File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
  • Find via Find It@HKUL
Supplementary

Article: Discovering Precision AD Biomarkers with Varying Prognosis Effects in Genetics Driven Subpopulations

TitleDiscovering Precision AD Biomarkers with Varying Prognosis Effects in Genetics Driven Subpopulations
Authors
Issue Date16-Jun-2023
PublisherAmerican Medical Informatics Association
Citation
AMIA Joint Summits on Translational Science proceedings, 2023, p. 340-349 How to Cite?
Abstract

Alzheimer’s Disease (AD) is a highly heritable neurodegenerative disorder characterized by memory impairments. Understanding how genetic factors contribute to AD pathology may inform interventions to slow or prevent the progression of AD. We performed stratified genetic analyses of 1,574 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants to examine associations between levels of quantitative traits (QT’s) and future diagnosis. The Chow test was employed to determine if an individual’s genetic profile affects identified predictive relationships between QT’s and future diagnosis. Our chow test analysis discovered that cognitive and PET-based biomarkers differentially predicted future diagnosis when stratifying on allelic dosage of AD loci. Post-hoc bootstrapped and association analyses of biomarkers confirmed differential effects, emphasizing the necessity of stratified models to realize individualized AD diagnosis prediction. This novel application of the Chow test allows for the quantification and direct comparison of genetic-based differences. Our findings, as well as the identified QT-future diagnosis relationships, warrant future investigation from a biological context.


Persistent Identifierhttp://hdl.handle.net/10722/337291
ISSN

 

DC FieldValueLanguage
dc.contributor.authorLee, BN-
dc.contributor.authorWang, J-
dc.contributor.authorNho, K-
dc.contributor.authorSaykin, AJ-
dc.contributor.authorShen, L-
dc.date.accessioned2024-03-11T10:19:32Z-
dc.date.available2024-03-11T10:19:32Z-
dc.date.issued2023-06-16-
dc.identifier.citationAMIA Joint Summits on Translational Science proceedings, 2023, p. 340-349-
dc.identifier.issn2153-4063-
dc.identifier.urihttp://hdl.handle.net/10722/337291-
dc.description.abstract<p>Alzheimer’s Disease (AD) is a highly heritable neurodegenerative disorder characterized by memory impairments. Understanding how genetic factors contribute to AD pathology may inform interventions to slow or prevent the progression of AD. We performed stratified genetic analyses of 1,574 Alzheimer’s Disease Neuroimaging Initiative (ADNI) participants to examine associations between levels of quantitative traits (QT’s) and future diagnosis. The Chow test was employed to determine if an individual’s genetic profile affects identified predictive relationships between QT’s and future diagnosis. Our chow test analysis discovered that cognitive and PET-based biomarkers differentially predicted future diagnosis when stratifying on allelic dosage of AD loci. Post-hoc bootstrapped and association analyses of biomarkers confirmed differential effects, emphasizing the necessity of stratified models to realize individualized AD diagnosis prediction. This novel application of the Chow test allows for the quantification and direct comparison of genetic-based differences. Our findings, as well as the identified QT-future diagnosis relationships, warrant future investigation from a biological context.<br></p>-
dc.languageeng-
dc.publisherAmerican Medical Informatics Association-
dc.relation.ispartofAMIA Joint Summits on Translational Science proceedings-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.titleDiscovering Precision AD Biomarkers with Varying Prognosis Effects in Genetics Driven Subpopulations-
dc.typeArticle-
dc.identifier.spage340-
dc.identifier.epage349-
dc.identifier.eissn2153-4063-
dc.identifier.issnl2153-4063-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats