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利用 ELOVL2 启动子的 DNA 甲基化改良和跨实验室实施及优化基于血液的单基因年龄预测模型。

Improvements and inter-laboratory implementation and optimization of blood-based single-locus age prediction models using DNA methylation of the ELOVL2 promoter.

机构信息

Laboratory for Bioinformatics, Foundation Jean Dausset-CEPH, Paris, France.

Laboratory of Excellence GenMed, Paris, France.

出版信息

Sci Rep. 2020 Sep 24;10(1):15652. doi: 10.1038/s41598-020-72567-6.

Abstract

Several blood-based age prediction models have been developed using less than a dozen to more than a hundred DNA methylation biomarkers. Only one model (Z-P1) based on pyrosequencing has been developed using DNA methylation of a single locus located in the ELOVL2 promoter, which is considered as one of the best age-prediction biomarker. Although multi-locus models generally present better performances compared to the single-locus model, they require more DNA and present more inter-laboratory variations impacting the predictions. Here we developed 17,018 single-locus age prediction models based on DNA methylation of the ELOVL2 promoter from pooled data of four different studies (training set of 1,028 individuals aged from 0 and 91 years) using six different statistical approaches and testing every combination of the 7 CpGs, aiming to improve the prediction performances and reduce the effects of inter-laboratory variations. Compared to Z-P1 model, three statistical models with the optimal combinations of CpGs presented improved performances (MAD of 4.41-4.77 in the testing set of 385 individuals) and no age-dependent bias. In an independent testing set of 100 individuals (19-65 years), we showed that the prediction accuracy could be further improved by using different CpG combinations and increasing the number of technical replicates (MAD of 4.17).

摘要

已经开发了几种基于血液的年龄预测模型,这些模型使用的 DNA 甲基化生物标志物数量从十几个到一百多个不等。只有一种基于焦磷酸测序的模型(Z-P1)使用位于 ELOVL2 启动子中的单个基因座的 DNA 甲基化,该模型被认为是最佳的年龄预测生物标志物之一。尽管多基因座模型通常比单基因座模型表现更好,但它们需要更多的 DNA,并且存在更多的实验室间变异,从而影响预测结果。在这里,我们从四个不同研究的合并数据中(训练集包含 1028 名年龄从 0 岁到 91 岁的个体),使用六种不同的统计方法,基于 ELOVL2 启动子的 DNA 甲基化,开发了 17018 个单基因座年龄预测模型,并测试了 7 个 CpG 的每一种组合,旨在提高预测性能并减少实验室间变异的影响。与 Z-P1 模型相比,三个具有最佳 CpG 组合的统计模型具有更好的性能(在包含 385 名个体的测试集中的 MAD 为 4.41-4.77),并且没有年龄依赖性偏差。在 100 名个体的独立测试集中(19-65 岁),我们表明,通过使用不同的 CpG 组合并增加技术重复次数,可以进一步提高预测精度(MAD 为 4.17)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/960d/7515898/7d5fe3b26022/41598_2020_72567_Fig1_HTML.jpg

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