• Genomics & Informatics
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• v.12 no.4
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• pp.216-224
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• 2014

Gastritis is a common but a serious disease with a potential risk of developing carcinoma. Helicobacter pylori infection is reported as the most common cause of gastritis, but other genetic and genomic factors exist, especially single-nucleotide polymorphisms (SNPs). Association studies between SNPs and gastritis disease are important, but results on epistatic interactions from multiple SNPs are rarely found in previous genome-wide association (GWA) studies. In this study, we performed computational GWA case-control studies for gastritis in Korea Associated Resource (KARE) data. By transforming the resulting SNP epistasis network into a gene-gene epistasis network, we also identified potential gene-gene interaction factors that affect the susceptibility to gastritis.

• Genomics & Informatics
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• v.11 no.4
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• pp.200-210
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• 2013

Studying biological networks, such as protein-protein interactions, is key to understanding complex biological activities. Various types of large-scale biological datasets have been collected and analyzed with high-throughput technologies, including DNA microarray, next-generation sequencing, and the two-hybrid screening system, for this purpose. In this review, we focus on network-based approaches that help in understanding biological systems and identifying biological functions. Accordingly, this paper covers two major topics in network biology: reconstruction of gene regulatory networks and network-based applications, including protein function prediction, disease gene prioritization, and network-based genome-wide association study.

• Genomics & Informatics
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• v.16 no.4
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• pp.38.1-38.3
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• 2018

Gene-gene interaction (GGI) analysis is known to play an important role in explaining missing heritability. Many previous studies have already proposed software to analyze GGI, but most methods focus on a binary phenotype in a case-control design. In this study, we developed "Hierarchical structural CoMponent analysis of Gene-Gene Interactions" (HisCoM-GGI) software for GGI analysis with a continuous phenotype. The HisCoM-GGI method considers hierarchical structural relationships between genes and single nucleotide polymorphisms (SNPs), enabling both gene-level and SNP-level interaction analysis in a single model. Furthermore, this software accepts various types of genomic data and supports data management and multithreading to improve the efficiency of genome-wide association study data analysis. We expect that HisCoM-GGI software will provide advanced accessibility to researchers in genetic interaction studies and a more effective way to understand biological mechanisms of complex diseases.

• Genomics & Informatics
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• v.19 no.4
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• pp.36.1-36.11
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• 2021

Predicting individual traits and diseases from genetic variants is critical to fulfilling the promise of personalized medicine. The genetic variants from genome-wide association studies (GWAS), including variants well below GWAS significance, can be aggregated into highly significant predictions across a wide range of complex traits and diseases. The recent arrival of large-sample public biobanks enables highly accurate polygenic predictions based on genetic variants across the whole genome. Various statistical methodologies and diverse computational tools have been introduced and developed to computed the polygenic risk score (PRS) more accurately. However, many researchers utilize PRS tools without a thorough understanding of the underlying model and how to specify the parameters for the best performance. It is advantageous to study the statistical models implemented in computational tools for PRS estimation and the formulas of parameters to be specified. Here, we review a variety of recent statistical methodologies and computational tools for PRS computation.

• BMB Reports
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• v.57 no.1
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• pp.66-70
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• 2024

Prime editors (PEs), which are CRISPR-Cas9 nickase (H840A)-reverse transcriptase fusion proteins programmed with prime editing guide RNAs (pegRNAs), can not only edit bases but also install transversions, insertions, or deletions without both donor DNA and double-strand breaks at the target DNA. As the demand for in-locus tagging is increasing, to reflect gene expression dynamics influenced by endogenous genomic contexts, we demonstrated that PEs can be used to introduce the hemagglutinin (HA) epitope tag to a target gene locus, enabling molecular and biochemical studies using in-locus tagged plants. To promote genome-wide in-locus tagging, we also implemented a publicly available database that designs pegRNAs for in-locus tagging of all the Arabidopsis genes.

• Korean Journal of Agricultural Science
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• v.40 no.2
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• pp.155-162
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• 2013

Genome-wide association study was performed on data from 266 Hanwoo steers derived from 66 sire using bovine 10K mapping chip in Hanwoo (Korean Cattle). SNPs were excluded from the analysis if they failed in over 5% of the genotypes, had median GC scores below 0.6, had GC scores under 0.6 in less than 90% of the samples, deviated in heterozygosity more than 3 standard deviations from the other SNPs and were out of Hardy-Weinberg equilibrium for a cutoff p-value of $1^{-15}$. Unmapped and SNPs on sex chromosomes were also excluded. A total of 4,522 SNPs were included in the analysis. To test an association between SNP and QTL, GWAS for five genetic mode (additive, dominant, overdominant, recessive and codominant) was implemented in this study. Three SNPs (rs29018694, ss46526851 and rs29018222) at a threshold p< $1.11{\times}10^{-5}$ were detected on BTA12 and BTA21 for dressing percentages in codominant and recessive genetic mode. The G allele for rs29018694 has 4.9% higher dressing percentage than A allele, while the T allele for ss46526851 has 2.57 % higher dressing percentage than C allele. Therefore, rs29018694 SNP showed a bigger effect than the other two SNPs (ss46526851 and rs29018222) in this study. In conclusion, this study identifies three loci with moderate effects and many loci with infinitesimally small effect across genome in Hanwoo.

• Journal of Animal Science and Technology
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• v.62 no.3
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• pp.306-312
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• 2020

Korean army dogs are raised for special purposes and have contributed much to society. However, several diseases occur in dogs. Canine hip dysplasia (CHD) is a musculoskeletal disorder that occurs frequently in Korean army dogs and interferes with their activities. If we could control CHD, this would have a positive effect on their performance. This study performed a genome-wide association study (GWAS) in 69 Korean army dogs to find significant loci for CHD using 170K single nucleotide polymorphisms (SNPs). CHD was classified according to the Norberg angle criterion. The control group comprised 62 dogs classified as relatively normal, and 7 dogs with severe CHD formed the case group. From the GWAS analysis, we concluded that SNPs present on chromosome 4 might have a significant impact on the overall expression of canine hip dysplasia.

• 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.

• Animal Bioscience
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• v.36 no.10
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• pp.1499-1507
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• 2023

Objective: The study investigated the origin of the Akhal-Teke horse using genome-wide single-nucleotide polymorphism (SNP) data and mitochondrial hypervariable region 1 (HVR-1) nucleotide sequences Methods: Genome-wide SNP data from 22 breeds (481 horses) and mitochondrial HVR-1 sequences from 24 breeds (544 sequences) worldwide to examine the origin of the Akhal-Teke horse. The data were analyzed using principal component analysis, linkage disequilibrium analysis, neighbor-joining dendrograms, and ancestry inference to determine the population relationships, ancestral source, genetic structure, and relationships with other varieties. Results: A close genetic relationship between the Akhal-Teke horse and horses from the Middle East was found. Analysis of mitochondrial HVR-1 sequences showed that there were no shared haplotypes between the Akhal-Teke and Tarpan horses, and the mitochondrial data indicated that the Akhal-Teke horse has not historically expanded its group. Ancestral inference suggested that Arabian and Caspian horses were the likely ancestors of the Akhal-Teke horse. Conclusion: The Akhal-Teke horse originated in the Middle East.

• Animal Bioscience
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• v.37 no.3
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• pp.451-460
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• 2024

Objective: The objective is to identify genomic regions and candidate genes associated with age to 105 kg (AGE), average daily gain (ADG), backfat thickness (BF), and eye muscle area (EMA) in Yorkshire pig. Methods: This study used a total of 104,380 records and 11,854 single nucleotide polymorphism (SNP) data obtained from Illumina porcine 60K chip. The estimated genomic breeding values (GEBVs) and SNP effects were estimated by single-step genomic best linear unbiased prediction (ssGBLUP). Results: The heritabilities of AGE, ADG, BF, and EMA were 0.50, 0.49, 0.49, and 0.23, respectively. We identified significant SNP markers surpassing the Bonferroni correction threshold (1.68×10^-6), with a total of 9 markers associated with both AGE and ADG, and 4 markers associated with BF and EMA. Genome-wide association study (GWAS) analyses revealed notable chromosomal regions linked to AGE and ADG on Sus scrofa chromosome (SSC) 1, 6, 8, and 16; BF on SSC 2, 5, and 8; and EMA on SSC 1. Additionally, we observed strong linkage disequilibrium on SSC 1. Finally, we performed enrichment analyses using gene ontology and Kyoto encyclopedia of genes and genomes (KEGG), which revealed significant enrichments in eight biological processes, one cellular component, one molecular function, and one KEGG pathway. Conclusion: The identified SNP markers for productive traits are expected to provide valuable information for genetic improvement as an understanding of their expression.