Large-scale integration of the plasma proteome with genetics and disease
The plasma proteome can help bridge the gap between the genome and diseases. Here we describe genome-wide association studies (GWASs) of plasma protein levels measured with 4,907 aptamers in 35,559 Icelanders. We found 18,084 associations between sequence variants and levels of proteins in plasma (p...
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| Vydáno v: | Nature genetics Ročník 53; číslo 12; s. 1712 - 1721 |
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| Hlavní autoři: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Nature Publishing Group US
01.12.2021
Nature Publishing Group |
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| ISSN: | 1061-4036, 1546-1718, 1546-1718 |
| On-line přístup: | Získat plný text |
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| Abstract | The plasma proteome can help bridge the gap between the genome and diseases. Here we describe genome-wide association studies (GWASs) of plasma protein levels measured with 4,907 aptamers in 35,559 Icelanders. We found 18,084 associations between sequence variants and levels of proteins in plasma (protein quantitative trait loci; pQTL), of which 19% were with rare variants (minor allele frequency (MAF) < 1%). We tested plasma protein levels for association with 373 diseases and other traits and identified 257,490 associations. We integrated pQTL and genetic associations with diseases and other traits and found that 12% of 45,334 lead associations in the GWAS Catalog are with variants in high linkage disequilibrium with pQTL. We identified 938 genes encoding potential drug targets with variants that influence levels of possible biomarkers. Combining proteomics, genomics and transcriptomics, we provide a valuable resource that can be used to improve understanding of disease pathogenesis and to assist with drug discovery and development.
A genome-wide association study of plasma protein levels measured with 4,907 aptamers in 35,559 Icelanders highlights links with over 370 disease endpoints and other traits. |
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| AbstractList | The plasma proteome can help bridge the gap between the genome and diseases. Here we describe genome-wide association studies (GWASs) of plasma protein levels measured with 4,907 aptamers in 35,559 Icelanders. We found 18,084 associations between sequence variants and levels of proteins in plasma (protein quantitative trait loci; pQTL), of which 19% were with rare variants (minor allele frequency (MAF) < 1%). We tested plasma protein levels for association with 373 diseases and other traits and identified 257,490 associations. We integrated pQTL and genetic associations with diseases and other traits and found that 12% of 45,334 lead associations in the GWAS Catalog are with variants in high linkage disequilibrium with pQTL. We identified 938 genes encoding potential drug targets with variants that influence levels of possible biomarkers. Combining proteomics, genomics and transcriptomics, we provide a valuable resource that can be used to improve understanding of disease pathogenesis and to assist with drug discovery and development.
A genome-wide association study of plasma protein levels measured with 4,907 aptamers in 35,559 Icelanders highlights links with over 370 disease endpoints and other traits. The proteome also introduces a new temporal quality: the levels of proteins may rise or fall as a function of time before or after an event or as disease progresses or regresses. [...]far, the largest pQTL study (n = 30,931) only assessed 90 proteins8, whereas the largest study with 200 or more proteins had less than 6,000 participants5. Here we report the analysis of the associations of 27.2 million sequence variants and 373 diseases and other traits with the levels of 4,907 plasma proteins measured in 35,559 Icelanders with SomaScan version 4 (Figs. 1 and 2). Accounting for multiple testing, the levels of 63% of the 4,907 proteins correlated positively with age and 18% correlated negatively with age (Supplementary Table 1). [...]levels of 33% of the proteins were higher in men and 23% were higher in women. Overall, the presence of PAVs in high LD, the absence of cis eQTL and lower protein levels associated with the alternative allele all increase the likelihood of an aptamer binding artifact (Fig. 4a,b and Extended Data Fig. 4). The plasma proteome can help bridge the gap between the genome and diseases. Here we describe genome-wide association studies (GWASs) of plasma protein levels measured with 4,907 aptamers in 35,559 Icelanders. We found 18,084 associations between sequence variants and levels of proteins in plasma (protein quantitative trait loci; pQTL), of which 19% were with rare variants (minor allele frequency (MAF) < 1%). We tested plasma protein levels for association with 373 diseases and other traits and identified 257,490 associations. We integrated pQTL and genetic associations with diseases and other traits and found that 12% of 45,334 lead associations in the GWAS Catalog are with variants in high linkage disequilibrium with pQTL. We identified 938 genes encoding potential drug targets with variants that influence levels of possible biomarkers. Combining proteomics, genomics and transcriptomics, we provide a valuable resource that can be used to improve understanding of disease pathogenesis and to assist with drug discovery and development. The plasma proteome can help bridge the gap between the genome and diseases. Here we describe genome-wide association studies (GWASs) of plasma protein levels measured with 4,907 aptamers in 35,559 Icelanders. We found 18,084 associations between sequence variants and levels of proteins in plasma (protein quantitative trait loci; pQTL), of which 19% were with rare variants (minor allele frequency (MAF) < 1%). We tested plasma protein levels for association with 373 diseases and other traits and identified 257,490 associations. We integrated pQTL and genetic associations with diseases and other traits and found that 12% of 45,334 lead associations in the GWAS Catalog are with variants in high linkage disequilibrium with pQTL. We identified 938 genes encoding potential drug targets with variants that influence levels of possible biomarkers. Combining proteomics, genomics and transcriptomics, we provide a valuable resource that can be used to improve understanding of disease pathogenesis and to assist with drug discovery and development.The plasma proteome can help bridge the gap between the genome and diseases. Here we describe genome-wide association studies (GWASs) of plasma protein levels measured with 4,907 aptamers in 35,559 Icelanders. We found 18,084 associations between sequence variants and levels of proteins in plasma (protein quantitative trait loci; pQTL), of which 19% were with rare variants (minor allele frequency (MAF) < 1%). We tested plasma protein levels for association with 373 diseases and other traits and identified 257,490 associations. We integrated pQTL and genetic associations with diseases and other traits and found that 12% of 45,334 lead associations in the GWAS Catalog are with variants in high linkage disequilibrium with pQTL. We identified 938 genes encoding potential drug targets with variants that influence levels of possible biomarkers. Combining proteomics, genomics and transcriptomics, we provide a valuable resource that can be used to improve understanding of disease pathogenesis and to assist with drug discovery and development. |
| Author | Oddsson, Asmundur Masson, Gisli Fridriksdottir, Run Norddahl, Gudmundur L. Rognvaldsson, Solvi Melsted, Pall Halldorsson, Bjarni V. Olafsdottir, Thorunn A. Atlason, Bjarni A. Sveinbjornsson, Gardar Gunnarsdottir, Kristbjorg Thorsteinsdottir, Unnur Saevarsdottir, Saedis Halldorsson, Gisli H. Steinthorsdottir, Valgerdur Rafnar, Thorunn Eiriksdottir, Thjodbjorg Holm, Hilma Lund, Sigrun H. Ferkingstad, Egil Styrmisdottir, Edda L. Gudbjartsson, Daniel F. Tragante, Vinicius Jensson, Brynjar O. Helgason, Agnar Stefansson, Kari Sulem, Patrick Jonsdottir, Ingileif Arnadottir, Gudny A. Katrinardottir, Hildigunnur Saemundsdottir, Jona Magnusson, Magnus I. Zink, Florian Magnusson, Magnus K. Gudjonsson, Sigurjon A. Stacey, Simon N. Ulfarsson, Magnus O. Juliusson, Kristinn Stefansson, Hreinn Magnusson, Olafur Th |
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givenname: Agnar orcidid: 0000-0002-8545-3767 surname: Helgason fullname: Helgason, Agnar organization: deCODE genetics/Amgen, Inc., Department of Anthropology, University of Iceland – sequence: 9 givenname: Asmundur orcidid: 0000-0002-4606-5163 surname: Oddsson fullname: Oddsson, Asmundur organization: deCODE genetics/Amgen, Inc – sequence: 10 givenname: Bjarni V. orcidid: 0000-0003-0756-0767 surname: Halldorsson fullname: Halldorsson, Bjarni V. organization: deCODE genetics/Amgen, Inc., School of Science and Engineering, Reykjavik University – sequence: 11 givenname: Brynjar O. surname: Jensson fullname: Jensson, Brynjar O. organization: deCODE genetics/Amgen, Inc – sequence: 12 givenname: Florian surname: Zink fullname: Zink, Florian organization: deCODE genetics/Amgen, Inc – sequence: 13 givenname: Gisli H. orcidid: 0000-0001-7067-9862 surname: Halldorsson fullname: Halldorsson, Gisli H. organization: deCODE genetics/Amgen, Inc – sequence: 14 givenname: Gisli surname: Masson fullname: Masson, Gisli organization: deCODE genetics/Amgen, Inc – sequence: 15 givenname: Gudny A. orcidid: 0000-0001-6571-423X surname: Arnadottir fullname: Arnadottir, Gudny A. organization: deCODE genetics/Amgen, Inc – sequence: 16 givenname: Hildigunnur surname: Katrinardottir fullname: Katrinardottir, Hildigunnur organization: deCODE genetics/Amgen, Inc – sequence: 17 givenname: Kristinn surname: Juliusson fullname: Juliusson, Kristinn organization: deCODE genetics/Amgen, Inc – sequence: 18 givenname: Magnus K. orcidid: 0000-0001-8593-4934 surname: Magnusson fullname: Magnusson, Magnus K. organization: deCODE genetics/Amgen, Inc., Faculty of Medicine, School of Health Sciences, University of Iceland – sequence: 19 givenname: Olafur Th surname: Magnusson fullname: Magnusson, Olafur Th organization: deCODE genetics/Amgen, Inc – sequence: 20 givenname: Run surname: Fridriksdottir fullname: Fridriksdottir, Run organization: deCODE genetics/Amgen, Inc – sequence: 21 givenname: Saedis orcidid: 0000-0001-9392-6184 surname: Saevarsdottir fullname: Saevarsdottir, Saedis organization: deCODE genetics/Amgen, Inc., Faculty of Medicine, School of Health Sciences, University of Iceland – sequence: 22 givenname: Sigurjon A. surname: Gudjonsson fullname: Gudjonsson, Sigurjon A. organization: deCODE genetics/Amgen, Inc – sequence: 23 givenname: Simon N. orcidid: 0000-0002-4732-7380 surname: Stacey fullname: Stacey, Simon N. organization: deCODE genetics/Amgen, Inc – sequence: 24 givenname: Solvi orcidid: 0000-0002-4376-3361 surname: Rognvaldsson fullname: Rognvaldsson, Solvi organization: deCODE genetics/Amgen, Inc – sequence: 25 givenname: Thjodbjorg orcidid: 0000-0003-1009-7398 surname: Eiriksdottir fullname: Eiriksdottir, Thjodbjorg organization: deCODE genetics/Amgen, Inc – sequence: 26 givenname: Thorunn A. surname: Olafsdottir fullname: Olafsdottir, Thorunn A. organization: deCODE genetics/Amgen, Inc., Faculty of Medicine, School of Health Sciences, University of Iceland – sequence: 27 givenname: Valgerdur orcidid: 0000-0003-1846-6274 surname: Steinthorsdottir fullname: Steinthorsdottir, Valgerdur organization: deCODE genetics/Amgen, Inc – sequence: 28 givenname: Vinicius orcidid: 0000-0002-8223-8957 surname: Tragante fullname: Tragante, Vinicius organization: deCODE genetics/Amgen, Inc – sequence: 29 givenname: Magnus O. orcidid: 0000-0002-0461-040X surname: Ulfarsson fullname: Ulfarsson, Magnus O. organization: deCODE genetics/Amgen, Inc., Faculty of Electrical and Computer Engineering, University of Iceland – sequence: 30 givenname: Hreinn orcidid: 0000-0002-9331-6666 surname: Stefansson fullname: Stefansson, Hreinn organization: deCODE genetics/Amgen, Inc – sequence: 31 givenname: Ingileif orcidid: 0000-0001-8339-150X surname: Jonsdottir fullname: Jonsdottir, Ingileif organization: deCODE genetics/Amgen, Inc., Faculty of Medicine, School of Health Sciences, University of Iceland – sequence: 32 givenname: Hilma orcidid: 0000-0002-9517-6636 surname: 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| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/34857953$$D View this record in MEDLINE/PubMed |
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| Snippet | The plasma proteome can help bridge the gap between the genome and diseases. Here we describe genome-wide association studies (GWASs) of plasma protein levels... The proteome also introduces a new temporal quality: the levels of proteins may rise or fall as a function of time before or after an event or as disease... |
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| Title | Large-scale integration of the plasma proteome with genetics and disease |
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