ukb_brainage

https://github.com/pjawinski/ukb_brainage

Science Score: 57.0%

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Last synced: 10 months ago · JSON representation ·

Repository

Basic Info

Host: GitHub
Owner: pjawinski
License: gpl-3.0
Language: Shell
Default Branch: main
Size: 133 MB

Statistics

Stars: 4
Watchers: 1
Forks: 0
Open Issues: 0
Releases: 0

Created over 2 years ago · Last pushed 11 months ago

Metadata Files

Readme License Citation

Genome-wide analysis of brain age gap identifies 59 associated loci and unveils relationships with mental and physical health

This repository provides the analysis scripts and resources required to reproduce the findings reported in our article, "Genome-wide analysis of brain age gap identifies 59 associated loci and unveils relationships with mental and physical health". The individual-level data incorporated in this work have been obtained from the UK Biobank and the LIFE-Adult study. Access to these datasets is restricted to researchers with approved projects. The GWAS summary statistics and polygenic score weights generated from our analyses are publicly available on Zenodo.

Abstract

Neuroimaging and machine learning are advancing the study of biological aging mechanisms. In this field, brain age gap has emerged as promising MRI-based biomarker quantifying the deviation between an individuals biological and chronological age of the brain. Here, we conducted an in-depth genomic analysis of brain age gap and its relationships with over 1,000 health traits. Genome-wide analyses in up to 56,348 individuals unveiled a 23-29% SNP-based heritability and highlighted 59 associated loci (39 novel). The leading locus encompasses MAPT, encoding the tau protein central to Alzheimer's disease. Genetic correlations revealed relationships with mental health (e.g., depressed mood), physical health (e.g., diabetes), lifestyle (e.g., alcohol intake), and socioeconomic traits (e.g., income). Mendelian Randomization indicated a causal role of high blood pressure and type-2-diabetes in accelerated brain aging. Our study highlights key genes and pathways related to neurogenesis, immune-system-related processes, and small GTPases binding, laying the foundation for further mechanistic exploration.

Keywords: aging, genetics, machine learning, mental health, MRI

Folder structure

code/ - contains preparation files, functions, and analysis scripts
envs/ - contains conda .yml files to recreate our environments
results/ - contains result files (individual-level results are not provided due to data privacy policies)
run.mri.sh - main analysis file for brain age gap estimations (phenotyping)
run.genetics.sh - main analysis file for genetic analyses

Software Environment

Analyses were run on Debian GNU/Linux 11 (bullseye) with kernel version 5.10.0-23-amd64. The code/prepare/ directory contains scripts to facilitate the installation of the necessary bioinformatic tools for reproducing our analyses. For managing conda environments, we recommend using mamba, which offers faster dependency resolution and package installation compared to conda.

Required Tools: Below is a list of the primary tools utilized in our analysis, along with their respective versions and roles:

R 3.5.1-4.4.1 | Statistical computing and plotting, included in conda environments
MATLAB R2021a | MRI preprocessing and age prediction
SPM12 r7487 | MRI preprocessing
CAT12 r1364 | MRI preprocessing
RVM-MATLAB v1.0.0 | Age prediction
XGBoost v0.82.1 | Age-prediction
PHESANT v1.1 | Cross-trait (phenome-wide) association analysis in UK Biobank
ENIGMA Toolbox v2.0.3 for MATLAB | Visualizing cortical and subcortical surface associations
PLINK 1.9 v1.90b6.8 64-bit | Genomic preprocessing
PLINK 2.0 v2.00a2LM 64-bit | Genomic preprocessing and genome-wide association analysis
METAL 2020-05-05 | Fixed-effects meta-analysis in European ancestry samples
MR-MEGA v0.2 | Multi-ancestry meta-analysis (meta-regression)
GWAMA v2.2.2 | Multi-ancestry meta-analysis (random-effects)
LOCUSZOOM v1.4 standalone | Regional association plots
GCTA v1.93.1f beta | Conditional analysis, gene-based analysis, and Mendelian randomization
SMR v1.3.0 | eQTL/sQTL Mendelian randomization
LDSTORE v2.0 x86/64 | Estimating and storing linkage disequilibrium data
FINEMAP v1.4.2 | Identification of causal variants
susieR v0.12.35 | Identification of causal variants
GCTB v2.5.2 | Identification of causal variants and polygenic score analysis using SBayesRC
PRSice2 v2.3.3 | Polygenic score analysis
MAGMA v1.10 | Gene-based analysis as input for PoPS
PoPS v0.2 | Gene prioritization
GOfuncR v1.14.0 | Gene set enrichment analysis
LDSC v1.0.1 | Heritability & partitioned heritability analysis, genetic correlations
GENESIS e4e6894 | Polygenicity analysis

Cloning the Repository

Navigate to your preferred local folder and clone this repository via the following commands: git clone https://github.com/pjawinski/ukb_brainage cd ukb_brainage

To preprocess MRI files, refer to the steps outlined in code/prepare.mri.sh
To prepare genetic data, follow the instructions in code/prepare.genetics.sh
For a step-by-step guide through individual analysis processes, see the main analysis files run.mri.sh and run.genetics.sh > Note: Direct downloads of MRI and genetic data are deprecated. All data access and preprocessing must now be performed using the UK Biobank Research Analysis Platform (RAP).

Results at a glance

Graphical Abstract

Contact

Philippe Jawinski | Humboldt-Universitt zu Berlin | philippe.jawinski[at]hu-berlin.de
Sebastian Markett | Humboldt-Universitt zu Berlin | sebastian.markett[at]hu-berlin.de

Owner

Login: pjawinski
Kind: user

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

Citation (CITATION.cff)

cff-version: 1.2.0
preferred-citation:
  type: article
  authors:
  - family-names: "Jawinski"
    given-names: "Philippe"
    orcid: "http://orcid.org/0000-0002-2994-3075"
  - family-names: "Forstbach"
    given-names: "Helena"
  - family-names: "Kirsten"
    given-names: "Holger"
    orcid: "http://orcid.org/0000-0002-3126-7950"
  - family-names: "Beyer"
    given-names: "Frauke"
    orcid: "http://orcid.org/0000-0001-5401-852X"
  - family-names: "Villringer"
    given-names: "Arno"
    orcid: "http://orcid.org/0000-0003-2604-2404"
  - family-names: "Witte"
    given-names: "A. Veronica"
    orcid: "http://orcid.org/0000-0001-9054-6688"
  - family-names: "Scholz"
    given-names: "Markus"
    orcid: "http://orcid.org/0000-0002-4059-1779"
  - family-names: "Ripke"
    given-names: "Stephan"
    orcid: "http://orcid.org/0000-0003-3622-835X"
  - family-names: "Markett"
    given-names: "Sebastian"
    orcid: "http://orcid.org/0000-0002-0841-3163"
  title: "Genome-wide analysis of brain age identifies 25 associated loci and unveils relationships with mental and physical health"
  doi: 10.1101/2023.12.26.23300533
  journal: "medRxiv"
  year: 2023
  month: 12

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