From Analysis:
Statistical Fine-Mapping of AD GWAS Loci to Identify Causal Variants
Can Bayesian fine-mapping of the top 25 AD GWAS loci identify credible sets of causal variants with high posterior probability?
Bayesian fine-mapping of the top 25 AD GWAS loci will identify credible sets significantly enriched for variants disrupting microglia-specific regulatory elements, reflecting microglial dysfunction as a central AD pathogenic mechanism. Credible sets at loci with known effector genes (APOE, TREM2, PLCG2) will be smaller (<10 variants) due to stronger functional constraints, while novel loci will have larger sets requiring integration with epigenomic data to prioritize causal variants. The highest posterior probability variants will predominantly map to non-coding regulatory regions active in myeloid cells rather than neuronal or astrocytic enhancers.
No AI visual card yet
[Error in hypothesis generation: complete() got an unexpected keyword argument 'tools']
The fundamental problem with this hypothesis is a category error: strong LD is a hindrance, not a help, for variant-level resolution. When variants are highly correlated, posterior probability diffuses across the LD block, making pinpointing the causal variant statistically harder, not easier. The hypothesis conflates "high statistical power to detect association" with "narrow credible sets."
The APOE/TOMM40 region is particularly problematic as an exemplar. De
[Error in expert assessment: complete() got an unexpected keyword argument 'tools']
{"ranked_hypotheses":[],"synthesis_summary":"Synthesis could not be completed due to errors in receiving inputs from component agents. The Theorist, Skeptic, and Expert modules all returned errors stating 'complete() got an unexpected keyword argument tools', indicating a technical issue with agent invocation. Without validated hypotheses, critique, or feasibility assessments, no ranking or synthesis can be produced. Please verify the agent configuration and retry the generation pipeline.","knowledge_edges":[]}
No clinical trials data available
Hypotheses receive an efficiency score (0-1) based on how many knowledge graph edges and citations they produce per token of compute spent.
High-efficiency hypotheses (score >= 0.8) get a price premium in the market, pulling their price toward $0.580.
Low-efficiency hypotheses (score < 0.6) receive a discount, pulling their price toward $0.420.
Monthly batch adjustments update all composite scores with a 10% weight from efficiency, and price signals are logged to market history.
No knowledge graph edges recorded
neurodegeneration | 2026-04-16 | completed
No comments yet. Be the first to comment!