Xia Shen

Xia Shen is a Professor in Statistical Genetics at the Greater Bay Area Institute of Precision Medicine, Fudan University, and an Honorary Fellow in Statistical Genetics at the Usher Institute, University of Edinburgh. His research focuses on statistical modeling of high-throughput omics big data, with interdisciplinary work spanning genetics, statistical inference, and computational biology. He leads the Shen Lab, which specializes in Statistical Genetics, Quantitative Genetics, Genetics of Complex Traits, Computational Biology, Biostatistics, and Bioinformatics.

Dr. Shen's research interests center on developing statistical methods for analyzing genetic data, particularly for understanding complex traits and diseases. His work includes:

• Statistical modeling of high-throughput omics data
• Genetic correlation and pleiotropy analysis
• Development of methods for genome-wide association studies
• Integration of multi-omics data for biomarker discovery
• Application of statistical genetics to understand diseases including cardiovascular conditions, cancer, and neurodegenerative disorders

Analysis of Dr. Shen's recent publications reveals a strong focus on applying advanced statistical genetics methods to understand the genetic basis of complex human traits and diseases. His work spans diverse areas including cardiovascular genetics, cancer biology, aging research, and host-pathogen interactions in infectious diseases like Covid-19. A notable trend is the integration of multiple omics data types (genomics, proteomics, epigenomics) to gain comprehensive insights into disease mechanisms.

Dr. Shen has developed several important software tools for statistical genetics:

• TGCA: For unified assessment of pleiotropic genetic effects
• HDL: For precise genetic correlation estimation
• MultiABEL: For multi-trait genome-wide association analysis
• SOJO: For fine-mapping using LASSO based on GWAS summary statistics
• hglm: For generalized linear models with random effects
• bigRR: For fast high-throughput generalized ridge regression