陈琳博士是芝加哥大学公共卫生科学系的生物统计学终身教授。她的研究开发了整合基因组学、因果推理和多组学数据分析的统计和计算方法，目标是揭示复杂性状和疾病背后的分子机制。陈博士在华盛顿大学获得生物统计学博士学位，并在弗雷德·哈钦森癌症研究中心完成博士后培训。在过去的十年中，陈博士广泛研究了多组学多背景整合方法、基因组数据的因果推断方法、跨组织的遗传调控、基因-环境相互作用、组学研究的缺失数据方法以及整合的蛋白基因组学和表观基因组学分析。她最近的工作包括确定跨组织、细胞类型、年龄组和疾病结果的因果分子效应的稳健和综合的孟德尔随机化框架。她是美国国立卫生研究院资助的发育基因型组织表达项目的综合多元基因组分析和方法项目的项目负责人。

:Mendelian randomization (MR) is widely used to infer causal effects of exposures on complex diseases. Increasingly, MR has been applied to molecular traits, such as gene expression, DNA methylation, splicing, and alternative polyadenylation, to identify putative disease genes and regulatory mechanisms. However, transcriptome-wide MR presents several challenges: cis-QTL instruments are often limited, horizontal pleiotropy is widespread, and emerging resources such as developmental transcriptomic datasets frequently have small sample sizes.

In this talk, I will present two related MR frameworks designed to improve causal inference for molecular traits. First, I will introduce FusioMR, a robust Bayesian MR framework applicable to both molecular and complex trait exposures. The single-outcome model, FusioMRs, incorporates gene-region-specific empirical priors informed by QTL strength, linkage disequilibrium, and consistency of genetic effects, enabling robust inference when instruments are sparse. The multi-outcome model, FusioMRm, jointly analyzes correlated diseases, subtypes, or comorbid outcomes, leveraging shared instruments and correlated pleiotropic effects to improve invalid instrument detection and enhance power, particularly for underpowered outcomes. Applications of FusioMR identify cell-type-specific gene expression traits associated with Alzheimer’s disease, alternative polyadenylation events affecting atrial fibrillation and ischemic stroke, and lipid effects on ischemic stroke across ancestries.

I will then discuss dynamicMR, a framework for identifying age-varying causal gene effects by integrating developmental GTEx, adult GTEx, and GWAS summary statistics. By borrowing information across developmental stages and tissues and incorporating gene-embedding-informed empirical priors, dynamicMR improves power under small-sample settings and distinguishes development-specific from adult-specific disease effects. Applications to asthma, pneumonia, and type 2 diabetes reveal distinct temporal patterns of genetic risk, highlighting the importance of developmental gene regulation in complex disease etiology.