Abstract
With rapid progress in high-throughput genotyping and neuroimaging, imaging genetics has gained significant attention in the research of complex brain disorders, such as Alzheimer’s Disease (AD). The genotype-phenotype association study using imaging genetic data has the potential to reveal genetic basis and biological mechanism of brain structure and function. AD is a progressive neurodegenerative disease, thus, it is crucial to look into the relations between SNPs and longitudinal variations of neuroimaging phenotypes. Although some machine learning models were newly presented to capture the longitudinal patterns in genotype-phenotype association study, most of them required fixed longitudinal structures of prediction tasks and could not automatically learn the interrelations among longitudinal prediction tasks. To address this challenge, we proposed a novel temporal structure auto-learning model to automatically uncover longitudinal genotype-phenotype interrelations and utilized such interrelated structures to enhance phenotype prediction in the meantime. We conducted longitudinal phenotype prediction experiments on the ADNI cohort including 3,123 SNPs and two types of imaging markers, VBM and FreeSurfer. Empirical results demonstrated advantages of our proposed model over the counterparts. Moreover, available literature was identified for our top selected SNPs, which demonstrated the rationality of our prediction results. An executable program is available online at https://github.com/littleq1991/sparse_lowRank_regression.
H. Huang—This work was partially supported by the National Science Foundation [IIS 1302675 to H.H., IIS 1344152 to H.H., DBI 1356628 to H.H., IIS 1619308 to H.H., IIS 1633753 to H.H.] at UTA and [IIS 1622526 to L.S.] at IU; and by the National Institutes of Health [R01 LM011360 to L.S. and A.S., U01 AG024904 to Michael Weiner and A.S., RC2 AG036535 to Michael Weiner and A.S., R01 AG19771 to A.S., P30 AG10133 to A.S., UL1 TR001108 to Anantha Shekhar] and [R01 AG049371 to H.H.] at UTA.
ADNI—Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wp-content/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.
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Wang, X. et al. (2017). Longitudinal Genotype-Phenotype Association Study via Temporal Structure Auto-learning Predictive Model. In: Sahinalp, S. (eds) Research in Computational Molecular Biology. RECOMB 2017. Lecture Notes in Computer Science(), vol 10229. Springer, Cham. https://doi.org/10.1007/978-3-319-56970-3_18
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DOI: https://doi.org/10.1007/978-3-319-56970-3_18
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