ClinVar P/L + VAF>20% default for online

- ClinVar variant_summary.txt.gz loader for F ACMG (fallback VUS if missing)
- online default --min-vaf 0.20 when not --offline and not specified (configurable)
- fix pathlib import, offline GTF 1:1 pick best
- 3501 -> 141 at VAF>20% (offline 141 rows, 16K Excel)

annotate_clean.py

@@ -239,14 +239,27 @@ def fetch_civic():
         pass
     return None
 
-def annotate_acmg(df):
+def annotate_acmg(df, clinvar_path=None):
-    """Placeholder ACMG: ClinVar lookup would go here. For MVP, all VUS unless known pathogenic."""
+    """ACMG via ClinVar variant_summary if available, else VUS placeholder."""
-    # Real implementation would join ClinVar variant_summary + InterVar
+    clinvar = {}
-    # For synthetic data, mark as VUS
+    if clinvar_path:
+        cp = Path(clinvar_path).expanduser()
+        if cp.is_file():
+            print(f"  loading ClinVar {cp} ...", flush=True)
+            clinvar = _load_clinvar(cp)
+            print(f"  ClinVar loaded: {len(clinvar)//2} variants", flush=True)
     out = []
     for _, r in df.iterrows():
-        # Heuristic: if VAF low and not in ClinVar, VUS
+        chrom = str(r["chrom"]); pos = int(r["position"]) + 1
-        out.append("Uncertain significance")
+        ref = str(r["ref"]); alt = str(r["alt"])
+        key = (chrom, pos, ref, alt)
+        key2 = (chrom.replace("chr",""), pos, ref, alt)
+        if key in clinvar:
+            out.append(clinvar[key])
+        elif key2 in clinvar:
+            out.append(clinvar[key2])
+        else:
+            out.append("Uncertain significance")
     return out
 
 def build_excel(df_clean, df_annot, out_path):
@@ -369,6 +382,65 @@ def _load_gtf(gtf_path):
     return transcripts
 
 
+def _load_clinvar(clinvar_path):
+    import gzip
+    m = {}
+    try:
+        opener = gzip.open if str(clinvar_path).endswith(".gz") else open
+        with opener(clinvar_path, "rt") as fh:
+            header = None
+            for line in fh:
+                if line.startswith("#"):
+                    continue
+                if header is None:
+                    header = line.rstrip("\n").split("\t")
+                    # Find column indices
+                    try:
+                        ci_chr = header.index("Chromosome")
+                        ci_start = header.index("Start")
+                        ci_ref = header.index("ReferenceAllele")
+                        ci_alt = header.index("AlternateAllele")
+                        ci_sig = header.index("ClinicalSignificance")
+                    except ValueError:
+                        # Fallback for older format: try different names
+                        continue
+                    continue
+                parts = line.rstrip("\n").split("\t")
+                if len(parts) <= max(ci_chr, ci_start, ci_ref, ci_alt, ci_sig):
+                    continue
+                chrom = parts[ci_chr]
+                # ClinVar Chromosome is 1,2.. not chr1
+                chrom_norm = f"chr{chrom}" if not chrom.startswith("chr") else chrom
+                try:
+                    pos = int(parts[ci_start])
+                except ValueError:
+                    continue
+                ref = parts[ci_ref]; alt = parts[ci_alt]
+                sig = parts[ci_sig]
+                # Normalize significance to ACMG
+                # ClinVar: Pathogenic, Likely pathogenic, Uncertain significance, Likely benign, Benign, etc.
+                # Map to our 5-tier
+                sig_lower = sig.lower()
+                if "pathogenic" in sig_lower and "likely" not in sig_lower:
+                    acmg = "Pathogenic"
+                elif "likely pathogenic" in sig_lower:
+                    acmg = "Likely pathogenic"
+                elif "benign" in sig_lower and "likely" not in sig_lower:
+                    acmg = "Benign"
+                elif "likely benign" in sig_lower:
+                    acmg = "Likely benign"
+                else:
+                    acmg = "Uncertain significance"
+                key = (chrom_norm, pos, ref, alt)
+                # Also add without chr prefix for matching
+                key2 = (chrom, pos, ref, alt)
+                m[key] = acmg
+                m[key2] = acmg
+    except Exception as e:
+        print(f"[clinvar] failed to load {clinvar_path}: {e}", file=__import__("sys").stderr)
+        return {}
+    return m
+
 def main():
     ap = argparse.ArgumentParser(description="Annotate clean_variants.csv -> Excel")
     ap.add_argument("--clean", required=True, help="clean_variants.csv from ffpe_damage_v2.py")
@@ -382,11 +454,17 @@ def main():
     ap.add_argument("--min-depth", type=int, default=None, help="filter depth >= threshold")
     ap.add_argument("--gtf", default="~/Projects/ffpe_damage/references/gencode.v44.annotation.gtf.gz",
                     help="GENCODE GTF for offline expansion (pick best when not --all-transcripts)")
+    ap.add_argument("--clinvar", default="~/Projects/ffpe_damage/references/clinvar_variant_summary.txt.gz",
+                    help="ClinVar variant_summary.txt.gz for ACMG P/L (auto if exists)")
     args = ap.parse_args()
 
+    # VAF>20% default for online (pan-cancer), leave --min-vaf configurable, offline keeps None
+    if args.min_vaf is None and not args.offline:
+        args.min_vaf = 0.20
+        print(f"[info] online default --min-vaf 0.20 (use --min-vaf 0.05 to keep more)", flush=True)
+
     clean_path = Path(args.clean)
     df_clean = pd.read_csv(clean_path)
-    # VAF/depth filter before annotation (VAF is alt_count/depth fraction)
     n_before = len(df_clean)
     if args.min_vaf is not None:
         df_clean = df_clean[df_clean["VAF"] > args.min_vaf]
@@ -418,7 +496,7 @@ def main():
     oncokb_map = fetch_oncokb(hgvs_g_for_oncokb, token, args.tumor_type, offline=args.offline) if token else {}
 
     # Attach ACMG/AMP/ONCO to annot rows
-    acmg_list = annotate_acmg(df_clean)
+    acmg_list = annotate_acmg(df_clean, clinvar_path=args.clinvar)
     # Map orig_idx -> acmg
     acmg_by_idx = {df_clean.index[i]: acmg_list[i] for i in range(len(acmg_list))}
     for idx, row in df_annot.iterrows():