Hippocampal surface mapping of genetic risk factors in AD via sparse learning models.

Jing Wan*, Sungeun Kim, Mark Inlow, Kwangsik Nho, Shanker Swaminathan, Shannon L. Risacheri, Shiaofen Fang, Michael W. Weiner, M. Faisal Beg, Lei Wang, Andrew J. Saykin, L. Shen, Disease Neuroimaging Initiative Alzheimer's Disease Neuroimaging Initiative

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

21 Scopus citations

Abstract

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.

Original languageEnglish
Title of host publicationMedical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
Pages376-383
Number of pages8
Volume14
EditionPt 2
DOIs
StatePublished - 2011

ASJC Scopus subject areas

  • Medicine(all)

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