Pirillo A, Casula M, Olmastroni E, Norata GD, Catapano AL. Global epidemiology of dyslipidaemias. Nat Rev Cardiol Springer, US. 2021;18:689–700. https://doi.org/10.1038/s41569-021-00541-4. Available from:
Olkowicz M, Cichon IC, Szupryczynska N, Kostogrys RB, Kochan Z, Debski J, et al. Multi-omic signatures of atherogenic dyslipidaemia: pre-clinical target identification and validation in humans. J Transl Med. 2021;19:1–23. https://doi.org/10.1186/s12967-020-02663-8. BioMed Central.
Weber C, Noels H. Atherosclerosis: current pathogenesis and therapeutic options. Nat Med. 2011;17(11):1410–22. https://doi.org/10.1038/nm.2538.
Kaptoge S, Pennells L, De Bacquer D, Cooney MT, Kavousi M, Stevens G, et al. World Health Organization cardiovascular disease risk charts: revised models to estimate risk in 21 global regions. Lancet Glob Heal. 2019;7:e1332–45.
Kastenmüller G, Raffler J, Gieger C, Suhre K. Genetics of human metabolism: an update. Hum Mol Genet. 2015;24:93–101.
Beuchel C, Becker S, Dittrich J, Kirsten H, Toenjes A, Stumvoll M, et al. Clinical and lifestyle related factors influencing whole blood metabolite levels – A comparative analysis of three large cohorts. Mol Metab. 2019;29:76–85.
Article CAS PubMed PubMed Central Google Scholar
Nassan FL, Kelly RS, Koutrakis P, Vokonas PS, Lasky-Su JA, Schwartz JD. Metabolomic signatures of the short-term exposure to air pollution and temperature. Environ Res. 2021;201:111553 Environmental Health.
Article CAS PubMed PubMed Central Google Scholar
Rhee EP, Ho JE, Chen M, Shen D, Larson MG, Ghorbani A, et al. A Genome-Wide Association Study of the Human Metabolome in a Community-Based Cohort. Cell Metab. 2014;18:130–43.
Shin S, Fauman EB, Petersen A, Krumsiek J, Santos R, Huang J, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46.
Draisma HHM, Pool R, Kobl M, Jansen R, Petersen A, Vaarhorst AAM, et al. Genome-wide association study identifies novel genetic variants contributing to variation in blood metabolite levels. Nat Commun. 2015;6:7208.
Article CAS PubMed Google Scholar
Kettunen J, Demirkan A, Würtz P, Draisma HHM, Haller T, Rawal R, et al. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA. Nat Commun. 2016;7: 11122.
Article CAS PubMed PubMed Central Google Scholar
Long T, Hicks M, Yu H, Biggs WH, Kirkness EF, Menni C, et al. Whole-genome sequencing identifies common-to-rare variants associated with human blood metabolites. Nat Genet Nat Pub Gr. 2017;49:568–78.
Gallois A, Mefford J, Ko A, Vaysse A, Julienne H, Ala-korpela M, et al. A comprehensive study of metabolite genetics reveals strong pleiotropy and heterogeneity across time and context. Nat Commun. 2019;10:4788. https://doi.org/10.1038/s41467-019-12703-7. Springer, US.
Article CAS PubMed PubMed Central Google Scholar
Margoliash J, Fuchs S, Li Y, Zhang X, Massarat A, Goren A, et al. Polymorphic short tandem repeats make widespread contributions to blood and serum traits. Cell Genomics. 2023;3:100458. https://doi.org/10.1016/j.xgen.2023.100458. The Author(s).
Article CAS PubMed PubMed Central Google Scholar
Cadby G, Giles C, Melton PE, Huynh K, Mellett NA, Duong T, et al. Comprehensive genetic analysis of the human lipidome identifies loci associated with lipid homeostasis with links to coronary artery disease. Nat Commun. 2022;13:1–17.
Cano-Gamez E, Trynka G. From GWAS to Function: Using Functional Genomics to Identify the Mechanisms Underlying Complex Diseases. Front Genet. 2020;11: 424.
Article CAS PubMed PubMed Central Google Scholar
Gusev A, Ko A, Shi H, Bhatia G, Chung W, Penninx BWJH, et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat Publ Gr. 2016;48:245–52. https://doi.org/10.1038/ng.3506. Available from.
Barbeira AN, Pividori M, Zheng J, Wheeler HE, Nicolae DL, Im HK. Integrating predicted transcriptome from multiple tissues improves association detection. Plos Genet. 2019;15:e1007889. https://doi.org/10.1371/journal.pgen.1007889.
Article CAS PubMed PubMed Central Google Scholar
Breschi A, Muñoz-Aguirre M, Wucher V, Davis CA, Garrido-Martín D, Djebali S, et al. A limited set of transcriptional programs define major cell types. Genome Res. 2020;30:1047–59.
Article CAS PubMed PubMed Central Google Scholar
Li B, Veturi Y, Bradford Y, Verma SS, Verma A, Lucas AM, et al. Influence of tissue context on gene prioritization for predicted transcriptome-wide association studies 1. Introduction Improving antiretroviral therapy ( ART ) efficacy and safety is an ongoing goal for addressing the HIV pandemic. According to the Joi. Pac Symp Biocomput. 2019;24:296–307.
PubMed PubMed Central Google Scholar
Li L, Chen Z, von Scheidt M, Li S, Steiner A, Güldener U, et al. Transcriptome-wide association study of coronary artery disease identifies novel susceptibility genes. Basic Res Cardiol. 2022;117:1–20. https://doi.org/10.1007/s00395-022-00917-8. Springer, Berlin Heidelberg.
Highland HM, Wojcik GL, Graff M, Nishimura KK, Hodonsky CJ, Baldassari AR, et al. Predicted gene expression in ancestrally diverse populations leads to discovery of susceptibility loci for lifestyle and cardiometabolic traits. Am J Hum Genet. 2022:1–11. https://doi.org/10.1016/j.ajhg.2022.02.013. American Society of Human Genetics.
Thompson M, Gordon MG, Lu A, Tandon A, Halperin E, Gusev A, et al. Multi-context genetic modeling of transcriptional regulation resolves novel disease loci. Nat Commun Springer, US. 2022;13:1–15.
Zhao Q, Liu R, Chen H, Yan X, Dong J, Bai M, et al. Transcriptome-wide association genes for coronary atherosclerosis. Front Cardiovasc Med. 2023:1–8. https://doi.org/10.3389/fcvm.2023.1149113.
Ndungu A, Payne A, Torres JM, van de Bunt M, McCarthy MI. A Multi-tissue Transcriptome Analysis of Human Metabolites Guides Interpretability of Associations Based on Multi-SNP Models for Gene Expression. Am J Hum Genet ElsevierCompany. 2020;106:188–201. https://doi.org/10.1016/j.ajhg.2020.01.003. Available from:
de Leeuw C, Werme J, Savage JE, Peyrot WJ, Posthuma D. On the interpretation of transcriptome-wide association studies. PLoS Genet. 2023;19:1–23. Available from: https://doi.org/10.1371/journal.pgen.1010921.
Obón-Santacana M, Vilardell M, Carreras A, Duran X, Velasco J, Galván-femenía I, et al. GCAT | Genomes for life: a prospective cohort study of the genomes of Catalonia. BMJ Open. 2018;8: e018324.
Article PubMed PubMed Central Google Scholar
Galván-Femenía I, Obón-Santacana M, Piñeyro D, Guindo-Martinez M, Duran X, Carreras A, et al. Multitrait genome association analysis identifies new susceptibility genes for human anthropometric variation in the GCAT cohort. J Med Genet. 2018;55:765–78.
Delaneau O, Marchini J, Zagury J-F. A linear complexity phasing method for thousands of genomes. Nat Methods. 2012;9:179–81.
Howie BN, Donnelly P, Marchini J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 2009;5:e1000529. Available from: http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2689936&tool=pmcentrez&rendertype=abstract. Cited 2014 Jan 22.
Article PubMed PubMed Central Google Scholar
The 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature. 2015;526:68–74.
Huang J, Howie B, Mccarthy S, Memari Y, Walter K, Min JL, et al. Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel. Nat Commun Nature Publishing Group. 2015;6:8111.
Deelen P, Menelaou A, Van Leeuwen EM, Kanterakis A, Van Dijk F, Medina-gomez C, et al. Improved imputation quality of low-frequency and rare variants in European samples using the ‘ Genome of The Netherlands.’ Eur J Hum Genet. 2014;22:1321–6.
Article CAS PubMed PubMed Central Google Scholar
McCarthy S, Das S, Kretzschmar W, Delaneau O, Wood AR, Teumer A, et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat Genet. 2016;48:1279–83.
留言 (0)