Identifying causal serum protein-cardiometabolic trait relationships using whole genome sequencing

. 2023 Apr 06 ; 32 (8) : 1266-1275.

Jazyk angličtina Země Velká Británie, Anglie Médium print

Typ dokumentu časopisecké články, práce podpořená grantem

Perzistentní odkaz   https://www.medvik.cz/link/pmid36349687

Grantová podpora
MC_UU_00007/10 Medical Research Council - United Kingdom
MR/R026408/1 Medical Research Council - United Kingdom
CZB/4/710 Chief Scientist Office - United Kingdom
ERC-2011-StG 280559- SEPI European Research Council - International
CZB/4/276 Chief Scientist Office - United Kingdom
Arthritis Research UK - United Kingdom
098051 Wellcome Trust - United Kingdom
U. MC_UU_00007/10 Medical Research Council - United Kingdom
Wellcome Trust - United Kingdom

Cardiometabolic diseases, such as type 2 diabetes and cardiovascular disease, have a high public health burden. Understanding the genetically determined regulation of proteins that are dysregulated in disease can help to dissect the complex biology underpinning them. Here, we perform a protein quantitative trait locus (pQTL) analysis of 248 serum proteins relevant to cardiometabolic processes in 2893 individuals. Meta-analyzing whole-genome sequencing (WGS) data from two Greek cohorts, MANOLIS (n = 1356; 22.5× WGS) and Pomak (n = 1537; 18.4× WGS), we detect 301 independently associated pQTL variants for 170 proteins, including 12 rare variants (minor allele frequency < 1%). We additionally find 15 pQTL variants that are rare in non-Finnish European populations but have drifted up in the frequency in the discovery cohorts here. We identify proteins causally associated with cardiometabolic traits, including Mep1b for high-density lipoprotein (HDL) levels, and describe a knock-out (KO) Mep1b mouse model. Our findings furnish insights into the genetic architecture of the serum proteome, identify new protein-disease relationships and demonstrate the importance of isolated populations in pQTL analysis.

Anogia Medical Centre Anogia 74150 Greece

Centre for Global Health Research Usher Institute University of Edinburgh Edinburgh EH8 9QN UK

Chair of Experimental Genetics TUM School of Life Sciences Technical University of Munich Freising 80333 Germany

Department of Nutrition and Dietetics School of Health Science and Education Harokopio University of Athens Athens 17671 Greece

Echinos Medical Centre Echinos 67300 Greece

German Center for Diabetes Research Neuherberg 40225 Germany

Institute for Pathobiochemistry University Medical Center of the Johannes Gutenberg University Mainz Mainz 55122 Germany

Institute of Biochemistry Unit for Degradomics of the Protease Web University of Kiel Kiel 24118 Germany

Institute of Experimental Genetics German Mouse Clinic Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg 85764 Germany

Institute of Molecular Animal Breeding and Biotechnology Gene Center Ludwig Maximilians University Munich Munich 80539 Germany

Institute of Molecular Genetics of the Czech Academy of Sciences Czech Centre for Phenogenomics Vestec 25250 Czech Republic

Institute of Translational Genomics Helmholtz Zentrum München German Research Center for Environmental Health Neuherberg 85764 Germany

MRC Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh EH8 9QN UK

Technical University of Munich and Klinikum Rechts der Isar TUM School of Medicine Munich 80333 Germany

Technical University of Munich School of Medicine Munich 80333 Germany

Zobrazit více v PubMed

Roth, G.A., Mensah, G.A., Johnson, C.O., Addolorato, G., Ammirati, E., Baddour, L.M., Barengo, N.C., Beaton, A.Z., Benjamin, E.J., Benziger, C.P.  et al. (2020) Global burden of cardiovascular diseases and risk factors, 1990–2019. J. Am. Coll. Cardiol., 76, 2982–3021. PubMed PMC

Lin, X., Xu, Y., Pan, X., Xu, J., Ding, Y., Sun, X., Song, X., Ren, Y. and Shan, P.-F. (2020) Global, regional, and national burden and trend of diabetes in 195 countries and territories: an analysis from 1990 to 2025. Sci. Rep., 10, 14790. PubMed PMC

Ferreira, J.P., Sharma, A., Mehta, C., Bakris, G., Rossignol, P., White, W.B. and Zannad, F. (2021) Multi-proteomic approach to predict specific cardiovascular events in patients with diabetes and myocardial infarction: findings from the EXAMINE trial. Clin. Res. Cardiol., 110, 1006–1019. PubMed

Feldreich, T., Nowak, C., Fall, T., Carlsson, A.C., Carrero, J.-J., Ripsweden, J., Qureshi, A.R., Heimbürger, O., Barany, P., Stenvinkel, P.  et al. (2019) Circulating proteins as predictors of cardiovascular mortality in end-stage renal disease. J. Nephrol., 32, 111–119. PubMed PMC

Cauwenberghs, N., Sabovčik, F., Magnus, A., Haddad, F. and Kuznetsova, T. (2021) Proteomic profiling for detection of early-stage heart failure in the community. ESC Heart Fail., 8, 2928–2939. PubMed PMC

Sun, B.B., Maranville, J.C., Peters, J.E., Stacey, D., Staley, J.R., Blackshaw, J., Burgess, S., Jiang, T., Paige, E., Surendran, P.  et al. (2018) Genomic atlas of the human plasma proteome. Nature, 558, 73–79. PubMed PMC

Yao, C., Chen, G., Song, C., Keefe, J., Mendelson, M., Huan, T., Sun, B.B., Laser, A., Maranville, J.C., Wu, H.  et al. (2018) Genome-wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease. Nat. Commun., 9, 3268. PubMed PMC

Folkersen, L., Gustafsson, S., Wang, Q., Hansen, D.H., Hedman, Å.K., Schork, A., Page, K., Zhernakova, D.V., Wu, Y., Peters, J.  et al. (2020) Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals. Nat Metab., 2, 1135–1148. PubMed PMC

Emilsson, V., Ilkov, M., Lamb, J.R., Finkel, N., Gudmundsson, E.F., Pitts, R., Hoover, H., Gudmundsdottir, V., Horman, S.R., Aspelund, T.  et al. (2018) Co-regulatory networks of human serum proteins link genetics to disease. Science, 361, 769–773. PubMed PMC

Gilly, A., Park, Y.-C., Png, G., Barysenka, A., Fischer, I., Bjørnland, T., Southam, L., Suveges, D., Neumeyer, S., Rayner, N.W.  et al. (2020) Whole-genome sequencing analysis of the cardiometabolic proteome. Nat. Commun., 11, 6336. PubMed PMC

Suhre, K., Arnold, M., Bhagwat, A.M., Cotton, R.J., Engelke, R., Raffler, J., Sarwath, H., Thareja, G., Wahl, A., DeLisle, R.K.  et al. (2017) Connecting genetic risk to disease end points through the human blood plasma proteome. Nat. Commun., 8, 14357. PubMed PMC

Pietzner, M., Wheeler, E., Carrasco-Zanini, J., Cortes, A., Koprulu, M., Wörheide, M.A., Oerton, E., Cook, J., Stewart, I.D., Kerrison, N.D.  et al. (2021) Mapping the proteo-genomic convergence of human diseases. Science, 374, eabj1541. PubMed PMC

McQuillan, R., Leutenegger, A.-L., Abdel-Rahman, R., Franklin, C.S., Pericic, M., Barac-Lauc, L., Smolej-Narancic, N., Janicijevic, B., Polasek, O., Tenesa, A.  et al. (2008) Runs of homozygosity in European populations. Am. J. Hum. Genet., 83, 359–372. PubMed PMC

Combadiere, C., Ahuja, S.K. and Murphy, P.M. (1995) Cloning and functional expression of a human eosinophil CC chemokine receptor. J. Biol. Chem., 270, 16491–16494. PubMed

Panoutsopoulou, K., Hatzikotoulas, K., Xifara, D.K., Colonna, V., Farmaki, A.-E., Ritchie, G.R.S., Southam, L., Gilly, A., Tachmazidou, I., Fatumo, S.  et al. (2014) Genetic characterization of Greek population isolates reveals strong genetic drift at missense and trait-associated variants. Nat. Commun., 5, 5345. PubMed PMC

Howard, E.W. and Banda, M.J. (1991) Binding of tissue inhibitor of metalloproteinases 2 to two distinct sites on human 72-kDa gelatinase. Identification of a stabilization site. J. Biol. Chem., 266, 17972–17977. PubMed

Zhong, F., Chen, Z., Zhang, L., Xie, Y., Nair, V., Ju, W., Kretzler, M., Nelson, R.G., Li, Z., Chen, H.  et al. (2018) Tyro3 is a podocyte protective factor in glomerular disease. JCI Insight, 3, 123482. PubMed PMC

Ochodnicky, P., Lattenist, L., Ahdi, M., Kers, J., Uil, M., Claessen, N., Leemans, J.C., Florquin, S., Meijers, J.C.M., Gerdes, V.E.A.  et al. (2017) Increased circulating and urinary levels of soluble TAM receptors in diabetic nephropathy. Am. J. Pathol., 187, 1971–1983. PubMed

Brown, M.S. and Goldstein, J.L. (1984) How LDL receptors influence cholesterol and atherosclerosis. Sci. Am., 251, 58–66. PubMed

Broder, C. and Becker-Pauly, C. (2013) The metalloproteases meprin α and meprin β: unique enzymes in inflammation, neurodegeneration, cancer and fibrosis. Biochem. J., 450, 253–264. PubMed PMC

Jefferson, T., Auf dem Keller, U., Bellac, C., Metz, V.V., Broder, C., Hedrich, J., Ohler, A., Maier, W., Magdolen, V., Sterchi, E.  et al. (2013) The substrate degradome of meprin metalloproteases reveals an unexpected proteolytic link between meprin β and ADAM10. Cell. Mol. Life Sci., 70, 309–333. PubMed PMC

Monami, M., Lamanna, C., Desideri, C.M. and Mannucci, E. (2012) DPP-4 inhibitors and lipids: systematic review and meta-analysis. Adv. Ther., 29, 14–25. PubMed

Pierrot, N., Tyteca, D., D’auria, L., Dewachter, I., Gailly, P., Hendrickx, A., Tasiaux, B., Haylani, L.E., Muls, N., Nkuli, F.  et al. (2013) Amyloid precursor protein controls cholesterol turnover needed for neuronal activity. EMBO Mol. Med., 5, 608–625. PubMed PMC

Gilly, A., Klaric, L., Park, Y.-C., Png, G., Barysenka, A., Marsh, J.A., Tsafantakis, E., Karaleftheri, M., Dedoussis, G., Wilson, J.F.  et al. (2022) Gene-based whole genome sequencing meta-analysis of 250 circulating proteins in three isolated European populations. Mol. Metab., 61, 101509. PubMed PMC

Pietzner, M., Wheeler, E., Carrasco-Zanini, J., Kerrison, N.D., Oerton, E., Koprulu, M., Luan, J., Hingorani, A.D., Williams, S.A., Wareham, N.J.  et al. (2021) Synergistic insights into human health from aptamer- and antibody-based proteomic profiling. Nat. Commun., 12, 6822. PubMed PMC

de  Auwera, G.A.V. and O’Connor, B.D. (2020) Genomics in the Cloud: Using Docker, GATK, and WDL in Terra, 1st edn. O’Reilly, Beijing, Boston, Farnham, Sebastopol, Tokyo.

Zhou, X. and Stephens, M. (2012) Genome-wide efficient mixed-model analysis for association studies. Nat. Genet., 44, 821–824. PubMed PMC

Willer, C.J., Li, Y. and Abecasis, G.R. (2010) METAL: fast and efficient meta-analysis of genomewide association scans. Bioinform. Oxf. Engl., 26, 2190–2191. PubMed PMC

Chang, C.C., Chow, C.C., Tellier, L.C., Vattikuti, S., Purcell, S.M. and Lee, J.J. (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. GigaScience, 4, 7. PubMed PMC

Yang, J., Lee, S.H., Goddard, M.E. and Visscher, P.M. (2011) GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet., 88, 76–82. PubMed PMC

Mägi, R. and Morris, A.P. (2010) GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics, 11, 288. PubMed PMC

Magi, R., Lindgren, C.M. and Morris, A.P. (2010) Meta-analysis of sex-specific genome-wide association studies. Genet. Epidemiol., 34, 846–853. PubMed PMC

Yang, J., Zaitlen, N.A., Goddard, M.E., Visscher, P.M. and Price, A.L. (2014) Advantages and pitfalls in the application of mixed-model association methods. Nat. Genet., 46, 100–106. PubMed PMC

Buniello, A., MacArthur, J.A.L., Cerezo, M., Harris, L.W., Hayhurst, J., Malangone, C., McMahon, A., Morales, J., Mountjoy, E., Sollis, E.  et al. (2019) The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res., 47, D1005–D1012. PubMed PMC

Howe, K.L., Achuthan, P., Allen, J., Allen, J., Alvarez-Jarreta, J., Amode, M.R., Armean, I.M., Azov, A.G., Bennett, R., Bhai, J.  et al. (2021) Ensembl 2021. Nucleic Acids Res., 49, D884–D891. PubMed PMC

McLaren, W., Gil, L., Hunt, S.E., Riat, H.S., Ritchie, G.R.S., Thormann, A., Flicek, P. and Cunningham, F. (2016) The Ensembl variant effect predictor. Genome Biol., 17, 122. PubMed PMC

Carithers, L.J., Ardlie, K., Barcus, M., Branton, P.A., Britton, A., Buia, S.A., Compton, C.C., DeLuca, D.S., Peter-Demchok, J., Gelfand, E.T.  et al. (2015) A novel approach to high-quality postmortem tissue procurement: the GTEx project. Biopreserv. Biobank., 13, 311–319. PubMed PMC

Giambartolomei, C., Vukcevic, D., Schadt, E.E., Franke, L., Hingorani, A.D., Wallace, C. and Plagnol, V. (2014) Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet., 10, e1004383. PubMed PMC

Staley, J.R., Blackshaw, J., Kamat, M.A., Ellis, S., Surendran, P., Sun, B.B., Paul, D.S., Freitag, D., Burgess, S., Danesh, J.  et al. (2016) PhenoScanner: a database of human genotype-phenotype associations. Bioinform. Oxf. Engl., 32, 3207–3209. PubMed PMC

Kamat, M.A., Blackshaw, J.A., Young, R., Surendran, P., Burgess, S., Danesh, J., Butterworth, A.S. and Staley, J.R. (2019) PhenoScanner V2: an expanded tool for searching human genotype-phenotype associations. Bioinform. Oxf. Engl., 35, 4851–4853. PubMed PMC

Ochoa, D., Hercules, A., Carmona, M., Suveges, D., Gonzalez-Uriarte, A., Malangone, C., Miranda, A., Fumis, L., Carvalho-Silva, D., Spitzer, M.  et al. (2021) Open targets platform: supporting systematic drug–target identification and prioritisation. Nucleic Acids Res., 49, D1302–D1310. PubMed PMC

Wishart, D.S. (2006) DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucleic Acids Res., 34, D668–D672. PubMed PMC

The International Mouse Phenotyping Consortium, Dickinson, M.E., Flenniken, A.M., Ji, X., Teboul, L., Wong, M.D., White, J.K., Meehan, T.F., Weninger, W.J., Westerberg, H.  et al. (2016) High-throughput discovery of novel developmental phenotypes. Nature, 537, 508–514. PubMed PMC

Hemani, G., Zheng, J., Elsworth, B., Wade, K.H., Haberland, V., Baird, D., Laurin, C., Burgess, S., Bowden, J., Langdon, R.  et al. (2018) The MR-Base platform supports systematic causal inference across the human phenome. eLife, 7, e34408. PubMed PMC

Norman, L.P., Jiang, W., Han, X., Saunders, T.L. and Bond, J.S. (2003) Targeted disruption of the meprin beta gene in mice leads to underrepresentation of knockout mice and changes in renal gene expression profiles. Mol. Cell. Biol., 23, 1221–1230. PubMed PMC

Gailus-Durner, V., Fuchs, H., Becker, L., Bolle, I., Brielmeier, M., Calzada-Wack, J., Elvert, R., Ehrhardt, N., Dalke, C., Franz, T.J.  et al. (2005) Introducing the German Mouse Clinic: open access platform for standardized phenotyping. Nat. Methods, 2, 403–404. PubMed

Fuchs, H., Gailus-Durner, V., Adler, T., Pimentel, J.A.A., Becker, L., Bolle, I., Brielmeier, M., Calzada-Wack, J., Dalke, C., Ehrhardt, N.  et al. (2009) The German Mouse Clinic: a platform for systemic phenotype analysis of mouse models. Curr. Pharm. Biotechnol., 10, 236–243. PubMed

Fuchs, H., Aguilar-Pimentel, J.A., Amarie, O.V., Becker, L., Calzada-Wack, J., Cho, Y.-L., Garrett, L., Hölter, S.M., Irmler, M., Kistler, M.  et al. (2018) Understanding gene functions and disease mechanisms: phenotyping pipelines in the German Mouse Clinic. Behav. Brain Res., 352, 187–196. PubMed

Fuchs, H., Gailus-Durner, V., Adler, T., Aguilar-Pimentel, J.A., Becker, L., Calzada-Wack, J., Da Silva-Buttkus, P., Neff, F., Götz, A., Hans, W.  et al. (2011) Mouse phenotyping. Methods, 53, 120–135. PubMed

Rathkolb, B., Hans, W., Prehn, C., Fuchs, H., Gailus-Durner, V., Aigner, B., Adamski, J., Wolf, E. and Hrabě de Angelis, M. (2013) Clinical chemistry and other laboratory tests on mouse plasma or serum. Curr. Protoc. Mouse Biol., 3, 69–100. PubMed

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