Co-sparse reduced-rank regression for association analysis between imaging phenotypes and genetic variants.

ABSTRACT:

Motivation

The association analysis between genetic variants and imaging phenotypes must be carried out to understand the inherited neuropsychiatric disorders via imaging genetic studies. Given the high dimensionality in imaging and genetic data, traditional methods based on massive univariate regression entail large computational cost and disregard many-to-many correlations between phenotypes and genetic variants. Several multivariate imaging genetic methods have been proposed to alleviate the above problems. However, most of these methods are based on the l1 penalty, which might cause the over-selection of variables and thus mislead scientists in analyzing data from the field of neuroimaging genetics.

Results

To address these challenges in both statistics and computation,

SUBMITTER: Wen C 

PROVIDER: S-EPMC7849977 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature