Identification of Genetic Risk Factors Based on Disease Progression Derived from Longitudinal Brain Imaging Phenotypes

Lei Du, Ying Zhao, Jianting Zhang, Muheng Shang, Jin Zhang, Junwei Han

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)928-939
Number of pages12
JournalIEEE Transactions on Medical Imaging
Volume43
Issue number3
DOIs
StatePublished - 1 Mar 2024

Keywords

  • Brain imaging genetics
  • disease progression
  • genetic risk factors
  • sparse multi-Task mixed-effects model

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