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Rmd 08ab4ff Jing Gu 2021-11-16 compare with lungs
html 1bd2749 Jing Gu 2021-11-12 Build site.
Rmd f375e53 Jing Gu 2021-11-12 compare lung and blood
html 63b3472 Jing Gu 2021-11-11 Build site.
Rmd 912c9b6 Jing Gu 2021-11-11 analyzed lung immune cells
html be4dede Jing Gu 2021-11-04 Build site.
Rmd dd720fe Jing Gu 2021-11-04 enrichment analysis for AAD traits
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Rmd db3c6f9 Jing Gu 2021-10-26 test enrichment for asthma risk variants

Asthama and Allergy diseases

The main goal is to identify causal variants, genes and cell types relevant to AAD by integrating omics data of lung samples. We hypothesize that open chromatin regions of lung-resident immune cells can explain broader heritability for AAD than those of blood immune cells. The disease associated variants annotated by these regulatory functions are more likely to contribute to disease risk, so this prior knowledge can be leveraged to prioritize risk variants in GWAS loci.

Dataset

ATAC-seq data for Blood immune cells (Caldero2019)

Dataset: ATAC-Seq profiles for FACS-sorted cells from the peripheral blood of up to 4 healthy donors

Output files:
1. An ATAC-seq count table with a union set of peaks as rows and individual cell as columns
2. A Sample QC table that includes number of peaks and cell type identity for each cell
3. Significant differentially accessible regions when compared to progenitor cells
4. Significant differentially accessible regions under stimulation

Procedure: I used the number of peaks after QC for each cell to extract its corresponding ATAC-Seq peaks from the count table.

Details of the procedure For the cells of interest, I sorted their corresponding columns of the count table and store the top N number of peaks according to the number of peaks from the QC table as cell type resolved peaks.

ATAC-seq data for hematopoietic cells (Ulirsch2019)

Dataset: ATAC-Seq profiles for FACS-sorted cells from human peripheral blood or bone marrow.

Output files:
1.peak files downloaded from: https://github.com/caleblareau/singlecell_bloodtraits/tree/master/data/bulk/ATAC/narrowpeaks

scATAC-seq data for lung tissues (Wang2020)

Dataset: scATAC-Seq and scRNA-seq profiles for small airway region of right middle lobe (RML) lung tissue from 3 donors at different ages

Output files:
1. peak files downloaded from web portal: https://www.lungepigenome.org/

scATAC-seq data for fetal hematopoietic cells (Ronzoni2021)

Dataset: scATAC-Seq and scRNA-seq profiles of human immunophenotypic blood cells from fetal liver and bone marrow

Output files:
Downloaded from gitlab page: to be added
1. A merged normalized peak table with a union set of peaks as rows and individual cell as columns
2. A meta table that includes number of peaks and predicted cell type identity for each cell
3. A raw count table with peaks as rows and individual cell as columns

Procedure: I used the number of peaks for each cell from the meta table to extract its corresponding ATAC-Seq peaks from the merged peak table. The number of peaks is equivalent to the amount of non-zero peaks.

TORUS run for individual Blood annotation

Motivation:

  1. perform QC check for the Caldero2019 dataset
  2. have a senes of which cell types are potentially relevant to AAD

Results: Overall, the magnitude of enrichment estimates are much smaller than that in figure 5b. The discrepancy can be due to different GWAS datasets and enrichment method. They used LDSC to estimate enrichment coefficients, which have a better control on the overlapping peaks between many annotations.

Version Author Date
be4dede Jing Gu 2021-11-04
5b65bd1 Jing Gu 2021-10-26

TORUS run for individual lung annotation

Motivation:

  1. perform QC check for this dataset
  2. have a senes of which cell types in lungs are potentially relevant to AAD

Results: Data look good as the open chromatin regions of most cell types in lungs are enriched with disease risk variants.

Version Author Date
63b3472 Jing Gu 2021-11-11
be4dede Jing Gu 2021-11-04
5b65bd1 Jing Gu 2021-10-26

TORUS joint run for lung vs blood immune cells

Motivation:
Test the hypothesis that open chromatin regions of lung-resident immune cells can explain broader heritability for AAD than those of blood immune cells.

Procedure: For each immune group (B cells, T cells, Myeloids, NK cells), I ran TORUS over pairs of annotations from lung and blood one at a time. Pairs of annotations as follows:

  • B cells (lung) vs B cells (blood)
  • T cells (lung) vs CD4+ T (blood)
  • T cells (lung) vs CD8+ T (blood)
  • NK cells (lung) vs NK cells (blood)
  • Fibroblast cells (lung) vs Myeloids (blood)

Results: Overall, we see lung-resident immune cells show significant enrichemnt conditional on blood immune cells in AAD, but not in RA. Compared with a control set of traits, there is a large difference between blood and lung enrichment estimates for Myeloid, CD4+ and CD8+ T cells in AAD. ### Caldero2019 dataset

Version Author Date
1bd2749 Jing Gu 2021-11-12
63b3472 Jing Gu 2021-11-11
be4dede Jing Gu 2021-11-04
5b65bd1 Jing Gu 2021-10-26

### Ulirsch2019 dataset Motivation:

  • Replicate the above result in a different blood dataset.

Results:
Overall, the enrichment of lung annotations dwindles in a great extent when jointly run with blood annotations. There is one exception that CD8+ T cells in lungs show significant enrichment with child-onset asthma.

Version Author Date
63b3472 Jing Gu 2021-11-11
be4dede Jing Gu 2021-11-04
5b65bd1 Jing Gu 2021-10-26

Joint TORUS run for annotation sets

major clusters in lung dataset

  • Mesenchymal cells
  • Immune cells
  • epithelial cells
  • endothelial cells
  • others(neuroendocrine cells, type I pneumocyte, ciliated cells) Motivation: determine how each cluster with similar chromatin accessibility patterns contribute to AAD heritability.

Results:
We observed some non-immune cell types such as epithelial cells, endothelia cells do contribute to genetic risks of AAD, though not as much as immune cells.

Version Author Date
63b3472 Jing Gu 2021-11-11
be4dede Jing Gu 2021-11-04
8a43e8f Jing Gu 2021-10-26
5b65bd1 Jing Gu 2021-10-26

annotation sets for Caldero2019

grouped by lineages

Motivation: Running annotation sets in a joint model enables us to idenity the relevant contribution in open chromatin regions of each immune cell type to traits. LSDC is a better tool to use as we expected many overlaps of acccessible peaks across the sub-clusters of immune cells, which can make the estimation of TORUS's joint model unstable. Here we grouped the cell types and used TORUS to get a quick run of which group of cell types have significant enrichment.

Procedure:
For Caldero2019 dataset: Immune cells were grouped into six main categories and merged across two conditions. The peaks in these groups can be overlapped.
For Ronzoni2021 dataset, I took a union set of peaks from all cells that were predicted to be granulocytes progenitors.

Results:
Consistent with prior knowledge, we see enrichment of granulocyte progenitors with genetic risk of allergy.

Version Author Date
63b3472 Jing Gu 2021-11-11
be4dede Jing Gu 2021-11-04

Summary:

  1. Overall, we see the differences in immune cell components that contribute to these three autoimmune diseases.
  2. GPs and CD4+ T cels are consistently significant in enrichment of risk variants for all three diseases.

grouped by lineages and conditions

Procedure: Immune cells were grouped first into six main categories and then separated by conditions. This set of 12 annotations were jointly tested via TORUS.

Version Author Date
1bd2749 Jing Gu 2021-11-12
63b3472 Jing Gu 2021-11-11

grouped by disjoint peaks

Motivation: Using disjoint groups of peaks from immune cell types to estimate separate contributions of immune components to disease heritability. These annotations are ideal predictors for the linear model used in either LDSC or TORUS to obtain unbiased enrichment estimators.
Procedure: The disjoint peaks were directly downloaded from the paper.

Version Author Date
1bd2749 Jing Gu 2021-11-12
63b3472 Jing Gu 2021-11-11

Disjoint immune peaks in blood vs. immune groups in lung

Motivation: There is a large fraction of overlaps for peaks across immune groups in blood. Thus we used disjoint peaks to build blood annotations and then ran TORUS together with main immune groups in lungs to better estimate their enrichment separetly.

Procedure:
I merged peaks from three groups of T-cells, which are T-stimulated, T-resting and BandT_stimulated cells. Same process for B cells. Then I ran TORUS on merged T cells, B cells, NK resting cells, myeloid resting cells from Caldero2019 with main immune groups in lungs.

Results:
NK disjoint peaks in blood was not displayed due to very large standard error. We can still see there is difference in T cells between blood and lung and the extent of difference varies across traits. Other immune cell types in blood do not show significance probably due to small number of disjoint peaks. Since the peaks in lungs are not disjoint, it is expected to see a bias towards lung. Nonetheless, compared with controls, we observe a larger difference in enrichment of risk variants in T cells for AAD.


sessionInfo()
R version 4.0.4 (2021-02-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so

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attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] knitr_1.31      ggplot2_3.3.3   workflowr_1.6.2

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