This application seeks funding for research to help understand the genetic influences on neuroanatomical shapes. Imaging genetics is a major new direction in brain mapping because it goes beyond simple descriptions to more mechanistic understandings of which genes and environmental factors affect disease expression or risk. Unique to our approach is access to an existing dataset collected on more than 1000 twins, all with GWAS and neuroimaging data. We will use this data to develop maps of: (1) the heritability of subcortical and cortical gray matter shapes, (2) the heritability of the shape of white matter tracts, (3) the influence of brain-derived neurotrophic factor (BDNF), catechol-O-methyltransferase (COMT), and fat mass and obesity-associated (FTO) polymorphisms on gray and white matter shapes, and (4) genome-wide association studies (GWAS) to identify new genes or single nucleotide polymorphisms (SNPs) that influence neuroanatomical shapes. The results generated will greatly advance our knowledge of how genes influence neuroanatomy. In addition, we will develop software tools to facilitate the community's use of neuroimaging data to characterize subtle genetic effects. Project deliverables include the dissemination of a complete, open- source multimodal imaging genetics toolkit. Through accelerating research in imaging genetics, and combining mathematical approaches from multiple fields, this project will invigorate biomedical research and expedite the merging of huge arsenals of neuroimaging data and GWAS data. The activities in the project are extremely responsive to the NIH's mission in advancing GWAS to identify common genetic factors that influence health and disease.
This unique large-scale investigation will improve understanding of the complex ways in which genetics exert influence over human neuroanatomy. By providing the larger research community with mathematically sophisticated software tools, we will create opportunities to advance progress in many psychiatric and neurological diseases.
|Ning, Kaida; Chen, Bo; Sun, Fengzhu et al. (2018) Classifying Alzheimer's disease with brain imaging and genetic data using a neural network framework. Neurobiol Aging 68:151-158|
|Sepehrband, Farshid; Lynch, Kirsten M; Cabeen, Ryan P et al. (2018) Neuroanatomical morphometric characterization of sex differences in youth using statistical learning. Neuroimage 172:217-227|
|Li, Junning; Gahm, Jin Kyu; Shi, Yonggang et al. (2018) Topological false discovery rates for brain mapping based on signal height. Neuroimage 167:478-487|
|Zhang, Guohao; Kochunov, Peter; Hong, Elliot et al. (2017) ENIGMA-Viewer: interactive visualization strategies for conveying effect sizes in meta-analysis. BMC Bioinformatics 18:253|
|Shi, Y; Toga, A W (2017) Connectome imaging for mapping human brain pathways. Mol Psychiatry 22:1230-1240|
|Brouwer, Rachel M; Panizzon, Matthew S; Glahn, David C et al. (2017) Genetic influences on individual differences in longitudinal changes in global and subcortical brain volumes: Results of the ENIGMA plasticity working group. Hum Brain Mapp 38:4444-4458|
|Palacios, E M; Martin, A J; Boss, M A et al. (2017) Toward Precision and Reproducibility of Diffusion Tensor Imaging: A Multicenter Diffusion Phantom and Traveling Volunteer Study. AJNR Am J Neuroradiol 38:537-545|
|Wang, Junyan; Aydogan, Dogu Baran; Varma, Rohit et al. (2017) Topographic Regularity for Tract Filtering in Brain Connectivity. Inf Process Med Imaging 10265:263-274|
|Li, Junning; Shi, Yonggang; Toga, Arthur W (2016) Transformation Invariant Control of Voxel-Wise False Discovery Rate. IEEE Trans Med Imaging 35:2243-2257|
|Leng, Yuan; Shi, Yonggang; Yu, Qiaowen et al. (2016) Phenotypic and Genetic Correlations Between the Lobar Segments of the Inferior Fronto-occipital Fasciculus and Attention. Sci Rep 6:33015|
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