• Genomics & Informatics
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• v.20 no.2
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• pp.16.1-16.12
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• 2022

Various methodologies for the genetic analysis of longitudinal data have been proposed and applied to data from large-scale genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with traits of interest and to detect SNP-time interactions. We recently proposed a grid-based Bayesian mixed model for longitudinal genetic data and showed that our Bayesian method increased the statistical power compared to the corresponding univariate method and well detected SNP-time interactions. In this paper, we further analyze longitudinal obesity-related traits such as body mass index, hip circumference, waist circumference, and waist-hip ratio from Korea Association Resource data to evaluate the proposed Bayesian method. We first conducted GWAS analyses of cross-sectional traits and combined the results of GWAS analyses through a meta-analysis based on a trajectory model and a random-effects model. We then applied our Bayesian method to a subset of SNPs selected by meta-analysis to further discover SNPs associated with traits of interest and SNP-time interactions. The proposed Bayesian method identified several novel SNPs associated with longitudinal obesity-related traits, and almost 25% of the identified SNPs had significant p-values for SNP-time interactions.

• Journal of Ecology and Environment
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• v.45 no.1
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• pp.24-39
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• 2021

Background: To address the lack of evidence supporting invasion by three invasive plant species (Imperata cylindrica, Lantana camara, and Chromolaena odorata) in tropical ecosystems, we compared the ecophysiological and leaf anatomical traits of these three invasive alien species with those of species native to Sempu Island, Indonesia. Data on four plant traits were obtained from the TRY Plant Trait Database, and leaf anatomical traits were measured using transverse leaf sections. Results: Two ecophysiological traits including specific leaf area (SLA) and seed dry weight showed significant association with plant invasion in the Sempu Island Nature Reserve. Invasive species showed higher SLA and lower seed dry weight than non-invasive species. Moreover, invasive species showed superior leaf anatomical traits including sclerenchymatous tissue thickness, vascular bundle area, chlorophyll content, and bundle sheath area. Principal component analysis (PCA) showed that leaf anatomical traits strongly influenced with cumulative variances (100% in grass and 88.92% in shrubs), where I. cylindrica and C. odorata outperformed non-invasive species in these traits. Conclusions: These data suggest that the traits studied are important for plant invasiveness since ecophysiological traits influence of light capture, plant growth, and reproduction while leaf anatomical traits affect herbivory, photosynthetic assimilate transport, and photosynthetic activity.

• Journal of Animal Science and Technology
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• v.65 no.6
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• pp.1180-1193
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• 2023

Most carcass and meat quality traits are moderate to highly heritable, indicating that they can be improved through selection. Genetic evaluation for these types of traits is performed using performance data obtained from commercial and progeny testing evaluation. The performance data from commercial farms are available in large volume, however, some drawbacks have been observed. The drawback of the commercial data is mainly due to sorting of animals based on live weight prior to slaughter, and this could lead to bias in the genetic evaluation of later measured traits such as carcass traits. The current study has two components to address the drawback of the commercial data. The first component of the study aimed to estimate genetic parameters for carcass and meat quality traits in Korean Hanwoo cattle using a large sample size of industry-based carcass performance records (n = 469,002). The second component of the study aimed to describe the impact of sorting animals into different contemporary groups based on an early measured trait and then examine the effect on the genetic evaluation of subsequently measured traits. To demonstrate our objectives, we used real performance data to estimate genetic parameters and simulated data was used to assess the bias in genetic evaluation. The results of our first study showed that commercial data obtained from slaughterhouses is a potential source of carcass performance data and useful for genetic evaluation of carcass traits to improve beef cattle performance. However, we observed some harvesting effect which leads to bias in genetic evaluation of carcass traits. This is mainly due to the selection of animal based on their body weight before arrival to slaughterhouse. Overall, the non-random allocation of animals into a contemporary group leads to a biased estimated breeding value in genetic evaluation, the severity of which increases when the evaluation traits are highly correlated.

• Asian-Australasian Journal of Animal Sciences
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• v.18 no.8
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• pp.1088-1097
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• 2005

Main objectives of this study were to investigate accuracy, bias and power of linear and threshold model segregation analysis methods for detection of major genes in categorical traits in farm animals. Maximum Likelihood Linear Model (MLLM), Bayesian Linear Model (BALM) and Bayesian Threshold Model (BATM) were applied to simulated data on normal, categorical and binary scales as well as to disease data in pigs. Simulated data on the underlying normally distributed liability (NDL) were used to create categorical and binary data. MLLM method was applied to data on all scales (Normal, categorical and binary) and BATM method was developed and applied only to binary data. The MLLM analyses underestimated parameters for binary as well as categorical traits compared to normal traits; with the bias being very severe for binary traits. The accuracy of major gene and polygene parameter estimates was also very low for binary data compared with those for categorical data; the later gave results similar to normal data. When disease incidence (on binary scale) is close to 50%, segregation analysis has more accuracy and lesser bias, compared to diseases with rare incidences. NDL data were always better than categorical data. Under the MLLM method, the test statistics for categorical and binary data were consistently unusually very high (while the opposite is expected due to loss of information in categorical data), indicating high false discovery rates of major genes if linear models are applied to categorical traits. With Bayesian segregation analysis, 95% highest probability density regions of major gene variances were checked if they included the value of zero (boundary parameter); by nature of this difference between likelihood and Bayesian approaches, the Bayesian methods are likely to be more reliable for categorical data. The BATM segregation analysis of binary data also showed a significant advantage over MLLM in terms of higher accuracy. Based on the results, threshold models are recommended when the trait distributions are discontinuous. Further, segregation analysis could be used in an initial scan of the data for evidence of major genes before embarking on molecular genome mapping.

• Journal of Information Technology Applications and Management
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• v.20 no.3
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• pp.19-30
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• 2013

The objective of this research is to identify a web portal's specific functions and layouts that are aligned with personality trait of an individual internet user. This first stage of the overall research project intends to check whether the research instrument, namely the NEO Five Factors Inventory (NEO-FFI), is applicable to assess the personality traits of Thai internet users. Based on these personality traits, text-based description of functions/layouts of a web portal was developed and given to professional designers to mock up example web portal pages. These web portal pages were in alignment with individual personality traits. Rating data on the functions/layouts corresponding to individual personality traits were collected from an online survey of 207 Thai internet users. Results showed that respondents gave more consistent rating to the functions/layouts close to their individual personality traits identified in the text-based descriptions than in the mocked-up web portal pages.

• Journal of Environmental Science International
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• v.20 no.9
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• pp.1183-1190
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• 2011

This study was aimed to establish the evaluating protocol and standard assessment for genetically modified (GM) hot pepper and to find out a proper statistic method to analyze for equality of agricultural characters between GM and non-GM pepper lines. GM and non-GM hot pepper lines were cultivated in two GMO fields in the middle region of Korea and total of 52 agricultural characters were collected during the plant growing season for 4 years, 2007 to 2010. Levene's test was conducted to confirm the homogeneity of raw data before statistic analysis. Two-way ANOVA in the multivariate tests and t-test were conducted to analyze 52 agricultural characters in order to find out the equality between H15 and P2377. From the statistical analysis through two-way ANOVA, 16 out of 16 plant growth traits, 9 out of 18 green fruit traits and 7 out of 18 red fruit traits among 4 years and 9 out of 16 plant growth traits, 4 out of 18 green fruit traits and 3 out of 18 red fruit traits between H15 and P2377 have shown the statistic differences. With the same raw data of 52 agricultural characters, t-test was also conducted. Based on the result from t-test, only 1 out of 16 plant growth traits, 2 out of 18 green fruit traits and 1 out of 18 red fruit traits have shown the differences between H15 and P2377, so that it was concluded that there is no statistic difference between H15 and P2377 in terms of agricultural characters. Also, the t-test is a proper statistic method to analyze each trait between GM and its control lines in order to evaluate agricultural characters.

• Asian-Australasian Journal of Animal Sciences
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• v.23 no.1
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• pp.1-6
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• 2010

The objectives of this study were to i) investigate genetic performance for linear type traits of individual Holstein dairy cows, especially focusing on comparative traits, and to estimate genetic variances for these traits using actual data, and ii) compare genetic performance and improvement of progeny by birth country of the cows. Linear type traits defined with five comparative traits on this study were general stature composite (GSC), dairy capacity composite (DCC), body size composite (BSC), foot and leg composite (FLC), and udder composite (UDC). These traits were scored from 1 to 6 with 1 = poor, 2 = fair, 3 = good, 4 = good plus, 5 = very good and 6 = excellent. Final scores (FS) were also included in this study. Data used was collected from the years 2000 to 2004 by the Korea Animal Improvement Association (KAIA). Only data of more than five tested cows by herd appraisal date and by sires having more than ten daughters were included to increase the reliability of the data analyses. A total of 30,204 records of the selected traits, which was collected from 26,701 individuals having pedigree information were used. Herd appraisal date, year of age, lactation stage (grouped by month), and time lagged for milking (in hours) were assumed as fixed effects on the model. Animal additive genetic effects considering pedigree relationship and residual errors were assumed with random effects. Year of age at appraisal date was classified from one to nine years of age, assigning the value of nine years of age for animals that were greater than or equal to nine years of age. From our results, the estimate for heritability was 0.463, 0.346, 0.473, 0.290, and 0.430 on GSC, DCC, BSC, FLC and UDC, respectively. The estimate for FS heritability was 0.539. The greatest breeding values for GSC were estimated for Canada, with the breeding values for American lines increasing for 10 years starting in 1989 but tending to decrease after that until 2004. For DCC, the breeding values for American and Canadian lines showed similar patterns until 1999, after which the breeding values for the American lines declined sharply. For BSC, data from Korea, Canada and the USA followed similar trends overall except when the breeding values of the American lines decreased starting in 1999. Overall, the methods used to evaluate genetic performance in this study were acceptable and allowed for the discovery of differences by country of genetic origin, likely due in part to the American use of selection indexes based primarily on milk yield traits until methods for evaluating other traits began to emerge.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.325-333
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• 2006

We describe tests for detecting and locating quantitative traits loci (QTL) for traits in Hanwoo. Lod scores and a permutation test have been described. From results of a permutation test to detect QTL, we select major DNA markers of BMS1167 microsatellite locus in Hanwoo chromosome 17 for further analysis. K-means clustering analysis applied to four traits and eight DNA markers in BMS1167 resulted in three cluster groups. We conclude that the major DNA markers of BMS1167 microsatellite locus in Hanwoo chromosome 17 are markers 100bp, 108bp and 110bp.

• Journal of the Korean Data and Information Science Society
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• v.16 no.4
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• pp.913-921
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• 2005

We describe tests for detecting and locating quantitative traits loci (QTL) for traits in Hanwoo. Lod scores and a permutation test have been described. From results of a permutation test to detect QTL, we select major DNA markers of BMS941 microsatellite locus in Hanwoo chromosome 17 for further analysis. K-means clustering analysis applied to four traits and eight DNA markers in BMS941 resulted in three cluster groups. We conclude that the major DNA markers of BMS941 microsatellite locus in Hanwoo chromosome 17 are markers 80bp, 85bp 90bp and 105bp.

• Journal of the Korea Society for Simulation
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• v.23 no.4
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• pp.219-228
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• 2014

Various computational resources in distributed environment are to build a high-performance computing environments through virtualization technology. Recently, there is a growing need for a complicated process due to the improvement of the user-level application, which has led to demand for high-performance computing. The requested job from users is composed of data. And because of each data has own characteristics, the classifier may consider the features of data. In this paper, we propose Job Classifying method based on Data Traits for Increased Efficiency of Computational Resources in Distributed Environment (JCDT). JCDT classifies the job by data traits of the users' request, is expected to improve the job processing time and increase the processing speed of the calculation resources.