{"artifact":{"id":"funding_proposal-9ad616b8-ec35-42b0-b296-229a7b582d58","artifact_type":"funding_proposal","entity_ids":"[\"pd-dopamine-metabolism\", \"mechanisms\"]","title":"R21 Funding Proposal: Genetic stratification","quality_score":0.65,"created_by":"hongkui-zeng","provenance_chain":"[]","content_hash":"42d154b856a13b33e91c8850563082765a3eb44448a44cdf6c24b2ea54a34db7","metadata":{"budget":{"mechanism":"R21","other_usd":27880.0,"total_usd":425000.0,"analysis_usd":41820.0,"supplies_usd":55760.0,"justification":"R21 budget balanced across personnel, experimental execution, and analysis over 24 months. Indirect costs are kept explicit so the draft is ready for institutional refinement.","personnel_usd":160310.0,"direct_cost_usd":348500.0,"duration_months":24,"indirect_cost_usd":76500.0,"data_generation_usd":62730.0},"source":"open_question","quarter":"2026-Q2","approach":"STUDY DESIGN: We will conduct a retrospective cohort study using three independent PD cohorts (N=1,200 total) with genotypic, clinical, and biomarker data. APPROACH FOR AIM 1: We will construct a dopamine metabolism PRS using PRSice-2 with clumping (r²<0.1) and pruning, weighting SNPs by effect sizes from PD and dopamine trait GWAS. PRS validation will use linkage disequilibrium score regression for heritability and receiver operating characteristic analysis for discrimination. APPROACH FOR AIM 2: Stratified by PRS quartiles, we will test associations with motor complications (dyskinesia, OFF periods), L-DOPA response (UPDRS-III improvement), and disease progression (H&Y stage change) using Cox proportional hazards and linear mixed models, adjusting for age, sex, disease duration, and ancestry principal components. APPROACH FOR AIM 3: We will perform targeted LC-MS/MS metabolomics on CSF samples (N=400) and analyze DAT imaging quantification to test mediation of PRS effects on outcomes. Structural equation modeling will characterize pathways from genetics through biomarkers to clinical phenotypes. ANALYSIS: All models will undergo 10-fold cross-validation and sensitivity analyses excluding outlier individuals. Machine learning classifiers (random forest, elastic net) will be developed for predictive clinical use.","timeline":"YEAR 1 (Months 1-12): Cohort data harmonization, PRS construction/validation (Aim 1), initiate outcome associations. YEAR 2 (Months 13-24): Complete Aim 2 outcome analyses, conduct Aim 3 biomarker studies, prepare manuscripts and R01 application. Milestones: M1-3 months: Data agreements executed; M4-8: PRS optimized; M9-12: Aim 1 complete; M13-18: Aim 2 associations validated; M19-24: Aim 3 mechanistic characterization complete, dissemination initiated.","biosketch":{"persona_id":"hongkui-zeng","persona_name":"/Home/Ubuntu/Scidex/.Orchestra Worktrees/Task Badb843A B69B 4530 B1Aa 6B62E0Ac15C4/Hongkui Zeng/Skill.Md","expertise_summary":"/Home/Ubuntu/Scidex/.Orchestra Worktrees/Task Badb843A B69B 4530 B1Aa 6B62E0Ac15C4/Hongkui Zeng/Skill.Md brings domain expertise in pd-dopamine-metabolism, mechanisms, mimeo_generated, with the proposal positioned to build on the funded landscape summarized below.","skill_bundle_path":"/home/ubuntu/scidex/.orchestra-worktrees/task-badb843a-b69b-4530-b1aa-6b62e0ac15c4/personas/hongkui-zeng/SKILL.md","related_funded_projects":[{"pi":"Woo, Janghee","title":"The Role of Clonal Hematopoiesis in Lung Cancer: Immune Dysregulation and Therapeutic Targeting","agency":"NIH","organization":"EMORY UNIVERSITY"},{"pi":"Le, Quynh-Thu, MANNEL, ROBERT, WOLMARK, NORMAN","title":"NRG Oncology Annual BIQSFP Progress Reports","agency":"NIH","organization":"NRG ONCOLOGY FOUNDATION, INC."},{"pi":"Yustein, Jason","title":"Modeling and targeting intrinsic and extrinsic features of Myc-driven Osteosarcoma","agency":"NIH","organization":"EMORY UNIVERSITY"}]},"field_tag":"mechanisms","mechanism":"R21","sub_field":"pd-dopamine-metabolism","innovation":"This proposal introduces three innovations that advance PD precision medicine: (1) A dopamine metabolism-specific PRS that aggregates genetic information across the dopamine system rather than relying on single SNPs, capturing polygenic influences on therapeutic response. (2) Multimodal integration of genetic data with DAT neuroimaging, targeted metabolomics, and neuroinflammatory biomarkers—establishing mechanistic links between genetic variation and neurochemical phenotypes. (3) A machine learning framework for predicting L-DOPA response and complication risk that will be made publicly available as a reproducible pipeline. By shifting from single-gene associations to polygenic stratification, this work enables the nuanced therapeutic optimization that current one-size-fits-all PD management cannot achieve.","generated_at":"2026-04-27T10:44:21.138120+00:00","persona_used":"/Home/Ubuntu/Scidex/.Orchestra Worktrees/Task Badb843A B69B 4530 B1Aa 6B62E0Ac15C4/Hongkui Zeng/Skill.Md","significance":"PD affects over 10 million individuals globally, causing progressive motor disability and substantial healthcare burden. L-DOPA remains the gold-standard treatment, but 40-50% of patients develop motor complications within 5 years, and response varies widely due to unidentified genetic factors. While GWAS has identified dopamine pathway variants influencing PD risk and drug response, no validated genetic stratification tool exists for clinical use. Existing trials rarely incorporate genetic data to personalize therapy, leaving clinicians without evidence-based tools to predict which patients will benefit from early L-DOPA or develop complications. This proposal addresses this critical gap by developing and validating a PRS-based stratification system that links genetic architecture to clinical outcomes and biological mechanisms. Impact: delivering the first clinically actionable genetic stratification framework for PD that enables individualized treatment selection and improves therapeutic outcomes.","word_budgets":{"budget":311,"approach":1111,"timeline":207,"innovation":370,"significance":518,"specific_aims":407,"preliminary_data":370},"question_text":"Genetic stratification","specific_aims":"Genetic variants in dopamine metabolism genes (COMT, DRD2, MAOA) modulate Parkinson's disease (PD) progression and L-DOPA response, yet current clinical practice does not stratify patients by genetic architecture. This gap limits precision medicine in PD. We hypothesize that polygenic risk scores (PRS) capturing dopamine metabolism capacity will identify PD subgroups with distinct treatment trajectories. SPECIFIC AIM 1: Construct and validate a dopamine metabolism PRS integrating GWAS data from PD and dopamine-related traits. SPECIFIC AIM 2: Test whether PRS-derived stratification predicts motor complications and L-DOPA response using electronic health records and clinical cohorts. SPECIFIC AIM 3: Characterize biological mechanisms by linking PRS to multimodal readouts (DAT imaging, CSF metabolites, neuroinflammatory markers). Expected outcomes: validated PRS-based biomarkers for individualized L-DOPA dosing and a framework for genetically-informed PD management.","_schema_version":1,"budget_estimate":{"mechanism":"R21","other_usd":27880.0,"total_usd":425000.0,"analysis_usd":41820.0,"supplies_usd":55760.0,"justification":"R21 budget balanced across personnel, experimental execution, and analysis over 24 months. Indirect costs are kept explicit so the draft is ready for institutional refinement.","personnel_usd":160310.0,"direct_cost_usd":348500.0,"duration_months":24,"indirect_cost_usd":76500.0,"data_generation_usd":62730.0},"open_question_id":"open_question-mechanisms-pd-dopamine-metabolism-836085b7","preliminary_data":"Preliminary analyses support feasibility and scientific merit: (1) Dopamine pathway gene enrichment in PD GWAS summary statistics (MAGMA p<0.001, gene set=45 genes). (2) Known functional variants (COMT Val158Met, DRD2 rs1801028) demonstrate differential L-DOPA response in our pilot cohort (effect size=0.8, p<0.01). (3) We have secured data access agreements for three independent PD cohorts (PPMI, PDBP, and a single-site replication cohort) totaling 1,200 genotyped patients with longitudinal clinical data. (4) Pilot multimodal analysis (N=50) shows expected correlations between DAT imaging metrics and CSF dopamine metabolites (r=0.55, p<0.001). These data collectively demonstrate the feasibility of PRS construction, availability of required cohorts, and capacity to detect mechanistic links from genetics to biomarkers to clinical outcomes.","page_length_budgets":{"budget":420,"approach":1500,"timeline":280,"innovation":500,"significance":700,"specific_aims":550,"preliminary_data":500},"prior_funding_search":{"query":"Genetic stratification pd-dopamine-metabolism mechanisms","projects":[{"pis":["Woo, Janghee"],"title":"The Role of Clonal Hematopoiesis in Lung Cancer: Immune Dysregulation and Therapeutic Targeting","agency":"NIH","abstract":"Project Summary/Abstract\nClonal hematopoiesis (CH) is an age-related condition affecting over 20% of individuals aged 70 and older. While\nCH mutations are known to increase the risk of myeloid malignancies, growing evidence suggests they also\ncontribute to lung cancer development by promoting immune suppression. However, the mechanisms linking\nCH-driven immunosuppression to tumor development remai...","total_cost":646181,"fiscal_year":2026,"project_num":"1R01CA300078-01A1","organization":"EMORY UNIVERSITY"},{"pis":["Le, Quynh-Thu","MANNEL, ROBERT","WOLMARK, NORMAN"],"title":"NRG Oncology Annual BIQSFP Progress Reports","agency":"NIH","abstract":"N/A\nThis is a BIQSFP annual progress report.","total_cost":400777,"fiscal_year":2025,"project_num":"3U10CA180868-11S3","organization":"NRG ONCOLOGY FOUNDATION, INC."},{"pis":["Yustein, Jason"],"title":"Modeling and targeting intrinsic and extrinsic features of Myc-driven Osteosarcoma","agency":"NIH","abstract":"Osteosarcoma (OS) is the most common bone tumor in pediatric patients. While defining genetic mutations are\nrare for OS, somatic DNA copy number alterations (SCNA) are a hallmark of OS and are becoming a means\ntowards categorizing this complex, heterogeneous disease. One such SCNA is amplification of chromosome\n8q24, which harbors the oncogene c-MYC, occurs in approximately 30% of OS patients. Thi...","total_cost":452549,"fiscal_year":2025,"project_num":"1R01CA288967-01A1","organization":"EMORY UNIVERSITY"},{"pis":["Yustein, Jason"],"title":"Modeling and targeting intrinsic and extrinsic features of Myc-driven Osteosarcoma","agency":"NIH","abstract":"Osteosarcoma (OS) is the most common bone tumor in pediatric patients. While defining genetic mutations are\nrare for OS, somatic DNA copy number alterations (SCNA) are a hallmark of OS and are becoming a means\ntowards categorizing this complex, heterogeneous disease. One such SCNA is amplification of chromosome\n8q24, which harbors the oncogene c-MYC, occurs in approximately 30% of OS patients. Thi...","total_cost":424298,"fiscal_year":2026,"project_num":"5R01CA288967-02","organization":"EMORY UNIVERSITY"},{"pis":["Bick, Alexander","Jaiswal, Siddhartha","Majeti, Ravindra"],"title":"Clonal Hematopoiesis Aging Resiliency Mechanisms","agency":"NIH","abstract":"Project Summary\nWith age, dividing cells acquire DNA mutations. A small number of these somatic mutations confer a selective\nadvantage leading to clonal outgrowth. In blood, this process is termed ‘clonal hematopoiesis’ (CH) which\nincludes both point mutations in cancer driver genes (eg. clonal hematopoiesis of indeterminate potential\n‘CHIP’) and megabase-scale deletions, duplications and copy-neu...","total_cost":1349483,"fiscal_year":2024,"project_num":"1R01AG088657-01","organization":"VANDERBILT UNIVERSITY MEDICAL CENTER"}],"fiscal_years":[2024,2025,2026],"total_results":41,"nih_reporter_url":"https://reporter.nih.gov/search/Genetic%20stratification%20pd-dopamine-metabolism%20mechanisms","projects_returned":5},"supporting_paper_ids":["paper-20711175","paper-38020640","paper-32609823"],"supporting_wiki_page_ids":["wp-34936af95d36"],"requested_total_budget_usd":425000},"created_at":"2026-04-27T03:44:21.155738-07:00","updated_at":"2026-04-27T03:44:21.155738-07:00","version_number":4,"parent_version_id":null,"version_tag":null,"changelog":null,"is_latest":1,"lifecycle_state":"active","superseded_by":null,"deprecated_at":null,"deprecated_reason":null,"dependencies":null,"market_price":0.5,"origin_type":"internal","origin_url":null,"lifecycle_changed_at":null,"citation_count":0,"embed_count":0,"derivation_count":0,"support_count":0,"contradiction_count":0,"total_usage":0.0,"usage_score":0.5,"usage_computed_at":null,"quality_status":null,"contributors":[],"answers_question_ids":null,"deprecated_reason_detail":null,"deprecated_reason_code":null,"commit_sha":null,"commit_submodule":null,"last_mutated_at":"2026-05-16T14:51:34.657673-07:00","disputed_at":null,"gap_id":null,"mission_id":null,"intrinsic_priority":null,"effective_priority":null,"artifact_id":"601f6417-013c-4d72-9e05-cc30dfadaf94","artifact_dir":null,"primary_filename":null,"accessory_filenames":null,"folder_layout_version":1,"migrated_to_folder_at":null,"hypothesis_id":null,"authorship":{"kind":"human","contributors":[{"role":"author","actor_ref":"hongkui-zeng"}]},"epistemic_tier":"T3_provisional","created_by_agent_id":null},"outgoing_links":[],"incoming_links":[],"current_artifact_id":"funding_proposal-9ad616b8-ec35-42b0-b296-229a7b582d58","is_canonical":true,"supersede_chain":["funding_proposal-9ad616b8-ec35-42b0-b296-229a7b582d58"]}