Objective: Sow longevity is important for efficient and profitable pig farming. Recently, there has been an increasing interest in social genetic effect (SGE) of pigs on stress-tolerance and behavior. The present study aimed to estimate genetic correlations among average daily gain (ADG), stayability (STAY), and number of piglets born alive at the first parity (NBA1) in Korean Yorkshire pigs, using a model including SGE. Methods: The phenotypic records of ADG and reproductive traits of 33,120 and 11,654 pigs, respectively, were evaluated. The variances and (co) variances of the studied traits were estimated by a multi-trait animal model applying the Bayesian with linear-threshold models using Gibbs sampling. Results: The direct and SGEs on ADG had a significantly negative (-0.30) and neutral (0.04) genetic relationship with STAY, respectively. In addition, the genetic correlation between the social effects on ADG and NBA1 tended to be positive (0.27), unlike the direct effects (-0.04). The genetic correlation of the total effect on ADG with that of STAY was negative (-0.23) but non-significant, owing to the social effect. Conclusion: These results suggested that total genetic effect on growth in the SGE model might reduce the negative effect on sow longevity because of the growth potential of pigs. We recommend including social effects as selection criteria in breeding programs to obtain satisfactory genetic changes in both growth and longevity.
An investigation was conducted on 729 Hariana cows maintained at Government Livestock Farm, Hisar, from 1973 to 1999, with an objective to compare the efficiency of various selection indices for attaining desired genetic gains in the index traits. The various traits included were age at first calving (AFC), service period (SP), calving interval (CI), days to first service (DFS), number of services per conception (NSPC), lactation milk yield (LY), peak yield (PY), dry period (DP). Except for LY, PY and AFC the heritabilities of all other traits were low. Desirable associations among reproductive traits are supportive of the fact that any one of these traits incorporated in simultaneous selection is expected to cause correlated response in other traits. Production traits (LY and PY) were positively correlated, while DP had low negative genetic correlation with LY, and high genetic correlation with PY. Thus, DP can be taken as additional criteria in selection index for better over all improvement. Almost all production traits except DP had low negative correlation with AFC, SP, DFS and CI meaning that reduction in reproduction traits up to certain level may increase production performance. While, the correlation of NSPC with LY and PY was moderate positive. Among four trait indices I23: incorporating PY, AFC, SP and NSPC and among three trait indices I1: incorporating LY, AFC and SP were the best as these required least number of generations (4.87 and 1.35, respectively) to attain desired goals. Next in order of preference were PY or LY along with DP and SP as the best indices (I20 and I16) of which, index with PY may be preferred instead of LY as it produced considerably high correlated response in LY and reduction in NSPC as well.
This study was aimed at a genetic evaluation of Iranian Holstein cattle for milk and fat yields and calculating daughter yield deviation (DYD) of bulls. The data file that was used in this research included 367,943 first three lactation records of 186,064 Holstein cows which calved between 1983 and 2006 in 11,806 herd-year-season groups. The model included herd-year-season of calving and age at calving as fixed effects and animal and permanent environment as random effects. Mean breeding values of cows for each year were regressed on birth year to estimate genetic trends. Genetic trends in milk and fat yields were greater for cows born after 1997 (59.38 kg/yr and 1.11 kg/yr for milk yield and fat yield, respectively). Animal evaluations were partitioned into contribution from parent average, yield deviation (YD) and progeny. DYD of bulls was calculated as described by VanRaden and Wiggans (1991). DYD provides an indication of the performance of the daughters of a bull without consideration of his parents or sons. Variance of bull DYD was greater than variance of their predicted transmitting ability (PTA). Correlation of bull DYD and PTA was dependent on the number of daughters and when this increased, the correlation of DYD and PTA was increased. Also as lactation number of daughters increased, the correlation of bull DYD and PTA was increased.
This study was conducted to analyze the responses of some agronomic traits to the different cultural environments and relations among the agronomic traits for selecting sesame varieties with higher cultural stabilities. The indexes for stability parameters measured were coefficient of variability, heritabilities, genetic correlations and path coefficients of agronomic traits according to locations and years in Korea. The heritabilities of agronomic traits showed different by locations and years. Number of seeds per capsule and 1000 seeds weight showed higher heritabilities, but stem length and seed weight per plant showed relatively lower heritabilities. Average heritabilities of some agronomic traits in 1998 were comparatively higher than those of 1999. Of six areas, Jinju area showed biggest coefficient of yield variability in 1998-1999. Iksan and Taegu areas showed higher heritabilities in 1998, but Iksan and Jinju areas showed lower heritabilities in 1999. Genetic correlations were slightly higher than corresponding phenotypic correlations. Stem length showed positive genetic correlation with the number of capsules per plant, and seed weight per plant and the number of capsule per plant showed positive genetic correlation with seed weight per plant. On the analysis of path coefficients, stem length and number of capsules effected highly on grain yield. Great regional variations were observed on the effects of agronomic traits on grain yield. Higher direct effects of stem length on grain yield were observed at Suwon, Chungwon, Taegu, Jinju and Naju areas, but in Iksan area was observed higher direct effect of the number of capsules per plant on grain yield in 1998. In 1999, higher direct effect of stem length on grain yield was observed at Chungwon and Suwon areas. Iksan and Taegu areas were also observed higher direct effect of the number of capsule per plant on grain yield.
Zeng, Wen-Li;Chen, Yao-Wu;Zhou, Hui;Zhou, Jue-Yu;Wei, Min;Shi, Rong
Asian Pacific Journal of Cancer Prevention
/
v.16
no.2
/
pp.513-517
/
2015
Background: Growing evidence suggests that the members of the ubiquitin-proteasome system (UPS) are important for tumorigenesis. HERC4, one component, is a recently identified ubiqutin ligase. However, the expression level and function role of HERC4 in lung cancer remain unknown. Our objective was to investigate any correlation between HERC4 and development of lung cancer and its clinical significance. Materials and Methods: To determine HERC4 expression in lung cancer, an immunohistochemistry analysis of a tissue microarray containing samples of 10 lung normal tissues, 15 pulmonary neuroendocrine carcinomas, 45 squamous epithelial cancers and 50 adenocarcinomas was conducted. Receiver operating characteristic (ROC) curve analysis was applied to obtain a cut-off point of 52.5%, above which the expression of HERC4 was regarded as "positive". Results: On the basis of ROC curve analysis, positive expression of HERC4 was detected in 0/10 (0.0%) of lung normal tissues, in 4/15 (26.7%) of pulmonary neuroendocrine carcinomas, in 13/45 (28.9%) of squamous epithelial cancers and in 19/50 (38.0%) of adenocarcinomas. It showed that lung tumors expressed more HERC4 protein than adjacent normal tissues (${\chi}^2$=4.675, p=0.031). Furthermore, HERC4 positive expression had positive correlation with pT status (${\chi}^2$=44.894, p=0.000), pN status (${\chi}^2$=43.628, p=0.000), histological grade (${\chi}^2$=7.083, p=0.029) and clinical stage (${\chi}^2$=72.484, p=0.000), but not age (${\chi}^2$=0.910, p=0.340). Conclusions: Our analysis suggested that HERC4 is likely to be a diagnostic biomarker for lung cancer.
Canaza-Cayo, Ali William;Lopes, Paulo Savio;da Silva, Marcos Vinicius Gualberto Barbosa;de Almeida Torres, Robledo;Martins, Marta Fonseca;Arbex, Wagner Antonio;Cobuci, Jaime Araujo
Asian-Australasian Journal of Animal Sciences
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v.28
no.10
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pp.1407-1418
/
2015
A total of 32,817 test-day milk yield (TDMY) records of the first lactation of 4,056 Girolando cows daughters of 276 sires, collected from 118 herds between 2000 and 2011 were utilized to estimate the genetic parameters for TDMY via random regression models (RRM) using Legendre's polynomial functions whose orders varied from 3 to 5. In addition, nine measures of persistency in milk yield ($PS_i$) and the genetic trend of 305-day milk yield (305MY) were evaluated. The fit quality criteria used indicated RRM employing the Legendre's polynomial of orders 3 and 5 for fitting the genetic additive and permanent environment effects, respectively, as the best model. The heritability and genetic correlation for TDMY throughout the lactation, obtained with the best model, varied from 0.18 to 0.23 and from -0.03 to 1.00, respectively. The heritability and genetic correlation for persistency and 305MY varied from 0.10 to 0.33 and from -0.98 to 1.00, respectively. The use of $PS_7$ would be the most suitable option for the evaluation of Girolando cattle. The estimated breeding values for 305MY of sires and cows showed significant and positive genetic trends. Thus, the use of selection indices would be indicated in the genetic evaluation of Girolando cattle for both traits.
Campos, Rafael Viegas;Cobuci, Jaime Araujo;Kern, Elisandra Lurdes;Costa, Claudio Napolis;McManus, Concepta Margaret
Asian-Australasian Journal of Animal Sciences
/
v.28
no.4
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pp.476-484
/
2015
The objective of this study was to estimate genetic and phenotypic parameters for linear type traits, as well as milk yield (MY), fat yield (FY) and protein yield (PY) in 18,831 Holstein cows reared in 495 herds in Brazil. Restricted maximum likelihood with a bivariate model was used for estimation genetic parameters, including fixed effects of herd-year of classification, period of classification, classifier and stage of lactation for linear type traits and herd-year of calving, season of calving and lactation order effects for production traits. The age of cow at calving was fitted as a covariate (with linear and quadratic terms), common to both models. Heritability estimates varied from 0.09 to 0.38 for linear type traits and from 0.17 to 0.24 for production traits, indicating sufficient genetic variability to achieve genetic gain through selection. In general, estimates of genetic correlations between type and production traits were low, except for udder texture and angularity that showed positive genetic correlations (>0.29) with MY, FY, and PY. Udder depth had the highest negative genetic correlation (-0.30) with production traits. Selection for final score, commonly used by farmers as a practical selection tool to improve type traits, does not lead to significant improvements in production traits, thus the use of selection indices that consider both sets of traits (production and type) seems to be the most adequate to carry out genetic selection of animals in the Brazilian herd.
Choi, Jae Gwan;Cho, Chung Il;Choi, Im Soo;Lee, Seung Soo;Choi, Tae Jeong;Cho, Kwang Hyun;Park, Byoung Ho;Choy, Yun Ho
Asian-Australasian Journal of Animal Sciences
/
v.26
no.4
/
pp.470-475
/
2013
The objective of this study was to estimate genetic parameters that are to be used for across-herd genetic evaluations of seed stock pigs at GGP level. Performance data with pedigree information collected from swine breeder farms in Korea were provided by Korea Animal Improvement Association (AIAK). Performance data were composed of final body weights at test days and ultrasound measures of back fat thickness (BF), rib eye area (EMA) and retail cut percentage (RCP). Breeds of swine tested were Landrace, Yorkshire and Duroc. Days to 90 kg body weight (DAYS90) were estimated with linear function of age and ADG calculated from body weights at test days. Ultrasound measures were taken with A-mode ultrasound scanners by trained technicians. Number of performance records after censoring outliers and keeping records pigs only born from year 2000 were of 78,068 Duroc pigs, 101,821 Landrace pigs and 281,421 Yorkshire pigs. Models included contemporary groups defined by the same herd and the same seasons of births of the same year, which was regarded as fixed along with the effect of sex for all traits and body weight at test day as a linear covariate for ultrasound measures. REML estimation was processed with REMLF90 program. Heritability estimates were 0.40, 0.32, 0.21 0.39 for DAYS90, ADG, BF, EMA, RCP, respectively for Duroc population. Respective heritability estimates for Landrace population were 0.43, 0.41, 0.22, and 0.43 and for Yorkshire population were 0.36, 0.38, 0.22, and 0.42. Genetic correlation coefficients of DAYS90 with BF, EMA, or RCP were estimated to be 0.00 to 0.09, -0.15 to -0.25, 0.22 to 0.28, respectively for three breeds populations. Genetic correlation coefficients estimated between BF and EMA was -0.33 to -0.39. Genetic correlation coefficient estimated between BF and RCP was high and negative (-0.78 to -0.85) but the environmental correlation coefficients between these two traits was medium and negative (near -0.35), which describes a highly correlated genetic response to selection on one or the other of these traits. Genetic Trends of all three breeds tend to be towards bigger EMA or greater RCP and shorter DAYS90 especially from generations born after year 2000.
This study was conducted to estimate genetic parameter of Duroc, Landrace and Yorkshire breeds based on the on-farm performance tested records of 57,316 pigs under the supervision of Korean Animal Improvement Association from 1992 to 1999. Genetic parameters were estimated with a multiple trait animal model by using DF - REML. The result obtained in this study was summarized as follow ; The estimated heritabilities of Duroc, Landrace and Yorkshire were 0.46${\sim}$0.65 for the average backfat thickness, 0.28${\sim}$0.31 for loin depth, 0.50~0.60 for percent lean, 0.45${\sim}$0.55 for the average daily gain, 0.38${\sim}$0.50 for age at 90kg, respectively. Phenotypic correlation of average backfat thickness with loin depth, percent lean, average daily gain and age at 90㎏ for the three breeds were -0.12${\sim}$-0.01, -0.81${\sim}$-0.76, 0.34${\sim}$0.46, and -0.41${\sim}$-0.33, respectively. Phenotypic correlation of loin depth with percent lean, average daily gain and age at 90kg were 0.12${\sim}$0.23, 0.03${\sim}$0.21, and -0.17${\sim}$-0.03, respectively. Phenotypic correlation of percent lean with average daily gain and age at 90kg were -0.37${\sim}$-0.26 and 0.26~0.35, respectively. Phenotypic correlation of average daily gain with age at 90kg was -0.97${\sim}$-0.95. The estimated genetic correlation coefficients of average backfat thickness with loin depth, percent lean, average daily gain and age at 90kg estimated for the three breeds were -0.17${\sim}$0.03, -0.79${\sim}$-0.69, 0.24${\sim}$0.45 and -0.41${\sim}$-0.19, respectively. The estimated genetic correlation coefficients of loin depth with percent lean, average daily gain and age at 90kg were 0.11~0.19, 0.23 and -0.30~-0.20, respectively. The estimated correlation coefficients of percent lean with average daily gain and age at 90kg were -0.36${\sim}$-0.13 and 0.10~0.34, respectively. The estimated genetic correlation coefficients of average daily gain with age at 90㎏ was -0.96${\sim}$-0.95.
Recently, there have been many studies in medicine related to genetic analysis. Many genetic studies have been performed to find genes associated with complex diseases. To find out how genes are related to disease, we need to understand not only the simple relationship of genotypes but also the way they are related to phenotype. Multi-block data, which is a summation form of variable sets, is used for enhancing the analysis of the relationships of different blocks. By identifying relationships through a multi-block data form, we can understand the association between the blocks in comprehending the correlation between them. Several statistical analysis methods have been developed to understand the relationship between multi-block data. In this paper, we will use generalized canonical correlation methodology to analyze multi-block data from the Korean Association Resource project, which has a combination of single nucleotide polymorphism blocks, phenotype blocks, and disease blocks.
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