Quantitative trait loci identification for brain endophenotypes via new additive model with random networks
Year: 2018
Type: article
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-linear associations between SNPs and brain endophenotypes in Alzheimer's disease. Our model can be flexibly employed to explain the non-linear influence of QTLs, thus is more adaptive for the complex distribution of the high-throughput biological data. Meanwhile, as an important computational learning theory contribution, we provide the generalization error analysis for the proposed approach. Unlike most previous theoretical analysis under independent and identically distributed samples assumption, our error bound is based on m-dependent observations, which is more appropriate for the high-throughput and noisy biological data. Experiments on the data from Alzheimer's Disease Neuroimaging Initiative (ADNI) cohort demonstrate the promising performance of our approach for identifying biological meaningful SNPs. Availability and implementation: An executable is available at https://github.com/littleq1991/additive_FNNRW.
Source: Bioinformatics
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
Awards: 子采样排序与相关熵排序的学习理论研究, G104586243, G107491585, G1221608705, Indiana Alzheimer Disease Center, G1576357080, ABI Innovation: A New Automated Data Integration, Annotations, and Interaction Network Inference System for Analyzing Drosophila Gene Expression, III: Medium: Collaborative Research: Robust Large-Scale Electronic Medical Record Data Mining Framework to Conduct Risk Stratification for Personalized Intervention, BIGDATA: Collaborative Research: IA: Big Imaging-Omics Data Mining Framework for Precision Medicine, Alzheimer's Disease Neuroimaging Initiative, G7378543102, III: Small: Robust Large-Scale Data Mining for Knowledge Discovery in Depression Thought Records, G7619289898, Amyloid Imaging, VMCI, and Analysis for ADNI, SCH: EXP: Collaborative Research: Privacy-Preserving Framework for Publishing Electronic Healthcare Records, G9000039018, G9000077669
OpenAlex ID: W2890070664