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 journalArticle

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
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
DOIs
StatePublished - 2011

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Risk Factors
Nucleotides
Polymorphism
Single nucleotide Polymorphism
Elastic Net
Shape Feature
Multiple Regression
Canonical Correlation Analysis
Model
Biomarkers
Shape Analysis
Multivariate Models
Learning
Registration
Disorder
Predictors

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Hippocampal surface mapping of genetic risk factors in AD via sparse learning models. / Wan, Jing; Kim, Sungeun; Inlow, Mark; Nho, Kwangsik; Swaminathan, Shanker; Risacher, Shannon L.; Fang, Shiaofen; Weiner, Michael W.; Beg, M. Faisal; Wang, Lei; Saykin, Andrew J.; Shen, Li.

In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 6892 LNCS, No. PART 2, 2011, p. 376-383.

Research output: Contribution to journalArticle

Wan, J, Kim, S, Inlow, M, Nho, K, Swaminathan, S, Risacher, SL, Fang, S, Weiner, MW, Beg, MF, Wang, L, Saykin, AJ & Shen, L 2011, 'Hippocampal surface mapping of genetic risk factors in AD via sparse learning models', Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6892 LNCS, no. PART 2, pp. 376-383. https://doi.org/10.1007/978-3-642-23629-7_46
Wan, Jing ; Kim, Sungeun ; Inlow, Mark ; Nho, Kwangsik ; Swaminathan, Shanker ; Risacher, Shannon L. ; Fang, Shiaofen ; Weiner, Michael W. ; Beg, M. Faisal ; Wang, Lei ; Saykin, Andrew J. ; Shen, Li. / Hippocampal surface mapping of genetic risk factors in AD via sparse learning models. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2011 ; Vol. 6892 LNCS, No. PART 2. pp. 376-383.
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