IntroductionVariance sources are the basis for organization of progeny testing, calculation of genetic-environment interaction, construction of selection index, calculation of mixed model BLUP, estimation of phenotype-environment correlation, planning of improvement program with identified genetic structure in quantitative characters, estimation of variance components, and estimation of accurate breeding value [1,2]. Accuracy of estimation of variance components depends on factors that include observations, statistical model used, and method [3]. Therefore, many researchers have tried to improve different methods for estimation of variance components [4][5][6][7][8][9]. ANOVA, ML, and REML are the most used methods for estimation of genetic variance [10]. These methods have been called classical approaches (frequentist, Berkeley methods) and are based on normality assumption. However, the existence of threshold traits and the observation of binary data in animal breeding violates the rule of normality assumption [11,12]. Therefore, the Bayesian approach, another alternative method to overcome this concern, has been developed and this approach that does not require normality can be alternatively used to estimate variance components by using posterior distribution in discrete and continuous distributed traits [13][14][15][16]. In this respect, the Bayesian approach has more advantages than the classical approach in practice [17]. At the same time, no negative variance can be estimated under the Bayesian approach [18]. In the genetic evaluation of animals, the use of the MCMC algorithm in Bayesian approach has been a good option and this approach has been reducing bias in the estimations even when the dataset is too small [19]. At the same time, the Bayesian approach is statistically more flexible for estimations of variance components than the REML procedure [15]. In addition, some researchers have suggested that the use of the MCMC algorithm in the Bayesian approach is more feasible although it is computationally more expensive [20,21]. However, with the developed computer technology, various programs such as MTGSAM [22], GIBANAL [23], MCMCglmm [24], and FlexQTLs [25,26] have increased the popularity of the Bayesian approach. Under the National Small

This study was conducted to investigate the maternal genetic diversity using the mtDNA D -loop region 842 base pair (bp) polymorphism in Şavak Akkaraman sheep raised in the province of Erzincan. According to the results, in the mtDNA D -loop of the Şavak Akkaraman breed, 27 polymorphic sites and 20 haplotypes were identified and then, 11 of the 20 haplotypes identified in Şavak sheep were in haplogroup B (55%), 6 in haplogroup A (30%), and 3 in haplogroup C (15%). Haplogroup B was the most frequent haplogroup in Şavak Akkaraman sheep. Haplotype and nucleotide diversities were estimated to be 0.995 ± 0.004 and 0.0146 ± 0.0003, respectively. The Şavak Akkaraman breed was compared with the mtDNA sequencing of different breeds and wild sheep, and the contribution of the breed to biodiversity was considered.

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This study was carried out in order to determine whether there was any relationship between udder sagging and milk yield of Kilis Goats, which were cultivated by farmers in Polateli district of Kilis. For this purpose, of the 73 Kilis goats udder upper height (UUH), udder length up to the teat (ULT), udder cistern depth (UCD), udder depth (UD), the distance between teats (DBT), udder bottom height (UBH) and teat height (TH). daily milk yields (DMY) of determined goats were determined. The mean squares of these udder measurement were determined as 47.66±0.36 cm, 23.99±0.36 cm, 21.35±0.58 cm, 15.53±0.47 cm, 21.59±0.57 cm, 26.31±0.59 cm and 23.67±0.60 cm, respectively. Daily milk yield was calculated as 687.09±32.37 g. Correlation analysis was performed to determine the relationship between udder measurements and their daily milk yield. It was found positively, moderately strong, and significant correlations between DMY and UCD and UD, but it was negatively found between DMY and UBH and TH. The regression coefficients between DMY and UCD and UD were 32.79±5.63 and 40.31±6.69, respectively; The regression coefficients between DMY and UBH and TH were -24.56±6.36 and -25.91±6.14, respectively. It was seem that the goats with very low UD and UCD to be very low milk yield and the goats with low UBH and TH were more milk yield. Therefore, caution should be exercised as the culling of goats that udder is near ground may decrease milk yield.