成人VR视频

Zhonghua Liu, PhD

Assistant Professor
Department of Biostatistics |
Columbia University

WHEN: Monday, September 23, 2024, from 3:30 to 4:30 p.m.
WHERE:聽Hybrid | 2001 成人VR视频 College Avenue, Room 1201;
NOTE: Zhonghua Liu will be presenting in-person

Abstract

Hidden confounding bias is a major threat in identifying causal protein biomarkers for Alzheimer鈥檚 disease in non-randomized studies. Mendelian randomization (MR) framework holds the promise of removing such hidden confounding bias by leveraging protein quantitative trait loci (pQTLs) as instrumental variables (IVs) for establishing causal relationships. However, some pQTLs might violate core IV assumptions, leading to biased causal inference and misleading scientific conclusions. To address this urgent challenge, we propose a novel MR method called MR-SPI that first Selects valid pQTL IVs under the Anna Karenina Principle and then performs valid Post-selection Inference that is robust to possible pQTL selection error. We further develop a computationally efficient pipeline by integrating MR-SPI and AlphaFold3 to automatically identify causal protein biomarkers and predict protein 3D structural alterations. We apply this pipeline to analyze genome-wide summary statistics for 912 plasma proteins in 54,306 participants from UK Biobank and for Alzheimer鈥檚 disease (AD) in 455,258 samples. We identified seven proteins associated with Alzheimer's disease - TREM2, PILRB, PILRA, EPHA1, CD33, RET, and CD55 - whose 3D structures are altered by missense genetic variations. This discovery offers novel insights into their biological roles in AD development and may aid in identifying potential drug targets.

Speaker bio

Dr. Zhonghua Liu is currently Assistant Professor in the Department of Biostatistics at Columbia University. His primary research interests include causal inference, semiparametric efficiency theory, machine (deep) learning theory and applications, statistical genetics/genomics, causal mediation analysis, Mendelian randomization. He obtained his doctorate in Biostatistics from Harvard University advised by Professor Xihong Lin.