Determination of genetic variability, population structure and genetic differentiation of indigenous Turkish goat breeds based on SSR loci

Karslı, Taki; Demir, Eymen; Fidan, Hüseyin Göktuğ; Aslan, Mehmet; Karsli, Bahar Argun; Arik, I. Zafer; Semerci, Emine Şahin; Karabağ, Kemal; Balcıoğlu, Murat Soner

doi:10.1016/j.smallrumres.2020.106147

Cited by 11 publications

(13 citation statements)

References 22 publications

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“…Compared to the goat breeds raised in China (Salles et al 2011;Guang-Xin et al 2015), lower inbreeding was detected in native Turkish goats via MHC-linked microsatellite markers. Similarly, low inbreeding was reported in previous microsatellite studies on native Turkish goats (Gül et al 2020;Karsli et al 2020;Tefiel et al 2020). As highlighted by Argun Karsli et al (2020), higher null allele frequency (> 0.2) may affect estimates of observed heterozygosity and inbreeding coefficient parameters.…”

Section: Genetic Diversity Among Native Turkish Goat Populations Base...supporting

confidence: 70%

“…Additionally, mean observed heterozygosity values for native Turkish goat populations were higher than the values reported for Xuhuai, Pashmina, Changthangi, Vendi, Galla, Small East African goat breeds (Salles et al 2011). On the other hand, higher genetic diversity was reported in previous studies conducted on native Turkish goats breeds in which Karsli et al (2020) used 20 microsatellite markers, while Gül et al (2020) preferred a total 22 microsatellite markers to reveal genetic structure of studied goat breeds. It is not surprising, since genetic diversity may increase by using more microsatellite markers with higher population size.…”

Section: Genetic Diversity Among Native Turkish Goat Populations Base...contrasting

confidence: 63%

“…In Turkey, goat raising is practised by smallholder farmers across the country in order to obtain specific products such as milk, meat, cashmere and angora. Although, large part of goat population (more than 90%) is Hair (HAI) goat, it possesses different varieties [Kabakulak (KBK), Pavga and Çandır] adapted to different environmental conditions (Karsli et al 2020). There are differences between HAI and its varieties in terms of body size, fertility and milk yield (Erduran & Kırbas 2010).…”

Section: Introductionmentioning

confidence: 99%

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Microsatellite diversity and restriction enzyme-based polymorphisms of MHC loci in native Turkish goats

Karslı¹,

Aslan²,

Demir³

2021

Tarım Bilimleri Dergisi

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Section: Genetic Diversity Among Native Turkish Goat Populations Base...supporting

confidence: 70%

Section: Genetic Diversity Among Native Turkish Goat Populations Base...contrasting

confidence: 63%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Microsatellite diversity and restriction enzyme-based polymorphisms of MHC loci in native Turkish goats

Karslı¹,

Aslan²,

Demir³

2021

Tarım Bilimleri Dergisi

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“…The aforementioned methods are utilized for many purposes in farm animals, including revealing genetic origins (Upadhyay et al ., 2017), selection signatures (Guo et al ., 2018), genetic diversity (Demir and Balcioğlu, 2019), population structure (Karsli et al ., 2020), population difference (Ceccobelli et al ., 2020), detecting genes related to economically important traits (Wang et al ., 2021), identifying genes related to resistance/susceptibility to specific infectious diseases (Bermingham et al ., 2014), carrier animals of genetic diseases (Gebreselassie et al ., 2019), analyzing phylogenetic relationships (Özşensoy et al ., 2019), and conducting parentage testing (Keskin et al ., 2019).…”

Section: Advances In Molecular Genetics and Its Implications In Fawmentioning

confidence: 99%

Direct and indirect contributions of molecular genetics to farm animal welfare: a review

Demir

Bilginer

Balcıoğlu

et al. 2021

Anim. Health. Res. Rev.

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Since domestication, farm animals have played a key role to increase the prosperity of humankind, while animal welfare (AW) is debated even today. This paper aims to comprehensively review the contributions of developing molecular genetics to farm animal welfare (FAW) and to raise awareness among both scientists and farmers about AW. Welfare is a complex trait affected by genetic structure and environmental factors. Therefore, the best welfare status can be achieved not only to enhance environmental factors such as management and feeding practices, but also the genetic structure of animals must be improved. In this regard, advances in molecular genetics have made great contributions to improve the genetic structure of farm animals, which has increased AW. Today, by sequencing and/or molecular markers, genetic diseases may be detected and eliminated in local herds. Additionally, genes related to diseases or adaptations are investigated by molecular techniques, and the frequencies of desired genotypes are increased in farm animals to keep welfare at an optimized level. Furthermore, stress on animals can be reduced with DNA extraction from stool and feather samples which reduces physical contact between animals and veterinarians. Together with molecular genetics, advances in genome editing tools and biotechnology are promising to improve FAW in the future.

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“…Honamli goat, which is de ned as a new breed by animal genetic resources, was taken under protection by the General Directorate of Agricultural Research and Policies in 2015 (Sozuer, 2019). After this development, it was reported that the phylogenetic similarity of Honamli and Hair goats to each other was over 85% and that these breeds could not be distinguished with the help of microsatellite markers (Bulut, et al, 2016;Karadag, 2016;Gumus, 2018;Karsli, et al, 2020;Gul, et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Phenotypic Characterization of Hair and Honamli Goats by Using Classification Trees Algorithms and Multivariate Adaptive Regression Splines (Mars)

Altay

2021

Preprint

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In order to meet the food demand of the increasing world population, it is very important to define the animal breeds and species raised in tropical and subtropical regions and to organize breeding programs for this. Discrimination animal breeds by morphological classification are a widely used method for a century. Although Honamli and Hair goats are very similar to each other morphologically, they can be subjectively distinguished by experienced breeders with some distinctive morphological markers. In the current study, certain body characteristics of Hair goats, which have a large portion of the population in Turkey, and Honamli goat, which has recently been registered as a new breed were used. Phenotypic characterization of these breeds has been made using data mining methods such as Classification and regression tree (CART), chi-square automatic interaction detector (CHAID), Exhaustive CHAID, Quick Unbiased, Efficient Statistical Tree, (QUEST), and multivariate adaptive regression splines (MARS) algorithms. In other words, the current study is the first data mining algorithms used for phenotypic characterization in Hair and Honamli goat breeds. Goats’ morphological characteristics such as live weight (LW), withers height (WH), back height (BH), rump height (RH), chest Depth (CD), body length (BL), chest girth (CG), leg girth (LG), head length (HL), fore head (FH), ear length (EL), and tail length (TL), used in diagnosis of discrimination on breeds, were used as a binary response variable in Honamli and Hair breeds. Here, the independent variables used in data mining algorithms are the morphological characteristics of goats. CHAID, Exhaustive CHAID, CART, QUEST, and MARS were used as data mining algorithms to make an accurate decision in detecting effective morphological traits in breed discrimination. The success of the CHAID, Exhaustive CHAID, CART, QUEST and MARS algorithms in breed discrimination is 87.80%, 85.80%, 87.80%, 77.00%, and 88.51%, respectively, while the area under the ROC curve is 0.880, 0.853, 0.868, 0.784 and 0.942, respectively. As a result, using data mining methods for some body measurements of Honamli and Hair goats, whose morphological distinction is not exactly accurate, phenotype characterization separation was performed with high success in MARS and CHAID algorithms compared with the other methods. The outputs of this study can be used for breeding material by enabling pure Honamli goat breeding. Also, data mining algorithms can be included in gene resource conservation programs.

show abstract

Determination of genetic variability, population structure and genetic differentiation of indigenous Turkish goat breeds based on SSR loci

Cited by 11 publications

References 22 publications

Microsatellite diversity and restriction enzyme-based polymorphisms of MHC loci in native Turkish goats

Microsatellite diversity and restriction enzyme-based polymorphisms of MHC loci in native Turkish goats

Direct and indirect contributions of molecular genetics to farm animal welfare: a review

Phenotypic Characterization of Hair and Honamli Goats by Using Classification Trees Algorithms and Multivariate Adaptive Regression Splines (Mars)

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