Academic Report of School of Mathematical Sciences [2026] No. 070

(Series Report for High-Level University Construction No. 1329)

Title：Powerful Statistical Frameworks for Genome-Wide Survival Association Analysis

Speaker：Shiyang Ma, Associate Researcher (Shanghai Jiao Tong University)

Time：11:00-12:00, June 30, 2026

Location：Room 1, Huixing Building, Yuehai Campus, Shenzhen University

Abstract: In genome-wide survival association analysis, time-to-event traits offer the advantage of capturing both diagnosis status and timing, facilitating the identification of genetic variants associated with age of onset, disease progression, and lifespan. However, existing methods primarily focus on quantitative and binary traits, which do not leverage censored time information and have limitations in detecting rare variants associated with disease progression. To address these challenges, we propose a set of powerful statistical frameworks for genome-wide survival analysis, including SurvSTAAR and Cox-MK. SurvSTAAR provides a scalable pipeline for rare variant analysis of TTE traits, accounting for sample relatedness, population structure, and functional annotations. Cox-MK integrates model-X knockoff inference with saddlepoint approximation to achieve SNP-level false discovery rate (FDR) control. Extensive simulations and applications to UK Biobank data demonstrate that our methods improve statistical power while maintaining well-calibrated FDR control compared to existing approaches. Notably, our frameworks identify novel associations for Alzheimer’s disease, as well as additional candidate genes for asthma and ischemic heart disease.

Speaker Profile：Shiyang Ma is an Associate Researcher at the School of Medicine and the School of Mathematical Sciences at Shanghai Jiao Tong University, a doctoral advisor, and Vice Dean of the Institute of Clinical Medicine at Shanghai Jiao Tong University School of Medicine. In 2023, she was selected for the National High-Level Overseas Youth Talent Program and the Shanghai High-Level Overseas Talent Program. She received his bachelor’s degree from the School of Mathematics at Sichuan University in 2014 and his Ph.D. in Statistics from the University of Rochester in the United States in 2019. She subsequently conducted postdoctoral research under the guidance of Professor Iuliana Ionita-Laza, a renowned genetic statistician in the Department of Biostatistics at Columbia University, before returning to China to join Shanghai Jiao Tong University in late 2022. Her primary research interests are genetic statistics and biomedical statistics. Her papers have been published in high-impact academic journals such as PNAS, JASA, Nature Communications, Genome Biology, American Journal of Human Genetics, and Statistics in Medicine. Several of her papers have been widely cited within the field, with the most cited paper receiving over 100 citations. She has led the Shanghai Rising Star Project, a general project under the Shanghai Municipal Education Commission’s “Artificial Intelligence to Promote Scientific Research Paradigm Reform and Empower Disciplinary Advancement” initiative, a clinical research special project of the Shanghai Municipal Health Commission, and the Shanghai Jiao Tong University “Jiao Tong Star” Program’s Interdisciplinary Research Fund in Medicine and Engineering. She has also participated in a key research and development project of the Ministry of Science and Technology. She serves as Deputy Secretary-General of the Medical Mathematics Committee of the Chinese Mathematical Society and as a member of the Large Population Health and Common Diseases Genetics Branch of the Chinese Society of Genetics. In the area of teaching, she received the First Prize in the Shanghai Jiao Tong University Teaching Achievement Award and co-authored Biostatistics, a core textbook for the Ministry of Education’s “101 Program” in statistics.

Faculty and students are welcome to attend!

Invited by: Xianghong Hu

School of Mathematical Sciences

June 26, 2026