• Asian-Australasian Journal of Animal Sciences
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• v.29 no.11
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• pp.1555-1561
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• 2016

Shank skin color of Korean native chicken (KNC) shows large color variations. It varies from white, yellow, green, bluish or grey to black, whilst in the majority of European breeds the shanks are typically yellow-colored. Three shank skin color-related traits (i.e., lightness [$L^*$], redness [$a^*$], and yellowness [$b^*$]) were measured by a spectrophotometer in 585 progeny from 68 nuclear families in the KNC resource population. We performed genome scan linkage analysis to identify loci that affect quantitatively measured shank skin color traits in KNC. All these birds were genotyped with 167 DNA markers located throughout the 26 autosomes. The SOLAR program was used to conduct multipoint variance-component quantitative trait locus (QTL) analyses. We detected a major QTL that affects $b^*$ value (logarithm of odds [LOD] = 47.5, $p=1.60{\times}10^{-49}$) on GGA24 (GGA for Gallus gallus). At the same location, we also detected a QTL that influences $a^*$ value (LOD = 14.2, $p=6.14{\times}10^{-16}$). Additionally, beta-carotene dioxygenase 2 (BCDO2), the obvious positional candidate gene under the linkage peaks on GGA24, was investigated by the two association tests: i.e., measured genotype association (MGA) and quantitative transmission disequilibrium test (QTDT). Significant associations were detected between BCDO2 g.9367 A>C and $a^*$ ($P_{MGA}=1.69{\times}10^{-28}$; $P_{QTDT}=2.40{\times}10^{-25}$). The strongest associations were between BCDO2 g.9367 A>C and $b^*$ ($P_{MGA}=3.56{\times}10^{-66}$; $P_{QTDT}=1.68{\times}10^{-65}$). However, linkage analyses conditional on the single nucleotide polymorphism indicated that other functional variants should exist. Taken together, we demonstrate for the first time the linkage and association between the BCDO2 locus on GGA24 and quantitatively measured shank skin color traits in KNC.

• Management & Information Systems Review
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• v.38 no.1
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• pp.181-199
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• 2019

The purpose of this study is to examine whether or not the volatility of the 1997~1998 Asian crisis still affects the monthly stock returns of Korea, Japan, Singapore, Hong Kong and China from 1980 to 2018. This study investigated whether the volatility has already fallen to pre-crisis levels. To illustrate the possible structural changes in the unconditioned variance due to the Asian financial crisis, we use the MRS-GARCH model, which is a regime switching model. The main results of this study were as follows: First, the stock return of each country was weak in the high volatility regime except Japan resulted by the Asian financial crisis from 1997 to 1998 until March 2018, and the Asian stock market has not yet calmed down except for the global financial crisis period of 2007 and 2008. Second, the conditional volatility has been significantly and persistently decreased and eliminated after the Asian financial crisis. Thus, we could be judged that the Asian stock market was not fully recovered(stable) due to the Asian crisis including the capital liberalization high inflation, worsening current account deficit, overseas low interest rates and expansion of credit growth in 1997 and 1998, but the Asian stock market was largely settled down, except for the 2007 and 2008 in Global financial crises. Considering the similarity between the Asian stock markets and the similar correlation of the regime switching, it may be worthwhile to analyze the MRS-GARCH model.

• The Korean Journal of Applied Statistics
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• v.36 no.5
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• pp.381-397
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• 2023

The most representative design used in clinical trials is randomization, which is used to accurately estimate the treatment effect. However, comparison between the treatment group and the control group in an observational study without randomization is biased due to various unadjusted differences, such as characteristics between patients. Propensity score weighting is a widely used method to address these problems and to minimize bias by adjusting those confounding and assess treatment effects. Inverse probability weighting, the most popular method, assigns weights that are proportional to the inverse of the conditional probability of receiving a specific treatment assignment, given observed covariates. However, this method is often suffered by extreme propensity scores, resulting in biased estimates and excessive variance. Several alternative methods including trimming, overlap weights, and matching weights have been proposed to mitigate these issues. In this paper, we conduct a simulation study to compare performance of various propensity score weighting methods under diverse situation, such as limited overlap, misspecified propensity score, and treatment contrary to prediction. From the simulation results overlap weights and matching weights consistently outperform inverse probability weighting and trimming in terms of bias, root mean squared error and coverage probability.