Zhang Pei, Albert Paul S, Hong Hyokyoung G
Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute.
Ann Appl Stat. 2025 Sep;19(3):2070-2087. doi: 10.1214/25-aoas2033. Epub 2025 Aug 28.
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.
个体水平上估计遗传效应的方法通常侧重于分析单个时间点的表型，而对纵向表型的关注较少。本文介绍了一种混合建模方法，该方法包括遗传和个体特异性随机效应，旨在估计纵向轨迹基线和斜率上的遗传效应。对基线和斜率同时纳入遗传效应，再加上遗传和个体特异性随机效应的交叉结构，在所有受试者的重复测量中产生了复杂的依赖性。这些复杂性使得有必要开发新的估计程序，用于参数估计以及对基线和斜率上遗传效应的个体特异性预测。我们采用平均信息约束最大似然（AI-ReML）算法来估计纵向表型基线水平和变化率对应的遗传和个体特异性效应的方差分量。该算法用于刻画前列腺、肺、结肠和卵巢（PLCO）癌症筛查试验中未患前列腺癌参与者的前列腺特异性抗原（PSA）轨迹。了解该人群中的遗传和个体特异性变异将为确定遗传学在癌症筛查中的作用提供见解。我们的结果揭示了对初始PSA水平及其随时间进展的显著遗传贡献，突出了这些遗传因素在未受影响个体中PSA变异性上的作用。我们展示了如何利用遗传因素在未患前列腺癌的个体中识别出易于出现高基线和PSA值上升轨迹的个体。反过来，我们可以利用该生物标志物的水平和变化率，根据已确立的早期检测临界值，识别出前列腺癌假阳性筛查概率高的个体群体。结果证明了纳入遗传因素以监测PSA用于更准确的前列腺癌检测的重要性。
