, 2001). In Ghana guinea fowls are mostly raised in the backyard system (as is the case in most developing countries) where they may be fed by their owners, but are mainly allowed to scavange for food during the day and return to their owner’s yard to roost at night (Teye and Gyawu, University of Ghana http://ugspace.ug.edu.gh 10 2001; Ikani and Dafwang, 2004). Though this type of poultry keeping is not labour intensive, it is economical in terms of feed cost. The demerits of this system are that the birds are easily lost through thefts and predation and the eggs may also be lost since the birds do not necessarily lay in the owner’s yard. Such losses may be colossal and deprive the farmers of the full benefit of rearing these birds (Mallia, 1999; Teye and Gyawu, 2001). According to Teye and Gyawu (2001) improved guinea fowls, when kept in the intensive system, however, enables farmers to make more profit, as they can monitor the eggs and the birds. Although the intensive system may improve overall production in local guinea fowls, Agbolosu et al. (2012) reported that generally, guinea fowl production is hampered by poor egg hatchability, male infertility, high keet mortality, difficulty in sexing keets and slow growth rate. Sexual dimorphism of guinea fowls is not very clear (Agbolosu et al., 2012), although the cry of the cock (one syllabus cry) is distinct from that of the hen (two syllabus cry) at nine weeks (Teye and Gyawu, 2002). Other methods of sex identification including vent sexing, laparoscopy and polymerase chain reaction (PCR) have been used to differentiate between the sexes. The PCR method of sexing has been reported to be the most reliable as it deals with direct identification of the sex chromosomes or genes (Itoh et al., 2001). Generally guinea fowls are seasonal breeders, but have the potential to lay eggs all through the year provided sufficient supplementary feeding and water are available (Konlan et al., 2011). Hens can lay 12 to 15 eggs in a clutch (between 90- 120 eggs per annum) which will take about 24 to 30 days to hatch into keets weighing 24 to 25 g each (Farrell, 2010). According to Teye et al. (2001) and Apiiga (2004), with good feeding and management practices including health care and intensive selection, hens University of Ghana http://ugspace.ug.edu.gh 11 can lay 150 to 220 eggs per annum with keet weight of 1.48 kg at 18 weeks (Moreki, 2009). Related studies by Agbolosu et al. (2012) also revealed a similar trend of increasing guinea fowl performance with improved management practices. Guinea fowl eggs are protected in hard shells and this helps to extend the shelf life by reducing spoilage. The hard shells also facilitate transportation over long distances and therefore reduce production losses (Moreki, 2009). Although the hens are bad brooders, the eggs are hatched artificially using incubators or other good brooding poultry species including chicken (Adjetey, 2006; Apiiga, 2007). Konlan et al. (2011), in a study which involved increasing guinea fowl egg hatchability, reported a 69% rate with artificial incubation. At six months, guinea fowls reach a slaughter weight of 1.5 kg to 2 kg but this relies greatly on the geographical location and management system (Koney, 2004; Ikani and Dafwang, 2004). Guinea fowl meat is a delicacy as it has a gamey taste and can be used as a substitute for wild birds. Moreover, both the meat (134 kcal per 100 gram) and eggs are healthier (lower cholesterol) compared to other poultry and are rich in minerals (magnesium, calcium and iron), vitamins (E, B1 and B2) and high in essential fatty acids (Moreki, 2009). Guinea fowls are hardy and are tolerant to mycotoxin and aflatoxin (Moreki, 2009). They are also resistant to most of the common poultry diseases, including coccidiosis, Newcastle disease, fowlpox and gumboro. Furthermore, they also easily adapt to harsh weather conditions as experienced in the tropics (Moreki, 2009; Singh et al., 2010). University of Ghana http://ugspace.ug.edu.gh 12 2.2 Genetic Markers Genetic markers are DNA sequences linked to specific locations on chromosomes and related to specific traits (Moore and Hansen, 2003). Although biochemical and molecular markers are the two types of genetic markers, FAO (2011) insists on the application of current advanced technologies because they are most informative. The most advanced and current techniques are molecular genetic markers, which include mitochondrial DNA (mtDNA) sequences (which are maternally inherited), Y- chromosomal haplotype (which are paternal linked) and autosomal DNA which are related more to phenotype (Lenstra et al., 2012). 2.2.1 Mitochondrial DNA and Y-chromosomal haplotype The mtDNA are maternally inherited, circular DNA molecules located outside the nucleus, and are capable of evolving 5 to 10 times more rapidly than nuclear (autosomal) DNA, especially the displacement-loop (D-loop) region which is the control region of mtDNA (Garrime, 2007) located in the non-coding region. Most studies, however, emphasise on the highly polymorphic D-loop, but whole genome sequences have been reported as informative (Achilli et al., 2008). mtDNA can easily be isolated but rely on the recognition of nuclear mtDNA insertions (Hassanin et al., 2010; Calvignac et al., 2011), especially when diverse species-specific primers are used (Den Tex et al., 2010). The D-loop is usually used for intraspecies diversity studies. The cytochrome b gene, on the other hand, is located within the coding region and evolves slowly in terms of non-synonymous substitutions. It is usually applied in interspecies genetic diversity studies (Mburu and Hanotte, 2005). In contrast to mitochondrial DNA, Y-chromosome is paternally inherited and is a large linear molecule located in the nucleus (Mburu and Hanotte, 2005). The Y- University of Ghana http://ugspace.ug.edu.gh 13 chromosomal haplotypes have slow mutation rates and are powerful tools used to trace gene flow by male introgression and thus identify paternal lineages in populations (Petit et al., 2002). 2.2.2 Autosomal DNA According to Baker (1980), initial genetic diversity studies relied on blood groups and protein polymorphisms. Recently, autosomal DNA (Ellegren, 2004; Whittaker et al., 2003) are the most used markers (Bruford et al., 2003; Schlötterer, 2004; Soller et al., 2006). Autosomal markers include Amplified Fragment Length Polymorphism (AFLP), Random Amplification of Polymorphic DNA (RAPD), Restriction Fragment Length Polymorphism (RFLP), Single nucleotide polymorphism (SNP) and microsatellite markers (FAO, 2007). Microsatellite markers or simple sequence repeats (SSR) and SNPs are the most recent autosomal DNA markers, however microsatellite markers have been identified as the most powerful markers (Tóth et al., 2000; Ellegren, 2004; FAO, 2007; Tenva, 2009). 2.2.2.1 Amplified Fragment Length Polymorphism (AFLP) According to FAO (2007), this technique involves the digestion of DNA with restriction enzymes and the selective amplification of the digested fragments using a PCR. The output is a significant number of informative markers which can be located reliably in the genome allowing a quick scan of the entire genome. These markers are biallelic (Vos et al., 1995), easily reproducible (Table 1) and are capable of estimating relationships between breeds (Ajmone-Marsan et al., 2002; Negrini et al., 2006) and related species (Buntjer et al., 2002). Although this technology yields a large amount of information per run it is not possible to distinguish between heterozygotes and homozygotes (dominant markers) and this makes the use of these markers technically University of Ghana http://ugspace.ug.edu.gh 14 demanding and labour intensive (Vos et al., 1995; FAO, 2007). Genetic diversity studies using AFLP have been reported in pigs (SanCristobal et al., 2006), goats (Ajmone-Marsan et al., 2002; Negrini et al., 2006) and cattle (Buntjer et al., 2002). 2.2.2.2 Random Amplification of Polymorphic DNA (RAPD) RAPD markers, first described by Williams et al. (1990), are detected using random PCR primers. They are the most popular molecular tools capable of recognizing polymorphisms in large portions of the genome based on minute quantities of DNA. RAPD analysis is quick and simple, because a single RAPD primer can anneal to various locations in a genome (multiple loci). Although results are sensitive to laboratory conditions, this technique is fast and cost effective compared to RFLPs (Table 1). Although RAPD are dominant markers (Table 1), they however have the tendency to underestimate genetic variability and are not easily reproducible (Plotsky et al., 1995). Sharma et al. (1998) estimated the intra and inter varietal genetic variation in three varieties of guinea fowl (Lavender, Pearl and White) with RAPD markers. The results showed a very low level of intra and inter varietal genetic variation in the three guinea fowl varieties, implying low genetic variation between the populations. The genetic homogeneity found in the study was attributed to the fact that the guinea fowl populations were diluted, closed, reproduced from small number of sires and subjected to similar type of selection programmes. The ability of RAPD markers to underestimate genetic variability as reported by Plotsky et al. (1995) was also indicated as a reason for the low genetic diversity realized in the above study. University of Ghana http://ugspace.ug.edu.gh 15 Table 1: Characteristics of the main types of molecular markers. a: Both cost and technical difficulty are highly dependent on the chosen method of visualization. Source: Karp et al.(1997); De Vienne (1998). Characteristic RFLP SSR (Microsatellite) AFLP RAPD SNP Type of visualization Single locus Single locus Multi-loci Multi-loci Single locus Type of polymorphism Sequence No. of repeats Sequence Sequence Sequence Level of polymorphism Good Excellent Excellent Good Excellent Polymorphism at the locus 2 to 5 alleles Multiple alleles Presence/absence Presence/absence 2 alleles Dominance Co-dominant Co-dominant Dominant Dominant Co-dominant Quantity of DNA needed Large Small Small Small Small Quality of DNA needed Good No restrictions Good Good Good Reproducibility Good Good Good Low Good Time Long Fast, once developed Fast Fast Fast, once developed Cost Expensive Average Average Average Expensivea Technical difficulty High Low Medium Medium Higha University of Ghana http://ugspace.ug.edu.gh 16 2.2.2.3 Restriction Fragment Length Polymorphism (RFLP) RFLPs were discovered in 1980 (Botstein et al., 1980; Schimenti, 1998). Similar to AFLP markers, RFLP markers also rely on the use of restriction enzymes and occur as variations in the length of DNA fragments, after restriction enzyme digestion at precise restriction sites (FAO, 2007). The difference between RFLP and AFLP markers is that the PCR is done prior to restriction enzyme digestion in RFLPs (FAO, 2007). The advantages of this technique include its ability to discriminate between homozygotes and heterozygotes (co-dominant markers) (Table 1). They are also stable markers and therefore produce reproducible results (Table 1). However, although RLFPS are good markers, the methodology is long, labourious and demands the use of DNA of both high quality and quantity. They are also non-informative and therefore are unable to identify whole genome variation especially when inbreeding is high (Tenva, 2009). 2.2.2.4 Single Nucleotide Polymorphism (SNP) SNPs are single base or nucleotide (A, T, G or C) variations or alterations that occur in DNA sequence (Vignal et al., 2002) and do not directly affect the phenotype of organisms (FAO, 2007). They are abundant in genomes (mostly in non-coding regions) and this makes them easy to find. Sachinandam et al. (2001) reported one SNP per 1,000 bp in the human genome. Stoneking (2001) and Vignal et al. (2002) have also reported that in most genomes SNPs occur as one SNP per 1,000 bp in both coding and non-coding regions. Although SNPs are biallelic and stable with low mutation rates, they are highly non-informative (Table 1), compared to microsatellites (Tenva, 2009). Studies involving SNPs are costly and require high numbers of the markers to provide little information (FAO, 2007). University of Ghana http://ugspace.ug.edu.gh 17 2.3 Microsatellite Markers Microsatellite markers are defined as a class of highly informative, repetitive DNA sequences, based on nucleotide repeats (Griffiths et al., 2000; Gurdebecke and Maelfait, 2002). According to Mburu and Hanotte (2005) microsatellites markers are also referred to as short tandem repeats (STR), simple sequence tandem repeats (SSTR), variable number tandem repeats (VNTR), simple sequence length polymorphisms (SSLP), simple sequence repeats (SSR) and sequence tagged microsatellites (STM). Two types of microsatellites have been described: the rare Type I markers which characterise genes of specific functions and are informative in gene mapping for evolutionary genome studies (Vignal et al., 2002) and Type II markers which are of no known function but are more polymorphic than Type I markers (Odeny, 2006). Microsatellites can range from between two to six base pairs in length (Wang et al., 2010). The most popular class of microsatellites (Table 2) are dinucleotides (Ellegren, 2004; Adenyo et al., 2012), followed by tri-, tetra-, penta and hexanucleotide repeats (Schlötterer and Harr, 2001). Dinucleotide microsatellites have been reported as the most polymorphic and are known to be characterised by higher repeat numbers (Ellegren, 2004; Li et al., 2004) with low repeat numbers being observed in trinucleotide repeats (Tóth et al., 2000; Thiel et al., 2003). According to Tong et al. (2009), dinucleotides occur more frequently in vertebrates whilst in plants the commonest class of repeats are trinucleotides. University of Ghana http://ugspace.ug.edu.gh 18 Table 2: Common classes of microsatellites Class of microsatellite Repeat motif Microsatellite sequence Dinucleotide (GT)8 GTGTGTGTGTGTGTGT Trinucleotide (GAT)7 GATGATGATGATGATGATGAT Tetranucleotide (CTAG)6 CTAGCTAGCTAGCTAGCTAGCTAG Pentanucleotide (CATTG)5 CATTGCATTGCATTGCATTGCATTG Hexanucleotide (GGATCC)4 GGATCCGGATCCGGATCCGGATCC Source: Schlötterer and Harr (2001). Within the classes of repeats, longer reads have a higher probability of producing microsatellites with flanking regions critical for primer development (Mallory, 2007; Schoebel et al., 2013) which give polymorphic PCR products with higher polymorphism information contents or low probability of identity values (Qi et al., 2001). In most genomes, the class of repeat is inversely proportional to the number of microsatellites found (Meglécz et al., 2012). According to Tóth et al. (2000) and Ellegren (2004), who surveyed microsatellites in different eukaryotic genomes, there is a higher proportion of tetranucleotide repeats than trinucleotide repeats in vertebrate genomes. There are four types of dinucleotide repeats CA/AC/GT/TG, GA/AG/CT/TC, AT/TA, and GC/CG (Ellegren, 2004). However, the most common repeat in most eukaryotic genomes is CA and its complement GT (Tóth et al., 2000; Ellegren, 2004) while AT repeats occur most in plants (Meglécz et al., 2012). Microsatellites are presumed to originate from single or multiple mutational events including duplications during replication, insertion/deletions, unequal recombination University of Ghana http://ugspace.ug.edu.gh 19 of chromatids and base substitutions (Gupta et al., 1996; Zane et al., 2002). According to Weber (1990) and Schlötterer and Harr (2001) microsatellites classes can further be grouped into three categories (Table 3), perfect, imperfect (disrupted by base substitutions) and compound microsatellites (consist of more than a single repeat type). Perfect microsatellites are usually the most abundant and the most polymorphic among the three (Kayang et al., 2000; Schlötterer and Harr, 2001). Kutil and Williams (2001) have reported that in genomes, compound microsatellites occur less frequently than perfect microsatellites because they contain more imperfections and deletions and may signify the last stage prior to degradation. Sequence stability and conservation to some extent can be deduced from the nature of repeats (Moriguchi et al., 2003) and be used for studying the evolutionary patterns of genomes (Zhang et al., 2012). The proportion of perfect to imperfect repeat is also directly influenced by the enrichment procedure used in the microsatellite isolation process (Van de Wiel et al., 1999; Moriguchi et al., 2003). Table 3: Categories of microsatellite repeats Category of microsatellite Repeat motif Microsatellite sequence Imperfect microsatellite (GT)5A(GT)6 GTGTGTGTGTAGTGTGTGTGTGT Interrupted microsatellite (GT)4CCC(GT)5 GTGTGTGTCCCGTGTGTGTGT Compound microsatellite (GT)5(CT)7 TGTGTGTGTCTCTCTCTCTCTCT Source: Schlötterer and Harr (2001). Microsatellites occur in both coding and non-coding regions. However, tri- and hexanucleotide repeats occur mostly in coding regions (exons) whilst the other classes University of Ghana http://ugspace.ug.edu.gh 20 of repeats occur in intergenic regions and introns (Tóth et al., 2000). Microsatellites also belong to a class of genomic sequences called variable number of tandem repeat (VNTR) elements (Buschiazzo and Gemmel, 2006), which are highly mutable, thus their polymorphic nature evident in both prokaryotic and eukaryotic organisms (Katti et al., 2001). Research has shown that it is easier to identify and develop microsatellite markers if the frequency of occurrence is high in an organism (Zane et al., 2002; Selkoe and Toonen, 2006). Even though these sequences are common in eukaryotic and prokaryotic genomes (Chambers and MacAvoy, 2000), they occur at low frequencies in some species including corals, some insects, bats and birds (Neff and Gross, 2001; Baums et al., 2005; Primmer et al., 1997). In a study by Abdelkrim et al. (2009) to describe the use of genomic sequencing for the development of microsatellite markers in Blue ducks, only 231 of a total of 17,215 microsatellite sequences, were di-, tri- and tetranucleotide repeats. Santana et al. (2009) also reported that there were no pentanucleotide repeats in a study involving microsatellite development for Sirex noctilio (a pine-damaging wasp), and attributed this finding to the low abundance of the markers in insects. In animals, there is a positive correlation between genome size and microsatellite abundance (Hancock, 1996; Katti et al., 2001) while in plants, there is a negative correlation (Morgante et al., 2002). Unlike human genomes, there are limiting numbers of Poly-A tails in avian genomes due to the low abundance of interspersed elements which aid in the transition of Poly-A tails into various repeats (Primmer et al., 1997). 2.3.1 Merits of microsatellite markers Microsatellites offer a variety of advantages in contrast to other molecular markers (Table 1). Among all the marker types, microsatellites are the best markers for genetic University of Ghana http://ugspace.ug.edu.gh 21 studies because they have higher heterozygosity or exhibit higher polymorphism (Edwards et al., 1996; Liu and Cordes, 2004). Although SNPs, a new class of good markers, have been developed, microsatellites remain the markers of choice for various reasons (FAO, 2007). SNPs have rather low heterozygosity and therefore more of these markers need to be typed to yield better results (FAO, 2007). Microsatellite markers, on the other hand, are highly reproducible, can be amplified easily by the polymerase chain reaction (PCR) using two unique sequences which are complementary to the flanking regions as primers and require very little amount of DNA as starting material (Liu and Cordes, 2004; Selkoe and Toonen, 2006). Due to the species-specificity of microsatellites, issues with cross-contamination by non- target DNA are reduced in contrast to techniques that employ universal primers (Liu and Cordes, 2004). Microsatellite markers are also co-dominant, therefore the heterozygote can easily be differentiated from the homozygote (Zane et al., 2002; FAO, 2007). Finally, microsatellites have a high tendency to mutate (15 or more alleles in any given population) increasing the ease of establishing allelic identity-by- descent and linkage (FAO, 2007). 2.3.2 Limitations of Microsatellite Markers Microsatellites, though versatile molecular markers, particularly for population analysis, are not without limitations. Although it is possible to cross amplify microsatellites in closely related species, the percentage of loci that successfully amplify may decrease with increasing genetic distance (Jarne and Lagoda, 1996). Null alleles may occur as a result of point mutations (Jarne and Lagoda, 1996; Dakin and Avise, 2004). Sequence variation in flanking regions can result in poor primer annealing, especially at the 3’ region, which is the starting point of sequence University of Ghana http://ugspace.ug.edu.gh 22 extension (Jarne and Lagoda, 1996; Dakin and Avise, 2004). Due to the competitive nature of PCR there may be bias amplification of certain allele sizes therefore increasing the possibility of heterozygous individuals being scored for homozygotes (partial null) (Dakin and Avise, 2004). Null alleles which are technical problems complicate the elucidation of microsatellite allele frequencies and thus make assessment of relatedness faulty (Dakin and Avise, 2004; Oddou-Muratorio et al., 2008). Although null alleles change allele frequencies, random sampling (which is a natural phenomenon) during mating may also alter allele frequencies so that an excessive frequency of homozygotes results in a departure from Hardy-Weinberg equilibrium expectations (Dakin and Avise, 2004). It is therefore important to distinguish between them if excess homozygotes are observed. Identification and development of microsatellite markers is quite challenging, especially in organisms where little or no sequence data is available. In genomes with low abundance of microsatellites such as birds, the degree of difficulty is elevated (Primmer et al., 1997). Generally, the process could be expensive, time-consuming and labour-intensive, requiring construction of a genomic library enriched for repeated motifs, isolation and sequencing of candidate clones, primer design, PCR amplification, and testing for polymorphisms in unrelated individuals (Queller et al., 1993; Jarne and Lagoda, 1996; Santana et al., 2009). 2.3.3 Development of Microsatellite Markers Among a number of available methods to identify microsatellites (Dutech et al., 2007), the most commonly used methods are based on targeted enrichment of DNA for microsatellites (Zane et al., 2002; Selkoe and Toonen, 2006), for example inter simple sequence repeat PCR (ISSR-PCR) (Zietkiewicz et al., 1994). The genome University of Ghana http://ugspace.ug.edu.gh 23 region between microsatellite loci is the ISSR. This has been improved by the advancement of technology including the Next-Generation Sequencing technique (Zane et al., 2002; Glenn and Schable, 2005). 2.3.3.1. Microsatellite isolation using traditional method The traditional method of marker isolation (Figure 1) entails cloning of small genomic DNA fragments from existing partial genomic libraries of the target species (Queller et al., 1993; Jarne and Lagoda, 1996). In the absence of an existing library, one must be constructed by extracting DNA from the species of interest. Enriched libraries have been proposed to increase success rates of isolation (Karagyozov et al., 1993; Billotte et al., 1999; Edwards et al., 1996). Prior to cloning, the library is fragmented and then adaptors (double-stranded DNA segments, usually ≈10 – 12 bp long, that contain the recognition site for a particular restriction enzyme) are attached to both ends of the fragments. The fragments are cloned into vectors. Common vectors used in cloning include plasmids, cosmid, lamda phage, bacterial artificial chromosomes (BAC) and yeast artificial chromosomes (YAC) (Primrose and Twyman, 2006). The vectors are then transformed into industrially produced competent bacterial cells and cultured on media. University of Ghana http://ugspace.ug.edu.gh 24 Figure 1: Schematic representation of traditional marker development Source: Zane et al. (2002). University of Ghana http://ugspace.ug.edu.gh 25 Clones containing fragments are screened through colony hybridisation with probes (Powell et al., 1996; Chen et al., 1997) possibly bound to a nylon membrane (Stajner et al., 2005) or biotinylated and bound to streptavidin-coated beads (Yaish and de la Vega, 2003). Plasmids are then extracted and elctrophoresed by Sanger sequencing to confirm microsatellite containing clones (Temnykh et al., 2001), followed by primer design and optimisation. This procedure is more efficient for species with abundant SSRs in contrast to genomes with low frequency of SSR such as birds (Primmer et al., 1997; B.B. Kayang, personal communication, June 19, 2013). Generally, the efficiency of this method of marker development is low. Kayang et al. (2000) for instance, used this method in Japanese quail, and found only 29.2% (372 of 1273 clones) recording a positive signal for microsatellite after hybridization. The isolation process could also be time-consuming (several months), technically demanding and considerably more costly (Croooijimas et al., 1997; Santana et al., 2009; Andrés and Bogdanowicz, 2011; Blair et al., 2012). 2.3.3.2 Microsatellite isolation using 454 next-generation sequencing (NGS) technique The 454 next-generation sequencing technique combines three main molecular techniques (PCR, Shotgun and Pyrosequencing Sequencing) to convert DNA from the genome into sequence data (Margulies et al., 2005). The 454 sequencing method involves three main steps, namely, DNA Rapid Library Preparation, Emulsion PCR (emPCR) and Sequencing (Margulies et al., 2005). During the DNA library preparation, pure genomic DNA is fragmented through a partial shearing process, adapters ligated and the double strands separated into single strands. University of Ghana http://ugspace.ug.edu.gh 26 In an emPCR (Figure 2), the fragments are then cloned, mixed with DNA micro capture beads and loaded into cylindrical wells which contain synthetic oil and enzyme reagents in a water mixture (Margulies et al., 2005). The water mixture forms droplets around the beads, (emulsion) with each droplet containing only one DNA fragment. Enzymes cause the single and isolated DNA fragment in the droplets to be amplified into millions of identical copies (≈ 10 million) of the fragments per bead (Margulies et al., 2005). Figure 2: Schematic diagram of emulsion PCR process Source: Margulies et al. (2005) University of Ghana http://ugspace.ug.edu.gh 27 The DNA-capture beads are loaded onto a picoTiterplate with a pipette (Figure 3), and placed into the genome sequencing system instrument (Figure 4). The instrument washes the plate sequentially with various reagents, including the four nucleotides; A, T, G and C (Margulies et al., 2005). Upon incorporation of the nucleotides, the bead- bound enzymes contained in each plate well converts the chemicals generated into light (which has an intensity directly proportional to the consecutive number of complementary nucleotides on the single stranded DNA fragment) in a chemi- luminescent signal which is detected by an in-built CCD camera (Margulies et al., 2005). The signals are then analysed on the 454 sequencing system software to generate billions of sequenced bases per hour from a single run and then primers are designed with bioinformatics and subsequently optimized (Lim et al., 2004; Glenn and Schable, 2005). Figure 3: Schematic diagram of bead deposition into PicoTiter Plate Source: Margulies et al. (2005). University of Ghana http://ugspace.ug.edu.gh 28 Figure 4: Bench top Next-Generation Sequencer (Roche Genome sequencer junior) Source: Molecular Genetics Laboratory, Wildlife Research Center, Kyoto University, Japan. 2.3.3.2 Primer design and optimisation The microsatellite containing sequences in the obtained library are exported as FASTA sequences (Figure 5) and primers are then designed from the unique DNA that flanks microsatellite motifs (Glenn and Schable, 2005) with bioinformatics (Lim et al., 2004). For example, for a desired (GAT)4 microsatellite repeat, the flanking regions (Figure 5) are targeted to enable this particular repeat to be identified in the genome of the organism which contains other similar repeats but probably in different locations. Softwares such as PRIMER 3 (Rozen and Skaletsky, 2000), DNAstar and FASTPCR (Tong et al., 2009) can be used to design primers. Primers are then tested for optimal cycling conditions in a PCR using DNA of the target species (Lim et al., 2004; Glenn and Schable, 2005). Primers with clear amplified patterns (usually less than 100%) are selected at the appropriate annealing University of Ghana http://ugspace.ug.edu.gh 29 temperatures and used for subsequent analysis. For instance, in a study by Schoebel et al. (2013), to develop microsatellite markers for the El Oro parakeet (an endangered parrot species) and Blackcap (a songbird), 86% of 22 primers and 78% of 51 primers respectively amplified successfully in a primer test. According to Mitsuhashi (1996), the G-C content of primer sequence increases stability of the primers, therefore higher melting temperatures will be required for primers in contrast to those with a higher AT content and vice versa (www.premierbiosoft.com, accessed February 31, 2013). Studies by Callen et al. (1993) and Smulders et al. (1997) have also shown that the occurrence of null alleles could result in the failure of primers to amplify during optimization. Figure 5: Primer design with bioinformatics 2.3.4 Application of 454 sequencing in microsatellite marker development Microsatellite marker isolation with 454 sequencing is automated and this reduces the chances of sample contamination and avoids the time consuming cloning step Forward Primer =TGTATTTTAGTGCAGGTTCTGA Reverse Primer = CTCAGTTCTATTCTGGTTGGA University of Ghana http://ugspace.ug.edu.gh 30 involved in microsatellite isolation (Allentoft et al., 2008; Abdelkrim et al., 2009; Santana et al., 2009). Adenyo et al. (2012) developed 33 novel microsatellite markers, with number of alleles ranging from 3 to 11 (mean = 6.4), for grasscutter, using the next-generation sequencing technology. From the library screening, 156,966 reads were obtained containing 95,805 microsatellite sites. Subsequently, the primers developed recorded very low cumulative probability of identity (PI) for all loci (3.1 x 10-33) which indicates that they were highly informative. An and Lee (2012), also confirmed the efficiency of 454 sequencing by using this technology to develop microsatellite markers for Mytilus coruscus (a Korean mussel) and obtained a total of 176,327 unique sequences (mean length = 381 bp) containing 2,569 (1.45%) microsatellite sites. Due to the massive amounts of sequence data generated in a single run, the technique can be applied to genomes where microsatellite frequencies are low (Abdelkrim et al., 2009). The isolation procedure can be done in less than a week, since almost every step is automated (C. Adenyo, personal communication, September 10, 2012). Abdelkrim et al. (2009), also reported a total of 17,215 containing 231 (1.3%) microsatellite sequences for Blue ducks with 454 sequencing and confirmed the efficiency of the method. Carvalho et al. (2011) also reported a total of 145,071 reads through the NGS, for the threatened Yarra pygmy perch (Nannoperca obscura), containing 9,476 microsatellite sites, from which 858 primers were designed. 2.3.5 Measures of microsatellite variation Good indicators of genetic variation within populations include the mean number of alleles (average number of alleles observed) and the expected heterozygosities University of Ghana http://ugspace.ug.edu.gh 31 (proportion of heterozygotes observed) detected in each population (FAO, 2007). According to Powell et al. (1996) higher values of expected heterozygosity (also known as diversity index) implies more allelic variation and is affected by the number of alleles per locus. The Hardy-Weinberg equilibrium (HWE) law states that in a large random mating population, in the absence of migration, selection and mutation, gene and genotype frequencies remain the same from generation to generation (Falconer and McKay, 1996). Conformity to HWE is the most commonly reported test in which observed and expected genotype frequencies for an ideal population are compared (Selkoe and Toonen, 2006). An excess of heterozygote (homozygote deficit) is recorded when fewer homozygotes occur than expected under HWE, whilst a heterozygote deficit is recorded when the opposite of this phenomenon is recorded. Biological factors including selection against a particular allele or inbreeding (F statistic) can cause significant heterozygote deficits (the most common direction of HWE) relative to HWE (Selkoe and Toonen, 2006). On the other hand, when two genetically different populations are consolidated into a sampling unit, a homozygote excess will be observed under HWE (Wahlund effect) (Chakraborty et al., 1992; Nielsen et al., 2003; Latip et al., 2010). In both cases, all loci, instead of just one or a few should be affected by the deficit. Although null alleles are also the common causes of deviations from HWE (Jarne and Lagoda, 1996; Dakin and Avise, 2004), only one few loci are implicated by the deficit. Although software such as FreeNA (Chapuis and Estoup, 2007) and MICROCHECKER (Van Oosterhout et al., 2004) can be used to identify null alleles, a more technical way to detect null alleles is to examine inheritance patterns in a pedigree (Paetkau and Strobeck, 1995). According to a model study by Chapuis and Estoup (2007) disregarding the existence of null alleles will only University of Ghana http://ugspace.ug.edu.gh 32 considerably bias estimates of population differentiation if the frequency is between 5–8% across loci (Oddou-Muratorio et al., 2008). Dakin and Avise (2004) in another model study reported a similar tolerable range of 5–8% and found a less than 5% risk of falsely excluding an actual parent of a heterozygous offspring in parentage/ paternity analyses when such alleles are used. Therefore, failure to meet HWE expectations is not a basis to reject a locus (Selkoe and Toonen, 2006). The polymorphism information content (PIC) is a measure of genetic diversity that refers to the ability of a marker to detect polymorphism within a population. PIC depends on the number of observed alleles and their frequencies. Botstein et al. (1980) classified PIC values into three groups: slightly informative (PIC < 0.25), reasonably informative (0.50 > PIC > 0.25) and highly informative (PIC >0.5). Preferably, microsatellite markers with PIC values higher than 0.70 are very constructive in genetic linkage studies (Barker et al., 2001). 2.3.6 Microsatellite marker development in some birds Microsatellites have been reported for several livestock species and poultry. In turkeys, Reed et al. (2002) characterized 12 microsatellite loci and reported 7 polymorphic (out of 12 loci) with number of alleles ranging from 1 to 6 (average of 2.7) per locus. In a similar study, Kayang et al. (2002), genotyped 20 unrelated quails with 100 Japanese quail microsatellite markers and found 98 to be polymorphic with 1 to 6 alleles per locus (average of 3.7 alleles). The allele sizes were between 87 bp and 298 bp (mean range 12.6) with the effective number of alleles ranging from 1.0 to 4.3 (mean 2.45). The observed and expected heterozygosities ranged from 0.00 to 0.95 (mean 0.423) and 0.00 to 0.77 (mean 0.527), respectively, with PIC values varying University of Ghana http://ugspace.ug.edu.gh 33 between 0.000 and 0.729 (mean 0.4769). In conclusion, 59.2% (58/98) of the polymorphic markers were highly informative (PIC > 0.50), 28.6% (28/98) were reasonably informative (0.50 > PIC > 0.25), and 12.2% (12/98) were slightly informative (PIC < 0.25). Tang et al. (2003) characterized 70 of 94 microsatellites and used them to detect polymorphisms in 17 unrelated ostrich individuals. Sixty-one of the markers were polymorphic in the individuals tested. A total of 35 primers were developed and used to detect polymorphisms in 31 unrelated Peking ducks (Huang et al., 2005). Twenty-eight loci were polymorphic covering 117 alleles ranging from 2 to 14 (average of 4.18) per locus. The frequencies of the 117 alleles ranged from 0.02 to 0.98. The observed heterozygosity ranged from 0.97 to 0.04 with a mean polymorphism information content (PIC) value of 0.42 (range of 0.04 to 0.88). Kopps et al. (2013) designed 48 primers and screened for polymorphism in 15 Noisy Miners. Fifteen polymorphic loci were reported in this study, with alleles ranging between 3 to10 (average = 5.1). The study revealed that none of the 15 loci conformed to Hardy-Weinberg expectations after sequential Bonferoni correction (Rice, 1989). University of Ghana http://ugspace.ug.edu.gh 34 CHAPTER THREE 3.0 MATERIALS AND METHODS 3.1 Sampling A heparinised syringe was used to draw approximately 2 ml of blood from the wing vein of one female guinea fowl [the heterogametic sex (ZW)] for microsatellite marker development. Feather samples collected from 36 unrelated guinea fowls (18 males and 18 females) from the Northern and Upper West Regions of Ghana, Benin and the Livestock and Poultry Research Centre (LIPREC) of the University of Ghana (Appendix I) (Kayang et al., 2010) were used to test for marker polymorphism. 3.2 DNA Extraction and quality assessment DNA was extracted from both blood and feather samples using the QIAGEN DNeasy Blood and Tissue Kit (QIAGEN, Valencia, CA, USA) according to the manufacture’s protocol (Appendix II). The DNA samples were then analysed on a 1.5% agarose gel to check for the presence of DNA as well as the quality of the DNA samples. The gels were prepared by melting 0.45 g of agarose powder in 30 ml TBE buffer in a microwave. 5 µl of DNA was stained with 1 µl of loading dye containing gel red and loaded onto the gel in an electrophoresis tank. A 100 bp molecular ladder (Thermo Scientific, Wilmington, DE, USA) was used as size standard. The samples were run at 100 V for 30 minutes and the gels observed in a UV Transilluminator (Thermo Scientific). The concentrations of the DNA samples were checked using Nanodrop Spectrophotometer (Thermo Scientific). A concentration of 394.9 ng/µl, was recorded for the blood sample. This was within the range required by the Genome Sequencer Junior for sequencing. University of Ghana http://ugspace.ug.edu.gh 35 3.3 Marker Development Pure DNA extracted from blood sample was processed and sequenced, adopting the shotgun sequencing technique using the Roche 454 Genome Sequencer Junior (GS Junior) with the Titanium Sequencing kit (Roche, Penzburg, Germany) (Margulies et al., 2005). 3.3.1 DNA Rapid Library (RL) Preparation The Individual Sample Cleanup method was used to prepare the library in this study. The DNA sample used in library development was double stranded with optical density of OD260/280 = 1.86. The DNA Rapid Library preparation involved the following steps: DNA Fragmentation by Nebulisation, Fragment End Repair, AMPure Bead Preparation, Adapter Ligation, Small Fragment Removal, Agilent Library Assessment and Flourometer Library Quantitation. 3.3.1.1 DNA Fragmentation by Nebulisation 1.3 µl of the pure DNA sample (from blood) was diluted with TE buffer to a concentration of 500 ng, the required concentration for library development with the Genome Sequencer Junior. The sample was diluted again with TE buffer to top it up to 100 µl. The 100 µl sample was pipetted into a nebulizer cap and after 500 µl of nebulization buffer was added, the solution was mixed by pipetting up and down. The nebulizer cap was connected to a nitrogen tank and 30 psi (2.4 bar) of nitrogen was applied for 1 minute. 2.5 ml of PBI buffer was added, mixed and purified using the QIAGEN MinElute PCR Purification kit (QIAGEN). The DNA was eluted with 17 µl of TE buffer. 1 µl of the DNA was reserved for the bioanalyser step while the rest of the 16 µl was transferred into a 200 µl PCR tube (Margulies et al., 2005). University of Ghana http://ugspace.ug.edu.gh 36 3.3.1.2 Fragment End Repair To repair the ends of the DNA fragments, a 9 µl volume PCR mix was prepared by adding 2.5 µl RL 10x buffer, 2.5 µl RL ATP, 1 µl RL dNTP, 1 µl RL T4 polymerase, 1 µl RL PNK and 1 µl RL Taq Polymerase. The 9 µl mixture was added to the DNA sample, vortexed for 5 seconds and centrifuged for 2 seconds in a mini centrifuge. The PCR sample was run on a thermal cycler using the following cycling conditions: 25°C for 20 min., 72°C for 20 min. and then 4°C hold (Margulies et al., 2005). 3.3.1.3 AMPure Bead Preparation 125 µl of AMPure beads was pipetted into a 2 ml centrifuge tube and placed on a Magnetic Particle Concentrator (MPC) to allow the beads to pellet on one side of the tube. The supernatant was discarded and 73 µl of TE Buffer added and vortexed for 5 seconds. 500 µl of Sizing solution was added to the beads, vortexed for 5 seconds and centrifuged in a mini centrifuge for 2 seconds. The beads were then kept on ice (Margulies et al., 2005). 3.3.1.4 Adapter Ligation 1 µl of RL Adaptor was added to the reaction tube from the fragment end repair. 1 µl of RL Ligase was also added, vortexed for 5 seconds, centrifuged for 2 seconds and then incubated at 25°C for 10 minutes on a thermocycler (Thermo scientific) (Margulies et al., 2005). 3.3.1.5 Small Fragment Removal The sample was then added to the AMPure beads, vortexed for 5 seconds, centrifuged for 2 seconds and incubated at room temperature for 5 minutes. It was then transferred to the MPC to pellet the beads on the wall of the tube and the supernatant was once again discarded. 100 µl and 500 µl, of TE Buffer and Sizing Solution respectively University of Ghana http://ugspace.ug.edu.gh 37 were added, followed by vortexing for 5 seconds after every addition. The sample was incubated at room temperature for 5 minutes and placed on the MPC to pellet the beads. After the beads had pelleted, 100 µl of TE Buffer and 500 µl of Sizing Solution was added again and then incubated and returned to the MPC. Still keeping the beads on the MPC, the beads were washed twice with 1 µl of 70% ethanol, the tube air dried at room temperature for 2 minutes, after which the tube was removed and used in the library assessment step (Margulies et al., 2005). 3.3.1.6 Library Assessment This was done using the Agilent Bioanalyser Method. In brief, 53 µl of TE Buffer was added to the tube from the previous step, vortexed for 5 seconds and centrifuged for 2 seconds. The tube was placed on the MPC to pellet the beads to one side of the tube and then 51 µl of the supernatant was transferred to a new labeled 2 ml tube, leaving the beads behind. 1 µl of the DNA before ligation (Figure 6) and another 1 µl of DNA after ligation (Figure 7) was loaded onto the Agilent Bioanalyser high sensitivity DNA chip (Agilent Technologies, Santa Clara, CA, USA) and the two profiles were compared after 30 minutes to assess the quality of the library. The following characteristics were expected: an average fragment length between 600 to 900 bp and a lower size cut-off <10% below 350 bp (Margulies et al., 2005). University of Ghana http://ugspace.ug.edu.gh 38 Figure 6: High sensitivity chip profile of sample before ligation Figure 7: High sensitivity chip profile of sample after ligation The profiles in Figures 6 and 7 indicate that the majority of the sequences were between 600 bp and 800 bp after ligation and therefore fell within the ideal range of 600 bp to 900 bp after ligation. University of Ghana http://ugspace.ug.edu.gh 39 3.3.1.7 Library Quantitation The library was quantitised using the Flourometer method. In this procedure, 50 µl of the 8 dilutions of the RL (Rapid Library) Standard was transferred into 8 cuvettes. 50 µl of TE Buffer was pipetted into a cuvette and used as blank. The TBS 380 flourometer was set on the blue channel with the blue cuvette holder inset and then the standard value set to 250. The flourometer was calibrated with the blank and 2.5 x 109 molecule /µl solution RL standard and the Relative Fluorescence units (RFU) of each dilution recorded. 50 µl of the sample library was pipetted into a cuvette and the RFU recorded. The sample was transferred back into its tube and kept for further analysis (Margulies et al., 2005). The Microsoft EXCEL 2007 spreadsheet was used to generate a standard curve and calculate the sample concentration in this study. Using the fluorescence readings as the X-axis and the RL concentrations as the Y-axis, a scatter plot was drawn (Figure 8). From the graph, the R2 (correlation coefficient for linear regression) was calculated as 0.9 and this was within the appropriate range suggested by Margulies et al. (2005) for library development. In the library quantitation step, the results from the fluorometer were used to generate an RL Standard curve in Excel spread sheet (Figure 8). University of Ghana http://ugspace.ug.edu.gh 40 Figure 8: Rapid Library (RL) standard curve 3.3.2 Emulsion PCR Amplification (emPCR) This process involved the following steps: DNA Library Capture, Emulsification, Amplification, Bead Recovery and DNA Library Bead Enrichment. 3.3.2.1 DNA Library Capture The beads were pelleted in a centrifuge at 180°C for 20 seconds and the supernatant was discarded. The capture beads were washed twice with 1 ml 1x Wash Buffer, vortexed and centrifuged to resuspend the beads, after which the supernatant was discarded. The DNA library from the rapid library preparation step was then heat denatured on a thermocycler at 95°C for 2 minutes and 4°C hold (Margulies et al., 2005). The volume of DNA library needed was calculated as follows: y = 1E+07x - 9E+08 R² = 0.8805 0.00E+00 5.00E+08 1.00E+09 1.50E+09 2.00E+09 2.50E+09 3.00E+09 0.00 50.00 100.00 150.00 200.00 250.00 300.00 R L s ta n d a rd (m o l/ u l) Relative Fluorescence units RL standard(mol/ul) Linear (RL standard(mol/ul)) University of Ghana http://ugspace.ug.edu.gh 41 Volume of DNA library per tube = desired molecules per bead x 10 million beads Library concentration (molecules/ µl) = 2 (standard) x 10 million beads 2 million molecules/ µl =10 µl Based on the calculation, 10 µl of the library was added to the washed Capture beads and mixed for 5 seconds. 3.3.2.2 Emulsification 1,347 ml Live Amp Mix for Rapid and cDNA Rapid Libraries was prepared by adding 410 µl of Molecular Biology Grade Water, 515 µl of Additive, 270 µl of Amp Mix, 80 µl of Amp Primer, 70 µl of Enzyme Mix and 2 µl of PPiase to a 1.5 ml tube, vortexed and stored on ice. 1.2 µl of the Live Amp Mix was added to the tube of captured DNA library, vortexed and transferred into a Turrax stiring tube. The tube was placed into an Ultra Turrax Tube Drive (UTTD) at 2000 rpm for 5 minutes (Margulies et al., 2005). 3.3.2.3 Amplification A Combitip was used to alliquote 100 µl of the emulsion into each well of a 96-well plate. The plate was put into a thermocycler using the following cycling conditions: 4 minutes at 94°C, 50 cycles of 30 seconds at 94°C, 4.5 minutes at 58°C and 30 seconds at 68°C, and a final hold at 10°C (Margulies et al., 2005). 3.3.2.4 Bead Recovery In a hood, a 50 ml tube was connected to a vacuum and a transpette. The transpette was dipped into the wells of the 96-well plate and the emulsion aspirated from each well with the aid of the vacuum into the 50 ml tube. The transpette was turned upside University of Ghana http://ugspace.ug.edu.gh 42 down to facilitate draining the emulsion into the collection tube. The wells were rinsed twice with 100 µl of isopropanol per well and then the rinse aspirated into the 50 ml tube. About 5 ml of isopropanol was aspirated to collect any beads trapped in the tubing (Margulies et al., 2005). The 50 ml tube was then removed and the contents vortexed. Isopropanol was added to a final volume of 35 ml, vortexed to resuspend the bead pellet and centrifuged at 930 x g for 5 minutes, after which the supernatant was discarded. 10 ml of Enhancing Buffer was added, vortexed to resuspend the pellet and 40 ml isopropanol added to a final volume of 40 ml. The sample was vortexed again and centrifuged at 930 x g for 5 minutes, after which supernatant was discarded. Absolute ethanol was added to attain a final volume of 35 ml. After vortexing, the sample was centrifuged again at 930 x g for 5 minutes and the supernatant discarded. Enhancing Buffer was added to a final volume of 35 ml and vortexed again. The sample was centrifuged at 930 x g for 5 minutes and the supernatant discarded leaving approximately 2 ml of Enhancing Buffer. The DNA-bead-suspension was then transferred into a 1.7 ml micro- centrifuge tube and the supernatant discarded after a process of ‘spin-rotate-spin’. The 50 ml tube was rinsed with 1 ml of Enhancing Buffer and the rinse added to the 1.7 ml tube and the process of ‘spin-rotate-spin’ was repeated and the supernatant discarded. Then the 1.7 ml tube was rinsed thoroughly twice with 1 ml Enhancing Buffer and the supernatant discarded after a process of ‘spin-rotate-spin’ (Margulies et al., 2005). 3.3.2.5 DNA Library Bead Enrichment A tube of brown Enrichment Beads was resuspended and placed in a Magnetic Particle Concentrator (MPC) for 3 minutes to pellet the Enrichment Beads. The supernatant was discarded. 500 µl of Enhancing Buffer was added, vortexed and the University of Ghana http://ugspace.ug.edu.gh 43 tube placed on the MPC after which the supernatant was discarded. This was repeated and the supernatant discarded. 80 µl of Enhancing Buffer was finally added, mixed and the mixture added to the 1.7 ml tube of beads (from the Bead Wash Discovery step) and mixed completely. The tube was placed in the MPC to pellet the beads for 3 to 5 minutes and the supernatant carefully discarded. The beads were washed again with Enhancing Buffer, pelleted and the supernatant carefully discarded (Margulies et al., 2005). A 700 µl Melt Solution comprising 125 µl of NaOH and 9.875 ml of Molecular Biology Grade Water was used to resuspend the beads. The tube was returned to the MPC after vortexing for 5 seconds and the supernatant was transferred into a new 1.7 ml microcentrifuge tube. 700 µl of Melt Solution was again added to the beads, vortexed and the supernatant transferred to the same new 1.7 ml tube. The enrichment tube was then discarded. The supernatant was also discarded after a process of spin- rotate-spin. 1 ml Annealing Buffer was added to the tube 10 times, each time the supernatant was discarded after a process of spin-rotate-spin. Finally, 100 µl of Annealing Buffer was used to resuspend the beads in the tube (Margulies et al., 2005). 25 µl of Sequence Primer was added to the 1.7 ml tube, mixed and incubated for 5 minutes at 65°C and promptly cooled on ice for 2 minutes. 1 ml Annealing Buffer was mixed with the bead pellet and then centrifuged briefly (“spin-rotate-spin”) (Margulies et al., 2005). The amount of enriched beads was then evaluated by placing the tube into the GS Junior Bead Counter (Roche) and the level and quality of the emulsion accessed (Margulies et al., 2005). University of Ghana http://ugspace.ug.edu.gh 44 3.3.3 Sequencing Method The 175 µl of Packing Beads containing Polymerase, Polymerase Cofactor and Bead Buffer was mixed with enriched DNA beads (from the emulsion PCR) and incubated for 5 minutes at room temperature in a laboratory rotator. The Pico-Titer Plate was assembled into the Bead Deposition Device (BDD) and the plate loaded by injection with a pipette through the loading port on the BDD with four layers of beads. The layers were 350 µl each of Enzyme beads (pre-layer), enriched DNA beads + Packing Beads, Enzyme beads (Post-layer) and PPiase beads respectively. Centrifugal sedimentation was used to settle the beads at the bottom of the Pico-Titer Plate at 4,013 rpm for 5, 10, 10 and 5 minutes, respectively, for the various layers. After each sedimentation process, excess supernatant was discarded from the device using a pipette. The instrument protocol was followed and all the parameters entered manually. The number of nucleotide cycles used was 200. The BDD was removed and only the Pico-Titer Plate was loaded onto the cartridge in the sequencer and run for 9 hours to generate the sequence data of the library (Margulies et al., 2005). 3.3.4 Library Screening FASTA sequences obtained in the library were screened for possible microsatellites. Reads containing repeat motifs were screened and selected with MSATCOMMANDER software (Faircloth, 2008). The microsatellites were classified into di-, tri- and tetra-nucleotides, etc. 3.3.5 Primer Design The PRIMER 3 software (Rozen and Skaletsky, 2000) was used to design 154 primer pairs to flank the repeat motifs which comprised di-, tri- and tetra- nucleotides. The primers were designed to meet the following criteria: primer length range of 18 to 25 bp, amplification product size of 100 –250 bp, GC content of 50%, optimal melting University of Ghana http://ugspace.ug.edu.gh 45 temperature of 55 ºC (range 52 ºC–60 ºC), low levels of self- or pair- complementarity and maximum end stability (ΔG) of 8.0 (Faircloth, 2008). The primers were synthesized and used in subsequent tests. The forward primers of 20 primer pairs were labeled with fluorescent dyes: FAM, HEX and NED (Applied Biosystems, Foster City, CA). For all the remaining primers (134), an M13 (-21) universal leading sequence (5-TGTAAAACGACGGCCAGT-3) was added to the 5' end of each forward primer (Schuelke, 2000). 3.3.6 Primer Optimisation One hundred and fifty two synthesised primer pairs were tested in a PCR at two temperatures (55 ºC and 60˚C), to determine optimal cycling conditions using four guinea fowl DNA samples collected from LIPREC. Owing to limitation of time and resources, only two primer annealing temperatures were used as the basis for determining the optimal cycling conditions. A 10 µl volume PCR containing 20 ng of DNA, 0.75 U of LA-Taq DNA polymerase (TaKaRa Biomedicals,Tokyo, Japan), PCR buffer, 400 µM of each dNTP and 0.4 µM of forward and reverse primers was prepared for each primer pair. Cycling conditions were as follows: initial denaturing at 95 ºC for 2 minutes, followed by 35 cycles of denaturation at 95 ºC for 30 seconds, annealing at 55 ºC or 60 ºC for 30 seconds, extension at 74 ºC for 1 minute, and a final extension of 74 ºC for 10 minutes. The PCR products were analysed on 1.5% agarose gel electrophoresis and viewed in a UV Transilluminator (Thermo Scientific). The appropriate annealing temperature (i.e. 55 ºC or 60 ˚C) was selected for each primer based on the quality of the bands in the image. Primers that successfully amplified in at least two individual guinea fowl DNA were selected for genotyping. University of Ghana http://ugspace.ug.edu.gh 46 3.3.7 Genotyping 3.3.7.1 PCR Polymorphism at each microsatellite locus was determined using DNA isolated from the 32 unrelated guinea fowls. A 10 µl PCR was prepared containing 0.75 U of LA- Taq DNA polymerase (TaKaRa), PCR buffer, 400 µM of each dNTP, 0.4 µM of forward and reverse primers, 0.1 µg of T4 M13 universal tag sequence and 20 ng of template DNA. Also, three individual multiplex PCRs (Table 4) were prepared for HEX and FAM fluorescently labeled primers based on the expected product sizes. Each 10 µl volume contained 5 µl Master mix, 2x multiplex master mix (QIAGEN), 3 µl dH2O, 0.01 µM of the forward primer, 0.15 µM of reverse primer and 20 ng of the genomic DNA. General PCR conditions were initial denaturation of 95 ºC for 2 minutes, 35 cycles of 95 ºC for 30 seconds, 55 ºC or 60 ºC for 30 seconds, 74 ºC for 1 minute and a final extension at 74 ºC for 10 minutes. University of Ghana http://ugspace.ug.edu.gh 47 Table 4: Multiplex PCR and primer information for nine primers PCR Locus Primer sequence Dye Product Size Multiplex 1 Tm 60˚C GF12F GCACTAATAGTAGAGTACGCAGAA FAM 110 GF17F GATGGCTATTGGGAAATACA FAM 214 GF18F GGACCTTTCTCTGGAGACTT HEX 167 Multiplex 2 Tm 55˚C GF16F TGAGAGTGAAATACCTGCAA FAM 175 GF5F GTCTTCTCTGACTTTTGGAAAT HEX 173 GF8F ATGTCCCAAAATTCTAAGCA HEX 248 Multiplex 3 Tm 55˚C GF19F GTCTCCGAGATGTTGGTTT FAM 151 GF20F TCTTGTTCCAGTTGTCATCA HEX 119 GF2F CATCCAATACCCTGAACCTA HEX 196 3.3.7.2 Fragment Analysis 1 µl of each PCR product was diluted with 100 µl of water and mixed. 160 µl of Hi Dye Formamide (HDFA) and 0.4 µl of Rox500 size standard (Applied Biosystems, Foster City, CA, USA) were mixed per run (i.e. 16 samples). 10 µl of the mixture was pipette into each well on a 96 well sequencing plate and 1 µl of the diluted PCR product added to the wells on the plate. The plate was covered, spun down and incubated at 95 ºC for 5 minutes to denature the DNA. The plate was then quickly transferred onto ice for 5 minutes and loaded onto an ABI Prism 3130 XL Genetic Analyser 16 Capillary System (Applied Biosystems) for sequencing using GENESCAN software. Electrophoregrams were analyzed using PEAK SCANNER version 1 (Applied Biosystems). University of Ghana http://ugspace.ug.edu.gh 48 3.3.8 Data Analysis Population genetics parameters were estimated for all polymorphic loci with clear electropherograms. The observed heterozygosity (Ho), expected heterozygosity (He), number of alleles (Na), effective population size (Ne), probability of identity (PI), Shannon’s informative index (I), fixation index (F) and deviations from Hardy- Weinberg Equilibrium (HWE) were calculated using GENEALEX software version 6.41 (Peakall and Smouse, 2006). Null allele frequency (NAF) was determined for all loci using FreeNA (Chapuis and Estoup, 2007). The polymorphism information content (PIC) of the markers was calculated using Microsoft Office EXCEL 2007 based on the formula: ��� = 1 − ���� � � ��� � −� � 2�� ��� � � ����� ��� ��� where: pi and pj are the frequencies of the i-th and j-th alleles of a given microsatellite and n is the total number of alleles detected for that microsatellite (Botstein et al., 1980). University of Ghana http://ugspace.ug.edu.gh 49 CHAPTER FOUR 4.0 RESULTS 4.1 Library development and screening A total number of 105,015 reads were generated by the Genome Sequencer Junior instrument. All reads were exported as FASTA sequences, 31 of which are shown in Appendix III. Preliminary library screening with the MSATCOMMANDER software showed that the reads ranged from 40 bp to 757 bp in length, with an average of 393.27 bp. The total number of microsatellite sites obtained was 1,234, comprising 5 classes of repeats. Subsequent library screening showed di-, tri-, tetra-, penta and hexa- repeats in the proportions shown in Figure 9. It was observed that the number of microsatellites decreased sharply with increasing class of repeat. Figure 9: Distribution of microsatellites in five classes of repeats 520 356 179 155 24 0 100 200 300 400 500 600 di- tri- tetra- penta- hexa- N o . o f m ic ro s a te ll it e s it e s Class of nucleotide repeats University of Ghana http://ugspace.ug.edu.gh 50 4.2 Primer Design Using the PRIMER 3 software, sequences with sufficient flanking regions were used to design 154 primers out of 584 microsatellite sequences. Of the total number of primers designed, 38.3% (59 of 154) were di-, 60.4% (93 of 154) were tri- and 1.2% (2 of 154) were tetranucleotide repeats (Table 5). The primers were made up of seven types of repeats comprising CA/GT, ATC/GAT, CAT, CTG, GCC/CGG, AAAC and ACAT. The primer sequence information for each of the 154 primers is shown in Table 5. Table 5: Number of primers designed Class of repeat Total number Repeat length selected Type of repeat Number of reads Number designed* Di- nucleotide 520 >7 CA/GT 200 59 GA 94 - Tri- nucleotide 356 >4 ATC/GAT 46 35 CAT 25 19 GCT 105 - CTG 104 35 GCC/CGG 8 4 Tetra- nucleotide 179 >4 AAAC 1 1 ACAT 1 1 TOTAL 1055 584 154 * - : No primers designed. University of Ghana http://ugspace.ug.edu.gh 51 Following the criteria described by Weber (1990) and Schlötterer and Harr (2001), majority of the primers (142) were perfect repeats whilst the rest (loci GF1, GF3, GF4, GF6, GF19, GF126, GF154, GF181, GF202, GF207, GF210 and GF214) were imperfect repeats (Figure 10) with one or 2 nucleotide interruptions. No compound repeats were designed in this study. Figure 10: Characteristics of microsatellite repeats 4.3 Primer Testing Out of a total of 152 primers tested, 122 primers (Table 6) showed clear, distinct amplification patterns, an example of which is shown in Figure 11. Optimisation could not be done for 30 primers, which included 16 di- and 14 trinucleotide repeats. The annealing temperatures for each of the 122 primers are shown in Table 7. Of the 92% 8% Perfect repeats University of Ghana http://ugspace.ug.edu.gh 52 122 primers (43 di-, 77 tri- and 2 tetranucleotide repeats) that amplified at either of the two temperatures, 114 were perfect repeats whilst eight were imperfect repeats. Table 6: PCR optimization at 55°C and 60°C Class of repeat Type of repeat Annealing temperature Total 55°C 60°C Dinucleotide CA/GT 26 17 43 Trinucleotide ATC/GAT 17 11 28 CAT 9 5 14 CTG 20 11 31 CGG/CCG 3 1 4 Tetranucleotide AAAC 1 - 1 ACAT 1 - 1 Total no. of primers optimized 77 45 122 University of Ghana http://ugspace.ug.edu.gh 53 Figure 11: Gel image showing PCR optimization results of four primers with DNA from four individuals. The optimized annealing temperature was 55 °C for Primers GF2 and GF13 and 60 °C for Primers GF12 and GF16. University of Ghana http://ugspace.ug.edu.gh 54 Table 7: Primer sequence and amplification information for 154 designed microsatellite primers Serial Number Sequence ID Type Repeats Forward Primer Reverse Primer Product size (bp) PCR Ta GF1 G4QAAT301AA5ZK (CA)9AA(CA)5 CAACCAACGGCACAACAG TCCCTAGACGTAAATTGCAC 198 NA GF2 G4QAAT301AQSGU (CA)10 CATCCAATACCCTGAACCTA TTAAACCACAAGACCATTCC 196 55°c GF3 G4QAAT301BOVV7 (CA)7GA(CA)8 TCCTCTGAATGAAGGAAGAA TGTGATTCACTCTTTGGTCA 183 NA GF4 G4QAAT301ALJNI (CA)12GA(CA)5 TGAACACGGGCTTAGATAGT GCTTTAGATGGCAATAGTGG 215 55°c GF5 G4QAAT301BHEM5 (CA)13 GTCTTCTCTGACTTTTGGAAAT TACCCACACTGGTACTCTCC 173 55°c GF6 G4QAAT301AMILO (CA)16CG(CA)5 GTAGACCTGCACCTGAACAT GACTCTGACATTACCCTGGA 140 NA GF7 G4QAAT301BGU6K (CA)10 TTATCCAGACCTCCACTGTC GGACCTTTCTCTGGAGACTT 165 NA GF8 G4QAAT301A9VSB (CA)13 ATGTCCCAAAATTCTAAGCA TCTGTGCAAGTATGATCAGC 248 55°c GF9 G4QAAT301BURPT (CA)13 TCCCTGTAGTCCTGAACAAG CAGTTAGGAGAGCTGTAGCC 124 NA GF10 G4QAAT301A4HM7 (CA)10 TGCTAAATTATGTGCAGCAG TGGAACCAGAAGATTTTACG 181 NA GF11 G4QAAT301AJ5J8 (CA)15 GTAATTTTGCAGGGTACAGC GCGTAGCAATTGTATGATGA 196 NA GF12 G4QAAT301BPAU2 (CA)11 GCACTAATAGTAGAGTACGCAGAA TGCTAACTCCAAATGACACA 110 60°c GF13 G4QAAT301BX99P (CA)14 TGTACATGGTGCGTGTTTAT CGTTTTTGTCCGTACTCAAC 120 55°c GF14 G4QAAT301AL1V2 (CA)17 ATGATTGTTGGTTTTTACCG TTGGTAGAGTTTGGTTTCGT 202 NA GF15 G4QAAT301A3ZTS (CA)11 TGCAAATCATCTTTTTCCTT TCCTCTGACTTATACCAGTTGA 175 55°c University of Ghana http://ugspace.ug.edu.gh 55 GF16 G4QAAT301BSWC2 (CAT)7 TGAGAGTGAAATACCTGCAA GATCTGTTAGGGCTGCTAGA 175 60°c GF17 G4QAAT301BJUZI (GT)11 GATGGCTATTGGGAAATACA CTGGCTTACATATCCTTCCA 214 60°c GF18 G4QAAT301AXXZG (GT)10 GGACCTTTCTCTGGAGACTT TTATCCAGACCTCCACTGTC 167 60°c GF19 G4QAAT301BFUPO (GT)13T(GT) GTCTCCGAGATGTTGGTTT AATCTTTCGCCTCTTACACA 151 55°c GF20 G4QAAT301A3MWX (GT)13 TCTTGTTCCAGTTGTCATCA ATGCCTCTGCAAATTAGTGT 119 55°c GF21 G4QAAT301AIKN9 (GT)10 AAGTTTTCAGCAAAATCCAG CACATACAGATCATGGGACA 221 60°c GF22 G4QAAT301AO8TM (GT)12 GAGAACAACTTTTTGCATCC GCATTAAGCCGGTAAGTAAA 199 NA GF23 G4QAAT301AXAGK (GT)12 TACATTCGGGATATTGTTCC TATTGCTGGGTAATGGAGTC 209 60°c GF24 G4QAAT301AL1WL (GT)17 GCTGGAACAAGCTAAGAAGA TGGTAGAAGGCTTTTGTCAT 228 NA GF25 G4QAAT301BTSFA (GT)11 ATTCTAAAACAAT