The DOI matches its Crossref record; 67 of the 68 references it cites were found in a registry or an index.
Bayesian Test for Colocalisation between Pairs of Genetic Association Studies Using Summary Statistics
Open accessAbstract
Genetic association studies, in particular the genome-wide association study (GWAS) design, have provided a wealth of novel insights into the aetiology of a wide range of human diseases and traits, in particular cardiovascular diseases and lipid biomarkers. The next challenge consists of understanding the molecular basis of these associations. The integration of multiple association datasets, including gene expression datasets, can contribute to this goal. We have developed a novel statistical methodology to assess whether two association signals are consistent with a shared causal variant. An application is the integration of disease scans with expression quantitative trait locus (eQTL) studies, but any pair of GWAS datasets can be integrated in this framework. We demonstrate the value of the approach by re-analysing a gene expression dataset in 966 liver samples with a published meta-analysis of lipid traits including >100,000 individuals of European ancestry. Combining all lipid biomarkers, our re-analysis supported 26 out of 38 reported colocalisation results with eQTLs and identified 14 new colocalisation results, hence highlighting the value of a formal statistical test. In three cases of reported eQTL-lipid pairs (SYPL2, IFT172, TBKBP1) for which our analysis suggests that the eQTL pattern is not consistent with the lipid association, we identify alternative colocalisation results with SORT1, GCKR, and KPNB1, indicating that these genes are more likely to be causal in these genomic intervals. A key feature of the method is the ability to derive the output statistics from single SNP summary statistics, hence making it possible to perform systematic meta-analysis type comparisons across multiple GWAS datasets (implemented online at http://coloc.cs.ucl.ac.uk/coloc/). Our methodology provides information about candidate causal genes in associated intervals and has direct implications for the understanding of complex diseases as well as the design of drugs to target disease pathways.
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
“because the presence of multiple associated variants in a region violates the assumptions of COLOC and can lead to false positives or false negatives [13].”
From Multi-trait association studies discover pleiotropic loci between Alzheimer’s disease and cardiometabolic traits (2021) · cites this paper as [13]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Considering the 106 miRNA-host pairs (103 mature miRNAs from 77 precursors in 71 host genes) for which the miRNA had miR-QTLs and the host gene had eQTLs, we found evidence of colocalization between miR-QTLs and host gene eQTLs (coloc PP.H4 > 0.5) for 17 pairs and no evidence of colocalization for 89 pairs (Supplementary Table 9).”
From Mapping the microRNA landscape in the older adult brain and its genetic contribution to neuropsychiatric conditions (2024) · cites this paper as [20]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“To estimate the prior likelihood of each genomic locus having a single variant affecting both the protein and the phenotype, we used a strict Bayesian model (colocalization analysis) using the coloc package in R (Giambartolomei et al, 2014).”
From The genetic regulation of protein expression in cerebrospinal fluid (2023) · cites this paper as Giambartolomei et al, 2014
The cited source says what the claim attributes to it.
In the citing paper
“Finally, we assessed colocalization48,49 between each of our 2,477 significant pQTL associations (cis and trans) and the AD GWAS. After exclusion of pleiotropic regions, 32 proteins had QTL that colocalized with AD risk (PP.H4>0.8, Fig. 4A, Supp. Tables S43&44).”
From Proteogenomic analysis of human cerebrospinal fluid identifies neurologically relevant regulation and implicates causal proteins for Alzheimer’s disease (2024) · cites this paper as [48]
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Further, SPI1, MS4A4A, MS4A6A, and SELL are specifically expressed in microglia based on RNA-Seq data29–31”
From A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease (2017) · cites this paper as [31]
Experimental — accuracy not yet measured
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Considering the 106 miRNA-host pairs (103 mature miRNAs from 77 precursors in 71 host genes) for which the miRNA had miR-QTLs and the host gene had eQTLs, we found evidence of colocalization between miR-QTLs and host gene eQTLs (coloc PP.H4 > 0.5) for 17 pairs and no evidence of colocalization for 89 pairs (Supplementary Table 9).”
From Mapping the microRNA landscape in the older adult brain and its genetic contribution to neuropsychiatric conditions (2024) · cites this paper as [20]
Experimental — accuracy not yet measuredIn the citing paper
“We also found a significant overlap between H-MAGMA and two widely used expression quantitative trait loci (eQTL)-based gene mapping tools, coloc9 and TWAS10.”
From A computational tool (H-MAGMA) for improved prediction of brain-disorder risk genes by incorporating brain chromatin interaction profiles (2020) · cites this paper as [9]
In the citing paper
“Alternatively, Bayesian frameworks for colocalization analysis consider GWAS summary statistics for both traits across multiple SNPs in the region around the IV and assess either the evidence that each variant is jointly causal115 or consider shared causal variants as one of five competing hypotheses.116”
From Mendelian randomization (2022) · cites this paper as [115]
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
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,893
Introduction to Quantitative Genetics.
Edwards, A. W. F., & Falconer, D. S. (1982) · Biometrics · doi:10.2307/2529912 · Cited by 21,447
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072
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
Self-citationAn 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
Introduction to Quantitative Genetics
J.-M., G., & Falconer, D. S. (1962) · Population · doi:10.2307/1525780 · Cited by 3,773
Biological, clinical and population relevance of 95 loci for blood lipids
Teslovich, T. M., Musunuru, K., Smith, A. V., Edmondson, A. C., Stylianou, I. M., Koseki, M., Pirruccello, J. P., Ripatti, S., Chasman, D. I., Willer, C. J., et al. (2010) · Nature · doi:10.1038/nature09270 · Cited by 3,487
Self-citationWiller (2013) · doi:10.1038/ng.2797 · Cited by 2,983
LocusZoom: regional visualization of genome-wide association scan results
Pruim, R. J., Welch, R. P., Sanna, S., Teslovich, T. M., Chines, P. S., Gliedt, T. P., Boehnke, M., Abecasis, G. R., & Willer, C. J. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq419 · Cited by 2,707
A new multipoint method for genome-wide association studies by imputation of genotypes
Marchini, J., Howie, B., Myers, S., McVean, G., & Donnelly, P. (2007) · Nature Genetics · doi:10.1038/ng2088 · Cited by 2,609
Flagged first, then by citations
1 of the 68 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
Items 301–400 of 1,016
Inshaw, J. R. J., Cutler, A. J., Crouch, D. J. M., Wicker, L. S., & Todd, J. A. (2019) · Diabetes Care · doi:10.2337/dc19-0803 · Cited by 104
Klimentidis, Y. C., Arora, A., Newell, M., Zhou, J. J., Ordovás, J. M., Renquist, B. J., & Wood, A. C. (2020) · Diabetes · doi:10.2337/db19-1134 · Cited by 108
Cross-Ancestry Investigation of Venous Thromboembolism Genomic Predictors
Thibord, F., Klarin, D. M., Brody, J. A., Chen, M., Levin, M. G., Chasman, D. I., Goode, E. L., Hveem, K., Teder‐Laving, M., Martínez-Pérez, Á., et al. (2022) · Circulation · doi:10.1161/circulationaha.122.059675 · Cited by 105
Roychowdhury, T., Klarin, D. M., Levin, M. G., Spin, J. M., Rhee, Y. H., Deng, A. C., Headley, C. A., Tsao, N. L., Gellatly, C., Zuber, V., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01510-y · Cited by 115
TIGER: The gene expression regulatory variation landscape of human pancreatic islets
Alonso, L., Piron, A., Morán, I., Guindo-Martínez, M., Bonàs‐Guarch, S., Atla, G., Miguel-Escalada, I., Royo, R., Puiggròs, M., Garcia-Hurtado, X., et al. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109807 · Cited by 99
Genetic and molecular architecture of complex traits
Lappalainen, T., Li, Y., Ramachandran, S., & Gusev, A. (2024) · Cell · doi:10.1016/j.cell.2024.01.023 · Cited by 125
High-resolution genetic mapping of putative causal interactions between regions of open chromatin
Kumasaka, N., Knights, A. J., & Gaffney, D. J. (2018) · Nature Genetics · doi:10.1038/s41588-018-0278-6 · Cited by 103
A molecular quantitative trait locus map for osteoarthritis
Steinberg, J., Southam, L., Butterfield, N. C., Roumeliotis, T. I., Fontalis, A., Clark, M. J. R., Jayasuriya, R. L., Swift, D., Shah, K. M., Curry, K. F., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21593-7 · Cited by 103
Genetic effects on the timing of parturition and links to fetal birth weight
Solé-Navais, P., Flatley, C. J., Steinthórsdóttir, V., Vaudel, M., Juodakis, J., Chen, J., Laisk, T., LaBella, A. L., Westergaard, D., Bačelis, J., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01343-9 · Cited by 105
Burgess, S. (2018) · Apollo (University of Cambridge) · doi:10.17863/cam.26250 · Cited by 118
Reynolds, R. H., Botía, J. A., Nalls, M. A., Noyce, A. J., Nicolas, A., Cookson, M., Bandrés‐Ciga, S., Gibbs, J. R., Hernandez, D. G., Singleton, A., et al. (2019) · npj Parkinson s Disease · doi:10.1038/s41531-019-0076-6 · Cited by 101
Schmiedel, B. J., González-Colín, C., Fajardo, V., Rocha, J., Madrigal, A., Ramírez-Suástegui, C., Bhattacharyya, S., Simon, H. A., Greenbaum, J., Peters, B., et al. (2022) · Science Immunology · doi:10.1126/sciimmunol.abm2508 · Cited by 98
Integrative analysis of the plasma proteome and polygenic risk of cardiometabolic diseases
Ritchie, S. C., Lambert, S. A., Arnold, M., Teo, S. M., Lim, S. I., Šćepanović, P., Marten, J., Zahid, S., Chaffin, M. D., Liu, Y., et al. (2021) · Nature Metabolism · doi:10.1038/s42255-021-00478-5 · Cited by 96
Genetics of common cerebral small vessel disease
Bordes, C., Sargurupremraj, M., Mishra, A., & Debette, S. (2022) · Nature Reviews Neurology · doi:10.1038/s41582-021-00592-8 · Cited by 108
Sun, X., Chen, B., Qi, Y., Wei, M., Chen, W., Wu, X., Wang, Q., Li, J., Lei, X., & Luo, G. (2024) · The Journal of Headache and Pain · doi:10.1186/s10194-024-01828-w · Cited by 105
Zhang, W., Voloudakis, G., Rajagopal, V. M., Readhead, B., Dudley, J. T., Schadt, E. E., Björkegren, J., Kim, Y., Fullard, J. F., Hoffman, G. E., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11874-7 · Cited by 90
Self-citationKrohn, L., Heilbron, K., Blauwendraat, C., Reynolds, R. H., Yu, E., Senkevich, K. A., Rudakou, U., Estiar, M. A., Gustavsson, E. K., Brolin, K. A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-34732-5 · Cited by 104
Molecular quantitative trait loci
Aguet, F., Alasoo, K., Li, Y. I., Battle, A., Im, H. K., Montgomery, S. B., & Lappalainen, T. (2023) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00188-6 · Cited by 98
A genome-wide association analysis reveals new pathogenic pathways in gout
Major, T. J., Takei, R., Matsuo, H., Leask, M., Sumpter, N. A., Topless, R., Shirai, Y., Wang, W., Cadzow, M. J., Phipps‐Green, A. J., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01921-5 · Cited by 106
Mooney, M. A., Ryabinin, P., Wilmot, B., Bhatt, P., Mill, J., & Nigg, J. T. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0710-4 · Cited by 91
Peters, 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 97
Zhao, H., Rasheed, H., Nøst, T. H., Cho, Y., Liu, Y., Bhatta, L., Bhattacharya, A., Initiative, G. B. M.-A., Hemani, G., Smith, G. D., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100195 · Cited by 90
Genetic Regulatory Mechanisms of Smooth Muscle Cells Map to Coronary Artery Disease Risk Loci
Liu, B., Pjanic, M., Wang, T., Nguyen, T., Gloudemans, M., Rao, A., Castano, V. G., Nurnberg, S., Rader, D. J., Elwyn, S., et al. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.08.001 · Cited by 94
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 90
Sex differences in brain protein expression and disease
Wingo, A. P., Liu, Y., Gerasimov, E. S., Vattathil, S. M., Liu, J., Cutler, D. J., Epstein, M. P., Blokland, G. A. M., Thambisetty, M., Troncoso, J. C., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02509-y · Cited by 94
Li, X., Christenson, S. A., Modena, B. D., Li, H., Busse, W. W., Castro, M., Denlinger, L. C., ERZURUM, S. C., Fahy, J. V., Gaston, B. M., et al. (2020) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2020.07.030 · Cited by 95
Reay, W. R., Kiltschewskij, D. J., Geaghan, M. P., Atkins, J., Carr, V. J., Green, M. J., & Cairns, M. J. (2022) · Science Advances · doi:10.1126/sciadv.abj8969 · Cited by 91
The long noncoding RNA ROCKI regulates inflammatory gene expression
Zhang, Q., Chao, T., Patil, V. S., Qin, Y., Tiwari, S. K., Chiou, J., Dobin, A., Tsai, C., Li, Z., Dang, J. W., et al. (2019) · The EMBO Journal · doi:10.15252/embj.2018100041 · Cited by 92
Step by step: towards a better understanding of the genetic architecture of Alzheimer’s disease
Lambert, J.-C., Ramirez, A., Grenier-Boley, B., & Bellenguez, C. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-02076-1 · Cited by 87
Pouget, J. G., Han, B., Wu, Y., Mignot, E. J.-M., Ollila, H. M., Barker, J., Spain, S. L., Dand, N., Trembath, R. C., Martin, J. J., et al. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz145 · Cited by 95
Thériault, S., Dina, C., Messika-Zeitoun, D., Le Scouarnec, S., Capoulade, R., Gaudreault, N., Rigade, S., Li, Z., Simonet, F., Lamontagne, M., et al. (2019) · Circulation: Genomic and Precision Medicine · doi:10.1161/circgen.119.002617 · Cited by 90
Tang, A. S., Rankin, K. P., Cerono, G., Miramontes, S., Mills, H., Roger, J., Zeng, B., Nelson, C., Soman, K., Woldemariam, S., et al. (2024) · Nature Aging · doi:10.1038/s43587-024-00573-8 · Cited by 86
Genome‐wide association studies of multiple sclerosis
Cotsapas, C., & Mitrovic, M. (2018) · Clinical & Translational Immunology · doi:10.1002/cti2.1018 · Cited by 89
Pierce, B. L., Lin, T., Argos, M., Demanelis, K., Jasmine, F., Rakibuz‐Zaman, M., Sarwar, G., Islam, T., Shahriar, H., Islam, T., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03209-9 · Cited by 87
Dorajoo, R., Chang, X., Gurung, R. L., Li, Z., Wang, L., Wang, R., Beckman, K. B., Adams‐Haduch, J. M., Yiamunaa, M., Liu, S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-10443-2 · Cited by 84
Genetic Markers of ADHD-Related Variations in Intracranial Volume
Klein, M., Walters, R. K., Demontis, D., Stein, J. L., Hibar, D. P., Adams, H. H. H., Bralten, J., Mota, N. R., Schachar, R. J., Sonuga-Barke, E. J. S., et al. (2019) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2018.18020149 · Cited by 91
Genetic regulation of gene expression across multiple tissues in chickens
Guan, D., Bai, Z., Zhu, X., Zhong, C., Hou, Y., Zhu, D., Li, H., Lan, F., Diao, S., Yao, Y., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02155-9 · Cited by 99
Genetic variation at 16q24.2 is associated with small vessel stroke
Traylor, M., Malik, R., Nalls, M. A., Cotlarciuc, I., Radmanesh, F., Þorleifsson, G., Hanscombe, K. B., Langefeld, C. D., Saleheen, D., Rost, N. S., et al. (2016) · Annals of Neurology · doi:10.1002/ana.24840 · Cited by 90
Pollack, S. J., Igo, R. P., Jensen, R. A., Christiansen, M. W., Li, X., Cheng, C., Ng, M. C. Y., Smith, A. V., Rossin, E. J., Segrè, A. V., et al. (2018) · Diabetes · doi:10.2337/db18-0567 · Cited by 86
Hu, Y., Sun, J., Zhang, Y., Zhang, H., Gao, S., Wang, T., Han, Z., Wang, L., Sun, B., & Liu, G. (2021) · BMC Medicine · doi:10.1186/s12916-020-01883-5 · Cited by 89
Using human genetics to improve safety assessment of therapeutics
Carss, K., Deaton, A. M., Río-Espínola, A. D., Diogo, D., Fielden, M. R., Kulkarni, D. A., Moggs, J. G., Newham, P., Nelson, M. R., Sistare, F. D., et al. (2022) · Nature Reviews Drug Discovery · doi:10.1038/s41573-022-00561-w · Cited by 90
Association of Lipid-Lowering Drugs With Risk of Psoriasis
Zhao, S. S., Yiu, Z. Z. N., Barton, A. C., & Bowes, J. (2023) · JAMA Dermatology · doi:10.1001/jamadermatol.2022.6051 · Cited by 87
Multi-omics data integration methods and their applications in psychiatric disorders
Sathyanarayanan, A., Mueller, T. T., Moni, M. A., Schueler, K., Baune, B. T., Lió, P., Mehta, D., Baune, B. T., Dierssen, M., Ebert, B., et al. (2023) · European Neuropsychopharmacology · doi:10.1016/j.euroneuro.2023.01.001 · Cited by 93
Schlosser, P., Scherer, N., Grundner-Culemann, F., Monteiro-Martins, S., Haug, S., Steinbrenner, I., Uluvar, B., Wuttke, M., Cheng, Y., Ekici, A. B., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01409-8 · Cited by 92
Regulatory controls of duplicated gene expression during fiber development in allotetraploid cotton
You, J., Liu, Z., Qi, Z., Ma, Y., Sun, M., Su, L., Niu, H., Peng, Y., Luo, X., Zhu, M., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01530-8 · Cited by 89
Translational genomics of osteoarthritis in 1,962,069 individuals
Hatzikotoulas, K., Southam, L., Stefánsdóttir, L., Boer, C. G., McDonald, M., Pett, J. P., Park, Y., Tuerlings, M., Mulders, R., Barysenka, A., et al. (2025) · Nature · doi:10.1038/s41586-025-08771-z · Cited by 99
Alemu, R. G., Sharew, N. T., Arsano, Y. Y., Ahmed, M. B., Ayele, F. T., Baye, T. M., & Amare, A. T. (2025) · Human Genomics · doi:10.1186/s40246-025-00718-9 · Cited by 99
Endo, C., Johnson, T. A., Morino, R., Nakazono, K., Kamitsuji, S., Akita, M., Kawajiri, M., Yamasaki, T., Kami, A., Hoshi, Y., et al. (2018) · Scientific Reports · doi:10.1038/s41598-018-27145-2 · Cited by 85
Sex-specific and pleiotropic effects underlying kidney function identified from GWAS meta-analysis
Graham, S. E., Nielsen, J. B., Zawistowski, M., Zhou, W., Fritsche, L. G., Gabrielsen, M. E., Skogholt, A. H., Surakka, I. L., Hornsby, W., Fermin, D., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-09861-z · Cited by 82
Wang, L., Balmat, T. J., Antonia, A. L., Constantine, F. J., Henao, R., Burke, T. W., Ingham, A., McClain, M. T., Tsalik, E. L., Ko, E. R., et al. (2021) · Genome Medicine · doi:10.1186/s13073-021-00904-z · Cited by 85
Narzo, A. D., Houten, S. M., Kosoy, R., Huang, R., Vaz, F. M., Hou, R., Wei, G., Wang, W., Comella, P., Dodatko, T., et al. (2021) · Gastroenterology · doi:10.1053/j.gastro.2021.11.015 · Cited by 91
Self-citationThe genetics of bone mass and susceptibility to bone diseases
Karasik, D., Rivadeneira, F., & Johnson, M. L. (2016) · Nature Reviews Rheumatology · doi:10.1038/nrrheum.2016.48 · Cited by 88
The use of Mendelian randomisation to identify causal cancer risk factors: promise and limitations
Gala, H., & Tomlinson, I. P. M. (2020) · The Journal of Pathology · doi:10.1002/path.5421 · Cited by 86
Park, C. Y., Zhou, J., Wong, A. K., Chen, K., Theesfeld, C. L., Darnell, R. B., & Troyanskaya, O. G. (2021) · Nature Genetics · doi:10.1038/s41588-020-00761-3 · Cited by 80
Zhao, S., Crouse, W. L., Qian, S., Luo, K., Stephens, M., & He, X. (2024) · Nature Genetics · doi:10.1038/s41588-023-01648-9 · Cited by 79
Limited overlap of eQTLs and GWAS hits due to systematic differences in discoveryPreprint
Mostafavi, H., Spence, J. P., Naqvi, S., & Pritchard, J. K. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.05.07.491045 · Cited by 86
Transcriptome‐wide association studies accounting for colocalization using Egger regression
Barfield, R., Feng, H., Gusev, A., Wu, L., Zheng, W., Paşaniuc, B., & Kraft, P. L. (2018) · Genetic Epidemiology · doi:10.1002/gepi.22131 · Cited by 82
Multi-Omics Approaches in Immunological Research
Chu, X., Zhang, B., Koeken, V. A. C. M., Gupta, M. K., & Li, Y. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.668045 · Cited by 87
Eye-brain connections revealed by multimodal retinal and brain imaging genetics
Zhao, B., Li, Y., Fan, Z., Wu, Z., Shu, J., Yang, X., Yang, Y., Wang, X., Li, B., Wang, X., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-50309-w · Cited by 92
Yang, Q., Sanderson, E., Tilling, K., Borges, M. C., & Lawlor, D. A. (2019) · doi:10.1101/19009605 · Cited by 85
Peng, S., Deyssenroth, M. A., Narzo, A. F. D., Cheng, H. M., Zhang, Z., Lambertini, L., Ruusalepp, A., Kovacic, J. C., Björkegren, J., Marsit, C. J., et al. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007799 · Cited by 83
Richardson, T. G., Hemani, G., Gaunt, T. R., Relton, C. L., & Smith, G. D. (2020) · Nature Communications · doi:10.1038/s41467-019-13921-9 · Cited by 84
Baird, D. A., Liu, J. Z., Zheng, J., Sieberts, S. K., Perumal, T. M., Elsworth, B., Richardson, T. G., Chen, C., Carrasquillo, M. M., Allen, M., et al. (2021) · PLoS Genetics · doi:10.1371/journal.pgen.1009224 · Cited by 80
Zhang, Y., Yang, Z., He, Y., Liu, D., Liu, Y., Liang, C., Xie, M., Jia, Y., Ke, Q., Zhou, Y., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01957-7 · Cited by 91
Large-Scale Identification of Common Trait and Disease Variants Affecting Gene Expression
Hauberg, M. E., Zhang, W., Giambartolomei, C., Franzén, O., Morris, D. L., Vyse, T. J., Ruusalepp, A., Sklar, P. B., Schadt, E. E., Björkegren, J., et al. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.04.016 · Cited by 78
Self-citationThareja, G., Al-Sarraj, Y. A., Belkadi, A., Al-Motawa, M., Qatar Genome Project Management, Ismail, S. I., Al‐Muftah, W., Badji, R. M., Mbarek, H., Darwish, D., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-21381-3 · Cited by 83
Doke, T., Huang, S., Qiu, C., Liu, H., Guan, Y., Hu, H., Ma, Z., Wu, J., Miao, Z., Sheng, X., et al. (2021) · Journal of Clinical Investigation · doi:10.1172/jci141801 · Cited by 82
Georges, A. B., Yang, M., Berrandou, T.-E., Bakker, M. K., Dikilitas, O., Kiando, S. R., Ma, L., Satterfield, B. A., Sengupta, S., Yu, M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26174-2 · Cited by 81
Taub, M. A., Conomos, M. P., Keener, R., Iyer, K. R., Weinstock, J. S., Yanek, L. R., Lane, J., Miller‐Fleming, T. W., Brody, J. A., McHugh, C., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2021.100084 · Cited by 83
From genetic associations to genes: methods, applications, and challenges
Qi, T., Song, L., Guo, Y., Chen, C., & Yang, J. (2024) · Trends in Genetics · doi:10.1016/j.tig.2024.04.008 · Cited by 85
Genome-wide analyses identify 30 loci associated with obsessive–compulsive disorder
Strom, N. I., Gerring, Z. F., Galimberti, M., Yu, D., Halvorsen, M., Abdellaoui, A., Rodriguez‐Fontenla, C., Sealock, J., Bigdeli, T. B., Coleman, J. R. I., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02189-z · Cited by 123
Williamson, A., Norris, D. M., Yin, X., Broadaway, K. A., Moxley, A. H., Vadlamudi, S., Wilson, E. P., Jackson, A., Ahuja, V., Andersen, M. K., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01408-9 · Cited by 84
Rosoff, D. B., Mavromatis, L. A., Bell, A. S., Wagner, J., Jung, J., Marioni, R. E., Smith, G. D., Horvath, S., & Lohoff, F. W. (2023) · Nature Aging · doi:10.1038/s43587-023-00455-5 · Cited by 89
Molecular mechanisms underlying variations in lung function: a systems genetics analysis
Obeidat, M., Hao, K., Bossé, Y., Nickle, D. C., Nie, Y., Postma, D. S., Laviolette, M., Sandford, A. J., Daley, D., Hogg, J. C., et al. (2015) · The Lancet Respiratory Medicine · doi:10.1016/s2213-2600(15)00380-x · Cited by 85
Pain, O., Pocklington, A. J., Holmans, P., Bray, N. J., O’Brien, H., Hall, L. S., Pardiñas, A. F., O’Donovan, M. C., Owen, M. J., & Anney, R. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.04.034 · Cited by 75
Raulerson, C. K., Ko, A., Kidd, J., Currin, K. W., Brotman, S. M., Cannon, M. E., Wu, Y., Spracklen, C. N., Jackson, A., Stringham, H. M., et al. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2019.09.001 · Cited by 75
Sources of gene expression variation in a globally diverse human cohort
Taylor, D. J., Chhetri, S. B., Tassia, M. G., Biddanda, A., Yan, S. M., Wojcik, G. L., Battle, A. J., & McCoy, R. C. (2024) · Nature · doi:10.1038/s41586-024-07708-2 · Cited by 83
Gupta, P., Galimberti, M., Liu, Y., Beck, S., Wingo, A. P., Wingo, T. S., Adhikari, K., Kranzler, H. R., Program, V. M. V., Stein, M. B., et al. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01951-3 · Cited by 111
Chromosome contacts in activated T cells identify autoimmune disease candidate genes
Burren, O. S., Rubio García, A., Javierre, B.-M., Rainbow, D. B., Cairns, J., Cooper, N. J., Lambourne, J. J., Schofield, E., Castro Dopico, X., Ferreira, R. C., et al. (2017) · Genome Biology · doi:10.1186/s13059-017-1285-0 · Cited by 80
Self-citationMendelian randomisation for psychiatry: how does it work, and what can it tell us?
Wootton, R. E., Jones, H. J., & Sallis, H. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01173-3 · Cited by 79
Arvanitis, M., Tayeb, K., Strober, B. J., & Battle, A. J. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.01.002 · Cited by 80
Luo, S., Liang, Y., Wong, T. H. T., Schooling, C. M., & Yeung, S. L. A. (2022) · International Journal of Epidemiology · doi:10.1093/ije/dyac076 · Cited by 78
Tissue-specific impacts of aging and genetics on gene expression patterns in humans
Yamamoto, R., Chung, R., Vázquez, J. M., Sheng, H., Steinberg, P. L., Ioannidis, N. M., & Sudmant, P. H. (2022) · Nature Communications · doi:10.1038/s41467-022-33509-0 · Cited by 82
Wainberg, M., Andrews, S. J., & Tripathy, S. J. (2023) · Alzheimer s Research & Therapy · doi:10.1186/s13195-023-01244-3 · Cited by 83
Hillary, R. F., McCartney, D. L., Harris, S. E., Stevenson, A. J., Seeboth, A., Zhang, Q., Liewald, D. C., Evans, K. L., Ritchie, C. W., Tucker-Drob, E. M., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11177-x · Cited by 78
Gong, J., Wang, F., Xiao, B., Panjwani, N., Fan, L., Keenan, K., Avolio, J., Esmaeili, M., Zhang, L., He, G., et al. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008007 · Cited by 79
Genome-wide association and multi-omic analyses reveal ACTN2 as a gene linked to heart failure
Arvanitis, M., Tampakakis, E., Zhang, Y., Wang, W., Auton, A., Agee, M., Aslibekyan, S. W., Bell, R. K., Bryc, K., Clark, S., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14843-7 · Cited by 76
Shah, A. M., Myhre, P. L., Arthur, V., Dorbala, P., Rasheed, H., Buckley, L. F., Claggett, B. L., Liu, G., Ma, J., Nguyen, N. Q., et al. (2024) · Nature Communications · doi:10.1038/s41467-023-44680-3 · Cited by 89
Small, A. M., Peloso, G. M., Linefsky, J., Aragam, J., Galloway, A., Tanukonda, V., Wang, L.-C., Yu, Z., Sunitha Selvaraj, M., Farber-Eger, E. H., et al. (2023) · Circulation · doi:10.1161/circulationaha.122.061451 · Cited by 76
Interpreting non-coding disease-associated human variants using single-cell epigenomics
Gaulton, K. J., Preißl, S., & Ren, B. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00598-6 · Cited by 81
Making Sense of the Epigenome Using Data Integration Approaches
Cazaly, E., Saad, J. J., Wang, W., Heckman, C. A., Ollikainen, M., & Tang, J. P. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00126 · Cited by 77
Proteomic profiling reveals biomarkers and pathways in type 2 diabetes risk
Ngo, D., Benson, M. D., Long, J. Z., Chen, Z., Wang, R., Nath, A. K., Keyes, M. J., Shen, D., Sinha, S., Kuhn, E. W., et al. (2021) · JCI Insight · doi:10.1172/jci.insight.144392 · Cited by 77
Genetic architecture of heart failure with preserved versus reduced ejection fraction
Joseph, J., Liu, C., Hui, Q., Aragam, K. G., Wang, Z., CHAREST, B. R., Huffman, J. E., Keaton, J. M., Edwards, T. L., Demissie, S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-35323-0 · Cited by 79
Shared genetic risk factors and causal association between psoriasis and coronary artery disease
Patrick, M. T., Li, Q., Wasikowski, R., Mehta, N. N., Gudjonsson, J. E., Elder, J. T., Zhou, X., & Tsoi, L. C. (2022) · Nature Communications · doi:10.1038/s41467-022-34323-4 · Cited by 78
Shen, C., Zhang, R., Yu, J., Sahakian, B. J., Cheng, W., & Feng, J. (2025) · Nature Human Behaviour · doi:10.1038/s41562-024-02078-1 · Cited by 86
Gong, H., Xiao, S., Li, W., Huang, T., Huang, X., Yan, G., Huang, Y., Qiu, H., Jiang, K., Wang, X., et al. (2018) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12365 · Cited by 74
Crawford, A. A., Bankier, S., Altmaier, E., Barnes, C. L. K., Clark, D. W., Ermel, R., Friedrich, N., van der Harst, P., Joshi, P. K., Karhunen, V., et al. (2021) · Journal of Human Genetics · doi:10.1038/s10038-020-00895-6 · Cited by 74
Ghouse, J., Sveinbjörnsson, G., Vujković, M., Seidelin, A.-S., Gellert‐Kristensen, H., Ahlberg, G., Tragante, V., Rand, S. A., Brancale, J., Vilarinho, S., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01720-y · Cited by 79
Expression quantitative trait locus analysis for translational medicine
Gibson, G. C., Powell, J. E., & Marigorta, U. M. (2015) · Genome Medicine · doi:10.1186/s13073-015-0186-7 · Cited by 77
The impact of cell type and context-dependent regulatory variants on human immune traits
Mu, Z., Wei, W., Fair, B. J., Miao, J., Zhu, P., & Li, Y. (2021) · Genome biology · doi:10.1186/s13059-021-02334-x · Cited by 67
Metadata
- Authors
- Giambartolomei, C., Vukcevic, D., Schadt, E. E., Franke, L., Hingorani, A. D., Wallace, C., & Plagnol, V.
- Affiliations
- University College London (GB)
- Royal Children's Hospital (AU)
- Icahn School of Medicine at Mount Sinai (US)
- University Medical Center Groningen (NL)
- University of Cambridge (GB)
- Corresponding author
- Giambartolomei, Claudia
- ORCID
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2014-05-15
- Volume
- 10
- Issue
- 5
- Pages
- e1004383
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC4022491
- OpenAlex
- W2073836327
- Cited by
- 4,261
- References
- 68
- Citations by year
- 2026: 797 · 2025: 949 · 2024: 871 · 2023: 441 · 2022: 343 · 2021: 323 · 2020: 195 · 2019: 120 · 2018: 105 · 2017: 59 · 2016: 30 · 2015: 20 · 2014: 4
- FWCI
- 4.99
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- University of Groningen research database (University of Groningen / Centre for Information Technology) (submittedVersion, cc-by)
- arXiv (Cornell University)
- Apollo (cc-by)
- Funders
- Wellcome Trust (091157, 089989, 100140, 091157/Z/10/Z, 100140/Z/12/Z, 091157/Z/10/B, 089989/Z/09/Z)
- National Institute for Health and Care Research
- British Heart Foundation
- Moorfields Eye Hospital NHS Foundation Trust
- Medical Research Council (MR/K006584/1)
- Cambridge Institute for Medical Research, University of Cambridge (100140)
- NIHR Cambridge Biomedical Research Centre
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- genetic association summary statistics, expression quantitative trait loci, lipid biomarkers, hepatic gene expression, causal variant identification, SORT1, GCKR, KPNB1, cardiovascular genetics
- MeSH terms
- Bayes Theorem, Humans, Sample Size, Genome-Wide Association Study
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
