Biology subjects

Sim, K. S.

Publications and source records attributed to Sim, K. S..

3 recordsLinked to original sources

bioRxiv2026

Integrative Genomic Analyses Identify COL21A1 and ENPEP-FGF5 Regulatory Pathways for Blood Pressure Variation in East Asians

BackgroundHypertension is a highly heritable cardiovascular disorder and a major determinant of cardiometabolic disease, including diabetes. However, the regulatory genes and tissue-specific mechanisms underlying blood pressure variations remain incompletely understood. MethodsLeveraging a well-characterized prospective population-based cohort comprised of 27,308 participants from the Singapore Chinese Health Study (SCHS), we evaluated genome-wide genetic associations for five blood pressure traits: hypertension status, systolic blood pressure, diastolic blood pressure, mean arterial pressure (MAP) and pulse pressure (PP). Additionally, we conducted a transcriptome-wide association study (TWAS), integrating gene expression data from 49 tissues, followed by colocalization and fine-mapping to prioritize regulatory genes. Association of identified variants with incident diabetes was additionally evaluated in the longitudinal data. ResultsWe validated 10 blood pressure loci (P between 1.64 x 10-20 - 4.10 x 10-8) and identified an East-Asian specific splice donor variant at the COL21A1 gene associated with PP (rs149344559, P = 6.78 x 10-10). Integrative analyses prioritized FGF5 in kidney cortex and ENPEP in pituitary tissue as candidate regulatory genes. The blood pressure-lowering allele at ENPEP (T allele, rs1879056) was associated with reduced risk of incident diabetes. Mediation analysis demonstrated that approximately 21% of the genetic association with diabetes was mediated through MAP (Pindirect-effect = 2 x 10-16). ConclusionThis study refines genetic predispositions for blood pressure among East-Asians. We further delineate tissue-specific regulatory pathways underlying blood pressure variations and identify ENPEP-mediated dysfunctions linking blood pressure genetics to diabetes risk, underscoring integrated disease mechanisms.

genetics↗

bioRxiv2022

RAPTOR: A Five-Safes approach to a secure, cloud native and serverless genomics data repository

Genomic researchers are increasingly utilizing commercial cloud platforms (CCPs) to manage their data and analytics needs. Commercial clouds allow researchers to grow their storage and analytics capacity on demand, keeping pace with expanding project data footprints and enabling researchers to avoid large capital expenditures while paying only for IT capacity consumed by their project. Cloud computing also allows researchers to overcome common network and storage bottlenecks encountered when combining or re-analysing large datasets. However, cloud computing presents a new set of challenges. Without adequate security controls, the risk of unauthorised access may be higher for data stored on the cloud. In addition, regulators are increasingly mandating data access patterns and specific security protocols on the storage and use of genomic data to safeguard rights of the study participants. While CCPs provide tools for security and regulatory compliance, utilising these tools to build the necessary controls required for cloud solutions is not trivial as such skill sets are not commonly found in a genomics lab. The Research Assets Provisioning and Tracking Online Repository (RAPTOR) by the Genome Institute of Singapore is a cloud native genomics data repository and analytics platform focusing on security and regulatory compliance. Using a "five-safes" framework (Safe Purpose, Safe People, Safe Settings, Safe Data and Safe Output), RAPTOR provides security and governance controls to data contributors and users leveraging cloud computing for sharing and analysis of large genomic datasets without the risk of security breaches or running afoul of regulations. RAPTOR can also enable data federation with other genomic data repositories using GA4GH community-defined standards, allowing researchers to boost the statistical power of their work and overcome geographic and ancestry limitations of data sets

genomics↗

bioRxiv2022

Direct inference and control of genetic population structure from RNA sequencing data

RNAseq data can be used to infer genetic variants, yet its use for estimating genetic population structure remains underexplored. Here, we construct a freely available computational tool (RGStraP) to estimate RNAseq-based genetic principal components (RG-PCs) and assess whether RG-PCs can be used to control for population structure in gene expression analyses. Using whole blood samples from understudied Nepalese populations and the Geuvadis study, we show that RG-PCs had comparable results to paired array-based genotypes, with high genotype concordance and high correlations of genetic principal components, capturing subpopulations within the dataset. In differential gene expression analysis, we found that inclusion of RG-PCs as covariates reduced test statistic inflation. Our paper demonstrates that genetic population structure can be directly inferred and controlled for using RNAseq data, thus facilitating improved retrospective and future analyses of transcriptomic data.

bioinformatics↗