eCAVIAR

gentropy.method.colocalisation.ECaviar

Bases: ColocalisationMethodInterface

ECaviar-based colocalisation analysis.

It extends CAVIAR framework to explicitly estimate the posterior probability that the same variant is causal in 2 studies while accounting for the uncertainty of LD. eCAVIAR computes the colocalization posterior probability (CLPP) by utilizing the marginal posterior probabilities. This framework allows for multiple variants to be causal in a single locus.

Source code in src/gentropy/method/colocalisation.py

class ECaviar(ColocalisationMethodInterface):
    """ECaviar-based colocalisation analysis.

    It extends [CAVIAR](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5142122/#bib18) framework to explicitly estimate the posterior probability that the same variant is causal in 2 studies while accounting for the uncertainty of LD. eCAVIAR computes the colocalization posterior probability (**CLPP**) by utilizing the marginal posterior probabilities. This framework allows for **multiple variants to be causal** in a single locus.
    """

    METHOD_NAME: str = "eCAVIAR"
    METHOD_METRIC: str = "clpp"

    @staticmethod
    def _get_clpp(left_pp: Column, right_pp: Column) -> Column:
        """Calculate the colocalisation posterior probability (CLPP).

        If the fact that the same variant is found causal for two studies are independent events,
        CLPP is defined as the product of posterior porbabilities that a variant is causal in both studies.

        Args:
            left_pp (Column): left posterior probability
            right_pp (Column): right posterior probability

        Returns:
            Column: CLPP

        Examples:
            >>> d = [{"left_pp": 0.5, "right_pp": 0.5}, {"left_pp": 0.25, "right_pp": 0.75}]
            >>> df = spark.createDataFrame(d)
            >>> df.withColumn("clpp", ECaviar._get_clpp(f.col("left_pp"), f.col("right_pp"))).show()
            +-------+--------+------+
            |left_pp|right_pp|  clpp|
            +-------+--------+------+
            |    0.5|     0.5|  0.25|
            |   0.25|    0.75|0.1875|
            +-------+--------+------+
            <BLANKLINE>

        """
        return left_pp * right_pp

    @classmethod
    def colocalise(
        cls: type[ECaviar],
        overlapping_signals: StudyLocusOverlap,
        **kwargs: Any,
    ) -> Colocalisation:
        """Calculate bayesian colocalisation based on overlapping signals.

        Args:
            overlapping_signals (StudyLocusOverlap): overlapping signals.
            **kwargs (Any): Additional parameters passed to the colocalise method.

        Returns:
            Colocalisation: colocalisation results based on eCAVIAR.
        """
        return Colocalisation(
            _df=(
                overlapping_signals.df.withColumns(
                    {
                        "clpp": ECaviar._get_clpp(
                            f.col("statistics.left_posteriorProbability"),
                            f.col("statistics.right_posteriorProbability"),
                        ),
                        "tagVariantSource": get_tag_variant_source(f.col("statistics")),
                    }
                )
                .groupBy(
                    "leftStudyLocusId",
                    "rightStudyLocusId",
                    "rightStudyType",
                    "chromosome",
                )
                .agg(
                    # Count the number of tag variants that can be found in both loci:
                    f.size(
                        f.filter(
                            f.collect_list(f.col("tagVariantSource")),
                            lambda x: x == "both",
                        )
                    )
                    .cast(t.LongType())
                    .alias("numberColocalisingVariants"),
                    f.sum(f.col("clpp")).alias("clpp"),
                )
                .withColumn("colocalisationMethod", f.lit(cls.METHOD_NAME))
                .join(
                    overlapping_signals.calculate_beta_ratio(),
                    on=["leftStudyLocusId", "rightStudyLocusId", "chromosome"],
                    how="left",
                )
            ),
            _schema=Colocalisation.get_schema(),
        )

colocalise(overlapping_signals: StudyLocusOverlap, **kwargs: Any) -> Colocalisation classmethod

Calculate bayesian colocalisation based on overlapping signals.

Parameters:

Name	Type	Description	Default
overlapping_signals	StudyLocusOverlap	overlapping signals.	required
**kwargs	Any	Additional parameters passed to the colocalise method.	{}

Returns:

Name	Type	Description
Colocalisation	Colocalisation	colocalisation results based on eCAVIAR.

Source code in src/gentropy/method/colocalisation.py

@classmethod
def colocalise(
    cls: type[ECaviar],
    overlapping_signals: StudyLocusOverlap,
    **kwargs: Any,
) -> Colocalisation:
    """Calculate bayesian colocalisation based on overlapping signals.

    Args:
        overlapping_signals (StudyLocusOverlap): overlapping signals.
        **kwargs (Any): Additional parameters passed to the colocalise method.

    Returns:
        Colocalisation: colocalisation results based on eCAVIAR.
    """
    return Colocalisation(
        _df=(
            overlapping_signals.df.withColumns(
                {
                    "clpp": ECaviar._get_clpp(
                        f.col("statistics.left_posteriorProbability"),
                        f.col("statistics.right_posteriorProbability"),
                    ),
                    "tagVariantSource": get_tag_variant_source(f.col("statistics")),
                }
            )
            .groupBy(
                "leftStudyLocusId",
                "rightStudyLocusId",
                "rightStudyType",
                "chromosome",
            )
            .agg(
                # Count the number of tag variants that can be found in both loci:
                f.size(
                    f.filter(
                        f.collect_list(f.col("tagVariantSource")),
                        lambda x: x == "both",
                    )
                )
                .cast(t.LongType())
                .alias("numberColocalisingVariants"),
                f.sum(f.col("clpp")).alias("clpp"),
            )
            .withColumn("colocalisationMethod", f.lit(cls.METHOD_NAME))
            .join(
                overlapping_signals.calculate_beta_ratio(),
                on=["leftStudyLocusId", "rightStudyLocusId", "chromosome"],
                how="left",
            )
        ),
        _schema=Colocalisation.get_schema(),
    )

---

• 2023-01-20
• 2024-01-17
• Contributors