• Journal of Preventive Medicine and Public Health
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• 제42권6호
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• pp.356-359
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• 2009

In the more than 100 genome wide association studies (GWAS) conducted in the past 5 years, more than 250 genetic loci contributing to more than 40 common diseases and traits have been identified. Whilst many genes have been linked to a trait, both their individual and combined effects are small and unable to explain earlier estimates of heritability. Given the rapid changes in disease incidence that cannot be accounted for by changes in diagnostic practises, there is need to have well characterized exposure information in addition to genomic data for the study of gene-environment interactions. The case-control and cohort study designs are most suited for studying associations between risk factors and occurrence of an outcome. However, the case control study design is subject to several biases and hence the preferred choice of the prospective cohort study design in investigating geneenvironment interactions. A major limitation of utilising the prospective cohort study design is the long duration of follow-up of participants to accumulate adequate outcome data. The GWAS paradigm is a timely reminder for traditional epidemiologists who often perform one- or few-at-a-time hypothesis-testing studies with the main hallmarks of GWAS being the agnostic approach and the massive dataset derived through large-scale international collaborations.

• Journal of Genetic Medicine
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• 제17권1호
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• pp.1-10
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• 2020

Polycystic ovarian syndrome (PCOS) is the most common endocrine disorder in women, which is characterized by the oligo/anovulation, hyperandrogenism (HA) and polycystic ovarian morphology which are diagnostic criteria. PCOS has diverse clinical aspects in addition to those diagnostic criteria including increased risk for cardiovascular diseases, metabolic syndrome, dyslipidemia, type 2 diabetes and impaired fertility. Because of the heterogeneity of the disease, the pathogenesis of the disease has not been elucidated yet. Therefore, there is no cure for the endocrinopathy. HA and insulin resistance (IR) has been considered two major pillars of the pathogenesis of PCOS. Recent advances in animal studies revealed the critical role of neuroendocrine abnormalities in developing PCOS. Several pathways related to neuroendocrine origin have been investigated such as hypothalamus pituitary ovarian axis, hypothalamus pituitary adrenal axis and hypothalamus pituitary adipose axis. This review summarizes the current knowledge about the role of HA and IR in developing PCOS. In addition, we review the results of recent genome wide association studies for PCOS. This new perspective improves our understanding of the role of neuroendocrine origins in PCOS and suggest a novel potential therapeutic target for the treatment of PCOS.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권3호
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• pp.1028-1046
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• 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.

• Asian-Australasian Journal of Animal Sciences
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• 제30권1호
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• pp.1-7
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• 2017

Objective: Three genome-wide association studies (GWAS) and a meta-analysis of GWAS were conducted to explore the genetic mechanisms underlying variation in pig teat number. Methods: We performed three GWAS and a meta-analysis for teat number on three pig populations, including a White Duroc${\times}$Erhualian $F_2$ resource population (n = 1,743), a Chinese Erhualian pig population (n = 320) and a Chinese Sutai pig population (n = 383). Results: We detected 24 single nucleotide polymorphisms (SNPs) that surpassed the genome-wide significant level on Sus Scrofa chromosomes (SSC) 1, 7, and 12 in the $F_2$ resource population, corresponding to four loci for pig teat number. We highlighted vertnin (VRTN) and lysine demethylase 6B (KDM6B) as two interesting candidate genes at the loci on SSC7 and SSC12. No significant associated SNPs were identified in the meta-analysis of GWAS. Conclusion: The results verified the complex genetic architecture of pig teat number. The causative variants for teat number may be different in the three populations

• Asian-Australasian Journal of Animal Sciences
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• 제27권9호
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• pp.1328-1335
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• 2014

Significant SNPs associated with Warner-Bratzler (WB) shear force and sensory traits were confirmed for Hanwoo beef (Korean cattle). A Bonferroni-corrected genome-wide significant association (p< $1.3{\times}10^{-6}$) was detected with only one single nucleotide polymorphism (SNP) on chromosome 5 for WB shear force. A slightly higher number of SNPs was significantly (p<0.001) associated with WB shear force than with other sensory traits. Further, 50, 25, 29, and 34 SNPs were significantly associated with WB shear force, tenderness, juiciness, and flavor likeness, respectively. The SNPs between p = 0.001 and p = 0.0001 thresholds explained 3% to 9% of the phenotypic variance, while the most significant SNPs accounted for 7% to 12% of the phenotypic variance. In conclusion, because WB shear force and sensory evaluation were moderately affected by a few loci and minimally affected by other loci, further studies are required by using a large sample size and high marker density.

• Asian-Australasian Journal of Animal Sciences
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• 제27권3호
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• pp.303-309
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• 2014

The increasing importance of meat quality has implications for animal breeding programs. Research has revealed much about the genetic background of pigs, and many studies have revealed the importance of various genetic factors. Since meat quality is a complex trait which is affected by many factors, consideration of the overall phenotype is very useful to study meat quality. For integrating the phenotypes, we used principle component analysis (PCA). The significant SNPs refer to results of the GRAMMAR method against PC1, PC2 and PC3 of 14 meat quality traits of 181 Duroc pigs. The Genome-wide association study (GWAS) found 26 potential SNPs affecting various meat quality traits. The loci identified are located in or near 23 genes. The SNPs associated with meat quality are in or near five genes (ANK1, BMP6, SHH, PIP4K2A, and FOXN2) and have been reported previously. Twenty-five of the significant SNPs also located in meat quality-related QTL regions, these result supported the QTL effect indirectly. Each single gene typically affects multiple traits. Therefore, it is a useful approach to use integrated traits for the various traits at the same time. This innovative approach using integrated traits could be applied on other GWAS of complex-traits including meat-quality, and the results will contribute to improving meat-quality of pork.

• 한국작물학회:학술대회논문집
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• 한국작물학회 2017년도 9th Asian Crop Science Association conference
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• pp.158-158
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• 2017

The purpose of this study was to identify the candidate genes involved in selenium content in brown rice. Rice (Oryza sativa L.) was important crop including diverse functional substance such as carbohydrate, protein, lysine and tocopherol, mineral. Especially, selenium as nutritionally important minerals, it was known to activate the immune system, antioxidant effect and inhibition of carcinogenesis. Also recommended daily requirements of the United States and the United Kingdom were 55 to 90 ug for selenium. Therefore, selenium content in brown rice of core-set were analyzed by using ICP-MS (Inductively Coupled Plasma Mass Spectrometer) and GWAS (Genome Wide Association Study) was conducted to search for candidate genes in this study. The new natural variants identified through haplotyping analysis would be useful to develop new rice varieties with improved storage ability of the valuable mineral through the future molecular breeding.

• Genomics & Informatics
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• 제10권4호
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• pp.239-243
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• 2012

Gestational diabetes mellitus (GDM) is a complex metabolic disorder of pregnancy that is suspected to have a strong genetic predisposition. It is associated with poor perinatal outcome, and both GDM women and their offspring are at increased risk of future development of type 2 diabetes mellitus (T2DM). During the past several years, there has been progress in finding the genetic risk factors of GDM in relation to T2DM. Some of the genetic variants that were proven to be significantly associated with T2DM are also genetic risk factors of GDM. Recently, a genome-wide association study of GDM was performed and reported that genetic variants in CDKAL1 and MTNR1B were associated with GDM at a genome-wide significance level. Current investigations using next-generation sequencing will improve our insight into the pathophysiology of GDM. It would be important to know whether genetic information revealed from these studies could improve our prediction of GDM and the future development of T2DM. We hope further research on the genetics of GDM would ultimately lead us to personalized genomic medicine and improved patient care.

• Asian-Australasian Journal of Animal Sciences
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• 제31권6호
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• pp.784-790
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• 2018

Objective: The genetic effects of an individual on the phenotypes of its social partners, such as its pen mates, are known as social genetic effects. This study aims to identify the candidate genes for social (pen-mates') average daily gain (ADG) in pigs by using the genome-wide association approach. Methods: Social ADG (sADG) was the average ADG of unrelated pen-mates (strangers). We used the phenotype data (16,802 records) after correcting for batch (week), sex, pen, number of strangers (1 to 7 pigs) in the pen, full-sib rate (0% to 80%) within pen, and age at the end of the test. A total of 1,041 pigs from Landrace breeds were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel, which comprised 61,565 single nucleotide polymorphism (SNP) markers. After quality control, 909 individuals and 39,837 markers remained for sADG in genome-wide association study. Results: We detected five new SNPs, all on chromosome 6, which have not been associated with social ADG or other growth traits to date. One SNP was inside the prostaglandin $F2{\alpha}$ receptor (PTGFR) gene, another SNP was located 22 kb upstream of gene interferon-induced protein 44 (IFI44), and the last three SNPs were between 161 kb and 191 kb upstream of the EGF latrophilin and seven transmembrane domain-containing protein 1 (ELTD1) gene. PTGFR, IFI44, and ELTD1 were never associated with social interaction and social genetic effects in any of the previous studies. Conclusion: The identification of several genomic regions, and candidate genes associated with social genetic effects reported here, could contribute to a better understanding of the genetic basis of interaction traits for ADG. In conclusion, we suggest that the PTGFR, IFI44, and ELTD1 may be used as a molecular marker for sADG, although their functional effect was not defined yet. Thus, it will be of interest to execute association studies in those genes.

• Genomics & Informatics
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• 제20권1호
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• pp.8.1-8.14
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• 2022

Despite the success of recent genome-wide association studies investigating longitudinal traits, a large fraction of overall heritability remains unexplained. This suggests that some of the missing heritability may be accounted for by gene-gene and gene-time/environment interactions. In this paper, we develop a Bayesian variable selection method for longitudinal genetic data based on mixed models. The method jointly models the main effects and interactions of all candidate genetic variants and non-genetic factors and has higher statistical power than previous approaches. To account for the within-subject dependence structure, we propose a grid-based approach that models only one fixed-dimensional covariance matrix, which is thus applicable to data where subjects have different numbers of time points. We provide the theoretical basis of our Bayesian method and then illustrate its performance using data from the 1000 Genome Project with various simulation settings. Several simulation studies show that our multivariate method increases the statistical power compared to the corresponding univariate method and can detect gene-time/ environment interactions well. We further evaluate our method with different numbers of individuals, variants, and causal variants, as well as different trait-heritability, and conclude that our method performs reasonably well with various simulation settings.