• Title/Summary/Keyword: Teat Number

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QTL Analysis of Teat Number Traits in an F2 Intercross between Landrace And Korean Native Pigs

  • Park, Hee-Bok;Han, Sang-Hyun;Yoo, Chae-Kyoung;Lee, Jae-Bong;Cho, Sang-Rae;Cho, In-Cheol
    • Journal of Embryo Transfer
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    • v.31 no.4
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    • pp.313-318
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    • 2016
  • The aim of this study was to identify quantitative trait loci (QTLs) influencing teat number traits in an $F_2$ intercross between Landrace and Korean native pigs (KNP). Three teat number traits (left;right;and total) were measured in 1105 $F_2$ progeny. All experimental animals were genotyped with 173 informative microsatellite markers located throughout the pig genome. We detect that seven chromosomes harbored QTLs for teat number traits: genome regions on SSC1;3;7;8;10;11;and 13. Six of fourteen identified QTL reached genome-wide significance. In SSC7;we identified a major QTL affecting total teat number that accounted for 5.6 % of the phenotypic variance;which was the highest test statistic (F-ratio = 61.1 under the additive model;nominal $P=1.3{\times}10^{-14}$) observed in this study. In this region;QTL for left and right teat number were also detected with genome-wide significance. With exception of the QTL in SSC10;the allele from KNP in all 6 identified QTLs was associated with decreased phenotypic values. In conclusion;our study identified both previously reported and novel QTL affecting teat number traits. These results can play an important role in determining the genetic structure underlying the variation of teat number in pigs.

Identification of Quantitative Trait Loci (QTL) Affecting Teat Number in Pigs

  • Kim, Tae-Hun;Choi, Bong-Hwan;Yoon, Du-Hak;Park, Eung-Woo;Jeon, Jin-Tae;Han, Jae-Young;Oh, Sung-Jong;Cheong, Il-Cheong
    • Asian-Australasian Journal of Animal Sciences
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    • v.17 no.9
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    • pp.1210-1213
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    • 2004
  • Quantitative trait loci (QTL) mapping can be applied to detect chromosomal locations that control economic traits in farm animals. Teat number has been considered as one of the most important factors to evaluate mothering ability of sow. Especially, teat number is more important when the number is less than the litter size. This study was conducted to identify QTL affecting teat number in the Korean native pig${\times}$Landrace resource family. A total of 240 animals was genotyped for 132 polymorphic microsatellites covering the 18 pig autosomes. Mean and standard deviation of teat number in $F_2$animals is 13.46${\pm}$1.40. QTL was analyzed using F2 QTL Analysis Servlet of QTL express. A QTL for teat number on SSC9 was significant at the 1% chromosome-wide level and three suggestive QTL were detected on SSC3, 7 and 14. All QTL detected in this study had additive effect and Landrace alleles were associated with higher teat number in comparison with Korean native pig for three of four QTL.

Genetic Parameter Estimation with Normal and Poisson Error Mixed Models for Teat Number of Swine

  • Lee, C.;Wang, C.D.
    • Asian-Australasian Journal of Animal Sciences
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    • v.14 no.7
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    • pp.910-914
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    • 2001
  • The teat number of a sow plays an important role for weaning pigs and has been utilized in selection of swine breeding stock. Various linear models have been employed for genetic analyses of teat number although the teat number can be considered as a count trait. Theoretically, Poisson error mixed models are more appropriate for count traits than Normal error mixed models. In this study, the two models were compared by analyzing data simulated with Poisson error. Considering the mean square errors and correlation coefficients between observed and fitted values, the Poisson generalized linear mixed model (PGLMM) fit the data better than the Normal error mixed model. Also these two models were applied to analyzing teat numbers in four breeds of swine (Landrace, Yorkshire, crossbred of Landrace and Yorkshire, crossbred of Landrace, Yorkshire, and Chinese indigenous Min pig) collected in China. However, when analyzed with the field data, the Normal error mixed model, on the contrary, fit better for all the breeds than the PGLMM. The results from both simulated and field data indicate that teat numbers of swine might not have variance equal to mean and thus not have a Poisson distribution.

The Possibility of TBC1D21 as a Candidate Gene for Teat Numbers in Pigs

  • Jin, S.;Lee, J.B.;Kang, K.;Yoo, C.K.;Kim, B.M.;Park, H.B.;Lim, H.T.;Cho, I.C.;Maharani, D.;Lee, J.H.
    • Asian-Australasian Journal of Animal Sciences
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    • v.26 no.10
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    • pp.1374-1378
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    • 2013
  • Based on a quantitative traits locus (QTL) study using a $F_2$ intercross between Landrace and Korean native pigs, a significant QTL affecting teat numbers in SSC7 was identified. The strong positional candidate gene, TBC1D21, was selected due to its biological function for epithelial mesenchymal cell development. Sequence analysis revealed six single nucleotide polymorphisms (SNPs) in the TBC1D21 gene. Among these, two SNP markers, one silent mutation (SNP01) for g.13,050A>G and one missense mutation (SNP04) for c.829A>T (S277C), were genotyped and they showed significant associations with teat number traits (p value = 6.38E-05 for SNP01 and p value = 1.06E-07 for SNP04 with total teat numbers). Further functional validation of these SNPs could give valuable information for understanding the teat number variation in pigs.

Association of Novel Polymorphisms in Lymphoid Enhancer Binding Factor 1 (LEF-1) Gene with Number of Teats in Different Breeds of Pig

  • Xu, Ru-Xiang;Wei, Ning;Wang, Yu;Wang, Guo-Qiang;Yang, Gong-She;Pang, Wei-Jun
    • Asian-Australasian Journal of Animal Sciences
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    • v.27 no.9
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    • pp.1254-1262
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    • 2014
  • Lymphoid enhancer binding factor 1 (LEF-1) is a member of the T-cell specific factor (TCF) family, which plays a key role in the development of breast endothelial cells. Moreover, LEF-1 gene has been identified as a candidate gene for teat number trait. In the present study, we detected two novel mutations (NC_010450.3:g. 99514A>G, 119846C>T) by DNA sequencing and polymerase chain reaction-restriction fragment length polymorphism in exon 4 and intron 9 of LEF-1 in Guanzhong Black, Hanjiang Black, Bamei and Large White pigs. Furthermore, we analyzed the association between the genetic variations with teat number trait in these breeds. The 99514A>G mutation showed an extremely significant statistical relevance between different genotypes and teat number trait in Guanzhong (p<0.001) and Large White (p = 0.002), and significant relevance in Hanjiang (p = 0.017); the 119846C>T mutation suggested significant association in Guanzhong Black pigs (p = 0.042) and Large White pigs (p = 0.003). The individuals with "AG" or "GG" genotype displayed more teat numbers than those with "AA"; the individuals with "TC" or "CC" genotype showed more teat numbers than those with "TT". Our findings suggested that the 99514A>G and 119846C>T mutations of LEF-1 affected porcine teat number trait and could be used in breeding strategies to accelerate porcine teat number trait improvement of indigenous pigs breeds through molecular marker assisted selection.

Identification of loci affecting teat number by genome-wide association studies on three pig populations

  • Tang, Jianhong;Zhang, Zhiyan;Yang, Bin;Guo, Yuanmei;Ai, Huashui;Long, Yi;Su, Ying;Cui, Leilei;Zhou, Liyu;Wang, Xiaopeng;Zhang, Hui;Wang, Chengbin;Ren, Jun;Huang, Lusheng;Ding, Nengshui
    • Asian-Australasian Journal of Animal Sciences
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    • v.30 no.1
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    • pp.1-7
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    • 2017
  • Objective: Three genome-wide association studies (GWAS) and a meta-analysis of GWAS were conducted to explore the genetic mechanisms underlying variation in pig teat number. Methods: We performed three GWAS and a meta-analysis for teat number on three pig populations, including a White Duroc${\times}$Erhualian $F_2$ resource population (n = 1,743), a Chinese Erhualian pig population (n = 320) and a Chinese Sutai pig population (n = 383). Results: We detected 24 single nucleotide polymorphisms (SNPs) that surpassed the genome-wide significant level on Sus Scrofa chromosomes (SSC) 1, 7, and 12 in the $F_2$ resource population, corresponding to four loci for pig teat number. We highlighted vertnin (VRTN) and lysine demethylase 6B (KDM6B) as two interesting candidate genes at the loci on SSC7 and SSC12. No significant associated SNPs were identified in the meta-analysis of GWAS. Conclusion: The results verified the complex genetic architecture of pig teat number. The causative variants for teat number may be different in the three populations

Estimation of Genetic Parameters for Direct and Maternal Effects on Litter Size and Teat Numbers in Korean Seedstock Swine Population

  • Song, Guy-Bong;Lee, Jun-Ho;Lee, Deuk-Hwan
    • Journal of Animal Science and Technology
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    • v.52 no.3
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    • pp.187-190
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    • 2010
  • The objective of this study was to estimate genetic parameters for total number of born (TNB), number of born alive (NBA) and teat numbers (TN) of Landrace and Yorkshire breeds in Korean swine population using multiple trait animal model procedures. Total numbers of 4,653 records for teat numbers and 8,907 records for TNB and NBA collected from 2004 to 2008 on imported breeding pigs and their litter size records were used in this study. To find the appropriate model for estimation of genetic parameters (heritabilities and genetic correlations), five statistical models (two models for reproductive traits, two models for teat numbers, one model for combining these traits) considering only direct additive genetic effects, including maternal effects were used and Akaike information criteria (AIC) of each two models for reproductive traits and teat trait were compared. The means and standard deviations of TNB, NBA, and TN were $11.52{\pm}3.34$, $10.55{\pm}2.96$ and $14.30{\pm}0.83$, respectively. Estimated heritabilities for TNB and NBA traits using the model which considered only additive genetic effect were low (0.06 and 0.05, respectively). However, estimated heritabilities considering maternal genetic effects were a little bit higher than that of the model considering only additive genetic effect (0.09 for TNB and NBA, respectively). Estimated heritability for TN using the model which considered only additive genetic effect was 0.40. However, estimated heritability of direct genetic effects from a model considering maternal genetic effect was high (0.60). All results of AIC statistics, the models considering maternal effect was more appropriate than the models considering only additive genetic effect. Genetic correlations of direct additive genetic effect between litter size (TNB, NBA) and teat numbers were low (-0.18 and -0.14, respectively). However, genetic correlations of maternal effect between litter size (TNB, NBA) and teat numbers were a little bit higher than those of direct additive genetic effect (0.08 and 0.16, respectively).

Bayesian Inference on Variance Components Using Gibbs Sampling with Various Priors

  • Lee, C.;Wang, C.D.
    • Asian-Australasian Journal of Animal Sciences
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    • v.14 no.8
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    • pp.1051-1056
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    • 2001
  • Data for teat number for Landrace (L), Yorkshire (Y), crossbred of Landrace and Yorkshire (LY), and crossbred of Landrace, Yorkshire and Chinese indigenous Min Pig (LYM) were analyzed using Gibbs sampling. In Bayesian inference, flat priors and some informative priors were used to examine their influence on posterior estimates. The posterior mean estimates of heritabilities with flat priors were $0.661{\pm}0.035$ for L, $0.540{\pm}0.072$ for Y, $0.789{\pm}0.074$ for LY, and $0.577{\pm}0.058$ for LYM, and they did not differ (p>0.05) from their corresponding estimates of REML. When inverse Gamma densities for variance components were used as priors with the shape parameter of 4, the posterior estimates were still corresponding (p>0.05) to REML estimates and mean estimates using Gibbs sampling with flat priors. However, when the inverse Gamma densities with the shape parameter of 10 were utilized, some posterior estimates differed (p<0.10) from REML estimates and/or from other Gibbs mean estimates. The use of moderate degree of belief was influential to the posterior estimates, especially for Y and for LY where data sizes were small. When the data size is small, REML estimates of variance components have unknown distributions. On the other hand, Bayesian approach gives exact posterior densities of variance components. However, when the data size is small and prior knowledge is lacked, researchers should be careful with even moderate priors.

Whole-genome association and genome partitioning revealed variants and explained heritability for total number of teats in a Yorkshire pig population

  • Uzzaman, Md. Rasel;Park, Jong-Eun;Lee, Kyung-Tai;Cho, Eun-Seok;Choi, Bong-Hwan;Kim, Tae-Hun
    • Asian-Australasian Journal of Animal Sciences
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    • v.31 no.4
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    • pp.473-479
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    • 2018
  • Objective: The study was designed to perform a genome-wide association (GWA) and partitioning of genome using Illumina's PorcineSNP60 Beadchip in order to identify variants and determine the explained heritability for the total number of teats in Yorkshire pig. Methods: After screening with the following criteria: minor allele frequency, $MAF{\leq}0.01$; Hardy-Weinberg equilibrium, $HWE{\leq}0.000001$, a pair-wise genomic relationship matrix was produced using 42,953 single nucleotide polymorphisms (SNPs). A genome-wide mixed linear model-based association analysis (MLMA) was conducted. And for estimating the explained heritability with genome- or chromosome-wide SNPs the genetic relatedness estimation through maximum likelihood approach was used in our study. Results: The MLMA analysis and false discovery rate p-values identified three significant SNPs on two different chromosomes (rs81476910 and rs81405825 on SSC8; rs81332615 on SSC13) for total number of teats. Besides, we estimated that 30% of variance could be explained by all of the common SNPs on the autosomal chromosomes for the trait. The maximum amount of heritability obtained by partitioning the genome were $0.22{\pm}0.05$, $0.16{\pm}0.05$, $0.10{\pm}0.03$ and $0.08{\pm}0.03$ on SSC7, SSC13, SSC1, and SSC8, respectively. Of them, SSC7 explained the amount of estimated heritability along with a SNP (rs80805264) identified by genome-wide association studies at the empirical p value significance level of 2.35E-05 in our study. Interestingly, rs80805264 was found in a nearby quantitative trait loci (QTL) on SSC7 for the teat number trait as identified in a recent study. Moreover, all other significant SNPs were found within and/or close to some QTLs related to ovary weight, total number of born alive and age at puberty in pigs. Conclusion: The SNPs we identified unquestionably represent some of the important QTL regions as well as genes of interest in the genome for various physiological functions responsible for reproduction in pigs.

Analytical studies of bovine mastitis management by standard plate counts(SPC) and somatic cell counts(SCC) (젖소 유방염 관리에 따른 세균 및 체세포수 등급 실태 조사 분석)

  • 허정호;정명호;박영호;조명희;이주홍
    • Korean Journal of Veterinary Service
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    • v.21 no.3
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    • pp.285-300
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    • 1998
  • 1. The number of average milking cows, clinical forms of mastitis, mastitis-developing cows, and cows killed by mastitis a year were 25.7, 1.8(7%), 6.3(26%), and 2.7(10.1%)heads, respectively. The annual grade changes of standard plate counts(SPC) and somatic cell counts(SCC) showed the grade 1A of SPC diminished sharply from April to August, we think it was due to the lack of proper management in farming season and the grade 3 of SCC indirectly influenced increased in huge during August. 2. The average number of parturitions of farms was 2.3, but 50% of below 1 parturition were 22 farms(31%), 50% of above 3 parturitions were 16(23%) out of 71 farms. According to grades of the number of parturitions of milking cows per each farm, the farms' grades recording 3 parturitions and 50% were little bit excellent. 3. The actual situation research of foremilking CMT revealed 35 out of 74 farmer didn't do CMT Among them(35 out of 74 farmers), 80% did not test thanks to the troublesome process of the CMT. SCC grade 3, among farms who did foremilking CMT once or twice a month and who did not were 29% and 40% respectively and SPC grade 1A were 55% and 9%, respectively. 4. The research of actual situation on milking management let us know 29 farms(39%) did not do lastmilking, 37 farms(49%) usually did overmilking, and 34 farms(46%) did milking for 4 or 5 minutes. Grades according to average requiring times of milking showed SCC grade 1 of farms milking within 7 minutes was 11% and SPC grade 1A was 34%, on the other side, farms milking more than 7 minutes were 0% in SCC grade 1 and 13% in SPC grade 1A. Grades according to the starting time of milking after rubbing teats showed SPC grade 1A of farms starting milking at about 1 minute and over 2 minutes were 50% and 20%, respectively. 5. The research of actual situation on hygienic milking management uncovered 65 farms(88%) were using one towel which was used in washing teats and udders to wash more than 3 to 4 cows, and 53 farms(72%) were using one dried towel to dry udders not for each cow but for more than 3 to 4 cows after washing. Also, on milking turns disclosed 30 farms(40%) were milking cows in the order of incoming without isolation of a dominant group. According to grades of towels used in washing teats and udders, farms using a towel for each cow were 56% and a towel for over 3 cows were 31% in SPC grade 1A. According to using-or-not grades of dried towels after washing udders, farms using a towel for each cow were 79% and a towel for over 3 cows were 21% in SPC grade 1A. 6. Farms doing teat-dipping before milking were 7(10%), not doing teat-dipping after milking, or doing sometimes were 9(12%), and doing right after milking were 57(77%). And farms doing teat-dipping after dry cows and before delivery were 21(28a ). Farms using bethadine as an antiseptic solution were 70(95%), 40 farms(59%) diluted it with water as weak as 5 to 10 times, and on drying cows 64 farms(87%) slowly did it more than 2 days. Grade 1A of SPC of farms doing teat-dipping at every milking was 38%, farms doing occasionally or not was 33%, and farms doing it right after milking was 37% and doing after milking more than 5 cows was 20%. Grade 1A of SPC among farms diluting bethadine 5 times and diluting 5 to 10 times with water were 36% and 33%, respectively, and Grade 3 of SCC were 35% and 32%, respectively. 7. Studies on nonlactating period medical treatment, as the cows were on dry, 54 farms treated with their own hands.73 farms(98%) had bovine mastitis treated for themselves. And on applying medicines against mastitis, 55 farmers chose them on the basis of their own experience, 42 farms(57%) were treated more than 3 days. 41 farms(55%) dumped away the mastitis infected milk separately, 24 farms(32%) were feeding and milking at the same time. 8. Fifty-six farms(76%) always washed and disinfected milking machines after milking. Farms using the milking machines at low, or variable vacuum pressures, or at the vacuum pressure, set at the moment of its installation were 31(42%), and farms that did not know pulsation ratio were 27(37%). Farms changing liners when they were torn 8(11%), 58 farms(78%) said they checked milking system when there were wrong with them, 31 farms(42%) changed milking hoses when they found out problems, and 42 farms(57%) cleaned vacuum and milking systems when they felt dirty. The SPC grade 1A of farms washing and sterilizing milking machines was 38% and farms only washing was 28%.

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