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
Link To Document :
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