• DocumentCode
    139884
  • Title

    Effects of genetic variation on the dynamics of neurodegeneration in Alzheimer´s disease

  • Author

    Printy, Blake P. ; Verma, Naveen ; Cowperthwaite, Matthew C. ; Markey, Mia K.

  • Author_Institution
    Dept. of Biomed. Eng., Univ. of Texas at Austin, Austin, TX, USA
  • fYear
    2014
  • fDate
    26-30 Aug. 2014
  • Firstpage
    2464
  • Lastpage
    2467
  • Abstract
    Although many genetic markers are identified as being associated with Alzheimer´s disease (AD), not much is known about their association with the structural changes that happen as the disease progresses. In this study, we investigate the genetic etiology of neurodegeneration in AD by associating genetic markers with atrophy profiles obtained using patient data from the Alzheimer´s Disease Neuroimaging Initiative (ADNI) cohort. The atrophy profiles were quantified using a linear least-squares regression model over the span of patient enrollment, and used as imaging features throughout the analysis. A subset of the imaging features were selected for genetic association based on their ability to discriminate between healthy individuals and AD patients in a Support Vector Machines (SVM) classifier. Each imaging feature was associated with single-nucleotide polymorphisms (SNPs) using a linear model that included age and cognitive impairment scores as covariates to correct for normal disease progression. After false discovery rate correction, we observed 53 significant associations between SNPs and our imaging features, including associations of ventricular enlargement with SNPs on estrogen receptor 1 (ESR1) and sortilin-related VPS10 domain containing receptor 1 (SORCS1), hippocampal atrophy with SNPs on ESR1, and cerebral atrophy with SNPs on transferrin (TF) and amyloid beta precursor protein (APP). This study provides important insights into genetic predictors of specific types of neurodegeneration that could potentially be used to improve the efficacy of treatment strategies for the disease and allow the development of personalized treatment plans based on each patient´s unique genetic profile.
  • Keywords
    associative processing; biochemistry; bioinformatics; brain; cognition; demography; diseases; feature extraction; feature selection; genetics; genomics; image classification; least squares approximations; medical disorders; medical image processing; molecular biophysics; neurophysiology; proteins; regression analysis; support vector machines; AD genetic marker identification; AD patient discrimination; ADNI cohort; APP; Alzheimer disease progression; Alzheimer´s Disease Neuroimaging Initiative cohort; ESR1; SORCS1; SVM classifier; age scores; amyloid beta precursor protein; atrophy profile quantification; brain structural changes; cerebral atrophy; cognitive impairment scores; estrogen receptor 1; false discovery rate correction; genetic association; genetic etiology; genetic predictors; genetic variation effects; hippocampal atrophy; imaging feature selection; imaging feature-SNP association; linear least-squares regression model; linear model; neurodegeneration dynamics; neurodegeneration predictors; neurodegeneration types; normal disease progression; patient data analysis; patient enrollment; patient genetic profile; personalized treatment plan development; single nucleotide polymorphisms; sortilin-related VPS10 domain containing receptor 1; support vector machines classifier; transferrin; treatment strategy efficacy; ventricular enlargement-SNP associations; Alzheimer´s disease; Atrophy; Genetics; Imaging; Neuroimaging; Support vector machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Engineering in Medicine and Biology Society (EMBC), 2014 36th Annual International Conference of the IEEE
  • Conference_Location
    Chicago, IL
  • ISSN
    1557-170X
  • Type

    conf

  • DOI
    10.1109/EMBC.2014.6944121
  • Filename
    6944121