bd-lithium

A collection of source codes for network-based multi-omics analysis using integrated genome-wide association studies (GWAS) and transcriptomic data to identify genetic contribution into lithium response in patients with bipolar disorder (BD).

https://github.com/vniems/bd-lithium

Keywords

bipolar-disorder drug-response functional-enrichment genome-wide-association-studies induced-pluripotent-stem-cells ipsc lithium multi-omics network-analysis pathview pharmacogenetics rna-sequencing transcriptomics

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A collection of source codes for network-based multi-omics analysis using integrated genome-wide association studies (GWAS) and transcriptomic data to identify genetic contribution into lithium response in patients with bipolar disorder (BD).

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Owner: vniems
License: gpl-3.0
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bipolar-disorder drug-response functional-enrichment genome-wide-association-studies induced-pluripotent-stem-cells ipsc lithium multi-omics network-analysis pathview pharmacogenetics rna-sequencing transcriptomics

Created over 3 years ago · Last pushed over 3 years ago

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Readme License Citation

README.md

Bipolar Disorder and Lithium Response: Pharmacogenomic Study

This is the README for the project of Bipolar Disorder and Lithium Response: Pharmacogenomic Study.

Note that this project is temporality under development, and coming soon. Some info is not completed.

About Our Project

The project is a collection of source codes being used in the study that aimed to identify the genetic contribution into lithium response in patients with bipolar disorder (BD) using multi-omics analysis of integrated between genome-wide association study (GWAS) and RNA-sequencing (RNA-seq) data.

Here, we make available the analytic tools employed as part of the main study, of which the manuscript, entitled "Focal adhesion is associated with lithium response in bipolar disorder: evidence from a network-based multi-omics analysis", has been published in Molecular Psychiatry. [doi will be added after a paper is published]

Project Aim

The purpose of the project is to distribute the tool (code development) that had been used in the multi-omics analysis of integrated data between BD GWAS and RNA-seq of patients-derived induced pluripotent stem cell (iPSC) neurons.

The study workflow including detailed methods can be found in 1) the Method section in the main text and 2) the Supplemental Methods of the manuscript [doi will be added after a paper is published].

The Project Workflow and File Descriptions

Source codes of this project can be found in a 'DIRECTORIES' folder, containing three subdirectories based on the analytic steps as shown below.

Brief description is provided for each file, including additional/required data, which will be a useful resource for users.

Step I

Genome-wide association study of patients with BD

Genome-wide association study (GWAS) analysis & imputation: we did not provide the code here. A summary is described as follows.

DNA from 1106 subjects was genotyped on the Illumina PsychChip (https://sites.google.com/a/broadinstitute.org/psych-chip-resources/home).
Genotype data was screened for low call rate, low genotyping rate, and Hardy-Weinberg equilibrium. Imputation was performed using IMPUTE2 (Howie et al. 2009). Association was tested by linear regression as implemented in PLINK (Purcell et al. 2007).

Gene-based analysis: we used a Versatile Gene-based Association Test (VEGAS) (Mishra and Macgregor 2015), a web-based tool, https://vegas2.qimrberghofer.edu.au/.

GWAS prioritizing analysis: the GWAS results were reprioritized by using network information and the algorithms implemented in genome-wide boosting analysis (GWAB) (Shim et al. 2017) and NetWAS (Greene et al. 2015) methods.

Step II

Transcriptomic analysis of iPSC-derived neurons

Raw data of RNA-sequencing (RNA-seq) stored in NCBI's Gene Expression Omnibus (Edgar et al. 2002) and are accessible through GEO Series accession number GSE205422.

RNA-seq analysis:

Differential expression analysis: the functions are used for RNA-seq differential expression analysis and downstream analysis.
RNA-seq input files:

Data #1: Phenotypic data - kelsoe_metadata_111.csv.

Data #2: Transcriptomic data from the RNA-seq raw data after quality control and alignment processes - all_genes_results_111.txt. Note that data #2 was not stored in this project, read more detail here.

Step III

Network and functional enrichment analyses

Network propagation analysis

Cluster analysis

KEGG pathway analysis

Pathview creation

Network input files:

Data #3: A list of significantly differentially expressed (DE) genes in Li responders vs Li non-responders (LR vs NR), n = 41 genes - DE_Genes_Li and CTRL_LRvsNR.xlsx.
Data #4: A list of the top 5% prioritized genes obtained from VEGAS, GWAB, and NetWAS - prioritized_genes_boosting_methods_5percent_190708.xlsx.

Feature Guidance

All source codes shown here can be found in this GitHub project:

| Features |||Additional software required| |------------------------------|:--------:|:--------:|:------------------------:| | NetWAS analysis | | | | | RNA-seq analysis | | | | | Network propagation analysis | | | Cytoscape | | Cluster analysis | | | | | KEGG pathway analysis | | | | | Pathview creation | | | |

Contributing

Contributions for this project are:

Code development team:
- Kathleen M. Fisch, PhD
- Sara Brin Rosenthal, PhD
Executive manager and sponsor:
- John R. Kelsoe, MD
Administrator:
- Vipavee Niemsiri, MD, PhD

License

Contents in this project are under licenses as follows:

Source codes:
The source codes in this project is free to be used and/or modify under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or any later version. Please see the policy and term of use here.
Other materials except the source codes:
Other contents, such as input data, are part of the study published in the manuscript entitled, Focal adhesion is associated with lithium response in bipolar disorder: evidence from a network-based multi-omics analysis", which are under the term of a CC BY license (Creative Commons Attribution 4.0 International License).

Artwork:
Copyright 2022 by Vipavee N. The artwork in this project is licensed under the term of a CC BY-NC-SA 4.0 license (Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License).

Citation

If you use the code from this project, please cite with the data from the CITATION.cff file (see here) or from the Council of Science Editors (CSE) citation styles as shown below.

CSE Bibliography Format:
Niemsiri V, Rosenthal SB, Fisch KM, and Kelsoe JR. 2022. Bipolar Disorder and Lithium Response: Pharmacogenomic Study. San Francisco (CA): GitHub; [accessed 2022 Jun 19]. https://github.com/vniems/BD-Lithium.

References

Edgar R, Domrachev M, Lash AE. 2002. Gene Expression Omnibus: NCBI gene expression and hybridization array data repository. Nucleic Acids Res. 30(1):207-210.

Greene CS, Krishnan A, Wong AK, Ricciotti E, Zelaya RA, Himmelstein DS, Zhang R, Hartmann BM, Zaslavsky E, Sealfon SC et al. 2015. Understanding multicellular function and disease with human tissue-specific networks. Nat Genet. 47(6):569-576.

Howie BN, Donnelly P, Marchini J. 2009. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5(6):e1000529.

Mishra A, Macgregor S. 2015. VEGAS2: Software for more flexible gene-based testing. Twin Res Hum Genet. 18(1):86-91.

Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ et al. 2007. PLINK: A tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet. 81(3):559-575.

Shim JE, Bang C, Yang S, Lee T, Hwang S, Kim CY, Singh-Blom UM, Marcotte EM, Lee I. 2017. GWAB: A web server for the network-based boosting of human genome-wide association data. Nucleic Acids Res. 45(W1):W154-W161.

Owner

Name: WithAmbition
Login: vniems
Kind: user

Repositories: 1
Profile: https://github.com/vniems

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  Bipolar Disorder and Lithium Response:
  Pharmacogenomic Study
message: Please cite this software using these metadata.
type: software
authors:
  - given-names: Vipavee
    name-particle: Niemsiri
    affiliation: >-
      Department of Psychiatry, University of
      California, San Diego, USA
  - given-names: Sara Brin
    name-particle: Rosenthal
    affiliation: >-
      Center for Computational Biology and
      Bioinformatics, University of California, San
      Diego, USA
  - given-names: Kathleen M.
    name-particle: Fisch
    affiliation: >-
      Center for Computational Biology and
      Bioinformatics, University of California, San
      Diego, USA
  - given-names: John R.
    name-particle: Kelsoe
    affiliation: >-
      Department of Psychiatry, University of
      California, San Diego, USA
identifiers:
  - type: url
    value: 'https://github.com/vniems/BD-Lithium'
    description: The repository of this project.
url: 'https://github.com/vniems/BD-Lithium'
abstract: >-
  A source code for network-based multi-omics
  analysis of lithium response in bipolar disorder,
  using integrative genome-wide association studies
  (GWAS) and transcriptomics data. 
keywords:
  - Network-Based Analysis
  - Multi-Omics
  - Genome-Wide Association Study
  - Transcriptomics
  - RNA Sequencing
  - Bipolar Disorder
  - Lithium
  - 'Biomarkers, Drug Response '
  - Pharmacogenetics
  - Transcriptome
  - Gene expression
  - Functional Enrichment Analysis
license: GPL-3.0-or-later
version: 1.1.0
date-released: '2022-06-19'

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