Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies.
Genome-wide association studies (GWASs) have identified many SNPs associated with various common diseases. Understanding the biological functions of these identified SNP associations requires identifying disease/trait relevant tissues or cell types. Here, we develop a network method, CoCoNet, to fac...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2020-04-01
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| Series: | PLoS Genetics |
| Online Access: | https://doi.org/10.1371/journal.pgen.1008734 |
| _version_ | 1797818290126454784 |
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| author | Lulu Shang Jennifer A Smith Xiang Zhou |
| author_facet | Lulu Shang Jennifer A Smith Xiang Zhou |
| author_sort | Lulu Shang |
| collection | DOAJ |
| description | Genome-wide association studies (GWASs) have identified many SNPs associated with various common diseases. Understanding the biological functions of these identified SNP associations requires identifying disease/trait relevant tissues or cell types. Here, we develop a network method, CoCoNet, to facilitate the identification of trait-relevant tissues or cell types. Different from existing approaches, CoCoNet incorporates tissue-specific gene co-expression networks constructed from either bulk or single cell RNA sequencing (RNAseq) studies with GWAS data for trait-tissue inference. In particular, CoCoNet relies on a covariance regression network model to express gene-level effect measurements for the given GWAS trait as a function of the tissue-specific co-expression adjacency matrix. With a composite likelihood-based inference algorithm, CoCoNet is scalable to tens of thousands of genes. We validate the performance of CoCoNet through extensive simulations. We apply CoCoNet for an in-depth analysis of four neurological disorders and four autoimmune diseases, where we integrate the corresponding GWASs with bulk RNAseq data from 38 tissues and single cell RNAseq data from 10 cell types. In the real data applications, we show how CoCoNet can help identify specific glial cell types relevant for neurological disorders and identify disease-targeted colon tissues as relevant for autoimmune diseases. |
| first_indexed | 2024-03-13T09:05:58Z |
| format | Article |
| id | doaj.art-80127b6d710f430e9a55e0dafbb34926 |
| institution | Directory Open Access Journal |
| issn | 1553-7390 1553-7404 |
| language | English |
| last_indexed | 2024-03-13T09:05:58Z |
| publishDate | 2020-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Genetics |
| spelling | doaj.art-80127b6d710f430e9a55e0dafbb349262023-05-28T05:31:00ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042020-04-01164e100873410.1371/journal.pgen.1008734Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies.Lulu ShangJennifer A SmithXiang ZhouGenome-wide association studies (GWASs) have identified many SNPs associated with various common diseases. Understanding the biological functions of these identified SNP associations requires identifying disease/trait relevant tissues or cell types. Here, we develop a network method, CoCoNet, to facilitate the identification of trait-relevant tissues or cell types. Different from existing approaches, CoCoNet incorporates tissue-specific gene co-expression networks constructed from either bulk or single cell RNA sequencing (RNAseq) studies with GWAS data for trait-tissue inference. In particular, CoCoNet relies on a covariance regression network model to express gene-level effect measurements for the given GWAS trait as a function of the tissue-specific co-expression adjacency matrix. With a composite likelihood-based inference algorithm, CoCoNet is scalable to tens of thousands of genes. We validate the performance of CoCoNet through extensive simulations. We apply CoCoNet for an in-depth analysis of four neurological disorders and four autoimmune diseases, where we integrate the corresponding GWASs with bulk RNAseq data from 38 tissues and single cell RNAseq data from 10 cell types. In the real data applications, we show how CoCoNet can help identify specific glial cell types relevant for neurological disorders and identify disease-targeted colon tissues as relevant for autoimmune diseases.https://doi.org/10.1371/journal.pgen.1008734 |
| spellingShingle | Lulu Shang Jennifer A Smith Xiang Zhou Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies. PLoS Genetics |
| title | Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies. |
| title_full | Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies. |
| title_fullStr | Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies. |
| title_full_unstemmed | Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies. |
| title_short | Leveraging gene co-expression patterns to infer trait-relevant tissues in genome-wide association studies. |
| title_sort | leveraging gene co expression patterns to infer trait relevant tissues in genome wide association studies |
| url | https://doi.org/10.1371/journal.pgen.1008734 |
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