A cross-platform approach identifies genetic regulators of human metabolism and health
In cross-platform analyses of 174 metabolites, we identify 499 associations ( P
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| Veröffentlicht in: | Nature genetics 2021-01, Vol.53 (1), p.54-64 |
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| creator | Lotta, Luca A. Pietzner, Maik Stewart, Isobel D. Wittemans, Laura B. L. Li, Chen Bonelli, Roberto Raffler, Johannes Biggs, Emma K. Oliver-Williams, Clare Auyeung, Victoria P. W. Luan, Jian’an Wheeler, Eleanor Paige, Ellie Surendran, Praveen Michelotti, Gregory A. Scott, Robert A. Burgess, Stephen Zuber, Verena Sanderson, Eleanor Koulman, Albert Imamura, Fumiaki Forouhi, Nita G. Khaw, Kay-Tee Griffin, Julian L. Wood, Angela M. Kastenmüller, Gabi Danesh, John Butterworth, Adam S. Gribble, Fiona M. Reimann, Frank Bahlo, Melanie Fauman, Eric Wareham, Nicholas J. Langenberg, Claudia |
| description | In cross-platform analyses of 174 metabolites, we identify 499 associations (
P
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| doi_str_mv | 10.1038/s41588-020-00751-5 |
| format | Article |
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P
< 4.9 × 10
−10
) characterized by pleiotropy, allelic heterogeneity, large and nonlinear effects and enrichment for nonsynonymous variation. We identify a signal at
GLP2R
(p.Asp470Asn) shared among higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes, with β-arrestin signaling as the underlying mechanism. Genetically higher serine levels are shown to reduce the likelihood (by 95%) and predict development of macular telangiectasia type 2, a rare degenerative retinal disease. Integration of genomic and small molecule data across platforms enables the discovery of regulators of human metabolism and translation into clinical insights.
A large-scale genome-wide meta-analysis conducted across different platforms identifies genetic loci regulating levels of circulating metabolites.</description><identifier>ISSN: 1061-4036</identifier><identifier>EISSN: 1546-1718</identifier><identifier>DOI: 10.1038/s41588-020-00751-5</identifier><identifier>PMID: 33414548</identifier><language>eng</language><publisher>New York: Nature Publishing Group US</publisher><subject>45 ; 45/43 ; 631/208/205 ; 692/308/575 ; Agriculture ; Animal Genetics and Genomics ; Arrestin ; Biomedical and Life Sciences ; Biomedicine ; Body mass index ; Body size ; Cancer Research ; Citrulline ; Diabetes mellitus (non-insulin dependent) ; Diabetes Mellitus, Type 2 - genetics ; Eye Diseases - genetics ; Gene Frequency - genetics ; Gene Function ; Gene loci ; Genetic aspects ; Genetic Loci ; Genetic Pleiotropy ; Genetic regulation ; Genome, Human ; Genomes ; Glucagon-Like Peptide-2 Receptor - genetics ; Glycine - metabolism ; Health ; Health aspects ; Heterogeneity ; Human Genetics ; Humans ; Linear Models ; Mendelian Randomization Analysis ; Metabolism ; Metabolism - genetics ; Metabolism, Inborn Errors - genetics ; Metabolites ; Metabolome - genetics ; Mutation, Missense - genetics ; Phenotype ; Pleiotropy ; Polymorphism, Single Nucleotide - genetics ; Principal components analysis ; Regulators ; Retinal Telangiectasis - genetics ; Sample Size ; Serine ; Serine - metabolism</subject><ispartof>Nature genetics, 2021-01, Vol.53 (1), p.54-64</ispartof><rights>The Author(s), under exclusive licence to Springer Nature America, Inc. 2021</rights><rights>COPYRIGHT 2021 Nature Publishing Group</rights><rights>Copyright Nature Publishing Group Jan 2021</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c620t-944517e2b4894e20a20063d06ce298d09d7ae8001fb2576db2d35b33f3ef6e363</citedby><cites>FETCH-LOGICAL-c620t-944517e2b4894e20a20063d06ce298d09d7ae8001fb2576db2d35b33f3ef6e363</cites><orcidid>0000-0003-2495-4020 ; 0000-0001-5188-5775 ; 0000-0001-9399-6377 ; 0000-0001-9827-1877 ; 0000-0002-6915-9015 ; 0000-0003-0855-9872 ; 0000-0002-2368-7322 ; 0000-0002-6841-8396 ; 0000-0001-9998-051X ; 0000-0001-5132-0774 ; 0000-0002-9739-0249 ; 0000-0002-8616-6444 ; 0000-0001-5365-8760 ; 0000-0003-1422-2993 ; 0000-0002-0823-3963 ; 0000-0003-3634-3016 ; 0000-0002-5017-7344 ; 0000-0002-5041-248X ; 0000-0003-3137-6337</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1038/s41588-020-00751-5$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1038/s41588-020-00751-5$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,776,780,27901,27902,41464,42533,51294</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33414548$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lotta, Luca A.</creatorcontrib><creatorcontrib>Pietzner, Maik</creatorcontrib><creatorcontrib>Stewart, Isobel D.</creatorcontrib><creatorcontrib>Wittemans, Laura B. L.</creatorcontrib><creatorcontrib>Li, Chen</creatorcontrib><creatorcontrib>Bonelli, Roberto</creatorcontrib><creatorcontrib>Raffler, Johannes</creatorcontrib><creatorcontrib>Biggs, Emma K.</creatorcontrib><creatorcontrib>Oliver-Williams, Clare</creatorcontrib><creatorcontrib>Auyeung, Victoria P. 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P
< 4.9 × 10
−10
) characterized by pleiotropy, allelic heterogeneity, large and nonlinear effects and enrichment for nonsynonymous variation. We identify a signal at
GLP2R
(p.Asp470Asn) shared among higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes, with β-arrestin signaling as the underlying mechanism. Genetically higher serine levels are shown to reduce the likelihood (by 95%) and predict development of macular telangiectasia type 2, a rare degenerative retinal disease. Integration of genomic and small molecule data across platforms enables the discovery of regulators of human metabolism and translation into clinical insights.
A large-scale genome-wide meta-analysis conducted across different platforms identifies genetic loci regulating levels of circulating metabolites.</description><subject>45</subject><subject>45/43</subject><subject>631/208/205</subject><subject>692/308/575</subject><subject>Agriculture</subject><subject>Animal Genetics and Genomics</subject><subject>Arrestin</subject><subject>Biomedical and Life Sciences</subject><subject>Biomedicine</subject><subject>Body mass index</subject><subject>Body size</subject><subject>Cancer Research</subject><subject>Citrulline</subject><subject>Diabetes mellitus (non-insulin dependent)</subject><subject>Diabetes Mellitus, Type 2 - genetics</subject><subject>Eye Diseases - genetics</subject><subject>Gene Frequency - genetics</subject><subject>Gene Function</subject><subject>Gene loci</subject><subject>Genetic aspects</subject><subject>Genetic Loci</subject><subject>Genetic Pleiotropy</subject><subject>Genetic regulation</subject><subject>Genome, Human</subject><subject>Genomes</subject><subject>Glucagon-Like Peptide-2 Receptor - genetics</subject><subject>Glycine - metabolism</subject><subject>Health</subject><subject>Health aspects</subject><subject>Heterogeneity</subject><subject>Human Genetics</subject><subject>Humans</subject><subject>Linear Models</subject><subject>Mendelian Randomization Analysis</subject><subject>Metabolism</subject><subject>Metabolism - genetics</subject><subject>Metabolism, Inborn Errors - genetics</subject><subject>Metabolites</subject><subject>Metabolome - genetics</subject><subject>Mutation, Missense - genetics</subject><subject>Phenotype</subject><subject>Pleiotropy</subject><subject>Polymorphism, Single Nucleotide - genetics</subject><subject>Principal components analysis</subject><subject>Regulators</subject><subject>Retinal Telangiectasis - genetics</subject><subject>Sample Size</subject><subject>Serine</subject><subject>Serine - metabolism</subject><issn>1061-4036</issn><issn>1546-1718</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>8G5</sourceid><sourceid>BENPR</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNqNkU1rFTEUhgex2Fr9Ay4k4KqLaU8-J3d5KVoLhUKr3YbM5GRuynxckwzUf2_aWy0XRCSLhOR5Tw7nqaoPFE4pcH2WBJVa18CgBmgkreWr6ohKoWraUP26nEHRWgBXh9XblO4BqBCg31SHnAsqpNBH1d2adHFOqd4ONvs5jsRut3G23YYEh1MOPmAiPU6YQ0ci9kvh5pjI7MlmGe1ERsy2nYeQSnRyZIN2yJt31YG3Q8L3z_tx9f3L52_nX-ur64vL8_VV3SkGuV4JIWmDrBV6JZCBZQCKO1AdspV2sHKNRV369i2TjXItc1y2nHuOXiFX_Lj6tKtbev6xYMrmfl7iVL40TDQN01JzeKF6O6AJk59ztN0YUmfWSgJtlKKyUKd_ocpyOIZuntCHcr8XONkLFCbjQ-7tkpK5vL35f_b6bp9lO_bJTERvtjGMNv40FMyjebMzb4p582TePIY-Pk9iaUd0fyK_VReA74BUnqYe48uo_lH2F6aetSE</recordid><startdate>20210101</startdate><enddate>20210101</enddate><creator>Lotta, Luca A.</creator><creator>Pietzner, Maik</creator><creator>Stewart, Isobel D.</creator><creator>Wittemans, Laura B. 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L.</au><au>Li, Chen</au><au>Bonelli, Roberto</au><au>Raffler, Johannes</au><au>Biggs, Emma K.</au><au>Oliver-Williams, Clare</au><au>Auyeung, Victoria P. W.</au><au>Luan, Jian’an</au><au>Wheeler, Eleanor</au><au>Paige, Ellie</au><au>Surendran, Praveen</au><au>Michelotti, Gregory A.</au><au>Scott, Robert A.</au><au>Burgess, Stephen</au><au>Zuber, Verena</au><au>Sanderson, Eleanor</au><au>Koulman, Albert</au><au>Imamura, Fumiaki</au><au>Forouhi, Nita G.</au><au>Khaw, Kay-Tee</au><au>Griffin, Julian L.</au><au>Wood, Angela M.</au><au>Kastenmüller, Gabi</au><au>Danesh, John</au><au>Butterworth, Adam S.</au><au>Gribble, Fiona M.</au><au>Reimann, Frank</au><au>Bahlo, Melanie</au><au>Fauman, Eric</au><au>Wareham, Nicholas J.</au><au>Langenberg, Claudia</au><aucorp>MacTel Consortium</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A cross-platform approach identifies genetic regulators of human metabolism and health</atitle><jtitle>Nature genetics</jtitle><stitle>Nat Genet</stitle><addtitle>Nat Genet</addtitle><date>2021-01-01</date><risdate>2021</risdate><volume>53</volume><issue>1</issue><spage>54</spage><epage>64</epage><pages>54-64</pages><issn>1061-4036</issn><eissn>1546-1718</eissn><abstract>In cross-platform analyses of 174 metabolites, we identify 499 associations (
P
< 4.9 × 10
−10
) characterized by pleiotropy, allelic heterogeneity, large and nonlinear effects and enrichment for nonsynonymous variation. We identify a signal at
GLP2R
(p.Asp470Asn) shared among higher citrulline levels, body mass index, fasting glucose-dependent insulinotropic peptide and type 2 diabetes, with β-arrestin signaling as the underlying mechanism. Genetically higher serine levels are shown to reduce the likelihood (by 95%) and predict development of macular telangiectasia type 2, a rare degenerative retinal disease. Integration of genomic and small molecule data across platforms enables the discovery of regulators of human metabolism and translation into clinical insights.
A large-scale genome-wide meta-analysis conducted across different platforms identifies genetic loci regulating levels of circulating metabolites.</abstract><cop>New York</cop><pub>Nature Publishing Group US</pub><pmid>33414548</pmid><doi>10.1038/s41588-020-00751-5</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-2495-4020</orcidid><orcidid>https://orcid.org/0000-0001-5188-5775</orcidid><orcidid>https://orcid.org/0000-0001-9399-6377</orcidid><orcidid>https://orcid.org/0000-0001-9827-1877</orcidid><orcidid>https://orcid.org/0000-0002-6915-9015</orcidid><orcidid>https://orcid.org/0000-0003-0855-9872</orcidid><orcidid>https://orcid.org/0000-0002-2368-7322</orcidid><orcidid>https://orcid.org/0000-0002-6841-8396</orcidid><orcidid>https://orcid.org/0000-0001-9998-051X</orcidid><orcidid>https://orcid.org/0000-0001-5132-0774</orcidid><orcidid>https://orcid.org/0000-0002-9739-0249</orcidid><orcidid>https://orcid.org/0000-0002-8616-6444</orcidid><orcidid>https://orcid.org/0000-0001-5365-8760</orcidid><orcidid>https://orcid.org/0000-0003-1422-2993</orcidid><orcidid>https://orcid.org/0000-0002-0823-3963</orcidid><orcidid>https://orcid.org/0000-0003-3634-3016</orcidid><orcidid>https://orcid.org/0000-0002-5017-7344</orcidid><orcidid>https://orcid.org/0000-0002-5041-248X</orcidid><orcidid>https://orcid.org/0000-0003-3137-6337</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1061-4036 |
| ispartof | Nature genetics, 2021-01, Vol.53 (1), p.54-64 |
| issn | 1061-4036 1546-1718 |
| language | eng |
| recordid | cdi_proquest_journals_2477285830 |
| source | MEDLINE; SpringerLink Journals; Nature Journals Online |
| subjects | 45 45/43 631/208/205 692/308/575 Agriculture Animal Genetics and Genomics Arrestin Biomedical and Life Sciences Biomedicine Body mass index Body size Cancer Research Citrulline Diabetes mellitus (non-insulin dependent) Diabetes Mellitus, Type 2 - genetics Eye Diseases - genetics Gene Frequency - genetics Gene Function Gene loci Genetic aspects Genetic Loci Genetic Pleiotropy Genetic regulation Genome, Human Genomes Glucagon-Like Peptide-2 Receptor - genetics Glycine - metabolism Health Health aspects Heterogeneity Human Genetics Humans Linear Models Mendelian Randomization Analysis Metabolism Metabolism - genetics Metabolism, Inborn Errors - genetics Metabolites Metabolome - genetics Mutation, Missense - genetics Phenotype Pleiotropy Polymorphism, Single Nucleotide - genetics Principal components analysis Regulators Retinal Telangiectasis - genetics Sample Size Serine Serine - metabolism |
| title | A cross-platform approach identifies genetic regulators of human metabolism and health |
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