Pseudo metrics

pseudo_pearson_r(test_gdl, prs_beta_table)

Perform pseudo-validation of the inferred effect sizes by comparing them to standardized marginal betas from an independent validation set. Here, we follow the pseudo-validation procedures outlined in Mak et al. (2017) and Yang and Zhou (2020), where the correlation between the PRS and the phenotype in an independent validation cohort can be approximated with:

Corr(PRS, y) ~= r'b / sqrt(b'Sb)

Where r is the standardized marginal beta from a validation set, b is the posterior mean for the effect size of each variant and S is the LD matrix.

Parameters:

Name	Type	Description	Default
test_gdl		An instance of GWADataLoader with the summary statistics table initialized.	required
prs_beta_table		A pandas DataFrame with the PRS effect sizes. Must contain the columns: CHR, SNP, A1, A2, BETA.	required

Source code in viprs/eval/pseudo_metrics.py

def pseudo_pearson_r(test_gdl, prs_beta_table):
    """
    Perform pseudo-validation of the inferred effect sizes by comparing them to
    standardized marginal betas from an independent validation set. Here, we follow the pseudo-validation
    procedures outlined in Mak et al. (2017) and Yang and Zhou (2020), where
    the correlation between the PRS and the phenotype in an independent validation
    cohort can be approximated with:

    Corr(PRS, y) ~= r'b / sqrt(b'Sb)

    Where `r` is the standardized marginal beta from a validation set,
    `b` is the posterior mean for the effect size of each variant and `S` is the LD matrix.

    :param test_gdl: An instance of `GWADataLoader` with the summary statistics table initialized.
    :param prs_beta_table: A pandas DataFrame with the PRS effect sizes. Must contain
    the columns: CHR, SNP, A1, A2, BETA.
    """

    std_beta, prs_beta, q = _match_variant_stats(test_gdl, prs_beta_table)

    rb = np.sum((prs_beta.T * std_beta).T, axis=0)
    bsb = np.sum(prs_beta * q, axis=0)

    return rb / np.sqrt(bsb)

pseudo_r2(test_gdl, prs_beta_table)

Compute the R-Squared metric (proportion of variance explained) for a given PRS using standardized marginal betas from an independent test set. Here, we follow the pseudo-validation procedures outlined in Mak et al. (2017) and Yang and Zhou (2020), where the proportion of phenotypic variance explained by the PRS in an independent validation cohort can be approximated with:

R2(PRS, y) ~= 2*r'b - b'Sb

Where r is the standardized marginal beta from a validation/test set, b is the posterior mean for the effect size of each variant and S is the LD matrix.

Parameters:

Name	Type	Description	Default
test_gdl		An instance of GWADataLoader with the summary statistics table initialized.	required
prs_beta_table		A pandas DataFrame with the PRS effect sizes. Must contain the columns: CHR, SNP, A1, A2, BETA.	required

Source code in viprs/eval/pseudo_metrics.py

def pseudo_r2(test_gdl, prs_beta_table):
    """
    Compute the R-Squared metric (proportion of variance explained) for a given
    PRS using standardized marginal betas from an independent test set.
    Here, we follow the pseudo-validation procedures outlined in Mak et al. (2017) and
    Yang and Zhou (2020), where the proportion of phenotypic variance explained by the PRS
    in an independent validation cohort can be approximated with:

    R2(PRS, y) ~= 2*r'b - b'Sb

    Where `r` is the standardized marginal beta from a validation/test set,
    `b` is the posterior mean for the effect size of each variant and `S` is the LD matrix.

    :param test_gdl: An instance of `GWADataLoader` with the summary statistics table initialized.
    :param prs_beta_table: A pandas DataFrame with the PRS effect sizes. Must contain
    the columns: CHR, SNP, A1, A2, BETA.
    """

    std_beta, prs_beta, q = _match_variant_stats(test_gdl, prs_beta_table)

    rb = np.sum((prs_beta.T * std_beta).T, axis=0)
    bsb = np.sum(prs_beta*q, axis=0)

    return 2*rb - bsb