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fix(susie_finemapper): fix in the fine-mapper in case of sum stat imputation is False #627

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merged 2 commits into from
May 29, 2024
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fix(susie_finemapper): fix in the fine-mapper in case of sum stat imputation is False #627

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75f2412
fix(susie_finemapper): fix in the FMer in case ss_imp=False
addramir May 29, 2024
03e5290
fix: small fix in docs
addramir May 29, 2024
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75 changes: 59 additions & 16 deletions src/gentropy/susie_finemapper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -120,16 +120,18 @@ def __init__(
ld_score_threshold=ld_score_threshold,
)
)
# Write result
result_logging["study_locus"].df.write.mode(session.write_mode).parquet(
output_path + "/" + study_locus_to_finemap
)
# Write log
result_logging["log"].to_parquet(
output_path_log + "/" + study_locus_to_finemap + ".parquet",
engine="pyarrow",
index=False
)

if result_logging is not None:
# Write result
result_logging["study_locus"].df.write.mode(session.write_mode).parquet(
output_path + "/" + study_locus_to_finemap
)
# Write log
result_logging["log"].to_parquet(
output_path_log + "/" + study_locus_to_finemap + ".parquet",
engine="pyarrow",
index=False,
)
else:
result = self.susie_finemapper_ss_gathered(
session=session,
Expand Down Expand Up @@ -1009,7 +1011,7 @@ def susie_finemapper_one_studylocus_row_v3_dev_ss_gathered(
purity_mean_r2_threshold: float = 0,
purity_min_r2_threshold: float = 0.25,
cs_lbf_thr: float = 2,
) -> dict[str, Any]:
) -> dict[str, Any] | None:
"""Susie fine-mapper function that uses study-locus row with collected locus, chromosome and position as inputs.

Args:
Expand All @@ -1032,7 +1034,7 @@ def susie_finemapper_one_studylocus_row_v3_dev_ss_gathered(
cs_lbf_thr (float): credible set logBF threshold for filtering credible sets, default is 2

Returns:
dict[str, Any]: dictionary with study locus, number of GWAS variants, number of LD variants, number of variants after merge, number of outliers, number of imputed variants, number of variants to fine-map
dict[str, Any] | None: dictionary with study locus, number of GWAS variants, number of LD variants, number of variants after merge, number of outliers, number of imputed variants, number of variants to fine-map, or None
"""
# PLEASE DO NOT REMOVE THIS LINE
pd.DataFrame.iteritems = pd.DataFrame.items
Expand Down Expand Up @@ -1077,6 +1079,11 @@ def susie_finemapper_one_studylocus_row_v3_dev_ss_gathered(
.filter(f.col("z").isNotNull())
)

# Remove ALL duplicated variants from GWAS DataFrame - we don't know which is correct
variant_counts = gwas_df.groupBy("variantId").count()
unique_variants = variant_counts.filter(f.col("count") == 1)
gwas_df = gwas_df.join(unique_variants, on="variantId", how="left_semi")

ld_index = (
GnomADLDMatrix()
.get_locus_index(
Expand All @@ -1097,14 +1104,50 @@ def susie_finemapper_one_studylocus_row_v3_dev_ss_gathered(
).cast("string"),
)
)

gnomad_ld = GnomADLDMatrix.get_numpy_matrix(
ld_index, gnomad_ancestry=major_population
# Remove ALL duplicated variants from ld_index DataFrame - we don't know which is correct
variant_counts = ld_index.groupBy("variantId").count()
unique_variants = variant_counts.filter(f.col("count") == 1)
ld_index = ld_index.join(unique_variants, on="variantId", how="left_semi").sort(
"idx"
)

if not run_sumstat_imputation:
# Filtering out the variants that are not in the LD matrix, we don't need them
gwas_index = gwas_df.join(
ld_index.select("variantId", "alleles", "idx"), on="variantId"
).sort("idx")
gwas_df = gwas_index.select(
"variantId",
"z",
"chromosome",
"position",
"beta",
"StandardError",
)
gwas_index = gwas_index.drop(
"z", "chromosome", "position", "beta", "StandardError"
)
if gwas_index.rdd.isEmpty():
logging.warning("No overlapping variants in the LD Index")
return None
gnomad_ld = GnomADLDMatrix.get_numpy_matrix(
gwas_index, gnomad_ancestry=major_population
)
else:
gwas_index = gwas_df.join(
ld_index.select("variantId", "alleles", "idx"), on="variantId"
).sort("idx")
if gwas_index.rdd.isEmpty():
logging.warning("No overlapping variants in the LD Index")
return None
gwas_index = ld_index
gnomad_ld = GnomADLDMatrix.get_numpy_matrix(
gwas_index, gnomad_ancestry=major_population
)

out = SusieFineMapperStep.susie_finemapper_from_prepared_dataframes(
GWAS_df=gwas_df,
ld_index=ld_index,
ld_index=gwas_index,
gnomad_ld=gnomad_ld,
L=max_causal_snps,
session=session,
Expand Down