/gpcr_missense_constraint

Primary LanguageJupyter Notebook

Mapping patterns of mutational constraint in GPCR drug targets

Overview

  • gnomadIC (gnomAD Inference of Constraint) is a package for estimation of constraint from the gnomAD dataset, using custom annotations and aggregation groups. The constraint computation pipeline is written in Hail 0.2 and is based on the workflow from the gnomAD flagship manuscript Karczewski et al., 2019
  • Analysis can be run using a set of chosen target genes using constraint_analysis.py --targets data/targets/targets.txt. You can either keep standard settings (aggegrating missense variants using provided PolyPhen2 labels) or add your own annotations. Custom annotations must be linked to HGVSP ids for variants of your choice. These can then be used to modify the annotation column before aggregation.
  • This repo also contains notebooks required to perform the analyses detailed in our manuscript (Boardman et al, in preparation).

Contents

  • gnomadIC contains code for custom constraint calculations
  • data contains constraint values, gene lists and annotations for analysis
  • scripts contains notebooks for analysis of constraint values
  • plots and results contain plots and data tables generated by scripts
  • slides contains marp slidedeck overview for this project

Data summary

Description File
Constraint for 19,704 gene regions from gnomAD (v2.1) all_genes_gnomad_constraint.tsv
Essential genes from gnomAD all_genes_essential_gnomad.tsv
List of 391 GPCR genes from GPCRdb gpcr_genes_human_gpcrdb.tsv
Constraint from gnomAD release for 391 GPCR genes gpcr_genes_gnomad_constraint.tsv
Essential GPCR genes from gnomAD paper & literature gpcr_genes_essential_curated.tsv
GPCR genes with disease associations from OMIM & OpenTargets GWAS gpcr_genes_disease_associated.tsv
GPCR drug targets with associated adverse events gpcr_drug_targets_w_adverse_events.tsv

Script summary

Description Script
Description of data gathering get_data.ipynb
Distribution of constraint in GPCRs Fig1_constraint.ipynb
Correlation between constraint and essentiality Fig2_essentiality.ipynb
Constraint in drug targets Fig3_drug_targets.ipynb

Analysis notebooks

  • underpowered_genes_by_group.R counts underpowered genes by group

    • It takes the full set of HGNC genes (data/hgnc_groups/hgnc_complete_set.txt) and matches this to the set of HGNC groups (data/hgnc_groups/hgnc_gene_families.txt).
    • It then identifies underpowered genes from the gnomAD summary statistics (data/constraint/precalc_obs_exp.tsv.gz) and counts these by group.
    • It then identifies underpowered GPCR genes using the gene symbol table (data/gene_lists/gpcr_gene_symbols.tsv) and counts these by family.

  • check_variants_and_constraint.ipynb

    • Check that constraint values match gnomAD

  • figure_2_variant_impact.ipynb

    • Analyse the evidence for constraint against different types of variants, for GPCRs and controls

  • figure_3_constraint_by_family.ipynb

    • This plots the constraint by gene across GPCR families

  • figure_4_phenotypes_venn_diagram.ipynb

    • analyses phenotype gene lists and makes Venn diagram

  • figure_5_phenotype_constraint_associations.ipynb

    • Compute ROC curves and perform analysis of classification

  • figure_6_case_studies.ipynb

    • Analyse region-level constraint in selected chemokine receptors