cellink.tl.external.estimate_genetic_correlation

cellink.tl.external.estimate_genetic_correlation(sumstats_files, ref_ld_chr, w_ld_chr, out_prefix, overlap_annot=False, frqfile_chr=None, not_m_5_50=False, print_coefficients=False, print_delete_vals=False, samp_prev=None, pop_prev=None, intercept_h2=None, intercept_gencov=None, no_intercept=False, run=True, runner=None, **kwargs)

Estimate genetic correlation using LD Score regression

Convenience wrapper around run_ldsc_genetic_correlation with validation and structured output.

Parameters:

• sumstats_files (list[str]) – List of paths to munged summary statistics files (.sumstats.gz)

• ref_ld_chr (str) – Prefix for reference LD scores (with @, e.g., “baseline.”)

• w_ld_chr (str) – Prefix for regression weights (with @, e.g., “weights.”)

• out_prefix (str) – Prefix for output files

• overlap_annot (bool, default False) – Use overlapping annotation model

• frqfile_chr (str, optional) – Prefix for allele frequency files (required with overlap_annot)

• not_m_5_50 (bool, default False) – Don’t restrict to common SNPs

• print_coefficients (bool, default False) – Print coefficient estimates

• print_delete_vals (bool, default False) – Print delete values

• samp_prev (list[float], optional) – Sample prevalences for each trait (use None for quantitative traits)

• pop_prev (list[float], optional) – Population prevalences for each trait

• intercept_h2 (list[float], optional) – Constrain h2 intercepts for each trait

• intercept_gencov (list[float], optional) – Constrain genetic covariance intercepts

• no_intercept (bool, default False) – Force intercepts to 1 and 0

• run (bool, default True) – Whether to execute the command or just return it

• runner (LDSCRunner, optional) – Runner instance to use

• **kwargs – Additional arguments passed to ldsc.py

Return type:

dict[str, Any]

Returns:

dict Results dictionary with: - ‘log_file’: Path to log file (if run=True) - ‘files_created’: List of created files (if run=True) - ‘command’: Command string (if run=False)

Examples

>>> result = estimate_genetic_correlation(
...     sumstats_files=["height_munged.sumstats.gz", "bmi_munged.sumstats.gz"],
...     ref_ld_chr="baseline_v1.2/baseline.",
...     w_ld_chr="weights_hm3_no_hla/weights.",
...     out_prefix="height_bmi_rg",
... )