基于阵列的年龄预测模型应用于死后组织样本
Jeong Min Lee1, Sang Un Park1, Soong Deok Lee2
1Department of Forensic Medicine, Seoul National University College of Medicine, Seoul, Korea.
Forensic science international. Genetics
|October 19, 2023
概括
使用DNA甲基化,表观遗传时钟准确地预测韩国人的各种人体组织的年龄. 这些法医工具即使在退化的DNA样本上也保持了强大.
科学领域:
- 法医科学 法医科学 法医科学
- 表观遗传学 在表观遗传学中,表观遗传学是指表观遗传学.
- 基因组学就是基因组学.
背景情况:
- DNA甲基化模式与时间年龄有很强的相关性.
- 基于甲基化数据的表观遗传钟是预测年龄的宝贵工具.
- 这些时钟在法医调查中有潜在的应用,用于识别未知的个人.
研究的目的:
- 在韩国人口中评估现有的基于DNA甲基化的年龄预测模型的性能.
- 评估9种不同的死后组织类型的年龄预测准确性.
- 研究DNA数量和质量对年龄预测模型的影响.
主要方法:
- 从20名韩国人 (年龄在18-78岁之间) 收集了180个死后组织样本.
- 使用Infinium MethylationEPIC阵列进行DNA甲基化分析.
- 测试了多个已建立的基于阵列的年龄预测模型,包括全组织,组织特定,汉纳姆,皮肤和血液,张,PhenoAge和MEAT时钟.
主要成果:
- 全组织时钟显示中等准确度 (MAE=8.7年).
- 汉努姆,皮肤与血液和张钟在血液样本 (MAE <5年) 中实现了高精度.
- 肌肉特定的MEAT时钟在肌肉组织中显示出高精度 (MAE=4.7年).
- 模型在韩国人中表现良好,尽管在欧洲队伍中先前有发展.
- 在低DNA数量和碎片化条件下,年龄预测仍然强大.
结论:
- 已建立的表观遗传时钟在韩国死后组织中有效预测年龄.
- 组织特定的时钟为相关样本类型提供了高精度.
- 基因甲基化年龄预测模型是有价值的法医工具,即使有受损的DNA样本.
相关概念视频
01:22Life Tables
A life table is a statistical tool that summarizes the mortality and survival patterns of a population, providing detailed insights into the likelihood of survival or death across different age intervals within a cohort. By organizing data on survival probabilities and mortality rates, life tables offer a clear snapshot of population dynamics over time. They are extensively used in demography, public health, actuarial science, and ecology to analyze life expectancy, design health interventions,...
01:20Actuarial Approach
The actuarial approach, a statistical method originally developed for life insurance risk assessment, is widely used to calculate survival rates in clinical and population studies. This method accounts for participants lost to follow-up or those who die from causes unrelated to the study, ensuring a more accurate representation of survival probabilities.
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
Consider the example of a high-risk surgical procedure with significant early-stage mortality. A two-year clinical study is conducted,...
01:24Kaplan-Meier Approach
The Kaplan-Meier estimator is a non-parametric method used to estimate the survival function from time-to-event data. In medical research, it is frequently employed to measure the proportion of patients surviving for a certain period after treatment. This estimator is fundamental in analyzing time-to-event data, making it indispensable in clinical trials, epidemiological studies, and reliability engineering. By estimating survival probabilities, researchers can evaluate treatment effectiveness,...
01:20Comparing the Survival Analysis of Two or More Groups
Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
01:22Applications of Life Tables
Life tables are versatile across various fields, providing a quantitative basis for analyzing mortality and survival rates. Whether used by demographers, actuaries, epidemiologists, or sociologists, life tables offer valuable insights into the dynamics of life and death, facilitating informed decisions in public health, insurance, conservation, and beyond. Their broad applicability highlights the interconnectedness of demographic data with practical outcomes in everyday life and strategic...
01:21Cancer Survival Analysis
Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...