Sign disagreement between published QTL feathers and Supp. Table 4 for caqtl_microglia and dsqtl_yoruba

## Sign disagreement between published QTL feathers and Supp. Table 4 for caqtl_microglia and dsqtl_yoruba

Hi! While reproducing the QTL coefficient evaluation against your paper's Suppl. Table 4 numbers, I ran into an inconsistency that I think is most likely a feather export bug, but it could also be that there's a documented sign-correction step I'm missing. Wanted to flag it either way.

### Summary

For 3 of the 5 caQTL/dsQTL coefficient datasets you publish at `gs://alphagenome/evals/`, the signed `pearsonr(prediction, target)` computed directly on the feather matches the paper's Suppl. Table 4 value. For 2 datasets (`caqtl_microglia` and `dsqtl_yoruba`), the magnitude matches but the **sign is opposite**.

| Dataset | Direct from feather | Suppl. Table 4 |
|---|---|---|
| caqtl_african | **+0.7367** | +0.7368 ✅ |
| caqtl_european | **+0.5914** | +0.5916 ✅ |
| caqtl_smc | **+0.6870** | +0.6870 ✅ |
| **caqtl_microglia** | **−0.6354** | **+0.6357** ❌ |
| **dsqtl_yoruba** | **−0.8323** | **+0.8323** ❌ |

### Reproducer (no install needed, runs on any machine with pandas + scipy)

```python
import os
import pandas as pd
from scipy.stats import pearsonr
from urllib.request import urlretrieve

BASE = "https://storage.googleapis.com/alphagenome/evals/"
cases = [
("caqtl_african", "caqtl_african_variant_coefficient_human_predictions"),
("caqtl_european", "caqtl_european_variant_coefficient_human_predictions"),
("caqtl_smc", "caqtl_smc_variant_coefficient_human_predictions"),
("caqtl_microglia", "caqtl_microglia_variant_coefficient_human_predictions"),
("dsqtl_yoruba", "dsqtl_yoruba_variant_coefficient_human_predictions"),
]
for label, name in cases:
local = f"/tmp/{name}.feather"
if not os.path.exists(local):
urlretrieve(BASE + name + ".feather", local)
df = pd.read_feather(local)
r, _ = pearsonr(df["prediction"], df["target"])
print(f"{label:<20} pearsonr(prediction, target) = {r:+.4f}")
```

Output:
```
caqtl_african pearsonr(prediction, target) = +0.7367
caqtl_european pearsonr(prediction, target) = +0.5914
caqtl_smc pearsonr(prediction, target) = +0.6870
caqtl_microglia pearsonr(prediction, target) = -0.6354
dsqtl_yoruba pearsonr(prediction, target) = -0.8323
```

### What I checked

- The paper distinguishes signed vs unsigned Pearson explicitly (e.g. caQTL Fig. 5d: *"Signed Pearson r = 0.74; unsigned Pearson r = 0.45"*), so Suppl. Table 4's `pearsonr` column is clearly the signed version — the magnitudes match perfectly across all 5 datasets, only the sign disagrees on 2.
- I separately verified by running `model.predict_variant` from this SDK on a few `caqtl_microglia` variants — the values reproduce the feather's `prediction` column to within bf16 numerics (`r ≈ 0.999`). So the **`prediction` column is faithful to model output**; the apparent disagreement is between `target` and the paper number.
- The other 3 datasets process through the same loading code I'm using (`target` → effect_size, `prediction` → score) and they line up with the paper exactly. So this isn't something on my end being applied inconsistently.

### Question

Is there a per-dataset sign-correction step in your table-generation pipeline that the published feathers don't reflect (e.g. a polarity flip based on which allele was assigned `REF` in the upstream QTL study for those two)? Or is the `target` column for `caqtl_microglia` and `dsqtl_yoruba` simply exported with the wrong sign?

Either is fine — either the feathers should be re-exported, or the convention should be documented somewhere users can find it. Right now anyone naively running `pearsonr(prediction, target)` on the published artifacts gets the opposite sign of the paper for those two datasets.

Happy to send a PR with whatever fix you prefer (re-sign the feather column, or add a documentation note + a small `apply_sign_convention()` utility).

### Why this matters downstream

Anyone benchmarking against your numbers using `gs://alphagenome/evals/` directly hits this — for instance our radical-eval pipeline cross-published baselines for all 5 datasets and the microglia/dsqtl_yoruba ones came out with the wrong sign, traceable entirely to this.

Thanks!

Start by running the provided pandas/scipy reproducer against the five feather artifacts in gs://alphagenome/evals/ and compare the results with Supplementary Table 4. Then trace the evaluation table-generation and sign-convention handling for caqtl_microglia and dsqtl_yoruba; done means the published artifacts and documented convention produce the reported signed correlations.

Written by the indexing model from the issue text.