TY - GEN
T1 - Identifying Disease-related Brain Imaging Quantitative Traits and Related Genetic Variations via A Bidirectional Association Learning Method
AU - Shang, Muheng
AU - Yang, Yan
AU - Zhang, Minjianan
AU - Zhang, Jin
AU - Xi, Duo
AU - Guo, Lei
AU - Du, Lei
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Discovering critical genetic biomarkers of Alzheimer's disease (AD) by detecting the complex associations between genotypes (i.e. single nucleotide polymorphism, SNP) and phenotypes (i.e. quantitative trait, QT) is a long-standing and beneficial task for the diagnosis and the follow-up treatment of patients. The function of genes and their relationships with phenotypes are extremely complex. A lot of imaging genetic methods have been designed to uncover the association between brain imaging QTs and SNPs. However, most of them are focused on the effect of a single SNP, which may have limited ability due to the oligogenic or polygenic characteristic of AD. In this paper, we propose a deep reconstruction bidirectional association with feature selection (DRBA-FS) method to explore the multi-SNPmulti-QT associations. In this method, the co-effect of multiple AD-related genetic variations is identified and aggregated, and their high-level genetic associations to brain imaging QTs are jointly modeled. Experiment results on real neuroimaging genetic data from Alzheimer's Disease Neuroimaging Initiative (ADNI) show that the identified biomarkers are all related to AD. Interestingly, our method can learn the joint effect of multiple AD-related genetic variations across the genome, and thus has significant potential in understanding the genetic mechanism of AD.
AB - Discovering critical genetic biomarkers of Alzheimer's disease (AD) by detecting the complex associations between genotypes (i.e. single nucleotide polymorphism, SNP) and phenotypes (i.e. quantitative trait, QT) is a long-standing and beneficial task for the diagnosis and the follow-up treatment of patients. The function of genes and their relationships with phenotypes are extremely complex. A lot of imaging genetic methods have been designed to uncover the association between brain imaging QTs and SNPs. However, most of them are focused on the effect of a single SNP, which may have limited ability due to the oligogenic or polygenic characteristic of AD. In this paper, we propose a deep reconstruction bidirectional association with feature selection (DRBA-FS) method to explore the multi-SNPmulti-QT associations. In this method, the co-effect of multiple AD-related genetic variations is identified and aggregated, and their high-level genetic associations to brain imaging QTs are jointly modeled. Experiment results on real neuroimaging genetic data from Alzheimer's Disease Neuroimaging Initiative (ADNI) show that the identified biomarkers are all related to AD. Interestingly, our method can learn the joint effect of multiple AD-related genetic variations across the genome, and thus has significant potential in understanding the genetic mechanism of AD.
KW - Alzheimer's disease
KW - Brain imaging genetics
KW - co-effect of genetic variations
KW - feature selection
UR - https://www.scopus.com/pages/publications/85184900353
U2 - 10.1109/BIBM58861.2023.10385388
DO - 10.1109/BIBM58861.2023.10385388
M3 - 会议稿件
AN - SCOPUS:85184900353
T3 - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
SP - 616
EP - 621
BT - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
A2 - Jiang, Xingpeng
A2 - Wang, Haiying
A2 - Alhajj, Reda
A2 - Hu, Xiaohua
A2 - Engel, Felix
A2 - Mahmud, Mufti
A2 - Pisanti, Nadia
A2 - Cui, Xuefeng
A2 - Song, Hong
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Y2 - 5 December 2023 through 8 December 2023
ER -