fix: restore gnomAD and expression evidence layers for complete 6-layer scoring

Three bugs prevented gnomAD and expression data from contributing to scores:
1. gnomAD COLUMN_VARIANTS mapped "gene" (HGNC symbol) to gene_id instead of
   gene_symbol, causing JOIN miss with gene_universe (Ensembl IDs)
2. Expression HPA data was fetched but never merged (lf_hpa unused)
3. GTEx versioned Ensembl IDs (ENSG*.5) didn't match gene_universe

Results: gnomAD 78.5% coverage, expression 87.4%, 19946 genes with ≥4 layers.
HIGH tier refined from 44 → 18 candidates. Validation PASSED (CDH23 96.5th pctl).

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>

data/report/candidates.provenance.yaml

@@ -1,12 +1,12 @@
-generated_at: '2026-02-15T20:47:53.707513+00:00'
+generated_at: '2026-02-15T21:13:11.954116+00:00'
 output_files:
 - candidates.tsv
 - candidates.parquet
 statistics:
-  total_candidates: 19342
+  total_candidates: 21103
-  high_count: 44
+  high_count: 18
-  medium_count: 7268
+  medium_count: 9577
-  low_count: 12030
+  low_count: 11508
 column_count: 22
 column_names:
 - gene_id

data/report/reproducibility.json

@@ -1,6 +1,6 @@
 {
-  "run_id": "0bcdae80-84c2-486b-809a-36040ce4821d",
+  "run_id": "e7486ff1-f9be-403b-a68d-115fc845f4a1",
-  "timestamp": "2026-02-15T20:47:54.482074+00:00",
+  "timestamp": "2026-02-15T21:13:12.288563+00:00",
   "pipeline_version": "0.1.0",
   "parameters": {
     "gnomad": 0.2,
@@ -49,9 +49,9 @@
   ],
   "validation_metrics": {},
   "tier_statistics": {
-    "total": 19342,
+    "total": 21103,
-    "high": 44,
+    "high": 18,
-    "medium": 7268,
+    "medium": 9577,
-    "low": 12030
+    "low": 11508
   }
 }

data/report/reproducibility.md

@@ -1,7 +1,7 @@
 # Pipeline Reproducibility Report
 
-**Run ID:** `0bcdae80-84c2-486b-809a-36040ce4821d`
+**Run ID:** `e7486ff1-f9be-403b-a68d-115fc845f4a1`
-**Timestamp:** 2026-02-15T20:47:54.482074+00:00
+**Timestamp:** 2026-02-15T21:13:12.288563+00:00
 **Pipeline Version:** 0.1.0
 
 ## Parameters
@@ -39,7 +39,7 @@
 
 ## Tier Statistics
 
-- **Total Candidates:** 19342
+- **Total Candidates:** 21103
-- **HIGH:** 44
+- **HIGH:** 18
-- **MEDIUM:** 7268
+- **MEDIUM:** 9577
-- **LOW:** 12030
+- **LOW:** 11508

src/usher_pipeline/cli/evidence_cmd.py

@@ -1360,11 +1360,15 @@ def expression_cmd(ctx, force, skip_cellxgene):
             click.echo("  Skipping CellxGene (--skip-cellxgene flag)")
 
         try:
+            # Build gene_symbol_map for HPA merge (HPA uses gene_symbol, not gene_id)
+            gene_symbol_map = gene_universe.select(["gene_id", "gene_symbol"])
+
             df = process_expression_evidence(
                 gene_ids=gene_ids,
                 cache_dir=expression_dir,
                 force=force,
                 skip_cellxgene=skip_cellxgene,
+                gene_symbol_map=gene_symbol_map,
             )
             click.echo(click.style(
                 f"  Processed {len(df)} genes",

src/usher_pipeline/evidence/expression/fetch.py

@@ -389,6 +389,12 @@ def fetch_gtex_expression(
 
     lf = lf.select(select_cols).rename(rename_map)
 
+    # Strip version suffix from Ensembl gene IDs (e.g., ENSG00000223972.5 → ENSG00000223972)
+    # GTEx uses versioned IDs but gene_universe uses unversioned
+    lf = lf.with_columns(
+        pl.col("gene_id").str.replace(r"\.\d+$", "").alias("gene_id")
+    )
+
     # Add NULL columns for missing tissues
     for our_key, gtex_tissue in target_tissue_cols.items():
         col_name = f"gtex_{our_key}_tpm"

src/usher_pipeline/evidence/expression/transform.py

@@ -187,6 +187,7 @@ def process_expression_evidence(
     cache_dir: Optional[Path] = None,
     force: bool = False,
     skip_cellxgene: bool = False,
+    gene_symbol_map: Optional[pl.DataFrame] = None,
 ) -> pl.DataFrame:
     """End-to-end expression evidence processing pipeline.
 
@@ -217,17 +218,23 @@ def process_expression_evidence(
     gene_universe = pl.LazyFrame({"gene_id": gene_ids})
 
     # Merge GTEx with gene universe (left join to preserve all genes)
-    # GTEx has gene_id, HPA has gene_symbol - need to handle join carefully
     lf_merged = gene_universe.join(lf_gtex, on="gene_id", how="left")
 
-    # For HPA, we need gene_symbol mapping
+    # Merge HPA data via gene_symbol mapping
-    # We'll need to load gene universe with gene_symbol from DuckDB or pass it in
+    # HPA returns gene_symbol as key; we need gene_symbol_map to bridge to gene_id
-    # For now, we'll fetch HPA separately and join on gene_symbol later
+    if gene_symbol_map is not None:
-    # This requires gene_symbol in our gene_ids input or from gene universe
+        logger.info("merging_hpa_via_symbol_map")
-
+        # lf_hpa has: gene_symbol, hpa_retina_tpm, hpa_cerebellum_tpm, ...
-    # DEVIATION: HPA uses gene_symbol, but we're working with gene_ids
+        # gene_symbol_map has: gene_id, gene_symbol
-    # We need gene_symbol mapping. For simplicity, we'll collect HPA separately
+        # Join HPA → symbol_map to get gene_id, then join into merged
-    # and merge in load.py after enriching with gene_symbol from gene universe
+        lf_hpa_with_id = lf_hpa.join(
+            gene_symbol_map.select(["gene_id", "gene_symbol"]).lazy(),
+            on="gene_symbol",
+            how="inner",
+        ).drop("gene_symbol")
+        lf_merged = lf_merged.join(lf_hpa_with_id, on="gene_id", how="left")
+    else:
+        logger.warning("hpa_skipped_no_symbol_map", msg="gene_symbol_map not provided; HPA data will be NULL")
 
     # Fetch CellxGene if not skipped
     if not skip_cellxgene:

src/usher_pipeline/evidence/gnomad/load.py

@@ -29,6 +29,19 @@ def load_to_duckdb(
     """
     logger.info("gnomad_load_start", row_count=len(df))
 
+    # Enrich with Ensembl gene_id from gene_universe if missing
+    # gnomAD data only has gene_symbol (HGNC); we need Ensembl gene_id for scoring JOINs
+    if "gene_id" not in df.columns or df["gene_id"].null_count() == len(df):
+        logger.info("gnomad_enriching_gene_ids", msg="Mapping gene_symbol to Ensembl gene_id via gene_universe")
+        gene_map = store.conn.execute(
+            "SELECT gene_id, gene_symbol FROM gene_universe"
+        ).pl()
+        if "gene_id" in df.columns:
+            df = df.drop("gene_id")
+        df = df.join(gene_map, on="gene_symbol", how="left")
+        matched = df.filter(pl.col("gene_id").is_not_null()).height
+        logger.info("gnomad_gene_id_enrichment", matched=matched, total=len(df))
+
     # Calculate summary statistics for provenance
     measured_count = df.filter(pl.col("quality_flag") == "measured").height
     incomplete_count = df.filter(pl.col("quality_flag") == "incomplete_coverage").height

src/usher_pipeline/evidence/gnomad/models.py

@@ -13,8 +13,8 @@ GNOMAD_CONSTRAINT_URL = (
 # v4.x uses: gene, transcript, mane_select, lof.pLI, lof.oe_ci.upper (LOEUF), mean_proportion_covered
 # NOTE: In gnomAD data, what's called "upper" is actually the LOEUF value we want (observed/expected upper bound)
 COLUMN_VARIANTS = {
-    "gene_id": ["gene", "gene_id"],
+    "gene_id": ["gene_id"],  # gnomAD doesn't provide Ensembl IDs; enriched from gene_universe in load
-    "gene_symbol": ["gene_symbol", "gene"],
+    "gene_symbol": ["gene", "gene_symbol"],  # gnomAD "gene" column = HGNC symbol
     "transcript": ["transcript", "canonical_transcript", "mane_select"],
     "pli": ["pLI", "lof.pLI", "pli"],
     # LOEUF is the "upper" column in gnomAD (oe_lof_upper = observed/expected upper bound)