About
A statistical geneticist turning data into therapies.
I am Shicheng Guo, Ph.D., Senior Director of Translational Genetics & Data Science at Arrowhead Pharmaceuticals, where I lead human-genetics-driven target discovery and biomarker strategy for RNA-based therapeutics. My work sits where computational genomics, statistical genetics, and machine learning meet drug development — translating population-scale biology into medicines.
Background
I earned my Ph.D. at Fudan University in 2014 under Prof. Li Jin, with the support of Prof. Jiucun Wang and Prof. Momiao Xiong. I completed postdoctoral training at the University of Texas Health Science Center at Houston (2014–2015) and the University of California, San Diego (2015–2017), where I contributed to landmark studies of the human PBMC methylome, the silkworm methylome, hepatocellular and pancreatic cancer methylomes, CD4+ methylomes in rheumatoid arthritis, and tissue-of-origin mapping from cell-free circulating DNA methylation.
From 2017, I worked at the Marshfield Clinic Research Institute and the University of Wisconsin–Madison on genetic epidemiology and the diagnostic and prognostic roles of epigenetic variation in human complex disease — especially autoimmune disease and cancer. There I applied large-scale bioinformatics and data-mining to the Personalized Medicine Research Project (PMRP) cohort alongside Roadmap, eMERGE, GTEx, UK Biobank, TCGA, and other major resources to map disease susceptibility genes and biomarkers. In 2019, I introduced a novel gene-based recessive diplotype approach that identified FGF6 as a new iron-metabolism gene — work published in Blood.
Focus today
At Arrowhead, I connect human genetics, functional genomics, and AI/ML to:
- Identify and validate drug targets grounded in human-genetic evidence and the druggable genome
- Build biomarker and patient-stratification strategies for RNA therapeutics
- Scale data science infrastructure across multi-omic and clinical datasets to support program decisions
Across my career I have applied case-control, pedigree-based linkage, association, and transmission disequilibrium analyses under additive, dominant, recessive, and compound-heterozygous models — with mixed-model correction for population structure and relatedness, multi-phenotype joint analysis, and causal inference — to find and validate disease genes.