Cited by 849Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsGenetic and phenotypic traits in livestock

The DOI matches its Crossref record; 113 of the 115 references it cites were found in a registry or an index.

Mapping complex disease traits with global gene expression

Open access

Cookson, William, Liang, Liming, Abecasis, Gonçalo, Moffatt, Miriam, Lathrop, Mark·2009·Nature Reviews Genetics

DOI 10.1038/nrg2537PMC PMC4550035

Open this paper in CoChat — free account

Abstract

Variation in gene expression is an important mechanism underlying susceptibility to complex disease. The simultaneous genome-wide assay of gene expression and genetic variation allows the mapping of the genetic factors that underpin individual differences in quantitative levels of expression (expression QTLs; eQTLs). The availability of systematically generated eQTL information could provide immediate insight into a biological basis for disease associations identified through genome-wide association (GWA) studies, and can help to identify networks of genes involved in disease pathogenesis. Although there are limitations to current eQTL maps, understanding of disease will be enhanced with novel technologies and international efforts that extend to a wide range of new samples and tissues.

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

Crossref · 10.1038/nrg2537

Cites

113 of 113 references verified0 citation errors · 0 not checkable

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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
Genetics of Food Intake Self-Regulation in Childhood: Literature Review and Research Opportunities
Faith, M. S., Carnell, S., & Kral, T. V. E. (2013) · Human Heredity · doi:10.1159/000353879 · Cited by 7,633
A General Framework for Weighted Gene Co-Expression Network Analysis
Zhang, B., & Horvath, S. (2005) · Statistical Applications in Genetics and Molecular Biology · doi:10.2202/1544-6115.1128 · Cited by 5,684
Association of NOD2 leucine-rich repeat variants with susceptibility to Crohn's disease
Hugot, J.-P., Chamaillard, M., Zouali, H., Lesage, S., Cézard, J.-P., Belaiche, J., Almer, S., Tysk, C., O'Morain, C. A., Gassull, M., et al. (2001) · Nature · doi:10.1038/35079107 · Cited by 5,591
The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease
Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., et al. (2006) · Science · doi:10.1126/science.1132939 · Cited by 5,257
The human disease network
Goh, K.-I., Cusick, M. E., Valle, D., Childs, B., Vidal, M., & Barabási, A.-L. (2007) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0701361104 · Cited by 3,242
Common loss-of-function variants of the epidermal barrier protein filaggrin are a major predisposing factor for atopic dermatitis
Palmer, C. N. A., Irvine, A. D., Terron-Kwiatkowski, A., Zhao, Y., Liao, H., Lee, S. P., Goudie, D. R., Sandilands, A., Campbell, L. E., Smith, F. J. D., et al. (2006) · Nature Genetics · doi:10.1038/ng1767 · Cited by 2,978
Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease
the NIDDK IBD Genetics Consortium, Barrett, J. C., the Belgian-French IBD Consortium, the Wellcome Trust Case Control Consortium, Hansoul, S., Nicolae, D. L., Cho, J. H., Duerr, R. H., Rioux, J. D., Brant, S. R., et al. (2008) · Nature Genetics · doi:10.1038/ng.175 · Cited by 2,593
Self-citation
Heritability in the genomics era — concepts and misconceptions
Visscher, P. M., Hill, W. G., & Wray, N. R. (2008) · Nature Reviews Genetics · doi:10.1038/nrg2322 · Cited by 1,921
An Oncogene-Induced DNA Damage Model for Cancer Development
Halazonetis, T. D., Gorgoulis, V. G., & Bartek, J. (2008) · Science · doi:10.1126/science.1140735 · Cited by 1,851

Show all 115 references

Research works printed but not linked: 1

Thein, S. L. et al. Intergenic variants of HBS1L–MYB are responsible for a major QTL on chromosome 6q23 influencing HbF levels in adults. Proc. Natl Acad. Sci. USA (in the press).

Sorted by citations

Items 501–600 of 848

Leveraging drug perturbation to reveal genetic regulators of hepatic gene expression in African Americans
Zhong, Y., De, T., Mishra, M., Avitia, J., Alarcón, C., & Perera, M. A. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2022.12.005 · Cited by 8
RTeQTL: Real-Time Online Engine for Expression Quantitative Trait Loci Analyses
Ma, B., Huang, J. J., & Liang, L. (2014) · Database · doi:10.1093/database/bau066 · Cited by 9
Self-citation
Allelic expression analysis of the osteoarthritis susceptibility locus that MAPS to MICAL3
Ratnayake, M., Reynard, L. N., Raine, E. V. A., Santibanez‐Koref, M., & Loughlin, J. A. (2012) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2012.02.321 · Cited by 8
Fine Mapping of the Insulin-Induced Gene 2 Identifies a Variant Associated With LDL Cholesterol and Total Apolipoprotein B Levels
Do, R., Bailey, S. D., Paré, G., Montpetit, A., Desbiens, K., Hudson, T. J., Yusuf, S., Bouchard, C., Gaudet, D., Përusse, L., et al. (2010) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.109.917039 · Cited by 8
Spatio-Temporal Roles of ASD-Associated Variants in Human Brain Development
Kim, Y., & An, J. (2020) · Genes · doi:10.3390/genes11050535 · Cited by 8
Natural Selection and Functional Potentials of Human Noncoding Elements Revealed by Analysis of Next Generation Sequencing Data
Jha, P., Lu, D., & Xu, S. (2015) · PLoS ONE · doi:10.1371/journal.pone.0129023 · Cited by 9
The genetic architecture of autism spectrum disorders (ASDs) and the potential importance of common regulatory genetic variants
Saffen, D. W. (2015) · Science China Life Sciences · doi:10.1007/s11427-012-4336-5 · Cited by 8
Functional analysis of HapMap SNPs
Liu, C., Lin, H., & Lin, H. (2012) · Gene · doi:10.1016/j.gene.2012.09.075 · Cited by 8
New aQTL SNPs for the CYP2D6 Identified by a Novel Mediation Analysis of Genome-Wide SNP Arrays, Gene Expression Arrays, and CYP2D6 Activity
Jiang, G., Chakraborty, A., Wang, Z. P., Boustani, M. A., Liu, Y., Skaar, T. C., & Li, L. (2013) · BioMed Research International · doi:10.1155/2013/493019 · Cited by 8
Expression Quantitative Trait Loci (eQTL) Analysis in Cancer
Liu, Y., Ye, Y., Gong, J., & Han, L. (2019) · Methods in molecular biology · doi:10.1007/978-1-0716-0026-9_13 · Cited by 8
Using “-omics” Data to Inform Genome-wide Association Studies (GWASs) in the Osteoporosis Field
Abood, A., & Farber, C. R. (2021) · Current Osteoporosis Reports · doi:10.1007/s11914-021-00684-w · Cited by 8
Inguinal Ring RNA Sequencing Reveals Downregulation of Muscular Genes Related to Scrotal Hernia in Pigs
Romano, G., Ibelli, A. M. G., Lorenzetti, W. R., Weber, T., Peixoto, J. D. O., Cantão, M. E., Morés, M. A. Z., Morés, N., Pedrosa, V. B., Coutinho, L. L., et al. (2020) · Genes · doi:10.3390/genes11020117 · Cited by 8
Current Research on the Genetic Contributors to Schizophrenia
Pogue‐Geile, M. F., & Yokley, J. L. (2010) · Current Directions in Psychological Science · doi:10.1177/0963721410378490 · Cited by 9
Identifying genetic regulatory variants that affect transcription factor activity
Li, X., Lappalainen, T., & Bussemaker, H. J. (2023) · Cell Genomics · doi:10.1016/j.xgen.2023.100382 · Cited by 7
Genetic dissection of intermediate phenotypes as a way to discover novel cancer susceptibility alleles
Carvajal‐Carmona, L. G. (2010) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2010.03.013 · Cited by 7
Linking genotype to phenotype in a changing ocean: inferring the genomic architecture of a blue mussel stress response with genome‐wide association
Kingston, S. E., Martino, P. A., Melendy, M., Reed, F. A., & Carlon, D. B. (2017) · Journal of Evolutionary Biology · doi:10.1111/jeb.13224 · Cited by 7
Cross-tissue eQTL enrichment of associations in schizophrenia
Bettella, F., Brown, A. A., Smeland, O. B., Wang, Y., Witoelar, A., Demur, A. A. B., Thompson, W. K., Zuber, V., Dale, A. M., Djurovic, S., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0202812 · Cited by 7
Developing Peripheral Blood Gene Expression-Based Diagnostic Tests for Coronary Artery Disease: a Review
Rhees, B. K., & Wingrove, J. A. (2015) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-015-9641-5 · Cited by 7
Effects of copy number variable regions on local gene expression in white blood cells of Mexican Americans
Blackburn, A. N., Almeida, M., Dean, A. K., Curran, J. E., Johnson, M. P., Moses, E. K., Abraham, L. J., Carless, M. A., Dyer, T. D., Kumar, S., et al. (2015) · European Journal of Human Genetics · doi:10.1038/ejhg.2014.280 · Cited by 7
Networking complex traits
Swami, M. (2009) · Nature Reviews Genetics · doi:10.1038/nrg2566 · Cited by 7
Nuclear Receptor Variants in Liver Disease
Müllenbach, R., Weber, S. N., & Lammert, F. (2012) · Journal of Lipids · doi:10.1155/2012/934707 · Cited by 7
Enhancing the usability and performance of structured association mapping algorithms using automation, parallelization, and visualization in the GenAMap software system
Curtis, R. E., Goyal, A. K., & Xing, E. P. (2012) · BMC Genetics · doi:10.1186/1471-2156-13-24 · Cited by 7
GWAS in a Box: Statistical and Visual Analytics of Structured Associations via GenAMap
Xing, E. P., Curtis, R. E., Schoenherr, G. P., Lee, S., Yin, J., Puniyani, K., Wu, W., & Kinnaird, P. (2014) · PLoS ONE · doi:10.1371/journal.pone.0097524 · Cited by 7
The identification of trans-associations between prostate cancer GWAS SNPs and RNA expression differences in tumor-adjacent stroma
Chen, X., McClelland, M., Jia, Z., Rahmatpanah, F. B., Sawyers, A., Trent, J. M., Duggan, D. J., & Mercola, D. (2015) · Oncotarget · doi:10.18632/oncotarget.2763 · Cited by 7
Prediction of fine-tuned promoter activity from DNA sequence
Siwo, G., Rider, A. K., Tan, A., Pinapati, R. S., Emrich, S., Chawla, N. V., & Ferdig, M. T. (2016) · F1000Research · doi:10.12688/f1000research.7485.1 · Cited by 7
Experimental Evolution and Heart Function inDrosophila
Shahrestani, P., Burke, M. K., Birse, R. T., Kezos, J. N., Ocorr, K. A., Mueller, L. D., Rose, M. R., & Bodmer, R. (2016) · Physiological and Biochemical Zoology · doi:10.1086/689288 · Cited by 7
A cis-eQTL allele regulating reduced expression of CHI3L1 is associated with late-onset adult asthma in Japanese cohorts
Kanazawa, J., Kitazawa, H., Masuko, H., Yatagai, Y., Sakamoto, T., Kaneko, Y., Iijima, H., Naito, T., Saito, T., Noguchi, E., et al. (2019) · BMC Medical Genetics · doi:10.1186/s12881-019-0786-y · Cited by 7
Celiac disease associated SNP rs17810546 is located in a gene silencing region
Zwiers, A., van Wanrooij, R. L. J., Dieckman, T., Nijeboer, P., Kraal, G., & Bouma, G. (2019) · Gene · doi:10.1016/j.gene.2019.144165 · Cited by 7
Dynamic patterns of the translatome in a hybrid triplet show translational fractionation of the maize subgenomes
Zhu, W., Chen, S., Zhang, T., Qian, J., Luo, Z. W., Zhao, H. Q., Zhang, Y., & Li, L. (2021) · The Crop Journal · doi:10.1016/j.cj.2021.02.002 · Cited by 7
Inference of a Dynamic Aging-related Biological Subnetwork via Network Propagation
Newaz, K., & Milenković, T. (2020) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2020.3022767 · Cited by 7
Are complex traits underpinned by polygenic molecular traits? A reflection on the complexity of gene expression
Hajheidari, M., Sunyaev, S. R., & de Meaux, J. (2024) · Plant and Cell Physiology · doi:10.1093/pcp/pcae140 · Cited by 6
Targeting the epigenome and tumor heterogeneity: advances in immunotherapy for chemoresistant metastatic colorectal cancer
Cao, Y. J., Beeraka, N. M., Efetov, S. K., Liu, Z., Otabekov, A. A., Basappa, B., Wang, W., & Ma, D. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1623117 · Cited by 6
A human minisatellite hosts an alternative transcription start site for NPRL3 driving its expression in a repeat number‐dependent manner
Bertuzzi, M., Tang, D. T. P., Calligaris, R., Vlachouli, C., Finaurini, S., Sanges, R., Goldwurm, S., Catalan, M., Antonutti, L., Manganotti, P., et al. (2020) · Human Mutation · doi:10.1002/humu.23974 · Cited by 8
Modeling the Aneuploidy Control of Cancer
Li, Y., Berg, A. S., Wu, L. R., Wang, Z., Chen, G., & Wu, R. (2010) · BMC Cancer · doi:10.1186/1471-2407-10-346 · Cited by 6
iFORM/eQTL: an ultrahigh-dimensional platform for inferring the global genetic architecture of gene transcripts
Gosik, K., Kong, L., Chinchilli, V. M., & Wu, R. (2016) · Briefings in Bioinformatics · doi:10.1093/bib/bbw014 · Cited by 6
Blueprint of differentially expressed genes reveals the dynamic gene expression landscape and the gender biases in long COVID
Chakraborty, C., Bhattacharya, M., Alshammari, A., & Albekairi, T. H. (2024) · Journal of Infection and Public Health · doi:10.1016/j.jiph.2024.02.018 · Cited by 6
Identification of differentially expressed genes in matched formalin‐fixed paraffin‐embedded primary and metastatic melanoma tumor pairs
Jewell, R., Mitra, A., Conway, C., Iremonger, J., Walker, C., de Kort, F., Cook, M. G., Boon, A., Speirs, V., & Newton‐Bishop, J. (2011) · Pigment Cell & Melanoma Research · doi:10.1111/j.1755-148x.2011.00965.x · Cited by 6
Genetic Analysis Identifies DDR2 as a Novel Gene Affecting Bone Mineral Density and Osteoporotic Fractures in Chinese Population
Guo, Y., Yang, T., Dong, S., Yan, H., Hao, R.-H., Chen, X., Chen, J., Tian, Q., Li, J., Shen, H., et al. (2015) · PLoS ONE · doi:10.1371/journal.pone.0117102 · Cited by 6
Cause and express
Kruglyak, L., & Storey, J. D. (2009) · Nature Biotechnology · doi:10.1038/nbt0609-544 · Cited by 6
From circulating biomarkers to genomics and imaging in the prediction of cardiovascular events in the general population
Fava, C., Montagnana, M., Guidi, G. C., & Melander, O. (2011) · Annals of Medicine · doi:10.3109/07853890.2011.582511 · Cited by 6
Genetic predisposition for sudden cardiac death in myocardial ischaemia: the Arrhythmia Genetics in the NEtherlandS study
Marsman, R. F. J., Wilde, A. A. M., & Bezzina, C. R. (2011) · Netherlands Heart Journal · doi:10.1007/s12471-010-0070-4 · Cited by 6
Understanding Complex Transcriptome Dynamics in Schizophrenia and Other Neurological Diseases Using RNA Sequencing
Wang, X., & Cairns, M. J. (2014) · International review of neurobiology · doi:10.1016/b978-0-12-801105-8.00006-0 · Cited by 6
First-degree relationships and genotyping errors deciphered by a high-density SNP array in a Duroc × Iberian pig cross
Gomez‐Raya, L., Izquierdo, E. G., de Mercado, E., García-Ruiz, F., & Rauw, W. M. (2022) · BMC Genomic Data · doi:10.1186/s12863-022-01025-1 · Cited by 6
Systems Genetics of Complex Diseases Using RNA-Sequencing Methods
Mazzoni, G., Kogelman, L. J. A., Suravajhala, P., & Kadarmideen, H. N. (2015) · International Journal of Bioscience Biochemistry and Bioinformatics · doi:10.17706/ijbbb.2015.5.4.264-279 · Cited by 8
Mapping Tumor-Specific Expression QTLs in Impure Tumor Samples
Wilson, D., Ibrahim, J., & Sun, W. (2019) · Journal of the American Statistical Association · doi:10.1080/01621459.2019.1609968 · Cited by 6
JEPEGMIX2: improved gene-level joint analysis of eQTLs in cosmopolitan cohorts
Chatzinakos, C., Lee, D., Webb, B. T., Vladimirov, V. I., Kendler, K. S., & Bacanu, S. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx509 · Cited by 6
Schizophrenia risk alleles often affect the expression of many genes and each gene may have a different effect on the risk: A mediation analysis
Peng, X., Bader, J. S., & Avramopoulos, D. (2021) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32841 · Cited by 6
Modern Advances in Tree Breeding
El-Kassaby, Y. A., Işik, F., & Whetten, R. W. (2013) · Forestry sciences · doi:10.1007/978-94-007-7076-8_18 · Cited by 6
AD Gene 3-D: Moving Past Single Layer Genetic Information to Map Novel Loci Involved in Alzheimer's Disease
Myers, A. (2012) · Journal of Alzheimer s Disease · doi:10.3233/jad-2012-129013 · Cited by 6
Analysis of Nonlinear Gene Expression Progression Reveals Extensive Pathway and Age-Specific Transitions in Aging Human Brains
Cao, K., Ryvkin, P., Hwang, Y.-C., Johnson, F. B., & Wang, L. (2013) · PLoS ONE · doi:10.1371/journal.pone.0074578 · Cited by 5
A statistical framework for revealing signaling pathways perturbed by DNA variants
Wilentzik, R., & Gat‐Viks, I. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv203 · Cited by 5
Genome-wide associations of signaling pathways in glioblastoma multiforme
Wuchty, S., Vázquez, A., Bozdag, S., & Bauer, P. O. (2013) · BMC Medical Genomics · doi:10.1186/1755-8794-6-11 · Cited by 5
Efficiently Identifying Significant Associations in Genome-Wide Association Studies
Kostem, E., & Eskin, E. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-37195-0_10 · Cited by 5
Familial resemblances in human whole blood transcriptome
Tremblay, B. L., Guénard, F., Lamarche, B., Përusse, L., & Vohl, M. (2018) · BMC Genomics · doi:10.1186/s12864-018-4698-6 · Cited by 5
Understanding of the various aspects of gene regulatory networks related to crop improvement
Ahmed, B., & Kabir, A. H. (2022) · Gene · doi:10.1016/j.gene.2022.146556 · Cited by 5
Mapping the Potential Genes and Associated Pathways Involved in Long COVID-Associated Brain Fog Using Integrative Bioinformatics and Systems Biology Strategy
Chakraborty, C., Bhattacharya, M., Alshammari, A., Albekairi, N. A., & Lee, S. (2024) · Molecular Biotechnology · doi:10.1007/s12033-024-01324-1 · Cited by 6
Network Medicine: New Paradigm in the -Omics Era
Guo, N. L. (2011) · Anatomy & Physiology · doi:10.4172/2161-0940.1000e106 · Cited by 5
Dissecting genetic architecture of startle response in Drosophila melanogaster using multi-omics information
Xue, A., Wang, H., & Zhu, J. (2017) · Scientific Reports · doi:10.1038/s41598-017-11676-1 · Cited by 5
Assessment of RNA Stability in Postmortem Tissue from New-Born Lambs
McGovern, F., Boland, T. M., Ryan, M. T., & Sweeney, T. (2017) · Animal Biotechnology · doi:10.1080/10495398.2017.1378226 · Cited by 5
The role of transposable elements in human evolution and methods for their functional analysis: current status and future perspectives
Fukuda, K. (2023) · Genes & Genetic Systems · doi:10.1266/ggs.23-00140 · Cited by 5
A functional schizophrenia-associated genetic variant near the TSNARE1 and ADGRB1 genes
Wahbeh, M. H., Boyd, R. J., Yovo, C., Rike, B., McCallion, A. S., & Avramopoulos, D. (2024) · Human Genetics and Genomics Advances · doi:10.1016/j.xhgg.2024.100303 · Cited by 5
Induced pluripotent stem cell modeling of complex genetic diseases
Hinson, J. T., Nakamura, K., & Wu, S. M. (2012) · Drug Discovery Today Disease Models · doi:10.1016/j.ddmod.2012.04.002 · Cited by 5
Effective SNP ranking improves the performance of eQTL mapping
Jeng, X. J., Rhyne, J., Zhang, T., & Tzeng, J. (2020) · Genetic Epidemiology · doi:10.1002/gepi.22293 · Cited by 5
Systematic Evaluation of DNA Sequence Variations on in vivo Transcription Factor Binding Affinity
Jin, Y., Jiang, J., Wang, R., & Qin, Z. S. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.667866 · Cited by 5
Genotypic analysis of gene expression in the dissection of the aetiology of complex neurological and psychiatric diseases
Ryten, M., Trabzuni, D., & Hardy, J. (2009) · Briefings in Functional Genomics and Proteomics · doi:10.1093/bfgp/elp028 · Cited by 5
SPATS2L is a positive feedback regulator of the type I interferon signaling pathway and plays a vital role in lupus
Chen, M., Zhang, Y., Shi, W., Song, X., Yang, Y., Hou, G., Ding, H., Chen, S., Yang, W., Shen, N., et al. (2024) · Acta Biochimica et Biophysica Sinica · doi:10.3724/abbs.2024132 · Cited by 4
Probing the limits of cis-acting gene regulation using a model of allelic imbalance quantitative trait loci
Seoighe, C., Connaire, S., & Chopra, M. (2025) · PLoS Genetics · doi:10.1371/journal.pgen.1011446 · Cited by 4
Epigenetic Alterations Induced by Air Pollution: A Key Driver in Atherosclerosis Development
Goleij, P., Tabari, M. A. K., Sanaye, P. M., Moradi, A., Karimi, H., Rezaee, A., Kumar, A. P., & Khan, H. Z. (2025) · Cardiovascular Toxicology · doi:10.1007/s12012-025-10036-0 · Cited by 4
Generative prediction of causal gene sets responsible for complex traits
Kuznets-Speck, B., Ogonor, B. K., Wytock, T. P., & Motter, A. E. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2415071122 · Cited by 6
The genetics of latent inhibition: studies of inbred and mutant mice
Lipina, T. V., & Roder, J. (2010) · Cambridge University Press eBooks · doi:10.1017/cbo9780511730184.012 · Cited by 4
Functional Mapping of Expression Quantitative Trait Loci that Regulate Oscillatory Gene Expression
Berg, A. S., Li, N., Tong, C., Wang, Z., Berceli, S. A., & Wu, R. (2011) · Methods in molecular biology · doi:10.1007/978-1-61779-086-7_12 · Cited by 4
Interconnectivity of human cellular metabolism and disease prevalence
Lee, D. (2010) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/2010/12/p12015 · Cited by 4
Detection of Regulator Genes and eQTLs in Gene Networks
Wang, L., & Michoel, T. (2016) · Springer eBooks · doi:10.1007/978-3-319-43335-6_1 · Cited by 5
Application of integrative genomics and systems biology to conventional and in vitro reproductive traits in cattle
Mazzoni, G., Pedersen, H. S., Júnior, G. A. O., Alexandre, P. A., Razza, E. M., Callesen, H., Hyttel, P., Nogueira, M. F. G., Ferraz, J. B. S., & Kadarmideen, H. N. (2017) · Animal Reproduction · doi:10.21451/1984-3143-ar993 · Cited by 4
Cardiac physiology and metabolic gene expression during late organogenesis among F. heteroclitus embryo families from crosses between pollution-sensitive and -resistant parents
Bozinovic, G., Feng, Z., Shea, D., & Oleksiak, M. F. (2022) · BMC Ecology and Evolution · doi:10.1186/s12862-022-01959-1 · Cited by 4
Genetics of Psychiatric Disorders Methods: Molecular Approaches
Avramopoulos, D. (2010) · Clinics in Laboratory Medicine · doi:10.1016/j.cll.2010.07.007 · Cited by 4
Leveraging molecular quantitative trait loci to comprehend complex diseases/traits from the omics perspective
Zhu, Z., Chen, X., Zhang, S., Yu, R., Qi, C., Cheng, L., & Zhang, X. (2023) · Human Genetics · doi:10.1007/s00439-023-02602-9 · Cited by 5
Unsupervised pattern identification in spatial gene expression atlas reveals mouse brain regions beyond established ontology
Cahill, R., Wang, Y., Xian, R. P., Lee, A. J., Zeng, H., Yu, B., Tasic, B., & Abbasi-Asl, R. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2319804121 · Cited by 4
Autism and autism spectrum disorders
Geschwind, D. H., & Alarcón, M. (2012) · Cambridge University Press eBooks · doi:10.1017/cbo9781139025997.017 · Cited by 4
Mapping QTL for Multiple Traits
Xu, S. (2012) · Springer eBooks · doi:10.1007/978-0-387-70807-2_14 · Cited by 4
Novel Algorithm for Multiple Quantitative Trait Loci Mapping by Using Bayesian Variable Selection Regression
Yuan, L., Han, K., & Huang, D.-S. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-42297-8_80 · Cited by 4
eQTL mapping of rare variant associations using RNA-seq data: An evaluation of approaches
Lutz, S. M., Thwing, A., & Fingerlin, T. E. (2019) · PLoS ONE · doi:10.1371/journal.pone.0223273 · Cited by 4
Expression Quantitative Trait Loci Analysis in Multiple Tissues
Li, G. (2019) · Methods in molecular biology · doi:10.1007/978-1-0716-0026-9_16 · Cited by 4
Assigning Co-Regulated Human Genes and Regulatory Gene Clusters
Strunz, T., Kellner, M., Kiel, C., & Weber, B. H. F. (2021) · Cells · doi:10.3390/cells10092395 · Cited by 5
QTL mapping and identification of candidate genes linked to red rot resistance in sugarcane
Banerjee, N., Khan, M. S., Swapna, M., Yadav, S., Tiwari, G. J., Jena, S. N., Patel, J. D., Manimekalai, R., Kumar, S., Dattamajuder, S. K., et al. (2023) · 3 Biotech · doi:10.1007/s13205-023-03481-7 · Cited by 4
Efficiently Identifying Significant Associations in Genome-wide Association Studies
Kostem, E., & Eskin, E. (2013) · Journal of Computational Biology · doi:10.1089/cmb.2013.0087 · Cited by 4
From parasite genomes to one healthy world: Are we having fun yet?
Zarlenga, D. S., & Gasbarre, L. C. (2009) · Veterinary Parasitology · doi:10.1016/j.vetpar.2009.06.010 · Cited by 4
The future of mouse genetics in osteoporosis research
Ackert‐Bicknell, C. L., & Rosen, C. J. (2009) · IBMS BoneKEy · doi:10.1138/20090380 · Cited by 4
FeedOmics, an approach to evaluate the functional properties of protein containing feed ingredients
Kar, S. K. (2017) · doi:10.18174/416108 · Cited by 4
Gene Expression Imputation with Generative Adversarial Imputation NetsPreprint
Viñas, R., Azevedo, T., Gamazon, E. R., & Liò, P. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.06.09.141689 · Cited by 5
Phenotypic and expression QTL integration allows the refinement of loci associated with pig carcass and meat quality traitsPreprint
Herault, F., Vincent, A., Randriamanantena, A. Y., Damon, M., Cherel, P., & Roy, P. L. (2021) · Research Square · doi:10.21203/rs.3.rs-886438/v1 · Cited by 4
Illuminating the enigmatic pathogenesis of Kawasaki disease: Unveiling novel therapeutic avenues by targeting FCGR3B-S100A12 pathway
Wu, M., Wang, W., Yang, Z., Long, G., Zhang, Y., & Yang, D. (2024) · European Journal of Pharmacology · doi:10.1016/j.ejphar.2024.177154 · Cited by 3
POMO - Plotting Omics analysis results for Multiple Organisms
Lin, J., Kreisberg, R., Kallio, A., Dudley, A. M., Nykter, M., Shmulevich, I., May, P., & Autio, R. (2013) · BMC Genomics · doi:10.1186/1471-2164-14-918 · Cited by 3
Genes and Pathways Contributing to Obesity
Drake, T. A. (2010) · Progress in molecular biology and translational science · doi:10.1016/b978-0-12-375003-7.00002-9 · Cited by 3
Hierarchical probabilistic models for multiple gene/variant associations based on next-generation sequencing data
Vavoulis, D. V., Taylor, J. C., & Schuh, A. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx355 · Cited by 3
Statistical considerations for the analysis of massively parallel reporter assays data
Qiao, D., Zigler, C. M., Cho, M. H., Silverman, E. K., Zhou, X., Castaldi, P. J., & Laird, N. M. (2020) · Genetic Epidemiology · doi:10.1002/gepi.22337 · Cited by 3
Detection of Disease-associated Mutations and Biomarkers Using Next-generation Sequencing
Yan, J., & Wang, X. (2020) · Royal Society of Chemistry eBooks · doi:10.1039/9781788019958-00117 · Cited by 3
Genetic Susceptibility to Dry Skin in a General Middle-Aged to Elderly Population: A GWAS
Mekić, S., Gunn, D. A., Jacobs, L. C., Hijnen, D., Ikram, M. A., Mayes, A. E., Nijsten, T. E. C., & Pardo, L. M. (2021) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.12.033 · Cited by 3
Deciphering the Role of the Non-Coding Genome in Regulating Gene-Diet Interactions
Law, P. P., & Holland, M. L. (2018) · Nutrients · doi:10.3390/nu10121831 · Cited by 3
The statistical practice of the GTEx Project: from single to multiple tissues
Liao, X., Chai, X., Shi, X., Chen, L., & Liu, J. (2020) · Quantitative Biology · doi:10.1007/s40484-020-0210-9 · Cited by 3

Authors: Cookson, W., Liang, L., Abecasis, G., Moffatt, M., & Lathrop, M.
Affiliations: Imperial College London (GB)
University of Michigan (US)
Commissariat à l'Énergie Atomique et aux Énergies Alternatives (FR)
Corresponding author: Cookson, William
Liang, Liming
Abecasis, Gonçalo
Moffatt, Miriam
Lathrop, Mark
ORCID: Cookson, William
Liang, Liming
Abecasis, Gonçalo
Moffatt, Miriam
Lathrop, Mark
Venue: Nature Reviews Genetics
Publisher: Springer Science and Business Media LLC
Published: 2009-03
Volume: 10
Issue: 3
Pages: 184-194
ISSN: 1471-0056 (ISSN-L)
1471-0064
DOI: 10.1038/nrg2537
PMC: PMC4550035
OpenAlex: W2142260323
Cited by: 849
References: 115
Citations by year: 2026: 8 · 2025: 15 · 2024: 30 · 2023: 27 · 2022: 29 · 2021: 41 · 2020: 37 · 2019: 47 · 2018: 36 · 2017: 40 · 2016: 52 · 2015: 62 · 2014: 59 · 2013: 79 · 2012: 102
FWCI: 8.99
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
Locations: Nature Reviews Genetics (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: Wellcome Trust
National Heart, Lung, and Blood Institute (U01HL084729)
National Human Genome Research Institute (R01HG002651)
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: expression quantitative trait loci, eQTL mapping, gene expression variation, complex disease susceptibility, disease risk loci, genome-wide association study, mRNA levels, gene regulatory networks, pathogenesis, tissue-specific gene expression
MeSH terms: Animals, Disease, Humans, Genetic Variation, Quantitative Trait Loci, Genome-Wide Association Study

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Mapping complex disease traits with global gene expression

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks