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312 | @dataclass
class FinnGenFinemapping:
"""SuSIE finemapping dataset for FinnGen.
Credible sets from SuSIE are extracted and transformed into StudyLocus objects:
- Study ID in the special format (e.g. FINNGEN_R10_*)
- Credible set specific finemapping statistics (e.g. LogBayesFactors, Alphas/Posterior)
- Additional credible set level BayesFactor filtering is applied (LBF > 2)
- StudyLocusId is annotated for each credible set.
Finemapping method is populated as a constant ("SuSIE").
"""
raw_schema: t.StructType = StructType(
[
StructField("trait", StringType(), True),
StructField("region", StringType(), True),
StructField("v", StringType(), True),
StructField("rsid", StringType(), True),
StructField("chromosome", StringType(), True),
StructField("position", StringType(), True),
StructField("allele1", StringType(), True),
StructField("allele2", StringType(), True),
StructField("maf", StringType(), True),
StructField("beta", StringType(), True),
StructField("se", StringType(), True),
StructField("p", StringType(), True),
StructField("mean", StringType(), True),
StructField("sd", StringType(), True),
StructField("prob", StringType(), True),
StructField("cs", StringType(), True),
StructField("cs_specific_prob", DoubleType(), True),
StructField("low_purity", StringType(), True),
StructField("lead_r2", StringType(), True),
StructField("mean_99", StringType(), True),
StructField("sd_99", StringType(), True),
StructField("prob_99", StringType(), True),
StructField("cs_99", StringType(), True),
StructField("cs_specific_prob_99", StringType(), True),
StructField("low_purity_99", StringType(), True),
StructField("lead_r2_99", StringType(), True),
StructField("alpha1", DoubleType(), True),
StructField("alpha2", DoubleType(), True),
StructField("alpha3", DoubleType(), True),
StructField("alpha4", DoubleType(), True),
StructField("alpha5", DoubleType(), True),
StructField("alpha6", DoubleType(), True),
StructField("alpha7", DoubleType(), True),
StructField("alpha8", DoubleType(), True),
StructField("alpha9", DoubleType(), True),
StructField("alpha10", DoubleType(), True),
StructField("mean1", StringType(), True),
StructField("mean2", StringType(), True),
StructField("mean3", StringType(), True),
StructField("mean4", StringType(), True),
StructField("mean5", StringType(), True),
StructField("mean6", StringType(), True),
StructField("mean7", StringType(), True),
StructField("mean8", StringType(), True),
StructField("mean9", StringType(), True),
StructField("mean10", StringType(), True),
StructField("sd1", StringType(), True),
StructField("sd2", StringType(), True),
StructField("sd3", StringType(), True),
StructField("sd4", StringType(), True),
StructField("sd5", StringType(), True),
StructField("sd6", StringType(), True),
StructField("sd7", StringType(), True),
StructField("sd8", StringType(), True),
StructField("sd9", StringType(), True),
StructField("sd10", StringType(), True),
StructField("lbf_variable1", DoubleType(), True),
StructField("lbf_variable2", DoubleType(), True),
StructField("lbf_variable3", DoubleType(), True),
StructField("lbf_variable4", DoubleType(), True),
StructField("lbf_variable5", DoubleType(), True),
StructField("lbf_variable6", DoubleType(), True),
StructField("lbf_variable7", DoubleType(), True),
StructField("lbf_variable8", DoubleType(), True),
StructField("lbf_variable9", DoubleType(), True),
StructField("lbf_variable10", DoubleType(), True),
]
)
summary_schema: t.StructType = StructType(
[
StructField("trait", StringType(), True),
StructField("region", StringType(), True),
StructField("cs", StringType(), True),
StructField("cs_log10bf", DoubleType(), True),
]
)
@classmethod
def from_finngen_susie_finemapping(
cls: type[FinnGenFinemapping],
spark: SparkSession,
finngen_finemapping_df: (str | list[str]),
finngen_finemapping_summaries: (str | list[str]),
finngen_release_prefix: str,
credset_lbf_threshold: float = 0.8685889638065036,
) -> StudyLocus:
"""Process the SuSIE finemapping output for FinnGen studies.
Args:
spark (SparkSession): Spark session object.
finngen_finemapping_df (str | list[str]): SuSIE finemapping output filename(s).
finngen_finemapping_summaries (str | list[str]): filename of SuSIE finemapping summaries.
finngen_release_prefix (str): FinnGen study prefix.
credset_lbf_threshold (float, optional): Filter out credible sets below, Default 0.8685889638065036 == np.log10(np.exp(2)), this is threshold from publication.
Returns:
StudyLocus: Processed SuSIE finemapping output in StudyLocus format.
"""
processed_finngen_finemapping_df = (
spark.read.schema(cls.raw_schema)
.option("delimiter", "\t")
.option("compression", "gzip")
.csv(finngen_finemapping_df, header=True)
# Drop rows which don't have proper position.
.filter(f.col("position").cast(t.IntegerType()).isNotNull())
# Drop non credible set SNPs:
.filter(f.col("cs").cast(t.IntegerType()) > 0)
.select(
# Add study idenfitier.
f.concat(f.lit(finngen_release_prefix), f.col("trait"))
.cast(t.StringType())
.alias("studyId"),
f.col("region"),
# Add variant information.
f.regexp_replace(f.col("v"), ":", "_").alias("variantId"),
f.col("cs").cast("integer").alias("credibleSetIndex"),
f.regexp_replace(f.col("chromosome"), "^chr", "")
.cast(t.StringType())
.alias("chromosome"),
f.col("position").cast(t.IntegerType()),
f.col("allele1").cast(t.StringType()).alias("ref"),
f.col("allele2").cast(t.StringType()).alias("alt"),
# Parse p-value into mantissa and exponent.
*parse_pvalue(f.col("p")),
# Add beta, standard error, and allele frequency information.
f.col("beta").cast("double"),
f.col("se").cast("double").alias("standardError"),
f.col("maf").cast("float").alias("effectAlleleFrequencyFromSource"),
f.lit("SuSie").cast("string").alias("finemappingMethod"),
*[
f.col(f"alpha{i}").cast(t.DoubleType()).alias(f"alpha_{i}")
for i in range(1, 11)
],
*[
f.col(f"lbf_variable{i}").cast(t.DoubleType()).alias(f"lbf_{i}")
for i in range(1, 11)
],
)
.withColumn(
"posteriorProbability",
f.when(f.col("credibleSetIndex") == 1, f.col("alpha_1"))
.when(f.col("credibleSetIndex") == 2, f.col("alpha_2"))
.when(f.col("credibleSetIndex") == 3, f.col("alpha_3"))
.when(f.col("credibleSetIndex") == 4, f.col("alpha_4"))
.when(f.col("credibleSetIndex") == 5, f.col("alpha_5"))
.when(f.col("credibleSetIndex") == 6, f.col("alpha_6"))
.when(f.col("credibleSetIndex") == 7, f.col("alpha_7"))
.when(f.col("credibleSetIndex") == 8, f.col("alpha_8"))
.when(f.col("credibleSetIndex") == 9, f.col("alpha_9"))
.when(f.col("credibleSetIndex") == 10, f.col("alpha_10")),
)
.drop(
"alpha_1",
"alpha_2",
"alpha_3",
"alpha_4",
"alpha_5",
"alpha_6",
"alpha_7",
"alpha_8",
"alpha_9",
"alpha_10",
)
.withColumn(
"logBF",
f.when(f.col("credibleSetIndex") == 1, f.col("lbf_1"))
.when(f.col("credibleSetIndex") == 2, f.col("lbf_2"))
.when(f.col("credibleSetIndex") == 3, f.col("lbf_3"))
.when(f.col("credibleSetIndex") == 4, f.col("lbf_4"))
.when(f.col("credibleSetIndex") == 5, f.col("lbf_5"))
.when(f.col("credibleSetIndex") == 6, f.col("lbf_6"))
.when(f.col("credibleSetIndex") == 7, f.col("lbf_7"))
.when(f.col("credibleSetIndex") == 8, f.col("lbf_8"))
.when(f.col("credibleSetIndex") == 9, f.col("lbf_9"))
.when(f.col("credibleSetIndex") == 10, f.col("lbf_10")),
)
.drop(
"lbf_1",
"lbf_2",
"lbf_3",
"lbf_4",
"lbf_5",
"lbf_6",
"lbf_7",
"lbf_8",
"lbf_9",
"lbf_10",
)
)
# drop credible sets where logbf > 2. Except when there's only one credible set in region:
# 0.8685889638065036 corresponds to np.log10(np.exp(2)), to match the orginal threshold in publication.
finngen_finemapping_summaries_df = (
# Read credible set level lbf, it is output as a different file which is not ideal.
spark.read.schema(cls.summary_schema)
.option("delimiter", "\t")
.csv(finngen_finemapping_summaries, header=True)
.select(
f.col("region"),
f.col("trait"),
f.col("cs").cast("integer").alias("credibleSetIndex"),
f.col("cs_log10bf").cast("double").alias("credibleSetlog10BF"),
)
.filter(
(f.col("credibleSetlog10BF") > credset_lbf_threshold)
| (f.col("credibleSetIndex") == 1)
)
.withColumn(
"studyId", f.concat(f.lit(finngen_release_prefix), f.col("trait"))
)
)
processed_finngen_finemapping_df = processed_finngen_finemapping_df.join(
finngen_finemapping_summaries_df,
on=["studyId", "region", "credibleSetIndex"],
how="inner",
)
toploci_df = get_top_ranked_in_window(
processed_finngen_finemapping_df,
Window.partitionBy("studyId", "region", "credibleSetIndex").orderBy(
f.desc("posteriorProbability")
),
).select(
"variantId",
"chromosome",
"position",
"studyId",
"beta",
"pValueMantissa",
"pValueExponent",
"effectAlleleFrequencyFromSource",
"standardError",
"region",
"credibleSetIndex",
"finemappingMethod",
"credibleSetlog10BF",
)
processed_finngen_finemapping_df = (
processed_finngen_finemapping_df.groupBy(
"studyId", "region", "credibleSetIndex"
)
.agg(
f.collect_list(
f.struct(
f.col("variantId").cast("string").alias("variantId"),
f.col("posteriorProbability")
.cast("double")
.alias("posteriorProbability"),
f.col("logBF").cast("double").alias("logBF"),
f.col("pValueMantissa").cast("float").alias("pValueMantissa"),
f.col("pValueExponent").cast("integer").alias("pValueExponent"),
f.col("beta").cast("double").alias("beta"),
f.col("standardError").cast("double").alias("standardError"),
)
).alias("locus"),
)
.select(
"studyId",
"region",
"credibleSetIndex",
"locus",
)
.join(
toploci_df,
on=["studyId", "region", "credibleSetIndex"],
how="inner",
)
).withColumn(
"studyLocusId",
StudyLocus.assign_study_locus_id(f.col("studyId"), f.col("variantId")),
)
return StudyLocus(
_df=processed_finngen_finemapping_df,
_schema=StudyLocus.get_schema(),
).annotate_credible_sets()
|
