Assessment of Genetic Diversity of Indigenous Chicken Ecotypes in Selected Areas of Tanzania

Assessment of Genetic Diversity of Indigenous Chicken Ecotypes in Selected Areas of Tanzania | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessment of Genetic Diversity of Indigenous Chicken Ecotypes in Selected Areas of Tanzania RHODA LUCAS, CHRISTOPHER JACOB KASANGA, ELISA DANIEL MWEGA, SISSA GOTIFRED ACKREY This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4109103/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract The sustainable usage and conservation of indigenous chickens require a genotypic characterization. This study was conducted in the Bahi and Songea districts of Tanzania. Blood samples of 100 indigenous chickens were collected and genotyped using eighteen microsatellite markers recommended by the International Society for Animal Genetics, Food and Agriculture Organization Advisory Group for Animal Genetics. Data were analyzed using GenAIEXv.6.5 software. Results showed that Bahi had a total of 117 alleles while Songea had 111 alleles. There were 6.5 alleles on average per locus for Bahi and 6.1 for Songea. The locus with the least number of alleles was MCW0078, with just 5 alleles, whereas the MCW0111F locus was highly polymorphic with 20 alleles. The mean number of different alleles (Na) was 7.250 ± 0.544 for Bahi and 7.000 ± 0.742 for Songea, with an overall average of 7.125 ± 0.515. The number of private alleles was 31 and 37 for Bahi and Songea, respectively. Genetic variation among the population was 17%, while within the population it was 83%. Nei’s genetic distance was calculated to be 0.589, and the principal of coordinate analysis (PCoA) revealed two clusters with some intermixes. At p < 0.05, divergence from Hard Weinberg equilibrium was significant in all loci. In conclusion, there was a considerable genetic variation between the two study populations hence, they are very useful in conservation programs of the indigenous chickens. Genetic diversity Indigenous chicken Microsatellite marker Bahi district Songea district Figures Figure 1 Figure 2 Figure 3 Introduction In Tanzania, most rural households keep indigenous chickens (ICs), which are common and significant species of fowl (Mwambene et al., 2019 ). Rukambile et al. ( 2020 ) reported that they are estimated to be above 37 million. According to Mpenda et al. ( 2019 ), most of the indigenous chickens are adapted to stressful and harsh environmental conditions. They provide a higher proportion of animal protein in the human diet and their meat is usually preferable to exotic species due to the pigmentation, taste, flavour and lower lipid contents (Haunshi et al., 2022 ). In addition, indigenous chickens act as a source of income and poverty alleviation at the family level, as Manyelo et al. ( 2020 ) noted. Unregulated distribution of exotic breeds to the community without a systematic genetic improvement strategy has brought about the indigenous chicken's significant loss of genetic diversity, according to Sabry et al. ( 2021 ). Characterizing a specific domestic animal population is essential for its conservation, sustainable management, and advancement (Gamaniel and Gwaza, 2017 ). These differences between individuals and groups serve as the foundation for selection and breeding (Faruque et al., 2010 ). Given that indigenous chickens are found in every place with human settlements, most of them are reported to be kept in the central corridor region of Tanzania (Moto and Rubanza, 2019 ). It is expected that these ecotypes will have a high degree of genetic diversity and distinct gene allele combinations that could enable environmental adaptability. Few studies on genetic diversity that employ microsatellite markers have been reported in Tanzania, (Lyimo et al., 2013 ), (Moto and Rubanza, 2019 ). The studies pointed out a potential significant genetic variability of indigenous chicken, though, it was not clear that the population was at equilibrium. Therefore, this study aimed to assess the genetic diversity of these indigenous chickens using microsatellite markers in central and Southern highlands Tanzania. The obtained information may advance the understanding of the genetic variety of indigenous chickens and encourage their sustainable use and conservation purposes. Materials and methods Study area This study was conducted in two different districts: Bahi in the Dodoma region, and Songea in the Ruvuma region, both of them are located in Tanzania, specifically, Bahi is situated in the central part of Tanzania, with coordinates of 05⁰57’10’’S35⁰18’43’’E. On the other hand, Songea is located in the southern highlands of Tanzania, with coordinates of 10⁰41’S35⁰39’E as identified in Fig. 1 . Sample collection The study involved a random collection of around 100 free-range indigenous chickens, both male and female, aged between 6 to 18 months collected at a distance of 0.5 km between each household and only one chicken per household. Various qualitative and quantitative phenotypic characteristics were recorded. Approximately one milliliter (1ml) of whole blood sample was collected through the wing vein puncture, then transferred into a heparinized vacutainer tube and stored in a refrigerator at 4 o C. Laboratory analysis was conducted at the Genomic Science Center, Sokoine University of Agriculture. DNA Extraction Extraction of the genomic DNA adopted a PrepFiler DNA extraction kit ( Applied Biosystem ™) by following the manufacturer's instructions. DNA quantity was ascertained using a Nanodrop spectrophotometer. Polymerase chain reaction (PCR) The DNA extracts obtained were exposed to a Polymerase chain reaction, followed by genotyping using 18 microsatellite markers that are suggested by the International Society for Animal Genetics (ISAG) and Food and Agricultural Organization (FAO) project for surveying chicken biodiversity, as noted by FAO, (2012), presented in Table 1 . The PCR reaction was carried out with a total volume of 25 µl, containing 12.5 µl PCR premix, 0.5 µl of forward and reverse primer, 7.5µl nuclease-free water, and 4µl of DNA template. Amplification was carried out in a thermocycler ( ProFlex PCR system - Applied Biosystems ™). The PCR products were separated by gel electrophoresis, followed by allele scoring. Data analysis GenAIEX v.6.5 was used in measurement of genetic diversity parameters and genetic relationship. Analysis of molecular variance (AMOVA) was computed to analyze the variation within and between the populations and the distance between the populations was analyzed by Nei’s genetic distance. Results Genetic diversity A total of 149 alleles were identified at the eighteen loci, averaging 8.2 alleles per microsatellite marker. The smallest number of alleles was 5 in the MCW0078 locus, while the MCW0111F locus was highly polymorphic with 20 alleles as shown in Fig. 3 . The Bahi chicken had a total of 111 alleles having a mean of 6.1 alleles per microsatellite marker, while the chicken from Songea had 118 alleles with an average of 6.5 alleles per microsatellite marker. We observed that the mean number of different alleles (Na) was 6.938 ± 0.798 for Bahi and 7.313 ± 0.681 for Songea, having an average of 7.125 ± 0.517. The effective number of alleles (Ne) was 3.331 ± 0.310 and 3.39 ± 0.281 for Bahi and Songea, respectively, with an overall mean of 3.335 ± 0.209. The heterozygosity (He) for Bahi and Songea was 0.667 ± 0.025 and 0.668 ± 0.027, respectively, with a grand mean of 0.668 ± 0.018. Additionally, the private alleles were 68, with a mean of 2.187 ± 0.481 alleles per locus, whereas MCW0111F revealed a high number of private alleles (12) indicated in Table 2 . The mean number of information index was 1.408. Moreover, the inbreeding coefficient (F IT factor) ranged between 0.894 and 1.000 with a mean of 0.986 ± 0.008, while the global heterozygosity index (F Is ) ranged from 0.912 and 1.000 with a mean of 0.988 ± 0.007. The degree of differentiation among the population (F ST ) was estimated as 0.098 ± 0.017 presented in Table 4 Genetic relationship There was a variation of 83% within a group and only 17% among the population as identified in Table 3 . However, the pairwise of Nei’s genetic distance was 0.589 between the two populations. Principal coordinate analysis (PCoA) observed a few intermixes of chicken from Bahi to Songea as indicated in Fig. 2 , additionally, there was a deviation from Hardy Weinberg Equilibrium at P < 0.05 Table 1 List of the eighteen selected microsatellite molecular markers used in the genotyping of indigenous chickens. Marker Oligo Seq. Chromosome location Annealing Temperature (ᵒC) Allele range (bp) ADL0268 CTC CAC CCC TCT CAG AAC TA CAA CTT CCC ATC TAC CTA CT 1 60 102–116 ADL0278 CCA GCA GTC TAC CTT CCT AT TGT CAT CCA AGA ACA GTG TG 8 50 114–126 MCW0295 ATC ACT ACA GAA CAC CCT CTC TAT GTA TGC ACG CAG ATA TCC 4 60 88–106 MCW0081 GTT GCT GAG AGC CTG GTG CAG CCT GTA TGT GGA ATT ACT TCT C 5 60 112–135 MCW0069 GCA CTC GAG AAA ACT TCC TGC G ATT GCT TCA GCA AGC ATG GGA GGA 26 60 158–185 MCW0183 ATC CCA GTG TCG AGT ATC CGA TGA GAT TTA CTG GAG CCT GCC 7 55 290–326 MCW0014 TAT TGG CTC TAG GAA CTG TC GAA ATG AAG GTA AGA CTA GC 6 55 162–182 MCW0067 GCA CTA CTG TGT GCT GCA GTT T GAG ATG TAG TTG CCA CAT TCC GAC 10 60 175–186 MCW0123 CCA CTA GAA AAG AAC ATC CTC GGC TGA TGT AAG AAG GGA TGA 14 50 75–100 MCW0016 ATG GCG CAG AAG GCA AAG CGA TAT TGG CTT CTG AAG CAG TTG CTA TGG 3 60 155–206 MCW0248 GTT GTT CAA AAG AAG ATG CAT G TTG CAT TAA CTG GGC ACT TTC 1 50 205–225 MCW0034 TGC ACG CAC TTA CAT ACT TAG AGA TGT CCT TCC AAT TAC ATT CAT GGG 2 60 212–248 MCW0037 ACC GGT GCC ATC AAT TAC CTA TTA GAA AGC TCA CAT GAC ACT GCG AAA 3 60 152–170 MCW0111 GCT CCA TGT GAA GTG GTT TA ATG TCC ACT TGT CAA TGA TG 1 50 96–120 MCW0078 CCA CAC GGA GAG GAG AAG GTC T TAG CAT ATG AGT GTA CTG AGC TTC 5 60 132–240 MCW0330 TGG ACC TCA TCA GTC TGA CAG AAT GTT CTC ATA GAG TTC CTG C 17 50 256–300 LEI0094 GAT CTC ACC AGT ATG AGC TGC TCT CAC ACT GTA ACA CAG TGC 4 50 247–287 LEI0234 ATG CAT CAG ATT GGT ATT CAA CGT GGC TGT GAA CAA ATA TG 2 50 216–364 Table 2 Genetic diversity parameters estimated from two different indigenous chicken populations (Bahi and Songea districts) were presented as the mean and standard error of the mean. N = 100, p =5% Ne I No. Private alleles/locus Ho He uHe F % PL Bahi Mean 6.938 4.375 3.331 1.398 2.000 0.012 0.667 0.674 0.984 100 SEM 0.798 0.417 0.310 0.086 0.599 0.007 0.025 0.025 0.009 Songea Mean 7.313 4.063 3.339 1.418 2.375 0.007 0.668 0.675 0.990 100 SEM 0.681 0.295 0.281 0.190 0.364 0.007 0.027 0.027 0.010 Total 7.125 4.219 3.335 1.408 2.187 0.009 0.668 0.674 0.987 100 0.517 0.356 0.206 0.061 0.481 0.005 0.018 0.018 0.007 Key Ho = Observed heterozygosity, He = Expected heterozygosity, uHe = unbiased expected heterozygosity, F = fixation index, %PL = Percentage polymorphic loci. Table 3 Analysis of molecular Variance (AMOVA) showing the percentage variation among the population and within the population. There was a significant variation in allelic patterns within the group. P < 0.05, n = 100. Source df SS MS Est. Var. % Among Pops 1 114.935 114.935 1.095 17% Within Pops 198 1081.730 5.463 5.463 83% Total 199 1196.665 6.558 100% Table 4 F-Statistics and Estimates of Nm over All Populations for each Locus. N = 100, SEM = Standard error of the mean. All Pops. Locus F IS F IT F ST Nm ADL0268 1.000 1.000 0.140 1.533 MCW0295 1.000 1.000 0.023 10.706 MCW0081 1.000 1.000 0.022 10.956 MCW0016 1.000 1.000 0.107 2.082 MCW0034 0.894 0.912 0.165 1.264 MCW0078 1.000 1.000 0.114 1.935 MCW0067 0.934 0.936 0.024 10.201 MCW0069 1.000 1.000 0.088 2.597 MCW0037 1.000 1.000 0.036 6.627 MCW0123 0.965 0.970 0.156 1.356 MCW0330 1.000 1.000 0.045 5.328 MCW0248 1.000 1.000 0.197 1.018 MCW0111F 1.000 1.000 0.155 1.360 LEI0234 1.000 1.000 0.094 2.401 MCW0014 0.983 0.987 0.202 0.987 ADL0278 1.000 1.000 0.004 67.915 Mean 0.986 0.988 0.098 8.017 SEM 0.008 0.007 0.017 4.095 Key F IT = Inbreeding coefficient, F ST = degree of differentiation among the population, F Is = global heterozygosity index, Nm = gene flow. Discussion Genetic diversity was analysed and a high level of allele polymorphism was identified. This is evident through the total number of alleles identified per marker. It indicates an unlimited gene pool and high gene flow. The current findings are in line with the value estimated in six populations in Central Tanzania as reported by Moto and Rubanza ( 2019 ) and were in a similar range as reported by Nxumalo et al. ( 2020 ). Moreover, high alleles per marker were identified in Bangladesh and China (Rashid et al., 2020 ), (Azimu et al., 2018 ). In contrast to the work published by Yacouba et al. ( 2022 ), the mean number of distinct alleles (Na) discovered in this investigation was greater on the other hand was lower compared to the report posed by Luis-Chincoya et al. ( 2021 ). Additionally, the number of effective alleles (Ne) looks to be higher compared to the approximate amount disclosed by Yacouba et al. ( 2022 ). The current findings show the allelic diversity and genetic variants for a given gene locus in the Tanzania chicken population which might be influenced by migration of individuals between populations and aspects related to natural selection. The existing variation in heterozygosity might be influenced by nonrandom mating. The expected heterozygosity concurs with the study conducted by Habimana et al. ( 2020 ) but, it persists persisting in a higher range compared to what was mentioned in traditional Dutch chicken by Teinlek et al. ( 2018 ) and 22 population of Chinese gamecock chicken. Interestingly, the expected heterozygosity in this study seems low compared to Central Tanzania as mentioned by Moto and Rubanza, ( 2019 ). On the other hand, the private alleles identified correspond with the study done by Habimana et al., ( 2020 a), and also were higher than that observed in Norfa chickens and Sinai chickens as reported by Soltan et al. ( 2018 ). These findings show genetic variants within a population that describes population relationships and structures. Based on this study, Wright’s F-statistics (F IS ) shows a greater range than what was estimated in several reports including one from Egypt under the study conducted by Abdelaziz et al. ( 2019 ), Alsoufi and Changrong, ( 2022 ), and Hata et al. , (2020). However, the genetic differentiation among the populations, was estimated lower amount than the value estimated in Egypt by Mekky et al. ( 2021 ) and China by Alsoufi and Changrong ( 2022 ). Additionally, the lower F ST value was reported by Yacouba et al. ( 2022 ), Okumu et al. ( 2017 ) and Soltan et al., ( 2018 ). The current findings indicate inbreeding which might be caused by migration. A great genetic variation within a population and only a small difference among the groups were identified by (AMOVA) which might be influenced by several factors including; random mating and the absence of mating restrictions within the population. Moreover, low variation between the groups might be caused by the presence of a close genetic relationship between the two populations. However, these findings were consistent with the findings from Sri Lanka (Samaraweera et al., 2021 ). In addition, Mwambene et al. ( 2019 ) identified a greater variation than the current findings within the population and few variations among the population of Southern highland Tanzania. The Nei’s genetic distance between the two populations observed in this study concurs with the findings estimated between populations of Tabora and Kondoa Tanzania, as reported by Moto and Rubanza, ( 2019 ). Asia reported the same value between the populations of Korean greyish brown and Mongolian Muthiinbor (Roh et al., 2020 ). Also, a greater value than the current finding was reported in Nigeria between populations of Shika brown and the Noiler chicken (Bakare et al., 2021 ). These results point to the genetic variation among the populations but not extremely distinct from each other, the value indicates a moderate level of genetic differentiation between the populations. Population distribution and the genetic relationship among the two populations through a Principal of Coordinate Analysis (PCoA) showed the existence of two clusters with some intermixes. The existence of two clusters might be influenced by geographical distances that create physical barriers and local adaptation due to different pressures from the environment. A similar study was conducted by Lyimo et al. ( 2013 ) on five ecotypes of chicken native to Tanzanian from eight regions and found three clusters. The deviation of all loci from HWE in the populations was significant. It indicates that the indigenous chicken populations were not in HWE. After all, the current findings might be caused by inbreeding which increases the likelihood of homozygosity, population substructures that might have different allele frequencies, random fluctuations in allele frequencies (genetic drift), a natural and artificial selection that selects and favours certain alleles based on their adaptation and fitness over the others, a mutation which changes the DNA sequence through the addition or deletion of a nucleotide hence allele’s change. Furthermore, the movement between populations (migration) with different allele frequencies might have disrupted the allele frequencies through the introduction or change of the existing frequencies (Haunshi et al., 2020 ). Similar results were reported in Southern highland Tanzania whereby 6 ecotypes out of 10 ecotypes deviated from HWE (Mwambene et al., 2019 ), The study conducted in India revealed that 22 microsatellite markers out of 24 were found to deviate from HWE in Mewari chicken (Parmar et al., 2022 ), Nigeria reported the significant microsatellite deviation from HWE in Nigerian indigenous chicken (Ajibike et al., 2022 ). The study concluded with the identification of genetic variants and relatedness which is supported by Nei genetic distance which suggests the genetic differences between the populations but not extremely distinct from each other, analysis of molecular variance (AMOVA) suggests the high variations within the population contrasted to among the populations. The research observed deviation of all loci from HWE in the populations which might be caused by natural and artificial selection, genetic drift, mutation and migration. These traits could then be integrated into breeding programs to enhance and select specific chicken characteristics for conservation and sustainable use. Moreover, two clusters identified in the study might be kept apart to preserve their genetic diversity although, more markers, sample size and regions from different zones can be employed in diversity studies. Declarations Acknowledgement I acknowledge The Government chemist laboratory authority under the training committee for supporting this study. The Sokoine University of Agriculture Genomic Science Centre for the laboratory analysis services and District livestock officers for their support during the fieldwork. Author contribution. Rhoda Lucas is a principal investigator, data collection and analysis and manuscript writing. Christopher J. Kasanga and Elisa D. Mwega participated in the design and played a supervisory role. Sissa Gotifred Ackrey participated in sample collection and data analysis. All authors have read and agreed to submit for publication. Ethical statement The care and use of animals were conducted following all relevant national, international and institutional guidelines. The research clearance for conducting the study was obtained from the Sokoine University of Agriculture reference number. SUA/DPRTC/R/186 VOL IV 65 and permission to conduct the study at Bahi-Dodoma and Songea-Ruvuma were granted by the President's Office regional administration and local authority reference number. AB.307/323/01/187. Funding The authorreceives fund from Government Chemist Laboratory Authority (GCLA) Data availability Data generated in this study are included in the manuscript, but if more data are required will be available. Conflict of interest No conflict of interest exists according to the authors References Abdelaziz, N., Marey, N., Duksi, F., Rebouh, N., Boli, B., Gadzhikurbanov, A., . . . Kulikov, E. (2019). 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4109103","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":288428178,"identity":"cfae7ded-676c-41e8-b73a-da05446f25ee","order_by":0,"name":"RHODA LUCAS","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACPgh1AIj4Pz4Asnj4CGlhQ2hhMDYAaWEjRYuZBJIIHi0SyQ8//txzR47v+IG0yq85djJsDMwPH93AqyXNWJrn2TNjyTMJx27LbksGOozN2DgHr5YcBmmGA4cTNxxIbLstuY0ZqIWHTZqAFuafP0Bazj9mK5bcVk+UFjYJHpCWG2lsjB+3HSZCC88zM2ugFmPJG2+YpRm3HedhYybgF3725Mc3gQ6T4zufw/jx57Zqe3725oeP8WlBAcw8YJJY5SDA+IMU1aNgFIyCUTBiAACKDkf99la/uwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0003-5637-5004","institution":"Sokoine University of Agriculture College of Veterinary Medicine and Biomedical Sciences","correspondingAuthor":true,"prefix":"","firstName":"RHODA","middleName":"","lastName":"LUCAS","suffix":""},{"id":288428179,"identity":"2f9a020c-3474-4601-ae15-3af1f91b791f","order_by":1,"name":"CHRISTOPHER JACOB KASANGA","email":"","orcid":"","institution":"Sokoine University of Agriculture College of Veterinary Medicine and Biomedical Sciences","correspondingAuthor":false,"prefix":"","firstName":"CHRISTOPHER","middleName":"JACOB","lastName":"KASANGA","suffix":""},{"id":288428180,"identity":"34f2c3b8-df8d-44b3-a28f-9b05b993f6cd","order_by":2,"name":"ELISA DANIEL MWEGA","email":"","orcid":"","institution":"Sokoine University of Agriculture College of Veterinary Medicine and Biomedical Sciences","correspondingAuthor":false,"prefix":"","firstName":"ELISA","middleName":"DANIEL","lastName":"MWEGA","suffix":""},{"id":288428181,"identity":"7ff126f3-56bb-4f01-81cd-2c7dcd18bb4e","order_by":3,"name":"SISSA GOTIFRED ACKREY","email":"","orcid":"","institution":"University of Dar es Salaam","correspondingAuthor":false,"prefix":"","firstName":"SISSA","middleName":"GOTIFRED","lastName":"ACKREY","suffix":""}],"badges":[],"createdAt":"2024-03-15 15:50:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4109103/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4109103/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":54411380,"identity":"9103bc27-69c8-4790-b380-87b0fbf63c84","added_by":"auto","created_at":"2024-04-10 05:34:18","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":93704,"visible":true,"origin":"","legend":"\u003cp\u003eTanzania sketch map showing Dodoma and Ruvuma regions portraying two study sites: Bahi and Songea district with their respective Wards from which, sampling of indigenous chicken was conducted from December 2022 and July 2023.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4109103/v1/bffab3da89cb695b0fa5fe49.jpeg"},{"id":54411058,"identity":"f4b94b27-55ff-4a0a-a92d-7978c7e0d778","added_by":"auto","created_at":"2024-04-10 05:26:17","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":13514,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution and population structure showing two clusters for two indigenous chicken populations collected from Bahi and Songea.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4109103/v1/15f48b166afbcf906c24361c.png"},{"id":54411060,"identity":"0b8cec0e-81a5-4267-9b37-43ef69f81dab","added_by":"auto","created_at":"2024-04-10 05:26:18","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":100241,"visible":true,"origin":"","legend":"\u003cp\u003eA number of alleles and allele frequencies identified in the highly polymorphic locus MCW0111F with twenty alleles and the least was MCW0078 with five alleles. n = 100.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4109103/v1/2e0cac8e09a8ab211c786abe.jpeg"},{"id":54411613,"identity":"5712dc77-5c7a-4b2c-bc88-9af672537381","added_by":"auto","created_at":"2024-04-10 05:42:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":429426,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4109103/v1/51875f24-37ab-466b-a16b-144820717cf4.pdf"}],"financialInterests":"","formattedTitle":"\u003cp\u003eAssessment of Genetic Diversity of Indigenous Chicken Ecotypes in Selected Areas of Tanzania\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn Tanzania, most rural households keep indigenous chickens (ICs), which are common and significant species of fowl (Mwambene et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Rukambile et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) reported that they are estimated to be above 37\u0026nbsp;million. According to Mpenda et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), most of the indigenous chickens are adapted to stressful and harsh environmental conditions. They provide a higher proportion of animal protein in the human diet and their meat is usually preferable to exotic species due to the pigmentation, taste, flavour and lower lipid contents (Haunshi et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In addition, indigenous chickens act as a source of income and poverty alleviation at the family level, as Manyelo et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) noted.\u003c/p\u003e \u003cp\u003e Unregulated distribution of exotic breeds to the community without a systematic genetic improvement strategy has brought about the indigenous chicken's significant loss of genetic diversity, according to Sabry et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Characterizing a specific domestic animal population is essential for its conservation, sustainable management, and advancement (Gamaniel and Gwaza, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). These differences between individuals and groups serve as the foundation for selection and breeding (Faruque et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven that indigenous chickens are found in every place with human settlements, most of them are reported to be kept in the central corridor region of Tanzania (Moto and Rubanza, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). It is expected that these ecotypes will have a high degree of genetic diversity and distinct gene allele combinations that could enable environmental adaptability. Few studies on genetic diversity that employ microsatellite markers have been reported in Tanzania, (Lyimo et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), (Moto and Rubanza, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The studies pointed out a potential significant genetic variability of indigenous chicken, though, it was not clear that the population was at equilibrium. Therefore, this study aimed to assess the genetic diversity of these indigenous chickens using microsatellite markers in central and Southern highlands Tanzania. The obtained information may advance the understanding of the genetic variety of indigenous chickens and encourage their sustainable use and conservation purposes.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThis study was conducted in two different districts: Bahi in the Dodoma region, and Songea in the Ruvuma region, both of them are located in Tanzania, specifically, Bahi is situated in the central part of Tanzania, with coordinates of 05⁰57\u0026rsquo;10\u0026rsquo;\u0026rsquo;S35⁰18\u0026rsquo;43\u0026rsquo;\u0026rsquo;E. On the other hand, Songea is located in the southern highlands of Tanzania, with coordinates of 10⁰41\u0026rsquo;S35⁰39\u0026rsquo;E as identified in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSample collection\u003c/h3\u003e\n\u003cp\u003eThe study involved a random collection of around 100 free-range indigenous chickens, both male and female, aged between 6 to 18 months collected at a distance of 0.5 km between each household and only one chicken per household. Various qualitative and quantitative phenotypic characteristics were recorded. Approximately one milliliter (1ml) of whole blood sample was collected through the wing vein puncture, then transferred into a heparinized vacutainer tube and stored in a refrigerator at 4\u003csup\u003eo\u003c/sup\u003eC. Laboratory analysis was conducted at the Genomic Science Center, Sokoine University of Agriculture.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eDNA Extraction\u003c/h2\u003e \u003cp\u003eExtraction of the genomic DNA adopted a PrepFiler DNA extraction kit (\u003cem\u003eApplied Biosystem\u003c/em\u003e\u0026trade;) by following the manufacturer's instructions. DNA quantity was ascertained using a Nanodrop spectrophotometer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePolymerase chain reaction (PCR)\u003c/h2\u003e \u003cp\u003eThe DNA extracts obtained were exposed to a Polymerase chain reaction, followed by genotyping using 18 microsatellite markers that are suggested by the International Society for Animal Genetics (ISAG) and Food and Agricultural Organization (FAO) project for surveying chicken biodiversity, as noted by FAO, (2012), presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The PCR reaction was carried out with a total volume of 25 \u0026micro;l, containing 12.5 \u0026micro;l PCR premix, 0.5 \u0026micro;l of forward and reverse primer, 7.5\u0026micro;l nuclease-free water, and 4\u0026micro;l of DNA template. Amplification was carried out in a thermocycler (\u003cem\u003eProFlex PCR system - Applied Biosystems\u003c/em\u003e\u0026trade;). The PCR products were separated by gel electrophoresis, followed by allele scoring.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eGenAIEX v.6.5 was used in measurement of genetic diversity parameters and genetic relationship. Analysis of molecular variance (AMOVA) was computed to analyze the variation within and between the populations and the distance between the populations was analyzed by Nei\u0026rsquo;s genetic distance.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eGenetic diversity\u003c/h2\u003e \u003cp\u003eA total of 149 alleles were identified at the eighteen loci, averaging 8.2 alleles per microsatellite marker. The smallest number of alleles was 5 in the MCW0078 locus, while the MCW0111F locus was highly polymorphic with 20 alleles as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The Bahi chicken had a total of 111 alleles having a mean of 6.1 alleles per microsatellite marker, while the chicken from Songea had 118 alleles with an average of 6.5 alleles per microsatellite marker. We observed that the mean number of different alleles (Na) was 6.938\u0026thinsp;\u0026plusmn;\u0026thinsp;0.798 for Bahi and 7.313\u0026thinsp;\u0026plusmn;\u0026thinsp;0.681 for Songea, having an average of 7.125\u0026thinsp;\u0026plusmn;\u0026thinsp;0.517. The effective number of alleles (Ne) was 3.331\u0026thinsp;\u0026plusmn;\u0026thinsp;0.310 and 3.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.281 for Bahi and Songea, respectively, with an overall mean of 3.335\u0026thinsp;\u0026plusmn;\u0026thinsp;0.209. The heterozygosity (He) for Bahi and Songea was 0.667\u0026thinsp;\u0026plusmn;\u0026thinsp;0.025 and 0.668\u0026thinsp;\u0026plusmn;\u0026thinsp;0.027, respectively, with a grand mean of 0.668\u0026thinsp;\u0026plusmn;\u0026thinsp;0.018. Additionally, the private alleles were 68, with a mean of 2.187\u0026thinsp;\u0026plusmn;\u0026thinsp;0.481 alleles per locus, whereas MCW0111F revealed a high number of private alleles (12) indicated in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The mean number of information index was 1.408. Moreover, the inbreeding coefficient (F\u003csub\u003eIT\u003c/sub\u003e factor) ranged between 0.894 and 1.000 with a mean of 0.986\u0026thinsp;\u0026plusmn;\u0026thinsp;0.008, while the global heterozygosity index (F\u003csub\u003eIs\u003c/sub\u003e) ranged from 0.912 and 1.000 with a mean of 0.988\u0026thinsp;\u0026plusmn;\u0026thinsp;0.007. The degree of differentiation among the population (F\u003csub\u003eST\u003c/sub\u003e) was estimated as 0.098\u0026thinsp;\u0026plusmn;\u0026thinsp;0.017 presented in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eGenetic relationship\u003c/h2\u003e \u003cp\u003eThere was a variation of 83% within a group and only 17% among the population as identified in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. However, the pairwise of Nei\u0026rsquo;s genetic distance was 0.589 between the two populations. Principal coordinate analysis (PCoA) observed a few intermixes of chicken from Bahi to Songea as indicated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, additionally, there was a deviation from Hardy Weinberg Equilibrium at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of the eighteen selected microsatellite molecular markers used in the genotyping of indigenous chickens.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOligo Seq.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChromosome\u003c/p\u003e \u003cp\u003elocation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAnnealing\u003c/p\u003e \u003cp\u003eTemperature\u003c/p\u003e \u003cp\u003e(ᵒC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAllele range\u003c/p\u003e \u003cp\u003e(bp)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADL0268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTC CAC CCC TCT CAG AAC TA\u003c/p\u003e \u003cp\u003eCAA CTT CCC ATC TAC CTA CT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e102\u0026ndash;116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eADL0278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCA GCA GTC TAC CTT CCT AT\u003c/p\u003e \u003cp\u003eTGT CAT CCA AGA ACA GTG TG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e114\u0026ndash;126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCW0295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATC ACT ACA GAA CAC CCT CTC\u003c/p\u003e \u003cp\u003eTAT GTA TGC ACG CAG ATA TCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e88\u0026ndash;106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCW0081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTT GCT GAG AGC CTG GTG CAG\u003c/p\u003e \u003cp\u003eCCT GTA TGT GGA ATT ACT TCT C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e112\u0026ndash;135\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCW0069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCA CTC GAG AAA ACT TCC TGC G\u003c/p\u003e \u003cp\u003eATT GCT TCA GCA AGC ATG GGA GGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e158\u0026ndash;185\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCW0183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATC CCA GTG TCG AGT ATC CGA\u003c/p\u003e \u003cp\u003eTGA GAT TTA CTG GAG CCT GCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e290\u0026ndash;326\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCW0014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTAT TGG CTC TAG GAA CTG TC\u003c/p\u003e \u003cp\u003eGAA ATG AAG GTA AGA CTA GC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e162\u0026ndash;182\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCW0067\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCA CTA CTG TGT GCT GCA GTT T\u003c/p\u003e \u003cp\u003eGAG ATG TAG TTG CCA CAT TCC GAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e175\u0026ndash;186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCW0123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCA CTA GAA AAG AAC ATC CTC\u003c/p\u003e \u003cp\u003eGGC TGA TGT AAG AAG GGA TGA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75\u0026ndash;100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCW0016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATG GCG CAG AAG GCA AAG CGA TAT\u003c/p\u003e \u003cp\u003eTGG CTT CTG AAG CAG TTG CTA TGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e155\u0026ndash;206\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCW0248\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGTT GTT CAA AAG AAG ATG CAT G\u003c/p\u003e \u003cp\u003eTTG CAT TAA CTG GGC ACT TTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e205\u0026ndash;225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCW0034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGC ACG CAC TTA CAT ACT TAG AGA\u003c/p\u003e \u003cp\u003eTGT CCT TCC AAT TAC ATT CAT GGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e212\u0026ndash;248\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCW0037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eACC GGT GCC ATC AAT TAC CTA TTA\u003c/p\u003e \u003cp\u003eGAA AGC TCA CAT GAC ACT GCG AAA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152\u0026ndash;170\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCW0111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGCT CCA TGT GAA GTG GTT TA\u003c/p\u003e \u003cp\u003eATG TCC ACT TGT CAA TGA TG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e96\u0026ndash;120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCW0078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCCA CAC GGA GAG GAG AAG GTC T\u003c/p\u003e \u003cp\u003eTAG CAT ATG AGT GTA CTG AGC TTC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e132\u0026ndash;240\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCW0330\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTGG ACC TCA TCA GTC TGA CAG\u003c/p\u003e \u003cp\u003eAAT GTT CTC ATA GAG TTC CTG C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e256\u0026ndash;300\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEI0094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGAT CTC ACC AGT ATG AGC TGC\u003c/p\u003e \u003cp\u003eTCT CAC ACT GTA ACA CAG TGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e247\u0026ndash;287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLEI0234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATG CAT CAG ATT GGT ATT CAA\u003c/p\u003e \u003cp\u003eCGT GGC TGT GAA CAA ATA TG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e216\u0026ndash;364\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGenetic diversity parameters estimated from two different indigenous chicken populations (Bahi and Songea districts) were presented as the mean and standard error of the mean. N\u0026thinsp;=\u0026thinsp;100, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNa\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNa Freq.\u0026gt;=5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo. Private alleles/locus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003euHe\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\"\u003e \u003cp\u003e%\u003c/p\u003e \u003cp\u003ePL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBahi\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.417\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.599\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSongea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.375\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSEM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.681\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.674\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c12\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.517\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.206\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eKey\u003c/strong\u003e \u003cp\u003eHo\u0026thinsp;=\u0026thinsp;Observed heterozygosity, He\u0026thinsp;=\u0026thinsp;Expected heterozygosity, uHe\u0026thinsp;=\u0026thinsp;unbiased expected heterozygosity, F\u0026thinsp;=\u0026thinsp;fixation index, %PL\u0026thinsp;=\u0026thinsp;Percentage polymorphic loci.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of molecular Variance (AMOVA) showing the percentage variation among the population and within the population. There was a significant variation in allelic patterns within the group. P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, n\u0026thinsp;=\u0026thinsp;100.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEst. Var.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAmong Pops\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e114.935\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWithin Pops\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1081.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e83%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1196.665\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eF-Statistics and Estimates of Nm over All Populations for each Locus. N\u0026thinsp;=\u0026thinsp;100, SEM\u0026thinsp;=\u0026thinsp;Standard error of the mean.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll Pops.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLocus\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eF\u003csub\u003eIS\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eF\u003csub\u003eIT\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003csub\u003eST\u003c/sub\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNm\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eADL0268\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.533\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMCW0295\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.706\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMCW0081\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.956\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMCW0016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMCW0034\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.894\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.264\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMCW0078\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.114\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.935\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMCW0067\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.201\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMCW0069\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.597\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMCW0037\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.627\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMCW0123\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.965\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.156\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.356\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMCW0330\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.328\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMCW0248\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMCW0111F\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.360\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eLEI0234\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.401\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMCW0014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.983\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eADL0278\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e67.915\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSEM\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.095\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eKey\u003c/strong\u003e \u003cp\u003eF\u003csub\u003eIT\u003c/sub\u003e = Inbreeding coefficient, F\u003csub\u003eST\u003c/sub\u003e = degree of differentiation among the population, F\u003csub\u003eIs\u003c/sub\u003e = global heterozygosity index, Nm\u0026thinsp;=\u0026thinsp;gene flow.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eGenetic diversity was analysed and a high level of allele polymorphism was identified. This is evident through the total number of alleles identified per marker. It indicates an unlimited gene pool and high gene flow. The current findings are in line with the value estimated in six populations in Central Tanzania as reported by Moto and Rubanza (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and were in a similar range as reported by Nxumalo et al. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, high alleles per marker were identified in Bangladesh and China (Rashid et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), (Azimu et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast to the work published by Yacouba et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the mean number of distinct alleles (Na) discovered in this investigation was greater on the other hand was lower compared to the report posed by Luis-Chincoya et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Additionally, the number of effective alleles (Ne) looks to be higher compared to the approximate amount disclosed by Yacouba et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The current findings show the allelic diversity and genetic variants for a given gene locus in the Tanzania chicken population which might be influenced by migration of individuals between populations and aspects related to natural selection.\u003c/p\u003e \u003cp\u003eThe existing variation in heterozygosity might be influenced by nonrandom mating. The expected heterozygosity concurs with the study conducted by Habimana et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) but, it persists persisting in a higher range compared to what was mentioned in traditional Dutch chicken by Teinlek et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and 22 population of Chinese gamecock chicken. Interestingly, the expected heterozygosity in this study seems low compared to Central Tanzania as mentioned by Moto and Rubanza, (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, the private alleles identified correspond with the study done by Habimana et al., (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003ea), and also were higher than that observed in Norfa chickens and Sinai chickens as reported by Soltan et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These findings show genetic variants within a population that describes population relationships and structures.\u003c/p\u003e \u003cp\u003eBased on this study, Wright\u0026rsquo;s F-statistics (F\u003csub\u003eIS\u003c/sub\u003e) shows a greater range than what was estimated in several reports including one from Egypt under the study conducted by Abdelaziz et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), Alsoufi and Changrong, (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and Hata \u003cem\u003eet al.\u003c/em\u003e, (2020). However, the genetic differentiation among the populations, was estimated lower amount than the value estimated in Egypt by Mekky et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and China by Alsoufi and Changrong (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Additionally, the lower F\u003csub\u003eST\u003c/sub\u003e value was reported by Yacouba et al. (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Okumu et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and Soltan et al., (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). The current findings indicate inbreeding which might be caused by migration. A great genetic variation within a population and only a small difference among the groups were identified by (AMOVA) which might be influenced by several factors including; random mating and the absence of mating restrictions within the population. Moreover, low variation between the groups might be caused by the presence of a close genetic relationship between the two populations. However, these findings were consistent with the findings from Sri Lanka (Samaraweera et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, Mwambene et al. (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) identified a greater variation than the current findings within the population and few variations among the population of Southern highland Tanzania.\u003c/p\u003e \u003cp\u003eThe Nei\u0026rsquo;s genetic distance between the two populations observed in this study concurs with the findings estimated between populations of Tabora and Kondoa Tanzania, as reported by Moto and Rubanza, (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Asia reported the same value between the populations of Korean greyish brown and Mongolian Muthiinbor (Roh et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Also, a greater value than the current finding was reported in Nigeria between populations of Shika brown and the Noiler chicken (Bakare et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These results point to the genetic variation among the populations but not extremely distinct from each other, the value indicates a moderate level of genetic differentiation between the populations.\u003c/p\u003e \u003cp\u003ePopulation distribution and the genetic relationship among the two populations through a Principal of Coordinate Analysis (PCoA) showed the existence of two clusters with some intermixes. The existence of two clusters might be influenced by geographical distances that create physical barriers and local adaptation due to different pressures from the environment. A similar study was conducted by Lyimo et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) on five ecotypes of chicken native to Tanzanian from eight regions and found three clusters.\u003c/p\u003e \u003cp\u003eThe deviation of all loci from HWE in the populations was significant. It indicates that the indigenous chicken populations were not in HWE. After all, the current findings might be caused by inbreeding which increases the likelihood of homozygosity, population substructures that might have different allele frequencies, random fluctuations in allele frequencies (genetic drift), a natural and artificial selection that selects and favours certain alleles based on their adaptation and fitness over the others, a mutation which changes the DNA sequence through the addition or deletion of a nucleotide hence allele\u0026rsquo;s change. Furthermore, the movement between populations (migration) with different allele frequencies might have disrupted the allele frequencies through the introduction or change of the existing frequencies (Haunshi et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Similar results were reported in Southern highland Tanzania whereby 6 ecotypes out of 10 ecotypes deviated from HWE (Mwambene et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), The study conducted in India revealed that 22 microsatellite markers out of 24 were found to deviate from HWE in Mewari chicken (Parmar et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Nigeria reported the significant microsatellite deviation from HWE in Nigerian indigenous chicken (Ajibike et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study concluded with the identification of genetic variants and relatedness which is supported by Nei genetic distance which suggests the genetic differences between the populations but not extremely distinct from each other, analysis of molecular variance (AMOVA) suggests the high variations within the population contrasted to among the populations. The research observed deviation of all loci from HWE in the populations which might be caused by natural and artificial selection, genetic drift, mutation and migration. These traits could then be integrated into breeding programs to enhance and select specific chicken characteristics for conservation and sustainable use. Moreover, two clusters identified in the study might be kept apart to preserve their genetic diversity although, more markers, sample size and regions from different zones can be employed in diversity studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eI acknowledge The Government chemist laboratory authority under the training committee for supporting this study. The Sokoine University of Agriculture Genomic Science Centre for the laboratory analysis services and District livestock officers for their support during the fieldwork.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution.\u0026nbsp;\u003c/strong\u003eRhoda Lucas is a principal investigator, data collection and analysis and manuscript writing. Christopher J. Kasanga and Elisa D. Mwega participated in the design and played a supervisory role. Sissa Gotifred Ackrey participated in sample collection and data analysis. All authors have read and agreed to submit for publication. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe care and use of animals were conducted following all relevant national, international and institutional guidelines. The research clearance for conducting the study was obtained from the Sokoine University of Agriculture reference number. SUA/DPRTC/R/186 VOL IV 65 and permission to conduct the study at Bahi-Dodoma and Songea-Ruvuma were granted by the President's Office regional administration and local authority reference number. AB.307/323/01/187.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003eThe authorreceives fund from Government Chemist Laboratory Authority (GCLA)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e Data generated in this study are included in the manuscript, but if more data are required will be available.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e No conflict of interest exists according to the authors\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdelaziz, N., Marey, N., Duksi, F., Rebouh, N., Boli, B., Gadzhikurbanov, A., . . . Kulikov, E. (2019). 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Genetic Diversity and Population Structure of Local Chicken Ecotypes in Burkina Faso Using Microsatellite Markers. \u003cem\u003eGenes, 13\u003c/em\u003e(9), 1523.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Genetic diversity, Indigenous chicken, Microsatellite marker, Bahi district, Songea district","lastPublishedDoi":"10.21203/rs.3.rs-4109103/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4109103/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe sustainable usage and conservation of indigenous chickens require a genotypic characterization. This study was conducted in the Bahi and Songea districts of Tanzania. Blood samples of 100 indigenous chickens were collected and genotyped using eighteen microsatellite markers recommended by the International Society for Animal Genetics, Food and Agriculture Organization Advisory Group for Animal Genetics. Data were analyzed using GenAIEXv.6.5 software. Results showed that Bahi had a total of 117 alleles while Songea had 111 alleles. There were 6.5 alleles on average per locus for Bahi and 6.1 for Songea. The locus with the least number of alleles was MCW0078, with just 5 alleles, whereas the MCW0111F locus was highly polymorphic with 20 alleles. The mean number of different alleles (Na) was 7.250\u0026thinsp;\u0026plusmn;\u0026thinsp;0.544 for Bahi and 7.000\u0026thinsp;\u0026plusmn;\u0026thinsp;0.742 for Songea, with an overall average of 7.125\u0026thinsp;\u0026plusmn;\u0026thinsp;0.515. The number of private alleles was 31 and 37 for Bahi and Songea, respectively. Genetic variation among the population was 17%, while within the population it was 83%. Nei\u0026rsquo;s genetic distance was calculated to be 0.589, and the principal of coordinate analysis (PCoA) revealed two clusters with some intermixes. At p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, divergence from Hard Weinberg equilibrium was significant in all loci. In conclusion, there was a considerable genetic variation between the two study populations hence, they are very useful in conservation programs of the indigenous chickens.\u003c/p\u003e","manuscriptTitle":"Assessment of Genetic Diversity of Indigenous Chicken Ecotypes in Selected Areas of Tanzania","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-10 05:26:13","doi":"10.21203/rs.3.rs-4109103/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision with re-assessment","date":"2024-07-17T13:09:00+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-04-15T05:13:53+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-04-07T11:31:46+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-03-26T04:50:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"Tropical Animal Health and Production","date":"2024-03-23T10:54:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"tropical-animal-health-and-production","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"trop","sideBox":"Learn more about [Tropical Animal Health and Production](https://www.springer.com/journal/11250)","snPcode":"11250","submissionUrl":"https://submission.nature.com/new-submission/11250/3","title":"Tropical Animal Health and Production","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e1bb8954-96fc-4cda-bc43-4237301ce7cd","owner":[],"postedDate":"April 10th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2024-09-20T08:30:45+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-10 05:26:13","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4109103","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4109103","identity":"rs-4109103","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}