I am a National Library of Medicine (NLM) Biomedical Informatics (BMI) fellow at both the Regenstrief Institute and the Center for Neuroimaging at Indiana University School of Medicine (IUSM). My career goal is to become a successful independent investigator with a focus on integrating bio-, medical, and imaging informatics approaches, including next generation sequencing (NGS) and advanced neuroimaging, to enhance the understanding of complex disease mechanisms and thereby facilitate development of novel treatments. The NLM BMI training program has been a priceless experience that enabled me to successfully transition my career from computational physics to biomedical informatics. During my three year NLM BMI fellowship, I gained extensive experience in advanced medical imaging analysis, medical informatics, bioinformatics, genomics, and statistics to prepare me to achieve my career goal. In order to consolidate my transdisciplinary skills and complete the transition to an independent researcher, my immediate career objective is to apply and expand my methodological skills and gain a broader understanding of current biological knowledge and future computational challenges in BMI. Using whole- exome sequencing pilot project data from the ongoing Alzheimer's Disease Neuroimaging Initiative (ADNI) as an initial learning platform, I will employ bio-, medical, and imaging informatics strategies in an integrated manner to identify functional rare variants associated with intermediate multi-modality phenotypes related to progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). Prior studies have identified a large number of common genetic variants but a substantial proportion of the heritability of AD remains unexplained by the known susceptibility genes. NGS can help close the gap by rare variant detection. The proposed research plan builds on my previous work in genome-wide association studies (GWAS) using imaging and medical informatics phenotypes and will expand my expertise to NGS. The rich ADNI data set provides a unique opportunity to address the challenges proposed in my specific aims and the IU Center for Neuroimaging serves as the ADNI Genetics Core which greatly facilitates data access and expert assistance.
Specific aims : (1) identify a subset of intermediate phenotypic biomarkers associated with a diagnosis of MCI/AD for genetic association analysis in the full ADNI sample set; (2) perform gene set- based association analysis of rare variants with MCI/AD-related intermediate phenotypic biomarkers in the ADNI whole-exome sequencing pilot project; and (3) validate novel variants and risk genes by imputing and genotyping target regions of detected genes in the full ADNI sample set.
TO PUBLIC HEALTH: AD is an increasingly common neurodegenerative disease and the sixth leading cause of death in the United States. Identifying new susceptibility loci with functional impact on MCI progression will enhance mechanistic knowledge regarding AD pathways and may enable earlier diagnosis and treatment.
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| Toledo, Jon B; Arnold, Matthias; Kastenmüller, Gabi et al. (2017) Metabolic network failures in Alzheimer's disease: A biochemical road map. Alzheimers Dement 13:965-984 |
| Zahodne, Laura B; Gilsanz, Paola; Glymour, M Maria et al. (2017) Comparing Variability, Severity, and Persistence of Depressive Symptoms as Predictors of Future Stroke Risk. Am J Geriatr Psychiatry 25:120-128 |
| Horgusluoglu, Emrin; Nudelman, Kelly; Nho, Kwangsik et al. (2017) Adult neurogenesis and neurodegenerative diseases: A systems biology perspective. Am J Med Genet B Neuropsychiatr Genet 174:93-112 |
| Yan, Jingwen; Risacher, Shannon L; Nho, Kwangsik et al. (2017) IDENTIFICATION OF DISCRIMINATIVE IMAGING PROTEOMICS ASSOCIATIONS IN ALZHEIMER'S DISEASE VIA A NOVEL SPARSE CORRELATION MODEL. Pac Symp Biocomput 22:94-104 |
| Kim, Dokyoon; Basile, Anna O; Bang, Lisa et al. (2017) Knowledge-driven binning approach for rare variant association analysis: application to neuroimaging biomarkers in Alzheimer's disease. BMC Med Inform Decis Mak 17:61 |
| Nho, Kwangsik; Kim, Sungeun; Horgusluoglu, Emrin et al. (2017) Association analysis of rare variants near the APOE region with CSF and neuroimaging biomarkers of Alzheimer's disease. BMC Med Genomics 10:29 |
| Yao, Xiaohui; Yan, Jingwen; Risacher, Shannon et al. (2017) NETWORK-BASED GENOME WIDE STUDY OF HIPPOCAMPAL IMAGING PHENOTYPE IN ALZHEIMER'S DISEASE TO IDENTIFY FUNCTIONAL INTERACTION MODULES. Proc IEEE Int Conf Acoust Speech Signal Process 2017:6170-6174 |
| Mez, Jesse; Marden, Jessica R; Mukherjee, Shubhabrata et al. (2017) Alzheimer's disease genetic risk variants beyond APOE ?4 predict mortality. Alzheimers Dement (Amst) 8:188-195 |
| Hibar, Derrek P (see original citation for additional authors) (2017) Novel genetic loci associated with hippocampal volume. Nat Commun 8:13624 |
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