Data synthesis and method evaluation for brain imaging genetics
Year: 2014
Type: conference-paper
Abstract: Brain imaging genetics is an emergent research field where the association between genetic variations such as single nucleotide polymorphisms (SNPs) and neuroimaging quantitative traits (QTs) is evaluated. Sparse canonical correlation analysis (SCCA) is a bi-multivariate analysis method that has the potential to reveal complex multi-SNP-multi-QT associations. We present initial efforts on evaluating a few SCCA methods for brain imaging genetics. This includes a data synthesis method to create realistic imaging genetics data with known SNP-QT associations, application of three SCCA algorithms to the synthetic data, and comparative study of their performances. Our empirical results suggest, approximating covariance structure using an identity or diagonal matrix, an approach used in these SCCA algorithms, could limit the SCCA capability in identifying the underlying imaging genetics associations. An interesting future direction is to develop enhanced SCCA methods that effectively take into account the covariance structures in the imaging genetics data.
Institutions: Indiana University, Dartmouth College
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Awards: G104586243, Indiana Alzheimer Disease Center, Bioinformatics Strategies for Multidimensional Brain Imaging Genetics, Indiana Clinical and Translational Sciences Institute, Machine Learning Prediction of Cancer Susceptibility, Bioinformatics Strategies for Genome Wide Association Studies, Alzheimer's Disease Neuroimaging Initiative, Memory Circuitry in MCI and Early Alzheimerâs Disease Prodrome: Molecular Drivers, Amyloid Imaging, VMCI, and Analysis for ADNI, Alzheimer's Disease Neuroimaging Initiative (ADNI4)
OpenAlex ID: W2085791172