Refactor: Replace scaffolding with working analysis scripts
- Add trio_analysis.py for trio-based variant analysis with de novo detection - Add clinvar_acmg_annotate.py for ClinVar/ACMG annotation - Add gwas_comprehensive.py with 201 SNPs across 18 categories - Add pharmgkb_full_analysis.py for pharmacogenomics analysis - Add gwas_trait_lookup.py for basic GWAS trait lookup - Add pharmacogenomics.py for basic PGx analysis - Remove unused scaffolding code (src/, configs/, docs/, tests/) - Update README.md with new documentation 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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365
gwas_trait_lookup.py
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365
gwas_trait_lookup.py
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#!/usr/bin/env python3
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"""
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GWAS Trait Lookup Script
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Searches for trait-associated variants in VCF data using GWAS Catalog data.
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"""
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import gzip
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import sys
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import os
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from collections import defaultdict
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from dataclasses import dataclass
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from typing import Dict, List, Optional, Set, Tuple
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@dataclass
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class GWASAssociation:
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"""GWAS association entry"""
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rsid: str
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chrom: str
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pos: int
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risk_allele: str
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trait: str
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p_value: float
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odds_ratio: Optional[float]
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beta: Optional[float]
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pubmed_id: str
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study: str
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@dataclass
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class TraitResult:
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"""Result for a specific trait"""
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trait: str
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variants_found: int
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risk_variants: int
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protective_variants: int
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details: List[dict]
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# Common trait-associated SNPs (curated from GWAS Catalog)
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# Format: rsid -> (chrom, pos, risk_allele, trait, effect_direction)
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TRAIT_SNPS = {
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# Sleep quality / Insomnia
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"rs113851554": ("2", 66799986, "T", "Insomnia", "risk"),
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"rs12927162": ("16", 68856985, "A", "Sleep duration", "shorter"),
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"rs1823125": ("1", 205713532, "G", "Chronotype (morning person)", "morning"),
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"rs10493596": ("1", 215803417, "T", "Insomnia", "risk"),
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"rs3104997": ("6", 27424938, "C", "Sleep duration", "shorter"),
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"rs73598374": ("4", 94847526, "A", "Insomnia", "risk"),
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"rs2302729": ("5", 35857091, "G", "Insomnia", "risk"),
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# Skin conditions
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"rs1800629": ("6", 31543031, "A", "Psoriasis", "risk"),
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"rs20541": ("5", 131995964, "A", "Atopic dermatitis", "risk"),
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"rs2066808": ("6", 31540784, "A", "Psoriasis", "risk"),
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"rs3093662": ("6", 31574339, "G", "Psoriasis", "risk"),
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"rs10484554": ("6", 31271836, "A", "Psoriasis", "risk"),
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"rs1295686": ("5", 131996447, "A", "Atopic dermatitis", "risk"),
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"rs2227956": ("6", 31783279, "T", "Psoriasis", "risk"),
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"rs6906021": ("6", 32051991, "C", "Atopic dermatitis", "risk"),
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"rs661313": ("1", 152285861, "G", "Ichthyosis vulgaris (FLG)", "risk"),
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"rs2065958": ("11", 35300406, "C", "Atopic dermatitis", "risk"),
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# Cardiovascular
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"rs10757274": ("9", 22096055, "G", "Coronary artery disease", "risk"),
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"rs1333049": ("9", 22125503, "C", "Coronary artery disease", "risk"),
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"rs4665058": ("2", 43845437, "C", "Coronary artery disease", "risk"),
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"rs17465637": ("1", 222823529, "A", "Coronary artery disease", "risk"),
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"rs6725887": ("2", 203828796, "C", "Coronary artery disease", "risk"),
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# Type 2 Diabetes
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"rs7903146": ("10", 114758349, "T", "Type 2 diabetes", "risk"),
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"rs12255372": ("10", 114808902, "T", "Type 2 diabetes", "risk"),
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"rs1801282": ("3", 12393125, "C", "Type 2 diabetes", "risk"),
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"rs5219": ("11", 17409572, "T", "Type 2 diabetes", "risk"),
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"rs13266634": ("8", 118184783, "C", "Type 2 diabetes", "risk"),
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# Obesity/BMI
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"rs9939609": ("16", 53820527, "A", "Obesity (FTO)", "risk"),
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"rs17782313": ("18", 57851097, "C", "Obesity (MC4R)", "risk"),
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"rs6548238": ("2", 634905, "C", "BMI", "higher"),
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"rs10938397": ("4", 45186139, "G", "BMI", "higher"),
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# Hair loss / Baldness
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"rs2180439": ("20", 22162468, "T", "Male pattern baldness", "risk"),
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"rs1160312": ("X", 67052952, "A", "Male pattern baldness", "risk"),
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"rs6625163": ("X", 67177092, "A", "Male pattern baldness", "risk"),
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# Eye conditions (relevant to Usher)
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"rs10490924": ("10", 124214448, "T", "Age-related macular degeneration", "risk"),
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"rs1061170": ("1", 196659237, "C", "Age-related macular degeneration", "risk"),
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"rs9621532": ("22", 38477587, "C", "Myopia", "risk"),
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# Caffeine metabolism
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"rs762551": ("15", 75041917, "C", "Caffeine metabolism (slow)", "slow"),
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"rs2472297": ("15", 75027880, "T", "Caffeine consumption", "higher"),
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# Alcohol metabolism
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"rs671": ("12", 112241766, "A", "Alcohol flush reaction", "risk"),
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"rs1229984": ("4", 100239319, "T", "Alcohol metabolism (fast)", "fast"),
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# Lactose intolerance
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"rs4988235": ("2", 136608646, "G", "Lactose intolerance", "risk"),
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# Vitamin D
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"rs2282679": ("4", 72608383, "C", "Vitamin D deficiency", "risk"),
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"rs12785878": ("11", 71167449, "T", "Vitamin D levels (lower)", "lower"),
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# Alzheimer's Disease / Dementia
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"rs429358": ("19", 45411941, "C", "Alzheimer's disease (APOE e4)", "risk"), # APOE e4
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"rs7412": ("19", 45412079, "T", "Alzheimer's disease (APOE e2)", "protective"), # APOE e2
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"rs3865444": ("19", 51727962, "C", "Alzheimer's disease (CD33)", "risk"),
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"rs744373": ("2", 127892810, "G", "Alzheimer's disease (BIN1)", "risk"),
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"rs3851179": ("11", 85868640, "T", "Alzheimer's disease (PICALM)", "protective"),
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"rs670139": ("11", 59939307, "G", "Alzheimer's disease (MS4A)", "risk"),
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"rs9349407": ("6", 47487762, "C", "Alzheimer's disease (CD2AP)", "risk"),
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"rs11136000": ("8", 27468503, "C", "Alzheimer's disease (CLU)", "protective"),
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"rs3764650": ("19", 1063443, "G", "Alzheimer's disease (ABCA7)", "risk"),
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"rs3818361": ("1", 207692049, "A", "Alzheimer's disease (CR1)", "risk"),
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# Depression / Major Depressive Disorder
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"rs1545843": ("1", 72761657, "A", "Major depression (NEGR1)", "risk"),
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"rs7973260": ("12", 118364392, "A", "Major depression (KSR2)", "risk"),
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"rs10514299": ("5", 87992715, "T", "Major depression (TMEM161B)", "risk"),
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"rs2422321": ("15", 88945878, "G", "Major depression (NTRK3)", "risk"),
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"rs301806": ("1", 8477981, "A", "Major depression (RERE)", "risk"),
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"rs1432639": ("3", 117115304, "G", "Major depression (LSAMP)", "risk"),
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"rs9530139": ("13", 53645407, "G", "Major depression", "risk"),
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"rs4543289": ("10", 106610839, "T", "Major depression (SORCS3)", "risk"),
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# Anxiety
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"rs1709393": ("1", 34774088, "A", "Anxiety disorder", "risk"),
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"rs7688285": ("4", 123372626, "A", "Anxiety disorder", "risk"),
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# Bipolar disorder
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"rs4765913": ("12", 2345295, "A", "Bipolar disorder (CACNA1C)", "risk"),
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"rs10994336": ("10", 64649959, "T", "Bipolar disorder (ANK3)", "risk"),
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"rs9804190": ("11", 79077426, "C", "Bipolar disorder (ODZ4)", "risk"),
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# Schizophrenia
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"rs1625579": ("8", 130635575, "T", "Schizophrenia (MIR137)", "risk"),
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"rs2007044": ("6", 28626894, "G", "Schizophrenia (HIST1H2BJ)", "risk"),
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"rs6932590": ("6", 27243984, "T", "Schizophrenia", "risk"),
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}
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def get_genotype_class(gt: str) -> str:
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"""Classify genotype"""
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if gt in ['./.', '.|.', '.']:
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return 'MISSING'
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import re
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alleles = re.split('[/|]', gt)
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if all(a == '0' for a in alleles):
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return 'HOM_REF'
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elif all(a != '0' and a != '.' for a in alleles):
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return 'HOM_ALT'
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else:
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return 'HET'
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def parse_vcf_for_traits(vcf_path: str, proband_idx: int = 2) -> Dict[str, dict]:
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"""Parse VCF and look for trait-associated SNPs"""
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print(f"Scanning VCF for trait-associated variants...")
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# Build position lookup
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pos_to_snp = {}
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for rsid, (chrom, pos, risk_allele, trait, effect) in TRAIT_SNPS.items():
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key = f"{chrom}-{pos}"
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pos_to_snp[key] = (rsid, risk_allele, trait, effect)
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found_variants = {}
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samples = []
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open_func = gzip.open if vcf_path.endswith('.gz') else open
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mode = 'rt' if vcf_path.endswith('.gz') else 'r'
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with open_func(vcf_path, mode) as f:
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for line in f:
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if line.startswith('##'):
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continue
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elif line.startswith('#CHROM'):
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parts = line.strip().split('\t')
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samples = parts[9:]
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continue
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parts = line.strip().split('\t')
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if len(parts) < 10:
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continue
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chrom, pos, rsid_vcf, ref, alt, qual, filt, info, fmt = parts[:9]
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gt_fields = parts[9:]
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# Check if this position has a known trait SNP
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key = f"{chrom}-{pos}"
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if key not in pos_to_snp:
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continue
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rsid, risk_allele, trait, effect = pos_to_snp[key]
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# Get proband genotype
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fmt_parts = fmt.split(':')
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gt_idx = fmt_parts.index('GT') if 'GT' in fmt_parts else 0
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if proband_idx < len(gt_fields):
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gt_data = gt_fields[proband_idx].split(':')
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gt = gt_data[gt_idx] if gt_idx < len(gt_data) else './.'
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else:
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gt = './.'
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gt_class = get_genotype_class(gt)
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# Determine risk
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alleles = [ref] + alt.split(',')
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# Check if risk allele is present
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has_risk = False
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risk_copies = 0
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if gt_class != 'MISSING':
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import re
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gt_alleles = re.split('[/|]', gt)
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for a in gt_alleles:
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if a.isdigit():
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allele_idx = int(a)
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if allele_idx < len(alleles) and alleles[allele_idx] == risk_allele:
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has_risk = True
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risk_copies += 1
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found_variants[rsid] = {
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'rsid': rsid,
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'chrom': chrom,
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'pos': pos,
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'ref': ref,
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'alt': alt,
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'genotype': gt,
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'genotype_class': gt_class,
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'risk_allele': risk_allele,
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'trait': trait,
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'effect': effect,
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'has_risk_allele': has_risk,
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'risk_copies': risk_copies
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}
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return found_variants, samples
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def generate_trait_report(found_variants: Dict, output_path: str):
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"""Generate trait analysis report"""
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# Group by trait
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traits = defaultdict(list)
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for rsid, var in found_variants.items():
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traits[var['trait']].append(var)
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with open(output_path, 'w') as f:
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f.write("# GWAS Trait Analysis Report\n")
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f.write("# Based on curated GWAS Catalog associations\n\n")
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f.write("=" * 80 + "\n")
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f.write("SUMMARY BY TRAIT CATEGORY\n")
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f.write("=" * 80 + "\n\n")
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# Categorize traits
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categories = {
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"Sleep": ["Insomnia", "Sleep duration", "Chronotype (morning person)"],
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"Skin": ["Psoriasis", "Atopic dermatitis", "Ichthyosis vulgaris (FLG)"],
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"Cardiovascular": ["Coronary artery disease"],
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"Metabolic": ["Type 2 diabetes", "Obesity (FTO)", "Obesity (MC4R)", "BMI"],
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"Eye": ["Age-related macular degeneration", "Myopia"],
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"Neuropsychiatric": [
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"Alzheimer's disease (APOE e4)", "Alzheimer's disease (APOE e2)",
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"Alzheimer's disease (CD33)", "Alzheimer's disease (BIN1)",
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"Alzheimer's disease (PICALM)", "Alzheimer's disease (MS4A)",
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"Alzheimer's disease (CD2AP)", "Alzheimer's disease (CLU)",
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"Alzheimer's disease (ABCA7)", "Alzheimer's disease (CR1)",
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"Major depression (NEGR1)", "Major depression (KSR2)",
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"Major depression (TMEM161B)", "Major depression (NTRK3)",
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"Major depression (RERE)", "Major depression (LSAMP)",
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"Major depression", "Major depression (SORCS3)",
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"Anxiety disorder",
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"Bipolar disorder (CACNA1C)", "Bipolar disorder (ANK3)", "Bipolar disorder (ODZ4)",
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"Schizophrenia (MIR137)", "Schizophrenia (HIST1H2BJ)", "Schizophrenia"
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],
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"Other": ["Caffeine metabolism (slow)", "Caffeine consumption", "Alcohol flush reaction",
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"Alcohol metabolism (fast)", "Lactose intolerance", "Vitamin D deficiency",
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"Vitamin D levels (lower)", "Male pattern baldness"]
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}
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for category, trait_list in categories.items():
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f.write(f"\n## {category}\n")
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f.write("-" * 40 + "\n")
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category_risk = 0
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category_total = 0
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for trait in trait_list:
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if trait in traits:
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variants = traits[trait]
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risk_count = sum(1 for v in variants if v['has_risk_allele'])
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category_risk += risk_count
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category_total += len(variants)
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for v in variants:
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status = "RISK" if v['has_risk_allele'] else "OK"
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copies = f"({v['risk_copies']} copies)" if v['has_risk_allele'] else ""
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f.write(f" {v['trait']}: {v['rsid']} [{status}] {copies}\n")
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f.write(f" Genotype: {v['genotype']} | Risk allele: {v['risk_allele']}\n")
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if category_total > 0:
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f.write(f"\n Category summary: {category_risk}/{category_total} risk variants found\n")
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# Detailed results
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f.write("\n" + "=" * 80 + "\n")
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f.write("DETAILED RESULTS\n")
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f.write("=" * 80 + "\n\n")
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f.write("RSID\tCHROM\tPOS\tGENOTYPE\tRISK_ALLELE\tHAS_RISK\tCOPIES\tTRAIT\tEFFECT\n")
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for rsid, var in sorted(found_variants.items(), key=lambda x: (x[1]['trait'], x[0])):
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f.write(f"{var['rsid']}\t{var['chrom']}\t{var['pos']}\t{var['genotype']}\t")
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f.write(f"{var['risk_allele']}\t{var['has_risk_allele']}\t{var['risk_copies']}\t")
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f.write(f"{var['trait']}\t{var['effect']}\n")
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print(f"Report saved to: {output_path}")
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def main():
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vcf_path = sys.argv[1] if len(sys.argv) > 1 else '/Volumes/NV2/genomics_analysis/vcf/trio_joint.snpeff.vcf'
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output_path = sys.argv[2] if len(sys.argv) > 2 else '/Volumes/NV2/genomics_analysis/gwas_trait_report.txt'
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proband_idx = int(sys.argv[3]) if len(sys.argv) > 3 else 2
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print(f"GWAS Trait Analysis")
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print(f"VCF: {vcf_path}")
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print(f"Proband index: {proband_idx}")
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print(f"Searching for {len(TRAIT_SNPS)} trait-associated SNPs...\n")
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found_variants, samples = parse_vcf_for_traits(vcf_path, proband_idx)
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print(f"\nFound {len(found_variants)} trait-associated variants in VCF")
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# Quick summary
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risk_count = sum(1 for v in found_variants.values() if v['has_risk_allele'])
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print(f"Variants with risk allele: {risk_count}")
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generate_trait_report(found_variants, output_path)
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# Print summary to console
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print("\n" + "=" * 60)
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print("QUICK SUMMARY")
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print("=" * 60)
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traits = defaultdict(list)
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for rsid, var in found_variants.items():
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traits[var['trait']].append(var)
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for trait in sorted(traits.keys()):
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variants = traits[trait]
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risk_vars = [v for v in variants if v['has_risk_allele']]
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if risk_vars:
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print(f"\n{trait}:")
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for v in risk_vars:
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print(f" {v['rsid']}: {v['genotype']} (risk allele: {v['risk_allele']}, copies: {v['risk_copies']})")
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if __name__ == '__main__':
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main()
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