"""
Variant Annotation Integration for TWAS

This module integrates TWAS genes with the genetic-variant-annotation workflow
to identify mechanistic insights for expression-trait associations.
"""

import pandas as pd
from pathlib import Path


def integrate_with_variant_annotation(gene, gwas_sumstats, eqtl_data, coloc_results,
                                       vcf_file=None, window_kb=500):
    """
    Integrate TWAS gene with variant annotation to identify causal mechanisms.

    This function extracts lead eQTL variants for a gene and annotates them
    using the genetic-variant-annotation workflow.

    Parameters
    ----------
    gene : str
        Gene symbol
    gwas_sumstats : pandas.DataFrame
        GWAS summary statistics
    eqtl_data : pandas.DataFrame or str
        eQTL summary statistics
    coloc_results : pandas.DataFrame
        Colocalization results
    vcf_file : str, optional
        Path to VCF file for detailed annotation
    window_kb : int
        Window size around gene (default: 500kb)

    Returns
    -------
    dict
        Dictionary with variant details and mechanistic interpretation
    """
    print(f"\nIntegrating variant annotation for {gene}...")

    # Get lead eQTL variant
    if isinstance(eqtl_data, str):
        eqtl_df = pd.read_csv(eqtl_data, sep='\t')
    else:
        eqtl_df = eqtl_data

    # Find lead variant (lowest P-value)
    lead_variant_row = eqtl_df.loc[eqtl_df['P'].idxmin()]
    lead_variant = lead_variant_row['SNP']
    lead_chr = lead_variant_row['CHR']
    lead_pos = lead_variant_row['BP']

    print(f"  Lead eQTL variant: {lead_variant} (chr{lead_chr}:{lead_pos})")

    # Get variant details
    variant_details = annotate_variant(
        variant_id=lead_variant,
        chrom=lead_chr,
        position=lead_pos,
        vcf_file=vcf_file
    )

    # Get colocalization support
    gene_coloc = coloc_results[coloc_results['GENE'] == gene]
    if not gene_coloc.empty:
        pp4 = gene_coloc.iloc[0]['PP.H4']
        variant_details['colocalization_pp4'] = pp4
        variant_details['colocalization_support'] = pp4 > 0.8

    # Determine mechanistic interpretation
    mechanism = interpret_mechanism(variant_details, gene)
    variant_details['mechanism'] = mechanism

    return variant_details


def annotate_variant(variant_id, chrom, position, vcf_file=None):
    """
    Annotate variant with functional consequences.

    This is a placeholder that would call the genetic-variant-annotation workflow.

    Parameters
    ----------
    variant_id : str
        Variant ID (rsID)
    chrom : int or str
        Chromosome
    position : int
        Genomic position
    vcf_file : str, optional
        VCF file for annotation

    Returns
    -------
    dict
        Variant annotation details
    """
    # Placeholder - would call genetic-variant-annotation workflow
    # Example: subprocess.run(['claude', 'annotate_variant', variant_id])

    # Example annotation
    annotation = {
        'lead_variant': variant_id,
        'chrom': chrom,
        'position': position,
        'vep_consequence': 'missense_variant',  # or 'regulatory_region_variant', etc.
        'regulomedb_score': '1a',
        'cadd_score': 25.3,
        'allele_change': 'C>A',
        'ref_allele': 'C',
        'alt_allele': 'A'
    }

    print(f"    Consequence: {annotation['vep_consequence']}")
    print(f"    RegulomeDB: {annotation['regulomedb_score']}")
    print(f"    CADD: {annotation['cadd_score']}")

    return annotation


def interpret_mechanism(variant_details, gene):
    """
    Interpret mechanistic basis for expression-trait association.

    Parameters
    ----------
    variant_details : dict
        Variant annotation details
    gene : str
        Gene symbol

    Returns
    -------
    str
        Mechanistic interpretation
    """
    consequence = variant_details.get('vep_consequence', '')

    if 'missense' in consequence or 'splice' in consequence:
        mechanism = f"Coding variant alters {gene} protein structure/function"
    elif 'regulatory' in consequence or 'promoter' in consequence:
        mechanism = f"Regulatory variant modulates {gene} expression"
    elif 'utr' in consequence.lower():
        mechanism = f"UTR variant affects {gene} mRNA stability or translation"
    else:
        mechanism = f"Variant affects {gene} through unknown regulatory mechanism"

    print(f"    Predicted mechanism: {mechanism}")

    return mechanism


def batch_integrate_variants(genes, gwas_sumstats, eqtl_dir, coloc_results):
    """
    Integrate variant annotations for multiple genes.

    Parameters
    ----------
    genes : list
        List of gene symbols
    gwas_sumstats : pandas.DataFrame
        GWAS summary statistics
    eqtl_dir : str
        Directory containing eQTL data
    coloc_results : pandas.DataFrame
        Colocalization results

    Returns
    -------
    pandas.DataFrame
        Variant annotation details for all genes
    """
    results = []

    for gene in genes:
        eqtl_file = Path(eqtl_dir) / f"{gene}.eqtl.txt"

        if not eqtl_file.exists():
            print(f"WARNING: eQTL data not found for {gene}")
            continue

        variant_info = integrate_with_variant_annotation(
            gene=gene,
            gwas_sumstats=gwas_sumstats,
            eqtl_data=str(eqtl_file),
            coloc_results=coloc_results
        )

        variant_info['gene'] = gene
        results.append(variant_info)

    return pd.DataFrame(results)