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. Risacher, Shiaofen Fang, Michael W. Weiner, M. Faisal Beg, Lei Wang, Andrew J. Saykin, Li Shen

Research output: Contribution to journalConference article

21 Scopus citations


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 (US)
Pages (from-to)376-383
Number of pages8
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6892 LNCS
Issue numberPART 2
StatePublished - Oct 11 2011
Event14th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: Sep 18 2011Sep 22 2011

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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