The goal of this project is to develop integrative approaches for the detection of biomarkers from multiscale genomic and imaging data, so that multiple mental illnesses such as schizophrenia (SC), Unipolar (UD) and bipolar (BI) disorder can be better identified. Imaging genetics is an emerging technique, which integrates imaging and genomic approaches to explore the association between genetic variations and brain functions and behaviors. Although it promises a better and more powerful approach for disease diagnosis and prognosis, the field is facing several major challenges: 1) First, most of current imaging genetics studies focus on pair-wise data correlation and integration;other important genetic factors such as epigenomics and genetic interactions (epistasis) have not been incorporated. 2) Second, multiscale imaging genetics data often exhibit specific characteristics such as inter- correlations, but this prior knowledge has not been incorporated into existing integrative models. 3) Finally, there is a high dimensionality problem with the analysis of imaging genetic data the number of sample is always significantly less than that of features. The solution of these problems necessitates a paradigm shift in computational models by considering the specific characteristics of these multiscale and multimodal data. Our multidisciplinary research team consisting of imaging scientist (Dr. Calhoun), statistical geneticist (Dr. Deng), biomedical engineer and bioimaging informatician (Dr. Wang), and psychiatrist (Dr. Pearson) has worked productively and creatively over the past few years in developing a number of data integration methods for fusion of imaging and genomic data. Building on our initial success, we will accomplish the following specific aims: 1) to study the correlation between multiple imaging and genomic data for the detection of epistasis factors or interaction networks;2) to integrate multiscale imaging and genomic data, especially incorporating epistasis factors, for the identification of biomarkers, from which risk genes can be better detected;3) to apply the detected biomarkers for the classification of multiple mental illnesses that are currently based on symptoms and are often misdiagnosed;and 4) to develop and disseminate an open source sparse model based data integration toolbox to the broad research community. The project will make significant impact on more accurate classification of clinically cryptic subgroups (e.g., SC, UD, BI) with an innovative and integrative paradigm by taking into account specific features of multiscale imaging genomic data and incorporation of prior knowledge. This will bring transformative changes on the current diagnosis of these mental illnesses (e.g., primarily based on imaging symptoms, which are often inaccurate), promising for personalized and optimal treatments. The developed methodology and tools are also applicable to many other neurological and psychiatric disorders. By the dissemination of the developed software tools to the research community, the project will have a broad and sustained impact.
This project will challenge the current paradigm on mental illness diagnosis, which relies primarily on symptom assessment or single modality imaging. Our integrative approach (e.g., combining imaging with genomic and epigenomic biomarkers) promises a better discrimination of multiple mental illnesses, translating into individualized and optimal disease management and treatment.
|Hu, Wenxing; Lin, Dongdong; Cao, Shaolong et al. (2018) Adaptive Sparse Multiple Canonical Correlation Analysis With Application to Imaging (Epi)Genomics Study of Schizophrenia. IEEE Trans Biomed Eng 65:390-399|
|Meng, Xiang-He; Shen, Hui; Chen, Xiang-Ding et al. (2018) Inferring causal relationships between phenotypes using summary statistics from genome-wide association studies. Hum Genet 137:247-255|
|Zhao, Yan; Ning, Yujie; Zhang, Feng et al. (2018) PCA-based GRS analysis enhances the effectiveness for genetic correlation detection. Brief Bioinform :|
|Gossmann, Alexej; Cao, Shaolong; Brzyski, Damian et al. (2018) A Sparse Regression Method for Group-Wise Feature Selection with False Discovery Rate Control. IEEE/ACM Trans Comput Biol Bioinform 15:1066-1078|
|Zhou, Yu; Gao, Yunlong; Xu, Chao et al. (2018) A novel approach for correction of crosstalk effects in pathway analysis and its application in osteoporosis research. Sci Rep 8:668|
|Liu, Hui-Min; He, Jing-Yang; Zhang, Qiang et al. (2018) Improved detection of genetic loci in estimated glomerular filtration rate and type 2 diabetes using a pleiotropic cFDR method. Mol Genet Genomics 293:225-235|
|Lin, Dongdong; Chen, Jiayu; Ehrlich, Stefan et al. (2018) Cross-Tissue Exploration of Genetic and Epigenetic Effects on Brain Gray Matter in Schizophrenia. Schizophr Bull 44:443-452|
|Cai, Biao; Zille, Pascal; Stephen, Julia M et al. (2018) Estimation of Dynamic Sparse Connectivity Patterns From Resting State fMRI. IEEE Trans Med Imaging 37:1224-1234|
|Liang, Xiao; Wu, CuiYan; Zhao, Hongmou et al. (2018) Assessing the genetic correlations between early growth parameters and bone mineral density: A polygenic risk score analysis. Bone 116:301-306|
|Liu, Li; Wen, Yan; Zhang, Lei et al. (2018) Assessing the Associations of Blood Metabolites With Osteoporosis: A Mendelian Randomization Study. J Clin Endocrinol Metab 103:1850-1855|
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