Integrating gene expression data boosts statistical power in rare variant association analysis

主讲人：Hanmin Guo

Hanmin Guo is an assistant professor in Beijing International Center for Mathematical Research at Peking University. Before joining Peking University, he was a postdoctoral fellow at Stanford University, jointly supervised by Dr. Wing Hung Wong and Dr. Alexander Eckehart Urban. He obtained his Ph.D. in statistics from Tsinghua University, where he was supervised by Dr. Lin Hou. He received a bachelor’s degree in mathematics from Tsinghua University. His research focuses on developing statistical methods for high-dimensional multi-omics data and statistical genetics. His works have been published in Cell, Nature Communications, PNAS, and PLoS Genetics.

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报告信息

时间

2026年10月28日（周三）10:00

地点

中国人民大学中关村校区明德主楼1016会议室为线下会场；

中国人民大学通州校区经济学部楼215会议室同步设置观看点。

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报告摘要

Rare variants constitute the vast majority of human genetic variations. They tend to be more deleterious than common variants and may account for a large portion of the missing heritability. However, existing rare variant association methods lack statistical power, especially when sample size is small. We introduce a novel statistical framework, the carrier statistic, which combines genetic variation with gene expression data to overcome this limitation. By using gene expression as a functional readout, our method captures the functional impact of rare variants that cannot be inferred from sequence information alone and prioritizes variants with strong functional effects in patients. Simulations show that carrier statistic provides well-calibrated FDR, significantly improves sensitivity over existing approaches, and distinguishes disease-causing expression changes from disease-induced ones. Applied to Alzheimer’s disease, our method identifies 16 rare variants across 15 genes that are undetectable using genetic data alone. The carrier statistic is broadly applicable across variant types and adaptable to other functional genomics readouts, offering a powerful tool for uncovering molecular mechanisms of complex diseases.

“统计大讲堂”由中国人民大学统计学院与应用统计科学研究中心联合主办，旨在搭建学界与业界的交流平台，促进统计理论与实践的深度融合，推动统计学科研创新，服务国家经济社会发展。作为统计学院人才培养体系的核心组成部分，“统计大讲堂”通过系列学术讲座、专题研讨等形式，为统计学子提供前沿知识传授与学术思维训练，营造开放包容的学术氛围，以培养具有国际视野、创新能力和实践精神的复合型统计人才。