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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The presence of multiple associated variants in a region violates the assumptions of COLOC and can lead to false positives or false negatives.
“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]
The Results sections on several independent associations and on comparison with existing tests state that COLOC assumes at most one causal variant per trait per locus, and that multiple signals can affect PP4 or give strong support to either H3 or H4. They do not establish both false positives and false negatives as stated: the paper cautions that high PP4 in this setting supports ‘at least one causal variant’ being shared, while weaker secondary signals have negligible impact. (LLM, conf=0.84)
Experimental — accuracy not yet measuredColocalization analysis used a strict Bayesian model with the coloc package in R.
“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
After exclusion of pleiotropic regions, 32 proteins had QTL that colocalized with AD risk (PP.H4>0.8).
“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]
SPI1, MS4A4A, MS4A6A, and SELL are specifically expressed in microglia based on RNA-Seq data.
“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]
The Materials and Methods section (Expression dataset; page unavailable) describes gene-expression arrays from human liver samples, not microglial RNA-Seq. Searches for SPI1, MS4A4A, MS4A6A, SELL, and microglial RNA-Seq found no passage in our copy addressing the claimed cell-specific expression. (LLM, conf=0.76)
Experimental — accuracy not yet measuredNo evidence of colocalization between miR-QTLs and host gene eQTLs was found for 89 of 106 miRNA-host pairs.
“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]
The Materials and Methods (eQTL analysis; page unavailable) describes the paper’s colocalisation procedure, while the Results report analyses of lipid traits and liver eQTLs. A search for miR-QTLs, miRNA-host pairs, and the 89-of-106 result found no passage addressing that finding. (LLM, conf=0.76)
Experimental — accuracy not yet measured
H-MAGMA and two eQTL-based gene mapping tools, coloc and TWAS, show significant overlap.
“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]
Bayesian frameworks for colocalization analysis consider GWAS summary statistics for both traits across multiple SNPs in the region around the instrumental variable and assess either the evidence that each variant is jointly causal or consider shared causal variants as one of five competing hypotheses.
“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]
Evidence of colocalization between miR-QTLs and host gene eQTLs (coloc PP.H4 > 0.5) was found for 17 of 106 miRNA-host pairs.
“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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Cites
R: A Language and Environment for Statistical Computing
R Core Team (2000) · doi:10.32614/r.manuals · Cited by 355,706 · see citing works
Introduction to Quantitative Genetics.
Edwards, A. W. F., & Falconer, D. S. (1982) · Biometrics · doi:10.2307/2529912 · Cited by 21,447 · see citing works
An integrated encyclopedia of DNA elements in the human genome
The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072 · see citing works
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 · see citing works
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 · see citing works
Introduction to Quantitative Genetics
J.-M., G., & Falconer, D. S. (1962) · Population · doi:10.2307/1525780 · Cited by 3,773 · see citing works
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 · see citing works
Self-citationWiller (2013) · doi:10.1038/ng.2797 · Cited by 2,983 · see citing works
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 · see citing works
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 · see citing works
Fast and accurate genotype imputation in genome-wide association studies through pre-phasing
Howie, B., Fuchsberger, C., Stephens, M., Marchini, J., & Abecasis, G. R. (2012) · Nature Genetics · doi:10.1038/ng.2354 · Cited by 1,737 · see citing works
Genetic Investigation of ANthropometric Traits (GIANT) Consortium, Yang, J., DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Ferreira, T., Morris, A. P., Medland, S. E., Madden, P. A. F., Heath, A. C., Martin, N. G., Montgomery, G. W., et al. (2012) · Nature Genetics · doi:10.1038/ng.2213 · Cited by 1,712 · see citing works
Genotype imputation for genome-wide association studies
Marchini, J., & Howie, B. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2796 · Cited by 1,597 · see citing works
Genomewide Association Studies and Assessment of the Risk of Disease
Manolio, T. A. (2010) · New England Journal of Medicine · doi:10.1056/nejmra0905980 · Cited by 1,512 · see citing works
Architecture of the human regulatory network derived from ENCODE data
Gerstein, M., Kundaje, A. B., Hariharan, M., Landt, S. G., Yan, K., Cheng, C., Mu, X. J., Khurana, E., Rozowsky, J. S., Alexander, R. P., et al. (2012) · Nature · doi:10.1038/nature11245 · Cited by 1,418 · see citing works
Trait-associated SNPs are more likely to be eQTLs: annotation to enhance discovery from GWAS
Nicolae, D. L., Gamazon, E., Zhang, W., Duan, S., Dolan, M. E., & Cox, N. J. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1000888 · Cited by 1,292 · see citing works
Understanding mechanisms underlying human gene expression variation with RNA sequencing
Pickrell, J. K., Marioni, J. C., Pai, A. A., Degner, J. F., Engelhardt, B. E., Nkadori, E., Veyrieras, J.-B., Stephens, M., Gilad, Y., & Pritchard, J. K. (2010) · Nature · doi:10.1038/nature08872 · Cited by 1,256 · see citing works
Multiple common variants for celiac disease influencing immune gene expression
Dubois, P. C. A., Trynka, G., Franke, L., Hunt, K. A., Romanos, J., Curtotti, A., Zhernakova, A., Heap, G., Ádány, R., Aromaa, A. J., et al. (2010) · Nature Genetics · doi:10.1038/ng.543 · Cited by 1,010 · see citing works
Self-citationGenotype Imputation with Thousands of Genomes
Howie, B., Marchini, J., & Stephens, M. (2011) · G3 Genes|Genomes|Genetics · doi:10.1534/g3.111.001198 · Cited by 966 · see citing works
Mapping the Genetic Architecture of Gene Expression in Human Liver
Schadt, E. E., Molony, C. M., Chudin, E., Hao, K., Yang, X., Lum, P. Y., Kasarskis, A., Zhang, B., Wang, S. S.-M., Suver, C., et al. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060107 · Cited by 909 · see citing works
Self-citationMapping complex disease traits with global gene expression
Cookson, W., Liang, L., Abecasis, G., Moffatt, M., & Lathrop, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2537 · Cited by 849 · see citing works
Trynka, G., PreventCD Study Group, Hunt, K. A., Bockett, N. A., Romanos, J., Mistry, V., Szperl, A., Bakker, S. F., Bardella, M. T., Bhaw, L., et al. (2011) · Nature Genetics · doi:10.1038/ng.998 · Cited by 825 · see citing works
Self-citationCommon Regulatory Variation Impacts Gene Expression in a Cell Type–Dependent Manner
Dimas, A. S., Deutsch, S., Stranger, B. E., Montgomery, S. B., Borel, C., Attar, H., Ingle, C., Beazley, C., Arcelus, M. G., Sekowska, M., et al. (2009) · Science · doi:10.1126/science.1174148 · Cited by 729 · see citing works
Newly identified genetic risk variants for celiac disease related to the immune response
Hunt, K. A., Zhernakova, A., Turner, G., Heap, G., Franke, L., Bruinenberg, M., Romanos, J., Dinesen, L. C., Ryan, A. W., Panesar, D., et al. (2008) · Nature Genetics · doi:10.1038/ng.102 · Cited by 675 · see citing works
Self-citationPervasive Sharing of Genetic Effects in Autoimmune Disease
Cotsapas, C., Voight, B. F., Rossin, E., Lage, K., Neale, B. M., Wallace, C., Abecasis, G. R., Barrett, J. C., Behrens, T., Cho, J., et al. (2011) · PLoS Genetics · doi:10.1371/journal.pgen.1002254 · Cited by 606 · see citing works
Self-citationLocalization of type 1 diabetes susceptibility to the MHC class I genes HLA-B and HLA-A
Nejentsev, S., Howson, J. M. M., Walker, N. M., Szeszko, J. S., Field, S. F., Stevens, H. E., Reynolds, P., Hardy, M. P., King, E., Masters, J., et al. (2007) · Nature · doi:10.1038/nature06406 · Cited by 568 · see citing works
Bayesian statistical methods for genetic association studies
Stephens, M., & Balding, D. J. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2615 · Cited by 465 · see citing works
Voight, B. F., Kang, H. M., Ding, J., Palmer, C. D., Sidore, C., Chines, P. S., Burtt, N. P., Fuchsberger, C., Li, Y., Erdmann, J., et al. (2012) · PLoS Genetics · doi:10.1371/journal.pgen.1002793 · Cited by 464 · see citing works
Nica, A. C., Montgomery, S. B., Dimas, A. S., Stranger, B. E., Beazley, C., Barroso, I., & Dermitzakis, E. T. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1000895 · Cited by 423 · see citing works
Bayes factors for genome‐wide association studies: comparison with P ‐values
Wakefield, J. (2008) · Genetic Epidemiology · doi:10.1002/gepi.20359 · Cited by 397 · see citing works
A Statistical Framework for Joint eQTL Analysis in Multiple Tissues
Flutre, T., Wen, X., Pritchard, J. K., & Stephens, M. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003486 · Cited by 252 · see citing works
Sherlock: Detecting Gene-Disease Associations by Matching Patterns of Expression QTL and GWAS
He, X., Fuller, C. K., Song, Y., Meng, Q., Zhang, B., Yang, X., & Li, H. (2013) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2013.03.022 · Cited by 241 · see citing works
Trabzuni, D., Ryten, M., Walker, R., Smith, C., Imran, S., Ramasamy, A., Weale, M. E., & Hardy, J. (2011) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.2011.07432.x · Cited by 229 · cited 1× · see citing works
“The database and browser currently include the possibility of investigating colocalisation only with liver expression data, however the resource will soon be extended to include expression in different tissues (including brain, [24, 25]), a multiplicity of molecular phenotypes and clinical variables, and different dis…”
BackgroundHaptoglobin: function and polymorphism.
Wassell, J. (2000) · PubMed · Cited by 204 · see citing works
Gene-centric Association Signals for Lipids and Apolipoproteins Identified via the HumanCVD BeadChip
Talmud, P. J., Drenos, F., Shah, S. H., Shah, T., Palmen, J., Verzilli, C. J., Gaunt, T. R., Pallas, J. A., Lovering, R. C., Li, K., et al. (2009) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.10.014 · Cited by 193 · see citing works
Self-citationWilliams, D. A., McCullagh, P. J., & Nelder, J. A. (1984) · Biometrics · doi:10.2307/2531415 · Cited by 183 · see citing works
Ding, J., Gudjonsson, J. E., Liang, L., Stuart, P. E., Li, Y., Chen, W., Weichenthal, M., Ellinghaus, E., Franke, A., Cookson, W., et al. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.10.024 · Cited by 180 · see citing works
Brown, C. D., Mangravite, L. M., & Engelhardt, B. E. (2013) · PLoS Genetics · doi:10.1371/journal.pgen.1003649 · Cited by 173 · see citing works
Practical Issues in Imputation-Based Association Mapping
Guan, Y., & Stephens, M. (2008) · PLoS Genetics · doi:10.1371/journal.pgen.1000279 · Cited by 171 · see citing works
Fibrates and future PPARα agonists in the treatment of cardiovascular disease
Staels, B., Maes, M., & Zambon, A. (2008) · Nature Clinical Practice Cardiovascular Medicine · doi:10.1038/ncpcardio1278 · Cited by 160 · see citing works
Bayesian method to predict individual SNP genotypes from gene expression data
Schadt, E. E., Woo, S., & Hao, K. (2012) · Nature Genetics · doi:10.1038/ng.2248 · Cited by 150 · see citing works
Self-citationHernandez, D. G., Nalls, M. A., Moore, M., Chong, S., Dillman, A., Trabzuni, D., Gibbs, J. R., Ryten, M., Arepalli, S., Weale, M. E., et al. (2012) · Neurobiology of Disease · doi:10.1016/j.nbd.2012.03.020 · Cited by 128 · see citing works
Sarría, A. J., Panini, S. R., & Evans, R. M. (1992) · Journal of Biological Chemistry · doi:10.1016/s0021-9258(18)41797-8 · Cited by 126 · see citing works
Richards, A. L., Jones, L., Moskvina, V, Kirov, G., Gejman, P. V., Levinson, D. F., Sanders, A. R., Molecular Genetics of Schizophrenia Collaboration (MGS), International Schizophrenia Consortium (ISC), Purcell, S., et al. (2012) · Molecular Psychiatry · doi:10.1038/mp.2011.11 · Cited by 123 · see citing works
Johnson, M. P., Brennecke, S. P., East, C. E., Göring, H. H. H., Kent, J. W., Dyer, T. D., Said, J. M., Roten, L. T., Iversen, A., Abraham, L. J., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0033666 · Cited by 122 · see citing works
Statistical Testing of Shared Genetic Control for Potentially Related Traits
Wallace, C. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21765 · Cited by 115 · see citing works
Self-citationPlagnol, V., Smyth, D. J., Todd, J. A., & Clayton, D. G. (2009) · Biostatistics · doi:10.1093/biostatistics/kxn039 · Cited by 113 · see citing works
Self-citationHaptoglobin-related protein is a high-affinity hemoglobin-binding plasma protein
Nielsen, M. J., Petersen, S. V., Jacobsen, C. B., Oxvig, C., Rees, D. C., Møller, H. J., & Moestrup, S. K. (2006) · Blood · doi:10.1182/blood-2006-05-022327 · Cited by 107 · see citing works
Using gene expression to investigate the genetic basis of complex disorders
Nica, A. C., & Dermitzakis, E. T. (2008) · Human Molecular Genetics · doi:10.1093/hmg/ddn285 · Cited by 106 · see citing works
Genomewide Association Studies and Assessment of Risk of Disease
Rienhoff, H. Y. (2010) · New England Journal of Medicine · doi:10.1056/nejmc1010310 · Cited by 104 · see citing works
Statistical colocalization of monocyte gene expression and genetic risk variants for type 1 diabetes
Wallace, C., Rotival, M., Cooper, J. D., Rice, C. M., Yang, J. H. M., McNeill, M., Smyth, D. J., Niblett, D., Cambien, F., Tiret, L., et al. (2012) · Human Molecular Genetics · doi:10.1093/hmg/dds098 · Cited by 101 · see citing works
Self-citationBurren, O. S., Adlem, E., Achuthan, P., Christensen, M. B., Coulson, R., & Todd, J. A. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq912 · Cited by 79 · see citing works
Garver, W. S., Krishnan, K., Gallagos, J. R., Michikawa, M., Francis, G. A., & Heidenreich, R. A. (2002) · Journal of Lipid Research · doi:10.1016/s0022-2275(20)31487-5 · Cited by 78 · see citing works
Ramasamy, A., Trabzuni, D., Gibbs, J. R., Dillman, A., Hernandez, D. G., Arepalli, S., WALKER, R. E., Smith, C., Ilori, G. P., Shabalin, A. A., et al. (2013) · Nucleic Acids Research · doi:10.1093/nar/gkt069 · Cited by 77 · see citing works
The ubiquitin-like (UBX)-domain-containing protein Ubx2/ Ubxd8 regulates lipid droplet homeostasis
Wang, C.-W., & Lee, S. (2012) · Journal of Cell Science · doi:10.1242/jcs.100230 · Cited by 76 · see citing works
Regulation of Hepatic Cytochromes P450 by Lipids and Cholesterol
Hafner, M., Režen, T., & Rozman, D. (2011) · Current Drug Metabolism · doi:10.2174/138920011795016890 · Cited by 55 · see citing works
Wen, X., & Stephens, M. (2014) · The Annals of Applied Statistics · doi:10.1214/13-aoas695 · Cited by 53 · see citing works
Nasarre, L., Juan‐Babot, O., Gastelurrutia, P., Valldeperas, A. L., Badimón, L., Bayés‐Genís, A., & Llorente‐Cortés, V. (2012) · Acta Diabetologica · doi:10.1007/s00592-012-0436-8 · Cited by 44 · see citing works
Disease model distortion in association studies
Vukcevic, D., Hechter, E., Spencer, C. C. A., & Donnelly, P. (2011) · Genetic Epidemiology · doi:10.1002/gepi.20576 · Cited by 32 · see citing works
Self-citationSaito, K., Adachi, N., Koyama, H., & Matsushita, M. (2010) · FEBS Letters · doi:10.1016/j.febslet.2010.06.015 · Cited by 29 · see citing works
Knight, J., Barnes, M. R., Breen, G. D., & Weale, M. E. (2011) · PLoS ONE · doi:10.1371/journal.pone.0014808 · Cited by 27 · see citing works
Johansson, M., Roberts, A., Chen, D., Li, Y., Delahaye-Sourdeix, M., Aswani, N., Greenwood, M., Benhamou, S., Λάγιου, Π., Holcátová, I., et al. (2012) · PLoS ONE · doi:10.1371/journal.pone.0036888 · Cited by 25 · see citing works
Yilmaz, Y. N., Eren, F., Çolak, Y., Şenateş, E., Çelikel, Ç., & İmeryüz, N. (2012) · Scandinavian Journal of Gastroenterology · doi:10.3109/00365521.2012.725093 · Cited by 21 · see citing works
Complexity of a complex trait locus: HP, HPR, haemoglobin and cholesterol
Guthrie, P. A. I., Rodrı́guez, S., Gaunt, T. R., Lawlor, D. A., Smith, G. D., & Day, I. N. M. (2012) · Gene · doi:10.1016/j.gene.2012.03.034 · Cited by 21 · see citing works
Trabzuni, D., Ryten, M., Walker, R., Smith, C., Imran, S., Ramasamy, A., Weale, M., & Hardy, J. (2012) · Journal of Neurochemistry · doi:10.1111/j.1471-4159.2011.07602.x · Cited by 13 · see citing works
Unbiased statistical testing of shared genetic control for potentially related traitsPreprint
Wallace, C. (2013) · arXiv (Cornell University) · Cited by 1 · see citing works
Self-citationYilmaz, Y. N., Eren, F., Çolak, Y., Şenateş, E., Çelikel, Ç., & İmeryüz, N. (2013) · Journal of Hepatology · doi:10.1016/s0168-8278(13)61332-x · Cited by 1 · see citing works
Flagged first, then by citations
1 of the 68 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
The MR-Base platform supports systematic causal inference across the human phenome
Hemani, G., Zheng, J., Elsworth, B., Wade, K. H., Haberland, V., Baird, D., Laurin, C., Burgess, S., Bowden, J., Langdon, R., et al. (2018) · eLife · doi:10.7554/elife.34408 · Cited by 7,959 · see citing works
The GTEx Consortium atlas of genetic regulatory effects across human tissues
The GTEx Consortium, Aguet, F., Anand, S., Ardlie, K. G., Gabriel, S., Getz, G. A., Graubert, A., Hadley, K., Handsaker, R. E., Huang, K. H., et al. (2020) · Science · doi:10.1126/science.aaz1776 · Cited by 5,547 · see citing works
Genetic effects on gene expression across human tissues
GTEx Consortium (2017) · Nature · doi:10.1038/nature24277 · Cited by 4,036 · see citing works
Integration of summary data from GWAS and eQTL studies predicts complex trait gene targets
Zhu, Z., Zhang, F., Hu, H., Bakshi, A., Robinson, M. R., Powell, J. E., Montgomery, G. W., Goddard, M. E., Wray, N. R., Visscher, P. M., et al. (2016) · Nature Genetics · doi:10.1038/ng.3538 · Cited by 3,352 · see citing works
Alzheimer Disease Genetics Consortium (ADGC), Kunkle, B. W., The European Alzheimer’s Disease Initiative (EADI), Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium (CHARGE), Genetic and Environmental Risk in AD/Defining Genetic, P. A. E. R. F. A. D. C. (., Grenier-Boley, B., Sims, R., Bis, J. C., Damotte, V., Naj, A. C., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0358-2 · Cited by 2,946 · see citing works
A survey of best practices for RNA-seq data analysis
Conesa, A., Madrigal, P., Tarazona, S., Gomez-Cabrero, D., Cervera, A., McPherson, A., Szcześniak, M. W., Gaffney, D. J., Elo, L. L., Zhang, X., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0881-8 · Cited by 2,794 · see citing works
Võsa, U., Claringbould, A., Westra, H.-J., Bonder, M. J., Deelen, P., Zeng, B., Kirsten, H., Saha, A., Kreuzhuber, R., Yazar, S., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00913-z · Cited by 2,607 · see citing works
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 · see citing works
Genomic atlas of the human plasma proteome
Sun, B. B., Maranville, J. C., Peters, J. E., Stacey, D., Staley, J. R., Blackshaw, J., Burgess, S., Jiang, T., Paige, E., Surendran, P., et al. (2018) · Nature · doi:10.1038/s41586-018-0175-2 · Cited by 2,268 · see citing works
Sanderson, E., Glymour, M. M., Holmes, M. V., Kang, H., Morrison, J., Munafò, M. R., Palmer, T., Schooling, C. M., Wallace, C., Zhao, Q., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00092-5 · Cited by 2,229 · see citing works
Self-citationLarge-scale association analyses identify host factors influencing human gut microbiome composition
Kurilshikov, A., Medina-Gomez, C., Bacigalupe, R., Radjabzadeh, D., Wang, J., Demirkan, A., Le Roy, C. I., Raygoza Garay, J. A., Finnicum, C. T., Liu, X., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00763-1 · Cited by 1,786 · see citing works
Self-citationGenome-wide association studies
Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D. (2021) · Nature Reviews Methods Primers · doi:10.1038/s43586-021-00056-9 · Cited by 1,849 · see citing works
Guidelines for performing Mendelian randomization investigations
Burgess, S., Smith, G. D., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., Hartwig, F. P., Holmes, M. V., Minelli, C., Relton, C. L., et al. (2019) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15555.1 · Cited by 1,816 · see citing works
Evaluating the potential role of pleiotropy in Mendelian randomization studies
Hemani, G., Bowden, J., & Davey Smith, G. (2018) · Human Molecular Genetics · doi:10.1093/hmg/ddy163 · Cited by 1,496 · see citing works
de Lange, K. M., Moutsianas, L., Lee, J. C., Lamb, C. A., Luo, Y., Kennedy, N. A., Jostins, L., Rice, D. L., Gutierrez-Achury, J., Ji, S.-G., et al. (2017) · Nature Genetics · doi:10.1038/ng.3760 · Cited by 1,445 · see citing works
A global overview of pleiotropy and genetic architecture in complex traits
Watanabe, K., Stringer, S., Frei, O., Umićević Mirkov, M., de Leeuw, C., Polderman, T. J. C., van der Sluis, S., Andreassen, O. A., Neale, B. M., & Posthuma, D. (2019) · Nature Genetics · doi:10.1038/s41588-019-0481-0 · Cited by 1,332 · see citing works
Complex genetic signatures in immune cells underlie autoimmunity and inform therapy
Orrù, V., Steri, M., Sidore, C., Marongiu, M., Serra, V., Olla, S., Sole, G., Lai, S., Dei, M., Mulas, A., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0684-4 · Cited by 1,246 · see citing works
Detection and interpretation of shared genetic influences on 42 human traits
Pickrell, J. K., Berisa, T., Liu, J. Z., Ségurel, L., Tung, J. Y., & Hinds, D. A. (2016) · Nature Genetics · doi:10.1038/ng.3570 · Cited by 1,151 · see citing works
Guidelines for performing Mendelian randomization investigations
Burgess, S., Davey Smith, G., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., Hartwig, F. P., Holmes, M. V., Minelli, C., Relton, C. L., et al. (2020) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15555.2 · Cited by 1,273 · see citing works
A meta-analysis of genome-wide association studies identifies 17 new Parkinson's disease risk loci
Chang, D., Nalls, M. A., Hallgrímsdóttir, I. B., Hunkapiller, J., van der Brug, M., Cai, F., International Parkinson's Disease Genomics Consortium, 23andMe Research Team, Kerchner, G. A., Ayalon, G., et al. (2017) · Nature Genetics · doi:10.1038/ng.3955 · Cited by 1,112 · see citing works
Wightman, D. P., Jansen, I. E., Savage, J. E., Shadrin, A. A., Bahrami, S., Holland, D., Rongve, A., Børte, S., Winsvold, B. S., Drange, O. K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00921-z · Cited by 1,011 · see citing works
Comprehensive functional genomic resource and integrative model for the human brain
Wang, D., Liu, S., Warrell, J., Won, H., Shi, X., Navarro, F. C. P., Clarke, D., Gu, M., Emani, P., Yang, Y. T., et al. (2018) · Science · doi:10.1126/science.aat8464 · Cited by 965 · see citing works
Hou, L., Kellis, M., Liu, Y., Park, Y., & Rinaldi, N. J. (2018) · DSpace@MIT (Massachusetts Institute of Technology) · doi:10.34944/dspace/4648 · Cited by 1,082 · see citing works
The role of regulatory variation in complex traits and disease
Albert, F. W., & Kruglyak, L. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3891 · Cited by 1,047 · see citing works
The druggable genome and support for target identification and validation in drug development
Finan, C., Gaulton, A., Krüger, F. A., Lumbers, R. T., Shah, T., Engmann, J., Galver, L., Kelley, R., Karlsson, A., Santos, R., et al. (2017) · Science Translational Medicine · doi:10.1126/scitranslmed.aag1166 · Cited by 1,011 · see citing works
Self-citationGenomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases
Chen, Y., Lu, T., Pettersson, U., Stewart, I. D., Butler‐Laporte, G., Nakanishi, T., Cerani, A., Liang, K. Y. H., Yoshiji, S., Willett, J., et al. (2023) · Nature Genetics · doi:10.1038/s41588-022-01270-1 · Cited by 980 · see citing works
The power of genetic diversity in genome-wide association studies of lipids
Graham, S. E., Clarke, S. L., Wu, K.-H. H., Kanoni, S., Zajac, G. J. M., Ramdas, S., Surakka, I., Ntalla, I., Vedantam, S., Winkler, T. W., et al. (2021) · Nature · doi:10.1038/s41586-021-04064-3 · Cited by 972 · see citing works
“Expression quantitative trait loci colocalization was performed using coloc 61 Field-specific reporting Please select the one below that is the best fit for your research.”
MethodThe MRC IEU OpenGWAS data infrastructurePreprint
Elsworth, B., Lyon, M., Alexander, T., Liu, Y., Matthews, P., Hallett, J., Bates, P., Palmer, T., Haberland, V., Smith, G. D., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.10.244293 · Cited by 932 · see citing works
Opportunities and challenges for transcriptome-wide association studies
Wainberg, M., Sinnott-Armstrong, N., Mancuso, N., Barbeira, A., Knowles, D. A., Golan, D., Ermel, R., Ruusalepp, A., Quertermous, T., Hao, K., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0385-z · Cited by 929 · see citing works
Xue, A., Wu, Y., Zhu, Z., Zhang, F., Kemper, K. E., Zheng, Z., Yengo, L., Lloyd‐Jones, L. R., Sidorenko, J., Wu, Y., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04951-w · Cited by 884 · see citing works
Self-citationFrom genome-wide associations to candidate causal variants by statistical fine-mapping
Schaid, D. J., Chen, W., & Larson, N. B. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0016-z · Cited by 897 · see citing works
A catalog of genetic loci associated with kidney function from analyses of a million individuals
Study, L. C., Wuttke, M., Program, V. A. M. V., Li, Y., Li, M., Sieber, K. B., Feitosa, M. F., Gorski, M., Tin, A., Wang, L., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0407-x · Cited by 831 · see citing works
Shah, S. H., Henry, A., Roselli, C., Lin, H., Sveinbjörnsson, G., Fatemifar, G., Hedman, Å. K., Wilk, J. B., Morley, M. P., Chaffin, M. D., et al. (2020) · Nature Communications · doi:10.1038/s41467-019-13690-5 · Cited by 832 · see citing works
Mendelian Randomization: Concepts and Scope
Richmond, R. C., & Davey Smith, G. (2022) · Cold Spring Harbor Perspectives in Medicine · doi:10.1101/cshperspect.a040501 · Cited by 841 · see citing works
Zheng, J., Haberland, V., Baird, D., Walker, V., Haycock, P., Hurle, M., Gutteridge, A., Erola, P., Liu, Y., Luo, S., et al. (2019) · doi:10.1101/627398 · Cited by 812 · see citing works
Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits
Wu, Y., Zeng, J., Zhang, F., Zhu, Z., Qi, T., Zheng, Z., Lloyd-Jones, L. R., Marioni, R. E., Martin, N. G., Montgomery, G. W., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03371-0 · Cited by 685 · see citing works
Guidelines for performing Mendelian randomization investigations: update for summer 2023
Burgess, S., Smith, G. D., Davies, N. M., Dudbridge, F., Gill, D., Glymour, M. M., Hartwig, F. P., Kutalik, Z., Holmes, M. V., Minelli, C., et al. (2023) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15555.3 · Cited by 737 · see citing works
Mahajan, A., Spracklen, C. N., Zhang, W., Ng, M. C. Y., Petty, L. E., Kitajima, H., Yu, G. Z., Rüeger, S., Speidel, L., Kim, Y. J., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01058-3 · Cited by 704 · see citing works
Mapping the proteo-genomic convergence of human diseases
Pietzner, M., Wheeler, E., Carrasco-Zanini, J., Cortes, A., Koprulu, M., Wörheide, M. A., Oerton, E., Cook, J., Stewart, I. D., Kerrison, N. D., et al. (2021) · Science · doi:10.1126/science.abj1541 · Cited by 657 · see citing works
Self-citationColocalization of GWAS and eQTL Signals Detects Target Genes
Hormozdiari, F., van de Bunt, M., Segrè, A. V., Li, X., Joo, J. W. J., Bilow, M., Sul, J. H., Sankararaman, S., Pasaniuc, B., & Eskin, E. (2016) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2016.10.003 · Cited by 697 · see citing works
The impact of sex on gene expression across human tissues
Oliva, M., Muñoz-Aguirre, M., Kim-Hellmuth, S., Wucher, V., Gewirtz, A. D., Cotter, D. J., Parsana, P., Kasela, S., Balliu, B., Viñuela, A., et al. (2020) · Science · doi:10.1126/science.aba3066 · Cited by 679 · see citing works
A robust and efficient method for Mendelian randomization with hundreds of genetic variants
Burgess, S., Foley, C. N., Allara, E., Staley, J. R., & Howson, J. M. M. (2020) · Nature Communications · doi:10.1038/s41467-019-14156-4 · Cited by 681 · see citing works
Genetic Drivers of Epigenetic and Transcriptional Variation in Human Immune Cells
Chen, L., Ge, B., Casale, F. P., Vasquez, L., Kwan, T., Garrido-Martín, D., Watt, S., Yan, Y., Kundu, K., Ecker, S., et al. (2016) · Cell · doi:10.1016/j.cell.2016.10.026 · Cited by 679 · see citing works
Önengüt-Gümüşcü, S., Chen, W., Burren, O. S., Cooper, N. J., Quinlan, A. R., Mychaleckyj, J. C., Farber, E., Bonnie, J. K., Szpak, M., Schofield, E. C., et al. (2015) · Nature Genetics · doi:10.1038/ng.3245 · Cited by 675 · see citing works
Self-citationCommon genetic variation drives molecular heterogeneity in human iPSCs
Kilpinen, H., Goncalves, A., Leha, A., Afzal, V., Alasoo, K., Ashford, S., Bala, S., Bensaddek, D., Casale, F. P., Culley, O. J., et al. (2017) · Nature · doi:10.1038/nature22403 · Cited by 586 · see citing works
Large-scale plasma proteomics comparisons through genetics and disease associations
Eldjarn, G. H., Ferkingstad, E., Lund, S. H., Helgason, H., Magnusson, O. T., Gunnarsdottir, K., Olafsdottir, T. A., Halldorsson, B. V., Olason, P. I., Zink, F., et al. (2023) · Nature · doi:10.1038/s41586-023-06563-x · Cited by 583 · see citing works
Paternoster, L., Kemp, J. P., Zheng, J., Smith, G. D., Relton, C. L., Evans, D. M., McArdle, W. L., Henderson, A. J., Standl, M., Tiesler, C. M. T., et al. (2015) · Nature Genetics · doi:10.1038/ng.3424 · Cited by 614 · see citing works
Fine-mapping inflammatory bowel disease loci to single-variant resolution
International Inflammatory Bowel Disease Genetics Consortium, Huang, H., Fang, M., Jostins, L., Umićević Mirkov, M., Boucher, G., Anderson, C. A., Andersen, V., Cleynen, I., Cortes, A., et al. (2017) · Nature · doi:10.1038/nature22969 · Cited by 611 · see citing works
Stroke genetics informs drug discovery and risk prediction across ancestries
Mishra, A., Malik, R., Hachiya, T., Jürgenson, T., Namba, S., Posner, D. C., Kamanu, F., Koido, M., Grand, Q. L., Shi, M., et al. (2022) · Nature · doi:10.1038/s41586-022-05165-3 · Cited by 593 · see citing works
Warrington, N. M., Beaumont, R. N., Horikoshi, M., Day, F. R., Helgeland, Ø., Laurin, C., Bačelis, J., Peng, S., Hao, K., Feenstra, B., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0403-1 · Cited by 574 · see citing works
Cano-Gamez, E., & Trynka, G. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00424 · Cited by 608 · see citing works
Mountjoy, E., Schmidt, E. M., Carmona, M., Schwartzentruber, J., Peat, G., Miranda, A., Fumis, L., Hayhurst, J., Buniello, A., Karim, M. A., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00945-5 · Cited by 581 · see citing works
Gusev, A., Mancuso, N., Won, H., Kousi, M., Finucane, H. K., Reshef, Y., Song, L., Safi, A., Schizophrenia Working Group of the Psychiatric Genomics Consortium, McCarroll, S., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0092-1 · Cited by 491 · see citing works
A more accurate method for colocalisation analysis allowing for multiple causal variants
Wallace, C. (2021) · PLOS Genetics · doi:10.1371/journal.pgen.1009440 · Cited by 567 · see citing works
Self-citationFoley, C. N., Staley, J. R., Breen, P. G., Sun, B. B., Kirk, P., Burgess, S., & Howson, J. M. M. (2021) · Nature Communications · doi:10.1038/s41467-020-20885-8 · Cited by 542 · see citing works
Transcriptional and Cellular Diversity of the Human Heart
Tucker, N. R., Chaffin, M., Fleming, S. J., Hall, A. W., Parsons, V. A., Bedi, K. C., Akkad, A.-D., Herndon, C. N., Arduini, A., Papangeli, I., et al. (2020) · Circulation · doi:10.1161/circulationaha.119.045401 · Cited by 554 · see citing works
van Rheenen, W., van der Spek, R. A. A., Bakker, M. K., van Vugt, J. J. F. A., Hop, P. J., Zwamborn, R. A. J., de Klein, N., Westra, H.-J., Bakker, O. B., Deelen, P., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00973-1 · Cited by 542 · see citing works
Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis
Võsa, U., Claringbould, A., Westra, H.-J., Bonder, M. J., Deelen, P., Zeng, B., Kirsten, H., Saha, A., Kreuzhuber, R., Kasela, S., et al. (2018) · doi:10.1101/447367 · Cited by 2,607 · see citing works
Self-citationCell type–specific genetic regulation of gene expression across human tissues
Kim-Hellmuth, S., Aguet, F., Oliva, M., Muñoz-Aguirre, M., Kasela, S., Wucher, V., Castel, S. E., Hamel, A. R., Viñuela, A., Roberts, A. L., et al. (2020) · Science · doi:10.1126/science.aaz8528 · Cited by 530 · see citing works
Zuber, V., Grinberg, N. F., Gill, D., Manipur, I., Slob, E. A. W., Patel, A., Wallace, C., & Burgess, S. (2022) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.04.001 · Cited by 532 · see citing works
Self-citationJoint Analysis of Functional Genomic Data and Genome-wide Association Studies of 18 Human Traits
Pickrell, J. K. (2014) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2014.03.004 · Cited by 520 · see citing works
Schwartzentruber, J., Cooper, S., Liu, J. Z., Barrio-Hernandez, I., Bello, E., Kumasaka, N., Young, A. M. H., Franklin, R. J. M., Johnson, T., Estrada, K., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00776-w · Cited by 532 · see citing works
Ishigaki, K., Akiyama, M., Kanai, M., Takahashi, A., Kawakami, E., Sugishita, H., Sakaue, S., Matoba, N., Low, S.-K., Okada, Y., et al. (2020) · Nature Genetics · doi:10.1038/s41588-020-0640-3 · Cited by 508 · see citing works
Determinants of telomere length across human tissues
Demanelis, K., Jasmine, F., Chen, L. S., Chernoff, M., Tong, L., Delgado, D., Zhang, C., Shinkle, J., Sabarinathan, M., Lin, H., et al. (2020) · Science · doi:10.1126/science.aaz6876 · Cited by 515 · see citing works
Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation
Min, J. L., Hemani, G., Hannon, E., Dekkers, K. F., Castillo-Fernandez, J., Luijk, R., Carnero-Montoro, E., Lawson, D. J., Burrows, K., Suderman, M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00923-x · Cited by 506 · see citing works
arcOGEN Consortium, Tachmazidou, I., Hatzikotoulas, K., Southam, L., Esparza-Gordillo, J., Haberland, V., Zheng, J., Johnson, T., Koprulu, M., Zengini, E., et al. (2019) · Nature Genetics · doi:10.1038/s41588-018-0327-1 · Cited by 516 · see citing works
Association of host genome with intestinal microbial composition in a large healthy cohort
GEM Project Research Consortium, Turpin, W., Espin-Garcia, O., Xu, W., Silverberg, M. S., Kevans, D., Smith, M. I., Guttman, D. S., Griffiths, A., Panaccione, R., et al. (2016) · Nature Genetics · doi:10.1038/ng.3693 · Cited by 460 · see citing works
Chia, R., Sabir, M. S., Bandres-Ciga, S., Saez-Atienzar, S., Reynolds, R. H., Gustavsson, E., Walton, R. L., Ahmed, S., Viollet, C., Ding, J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00785-3 · Cited by 438 · see citing works
A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer's disease
The International Genomics of Alzheimer's Project, Huang, K.-L., Initiative, T. A. D. N., Marcora, E., Pimenova, A. A., Di Narzo, A. F., Kapoor, M., Jin, S. C., Harari, O., Bertelsen, S., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4587 · Cited by 423 · see citing works
Interpreting type 1 diabetes risk with genetics and single-cell epigenomics
Chiou, J., Geusz, R. J., Okino, M.-L., Han, J. Y., Miller, M., Melton, R. L., Beebe, E., Benaglio, P., Huang, S., Korgaonkar, K., et al. (2021) · Nature · doi:10.1038/s41586-021-03552-w · Cited by 437 · see citing works
A compendium of uniformly processed human gene expression and splicing quantitative trait loci
Kerimov, N., Hayhurst, J. D., Peikova, K., Manning, J. R., Walter, P., Kolberg, L., Samoviča, M., Sakthivel, M. P., Kuzmin, I., Trevanion, S. J., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00924-w · Cited by 440 · see citing works
Dissecting the genetics of complex traits using summary association statistics
Paşaniuc, B., & Price, A. L. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.142 · Cited by 444 · see citing works
Fine-mapping from summary data with the “Sum of Single Effects” model
Zou, Y., Carbonetto, P., Wang, G., & Stephens, M. (2022) · PLOS Genetics · doi:10.1371/journal.pgen.1010299 · Cited by 429 · see citing works
Levey, D. F., Stein, M. B., Wendt, F. R., Pathak, G. A., Zhou, H., Aslan, M., Quaden, R., Harrington, K. M., Nuñez, Y. Z., Overstreet, C., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-021-00860-2 · Cited by 422 · see citing works
Lo, M., Hinds, D. A., Tung, J. Y., Franz, C. E., Fan, C.-C., Wang, Y., Smeland, O. B., Schork, A. J., Holland, D., Kauppi, K., et al. (2016) · Nature Genetics · doi:10.1038/ng.3736 · Cited by 452 · see citing works
Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses
Wallace, C. (2020) · PLOS Genetics · doi:10.1371/journal.pgen.1008720 · Cited by 441 · see citing works
Self-citationDeciphering osteoarthritis genetics across 826,690 individuals from 9 populations
Boer, C. G., Hatzikotoulas, K., Southam, L., Stefánsdóttir, L., Zhang, Y., de Almeida, R. C., Wu, T. T., Zheng, J., Hartley, A. E., Teder‐Laving, M., et al. (2021) · Cell · doi:10.1016/j.cell.2021.07.038 · Cited by 441 · see citing works
Global Biobank Meta-analysis Initiative: Powering genetic discovery across human disease
Zhou, W., Kanai, M., Wu, K.-H., Rasheed, H., Tsuo, K., Hirbo, J. B., Wang, Y., Bhattacharya, A., Zhao, H., Namba, S., et al. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100192 · Cited by 414 · see citing works
Self-citationYao, C., Chen, G., Song, C., Keefe, J., Mendelson, M., Huan, T., Sun, B. B., Laser, A., Maranville, J. C., Wu, H., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05512-x · Cited by 431 · see citing works
Using genetic data to strengthen causal inference in observational research
Pingault, J.-B., O’Reilly, P. F., Schoeler, T., Ploubidis, G. B., Rijsdijk, F., & Dudbridge, F. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0020-3 · Cited by 444 · see citing works
Urbut, S., Wang, G. T., Carbonetto, P. S., & Stephens, M. (2018) · Nature Genetics · doi:10.1038/s41588-018-0268-8 · Cited by 424 · see citing works
Ligthart, S., Vaez, A., Võsa, U., Stathopoulou, M. G., de Vries, P. S., Prins, B. P., van der Most, P. J., Tanaka, T., Naderi, E., Rose, L. M., et al. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.09.009 · Cited by 416 · see citing works
Self-citationAn integrated framework for local genetic correlation analysis
Werme, J., van der Sluis, S., Posthuma, D., & de Leeuw, C. (2022) · Nature Genetics · doi:10.1038/s41588-022-01017-y · Cited by 422 · see citing works
A statistical framework for cross-tissue transcriptome-wide association analysis
Hu, Y., Li, M., Lu, Q., Weng, H., Wang, J., Zekavat, S. M., Yu, Z., Li, B., Gu, J., Muchnik, S. K., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0345-7 · Cited by 400 · see citing works
Probabilistic fine-mapping of transcriptome-wide association studies
Mancuso, N., Freund, M. K., Johnson, R., Shi, H., Kichaev, G., Gusev, A., & Paşaniuc, B. (2019) · Nature Genetics · doi:10.1038/s41588-019-0367-1 · Cited by 384 · see citing works
Target genes, variants, tissues and transcriptional pathways influencing human serum urate levels
Study, G. C. K. D., Tin, A., Study, L. C., Program, V. A. M. V., Marten, J., Kuhns, V. L. H., Li, Y., Wuttke, M., Kirsten, H., Sieber, K. B., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0504-x · Cited by 393 · see citing works
Hautakangas, H. M., Winsvold, B. S., Ruotsalainen, S., Björnsdóttir, G., Harder, A. V. E., Kogelman, L. J. A., Thomas, L. F., Noordam, R., Benner, C., Gormley, P., et al. (2022) · Nature Genetics · doi:10.1038/s41588-021-00990-0 · Cited by 403 · see citing works
COPDGene Investigators, Hobbs, B. D., ECLIPSE Investigators, LifeLines Investigators, International COPD Genetics Network Investigators, UK BiLEVE Investigators, de Jong, K., Lamontagne, M., Bossé, Y., Shrine, N. R. G., et al. (2017) · Nature Genetics · doi:10.1038/ng.3752 · Cited by 399 · see citing works
AlphaGenome: advancing regulatory variant effect prediction with a unified DNA sequence model
Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2025) · doi:10.1101/2025.06.25.661532 · Cited by 395 · see citing works
Zhang, J., Dutta, D., Köttgen, A., Tin, A., Schlosser, P., Grams, M. E., Harvey, B., CKDGen Consortium, Yu, B., Boerwinkle, E., et al. (2022) · Nature Genetics · doi:10.1038/s41588-022-01051-w · Cited by 373 · see citing works
The UK-PSC Consortium, Ji, S.-G., The International IBD Genetics Consortium, The International PSC Study Group, Juran, B. D., Mucha, S., Folseraas, T., Jostins, L., Melum, E., Kumasaka, N., et al. (2017) · Nature Genetics · doi:10.1038/ng.3745 · Cited by 357 · see citing works
Selecting instruments for Mendelian randomization in the wake of genome-wide association studies.
Swerdlow, D. I., Kuchenbaecker, K. B., Shah, S., Sofat, R., Holmes, M. V., White, J., Mindell, J. S., Kivimaki, M., Brunner, E. J., Whittaker, J. C., et al. (2016) · International Journal of Epidemiology · doi:10.1093/ije/dyw088 · Cited by 387 · see citing works
Self-citationWingo, A. P., Liu, Y., Gerasimov, E. S., Gockley, J., Logsdon, B. A., Duong, D. M., Dammer, E. B., Robins, C., Beach, T. G., Reiman, E. M., et al. (2021) · Nature Genetics · doi:10.1038/s41588-020-00773-z · Cited by 366 · see citing works
Polygenic basis and biomedical consequences of telomere length variation
Codd, V., Wang, Q., Allara, E., Musicha, C., Kaptoge, S. K., Stoma, S., Jiang, T., Hamby, S. E., Braund, P. S., Bountziouka, V. P., et al. (2021) · Nature Genetics · doi:10.1038/s41588-021-00944-6 · Cited by 376 · see citing works
A meta-analysis of genome-wide association studies identifies multiple longevity genes
Deelen, J., Evans, D. S., Arking, D. E., Tesi, N., Nygaard, M., Liu, X., Wojczynski, M. K., Biggs, M. L., van der Spek, A., Atzmon, G., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-11558-2 · Cited by 370 · see citing works
Advancing regulatory variant effect prediction with AlphaGenome
Avsec, Ž., Latysheva, N., Cheng, J., Novati, G., Taylor, K. R., Ward, T., Bycroft, C., Nicolaisen, L., Arvaniti, E., Pan, J., et al. (2026) · Nature · doi:10.1038/s41586-025-10014-0 · Cited by 373 · see citing works
Abundant associations with gene expression complicate GWAS follow-up
Liu, B., Gloudemans, M. J., Rao, A. S., Ingelsson, E., & Montgomery, S. B. (2019) · Nature Genetics · doi:10.1038/s41588-019-0404-0 · Cited by 370 · see citing works
Tcheandjieu, C., Zhu, X., Hilliard, A. T., Clarke, S. L., Napolioni, V., Ma, S., Lee, K. M., Fang, H., Chen, F., Lu, Y., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01891-3 · Cited by 356 · see citing works
Integrating predicted transcriptome from multiple tissues improves association detection
Barbeira, A., Pividori, M., Zheng, J., Wheeler, H. E., Nicolae, D. L., & Im, H. K. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1007889 · Cited by 334 · see citing works
- 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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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.
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