<mods:mods xmlns:mods="http://www.loc.gov/mods/v3" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-7.xsd"><mods:titleInfo><mods:title>An Exploration of the Biology Underlying Epigenetic Clocks</mods:title></mods:titleInfo><mods:typeOfResource authority="primo">dissertations</mods:typeOfResource><mods:name type="personal"><mods:namePart>Skvir, Nicholas John</mods:namePart><mods:role><mods:roleTerm type="text">creator</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart>Neretti, Nicola</mods:namePart><mods:role><mods:roleTerm type="text">Advisor</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart>Wu, Zhijin</mods:namePart><mods:role><mods:roleTerm type="text">Reader</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart>Kelsey, Karl</mods:namePart><mods:role><mods:roleTerm type="text">Reader</mods:roleTerm></mods:role></mods:name><mods:name type="personal"><mods:namePart>Istrail, Sorin</mods:namePart><mods:role><mods:roleTerm type="text">Reader</mods:roleTerm></mods:role></mods:name><mods:name type="corporate"><mods:namePart>Brown University. Center for Computational Molecular Biology</mods:namePart><mods:role><mods:roleTerm type="text">sponsor</mods:roleTerm></mods:role></mods:name><mods:originInfo><mods:copyrightDate>2022</mods:copyrightDate></mods:originInfo><mods:physicalDescription><mods:extent>xv, 82 p.</mods:extent><mods:digitalOrigin>born digital</mods:digitalOrigin></mods:physicalDescription><mods:note type="thesis">Thesis (Ph. D.)--Brown University, 2022</mods:note><mods:genre authority="aat">theses</mods:genre><mods:abstract>Abstract of An Exploration of the Biology Underlying Epigenetic Clocks
by Nicholas J. Skvir, Ph.D., Brown University, October 2022.
Epigenetic clocks have gained popularity over the years as an accurate way to predict the age of tissues
by measuring the DNA methylation levels at specific CpG dinucleotides that have been determined to be
predictive by underlying models. Models such as these are useful in the field of aging biology for their
ability to measure changes in predicted ‘biological age’ relative to chronological age, and to observe
whether changes are present with anti-aging interventions or with the onset of disease. Despite the
widespread use of these models, it was not until very recently that specific explanations were offered to
elucidate exactly how they worked and what biological mechanisms were being measured.
Here we reconstruct one of the original predictors and expand upon it with over one hundred
subsequently-generated clock models across multiple platforms, to create a larger, more comprehensive
library of strongly age-predictive CpG sites for pan-tissue and whole-blood-specific datasets. We use this
library to show correlation with specific genomic features from a large panel of references. We then
compare our libraries of age-predictive CpGs with subsets of CpGs used in cell mixture deconvolution (as
well as other significant lists from recent literature), to investigate whether proportional change in cell
types with age is truly the primary factor contributing to the predictions made by these models.</mods:abstract><mods:subject><mods:topic>epigenetics</mods:topic></mods:subject><mods:subject authority="fast" authorityURI="http://id.worldcat.org/fast" valueURI="http://id.worldcat.org/fast/00886570"><mods:topic>DNA--Methylation</mods:topic></mods:subject><mods:subject authority="fast" authorityURI="http://id.worldcat.org/fast" valueURI="http://id.worldcat.org/fast/00800293"><mods:topic>Aging</mods:topic></mods:subject><mods:language><mods:languageTerm authority="iso639-2b">English</mods:languageTerm></mods:language><mods:recordInfo><mods:recordContentSource authority="marcorg">RPB</mods:recordContentSource><mods:recordCreationDate encoding="iso8601">20221018</mods:recordCreationDate></mods:recordInfo></mods:mods>