TY - CHAP
T1 - Hippocampal surface mapping of genetic risk factors in AD via sparse learning models.
AU - Wan, Jing
AU - Kim, Sungeun
AU - Inlow, Mark
AU - Nho, Kwangsik
AU - Swaminathan, Shanker
AU - Risacheri, Shannon L.
AU - Fang, Shiaofen
AU - Weiner, Michael W.
AU - Beg, M. Faisal
AU - Wang, Lei
AU - Saykin, Andrew J.
AU - Shen, L.
AU - Alzheimer's Disease Neuroimaging Initiative, Disease Neuroimaging Initiative
PY - 2011
Y1 - 2011
N2 - Genetic mapping of hippocampal shape, an under-explored area, has strong potential as a neurodegeneration biomarker for AD and MCI. This study investigates the genetic effects of top candidate single nucleotide polymorphisms (SNPs) on hippocampal shape features as quantitative traits (QTs) in a large cohort. FS+LDDMM was used to segment hippocampal surfaces from MRI scans and shape features were extracted after surface registration. Elastic net (EN) and sparse canonical correlation analysis (SCCA) were proposed to examine SNP-QT associations, and compared with multiple regression (MR). Although similar in power, EN yielded substantially fewer predictors than MR. Detailed surface mapping of global and localized genetic effects were identified by MR and EN to reveal multi-SNP-single-QT relationships, and by SCCA to discover multi-SNP-multi-QT associations. Shape analysis identified stronger SNP-QT correlations than volume analysis. Sparse multivariate models have greater power to reveal complex SNP-QT relationships. Genetic analysis of quantitative shape features has considerable potential for enhancing mechanistic understanding of complex disorders like AD.
AB - Genetic mapping of hippocampal shape, an under-explored area, has strong potential as a neurodegeneration biomarker for AD and MCI. This study investigates the genetic effects of top candidate single nucleotide polymorphisms (SNPs) on hippocampal shape features as quantitative traits (QTs) in a large cohort. FS+LDDMM was used to segment hippocampal surfaces from MRI scans and shape features were extracted after surface registration. Elastic net (EN) and sparse canonical correlation analysis (SCCA) were proposed to examine SNP-QT associations, and compared with multiple regression (MR). Although similar in power, EN yielded substantially fewer predictors than MR. Detailed surface mapping of global and localized genetic effects were identified by MR and EN to reveal multi-SNP-single-QT relationships, and by SCCA to discover multi-SNP-multi-QT associations. Shape analysis identified stronger SNP-QT correlations than volume analysis. Sparse multivariate models have greater power to reveal complex SNP-QT relationships. Genetic analysis of quantitative shape features has considerable potential for enhancing mechanistic understanding of complex disorders like AD.
UR - http://www.scopus.com/inward/record.url?scp=82255183616&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=82255183616&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-23629-7_46
DO - 10.1007/978-3-642-23629-7_46
M3 - Chapter
C2 - 21995051
AN - SCOPUS:82255183616
VL - 14
SP - 376
EP - 383
BT - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
ER -