TY - JOUR
T1 - Discrimination of DNA methylation signal from background variation for clinical diagnostics
AU - Sanchez, Robersy
AU - Yang, Xiaodong
AU - Maher, Thomas
AU - Mackenzie, Sally A.
N1 - Funding Information:
Funding: This work was supported by funding from the Bill and Melinda Gates Foundation (OPP1088661) and NIH (R01 GM134056-01) to S.A.M.
Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2019/11/1
Y1 - 2019/11/1
N2 - Advances in the study of human DNA methylation variation offer a new avenue for the translation of epigenetic research results to clinical applications. Although current approaches to methylome analysis have been helpful in revealing an epigenetic influence in major human diseases, this type of analysis has proven inadequate for the translation of these advances to clinical diagnostics. As in any clinical test, the use of a methylation signal for diagnostic purposes requires the estimation of an optimal cutoff value for the signal, which is necessary to discriminate a signal induced by a disease state from natural background variation. To address this issue, we propose the application of a fundamental signal detection theory and machine learning approaches. Simulation studies and tests of two available methylome datasets from autism and leukemia patients demonstrate the feasibility of this approach in clinical diagnostics, providing high discriminatory power for the methylation signal induced by disease, as well as high classification performance. Specifically, the analysis of whole biomarker genomic regions could suffice for a diagnostic, markedly decreasing its cost.
AB - Advances in the study of human DNA methylation variation offer a new avenue for the translation of epigenetic research results to clinical applications. Although current approaches to methylome analysis have been helpful in revealing an epigenetic influence in major human diseases, this type of analysis has proven inadequate for the translation of these advances to clinical diagnostics. As in any clinical test, the use of a methylation signal for diagnostic purposes requires the estimation of an optimal cutoff value for the signal, which is necessary to discriminate a signal induced by a disease state from natural background variation. To address this issue, we propose the application of a fundamental signal detection theory and machine learning approaches. Simulation studies and tests of two available methylome datasets from autism and leukemia patients demonstrate the feasibility of this approach in clinical diagnostics, providing high discriminatory power for the methylation signal induced by disease, as well as high classification performance. Specifically, the analysis of whole biomarker genomic regions could suffice for a diagnostic, markedly decreasing its cost.
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U2 - 10.3390/ijms20215343
DO - 10.3390/ijms20215343
M3 - Article
C2 - 31717838
AN - SCOPUS:85074374595
VL - 20
JO - International Journal of Molecular Sciences
JF - International Journal of Molecular Sciences
SN - 1661-6596
IS - 21
M1 - 5343
ER -