MIXED MODELING APPROACH FOR CHARACTERIZING THE GENETIC EFFECTS IN A LONGITUDINAL PHENOTYPE

• 成果类型: Article
• 署名作者: Zhang, Pei; Albert, Paul S.; Hong, Hyokyoung G.
• 署名单位: National Institutes of Health (NIH) - USA; NIH National Cancer Institute (NCI); NIH National Cancer Institute- Division of Cancer Epidemiology & Genetics
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157; 1941-7330
• DOI: 10.1214/25-AOAS2033
• 发表日期: 2025-09
• 页码: 2070-2087
• 关键词: Longitudinal Data; genetic effects; AI-ReML algorithm; prostate-specific antigen; polygenic risk scores; AVERAGE INFORMATION REML; variance-components; prostate; prediction; association; traits

摘要

Approaches for estimating genetic effects at the individual level often focus on analyzing phenotypes at a single time point, with less attention given to longitudinal phenotypes. This paper introduces a mixed modeling approach that includes both genetic and individual-specific random effects and is designed to estimate genetic effects on both the baseline and slope for a longitudinal trajectory. The inclusion of genetic effects on both baseline and slope, combined with the crossed structure of genetic and individual-specific random effects, creates complex dependencies across repeated measurements for all subjects. These complexities necessitate the development of novel estimation procedures for parameter estimation and individual-specific predictions of genetic effects on both baseline and slope. We employ an Average Information Restricted Maximum Likelihood (AI-ReML) algorithm to estimate the variance components corresponding to genetic and individual-specific effects for the baseline levels and rates of change for a longitudinal phenotype. The algorithm is used to characterizes the prostate-specific antigen (PSA) trajectories for participants who remained prostate cancer-free in the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial. Understanding genetic and individual-specific variation in this population will provide insights for determining the role of genetics in cancer screening. Our results reveal significant genetic contributions to both the initial PSA levels and their progression over time, highlighting the role of these genetic factors on the variability of PSA across unaffected individuals. We show how genetic factors can be used to identify individuals prone to large baseline and increasing trajectories PSA values among individuals who are prostate cancer-free. In turn, we can identify groups of individuals who have a high probability of falsely screening positive for prostate cancer using well-established cutoffs for early detection based on the level and rate of change in this biomarker. The results demonstrate the importance of incorporating genetic factors for monitoring PSA for more accurate prostate cancer detection.