Assessing the genetic diversity of five Tanzanian chicken ecotypes using molecular tools

Lyimo, Charles Moses; Weigend, Annett; Janssen-Tapken, Ulrike; Msoffe, Peter L. M.; Simianer, Henner; Weigend, Steffen

doi:10.4314/sajas.v43i4.7

Cited by 35 publications

(46 citation statements)

References 41 publications

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“…; Lyimo et al . ). The traditional management system still in use at the present is defined by the absence of selection practice and by uncontrolled breeding management.…”

Section: Discussionmentioning

confidence: 97%

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Global diversity and genetic contributions of chicken populations from African, Asian and European regions

et al. 2014

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Genetic diversity and population structure of 113 chicken populations from Africa, Asia and Europe were studied using 29 microsatellite markers. Among these, three populations of wild chickens and nine commercial purebreds were used as reference populations for comparison. Compared to commercial lines and chickens sampled from the European region, high mean numbers of alleles and a high degree of heterozygosity were found in Asian and African chickens as well as in Red Junglefowl. Population differentiation (FST ) was higher among European breeds and commercial lines than among African, Asian and Red Junglefowl populations. Neighbour-Net genetic clustering and structure analysis revealed two main groups of Asian and north-west European breeds, whereas African populations overlap with other breeds from Eastern Europe and the Mediterranean region. Broilers and brown egg layers were situated between the Asian and north-west European clusters. structure analysis confirmed a lower degree of population stratification in African and Asian chickens than in European breeds. High genetic differentiation and low genetic contributions to global diversity have been observed for single European breeds. Populations with low genetic variability have also shown a low genetic contribution to a core set of diversity in attaining maximum genetic variation present from the total populations. This may indicate that conservation measures in Europe should pay special attention to preserving as many single chicken breeds as possible to maintain maximum genetic diversity given that higher genetic variations come from differentiation between breeds.

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“…; Lyimo et al . ). The traditional management system still in use at the present is defined by the absence of selection practice and by uncontrolled breeding management.…”

Section: Discussionmentioning

confidence: 97%

“…; Lyimo et al . ). Chicken populations included in this study encompassed various categories of management ranging from unmanaged backyard chickens to highly specialized commercial purebred chicken lines, sampled in various regions of Africa, Asia and Europe.…”

Section: Methodsmentioning

confidence: 97%

Global diversity and genetic contributions of chicken populations from African, Asian and European regions

et al. 2014

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“…ICs are anticipated to have multiple origins from wild ancestors in South Asia and Island Southeast Asia and have been introduced to Kenya through several entry points (Lyimo et al, 2013;Mwacharo et al, 2013aMwacharo et al, , 2013b. Number of alleles including private alleles and their apparent difference in distribution across the ecotypes indicated the existence of genetic diversity between the populations.…”

Section: Genetic Variabilitymentioning

confidence: 99%

“…Number of alleles including private alleles and their apparent difference in distribution across the ecotypes indicated the existence of genetic diversity between the populations. Kuchi chicken in Tanzania is similar to Shamo game birds from "Kōchi" Prefecture of Shikoku Island in Japan and its thought to be its origin (Lyimo et al, 2013). ICs are anticipated to have multiple origins from wild ancestors in South Asia and Island Southeast Asia and have been introduced to Kenya through several entry points (Lyimo et al, 2013;Mwacharo et al, 2013aMwacharo et al, , 2013b.…”

Section: Genetic Variabilitymentioning

confidence: 99%

Genetic diversity of different indigenous chicken ecotypes using highly polymorphic MHC-linked and non-MHC microsatellite markers

Ngeno

Waaij

Megens

et al. 2014

Anim. Genet. Resour.

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The study investigated the genetic make-up of different ecotypes of indigenous chickens (ICs) in Kenya based on major histocompatibility complex (MHC)-linked and non-MHC microsatellite markers. Blood samples were collected from eight regions (48 birds per region) of Kenya: Kakamega (KK), Siaya (BN), West Pokot (WP), Turkana (TK), Bomet (BM), Narok (NR), Lamu (LM) and Taita-Taveta (TT) and genotyped using two MHC-linked and ten non-MHC markers. All MHC-linked and non-MHC markers were polymorphic with a total of 140 alleles, of which 56 were identified in MHC-linked markers. Mean number of alleles (Na and Ne), private alleles, heterozygosity and genetic distances were higher for MHC-linked markers compared with non-MHC markers. The ad hoc statistic ΔK detected the true numbers of clusters to be three for MHC-linked markers and two in non-MHC markers. In conclusion, Kenyan ICs belong into two to three genetically distinct groups. Different markers systems have different clustering system. MHC-linked markers divided ICs into three mixed clusters, composing of individuals from the different ecotypes whereas non-MHC markers grouped ICs into two groups. These IC ecotypes host many and highly diverse MHC-linked alleles. Higher allelic diversity indicated a huge amount of genetic variation in the MHC region of ICs and supported their reputation of being hardy and resistant to diseases. RésuméCette étude a cherché à connaître la configuration génétique de différents écotypes de poules autochtones du Kenya, sur la base de marqueurs microsatellites associés ou non au Complexe Majeur d'Histocompatibilité (CMH). Des échantillons sanguins ont été prélevés dans huit régions du Kenya (48 volailles par région): Kakamega (KK), Siaya (BN), Pokot Occidental (PO), Turkana (TK), Bomet (BM), Narok (NR), Lamu (LM) et Taita-Taveta (TT). Les échantillons ont été génotypés en utilisant deux marqueurs associés au CMH et 10 marqueurs non associés. Tous les marqueurs, aussi bien ceux associés au CMH que ceux non associés à celui-ci, ont été polymorphes avec un total de 140 allèles, dont 56 ont été identifiés avec des marqueurs associés au CMH. Le nombre moyen d'allèles (Na et Ne) et celui d'allèles privés, l'hétérozygotie et les distances génétiques ont été plus élevés pour les marqueurs associés au CMH que pour ceux non associés. La mesure statistique ad hoc ΔK a révélé que le vrai nombre de groupes est de trois pour les marqueurs associés au CMH et de deux pour les marqueurs non associés. En conclusion, les poules autochtones kényanes appartiennent à 2-3 groupes génétiques différents. Des systèmes différents de marqueurs présentent des méthodes différentes de groupement. Les marqueurs associés au CMH ont divisé les populations de poules autochtones en trois groupes mixtes, constitués d'individus provenant des différents écotypes, alors que les marqueurs non associés au CMH ont rassemblé les poules autochtones en deux groupes. Ces écotypes de poules autochtones abritent de nombreux et très divers allèles associés au CMH. Une plus grande diver...

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“…This selective sweep hypothesis is reasonable because of the harsh natural environment to which local chickens are subject in this area of the world. The geographical location of the chicken lines may elevate inbreeding frequency and isolate them from their ancestral population, which could contribute to this low genetic variation, as discussed by Peters et al (2012) and Lyimo et al (2014a). Recent reports about the European chicken population over the past 150 years suggest that these birds are directly descended from Asian chicken breeds or have been crossed with them (Lyimo et al, 2014b).…”

Section: Discussionmentioning

confidence: 99%

Genetic diversity of Hajar1 and Hajar2 local Saudi chicken lines using mitochondrial DNA D-loop markers

Ahmed¹,

Alhudaib²,

Soliman³

2016

SA J. An. Sci.

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This study was conducted to assess genetic diversity of Hajar1 and Hajar2 local Saudi chicken lines using mitochondrial DNA (mtDNA) D-loop partial sequences. One hundred blood samples were obtained equally from Hajar1 and Hajar2 Saudi chicken lines as 50 samples from each line. The D-loop region was partially amplified from genomic DNA with a conserved primer set, and the fragments were sequenced. Eight published reference mtDNA sequence data from the GenBank were used for comparisons, and multiple alignments were performed. The most common haplotype was assigned as a basic sequence for comparing within each line. Entropy plot and conserved region analysis were performed. Genetic distances and neighbour-joining (NJ) phylogenetic trees were estimated. The results indicated haplotype variations within and between local Saudi chicken lines, which could explain the phenotypic variation reported earlier. A close genetic relationship was shown between the Saudi local chicken lines. Unique conserved regions and nucleotide substitutions were observed between the two lines. Both lines have a close relationship with the reference Asian local chicken population, especially local Chinese and Indian chicken breeds. The current results are considered the first report of mtDNA sequence diversity for Hajar1 and Hajar2 lines. Further detailed molecular genetic studies of both lines are indispensable to genetic conservation and development. ______________________________________________________________________________________

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Assessing the genetic diversity of five Tanzanian chicken ecotypes using molecular tools

Cited by 35 publications

References 41 publications

Global diversity and genetic contributions of chicken populations from African, Asian and European regions

Global diversity and genetic contributions of chicken populations from African, Asian and European regions

Genetic diversity of different indigenous chicken ecotypes using highly polymorphic MHC-linked and non-MHC microsatellite markers

Genetic diversity of Hajar1 and Hajar2 local Saudi chicken lines using mitochondrial DNA D-loop markers

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