feat(studyLocus validation): adding validation logic to studyLocus dataset

[DSuveges]

🛠 What does this PR implement

StudyLocus dataset now can be validated based on the p-value of the lead variant or the study quality flags of the study the locus was discovered in. As related effort the p-value quality control logic was moved from gwas catalog ingestion module and was moved to the study-locus dataset and was wrapped as a stand-alone validation step.

🙈 Missing

Important: This is not a step! This is pure business logic. Writing the orchestration is a separate effort.

🚦 Before submitting

• Do these changes cover one single feature (one change at a time)?
• Did you read the contributor guideline?
• Did you make sure to update the documentation with your changes?
• Did you make sure there is no commented out code in this PR?
• Did you follow conventional commits standards in PR title and commit messages?
• Did you make sure the branch is up-to-date with the dev branch?
• Did you write any new necessary tests?
• Did you make sure the changes pass local tests (make test)?
• Did you make sure the changes pass pre-commit rules (e.g poetry run pre-commit run --all-files)?

[DSuveges]

Usage:

study_index.df.show(truncate=False)
study_locus.df.show(truncate=False)
(
    study_locus
    .validate_lead_pvalue(1e-4)
    .validate_study(study_index)
    .df
    .show(truncate=False)
)

Output:

+-------+---------+---------+---------------+
|studyId|projectId|studyType|qualityControls|
+-------+---------+---------+---------------+
|s1     |p1       |gwas     |[]             |
|s2     |p1       |gwas     |[some_flag]    |
|s3     |p1       |gwas     |[some_flag]    |
+-------+---------+---------+---------------+

+------------+---------+-------+--------------+--------------+---------------+
|studyLocusId|variantId|studyId|pValueMantissa|pValueExponent|qualityControls|
+------------+---------+-------+--------------+--------------+---------------+
|1           |v1       |s1     |1.0           |-8            |[]             |
|3           |v2       |s2     |1.0           |-3            |[]             |
|2           |v2       |s3     |1.0           |-5            |[]             |
|2           |v2       |s4     |1.0           |-4            |[]             |
+------------+---------+-------+--------------+--------------+---------------+

+------------+---------+-------+--------------+--------------+-----------------------------------------------------------+
|studyLocusId|variantId|studyId|pValueMantissa|pValueExponent|qualityControls                                            |
+------------+---------+-------+--------------+--------------+-----------------------------------------------------------+
|1           |v1       |s1     |1.0           |-8            |[]                                                         |
|3           |v2       |s2     |1.0           |-3            |[Subsignificant p-value, Study has failed quality controls]|
|2           |v2       |s3     |1.0           |-5            |[Study has failed quality controls]                        |
|2           |v2       |s4     |1.0           |-4            |[Study not found in the study index]                       |
+------------+---------+-------+--------------+--------------+-----------------------------------------------------------+

[d0choa]

Once we start putting these functions into use, we might require minor tweaks. No point on trying to anticipate any of that.