TY - JOUR
T1 - Identification of Genetic Risk Factors Based on Disease Progression Derived from Longitudinal Brain Imaging Phenotypes
AU - Du, Lei
AU - Zhao, Ying
AU - Zhang, Jianting
AU - Shang, Muheng
AU - Zhang, Jin
AU - Han, Junwei
N1 - Publisher Copyright:
© 1982-2012 IEEE.
PY - 2024/3/1
Y1 - 2024/3/1
N2 - Neurodegenerative disorders usually happen stage-by-stage rather than overnight. Thus, cross-sectional brain imaging genetic methods could be insufficient to identify genetic risk factors. Repeatedly collecting imaging data over time appears to solve the problem. But most existing imaging genetic methods only use longitudinal imaging phenotypes straightforwardly, ignoring the disease progression trajectory which might be a more stable disease signature. In this paper, we propose a novel sparse multi-Task mixed-effects longitudinal imaging genetic method (SMMLING). In our model, disease progression fitting and genetic risk factors identification are conducted jointly. Specifically, SMMLING models the disease progression using longitudinal imaging phenotypes, and then associates fitted disease progression with genetic variations. The baseline status and changing rate, i.e., the intercept and slope, of the progression trajectory thus shoulder the responsibility to discover loci of interest, which would have superior and stable performance. To facilitate the interpretation and stability, we employ \ell _{{2},{1}}$-norm and the fused group lasso (FGL) penalty to identify loci at both the individual level and group level. SMMLING can be solved by an efficient optimization algorithm which is guaranteed to converge to the global optimum. We evaluate SMMLING on synthetic data and real longitudinal neuroimaging genetic data. Both results show that, compared to existing longitudinal methods, SMMLING can not only decrease the modeling error but also identify more accurate and relevant genetic factors. Most risk loci reported by SMMLING are missed by comparison methods, implicating its superiority in genetic risk factors identification. Consequently, SMMLING could be a promising computational method for longitudinal imaging genetics.
AB - Neurodegenerative disorders usually happen stage-by-stage rather than overnight. Thus, cross-sectional brain imaging genetic methods could be insufficient to identify genetic risk factors. Repeatedly collecting imaging data over time appears to solve the problem. But most existing imaging genetic methods only use longitudinal imaging phenotypes straightforwardly, ignoring the disease progression trajectory which might be a more stable disease signature. In this paper, we propose a novel sparse multi-Task mixed-effects longitudinal imaging genetic method (SMMLING). In our model, disease progression fitting and genetic risk factors identification are conducted jointly. Specifically, SMMLING models the disease progression using longitudinal imaging phenotypes, and then associates fitted disease progression with genetic variations. The baseline status and changing rate, i.e., the intercept and slope, of the progression trajectory thus shoulder the responsibility to discover loci of interest, which would have superior and stable performance. To facilitate the interpretation and stability, we employ \ell _{{2},{1}}$-norm and the fused group lasso (FGL) penalty to identify loci at both the individual level and group level. SMMLING can be solved by an efficient optimization algorithm which is guaranteed to converge to the global optimum. We evaluate SMMLING on synthetic data and real longitudinal neuroimaging genetic data. Both results show that, compared to existing longitudinal methods, SMMLING can not only decrease the modeling error but also identify more accurate and relevant genetic factors. Most risk loci reported by SMMLING are missed by comparison methods, implicating its superiority in genetic risk factors identification. Consequently, SMMLING could be a promising computational method for longitudinal imaging genetics.
KW - Brain imaging genetics
KW - disease progression
KW - genetic risk factors
KW - sparse multi-Task mixed-effects model
UR - http://www.scopus.com/inward/record.url?scp=85174836366&partnerID=8YFLogxK
U2 - 10.1109/TMI.2023.3325380
DO - 10.1109/TMI.2023.3325380
M3 - 文章
C2 - 37847615
AN - SCOPUS:85174836366
SN - 0278-0062
VL - 43
SP - 928
EP - 939
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
IS - 3
ER -