Asthama and Allergy diseases
Last updated: 2021-12-01
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | 88aeee6 | Jing Gu | 2021-12-01 | updated based on feedbacks |
| html | 5070974 | Jing Gu | 2021-11-29 | Build site. |
| html | c5e2f0d | Jing Gu | 2021-11-29 | Build site. |
| Rmd | 6e5eb4b | Jing Gu | 2021-11-29 | added LDSC results |
| html | 0c1aaaf | Jing Gu | 2021-11-29 | Build site. |
| Rmd | dc89e9f | Jing Gu | 2021-11-29 | added LDSC results |
| html | 0f707f7 | Jing Gu | 2021-11-18 | Build site. |
| Rmd | eadf25d | Jing Gu | 2021-11-18 | estimate heritability |
| html | 1c01788 | Jing Gu | 2021-11-18 | Build site. |
| Rmd | ad7cd6c | Jing Gu | 2021-11-18 | estimate heritability |
| html | f427975 | Jing Gu | 2021-11-17 | Build site. |
| Rmd | 093135b | Jing Gu | 2021-11-17 | estimate heritability |
| html | 6ce4a8d | Jing Gu | 2021-11-16 | Build site. |
| Rmd | 0c42fee | Jing Gu | 2021-11-16 | compare with lungs |
| html | a6ca0c7 | Jing Gu | 2021-11-16 | Build site. |
| 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 |
| html | 8a43e8f | Jing Gu | 2021-10-26 | Build site. |
| Rmd | 1892d30 | Jing Gu | 2021-10-26 | wflow_publish("analysis/asthma_prelim_results.Rmd") |
| html | 2945369 | Jing Gu | 2021-10-26 | Build site. |
| Rmd | b3259c5 | Jing Gu | 2021-10-26 | wflow_publish("analysis/asthma_prelim_results.Rmd") |
| html | 5b65bd1 | Jing Gu | 2021-10-26 | Build site. |
| Rmd | db3c6f9 | Jing Gu | 2021-10-26 | test enrichment for asthma risk variants |
Overview
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.
Summary of peaks called across open chromatin regions
Peak size - not shown
TORUS run for individual Blood annotation
Motivation:
- perform QC check for the Caldero2019 dataset
- 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. 
TORUS run for individual lung annotation
Motivation:
- perform QC check for this dataset
- 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. 
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. 
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. 
Summary:
- Overall, we see the differences in immune cell components that contribute to these three autoimmune diseases.
- GPs and CD4+ T cels are consistently significant in enrichment of risk variants for all three diseases.
grouped by disjoint peaks
Enrichment of each disjoint set of 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. 
Percent of causal variants in each peak set
Motivation: To not only test for enrichment, but also estimate the percent of genetic signals explained by each set of peaks
Procedure:
For each disjoint set of peaks, I multiplied the percent of genome-wide SNPs that occur in each peak set with the exponentials of Torus enrichment estimates. The number in parenthesis is the percent of SNPs in peaks.
Results: 
Disjoint immune peaks in blood vs. immune groups in lung
Motivation:
As the OCRs are largely shared across immune cell types, the use of multiple regression to estimate the effects of the openness of immune cells on disease risks can be hard to interpret. To address this issue, we generated disjoint sets of annotations as independent predictors, which can directly tell us how each immune component explains the genetic associations.
Procedure:
I partitioned peaks from lung and blood into three disjoint peak sets - only found in blood, only in lung and the shared. Specifically, I used bedtools intersect to call the overlapped peaks and peaks that are unique to either side for lung and blood peaks. Then, the overlapped peaks will be merged.
Summary of the number of overlapped peaks
Ulirsch2019 dataset 
Test the enrichment of disjoint peak sets 
Percent of causal variants in each peak set 
stratified-LDSC for partitioning h2g
Joint run of all annotations in Wang2020
Ulirsch2019
Enrichment estimates
| Version | Author | Date |
|---|---|---|
| 0c1aaaf | Jing Gu | 2021-11-29 |
h2 explained 
| Version | Author | Date |
|---|---|---|
| 0c1aaaf | Jing Gu | 2021-11-29 |
sessionInfo()R version 4.0.4 (2021-02-15)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)
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