The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index.
Colocalization of GWAS and eQTL Signals Detects Target Genes
Open accessPublished version of a preprint
Abstract
The vast majority of genome-wide association study (GWAS) risk loci fall in non-coding regions of the genome. One possible hypothesis is that these GWAS risk loci alter the individual's disease risk through their effect on gene expression in different tissues. In order to understand the mechanisms driving a GWAS risk locus, it is helpful to determine which gene is affected in specific tissue types. For example, the relevant gene and tissue could play a role in the disease mechanism if the same variant responsible for a GWAS locus also affects gene expression. Identifying whether or not the same variant is causal in both GWASs and expression quantitative trail locus (eQTL) studies is challenging because of the uncertainty induced by linkage disequilibrium and the fact that some loci harbor multiple causal variants. However, current methods that address this problem assume that each locus contains a single causal variant. In this paper, we present eCAVIAR, a probabilistic method that has several key advantages over existing methods. First, our method can account for more than one causal variant in any given locus. Second, it can leverage summary statistics without accessing the individual genotype data. We use both simulated and real datasets to demonstrate the utility of our method. Using publicly available eQTL data on 45 different tissues, we demonstrate that eCAVIAR can prioritize likely relevant tissues and target genes for a set of glucose- and insulin-related trait loci.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“One colocalization method originally proposed by Plagnol et al 112 frames shared causality as the null hypothesis and rejection of this would indicate violation of IV condition 2, i.e. that there are no common causes of the instrument and the outcome.112–114”
From Mendelian randomization (2022) · cites this paper as [114]
Experimental — accuracy not yet measuredIn the citing paper
“Modern human genetics methods, such as genetic colocalization163 and Mendelian randomization164, can also be used to test causality in ageing clock models.”
From Measuring biological age using omics data (2022) · cites this paper as [163]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls
The Wellcome Trust Case Control Consortium, Management Committee, Burton, P. R., Clayton, D. G., Cardon, L. R., Craddock, N., Deloukas, P., Duncanson, A., Kwiatkowski, D. P., McCarthy, M. I., et al. (2007) · Nature · doi:10.1038/nature05911 · Cited by 9,442
A map of human genome variation from population-scale sequencing.
The 1000 Genomes Project Consortium (2010) · Nature · doi:10.1038/nature09534 · Cited by 7,877
An integrated map of genetic variation from 1,092 human genomes
The 1000 Genomes Project Consortium (2012) · Nature · doi:10.1038/nature11632 · Cited by 7,711
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
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
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
The NHGRI GWAS Catalog, a curated resource of SNP-trait associations
Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., Klemm, A., Flicek, P., Manolio, T., Hindorff, L., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1229 · Cited by 2,707
Integrative approaches for large-scale transcriptome-wide association studies
Gusev, A., Ko, A., Shi, H., Bhatia, G., Chung, W., Penninx, B. W. J. H., Jansen, R., de Geus, E. J. C., Boomsma, D. I., Wright, F. A., et al. (2016) · Nature Genetics · doi:10.1038/ng.3506 · Cited by 2,479
Visscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.11.029 · Cited by 2,425
New 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
Cited by
Sorted by citations
Items 401–500 of 716
Integrative multi-omics QTL colocalization maps regulatory architecture in aging human brainPreprint
Cao, X., Sun, H., Feng, R., Mazumder, R., Najar, C. F. B. A., Li, Y., Jager, P. L. D., Bennett, D. A., Dey, K. K., & Wang, G. (2025) · medRxiv · doi:10.1101/2025.04.17.25326042 · Cited by 10
Prabhakar, V., & Liu, K. (2023) · Archives of Clinical and Biomedical Research · doi:10.26502/acbr.50170342 · Cited by 8
Chignon, A., & Lettre, G. (2025) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2024.118621 · Cited by 9
Dudek, M. F., Wenz, B. M., Brown, C. D., Voight, B. F., Almasy, L. A., & Grant, S. F. A. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.03.019 · Cited by 8
The Importance of Regulatory Network Structure for Complex Trait Heritability and Evolution
Stone, K. L., Platig, J., Quackenbush, J. F., & Fagny, M. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msaf174 · Cited by 9
Epigenomic and Transcriptomic Prioritization of Candidate Obesity-Risk Regulatory GWAS SNPs
Zhang, X., Li, T., Xiao, H., Ehrlich, K. C., Shen, H., Deng, H., & Ehrlich, M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23031271 · Cited by 8
Derrien, A., Houy, A., Ganier, O., Dingli, F., Ningarhari, M., Mobuchon, L., Díaz, M. I. E., Loew, D., Cassoux, N., Cussenot, O., et al. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.11.004 · Cited by 8
Okamoto, J., Yin, X., Ryan, B., Chiou, J., Luca, F., Piqué-Regi, R., Im, H. K., Morrison, J., Burant, C., Fauman, E. B., et al. (2025) · Genome biology · doi:10.1186/s13059-025-03480-2 · Cited by 9
Another Round of “Clue” to Uncover the Mystery of Complex Traits
Verma, S. S., & Ritchie, M. D. (2018) · Genes · doi:10.3390/genes9020061 · Cited by 9
Informing disease modelling with brain-relevant functional genomic annotations
Reynolds, R. H., Hardy, J., Ryten, M., & Taliun, S. A. G. (2019) · Brain · doi:10.1093/brain/awz295 · Cited by 8
Ludwig‐Słomczyńska, A. H., Seweryn, M. T., Kapusta, P., Pitera, E., Mantaj, U., Cyganek, K., Gutaj, P., Dobrucka, Ł., Wender-Ożegowska, E., Malecki, M. T., et al. (2021) · Molecular Medicine · doi:10.1186/s10020-020-00266-z · Cited by 8
Transcriptome wide association studies: general framework and methods
Xie, Y., Shan, N., Zhao, H., & Hou, L. (2021) · Quantitative Biology · doi:10.15302/j-qb-020-0228 · Cited by 8
Shieh, M., Hayeck, T. J., Dinh, A., Duke, J. L., Chitnis, N., Mosbruger, T. L., Morlen, R. P., Ferriola, D., Kneib, C., Hu, T., et al. (2020) · Transplantation · doi:10.1097/tp.0000000000003272 · Cited by 8
Guo, C., Sieber, K. B., Esparza-Gordillo, J., Hurle, M. R., Song, K. S., Yeo, A. J. M., Yerges-Armstrong, L. M., Johnson, T., & Nelson, M. R. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/808444 · Cited by 6
Multi-scale Inference of Genetic Trait Architecture using Biologically Annotated Neural NetworksPreprint
Demetçi, P., Cheng, W., Darnell, G., Zhou, X., Ramachandran, S., & Crawford, L. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.07.02.184465 · Cited by 8
Matoba, N., & Stein, J. L. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00852-2 · Cited by 7
Genetic influence on vascular smooth muscle cell apoptosis
McVey, D. G., Andreadi, C., Gong, P., Stanczyk, P. J., Solomon, C. U., Turner, L., Liu, Y., Chen, R., Cao, J., Nelson, C. P., et al. (2024) · Cell Death and Disease · doi:10.1038/s41419-024-06799-z · Cited by 8
Variant-to-function approaches for adipose tissue: Insights into cardiometabolic disorders
Metz, S., Belanich, J. R., Claussnitzer, M., & Kilpeläinen, T. O. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100844 · Cited by 7
Translating non-coding genetic associations into a better understanding of immune-mediated disease
Stankey, C. T., & Lee, J. C. (2023) · Disease Models & Mechanisms · doi:10.1242/dmm.049790 · Cited by 9
Bielczyk-Maczyńska, E., Sharma, D., Blencowe, M., Saliba‐Gustafsson, P., Gloudemans, M. J., Yang, X., Carcamo‐Orive, I., Wabitsch, M., Svensson, K. J., Park, C. Y., et al. (2023) · American Journal of Physiology-Cell Physiology · doi:10.1152/ajpcell.00148.2023 · Cited by 8
scTWAS Atlas: an integrative knowledgebase of single-cell transcriptome-wide association studies
Mai, J., Qian, Q., Gao, H., Fan, Z., Zeng, J., & Xiao, J. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae931 · Cited by 7
PWAS Hub: exploring gene-based associations of complex diseases with sex dependency
Zucker, R., Kelman, G., & Linial, M. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae1125 · Cited by 7
Association of CDKN2A/CDKN2B with inflammatory bowel disease in Koreans
Lee, H., Lee, S., Kim, B. M., Hong, M., Jung, S., Hong, J., Baek, J., Han, B., Oh, S. H., Kim, K. M., et al. (2017) · Journal of Gastroenterology and Hepatology · doi:10.1111/jgh.14031 · Cited by 8
NKG2D gene variation and susceptibility to viral bronchiolitis in childhood
Pasanen, A., Karjalainen, M. K., Kummola, L., Waage, J., Bønnelykke, K., Ruotsalainen, M., Piippo‐Savolainen, E., Goksör, E., Nuolivirta, K., Chawes, B., et al. (2018) · Pediatric Research · doi:10.1038/s41390-018-0086-9 · Cited by 7
Learning gene networks underlying clinical phenotypes using SNP perturbation
McCarter, C., Howrylak, J. A., & Kim, S.-Y. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1007940 · Cited by 7
Pahl, M. C., Grant, S. F. A., Leibel, R. L., & Stratigopoulos, G. (2023) · Obesity Reviews · doi:10.1111/obr.13558 · Cited by 8
Methods for fine-mapping with chromatin and expression data
Roytman, M., Kichaev, G., Gusev, A., & Paşaniuc, B. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007240 · Cited by 6
Cardinale, C. J., March, M., Lin, X., Liu, Y., Spruce, L. A., Bradfield, J. P., Wei, Z., Seeholzer, S. H., Grant, S. F. A., & Hakonarson, H. H. (2020) · Journal of Crohn s and Colitis · doi:10.1093/ecco-jcc/jjz213 · Cited by 7
Measurement of selective constraint on human gene expressionPreprint
Glassberg, E. C., Gao, Z., Harpak, A., Lant, X., & Pritchard, J. K. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/345801 · Cited by 7
RACER: A data visualization strategy for exploring multiple genetic associationsPreprint
Sabik, O. L., & Farber, C. R. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/495366 · Cited by 6
131 genetic loci highlight immunological pathways and tissues in nasal polyposis and asthma
Saarentaus, E., Fischer‐Rasmussen, K., Sliz, E., Bizaki-Vallaskangas, A. J., FinnGen, Sliz, E., Bizaki-Vallaskangas, A., Toppila-Salmi, S. K., Kankaanranta, H., Kettunen, J., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-64847-4 · Cited by 7
Genetic analysis of circulating metabolic traits in 619,372 individuals
Tambets, R., Jesse, M., Kronberg, J., van der Graaf, A., Abner, E., Võsa, U., Rahu, I., Taba, N., Kolde, A., Yarish, D., et al. (2026) · Nature · doi:10.1038/s41586-026-10532-5 · Cited by 28
Shared genetic architecture and causal relationship between liver and heart disease
Fang, Z., Jia, S., Mou, X., Li, Z., Hu, T., Tu, Y., Zhao, J., Zhang, T., Lin, W., Lu, Y., et al. (2024) · iScience · doi:10.1016/j.isci.2024.109431 · Cited by 7
CSGDN: contrastive signed graph diffusion network for predicting crop gene–phenotype associations
Pan, Y., Ji, X., You, J., Li, L., Liu, Z., Zhang, X., Zhang, Z., & Wang, M. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf062 · Cited by 6
Ratnapriya, R., Grassman, F., Chen, R., Hewitt, A. W., Du, J., Saban, D. R., Klaver, C. C. W., Ash, J. D., Stambolian, D., Tumminia, S. J., et al. (2025) · Experimental Eye Research · doi:10.1016/j.exer.2025.110344 · Cited by 6
A unifying framework for joint trait analysis under a non-infinitesimal model
Johnson, R., Shi, H., Paşaniuc, B., & Sankararaman, S. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty254 · Cited by 6
Self-citationFunctional genomics, genetic risk profiling and cell phenotypes in neurodegenerative disease
Finkbeiner, S. M. (2020) · Neurobiology of Disease · doi:10.1016/j.nbd.2020.105088 · Cited by 8
Abdalla, M., Abdalla, M., McCarthy, M. I., & Holmes, C. C. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010028 · Cited by 7
Imaging Genetics in Epilepsy: Current Knowledge and New Perspectives
Wang, G., Wu, W., Xu, Y., Yang, Z., Xiao, B., & Long, L. (2022) · Frontiers in Molecular Neuroscience · doi:10.3389/fnmol.2022.891621 · Cited by 8
A translational framework of genoproteomic studies for cardiovascular drug discovery
Yang, Z., Zhao, J. V., Qi, Y., Deng, X., Ji, Z., & Liu, J. (2024) · npj Cardiovascular Health · doi:10.1038/s44325-024-00015-9 · Cited by 6
Genomics and Systems Biology Approaches in the Study of Lipid Disorders
Rodríguez, A., & Pajukanta, P. (2018) · Revista de investigaci�n Cl�nica · doi:10.24875/ric.18002576 · Cited by 7
Yin, L., Chau, C. K., Lin, Y., Rao, S., Xiang, Y., Sham, P. C., & So, H. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab417 · Cited by 6
Gedik, H., Peterson, R. E., Riley, B. P., Vladimirov, V. I., & Bacanu, S. (2023) · Complex Psychiatry · doi:10.1159/000530223 · Cited by 6
Wnt activity reveals context-specific genetic effects on gene regulation in neural progenitorsPreprint
Matoba, N., Le, B. D., Valone, J. M., Wolter, J. M., Mory, J. T., Liang, D., Aygün, N., Broadaway, K. A., Bond, M. L., Mohlke, K. L., et al. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.02.07.527357 · Cited by 6
EPIraction - an atlas of candidate enhancer-gene interactions in human tissues and cell linesPreprint
Nurtdinov, R., & Guigó, R. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.02.18.638885 · Cited by 7
Zhang, C., Li, M., Sun, P., Hui, L., Gao, Y., Yang, J., Zhang, N., Feng, X., Wu, Y., Guo, L., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02147-2 · Cited by 7
Self-citationGenetic control of non-coding RNAs in the human brain and their implications for complex traits
Chen, L., Guo, Y., Hou, J., Yang, W., Qi, T., & Yang, J. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.11.012 · Cited by 5
Functional Genomics Analysis to Disentangle the Role of Genetic Variants in Major Depression
Pérez‐Granado, J., Piñero, J., Medina-Rivera, A., & Furlong, L. I. (2022) · Genes · doi:10.3390/genes13071259 · Cited by 5
Gnanapragasam, A., Kirbizakis, E., Li, A., White, K. H., Mortenson, K. L., de Moura, J. C., Jawhar, W., Yan, Y., Falter, R., Russett, C. S., et al. (2024) · Cancer Research · doi:10.1158/0008-5472.can-23-2397 · Cited by 5
Quan, L., Luo, X., Meng, C., Liu, J., Ju, J., Yang, Z., Shuai, Y., Wei, T., Meng, J., & Yuan, P. (2025) · Communications Biology · doi:10.1038/s42003-025-08046-3 · Cited by 5
Wang, L., Hu, L., Sun, J., Zhao, J., Zhou, S., Liu, L., Wei, Y., Hu, Y., Zhou, D., Meng, X., et al. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00906-9 · Cited by 5
Self-citationGloudemans, M. J., Balliu, B., Nachun, D. C., Durrant, M. G., Ingelsson, E., Wabitsch, M., Quertermous, T., Montgomery, S. B., Knowles, J. W., & Carcamo‐Orive, I. (2022) · Metabolism · doi:10.1016/j.metabol.2021.155025 · Cited by 5
Benchmarking post-GWAS analysis tools in major depression: Challenges and implications
Pérez‐Granado, J., Piñero, J., & Furlong, L. I. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.1006903 · Cited by 7
Abbatangelo, C. L., Lona‐Durazo, F., Wendt, F. R., Parra, E. J., & Novroski, N. M. M. (2023) · Forensic Genomics · doi:10.1089/forensic.2023.0006 · Cited by 5
Brümmer, A., & Bergmann, S. (2024) · Nature Communications · doi:10.1038/s41467-024-48244-x · Cited by 5
A Unifying Framework for Imputing Summary Statistics in Genome-Wide Association Studies
Wu, Y., Eskin, E., & Sankararaman, S. (2020) · Journal of Computational Biology · doi:10.1089/cmb.2019.0449 · Cited by 5
Self-citationShared associations identify causal relationships between gene expression and immune cell phenotypes
Gasperi, C., Chun, S., Sunyaev, S. R., & Cotsapas, C. J. (2021) · Communications Biology · doi:10.1038/s42003-021-01823-w · Cited by 5
Jeong, Y., Song, J., Lee, Y., Choi, E., Won, Y., Kim, B., & Jang, W. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms241411717 · Cited by 6
Examining the Impact of Imputation Errors on Fine-Mapping Using DNA Methylation QTL as a Model Trait
Chundru, V. K., Marioni, R. E., Prendergast, J. G. D., Vallerga, C. L., Lin, T., Beveridge, A. J., Gratten, J., Hume, D. A., Deary, I. J., Wray, N. R., et al. (2019) · Genetics · doi:10.1534/genetics.118.301861 · Cited by 6
Rotival, M. (2019) · HLA · doi:10.1111/tan.13598 · Cited by 7
Gerring, Z. F., Mina-Vargas, A., Gamazon, E. R., & Derks, E. M. (2020) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32829 · Cited by 5
Molstad, A. J., Sun, W., & Hsu, L. (2021) · The Annals of Applied Statistics · doi:10.1214/20-aoas1432 · Cited by 5
A Bayesian model selection approach to mediation analysisPreprint
Crouse, W. L., Keele, G. R., Gastonguay, M. S., Churchill, G. A., & Valdar, W. S. J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.07.19.452969 · Cited by 5
Inferring cell-type-specific causal gene regulatory networks during human neurogenesisPreprint
Aygün, N., Liang, D., Crouse, W. L., Keele, G. R., Love, M. I., & Stein, J. L. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.25.488920 · Cited by 5
Zhao, S., Crouse, W. L., Qian, S., Luo, K., Stephens, M., & He, X. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.09.27.509700 · Cited by 5
Mitigating inconsistencies in GWAS follow-up analyses with LocusCompare2
Liu, F., Gao, J., Lin, Y., Meng, H., Wang, W., Luo, Y., Cao, Y., Wang, W., Guan, F., Wang, P., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02331-x · Cited by 4
eQTL analysis: A bridge from genome to mechanism
Jia, Z., Xu, J., Ma, Y., Wei, S., Sun, C., Chen, X., Kang, J., Chen, H., Zhang, C., Dong, Y., et al. (2025) · Genes & Diseases · doi:10.1016/j.gendis.2025.101850 · Cited by 5
Identification and catalog of viral transcriptional regulators in human diseases
Citu, C., Chang, L., Manuel, A. M., Enduru, N., & Zhao, Z. (2025) · iScience · doi:10.1016/j.isci.2025.112081 · Cited by 4
Yang, Z., Fan, H., Chen, Y., Li, J., Chen, M., Tan, Y. L., Guo, L., Li, J., Liu, K., & Yang, Q. (2025) · Advanced Science · doi:10.1002/advs.202512036 · Cited by 4
Buren, E. V., Zhang, Y., Li, X., Selvaraj, M. S., Li, Z., Zhou, H., Palmer, N. D., Arnett, D. K., Blangero, J., Boerwinkle, E., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02919-5 · Cited by 4
Self-citationBeyond the baseline: mapping the context-specific regulatory landscape of disease
Gilad, Y., & Battle, A. J. (2026) · Trends in Genetics · doi:10.1016/j.tig.2026.01.010 · Cited by 7
Buffering of gene dosage response curves for human complex traits
Milind, N., Smith, C. J., Zhu, H., Gjorgjieva, T., Spence, J. P., & Pritchard, J. K. (2026) · Cell Genomics · doi:10.1016/j.xgen.2026.101221 · Cited by 21
Casazza, W., Inkster, A. M., Gobbo, G. F. D., Yuan, V., Delahaye, F., Marsit, C. J., Park, Y., Robinson, W. P., Mostafavi, S., & Dennis, J. K. (2024) · iScience · doi:10.1016/j.isci.2024.109047 · Cited by 4
Multivariate Bayesian variable selection for multi-trait genetic fine mapping
Canida, T. A., Ke, H., Chen, S., Ye, Z., & Ma, T. (2024) · Journal of the Royal Statistical Society Series C (Applied Statistics) · doi:10.1093/jrsssc/qlae055 · Cited by 7
Dalapati, T., Wang, L., Jones, A. G., Cardwell, J., Konigsberg, I. R., Bossé, Y., Sin, D. D., Timens, W., Hao, K., Yang, I. V., et al. (2025) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2025.100410 · Cited by 5
MARS: leveraging allelic heterogeneity to increase power of association testing
Hormozdiari, F., Jung, J., Eskin, E., & Joo, J. W. J. (2021) · Genome biology · doi:10.1186/s13059-021-02353-8 · Cited by 4
Self-citationA unifying statistical framework to discover disease genes from GWASs
McManus, J. N. J., Lovelett, R. J., Lowengrub, D., & Christensen, S. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100264 · Cited by 5
Aberra, Y. T., Ma, L., Björkegren, J., & Civelek, M. (2023) · eLife · doi:10.7554/elife.79834 · Cited by 4
Zhu, Z., Chen, X., Zhang, S., Yu, R., Qi, C., Cheng, L., & Zhang, X. (2023) · Human Genetics · doi:10.1007/s00439-023-02602-9 · Cited by 5
Assigning Co-Regulated Human Genes and Regulatory Gene Clusters
Strunz, T., Kellner, M., Kiel, C., & Weber, B. H. F. (2021) · Cells · doi:10.3390/cells10092395 · Cited by 5
Shared genetic contribution to type 1 and type 2 diabetes riskPreprint
Aylward, A., Chiou, J., Okino, M.-L., Kadakia, N., & Gaulton, K. J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/285304 · Cited by 4
Leveraging functional annotation to identify genes associated with complex diseasesPreprint
Liu, W., Li, M., Zhang, W., Zhou, G., Wu, X., Wang, J., Lu, Q., & Zhao, H. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/529297 · Cited by 3
Computational Tools for Causal Inference in GeneticsPreprint
Richardson, T. G., Zheng, J., & Gaunt, T. R. (2020) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a039248 · Cited by 4
Analysis of overlapping genetic association in type 1 and type 2 diabetesPreprint
Inshaw, J. R. J., Sidore, C., Cucca, F., Stefana, M. I., Crouch, D. J. M., McCarthy, M. I., Mahajan, A., & Todd, J. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.17.156778 · Cited by 4
Genetic determinants of polygenic prediction accuracy within a populationPreprint
Lu, T., Forgetta, V., Richards, J. B., & Greenwood, C. M. T. (2022) · Research Square · doi:10.21203/rs.3.rs-1157621/v1 · Cited by 4
Aherrahrou, R., Lue, D., Perry, R. N., Aberra, Y. T., Khan, M. D., Soh, J. Y., Örd, T., Singha, P., Gilani, H., Benavente, E. D., et al. (2022) · bioRxiv · doi:10.1101/2022.01.24.477536 · Cited by 8
Lessard, S., Chao, M. J., Reis, K., Beauvais, M., Rajpal, D. K., Shankara, S., Sloane, J., Palta, P., Klinger, K. W., de Rinaldis, E., et al. (2023) · medRxiv · doi:10.1101/2023.11.01.23297926 · Cited by 4
Joint-tissue integrative analysis identifies high-risk genes for Parkinson’s disease
Wu, Y.-S., Zheng, W.-H., Liu, T., Sun, Y., Xu, Y., Shao, L., Cai, Q., & Tang, Y. Q. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1309684 · Cited by 3
Sanders, K. L., Manuel, A. M., Liu, A., Leng, B., Chen, X., & Zhao, Z. (2024) · Epigenomes · doi:10.3390/epigenomes8020014 · Cited by 3
Liefferinckx, C., Stern, D., Perée, H., Bottieau, J., Mayer, A., Dubussy, C., Quertinmont, E., Tafciu, V., Minsart, C., Петров, В. А., et al. (2025) · PLoS Genetics · doi:10.1371/journal.pgen.1011599 · Cited by 6
Advancing causal inference in medicine using biobank data
Kaufman, H., Rappoport, N., Gilad, A., & Linial, M. (2025) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2025.104903 · Cited by 3
Zhang, T., & Wang, Y. (2026) · npj Parkinson s Disease · doi:10.1038/s41531-025-01250-2 · Cited by 3
FastGxC: Fast and powerful context-specific eQTL mapping in bulk and single-cell data
Krockenberger, L., Lu, A. Y., Thompson, M., Tucker, L. A., Liu, C., Gordon, M. G., Ranta, A., Carcamo‐Orive, I., Knowles, J. W., Dahl, A., et al. (2026) · Cell Genomics · doi:10.1016/j.xgen.2026.101250 · Cited by 14
Cao, R., Xu, S., Xiang, J., Qu, B., Zhuang, Z., & Chu, T. (2026) · International Dental Journal · doi:10.1016/j.identj.2026.109678 · Cited by 3
Zang, K., Brossard, M., Wilson, T., Ali, S. A., & Espin‐Garcia, O. (2024) · Osteoarthritis and Cartilage Open · doi:10.1016/j.ocarto.2024.100540 · Cited by 3
Hakim, A., Connally, N. J., Schnitzler, G. R., Cho, M., Jiang, Z. G., Sunyaev, S. R., & Gupta, R. M. (2025) · Genes · doi:10.3390/genes16010084 · Cited by 3
Chen, L. (2025) · DNA Research · doi:10.1093/dnares/dsaf024 · Cited by 4
Paré, G., Mao, S., & Deng, W. Q. (2018) · Genetic Epidemiology · doi:10.1002/gepi.22149 · Cited by 4
An integrated strategy to identify COVID‐19 causal genes and characteristics represented by LRRC37A2
Zhu, Z., Chen, X., Wang, C., Zhang, S., Yu, R., Xie, Y., Yuan, S., Cheng, L., Shi, L., & Zhang, X. (2023) · Journal of Medical Virology · doi:10.1002/jmv.28585 · Cited by 3
Sayaman, R. W., Saad, M., Heimann, C., Hu, D., Kunji, K. B., Roelands, J. P., Wolf, D. M., Huntsman, S., Ceccarelli, M., Thórsson, V., et al. (2022) · STAR Protocols · doi:10.1016/j.xpro.2022.101809 · Cited by 3
Metadata
- Authors
- 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.
- Affiliations
- University of California, Los Angeles (US)
- Broad Institute (US)
- Corresponding author
- Eskin, Eleazar
- ORCID
- Venue
- The American Journal of Human Genetics
- Publisher
- Elsevier BV
- Published
- 2016-12
- Volume
- 99
- Issue
- 6
- Pages
- 1245-1260
- ISSN
- 0002-9297 (ISSN-L)
- PMC
- PMC5142122
- OpenAlex
- W2555614465
- Cited by
- 697
- References
- 50
- Citations by year
- 2026: 69 · 2025: 76 · 2024: 75 · 2023: 90 · 2022: 81 · 2021: 100 · 2020: 78 · 2019: 59 · 2018: 46 · 2017: 22 · 2016: 1
- FWCI
- 34.43
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- The American Journal of Human Genetics (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- National Institutes of Health (K25-HL080079, P01-HL28481, P01-HL30568, R01 -ES022282, R01-ES021801, R01-GM083198, R01-MH101782, U01-DA024417, U54EB020403)
- Broad Institute (R00-GM 111744-03)
- Novo Nordisk (HHSN268201000029C)
- National Science Foundation (0513612, 0729049, 0731455, 0916676, 1065276, 1302448, 1320589, 1331176)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- expression quantitative trait loci, GWAS risk loci, colocalization, causal variants, linkage disequilibrium, tissue-specific gene expression, target gene identification, summary statistics, non-coding variants
- MeSH terms
- Gene Expression Regulation, Genotype, Glucose, Humans, Insulin, Models, Genetic, Organ Specificity, Probability, Models, Statistical, Linkage Disequilibrium, Sample Size, Genetic Predisposition to Disease, Quantitative Trait Loci, Genome-Wide Association Study, Datasets as Topic
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
