Estimating the heritability of longitudinal rate-of-change: genetic insights into PSA velocity in prostate cancer-free individuals.

IF 2.1 3区 数学 Q3 MATHEMATICAL & COMPUTATIONAL BIOLOGY
Pei Zhang, Xiaoyu Wang, Jianxin Shi, Paul S Albert
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引用次数: 0

Abstract

Serum prostate-specific antigen (PSA) is widely used for prostate cancer screening. While the genetics of PSA levels have been studied to enhance screening accuracy, the genetic basis of PSA velocity, the rate of PSA change over time, remains unclear. The Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial, a large, randomized study with longitudinal PSA data (15,260 cancer-free males, averaging 5.34 samples per subject) and genome-wide genotype data, provides a unique opportunity to estimate PSA velocity heritability. We developed a mixed model to jointly estimate the heritability of PSA levels at age 54 and PSA velocity. To accommodate the large dataset, we implemented 2 efficient computational approaches: a partitioning and meta-analysis strategy using average information restricted maximum likelihood (AI-REML) and a fast restricted Haseman-Elston (REHE) regression method. Simulations showed that both methods yield unbiased estimates of both heritability metrics, with AI-REML providing smaller variability in the estimation of velocity heritability than REHE. Applying AI-REML to PLCO data, we estimated heritability at 0.32 (s.e. = 0.07) for baseline PSA and 0.45 (s.e. = 0.18) for PSA velocity. These findings reveal a substantial genetic contribution to PSA velocity, supporting future genome-wide studies to identify variants affecting PSA dynamics and improve PSA-based screening.

估计纵向变化率的遗传性:前列腺癌无个体中PSA速度的遗传见解。
血清前列腺特异性抗原(PSA)被广泛用于前列腺癌筛查。虽然已经研究了PSA水平的遗传学以提高筛查的准确性,但PSA速度的遗传基础,即PSA随时间变化的速率,仍不清楚。前列腺、肺、结直肠和卵巢(PLCO)癌症筛查试验是一项大型随机研究,采用纵向PSA数据(15,260名无癌男性,平均每个受试者5.34个样本)和全基因组基因型数据,为估计PSA速度遗传性提供了独特的机会。我们开发了一个混合模型来共同估计54岁时PSA水平和PSA速度的遗传性。为了适应大型数据集,我们实施了2种有效的计算方法:使用平均信息限制最大似然(AI-REML)的分区和元分析策略以及快速限制Haseman-Elston (REHE)回归方法。模拟结果表明,这两种方法都能对两种遗传率指标产生无偏估计,其中AI-REML在估计速度遗传率方面的变异性比REHE小。将AI-REML应用于PLCO数据,我们估计基线PSA的遗传率为0.32 (s.e. = 0.07), PSA速度的遗传率为0.45 (s.e. = 0.18)。这些发现揭示了PSA速度的重要遗传贡献,支持未来全基因组研究确定影响PSA动态的变异并改进基于PSA的筛查。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Biostatistics
Biostatistics 生物-数学与计算生物学
CiteScore
5.10
自引率
4.80%
发文量
45
审稿时长
6-12 weeks
期刊介绍: Among the important scientific developments of the 20th century is the explosive growth in statistical reasoning and methods for application to studies of human health. Examples include developments in likelihood methods for inference, epidemiologic statistics, clinical trials, survival analysis, and statistical genetics. Substantive problems in public health and biomedical research have fueled the development of statistical methods, which in turn have improved our ability to draw valid inferences from data. The objective of Biostatistics is to advance statistical science and its application to problems of human health and disease, with the ultimate goal of advancing the public''s health.
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