xiekunwhy/emagma-tutorial

A step by step guide on how to use eMAGMA, an approach to conducting eQTL informed gene-based tests.

eMAGMA-TUTORIAL

This tutorial is a step by step guide on how to use eMAGMA, an approach to conducting eQTL informed gene-based tests by assigning SNPs to tissue-specific eGenes as presented in Gerring et al., 2019a, Gerring et al., 2019b. Here we provide the scripts and files to use the eMAGMA methodology which generates a list of disease-associated eGenes using genome-wide summary statistics. In this tutorial, we will show how to apply eMAGMA using GWAS summary statistics of Major Depression Disorder (MDD) as example data; these summary statistics are publicly available from the Psychiatric Genomic Consortium (PGC) website.

The tutorial is divided into two parts. Part 1 conducts eMAGMA gene-based analysis, this analysis integrates SNP-gene associations from an eQTL reference dataset with GWAS summary statistics. We generated annotation files in which SNPs are assigned to genes based on their association with gene expression. The SNP-gene associations are tissue specific; hence we can estimate what genes are more highly associated with a disease at the tissue level. Part 2 conducts eMAGMA gene-set analysis, testing for the enrichment of association in co-expression networks. The aim of this analysis is to identify modules (sets of highly correlated genes) that are highly associated with disease risk. Tissue-specific annotation files and co-expression network files (for 48 tissues) are shared as part of this tutorial. Explanation of the methods and resources used in this tutorial are provided in the publication accompanying this tutorial, Gerring et al., 2009a.

Requirements

This tutorial is executable in Unix, it is assumed that users are familiar with the Unix environment and command line. You can type or copy paste the commands or re-structure them as you wish. This is a hands-on tutorial with minimum theoretical explanations. It is essential that the user reads through the publications that accompany the tutorial (Gerring et al. 2019a, Gerring et al., 2019b) as they provide the theoretical background for the analyses. Knowledge of GWAS and GWA-summary analysis is required. We have previously generated a tutorial on the execution of GWAS analysis through another Github repository https://github.com/MareesAT/GWA_tutorial (Marees et al., 2018).

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Setting Up

Start by creating an eMAGMA folder with all the files we will use throughout the tutorial.

   cd /path/to-yourworking folder
    mkdir eMAGMA
    cd eMAGMA

The analysis is done using MAGMA v1.07b (de Leeuw, Neale, Heskes, & Posthuma, 2016). MAGMA and auxiliary files can be downloaded from the program website: https://ctg.cncr.nl/software/magma. Two auxiliary files are required: a file with gene locations for protein-coding genes from NCBI and a genome reference file. For this tutorial we use build 37(hg19) that matches the build of the summary data (MDD2018_excluding23andMe) and the reference file for the European population. Gene location files for build 36, 37, & 38 are available from the MAGMA website. You can use wget o curl to import the files directly into your directory, for example:

MAGMA

wget https:// https://ctg.cncr.nl/software/MAGMA/prog/magma_v1.07b_static.zip

Auxiliary files for 37(hg19)

wget https://ctg.cncr.nl/software/MAGMA/aux_files/NCBI37.3.zip

Reference data

wget https://ctg.cncr.nl/software/MAGMA/ref_data/g1000_eur.zip

GWAS summary = MDD2018_ex23andMe from PGC web site

 https://www.med.unc.edu/pgc/results-and-downloads/

Notice: If you are using your own data, make sure to download the auxiliary files that correspond to the genome build of your data.

This tutorial provides gene annotation and co-expression networks for 48 tissues, including 13 brain tissues and whole blood. At the end of the tutorial you will be able to apply the eMAGMA approach to your own data using these files.

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LIST OF FILES SHARED WITH THIS TUTORIAL:

eMAGMA Annotation files for 48 tissues:

Batch1.annotation: Brain_Amygdala.genes.annot, Brain_Hippocampus.genes.annot, Brain_Anterior_cingulate_cortex_BA24.genes.annot, Brain_Hypothalamus.genes.annot, Brain_Caudate_basal_ganglia.genes.annot, Brain_Nucleus_accumbens_basal_ganglia.genes.annot Brain_Cerebellar_Hemisphere.genes.annot, Brain_Putamen_basal_ganglia.genes.annot, Brain_Cerebellum.genes.annot Brain_Spinal_cord_cervical_c-1.genes.annot, Brain_Cortex.genes.annot, Brain_Substantia_nigra.genes.annot, Brain_Frontal_Cortex_BA9.genes.annot.

Batch2.annotation: Adipose_Subcutaneous.genes.annot, Artery_Aorta.genes.annot, Breast_Mammary_Tissue.genes.annot, Adipose_Visceral_Omentum.genes.annot, Artery_Coronary.genes.annot, Adrenal_Gland.genes.annot, Artery_Tibial.genes.annot.

Batch3.annotation: Cells_EBV-transformed_lymphocytes.genes.annot, Esophagus_Gastroesophageal_Junction.genes.annot, Cells_Transformed_fibroblasts.genes.annot, Esophagus_Mucosa.genes.annot, Colon_Sigmoid.genes.annot, Esophagus_Muscularis.genes.annot Colon_Transverse.genes.annot.

Batch4.annotation: Heart_Atrial_Appendage.genes.annot, Liver.genes.annot, Minor_Salivary_Gland.genes.annot, Nerve_Tibial.genes.annot, Heart_Left_Ventricle.genes.annot, Lung.genes.annot, Muscle_Skeletal.genes.annot.

Batch5.annotation: Pancreas.genes.annot, Skin_Not_Sun_Exposed_Suprapubic.genes.annot, Spleen.genes.annot, Pituitary.genes.annot, Skin_Sun_Exposed_Lower_leg.genes.annot, Stomach.genes.annot, Prostate.genes.annot, Small_Intestine_Terminal_Ileum.genes.annot.

Batch6.annotation: Ovary.genes.annot, Testis.genes.annot, Thyroid.genes.annot Uterus.genes.annot, Vagina.genes.annot, Whole_Blood.genes.annot.

eMAGMA Co-expression network files for 48 tissues:

network_files.zip: Adipose_Subcutaneous_entrez_gtex_v7_normalised.txt Adipose_Visceral_Omentum_entrez_gtex_v7_normalised.txt Adrenal_Gland_entrez_gtex_v7_normalised.txt Artery_Aorta_entrez_gtex_v7_normalised.txt Artery_Coronary_entrez_gtex_v7_normalised.txt Artery_Tibial_entrez_gtex_v7_normalised.txt Brain_Amygdala_entrez_gtex_v7_normalised.txt Brain_Anterior_cingulate_cortex_BA24_entrez_gtex_v7_normalised.txt Brain_Caudate_basal_ganglia_entrez_gtex_v7_normalised.txt Brain_Cerebellar_Hemisphere_entrez_gtex_v7_normalised.txt Brain_Cerebellum_entrez_gtex_v7_normalised.txt Brain_Cortex_entrez_gtex_v7_normalised.txt Brain_Frontal_Cortex_BA9_entrez_gtex_v7_normalised.txt Brain_Hippocampus_entrez_gtex_v7_normalised.txt Brain_Hypothalamus_entrez_gtex_v7_normalised.txt Brain_Nucleus_accumbens_basal_ganglia_entrez_gtex_v7_normalised.txt Brain_Putamen_basal_ganglia_entrez_gtex_v7_normalised.txt Brain_Spinal_cord_cervical_c-1_entrez_gtex_v7_normalised.txt Brain_Substantia_nigra_entrez_gtex_v7_normalised.txt Breast_Mammary_Tissue_entrez_gtex_v7_normalised.txt Cells_EBV-transformed_lymphocytes_entrez_gtex_v7_normalised.txt Cells_Transformed_fibroblasts_entrez_gtex_v7_normalised.txt Colon_Sigmoid_entrez_gtex_v7_normalised.txt Colon_Transverse_entrez_gtex_v7_normalised.txt Esophagus_Gastroesophageal_Junction_entrez_gtex_v7_normalised.txt Esophagus_Mucosa_entrez_gtex_v7_normalised.txt Esophagus_Muscularis_entrez_gtex_v7_normalised.txt Heart_Atrial_Appendage_entrez_gtex_v7_normalised.txt Heart_Left_Ventricle_entrez_gtex_v7_normalised.txt Liver_entrez_gtex_v7_normalised.txt Lung_entrez_gtex_v7_normalised.txt Minor_Salivary_Gland_entrez_gtex_v7_normalised.txt Muscle_Skeletal_entrez_gtex_v7_normalised.txt Nerve_Tibial_entrez_gtex_v7_normalised.txt Ovary_entrez_gtex_v7_normalised.txt Pancreas_entrez_gtex_v7_normalised.txt Pituitary_entrez_gtex_v7_normalised.txt Prostate_entrez_gtex_v7_normalised.txt Skin_Not_Sun_Exposed_Suprapubic_entrez_gtex_v7_normalised.txt Skin_Sun_Exposed_Lower_leg_entrez_gtex_v7_normalised.txt Small_Intestine_Terminal_Ileum_entrez_gtex_v7_normalised.txt Spleen_entrez_gtex_v7_normalised.txt Stomach_entrez_gtex_v7_normalised.txt Testis_entrez_gtex_v7_normalised.txt Thyroid_entrez_gtex_v7_normalised.txt Uterus_entrez_gtex_v7_normalised.txt Vagina_entrez_gtex_v7_normalised.txt Whole_Blood_entrez_gtex_v7_normalised.txt

Tutorial Output files:

Amygdala_outputs: Amygdala_emagma.genes.out, Amygdala_emagma.genes.raw, Amygdala_emagma.gsa.out, Amygdala_emagma.log, Amygdala_signif_genes.txt.

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References

a Zachary F Gerring, Angela Mina-Vargas, Nicholas G Martin2, Eric R Gamazon3-5, Eske M Derks. eMAGMA: An eQTL-informed method to identify risk genes using genome-wide association study summary statistics. doi: https://doi.org/10.1101/854315.

b Gerring ZF, Gamazon ER, Derks EM, for the Major Depressive Disorder Working Group of the Psychiatric Genomics Consortium (2019) A gene co-expression network-based analysis of multiple brain tissues reveals novel genes and molecular pathways underlying major depression. PLOS Genetics 15(7): e1008245. https://doi.org/10.1371/journal.pgen.1008245

Marees, AT, de Kluiver, H, Stringer, S, et al. A tutorial on conducting genome‐wide association studies: Quality control and statistical analysis. Int J Methods Psychiatr Res. 2018; 27:e1608. https://doi.org/10.1002/mpr.1608

de Leeuw C, Mooij J, Heskes T, Posthuma D (2015): MAGMA: Generalized gene-set analysis of GWAS data. PLoS Comput Biol 11(4): e1004219. doi:10.1371/journal.pcbi.1004219