Genomic and molecular characterization of preterm birth
Preterm birth (PTB) complications are the leading cause of long-term morbidity and mortality in children. By using whole blood samples, we integrated whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and DNA methylation data for 270 PTB and 521 control families. We analyzed this combined data...
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creator | Knijnenburg, Theo A. Vockley, Joseph G. Chambwe, Nyasha Gibbs, David L. Humphries, Crystal Huddleston, Kathi C. Klein, Elisabeth Kothiyal, Prachi Tasseff, Ryan Dhankani, Varsha Bodian, Dale L. Wong, Wendy S. W. Glusman, Gustavo Mauldin, Denise E. Miller, Michael Slagel, Joseph Elasady, Summer Roach, Jared C. Kramer, Roger Leinonen, Kalle Linthorst, Jasper Baveja, Rajiv Baker, Robin Solomon, Benjamin D. Eley, Greg Iyer, Ramaswamy K. Maxwell, George L. Bernard, Brady Shmulevich, Ilya Hood, Leroy Niederhuber, John E. |
description | Preterm birth (PTB) complications are the leading cause of long-term morbidity and mortality in children. By using whole blood samples, we integrated whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and DNA methylation data for 270 PTB and 521 control families. We analyzed this combined dataset to identify genomic variants associated with PTB and secondary analyses to identify variants associated with very early PTB (VEPTB) as well as other subcategories of disease that may contribute to PTB. We identified differentially expressed genes (DEGs) and methylated genomic loci and performed expression and methylation quantitative trait loci analyses to link genomic variants to these expression and methylation changes. We performed enrichment tests to identify overlaps between new and known PTB candidate gene systems. We identified 160 significant genomic variants associated with PTB-related phenotypes. The most significant variants, DEGs, and differentially methylated loci were associated with VEPTB. Integration of all data types identified a set of 72 candidate biomarker genes for VEPTB, encompassing genes and those previously associated with PTB. Notably, PTB-associated genes RAB31 and RBPJ were identified by all three data types (WGS, RNA-seq, and methylation). Pathways associated with VEPTB include EGFR and prolactin signaling pathways, inflammation- and immunity-related pathways, chemokine signaling, IFN-γ signaling, and Notch1 signaling. Progress in identifying molecular components of a complex disease is aided by integrated analyses of multiple molecular data types and clinical data. With these data, and by stratifying PTB by subphenotype, we have identified associations between VEPTB and the underlying biology. |
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W. ; Glusman, Gustavo ; Mauldin, Denise E. ; Miller, Michael ; Slagel, Joseph ; Elasady, Summer ; Roach, Jared C. ; Kramer, Roger ; Leinonen, Kalle ; Linthorst, Jasper ; Baveja, Rajiv ; Baker, Robin ; Solomon, Benjamin D. ; Eley, Greg ; Iyer, Ramaswamy K. ; Maxwell, George L. ; Bernard, Brady ; Shmulevich, Ilya ; Hood, Leroy ; Niederhuber, John E.</creator><creatorcontrib>Knijnenburg, Theo A. ; Vockley, Joseph G. ; Chambwe, Nyasha ; Gibbs, David L. ; Humphries, Crystal ; Huddleston, Kathi C. ; Klein, Elisabeth ; Kothiyal, Prachi ; Tasseff, Ryan ; Dhankani, Varsha ; Bodian, Dale L. ; Wong, Wendy S. W. ; Glusman, Gustavo ; Mauldin, Denise E. ; Miller, Michael ; Slagel, Joseph ; Elasady, Summer ; Roach, Jared C. ; Kramer, Roger ; Leinonen, Kalle ; Linthorst, Jasper ; Baveja, Rajiv ; Baker, Robin ; Solomon, Benjamin D. ; Eley, Greg ; Iyer, Ramaswamy K. ; Maxwell, George L. ; Bernard, Brady ; Shmulevich, Ilya ; Hood, Leroy ; Niederhuber, John E.</creatorcontrib><description>Preterm birth (PTB) complications are the leading cause of long-term morbidity and mortality in children. By using whole blood samples, we integrated whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and DNA methylation data for 270 PTB and 521 control families. We analyzed this combined dataset to identify genomic variants associated with PTB and secondary analyses to identify variants associated with very early PTB (VEPTB) as well as other subcategories of disease that may contribute to PTB. We identified differentially expressed genes (DEGs) and methylated genomic loci and performed expression and methylation quantitative trait loci analyses to link genomic variants to these expression and methylation changes. We performed enrichment tests to identify overlaps between new and known PTB candidate gene systems. We identified 160 significant genomic variants associated with PTB-related phenotypes. The most significant variants, DEGs, and differentially methylated loci were associated with VEPTB. Integration of all data types identified a set of 72 candidate biomarker genes for VEPTB, encompassing genes and those previously associated with PTB. Notably, PTB-associated genes RAB31 and RBPJ were identified by all three data types (WGS, RNA-seq, and methylation). Pathways associated with VEPTB include EGFR and prolactin signaling pathways, inflammation- and immunity-related pathways, chemokine signaling, IFN-γ signaling, and Notch1 signaling. Progress in identifying molecular components of a complex disease is aided by integrated analyses of multiple molecular data types and clinical data. With these data, and by stratifying PTB by subphenotype, we have identified associations between VEPTB and the underlying biology.</description><identifier>ISSN: 0027-8424</identifier><identifier>EISSN: 1091-6490</identifier><identifier>DOI: 10.1073/pnas.1716314116</identifier><identifier>PMID: 30833390</identifier><language>eng</language><publisher>United States: National Academy of Sciences</publisher><subject>Biological Sciences ; Biomarkers ; Birth ; Chemokines ; Deoxyribonucleic acid ; DNA ; DNA methylation ; DNA sequencing ; Epidermal growth factor receptors ; Gene expression ; Gene loci ; Gene mapping ; Genes ; Genomes ; Genomics ; Immunity ; Morbidity ; Notch1 protein ; Phenotypes ; PNAS Plus ; Premature birth ; Prolactin ; Quantitative trait loci ; Ribonucleic acid ; RNA ; Signal transduction ; Signaling ; γ-Interferon</subject><ispartof>Proceedings of the National Academy of Sciences - PNAS, 2019-03, Vol.116 (12), p.5819-5827</ispartof><rights>Copyright © 2019 the Author(s). Published by PNAS.</rights><rights>Copyright National Academy of Sciences Mar 19, 2019</rights><rights>Copyright © 2019 the Author(s). Published by PNAS. 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c443t-aedc6e19520cb2d94bd33817cd28b3d62453327df8a4c35c5f4cc7f3fef7a6543</citedby><cites>FETCH-LOGICAL-c443t-aedc6e19520cb2d94bd33817cd28b3d62453327df8a4c35c5f4cc7f3fef7a6543</cites><orcidid>0000-0002-2812-0122 ; 0000-0001-8060-5955 ; 0000-0002-4410-8780</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/26696590$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/26696590$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>230,314,723,776,780,799,881,27901,27902,53766,53768,57992,58225</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30833390$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Knijnenburg, Theo A.</creatorcontrib><creatorcontrib>Vockley, Joseph G.</creatorcontrib><creatorcontrib>Chambwe, Nyasha</creatorcontrib><creatorcontrib>Gibbs, David L.</creatorcontrib><creatorcontrib>Humphries, Crystal</creatorcontrib><creatorcontrib>Huddleston, Kathi C.</creatorcontrib><creatorcontrib>Klein, Elisabeth</creatorcontrib><creatorcontrib>Kothiyal, Prachi</creatorcontrib><creatorcontrib>Tasseff, Ryan</creatorcontrib><creatorcontrib>Dhankani, Varsha</creatorcontrib><creatorcontrib>Bodian, Dale L.</creatorcontrib><creatorcontrib>Wong, Wendy S. W.</creatorcontrib><creatorcontrib>Glusman, Gustavo</creatorcontrib><creatorcontrib>Mauldin, Denise E.</creatorcontrib><creatorcontrib>Miller, Michael</creatorcontrib><creatorcontrib>Slagel, Joseph</creatorcontrib><creatorcontrib>Elasady, Summer</creatorcontrib><creatorcontrib>Roach, Jared C.</creatorcontrib><creatorcontrib>Kramer, Roger</creatorcontrib><creatorcontrib>Leinonen, Kalle</creatorcontrib><creatorcontrib>Linthorst, Jasper</creatorcontrib><creatorcontrib>Baveja, Rajiv</creatorcontrib><creatorcontrib>Baker, Robin</creatorcontrib><creatorcontrib>Solomon, Benjamin D.</creatorcontrib><creatorcontrib>Eley, Greg</creatorcontrib><creatorcontrib>Iyer, Ramaswamy K.</creatorcontrib><creatorcontrib>Maxwell, George L.</creatorcontrib><creatorcontrib>Bernard, Brady</creatorcontrib><creatorcontrib>Shmulevich, Ilya</creatorcontrib><creatorcontrib>Hood, Leroy</creatorcontrib><creatorcontrib>Niederhuber, John E.</creatorcontrib><title>Genomic and molecular characterization of preterm birth</title><title>Proceedings of the National Academy of Sciences - PNAS</title><addtitle>Proc Natl Acad Sci U S A</addtitle><description>Preterm birth (PTB) complications are the leading cause of long-term morbidity and mortality in children. By using whole blood samples, we integrated whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and DNA methylation data for 270 PTB and 521 control families. We analyzed this combined dataset to identify genomic variants associated with PTB and secondary analyses to identify variants associated with very early PTB (VEPTB) as well as other subcategories of disease that may contribute to PTB. We identified differentially expressed genes (DEGs) and methylated genomic loci and performed expression and methylation quantitative trait loci analyses to link genomic variants to these expression and methylation changes. We performed enrichment tests to identify overlaps between new and known PTB candidate gene systems. We identified 160 significant genomic variants associated with PTB-related phenotypes. The most significant variants, DEGs, and differentially methylated loci were associated with VEPTB. Integration of all data types identified a set of 72 candidate biomarker genes for VEPTB, encompassing genes and those previously associated with PTB. Notably, PTB-associated genes RAB31 and RBPJ were identified by all three data types (WGS, RNA-seq, and methylation). Pathways associated with VEPTB include EGFR and prolactin signaling pathways, inflammation- and immunity-related pathways, chemokine signaling, IFN-γ signaling, and Notch1 signaling. Progress in identifying molecular components of a complex disease is aided by integrated analyses of multiple molecular data types and clinical data. With these data, and by stratifying PTB by subphenotype, we have identified associations between VEPTB and the underlying biology.</description><subject>Biological Sciences</subject><subject>Biomarkers</subject><subject>Birth</subject><subject>Chemokines</subject><subject>Deoxyribonucleic acid</subject><subject>DNA</subject><subject>DNA methylation</subject><subject>DNA sequencing</subject><subject>Epidermal growth factor receptors</subject><subject>Gene expression</subject><subject>Gene loci</subject><subject>Gene mapping</subject><subject>Genes</subject><subject>Genomes</subject><subject>Genomics</subject><subject>Immunity</subject><subject>Morbidity</subject><subject>Notch1 protein</subject><subject>Phenotypes</subject><subject>PNAS Plus</subject><subject>Premature birth</subject><subject>Prolactin</subject><subject>Quantitative trait loci</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>Signal transduction</subject><subject>Signaling</subject><subject>γ-Interferon</subject><issn>0027-8424</issn><issn>1091-6490</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNpdkc1LwzAYh4Mobk7PnpSCFy_d3nw0aS-CDJ3CwIueQ5qmrqNtatIK-tebsTk_ToH8njy8b34InWOYYhB01rXKT7HAnGKGMT9AYwwZjjnL4BCNAYiIU0bYCJ14vwaALEnhGI0opJTSDMZILExrm0pHqi2ixtZGD7VykV4pp3RvXPWp-sq2kS2jzplw0UR55frVKToqVe3N2e6coJf7u-f5Q7x8WjzOb5exZoz2sTKF5gZnCQGdkyJjeUFpioUuSJrTghOWUEpEUaaKaZropGRai5KWphSKJ4xO0M3W2w15E2Sm7Z2qZeeqRrkPaVUl_yZttZKv9l1yRjHOcBBc7wTOvg3G97KpvDZ1rVpjBy8JTlMC4ZNEQK_-oWs7uDasJwkBEMHHNxPNtpR21ntnyv0wGOSmFLkpRf6UEl5c_t5hz3-3EICLLbD2vXX7nHCe8STkX_23kmc</recordid><startdate>20190319</startdate><enddate>20190319</enddate><creator>Knijnenburg, Theo A.</creator><creator>Vockley, Joseph G.</creator><creator>Chambwe, Nyasha</creator><creator>Gibbs, David L.</creator><creator>Humphries, Crystal</creator><creator>Huddleston, Kathi C.</creator><creator>Klein, Elisabeth</creator><creator>Kothiyal, Prachi</creator><creator>Tasseff, Ryan</creator><creator>Dhankani, Varsha</creator><creator>Bodian, Dale L.</creator><creator>Wong, Wendy S. 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W. ; Glusman, Gustavo ; Mauldin, Denise E. ; Miller, Michael ; Slagel, Joseph ; Elasady, Summer ; Roach, Jared C. ; Kramer, Roger ; Leinonen, Kalle ; Linthorst, Jasper ; Baveja, Rajiv ; Baker, Robin ; Solomon, Benjamin D. ; Eley, Greg ; Iyer, Ramaswamy K. ; Maxwell, George L. ; Bernard, Brady ; Shmulevich, Ilya ; Hood, Leroy ; Niederhuber, John E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c443t-aedc6e19520cb2d94bd33817cd28b3d62453327df8a4c35c5f4cc7f3fef7a6543</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Biological Sciences</topic><topic>Biomarkers</topic><topic>Birth</topic><topic>Chemokines</topic><topic>Deoxyribonucleic acid</topic><topic>DNA</topic><topic>DNA methylation</topic><topic>DNA sequencing</topic><topic>Epidermal growth factor receptors</topic><topic>Gene expression</topic><topic>Gene loci</topic><topic>Gene mapping</topic><topic>Genes</topic><topic>Genomes</topic><topic>Genomics</topic><topic>Immunity</topic><topic>Morbidity</topic><topic>Notch1 protein</topic><topic>Phenotypes</topic><topic>PNAS Plus</topic><topic>Premature birth</topic><topic>Prolactin</topic><topic>Quantitative trait loci</topic><topic>Ribonucleic acid</topic><topic>RNA</topic><topic>Signal transduction</topic><topic>Signaling</topic><topic>γ-Interferon</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Knijnenburg, Theo A.</creatorcontrib><creatorcontrib>Vockley, Joseph G.</creatorcontrib><creatorcontrib>Chambwe, Nyasha</creatorcontrib><creatorcontrib>Gibbs, David L.</creatorcontrib><creatorcontrib>Humphries, Crystal</creatorcontrib><creatorcontrib>Huddleston, Kathi C.</creatorcontrib><creatorcontrib>Klein, Elisabeth</creatorcontrib><creatorcontrib>Kothiyal, Prachi</creatorcontrib><creatorcontrib>Tasseff, Ryan</creatorcontrib><creatorcontrib>Dhankani, Varsha</creatorcontrib><creatorcontrib>Bodian, Dale L.</creatorcontrib><creatorcontrib>Wong, Wendy S. 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W.</au><au>Glusman, Gustavo</au><au>Mauldin, Denise E.</au><au>Miller, Michael</au><au>Slagel, Joseph</au><au>Elasady, Summer</au><au>Roach, Jared C.</au><au>Kramer, Roger</au><au>Leinonen, Kalle</au><au>Linthorst, Jasper</au><au>Baveja, Rajiv</au><au>Baker, Robin</au><au>Solomon, Benjamin D.</au><au>Eley, Greg</au><au>Iyer, Ramaswamy K.</au><au>Maxwell, George L.</au><au>Bernard, Brady</au><au>Shmulevich, Ilya</au><au>Hood, Leroy</au><au>Niederhuber, John E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Genomic and molecular characterization of preterm birth</atitle><jtitle>Proceedings of the National Academy of Sciences - PNAS</jtitle><addtitle>Proc Natl Acad Sci U S A</addtitle><date>2019-03-19</date><risdate>2019</risdate><volume>116</volume><issue>12</issue><spage>5819</spage><epage>5827</epage><pages>5819-5827</pages><issn>0027-8424</issn><eissn>1091-6490</eissn><abstract>Preterm birth (PTB) complications are the leading cause of long-term morbidity and mortality in children. By using whole blood samples, we integrated whole-genome sequencing (WGS), RNA sequencing (RNA-seq), and DNA methylation data for 270 PTB and 521 control families. We analyzed this combined dataset to identify genomic variants associated with PTB and secondary analyses to identify variants associated with very early PTB (VEPTB) as well as other subcategories of disease that may contribute to PTB. We identified differentially expressed genes (DEGs) and methylated genomic loci and performed expression and methylation quantitative trait loci analyses to link genomic variants to these expression and methylation changes. We performed enrichment tests to identify overlaps between new and known PTB candidate gene systems. We identified 160 significant genomic variants associated with PTB-related phenotypes. The most significant variants, DEGs, and differentially methylated loci were associated with VEPTB. Integration of all data types identified a set of 72 candidate biomarker genes for VEPTB, encompassing genes and those previously associated with PTB. Notably, PTB-associated genes RAB31 and RBPJ were identified by all three data types (WGS, RNA-seq, and methylation). Pathways associated with VEPTB include EGFR and prolactin signaling pathways, inflammation- and immunity-related pathways, chemokine signaling, IFN-γ signaling, and Notch1 signaling. Progress in identifying molecular components of a complex disease is aided by integrated analyses of multiple molecular data types and clinical data. With these data, and by stratifying PTB by subphenotype, we have identified associations between VEPTB and the underlying biology.</abstract><cop>United States</cop><pub>National Academy of Sciences</pub><pmid>30833390</pmid><doi>10.1073/pnas.1716314116</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0002-2812-0122</orcidid><orcidid>https://orcid.org/0000-0001-8060-5955</orcidid><orcidid>https://orcid.org/0000-0002-4410-8780</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Biological Sciences Biomarkers Birth Chemokines Deoxyribonucleic acid DNA DNA methylation DNA sequencing Epidermal growth factor receptors Gene expression Gene loci Gene mapping Genes Genomes Genomics Immunity Morbidity Notch1 protein Phenotypes PNAS Plus Premature birth Prolactin Quantitative trait loci Ribonucleic acid RNA Signal transduction Signaling γ-Interferon |
title | Genomic and molecular characterization of preterm birth |
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