Quantitative trait loci identification for brain endophenotypes via new additive model with random networks.

ABSTRACT:

Motivation

The identification of quantitative trait loci (QTL) is critical to the study of causal relationships between genetic variations and disease abnormalities. We focus on identifying the QTLs associated to the brain endophenotypes in imaging genomics study for Alzheimer's Disease (AD). Existing research works mainly depict the association between single nucleotide polymorphisms (SNPs) and the brain endophenotypes via the linear methods, which may introduce high bias due to the simplicity of the models. Since the influence of QTLs on brain endophenotypes is quite complex, it is desired to design the appropriate non-linear models to investigate the associations of genotypes and endophenotypes.

Results

In this paper, we propose a new additive model to learn the non-linea

SUBMITTER: Wang X 

PROVIDER: S-EPMC6129276 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature