1.Bayesian analysis of longitudinal traits in the Korea Association Resource (KARE) cohort
Wonil CHUNG ; Hyunji HWANG ; Taesung PARK
Genomics & Informatics 2022;20(2):e16-
Various methodologies for the genetic analysis of longitudinal data have been proposed and applied to data from large-scale genome-wide association studies (GWAS) to identify single nucleotide polymorphisms (SNPs) associated with traits of interest and to detect SNP-time interactions. We recently proposed a grid-based Bayesian mixed model for longitudinal genetic data and showed that our Bayesian method increased the statistical power compared to the corresponding univariate method and well detected SNP-time interactions. In this paper, we further analyze longitudinal obesity-related traits such as body mass index, hip circumference, waist circumference, and waist-hip ratio from Korea Association Resource data to evaluate the proposed Bayesian method. We first conducted GWAS analyses of cross-sectional traits and combined the results of GWAS analyses through a meta-analysis based on a trajectory model and a random-effects model. We then applied our Bayesian method to a subset of SNPs selected by meta-analysis to further discover SNPs associated with traits of interest and SNP-time interactions. The proposed Bayesian method identified several novel SNPs associated with longitudinal obesity-related traits, and almost 25% of the identified SNPs had significant p-values for SNP-time interactions.
2.Statistical models and computational tools for predicting complex traits and diseases
Genomics & Informatics 2021;19(4):e36-
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.
3.Bayesian mixed models for longitudinal genetic data: theory, concepts, and simulation studies
Genomics & Informatics 2022;20(1):e8-
Despite the success of recent genome-wide association studies (GWAS) 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.
4.Pneumoconiosis in a polytetrafluoroethylene (PTFE) spray worker: a case report with an occupational hygiene study
Namhoon LEE ; Kiook BAEK ; Soohyun PARK ; Inho HWANG ; Insung CHUNG ; Wonil CHOI ; Hyera JUNG ; Miyoung LEE ; Seonhee YANG
Annals of Occupational and Environmental Medicine 2018;30(1):37-
BACKGROUND: Using analysis of air samples from the workplace, we report on one case of pneumoconiosis in an individual who has been working in a polytetrafluoroethylene (PTFE) spraying process for 28 years. CASE PRESENTATION: The patient was diagnosed with granulomatous lung disease caused by PTFE using computed tomography (CT), lung biopsy and electron microscopy. To assess the qualitative and quantitative exposure to PTFE in workplace, Fourier transform infrared spectroscopy (FT-IR), energy-dispersive X-ray spectroscopy (EDX) and thermogravimetric analysis (TGA) were performed on air samples from the workplace. The presence of PTFE particles was confirmed, and the airborne concentration of PTFE was estimated to be 0.75 mg/m3. CONCLUSIONS: This case demonstrates that long-term exposure to PTFE spraying can cause granulomatous lung lesions such as pneumoconiosis; such lesions appear to be caused not by the degradation products of PTFE from high temperatures but by spraying the particles of PTFE. Along with air-sampling analysis, we suggest monitoring the concentration of airborne PTFE particles related to chronic lung disease.
Biopsy
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Humans
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Hygiene
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Lung
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Lung Diseases
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Microscopy, Electron
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Occupational Diseases
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Pneumoconiosis
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Polytetrafluoroethylene
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Spectroscopy, Fourier Transform Infrared
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Spectrum Analysis
5.Human umbilical cord blood mesenchymal stem cells engineered to overexpress growth factors accelerate outcomes in hair growth.
Dong Ho BAK ; Mi Ji CHOI ; Soon Re KIM ; Byung Chul LEE ; Jae Min KIM ; Eun Su JEON ; Wonil OH ; Ee Seok LIM ; Byung Cheol PARK ; Moo Joong KIM ; Jungtae NA ; Beom Joon KIM
The Korean Journal of Physiology and Pharmacology 2018;22(5):555-566
Human umbilical cord blood mesenchymal stem cells (hUCB-MSCs) are used in tissue repair and regeneration; however, the mechanisms involved are not well understood. We investigated the hair growth-promoting effects of hUCB-MSCs treatment to determine whether hUCB-MSCs enhance the promotion of hair growth. Furthermore, we attempted to identify the factors responsible for hair growth. The effects of hUCB-MSCs on hair growth were investigated in vivo, and hUCB-MSCs advanced anagen onset and hair follicle neogeneration. We found that hUCB-MSCs co-culture increased the viability and up-regulated hair induction-related proteins of human dermal papilla cells (hDPCs) in vitro. A growth factor antibody array revealed that secretory factors from hUCB-MSCs are related to hair growth. Insulin-like growth factor binding protein-1 (IGFBP-1) and vascular endothelial growth factor (VEGF) were increased in co-culture medium. Finally, we found that IGFBP-1, through the co-localization of an IGF-1 and IGFBP-1, had positive effects on cell viability; VEGF secretion; expression of alkaline phosphatase (ALP), CD133, and β-catenin; and formation of hDPCs 3D spheroids. Taken together, these data suggest that hUCB-MSCs promote hair growth via a paracrine mechanism.
Alkaline Phosphatase
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Alopecia
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Cell Survival
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Coculture Techniques
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Fetal Blood*
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Hair Follicle
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Hair*
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Humans*
;
In Vitro Techniques
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Insulin-Like Growth Factor Binding Protein 1
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Insulin-Like Growth Factor I
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Intercellular Signaling Peptides and Proteins*
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Mesenchymal Stromal Cells
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Regeneration
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Stem Cells*
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Umbilical Cord*
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Vascular Endothelial Growth Factor A