Apply window based clumping on summary statistics datasets.
Source code in src/gentropy/window_based_clumping.py
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70 | class WindowBasedClumpingStep:
"""Apply window based clumping on summary statistics datasets."""
def __init__(
self,
session: Session,
summary_statistics_input_path: str,
study_locus_output_path: str,
distance: int = WindowBasedClumpingStepConfig().distance,
gwas_significance: float = WindowBasedClumpingStepConfig().gwas_significance,
collect_locus: bool = WindowBasedClumpingStepConfig().collect_locus,
collect_locus_distance: int = WindowBasedClumpingStepConfig().collect_locus_distance,
inclusion_list_path: str
| None = WindowBasedClumpingStepConfig().inclusion_list_path,
recursive_file_lookup: bool = WindowBasedClumpingStepConfig().recursive_file_lookup,
) -> None:
"""Run window-based clumping step.
Args:
session (Session): Session object.
summary_statistics_input_path (str): Path to the harmonized summary statistics dataset.
study_locus_output_path (str): Output path for the resulting study locus dataset.
distance (int): Distance, within which tagging variants are collected around the semi-index. Optional.
gwas_significance (float): GWAS significance threshold. Defaults to 5e-8.
collect_locus (bool): Whether to collect locus around semi-indices. Optional.
collect_locus_distance (int): Distance, within which tagging variants are collected around the semi-index. Optional.
inclusion_list_path (str | None): Path to the inclusion list (list of white-listed study identifier). Optional.
recursive_file_lookup (bool): Whether to recursively look for summary statistics files in the input path. Defaults to `True`.
Note that if an inclusion list is provided, this flag is set to `True` always.
Check WindowBasedClumpingStepConfig object for default values.
"""
# If inclusion list path is provided, only these studies will be read:
if inclusion_list_path:
study_ids_to_ingest = [
f"{summary_statistics_input_path}/{row['studyId']}.parquet"
for row in session.spark.read.parquet(inclusion_list_path).collect()
]
# Force recursive file lookup if inclusion list is provided
recursive_file_lookup = True
else:
# If no inclusion list is provided, read all summary stats in folder:
study_ids_to_ingest = [summary_statistics_input_path]
ss = SummaryStatistics.from_parquet(
session, study_ids_to_ingest, recursiveFileLookup=recursive_file_lookup
)
# Clumping:
study_locus = ss.window_based_clumping(
distance=distance, gwas_significance=gwas_significance
)
# Optional locus collection:
if collect_locus:
# Collecting locus around semi-indices:
study_locus = study_locus.annotate_locus_statistics(
ss, collect_locus_distance=collect_locus_distance
)
study_locus.df.write.mode(session.write_mode).parquet(study_locus_output_path)
|