Evaluating the Contribution of Cell Type–Specific Alternative Splicing to Variation in Lipid Levels
Genome-wide association studies have identified hundreds of loci associated with lipid levels. However, the genetic mechanisms underlying most of these loci are not well-understood. Recent work indicates that changes in the abundance of alternatively spliced transcripts contribute to complex trait v...
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| Veröffentlicht in: | Circulation. Genomic and precision medicine 2023-06, Vol.16 (3), p.248-257 |
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| creator | Gawronski, Katerina A.B. Bone, William P. Park, YoSon Pashos, Evanthia E. Wenz, Brandon M. Dudek, Max F. Wang, Xiao Yang, Wenli Rader, Daniel J. Musunuru, Kiran Voight, Benjamin F. Brown, Christopher D. |
| description | Genome-wide association studies have identified hundreds of loci associated with lipid levels. However, the genetic mechanisms underlying most of these loci are not well-understood. Recent work indicates that changes in the abundance of alternatively spliced transcripts contribute to complex trait variation. Consequently, identifying genetic loci that associate with alternative splicing in disease-relevant cell types and determining the degree to which these loci are informative for lipid biology is of broad interest.
We analyze gene splicing in 83 sample-matched induced pluripotent stem cell (iPSC) and hepatocyte-like cell lines (n=166), as well as in an independent collection of primary liver tissues (n=96) to perform discovery of splicing quantitative trait loci (sQTLs).
We observe that transcript splicing is highly cell type specific, and the genes that are differentially spliced between iPSCs and hepatocyte-like cells are enriched for metabolism pathway annotations. We identify 1384 hepatocyte-like cell sQTLs and 1455 iPSC sQTLs at a false discovery rate of |
| doi_str_mv | 10.1161/CIRCGEN.120.003249 |
| format | Article |
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We analyze gene splicing in 83 sample-matched induced pluripotent stem cell (iPSC) and hepatocyte-like cell lines (n=166), as well as in an independent collection of primary liver tissues (n=96) to perform discovery of splicing quantitative trait loci (sQTLs).
We observe that transcript splicing is highly cell type specific, and the genes that are differentially spliced between iPSCs and hepatocyte-like cells are enriched for metabolism pathway annotations. We identify 1384 hepatocyte-like cell sQTLs and 1455 iPSC sQTLs at a false discovery rate of <5% and find that sQTLs are often shared across cell types. To evaluate the contribution of sQTLs to variation in lipid levels, we conduct colocalization analysis using lipid genome-wide association data. We identify 19 lipid-associated loci that colocalize either with an hepatocyte-like cell expression quantitative trait locus or sQTL. Only 2 loci colocalize with both a sQTL and expression quantitative trait locus, indicating that sQTLs contribute information about genome-wide association studies loci that cannot be obtained by analysis of steady-state gene expression alone.
These results provide an important foundation for future efforts that use iPSC and iPSC-derived cells to evaluate genetic mechanisms influencing both cardiovascular disease risk and complex traits in general.</description><identifier>ISSN: 2574-8300</identifier><identifier>EISSN: 2574-8300</identifier><identifier>DOI: 10.1161/CIRCGEN.120.003249</identifier><identifier>PMID: 37165871</identifier><language>eng</language><publisher>United States: Lippincott Williams & Wilkins</publisher><subject>Alternative Splicing ; Genome-Wide Association Study - methods ; Humans ; Lipids ; Original ; Quantitative Trait Loci ; RNA Splicing</subject><ispartof>Circulation. Genomic and precision medicine, 2023-06, Vol.16 (3), p.248-257</ispartof><rights>Lippincott Williams & Wilkins</rights><rights>2023 The Authors. 2023</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3996-ef63719385786a25ec924112429b0ab29187deea104831fef8aa6c46451c9903</cites><orcidid>0000-0002-9245-9876 ; 0000-0002-6205-9994 ; 0000-0002-0465-4744 ; 0000-0001-8521-9543 ; 0000-0003-3298-0368 ; 0000-0002-1873-2336 ; 0000-0002-9617-4624 ; 0000-0001-7502-8292</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>230,314,780,784,885,3685,27922,27923</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/37165871$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Gawronski, Katerina A.B.</creatorcontrib><creatorcontrib>Bone, William P.</creatorcontrib><creatorcontrib>Park, YoSon</creatorcontrib><creatorcontrib>Pashos, Evanthia E.</creatorcontrib><creatorcontrib>Wenz, Brandon M.</creatorcontrib><creatorcontrib>Dudek, Max F.</creatorcontrib><creatorcontrib>Wang, Xiao</creatorcontrib><creatorcontrib>Yang, Wenli</creatorcontrib><creatorcontrib>Rader, Daniel J.</creatorcontrib><creatorcontrib>Musunuru, Kiran</creatorcontrib><creatorcontrib>Voight, Benjamin F.</creatorcontrib><creatorcontrib>Brown, Christopher D.</creatorcontrib><title>Evaluating the Contribution of Cell Type–Specific Alternative Splicing to Variation in Lipid Levels</title><title>Circulation. Genomic and precision medicine</title><addtitle>Circ Genom Precis Med</addtitle><description>Genome-wide association studies have identified hundreds of loci associated with lipid levels. However, the genetic mechanisms underlying most of these loci are not well-understood. Recent work indicates that changes in the abundance of alternatively spliced transcripts contribute to complex trait variation. Consequently, identifying genetic loci that associate with alternative splicing in disease-relevant cell types and determining the degree to which these loci are informative for lipid biology is of broad interest.
We analyze gene splicing in 83 sample-matched induced pluripotent stem cell (iPSC) and hepatocyte-like cell lines (n=166), as well as in an independent collection of primary liver tissues (n=96) to perform discovery of splicing quantitative trait loci (sQTLs).
We observe that transcript splicing is highly cell type specific, and the genes that are differentially spliced between iPSCs and hepatocyte-like cells are enriched for metabolism pathway annotations. We identify 1384 hepatocyte-like cell sQTLs and 1455 iPSC sQTLs at a false discovery rate of <5% and find that sQTLs are often shared across cell types. To evaluate the contribution of sQTLs to variation in lipid levels, we conduct colocalization analysis using lipid genome-wide association data. We identify 19 lipid-associated loci that colocalize either with an hepatocyte-like cell expression quantitative trait locus or sQTL. Only 2 loci colocalize with both a sQTL and expression quantitative trait locus, indicating that sQTLs contribute information about genome-wide association studies loci that cannot be obtained by analysis of steady-state gene expression alone.
These results provide an important foundation for future efforts that use iPSC and iPSC-derived cells to evaluate genetic mechanisms influencing both cardiovascular disease risk and complex traits in general.</description><subject>Alternative Splicing</subject><subject>Genome-Wide Association Study - methods</subject><subject>Humans</subject><subject>Lipids</subject><subject>Original</subject><subject>Quantitative Trait Loci</subject><subject>RNA Splicing</subject><issn>2574-8300</issn><issn>2574-8300</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVkc1q3DAUhU1oSUKaF-iiaNmNp1c_lqVVCWaSBoYWmqFbodFcZ9RqLNeyJ2SXd8gb9kmqZKYhFQgJdM65V_crivcUZpRK-qm5_t5czb_OKIMZAGdCHxWnrKpFqTjAm1f3k-I8pZ8AQLXWksnj4oTXVFaqpqcFznc2THb03S0ZN0ia2I2DX02jjx2JLWkwBLK87_HPw-NNj8633pGLMOLQZdMOyU0fvHt2R_LDDt4-O31HFr73a7LAHYb0rnjb2pDw_HCeFcvL-bL5Ui6-XV03F4vS8dxaia3MnWmuqlpJyyp0mglKmWB6BXbFNFX1GtFSEIrTFltlrXRCioo6rYGfFZ_3sf202uLaYf6LDaYf_NYO9yZab_5_6fzG3MadocCUoFzmhI-HhCH-njCNZuuTyzOwHcYpGaYoq0DmlaVsL3VDTGnA9qUOBfOEyBwQmYzI7BFl04fXHb5Y_gHJArEX3MWnIadfYbrDwWzQhnFjgEEFUNclA8ZBZqZl3iD5XyR3nec</recordid><startdate>20230601</startdate><enddate>20230601</enddate><creator>Gawronski, Katerina A.B.</creator><creator>Bone, William P.</creator><creator>Park, YoSon</creator><creator>Pashos, Evanthia E.</creator><creator>Wenz, Brandon M.</creator><creator>Dudek, Max F.</creator><creator>Wang, Xiao</creator><creator>Yang, Wenli</creator><creator>Rader, Daniel J.</creator><creator>Musunuru, Kiran</creator><creator>Voight, Benjamin F.</creator><creator>Brown, Christopher D.</creator><general>Lippincott Williams & Wilkins</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-9245-9876</orcidid><orcidid>https://orcid.org/0000-0002-6205-9994</orcidid><orcidid>https://orcid.org/0000-0002-0465-4744</orcidid><orcidid>https://orcid.org/0000-0001-8521-9543</orcidid><orcidid>https://orcid.org/0000-0003-3298-0368</orcidid><orcidid>https://orcid.org/0000-0002-1873-2336</orcidid><orcidid>https://orcid.org/0000-0002-9617-4624</orcidid><orcidid>https://orcid.org/0000-0001-7502-8292</orcidid></search><sort><creationdate>20230601</creationdate><title>Evaluating the Contribution of Cell Type–Specific Alternative Splicing to Variation in Lipid Levels</title><author>Gawronski, Katerina A.B. ; Bone, William P. ; Park, YoSon ; Pashos, Evanthia E. ; Wenz, Brandon M. ; Dudek, Max F. ; Wang, Xiao ; Yang, Wenli ; Rader, Daniel J. ; Musunuru, Kiran ; Voight, Benjamin F. ; Brown, Christopher D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3996-ef63719385786a25ec924112429b0ab29187deea104831fef8aa6c46451c9903</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Alternative Splicing</topic><topic>Genome-Wide Association Study - methods</topic><topic>Humans</topic><topic>Lipids</topic><topic>Original</topic><topic>Quantitative Trait Loci</topic><topic>RNA Splicing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Gawronski, Katerina A.B.</creatorcontrib><creatorcontrib>Bone, William P.</creatorcontrib><creatorcontrib>Park, YoSon</creatorcontrib><creatorcontrib>Pashos, Evanthia E.</creatorcontrib><creatorcontrib>Wenz, Brandon M.</creatorcontrib><creatorcontrib>Dudek, Max F.</creatorcontrib><creatorcontrib>Wang, Xiao</creatorcontrib><creatorcontrib>Yang, Wenli</creatorcontrib><creatorcontrib>Rader, Daniel J.</creatorcontrib><creatorcontrib>Musunuru, Kiran</creatorcontrib><creatorcontrib>Voight, Benjamin F.</creatorcontrib><creatorcontrib>Brown, Christopher D.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Circulation. Genomic and precision medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Gawronski, Katerina A.B.</au><au>Bone, William P.</au><au>Park, YoSon</au><au>Pashos, Evanthia E.</au><au>Wenz, Brandon M.</au><au>Dudek, Max F.</au><au>Wang, Xiao</au><au>Yang, Wenli</au><au>Rader, Daniel J.</au><au>Musunuru, Kiran</au><au>Voight, Benjamin F.</au><au>Brown, Christopher D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluating the Contribution of Cell Type–Specific Alternative Splicing to Variation in Lipid Levels</atitle><jtitle>Circulation. Genomic and precision medicine</jtitle><addtitle>Circ Genom Precis Med</addtitle><date>2023-06-01</date><risdate>2023</risdate><volume>16</volume><issue>3</issue><spage>248</spage><epage>257</epage><pages>248-257</pages><issn>2574-8300</issn><eissn>2574-8300</eissn><abstract>Genome-wide association studies have identified hundreds of loci associated with lipid levels. However, the genetic mechanisms underlying most of these loci are not well-understood. Recent work indicates that changes in the abundance of alternatively spliced transcripts contribute to complex trait variation. Consequently, identifying genetic loci that associate with alternative splicing in disease-relevant cell types and determining the degree to which these loci are informative for lipid biology is of broad interest.
We analyze gene splicing in 83 sample-matched induced pluripotent stem cell (iPSC) and hepatocyte-like cell lines (n=166), as well as in an independent collection of primary liver tissues (n=96) to perform discovery of splicing quantitative trait loci (sQTLs).
We observe that transcript splicing is highly cell type specific, and the genes that are differentially spliced between iPSCs and hepatocyte-like cells are enriched for metabolism pathway annotations. We identify 1384 hepatocyte-like cell sQTLs and 1455 iPSC sQTLs at a false discovery rate of <5% and find that sQTLs are often shared across cell types. To evaluate the contribution of sQTLs to variation in lipid levels, we conduct colocalization analysis using lipid genome-wide association data. We identify 19 lipid-associated loci that colocalize either with an hepatocyte-like cell expression quantitative trait locus or sQTL. Only 2 loci colocalize with both a sQTL and expression quantitative trait locus, indicating that sQTLs contribute information about genome-wide association studies loci that cannot be obtained by analysis of steady-state gene expression alone.
These results provide an important foundation for future efforts that use iPSC and iPSC-derived cells to evaluate genetic mechanisms influencing both cardiovascular disease risk and complex traits in general.</abstract><cop>United States</cop><pub>Lippincott Williams & Wilkins</pub><pmid>37165871</pmid><doi>10.1161/CIRCGEN.120.003249</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-9245-9876</orcidid><orcidid>https://orcid.org/0000-0002-6205-9994</orcidid><orcidid>https://orcid.org/0000-0002-0465-4744</orcidid><orcidid>https://orcid.org/0000-0001-8521-9543</orcidid><orcidid>https://orcid.org/0000-0003-3298-0368</orcidid><orcidid>https://orcid.org/0000-0002-1873-2336</orcidid><orcidid>https://orcid.org/0000-0002-9617-4624</orcidid><orcidid>https://orcid.org/0000-0001-7502-8292</orcidid><oa>free_for_read</oa></addata></record> |
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| source | MEDLINE; American Heart Association Journals; Alma/SFX Local Collection |
| subjects | Alternative Splicing Genome-Wide Association Study - methods Humans Lipids Original Quantitative Trait Loci RNA Splicing |
| title | Evaluating the Contribution of Cell Type–Specific Alternative Splicing to Variation in Lipid Levels |
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