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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.
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“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.
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“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]
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“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
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“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]
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“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]
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“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]
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“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]
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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
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Items 701–800 of 1,016
Arthur, T. D., Nguyen, J., Henson, B. A., D’Antonio‐Chronowska, A., Jaureguy, J., Silva, N., Arias, A. D., Benaglio, P., Berggren, W. T., Borja, V., et al. (2025) · Cell Genomics · doi:10.1016/j.xgen.2025.100775 · Cited by 41
Fast and flexible joint fine-mapping of multiple traits via the Sum of Single Effects model
Zou, Y., Carbonetto, P. S., Xie, D., Wang, G., & Stephens, M. (2026) · Nature Genetics · doi:10.1038/s41588-025-02486-7 · Cited by 44
Veturi, Y., & Ritchie, M. D. (2017) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813235533_0021 · Cited by 37
Robinson, T., Martin, R. M., & Yarmolinsky, J. (2020) · International Journal of Cancer · doi:10.1002/ijc.32947 · Cited by 40
Shadrina, A. S., Shashkova, T., Torgasheva, A. A., Sharapov, S., Klarić, L., Pakhomov, E., Alexeev, D. G., Wilson, J. F., Tsepilov, Y. A., Joshi, P. K., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-67001-w · Cited by 38
Fine-mapping of Parkinson’s disease susceptibility loci identifies putative causal variants
Schilder, B. M., & Raj, T. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab294 · Cited by 38
Genetic regulation of post-translational modification of two distinct proteins
Landini, A., Trbojević‐Akmačić, I., Navarro, P., Tsepilov, Y. A., Sharapov, S., Vučković, F., Polašek, O., Hayward, C., Pribić, T., Vilaj, M. Ć., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29189-5 · Cited by 42
Wingo, T. S., Gerasimov, E. S., Liu, Y., Duong, D. M., Vattathil, S., Lori, A., Gockley, J., Breen, M. S., Maihofer, A. X., Nievergelt, C. M., et al. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01544-4 · Cited by 38
Wünnemann, F., Tadjo, T. F., Beaudoin, M., Lalonde, S., Lo, K. S., Kleinstiver, B. P., & Lettre, G. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010680 · Cited by 40
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
Ning, Z., Huang, Y., Lu, H., Zhou, Y., Tu, T., Ouyang, F., Liu, Y., & Liu, Q. (2023) · Cardiovascular Drugs and Therapy · doi:10.1007/s10557-023-07467-8 · Cited by 36
Multitissue H3K27ac profiling of GTEx samples links epigenomic variation to disease
Hou, L., Xiong, X., Park, Y., Boix, C. A., James, B. T., Sun, N., He, L., Patel, A., Zhang, Z., Molinié, B., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01509-5 · Cited by 44
Exploring noncoding variants in genetic diseases: from detection to functional insights
Wu, K., Bu, F., Wu, Y., Zhang, G., Wang, X., He, S., Liu, M., Chen, R., & Yuan, H. (2024) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2024.01.001 · Cited by 40
Venkatesh, S. S., Wittemans, L. B. L., Palmer, D. S., Baya, N. A., Ferreira, T. C., Hill, B., Lassen, F. H., Parker, M. J., Reibe, S., Elhakeem, A., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02156-8 · Cited by 48
Wen, J. (2025) · Nature Aging · doi:10.1038/s43587-025-00928-9 · Cited by 39
Bhalala, O. G., Nath, A. P., Inouye, M., & Sibley, C. R. (2018) · PLoS Genetics · doi:10.1371/journal.pgen.1007607 · Cited by 38
Huang, Q. Q., Tang, H. H. F., Teo, S. M., Mok, D., Ritchie, S. C., Nath, A. P., Brożyńska, M., Salim, A., Bakshi, A., Holt, B. J., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-17477-x · Cited by 39
Sabik, O. L., Calabrese, G. M., Taleghani, E., Ackert‐Bicknell, C. L., & Farber, C. R. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108145 · Cited by 40
Multi-ancestry Mendelian randomization of omics traits revealing drug targets of COVID-19 severity
Zheng, J., Zhang, Y., Zhao, H., Liu, Y., Baird, D. A., Karim, M. A., Ghoussaini, M., Schwartzentruber, J., Dunham, I., Elsworth, B., et al. (2022) · EBioMedicine · doi:10.1016/j.ebiom.2022.104112 · Cited by 38
Yarmolinsky, J., Bouras, E., Constantinescu, A., Burrows, K., Bull, C. J., Vincent, E. E., Martin, R. M., Dimopoulou, O., Lewis, S. J., Moreno, V. R., et al. (2023) · Diabetologia · doi:10.1007/s00125-023-05925-4 · Cited by 38
Kleeman, S. O., Thakir, T. M., Demestichas, B., Mourikis, N., Loiero, D., Ferrer, M., Bankier, S., Riazat-Kesh, Y. J. R. A., Lee, H., Chantzichristos, D., et al. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100347 · Cited by 37
Cross-ancestry genome-wide association meta-analyses of hippocampal and subfield volumes
Liu, N., Zhang, L. J., Tian, T., Cheng, J., Zhang, B., Qiu, S., Geng, Z., Cui, G., Zhang, Q., Liao, W., et al. (2023) · Nature Genetics · doi:10.1038/s41588-023-01425-8 · Cited by 39
An overview of detecting gene-trait associations by integrating GWAS summary statistics and eQTLs
Zhang, Y., Wang, M., Li, Z., Yang, X., Li, K., Xie, A., Dong, F., Wang, S., Yan, J., & Liu, J. (2024) · Science China Life Sciences · doi:10.1007/s11427-023-2522-8 · Cited by 42
Goes, F. S., Collado‐Torres, L., Zandi, P. P., Huuki-Myers, L. A., Tao, R., Jaffe, A. E., Pertea, G. M., Shin, J. H., Weinberger, D. R., Kleinman, J. E., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59115-4 · Cited by 40
Towards improved fine-mapping of candidate causal variants
Li, Z., & Zhou, X. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00869-4 · Cited by 38
How genetic risk contributes to autoimmune liver disease
Ellinghaus, D. (2022) · Seminars in Immunopathology · doi:10.1007/s00281-022-00950-8 · Cited by 40
Contribution of Common Genetic Variants to Risk of Early-Onset Ischemic Stroke
Jaworek, T. J., Xu, H., Gaynor, B. J., Cole, J. W., Rannikmäe, K., Stanne, T. M., Tomppo, L., Abedi, V., Amouyel, P., Armstrong, N. D., et al. (2022) · Neurology · doi:10.1212/wnl.0000000000201006 · Cited by 37
Systems genomics in age-related macular degeneration
den Hollander, A. I., Mullins, R. F., Orozco, L. D., Voigt, A. P., Chen, H.-H., Strunz, T., Graßmann, F., Haines, J. L., Kuiper, J. J. W., Tumminia, S. J., et al. (2022) · Experimental Eye Research · doi:10.1016/j.exer.2022.109248 · Cited by 38
Sadler, M. C., Auwerx, C., Lepik, K., Porcu, E., & Kutalik, Z. (2022) · Nature Communications · doi:10.1038/s41467-022-35196-3 · Cited by 35
Taylor, H. J., Hung, Y.-H., Narisu, N., Erdos, M. R., Kanke, M., Yan, T., Grenko, C. M., Swift, A. J., Bonnycastle, L. L., Sethupathy, P., et al. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2206797120 · Cited by 35
Sieberts, S. K., & Schadt, E. E. (2019) · Wiley eBooks · doi:10.1002/9781119487845.ch25 · Cited by 37
Self-citationHandunnetthi, L., Knezevic, B., Kasela, S., Burnham, K. L., Milani, L. A., Irani, S. R., Fang, H., & Knight, J. C. (2021) · Annals of Neurology · doi:10.1002/ana.26169 · Cited by 37
Proteomics: Progress and Promise of High-Throughput Proteomics in Chronic Kidney Disease
Schlosser, P., Grams, M. E., & Rhee, E. P. (2023) · Molecular & Cellular Proteomics · doi:10.1016/j.mcpro.2023.100550 · Cited by 36
Sethi, A., Ruby, J. G., Veras, M. A., Telis, N., & Melamud, E. (2023) · Nature Communications · doi:10.1038/s41467-023-38279-x · Cited by 36
Wang, Y., Zhang, Y., Zhu, Z., Wang, T.-Y., Morris, D. L., Shen, J. J., Zhang, H., Pan, H., Yang, J., Yang, S., et al. (2018) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2018-213093 · Cited by 35
Rare-Variant Studies to Complement Genome-Wide Association Studies
Sazonovs, A., & Barrett, J. C. (2018) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-083117-021641 · Cited by 37
Torres, J., Abdalla, M., Payne, A., Fernandez-Tajes, J. J., Thurner, M., Nylander, V., Gloyn, A. L., Mahajan, A., & McCarthy, M. I. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.10.009 · Cited by 36
Bonelli, R., Jackson, V. E., Prasad, A., Munro, J. E., Farashi, S., Heeren, T. F. C., Pontikos, N., Scheppke, L., Friedlander, M., Egan, C. A., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-01788-w · Cited by 35
ezQTL: A Web Platform for Interactive Visualization and Colocalization of QTLs and GWAS Loci
Zhang, T., Klein, A., Sang, J., Choi, J., & Brown, K. M. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.05.004 · Cited by 35
Pujol‐Gualdo, N., Läll, K., Lepamets, M., Metspalu, A., Nelis, M., Milani, L. A., Esko, T., Hudjashov, G., Rossi, H., Arffman, R. K., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31188-5 · Cited by 36
Bhattacharya, A., Hirbo, J. B., Zhou, D., Zhou, W., Zheng, J., Kanai, M., Initiative, T. G. B. M.-A., Paşaniuc, B., Gamazon, E. R., & Cox, N. J. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100180 · Cited by 36
Veturi, Y., Lucas, A., Bradford, Y., Hui, D., DUDEK, S. M., Theusch, E., Verma, A., Miller, J. E., Kullo, I. J., Hakonarson, H. H., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00879-y · Cited by 32
Proteome-wide mendelian randomization study implicates therapeutic targets in common cancers
Ren, F., Jin, Q., Liu, T., Xuelei, R., & Zhan, Y. (2023) · Journal of Translational Medicine · doi:10.1186/s12967-023-04525-5 · Cited by 36
Zhang, W., Ma, L., Zhou, Q., Gu, T., Zhang, X., & Xing, H. (2024) · Diabetes · doi:10.2337/db23-0564 · Cited by 35
Genetic architecture and risk prediction of gestational diabetes mellitus in Chinese pregnancies
Gu, Y., Zheng, H., Wang, P., Liu, Y., Guo, X., Wei, Y., Yang, Z., Cheng, S., Chen, Y., Hu, L., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-59442-6 · Cited by 40
Ketharnathan, S., Leask, M., Boocock, J., Phipps‐Green, A. J., Antony, J., O'Sullivan, J. M., Merriman, T. R., & Horsfield, J. A. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy295 · Cited by 35
simGWAS: a fast method for simulation of large scale case–control GWAS summary statistics
Fortune, M. D., & Wallace, C. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty898 · Cited by 33
Self-citationZeng, P., Hao, X., & Zhou, X. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty204 · Cited by 34
Genome-wide Association Study Identifies 2 New Loci Associated With Anti-NMDAR Encephalitis
Tietz, A. K., Angstwurm, K., Baumgartner, T. R., Doppler, K., Eisenhut, K., Elišák, M., Franke, A., Golombeck, K. S., Handreka, R., Kaufmann, M., et al. (2021) · Neurology Neuroimmunology & Neuroinflammation · doi:10.1212/nxi.0000000000001085 · Cited by 35
Gleason, K. J., Yang, F., & Chen, L. (2021) · Genetic Epidemiology · doi:10.1002/gepi.22380 · Cited by 35
Genome-wide association studies identify the role of caspase-9 in kidney disease
Doke, T., Huang, S., Qiu, C., Sheng, X., Seasock, M. J., Liu, H., Ma, Z., Palmer, M., & Suszták, K. (2021) · Science Advances · doi:10.1126/sciadv.abi8051 · Cited by 35
Sonehara, K., Sakaue, S., Maeda, Y., Hirata, J., Kishikawa, T., Yamamoto, K., Matsuoka, H., Yoshimura, M., Nii, T., Ohshima, S., et al. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab361 · Cited by 36
An efficient and robust tool for colocalisation: Pair-wise Conditional and Colocalisation (PWCoCo)Preprint
Robinson, J. W., Hemani, G., Babaei, M. S., Huang, Y., Baird, D. A., Tsai, E. A., Chen, C., Gaunt, T. R., & Zheng, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.08.503158 · Cited by 36
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
Gilchrist, J. J., Makino, S., Naranbhai, V., Sharma, P. K., Koturan, S., Tong, O., Taylor, C. A., Watson, R. A., Aires, A. V. D. L., Cooper, R. A., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-31626-4 · Cited by 29
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Sun, B. B., Loomis, S. J., Pizzagalli, F., Shatokhina, N., Painter, J. N., Foley, C. N., Biogen Biobank Team, Sun, B. B., Tsai, E. A., Bronson, P. G., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-33829-1 · Cited by 34
Vaher, H., Kingo, K., Kolberg, P., Pook, M., Raam, L., Laanesoo, A., Remm, A., Tenson, T., Alasoo, K., Mrowietz, U., et al. (2023) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2023.01.013 · Cited by 34
Zheng, J., Xu, M., Yang, Q., Hu, C., Walker, V. M., Lu, J., Wang, J., Liu, R., Xu, Y., Wang, T., et al. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104803 · Cited by 35
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Andrews, S. V., Kukkle, P. L., Menon, R., Geetha, T. S., Goyal, V., Kandadai, R. M., Kumar, H., Borgohain, R., Mukherjee, A., Wadia, P. M., et al. (2023) · Movement Disorders · doi:10.1002/mds.29676 · Cited by 35
Long-read sequencing for 29 immune cell subsets reveals disease-linked isoforms
Inamo, J., Suzuki, A., Ueda, M. T., Yamaguchi, K., Nishida, H., Suzuki, K., Kaneko, Y., Takeuchi, T., Hatano, H., Ishigaki, K., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-48615-4 · Cited by 37
Li, D., Wang, Q., Tian, Y., Lyv, X., Zhang, H., Sun, Y., Hong, H., Gao, H., Li, Y.-F., Zhao, C., et al. (2024) · Plant Communications · doi:10.1016/j.xplc.2024.101010 · Cited by 34
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Johnson, K. E., Heisel, T. J., Allert, M., Fürst, A., Yerabandi, N., Knights, D., Jacobs, K. M., Lock, E. F., Bode, L., Fields, D. A., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100638 · Cited by 37
Gorman, B. R., Ji, S., Francis, M., Sendamarai, A. K., Shi, Y., Devineni, P., Saxena, U., Partan, E. S., DeVito, A. K., Byun, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-52129-4 · Cited by 38
Humphrey, J., Brophy, E., Kosoy, R., Zeng, B., Coccia, E., Mattei, D., Ravi, A., Naito, T., Efthymiou, A. G., Navarro, E., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02099-0 · Cited by 37
Tokolyi, A., Persyn, E., Nath, A. P., Burnham, K. L., Marten, J., Vanderstichele, T., Tardáguila, M., Stacey, D., Farr, B. W., Iyer, V., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02096-3 · Cited by 37
Ray, A., Alabarse, P. V. G., Malik, R., Sargurupremraj, M., Bernhagen, J., Dichgans, M., Baumeister, S., & Georgakis, M. K. (2025) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2025.06.001 · Cited by 39
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You, J., Cui, X.-H., Chen, Y., Wang, Y., Li, H., Qiang, Y., Cheng, J.-Y., Deng, Y., Guo, Y., Ren, P., et al. (2025) · Nature Metabolism · doi:10.1038/s42255-025-01371-1 · Cited by 43
MRI-based multi-organ clocks for healthy aging and disease assessment
Cao, H., Song, Z., Duggan, M. R., Erus, G., Srinivasan, D., Tian, Y., Bai, W., Rafii, M. S., Aisen, P. S. S., Belsky, D. W., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03999-8 · Cited by 36
Single-cell atlas of the transcriptome and chromatin accessibility in the human retina
Li, J., Wang, J., Ibarra, I. L., Cheng, X., Luecken, M. D., Lu, J., Monavarfeshani, A., Yan, W., Zheng, Y., Zuo, Z., et al. (2026) · Nature Genetics · doi:10.1038/s41588-025-02454-1 · Cited by 77
Ndungu, A., Payne, A., Torres, J., van de Bunt, M., & McCarthy, M. I. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.01.003 · Cited by 28
Regulatory sites for splicing in human basal ganglia are enriched for disease-relevant information
Guelfi, S., D’Sa, K., Botía, J. A., Vandrovcová, J., Reynolds, R. H., Zhang, D., Trabzuni, D., Collado‐Torres, L., Thomason, A., Leyton, P. Q., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14483-x · Cited by 31
Jermusyk, A. A., Zhong, J., Connelly, K. E., Gordon, N., Perera, S., Abdolalizadeh, E., Zhang, T., O’Brien, A., Hoskins, J. W., Collins, I. J., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.09.002 · Cited by 34
Beyond GWAS: from simple associations to functional insights
Ishigaki, K. (2021) · Seminars in Immunopathology · doi:10.1007/s00281-021-00894-5 · Cited by 33
Aggregative trans-eQTL analysis detects trait-specific target gene sets in whole blood
Dutta, D., He, Y. M., Saha, A., Arvanitis, M., Battle, A. J., & Chatterjee, N. (2022) · Nature Communications · doi:10.1038/s41467-022-31845-9 · Cited by 32
Qin, C., Díaz-Gallo, L.-M., Tang, B., Wang, Y., Nguyen, T.-D., Harder, A., Lu, Y., Padyukov, L., Askling, J., & Hägg, S. (2023) · European Journal of Epidemiology · doi:10.1007/s10654-023-01000-9 · Cited by 35
On the interpretation of transcriptome-wide association studies
de Leeuw, C., Werme, J., Savage, J. E., Peyrot, W. J., & Posthuma, D. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010921 · Cited by 34
Impact of genetic similarity on imputation accuracy
Roshyara, N. R., & Scholz, M. R. (2015) · BMC Genetics · doi:10.1186/s12863-015-0248-2 · Cited by 34
Smeland, O. B., Frei, O., Fan, C.-C., Shadrin, A. A., Dale, A. M., & Andreassen, O. A. (2019) · Psychiatric Genetics · doi:10.1097/ypg.0000000000000234 · Cited by 34
Patient education strategies in dermatology: part 2: methods.
Zirwas, M., & Holder, J. L. (2009) · PubMed Central · Cited by 34
Bayesian meta‐analysis across genome‐wide association studies of diverse phenotypes
Trochet, H., Pirinen, M., Band, G., Jostins, L., McVean, G., & Spencer, C. C. A. (2019) · Genetic Epidemiology · doi:10.1002/gepi.22202 · Cited by 33
Zhou, X., Im, H. K., & Lee, S. (2020) · Nature Communications · doi:10.1038/s41467-020-18085-5 · Cited by 35
Lee, M. K., Huan, T., McCartney, D. L., Chittoor, G., de Vries, M., Lahousse, L., Nguyen, J. N., Brody, J. A., Fernandez, J. C., Terzikhan, N., et al. (2022) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.202108-1907oc · Cited by 33
Ardissino, M., Slob, E. A. W., Rajasundaram, S., Reddy, R. K., Woolf, B. A. R., Girling, J., Johnson, M. R., Ng, F. S., & Gill, D. (2022) · BMC Medicine · doi:10.1186/s12916-022-02483-1 · Cited by 33
Elevated plasma complement factor H related 5 protein is associated with venous thromboembolism
Iglesias, M. J., Sánchez‐Rivera, L., Ibrahim‐Kosta, M., Naudin, C., Munsch, G., Goumidi, L., Farm, M., Smith, P. M., Thibord, F., Kral-Pointner, J. B., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-38383-y · Cited by 38
Schlosser, P., Zhang, J., Liu, H., Surapaneni, A., Rhee, E. P., Arking, D. E., Yu, B., Boerwinkle, E., Welling, P. A., Chatterjee, N., et al. (2023) · Genome biology · doi:10.1186/s13059-023-02993-y · Cited by 36
Zuber, V., Lewin, A., Levin, M. G., Haglund, A., Ben‐Aicha, S., Emanueli, C., Damrauer, S. M., Burgess, S., Gill, D., & Bottolo, L. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.06.005 · Cited by 30
Keshawarz, A., Bui, H., Joehanes, R., Ma, J., Liu, C., Huan, T., Hwang, S., Tejada, B., Sooda, M., Courchesne, P. L., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-39936-3 · Cited by 32
Yap, C. X., Vo, D., Heffel, M. G., Bhattacharya, A., Wen, C. H., Yang, Y., Kemper, K. E., Zeng, J., Zheng, Z., Zhu, Z., et al. (2024) · Science Advances · doi:10.1126/sciadv.adn7655 · Cited by 29
Wang, W., Wei, Z., Li, Z., Ren, J., Song, Y. J., Xu, J., Liu, A., Li, X., Li, M., Fan, H., et al. (2024) · iMeta · doi:10.1002/imt2.234 · Cited by 39
Zheng, J., Maerz, W., Gergei, I., Kleber, M. E., Drechsler, C., Wanner, C., Brandenburg, V. M., Reppe, S., Gautvik, K. M., Medina‐Gómez, C., et al. (2019) · Journal of Bone and Mineral Research · doi:10.1002/jbmr.3803 · Cited by 33
Thynn, H. N., Chen, X., Hu, W.-X., Duan, Y.-Y., Zhu, D., Chen, H., Wang, N., Chen, H., Rong, Y., Lü, B.-J., et al. (2019) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2019.06.147 · Cited by 30
Fang, H., Chen, L., & Knight, J. C. (2019) · The Lancet Rheumatology · doi:10.1016/s2665-9913(19)30134-1 · Cited by 32
Lymphocyte DNA methylation mediates genetic risk at shared immune-mediated disease loci
Clark, A. D., Nair, N., Anderson, A. E., Thalayasingam, N., Naamane, N., Skelton, A. J., Diboll, J., Barton, A. C., Eyre, S., Isaacs, J. D., et al. (2020) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2019.12.910 · Cited by 32
Discovery of novel hepatocyte eQTLs in African Americans
Zhong, Y., De, T., Alarcón, C., Park, C. S., Lec, B., & Perera, M. A. (2020) · PLoS Genetics · doi:10.1371/journal.pgen.1008662 · Cited by 28
Wang, Y., Wei, W., Tangtanatakul, P., Zheng, L., Yao, L., Lin, Z., Qian, C., Qin, X., Hou, F., Zhang, X., et al. (2021) · Arthritis & Rheumatology · doi:10.1002/art.42021 · Cited by 33
Integrative Prioritization of Causal Genes for Coronary Artery Disease
Hao, K., Ermel, R., Sukhavasi, K., Cheng, H.-X., Ma, L.-J., Li, L., Amadori, L., Koplev, S., Franzén, O., d'Escamard, V., et al. (2021) · Circulation Genomic and Precision Medicine · doi:10.1161/circgen.121.003365 · Cited by 32
Methods for statistical fine-mapping and their applications to auto-immune diseases
Wang, Q. S., & Huang, H. (2022) · Seminars in Immunopathology · doi:10.1007/s00281-021-00902-8 · Cited by 37
Han, X., Zhu, Z., Xiao, Q., Li, J., Hong, X., Wang, X., Hasegawa, K., Camargo, C. A., & Liang, L. (2022) · Communications Biology · doi:10.1038/s42003-022-04070-9 · Cited by 36
Hanßen, R., Auwerx, C., Jõeloo, M., Sadler, M. C., Henning, E., Keogh, J. M., Bounds, R., Smith, M., Firth, H. V., Kutalik, Z., et al. (2023) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101155 · Cited by 32
Identifying therapeutic targets for cancer among 2074 circulating proteins and risk of nine cancers
Smith-Byrne, K., Hedman, Å., Dimitriou, M., Desai, T., Sokolov, A. V., Schioth, H. B., Koprulu, M., Pietzner, M., Langenberg, C., Atkins, J., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-46834-3 · Cited by 33
“All MR associations with p Wald 0.05 were subsequently evaluated for confounding by LD and the probability of a shared causal locus (PP4) between protein concentrations and cancer risk using two approaches: conventional colocalization 18,48 and conditional iterative colocalization 18 .”
MethodInfluential
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
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