/NeuroimaGene_R

R package for NeuroimaGene resource

Primary LanguageRGNU General Public License v3.0GPL-3.0

NeuroimaGene R project

Hi, Welcome to the NeuroimaGene project!

The goal of this project is to improve the interpretability of genetic studies relating to neurological and/or psychiatric traits. Analyses such as genome-wide association studies often highlight genetic variants that segregate with disease. Methods such as fine mapping, eQTL colocalization, and transcriptome-wide analyses (TWAS) have been developed to identify the gene level targets of these trait-associated variants. Once a set of gene targets has been identified from GWAS findings, it often remains challenging to identify the biological consequences of variation in these genes. Briefly, the NeuroimaGene R package makes it possible to identify the neuroimaging correlates of TWAS associations.

More specifically, NeuroimaGene can be used to (1) characterize individual genes or gene sets as relevant to the structure and function of the brain, (2) identify the region(s) of the brain or body in which expression of target gene(s) is neurologically relevant, (3) impute the brain features most impacted by user-defined gene sets such as those produced by cohort level gene association studies, and (4) generate publication level, modifiable visual plots of neuroimaGene associations.

Installation

We recommend installing the NeuroimaGene package directly from CRAN. Use the following script in the R console:

install.packages("neuroimaGene")

Github Installation

You can also install the NeuroimaGene package directly from GitHub using the devtools package. First, ensure you have devtools installed. Then, use the install_github function to install the package from the GitHub repository.

Step-by-Step Instructions

  1. Install devtools:

    install.packages("devtools")

  2. Install NeuroimaGene from GitHub: Use the install_github function from the devtools package to install NeuroimaGene:

    devtools::install_github("xbledsoe/NeuroimaGene_R")

  3. Load the Package: Once installed, you can load the NeuroimaGene package as follows:

    library(neuroimaGene)

Here is a complete example in R:

# Install devtools if not already installed
install.packages("devtools")

# Install the NeuroimaGene package from GitHub
devtools::install_github("xbledsoe/NeuroimaGene_R")

# Load the package
library(neuroimaGene)

Using the NeuroimaGene R package

The NeuroimaGene R package serves to identify, characterize, and visualize the neurological correlates of genetically regulated gene expression (GReX). The following functions serve this goal.

The main neuroimaGene query

The main package function, neuroimaGene() takes a vector of gene names (HUGO gene names or ensembl id’s) and returns a data table of GReX-NIDP associations that fit a number of user-defined parameters described below:

ng <- neuroimaGene( gene_list, modality='T1', atlas='Desikan', mtc='BH', nidps = NA, filename = NA)

Short Parameter Descriptions:

gene_list: a vector of gene names (HUGO gene names or ensembl id’s). Typically these are associated with a single trait of interest.

modality: the MRI modality used to define the NIDP in the initial UK Biobank imaging protocol. (See table below for options)

atlas: the parcellation atlas used to define the NIDPs from the MRI. We use the term “atlas” loosely recognizing that the fMRI data, dMRI data, and T1/T2 data are parcellated in diverse ways. (See table below for options)

mtc: Multiple Testing Correction. Options include Nominal (nom), Benjamini Hochberg (BH), or Bonferroni (BF)

nidps: A user-defined list of NIDPs to test. This option overrides the modality and atlas parameters. Default is NA.

filename: A user-defined pathname/filename for text output of the NeuroimaGene results.

Extended Parameter Descriptions

Modality and Atlas parameters

In the process of using the tool, the user is responsible for selecting a subset of NIDPs from the resource for analysis using the modality and atlas parameters. These parameters allow the user to target specific types of NIDPs such as hippocampal subfields, area and thickness of named cortical regions, fractional anisotropy of named white matter tracts etc. It is recommended to identify the type of brain measure one is interested in prior to performing the gene set analysis. The input options for the modality and atlas parameters are detailed in the dropdown table below

Multiple Testing Correction Parameter for statistical significance

In addition to selecting the atlas and modality, neuroimaGene requires a multiple testing threshold correction. Each imaging modality contains a different number of NIDPs (see table above). The Bonferroni correction (‘BF’) treats each of these NIDPs as independent even though we know through significant data analyses that this is not accurate. This is a highly conservative threshold that will yield high confidence associations but is likely to generate many false negatives. Recognizing the correlation of brain measures from the same modality and atlas, we recommend using the less stringent Benjamini Hochberg (‘BH’) False Discovery Rate for discovery analyses. The nominal option (‘nom’) will provide the uncorrected p-value.

Note that the mtc parameter represents a study-wide is dynamic, depending on the modality and atlas parameters provided in the initial neuroimaGene query.

  1. If a user provides an atlas, multiple testing correction will be calculated at an atlas-wide level.

  2. If the user provides a modality but sets the atlas as NA or ‘all’, multiple testing correction will be calculated at a modality-wide level.

  3. If the user sets the both the modality and atlas to NA or ‘all’, the correction will be applied to the entire data set.

The above bullet points apply to both the Bonferroni (BF) and Benjamini Hochberg (BH) corrections. Alpha is set to 0.05 in either case.

Providing a user selected set of NIDP names

If the user has a specific set of NIDPs that they would like to assess, the nidps parameter receives a vector of NIDPs by name. The NIDP names all include data about the modality, atlas, region, and hemisphere. The names have to be provided in character form and matching must be perfect. To aid with the user-provision of NIDPs, we include the helper utility listNIDPs(). This function takes as parameters modality and atlas and returns a list of all NIDP names that satisfy these criteria. The user can then manually curate a list of desired NIDPs.

Note that when providing a user-defined set of NIDPs, this option overrides the modality and atlas parameters. This applies both to the set of NIDPs analyze as well as the multiple testing correction. The nominal p-value will be reported as both the Bonferroni and Benjamini-Hochberg corrections that are encoded rely on knowing the set of NIDPs being tested. The user NIDP input violates this assumption and requires the user to identify their own multiple testing threshold.

NIDP modality and atlas descriptions:

See dropdown table for NIDP descriptions and source links

NIDP atlas descriptions and source links

Modality Atlas name # of NIDPs Description source
T1 all 1319 All measures recorded by the UKB neuroimaging study derived from T1 imaging see note*
T1 Destrieux 444 Destrieux atlas parcellation of cortical sulci and gyri Destrieux
T1 AmygNuclei 20 morphology of Nuclei of the amygdala Amygdala nuclei
T1 Subcortex 52 subcortical volumetric segmentation aseg_volume
T1 Broadmann 84 cortical morphology via Broadmann Areas Broadmann
T1 Desikan 202 Desikan Killiany atlas parcellation of cortical morphology Desikan
T1 DKT 186 DKT atlas parcellation of cortical morphology DKTatlas
T1 FAST 139 cortical morphology via FMRIB’s Automatic Segmentation Tool FAST
T1 FIRST 15 Subcortical morphology via FIRST FIRST
T1 HippSubfield 44 morphology of Hippocampal subfields HippSubfield
T1 pial 66 structure: Desikan Killiany atlas of the pial surface Desikan
T1 Brainstem 5 structure: Freesurfer brainstem parcellation Brainstem
T1 SIENAX 10 structure: Structural Image Evaluation of whole brain measures SIENAX
T1 ThalamNuclei 52 morphology of the Nuclei of the thalamus ThalamNuclei
dMRI all 675 All measures recorded by the UKB neuroimaging study derived from DWI imaging see note*
dMRI ProbtrackX 243 white matter mapping obtained via probabilistic tractography ProbtrackX*
dMRI TBSS 432 white matter mapping obtained via tract-based spatial statistics TBSS*
rfMRI ICA100 1485 functional connectivity using 100 cortical seeds see note*
rfMRI ICA25 210 functional connectivity using 25 cortical seeds see note*
rfMRI ICA-features 6 summary of functional connectivity components see note*
T2_FLAIR BIANCA 1 white matter hyperintensity classification algorithm BIANCA
T2star SWI 14 susceptibility-weighted imaging: microhemorrhage and hemosiderin deposits see note*

* original publication for details here (Alfaro-Almagro, Fidel, et al. “Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank.” Neuroimage 166 (2018): 400-424.)

Example NeuroimaGene Query:

library(neuroimaGene, quietly = TRUE)
gene_list <- c('TRIM35', 'PROSER3', 'EXOSC6', 'PICK1', 'UPK1A', 'ESPNL', 'ZIC4')
ng <- neuroimaGene(gene_list, atlas = NA, mtc = 'BH', vignette = TRUE)
gene gene_name gwas_phenotype training_model zscore mod_BHpval
ENSG00000223496 EXOSC6 aparc-a2009s_lh_thickness_G-front-middle JTI_Adrenal_Gland 4.160209 0.0357891
ENSG00000223496 EXOSC6 aparc-a2009s_lh_thickness_G-front-sup JTI_Adrenal_Gland 5.163521 0.0012963
ENSG00000223496 EXOSC6 aparc-a2009s_lh_thickness_G-parietal-sup JTI_Adrenal_Gland 5.099214 0.0016672
ENSG00000144488 ESPNL aparc-a2009s_lh_thickness_G-parietal-sup JTI_Adrenal_Gland -4.306351 0.0241802
ENSG00000144488 ESPNL aparc-a2009s_lh_thickness_G-pariet-inf-Angular JTI_Adrenal_Gland -6.103620 0.0000168
ENSG00000223496 EXOSC6 aparc-a2009s_lh_thickness_G-pariet-inf-Angular JTI_Adrenal_Gland 4.362468 0.0205838

Statistical visualizations

Genes

Users may wish to know the comparative contribution of each gene from the provided list to the NeuroimaGene results. We include a function to visualize the number of NIDPs per gene in the neuroimaGene package.

plot_gns(ng, maxGns = 15)

The legend describes the primary measurement of the NIDPs For structural data, this is the cortex, subcortex, CSF, or whole brain measurements. The parameter maxGns describes the maximum number of genes to display. It is set to a default of 15. Genes are selected by ranking the neuroimagene genes by their zscore magnitudes and selecting the top N genes.

Note that this plot does not reflect any information on the number of tissue contexts in which the GReX-NIDP association is significant. This data is better captured by the plot_gnNIDP() function.

Neuroimaging Derived Phenotypes (NIDPs)

It is also useful to see what NIDPs are most impacted by the genes provided. The plot_nidps() function calculates the mean zscore for each NIDP across all tissue models and genes. It amounts to an aggregated effect of the genes on the region of interest. This linear aggregation is an approximation best understood to show convergence in effect size magnitude and direction.

plot_nidps(ng, maxNidps = 20)

The legend describes a narrower descriptive profile of the NIDPs than the plot_gns() tool. The parameter maxNidps describes the maximum number of NIDPs to display. It is set to a default of 30. NIDPs are selected by ranking the NIDPs by their zscore magnitudes and selecting the top N NIDPs.

Genes and Neuroimaging Derived Phenotypes (NIDPs)

It is also useful to see the intersection of genes and NIDPs. The plot_gnNIDP() function shows the number of JTI models in which each GReX-NIDP association is significant.

plot_gnNIDP(ng, maxNidps = 20, maxGns = 15)

The parameter maxNidps describes the maximum number of NIDPs to display. It is set to a default of 20. NIDPs are selected by ranking the NIDPs by their zscore magnitudes and selecting the top N NIDPs. The parameter maxGns describes the maximum number of genes to display. It is set to a default of 15. Genes are selected by ranking the neuroimagene genes by their zscore magnitudes and selecting the top N genes.

2-Dimensional brain visualization of GReX-NIDP associations

It is often useful to provide a visual representation of neuroimaging features in their 2-dimensional biological context for presentation purposes. We include the neuro_vis() function for this purpose. This function calculates the mean normalized effect size for each NIDP in the neuroimaGene object. The user must provide a number of parameters listed below.

ng_obj  - NeuroimaGene object produced by neuroimaGene() function
atlas   - desired atlas for visualization. (Desikan[default], Subcortex, DKT, Destrieux)
lowcol  - color for low end of Zscore spectrum. Default is darkred
midcol  - color for middle of Zscore spectrum. Default is white
hicol   - color for top end of Zscore spectrum. Default is blue4

The function only reflects cortical and subcortical measures. It will automatically separate the NIDPs according to volume, thickness, and surface areas and plot each as its own facet. Please note that this function uses the ggseg package and select atlases from the ggsegExtra package. Please be sure to cite the creators of ggseg if using this feature in publications.

Mowinckel AM, Vidal-Piñeiro D (2019). “Visualisation of Brain Statistics with R-packages ggseg and ggseg3d.” 1912.08200.

The neuro_vis() function can be used as described below:

neuro_vis(ng, atlas = 'Desikan', lowcol = 'darkred', midcol = 'white', highcol = 'blue4')
#> merging atlas and data by 'label', 'atlas', 'roi'


neuro_vis(ng, atlas = 'Subcortex', lowcol = 'darkgreen', midcol = 'yellow2', highcol = 'darkorange')
#> merging atlas and data by 'label', 'atlas', 'side'

Limitations

There are several key limitations to this resource that we wish to highlight here.

  • First, neuroimaGene describes associations between NIDPs and predicted genetically regulated gene expression (GReX). GReX represents the proportion of gene expression that is genetically determined. Genes that affect a phenotype through protein dysfunction will not show up in neuroimaGene so long as the underlying mRNA expression remains unchanged.

  • Second, the tissue context of the GReX-NIDP association is not causal. GReX-NIDP associations are frequently significant in multiple tissue contexts. In these circumstances, we do not yet have sufficient methods to identify the causal tissue. Even when the GReX measure is only associated with the NIDP in a single tissue, it is important to note that GReX cannot be said to be causal in this tissue without further analyses.

  • Third, while GReX associations are derived from SNP data which is not affected by environment, the S-PrediXcan methodology is still susceptible to confounding from linkage disequilibrium of input SNPs. For causal inference, we recommend applying methods such as MR-JTI (Zhou et al 2020) to the GReX-NIDP associations as a post-hoc test to adjust the association statistics by quantifying and accounting for genetic heterogeneity.

Please see our publication for a more extended discussion on limitations.

Terminology

GWAS  - Genome Wide Association Study
TWAS  - Transcriptome Wide Association Study
GReX  - Genetically regulated gene expression
JTI   - Joint Tissue Imputation (link)
NIDP  - Neuroimaging Derived Phenotype
MRI   - Magnetic resonance imaging
T1    - MRI modality classically used for structural characterization of the brain
dMRI  - diffusion weighted MRI (used in our data for white matter tractography)
fMRI  - functional MRI used for examining coordinated activity across regions of the brain 
UKB   - United Kingdom Biobank
eQTL  - expression Quantitative Trait Locus
GTEx  - Genotype Tissue Expression Consortium

Associated papers for further reading

Barbeira, Alvaro N., et al. “Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics.” Nature communications 9.1 (2018): 1-20.

Zhou, Dan, et al. “A unified framework for joint-tissue transcriptome-wide association and Mendelian randomization analysis.” Nature genetics 52.11 (2020): 1239-1246.

Miller, Karla L., et al. “Multimodal population brain imaging in the UK Biobank prospective epidemiological study.” Nature neuroscience 19.11 (2016): 1523-1536.

Smith, Stephen M., et al. “An expanded set of genome-wide association studies of brain imaging phenotypes in UK Biobank.” Nature neuroscience 24.5 (2021): 737-745.

Elliott, Lloyd T., et al. “Genome-wide association studies of brain imaging phenotypes in UK Biobank.” Nature 562.7726 (2018): 210-216.

Gamazon, Eric R., et al. “Multi-tissue transcriptome analyses identify genetic mechanisms underlying neuropsychiatric traits.” Nature genetics 51.6 (2019): 933-940.

Mowinckel, Athanasia M., and Didac Vidal-Piñeiro. “Visualization of brain statistics with R packages ggseg and ggseg3d.” Advances in Methods and Practices in Psychological Science 3.4 (2020): 466-483.

Please direct all questions to me at the following email: xbledsoe22@gmail.com