Predicting gene targets from integrative analyses of summary data from GWAS and eQTL studies for 28 human complex traits
Open accessAbstract
Genome-wide association studies (GWAS) have identified hundreds of genetic variants associated with complex traits and diseases. However, elucidating the causal genes underlying GWAS hits remains challenging. We applied the summary data-based Mendelian randomization (SMR) method to 28 GWAS summary datasets to identify genes whose expression levels were associated with traits and diseases due to pleiotropy or causality (the expression level of a gene and the trait are affected by the same causal variant at a locus). We identified 71 genes, of which 17 are novel associations (no GWAS hit within 1 Mb distance of the genes). We integrated all the results in an online database ( http://www.cnsgenomics/shiny/SMRdb/ ), providing important resources to prioritize genes for further follow-up, for example in functional studies.
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
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?
One colocalization method frames shared causality as the null hypothesis and rejection of this would indicate violation of instrumental variable condition 2.
“One colocalization method originally proposed by Plagnol et al 112 frames shared causality as the null hypothesis and rejection of this would indicate violation of IV condition 2, i.e. that there are no common causes of the instrument and the outcome.112–114”
From Mendelian randomization (2022) · cites this paper as [113]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Mendelian randomization: Using genes as instruments for making causal inferences in epidemiology
Lawlor, D. A., Harbord, R. M., Sterne, J. A. C., Timpson, N., & Davey Smith, G. (2008) · Statistics in Medicine · doi:10.1002/sim.3034 · Cited by 4,618 · see citing works
Meta-Analysis of 74,046 Individuals Identifies 11 New Susceptibility Loci for Alzheimer’s Disease
(EADI), E. A. D. I., Lambert, J.-C., (GERAD), G. A. E. R. I. A. D., Alzheimer's Disease Genetic Consortium (ADGC), (CHARGE), C. F. H. A. A. R. I. G. E., Ibrahim-Verbaas, C. A., Harold, D., Naj, A. C., Sims, R., Bellenguez, C., et al. (2013) · Nature Genetics · doi:10.1038/ng.2802 · Cited by 4,336 · see citing works
Giambartolomei, C., Vukcevic, D., Schadt, E. E., Franke, L., Hingorani, A. D., Wallace, C., & Plagnol, V. (2014) · PLoS Genetics · doi:10.1371/journal.pgen.1004383 · Cited by 4,261 · see citing works
Hindorff, L. A., Sethupathy, P., Junkins, H. A., Ramos, E. M., Mehta, J. P., Collins, F. S., & Manolio, T. A. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0903103106 · Cited by 4,013 · 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
Self-citation(2013) · The Lancet · doi:10.1016/s0140-6736(12)62129-1 · Cited by 3,010 · see citing works
Willer (2013) · doi:10.1038/ng.2797 · Cited by 2,983 · see citing works
The NHGRI GWAS Catalog, a curated resource of SNP-trait associations
Welter, D., MacArthur, J., Morales, J., Burdett, T., Hall, P., Junkins, H., Klemm, A., Flicek, P., Manolio, T., Hindorff, L., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gkt1229 · Cited by 2,707 · see citing works
Liu, J. Z., van Sommeren, S., Huang, H., Ng, S. C., Alberts, R., Takahashi, A., Ripke, S., Lee, J. C., Jostins, L., Shah, T., et al. (2015) · Nature Genetics · doi:10.1038/ng.3359 · Cited by 2,603 · see citing works
Franke, A., McGovern, D. P. B., Barrett, J. C., Wang, K., Radford-Smith, G. L., Ahmad, T., Lees, C. W., Balschun, T., Lee, J., Roberts, R., et al. (2010) · Nature Genetics · doi:10.1038/ng.717 · Cited by 2,520 · 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
A comprehensive 1000 Genomes–based genome-wide association meta-analysis of coronary artery disease
the CARDIoGRAMplusC4D Consortium (2015) · Nature Genetics · doi:10.1038/ng.3396 · Cited by 2,447 · see citing works
Visscher, P. M., Brown, M. A., McCarthy, M. I., & Yang, J. (2012) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2011.11.029 · Cited by 2,425 · see citing works
Self-citationNew genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk
DIAGRAM Consortium, Dupuis, J., GIANT Consortium, Global BPgen Consortium, Anders Hamsten on behalf of Procardis Consortium, the MAGIC investigators, Langenberg, C., Prokopenko, I., Saxena, R., Soranzo, N., et al. (2010) · Nature Genetics · doi:10.1038/ng.520 · Cited by 2,166 · see citing works
Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk
The International Consortium for Blood Pressure Genome-Wide Association Studies (2011) · Nature · doi:10.1038/nature10405 · Cited by 2,033 · see citing works
the DIAbetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium, Morris, A. P., Voight, B. F., Teslovich, T. M., Ferreira, T., Segrè, A. V., Steinthorsdottir, V., Strawbridge, R. J., Khan, H., Grallert, H., et al. (2012) · Nature Genetics · doi:10.1038/ng.2383 · Cited by 1,912 · see citing works
A gene-based association method for mapping traits using reference transcriptome data
Gamazon, E. R., Wheeler, H. E., Shah, K. P., Mozaffari, S. V., Aquino-Michaels, K., Carroll, R. J., Eyler, A. E., Denny, J. C., Nicolae, D. L., Cox, N. J., et al. (2015) · Nature Genetics · doi:10.1038/ng.3367 · Cited by 1,762 · see citing works
Systematic identification of trans eQTLs as putative drivers of known disease associations
Westra, H.-J., Peters, M. J., Esko, T., Yaghootkar, H., Schurmann, C., Kettunen, J., Christiansen, M. W., Fairfax, B. P., Schramm, K., Powell, J. E., et al. (2013) · Nature Genetics · doi:10.1038/ng.2756 · Cited by 1,658 · see citing works
Pierce, B. L., & Burgess, S. (2013) · American Journal of Epidemiology · doi:10.1093/aje/kwt084 · Cited by 1,389 · see citing works
Psychiatric GWAS Consortium Bipolar Disorder Working Group, Sklar, P., Ripke, S., Scott, L. J., Andreassen, O. A., Cichon, S., Craddock, N., Edenberg, H. J., Nurnberger, J. I., Rietschel, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.943 · Cited by 1,344 · see citing works
Anderson, C. A., Boucher, G., Lees, C. W., Franke, A., D’Amato, M., Taylor, K. D., Lee, J. C., Goyette, P., Imielinski, M. B., Latiano, A., et al. (2011) · Nature Genetics · doi:10.1038/ng.764 · Cited by 1,312 · see citing works
A mega-analysis of genome-wide association studies for major depressive disorder
Major Depressive Disorder Working Group of the Psychiatric GWAS Consortium, Ripke, S., Wray, N. R., Lewis, C. M., Hamilton, S. P., Weissman, M. M., Breen, G., Byrne, E. M., Blackwood, D. H. R., Boomsma, D. I., et al. (2013) · Molecular Psychiatry · doi:10.1038/mp.2012.21 · Cited by 1,120 · see citing works
Self-citationGenome-wide meta-analyses identify multiple loci associated with smoking behavior
The Tobacco and Genetics Consortium (2010) · Nature Genetics · doi:10.1038/ng.571 · Cited by 1,098 · 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
GWAS of 126,559 Individuals Identifies Genetic Variants Associated with Educational Attainment
Rietveld, C. A., Medland, S. E., Derringer, J., Yang, J., Esko, T., Martin, N. W., Westra, H.-J., Shakhbazov, K., Abdellaoui, A., Agrawal, A., et al. (2013) · Science · doi:10.1126/science.1235488 · Cited by 827 · see citing works
Self-citationMendelian randomization studies: a review of the approaches used and the quality of reporting
Boef, A. G. C., Dekkers, O. M., & le Cessie, S. (2015) · International Journal of Epidemiology · doi:10.1093/ije/dyv071 · Cited by 610 · see citing works
A comprehensive 1000 Genomes-based genome-wide association meta-analysis of coronary artery disease
Nikpay, M., Goel, A., Won, H., Hall, L. M., Willenborg, C., Kanoni, S., Saleheen, D., Kyriakou, T., Nelson, C. P., Hopewell, J. C., et al. (2015) · EUR Research Repository (Erasmus University Rotterdam) · Cited by 581 · see citing works
Methodological Challenges in Mendelian Randomization
VanderWeele, T. J., Tchetgen Tchetgen, E. J., Cornelis, M., & Kraft, P. (2014) · Epidemiology · doi:10.1097/ede.0000000000000081 · Cited by 555 · see citing works
Two-Sample Instrumental Variables Estimators
Inoue, A., & Solon, G. (2010) · The Review of Economics and Statistics · doi:10.1162/rest_a_00011 · Cited by 377 · see citing works
Meta-analysis of genome-wide association studies for personality
de Moor, M. H. M., Costa, P. T., Terracciano, A., Krueger, R. F., de Geus, E. J. C., Toshiko, T., Penninx, B. W. J. H., Esko, T., Madden, P. A. F., Derringer, J. L., et al. (2010) · Molecular Psychiatry · doi:10.1038/mp.2010.128 · Cited by 370 · see citing works
Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general population
iPSYCH-SSI-Broad Autism Group, Robinson, E. B., St Pourcain, B., Anttila, V., Kosmicki, J. A., Bulik-Sullivan, B., Grove, J., Maller, J., Samocha, K. E., Sanders, S. J., et al. (2016) · Nature Genetics · doi:10.1038/ng.3529 · Cited by 367 · see citing works
de Moor, M. H. M., van den Berg, S. M., Verweij, K. J. H., Krueger, R. F., Luciano, M., Arias Vasquez, A., Matteson, L. K., Derringer, J., Esko, T., Amin, N., et al. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.0554 · Cited by 345 · see citing works
Childhood intelligence is heritable, highly polygenic and associated with FNBP1L
Benyamin, B., Pourcain, B., Davis, O. S. P., Davies, G., Hansell, N. K., Brion, M., Kirkpatrick, R. M., Cents, R. A. M., Franić, S., Miller, M. B., et al. (2013) · Molecular Psychiatry · doi:10.1038/mp.2012.184 · Cited by 250 · see citing works
Self-citationSherlock: 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
From genome to function by studying eQTLs
Westra, H.-J., & Franke, L. (2014) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2014.04.024 · Cited by 166 · see citing works
van den Berg, S. M., de Moor, M. H. M., McGue, M. K., Pettersson, E., Terracciano, A., Verweij, K. J. H., Amin, N., Derringer, J. L., Esko, T., van Grootheest, G., et al. (2014) · Behavior Genetics · doi:10.1007/s10519-014-9654-x · Cited by 123 · see citing works
Genetic risk for autism spectrum disorders and neuropsychiatric variation in the general populationPreprint
Robinson, E., Pourcain, B. S., Anttila, V., Kosmicki, J. A., Bulik-Sullivan, B. K., Grove, J., Maller, J., Samocha, K. E., Sanders, S., Ripke, S., et al. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/027771 · Cited by 34 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
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
Genome-wide association study identifies 30 loci associated with bipolar disorder
eQTLGen Consortium, Stahl, E. A., BIOS Consortium, the Bipolar Disorder Working Group of the Psychiatric Genomics Consortium, Breen, G., Forstner, A. J., McQuillin, A., Ripke, S., Trubetskoy, V., Mattheisen, M., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0397-8 · Cited by 1,519 · see citing works
Self-citationHou, 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
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
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
Qi, T., Wu, Y., Zeng, J., Zhang, F., Xue, A., Jiang, L., Zhu, Z., Kemper, K., Yengo, L., Zheng, Z., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04558-1 · Cited by 427 · see citing works
Self-citationMancuso, N., Shi, H., Goddard, P. C., Kichaev, G., Gusev, A., & Paşaniuc, B. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.01.031 · Cited by 265 · see citing works
Genetic control of RNA splicing and its distinct role in complex trait variation
Qi, T., Wu, Y., Fang, H., Zhang, F., Liu, S., Zeng, J., & Yang, J. (2022) · Nature Genetics · doi:10.1038/s41588-022-01154-4 · Cited by 237 · see citing works
Self-citationIBD risk loci are enriched in multigenic regulatory modules encompassing putative causative genes
Momozawa, Y., Dmitrieva, J., Théâtre, E., Deffontaine, V., Rahmouni, S., Charloteaux, B., Crins, F., Docampo, E., Elansary, M., Gori, A.-S., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04365-8 · Cited by 233 · see citing works
Fang, H., Knezevic, B., Burnham, K. L., & Knight, J. C. (2016) · Genome Medicine · doi:10.1186/s13073-016-0384-y · Cited by 173 · see citing works
Large-scale transcriptome-wide association study identifies new prostate cancer risk regions
Mancuso, N., Gayther, S. A., Gusev, A., Zheng, W., Penney, K. L., Kote‐Jarai, Z., Eeles, R. A., Freedman, M. L., Haiman, C. A., Paşaniuc, B., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-06302-1 · Cited by 145 · see citing works
Klarić, L., Tsepilov, Y. A., Stanton, C. M., Mangino, M., Sikka, T. T., Esko, T., Pakhomov, E., Salo, P. P., Deelen, J., McGurnaghan, S. J., et al. (2020) · Science Advances · doi:10.1126/sciadv.aax0301 · Cited by 139 · see citing works
Benn, M., & Nordestgaard, B. G. (2018) · Cardiovascular Research · doi:10.1093/cvr/cvy045 · Cited by 131 · see citing works
Wang, R., Xue, Y., Fan, J., Yao, J., Qin, M., Lin, T., Lian, Q., Zhang, M., Li, X., Li, J., et al. (2021) · Genome biology · doi:10.1186/s13059-021-02531-8 · Cited by 89 · see citing works
The integrated landscape of causal genes and pathways in schizophrenia
Ma, C., Gu, C., Huo, Y., Li, X., & Luo, X. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0114-x · Cited by 85 · see citing works
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 83 · see citing works
Genome-wide analysis of genetic predisposition to Alzheimer’s disease and related sex disparities
Nazarian, A., Yashin, A. I., & Kulminski, A. M. (2019) · Alzheimer s Research & Therapy · doi:10.1186/s13195-018-0458-8 · Cited by 80 · see citing works
Richardson, T. G., Zheng, J., Smith, G. D., Timpson, N. J., Gaunt, T. R., Relton, C. L., & Hemani, G. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.09.003 · Cited by 68 · see citing works
Barbeira, A. N., Dickinson, S. P., Bonazzola, R., Zheng, J., Wheeler, H. E., Torres, J. M., Torstenson, E. S., Shah, K. P., Garcia, T., Edwards, T. L., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-03621-1 · Cited by 69 · see citing works
Liu, D., Yang, J., Feng, B., Lu, W., Zhao, C., & Li, L. (2020) · Journal of Infection · doi:10.1016/j.jinf.2020.11.031 · Cited by 61 · see citing works
Self-citationYang, H., Liu, D., Zhao, C., Feng, B., Lu, W., Yang, X., Xu, M., Zhou, W., Jing, H., & Yang, J. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01348-0 · Cited by 56 · see citing works
Self-citationWu, L., Wang, J., Cai, Q., Cavazos, T. B., Emami, N. C., Long, J., Shu, X., Lu, Y., Guo, X., Bauer, J. A., et al. (2019) · Cancer Research · doi:10.1158/0008-5472.can-18-3536 · Cited by 59 · see citing works
Caramaschi, D., Sharp, G. C., Nohr, E. A., Berryman, K., Lewis, S. J., Smith, G. D., & Relton, C. L. (2017) · Human Molecular Genetics · doi:10.1093/hmg/ddx164 · Cited by 58 · see citing works
Liu, Y., Liu, X.-L., Zheng, Z.-W., Ma, T., Liu, Y., Long, H., Cheng, H., Fang, M., Gong, J., Li, X.-Y., et al. (2020) · Genetics Selection Evolution · doi:10.1186/s12711-020-00579-x · Cited by 42 · see citing works
Integrative analysis revealed potential causal genetic and epigenetic factors for multiple sclerosis
Mo, X., Lei, S., Qian, Q., Guo, Y., Zhang, Y., & Zhang, H. (2019) · Journal of Neurology · doi:10.1007/s00415-019-09476-w · Cited by 54 · see citing works
Ward, M. C., Banovich, N. E., Sarkar, A., Stephens, M., & Gilad, Y. (2021) · eLife · doi:10.7554/elife.57345 · Cited by 50 · see citing works
Chambers, T., Escott‐Price, V., Legge, S. E., Baker, E. A., Singh, K. D., Walters, J., Caseras, X., & Anney, R. (2022) · Molecular Psychiatry · doi:10.1038/s41380-022-01443-8 · Cited by 45 · see citing works
Including diverse and admixed populations in genetic epidemiology research
Caliebe, A., Ayele, F. T., Darst, B. F., Wang, X., Song, Y. E., Gui, J., Sebro, R., Balding, D. J., Saad, M., & Dubé, M. (2022) · Genetic Epidemiology · doi:10.1002/gepi.22492 · Cited by 45 · see citing works
Wolujewicz, P., Steele, J., Kaltschmidt, J. A., Finnell, R. H., & Ross, M. E. (2021) · genesis · doi:10.1002/dvg.23459 · Cited by 44 · see citing works
The kidney and hypertension: over 70 years of research
Campese, V. M., & Park, J. (2006) · Journal of Nephrology · doi:10.1093/joneph/19.6.691 · Cited by 40 · see citing works
Identification of Alzheimer's Disease-Related Genes Based on Data Integration Method
Hu, Y., Zhao, T., Zang, T., Zhang, Y., & Cheng, L. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2018.00703 · Cited by 38 · see citing works
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 · see citing works
Zeng, P., Dai, J., Jin, S., & Zhou, X. (2021) · Human Molecular Genetics · doi:10.1093/hmg/ddab056 · Cited by 35 · see citing works
The Gene scb-1 Underlies Variation in Caenorhabditis elegans Chemotherapeutic Responses
Evans, K. S., & Andersen, E. C. (2020) · G3 Genes Genomes Genetics · doi:10.1534/g3.120.401310 · Cited by 29 · see citing works
Genetic Insights of Schizophrenia via Single Cell RNA-Sequencing Analyses
Wu, Y., Zhang, C., Wang, L., Li, Y., & Xiao, X. (2023) · Schizophrenia Bulletin · doi:10.1093/schbul/sbad002 · Cited by 28 · see citing works
Meng, X.-H., Chen, X., Greenbaum, J., Zeng, Q., You, S.-L., Xiao, H., Tan, L., & Deng, H. (2018) · Bone · doi:10.1016/j.bone.2018.05.012 · Cited by 29 · see citing works
sn-spMF: matrix factorization informs tissue-specific genetic regulation of gene expression
He, Y. M., Chhetri, S. B., Arvanitis, M., Srinivasan, K., Aguet, F., Ardlie, K., Barbeira, A., Bonazzola, R., Im, H. K., Brown, C. D., et al. (2020) · Genome biology · doi:10.1186/s13059-020-02129-6 · Cited by 28 · see citing works
Mendelian randomization analysis revealed potential causal factors for systemic lupus erythematosus
Mo, X., Guo, Y., Qian, Q., Fu, M., Lei, S., Zhang, Y., & Zhang, H. (2019) · Immunology · doi:10.1111/imm.13144 · Cited by 24 · see citing works
Islam, T., Rahman, M. R., Khan, A., & Moni, M. A. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104345 · Cited by 22 · see citing works
Wang, L., Xia, Y., Chen, Y., Dai, R., Qiu, W., Meng, Q., Kuney, L., & Chen, C. (2019) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2019.02.002 · Cited by 23 · see citing works
Identification of multiple novel susceptibility genes associated with autoimmune thyroid disease
Liu, X., Miao, Y., Liu, C., Wan, L., Feng, Q., & Zhang, Q. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1161311 · Cited by 21 · see citing works
Ainsworth, H. F., Shin, S., & Cordell, H. J. (2017) · Genetic Epidemiology · doi:10.1002/gepi.22061 · Cited by 20 · see citing works
The Integrated Landscape of Biological Candidate Causal Genes in Coronary Artery Disease
Zheng, Q., Ma, Y., Chen, S., Che, Q., & Chen, D. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.00320 · Cited by 17 · see citing works
Convergent lines of evidence support BIN1 as a risk gene of Alzheimer’s disease
Zhu, J. L., Liu, X., Yin, H., Gao, Y., & Yu, H. (2021) · Human Genomics · doi:10.1186/s40246-021-00307-6 · Cited by 17 · see citing works
Integrative analysis identifies the association between CASZ1 methylation and ischemic stroke
Mo, X., Zhang, H., Wang, A., Xu, T., & Zhang, Y. (2020) · Neurology Genetics · doi:10.1212/nxg.0000000000000509 · Cited by 15 · see citing works
Sun, Q., Gao, Y. J., Yang, J., Lu, J., Feng, W., & Wen, Y. (2021) · Reproductive Sciences · doi:10.1007/s43032-021-00776-z · Cited by 15 · see citing works
Self-citationShi, J., Wu, L., Li, B., Lu, Y., Guo, X., Cai, Q., Long, J., Wen, W., Zheng, W., & Shu, X. (2018) · Reproductive Sciences · doi:10.1177/1933719118776788 · Cited by 15 · see citing works
Fernández‐Jiménez, N., & Bilbao, J. R. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz113 · Cited by 15 · see citing works
Considerations for integrative multi‐omic approaches to explore Alzheimer's disease mechanisms
Ma, Y., Klein, H., & Jager, P. L. D. (2020) · Brain Pathology · doi:10.1111/bpa.12878 · Cited by 15 · see citing works
Zhang, L., Liu, L., Wen, Y. F., Ma, M., Cheng, S., Yang, J., Li, P., Cheng, B., Du, Y., Liang, X., et al. (2018) · Autism Research · doi:10.1002/aur.2001 · Cited by 14 · see citing works
Self-citationDong, H., Zhou, W., Wang, P., Zuo, E., Ying, X., Chai, S., Fei, T., Jin, L., Chen, C., Ma, G., et al. (2020) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.00194 · Cited by 14 · see citing works
Wang, J., Li, X.-Y., Li, H., Liu, J.-W., Yao, Y., Li, M., Xiao, X., & Luo, X. (2021) · Schizophrenia Bulletin · doi:10.1093/schbul/sbab032 · Cited by 13 · see citing works
Deng, C., Xie, C., Li, Z., Mei, J., & Wang, K. (2025) · npj Precision Oncology · doi:10.1038/s41698-025-00895-9 · Cited by 11 · see citing works
Jayarathna, D. K., Rentería, M. E., Malik, A., Sauret, E., Batra, J., & Gandhi, N. S. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.716236 · Cited by 10 · see citing works
A transcriptome-wide association study provides new insights into the etiology of osteoarthritis
Wang, W., Ou, Z., Peng, J., Zhou, Y., & Wang, N. (2022) · Annals of Translational Medicine · doi:10.21037/atm-22-4471 · Cited by 11 · see citing works
Integrative analysis highlighted susceptibility genes for rheumatoid arthritis
Mo, X., Sun, Y.-H., Zhang, Y., & Lei, S. (2020) · International Immunopharmacology · doi:10.1016/j.intimp.2020.106716 · Cited by 11 · see citing works
Integrating Multi-Omics Data to Identify Novel Disease Genes and Single-Neucleotide Polymorphisms
Zhao, S., Jiang, H., Liang, Z., & Ju, H. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2019.01336 · Cited by 10 · see citing works
Zhang, J., Sun, M., Zhao, Y., Geng, G., & Hu, Y. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2020.624766 · Cited by 9 · see citing works
Wang, X., Gharahkhani, P., Levine, D., Fitzgerald, R. C., Gockel, I., Corley, D. A., Risch, H. A., Bernstein, L., Chow, W., Onstad, L., et al. (2022) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-22-0096 · Cited by 9 · see citing works
Huang, Y., Luo, J., Zhang, Y., Zhang, T., Fei, X., Chen, L., Zhu, Y., Li, S., Zhou, C.-Y., Xu, K., et al. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.02.001 · Cited by 8 · see citing works
Innovative strategies for annotating the “relationSNP” between variants and molecular phenotypes
Miller, J. E., Veturi, Y., & Ritchie, M. D. (2019) · BioData Mining · doi:10.1186/s13040-019-0197-9 · Cited by 9 · see citing works
Genetic and phenotypic links between obesity and extracellular vesicles
Zhai, R., Pan, L., Yang, Z., Li, T., Zheng, N., Yudi Pawitan, Y., Wilson, J. F., Wu, D., & Shen, X. (2022) · Human Molecular Genetics · doi:10.1093/hmg/ddac069 · Cited by 8 · see citing works
Mo, X., Zhang, Y.-H., & Lei, S. (2020) · Molecular Immunology · doi:10.1016/j.molimm.2020.12.021 · Cited by 8 · see citing works
Zhang, Q., Liu, H., Lv, W., He, J., Xia, X., Zhang, W., Deng, H., & Sun, C. (2018) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2018.09.003 · Cited by 7 · see citing works
Strathearn, L., Momany, A. M., Kovács, E. H. C., Guiler, W., & Ladd‐Acosta, C. (2022) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2023.107761 · Cited by 6 · see citing works
Zhang, H., Wang, A., Xu, T., Mo, X., & Zhang, Y. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2021.791146 · Cited by 6 · see citing works
Molstad, A. J., Sun, W., & Hsu, L. (2021) · The Annals of Applied Statistics · doi:10.1214/20-aoas1432 · Cited by 5 · see citing works
wen, D., Li, B., Guo, S., Chen, L., & Chen, B. (2024) · International Journal of Medical Sciences · doi:10.7150/ijms.98505 · Cited by 4 · see citing works
Luo, L., Pang, T., Zheng, H., Liufu, C., & Chang, S. (2024) · Journal of Translational Medicine · doi:10.1186/s12967-024-05065-2 · Cited by 4 · see citing works
Zhu, Z., Chen, X., Zhang, S., Yu, R., Qi, C., Cheng, L., & Zhang, X. (2023) · Human Genetics · doi:10.1007/s00439-023-02602-9 · Cited by 4 · see citing works
Self-citationIntegrating plasma proteome with genome reveals novel protein biomarkers in colorectal cancer
Ye, C., Xia, L., Gong, R., Chang, J., Sun, Q., Xu, J., & Li, F. (2024) · Clinical & Translational Oncology · doi:10.1007/s12094-024-03616-z · Cited by 4 · see citing works
Joint-Tissue Integrative Analysis Identified Hundreds of Schizophrenia Risk Genes
Wu, Y., Yu, X., Xiao, X., Li, M., & Li, Y. (2021) · Molecular Neurobiology · doi:10.1007/s12035-021-02572-x · Cited by 4 · see citing works
Leveraging Large-Scale Biobanks for Therapeutic Target DiscoveryPreprint
Ferolito, B. R., Dashti, H., Giambartolomei, C., Peloso, G. M., Golden, D. J., Gravel-Pucillo, K., Rasooly, D., Horimoto, A. R. V. R., Matty, R., Gaziano, L., et al. (2025) · medRxiv · doi:10.1101/2025.02.10.25321487 · Cited by 4 · see citing works
Joint-tissue integrative analysis identifies high-risk genes for Parkinson’s disease
Wu, Y.-S., Zheng, W.-H., Liu, T., Sun, Y., Xu, Y., Shao, L., Cai, Q., & Tang, Y. Q. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1309684 · Cited by 3 · see citing works
Ran, H., Li, C., Rizvi, S., Zhou, R., Kong, L., Shuangling, S., Shao, Y., Wu, K., Duan, C., Luo, J., et al. (2024) · Scientific Reports · doi:10.1038/s41598-024-83611-0 · Cited by 3 · see citing works
Genome-Wide Analysis of Disordered Eating Behavior in the Mexican Population
Martínez‐Magaña, J. J., Hernández, S., García, A. R. S., Cardoso-Barajas, V., Sarmiento, E., Camarena, B., Caballero, A., Gonzalez, L., Velázquez, J. A. V., Medina‐Mora, M. E., et al. (2022) · Nutrients · doi:10.3390/nu14020394 · Cited by 3 · see citing works
Expression Quantitative Trait Locus of Wood Formation-Related Genes in Salix suchowensis
Chen, L., Liu, L., Yang, G. X., Li, X., Dai, X., Xue, L., & Yin, T. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms25010247 · Cited by 3 · see citing works
Prioritizing Suggestive Candidate Genes in Migraine: An Opinion
Frederiksen, S. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2022.910366 · Cited by 2 · see citing works
Mancuso, N., Shi, H., Goddard, P. C., Kichaev, G., Gusev, A., & Paşaniuc, B. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/072967 · Cited by 2 · see citing works
Mechanisms of tissue-specific genetic regulation revealed by latent factors across eQTLsPreprint
He, Y. M., Chhetri, S. B., Arvanitis, M., Srinivasan, K., Aguet, F., Ardlie, K., Barbeira, A., Bonazzola, R., Im, H. K., Brown, C. D., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/785584 · Cited by 2 · see citing works
Yi, S., Liu, A., Zhang, X., Deng, Y., Chen, Y., & Huoshen, W. (2025) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2025.11.053 · Cited by 2 · see citing works
Leveraging large-scale biobanks for therapeutic target discovery
Ferolito, B. R., Dashti, H., Giambartolomei, C., Peloso, G. M., Golden, D. J., Gravel-Pucillo, K., Rasooly, D., Horimoto, A. R. V. R., Matty, R., Gaziano, L., et al. (2025) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2025.100556 · Cited by 1 · see citing works
Zhang, R., Fu, P., & Tian, H. (2025) · World Allergy Organization Journal · doi:10.1016/j.waojou.2025.101140 · Cited by 1 · see citing works
Ni, N., Wang, M., Yuan, Z., Zhang, L., Cai, J., Du, Q., Li, P., Gao, C. Q., Zhang, H., Li, Y., et al. (2026) · International Journal of Oral Science · doi:10.1038/s41368-025-00402-7 · Cited by 1 · see citing works
Gao, J., Huang, P., & Zhang, Y. (2026) · Clinical Epigenetics · doi:10.1186/s13148-026-02072-6 · Cited by 1 · see citing works
Li, Z., Liu, M., Li, J., Li, W., Min, T., Pan, R., Wang, W., Chen, Z., Li, W., & Huang, R. (2026) · Human Genomics · doi:10.1186/s40246-026-00984-1 · Cited by 2 · see citing works
Genkinger, J. M., Su, G. H., & Santella, R. M. (2020) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djz247 · Cited by 1 · see citing works
Chambers, T., Escott‐Price, V., Legge, S. E., Baker, E. A., Singh, K. D., Walters, J., Caseras, X., & Anney, R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.11.24.393249 · Cited by 1 · see citing works
Variants Around miR-451 Can Affect the Risk of Large-artery Atherosclerosis Stroke in ChinesePreprint
Gu, J., Tang, W., Shen, C., Li, Z., Li, J., Ren, Z., Chen, F., Zong, H., Wang, C., Chen, W., et al. (2021) · Research Square · doi:10.21203/rs.3.rs-450391/v1
Jayarathna, D. K. (2023) · Queensland University of Technology · doi:10.5204/thesis.eprints.237470
Functional Enrichment Analysis Identifying Regulatory Information Associated with Human Fracture
Meng, X., Liu, Z., Chen, X., Deng, A., & Mao, Z. (2023) · Calcified Tissue International · doi:10.1007/s00223-023-01108-w
Luo, L., Pang, T., Zheng, H., Liufu, C., & Chang, S. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4575822
Maimaiti, A., Turhon, M., Abulaiti, A., Abulizi, A., Maihemuti, M., Cheng, X., Maimaitili, A., Hu, Q., Wang, Z., & Wang, Y. (2023) · Research Square · doi:10.21203/rs.3.rs-3381677/v1
Wang, R., Xue, Y., Fan, J., Yao, J., Qin, M., Lin, T., Lian, Q., Zhang, M., Li, X., Li, J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.17010235
Wang, R., Xue, Y., Fan, J., Yao, J., Qin, M., Lin, T., Lian, Q., Zhang, M., Li, X., Li, J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.17010271
Wang, R., Xue, Y., Fan, J., Yao, J., Qin, M., Lin, T., Lian, Q., Zhang, M., Li, X., Li, J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.17010262
Liu, Y., Liu, X., Zheng, Z., Ma, T., Liu, Y., Long, H., Cheng, H., Fang, M., Gong, J., Li, X., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13076493
Wang, R., Xue, Y., Fan, J., Yao, J., Qin, M., Lin, T., Lian, Q., Zhang, M., Li, X., Li, J., et al. (2021) · Figshare · doi:10.6084/m9.figshare.17010280
Liu, Y., Liu, X., Zheng, Z., Ma, T., Liu, Y., Long, H., Cheng, H., Fang, M., Gong, J., Li, X., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13076475
Liu, Y., Liu, X., Zheng, Z., Ma, T., Liu, Y., Long, H., Cheng, H., Fang, M., Gong, J., Li, X., et al. (2020) · Figshare · doi:10.6084/m9.figshare.13076487
- Authors
- Pavlides, J. M. W., Zhu, Z., Gratten, J., McRae, A. F., Wray, N. R., & Yang, J.
- Affiliations
- The University of Queensland (AU)
- Corresponding author
- Yang, Jian
- ORCID
- Venue
- Genome Medicine
- Published
- 2016-08-09
- Volume
- 8
- Issue
- 1
- Pages
- 84-84
- ISSN
- 1756-994X (ISSN-L)
- PMC
- PMC4979185
- OpenAlex
- W2499155228
- Cited by
- 109
- References
- 36
- Citations by year
- 2026: 11 · 2025: 6 · 2024: 8 · 2023: 10 · 2022: 12 · 2021: 16 · 2020: 15 · 2019: 11 · 2018: 11 · 2017: 5 · 2016: 3
- FWCI
- 8.07
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- cc-by
- Locations
- Genome Medicine (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- Medical Research Council (1107258, 1078901)
- National Health and Medical Research Council (1083656, 1078901, 1087889, 1107258)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- expression quantitative trait loci, summary-data-based Mendelian randomization, complex traits, causal gene identification, pleiotropy, gene expression, gene prioritization
- MeSH terms
- Autism Spectrum Disorder, Alzheimer Disease, Coronary Artery Disease, Gene Expression Regulation, Genotype, Humans, Phenotype, Inflammatory Bowel Diseases, Models, Statistical, Genome, Human, Quantitative Trait, Heritable, Genetic Predisposition to Disease, Gene Expression Profiling, Databases, Genetic, Quantitative Trait Loci, Genome-Wide Association Study, Genetic Pleiotropy
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
