• Genomics & Informatics
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• v.8 no.3
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• pp.101-102
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• 2010

During the last decade, large community cohorts have been established by the Korea National Institutes of Health (KNIH), and enormous epidemiological and clinical data have been accumulated. Using these information and samples in the cohorts, KNIH set out to do a large-scale genome-wide association study (GWAS) in 2007, and the Korea Association REsource (KARE) consortium was launched to analyze the data to identify the underlying genetic risk factors of diseases and diverse health indexes, such as blood pressure, obesity, bone density, and blood biochemical traits. The consortium consisted of 6 research divisions, formed by 25 principal investigators in 19 organizations, including 18 universities, 2 institutes, and 1 company. Each division focused on one of the following subjects: the identification of genetic factors, the statistical analysis of gene-gene interactions, the genetic epidemiology of gene-environment interactions, copy number variation, the bioinformatics related to a GWAS, and a GWAS of nutrigenomics. In this special issue, the study results of the KARE consortium are provided as 9 articles. We hope that this special issue might encourage the genomics community to share data and scientists, including clinicians, to analyze the valuable Korean data of KARE.

• Asian-Australasian Journal of Animal Sciences
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• v.25 no.9
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• pp.1216-1222
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• 2012

Somatic cells count (SCC), milk yield (MY) and pedigree information of 2,791 first lactation cows that calved between 1990 and 2010 on 259 Thai farms were used to estimate genetic parameters and trends for SCC and its genetic association with MY. The SCC were log-transformed (lnSCC) to make them normally distributed. An average information-restricted maximum likelihood procedure was used to estimate variance components. A bivariate animal model that considered herd-yr-season, calving age, and regression additive genetic group as fixed effects, and animal and residual as random effects was used for genetic evaluation. Heritability estimates were 0.12 (SE = 0.19) for lnSCC, and 0.31 (SE = 0.06) for MY. The genetic correlation estimate between lnSCC and MY was 0.26 (SE = 0.59). Mean yearly estimated breeding values during the last 20 years increased for SCC (49.02 cells/ml/yr, SE = 26.81 cells/ml/yr; p = 0.08), but not for MY (0.37 kg/yr, SE = 0.87 kg/yr; p = 0.68). Sire average breeding values for SCC and MY were higher than those of cows and dams (p<0.01). Heritability estimates for lnSCC and MY and their low but positive genetic correlation suggested that selection for low SCC may be feasible in this population as it is in other populations of dairy cows. Thus, selection for high MY and low SCC should be encouraged in Thai dairy improvement programs to increase profitability by improving both cow health and milk yield.

• Communications for Statistical Applications and Methods
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• v.17 no.5
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• pp.733-743
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• 2010

Recently, disease-genetic association analyses using single nucleotide polymorphisms(SNPs) and haplotypes in family-based genetic study have come into the spotlight. In binary trait, the classic transmission disequilibrium test(TDT) can only be applied if genetic information of parents and their offspring is available. However, in case of diseases having a late age of onset such as dementia, the TDT cannot be applied due to the fact that parental genotype data are unavailable. For this reason, alternate methods using genetic sib information instead of parental genotype data are proposed. In this study, methods using genetic sib information are reviewed and power of analysis tests is also compared throughout simulation experiment.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.1
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• pp.13-18
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• 2006

Two different animal models, which differ in whether or not taking maternal genetic effect into account, for estimating genetic parameters of cashmere weight, live body weight, cashmere thickness, staple length, fiber diameter, and fiber length in Inner Mongolia White Cashmere Goat were compared via likelihood ratio test. The results indicate that maternal genetic effect has significant influence on live body weight and cashmere thickness, but no significant influence on the other traits. Using models suitable for each trait, both genetic parameters and trends were analyzed with the MTDFREML program. Heritability estimates from single trait models for cashmere weight, live body weight, cashmere thickness, staple length, fiber diameter and fiber length were found to be 0.30, 0.07, 0.21, 0.29, 0.28 and 0.21, respectively. Genetic correlation estimates from two-trait models between live body weight and all other traits (-0.06~0.07) was negligible, as were those between fiber diameter and all other traits (-0.01~0.03) except cashmere thickness (0.19). Cashmere weight and staple length had moderate to low genetic correlations with other traits (-0.24~0.39 and -0.24~0.34, respectively) except for live body weight and fiber diameter. Cashmere thickness had a strong genetic correlation with fiber length (0.81), and low genetic correlation with other traits (0.19~0.34) except live body weight. Genetic trend analysis suggests that selection for cashmere weight was very effective, which has led to the slow genetic progress of cashmere thickness and fiber length due to their genetic correlations with cashmere weight. The selection for live body weight was not effective, which was consistent with its low inheritability.

• Asian-Australasian Journal of Animal Sciences
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• v.15 no.2
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• pp.303-308
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• 2002

Genetic markers engendered by genome projects drew enormous interest in quantitative genetics, but knowledge on genetic architecture of complex traits is limited. Complexities in genetics will not allow us to easily clarify relationship between genotypes and phenotypes for quantitative traits. Quantitative genetics guides an important way in facing such challenges. It is our exciting task to find genes that affect complex traits. In this paper, landmark research and future prospects are discussed on genetic parameter estimation and quantitative trait locus (QTL) mapping as major subjects of interest.

• Biomedical Science Letters
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• v.24 no.2
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• pp.94-101
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• 2018

Tuberculosis (TB) is infectious disease caused by Mycobacterium tuberculosis (MTB) infection. It is known that not only the property of microorganism but also the genetic susceptibility of infected patients is controlled. Interleukin 2 (IL-2) is a cytokine belonging to type 1 T helper (Th1) activity. In addition, IL-2, when infected with MTB, binds IL-2 receptor and promotes T cell replication and is involved in granuloma formation. The aim of this study was to investigate the genetic polymorphisms of the IL-2 receptor gene in tuberculosis patients and normal individuals. We analyzed 22 SNPs in three genes using the genotype data of 443 tuberculosis cases and 3,228 healthy controls from the Korea Association Resource for their correlation with tuberculosis case. IL2RA, IL2RB, and IL2RG genes were genotyped of 16, 4, and 2 SNPs, respectively. Among three genes, only IL2RA gene polymorphisms showed statistically significant association with tuberculosis case. 6 SNPs with high significance were identified in the IL2RA gene. In addition, the linkage disequilibrium (LD) structure of IL2RA gene was confirmed. SNP imputation of IL2RA gene was performed, it was confirmed that more SNPs were significant between case and control. If we look at the results of IL2RA gene analysis above, we can see that genetic polymorphism in the gene expressing $IL-2R{\alpha}$ will regulate the expression level of $IL-2R{\alpha}$, and the change in the immune system involved in $IL-2R{\alpha}$. In this study, genetic polymorphism that may affect host immunity suggests that susceptibility to tuberculosis may be controlled.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.7
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• pp.2713-2717
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• 2015

Background: Possible associations between the single nucleotide polymorphism (SNP) rs8034191 in the aminoglycosidephosphotransferase domain containing 1 (AGPHD1) gene and lung cancer risk have been studied by many researchers but the results have been contradictory. Materials and Methods: A computerized search for publications on rs8034191 and lung cancer risk was performed. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to assess the association between rs8034191 and lung cancer risk with 13 selected case-control studies. Sensitivity analysis, test of heterogeneity, cumulative meta-analysis, and assessment of bias were also performed. Results: A significant association between rs8034191 and lung cancer susceptibility was found using the dominant genetic model (OR=1.344, 95% CI: 1.285-1.406), the additive genetic model (OR=1.613, 95% CI: 1.503-1.730), and the recessive genetic model (OR=1.408, 95% CI: 1.319-1.503). Moreover, an increased lung cancer risk was found with all genetic models after stratification of ethnicity. Conclusions: The association between rs8034191 and lung cancer risk was significant using multiple genetic models, suggesting that rs8034191 is a risk factor for lung cancer. Further functional studies of this polymorphism and lung cancer risk are warranted.

• Journal of Preventive Medicine and Public Health
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• v.40 no.2
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• pp.114-121
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• 2007

Power and sample size estimation is one of the crucially important steps in planning a genetic association study to achieve the ultimate goal, identifying candidate genes for disease susceptibility, by designing the study in such a way as to maximize the success possibility and minimize the cost. Here we review the optimal two-stage genotyping designs for genomewide association studies recently investigated by Wang et al(2006). We review two mathematical frameworks most commonly used to compute power in genetic association studies prior to the main study: Monte-Carlo and non-central chi-square estimates. Statistical powers are computed by these two approaches for case-control genotypic tests under one-stage direct association study design. Then we discuss how the linkage-disequilibrium strength affects power and sample size, and how to use empirically-derived distributions of important parameters for power calculations. We provide useful information on publicly available softwares developed to compute power and sample size for various study designs.

• Asian-Australasian Journal of Animal Sciences
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• v.22 no.1
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• pp.124-130
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• 2009

Estimating genetic interaction effects in animal genomics would be one of the most challenging studies because the phenotypic variation for economically important traits might be largely explained by interaction effects among multiple nucleotide sequence variants under various environmental exposures. Genetic improvement of economic animals would be expected by understanding multi-locus genetic interaction effects associated with economic traits. Most analyses in animal breeding and genetics, however, have excluded the possibility of genetic interaction effects in their analytical models. This review discusses a historical estimation of the genetic interaction and difficulties in analyzing the interaction effects. Furthermore, two recently developed methods for assessing genetic interactions are introduced to animal genomics. One is the restricted partition method, as a nonparametric grouping-based approach, that iteratively utilizes grouping of genotypes with the smallest difference into a new group, and the other is the Bayesian method that draws inferences about the genetic interaction effects based on their marginal posterior distributions and attains the marginalization of the joint posterior distribution through Gibbs sampling as a Markov chain Monte Carlo. Further developing appropriate and efficient methods for assessing genetic interactions would be urgent to achieve accurate understanding of genetic architecture for complex traits of economic animals.

• Asian-Australasian Journal of Animal Sciences
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• v.19 no.4
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• pp.475-481
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• 2006

The genetic polymorphism and relationships of Muscovy, Cherry Valley Meat ducks, Partridge ducks and their crossbreds $F_1$ and $F_2$, respectively, were studied using a random amplified polymorphic DNA (RAPD) technique. The results showed that RAPD markers were effective for the analysis of genetic relationships among ducks. Amplification with 20-primers gave 760 reproducible amplified fragments. The percentage of polymorphic marker band was 74.70%, which indicates that the RAPD technique had higher efficiency of polymorphism detection and sensitivity in studying the genetic variations among ducks and showed that the genetic polymorphism was abundant between two species of duck populations. The average index of genetic distance in hybrid $F_2$ was 0.2341 and higher than that of its parents, which indicates that the genetic diversity was improved by crossbreeding with Muscovy.