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Genetic control of the human brain proteome
Open accessAbstract
Alteration of protein abundance and conformation are widely believed to be the hallmark of neurodegenerative diseases. Yet relatively little is known about the genetic variation that controls protein abundance in the healthy human brain. The genetic control of protein abundance is generally thought to parallel that of RNA expression, but there is little direct evidence to support this view. Here, we performed a large-scale protein quantitative trait locus (pQTL) analysis using single nucleotide variants (SNVs) from whole-genome sequencing and tandem mass spectrometry-based proteomic quantification of 12,691 unique proteins (7,901 after quality control) from the dorsolateral prefrontal cortex (dPFC) in 144 cognitively normal individuals. We identified 28,211 pQTLs that were significantly associated with the abundance of 864 proteins. These pQTLs were compared to dPFC expression quantitative trait loci (eQTL) in cognitive normal individuals (n=169; 81 had protein data) and a meta-analysis of dPFC eQTLs (n=1,433). We found that strong pQTLs are generally only weak eQTLs, and that the majority of strong eQTLs are not detectable pQTLs. These results suggest that the genetic control of mRNA and protein abundance may be substantially distinct and suggests inference concerning protein abundance made from mRNA in human brain should be treated with caution.
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“No statistical methods were used to predetermine sample sizes within each tissue type, but our sample sizes are much larger or comparable to those in previous publications13,14,16.”
From Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders (2021) · cites this paper as [16]
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“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 [89]
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“CSF Siglec-9, plasma sICAM-1, and brain PPAC have been replicated in other studies16,29,31,32 using a different proteomic approach, which indicates that these findings may not be driven by platform.”
From Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders (2021) · cites this paper as [16]
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“For example, individuals carrying the APOE ε4 allele display distinct proteo-metabolomic signatures in lipid transport and immune pathways206,207.”
From Artificial intelligence-driven multi-omics approaches in Alzheimer's disease: Progress, challenges, and future directions (2025) · cites this paper as [207]
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Cites
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709
PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915
The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data
McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., et al. (2010) · Genome Research · doi:10.1101/gr.107524.110 · Cited by 27,895
Robust enumeration of cell subsets from tissue expression profiles
Newman, A. M., Liu, C. L., Green, M. R., Gentles, A. J., Feng, W., Xu, Y., Hoang, C. D., Diehn, M., & Alizadeh, A. A. (2015) · Nature Methods · doi:10.1038/nmeth.3337 · Cited by 13,224
A framework for variation discovery and genotyping using next-generation DNA sequencing data
DePristo, M. A., Banks, E., Poplin, R., Garimella, K. V., Maguire, J. R., Hartl, C., Philippakis, A. A., del Angel, G., Rivas, M. A., Hanna, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.806 · Cited by 11,456
The Clinical Dementia Rating (CDR): Current version and scoring rules
Morris, J. C. (1993) · Neurology · doi:10.1212/wnl.43.11.2412-a · Cited by 9,882
Statistical significance for genomewide studies
Storey, J. D., & Tibshirani, R. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1530509100 · Cited by 9,692
Global quantification of mammalian gene expression control
Schwanhäusser, B., Busse, D., Li, N., Dittmar, G., Schuchhardt, J., Wolf, J., Chen, W., & Selbach, M. (2011) · Nature · doi:10.1038/nature10098 · Cited by 6,566
O'Leary, N. A., Wright, M. W., Brister, J. R., Ciufo, S., Haddad, D., McVeigh, R., Rajput, B., Robbertse, B., Smith-White, B., Ako-Adjei, D., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1189 · Cited by 6,357
Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E., & Storey, J. D. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts034 · Cited by 6,231
voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r29 · Cited by 6,155
Population Structure and Eigenanalysis
Patterson, N., Price, A. L., & Reich, D. (2006) · PLoS Genetics · doi:10.1371/journal.pgen.0020190 · Cited by 5,280
The Genotype-Tissue Expression (GTEx) pilot analysis: Multitissue gene regulation in humans
The GTEx Consortium, Ardlie, K. G., Deluca, D. S., Segrè, A. V., Sullivan, T. J., Young, T. R., Gelfand, E. T., Trowbridge, C. A., Maller, J. B., Tukiainen, T., et al. (2015) · Science · doi:10.1126/science.1262110 · Cited by 5,062
mediation:R Package for Causal Mediation Analysis
Tingley, D., Yamamoto, T., Hirose, K., Keele, L., & Imai, K. (2014) · Journal of Statistical Software · doi:10.18637/jss.v059.i05 · Cited by 4,155
Genetic effects on gene expression across human tissues
GTEx Consortium (2017) · Nature · doi:10.1038/nature24277 · Cited by 4,036
BioGRID: a general repository for interaction datasets
Stark, C. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj109 · Cited by 3,960
CADD: predicting the deleteriousness of variants throughout the human genome
Rentzsch, P., Witten, D., Cooper, G. M., Shendure, J., & Kircher, M. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1016 · Cited by 3,534
GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists
Eden, E., Navon, R., Steinfeld, I., Lipson, D., & Yakhini, Z. (2009) · BMC Bioinformatics · doi:10.1186/1471-2105-10-48 · Cited by 3,356
Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets
Zhu, Z., Zhang, F., Hu, H., Bakshi, A., Robinson, M. R., Powell, J. E., Montgomery, G. W., Goddard, M. E., Wray, N. R., Visscher, P. M., et al. (2016) · Nature Genetics · doi:10.1038/ng.3538 · Cited by 3,352
Integrating common and rare genetic variation in diverse human populations
The International HapMap 3 Consortium (2010) · Nature · doi:10.1038/nature09298 · Cited by 3,026
ChromHMM: automating chromatin-state discovery and characterization
Ernst, J., & Kellis, M. (2012) · Nature Methods · doi:10.1038/nmeth.1906 · Cited by 2,475
Nalls, M. A., Blauwendraat, C., Vallerga, C. L., Heilbron, K., Bandres-Ciga, S., Chang, D., Tan, M., Kia, D. A., Noyce, A. J., Xue, A., et al. (2019) · The Lancet Neurology · doi:10.1016/s1474-4422(19)30320-5 · Cited by 2,454
The UCSC Table Browser data retrieval tool
Karolchik, D. (2004) · Nucleic Acids Research · doi:10.1093/nar/gkh103 · Cited by 2,316
Jansen, I. E., Savage, J. E., Watanabe, K., Bryois, J., Williams, D. M., Steinberg, S., Sealock, J., Karlsson, I. K., Hägg, S., Athanasiu, L., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0311-9 · Cited by 2,277
Genomic atlas of the human plasma proteome
Sun, B. B., Maranville, J. C., Peters, J. E., Stacey, D., Staley, J. R., Blackshaw, J., Burgess, S., Jiang, T., Paige, E., Surendran, P., et al. (2018) · Nature · doi:10.1038/s41586-018-0175-2 · Cited by 2,268
Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis
Leek, J. T., & Storey, J. D. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030161 · Cited by 1,930
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
A gene-based association method for mapping traits using reference transcriptome data
Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., Eyler, A. E., Denny, J. C., GTEx Consortium, Nicolae, D. L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3367 · Cited by 1,829
Proteogenomic characterization of human colon and rectal cancer
CPTAC, T. N., Zhang, B., Wang, J., Wang, X., Zhu, J., Liu, Q., Shi, Z., Chambers, M. C., Zimmerman, L. J., Shaddox, K. F., et al. (2014) · Nature · doi:10.1038/nature13438 · Cited by 1,462
A survey of human brain transcriptome diversity at the single cell level
Darmanis, S., Sloan, S. A., Zhang, Y., Enge, M., Caneda, C., Shuer, L. M., Hayden Gephart, M. G., Barres, B. A., & Quake, S. R. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1507125112 · Cited by 1,401
Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS
Nicolae, D. L., Gamazon, E., Zhang, W., Duan, S., Dolan, M. E., & Cox, N. J. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1000888 · Cited by 1,292
McAlister, G. C., Nusinow, D. P., Jedrychowski, M. P., Wühr, M., Huttlin, E. L., Erickson, B. K., Rad, R., Haas, W., & Gygi, S. P. (2014) · Analytical Chemistry · doi:10.1021/ac502040v · Cited by 1,263
Religious Orders Study and Rush Memory and Aging Project
Bennett, D. A., Buchman, A. S., Boyle, P. A., Barnes, L. L., Wilson, R. S., & Schneider, J. A. (2018) · Journal of Alzheimer's Disease · doi:10.3233/jad-179939 · Cited by 1,260
Self-citationUnderstanding mechanisms underlying human gene expression variation with RNA sequencing
Pickrell, J. K., Marioni, J. C., Pai, A. A., Degner, J. F., Engelhardt, B. E., Nkadori, E., Veyrieras, J.-B., Stephens, M., Gilad, Y., & Pritchard, J. K. (2010) · Nature · doi:10.1038/nature08872 · Cited by 1,256
Global signatures of protein and mRNA expression levels
de Sousa Abreu, R., Penalva, L. O., Marcotte, E. M., & Vogel, C. (2009) · Molecular BioSystems · doi:10.1039/b908315d · Cited by 1,248
Population genomics of human gene expression
Stranger, B. E., Nica, A. C., Forrest, M. S., Dimas, A., Bird, C. P., Beazley, C., Ingle, C. E., Dunning, M., Flicek, P., Koller, D., et al. (2007) · Nature Genetics · doi:10.1038/ng2142 · Cited by 1,157
Gene expression elucidates functional impact of polygenic risk for schizophrenia
Fromer, M., Roussos, P., Sieberts, S. K., Johnson, J. S., Kavanagh, D. H., Perumal, T. M., Ruderfer, D. M., Oh, E. C., Topol, A., Shah, H. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4399 · Cited by 1,091
Self-citationStegle, O., Parts, L., Piipari, M., Winn, J., & Durbin, R. (2012) · Nature Protocols · doi:10.1038/nprot.2011.457 · Cited by 1,049
Johnson, E. C. B., Dammer, E. B., Duong, D. M., Ping, L., Zhou, M., Yin, L., Higginbotham, L. A., Guajardo, A., White, B., Troncoso, J. C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-020-0815-6 · Cited by 1,001
Self-citationAlzheimer's disease: early alterations in brain DNA methylation at ANK1, BIN1, RHBDF2 and other loci
De Jager, P. L., Srivastava, G., Lunnon, K., Burgess, J., Schalkwyk, L. C., Yu, L., Eaton, M. L., Keenan, B. T., Ernst, J., McCabe, C., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3786 · Cited by 956 · cited 1×
“Annotation of the multi-sample VCF (n=1,196) was performed using Bystro [18] and supplemented by the Broad’s ChromHMM annotation of dPFC tissue [19, 20].”
Self-citationA genome-wide association study of global gene expression
Dixon, A. L., Liang, L., Moffatt, M. F., Chen, W., Heath, S., Wong, K. C. C., Taylor, J., Burnett, E., Gut, I., Farrall, M., et al. (2007) · Nature Genetics · doi:10.1038/ng2109 · Cited by 953
Opportunities and challenges for transcriptome-wide association studies
Wainberg, M., Sinnott-Armstrong, N., Mancuso, N., Barbeira, A. N., Knowles, D. A., Golan, D., Ermel, R., Ruusalepp, A., Quertermous, T., Hao, K., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0385-z · Cited by 917
Overview and Findings from the Rush Memory and Aging Project
A. Bennett, D., A. Schneider, J., S. Buchman, A., L. Barnes, L., A. Boyle, P., & S. Wilson, R. (2012) · Current Alzheimer Research · doi:10.2174/156720512801322663 · Cited by 862
Drugs, their targets and the nature and number of drug targets
Imming, P., Sinning, C., & Meyer, A. (2006) · Nature Reviews Drug Discovery · doi:10.1038/nrd2132 · Cited by 839
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
23andMe Research Team, Nagel, M., Jansen, P. R., Stringer, S., Watanabe, K., de Leeuw, C. A., Bryois, J., Savage, J. E., Hammerschlag, A. R., Skene, N. G., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0151-7 · Cited by 799
Cell type- and brain region-resolved mouse brain proteome
Sharma, K., Schmitt, S., Bergner, C. G., Tyanova, S., Kannaiyan, N., Manrique-Hoyos, N., Kongi, K., Cantuti, L., Hanisch, U.-K., Philips, M.-A., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4160 · Cited by 785
Comprehensive comparative analysis of strand-specific RNA sequencing methods
Levin, J. Z., Yassour, M., Adiconis, X., Nusbaum, C., Thompson, D. A., Friedman, N., Gnirke, A., & Regev, A. (2010) · Nature Methods · doi:10.1038/nmeth.1491 · Cited by 753
Colocalization of GWAS and eQTL Signals Detects Target Genes
Hormozdiari, F., van de Bunt, M., Segrè, A. V., Li, X., Joo, J. W. J., Bilow, M., Sul, J. H., Sankararaman, S., Pasaniuc, B., & Eskin, E. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.10.003 · Cited by 697
Co-regulatory networks of human serum proteins link genetics to disease
Emilsson, V., Ilkov, M., Lamb, J. R., Finkel, N., Gudmundsson, E. F., Pitts, R., Hoover, H., Gudmundsdottir, V., Horman, S. R., Aspelund, T., et al. (2018) · Science · doi:10.1126/science.aaq1327 · Cited by 680
Overview and Findings from the Religious Orders Study
A. Bennett, D., A. Schneider, J., Arvanitakis, Z., & S. Wilson, R. (2012) · Current Alzheimer Research · doi:10.2174/156720512801322573 · Cited by 672
Comparative genetic architectures of schizophrenia in East Asian and European populations
Schizophrenia Working Group of the Psychiatric Genomics Consortium, Lam, M., Indonesia Schizophrenia Consortium, Genetic REsearch on schizophreniA neTwork-China and the Netherlands (GREAT-CN), Chen, C.-Y., Li, Z., Martin, A. R., Bryois, J., Ma, X., Gaspar, H., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0512-x · Cited by 655
Genetic variability in the regulation of gene expression in ten regions of the human brain
UK Brain Expression Consortium, Ramasamy, A., North American Brain Expression Consortium, Trabzuni, D., Guelfi, S., Varghese, V., Smith, C., Walker, R., De, T., Coin, L., et al. (2014) · Nature Neuroscience · doi:10.1038/nn.3801 · Cited by 644
Removing technical variability in RNA-seq data using conditional quantile normalization
Hansen, K. D., Irizarry, R. A., & WU, Z. (2012) · Biostatistics · doi:10.1093/biostatistics/kxr054 · Cited by 607
A multi-omic atlas of the human frontal cortex for aging and Alzheimer’s disease research
De Jager, P. L., Ma, Y., McCabe, C., Xu, J., Vardarajan, B. N., Felsky, D., Klein, H.-U., White, C. C., Peters, M. A., Lodgson, B., et al. (2018) · Scientific Data · doi:10.1038/sdata.2018.142 · Cited by 598
Self-citationRevealing the architecture of gene regulation: the promise of eQTL studies
Gilad, Y., Rifkin, S. A., & Pritchard, J. K. (2008) · Trends in Genetics · doi:10.1016/j.tig.2008.06.001 · Cited by 544
Comparative analysis of RNA sequencing methods for degraded or low-input samples
Adiconis, X., Borges-Rivera, D., Satija, R., DeLuca, D. S., Busby, M. A., Berlin, A. M., Sivachenko, A., Thompson, D. A., Wysoker, A., Fennell, T., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2483 · Cited by 503
Stegle, O., Parts, L., Durbin, R., & Winn, J. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000770 · Cited by 497
Mertins, P., Tang, L. C., Krug, K., Clark, D. J., Gritsenko, M. A., Chen, L., Clauser, K. R., Clauss, T. R., Shah, P., Gillette, M. A., et al. (2018) · Nature Protocols · doi:10.1038/s41596-018-0006-9 · Cited by 479
Seyfried, N. T., Dammer, E. B., Swarup, V., Nandakumar, D., Duong, D. M., Yin, L., Deng, Q., Nguyen, T., Hales, C. M., Wingo, T., et al. (2017) · Cell Systems · doi:10.1016/j.cels.2016.11.006 · Cited by 477
Self-citationImpact 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
Arizona Study of Aging and Neurodegenerative Disorders and Brain and Body Donation Program
Beach, T. G., Adler, C. H., Sue, L. I., Serrano, G., Shill, H. A., Walker, D. G., Lue, L., Roher, A. E., Dugger, B. N., Maarouf, C., et al. (2015) · Neuropathology · doi:10.1111/neup.12189 · Cited by 460
Self-citationAn xQTL map integrates the genetic architecture of the human brain's transcriptome and epigenome
Ng, B., White, C. C., Klein, H.-U., Sieberts, S. K., McCabe, C., Patrick, E., Xu, J., Yu, L., Gaiteri, C., Bennett, D. A., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4632 · Cited by 452
Self-citationVariation 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
Defining the consequences of genetic variation on a proteome-wide scale
Chick, J. M., Munger, S. C., Simecek, P., Huttlin, E. L., Choi, K., Gatti, D. M., Raghupathy, N., Svenson, K. L., Churchill, G. A., & Gygi, S. P. (2016) · Nature · doi:10.1038/nature18270 · Cited by 361
Sieberts, S. K., Perumal, T. M., Carrasquillo, M. M., Allen, M., Reddy, J. S., Hoffman, G. E., Dang, K. K., Calley, J., Ebert, P. J., Eddy, J., et al. (2020) · Scientific Data · doi:10.1038/s41597-020-00642-8 · Cited by 318
Self-citationPrimate Transcript and Protein Expression Levels Evolve Under Compensatory Selection Pressures
Khan, Z., Ford, M. J., Cusanovich, D. A., Mitrano, A., Pritchard, J. K., & Gilad, Y. (2013) · Science · doi:10.1126/science.1242379 · Cited by 240
Johnson, E. C. B., Dammer, E. B., Duong, D. M., Yin, L., Thambisetty, M., Troncoso, J. C., Lah, J. J., Levey, A. I., & Seyfried, N. T. (2018) · Molecular Neurodegeneration · doi:10.1186/s13024-018-0282-4 · Cited by 225 · cited 1×
“The TMT labeling was performed as described by [23, 24], followed by high-pH fractionation performed as described by [25] with slight modification.”
MethodSelf-citationGlobal quantitative analysis of the human brain proteome in Alzheimer’s and Parkinson’s Disease
Ping, L., Duong, D. M., Yin, L., Gearing, M., Lah, J. J., Levey, A. I., & Seyfried, N. T. (2018) · Scientific Data · doi:10.1038/sdata.2018.36 · Cited by 221
Self-citationWingo, A. P., Dammer, E. B., Breen, M. S., Logsdon, B. A., Duong, D. M., Troncosco, J. C., Thambisetty, M., Beach, T. G., Serrano, G. E., Reiman, E. M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09613-z · Cited by 211 · cited 1×
“In addition to these hallmark proteins, the abundance of hundreds of other proteins have also been shown to correlate with neurodegenerative disease phenotypes such as rate of decline of cognition and diagnosis of AD [1, 2].”
BackgroundSelf-citationEstimating the causal tissues for complex traits and diseases
GTEx Consortium, Ongen, H., Brown, A. A., Delaneau, O., Panousis, N. I., Nica, A. C., & Dermitzakis, E. T. (2017) · Nature Genetics · doi:10.1038/ng.3981 · Cited by 198
Hormozdiari, F., Gazal, S., van de Geijn, B., Finucane, H. K., Ju, C. J. ‐., Loh, P., Schoech, A. P., Reshef, Y., Liu, X., O’Connor, L. J., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0148-2 · Cited by 188
Shared proteomic effects of cerebral atherosclerosis and Alzheimer’s disease on the human brain
Wingo, A. P., Fan, W., Duong, D. M., Gerasimov, E. S., Dammer, E. B., Liu, Y., Harerimana, N. V., White, B., Thambisetty, M., Troncoso, J. C., et al. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-0635-5 · Cited by 161
Self-citationGamazon, E. R., Badner, J. A., Cheng, L., Zhang, C., Zhang, D., Cox, N. J., Gershon, E. S., Kelsoe, J. R., Greenwood, T. A., Nievergelt, C. M., et al. (2012) · Molecular Psychiatry · doi:10.1038/mp.2011.174 · Cited by 158
Protein abundances are more conserved than mRNA abundances across diverse taxa
Laurent, J. M., Vogel, C., Kwon, T., Craig, S. A., Boutz, D. R., Huse, H. K., Nozue, K., Walia, H., Whiteley, M., Ronald, P. C., et al. (2010) · PROTEOMICS · doi:10.1002/pmic.201000327 · Cited by 156
O’Brien, H., Hannon, E. J., Hill, M., Toste, C., Robertson, M. J., Morgan, J., McLaughlin, G. M., Lewis, C. M., Schalkwyk, L. C., Hall, L. S., et al. (2018) · Genome biology · doi:10.1186/s13059-018-1567-1 · Cited by 145
Proitsi, P., Lupton, M. K., Velayudhan, L., Newhouse, S., Fogh, I., Tsolaki, M., Daniilidou, M., Pritchard, M., Kłoszewska, I., Soininen, H. S., et al. (2014) · PLoS Medicine · doi:10.1371/journal.pmed.1001713 · Cited by 101
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Metadata
- Authors
- 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., Lah, J. J., Levey, A. I., Seyfried, N. T., & Wingo, T. S.
- Affiliations
- Emory University (US)
- Columbia University Irving Medical Center (US)
- Rush University Medical Center (US)
- Corresponding author
- Seyfried, Nicholas T.
- Wingo, Thomas S.
- ORCID
- Venue
- The American Journal of Human Genetics
- Publisher
- openRxiv
- Published
- 2019-10-24
- Volume
- 108
- Issue
- 3
- Pages
- 400-410
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC8008492
- OpenAlex
- W3128936277
- Cited by
- 104
- References
- 89
- Citations by year
- 2026: 14 · 2025: 19 · 2024: 29 · 2023: 14 · 2022: 18 · 2021: 7 · 2020: 3
- FWCI
- 10.03
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The American Journal of Human Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- U.S. Department of Veterans Affairs (BX003853, I01BX003853)
- Michael J. Fox Foundation for Parkinson's Research
- Illinois Department of Public Health (R01AG017917, P30AG10161, R01AG15819, R01AG17917, R01AG30146, R01AG015819)
- Arizona Department of Health Services
- Translational Genomics Research Institute
- Arizona Biomedical Research Commission
- National Institutes of Health (T32 NS00748018, NS007480, AG057195, AG025688)
- National Institute on Aging (R01AG017917, P30-AG 10161, R01AG056533, U01 AG32984, R01AG030146, AG057195, R01 AG30146, R01‐AG17917, R01 AG15819, R01-AG36836, AG 025688, U01AG057195, U01‐AG61356, R01AG036836, U01AG046152, R01AG036042, R01AG015819, R56AG060757, P30AG010161, U01AG032984, U01AG061357, U01AG061356, P30AG019610, R56AG062256, RF1AG051633, AG056533, R56AG062633, P50AG025688, U01‐AG46152, AG051633, P30AG072980, P30AG066511)
- National Institute of Mental Health (U01MH115484)
- National Institute of Neurological Disorders and Stroke (T32NS007480, U24NS072026)
- 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 › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- protein quantitative trait loci, brain proteome, dorsolateral prefrontal cortex, postmortem brain, expression quantitative trait loci, non-synonymous variants, protein abundance, evolutionary constraints, protein-protein interaction
- MeSH terms
- Autopsy, Brain, Female, Gene Expression Regulation, Genotype, Humans, Male, RNA, Messenger, Genetic Variation, Proteome, Quantitative Trait Loci, Proteomics, Genome-Wide Association Study, Transcriptome
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
