Human Age Prediction Based on DNA Methylation Using a Gradient Boosting Regressor
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Collection and Processing
2.2. Methylation Quality Control
2.3. Selection of Age-Related CpG Sites
2.4. Algorithm
2.5. Statistical Measurements
3. Results
3.1. Healthy Blood Data Results
3.2. Disease Blood Data Results
3.3. Application of the Technique to Saliva
3.4. Analysis of the Selected Six CpG Sites
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Conflicts of Interest
References
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DNA Origin | Platform | No. | Age Range | Author and Publication Year | Availability |
---|---|---|---|---|---|
Whole Blood | 27K | 93 | (49, 74) | Rakyan (2010) | GSE20236 |
Blood CD4+CD14 | 27K | 50 | (16, 69) | Rakyan (2010) | GSE20242 |
Blood PBMC 1 | 27K | 398 | (3.6, 18) | Alisch (2012) | GSE27097 |
Blood Cord | 27K | 168 | (0, 0) | Adkins (2011) | GSE27317 |
Blood PBMC | 450K | 40 | (0, 103) | Heyn (2012) | GSE30870 |
Blood PBMC | 450K | 71 | (3.5, 76) | Harretal (2012) | GSE32149 |
Blood Cord | 27K | 84 | (0, 0) | Khulan (2012) | GSE34257 |
Blood Cord | 27K | 24 | (0, 0) | Mallon (2012) | GSE34869 |
Blood PBMC | 450K | 78 | (1, 16) | Alisch (2012) | GSE36064 |
Blood Cord | 27K | 123 | (0, 0) | Gordon (2012) | GSE36642 |
Blood Cord | 27K | 48 | (0, 0) | Turan (2012) | GSE36812 |
Blood PBMC | 27K | 91 | (24, 45) | Lam (2012) | GSE37008 |
Whole Blood | 450K | 500 | (26, 101) | Hannum (2012) | GSE40279 |
Whole Blood | 450K | 95 | (18, 65) | Horvath (2012) | GSE41169 |
Whole blood | 450K | 43 | (47, 59) | Bell (2013) | GSE53128 |
Blood | 450K | 16 | (21, 32) | Xu (2015) | GSE65638 |
DNA Origin | Platform | No. | Age Range | Author and Publication Year | Availability |
---|---|---|---|---|---|
Whole Blood | 27K | 203 | (50, 85) | Song (2010) | GSE19711 |
Whole Blood | 27K | 194 | (1, 32) | Teschendorff (2010) | GSE20067 |
Peripheral Blood | 450K | 46 | (3.5, 76) | Harris (2011) | GSE32148 |
Blood | 450K | 24 | (52, 88) | Athanasios (2012) | GSE40005 |
Whole Blood | 27K | 498 | (16, 86) | Horvath (2012) | GSE41037 |
Whole Blood | 450K | 500 | (18, 70) | Liu (2013) | GSE42861 |
Blood | 27K | 71 | (23, 85) | Day (2013) | GSE49904 |
Blood | 450K | 499 | (34, 72) | Polidoro (2013) | GSE51032 |
Peripheral Blood | 450K | 383 | (34, 93) | Lwe (2013) | GSE53740 |
CpG ID | Gene ID | Chromosome Location 1 | Gene Region 2 | Relation to GpG Island 3 | Correlation Status | Reference |
---|---|---|---|---|---|---|
cg09809672 | EDARADD | 1:236557682 | TSS1500 | N_Shore | Negative | [1,17,33] |
cg22736354 | NHLRC1 | 6:18122719 | 1stExon | Island | Positive | [2,7,18,19] |
cg02228185 | ASPA | 17:3379567 | 1stExon | -- | Negative | [7,26,33] |
cg01820374 | LAG3 | 12:6882083 | Body | N_Shore | Negative | [1] |
cg06493994 | SCGN | 6:25652602 | 1stExon | Island | Positive | [2,7,18,19] |
cg19761273 | CSNK1D | 17:80232096 | TSS1500 | S_Shore | Negative | [2] |
R2 | MAD | MSE | RMSE | |
---|---|---|---|---|
Training | ||||
Gradient Boosting Regressor | 0.9747 | 2.7171 | 20.7243 | 4.5524 |
BayesianRidge | 0.8055 | 10.2561 | 158.3044 | 12.5819 |
Support Vector Regression | 0.9267 | 5.1338 | 60.0420 | 7.7487 |
Multiple Linear Regression | 0.8055 | 10.2448 | 158.2800 | 12.5809 |
Testing | ||||
Gradient Boosting Regressor | 0.9523 | 4.0593 | 39.8269 | 6.3109 |
BayesianRidge | 0.8101 | 10.5654 | 157.8721 | 12.5647 |
Support Vector Regression | 0.9151 | 5.9267 | 71.2060 | 8.4384 |
Multiple Linear Regression | 0.8104 | 10.5510 | 157.6726 | 12.5568 |
MAD | MSE | RMSE | ||
---|---|---|---|---|
Training | ||||
Gradient Boosting Regressor | 0.8186 | 5.4401 | 63.0648 | 7.9413 |
BayesianRidge | 0.6844 | 7.8944 | 109.6227 | 10.4701 |
Support Vector Regression | 0.5333 | 9.8583 | 162.6949 | 12.7552 |
Multiple Linear Regression | 0.6844 | 7.8946 | 109.6222 | 10.4701 |
Testing | ||||
Gradient Boosting Regressor | 0.7374 | 7.0832 | 91.7887 | 9.5806 |
BayesianRidge | 0.6812 | 8.0786 | 111.2896 | 10.5494 |
Support Vector Regression | 0.5303 | 9.9573 | 164.6747 | 12.8326 |
Multiple Linear Regression | 0.6812 | 8.0795 | 111.3016 | 10.5500 |
R2 | MAD | MSE | RMSE | |
---|---|---|---|---|
Training | ||||
Gradient Boosting Regressor | 0.8539 | 2.1040 | 13.7795 | 3.7121 |
BayesianRidge | 0.4310 | 5.7483 | 52.5169 | 7.2469 |
Support Vector Regression | 0.0227 | 7.9369 | 99.5273 | 9.9763 |
Multiple Linear Regression | 0.4333 | 5.6775 | 52.3045 | 7.2322 |
Testing | ||||
Gradient Boosting Regressor | 0.4298 | 5.3478 | 56.1291 | 7.4919 |
BayesianRidge | 0.5423 | 5.5389 | 43.8468 | 6.6217 |
Support Vector Regression | 0.0308 | 8.4729 | 104.4403 | 10.2196 |
Multiple Linear Regression | 0.5479 | 5.4662 | 43.3933 | 6.5874 |
No. of CpG Sites | MAD | ||
---|---|---|---|
Multiple Linear Regression | 88 | 0.73 | 5.2 |
Gradient Boosting Regressor | 6 | 0.58 | 3.76 |
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Li, X.; Li, W.; Xu, Y. Human Age Prediction Based on DNA Methylation Using a Gradient Boosting Regressor. Genes 2018, 9, 424. https://doi.org/10.3390/genes9090424
Li X, Li W, Xu Y. Human Age Prediction Based on DNA Methylation Using a Gradient Boosting Regressor. Genes. 2018; 9(9):424. https://doi.org/10.3390/genes9090424
Chicago/Turabian StyleLi, Xingyan, Weidong Li, and Yan Xu. 2018. "Human Age Prediction Based on DNA Methylation Using a Gradient Boosting Regressor" Genes 9, no. 9: 424. https://doi.org/10.3390/genes9090424