Platform-dependent effects of genetic variants on plasma APOL1
Open accessPreprint — published as 10.1016/j.isci.2026.114953
Abstract
Mutations in apolipoprotein L1 (APOL1) are strongly associated with protection against parasitic infections and increased risk of kidney disease in individuals of African ancestry. To better understand the mechanisms underlying APOL1-related pathologies, we examined genetic drivers of circulating APOL1 in individuals of African and European ancestry from four independent cohorts (UK Biobank, AASK, deCODE, and Health ABC) using three proteomic technologies (Olink, SomaLogic, and mass spectrometry). We found that disease-associated APOL1 G1 and G2 variants are strong cis -pQTLs for plasma APOL1 measured by Olink and SomaLogic, but not mass spectrometry. Critically, the direction of variant effects differed between proteomic platforms, being positive with Olink and negative with SomaLogic. We identified an additional APOL1 missense variant (rs2239785), common in Europeans, exhibiting the same platform-dependent directional discrepancy. Furthermore, variants in the kallikrein-kinin system (KKS), involving KLKB1 , F12 , and KNG1 , and their genetic interactions showed strong trans -pQTL effects on APOL1 measured by Olink, but not SomaLogic. These platform-dependent discrepancies raise the possibility that both intrinsic APOL1 mutations and extrinsic KKS activity induce conformational changes in the APOL1 protein that are differentially recognized by the proteomic platforms.
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Several cis-pQTL signals driven by PAVs-most commonly missense variants-were observed on each platform.
“While the majority of cis-pQTL variants were non-coding on both platforms (Supplementary Fig. 8), we observe several cis-pQTL signals driven by protein altering variants (PAVs—most commonly missense variants) on each platform, raising concerns as to whether the signal captures abundances or differences in isoform, as has been shown in other studies18,24,25.”
From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [25]
The Introduction reports that APOL1 G1/G2 mutations have pQTL effects on both Olink and SomaLogic, and the Discussion describes a similar effect for V1. The paper supports protein-altering variants affecting measurements on both platforms, but its APOL1-focused analysis does not establish that missense variants are the most common drivers of cis-pQTL signals generally. (LLM, conf=0.84)
Experimental — accuracy not yet measuredStudies incorporating comparisons to MS protein measures, measures of gene transcript abundance, and/or functional validation have established cases where platform-discordant PAV cis-pQTL reflect differences in probe binding to genetically encoded isoforms, including for uromodulin and apolipoprotein-1.
“While PAVs may certainly alter a protein’s overall abundance through impacts on protein stability and other mechanisms, studies incorporating comparisons to MS protein measures, measures of gene transcript abundance, and/or functional validation have established cases where platform-discordant PAV cis-pQTL reflect differences in probe binding to genetically encoded isoforms, including for uromodul”
From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [25]
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Experimental — accuracy not yet measuredPAV-driven cis-pQTL signals raise concerns as to whether the signal captures protein abundances or differences in isoform.
“While the majority of cis-pQTL variants were non-coding on both platforms (Supplementary Fig. 8), we observe several cis-pQTL signals driven by protein altering variants (PAVs—most commonly missense variants) on each platform, raising concerns as to whether the signal captures abundances or differences in isoform, as has been shown in other studies18,24,25.”
From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [25]
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Research works printed but not linked: 70
- Adamson W.E. (2024) ‘ APOL1 variants G1, G2 and N264K affect APOL1 plasma protein concentration: a UK Biobank study’, medRxiv. Available at: 10.1101/2024.02.28.24303461. · DOI
- Andrews M. (2022) ‘ Variant APOL1 protein in plasma associates with larger particles in humans and mouse models of kidney injury’, PloS one, 17(10), p. e0276649. 36279295 PMC9591058
- Appel L.J. (2003) ‘ The rationale and design of the AASK cohort study’, Journal of the American Society of Nephrology: JASN, 14(7 Suppl 2), pp. S166– 72. 12819323
- Backman J.D. (2021) ‘ Exome sequencing and analysis of 454,787 UK Biobank participants’, Nature, 599(7886), pp. 628– 634. 34662886 PMC8596853
- Bailey J.N.C. (2014) ‘ Analysis of coding variants identified from exome sequencing resources for association with diabetic and non-diabetic nephropathy in African Americans’, Human genetics, 133(6), pp. 769– 779. 24385048 PMC4024071
- Bajaj A. (2020) ‘ Phenome-wide association analysis suggests the APOL1 linked disease spectrum primarily drives kidney-specific pathways’, Kidney international, 97(5), pp. 1032– 1041. 32247630 PMC7265573
- Baryshnikova A. (2013) ‘ Genetic interaction networks: toward an understanding of heritability’, Annual review of genomics and human genetics, 14, pp. 111– 133. 23808365
- Beckerman P. (2017) ‘ Transgenic expression of human APOL1 risk variants in podocytes induces kidney disease in mice’, Nature medicine, 23(4), pp. 429– 438. PMC5603285 28218918
- Benner C. (2016) ‘ FINEMAP: efficient variable selection using summary data from genome-wide association studies’, Bioinformatics (Oxford, England), 32(10), pp. 1493– 1501. 26773131 PMC4866522
- Bostrom M.A. (2012) ‘ Genetic association and gene-gene interaction analyses in African American dialysis patients with nondiabetic nephropathy’, American journal of kidney diseases: the official journal of the National Kidney Foundation, 59(2), pp. 210– 221. 22119407 PMC3259209
- Bruggeman L.A. (2014) ‘ Plasma apolipoprotein L1 levels do not correlate with CKD’, Journal of the American Society of Nephrology: JASN, 25(3), pp. 634– 644. 24231663 PMC3935593
- Bycroft C. (2018) ‘ The UK Biobank resource with deep phenotyping and genomic data’, Nature, 562(7726), pp. 203– 209. 30305743 PMC6786975
- Calafell F. (2010) ‘ Sequence variation and genetic evolution at the human F12 locus: mapping quantitative trait nucleotides that influence FXII plasma levels’, Human molecular genetics, 19(3), pp. 517– 525. 19933701 PMC2798724
- Chee Y.L. (2014) ‘ Coagulation’, The journal of the Royal College of Physicians of Edinburgh, 44(1), pp. 42– 45. 24995447
- Cooper A. (2017) ‘ renal risk variants have contrasting resistance and susceptibility associations with African trypanosomiasis’, eLife, 6. Available at: 10.7554/eLife.25461. PMC5495568 28537557 · DOI
- Cuypers B. (2016) ‘ Apolipoprotein L1 Variant Associated with Increased Susceptibility to Trypanosome Infection’, mBio, 7(2), pp. e02198– 15. 27073096 PMC4959518
- Egbuna O. (2023) ‘ Inaxaplin for Proteinuric Kidney Disease in Persons with Two Variants’, The New England journal of medicine, 388(11), pp. 969– 979. 36920755
- Eldjarn G.H. (2023) ‘ Large-scale plasma proteomics comparisons through genetics and disease associations’, Nature, 622(7982), pp. 348– 358. 37794188 PMC10567571
- Elias J.E. and Gygi S.P. (2007) ‘ Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry’, Nature methods, 4(3), pp. 207– 214. 17327847
- Eng J.K., McCormack A.L. and Yates J.R. (1994) ‘ An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database’, Journal of the American Society for Mass Spectrometry, 5(11), pp. 976– 989. 24226387
- España F., Berrettini M. and Griffin J.H. (1989) ‘ Purification and characterization of plasma protein C inhibitor’, Thrombosis research, 55(3), pp. 369– 384. 2551064
- Ferguson R. (1991) ‘ End-stage renal disease and race: an overview and perspective’, Journal of the National Medical Association, 83(9), pp. 794– 798. 1942113 PMC2627098
- Ferkingstad E. (2021) ‘ Large-scale integration of the plasma proteome with genetics and disease’, Nature genetics, 53(12), pp. 1712– 1721. 34857953
- Fredriksson S. (2002) ‘ Protein detection using proximity-dependent DNA ligation assays’, Nature biotechnology, 20(5), pp. 473– 477. 11981560
- Friedman D.J. and Pollak M.R. (2011) ‘ Genetics of kidney failure and the evolving story of APOL1’, The Journal of clinical investigation, 121(9), pp. 3367– 3374. 21881214 PMC3163957
- Friedman D.J. and Pollak M.R. (2020) ‘ and Kidney Disease: From Genetics to Biology’, Annual review of physiology, 82, pp. 323– 342. 31710572
- Gaun A. (2021) ‘ Automated 16-Plex Plasma Proteomics with Real-Time Search and Ion Mobility Mass Spectrometry Enables Large-Scale Profiling in Naked Mole-Rats and Mice’, Journal of proteome research, 20(2), pp. 1280– 1295. 33499602
- Gbadegesin R.A. (2025) ‘ Bi- and Monoallelic Variants and Chronic Kidney Disease in West Africans’, The New England journal of medicine, 392(3), pp. 228– 238. 39465900 PMC11735277
- Genovese G. (2010) ‘ Association of trypanolytic ApoL1 variants with kidney disease in African Americans’, Science (New York, N.Y.), 329(5993), pp. 841– 845. 20647424 PMC2980843
- Giovinazzo J.A. (2020) ‘ Apolipoprotein L-1 renal risk variants form active channels at the plasma membrane driving cytotoxicity’, eLife, 9. Available at: 10.7554/eLife.51185. PMC7292663 32427098 · DOI
- Gold L. (2010) ‘ Aptamer-based multiplexed proteomic technology for biomarker discovery’, PloS one, 5(12), p. e15004. 21165148 PMC3000457
- Gupta Y. (2023) ‘ Strong protective effect of the APOL1 p.N264K variant against G2-associated focal segmental glomerulosclerosis and kidney disease’, Nature communications, 14(1), p. 7836. PMC10689833 38036523
- Hardwick R.J. (2014) ‘ Haptoglobin (HP) and Haptoglobin-related protein (HPR) copy number variation, natural selection, and trypanosomiasis’, Human genetics, 133(1), pp. 69– 83. 24005574 PMC3898332
- Herrington W.G. (2017) ‘ Body-mass index and risk of advanced chronic kidney disease: Prospective analyses from a primary care cohort of 1.4 million adults in England’, PloS one, 12(3), p. e0173515. 28273171 PMC5342319
- Huttlin E.L. (2010) ‘ A tissue-specific atlas of mouse protein phosphorylation and expression’, Cell, 143(7), pp. 1174– 1189. 21183079 PMC3035969
- Karczewski K.J. (2024) ‘ Pan-UK Biobank GWAS improves discovery, analysis of genetic architecture, and resolution into ancestry-enriched effects’. Available at: 10.1101/2024.03.13.24303864. · DOI
- Katsuda I. (2007) ‘ A new type of plasma prekallikrein deficiency associated with homozygosity for Gly104Arg and Asn124Ser in apple domain 2 of the heavy-chain region’, European journal of haematology, 79(1), pp. 59– 68. 17598838
- Kozlitina J. (2016) ‘ Plasma Levels of Risk-Variant APOL1 Do Not Associate with Renal Disease in a Population-Based Cohort’, Journal of the American Society of Nephrology: JASN, 27(10), pp. 3204– 3219. 27005919 PMC5042671
- Lan X. (2014) ‘ APOL1 risk variants enhance podocyte necrosis through compromising lysosomal membrane permeability’, American journal of physiology. Renal physiology, 307(3), pp. F326– 36. 24899058 PMC4121568
- Laurell M. and Stenflo J. (1989) ‘ Protein C inhibitor from human plasma: characterization of native and cleaved inhibitor and demonstration of inhibitor complexes with plasma kallikrein’, Thrombosis and haemostasis, 62(3), pp. 885– 891. 2556811
- Madhavan S.M. (2017) ‘ APOL1 variants change C-terminal conformational dynamics and binding to SNARE protein VAMP8’, JCI insight, 2(14). Available at: 10.1172/jci.insight.92581. PMC5518555 28724794 · DOI
- Madhavan S.M. and Buck M. (2021) ‘ The Relationship between APOL1 Structure and Function: Clinical Implications’, Kidney360, 2(1), pp. 134– 140. 35368828 PMC8785724
- Madhavan S.M. and O’Toole J.F. (2014) ‘ The biology of APOL1 with insights into the association between APOL1 variants and chronic kidney disease’, Clinical and experimental nephrology, 18(2), pp. 238– 242. 24233469 PMC4022720
- Meijers J.C. (1988) ‘ Inactivation of human plasma kallikrein and factor XIa by protein C inhibitor’, Biochemistry, 27(12), pp. 4231– 4237. 2844223
- Motta G., Juliano L. and Chagas J.R. (2023) ‘ Human plasma kallikrein: roles in coagulation, fibrinolysis, inflammation pathways, and beyond’, Frontiers in physiology, 14, p. 1188816. 37711466 PMC10499198
- Newman A.B. (2023) ‘ The Health, Aging, and Body Composition (Health ABC) Study-Ground-Breaking Science for 25 Years and Counting’, The journals of gerontology. Series A, Biological sciences and medical sciences, 78(11), pp. 2024– 2034. 37431156 PMC10613019
- Nicholas J. (2025) ‘ Cross-Ancestry Comparison of Aptamer and Antibody Proteomics Measures’. Available at: 10.21203/rs.3.rs-5968391/v1. PMC12847899 41571639 · DOI
- Norris Bradley M. (2020) ‘ Quality over quantity: A qualitative, targeted bottom-up proteomics approach to genotyping apolipoprotein L1’, Clinical biochemistry, 82, pp. 58– 65. 32234366
- Obrador G.T. (2017) ‘ Genetic and environmental risk factors for chronic kidney disease’, Kidney international supplements, 7(2), pp. 88– 106. 30675423 PMC6341015
- Okada Y. and Wang Q.S. (2021) ‘ A massive effort links protein-coding gene variants to health’, Nature, 599(7886), pp. 561– 563. 34697483
- Olabisi O.A. (2016) ‘ APOL1 kidney disease risk variants cause cytotoxicity by depleting cellular potassium and inducing stress-activated protein kinases’, Proceedings of the National Academy of Sciences of the United States of America, 113(4), pp. 830– 837. 26699492 PMC4743809
- Pietzner M. (2020) ‘ Genetic architecture of host proteins involved in SARS-CoV-2 infection’, Nature communications, 11(1), p. 6397. PMC7744536 33328453
- Pihl R. (2021) ‘ ITIH4 acts as a protease inhibitor by a novel inhibitory mechanism’, Science advances, 7(2). Available at: 10.1126/sciadv.aba7381. PMC7793589 33523981 · DOI
- Schweppe D.K. (2020) ‘ Full-Featured, Real-Time Database Searching Platform Enables Fast and Accurate Multiplexed Quantitative Proteomics’, Journal of proteome research, 19(5), pp. 2026– 2034. 32126768 PMC7295121
- Sham P.C. and Purcell S.M. (2014) ‘ Statistical power and significance testing in large-scale genetic studies’, Nature reviews. Genetics, 15(5), pp. 335– 346. 24739678
- Shiflett A.M. (2005) ‘ Human high density lipoproteins are platforms for the assembly of multi-component innate immune complexes’, The Journal of biological chemistry, 280(38), pp. 32578– 32585. 16046400
- Sudlow C. (2015) ‘ UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age’, PLoS medicine, 12(3), p. e1001779. 25826379 PMC4380465
- Sun B.B. (2023) ‘ Plasma proteomic associations with genetics and health in the UK Biobank’, Nature, 622(7982), pp. 329– 338. 37794186 PMC10567551
- Surapaneni A. (2022) ‘ Identification of 969 protein quantitative trait loci in an African American population with kidney disease attributed to hypertension’, Kidney international, 102(5), pp. 1167– 1177. 35870639 PMC12997408
- Tin A. (2015) ‘ Hemostatic Factors, APOL1 Risk Variants, and the Risk of ESRD in the Atherosclerosis Risk in Communities Study’, Clinical journal of the American Society of Nephrology: CJASN, 10(5), pp. 784– 790. 25887069 PMC4422243
- Tzur S. (2010) ‘ Missense mutations in the APOL1 gene are highly associated with end stage kidney disease risk previously attributed to the MYH9 gene’, Human genetics, 128(3), pp. 345– 350. 20635188 PMC2921485
- Vanhollebeke B. and Pays E. (2010) ‘ The trypanolytic factor of human serum: many ways to enter the parasite, a single way to kill’, Molecular microbiology, 76(4), pp. 806– 814. 20398209
- Vasquez-Rios G., De Cos M. and Campbell K.N. (2023) ‘ Novel Therapies in -Mediated Kidney Disease: From Molecular Pathways to Therapeutic Options’, Kidney international reports, 8(11), pp. 2226– 2234. 38025220 PMC10658239
- Wang G. (2020) ‘ A simple new approach to variable selection in regression, with application to genetic fine mapping’, Journal of the Royal Statistical Society. Series B, Statistical methodology, 82(5), pp. 1273– 1300. 37220626 PMC10201948
- Weckerle A. (2016) ‘ Characterization of circulating APOL1 protein complexes in African Americans’, Journal of lipid research, 57(1), pp. 120– 130. 26586272 PMC4689339
- Wisniewski P., Gangnus T. and Burckhardt B.B. (2024) ‘ Recent advances in the discovery and development of drugs targeting the kallikrein-kinin system’, Journal of translational medicine, 22(1), p. 388. 38671481 PMC11046790
- Yu H. (1998) ‘ Identification of human plasma kallikrein gene polymorphisms and evaluation of their role in end-stage renal disease’, Hypertension (Dallas, Tex.: 1979), 31(4), pp. 906– 911. 9535413
- Yu H. (2000) ‘ Genomic structure of the human plasma prekallikrein gene, identification of allelic variants, and analysis in end-stage renal disease’, Genomics, 69(2), pp. 225– 234. 11031105
- Zhang D.Y. (2024) ‘ Protein-truncating variant in APOL3 increases chronic kidney disease risk in epistasis with APOL1 risk alleles’, JCI insight, 9(19). Available at: 10.1172/jci.insight.181238. PMC11466179 39163132 · DOI
- Zhang J. (2022) ‘ Plasma proteome analyses in individuals of European and African ancestry identify cis-pQTLs and models for proteome-wide association studies’, Nature genetics, 54(5), pp. 593– 602. 35501419 PMC9236177
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Cross-ancestry comparison of aptamer and antibody protein measures
Nicholas, J. C., Katz, D. H., Tahir, U. A., Debban, C. L., Aguet, F., Blackwell, T., Bowler, R. P., Broadaway, K. A., Chen, J., Clish, C. B., et al. (2026) · Nature Communications · doi:10.1038/s41467-025-67814-1 · Cited by 13 · see citing works
- Authors
- Wang, Q. S., Huang, J., Chan, L., Haste, N., Olsson, N., Gaun, A., McAllister, F., Madhireddy, D., Baruch, A., Cardone, K. M., Kumar, R., Ritchie, M., Susztak, K., Melamud, E., & Baryshnikova, A.
- Affiliations
- Calico Life Sciences (US)
- University of Pennsylvania (US)
- Renal Electrolyte and Hypertension Division, Department of Genetics, Penn/CHOP Kidney Innovation Center, Institute for Diabetes Obesity and Metabolism, University of Pennsylvania, Philadelphia, PA, USA
- Corresponding author
- Baryshnikova, Anastasia
- ORCID
- Venue
- iScience
- Publisher
- openRxiv
- Published
- 2025-02-02
- Volume
- 29
- Issue
- 3
- Pages
- 114953-114953
- ISSN
- 2589-0042 (ISSN-L)
- PMC
- PMC11838367
- OpenAlex
- W7128388111
- Cited by
- 3
- References
- 1
- Citations by year
- 2026: 1 · 2025: 2
- FWCI
- 4.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by-nc-nd
- creativecommons.org/licenses/by/4.0/ (vor)
- Locations
- iScience (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed Central (submittedVersion, cc-by)
- Funders
- National Institutes of Health (N01-AG-6-2103, N01‐AG‐6‐2106, N01-AG-6-2101)
- National Institute on Aging (N01‐AG‐6–2101, R01‐NR012459, R01AG028050, N01AG62106, N01-AG-62103)
- National Institute of Nursing Research (N01-AG-6–2101, N01-AG-6-2103, N01-AG-6–2106, R01NR012459)
- National Institute of Diabetes and Digestive and Kidney Diseases (R01DK087635, R01DK105821, R01DK076077)
- Calico Life Sciences
- Topics
- Health Sciences › Medicine › Nephrology › Renal Diseases and Glomerulopathies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Health Sciences › Medicine › Nephrology › Chronic Kidney Disease and Diabetes
- Keywords
- APOL1, plasma protein levels, genetic variants, cis-pQTL, Olink, mass spectrometry, kallikrein-kinin system, KLKB1, F12, KNG1, kidney disease, African ancestry, European ancestry, protein conformational change
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