Genomic atlas of the human plasma proteome
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Abstract
Although plasma proteins have important roles in biological processes and are the direct targets of many drugs, the genetic factors that control inter-individual variation in plasma protein levels are not well understood. Here we characterize the genetic architecture of the human plasma proteome in healthy blood donors from the INTERVAL study. We identify 1,927 genetic associations with 1,478 proteins, a fourfold increase on existing knowledge, including trans associations for 1,104 proteins. To understand the consequences of perturbations in plasma protein levels, we apply an integrated approach that links genetic variation with biological pathway, disease, and drug databases. We show that protein quantitative trait loci overlap with gene expression quantitative trait loci, as well as with disease-associated loci, and find evidence that protein biomarkers have causal roles in disease using Mendelian randomization analysis. By linking genetic factors to diseases via specific proteins, our analyses highlight potential therapeutic targets, opportunities for matching existing drugs with new disease indications, and potential safety concerns for drugs under development.
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The pleiotropic loci included well-established, previously identified pleiotropic loci such as MHC, ABO, ZFPM2, ARHGEF3, GCKR, SERPINA1, SH2B3 and ASGR1.
“These included well-established, previously identified [17][18][19][20] pleiotropic loci such as MHC, ABO, ZFPM2, ARHGEF3, GCKR, SERPINA1, SH2B3 and ASGR1”
From Plasma proteomic associations with genetics and health in the UK Biobank (2023) · cites this paper as [18]
In “trans pQTLs identify pathways to disease” (page unavailable), the paper identifies ABO as a well-known pleiotropic locus, describes ZFPM2 as associated with many correlated proteins, and reports associations between a SERPINA1 variant and multiple proteins. It mentions MHC elsewhere but does not identify it there as a pleiotropic locus; the passages found do not establish ARHGEF3, GCKR, SH2B3 or ASGR1 as examples. (LLM, conf=0.88)
Experimental — accuracy not yet measuredSeveral studies in blood plasma have shown that pQTLs using aptamer-based and antibody-based assays are common and can explain up to 30% of protein variance.
“several studies in blood plasma have consistently shown that genetic variants associated with protein levels, known as protein quantitative trait loci (pQTL), using both aptamer‐based (Sun et al, 2018; Ferkingstad et al, 2021) and antibody‐based assays (Folkersen et al, 2020) are common and can explain up to 30% of the protein variance.”
From The genetic regulation of protein expression in cerebrospinal fluid (2023) · cites this paper as Sun et al, 2018
The Methods (Protein measurements) describe the aptamer-based SOMAscan assay, and Genetic architecture of the plasma proteome reports 1,927 pQTLs, supporting the claim that such associations are common in plasma. That section reports a median of 5.8% of protein-level variation explained and more than 20% for 193 proteins, but does not establish the cited figure of up to 30%. The antibody-based studies are attributed to other citations in the sentence, not to Sun et al. (LLM, conf=0.83)
Experimental — accuracy not yet measuredOf 1021 independent autosomal lead pQTL variants for 1478 proteins from the large-scale pQTL study by Sun et al., 824 (80.7%) were present in the EstBB WGS dataset.
“We used data on 1021 independent autosomal lead pQTL variants for 1478 proteins from the large-scale pQTL study conducted by Sun et al.4; 824 (80.7%) of these variants were present in the EstBB WGS dataset.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as Sun et al. [4]
The ‘Genetic architecture of the plasma proteome’ section reports pQTL associations involving 1,478 proteins and describes sentinel variants, supporting the identification of Sun et al. as the pQTL study used. Our copy does not establish the specific count of 1,021 independent autosomal lead variants (searched: ‘1,021 independent autosomal lead pQTL variants’ and ‘1021 sentinel variants’). The 824-variant overlap with EstBB WGS is presented as the citing authors’ own finding, not a finding attributed to Sun et al. No page number was provided for the indexed passages. (LLM, conf=0.84)
Experimental — accuracy not yet measuredWe identified eight CNVs with possible effects on protein levels from the Sun et al. 2018 study.
“We identified eight CNVs with possible effects on protein levels (Table 2) from the Sun et al. 2018 study4.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [4]
The Genetic architecture of the plasma proteome section reports associations between genetic variants and protein levels, so Sun et al. is a relevant source for that part of the sentence. Our copy does not identify eight CNVs or establish that CNVs were among the variants used; the claim's CNV-specific link to this study is therefore unsupported. (LLM, conf=0.78)
Experimental — accuracy not yet measuredcis-pQTL effect sizes correlate with proximity to the protein coding gene.
“We also confirmed previous evidence10,13 that cis-pQTL effect sizes correlate with proximity to the protein coding gene (Fig. 2D; Supp. Fig. 3H).”
From Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer’s disease (2024) · cites this paper as [13]
The Genetic architecture of the plasma proteome section (page unavailable) reports that cis-pQTL association P values increased with distance from the protein-coding gene’s transcription start site. It does not report a correlation between effect sizes and distance; P values measure statistical significance, not effect size. (LLM, conf=0.82)
Experimental — accuracy not yet measured
Evidence suggests that candidate proteins can be identified for many neurological traits, even with smaller sample sizes.
“Evidence suggests that candidate proteins can be identified for many neurological traits, even with smaller sample sizes10,13”
From Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer’s disease (2024) · cites this paper as [13]
The ‘trans pQTLs identify pathways to disease’ section links plasma proteins to disease-associated genetic variants, supporting the general idea that candidate proteins can be identified for disease traits. The passages do not establish this for many neurological traits or show that it works with smaller sample sizes; the Methods describe an analysis of 3,301 participants, not a smaller-sample comparison. (LLM, conf=0.82)
Experimental — accuracy not yet measuredMost pQTLs to date have been discovered via genome-wide association studies, which predominantly focus on common variants.
“To date, most pQTLs have been discovered via genome-wide association studies (GWASs), which predominantly focus on common variants3,5–8.”
From Rare variant associations with plasma protein levels in the UK Biobank (2023) · cites this paper as [6]
Protein quantitative trait loci (pQTL) are commonly detected in large-scale studies.
“Protein quantitative trait loci (pQTL), or associations between circulating protein abundances and genetic variants either within or nearby (cis-) to the protein-encoding gene or elsewhere in the genome (trans-), are commonly detected in large-scale studies1–6.”
From Cross-ancestry comparison of aptamer and antibody protein measures (2026) · cites this paper as [4]
The overall genetic architecture of the CSF pQTLs was similar to findings reported for plasma pQTLs, gene expression QTLs, and a recent study on CSF pQTLs.
“The overall genetic architecture of the CSF pQTLs was similar to findings reported for plasma pQTLs (Sun et al, 2018), gene expression QTLs (Stranger et al, 2012), and a recent study on CSF pQTLs (Yang et al, 2021).”
From The genetic regulation of protein expression in cerebrospinal fluid (2023) · cites this paper as Sun et al, 2018
Of the 278 pQTLs, 184 (66.19%) were tested in the Sun et al. study; 103 (55.98%) were significant at Benjamini-Hochberg FDR < 0.05 and 89.32% (92/103) were directionally concordant.
“66.19% (184/278) of pQTLs were tested in the Sun et al. study4. Of them, 55.98% (103/184) were significant (Benjamini–Hochberg FDR < 0.05) and 89.32% (92/103) were directionally concordant (Supplementary Table S2).”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [4]
There was an inverse relationship between effect size and MAF.
“In concordance with previous studies4,9,65, there was an inverse relationship between the effect size and MAF (Fig. 2b), and the associations were the strongest for significant cis-pQTL variants located nearest to the TSSs of the relevant protein genes (Fig. 2c).”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [4]
The associations were strongest for significant cis-pQTL variants located nearest to the TSSs of the relevant protein genes.
“In concordance with previous studies4,9,65, there was an inverse relationship between the effect size and MAF (Fig. 2b), and the associations were the strongest for significant cis-pQTL variants located nearest to the TSSs of the relevant protein genes (Fig. 2c).”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [4]
Most pQTL studies have been conducted using serum or plasma measurements from blood samples, and only a limited number of studies has involved examination of liver and brain tissue-specific pQTLs.
“Second, most pQTL studies have been conducted using serum or plasma measurements from blood samples4,6,8,51 and only a limited number of studies has involved the examination of liver and brain tissue–specific pQTLs88,89.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [4]
ASGR1 also emerged as a hotspot in the UKB-PPP GWAS and other large pQTL studies.
“ASGR1, which encodes a subunit of the asialoglycoprotein receptor, also emerged as a hotspot in the UKB-PPP GWAS and other large pQTL studies5,6,8.”
From Rare variant associations with plasma protein levels in the UK Biobank (2023) · cites this paper as [6]
The ‘trans pQTLs identify pathways to disease’ section describes twelve hotspot regions and names examples, but does not identify ASGR1 as one. Searches for ASGR1 and the asialoglycoprotein receptor found no passage in our copy establishing the claimed ASGR1 hotspot. (LLM, conf=0.72)
Experimental — accuracy not yet measuredOnly one of those associations [proxy SNP rs10935473 with the CNV on chromosome 3 (98,410,653-98,414,807; deletion frequency = 0.651)] affecting FLT4/VEGF-sR3 levels, was identified in our study because the other proteins were not measured in our cohort.
“Only one of those associations [proxy SNP rs10935473 with the CNV on chromosome 3 (98,410,653–98,414,807; deletion frequency = 0.651)] affecting FLT4/VEGF-sR3 levels, was identified in our study because the other proteins were not measured in our cohort.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [4]
Split into 2 atoms — 0 confirmed, 0 partially supported, 0 disputed, 0 misattributed, 2 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredThe largest proportion of cis-pQTLs was located in intronic and intergenic regions.
“Consistent with previous findings4,6,8, the largest proportion of cis-pQTLs was located in intronic and intergenic regions.”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [4]
The Genetic architecture section reports that 44% of cis-pQTL sentinel variants were within the relevant gene, but does not give proportions for intronic and intergenic regions. The Methods section describes functional annotation, and the Extended Data Fig. 5 caption says consequences were plotted by class; neither passage establishes which classes had the largest proportions. Searches for the intronic/intergenic distribution found no decisive statement in our text copy. (LLM, conf=0.72)
Experimental — accuracy not yet measuredNascent efforts to capture transcriptomic, proteomic and metabolomic phenotypes are not yet at equivalent scale to genetic data.
“There are nascent efforts to capture transcriptomic, proteomic and metabolomic phenotypes, although these are not yet at equivalent scale to the genetic data79,80.”
From A brief history of human disease genetics (2020) · cites this paper as [79]
Connections between genetic variation and circulating proteomic and metabolomic data provide additional mechanistic links between cellular events and whole-body physiology.
“Connections between genetic variation and circulating proteomic and metabolomic data provide additional mechanistic links between cellular events and whole-body physiology79,80.”
From A brief history of human disease genetics (2020) · cites this paper as [79]
The aptamer-binding platform used to profile proteomics might be confounded by recognition difference instead of real protein abundance change.
“An important note is that we used an aptamer-binding platform to profile proteomics, which might be confounded by recognition difference instead of real protein abundance change, as already addressed by Sun et al10.”
From Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders (2021) · cites this paper as [10]
A study of HMGCR found no strong predicted effect modification.
“with a study of HMGCR finding no strong predicted effect modification 149,150 .”
From Integrating genomics with biomarkers and therapeutic targets to invigorate cardiovascular drug development (2021) · cites this paper as [149]
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Cites
Gene Ontology: tool for the unification of biology
Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · see citing works
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072 · see citing works
Second-generation PLINK: rising to the challenge of larger and richer datasets
Chang, C. C., Chow, C. C., Tellier, L. C., Vattikuti, S., Purcell, S. M., & Lee, J. J. (2015) · Gigascience · doi:10.1186/s13742-015-0047-8 · Cited by 12,709 · see citing works
STRING v10: protein–protein interaction networks, integrated over the tree of life
Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1003 · Cited by 10,436 · see citing works
Analysis of protein-coding genetic variation in 60,706 humans
Exome Aggregation Consortium, Lek, M., Karczewski, K. J., Minikel, E. V., Samocha, K. E., Banks, E., Fennell, T., O’Donnell-Luria, A. H., Ware, J. S., Hill, A. J., et al. (2016) · Nature · doi:10.1038/nature19057 · Cited by 9,587 · see citing works
Self-citationKEGG as a reference resource for gene and protein annotation
Kanehisa, M., Sato, Y., Kawashima, M., Furumichi, M., & Tanabe, M. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1070 · Cited by 7,387 · see citing works
Mendelian Randomization Analysis With Multiple Genetic Variants Using Summarized Data
Burgess, S., Butterworth, A., & Thompson, S. G. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21758 · Cited by 6,050 · see citing works
Self-citationMETAL: fast and efficient meta-analysis of genomewide association scans
Willer, C. J., Li, Y., & Abecasis, G. R. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq340 · Cited by 5,205 · see citing works
UniProt: a hub for protein information
The UniProt Consortium (2015) · Nucleic Acids Research · doi:10.1093/nar/gku989 · Cited by 5,106 · see citing works
Host–microbe interactions have shaped the genetic architecture of inflammatory bowel disease
The International IBD Genetics Consortium (IIBDGC), Jostins, L., Ripke, S., Weersma, R. K., Duerr, R. H., McGovern, D. P., Hui, K. Y., Lee, J. C., Philip Schumm, L., Sharma, Y., et al. (2012) · Nature · doi:10.1038/nature11582 · Cited by 4,722 · see citing works
Self-citationGENCODE: The reference human genome annotation for The ENCODE Project
Harrow, J., Frankish, A., Gonzalez, J. M., Tapanari, E., Diekhans, M., Kokocinski, F., Aken, B. L., Barrell, D., Zadissa, A., Searle, S., et al. (2012) · Genome Research · doi:10.1101/gr.135350.111 · Cited by 4,585 · see citing works
STRING v9.1: protein-protein interaction networks, with increased coverage and integration
Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Lin, J., Minguez, P., Bork, P., von Mering, C., et al. (2012) · Nucleic Acids Research · doi:10.1093/nar/gks1094 · Cited by 4,398 · see citing works
Software for Computing and Annotating Genomic Ranges
Lawrence, M. F., Huber, W. K., Pagès, H., Aboyoun, P., Carlson, M. R. J., Gentleman, R. C., Morgan, M. T., & Carey, V. J. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003118 · Cited by 4,284 · see citing works
Giambartolomei, C., Vukcevic, D., Schadt, E. E., Franke, L., Hingorani, A. D., Wallace, C., & Plagnol, V. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004383 · Cited by 4,261 · see citing works
Genetic effects on gene expression across human tissues
GTEx Consortium (2017) · Nature · doi:10.1038/nature24277 · Cited by 4,036 · see citing works
The NHGRI GWAS Catalog, a curated resource of SNP-trait associations
Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., Klemm, A., Flicek, P., Manolio, T., Hindorff, L., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1229 · Cited by 2,707 · see citing works
Amberger, J. S., Bocchini, C. A., Schiettecatte, F., Scott, A. F., & Hamosh, A. (2015) · Nucleic Acids Research · doi:10.1093/nar/gku1205 · Cited by 2,691 · see citing works
A new multipoint method for genome-wide association studies by imputation of genotypes
Marchini, J., Howie, B., Myers, S., McVean, G., & Donnelly, P. (2007) · Nature Genetics · doi:10.1038/ng2088 · Cited by 2,609 · see citing works
Liu, J. Z., van Sommeren, S., Huang, H., Ng, S. C., Alberts, R., Takahashi, A., Ripke, S., Lee, J. C., Jostins, L., Shah, T., et al. (2015) · Nature Genetics · doi:10.1038/ng.3359 · Cited by 2,603 · see citing works
A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease
the CARDIoGRAMplusC4D Consortium (2015) · Nature Genetics · doi:10.1038/ng.3396 · Cited by 2,447 · see citing works
The NIH Roadmap Epigenomics Mapping Consortium
Bernstein, B. E., Stamatoyannopoulos, J. A., Costello, J. F., Ren, B., Milosavljevic, A., Meissner, A., Kellis, M., Marra, M. A., Beaudet, A. L., Ecker, J. R., et al. (2010) · Nature Biotechnology · doi:10.1038/nbt1010-1045 · Cited by 1,892 · see citing works
Transcriptome and genome sequencing uncovers functional variation in humans
The Geuvadis Consortium, Lappalainen, T., Sammeth, M., Friedländer, M. R., ‘t Hoen, P. A. C., Monlong, J., Rivas, M. A., Gonzàlez-Porta, M., Kurbatova, N., Griebel, T., et al. (2013) · Nature · doi:10.1038/nature12531 · Cited by 1,888 · see citing works
Assarsson, E., Lundberg, M., Holmquist, G., Björkesten, J., Bucht Thorsen, S., Ekman, D., Eriksson, A., Rennel Dickens, E., Ohlsson, S., Edfeldt, G., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0095192 · Cited by 1,874 · see citing works
EPIC- InterAct Consortium, Burgess, S., Scott, R. A., Timpson, N. J., Davey Smith, G., & Thompson, S. G. (2015) · European Journal of Epidemiology · doi:10.1007/s10654-015-0011-z · Cited by 1,803 · see citing works
Self-citationAptamer-Based Multiplexed Proteomic Technology for Biomarker Discovery
Gold, L., Ayers, D., Bertino, J., Bock, C., Bock, A., Brody, E. N., Carter, J., Dalby, A. B., Eaton, B. E., Fitzwater, T., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0015004 · Cited by 1,754 · see citing works
Self-citationGenetic Investigation of ANthropometric Traits (GIANT) Consortium, Yang, J., DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Ferreira, T., Morris, A. P., Medland, S. E., Madden, P. A. F., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2012) · Nature Genetics · doi:10.1038/ng.2213 · Cited by 1,712 · see citing works
The support of human genetic evidence for approved drug indications
Nelson, M. R., Tipney, H., Painter, J. L., Shen, J., Nicoletti, P., Shen, Y., Floratos, A., Sham, P. C., Li, M. J., Wang, J., et al. (2015) · Nature Genetics · doi:10.1038/ng.3314 · Cited by 1,666 · see citing works
Systematic identification of trans eQTLs as putative drivers of known disease associations
Westra, H.-J., Peters, M. J., Esko, T., Yaghootkar, H., Schurmann, C., Kettunen, J., Christiansen, M. W., Fairfax, B. P., Schramm, K., Powell, J. E., et al. (2013) · Nature Genetics · doi:10.1038/ng.2756 · Cited by 1,658 · see citing works
Burgess, S., & Thompson, S. G. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwu283 · Cited by 1,605 · see citing works
Self-citationDeriving the consequences of genomic variants with the Ensembl API and SNP Effect Predictor
McLaren, W. M., Yates, B., Ríos, D., Chen, Y., Flicek, P., & Cunningham, F. M. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq330 · Cited by 1,586 · see citing works
PhenoScanner: a database of human genotype-phenotype associations.
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) · Bioinformatics · doi:10.1093/bioinformatics/btw373 · Cited by 1,479 · see citing works
Self-citationMyostatin Mutation Associated with Gross Muscle Hypertrophy in a Child
Schuelke, M., Wagner, K. R., Stolz, L. E., Hübner, C., Riebel, T., Kömen, W., Braun, T., Tobin, J. F., & Lee, S.-J. (2004) · New England Journal of Medicine · doi:10.1056/nejmoa040933 · Cited by 1,399 · see citing works
The Allelic Landscape of Human Blood Cell Trait Variation and Links to Common Complex Disease
Astle, W. J., Elding, H., Jiang, T., Allen, D., Ruklisa, D., Mann, A. L., Mead, D., Bouman, H., Riveros-Mckay, F., Kostadima, M. A., et al. (2016) · Cell · doi:10.1016/j.cell.2016.10.042 · Cited by 1,224 · see citing works
Self-citationBurgess, S., Dudbridge, F., & Thompson, S. G. (2016) · Statistics in Medicine · doi:10.1002/sim.6835 · Cited by 1,153 · see citing works
Self-citationThe role of regulatory variation in complex traits and disease
Albert, F. W., & Kruglyak, L. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3891 · Cited by 1,047 · see citing works
Javierre, B. M., Burren, O. S., Wilder, S. P., Kreuzhuber, R., Hill, S. M., Sewitz, S., Cairns, J., Wingett, S. W., Várnai, C., Thiecke, M. J., et al. (2016) · Cell · doi:10.1016/j.cell.2016.09.037 · Cited by 1,046 · see citing works
Self-citationThe UK10K project identifies rare variants in health and disease
The UK10K Consortium, Writing group, Walter, K., Min, J. L., Huang, J., Crooks, L., Memari, Y., McCarthy, S., Perry, J. R. B., Xu, C., et al. (2015) · Nature · doi:10.1038/nature14962 · Cited by 1,040 · see citing works
Self-citationGenetically Distinct Subsets within ANCA-Associated Vasculitis
Lyons, P., Rayner, T. F., Trivedi, S., Holle, J. U., Watts, R. A., Jayne, D. R. W., Baslund, B., Brenchley, P. E., Bruchfeld, A. N., Chaudhry, A., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1108735 · Cited by 1,007 · see citing works
Burgess, S., Dudbridge, F., & Thompson, S. G. (2015) · American Journal of Epidemiology · doi:10.1093/aje/kwv017 · Cited by 945 · see citing works
Self-citationMapping the Genetic Architecture of Gene Expression in Human Liver
Schadt, E. E., Molony, C. M., Chudin, E., Hao, K., Yang, X., Lum, P. Y., Kasarskis, A., Zhang, B., Wang, S. S.-M., Suver, C., et al. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060107 · Cited by 909 · see citing works
Visualizing Genomic Data Using Gviz and Bioconductor
Hahne, F., & Ivánek, R. (2016) · Methods in molecular biology · doi:10.1007/978-1-4939-3578-9_16 · Cited by 893 · see citing works
Multiple newly identified loci associated with prostate cancer susceptibility
Collaborators, T. U. G. P. C. S., Eeles, R. A., Collaborators, T. U. P. S., Kote‐Jarai, Z., Giles, G. G., Olama, A. A. A., Guy, M., Jugurnauth, S. K., Mulholland, S., Leongamornlert, D. A., et al. (2008) · Nature Genetics · doi:10.1038/ng.90 · Cited by 844 · see citing works
Connecting genetic risk to disease end points through the human blood plasma proteome
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) · Nature Communications · doi:10.1038/ncomms14357 · Cited by 800 · see citing works
Self-citationLundberg, M., Eriksson, A., Tran, B., Assarsson, E., & Fredriksson, S. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr424 · Cited by 744 · see citing works
Paternoster, L., Kemp, J. P., Zheng, J., Smith, G. D., Relton, C. L., Evans, D. M., McArdle, W. L., Henderson, A. J., Standl, M., Tiesler, C. M. T., et al. (2015) · Nature Genetics · doi:10.1038/ng.3424 · Cited by 614 · see citing works
Fine-mapping inflammatory bowel disease loci to single-variant resolution
International Inflammatory Bowel Disease Genetics Consortium, Huang, H., Fang, M., Jostins, L., Umićević Mirkov, M., Boucher, G., Anderson, C. A., Andersen, V., Cleynen, I., Cortes, A., et al. (2017) · Nature · doi:10.1038/nature22969 · Cited by 611 · see citing works
Self-citationGenetics and Beyond – The Transcriptome of Human Monocytes and Disease Susceptibility
Zeller, T., Wild, P. S., Szymczak, S., Rotival, M., Schillert, A., Castagné, R., Maouche, S., Germain, M., Lackner, K. J., Rossmann, H., et al. (2010) · PLoS ONE · doi:10.1371/journal.pone.0010693 · Cited by 584 · see citing works
A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease
Nikpay, M., Goel, A., Won, H., Hall, L. M., Willenborg, C., Kanoni, S., Saleheen, D., Kyriakou, T., Nelson, C. P., Hopewell, J. C., et al. (2015) · EUR Research Repository (Erasmus University Rotterdam) · Cited by 581 · see citing works
Self-citationModeling sample variables with an Experimental Factor Ontology
Malone, J., Holloway, E., Adamusiak, T., Kapushesky, M., Zheng, J., Kolesnikov, N. N., Zhukova, A., Brāzma, A., & Parkinson, H. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq099 · Cited by 571 · see citing works
Battle, A., Mostafavi, S., Zhu, X., Potash, J. B., Weissman, M. M., McCormick, C., Haudenschild, C. D., Beckman, K. B., Shi, J., Mei, R., et al. (2014) · Genome Research · doi:10.1101/gr.155192.113 · Cited by 547 · see citing works
Rohloff, J. C., Gelinas, A. D., Jarvis, T. C., Ochsner, U. A., Schneider, D. J., Gold, L., & Janjic, N. (2014) · Molecular Therapy - Nucleic Acids · doi:10.1038/mtna.2014.49 · Cited by 533 · see citing works
Self-citationA genome-wide association study identifies protein quantitative trait loci (pQTLs)
Melzer, D., Perry, J. R. B., Hernandez, D., Corsi, A.-M., Stevens, K., Rafferty, I., Lauretani, F., Murray, A., Gibbs, J. R., Paolisso, G., et al. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000072 · Cited by 480 · see citing works
Impact of regulatory variation from RNA to protein
Battle, A., Khan, Z., Wang, S. H., Mitrano, A., Ford, M. J., Pritchard, J. K., & Gilad, Y. (2015) · Science · doi:10.1126/science.1260793 · Cited by 471 · see citing works
Patterns of Cis Regulatory Variation in Diverse Human Populations
Stranger, B. E., Montgomery, S. B., Dimas, A. S., Parts, L., Stegle, O., Ingle, C. E., Sekowska, M., Smith, G. D., Evans, D., Gutierrez-Arcelus, M., et al. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002639 · Cited by 470 · see citing works
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) · Nature Communications · doi:10.1038/s41467-018-05512-x · Cited by 431 · see citing works
Self-citationGrönberg, H., Adolfsson, J., Aly, M., Nordström, T., Wiklund, P. N., Brandberg, Y., Thompson, J., Wiklund, F. E., Lindberg, J., Clements, M., et al. (2015) · The Lancet Oncology · doi:10.1016/s1470-2045(15)00361-7 · Cited by 410 · see citing works
New gene functions in megakaryopoiesis and platelet formation
Gieger, C., Radhakrishnan, A., Cvejic, A., Tang, W., Porcu, E., Pistis, G., Serbanovic‐Canic, J., Elling, U., Goodall, A. H., Labrune, Y., et al. (2011) · Nature · doi:10.1038/nature10659 · Cited by 404 · see citing works
Variation and genetic control of protein abundance in humans
Wu, L., Candille, S. I., Choi, Y., Xie, D., Jiang, L., Li-Pook-Than, J., Tang, H., & Snyder, M. (2013) · Nature · doi:10.1038/nature12223 · Cited by 364 · see citing works
Ganz, P., Heidecker, B., Hveem, K., Jonasson, C., Kato, S., Segal, M. R., Sterling, D. G., & Williams, S. A. (2016) · JAMA · doi:10.1001/jama.2016.5951 · Cited by 364 · see citing works
Quantitative variability of 342 plasma proteins in a human twin population
Liu, Y., Buil, A., Collins, B. C., Gillet, L. C., Blum, L. C., Cheng, L., Vitek, O., Mouritsen, J., Lachance, G., Spector, T. D., et al. (2015) · Molecular Systems Biology · doi:10.15252/msb.20145728 · Cited by 328 · see citing works
Albagha, O., Visconti, M. R., Alonso, N., Langston, A. L., Cundy, T., Dargie, R., Dunlop, M. G., Fraser, W. D., Hooper, M. J., Isaia, G., et al. (2010) · Nature Genetics · doi:10.1038/ng.562 · Cited by 280 · see citing works
Hingorani, A. D., & Humphries, S. E. (2005) · The Lancet · doi:10.1016/s0140-6736(05)67767-7 · Cited by 277 · see citing works
Angelantonio, E. D., Thompson, S. G., Kaptoge, S. K., Moore, C. S., Walker, M. R., Armitage, J., Ouwehand, W. H., Roberts, D. J., Danesh, J., Angelantonio, E. D., et al. (2017) · The Lancet · doi:10.1016/s0140-6736(17)31928-1 · Cited by 265 · see citing works
Self-citationSmith, R. N., Aleksic, J., Butano, D., et al. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts577 · Cited by 244 · see citing works
Alzheimer's disease biomarker discovery using SOMAscan multiplexed protein technology
Sattlecker, M., Kiddle, S. J., Newhouse, S., Proitsi, P., Nelson, S., Williams, S., Johnston, C., Killick, R., Simmons, A., Westman, E., et al. (2014) · Alzheimer's & Dementia · doi:10.1016/j.jalz.2013.09.016 · Cited by 223 · see citing works
Ngo, D., Sinha, S., Shen, D., Kuhn, E. W., Keyes, M. J., Shi, X. D., Benson, M. D., O’Sullivan, J., Keshishian, H., Farrell, L. A., et al. (2016) · Circulation · doi:10.1161/circulationaha.116.021803 · Cited by 214 · see citing works
From expression QTLs to personalized transcriptomics
Montgomery, S. B., & Dermitzakis, E. T. (2011) · Nature Reviews Genetics · doi:10.1038/nrg2969 · Cited by 189 · see citing works
Merkel, P. A., ., G. X., Monach, P. A., Ji, X., Ciavatta, D. J., Byun, J., Pinder, B. D., Zhao, A., Zhang, J., Tadesse, Y., et al. (2016) · Arthritis & Rheumatology · doi:10.1002/art.40034 · Cited by 178 · see citing works
Enroth, S., Johansson, Å., Enroth, S. B., & Gyllensten, U. B. (2014) · Nature Communications · doi:10.1038/ncomms5684 · Cited by 176 · see citing works
Moore, C. S., Sambrook, J., Walker, M. R., Tolkien, Z., Kaptoge, S. K., Allen, D., Mehenny, S., Mant, J., Angelantonio, E. D., Thompson, S. G., et al. (2014) · Trials · doi:10.1186/1745-6215-15-363 · Cited by 145 · see citing works
Self-citationCirculating Proteomic Signatures of Chronological Age
Menni, C., Kiddle, S. J., Mangino, M., Viñuela, A., Psatha, M., Steves, C., Sattlecker, M., Buil, A., Newhouse, S., Nelson, S., et al. (2015) · The Journals of Gerontology: Series A · doi:10.1093/gerona/glu121 · Cited by 144 · see citing works
Carrasquillo, M. M., Nicholson, A. M., Finch, N. A., Gibbs, J. R., Baker, M. C., Rutherford, N. J., Hunter, T. A., DeJesus‐Hernandez, M., Bisceglio, G. D., Mackenzie, I. R., et al. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.002 · Cited by 138 · see citing works
Gonçalves, I., Bengtsson, E., Colhoun, H. M., Shore, A. C., Palombo, C., Natali, A., Edsfeldt, A., Dunér, P., Fredrikson, G. N., Björkbacka, H., et al. (2015) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.115.305631 · Cited by 131 · see citing works
Nai, A., Pagani, A., Silvestri, L., Campostrini, N., Corbella, M., Girelli, D., Traglia, M., Toniolo, D., & Camaschella, C. (2011) · Blood · doi:10.1182/blood-2011-06-364034 · Cited by 113 · see citing works
Stacey, D., Fauman, E. B., Ziemek, D., Sun, B. B., Harshfield, E. L., Wood, A., Butterworth, A. S., Suhre, K., & Paul, D. S. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky837 · Cited by 107 · see citing works
Self-citationThe molecular and cellular pathology of α1-antitrypsin deficiency
Gooptu, B., Dickens, J. A., & Lomas, D. A. (2013) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2013.10.007 · Cited by 103 · see citing works
Emerging drugs for Duchenne muscular dystrophy
Malik, V., Rodino‐Klapac, L. R., & Mendell, J. R. (2012) · Expert Opinion on Emerging Drugs · doi:10.1517/14728214.2012.691965 · Cited by 98 · see citing works
Macrophages in cardiac homeostasis, injury responses and progenitor cell mobilisation
Pinto, A. R., Godwin, J. W., & Rosenthal, N. A. (2014) · Stem Cell Research · doi:10.1016/j.scr.2014.06.004 · Cited by 90 · see citing works
Traylor, M., Mäkelä, K.-M., Kilarski, L. L., Holliday, E. G., Devan, W. J., Nalls, M. A., Wiggins, K. L., Zhao, W., Cheng, Y.-C., Achterberg, S., et al. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004469 · Cited by 88 · see citing works
Dahl, R., Titlestad, I. L., Lindqvist, A. E., et al. (2012) · Pulmonary Pharmacology & Therapeutics · doi:10.1016/j.pupt.2011.12.011 · Cited by 85 · see citing works
Both WFIKKN1 and WFIKKN2 Have High Affinity for Growth and Differentiation Factors 8 and 11
Kondás, K., Szláma, G., Trexler, M., & Patthy, L. (2008) · Journal of Biological Chemistry · doi:10.1074/jbc.m803025200 · Cited by 81 · see citing works
Paget's disease of the bone after treatment with Denosumab: A case report
Schwarz, P., Rasmussen, A. Q., Kvist, T., Andersen, U. B., & Jørgensen, N. R. (2012) · Bone · doi:10.1016/j.bone.2012.01.020 · Cited by 61 · see citing works
Narzo, A. D., Telesco, S., Brodmerkel, C. M., Argmann, C., Peters, L. A., Li, K., Kidd, B. A., Dudley, J. T., Cho, J. H., Schadt, E. E., et al. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006565 · Cited by 51 · see citing works
Högler, W., Martin, D. D., Crabtree, N. J., Nightingale, P., Tomlinson, J., Metherell, L., Rosenfeld, R. G., Hwa, V., Rose, S., Walker, J. M., et al. (2014) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2013-3718 · Cited by 32 · see citing works
Whole-genome sequencing study of serum peptide levels: the Atherosclerosis Risk in Communities study
de Vries, P. S., Yu, B., Feofanova, E. V., Metcalf, G., Brown, M. R., Zeighami, A. L., Liu, X., Muzny, D. M., Gibbs, R. A., Boerwinkle, E., et al. (2017) · Human Molecular Genetics · doi:10.1093/hmg/ddx266 · Cited by 31 · see citing works
GARFIELD - GWAS Analysis of Regulatory or Functional Information Enrichment with LD correctionPreprint
Iotchkova, V., Ritchie, G. R. S., Geihs, M., Morganella, S., Min, J. L., Walter, K., Timpson, N. J., Dunham, I., Birney, E., & Soranzo, N. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/085738 · Cited by 28 · see citing works
Self-citationGenetic studies of plasma analytes identify novel potential biomarkers for several complex traits
Deming, Y., Xia, J., Cai, Y., Lord, J., Del-Aguila, J. L., Fernandez, M. V., Carrell, D., Black, K., Budde, J., Ma, S., et al. (2016) · Scientific Reports · doi:10.1038/srep18092 · Cited by 28 · see citing works
Self-citationYao, C., Chen, G., Song, C., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/136523 · Cited by 5 · see citing works
Stacey, D., Fauman, E. B., Ziemek, D., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/230094 · Cited by 3 · see citing works
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Plasma proteomic associations with genetics and health in the UK Biobank
Sun, B. B., Chiou, J., Traylor, M., Benner, C., Hsu, Y.-H., Richardson, T. G., Surendran, P., Mahajan, A., Robins, C., Vasquez-Grinnell, S. G., et al. (2023) · Nature · doi:10.1038/s41586-023-06592-6 · Cited by 1,740 · see citing works
Self-citationLarge-scale integration of the plasma proteome with genetics and disease
Ferkingstad, E., Sulem, P., Atlason, B. A., Sveinbjornsson, G., Magnusson, M. I., Styrmisdottir, E. L., Gunnarsdottir, K., Helgason, A., Oddsson, A., Halldorsson, B. V., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00978-w · Cited by 1,697 · see citing works
Undulating changes in human plasma proteome profiles across the lifespan
Lehallier, B., Gate, D., Schaum, N., Nanasi, T., Lee, S. E., Yousef, H., Moran Losada, P., Berdnik, D., Keller, A., Verghese, J., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0673-2 · Cited by 981 · see citing works
Mendelian Randomization: Concepts and Scope
Richmond, R. C., & Davey Smith, G. (2022) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a040501 · Cited by 841 · see citing works
Zheng, J., Haberland, V., Baird, D., Walker, V., Haycock, P., Hurle, M., Gutteridge, A., Erola, P., Liu, Y., Luo, S., et al. (2019) · doi:10.1101/627398 · Cited by 812 · see citing works
Self-citationA brief history of human disease genetics
Claussnitzer, M., Cho, J. H., Collins, R., Cox, N. J., Dermitzakis, E. T., Hurles, M. E., Kathiresan, S., Kenny, E. E., Lindgren, C. M., MacArthur, D. G., et al. (2020) · Nature · doi:10.1038/s41586-019-1879-7 · Cited by 697 · see citing works
Self-citationThe Polygenic and Monogenic Basis of Blood Traits and Diseases
Vuckovic, D., Bao, E. L., Akbari, P., Lareau, C. A., Mousas, A., Jiang, T., Chen, M.-H., Raffield, L. M., Tardaguila, M., Huffman, J. E., et al. (2020) · Cell · doi:10.1016/j.cell.2020.08.008 · Cited by 671 · see citing works
Self-citationPlasma protein patterns as comprehensive indicators of health
Williams, S. A., Kivimaki, M., Langenberg, C., Hingorani, A. D., Casas, J. P., Bouchard, C., Jonasson, C., Sarzynski, M. A., Shipley, M. J., Alexander, L., et al. (2019) · Nature Medicine · doi:10.1038/s41591-019-0665-2 · Cited by 506 · see citing works
Genetics meets proteomics: perspectives for large population-based studies
Suhre, K., McCarthy, M. I., & Schwenk, J. M. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-020-0268-2 · Cited by 478 · see citing works
Self-citationThe sequences of 150,119 genomes in the UK Biobank
Halldorsson, B. V., Eggertsson, H. P., Moore, K. H. S., Hauswedell, H., Eiriksson, O., Ulfarsson, M. O., Palsson, G., Hardarson, M. T., Oddsson, A., Jensson, B. O., et al. (2022) · Nature · doi:10.1038/s41586-022-04965-x · Cited by 448 · see citing works
Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses
Wallace, C. (2020) · PLOS Genetics · doi:10.1371/journal.pgen.1008720 · Cited by 441 · see citing works
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) · Nature Communications · doi:10.1038/s41467-018-05512-x · Cited by 431 · see citing works
Self-citationKarlsson Linnér, R., Mallard, T. T., Barr, P. B., Sanchez-Roige, S., Madole, J. W., Driver, M. N., Poore, H. E., de Vlaming, R., Grotzinger, A. D., Tielbeek, J. J., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00908-3 · Cited by 317 · see citing works
“Such discoveries include ALMS1 (previously associated with kidney function and urinary metabolites 30 ) and ERAP2 (blood protein levels and autoimmune disease 31,32 ).”
BackgroundYang, C., Farias, F. H. G., Ibanez, L., Suhy, A., Sadler, B., Fernandez, M. V., Wang, F., Bradley, J. L., Eiffert, B., Bahena, J. A., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00886-6 · Cited by 290 · see citing works
Self-citationPotential drug targets for multiple sclerosis identified through Mendelian randomization analysis
Lin, J., Zhou, J., & Xu, Y. (2023) · Brain · doi:10.1093/brain/awad070 · Cited by 271 · see citing works
de Klein, N., Tsai, E. A., Vochteloo, M., Baird, D., Huang, Y., Chen, C.-Y., van Dam, S., Oelen, R., Deelen, P., Bakker, O. B., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01300-6 · Cited by 272 · see citing works
Self-citationGDF15: a hormone conveying somatic distress to the brain
Lockhart, S. M., Saudek, V., & O’Rahilly, S. (2020) · Endocrine Reviews · doi:10.1210/endrev/bnaa007 · Cited by 256 · see citing works
Synergistic insights into human health from aptamer- and antibody-based proteomic profiling
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) · Nature Communications · doi:10.1038/s41467-021-27164-0 · Cited by 234 · see citing works
Brain Endothelial Cells Are Exquisite Sensors of Age-Related Circulatory Cues
Chen, M. B., Yang, A. C., Yousef, H., Lee, D., Chen, W., Schaum, N., Lehallier, B., Quake, S. R., & Wyss-Coray, T. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.03.012 · Cited by 233 · see citing works
“These ligands have also been reported to increase in human plasma with age (Sun et al., 2018; Tanaka et al., 2018).”
Plasma proteomic biomarker signature of age predicts health and life span
Tanaka, T., Basisty, N., Fantoni, G., Candia, J., Moore, A. Z., Biancotto, A., Schilling, B., Bandinelli, S., & Ferrucci, L. (2020) · eLife · doi:10.7554/elife.61073 · Cited by 213 · see citing works
Large-scale plasma proteomic analysis identifies proteins and pathways associated with dementia risk
Walker, K. A., Chen, J., Zhang, J., Fornage, M., Yang, Y., Zhou, L., Grams, M. E., Tin, A., Daya, N., Hoogeveen, R. C., et al. (2021) · Nature Aging · doi:10.1038/s43587-021-00064-0 · Cited by 212 · see citing works
Rare variant associations with plasma protein levels in the UK Biobank
Dhindsa, R. S., Burren, O. S., Sun, B. B., Prins, B. P., Matelska, D., Wheeler, E., Mitchell, J., Oerton, E., Hristova, V. A., Smith, K. R., et al. (2023) · Nature · doi:10.1038/s41586-023-06547-x · Cited by 193 · see citing works
Self-citationSystemic factors as mediators of brain homeostasis, ageing and neurodegeneration
Pluvinage, J. V., & Wyss-Coray, T. (2020) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0255-9 · Cited by 191 · see citing works
Holmes, M. V., Richardson, T. G., Ference, B. A., Davies, N. M., & Davey Smith, G. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-020-00493-1 · Cited by 184 · see citing works
Comparison of Proteomic Assessment Methods in Multiple Cohort Studies
Raffield, L. M., Dang, H., Pratte, K. A., Jacobson, S., Gillenwater, L. A., Ampleford, E., Barjaktarevic, I., Basta, P., Clish, C. B., Comellas, A. P., et al. (2020) · PROTEOMICS · doi:10.1002/pmic.201900278 · Cited by 184 · see citing works
Cordell, H. J., Fryett, J. J., Ueno, K., Darlay, R., Aiba, Y., Hitomi, Y., Kawashima, M., Nishida, N., Khor, S.-S., Gervais, O., et al. (2021) · Journal of Hepatology · doi:10.1016/j.jhep.2021.04.055 · Cited by 182 · see citing works
Johnson, A. A., Shokhirev, M. N., Wyss-Coray, T., & Lehallier, B. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101070 · Cited by 166 · see citing works
New insights into IL-6 family cytokines in metabolism, hepatology and gastroenterology
Giraldez, M. D., Carneros, D., Garbers, C., Rose-John, S., & Bustos, M. (2021) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-021-00473-x · Cited by 156 · see citing works
Walker, K. A., Chen, J., Shi, L., Yang, Y., Fornage, M., Zhou, L., Schlosser, P., Surapaneni, A., Grams, M. E., Duggan, M. R., et al. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.adf5681 · Cited by 148 · see citing works
Proteomics in aging research: A roadmap to clinical, translational research
Moaddel, R., Ubaida‐Mohien, C., Tanaka, T., Lyashkov, A., Basisty, N., Schilling, B., Semba, R. D., Franceschi, C., Gorospe, M., & Ferrucci, L. (2021) · Aging Cell · doi:10.1111/acel.13325 · Cited by 139 · see citing works
FLT3 stop mutation increases FLT3 ligand level and risk of autoimmune thyroid disease
Saevarsdottir, S., Olafsdottir, T. A., Ivarsdottir, E. V., Halldorsson, G. H., Gunnarsdottir, K., Sigurdsson, A., Johannesson, A., Sigurdsson, J. K., Juliusdottir, T., Lund, S. H., et al. (2020) · Nature · doi:10.1038/s41586-020-2436-0 · Cited by 137 · see citing works
Depression and Interleukin-6 Signaling: A Mendelian Randomization Study
Kelly, K. M., Smith, J. A., & Mezuk, B. (2021) · Brain, Behavior, and Immunity · doi:10.1016/j.bbi.2021.02.019 · Cited by 117 · see citing works
Western, D., Timsina, J., Wang, L., Wang, C., Yang, C., Phillips, B., Wang, Y., Liu, M., Ali, M., Beric, A., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01972-8 · Cited by 111 · see citing works
Lehallier, B., Shokhirev, M. N., Wyss‐Coray, T., & Johnson, A. A. (2020) · Aging Cell · doi:10.1111/acel.13256 · Cited by 106 · see citing works
Genetic control of the human brain proteome
Robins, C., Wingo, A. P., Fan, W., Duong, D. M., Meigs, J., Gerasimov, E. S., Dammer, E. B., Cutler, D. J., Jager, P. L. D., Bennett, D. A., et al. (2019) · doi:10.1101/816652 · Cited by 104 · see citing works
Tang, A. S., Rankin, K. P., Cerono, G., Miramontes, S., Mills, H., Roger, J., Zeng, B., Nelson, C., Soman, K., Woldemariam, S., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00573-8 · Cited by 86 · see citing works
Hemani, G., Bowden, J., Haycock, P., Zheng, J., Davis, O., Flach, P., Gaunt, T., & Smith, G. D. (2017) · doi:10.1101/173682 · Cited by 85 · see citing works
Cai, N., Gomez-Duran, A., Yonova-Doing, E., Kundu, K., Burgess, A. I., Golder, Z. J., Calabrese, C., Bonder, M. J., Camacho, M., Lawson, R. A., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01441-3 · Cited by 73 · see citing works
Self-citationHillary, R. F., McCartney, D. L., Harris, S. E., Stevenson, A. J., Seeboth, A., Zhang, Q., Liewald, D. C., Evans, K. L., Ritchie, C. W., Tucker-Drob, E. M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11177-x · Cited by 78 · see citing works
Associations of Adiposity, Circulating Protein Biomarkers, and Risk of Major Vascular Diseases
Pang, Y., Kartsonaki, C., Lv, J., Fairhurst-Hunter, Z., Millwood, I. Y., Yu, C., Guo, Y., Chen, Y., Bian, Z., Yang, L., et al. (2021) · JAMA Cardiology · doi:10.1001/jamacardio.2020.6041 · Cited by 75 · see citing works
Lindbohm, J. V., Mars, N., Walker, K. A., Singh‐Manoux, A., Livingston, G., Brunner, E. J., Sipilä, P. N., Saksela, K., Ferrie, J. E., Lovering, R. C., et al. (2022) · Alzheimer's & Dementia · doi:10.1002/alz.12419 · Cited by 70 · see citing works
Approaches and advances in the genetic causes of autoimmune disease and their implications
Inshaw, J. R. J., Cutler, A. J., Burren, O. S., Stefana, M. I., & Todd, J. A. (2018) · Nature Immunology · doi:10.1038/s41590-018-0129-8 · Cited by 70 · see citing works
Self-citationSliz, E., Kalaoja, M., Ahola-Olli, A., Raitakari, O., Perola, M., Salomaa, V., Lehtimäki, T., Karhu, T., Viinamäki, H., Salmi, M., et al. (2019) · Journal of Medical Genetics · doi:10.1136/jmedgenet-2018-105965 · Cited by 60 · see citing works
Schuermans, A., Pournamdari, A. B., Lee, J., Bhukar, R., Ganesh, S., Darosa, N., Small, A. M., Yu, Z., Hornsby, W., Koyama, S., et al. (2024) · Nature Cardiovascular Research · doi:10.1038/s44161-024-00567-0 · Cited by 71 · see citing works
Finkernagel, F., Reinartz, S., Schuldner, M., Malz, A., Jansen, J. M., Wagner, U., Worzfeld, T., Graumann, J., von Strandmann, E. P., & Müller, R. (2019) · Theranostics · doi:10.7150/thno.37549 · Cited by 55 · see citing works
The genetic regulation of protein expression in cerebrospinal fluid
Hansson, O., Kumar, A., Janelidze, S., Stomrud, E., Insel, P. S., Blennow, K., Zetterberg, H., Fauman, E., Hedman, Å. K., Nagle, M. W., et al. (2023) · EMBO Molecular Medicine · doi:10.15252/emmm.202216359 · Cited by 56 · see citing works
Lemmelä, S., Wigmore, E. M., Benner, C., Havulinna, A. S., Ong, R. M., Kempf, T., Wollert, K. C., Blankenberg, S., Zeller, T., Peters, J. E., et al. (2022) · eLife · doi:10.7554/elife.76272 · Cited by 28 · see citing works
Self-citationThe protein inputs of an ultra-predictive aging clock represent viable anti-aging drug targets
Johnson, A. A., Shokhirev, M. N., & Lehallier, B. (2021) · Ageing Research Reviews · doi:10.1016/j.arr.2021.101404 · Cited by 22 · see citing works
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
Self-citationGenetic determinants of plasma protein levels in the Estonian population
Kalnapenkis, A., Jõeloo, M., Lepik, K., Kukuškina, V., Kals, M., Alasoo, K., Estonian Biobank Research Team, Metspalu, A., Milani, L., Nelis, M., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-57966-3 · Cited by 11 · see citing works
Genetic evidence for serum amyloid P component as a drug target in neurodegenerative disorders
Schmidt, A. F., Finan, C., Chopade, S., Ellmerich, S., Rossor, M. N., Hingorani, A. D., & Pepys, M. B. (2024) · Open Biology · doi:10.1098/rsob.230419 · Cited by 11 · see citing works
“Genetic variant-specific estimates of the association with SAP values, measured by SomaLogic SomaScan assay version 4.1, were available from three independent studies: Interval, comprising 3301 participants [24]; AGES, with 5368 [26]; and DECODE, with 35 559 [25].”
InfluentialPhiltjens, S., Acri, D. J., Kim, B., Kim, H., & Kim, J. (2021) · doi:10.21203/rs.3.rs-171899/v1 · Cited by 1 · see citing works
“to a lack of statistical power, a low genetic diversity, or low-throughput protein level 57 screening platforms [1, 4-9].”
BackgroundWang, T., Wang, A., Chen, Z., & Lin, X. (2026) · Journal of Global Health · doi:10.7189/jogh.16.04259
- Authors
- Sun, B. B., Maranville, J. C., Peters, J. E., Stacey, D., Staley, J. R., Blackshaw, J., Burgess, S., Jiang, T., Paige, E., Surendran, P., Oliver-Williams, C., Kamat, M. A., Prins, B. P., Wilcox, S. K., Zimmerman, E. S., Chi, A., Bansal, N., Spain, S. L., Wood, A. M., Morrell, N. W., Bradley, J. R., Janjic, N., Roberts, D. J., Ouwehand, W. H., Todd, J. A., Soranzo, N., Suhre, K., Paul, D. S., Fox, C. S., Plenge, R. M., Danesh, J., Runz, H., & Butterworth, A. S.
- Affiliations
- University of Cambridge (GB)
- Merck & Co., Inc., Rahway, NJ, USA (United States) (US)
- Australian National University (AU)
- SomaLogic (United States) (US)
- Wellcome Sanger Institute (GB)
- National Health Service (GB)
- Centre for Human Genetics (GB)
- Weill Cornell Medical College in Qatar (QA)
- Corresponding author
- Danesh, John
- Butterworth, Adam S.
- ORCID
- Sun, Benjamin B.
- Maranville, Joseph C.
- Peters, James E.
- Stacey, David
- Staley, James R.
- Blackshaw, James
- Burgess, Stephen
- Paige, Ellie
- Surendran, Praveen
- Prins, Bram P.
- Wilcox, Sheri K.
- Chi, An
- Bansal, Narinder
- Spain, Sarah L.
- Wood, Angela M.
- Morrell, Nicholas W.
- Bradley, John R.
- Roberts, David J.
- Ouwehand, Willem H.
- Soranzo, Nicole
- Suhre, Karsten
- Paul, Dirk S.
- Fox, Caroline S.
- Plenge, Robert M.
- Danesh, John
- Runz, Heiko
- Butterworth, Adam S.
- Venue
- Nature
- Published
- 2018-06
- Volume
- 558
- Issue
- 7708
- Pages
- 73-79
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- PMC
- PMC6697541
- OpenAlex
- W2805712258
- Cited by
- 2,268
- References
- 89
- Citations by year
- 2026: 240 · 2025: 432 · 2024: 505 · 2023: 286 · 2022: 254 · 2021: 212 · 2020: 200 · 2019: 104 · 2018: 25 · 2017: 7 · 2012: 1
- FWCI
- 84.30
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Bristol Research (University of Bristol) (acceptedVersion, other-oa)
- RePEc: Research Papers in Economics (submittedVersion)
- ANU Open Research (Australian National University) (submittedVersion)
- Spiral (Imperial College London) (submittedVersion)
- Apollo (University of Cambridge) (submittedVersion)
- Apollo
- Funders
- Qatar Foundation
- NHS Blood and Transplant (11-01-GEN, NIHR BTRU-2014-10024)
- Wellcome Trust (204623/Z/16/Z, EU-FP7-, 105602, 105602/Z/14/Z, 091157, 084711/Z/08/Z, WT 098051, BTRU-2014-10024, 9-2011-253, 257082, WT091310)
- National Institute for Health and Care Research (NF-SI-0617-10113, NF-SI-0512-10165, NF-SI-0513-10143, NF-SI-0512-10014, NIHR BTRU‐2014‐10024, BTRU-2014–10024, WT098051)
- British Heart Foundation (SP/08/007/23628, G0800270, SP/09/002, FS/06/025, SP/02/002, CH/12/2/29428, RG/13/13/30194, SP/12/12/29836, RG/16/4/32218, RE/13/6/30180)
- European Commission (279143, EU-FP7, 282510, 257082, 279233)
- University of Sheffield (R/130423)
- Cambridge University Hospitals (3819-1516-29, BRC 2012-2017, 3819-1617-25, 146281)
- Weill Cornell Medical College
- Weill Cornell Medicine - Qatar
- Medical Research Council (SP/09/002, MC_qA137933, MR/L003120/1, MR/S004068/1, MR/S003746/1, G0800270, MR/K50127X/1, WT098051, MR/P013880/1, EU FP7)
- NIHR Cambridge Biomedical Research Centre (BTRU-2014-10024, RE/13/6/30180, 105602/Z/14/Z)
- National Institute of Neurological Disorders and Stroke (R21NS064908)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- plasma proteome, genetic architecture, protein quantitative trait loci, genetic association, trans-eQTL, expression quantitative trait loci, disease-associated loci, protein biomarkers, Mendelian randomization, drug repurposing, drug safety, therapeutic target
- MeSH terms
- Positive Regulatory Domain I-Binding Factor 1, alpha 1-Antitrypsin, Blood Proteins, Female, Humans, Male, Proto-Oncogene Proteins, Vasculitis, Inflammatory Bowel Diseases, Hepatocyte Growth Factor, Mutation, Missense, Proteome, Genomics, Quantitative Trait Loci, Myeloblastin
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