• Genomics & Informatics
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• v.10 no.4
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• pp.220-225
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• 2012

Recent rapid advances in genetic research are ushering us into the genome sequence era, where an individual's genome information is utilized for clinical practice. The most spectacular results of the human genome study have been provided by genome-wide association studies (GWASs). This is a review of the history of GWASs as related to my work. Further efforts are necessary to make full use of its potential power to medicine.

• Genomics & Informatics
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• v.20 no.4
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• pp.49.1-49.7
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• 2022

Many packages for a meta-analysis of genome-wide association studies (GWAS) have been developed to discover genetic variants. Although variations across studies must be considered, there are not many currently-accessible packages that estimate between-study heterogeneity. Thus, we propose a python based application called Beta-Meta which can easily process a meta-analysis by automatically selecting between a fixed effects and a random effects model based on heterogeneity. Beta-Meta implements flexible input data manipulation to allow multiple meta-analyses of different genotype-phenotype associations in a single process. It provides a step-by-step meta-analysis of GWAS for each association in the following order: heterogeneity test, two different calculations of an effect size and a p-value based on heterogeneity, and the Benjamini-Hochberg p-value adjustment. These methods enable users to validate the results of individual studies with greater statistical power and better estimation precision. We elaborate on these and illustrate them with examples from several studies of infertility-related disorders.

• Genomics & Informatics
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• v.12 no.4
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• pp.203-207
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• 2014

Hepatitis is a common and serious disease for the Korean population. It is caused by a virus, the A and B types of which are plentiful in Koreans. In this study, we tried to find genetic factors for hepatitis through genome-wide association studies. We took 368 cases and 1,500 controls from Anseong and Ansan cohort data. About 300,000 single-nucleotide polymorphisms and 20 epidemiological variables were analyzed. We did not find any meaningful significant single nucleotide polymorphisms, but we confirmed the influence of major epidemiological variables on hepatitis.

• The Korea Genome Conference
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• 2006.09a
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• pp.46-46
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• 2006

• Genomics & Informatics
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• v.14 no.4
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• pp.138-148
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• 2016

The success of genome-wide association studies (GWASs) has enabled us to improve risk assessment and provide novel genetic variants for diagnosis, prevention, and treatment. However, most variants discovered by GWASs have been reported to have very small effect sizes on complex human diseases, which has been a big hurdle in building risk prediction models. Recently, many statistical approaches based on penalized regression have been developed to solve the "large p and small n" problem. In this report, we evaluated the performance of several statistical methods for predicting a binary trait: stepwise logistic regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN). We first built a prediction model by combining variable selection and prediction methods for type 2 diabetes using Affymetrix Genome-Wide Human SNP Array 5.0 from the Korean Association Resource project. We assessed the risk prediction performance using area under the receiver operating characteristic curve (AUC) for the internal and external validation datasets. In the internal validation, SLR-LASSO and SLR-EN tended to yield more accurate predictions than other combinations. During the external validation, the SLR-SLR and SLR-EN combinations achieved the highest AUC of 0.726. We propose these combinations as a potentially powerful risk prediction model for type 2 diabetes.

• Animal Bioscience
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• v.36 no.1
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• pp.19-28
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• 2023

Objective: The objective of this study was to perform genome (genome wide association studies [GWAS]) and chromosome (CWAS) wide association analyses to identify single nucleotide polymorphisms (SNPs) associated with growth traits in registered Simmental and Simbrah cattle. Methods: The phenotypes were deregressed BLUP EBVs for birth weight, weaning weight direct, weaning weight maternal, and yearling weight. The genotyping was performed with the GGP Bovine 150k chip. After the quality control analysis, 105,129 autosomal SNP from 967 animals (473 Simmental and 494 Simbrah) were used to carry out genotype association tests. The two association analyses were performed per breed and using combined information of the two breeds. The SNP associated with growth traits were mapped to their corresponding genes at 100 kb on either side. Results: A difference in magnitude of posterior probabilities was found across breeds between genome and chromosome wide association analyses. A total of 110, 143, and 302 SNP were associated with GWAS and CWAS for growth traits in the Simmental-, Simbrah- and joint -data analyses, respectively. It stands out from the enrichment analysis of the pathways for RNA polymerase (POLR2G, POLR3E) and GABAergic synapse (GABRR1, GABRR3) for Simmental cattle and p53 signaling pathway (BID, SERPINB5) for Simbrah cattle. Conclusion: Only 6,265% of the markers associated with growth traits were found using CWAS and GWAS. The associated markers using the CWAS analysis, which were not associated using the GWAS, represents information that due to the model and priors was not associated with the traits.

• Genomics & Informatics
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• v.8 no.3
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• pp.159-163
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• 2010

Erythrocyte traits are heritable and indirect indicators of blood diseases caused by erythrocyte, but their genetic factors are largely unknown. So we performed genome-wide association study in 8,842 Korean individuals to identify genetic factors influencing erythrocyte traits. We identified 40 associations for three erythrocyte traits at genome-wide significance levels (p < $1{\times}10^{-6}$). We compared these associated loci with those reported in genome-wide association studies of European and Japanese. Our findings include previously identified loci(HBS1L-MYB, TMPRSS6, USP49 and CCND3) in other studies and novel associations (MRDS1/OFCC1, CSDE1, NRAS and 8 other loci). For example, SNP rs4895440 of HBS1L-MYB intergenic region on chromosome 6q23.3 is one of the most associations influencing erythrocyte traits (p=$8.33{\times}10^{-27}$).

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.10
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• pp.1371-1379
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• 2015

Undoubtedly livestock is one of the major contributors to the economy of any country. The economic value of livestock includes meat, dairy products, fiber, fertilizer etc. Understanding and identifying the associations of quantitative trait loci (QTL) with the economically important traits is believed to substantially benefit the livestock industry. The past two decades have seen a flurry of interest in mapping the QTL associated with traits of economic importance on the genome. With the availability of single nucleotide polymorphism chip of various densities it is possible to identify regions, QTL and genes on the genome that explain the association and its effect on the phenotype under consideration. Remarkable advancement has been seen in genome wide association studies (GWAS) since its inception till the present day. In this review we describe the progress and challenges of GWAS in various livestock species.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.295-308
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• 2015

We have experienced a substantial improvement in and cost-drop for genotyping that enables genetic epidemiological studies with large-scale genetic data. Genome-wide association studies have identified more than ten thousand causal variants. Many statistical methods based on linear mixed models have been developed for various goals such as estimating heritability and identifying disease susceptibility locus. Empirical results also repeatedly stress the importance of linear mixed models. Therefore, we review the statistical methods related with to linear mixed models and illustrate the meaning of their estimates.

• Genomics & Informatics
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• v.12 no.4
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• pp.187-194
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• 2014

Metabolic syndrome (METS) is a disorder of energy utilization and storage and increases the risk of developing cardiovascular disease and diabetes. To identify the genetic risk factors of METS, we carried out a genome-wide association study (GWAS) for 2,657 cases and 5,917 controls in Korean populations. As a result, we could identify 2 single nucleotide polymorphisms (SNPs) with genome-wide significance level p-values (< $5{\times}10^{-8}$), 8 SNPs with genome-wide suggestive p-values ($5{\times}10^{-8}{\leq}$ p < $1{\times}10^{-5}$), and 2 SNPs of more functional variants with borderline p-values ($5{\times}10^{-5}{\leq}$ p < $1{\times}10^{-4}$). On the other hand, the multiple correction criteria of conventional GWASs exclude false-positive loci, but simultaneously, they discard many true-positive loci. To reconsider the discarded true-positive loci, we attempted to include the functional variants (nonsynonymous SNPs [nsSNPs] and expression quantitative trait loci [eQTL]) among the top 5,000 SNPs based on the proportion of phenotypic variance explained by genotypic variance. In total, 159 eQTLs and 18 nsSNPs were presented in the top 5,000 SNPs. Although they should be replicated in other independent populations, 6 eQTLs and 2 nsSNP loci were located in the molecular pathways of LPL, APOA5, and CHRM2, which were the significant or suggestive loci in the METS GWAS. Conclusively, our approach using the conventional GWAS, reconsidering functional variants and pathway-based interpretation, suggests a useful method to understand the GWAS results of complex traits and can be expanded in other genomewide association studies.