Skills

Statistical genetics and omics analysis:

Statistical genetics analysis for complex traits (e.g., GWAS, PRS, statistical fine-mapping, colocalization, Mendelian randomization, QTL mapping, etc.)

Analysis for high-dimensional molecular data (e.g., EWAS, differential expression analysis using bulk RNA-sequencing data, weighted gene co-expression network analysis (WGCNA), similarity network fusion (SNF), etc.)

Other relevant analytical skills include pathway enrichment analysis, gene-by-environment interactions, etc.

Data quality control for epigenome-wide DNA methylation (Illumina array), proteomics (Olink platform), metabolomics (mass spectrometry), and genotyping array data.

Longitudinal and survival data analysis:

Mixed effects models, generalized estimating equations (GEE), latent growth mixture modelling, survival analysis (non-parametric, semi-parametric, parametric), mediation analysis.

Programming and computation:

R, Python, SAS, Linux and shell scripting, experience in working in the high-performance computing environment.

Coursework for biology and content knowledge:

Concepts of Molecular Biology, Principles of Immunology, Epidemiology of Aging, Epidemiology of Cardiovascular Disease, Introduction to Clinical Trials, Molecular Epidemiology and Biomarkers in Public Health

Coursework for biotech/pharma commercialization:

Duke University Regulatory Affairs Training Program (Fall 2023), Morrison Foerster Patent Law Course (Fall 2023)

Language and other skills:

Chinese (mandarin, native), English (fluent), excellent written and verbal communication, tennis (USTA NTRP 4.0)