| Autor: |
Garali I; Laboratory for Bioinformatics, Foundation Jean Dausset-CEPH, Paris, France.; Laboratory of Excellence GenMed, Paris, France., Sahbatou M; Laboratory for Human Genetics, Foundation Jean Dausset-CEPH, Paris, France., Daunay A; Laboratory for Genomics, Foundation Jean Dausset-CEPH, 75010, Paris, France., Baudrin LG; Laboratory of Excellence GenMed, Paris, France.; Laboratory for Genomics, Foundation Jean Dausset-CEPH, 75010, Paris, France., Renault V; Laboratory for Bioinformatics, Foundation Jean Dausset-CEPH, Paris, France., Bouyacoub Y; Laboratory of Excellence GenMed, Paris, France.; Laboratory for Genomics, Foundation Jean Dausset-CEPH, 75010, Paris, France., Deleuze JF; Laboratory for Bioinformatics, Foundation Jean Dausset-CEPH, Paris, France.; Laboratory of Excellence GenMed, Paris, France.; Laboratory for Human Genetics, Foundation Jean Dausset-CEPH, Paris, France.; Laboratory for Genomics, Foundation Jean Dausset-CEPH, 75010, Paris, France.; Centre National de Recherche en Génomique Humaine, CEA, Institut François Jacob, Evry, France., How-Kit A; Laboratory for Genomics, Foundation Jean Dausset-CEPH, 75010, Paris, France. alexandre.how-kit@fjd-ceph.org. |
| Abstrakt: |
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). |
