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  • Buniello, A. et al. The NHGRI-EBI GWAS Catalog of published genome-wide association studies, targeted arrays and summary statistics 2019. Nucleic Acids Res. 47, D1005–D1012 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Zhang, F. & Lupski, J. R. Non-coding genetic variants in human disease. Hum. Mol. Genet. 24, R102–R110 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Tak, Y. G. & Farnham, P. J. Making sense of GWAS: using epigenomics and genome engineering to understand the functional relevance of SNPs in non-coding regions of the human genome. Epigenetics chromatin 8, 57 (2015).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • Musunuru, K. et al. From noncoding variant to phenotype via SORT1 at the 1p13 cholesterol locus. Nature 466, 714–719 (2010).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Kumar, V. et al. Human disease-associated genetic variation impacts large intergenic non-coding RNA expression. PLoS Genet. 9, e1003201 (2013).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Lonsdale, J. et al. The genotype-tissue expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

    CAS 
    Article 

    Google Scholar
     

  • GTEx Consortium. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).

    PubMed Central 
    Article 

    Google Scholar
     

  • GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Hormozdiari, F. et al. Leveraging molecular quantitative trait loci to understand the genetic architecture of diseases and complex traits. Nat. Genet. 50, 1041–1047 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Gold, L. et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS One 5, e15004 (2010).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Williams, S. A. et al. Plasma protein patterns as comprehensive indicators of health. Nat. Med. 25, 1851–1857 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Anderson, N. L. & Anderson, N. G. The human plasma proteome: history, character, and diagnostic prospects. Mol. Cell. Proteom. 1, 845–867 (2002).

    CAS 
    Article 

    Google Scholar
     

  • Folkersen, L. et al. Genomic and drug target evaluation of 90 cardiovascular proteins in 30,931 individuals. Nat. Metab. 2, 1135–1148 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Emilsson, V. et al. Coding and regulatory variants affect serum protein levels and common disease. Preprint at BioRxiv https://doi.org/10.1101/2020.05.06.080440 (2021).

  • Yao, C. et al. Genome‐wide mapping of plasma protein QTLs identifies putatively causal genes and pathways for cardiovascular disease. Nat. Commun. 9, 1–11 (2018).

    Article 
    CAS 

    Google Scholar
     

  • Pietzner, M. et al. Genetic architecture of host proteins involved in SARS-CoV-2 infection. Nat. Commun. 11, 1–14 (2020).

    Article 
    CAS 

    Google Scholar
     

  • Zhou, S. et al. A Neanderthal OAS1 isoform protects individuals of European ancestry against COVID-19 susceptibility and severity. Nat. Med. 27, 659–667 (2021).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Yang, C. et al. Genomic and multi-tissue proteomic integration for understanding the biology of disease and other complex traits. Preprint at medRxiv https://doi.org/10.1101/2020.06.25.20140277 (2020).

  • He, B., Shi, J., Wang, X., Jiang, H. & Zhu, H. Genome-wide pQTL analysis of protein expression regulatory networks in the human liver. BMC Biol. 18, 97 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Wingo, A. P. Integrating human brain proteomes with genome-wide association data implicates new proteins in Alzheimer’s disease pathogenesis. Nat. Genet. 53, 143–146 (2021).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Bretherick, A. D. et al. Linking protein to phenotype with Mendelian randomization detects 38 proteins with causal roles in human diseases and traits. PLoS Genet. 16, e1008785 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Suhre, K. et al. Connecting genetic risk to disease end points through the human blood plasma proteome. Nat. Commun. 8, 14357 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Zheng, J. et al. Phenome-wide Mendelian randomization mapping the influence of the plasma proteome on complex diseases. Nat. Genet. 52, 1122–1131 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Gamazon, E. R. et al. A gene-based association method for mapping traits using reference transcriptome data. Nat. Genet. 47, 1091 (2015).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. Am. J. Epidemiol. 129, 687–702 (1989).

    Article 

    Google Scholar
     

  • Pietzner, M. et al. Cross-platform proteomics to advance genetic prioritisation strategies. Preprint at bioRxiv https://doi.org/10.1101/2021.03.18.435919 (2021).

  • Hemani, G., Bowden, J. & Davey Smith, G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum. Mol. Genet. 27, R195–R208 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Tin, A. et al. Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels.Nat. Genet. 51, 1459–1474 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Stegle, O., Parts, L., Durbin, R. & Winn, J. A Bayesian framework to account for complex non-genetic factors in gene expression levels greatly increases power in eQTL studies. PLoS Comput. Biol. 6, e1000770 (2010).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • 1000 Genomes Project Consortium. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Article 
    CAS 

    Google Scholar
     

  • Gassman, J. J. et al. Design and statistical aspects of the African American Study of Kidney Disease and Hypertension (AASK). J. Am. Soc. Nephrol. 14, S154–S165 (2003).

    PubMed 
    Article 

    Google Scholar
     

  • Park, J. et al. Distribution of allele frequencies and effect sizes and their interrelationships for common genetic susceptibility variants. Proc. Natl Acad. Sci. USA 108, 18026–18031 (2011).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Delaneau, O. et al. A complete tool set for molecular QTL discovery and analysis. Nat. Commun. 8, 15452 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T. & Delaneau, O. Fast and efficient QTL mapper for thousands of molecular phenotypes. Bioinformatics 32, 1479–1485 (2016).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Wang, G., Sarkar, A., Carbonetto, P. & Stephens, M. A simple new approach to variable selection in regression, with application to genetic fine mapping. J. R. Stat. Soc. Ser. B Stat. Methodol. 82, 1273–1300 (2020).

    Article 

    Google Scholar
     

  • Morris, A. P. Transethnic meta-analysis of genomewide association studies. Genet. Epidemiol. 35, 809–822 (2011).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • He, Z., Song, D., van Zalen, S. & Russell, J. E. Structural determinants of human ζ-globin mRNA stability. J. Hematol. Oncol. 7, 35 (2014).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • He, Z. & Russell, J. E. Effect of ζ-globin substitution on the O2-transport properties of Hb S in vitro and in vivo. Biochem. Biophys. Res. Commun. 325, 1376–1382 (2004).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Lafferty, J. D. et al. A multicenter trial of the effectiveness of ζ-globin enzyme-linked immunosorbent assay and hemoglobin H inclusion body screening for the detection of α0-thalassemia trait. Am. J. Clin. Pathol. 129, 309–315 (2008).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Watanabe, K. et al. A global overview of pleiotropy and genetic architecture in complex traits. Nat. Genet. 51, 1339–1348 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Astle, W. J. et al. The allelic landscape of human blood cell trait variation and links to common complex disease. Cell 167, 1415–1429. e19 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

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

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Wainberg, M. et al. Opportunities and challenges for transcriptome-wide association studies. Nat. Genet. 51, 592–599 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Anderson, L. & Seilhamer, J. A comparison of selected mRNA and protein abundances in human liver. Electrophoresis 18, 533–537 (1997).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Mancuso, N. et al. Integrating gene expression with summary association statistics to identify genes associated with 30 complex traits. Am. J. Hum. Genet. 100, 473–487 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Köttgen, A. et al. Genome-wide association analyses identify 18 new loci associated with serum urate concentrations. Nat. Genet. 45, 145–154 (2013).

    PubMed 
    Article 
    CAS 

    Google Scholar
     

  • Janssen, C. A. et al. Anakinra for the treatment of acute gout flares: a randomized, double-blind, placebo-controlled, active-comparator, non-inferiority trial. Rheumatology 58, 1344–1352 (2019).

    CAS 
    Article 

    Google Scholar
     

  • Wojcik, G. L. et al. Genetic analyses of diverse populations improves discovery for complex traits. Nature 570, 514–518 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Pietzner, M. et al. Mapping the proteo-genomic convergence of human diseases. Science 374, eabj1541 (2021).

    PubMed 
    Article 
    CAS 

    Google Scholar
     

  • Ferkingstad, E. et al. Large-scale integration of the plasma proteome with genetics and disease. Nat. Genet. 53, 1712–1721 (2021).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Durinck, S. et al. BioMart and Bioconductor: a powerful link between biological databases and microarray data analysis. Bioinformatics 21, 3439–3440 (2005).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • Kowalski, M. H. et al. Use of >100,000 NHLBI Trans-Omics for Precision Medicine (TOPMed) Consortium whole genome sequences improves imputation quality and detection of rare variant associations in admixed African and Hispanic/Latino populations. PLoS Genet. 15, e1008500 (2019).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • Howie, B. N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    CAS 
    PubMed 
    Article 

    Google Scholar
     

  • McLaren, W. et al. The ensembl variant effect predictor. Genome Biol. 17, 122 (2016).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • Karczewski, K. J. et al. The mutational constraint spectrum quantified from variation in 141,456 humans. Nature 581, 434–443 (2020).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Zerbino, D. R., Wilder, S. P., Johnson, N., Juettemann, T. & Flicek, P. R. The ensembl regulatory build. Genome Biol. 16, 56 (2015).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • Wen, X. Molecular QTL discovery incorporating genomic annotations using Bayesian false discovery rate control. Ann. Appl. Stat. 10, 1619–1638 (2016).

    Article 

    Google Scholar
     

  • Schaid, D. J., Chen, W. & Larson, N. B. From genome-wide associations to candidate causal variants by statistical fine-mapping. Nat. Rev. Genet. 19, 491–504 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Barbeira, A. N. et al. Exploring the phenotypic consequences of tissue specific gene expression variation inferred from GWAS summary statistics. Nat. Commun. 9, 1–20 (2018).

    CAS 
    Article 

    Google Scholar
     

  • Wang, Y. et al. Therapeutic target database 2020: enriched resource for facilitating research and early development of targeted therapeutics. Nucleic Acids Res. 48, D1031–D1041 (2020).

    CAS 
    PubMed 

    Google Scholar
     

  • Finan, C. The druggable genome and support for target identification and validation in drug development. Sci. Transl. Med. 9, eaag1166 (2017).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar
     

  • Navarro Gonzalez, J. et al. The UCSC Genome Browser database: 2021 update. Nucleic Acids Res. 49, D1046–D1057 (2021).

    PubMed 
    Article 
    CAS 

    Google Scholar
     

  • Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar
     

  • Zhang, J. & Dutta, D. Jingning-Zhang/PlasmaProtein: Custom code for: Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies. Zenodo https://doi.org/10.5281/zenodo.6332981 (2022).

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