The GTEx Consortium atlas of genetic regulatory effects across human tissues
Open accessAbstract
The Genotype-Tissue Expression (GTEx) project was established to characterize genetic effects on the transcriptome across human tissues and to link these regulatory mechanisms to trait and disease associations. Here, we present analyses of the version 8 data, examining 15,201 RNA-sequencing samples from 49 tissues of 838 postmortem donors. We comprehensively characterize genetic associations for gene expression and splicing in cis and trans, showing that regulatory associations are found for almost all genes, and describe the underlying molecular mechanisms and their contribution to allelic heterogeneity and pleiotropy of complex traits. Leveraging the large diversity of tissues, we provide insights into the tissue specificity of genetic effects and show that cell type composition is a key factor in understanding gene regulatory mechanisms in human tissues.
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The CSF variant was not significant in any plasma pQTL dataset or in brain/blood eQTL analyses.
“The CSF variant was not significant in any plasma pQTL dataset (9–11) or in brain/blood eQTL analyses (3–5).”
From Low overlap of plasma and CSF protein quantitative trait loci affects protein discovery for neurological disease (2026) · cites this paper as [5]
The QTL discovery section reports eQTL analyses across tissues, and the Tissue-specificity section identifies brain regions and whole blood among the GTEx tissues. Our copy does not identify the CSF variant or report that it was nonsignificant in those analyses; the plasma pQTL clause is attributed to other citations. (LLM, conf=0.82)
Experimental — accuracy not yet measuredThis variant is in high LD (R2=0.975) with the AD index variant (rs1140239, p.Gly48Ser in DOC2A) and is a strong splicing QTL for DOC2A in brain cortex and cerebellum according to GTEx.
“This variant is in high LD (R2 = 0.975) with the AD index variant (rs1140239, p.Gly48Ser in DOC2A) and is a strong splicing QTL for DOC2A in brain cortex and cerebellum according to GTEx (5).”
From Low overlap of plasma and CSF protein quantitative trait loci affects protein discovery for neurological disease (2026) · cites this paper as [5]
The QTL discovery section reports that GTEx mapped splicing QTLs across tissues, so this is an appropriate source for identifying the GTEx sQTL resource. Our copy does not identify DOC2A as a strong sQTL in brain cortex or cerebellum; searches for DOC2A and rs1140239 also did not substantiate the stated LD value. The specific variant-level claim therefore remains unsupported by this paper's text. (LLM, conf=0.76)
Experimental — accuracy not yet measuredCrucial regulatory elements governing gene expression, such as enhancers and promoters, were consistently detected by all models across multiple heads and layers.
“Crucial regulatory elements governing gene expression, such as enhancers and promoters41–43, were consistently detected by all models across multiple heads and layers.”
From Nucleotide Transformer: building and evaluating robust foundation models for human genomics (2025) · cites this paper as [42]
100 protein-coding genes were identified as aortic valve-specific compared to 45 other tissues from GTEx.
“We identified 100 specific protein-coding genes in the aortic valve compared to 45 other tissues from the Genotype-Tissue Expression (GTEx) project.”
From Aortic valve-specific genes dysregulated in calcific aortic valve stenosis as potential biomarkers and therapeutic targets (2025) · cites this paper as [22]
GTEx includes too small a sample (<2%) of Hispanic individuals for adequate comparison.
“Unfortunately, GTEx includes too small of a sample (<2%) of Hispanic individuals for adequate comparison, so these were removed from the GTEx sets and a different control group was used for white Hispanics in the SPARK group [25].”
From Enrichment of a subset of Neanderthal polymorphisms in autistic probands and siblings (2024) · cites this paper as [25]
eQTL mapping identified 13 genes mapped to the EAS EduYears loci through cis-eQTL using 13 brain tissue types from GTEx v8.
“For eQTL mapping, we identified 13 genes mapped to the EAS EduYears loci through cis-eQTL using 13 brain tissue types from the Genotype-Tissue Expression (GTEx) v8 dataset23”
From Shared genetic architectures of educational attainment in East Asian and European populations (2024) · cites this paper as [23]
Using the GTEx dataset, 739 cases of pQTL-eQTL colocalisation were found in multiple tissues.
“However, while using the GTEx dataset58, we also found 739 cases of pQTL–eQTL colocalisation in multiple tissues (Fig. 3, Supplementary Table S9).”
From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [58]
GTEx found that only 43% of GWAS hits colocalized with a cis-eQTL, suggesting that substantial regulatory information is missed when focusing solely on RNA.
“The Genotype-Tissue Expression (GTEx) consortium found that only 43% of GWAS hits colocalized with a cis-eQTL (5), suggesting that substantial regulatory information is missed when focusing solely on RNA.”
From Low overlap of plasma and CSF protein quantitative trait loci affects protein discovery for neurological disease (2026) · cites this paper as [5]
Analyses of transcript levels from blood and brain among other tissues have generated eQTLs, identifying potentially causal genes for various diseases.
“Analyses of transcript levels from blood and brain among other tissues have generated expression QTLs (eQTLs) (3–5), identifying potentially causal genes for various diseases.”
From Low overlap of plasma and CSF protein quantitative trait loci affects protein discovery for neurological disease (2026) · cites this paper as [5]
Blood eQTLs show low correlation of effects with other bodily contexts, suggesting that blood is an outlier compared with other tissues.
“Blood eQTLs show low correlation of effects with other bodily contexts, suggesting that blood is an outlier compared with other tissues (5).”
From Low overlap of plasma and CSF protein quantitative trait loci affects protein discovery for neurological disease (2026) · cites this paper as [5]
It is probable that the models, without any supervision, have learned to distinguish relevant regulatory genomic features associated with gene expression and methylation variation.
“it is probable that the models, without any supervision, have learned to distinguish relevant regulatory genomic features associated with gene expression and methylation variation.”
From Nucleotide Transformer: building and evaluating robust foundation models for human genomics (2025) · cites this paper as [42]
The Fine-mapping and Discussion sections discuss regulatory variants associated with gene expression, but neither reports that unsupervised models learned to distinguish genomic features. Searches for unsupervised learning and methylation variation found no passage addressing that claim; the available passages have no page numbers. (LLM, conf=0.76)
Experimental — accuracy not yet measuredLiver expression of SERPINA12 was higher in rs77060950-T carriers (P = 2.6 × 10 -8).
“Liver expression of SERPINA12 was also higher in rs77060950-T carriers (P = 2.6 × 10 -8 ) 31”
From Plasma proteomic associations with genetics and health in the UK Biobank (2023) · cites this paper as [31]
The QTL discovery section describes associations between genetic variants and gene expression across tissues, but does not report this specific association. Searches for SERPINA12, rs77060950, and the stated P value found no passage establishing higher liver expression in T carriers; page numbers were not supplied in our copy. (LLM, conf=0.72)
Experimental — accuracy not yet measuredrs9595404 was associated with lower CPB2 abundance in CSF and increased CPB2-AS1 expression in the brain but not with plasma CPB2 (P=0.41).
“For example, rs9595404 was associated with lower carboxypeptidase B2 (CPB2) abundance in CSF and increased CPB2-AS1 expression in the brain (5) but not with plasma CPB2 (P = 0.41; fig. S3A).”
From Low overlap of plasma and CSF protein quantitative trait loci affects protein discovery for neurological disease (2026) · cites this paper as [5]
The citation is attached to the claim that rs9595404 is associated with increased CPB2-AS1 expression in the brain; the plasma result is attributed to the citing paper’s fig. S3A. The atlas discusses brain-tissue genetic effects in “Tissue-specificity of genetic regulatory effects” (page unavailable), but searches for rs9595404 and CPB2-AS1 found no passage addressing that specific association. (LLM, conf=0.74)
Experimental — accuracy not yet measuredFor CD72, the CSF pQTL is a strong eQTL for CD72 RNA levels in the cerebellum, with the G allele increasing both RNA and CSF protein levels.
“For B cell differentiation antigen CD72 (CD72), the CSF pQTL is a strong eQTL for CD72 RNA levels in the cerebellum, with the G allele increasing both RNA and CSF protein levels (5).”
From Low overlap of plasma and CSF protein quantitative trait loci affects protein discovery for neurological disease (2026) · cites this paper as [5]
The QTL discovery and Fine-mapping sections discuss eQTLs generally, but the available passages do not report a CD72 eQTL in the cerebellum or an allele’s effect on CD72 RNA and CSF protein. Searches for CD72, cerebellum, the G allele, and CSF pQTLs found no passage addressing that specific result. (LLM, conf=0.73)
Experimental — accuracy not yet measuredThis variant is a strong splicing QTL for TMEM25 in multiple brain regions.
“This variant is a strong splicing QTL for TMEM25 in multiple brain regions (5).”
From Low overlap of plasma and CSF protein quantitative trait loci affects protein discovery for neurological disease (2026) · cites this paper as [5]
The QTL discovery and Tissue-specificity of genetic regulatory effects sections report splicing QTLs and their sharing across tissues, but do not identify a TMEM25-associated variant or establish its strength in multiple brain regions. Searches for TMEM25 and for the claimed brain-region sQTL found no passage addressing that specific association; the supplied passages have no page numbers. (LLM, conf=0.72)
Experimental — accuracy not yet measuredThe sample size of this study is similar to or larger than GTEx for brain-relevant tissues, and eQTL studies from GTEx do not see the same amount of coding-variant enrichment.
“However, the sample size of our study is similar to or larger than GTEx for brain-relevant tissues, and eQTL studies from GTEx do not see the same amount of coding-variant enrichment5.”
From Genomic atlas of the proteome from brain, CSF and plasma prioritizes proteins implicated in neurological disorders (2021) · cites this paper as [5]
Target expression data was primarily extracted from GTEx, the Human Protein Atlas, UniProt and TISSUES.
“Target expression data was primarily extracted from GTEx (12), the Human Protein Atlas (HPA) (13), UniProt and TISSUES (14).”
From TCRD and pharos 2021: mining the human proteome for disease biology (2021) · cites this paper as [12]
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Cites
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 · see citing works
HISAT: a fast spliced aligner with low memory requirements
Kim, D., Langmead, B., & Salzberg, S. L. (2015) · Nature Methods · doi:10.1038/nmeth.3317 · Cited by 24,112 · see citing works
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927 · 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
Self-citationDifferential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581 · see citing works
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 · see citing works
The UK Biobank resource with deep phenotyping and genomic data
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0579-z · Cited by 8,876 · see citing works
A scaling normalization method for differential expression analysis of RNA-seq data
Robinson, M. D., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r25 · Cited by 7,876 · see citing works
Biological insights from 108 schizophrenia-associated genetic loci
Schizophrenia Working Group of the Psychiatric Genomics Consortium (2014) · Nature · doi:10.1038/nature13595 · Cited by 7,577 · see citing works
Integrative analysis of 111 reference human epigenomes
Roadmap Epigenomics Consortium, Kundaje, A., Meuleman, W., Ernst, J., Bilenky, M., Yen, A., Heravi-Moussavi, A., Kheradpour, P., Zhang, Z., Wang, J., et al. (2015) · Nature · doi:10.1038/nature14248 · Cited by 6,368 · see citing works
Self-citationThe 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 · see citing works
Self-citationxCell: digitally portraying the tissue cellular heterogeneity landscape
Aran, D., Hu, Z., & Butte, A. J. (2017) · Genome Biology · doi:10.1186/s13059-017-1349-1 · Cited by 4,397 · see citing works
Meta-Analysis of 74,046 Individuals Identifies 11 New Susceptibility Loci for Alzheimer’s Disease
(EADI), E. A. D. I., Lambert, J.-C., (GERAD), G. A. E. R. I. A. D., Alzheimer's Disease Genetic Consortium (ADGC), (CHARGE), C. F. H. A. A. R. I. G. E., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., et al. (2013) · Nature Genetics · doi:10.1038/ng.2802 · Cited by 4,336 · see citing works
Self-citationGiambartolomei, 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
Buniello, A., MacArthur, J. A. L., Cerezo, M., Harris, L. W., Hayhurst, J., Malangone, C., McMahon, A., Morales, J., Mountjoy, E., Sollis, E., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1120 · Cited by 4,000 · see citing works
Self-citationGenome-wide efficient mixed-model analysis for association studies
Zhou, X., & Stephens, M. (2012) · Nature Genetics · doi:10.1038/ng.2310 · Cited by 3,627 · see citing works
Self-citationIntegrating common and rare genetic variation in diverse human populations
The International HapMap 3 Consortium (2010) · Nature · doi:10.1038/nature09298 · Cited by 3,026 · see citing works
Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis
Võsa, U., Claringbould, A., Westra, H.-J., Bonder, M. J., Deelen, P., Zeng, B., Kirsten, H., Saha, A., Kreuzhuber, R., Kasela, S., et al. (2018) · doi:10.1101/447367 · Cited by 2,607 · see citing works
Self-citationVõsa, U., Claringbould, A., Westra, H.-J., Bonder, M. J., Deelen, P., Zeng, B., Kirsten, H., Saha, A., Kreuzhuber, R., Yazar, S., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00913-z · Cited by 2,607 · see citing works
Self-citationPartitioning heritability by functional annotation using genome-wide association summary statistics
ReproGen Consortium, Finucane, H. K., Schizophrenia Working Group of the Psychiatric Genomics Consortium, The RACI Consortium, Bulik-Sullivan, B., Gusev, A., Trynka, G., Reshef, Y., Loh, P.-R., Anttila, V., et al. (2015) · Nature Genetics · doi:10.1038/ng.3404 · Cited by 2,604 · see citing works
Self-citationLiu, 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
Genetics of rheumatoid arthritis contributes to biology and drug discovery
the RACI consortium, Okada, Y., the GARNET consortium, Wu, D., Trynka, G., Raj, T., Terao, C., Ikari, K., Kochi, Y., Ohmura, K., et al. (2014) · Nature · doi:10.1038/nature12873 · Cited by 2,310 · see citing works
Self-citationNew genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk
DIAGRAM Consortium, Dupuis, J., GIANT Consortium, Global BPgen Consortium, Anders Hamsten on behalf of Procardis Consortium, the MAGIC investigators, Langenberg, C., Prokopenko, I., Saxena, R., Soranzo, N., et al. (2010) · Nature Genetics · doi:10.1038/ng.520 · Cited by 2,166 · see citing works
Self-citationTranscriptome 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
Self-citationThe Electronic Medical Records and Genomics (eMERGE) Consortium, Wood, A. R., The MIGen Consortium, The PAGE Consortium, Study, T. L. C., Esko, T., Yang, J., Vedantam, S., Pers, T. H., Gustafsson, S., et al. (2014) · Nature Genetics · doi:10.1038/ng.3097 · Cited by 1,853 · see citing works
Self-citationA 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., Nicolae, D. L., Cox, N. J., et al. (2015) · Nature Genetics · doi:10.1038/ng.3367 · Cited by 1,762 · see citing works
Self-citationSystematic 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
Association analysis identifies 65 new breast cancer risk loci
Michailidou, K., Lindström, S., Dennis, J., Beesley, J., Hui, S., Kar, S., Lemaçon, A., Soucy, P., Glubb, D. M., Rostamianfar, A., et al. (2017) · Nature · doi:10.1038/nature24284 · Cited by 1,380 · see citing works
Improved whole-chromosome phasing for disease and population genetic studies
Delaneau, O., Zagury, J., & Marchini, J. (2012) · Nature Methods · doi:10.1038/nmeth.2307 · Cited by 1,309 · see citing works
Genome-wide association study identifies 74 loci associated with educational attainment
Okbay, A., Beauchamp, J. P., Fontana, M. A., Lee, J. J., Pers, T. H., Rietveld, C. A., Turley, P., Chen, G.-B., Emilsson, V., Meddens, S. F. W., et al. (2016) · Nature · doi:10.1038/nature17671 · Cited by 1,297 · see citing works
Self-citationTrait-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 · see citing works
Self-citationThe 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-citationLandscape of X chromosome inactivation across human tissues
GTEx Consortium, Tukiainen, T., Villani, A.-C., Yen, A., Rivas, M. A., Marshall, J. L., Satija, R., Aguirre, M., Gauthier, L., Fleharty, M., et al. (2017) · Nature · doi:10.1038/nature24265 · Cited by 1,138 · see citing works
Self-citationStudy, L. C., Okbay, A., Baselmans, B. M. L., De Neve, J.-E., Turley, P., Nivard, M. G., Fontana, M. A., Meddens, S. F. W., Linnér, R. K., Rietveld, C. A., et al. (2016) · Nature Genetics · doi:10.1038/ng.3552 · Cited by 1,013 · see citing works
Self-citationBentham, J., Morris, D. L., Graham, D. S. C., Pinder, C. L., Tombleson, P., Behrens, T. W., Martin, J. J., Fairfax, B. P., Knight, J. C., Chen, L., et al. (2015) · Nature Genetics · doi:10.1038/ng.3434 · Cited by 990 · see citing works
Self-citationRNA-SeQC: RNA-seq metrics for quality control and process optimization
DeLuca, D. S., Levin, J. Z., Sivachenko, A., Fennell, T., Nazaire, M.-D., Williams, C., Reich, M., Winckler, W., & Getz, G. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts196 · Cited by 899 · see citing works
Self-citationCommon genetic variants influence human subcortical brain structures
Initiative, T. A. D. N., Hibar, D. P., The CHARGE Consortium, EPIGEN, IMAGEN, SYS, Stein, J. L., Renteria, M. E., Arias-Vasquez, A., Desrivières, S., et al. (2015) · Nature · doi:10.1038/nature14101 · Cited by 858 · see citing works
Self-citationThe Human Phenotype Ontology project: linking molecular biology and disease through phenotype data
Köhler, S., Doelken, S., Mungall, C. J., Bauer, S., Firth, H., Bailleul-Forestier, I., Black, G., Brown, D. L., Brudno, M., Campbell, J., et al. (2013) · Nucleic Acids Res. · doi:10.1093/nar/gkt1026 · Cited by 840 · see citing works
A Novel Approach to High-Quality Postmortem Tissue Procurement: The GTEx Project
Carithers, L. J., Ardlie, K., Barcus, M., Branton, P. A., Britton, A., Buia, S. A., Compton, C. C., DeLuca, D. S., Peter-Demchok, J., Gelfand, E. T., et al. (2015) · Biopreservation and Biobanking · doi:10.1089/bio.2015.0032 · Cited by 833 · see citing works
Self-citationAnnotation-free quantification of RNA splicing using LeafCutter
Li, Y. I., Knowles, D. A., Humphrey, J., Barbeira, A. N., Dickinson, S. P., Im, H. K., & Pritchard, J. K. (2018) · Nature Genetics · doi:10.1038/s41588-017-0004-9 · Cited by 765 · see citing works
Self-citationKettunen, J., Demirkan, A., Würtz, P., Draisma, H. H. M., Haller, T., Rawal, R., Vaarhorst, A., Kangas, A. J., Lyytikäinen, L.-P., Pirinen, M., et al. (2016) · Nature Communications · doi:10.1038/ncomms11122 · Cited by 738 · see citing works
Validating therapeutic targets through human genetics
Plenge, R. M., Scolnick, E. M., & Altshuler, D. (2013) · Nature Reviews Drug Discovery · doi:10.1038/nrd4051 · Cited by 712 · see citing works
Fisher, S., Barry, A. J., Abreu, J., Minie, B., Nolan, J., Delorey, T. M., Young, G., Fennell, T. J., Allen, A., Ambrogio, L., et al. (2011) · Genome biology · doi:10.1186/gb-2011-12-1-r1 · Cited by 683 · see citing works
Self-citationThe impact of sex on gene expression across human tissues
Oliva, M., Muñoz-Aguirre, M., Kim-Hellmuth, S., Wucher, V., Gewirtz, A. D., Cotter, D. J., Parsana, P., Kasela, S., Balliu, B., Viñuela, A., et al. (2020) · Science · doi:10.1126/science.aba3066 · Cited by 679 · see citing works
Self-citationRNA splicing is a primary link between genetic variation and disease
Li, Y. I., van de Geijn, B., Raj, A., Knowles, D. A., Petti, A. A., Golan, D., Gilad, Y., & Pritchard, J. K. (2016) · Science · doi:10.1126/science.aad9417 · Cited by 660 · 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
Self-citationWhole‐genome sequencing identifies EN1 as a determinant of bone density and fracture
Zheng, H., Forgetta, V., Hsu, Y., Estrada, K., Roselló‐Díez, A., Leo, P. J., Dahia, C. L., Park‐Min, K. H., Tobias, J. H., Kooperberg, C. L., et al. (2015) · Nature · doi:10.1038/nature14878 · Cited by 593 · see citing works
WASP: allele-specific software for robust molecular quantitative trait locus discovery
van de Geijn, B., McVicker, G., Gilad, Y., & Pritchard, J. K. (2015) · Nature Methods · doi:10.1038/nmeth.3582 · Cited by 575 · see citing works
Modeling 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
Fast and efficient QTL mapper for thousands of molecular phenotypes
Ongen, H., Buil, A., Brown, A. A., Dermitzakis, E. T., & Delaneau, O. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv722 · Cited by 571 · see citing works
Self-citationBattle, 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
Self-citationApproximately independent linkage disequilibrium blocks in human populations
Berisa, T., & Pickrell, J. K. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv546 · Cited by 534 · see citing works
Cell type–specific genetic regulation of gene expression across human tissues
Kim-Hellmuth, S., Aguet, F., Oliva, M., Muñoz-Aguirre, M., Kasela, S., Wucher, V., Castel, S. E., Hamel, A. R., Viñuela, A., Roberts, A. L., et al. (2020) · Science · doi:10.1126/science.aaz8528 · Cited by 530 · see citing works
Self-citationGazal, S., Finucane, H. K., Furlotte, N. A., Loh, P.-R., Palamara, P. F., Liu, X., Schoech, A., Bulik-Sullivan, B., Neale, B. M., Gusev, A., et al. (2017) · Nature Genetics · doi:10.1038/ng.3954 · Cited by 525 · see citing works
Polarization of the Effects of Autoimmune and Neurodegenerative Risk Alleles in Leukocytes
Raj, T., Rothamel, K., Mostafavi, S., Ye, C., Lee, M. N., Replogle, J. M., Feng, T., Lee, M., Asinovski, N., Frohlich, I., et al. (2014) · Science · doi:10.1126/science.1249547 · Cited by 506 · see citing works
Self-citationStegle, O., Parts, L., Durbin, R., & Winn, J. (2010) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1000770 · Cited by 497 · see citing works
Genome-wide associations for birth weight and correlations with adult disease
Horikoshi, M., Beaumont, R. N., Day, F. R., Warrington, N. M., Kooijman, M. N., Fernandez-Tajes, J. J., Feenstra, B., van Zuydam, N. R., Gaulton, K. J., Grarup, N., et al. (2016) · Nature · doi:10.1038/nature19806 · Cited by 495 · see citing works
Tewhey, R., Kotliar, D., Park, D. S., Liu, B., Winnicki, S., Reilly, S. K., Andersen, K. G., Mikkelsen, T. S., Lander, E. S., Schaffner, S. F., et al. (2016) · Cell · doi:10.1016/j.cell.2016.04.027 · Cited by 480 · 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
Self-citationIdentifying Causal Variants at Loci with Multiple Signals of Association
Hormozdiari, F., Kostem, E., Kang, E. Y., Pasaniuc, B., & Eskin, E. (2014) · Genetics · doi:10.1534/genetics.114.167908 · Cited by 465 · see citing works
Self-citationDisease variants alter transcription factor levels and methylation of their binding sites
Bonder, M. J., Luijk, R., Zhernakova, D. V., Moed, M., Deelen, P., Vermaat, M., van Iterson, M., van Dijk, F., van Galen, M., Bot, J. J., et al. (2016) · Nature Genetics · doi:10.1038/ng.3721 · Cited by 462 · see citing works
Identification of context-dependent expression quantitative trait loci in whole blood
Zhernakova, D. V., Deelen, P., Vermaat, M., van Iterson, M., van Galen, M., Arindrarto, W., van 't Hof, P., Mei, H., van Dijk, F., Westra, H.-J., et al. (2017) · Nature Genetics · doi:10.1038/ng.3737 · Cited by 441 · see citing works
Zerbino, D. R., Wilder, S. P., Johnson, N., Juettemann, T., & Flicek, P. R. (2015) · Genome Biology · doi:10.1186/s13059-015-0621-5 · Cited by 429 · see citing works
Urbut, S., Wang, G. T., Carbonetto, P. S., & Stephens, M. (2018) · Nature Genetics · doi:10.1038/s41588-018-0268-8 · Cited by 424 · see citing works
Self-citationGTEx Consortium, Gamazon, E. R., Segrè, A. V., van de Bunt, M., Wen, X., Xi, H. S., Hormozdiari, F., Ongen, H., Konkashbaev, A., Derks, E. M., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0154-4 · Cited by 412 · see citing works
Self-citationJones, S. E., Tyrrell, J., Wood, A. R., Beaumont, R. N., Ruth, K. S., Tuke, M. A., Yaghootkar, H., Hu, Y., Teder-Laving, M., Hayward, C., et al. (2016) · PLOS Genetics · doi:10.1371/journal.pgen.1006125 · Cited by 388 · see citing works
Large multiallelic copy number variations in humans
Handsaker, R. E., Doren, V. V., Berman, J. R., Genovese, G., Kashin, S., Boettger, L. M., & McCarroll, S. A. (2015) · Nature Genetics · doi:10.1038/ng.3200 · Cited by 382 · see citing works
Self-citationTools and best practices for data processing in allelic expression analysis.
Castel, S. E., Levy-Moonshine, A., Mohammadi, P., Banks, E., & Lappalainen, T. (2015) · Genome Biology · doi:10.1186/s13059-015-0762-6 · Cited by 375 · see citing works
Self-citationAbundant associations with gene expression complicate GWAS follow-up
Liu, B., Gloudemans, M. J., Rao, A. S., Ingelsson, E., & Montgomery, S. B. (2019) · Nature Genetics · doi:10.1038/s41588-019-0404-0 · Cited by 370 · see citing works
Self-citationBerthiaume, L. G. (2013) · Nature Chemical Biology · doi:10.1038/nchembio.1414 · Cited by 367 · see citing works
Heritability and genomics of gene expression in peripheral blood
Wright, F. A., Sullivan, P. F., Brooks, A. I., Zou, F., Sun, W., Xia, K., Madar, V., Jansen, R., Chung, W., Zhou, Y.-H., et al. (2014) · Nature Genetics · doi:10.1038/ng.2951 · Cited by 362 · see citing works
Self-citationIntegrating predicted transcriptome from multiple tissues improves association detection
Barbeira, A., Pividori, M., Zheng, J., Wheeler, H. E., Nicolae, D. L., & Im, H. K. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1007889 · Cited by 334 · see citing works
Self-citationHuang, Y.-F., Gulko, B., & Siepel, A. C. (2017) · Nature Genetics · doi:10.1038/ng.3810 · Cited by 329 · see citing works
Small, K. S., Grundberg, E., Soranzo, N., Ahmadi, K. R., Spector, T. D., Shin, S., Deloukas, P., Hedman, Å. K., Lindgren, C. M., McCarthy, M. I., et al. (2011) · Nature Genetics · doi:10.1038/ng.833 · Cited by 320 · see citing works
Self-citationExploiting the GTEx resources to decipher the mechanisms at GWAS loci
Barbeira, A., Bonazzola, R., Gamazon, E. R., Liang, Y., Park, Y., Kim-Hellmuth, S., Wang, G. T., Jiang, Z., Zhou, D., Hormozdiari, F., et al. (2021) · Genome biology · doi:10.1186/s13059-020-02252-4 · Cited by 307 · see citing works
Self-citationThe role of sex in the genomics of human complex traits
Khramtsova, E. A., Davis, L. K., & Stranger, B. E. (2019) · Nature Reviews Genetics · doi:10.1038/s41576-018-0083-1 · Cited by 303 · see citing works
Self-citationScaling computational genomics to millions of individuals with GPUs
Taylor-Weiner, A. N., Aguet, F., Haradhvala, N. J., Gosai, S. J., Anand, S. K., Kim, J., Ardlie, K., Allen, E. M. V., & Getz, G. A. (2019) · Genome biology · doi:10.1186/s13059-019-1836-7 · Cited by 302 · see citing works
Self-citationHammerschlag, A. R., Stringer, S., de Leeuw, C. A., Sniekers, S., Taskesen, E., Watanabe, K., Blanken, T. F., Dekker, K., te Lindert, B. H. W., Wassing, R., et al. (2017) · Nature Genetics · doi:10.1038/ng.3888 · Cited by 289 · see citing works
Wen, X., Piqué-Regi, R., & Luca, F. (2017) · PLoS Genetics · doi:10.1371/journal.pgen.1006646 · Cited by 269 · see citing works
Self-citationThe Genotype-Tissue Expression (GTEx) project
Lonsdale, J., Thomas, J., Salvatore, M., Phillips, R., Lo, E., Shad, S., Hasz, R., Walters, G., Garcia, F., Young, N., et al. (2013) · Nature Genetics · doi:10.1038/ng.2653 · Cited by 263 · see citing works
Self-citationGATA3: a master of many trades in immune regulation
Wan, Y. Y. (2014) · Trends in Immunology · doi:10.1016/j.it.2014.04.002 · Cited by 248 · see citing works
Identification of Novel Genetic Markers of Breast Cancer Survival
Guo, Q. J., Schmidt, M. K., Kraft, P., Canisius, S., Chen, C., Khan, S., Tyrer, J. P., Bolla, M. K., Wang, Q., Dennis, J., et al. (2015) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djv081 · Cited by 234 · see citing works
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Buil, A., Brown, A. A., Lappalainen, T., Viñuela, A., Davies, M. N., Zheng, H.-F., Richards, J. B., Glass, D., Small, K. S., Durbin, R. M., et al. (2014) · Nature Genetics · doi:10.1038/ng.3162 · Cited by 210 · see citing works
Self-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 · see citing works
Self-citationA Genetic Investigation of Sex Bias in the Prevalence of Attention-Deficit/Hyperactivity Disorder
Martin, J., Walters, R. K., Demontis, D., Mattheisen, M., Lee, S., Robinson, E., Brikell, I., Ghirardi, L., Larsson, H., Lichtenstein, P., et al. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.11.026 · Cited by 190 · see citing works
Fast and accurate imputation of summary statistics enhances evidence of functional enrichment
Paşaniuc, B., Zaitlen, N., Shi, H., Bhatia, G., Gusev, A., Pickrell, J. K., Hirschhorn, J. N., Strachan, D. P., Patterson, N. J., & Price, A. L. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu416 · Cited by 180 · see citing works
Efficient Integrative Multi-SNP Association Analysis via Deterministic Approximation of Posteriors
Wen, X., Lee, Y., Luca, F., & Piqué-Regi, R. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.03.029 · Cited by 178 · see citing works
Self-citationFemale Breast Cancer Incidence Among Asian and Western Populations: More Similar Than Expected
Sung, H., Rosenberg, P. S., Chen, W.-Q., et al. (2015) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djv107 · Cited by 175 · see citing works
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 174 · see citing works
Self-citationInterpreting Meta-Analyses of Genome-Wide Association Studies
Han, B., & Eskin, E. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002555 · Cited by 173 · see citing works
Self-citationChromatin three-dimensional interactions mediate genetic effects on gene expression
Delaneau, O., Zazhytska, M., Borel, C., Giannuzzi, G., Rey, G., Howald, C., Kumar, S., Ongen, H., Popadin, K., Marbach, D., et al. (2019) · Science · doi:10.1126/science.aat8266 · Cited by 168 · see citing works
Self-citationTranscriptomic signatures across human tissues identify functional rare genetic variation
Ferraro, N. M., Strober, B. J., Einson, J. E., Abell, N. S., Aguet, F., Barbeira, A., Brandt, M. K., Bućan, M., Castel, S. E., Davis, J. R., et al. (2020) · Science · doi:10.1126/science.aaz5900 · Cited by 155 · see citing works
Self-citationRare variant phasing and haplotypic expression from RNA sequencing with phASER
Castel, S., Mohammadi, P., Chung, W., Shen, Y.-F., & Lappalainen, T. (2016) · Nature Communications · doi:10.1038/ncomms12817 · Cited by 153 · see citing works
Self-citationQuantifying the regulatory effect size of cis -acting genetic variation using allelic fold changePreprint
Mohammadi, P., Castel, S. E., Brown, A. A., & Lappalainen, T. (2017) · Genome Research · doi:10.1101/gr.216747.116 · Cited by 146 · see citing works
Self-citationToland, A. E., Dai, Q., Gao, Y.-T., Nagase, H., Dunlop, M. G., Farrington, S. M., Barnetson, R. A., Anton‐Culver, H., Peel, D. A., Ziogas, A., et al. (2005) · Carcinogenesis · doi:10.1093/carcin/bgi085 · Cited by 144 · see citing works
Conditional eQTL analysis reveals allelic heterogeneity of gene expression
Jansen, R., Hottenga, J.-J., Nivard, M. G., Abdellaoui, A., Laport, B., de Geus, E. J., Wright, F. A., Penninx, B. W. J. H., & Boomsma, D. I. (2017) · Human Molecular Genetics · doi:10.1093/hmg/ddx043 · Cited by 141 · see citing works
Davis, J. R., Frésard, L., Knowles, D. A., Pala, M., Bustamante, C. D., Battle, A. J., & Montgomery, S. B. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.11.021 · Cited by 138 · see citing works
Self-citationGenome-wide rare variant analysis for thousands of phenotypes in over 70,000 exomes from two cohorts
Cirulli, E. T., White, S., Read, R. W., Elhanan, G., Metcalf, W. J., Tanudjaja, F., Fath, D. M., Sandoval, E., Isaksson, M., Schlauch, K. A., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14288-y · Cited by 135 · see citing works
A vast resource of allelic expression data spanning human tissues
Castel, S., Aguet, F., Mohammadi, P., Ardlie, K., Lappalainen, T., & GTEx Consortium (2019) · Genome Biology · doi:10.1186/s13059-020-02122-z · Cited by 117 · see citing works
Self-citationImpact of admixture and ancestry on eQTL analysis and GWAS colocalization in GTEx
Gay, N. R., Gloudemans, M. J., Antonio, M. L., Abell, N. S., Balliu, B., Park, Y., Martin, A. R., Musharoff, S. A., Rao, A. S., Aguet, F., et al. (2020) · Genome biology · doi:10.1186/s13059-020-02113-0 · Cited by 116 · see citing works
Self-citationSul, J. H., Han, B., Ye, C., Choi, T., & Eskin, E. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003491 · Cited by 112 · see citing works
Self-citationBrown, A. A., Viñuela, A., Delaneau, O., Spector, T. D., Small, K. S., & Dermitzakis, E. T. (2017) · Nature Genetics · doi:10.1038/ng.3979 · Cited by 108 · see citing works
Self-citationWen, X. (2016) · The Annals of Applied Statistics · doi:10.1214/16-aoas952 · Cited by 101 · see citing works
Self-citationPeters, J. E., Lyons, P., Lee, J. C., Richard, A. C., Fortune, M. D., Newcombe, P. J., Richardson, S. T., & Smith, K. G. C. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1005908 · Cited by 93 · see citing works
Self-citationGenetic regulatory variation in populations informs transcriptome analysis in rare disease
Mohammadi, P., Castel, S., Cummings, B., Einson, J., Sousa, C., Hoffman, P. J., Donkervoort, S., Jiang, Z.-X., Mohassel, P., Foley, A., et al. (2019) · Science · doi:10.1126/science.aay0256 · Cited by 93 · see citing works
Self-citationCBX8 Exhibits Oncogenic Activity via AKT/β-Catenin Activation in Hepatocellular Carcinoma
Zhang, C. Z., Chen, S.-L., Wang, C., He, Y.-F., Yang, X., Xie, D., & Yun, J. (2017) · Cancer Research · doi:10.1158/0008-5472.can-17-0700 · Cited by 91 · see citing works
Self-citationSaha, A., & Battle, A. J. (2018) · F1000Research · doi:10.12688/f1000research.17145.2 · Cited by 91 · see citing works
Self-citationCbx8 Acts Non-canonically with Wdr5 to Promote Mammary Tumorigenesis
Chung, C., Sun, Z., Mullokandov, G., Bosch, A., Qadeer, Z. A., Cihan, E., Rapp, Z., Parsons, R. E., Aguirre‐Ghiso, J. A., Farias, E., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.06.002 · Cited by 89 · see citing works
Widespread Allelic Heterogeneity in Complex Traits
Hormozdiari, F., Zhu, A., Kichaev, G., Ju, C. J. ‐., Segrè, A. V., Joo, J. W. J., Won, H., Sankararaman, S., Paşaniuc, B., Shifman, S., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.04.005 · Cited by 87 · see citing works
Self-citationDIST: direct imputation of summary statistics for unmeasured SNPs
Lee, D., Bigdeli, T. B., Riley, B. P., Fanous, A. H., & Bacanu, S. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt500 · Cited by 74 · see citing works
Yang, F., Wang, J., Pierce, B. L., & Chen, L. (2017) · Genome Research · doi:10.1101/gr.216754.116 · Cited by 74 · see citing works
Self-citationJordan, D. M., Verbanck, M., & Do, R. (2019) · Genome Biology · doi:10.1186/s13059-019-1844-7 · Cited by 73 · see citing works
Exploiting the GTEx resources to decipher the mechanisms at GWAS lociPreprint
Barbeira, A., Bonazzola, R., Gamazon, E. R., Liang, Y., Park, Y., Kim-Hellmuth, S., Wang, G. T., Jiang, Z., Zhou, D., Hormozdiari, F., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/814350 · Cited by 70 · see citing works
Self-citationBarbeira, A. N., Dickinson, S. P., Bonazzola, R., Zheng, J., Wheeler, H. E., Torres, J. M., Torstenson, E. S., Shah, K. P., Garcia, T., Edwards, T. L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03621-1 · Cited by 69 · see citing works
Self-citationAllelic mapping bias in RNA-sequencing is not a major confounder in eQTL studies
Panousis, N. I., Gutiérrez‐Arcelus, M., Dermitzakis, E. T., & Lappalainen, T. (2014) · Genome biology · doi:10.1186/s13059-014-0467-2 · Cited by 67 · see citing works
Self-citationFunctional Phe31Ile polymorphism in Aurora A and risk of breast carcinoma
Sun, T. (2004) · Carcinogenesis · doi:10.1093/carcin/bgh244 · Cited by 64 · see citing works
Deletion of mstna and mstnb impairs the immune system and affects growth performance in zebrafish
Wang, C., Chen, Y., Bian, W.-P., Xie, S., Qi, G.-L., Liu, L., Strauss, P. R., Zou, J., & Pei, D. (2017) · Fish & Shellfish Immunology · doi:10.1016/j.fsi.2017.11.040 · Cited by 62 · see citing works
CBX8 exhibits oncogenic properties and serves as a prognostic factor in hepatocellular carcinoma
Tang, B., Tian, Y., Liao, Y., Li, Z., Yu, S., Su, H., Zhong, F., Yuan, G., Wang, Y., Yu, H., et al. (2019) · Cell Death and Disease · doi:10.1038/s41419-018-1288-0 · Cited by 47 · see citing works
Yin, L., Coelho, S. G., Ebsen, D., Smuda, C., Mahns, A., Miller, S. A., Beer, J. Z., Kolbe, L., & Hearing, V. J. (2014) · Experimental Dermatology · doi:10.1111/exd.12518 · Cited by 45 · see citing works
Reconstructed Skin Models Revealed Unexpected Differences in Epidermal African and Caucasian Skin
Girardeau‐Hubert, S., Deneuville, C., Pageon, H., Abed, K., Tacheau, C., Cavusoglu, N., Donovan, M., Bernard, D., & Asselineau, D. (2019) · Scientific Reports · doi:10.1038/s41598-019-43128-3 · Cited by 44 · see citing works
Ruan, Y., Song, A., Wang, H., et al. (2011) · The Journal of Pathology · doi:10.1002/path.2902 · Cited by 38 · see citing works
Aurora Kinase A Is a Prognostic Marker in Colorectal Adenocarcinoma
Koh, H. M., Jang, B. G., Hyun, C. L., et al. (2016) · Journal of Pathology and Translational Medicine · doi:10.4132/jptm.2016.10.17 · Cited by 36 · see citing works
Empirical Bayes Matrix Factorization
Wang, W., & Stephens, M. (2018) · PubMed · doi:10.48550/arxiv.1802.06931 · Cited by 28 · see citing works
Self-citationDhanasekaran, K., Bose, A., Rao, V. J., Boopathi, R., Shankar, S. R., Rao, V. K., Swaminathan, A., Vasudevan, M., Taneja, R., & Kundu, T. K. (2018) · The FASEB Journal · doi:10.1096/fj.201800997 · Cited by 17 · see citing works
Siminoff, L. A., Wilson-Genderson, M., Gardiner, H. M., Mosavel, M., & Barker, K. L. (2018) · Biopreservation and Biobanking · doi:10.1089/bio.2017.0115 · Cited by 10 · see citing works
Self-citationLong non-coding RNA gene regulation and trait associations across human tissuesPreprint
de Goede, O. D., Ferraro, N., Nachun, D., Rao, A. S., Aguet, F., Barbeira, A., Castel, S., Kim-Hellmuth, S., Park, Y., Scott, A., et al. (2019) · bioRxiv · doi:10.1101/793091 · Cited by 9 · see citing works
Self-citationSmall, K. S., Hedman, Å. K., Grundberg, E., Nica, A. C., Þorleifsson, G., Kong, A., Thorsteindottir, U., Shin, S., Richards, H. B., Soranzo, N., et al. (2011) · Surrey Research Insight Open Access (The University of Surrey) · doi:10.1038/ng1011-1040c · Cited by 7 · see citing works
Self-citationLeveraging eQTLs to identify individual-level tissue of interest for a complex trait
Majumdar, A., Giambartolomei, C., Cai, N., Haldar, T., Schwarz, T., Gandal, M. J., Jonathan Flint, C., & Paşaniuc, B. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008915 · Cited by 5 · see citing works
broadinstitute/gtex-pipeline: GTEx v8
Aguet, F. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3727189 · Cited by 3 · see citing works
Self-citationbroadinstitute/gtex-v8: main_figures
Aguet, F. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.3930961 · Cited by 1 · see citing works
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- [22]Oliva M,, The role of sex in the human transcriptome. bioRxiv (2019).
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New insights into the genetic etiology of Alzheimer’s disease and related dementias
Bellenguez, C., Küçükali, F., Jansen, I. E., Kleineidam, L., Moreno-Grau, S., Amin, N., Naj, A. C., Campos-Martin, R., Grenier-Boley, B., Andrade, V., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01024-z · Cited by 2,498 · see citing works
Self-citationGenome-wide association studies
Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00056-9 · Cited by 1,849 · see citing works
Self-citationSingle-cell chromatin state analysis with Signac
Stuart, T., Srivastava, A., Madad, S., Lareau, C. A., & Satija, R. (2021) · Nature Methods · doi:10.1038/s41592-021-01282-5 · Cited by 1,769 · see citing works
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-citationBurley, S. K., Bhikadiya, C., Bi, C., Bittrich, S., Chen, L., Crichlow, G. V., Christie, C. H., Dalenberg, K., Di Costanzo, L., Duarte, J. M., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa1038 · Cited by 1,581 · see citing works
Self-citationEffective gene expression prediction from sequence by integrating long-range interactions
Avsec, Ž., Agarwal, V., Visentin, D., Ledsam, J. R., Grabska-Barwinska, A., Taylor, K. R., Assael, Y., Jumper, J., Kohli, P., & Kelley, D. R. (2021) · Nature Methods · doi:10.1038/s41592-021-01252-x · Cited by 1,326 · see citing works
Self-citationThe power of genetic diversity in genome-wide association studies of lipids
Graham, S. E., Clarke, S. L., Wu, K.-H. H., Kanoni, S., Zajac, G. J. M., Ramdas, S., Surakka, I., Ntalla, I., Vedantam, S., Winkler, T. W., et al. (2021) · Nature · doi:10.1038/s41586-021-04064-3 · Cited by 972 · see citing works
Self-citationhdWGCNA identifies co-expression networks in high-dimensional transcriptomics data
Morabito, S., Reese, F., Rahimzadeh, N., Miyoshi, E., & Swarup, V. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100498 · Cited by 773 · see citing works
Talquetamab, a T-Cell–Redirecting GPRC5D Bispecific Antibody for Multiple Myeloma
Chari, A., Minnema, M. C., Berdeja, J. G., Oriol, A., van de Donk, N. W. C. J., Rodríguez-Otero, P., Askari, E., Mateos, M.-V., Costa, L. J., Caers, J., et al. (2022) · New England Journal of Medicine · doi:10.1056/nejmoa2204591 · Cited by 744 · see citing works
Single-cell eQTL mapping identifies cell type–specific genetic control of autoimmune disease
Yazar, S., Alquicira-Hernandez, J., Wing, K., Senabouth, A., Gordon, M. G., Andersen, S., Lu, Q., Rowson, A., Taylor, T. R. P., Clarke, L., et al. (2022) · Science · doi:10.1126/science.abf3041 · Cited by 682 · see citing works
Haplotype-resolved diverse human genomes and integrated analysis of structural variation
Ebert, P., Audano, P. A., Zhu, Q., Rodriguez-Martin, B., Porubsky, D., Bonder, M. J., Sulovari, A., Ebler, J., Zhou, W., Serra Mari, R., et al. (2021) · Science · doi:10.1126/science.abf7117 · Cited by 702 · see citing works
Nucleotide Transformer: building and evaluating robust foundation models for human genomics
Dalla-Torre, H., Gonzalez, L., Mendoza-Revilla, J., Lopez Carranza, N., Grzywaczewski, A. H., Oteri, F., Dallago, C., Trop, E., de Almeida, B. P., Sirelkhatim, H., et al. (2025) · Nature Methods · doi:10.1038/s41592-024-02523-z · Cited by 656 · see citing works
Organ aging signatures in the plasma proteome track health and disease
Oh, H. S.-H., Rutledge, J., Nachun, D., Pálovics, R., Abiose, O., Moran-Losada, P., Channappa, D., Urey, D. Y., Kim, K., Sung, Y. J., et al. (2023) · Nature · doi:10.1038/s41586-023-06802-1 · Cited by 634 · see citing works
Self-citationA joint NCBI and EMBL-EBI transcript set for clinical genomics and research
Morales, J., Pujar, S., Loveland, J. E., Astashyn, A., Bennett, R., Berry, A., Cox, E., Davidson, C., Ermolaeva, O., Farrell, C. M., et al. (2022) · Nature · doi:10.1038/s41586-022-04558-8 · Cited by 571 · see citing works
Schwartzentruber, J., Cooper, S., Liu, J. Z., Barrio-Hernandez, I., Bello, E., Kumasaka, N., Young, A. M. H., Franklin, R. J. M., Johnson, T., Estrada, K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00776-w · Cited by 532 · see citing works
Determinants of telomere length across human tissues
Demanelis, K., Jasmine, F., Chen, L. S., Chernoff, M., Tong, L., Delgado, D., Zhang, C., Shinkle, J., Sabarinathan, M., Lin, H., et al. (2020) · Science · doi:10.1126/science.aaz6876 · Cited by 515 · see citing works
Self-citationA complete reference genome improves analysis of human genetic variation
Aganezov, S., Yan, S. M., Soto, D. C., Kirsche, M., Zarate, S., Avdeyev, P., Taylor, D. J., Shafin, K., Shumate, A., Xiao, C., et al. (2022) · Science · doi:10.1126/science.abl3533 · Cited by 441 · see citing works
The next-generation Open Targets Platform: reimagined, redesigned, rebuilt
Ochoa, D., Hercules, A., Carmona, M., Suveges, D., Baker, J., Malangone, C., Lopez, I., Miranda, A., Cruz-Castillo, C., Fumis, L., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1046 · Cited by 432 · see citing works
AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model
Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2025) · doi:10.1101/2025.06.25.661532 · Cited by 395 · see citing works
Advancing regulatory variant effect prediction with AlphaGenome
Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2026) · Nature · doi:10.1038/s41586-025-10014-0 · Cited by 373 · see citing works
Systematic differences in discovery of genetic effects on gene expression and complex traits
Mostafavi, H., Spence, J. P., Naqvi, S., & Pritchard, J. K. (2023) · Nature Genetics · doi:10.1038/s41588-023-01529-1 · Cited by 336 · see citing works
Li, Y., Ge, X., Peng, F., Li, W., & Li, J. J. (2022) · Genome Biology · doi:10.1186/s13059-022-02648-4 · Cited by 303 · see citing works
Self-citationWhere Are the Disease-Associated eQTLs?
Umans, B. D., Battle, A., & Gilad, Y. (2021) · Trends in Genetics · doi:10.1016/j.tig.2020.08.009 · Cited by 296 · see citing works
Self-citationYang, 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
Predicting RNA splicing from DNA sequence using Pangolin
Zeng, T., & Li, Y. I. (2022) · Genome Biology · doi:10.1186/s13059-022-02664-4 · Cited by 283 · see citing works
WILDS: A Benchmark of in-the-Wild Distribution Shifts
Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518 · see citing works
Lopes, K. D. P., Snijders, G. J. L., Humphrey, J., Allan, A., Sneeboer, M. A. M., Navarro, E., Schilder, B. M., Vialle, R. A., Parks, M., Missall, R., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00976-y · Cited by 273 · 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
Truncated stathmin-2 is a marker of TDP-43 pathology in frontotemporal dementia
Prudencio, M., Humphrey, J., Pickles, S., Brown, A.-L., Hill, S. E., Kachergus, J. M., Shi, J., Heckman, M. G., Spiegel, M. R., Cook, C., et al. (2020) · Journal of Clinical Investigation · doi:10.1172/jci139741 · Cited by 231 · see citing works
Self-citationTowards a structurally resolved human protein interaction network
Burke, D. F., Bryant, P., Barrio-Hernandez, I., Memon, D., Pozzati, G., Shenoy, A., Zhu, W., Dunham, A. S., Albanese, P., Keller, A., et al. (2023) · Nature Structural & Molecular Biology · doi:10.1038/s41594-022-00910-8 · Cited by 269 · 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
Locus for severity implicates CNS resilience in progression of multiple sclerosis
International Multiple Sclerosis Genetics Consortium, Harroud, A., Stridh, P., McCauley, J. L., Saarela, J., van den Bosch, A. M. R., Engelenburg, H. J., Beecham, A. H., Alfredsson, L., Alikhani, K., et al. (2023) · Nature · doi:10.1038/s41586-023-06250-x · Cited by 243 · 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
Discovery of target genes and pathways at GWAS loci by pooled single-cell CRISPR screens
Morris, J. A., Caragine, C., Daniloski, Z., Domingo, J., Barry, T., Lu, L., Davis, K., Ziosi, M., Glinos, D. A., Hao, S., et al. (2023) · Science · doi:10.1126/science.adh7699 · Cited by 208 · see citing works
Self-citationCombining SNP-to-gene linking strategies to identify disease genes and assess disease omnigenicity
Gazal, S., Weissbrod, O., Hormozdiari, F., Dey, K. K., Nasser, J., Jagadeesh, K. A., Weiner, D. J., Shi, H., Fulco, C. P., O’Connor, L. J., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01087-y · Cited by 181 · see citing works
Self-citationTCRD and pharos 2021: mining the human proteome for disease biology
Sheils, T. K., Mathias, S. L., Kelleher, K. J., Siramshetty, V. B., Nguyen, D.-T., Bologa, C. G., Jensen, L. J., Vidović, D., Koleti, A., Schürer, S. C., et al. (2021) · Nucleic Acids Research · doi:10.1093/nar/gkaa993 · Cited by 160 · see citing works
Self-citationThe genetic and biochemical determinants of mRNA degradation rates in mammals
Agarwal, V., & Kelley, D. R. (2022) · Genome Biology · doi:10.1186/s13059-022-02811-x · Cited by 155 · see citing works
Precise modulation of transcription factor levels identifies features underlying dosage sensitivity
Naqvi, S., Kim, S., Hoskens, H., Matthews, H. S., Spritz, R. A., Klein, O. D., Hallgrímsson, B., Swigut, T., Claes, P., Pritchard, J. K., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01366-2 · Cited by 132 · 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
miRNATissueAtlas2: an update to the human miRNA tissue atlas
Keller, A., Gröger, L., Tschernig, T., Solomon, J., Laham, O., Schaum, N., Wagner, V., Kern, F., Schmartz, G. P., Li, Y., et al. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkab808 · Cited by 143 · see citing works
Single-cell genomics meets human genetics
Cuomo, A. S. E., Nathan, A., Raychaudhuri, S., MacArthur, D. G., & Powell, J. E. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00599-5 · Cited by 124 · see citing works
Self-citationHumphrey, J., Venkatesh, S., Hasan, R., Herb, J. T., de Paiva Lopes, K., Küçükali, F., Byrska-Bishop, M., Evani, U. S., Narzisi, G., Fagegaltier, D., et al. (2023) · Nature Neuroscience · doi:10.1038/s41593-022-01205-3 · Cited by 138 · see citing works
Genome-Wide analysis of human long noncoding RNAs: a provocative review
Ponting, C. P., & Haerty, W. (2022) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-112921-123710 · Cited by 115 · see citing works
Pharos 2023: an integrated resource for the understudied human proteome
Kelleher, K. J., Sheils, T. K., Mathias, S. L., Yang, J. J., Metzger, V. T., Siramshetty, V. B., Nguyen, D.-T., Jensen, L. J., Vidović, D., Schürer, S. C., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1033 · Cited by 137 · see citing works
Self-citationXu, J., Mao, C., Hou, Y., Luo, Y., Binder, J. L., Zhou, Y., Bekris, L. M., Shin, J., Hu, M., Wang, F., et al. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.111717 · Cited by 115 · 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
scPower accelerates and optimizes the design of multi-sample single cell transcriptomic studies
Schmid, K. T., Höllbacher, B., Cruceanu, C., Böttcher, A., Lickert, H., Binder, E. B., Theis, F. J., & Heinig, M. (2021) · Nature Communications · doi:10.1038/s41467-021-26779-7 · Cited by 108 · see citing works
Liscovitch-Brauer, N., Montalbano, A., Deng, J., Méndez-Mancilla, A., Wessels, H.-H., Moss, N. G., Kung, C.-Y., Sookdeo, A., Guo, X., Geller, E., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00902-x · Cited by 107 · see citing works
Network analysis reveals rare disease signatures across multiple levels of biological organization
Buphamalai, P., Kokotovic, T., Nagy, V., & Menche, J. (2021) · Nature Communications · doi:10.1038/s41467-021-26674-1 · Cited by 95 · see citing works
Anderson, A. G., Rogers, B. B., Loupe, J. M., Rodriguez-Nunez, I., Roberts, S. C., White, L. M., Brazell, J. N., Bunney, W. E., Bunney, B. G., Watson, S. J., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100263 · Cited by 82 · see citing works
Zheng, D., Li, N., Hou, R., Zhang, X., Wu, L., Sundquist, J., Sundquist, K., & Ji, J. (2023) · BMC Medicine · doi:10.1186/s12916-023-02753-6 · Cited by 74 · see citing works
Bertoch, T., D’Aunno, D., McCoun, J., Solanki, D., Taber, L., Urban, J., Oswald, J., Swisher, M. W., Tian, S., Miao, X., et al. (2025) · Anesthesiology · doi:10.1097/aln.0000000000005460 · Cited by 72 · see citing works
Glycine decarboxylase maintains mitochondrial protein lipoylation to support tumor growth
Mukha, D., Fokra, M., Feldman, A., Sarvin, B., Sarvin, N., Nevo-Dinur, K., Besser, E., Hallo, E., Aizenshtein, E., Schug, Z. T., et al. (2022) · Cell Metabolism · doi:10.1016/j.cmet.2022.04.006 · Cited by 69 · see citing works
Borràs, D. M., Verbandt, S., Ausserhofer, M., Sturm, G., Lim, J., Verge, G. A., Vanmeerbeek, I., Laureano, R. S., Govaerts, J., Sprooten, J., et al. (2023) · Cell Discovery · doi:10.1038/s41421-023-00605-4 · Cited by 67 · see citing works
scQTLbase: an integrated human single-cell eQTL database
Ding, R., Wang, Q., Gong, L., Zhang, T., Zou, X., Xiong, K., Liao, Q., Plass, M., & Li, L. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkad781 · Cited by 62 · see citing works
Hivert, M.-F., White, F., Allard, C., James, K., Majid, S., Aguet, F., Ardlie, K. G., Florez, J. C., Edlow, A. G., Bouchard, L., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-02936-5 · Cited by 62 · see citing works
Self-citationThe lncRNA Toolkit: Databases and In Silico Tools for lncRNA Analysis
Pinkney, H. R., Wright, B. M., & Diermeier, S. D. (2020) · Non-Coding RNA · doi:10.3390/ncrna6040049 · Cited by 56 · see citing works
Fang, S., Yarmolinsky, J., Gill, D., Bull, C. J., Perks, C. M., the PRACTICAL Consortium, Davey Smith, G., Gaunt, T. R., & Richardson, T. G. (2023) · PLOS Medicine · doi:10.1371/journal.pmed.1003988 · Cited by 55 · see citing works
Kissel, L. T., & Werling, D. M. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.11.023 · Cited by 46 · see citing works
Denisovan introgression has shaped the immune system of present-day Papuans
Vespasiani, D. M., Jacobs, G. S., Cook, L. E., Brucato, N., Leavesley, M., Kinipi, C., Ricaut, F.-X., Cox, M. P., & Gallego Romero, I. (2022) · PLOS Genetics · doi:10.1371/journal.pgen.1010470 · Cited by 46 · see citing works
Thériault, S., Li, Z., Abner, E., Luan, J., Manikpurage, H. D., Houessou, U., Zamani, P., Briend, M., Estonian Biobank Research Team, Boudreau, D. K., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46639-4 · Cited by 49 · see citing works
Zheng, J., Wheeler, E., Pietzner, M., Andlauer, T. F. M., Yau, M. S., Hartley, A. E., Brumpton, B. M., Rasheed, H., Kemp, J. P., Frysz, M., et al. (2023) · Arthritis & Rheumatology · doi:10.1002/art.42538 · Cited by 44 · see citing works
Teyssonnière, E. M., Trébulle, P., Muenzner, J., Loegler, V., Ludwig, D., Amari, F., Mülleder, M., Friedrich, A., Hou, J., Ralser, M., et al. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2319211121 · Cited by 40 · see citing works
Combining genome-wide association studies highlight novel loci involved in human facial variation
Xiong, Z., Gao, X., Chen, Y., Feng, Z., Pan, S., Lu, H., Uitterlinden, A. G., Nijsten, T., Ikram, A., Rivadeneira, F., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-35328-9 · Cited by 37 · see citing works
Natri, H. M., Hudjashov, G., Jacobs, G., Kusuma, P., Saag, L., Darusallam, C. C., Metspalu, M., Sudoyo, H., Cox, M. P., Gallego Romero, I., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.11.017 · Cited by 33 · see citing works
Artificial intelligence for dementia genetics and omics
Bettencourt, C., Skene, N., Bandres‐Ciga, S., Anderson, E., Winchester, L. M., Foote, I. F., Schwartzentruber, J., Botia, J. A., Nalls, M., Singleton, A., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13427 · Cited by 30 · see citing works
Weigel, B., Tegethoff, J. F., Grieder, S. D., Lim, B., Nagarajan, B., Liu, Y.-C., Truberg, J., Papageorgiou, D., Adrian-Segarra, J. M., Schmidt, L. K., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-01959-7 · Cited by 31 · see citing works
Association of SPI1 haplotypes with altered SPI1 gene expression and Alzheimer’s disease risk
Cao, H., Initiative, A. D. N., Zhou, X., Chen, Y., Ip, F. C. F., Chen, Y., Lai, N. C. H., Lo, R. M. N., Tong, E. P. S., Mok, V. C. T., et al. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-215311 · Cited by 28 · see citing works
“6% of the donors are of European descent) 417 for SPI1 transcript level quantified by RNA-seq in 418 47 human tissues [18] and found that most human 419 tissues express SPI1 transcript (TPM: 2.”
FixItFelix: improving genomic analysis by fixing reference errors
Behera, S., LeFaive, J., Orchard, P., Mahmoud, M., Paulin, L. F., Farek, J., Soto, D. C., Parker, S. C. J., Smith, A. V., Dennis, M. Y., et al. (2023) · Genome Biology · doi:10.1186/s13059-023-02863-7 · Cited by 28 · see citing works
Self-citationWang, J., Zhou, X., Yu, P., Yao, J., Guo, P., Xu, Q., Zhao, Y., Wang, G., Li, Q., Zhu, X., et al. (2025) · Nature Aging · doi:10.1038/s43587-025-00886-2 · Cited by 28 · see citing works
Self-citationShared genetic architectures of educational attainment in East Asian and European populations
Chen, T.-T., Kim, J., Lam, M., Chuang, Y.-F., Chiu, Y.-L., Lin, S.-C., Jung, S.-H., Kim, B., Kim, S., Cho, C., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-023-01781-9 · Cited by 18 · see citing works
Impact of loss-of-function in angiopoietin-like 4 on the human phenome
Gagnon, E., Bourgault, J., Gobeil, É., Thériault, S., & Arsenault, B. J. (2024) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2024.117558 · Cited by 17 · see citing works
A signature of Neanderthal introgression on molecular mechanisms of environmental responses
Findley, A. S., Zhang, X., Boye, C., Lin, Y. L., Kalita, C. A., Barreiro, L., Lohmueller, K. E., Pique-Regi, R., & Luca, F. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009493 · Cited by 14 · 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-citationSmall, A. M., Yang, T.-Y., Itoh, S., Thériault, S., Dufresne, L., Kurosawa, R., Komuro, I., Matsuda, K., Vy, H. M. T., Farber-Eger, E. H., et al. (2026) · Nature Genetics · doi:10.1038/s41588-025-02417-6 · Cited by 12 · see citing works
“We also compared relative gene expression between the AV and 43 GTEx 55 tissues using previously calculated ESS 9 .”
InfluentialCRISPRmap: Sequencing-free optical pooled screens mapping multi-omic phenotypes in cells and tissue
Gu, J., Iyer, A., Wesley, B., Taglialatela, A., Leuzzi, G., Hangai, S., Decker, A., Gu, R., Klickstein, N., Shuai, Y., et al. (2023) · doi:10.1101/2023.12.26.572587 · Cited by 10 · see citing works
SingleBrain: A Meta-Analysis of Single-Nucleus eQTLs Linking Genetic Risk to Brain Disorders
Jang, B., Kailash, B., Tokolyi, A., Cuddleston, W. H., Ravi, A., Jung, S.-H., Naito, T., Kim, B., Kim, M. S., Cho, M., et al. (2025) · doi:10.1101/2025.03.06.25323424 · Cited by 11 · see citing works
Genetic 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
Houessou, U., Zamani, P., Manikpurage, H. D., Li, Z., Gaudreault, N., Dahmene, M., Dagenais, F., Couture, C., Clavel, M.-A., Pibarot, P., et al. (2025) · JACC: Basic to Translational Science · doi:10.1016/j.jacbts.2025.01.018 · Cited by 11 · see citing works
Enrichment of a subset of Neanderthal polymorphisms in autistic probands and siblings
Pauly, R., Johnson, L., Feltus, F. A., & Casanova, E. L. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02593-7 · Cited by 7 · see citing works
Establishing the relationship between brain cellular senescence and brain structure
Lund, A. N., Kopell, B. H., Golestani, N., Vornholt, E., Thompson, R. C., Liharska, L. E., Luo, Y., Cheng, E., Park, Y. J., Fennessy, B., et al. (2026) · Cell · doi:10.1016/j.cell.2025.10.014 · Cited by 6 · see citing works
Self-citationA genetic and proteomic comparison of key AD biomarkers across tissues
Marsh, T. W., Western, D., Timsina, J., Gorijala, P., Yang, C., Pastor, P., Liu, M., Morris, J. C., Bateman, R. J., Schindler, S. E., et al. (2024) · Alzheimer's & Dementia · doi:10.1002/alz.14139 · Cited by 6 · see citing works
Zamani, P., Houessou, U., Manikpurage, H. D., Li, Z., Dahmene, M., Gaudreault, N., Dagenais, F., Clavel, M.-A., Pibarot, P., Arsenault, B. J., et al. (2025) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2025.100448 · Cited by 3 · see citing works
Wu, C., Pu, Q., Zou, Y., Jin, J., Lei, Y., Hu, Y., Chen, Z., & Yu, L. (2025) · Scientific Reports · doi:10.1038/s41598-025-15525-4 · Cited by 3 · see citing works
“Finally, the cis-eQTL MR analysis was repeated using genetic instruments for relevant tissues derived from the Genotype-Tissue Expression project (GTEx-V8) 28 .”
Self-citationBurton, E. A., Argenziano, M., Cook, K., Ridler, M., Lu, S., Su, C., Manduchi, E., Littleton, S. H., Leonard, M. E., Hodge, K. M., et al. (2024) · doi:10.1101/2024.08.22.609230 · Cited by 3 · see citing works
Self-citationGenetic drivers of progression in Alzheimer’s disease are distinct from disease risk
Cohen, C. E., Fernandez, S., Yaman, U., Ehyaei, A. R., Kodosaki, E., Askarova, A., Porter, T., O’Brien, E., Study, A. I. B. A. L., Initiative, A. D. N., et al. (2026) · Alzheimer's Research & Therapy · doi:10.1186/s13195-026-02036-1 · Cited by 1 · see citing works
High false sign rates in transcriptome-wide association studies
Gerlach, P. A., Milind, N., Spence, J. P., & Pritchard, J. K. (2026) · Nature Genetics · doi:10.1038/s41588-026-02796-4 · Cited by 1 · see citing works
Western, D., Yang, C., Timsina, J., Liu, M., Wang, C., Campbell, M. C., Kotzbauer, P., Perlmutter, J. S., Schindler, S. E., Morris, J. C., et al. (2026) · Science Translational Medicine · doi:10.1126/scitranslmed.adz2016 · Cited by 1 · see citing works
Connecting proteomics and genomics to identify causal biomarkers specific for aortic valve stenosis
Zamani, P., Houessou, U., Manikpurage, H. D., Dahmene, M., Li, Z., Gaudreault, N., Clavel, M.-A., Pibarot, P., Mathieu, P., Arsenault, B. J., et al. (2025) · medRxiv · doi:10.1101/2025.11.27.25339923
“To determine whether the variants associated with gene expression and those associated with circulating protein levels were the same, we performed Bayesian colocalization analyses integrating aortic valve tissue eQTLs with pQTLs from the UKB-PPP 13 and aorta tissue eQTLs from GTEx (v8) 23 .”
Effects of introgressed Neanderthal alleles on present-day brain morphology
Zeloni, R., Amaolo, A., Morez Jacobs, A., Zapparoli, E., Akl, Y., Shafie, M., Huerta-Sanchez, E., Pizzagalli, F., Provero, P., Pagani, L., et al. (2026) · bioRxiv · doi:10.64898/2026.04.14.718380
“On chromosome 4, a large credible set composed at 93% by NSVs with an extensive open chromatin overlap and a large potential to alter chromatin accessibility in cortical neurons acts as eQTLs and sQTLs for PRDM5 (GTEx).”
TFXcan reveals transcription factor programs driving GWAS loci
Adeluwa, T., Salazar-Magaña, S., Sumner, S., Gona, S., Nyasimi, F., Carbonetto, P., Baca, S. C., Freedman, M., Gusev, A., Liu, B., et al. (2026) · Cell Genomics · doi:10.1016/j.xgen.2026.101398
Self-citation
- Authors
- The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G. A., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., Kashin, S., Li, X., MacArthur, D. G., Meier, S. R., Nedzel, J. L., Nguyen, D. T., Segrè, A. V., Todres, E., Balliu, B., Barbeira, A. N., Battle, A., Bonazzola, R., Brown, A., Brown, C. D., Castel, S. E., Conrad, D. F., Cotter, D. J., Cox, N., Das, S., de Goede, O. M., Dermitzakis, E. T., Einson, J., Engelhardt, B. E., Eskin, E., Eulalio, T. Y., Ferraro, N. M., Flynn, E. D., Fresard, L., Gamazon, E. R., Garrido-Martín, D., Gay, N. R., Gloudemans, M. J., Guigó, R., Hame, A. R., He, Y., Hoffman, P. J., Hormozdiari, F., Hou, L., Im, H. K., Jo, B., Kasela, S., Kellis, M., Kim-Hellmuth, S., Kwong, A., Lappalainen, T., Li, X., Liang, Y., Mangul, S., Mohammadi, P., Montgomery, S. B., Muñoz-Aguirre, M., Nachun, D. C., Nobel, A. B., Oliva, M., Park, Y., Park, Y., Parsana, P., Rao, A. S., Reverter, F., Rouhana, J. M., Sabatti, C., Saha, A., Stephens, M., Stranger, B. E., Strober, B. J., Teran, N. A., Viñuela, A., Wang, G., Wen, X., Wright, F., Wucher, V., Zou, Y., Ferreira, P. G., Li, G., Melé, M., Yeger-Lotem, E., Barcus, M. E., Bradbury, D., Krubit, T., McLean, J. A., Qi, L., Robinson, K., Roche, N. V., Smith, A. M., Sobin, L., Tabor, D. E., Undale, A., Bridge, J., Brigham, L. E., Foster, B. A., Gillard, B. M., Hasz, R., Hunter, M., Johns, C., Johnson, M., Karasik, E., Kopen, G., Leinweber, W. F., McDonald, A., Moser, M. T., Myer, K., Ramsey, K. D., Roe, B., Shad, S., Thomas, J. A., Walters, G., Washington, M., Wheeler, J., Jewell, S. D., Rohrer, D. C., Valley, D. R., Davis, D. A., Mash, D. C., Branton, P. A., Barker, L. K., Gardiner, H. M., Mosavel, M., Siminoff, L. A., Flicek, P., Haeussler, M., Juettemann, T., Kent, W. J., Lee, C. M., Powell, C. C., Rosenbloom, K. R., Ruffier, M., Sheppard, D., Taylor, K., Trevanion, S. J., Zerbino, D. R., Abell, N. S., Akey, J., Chen, L., Demanelis, K., Doherty, J. A., Feinberg, A. P., Hansen, K. D., Hickey, P. F., Jasmine, F., Jiang, L., Kaul, R., Kibriya, M. G., Li, J. B., Li, Q., Lin, S., Linder, S. E., Pierce, B. L., Rizzardi, L. F., Skol, A. D., Smith, K. S., Snyder, M., Stamatoyannopoulos, J., Tang, H., Wang, M., Carithers, L. J., Guan, P., Koester, S. E., Little, A. R., Moore, H. M., Nierras, C. R., Rao, A. K., Vaught, J. B., & Volpi, S.
- Affiliations
- Massachusetts General Hospital (US)
- University of Chicago (US)
- Northwestern University (US)
- King's College London (GB)
- ORCID
- Aguet, François
- Anand, Shankara
- Ardlie, Kristin G.
- Gabriel, Stacey
- Getz, Gad A.
- Graubert, Aaron
- Hadley, Kane
- Handsaker, Robert E.
- Huang, Katherine H.
- Kashin, Seva
- Li, Xiao
- MacArthur, Daniel G.
- Meier, Samuel R.
- Nedzel, Jared L.
- Nguyen, Duyen T.
- Segrè, Ayellet V.
- Todres, Ellen
- Balliu, Brunilda
- Barbeira, Alvaro N.
- Battle, Alexis
- Bonazzola, Rodrigo
- Brown, Andrew
- Brown, Christopher D.
- Castel, Stephane E.
- Conrad, Donald F.
- Cotter, Daniel J.
- Cox, Nancy
- Das, Sayantan
- Dermitzakis, Emmanouil T.
- Einson, Jonah
- Engelhardt, Barbara E.
- Eskin, Eleazar
- Eulalio, Tiffany Y.
- Ferraro, Nicole M.
- Flynn, Elise D.
- Gamazon, Eric R.
- Garrido-Martín, Diego
- Gay, Nicole R.
- Gloudemans, Michael J.
- Guigó, Roderic
- He, Yuan
- Hoffman, Paul J.
- Hormozdiari, Farhad
- Im, Hae Kyung
- Jo, Brian
- Kasela, Silva
- Kellis, Manolis
- Kim-Hellmuth, Sarah
- Kwong, Alan
- Lappalainen, Tuuli
- Li, Xin
- Liang, Yanyu
- Mangul, Serghei
- Mohammadi, Pejman
- Montgomery, Stephen B.
- Muñoz-Aguirre, Manuel
- Nachun, Daniel C.
- Nobel, Andrew B.
- Oliva, Meritxell
- Park, YoSon
- Park, Yongjin
- Parsana, Princy
- Rouhana, John M.
- Sabatti, Chiara
- Saha, Ashis
- Stephens, Matthew
- Stranger, Barbara E.
- Strober, Benjamin J.
- Teran, Nicole A.
- Viñuela, Ana
- Wang, Gao
- Wen, Xiaoquan
- Wright, Fred
- Wucher, Valentin
- Zou, Yuxin
- Ferreira, Pedro G.
- Li, Gen
- Melé, Marta
- Smith, Anna M.
- Tabor, David E.
- Foster, Barbara A.
- Gillard, Bryan M.
- Hasz, Richard
- Johns, Christopher
- Johnson, Mark
- Moser, Michael T.
- Jewell, Scott D.
- Rohrer, Daniel C.
- Davis, David A.
- Mash, Deborah C.
- Branton, Philip A.
- Gardiner, Heather M.
- Mosavel, Maghboeba
- Siminoff, Laura A.
- Flicek, Paul
- Haeussler, Maximilian
- Juettemann, Thomas
- Lee, Christopher M.
- Rosenbloom, Kate R.
- Ruffier, Magali
- Sheppard, Dan
- Taylor, Kieron
- Trevanion, Stephen J.
- Zerbino, Daniel R.
- Abell, Nathan S.
- Akey, Joshua
- Chen, Lin
- Demanelis, Kathryn
- Doherty, Jennifer A.
- Feinberg, Andrew P.
- Hansen, Kasper D.
- Hickey, Peter F.
- Jasmine, Farzana
- Jiang, Lihua
- Kaul, Rajinder
- Kibriya, Muhammad G.
- Li, Jin Billy
- Li, Qin
- Lin, Shin
- Pierce, Brandon L.
- Rizzardi, Lindsay F.
- Skol, Andrew D.
- Smith, Kevin S.
- Snyder, Michael
- Stamatoyannopoulos, John
- Tang, Hua
- Wang, Meng
- Little, A. Roger
- Moore, Helen M.
- Volpi, Simona
- Venue
- Science
- Published
- 2020-09-11
- Volume
- 369
- Issue
- 6509
- Pages
- 1318-1330
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC7737656
- OpenAlex
- W3133859624
- Cited by
- 5,547
- References
- 133
- Citations by year
- 2026: 1228 · 2025: 1173 · 2024: 1054 · 2023: 762 · 2022: 734 · 2021: 485 · 2020: 99 · 2019: 9 · 2012: 1
- FWCI
- 320.02
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Repositori digital de la UPF (Universitat Pompeu Fabra) (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion, public-domain)
- Dipòsit Digital de la Universitat de Barcelona (Universitat de Barcelona) (submittedVersion, public-domain)
- Discovery Research Portal (University of Dundee) (submittedVersion, other-oa)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, public-domain)
- Funders
- National Institute of Mental Health
- National Human Genome Research Institute (5U41HG002371-19)
- NIH Office of the Director (R01 MH107666, P30 DK020595, HG002585)
- National Cancer Institute
- European Molecular Biology Laboratory
- NHGRI (R01 HG006855)
- Leidos Biomedical Research (BOA No. 10XS1035)
- NIH/NIMH (R01MH101782)
- Swiss National Science Foundation
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA Research and Splicing
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Keywords
- gene expression regulation, cis-eQTL, trans-eQTL, splicing regulation, allelic heterogeneity, pleiotropy, complex traits, tissue specificity, cell type composition, transcriptome, GTEx
- MeSH terms
- Disease, Female, Gene Expression Regulation, Humans, Male, Organ Specificity, Sequence Analysis, RNA, Quantitative Trait Loci, Genome-Wide Association Study, Datasets as Topic
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