Identifying causal serum protein-cardiometabolic trait relationships using whole genome sequencing
Jazyk angličtina Země Velká Británie, Anglie Médium print
Typ dokumentu časopisecké články, práce podpořená grantem
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
PubMed
36349687
PubMed Central
PMC10077504
DOI
10.1093/hmg/ddac275
PII: 6812863
Knihovny.cz E-zdroje
- MeSH
- celogenomová asociační studie MeSH
- diabetes mellitus 2. typu * MeSH
- fenotyp MeSH
- kardiovaskulární nemoci * MeSH
- krevní proteiny genetika MeSH
- myši MeSH
- sekvenování celého genomu MeSH
- zvířata MeSH
- Check Tag
- myši MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Názvy látek
- krevní proteiny MeSH
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
Echinos Medical Centre Echinos 67300 Greece
German Center for Diabetes Research Neuherberg 40225 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