• Genomics & Informatics
• /
• v.20 no.1
• /
• pp.14.1-14.4
• /
• 2022

Permutation testing is a robust and popular approach for significance testing in genomic research that has the advantage of reducing inflated type 1 error rates; however, its computational cost is notorious in genome-wide association studies (GWAS). Here, we developed a supercomputing-aided approach to accelerate the permutation testing for GWAS, based on the message-passing interface (MPI) on parallel computing architecture. Our application, called MPI-GWAS, conducts MPI-based permutation testing using a parallel computing approach with our supercomputing system, Nurion (8,305 compute nodes, and 563,740 central processing units [CPUs]). For 10⁷ permutations of one locus in MPI-GWAS, it was calculated in 600 s using 2,720 CPU cores. For 10⁷ permutations of ~30,000-50,000 loci in over 7,000 subjects, the total elapsed time was ~4 days in the Nurion supercomputer. Thus, MPI-GWAS enables us to feasibly compute the permutation-based GWAS within a reason-able time by harnessing the power of parallel computing resources.

• Animal Bioscience
• /
• v.36 no.1
• /
• pp.19-28
• /
• 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
• /
• v.20 no.4
• /
• pp.49.1-49.7
• /
• 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
• /
• v.12 no.4
• /
• pp.187-194
• /
• 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.

• Genomics & Informatics
• /
• v.14 no.4
• /
• pp.149-159
• /
• 2016

With the success of the genome-wide association studies (GWASs), many candidate loci for complex human diseases have been reported in the GWAS catalog. Recently, many disease prediction models based on penalized regression or statistical learning methods were proposed using candidate causal variants from significant single-nucleotide polymorphisms of GWASs. However, there have been only a few systematic studies comparing existing methods. In this study, we first constructed risk prediction models, such as stepwise linear regression (SLR), least absolute shrinkage and selection operator (LASSO), and Elastic-Net (EN), using a GWAS chip and GWAS catalog. We then compared the prediction accuracy by calculating the mean square error (MSE) value on data from the Korea Association Resource (KARE) with body mass index. Our results show that SLR provides a smaller MSE value than the other methods, while the numbers of selected variables in each model were similar.

• Journal of the Korean Data and Information Science Society
• /
• v.25 no.5
• /
• pp.1127-1135
• /
• 2014

Genome-wide association studies (GWAS) are designed to discover genetic variants such as single nucleotide polymorphisms (SNPs) that are associated with human complex traits. Although there is an increasing interest in the application of GWAS methodologies to population-based cohorts, many published GWAS have adopted a case-control design, which raise an issue related to a sampling bias of both case and control samples. Because of unequal selection probabilities between cases and controls, the samples are not representative of the population that they are purported to represent. Therefore, non-random sampling in case-control study can potentially lead to inconsistent and biased estimates of SNP-trait associations. In this paper, we proposed inverse-probability of sampling weights based on disease prevalence to eliminate a case-control sampling bias in estimation and testing for association between SNPs and quantitative traits. We apply the proposed method to a data from the Korea Association Resource project and show that the standard estimators applied to the weighted data yield unbiased estimates.

• Genomics & Informatics
• /
• v.9 no.1
• /
• pp.1-4
• /
• 2011

Genome-wide association (GWA) studies are the method of choice for discovering loci associated with common diseases. More than a thousand GWA studies have reported successful identification of statistically significant association signals in human genomes for a variety of complex diseases. In this review, I discuss some of the issues related to the future of GWA studies and their biomedical applications.

• Genomics & Informatics
• /
• v.8 no.3
• /
• pp.101-102
• /
• 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
• /
• v.28 no.10
• /
• pp.1371-1379
• /
• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.16 no.3
• /
• pp.1028-1046
• /
• 2022

Genome-wide association studies (GWAS) aim to find the significant genetic variants for common complex disease. However, genotype data has privacy information such as disease status and identity, which make data sharing and research difficult. Differential privacy is widely used in the privacy protection of data sharing. The current differential privacy approach in GWAS pays no attention to raw data but to statistical data, and doesn't achieve equilibrium between utility and privacy, so that data sharing is hindered and it hampers the development of genomics. To share data more securely, we propose a differential privacy preserving approach of data sharing for GWAS, and achieve the equilibrium between privacy and data utility. Firstly, a reasonable disturbance interval for the genotype is calculated based on the expected utility. Secondly, based on the interval, we get the Nash equilibrium point between utility and privacy. Finally, based on the equilibrium point, the original genotype matrix is perturbed with differential privacy, and the corresponding random genotype matrix is obtained. We theoretically and experimentally show that the method satisfies expected privacy protection and utility. This method provides engineering guidance for protecting GWAS data privacy.