Ndumele, C. E. et al. Cardiovascular-kidney-metabolic health: a presidential advisory from the American Heart Association. Circulation 148, 1606–1635 (2023).
Google Scholar
Sullivan, K. M. & Susztak, K. Unravelling the complex genetics of common kidney diseases: from variants to mechanisms. Nat. Rev. Nephrol. 16, 628–640 (2020).
Google Scholar
Liu, H. et al. Epigenomic and transcriptomic analyses define core cell types, genes and targetable mechanisms for kidney disease. Nat. Genet. 54, 950–962 (2022).
Google Scholar
Sheng, X. et al. Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments. Nat. Genet. 53, 1322–1333 (2021).
Google Scholar
Qiu, C. et al. Renal compartment-specific genetic variation analyses identify new pathways in chronic kidney disease. Nat. Med. 24, 1721–1731 (2018).
Google Scholar
Ko, Y. A. et al. Genetic-variation-driven gene-expression changes highlight genes with important functions for kidney disease. Am. J. Hum. Genet. 100, 940–953 (2017).
Google Scholar
Eales, J. M. et al. Uncovering genetic mechanisms of hypertension through multi-omic analysis of the kidney. Nat. Genet. 53, 630–637 (2021).
Google Scholar
GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).
Sun, B. B. et al. Plasma proteomic associations with genetics and health in the UK Biobank. Nature 622, 329–338 (2023).
Google Scholar
Zhang, J. et al. Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies. Nat. Genet. 54, 593–602 (2022).
Google Scholar
Ferkingstad, E. et al. Large-scale integration of the plasma proteome with genetics and disease. Nat. Genet. 53, 1712–1721 (2021).
Google Scholar
Xu, X. et al. Genetic imputation of kidney transcriptome, proteome and multi-omics illuminates new blood pressure and hypertension targets. Nat. Commun. 15, 2359 (2024).
Google Scholar
Ritchie, S. C. et al. Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases. Nat. Metab. 3, 1476–1483 (2021).
Google Scholar
Inker, L. A. et al. New creatinine- and cystatin C–based equations to estimate GFR without race. N. Engl. J. Med. 385, 1737–1749 (2021).
Google Scholar
Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).
Google Scholar
McLaren, W. et al. The Ensembl Variant Effect Predictor. Genome Biol. 17, 122 (2016).
Google Scholar
Enroth, S., Hallmans, G., Grankvist, K. & Gyllensten, U. Effects of long-term storage time and original sampling month on biobank plasma protein concentrations. EBioMedicine 12, 309–314 (2016).
Google Scholar
Chesnaye, N. C., Carrero, J. J., Hecking, M. & Jager, K. J. Differences in the epidemiology, management and outcomes of kidney disease in men and women. Nat. Rev. Nephrol. 20, 7–20 (2024).
Google Scholar
Dhindsa, R. S. et al. Rare variant associations with plasma protein levels in the UK Biobank. Nature 622, 339–347 (2023).
Google Scholar
Shao, X. et al. CellTalkDB: a manually curated database of ligand–receptor interactions in humans and mice. Brief. Bioinform. 22, bbaa269 (2021).
Eldjarn, G. H. et al. Large-scale plasma proteomics comparisons through genetics and disease associations. Nature 622, 348–358 (2023).
Google Scholar
Hirohama, D. et al. Unbiased human kidney tissue proteomics identifies matrix metalloproteinase 7 as a kidney disease biomarker. J. Am. Soc. Nephrol. 34, 1279–1291 (2023).
Google Scholar
Battle, A. et al. Genomic variation. Impact of regulatory variation from RNA to protein. Science 347, 664–667 (2015).
Google Scholar
Assum, I. et al. Tissue-specific multi-omics analysis of atrial fibrillation. Nat. Commun. 13, 441 (2022).
Google Scholar
He, B., Shi, J., Wang, X., Jiang, H. & Zhu, H. J. Genome-wide pQTL analysis of protein expression regulatory networks in the human liver. BMC Biol. 18, 97 (2020).
Google Scholar
Pietzner, M. et al. Synergistic insights into human health from aptamer- and antibody-based proteomic profiling. Nat. Commun. 12, 6822 (2021).
Google Scholar
Visscher, P. M. et al. 10 years of GWAS discovery: biology, function, and translation. Am. J. Hum. Genet. 101, 5–22 (2017).
Google Scholar
Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).
Google Scholar
Liu, H. et al. Kidney multiome-based genetic scorecard reveals convergent coding and regulatory variants. Science 387, eadp4753 (2025).
Google Scholar
Giambartolomei, C. et al. A Bayesian framework for multiple trait colocalization from summary association statistics. Bioinformatics 34, 2538–2545 (2018).
Google Scholar
Guan, Y. et al. A single genetic locus controls both expression of DPEP1/CHMP1A and kidney disease development via ferroptosis. Nat. Commun. 12, 5078 (2021).
Google Scholar
Ku, E., Lee, B. J., Wei, J. & Weir, M. R. Hypertension in CKD: Core Curriculum 2019. Am. J. Kidney Dis. 74, 120–131 (2019).
Google Scholar
Klag, M. J. et al. Blood pressure and end-stage renal disease in men. N. Engl. J. Med. 334, 13–18 (1996).
Google Scholar
Jafar, T. H. et al. Progression of chronic kidney disease: the role of blood pressure control, proteinuria, and angiotensin-converting enzyme inhibition: a patient-level meta-analysis. Ann. Intern. Med. 139, 244–252 (2003).
Google Scholar
Zhu, Z. et al. Causal associations between risk factors and common diseases inferred from GWAS summary data. Nat. Commun. 9, 224 (2018).
Google Scholar
Raal, F. J. et al. Evinacumab for homozygous familial hypercholesterolemia. N. Engl. J. Med. 383, 711–720 (2020).
Google Scholar
Shimizugawa, T. et al. ANGPTL3 decreases very low density lipoprotein triglyceride clearance by inhibition of lipoprotein lipase. J. Biol. Chem. 277, 33742–33748 (2002).
Google Scholar
Freshour, S. L. et al. Integration of the Drug–Gene Interaction Database (DGIdb 4.0) with open crowdsource efforts. Nucleic Acids Res. 49, D1144–D1151 (2021).
Google Scholar
Raies, A. et al. DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets. Commun. Biol. 5, 1291 (2022).
Google Scholar
Xie, J. et al. The genetic architecture of membranous nephropathy and its potential to improve non-invasive diagnosis. Nat. Commun. 11, 1600 (2020).
Google Scholar
Pietzner, M. et al. Mapping the proteo-genomic convergence of human diseases. Science 374, eabj1541 (2021).
Google Scholar
Hoxha, E., Reinhard, L. & Stahl, R. A. K. Membranous nephropathy: new pathogenic mechanisms and their clinical implications. Nat. Rev. Nephrol. 18, 466–478 (2022).
Google Scholar
Coenen, M. J. et al. Phospholipase A2 receptor (PLA2R1) sequence variants in idiopathic membranous nephropathy. J. Am. Soc. Nephrol. 24, 677–683 (2013).
Google Scholar
Hoxha, E. et al. Enhanced expression of the M-type phospholipase A2 receptor in glomeruli correlates with serum receptor antibodies in primary membranous nephropathy. Kidney Int. 82, 797–804 (2012).
Google Scholar
Sukocheva, O. et al. Current insights into functions of phospholipase A2 receptor in normal and cancer cells: more questions than answers. Semin. Cancer Biol. 56, 116–127 (2019).
Google Scholar
Griveau, A. et al. Targeting the phospholipase A2 receptor ameliorates premature aging phenotypes. Aging Cell 17, e12835 (2018).
Google Scholar
Yang, B., Shen, F., Zhu, Y. & Cai, H. Downregulating ANGPTL3 by miR-144-3p promoted TGF-β1-induced renal interstitial fibrosis via activating PI3K/AKT signaling pathway. Heliyon 10, e24204 (2024).
Google Scholar
Ma, Y. et al. Podocyte protection by Angptl3 knockout via inhibiting ROS/GRP78 pathway in LPS-induced acute kidney injury. Int. Immunopharmacol. 105, 108549 (2022).
Google Scholar
Sarwar, N. et al. Interleukin-6 receptor pathways in coronary heart disease: a collaborative meta-analysis of 82 studies. Lancet 379, 1205–1213 (2012).
Google Scholar
Swerdlow, D. I. et al. The interleukin-6 receptor as a target for prevention of coronary heart disease: a Mendelian randomisation analysis. Lancet 379, 1214–1224 (2012).
Google Scholar
Du, X. Y. et al. The potential mechanism of INHBC and CSF1R in diabetic nephropathy. Eur. Rev. Med. Pharm. Sci. 24, 1970–1978 (2020).
Yang, H., Lian, D., Zhang, X., Li, H. & Xin, G. Key genes and signaling pathways contribute to the pathogensis of diabetic nephropathy. Iran. J. Kidney Dis. 13, 87–97 (2019).
Google Scholar
Schlosser, P. et al. Transcriptome- and proteome-wide association studies nominate determinants of kidney function and damage. Genome Biol. 24, 150 (2023).
Google Scholar
Graham, S. E. et al. The power of genetic diversity in genome-wide association studies of lipids. Nature 600, 675–679 (2021).
Google Scholar
Gold, L. et al. Aptamer-based multiplexed proteomic technology for biomarker discovery. PLoS ONE 5, e15004 (2010).
Google Scholar
Niewczas, M. A. et al. A signature of circulating inflammatory proteins and development of end-stage renal disease in diabetes. Nat. Med. 25, 805–813 (2019).
Google Scholar
Li, G. X. et al. Comprehensive proteogenomic characterization of rare kidney tumors. Cell Rep. Med. 5, 101547 (2024).
Google Scholar
Assarsson, E. et al. Homogenous 96-plex PEA immunoassay exhibiting high sensitivity, specificity, and excellent scalability. PLoS ONE 9, e95192 (2014).
Google Scholar
O’Brien, J. J. et al. A data analysis framework for combining multiple batches increases the power of isobaric proteomics experiments. Nat. Methods 21, 290–300 (2024).
Google Scholar
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Google Scholar
Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Google Scholar
Picard Tools. Broad Institute. (2018).
McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).
Google Scholar
Sherry, S. T. et al. dbSNP: the NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311 (2001).
Google Scholar
Abecasis, G. R. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).
Google Scholar
International HapMap Consortium. The International HapMap Project. Nature 426, 789–796 (2003).
Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Google Scholar
Mills, R. E. et al. An initial map of insertion and deletion (INDEL) variation in the human genome. Genome Res. 16, 1182–1190 (2006).
Google Scholar
Price, A. L. et al. Long-range LD can confound genome scans in admixed populations. Am. J. Hum. Genet. 83, 132–135; author reply 135–139 (2008).
Google Scholar
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
Google Scholar
Delaneau, O. et al. A complete tool set for molecular QTL discovery and analysis. Nat. Commun. 8, 15452 (2017).
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, 500–507 (2012).
Google Scholar
Storey, J. D. & Tibshirani, R. Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA 100, 9440–9445 (2003).
Google Scholar
Yang, J. et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565–569 (2010).
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).
Google Scholar
Wingo, A. P. et al. Sex differences in brain protein expression and disease. Nat. Med. 29, 2224–2232 (2023).
Google Scholar
Lee, S., Wu, M. C. & Lin, X. Optimal tests for rare variant effects in sequencing association studies. Biostatistics 13, 762–775 (2012).
Google Scholar
Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff. Fly (Austin) 6, 80–92 (2012).
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, S1–3 (2012).
Google Scholar
Xu, F. et al. Genome-wide genotype-serum proteome mapping provides insights into the cross-ancestry differences in cardiometabolic disease susceptibility. Nat. Commun. 14, 896 (2023).
Google Scholar
Sherman, B. T. et al. DAVID: a web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res. 50, W216–W221 (2022).
Google Scholar
Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
Google Scholar
Evangelou, E. et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat. Genet. 50, 1412–1425 (2018).
Google Scholar
Wallace, C. A more accurate method for colocalisation analysis allowing for multiple causal variants. PLoS Genet. 17, e1009440 (2021).
Google Scholar
Wang, C. et al. Genetic architecture of cerebrospinal fluid and brain metabolite levels and the genetic colocalization of metabolites with human traits. Nat. Genet. 56, 2685–2695 (2024).
Google Scholar
Zhu, Z. et al. Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets. Nat. Genet. 48, 481–487 (2016).
Google Scholar
Wu, Y. et al. Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat. Commun. 9, 918 (2018).
Google Scholar
Burgess, S., Davies, N. M. & Thompson, S. G. Bias due to participant overlap in two-sample Mendelian randomization. Genet. Epidemiol. 40, 597–608 (2016).
Google Scholar
Xue, A. et al. Unravelling the complex causal effects of substance use behaviours on common diseases. Commun. Med. (Lond.) 4, 43 (2024).
Google Scholar
Karczewski, K. J. et al. Pan-UK Biobank GWAS improves discovery, analysis of genetic architecture, and resolution into ancestry-enriched effects. Preprint at medRxiv (2024).
Pulit, S. L. et al. Meta-analysis of genome-wide association studies for body fat distribution in 694 649 individuals of European ancestry. Hum. Mol. Genet. 28, 166–174 (2019).
Google Scholar
Vujkovic, M. et al. A multiancestry genome-wide association study of unexplained chronic ALT elevation as a proxy for nonalcoholic fatty liver disease with histological and radiological validation. Nat. Genet. 54, 761–771 (2022).
Google Scholar
Haas, M. E. et al. Machine learning enables new insights into genetic contributions to liver fat accumulation. Cell Genom. 1, 100066 (2021).
Suzuki, K. et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature 627, 347–357 (2024).
Google Scholar
Chen, J. et al. The trans-ancestral genomic architecture of glycemic traits. Nat. Genet. 53, 840–860 (2021).
Google Scholar
Wuttke, M. et al. A catalog of genetic loci associated with kidney function from analyses of a million individuals. Nat. Genet. 51, 957–972 (2019).
Google Scholar
Aragam, K. G. et al. Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants. Nat. Genet. 54, 1803–1815 (2022).
Google Scholar
Levin, M. G. et al. Genome-wide association and multi-trait analyses characterize the common genetic architecture of heart failure. Nat. Commun. 13, 6914 (2022).
Google Scholar
Mishra, A. et al. Stroke genetics informs drug discovery and risk prediction across ancestries. Nature 611, 115–123 (2022).
Google Scholar
Hartiala, J. A. et al. Genome-wide analysis identifies novel susceptibility loci for myocardial infarction. Eur. Heart J. 42, 919–933 (2021).
Google Scholar
van Zuydam, N. R. et al. Genome-wide association study of peripheral artery disease. Circ. Genom. Precis. Med. 14, e002862 (2021).
Google Scholar
Pirruccello, J. P. et al. Analysis of cardiac magnetic resonance imaging in 36,000 individuals yields genetic insights into dilated cardiomyopathy. Nat. Commun. 11, 2254 (2020).
Google Scholar
Nauffal, V. et al. Genetics of myocardial interstitial fibrosis in the human heart and association with disease. Nat. Genet. 55, 777–786 (2023).
Google Scholar
Sakaue, S. et al. A cross-population atlas of genetic associations for 220 human phenotypes. Nat. Genet. 53, 1415–1424 (2021).
Google Scholar
Han, S. K. et al. Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs. Nat. Commun. 14, 2229 (2023).
Google Scholar
Hemani, G., Tilling, K. & Davey Smith, G. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 13, e1007081 (2017).
Google Scholar
Foley, C. N. et al. A fast and efficient colocalization algorithm for identifying shared genetic risk factors across multiple traits. Nat. Commun. 12, 764 (2021).
Google Scholar
Gill, D. et al. Common pitfalls in drug target Mendelian randomization and how to avoid them. BMC Med. 22, 473 (2024).
Google Scholar
Zuber, V. et al. Combining evidence from Mendelian randomization and colocalization: review and comparison of approaches. Am. J. Hum. Genet. 109, 767–782 (2022).
Google Scholar
Koplev, S. et al. A mechanistic framework for cardiometabolic and coronary artery diseases. Nat. Cardiovasc. Res. 1, 85–100 (2022).
Google Scholar
Brotman, S. M. et al. Adipose tissue eQTL meta-analysis highlights the contribution of allelic heterogeneity to gene expression regulation and cardiometabolic traits. Nat. Genet. 57, 180–192 (2025).
Google Scholar
de Klein, N. et al. Brain expression quantitative trait locus and network analyses reveal downstream effects and putative drivers for brain-related diseases. Nat. Genet. 55, 377–388 (2023).
Google Scholar
Abedini, A. et al. Single-cell multi-omic and spatial profiling of human kidneys implicates the fibrotic microenvironment in kidney disease progression. Nat. Genet. 56, 1712–1724 (2024).
Google Scholar
Sriworarat, C. et al. Performant web-based interactive visualization tool for spatially-resolved transcriptomics experiments. Biol. Imaging 3, e15 (2023).
Google Scholar
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:max_bytes(150000):strip_icc()/VWH-GettyImages-2157903338-69370a8ef92e449b8958c22cab26dd28.jpg "What Happens to Your Gut Health When You Eat Wild Blueberries Regularly")