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Genetic, transcriptomic, metabolic, and neuropsychiatric underpinnings of cortical functional gradients

This repo is for documentation of the work

Preprint: https://doi.org/10.1101/2025.03.03.25323242

$ python

  • py00: generating individual gradients using different sparsity scores, which are aligned to HCP group-level gradients.

  • py01: generating null group-level gradients by variogram, based on the HCP group-level geometric distance.

  • py02: cosine similarity, Euclidean distance, and Pearson r, between individual gradients and HCP group-level.

  • py03: integrating these scores from py03 into one file.

  • py04: preparing phenotype files (global-level) for the heritability analysis and GWAS.

  • py05: combining autochromosome and sexual chromosome results (because GWAS performed them separately) and filtering the MAF<1%.

  • py06: preparing phenotype files (region-level) for the heritability analysis and GWAS.

  • py07: imputing the missing gene expression, Allen Human Brain Atlas (AHBA), for Glasser parcellation, based on variogram of center coordinates of each parcel in volumetric space.

  • visualization: generating all figures and other statistics in the paper.

  • twin genetics: using the previous code from my repos (https://github.com/CNG-LAB/cngopen/tree/main/asymmetry_functional_gradients, https://github.com/wanb-psych/microstructural_asymmetry/blob/main/run_solar.sh)

$ bash

  • run00-03,05: working with python scripts and slurm using the high-performance computing service.

  • run04: GWAS code for gradients using GCTA64.

  • run06: extracting heritability scores from GCTA64 output.

$ r

  • manhtn: Manhattan plots for GWAS summaries using R package "CMplot".

Key toolboxes

Correspondence

Bin Wan (binwan.academic[at]gmail.com)
Sofie L. Valk (valk[at]cbs.mpg.de)

Neurobiosocial (NBS) & Cognitive Neurogenetics (CNG) lab,
Max Planck Institute for Human Cognitive and Brain Sciences &
Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich

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