Prob & Math-Fi seminar on 25 July

Title 1: tstrait: a quantitative trait simulator for ancestral recombination graphs
Abstract 1:
Ancestral recombination graphs (ARGs) encode the ensemble of correlated genealogical trees arising from recombination in a compact and efficient structure, and are of fundamental importance in population and statistical genetics. Recent breakthroughs have made it possible to simulate and infer ARGs at biobank scale, and there is now intense interest in using ARG-based methods across a broad range of applications, particularly in genome-wide association studies (GWAS). Sophisticated methods exist to simulate ARGs using population genetics models, but there is currently no software to simulate quantitative traits directly from these ARGs. To apply existing quantitative trait simulators users must export genotype data, losing important information about ancestral processes and producing prohibitively large files when applied to the biobank-scale datasets currently of interest in GWAS. We present tstrait, an open-source Python library to simulate quantitative traits on ARGs, and show how this user-friendly software can quickly simulate phenotypes for biobank-scale datasets on a laptop computer.

Speaker 2: Hau-Tieng Wu (NYU Courant Institute of Mathematical Sciences), 17:00-18:30

Title 2: Statistical Inference for Nonstationary Time Series via Phase-Driven Time-Frequency Analysis
Abstract 2:
Real-world time series are typically nonstationary and consist of multiple oscillatory components exhibiting complex statistical characteristics such as time-varying amplitude, frequency, and non-sinusoidal patterns. Signal quality is often compromised by intricate noise or artifacts. I will discuss recent advancements in addressing such time series using phase-driven nonlinear time-frequency analysis, highlighting recent statistical inference outcomes. Additionally, biomedical applications and unresolved mathematical challenges will be illustrated

立命館大学数理科学科