Statistical Fine-Mapping of AD GWAS Loci to Identify Causal Variants

Novel Hypotheses: Bayesian Fine-Mapping of Top 25 AD GWAS Loci

Hypothesis 1: Strong LD Structure Enables High-Posterior Credible Sets

Description: Loci exhibiting strong linkage disequilibrium patterns—particularly the APOE-TOMM40 region on chromosome 19 and the ABI3 locus—will yield narrow credible sets (<10 variants) with high posterior probability (>0.90). The LD structure creates natural statistical "bins" that Bayesian methods can exploit, combined with the relatively large effect sizes at these loci.

Target Gene/Protein: APOE (apolipoprotein E)

Confidence: 0.82

Rationale: The APOE ε4 allele demonstrates odds ratios of 3-4 for AD, providing substantial statistical power for fine-mapping. Preliminary fine-mapping studies (Karch et al., 2022) have already demonstrated posterior probabilities >0.85 for specific tagging variants.

Hypothesis 2: Multi-Omics Integration Will Sharpen Credible Sets by 40-60%

Description: Incorporating brain-derived epigenetic priors (ATAC-seq from microglia, H3K27ac ChIP-seq from neuronal nuclei) as annotation-informed Bayesian priors will substantially reduce credible set sizes. The hypothesis proposes that the combination of chromatin accessibility and active enhancer marks will concentrate posterior probability on functional regulatory variants rather than tagging SNPs.

Target Gene/Protein: INPP5D (phosphoinositide-5-phosphatase) / PLCG2 pathway

Confidence: 0.76

Rationale: Microglia-specific ATAC-seq from human brain (Nott et al., 2019) identified regulatory variants in AD loci that would not appear in blood-based assays. Integration of these data as priors is mathematically equivalent to informative prior specification in Bayesian frameworks.

Hypothesis 3: Allelic Heterogeneity Will Confound Single-Variant Credible Sets in 3-5 Loci

Description: Approximately 3-5 of the top 25 loci will demonstrate allelic heterogeneity—multiple independent causal variants with modest effects. Standard fine-mapping assumes a single causal variant per signal, but loci like BIN1 and CLU show evidence of multiple independent signals in conditional analyses. This will result in inflated credible set sizes and posterior probability dilution.

Target Gene/Protein: BIN1 (Bridging Integrator 1)

Confidence: 0.68

Rationale: Conditional GWAS analyses (Bellenguez et al., 2022) identified secondary signals at BIN1, CLU, and PTK2B. Standard fine-mapping implementations (FINEMAP, CAVIAR) can accommodate multiple causal variants but require larger sample sizes for stable estimation.

Hypothesis 4: Brain eQTL Colocalization Will Increase Variant-Level Confidence by Factor of 2.5

Description: Bayesian colocalization using brain-specific eQTL data (PsychENCODE, ROS/MAP) will identify variants with concordant GWAS and expression signals, effectively doubling or tripling the posterior probability that specific variants are causal. This represents a form of "triangulation" where statistical and functional evidence converge.

Target Gene/Protein: MS4A gene cluster (MS4A6A, MS4A4A)

Confidence: 0.74

Rationale: The MS4A locus demonstrates strong eQTL effects in brain tissue (Foltyn et al., 2022), and the lead GWAS variant rs6591561 sits in high LD with expression-modulating variants. Colocalization posterior probabilities (e.g., using coloc R package) typically exceed 0.80 when both signals are present.

Hypothesis 5: Low-LD Loci Will Yield Unacceptably Large Credible Sets (>50 variants)

Description: Loci in genomic regions with sparse LD architecture—particularly those near centromeres or chromosomal arms—will produce credible sets containing >50 variants, rendering mechanistic interpretation infeasible. The hypothesis suggests that for these loci, variant-level inference is statistically underpowered with current sample sizes.

Target Gene/Protein: CASS4 (Cas-related scaffolding protein 4)

Confidence: 0.71

Rationale: CASS4 demonstrates the smallest effect size among top AD loci (OR ~1.1) combined with a recombination hotspot flanking the gene. Without strong LD "anchors," posterior probability diffuses across many plausible candidates.

Hypothesis 6: Cell-Type Specific Regulatory Architecture Will Reveal Microglia-Predominant Causal Variants

Description: Fine-mapping will identify a subset of variants whose posterior probability increases specifically when microglia-specific regulatory annotations are incorporated. These variants likely affect enhancers active in myeloid cells, consistent with AD genetic architecture enrichment in microglia.

Target Gene/Protein: MEF2C (myocyte enhancer factor 2C)

Confidence: 0.79

Rationale: MEF2C shows microglia-specific expression quantitative trait effects, and functional studies demonstrate MEF2C regulates microglial homeostatic genes (Kobayashi et al., 2018). Bayesian priors emphasizing myeloid chromatin states should concentrate posterior mass on enhancers active in this lineage.

Hypothesis 7: Multi-Ancestry Meta-Analysis Will Halve Credible Set Sizes Through Haplotype Diversity

Description: Incorporating non-European ancestry cohorts (African American, East Asian, Hispanic) will reduce credible set sizes by approximately 50% due to different LD patterns and recombination histories. The hypothesis proposes that haplotype diversity across ancestries resolves causal variant identity in European-only analyses.

Target Gene/Protein: SORL1 (sortilin-related receptor 1)

Confidence: 0.66

Rationale: SORL1 rare variants demonstrate AD association across multiple ancestries (CampELO et al., 2019), suggesting consistent genetic architecture. Fine-mapping in admixed populations can break down LD correlations that obscure causal variant identification in homogeneous samples.

Summary Table

| Hypothesis | Primary Mechanism | Confidence |
|------------|-------------------|------------|
| 1 | Strong LD enables narrow credible sets | 0.82 |
| 2 | Multi-omics integration sharpens inference | 0.76 |
| 3 | Allelic heterogeneity confounds standard models | 0.68 |
| 4 | Brain eQTL colocalization doubles confidence | 0.74 |
| 5 | Sparse LD yields impractically large sets | 0.71 |
| 6 | Microglia-specific regulatory variants identified | 0.79 |
| 7 | Multi-ancestry analysis halves set sizes | 0.66 | Key Assumptions & Limitations:

• Sample sizes assumed at ~200K+ individuals (current largest AD GWAS meta-analyses)
• Functional annotation resources assumed to cover relevant brain cell types
• Allelic heterogeneity hypothesis requires multi-signal fine-mapping software (e.g., SuSiE, DAP)
• Multi-ancestry hypothesis depends on sufficient non-European sample sizes (currently limited for AD)

Critical Evaluation of AD Fine-Mapping Hypotheses

Hypothesis 1: Strong LD Enables Narrow Credible Sets

Weaknesses

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. Despite the strong effect size, fine-mapping has been notoriously contentious:

• The extended LD block spans APOE promoter regions, TOMM40, and downstream elements
• The "APOE ε4 tag SNP" problem has been debated for years—some argue rs429358/rs7412 (the functional APOE4 missense variants) should be the causal anchor, while others identify regulatory proxies
• Karch et al.'s posterior probability of >0.85 should be scrutinized: is this for the missense variants themselves or for tagging SNPs? These are fundamentally different claims.

Counter-Evidence

• Fine-mapping studies of the APOE region (e.g., Ridge et al., 2013; Corradini et al., 2020) have repeatedly failed to achieve single-digit credible sets despite decades of study
• The region defies easy resolution because LD spans both regulatory and coding variation
• Even the CASZ1 locus adjacent to APOE demonstrates how extended LD can confuse inference

Falsification Experiments

Revised Confidence: 0.58 (down from 0.82)

The confidence of 0.82 is substantially inflated. The stated mechanism is misunderstood—strong LD complicates rather than simplifies fine-mapping. The confidence should reflect the known difficulty of the APOE region despite its large effect size.

Hypothesis 2: Multi-Omics Integration Sharpens Credible Sets 40-60%

Weaknesses

1. The claimed magnitude (40-60%) lacks theoretical justification or empirical precedent.

The reduction from incorporating chromatin annotations is bounded by how much posterior mass currently concentrates on non-coding regulatory regions versus tagging SNPs. If 70% of posterior probability already falls on a variant in LD with the functional causal variant, annotation integration can only recover 30% at maximum—even this assumes perfect annotation calibration.

2. Informative priors from ATAC-seq and ChIP-seq carry substantial assumptions:

• Tissue specificity: ATAC-seq from bulk microglia contains mixed cell types; accessible chromatin may reflect multiple lineages
• Temporal specificity: AD-relevant chromatin states may differ from young adult tissue donors
• The assumption that "accessible = causal regulatory variant" conflates accessibility with actual regulatory function

• How functional annotations are weighted relative to statistical LD
• Prior specification choices (Laplace versus normal mixtures)
• Calibration of annotation weights against truth-known benchmarks

• INPP5D shows complex splicing patterns with multiple isoforms
• PLCG2 is a signaling enzyme with limited clear eQTL patterns compared to surface receptors
• These are not the canonical GWAS signal genes for AD—they appear secondary

Counter-Evidence

• The GARFIELD algorithm (Iotchkova et al., 2019) showed more modest improvements (~20-30%) from regulatory annotations in fine-mapping
• Enrichment of GWAS variants in regulatory elements doesn't translate linearly to posterior probability allocation
• Many "functional" regulatory variants in ATAC-seq peaks are not causal for trait differences

Falsification Experiments

Revised Confidence: 0.52 (down from 0.76)

The mechanism is plausible in principle but the claimed effect size (40-60%) is unsupported by theory or existing benchmarks. The target gene selection is also suboptimal. A confidence in the 0.50-0.55 range better reflects the uncertainty.

Hypothesis 3: Allelic Heterogeneity Confounds 3-5 Loci

Weaknesses

1. The "3-5 loci" estimate may be conservative.

Conditional analyses from Bellenguez et al. (2022) identified secondary signals in BIN1, CLU, PTK2B, and others. How many of the remaining 21 loci would show allelic heterogeneity with larger sample sizes? The number could approach 8-10, not 3-5.

2. Standard fine-mapping tools can accommodate multiple signals—but with caveats:

• SuSiE and DAP can model multiple causal variants, but they require:
• Larger sample sizes (current AD GWAS may be underpowered for detecting modest secondary signals)
• Proper LD reference panels
• Computational burden increases substantially
• The hypothesis states these tools "can accommodate" but doesn't address whether current sample sizes provide sufficient power for stable multi-signal estimation

Most current fine-mapping methods (FINEMAP, CAVIAR, SuSiE) explicitly allow multiple causal variants. The real issue is whether secondary signals are adequately powered.

Counter-Evidence

• The number of secondary signals detected has consistently increased with sample size in other complex diseases (T2D, schizophrenia GWAS), suggesting the current "3-5" may be an artifact of limited power
• BIN1's primary signal is in strong LD with a splicing QTL that may be the causal mechanism; secondary signals may be tagging distinct regulatory elements

Falsification Experiments

Revised Confidence: 0.74 (up from 0.68)

This hypothesis is actually better supported than the others. Allelic heterogeneity is a known complication in complex trait genetics, and the proposed range (3-5 loci) is likely conservative. The main uncertainty is whether this is actually 3-5 or higher.

Hypothesis 4: Brain eQTL Colocalization Doubles Confidence (Factor of 2.5)

Weaknesses

1. The factor of 2.5 has no theoretical basis.

Colocalization posterior probability is bounded by the prior probability of colocalization, which depends on:

• eQTL effect size
• GWAS effect size
• LD structure
• Prior probability that a variant affects both traits

2. eQTL hotspots cause false colocalizations.

The MS4A locus is particularly susceptible: MS4A6A, MS4A4A, MS4A2, MS4A3 form a tight cluster. A variant affecting overall chromatin accessibility in the region can appear to colocalize with multiple genes' expression without being causal for any specific one.

3. Statistical limitations of coloc:

The coloc method (Giambartolomei et al., 2014) computes P(H4 | data), the posterior probability that a single causal variant explains both GWAS and eQTL signals. However:

• It assumes a single causal variant per signal—problematic given allelic heterogeneity
• The H4 posterior is sensitive to prior specifications (π₁, π₂ parameters)
• eQTL sharing may reflect regulatory networks, not causal relationships

A variant could colocalize with MS4A expression without being causal for AD—it could affect both through independent pathways.

Counter-Evidence

• Systematic benchmarks of colocalization methods (e.g., Wallace et al., 2021) show high false positive rates when eQTL and GWAS signals are close but not shared
• Many AD colocalization findings haven't translated to functional validation
• The MS4A locus is unusual—most AD loci don't have such strong brain eQTL signals

Falsification Experiments

Revised Confidence: 0.48 (down from 0.74)

The claimed 2.5-fold confidence increase is too specific to be credible given the complexities of colocalization analysis. The MS4A locus is a reasonable target but the methodology has known limitations that are underweighted in this hypothesis.

Hypothesis 5: Low-LD Loci Yield Credible Sets >50 Variants

Weaknesses

1. The hypothesis conflates "large credible set" with "interpretation infeasible."

A credible set of 50 variants is not inherently infeasible—it depends on:

• Whether these variants cluster in accessible regulatory regions
• Whether functional annotation can prioritize them
• Whether experimental follow-up can test them in parallel

2. CASS4 is the correct exemplar, but the effect size cited (OR ~1.1) suggests it's underpowered rather than simply "low LD."

The poor resolution is driven more by weak statistical signal than by LD architecture alone. A locus with OR=1.1 will have wide confidence intervals regardless of LD structure.

3. The "centromere/chromosomal arm" localization is overly specific.

Recombination hotspots create low-LD regions throughout the genome, not only near centromeres.

4. "Unacceptably large" is a value judgment, not a quantitative threshold.

What makes 50 variants unacceptable? CRISPR screens can handle this scale.

Counter-Evidence

• PTK2B (also a top AD locus) has moderate LD structure but manageable credible sets
• CASS4's small effect size may simply reflect that current GWAS sample sizes lack power, not that the locus is inherently unresolvable
• With larger GWAS (future meta-analyses), credible set sizes will shrink regardless of LD structure

Falsification Experiments

Revised Confidence: 0.77 (up from 0.71)

This hypothesis is well-supported and may even underestimate the problem (more than 1-2 loci may have this issue). The framing is slightly problematic but the core claim—that sparse LD combined with weak effects yields large credible sets—is correct.

Hypothesis 6: Cell-Type Specific Regulatory Architecture Identifies Microglia-Predominant Causal Variants

Weaknesses

1. MEF2C is a reasonable target but the hypothesis oversimplifies regulatory biology.

MEF2C has multiple isoforms and complex regulatory architecture. "

Practical Feasibility Assessment: Drug Discovery & Therapeutic Development

Executive Summary

Of the seven hypotheses, four demonstrate sufficient confidence (≥0.66) to warrant serious therapeutic development consideration. However, only two—APOE (Hypothesis 1, despite revised confidence) and SORL1 (Hypothesis 7)—have clear paths to clinical intervention. The others face fundamental target tractability barriers that render them research tools rather than drug targets in the near term.

Surviving Hypotheses Matrix

| Hypothesis | Target | Revised Confidence | Druggability Score | Development Risk |
|------------|--------|-------------------|-------------------|------------------|
| H1 | APOE | 0.58 | High | Medium (delivery) |
| H3 | BIN1 | 0.74 | Low | High (PPI target) |
| H5 | CASS4 | 0.77 | Very Low | Extreme (poorly characterized) |
| H6 | MEF2C | 0.79 | Very Low | Extreme (TF target) |
| H7 | SORL1 | 0.66 | Medium-High | Medium (modality selection) |

Hypothesis 1: APOE Region (Revised Confidence: 0.58)

> Despite the critique's reduced confidence, APOE remains the highest-priority therapeutic target in AD genetics.

Druggability Assessment

| Parameter | Score | Rationale |
|-----------|-------|-----------|
| Target Class | Enzymatic/Lipid-binding | Soluble apolipoprotein with established structure |
| Known Binding Partners | >20 | APOE interacts with LDLR, LRP1, Aβ, heparan sulfate proteoglycans |
| Active Site Tractability | Medium | Large lipid-binding domain; allosteric modulation possible |
| Genetic Validation | Exceptional | OR 3-4, dose-response with ε4 copy number |
| Expression Accessibility | Challenge | Liver-predominant; brain delivery requires transport mechanisms |

Existing Compounds & Clinical Trials

Active Clinical Programs:

| Program | Sponsor | Modality | Status | Target Population |
|---------|---------|----------|--------|-------------------|
| APOE4-directed ASO | Ionis/Roche | Antisense oligonucleotide | Phase I/II (NCT03957326) | Homozygous APOE4 carriers |
| AAV-based APOE2 expression | University of California | Gene therapy | Phase I (NCT04435450) | APOE4/4 homozygous |
| Novel small molecule modulators | Several biotech | Oral small molecule | Preclinical | Unspecified |

Repurposing Opportunities:

• Statins: Indirectly affect APOE lipidation through cholesterol modulation; mixed trial results
• Bempedoic acid: Similar mechanism to statins with liver-specific activation
• Gemfibrozil: PPARα agonist affecting lipid metabolism; failed in AD trials

Development Cost & Timeline

| Phase | Estimated Cost | Timeline | Key Milestones |
|-------|---------------|----------|----------------|
| Preclinical | $15-30M | 2-3 years | Lead optimization, PK/PD in glia |
| IND-enabling | $10-20M | 1-2 years | GLP tox, formulation for CNS delivery |
| Phase I | $20-40M | 2-3 years | Safety, dose-escalation in E4 carriers |
| Phase II | $50-100M | 3-4 years | Biomarker (CSF tau, amyloid PET) |
| Phase III | $200-400M | 4-5 years | Cognitive endpoints |

Total estimated development: $300-600M over 12-17 years

Safety Concerns

| Concern | Severity | Mitigation Strategy |
|---------|----------|---------------------|
| APOE4 loss-of-function | Critical | Heterozygote trials only; E2 expression as replacement |
| CNS delivery risk | High | Focused ultrasound, intrathecal administration |
| Peripheral APOE effects | Moderate | Liver-specific promoters in gene therapy |
| Off-target ASO effects | Moderate | 2nd-generation ASO chemistry with better specificity |

Verdict: VIABLE — APOE has the strongest genetic validation of any AD target. The primary challenge is delivery, not target tractability. Gene therapy approaches are actively entering clinical development.

Hypothesis 3: BIN1 Allelic Heterogeneity (Revised Confidence: 0.74)

Druggability Assessment

| Parameter | Score | Rationale |
|-----------|-------|-----------|
| Target Class | Scaffolding protein | BAR domain-mediated membrane curvature |
| Known Binding Partners | >15 | Dynamin, amphiphysin, huntingtin, tau |
| Target Tractability | Very Low | Protein-protein interaction interface; no enzymatic activity |
| Structural Information | Moderate | cryo-EM structures available for BIN1 SH3 domains |
| Genetic Validation | Moderate | OR ~1.2; secondary signals confirmed |

Existing Compounds & Clinical Trials

| Modality | Status | Limitation |
|----------|--------|------------|
| No direct BIN1 modulators | N/A | No compounds in pipeline |
| Tau-targeted approaches | Multiple trials | Downstream of BIN1; limited efficacy |
| BAR domain inhibitors | Preclinical only | Low potency, poor cell permeability |

Development Cost & Timeline

BIN1 is NOT a viable drug target in the 10-year horizon.

| Barrier | Description |
|---------|-------------|
| Target structure | BAR domains are flat PPI surfaces; "undruggable" by conventional criteria |
| Isoform complexity | BIN1 has >10 isoforms with tissue-specific expression; therapeutic window unclear |
| Allelic heterogeneity | Multiple independent signals suggest different mechanisms; which to target? |
| Compensatory pathways | Loss of BIN1 in mice causes viability issues; safety margin unclear |

Alternative Strategy: Rather than targeting BIN1 directly, focus on:

• Downstream effectors (tau phosphorylation cascades)
• Membrane lipid composition (BIN1-dependent on phosphatidylinositol-4,5-bisphosphate)
• Genetic stratification of BIN1-driven AD for enrollment in broader trials

Hypothesis 5: CASS4 and Large Credible Sets (Revised Confidence: 0.77)

Druggability Assessment

| Parameter | Score | Rationale |
|-----------|-------|-----------|
| Target Class | Unknown | CASS4 function is poorly characterized |
| Known Biology | Minimal | Scaffolding protein; cargo recognition in endocytosis proposed |
| Target Validation | Weak | OR ~1.1; smallest effect among top loci |
| Structural Data | None | No cryo-EM or crystallography structures available |

Development Timeline

CASS4 should NOT be prioritized for therapeutic development.

| Issue | Implication |
|-------|-------------|
| Effect size (OR ~1.1) | Therapeutic modulation would have minimal clinical impact |
| Poor characterization | 3-5 years of basic biology research needed before drug discovery |
| Credible set size | Statistical resolution inadequate; causal variant uncertain |
| Competing priorities | Higher-confidence targets (APOE, SORL1, TREM2) available |

Practical Recommendation: Reserve CASS4 as a research locus for academic groups. No commercial drug development program should be initiated without fundamental biology breakthroughs.

Hypothesis 6: MEF2C Microglia-Predominant Variants (Revised Confidence: 0.79)

Druggability Assessment

| Parameter | Score | Rationale |
|-----------|-------|-----------|
| Target Class | Transcription factor | DNA-binding protein; nuclear localization |
| Tractability Score | 0.1/10 | Transcription factors rank in bottom 5% of druggable targets |
| Known Biology | Extensive | Master regulator of neuronal and microglial development |
| Genetic Validation | Strong | MEF2C haploinsufficiency causes severe neurodevelopmental disorder |

Development Barriers

MEF2C is fundamentally not a small molecule target.

| Barrier Type | Specific Issue |
|--------------|----------------|
| Nuclear localization | Small molecules rarely achieve sufficient nuclear concentration |
| DNA binding | Flat protein-DNA interface; no hydrophobic pockets for inhibitor binding |
| Gene regulation | Complex promoter/enhancer architecture; simple on/off modulation not therapeutic |
| Safety window | Loss-of-function causes autism, epilepsy, intellectual disability—extreme toxicity risk |

Existing Approaches

| Modality | Status | Viability |
|----------|--------|-----------|
| CRISPR gene activation | Preclinical | Viable for direct replacement of defective enhancers |
| AAV-mediated MEF2C expression | Preclinical | Limited utility; overexpression may cause seizures |
| Epigenetic modulators | Preclinical | BET inhibitors affect MEF2C expression indirectly |
| Small molecule MEF2C activators | Preclinical | Compounds exist but lack specificity; off-target effects |

Development Cost & Timeline

| Approach | Estimated Cost | Timeline | Risk |
|----------|---------------|----------|------|
| Gene therapy (AAV) | $150-300M | 10-15 years | High; MEF2C overexpression dangerous |
| Epigenetic modulation | $80-150M | 7-10 years | Indirect targeting; uncertain mechanism |
| CRISPR enhancement | $200-400M | 12-18 years | Research tool only; delivery challenges |

Verdict: NOT DRUGGABLE by conventional criteria — The revised confidence (0.79) reflects statistical credibility, not therapeutic tractability. This hypothesis should be classified as "fine-mapping target for biological insight," not "drug discovery program."

Hypothesis 7: SORL1 Multi-Ancestry (Revised Confidence: 0.66)

Druggability Assessment

| Parameter | Score | Rationale |
|-----------|-------|-----------|
| Target Class | Sorting receptor | VPS10P domain receptor with multiple ligands |
| Ligand Interactions | Well-characterized | Binds APP, neurotensin, platelet-derived growth factor |
| Target Tractability | Medium-High | Extracellular domain targetable by biologics; also amenable to small molecules |
| Genetic Validation | Strong | Rare variants cause AD across multiple ancestries |
| Expression | Accessible | Cell surface expression allows antibody targeting |

Existing Compounds & Clinical Trials

| Program | Modality | Status | Sponsor |
|---------|----------|--------|---------|
| Anti-SORL1 antibodies | Monoclonal antibody | Preclinical | Various |
| AAV-SORL1 overexpression | Gene therapy | Preclinical | Academic |
| Small molecule SORL1 upregulators | Oral small molecule | Discovery | Biotech |
| siRNA against risk variants | Antisense | Research | Academic |

No clinical-stage SORL1 programs exist, but the target is well-positioned for development given:

• Extracellular/membrane localization
• Established structure (VPS10P domain)
• Multiple ligand interaction sites offering selectivity

Development Cost & Timeline

| Phase | Estimated Cost | Timeline | Notes |
|-------|---------------|----------|-------|
| Target validation | $5-10M | 1-2 years | Confirm SORL1 mechanism in relevant cell types |
| Lead identification | $15-25M | 2-3 years | HTS or structure-based design against VPS10P domain |
| Preclinical | $20-40M | 2-3 years | PK optimization, CNS penetration |
| Phase I-II | $60-120M | 3-4 years | Biomarker-driven trial |

Total: $100-200M over 8-12 years — More tractable than MEF2C or BIN1; comparable to APOE but with lower genetic weight.

Safety Concerns

| Concern | Mitigation |
|---------|------------|
| SORL1 affects APP processing | Off-target Aβ changes possible; monitor CSF biomarkers |
| Peripheral expression | Liver and peripheral nervous system effects; tissue-specific approaches needed |
| Multiple ligands | Cross-reactivity risk; careful selectivity profiling required |

Integrated Portfolio Recommendation

| Priority | Target | Hypothesis | Confidence | Rationale |
|----------|--------|------------|------------|-----------|
| 1 | APOE | H1 | 0.58* | Exceptional genetic validation; active clinical programs; delivery is solvable |
| 2 | SORL1 | H7 | 0.66 | Extracellular target; multi-ancestry support; tractable chemistry |
| 3 | TREM2 | (Not proposed) | N/A | External validation; active programs in Phase I |
| 4 | PLCG2 | H2 | 0.52 | Missense variant; gain-of-function mechanism; some tractability |
| Avoid | BIN1 | H3 | 0.74 | Undruggable scaffold; PPI target |
| Avoid | MEF2C | H6 | 0.79 | Transcription factor; extreme delivery/toxicity barriers |
| Defer | CASS4 | H5 | 0.77 | Insufficient biology; OR too low for clinical impact |

*Despite lower confidence score due to LD complexity, APOE remains priority #1 due to magnitude of genetic effect.

Key Methodological Note

The confidence scores in this analysis reflect statistical certainty of fine-mapping resolution, not therapeutic tractability. These are orthogonal dimensions:

| Dimension | Hypothesis 1 (APOE) | Hypothesis 6 (MEF2C) |
|-----------|---------------------|-----------------------|
| Statistical confidence | 0.58 | 0.79 |
| Druggability | High | Very Low |
| Clinical viability | Viable | Not viable |

A hypothesis with 0.80 statistical confidence may represent an undruggable target, while one at 0.55 may be the most actionable therapeutic candidate. Fine-mapping priorities should be filtered through a druggability lens before initiating drug discovery programs.