Biology subjects

Chung, R.-H.

Publications and source records attributed to Chung, R.-H..

3 recordsLinked to original sources

bioRxiv2021

Whole genome sequence analysis of blood lipid levels in >66,000 individuals

Plasma lipids are heritable modifiable causal factors for coronary artery disease, the leading cause of death globally. Despite the well-described monogenic and polygenic bases of dyslipidemia, limitations remain in discovery of lipid-associated alleles using whole genome sequencing, partly due to limited sample sizes, ancestral diversity, and interpretation of potential clinical significance. Increasingly larger whole genome sequence datasets with plasma lipids coupled with methodologic advances enable us to more fully catalog the allelic spectrum for lipids. Here, among 66,329 ancestrally diverse (56% non-European ancestry) participants, we associate 428M variants from deep-coverage whole genome sequences with plasma lipids. Approximately 400M of these variants were not studied in prior lipids genetic analyses. We find multiple lipid-related genes strongly associated with plasma lipids through analysis of common and rare coding variants. We additionally discover several significantly associated rare non-coding variants largely at Mendelian lipid genes. Notably, we detect rare LDLR intronic variants associated with markedly increased LDL-C, similar to rare LDLR exonic variants. In conclusion, we conducted a systematic whole genome scan for plasma lipids expanding the alleles linked to lipids for multiple ancestries and characterize a clinically-relevant rare non-coding variant model for lipids.

genetics↗

bioRxiv2018

A multi-omics data simulator for complex disease studies and its application to evaluate multi-omics data analysis methods for disease classification

An integrative multi-omics analysis approach that combines multiple types of omics data including genomics, epigenomics, transcriptomics, proteomics, metabolomics, and microbiomics, has become increasing popular for understanding the pathophysiology of complex diseases. Although many multi-omics analysis methods have been developed for complex disease studies, there is no simulation tool that simulates multiple types of omics data and models their relationships with disease status. Without such a tool, it is difficult to evaluate the multi-omics analysis methods on the same scale and to estimate the sample size or power when planning a new multi-omics disease study. We developed a multi-omics data simulator OmicsSIMLA, which simulates genomics (i.e., SNPs and copy number variations), epigenomics (i.e., whole-genome bisulphite sequencing), transcriptomics (i.e., RNA-seq), and proteomics (i.e., normalized reverse phase protein array) data at the whole-genome level. Furthermore, the relationships between different types of omics data, such as meQTLs (SNPs influencing methylation), eQTLs (SNPs influencing gene expression), and eQTM (methylation influencing gene expression), were modeled. More importantly, the relationships between these multi-omics data and the disease status were modeled as well. We used OmicsSIMLA to simulate a multi-omics dataset for breast cancer under a hypothetical disease model, and used the data to compare the performance among existing multi-omics analysis methods in terms of disease classification accuracy and run time. Our results demonstrated that complex disease mechanisms can be simulated by OmicsSIMLA, and a random forest-based method showed the highest prediction accuracy when the multi-omics data were properly normalized.

bioinformatics↗

bioRxiv2018

pWGBSSimla: a profile-based whole-genome bisulphite sequencing data simulator incorporating methylation QTLs, allele-specific methylations and differentially methylated regions

MotivationDNA methylation plays an important role in regulating gene expression. There has been growing interest in investigating the roles that genetic variants play in changing the methylation levels (i.e., methylation quantitative trait loci or meQTLs), how methylation regulates the imprinting of gene expression (i.e., allele-specific methylation or ASM), and the differentially methylated regions (DMRs) among different cell types. However, none of the current simulation tools can generate whole-genome bisulphite sequencing (WGBS) data while modeling meQTLs, ASM, and DMRs.\n\nResultsWe developed pWGBSSimla, a profile-based WGBS data simulator, which simulates WGBS data for 29 cell types based on real data. meQTLs and ASM are modeled based on the block structures of methylation status at CpGs, and DMRs are simulated based on observations of methylation rates in real data.\n\nAvailabilitypWGBSSimla is available at http://omicssimla.sourceforge.io.

bioinformatics↗