/H-MAGMA

Primary LanguageR

H-MAGMA

Authors: Nancy Sey and Hyejung Won Created: 06/14/2019 Updated: 06/15/2023

H_MAGMA.sh

  • We have included the full protocol to generate the variant-gene annotation file needed to run H-MAGMA. Additionally, we have generated additional annotation files for 28 tissue and cell types using promoter-capture HiC data from Jung et al. 2019. You can access the materials following this link to Zenodo: https://doi.org/10.5281/zenodo.5503876

  • We updated the code and results using the updated version of MAGMA (MAGMA v.1.08) that better controls for the potential type I error rate inflation. Initial results were generated from MAGMA v.1.07b. Both versions of MAGMA can be downloaded from: https://ctg.cncr.nl/software/magma.

  • The following files are required to run H-MAGMA to assign non-coding SNPs to cognate genes. Follow the detailed script below to run H-MAGMA using Hi-C driven annotation files.

    • magma/1.07b/bin/magma: using MAGMA version 1.07b
    • magma/1.08/bin/magma: using MAGMA version 1.08
    • --bfiile g1000_eur: Reference file for European population, downloaded from [Reference data (https://ctg.cncr.nl/software/magma)]
    • --pval disorder1_GWAS.txt: P-values from GWAS summary statistics, see the GWAS summary statistics section below
    • use=rsid,P: use rsid and P columns in GWAS summary statistics for SNP IDs and P-values, respectively
    • ncol=N: use N column in GWAS summary statistics for the sample size
    • --gene-annot: Fetal_brain.genes.annot. Use gene-SNP pair annotation files from the Input_Files folder using the relevant tissue/cell type Hi-C data
    • --out disorder1_FB: output file name

  • Description of annotation files (files are provided in this repository under the Input_Files folder)

    • Fetal_brain.genes.annot: gene-SNP pairs based on the fetal brain Hi-C. Fetal paracentral cortex was used.
    • Adult_brain.genes.annot: gene-SNP pairs based on the adult brain Hi-C. Adult dorsolateral prefrontal cortex was used.
    • IPSC_derived_neuro.genes.annot: gene-SNP pairs based on the iPSC-derived neuron Hi-C.
    • IPSC_derived_astro.genes.annot: gene-SNP pairs based on the iPSC-derived astrocyte Hi-C.
    • Cortical_Neuron.genes.annot: gene-SNP pairs based on the cortical neuronal Hi-C. Neurons were sorted from the adult dorsolateral prefrontal cortex.
    • Midbrain_DA.genes.annot: gene-SNP pairs based on the adult midbrain dopaminergic Hi-C.
    • MAGMA.genes.annot: gene-SNP pairs based on conventional MAGMA.

GWAS summary statistics

  • GWAS Reference

    • Attention deficit/hyperactivity disorder (ADHD): Demontis, D. et al.(2019) PMID:30478444

    • Autism spectrum disorder (ASD): Grove, J. et al.(2019) PMID: 30804558

    • Bipolar disorder (BD): Stahl, E. et al.(2019) PMID: 31043756

    • Schizophrenia (SCZ): Pardiñas, A. F. et al.(2018) PMID: 29483656

    • Major depressive disorder (MDD): Howard, D. M. et al.(2019) PMID: 30718901

    • Alzheimer’s disease (AD): Jansen, I. E. et al.(2019) PMID: 30617256

    • Parkinson’s disease (PD): Nalls, M. A. et al.(2019) bioRxiv 388165 doi:10.1101/388165

    • Multiple sclerosis (MS): Andlauer, T. F. M. et al.(2016) PMID: 27386562

    • Amyotrophic lateral sclerosis (ALS): van Rheenen, W. et al.(2016) PMID: 27455348

    • Problematic alcohol use (PAU): Zhou, H. et al. (2020) PMID: 32451486

    • Drinks per week (DPW): Liu, M. et al. (2019) PMID: 30643251

    • Nicotine Dependence (ND): Quach, B.C. et al.(2020) PMID: 33144568

    • Cigarettes per day (CPD): Liu, M. et al. (2019) PMID: 30643251

MAGMA output files

  • Output files from H_MAGMA_v1.07.sh are provided in Output_Files/H-MAGMA_v1.07.
  • Output files from H_MAGMA_v1.08.sh are provided in Output_Files/H-MAGMA_v1.08.
  • Output files from MAGMAdefault_v1.07.sh are provided in Output_Files/MAGMA_v.1.07_default.
  • Columns
    • GENE: Gene ID
    • CHR: Chromosomal location
    • START: Chromosomal start location of gene
    • STOP: Chromosomal stop location of gene
    • NSNPS: Number of SNPS annotated to gene
    • NPARAM: Number of relevant parameters used in the model
    • N: Sample size
    • ZSTAT: Z-scores derived from P-values
    • P: Gene level P-values

RRHO.R

  • This script runs gene-level overlap between two disorders based on Z-scores from H-MAGMA outputs.
  • Here we provide an example code using fetal brain Hi-C MAGMA outputs for ADHD and ASD.
  • Input files are provided in RRHO_example_input folder as H-MAGMA_ADHD.FB.csv and H-MAGMA_ASD.FB.csv respectively.
    • alternative="enrichment" : One sided test
    • BY=TRUE : P-value corrected by the Benjamini and Yekutieli procedure
    • log10.ind=TRUE : P-value plotted in -log10

Pleiotropic_genes.R

  • This file is to identify a set of genes shared among at least 4 disorders.
  • To use this file, first run RRHO between pairs of disorders (e.g. ADHD vs ASD/BD/SCZ/MDD; ASD vs BD/SCZ/MDD; BD vs SCZ/MDD; SCZ vs MDD) to obtain most upregulated genes for each comparison. Then intersect gene lists to obtain pleiotropic genes (defined as genes shared in >3 disorder).
    • sharedlist : Most upregulated genes from RRHO. This list gives significantly associated genes in both disorders.
    • HGNC : We have provided geneAnno_allgenes.rda as an input file to convert ENSEMBL gene IDs to HGNC symbols.

Reference

Please cite this paper:

  • Sey, N.Y.A., Hu, B., Mah, W. et al. A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles. Nat Neurosci (2020). https://doi.org/10.1038/s41593-020-0603-0

  • Sey, N.Y.A., Pratt, B.M., Won, H. Annotating genetic variants to target genes using H-MAGMA. Nat Protocols (2023). https://doi.org/10.1038/s41596-022-00745-z

  • MAGMA

    • MAGMA: de Leeuw, C. A., Mooij, J. M., Heskes, T. & Posthuma, D. MAGMA: generalized gene-set analysis of GWAS data. PLoS Comput. Biol. 11, e1004219 (2015).
    • MAGMA update: de Leeuw, C. A., Sey, N.Y.A., Posthuma, D., Won, H. A response to Yurko et al: H-MAGMA, inheriting a shaky statistical foundation, yields excess false positives. bioRxiv (2020) doi 10.1101/2020.09.25.310722.

  • Hi-C

    • Promoter-capture Hi-C data from 28 tissues and cell types: Jung, I. et al. A compendium of promoter-centered long-range chromatin interactions in the human genome. Nature Genetics vol. 51 1442–1449 (2019).
    • Adult brain Hi-C: Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).
    • Fetal brain Hi-C: Won, H. et al. Chromosome conformation elucidates regulatory relationships in developing human brain. Nature 538, 523–527 (2016).
    • iPSC derived neurons and astrocytes: Rajarajan, P. et al. Neuron-specific Signatures in the Chromosomal Connectome Are Associated with Schizophrenia Risk. Science 362, eaat4311 (2018).
    • Cortical neuron: Hu, B. et al. Neuronal and glial 3D chromatin architecture illustrates cellular etiology of brain disorders. Nature Communications 12(1):3968 (2021).
    • Midbrain dopaminergic neuron: Sey, N.Y.A et al. Chromatin architecture in addiction circuitry identifies risk genes and potential biological mechanisms underlying cigarette smoking and alcohol use traits. Molecular Psychiatry 27(7):3085-3094 (2022).