Hippocampal surface mapping of genetic risk factors in AD via sparse learning models.
Med Image Comput Comput Assist Interv
; 14(Pt 2): 376-83, 2011.
Article
en En
| MEDLINE
| ID: mdl-21995051
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.
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
Enfermedad de Alzheimer
/
Hipocampo
/
Aprendizaje
Tipo de estudio:
Diagnostic_studies
/
Etiology_studies
/
Incidence_studies
/
Observational_studies
/
Prognostic_studies
/
Risk_factors_studies
Límite:
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
Med Image Comput Comput Assist Interv
Asunto de la revista:
DIAGNOSTICO POR IMAGEM
/
INFORMATICA MEDICA
Año:
2011
Tipo del documento:
Article
País de afiliación:
Estados Unidos