Cited by 1,849Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsGenetic and phenotypic traits in livestock

The DOI matches its Crossref record; 293 of the 293 references it cites were found in a registry or an index.

Genome-wide association studies

Open access

Uffelmann, Emil, Huang, Qin Qin, Munung, Nchangwi Syntia, de Vries, Jantina, Okada, Yukinori, Martin, Alicia R., Martin, Hilary C., Lappalainen, Tuuli, Posthuma, Danielle·2021·Nature Reviews Methods Primers

DOI 10.1038/s43586-021-00056-9

Open this paper in CoChat — free account Open PDF

Abstract

Genome-wide association studies (GWAS) test hundreds of thousands of genetic variants across many genomes to find those statistically associated with a specific trait or disease. This methodology has generated a myriad of robust associations for a range of traits and diseases, and the number of associated variants is expected to grow steadily as GWAS sample sizes increase. GWAS results have a range of applications, such as gaining insight into a phenotype’s underlying biology, estimating its heritability, calculating genetic correlations, making clinical risk predictions, informing drug development programmes and inferring potential causal relationships between risk factors and health outcomes. In this Primer, we provide the reader with an introduction to GWAS, explaining their statistical basis and how they are conducted, describe state-of-the art approaches and discuss limitations and challenges, concluding with an overview of the current and future applications for GWAS results. Uffelmann et al. describe the key considerations and best practices for conducting genome-wide association studies (GWAS), techniques for deriving functional inferences from the results and applications of GWAS in understanding disease risk and trait architecture. The Primer also provides information on the best practices for data sharing and discusses important ethical considerations when considering GWAS populations and data.

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/s43586-021-00056-9

Cites

293 of 293 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.

PLINK: a tool set for whole-genome association and population-based linkage analyses.
Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., Maller, J., Sklar, P., de Bakker, P. I. W., Daly, M. J., et al. (2007) · The American Journal of Human Genetics · doi:10.1086/519795 · Cited by 34,089
The FAIR Guiding Principles for scientific data management and stewardship
Wilkinson, M. D., Dumontier, M., Aalbersberg, I. J., Appleton, G., Axton, M., Baak, A., Blomberg, N., Boiten, J.-W., da Silva Santos, L. B., Bourne, P. E., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.18 · Cited by 18,423
A global reference for human genetic variation
The 1000 Genomes Project Consortium, authors, C., Auton, A., Abecasis, G. R., Steering committee, Altshuler, D. M., Durbin, R. M., Abecasis, G. R., Bentley, D. R., Chakravarti, A., et al. (2015) · Nature · doi:10.1038/nature15393 · Cited by 18,247
ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data
Wang, K., Li, M., & Hakonarson, H. H. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq603 · Cited by 14,724
Principal components analysis corrects for stratification in genome-wide association studies
Price, A. L., Patterson, N. J., Plenge, R. M., Weinblatt, M. E., Shadick, N. A., & Reich, D. (2006) · Nature Genetics · doi:10.1038/ng1847 · Cited by 10,248
The mutational constraint spectrum quantified from variation in 141,456 humans
Karczewski, K. J., Francioli, L. C., Tiao, G., Cummings, B. B., Alföldi, J., Wang, Q., Collins, R. L., Laricchia, K. M., Ganna, A., Birnbaum, D. P., et al. (2020) · Nature · doi:10.1038/s41586-020-2308-7 · Cited by 9,434
The UK Biobank resource with deep phenotyping and genomic data
Bycroft, C., Freeman, C., Petkova, D., Band, G., Elliott, L. T., Sharp, K., Motyer, A., Vukcevic, D., Delaneau, O., O’Connell, J., et al. (2018) · Nature · doi:10.1038/s41586-018-0579-z · Cited by 8,876
Detection of widespread horizontal pleiotropy in causal relationships inferred from Mendelian randomization between complex traits and diseases
Verbanck, M., Chen, C.-Y., Neale, B., & Do, R. (2018) · Nature Genetics · doi:10.1038/s41588-018-0099-7 · Cited by 8,591
Finding the missing heritability of complex diseases
Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., McCarthy, M. I., Ramos, E. M., Cardon, L. R., Chakravarti, A., et al. (2009) · Nature · doi:10.1038/nature08494 · Cited by 8,336
GCTA: a tool for genome-wide complex trait analysis
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.011 · Cited by 8,279

Show all 288 references

Research works printed but not linked: 7

Morris, T. T., Davies, N. M., Hemani, G. & Smith, G. D. Population phenomena inflate genetic associations of complex social traits. Sci. Adv. 6, eaay0328 (2020). · DOI · Paper page
Morris, J. A. et al. Discovery of target genes and pathways of blood trait loci using pooled CRISPR screens and single cell RNA sequencing. Preprint at bioRxiv https://doi.org/10.1101/2021.04.07.438882v1 (2021). · DOI
Kerimov, N. et al. eQTL Catalogue: a compendium of uniformly processed human gene expression and splicing QTLs. Preprint at bioRxiv https://doi.org/10.1101/2020.01.29.924266v1 (2020). · DOI
Márquez-Luna, C. et al. Modeling functional enrichment improves polygenic prediction accuracy in UK Biobank and 23andMe data sets. Preprint at bioRxiv https://doi.org/10.1101/375337v1 (2018). · DOI
Falconer, D. S. & Mackay, T. F. C. Introduction to Quantitative Genetics (Pearson, Prentice Hall, 2009).
Bernabeu, E. et al. Sexual differences in genetic architecture in UK Biobank. Preprint at bioRxiv https://doi.org/10.1101/2020.07.20.211813v1 (2020). · DOI
Wellcome Trust. Sharing Data from Large-scale Biological Research Projects: A System of Tripartite Responsibility Vol. 6 (Wellcome Trust, 2003).

Sorted by citations

Items 101–200 of 1,000

GWAS Central: an expanding resource for finding and visualising genotype and phenotype data from genome-wide association studies
Beck, T., Rowlands, T., Shorter, T., & Brookes, A. J. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1017 · Cited by 37
Genotype and phenotype data standardization, utilization and integration in the big data era for agricultural sciences
Deng, C. H., Naithani, S., Kumari, S., Cobo-Simón, I., Quezada-Rodríguez, E. H., Škrabišová, M., Gladman, N., Correll, M. J., Sikiru, A. B., Afuwape, O. O., et al. (2023) · Database · doi:10.1093/database/baad088 · Cited by 40
Overcoming Barriers to Discovery and Implementation of Equitable Pharmacogenomic Testing in Oncology
Shriver, S. P., Adams, D. V., McKelvey, B. A., McCune, J. S., Miles, D., Pratt, V. M., Ashcraft, K. C., McLeod, H. L., Williams, H., & Fleury, M. E. (2024) · Journal of Clinical Oncology · doi:10.1200/jco.23.01748 · Cited by 35
How quantum computing can enhance biomarker discovery
Flöther, F. F., Blankenberg, D. J., Demidik, M., Jansen, K., Krishnakumar, R., Krishnakumar, R., Laanait, N., Parida, L., Saab, C. Y., & Utro, F. (2025) · Patterns · doi:10.1016/j.patter.2025.101236 · Cited by 54
VSMCs in atherosclerosis: Implications on the role of inflammation and extracellular matrix remodelling
Jarad, S., Gill, G. S., Amadi, P. U., Gu, H.-M., & Zhang, D. (2025) · Pharmacological Research · doi:10.1016/j.phrs.2025.107833 · Cited by 40
Genetics of Diabetic Retinopathy
Han, J. M., Landó, L., Skowronska‐Krawczyk, D., & Chao, D. L. (2019) · Current Diabetes Reports · doi:10.1007/s11892-019-1186-6 · Cited by 35
Viral-Host Dependency Factors as Therapeutic Targets to Overcome Antiviral Drug-Resistance: A Focus on Innate Immune Modulation
Badía, R., García-Vidal, E., & Ballana, E. (2022) · Frontiers in Virology · doi:10.3389/fviro.2022.935933 · Cited by 39
Unlocking gene regulation with sequence-to-function models
Sasse, A., Chikina, M. D., & Mostafavi, S. (2024) · Nature Methods · doi:10.1038/s41592-024-02331-5 · Cited by 40
Factors Associated with the Development of Depression and the Influence of Obesity on Depressive Disorders: A Narrative Review
Alberti, A., Coelho, D. R. A., Vieira, W. F., Iser, B. P. M., Lampert, R., Traebert, E., da Silva, B. B., de Oliveira, B. H., Leão, G. M., de Souza, G., et al. (2024) · Biomedicines · doi:10.3390/biomedicines12091994 · Cited by 44
Personalized phosphoproteomics of skeletal muscle insulin resistance and exercise links MINDY1 to insulin action
Needham, E. J., Hingst, J. R., Onslev, J., Díaz‐Vegas, A., Leandersson, M. R., Huckstep, H., Kristensen, J. M., Kido, K., Richter, E. A., Højlund, K., et al. (2024) · Cell Metabolism · doi:10.1016/j.cmet.2024.10.020 · Cited by 36
Genetic risk factor clustering within and across neurodegenerative diseases
Koretsky, M. J., Alvarado, C. X., Makarious, M. B., Vitale, D., Levine, K. S., Bandrés‐Ciga, S., Dadu, A., Scholz, S. W., Sargent, L. J., Faghri, F., et al. (2023) · Brain · doi:10.1093/brain/awad161 · Cited by 31
Idiopathic pulmonary fibrosis and the role of genetics in the era of precision medicine
Alonso-González, A., Tosco-Herrera, E., Molina‐Molina, M., & Flores, C. (2023) · Frontiers in Medicine · doi:10.3389/fmed.2023.1152211 · Cited by 36
Polygenic risk scores for disease risk prediction in Africa: current challenges and future directions
Fatumo, S., Sathan, D., Samtal, C., Isewon, I., Tamuhla, T., Soremekun, C., Jafali, J., Panji, S., Tiffin, N., & Fakim, Y. J. (2023) · Genome Medicine · doi:10.1186/s13073-023-01245-9 · Cited by 36
Causal effects of fatty acids on depression: Mendelian randomization study
Zeng, L., Lv, H., Wang, X., Xue, R., Zhou, C., Liu, X., & Yu, H. (2022) · Frontiers in Nutrition · doi:10.3389/fnut.2022.1010476 · Cited by 33
A perspective on genetic and polygenic risk scores—advances and limitations and overview of associated tools
Schwarzerová, J., Hurta, M., Bartoň, V., Lexa, M., Walther, D., Provazník, I., & Weckwerth, W. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae240 · Cited by 32
Utilizing non-invasive prenatal test sequencing data for human genetic investigation
Liu, S. M., Liu, Y., Gu, Y., Lin, X., Zhu, H., Liu, H., Xu, Z., Cheng, S., Lan, X., Li, L., et al. (2024) · Cell Genomics · doi:10.1016/j.xgen.2024.100669 · Cited by 28
Functional genomics of human skeletal development and the patterning of height heritability
Richard, D. J., Muthuirulan, P., Young, M., Yengo, L., Vedantam, S., Marouli, E., Bartell, E., Hirschhorn, J. N., & Capellini, T. D. (2024) · Yearbook of pediatric endocrinology · doi:10.1016/j.cell.2024.10.040 · Cited by 27
New Research Perspectives on the Interplay Between Genes and Environment on Executive Function Development
Miguel, P. M., Meaney, M. J., & Silveira, P. P. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.01.008 · Cited by 53
The phenotype-genotype reference map: Improving biobank data science through replication
Bastarache, L. A., Delozier, S., Pandit, A., He, J., Lewis, A. J., Annis, A. C., LeFaive, J., Denny, J. C., Carroll, R. J., Altman, R. B., et al. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.07.012 · Cited by 30
SLiM 5: Eco-evolutionary Simulations Across Multiple Chromosomes and Full Genomes
Haller, B. C., Ralph, P. L., & Messer, P. W. (2025) · Molecular Biology and Evolution · doi:10.1093/molbev/msaf313 · Cited by 60
Genetic Susceptibility to Hepatocellular Carcinoma in Patients with Chronic Hepatitis Virus Infection
Yang, T.-H., Chan, C., Yang, P.-J., Huang, Y., & Lee, M. (2023) · Viruses · doi:10.3390/v15020559 · Cited by 31
Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study
Akbarzadeh, M., Alipour, N., Moheimani, H., Zahedi, A. S., Hosseini‐Esfahani, F., Lanjanian, H., Azizi, F., & DANESHPOUR, M. S. (2022) · Journal of Translational Medicine · doi:10.1186/s12967-022-03349-z · Cited by 30
Impact of genetic predisposition to late-onset neurodegenerative diseases on early life outcomes and brain structure
Ogonowski, N. S., García‐Marín, L. M., Fernando, A. S., Flores‐Ocampo, V., & Rentería, M. E. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02898-9 · Cited by 31
Opportunities and challenges for the use of common controls in sequencing studies
Wojcik, G. L., Murphy, J. I., Edelson, J. L., Gignoux, C. R., Ioannidis, A. G., Manning, A. K., Rivas, M. A., Buyske, S. G., & Hendricks, A. E. (2022) · Nature Reviews Genetics · doi:10.1038/s41576-022-00487-4 · Cited by 32
PheWAS-based clustering of Mendelian Randomisation instruments reveals distinct mechanism-specific causal effects between obesity and educational attainment
Darrous, L., Hemani, G., Smith, G. D., & Kutalik, Z. (2024) · Nature Communications · doi:10.1038/s41467-024-45655-8 · Cited by 29
Omics Approaches in Invasion Biology: Understanding Mechanisms and Impacts on Ecological Health
Qi, S., Wang, J., Zhang, Y., Naz, M., Afzal, M. R., Du, D., & Dai, Z. (2023) · Plants · doi:10.3390/plants12091860 · Cited by 29
Theoretical and empirical advances in understanding musical rhythm, beat and metre
Snyder, J. S., Gordon, R. L., & Hannon, E. E. (2024) · Nature Reviews Psychology · doi:10.1038/s44159-024-00315-y · Cited by 43
Use of whole genome sequencing for surveillance and control of foodborne diseases: status quo and quo vadis
Schadron, T., van den Beld, M. J. C., Mughini‐Gras, L., & Franz, E. (2024) · Frontiers in Microbiology · doi:10.3389/fmicb.2024.1460335 · Cited by 29
Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects
Wang, X., Li, F., Zhang, Y., Imoto, S., Shen, H., Li, S., Guo, Y., Yang, J., & Song, J. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae446 · Cited by 35
Genetics of SLE: does this explain susceptibility and severity across racial groups?
Demkova, K., Morris, D. L., & Vyse, T. J. (2022) · Rheumatology · doi:10.1093/rheumatology/keac695 · Cited by 32
From sub-Saharan Africa to China: Evolutionary history and adaptation of Drosophila melanogaster revealed by population genomics
Chen, J., Liu, C., Li, W., Zhang, W., Wang, Y., Clark, A. G., & Lü, J. (2024) · Science Advances · doi:10.1126/sciadv.adh3425 · Cited by 26
Adopting Mechanistic Molecular Biology Approaches in Exposome Research for Causal Understanding
Foreman, A., Warth, B., Hessel, E. V. S., Price, E. J., Schymanski, E., Cantelli, G., Parkinson, H., Hecht, H., Klánová, J., Vlaanderen, J. J., et al. (2024) · Environmental Science & Technology · doi:10.1021/acs.est.3c07961 · Cited by 28
Fungal Disease Tolerance with a Focus on Wheat: A Review
Maulenbay, A., & Rsaliyev, A. (2024) · Journal of Fungi · doi:10.3390/jof10070482 · Cited by 33
Role of Genetic Testing in Kidney Stone Disease: A Narrative Review
Geraghty, R. M., Lovegrove, C. E., Howles, S., & Sayer, J. A. (2024) · Current Urology Reports · doi:10.1007/s11934-024-01225-5 · Cited by 28
Valid inference for machine learning-assisted genome-wide association studies
Miao, J., Wu, Y., Sun, Z., Miao, X., Lu, T., Zhao, J., & Lu, Q. (2024) · Nature Genetics · doi:10.1038/s41588-024-01934-0 · Cited by 28
CNest: A novel copy number association discovery method uncovers 862 new associations from 200,629 whole-exome sequence datasets in the UK Biobank
Fitzgerald, T. W., & Birney, E. (2022) · Cell Genomics · doi:10.1016/j.xgen.2022.100167 · Cited by 26
Attention Deficit Hyperactivity Disorder (ADHD) and the gut microbiome: An ecological perspective
Cickovski, T., Mathee, K., Aguirre, G., Tatke, G., Hermida, A., Narasimhan, G., & Stollstorff, M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0273890 · Cited by 35
GWABLUP: genome-wide association assisted best linear unbiased prediction of genetic values
Meuwissen, T. H. E., Eikje, L. S., & Gjuvsland, A. B. (2024) · Genetics Selection Evolution · doi:10.1186/s12711-024-00881-y · Cited by 27
Host genetic basis of COVID-19: from methodologies to genes
Zguro, K., Fallerini, C., Fava, F., Furini, S., & Renieri, A. (2022) · European Journal of Human Genetics · doi:10.1038/s41431-022-01121-x · Cited by 27
Kidney omics in hypertension: from statistical associations to biological mechanisms and clinical applications
Tomaszewski, M., Morris, A. P., Howson, J. M. M., Franceschini, N., Eales, J., Xu, X., Dikalov, S., Guzik, T. J., Humphreys, B. D., Harrap, S. B., et al. (2022) · Kidney International · doi:10.1016/j.kint.2022.04.045 · Cited by 26
3D genome organization links non-coding disease-associated variants to genes
Orozco, G., Schoenfelder, S., Walker, N. J., Eyre, S., & Fraser, P. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.995388 · Cited by 30
Genome-wide association study of leaf-related traits in tea plant in Guizhou based on genotyping-by-sequencing
Chen, Y., Niu, S., Deng, X., Song, Q., He, L., Bai, D., & He, Y. (2023) · BMC Plant Biology · doi:10.1186/s12870-023-04192-0 · Cited by 29
Epigenetic Regulation of Nitrogen Signaling and Adaptation in Plants
Zhang, H., Zhang, X., & Xiao, J. Y. (2023) · Plants · doi:10.3390/plants12142725 · Cited by 27
Machine learning in onco-pharmacogenomics: a path to precision medicine with many challenges
Mondello, A., Bo, M. D., Toffoli, G., & Polano, M. (2024) · Frontiers in Pharmacology · doi:10.3389/fphar.2023.1260276 · Cited by 28
Application of Deep Learning Technology in Monitoring Plant Attribute Changes
Han, S., & Wang, H. (2025) · Sustainability · doi:10.3390/su17177602 · Cited by 32
Decoding biology with massively parallel reporter assays and machine learningPreprint
Fleur, A. L., Shi, Y., & Seelig, G. (2024) · Genes & Development · doi:10.1101/gad.351800.124 · Cited by 29
Modeling the longitudinal changes of ancestry diversity in the Million Veteran Program
Wendt, F. R., Pathak, G. A., Vahey, J. M., Qin, X., Koller, D., Cabrera‐Mendoza, B., Haeny, A. M., Harrington, K. M., Rajeevan, N., Duong, L. M., et al. (2023) · Human Genomics · doi:10.1186/s40246-023-00487-3 · Cited by 24
Power and reproducibility in the external validation of brain-phenotype predictions
Rosenblatt, M., Tejavibulya, L., Sun, H., Camp, C., Khaitova, M., Adkinson, B. D., Jiang, R., Westwater, M. L., Noble, S., & Scheinost, D. (2024) · Nature Human Behaviour · doi:10.1038/s41562-024-01931-7 · Cited by 26
Genome-wide discovery and integrative genomic characterization of insulin resistance loci using serum triglycerides to HDL-cholesterol ratio as a proxy
DeForest, N., Wang, Y., Zhu, Z., Dron, J. S., Koesterer, R., Natarajan, P., Flannick, J. A., Amariuta, T., Peloso, G. M., & Majithia, A. R. (2024) · Nature Communications · doi:10.1038/s41467-024-52105-y · Cited by 26
Progress in multi-omics studies of osteoarthritis
Wei, Y., He, Q., Zhang, X., Wang, J., Yan, H., Xiao, N., Zeng, S., Chen, B., Yang, Q., Lu, H., et al. (2025) · Biomarker Research · doi:10.1186/s40364-025-00732-y · Cited by 27
Genomic and neuroimaging approaches to bipolar disorder
Kohshour, M. O., Papiol, S., Ching, C. R. K., & Schulze, T. G. (2022) · BJPsych Open · doi:10.1192/bjo.2021.1082 · Cited by 28
Multi-model genome-wide association studies of leaf anatomical traits and vein architecture in rice
Narawatthana, S., Phansenee, Y., Thammasamisorn, B.-O., & Vejchasarn, P. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1107718 · Cited by 26
Enhancing nutritional quality in vegetables through breeding and cultivar choice in protected cultivation
Weiß, J., & Gruda, N. S. (2024) · Scientia Horticulturae · doi:10.1016/j.scienta.2024.113914 · Cited by 24
Food Allergy Genetics and Epigenetics: A Review of Genome‐Wide Association Studies
Arnau‐Soler, A., Tremblay, B. L., Sun, Y., Madore, A., Simard, M., Kersten, E. T. G., Ghauri, A., Marenholz, I., Eiwegger, T., Simons, E., et al. (2024) · Allergy · doi:10.1111/all.16429 · Cited by 27
Factors associated with: problems of using exploratory multivariable regression to identify causal risk factors
Lewer, D., Brothers, T. D., O’Nions, E., & Pickavance, J. (2025) · BMJ Medicine · doi:10.1136/bmjmed-2025-001375 · Cited by 27
The current review of adolescent obesity: the role of genetic factors
Yılmaz, B., & Karadağ, M. G. (2020) · Journal of Pediatric Endocrinology and Metabolism · doi:10.1515/jpem-2020-0480 · Cited by 27
Predicting Physical Appearance from DNA Data—Towards Genomic Solutions
Pośpiech, E., Teisseyre, P., Mielniczuk, J., & Branicki, W. (2022) · Genes · doi:10.3390/genes13010121 · Cited by 25
Computational Genomics in the Era of Precision Medicine: Applications to Variant Analysis and Gene Therapy
Wang, Y.-C., Wu, Y., Choi, J. Y., Allington, G., Zhao, S., Khanfar, M., Yang, K., Fu, P.-Y., Wrubel, M., Yu, X., et al. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12020175 · Cited by 23
Animal-SNPAtlas: a comprehensive SNP database for multiple animals
Gao, Y., Jiang, G., Yang, W., Jin, W., Gong, J., Xu, X., & Niu, X. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac954 · Cited by 24
Genetic Modifiers of Sickle Cell Disease
Pincez, T., Ashley‐Koch, A. E., Lettre, G., & Telen, M. J. (2022) · Hematology/Oncology Clinics of North America · doi:10.1016/j.hoc.2022.06.006 · Cited by 28
Current progress in understanding schizophrenia using genomics and pluripotent stem cells: A meta-analytical overview
Choudhary, A., Peles, D., Nayak, R., Mizrahi, L., & Stern, S. (2022) · Schizophrenia Research · doi:10.1016/j.schres.2022.11.001 · Cited by 21
Genetic evidence for causal effects of immune dysfunction in psychiatric disorders: where are we?
Iakunchykova, O. P., Leonardsen, E. H., & Wang, Y. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02778-2 · Cited by 24
Periodontal diseases in Africa
Olujitan, M., Ayanbadejo, P. O., Umeizudike, K. A., Oyapero, A., Okunseri, C. E., & Butali, A. (2024) · Periodontology 2000 · doi:10.1111/prd.12617 · Cited by 26
Advances in Managing Chytridiomycosis for Australian Frogs: Gradarius Firmus Victoria.
Berger, L., Skerratt, L., Kosch, T. A., Brannelly, L. A., Webb, R., & Waddle, A. W. (2024) · Annual Review of Animal Biosciences · doi:10.1146/annurev-animal-021122-100823 · Cited by 15
Polygenic risk scores in the clinic: a systematic review of stakeholders’ perspectives, attitudes, and experiences
Andreoli, L., Peeters, H., Steen, K. V., & Dierickx, K. (2024) · European Journal of Human Genetics · doi:10.1038/s41431-024-01747-z · Cited by 23
Recent advances in omics and the integration of multi-omics in osteoarthritis research
Liu, Y., Molchanov, V., Brass, D., & Yang, T. (2025) · Arthritis Research & Therapy · doi:10.1186/s13075-025-03563-2 · Cited by 23
A genome-wide analysis of the shared genetic risk architecture of complex neurological and psychiatric disorders
Smeland, O. B., Kutrolli, G., Bahrami, S., Fominykh, V., Parker, N., Fuhrer, J., Hindley, G., Rødevand, L. N., Jaholkowski, P. P., Tesfaye, M., et al. (2025) · Nature Neuroscience · doi:10.1038/s41593-025-02090-2 · Cited by 57
Genomic prediction of morphometric and colorimetric traits in Solanaceous fruits
Tong, H., Nankar, A. N., Liu, J., Todorova, V., Ganeva, D., Grozeva, S., Trıngovska, İ., Pasev, G., Radeva, V., Gechev, T., et al. (2022) · Horticulture Research · doi:10.1093/hr/uhac072 · Cited by 24
ULK4 in Neurodevelopmental and Neuropsychiatric Disorders
Luo, S., Zheng, N., & Lang, B. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.873706 · Cited by 21
Examining the biological mechanisms of human mental disorders resulting from gene-environment interdependence using novel functional genomic approaches
Silveira, P. P., & Meaney, M. J. (2023) · Neurobiology of Disease · doi:10.1016/j.nbd.2023.106008 · Cited by 26
Atopic polygenic risk score is associated with paradoxical eczema developing in patients with psoriasis treated with biologics
Al-Janabi, A., Eyre, S., Foulkes, A. C., Khan, A. R., Dand, N., Burova, E., DeSilva, B., Makrygeorgou, A., Davies, E., Smith, C. H., et al. (2023) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2023.01.021 · Cited by 23
A review article of inflammatory bowel disease treatment and pharmacogenomics
Eltantawy, N., El-Zayyadi, I. A. E.-H., Elberry, A. A., Salah, L. M., Abdelrahim, M. E. A., & Kassem, A. B. (2023) · Beni-Suef University Journal of Basic and Applied Sciences · doi:10.1186/s43088-023-00361-0 · Cited by 26
Improving grain yield and salt tolerance by optimizing plant height with beneficial haplotypes in rice (Oryza sativa)
Quan, R., Wang, J., Qin, H., Chen, L., Xiao, D., Zhao, Z., Zhang, Z., Zhu, X., Li, Z., & Huang, R. (2024) · Journal of Advanced Research · doi:10.1016/j.jare.2024.12.007 · Cited by 22
Genome-wide functional annotation of variants: a systematic review of state-of-the-art tools, techniques and resources
Pilalis, E., Zisis, D., Andrinopoulou, C., Karamanidou, T., Antonara, M., Stavropoulos, T. G., & Chatziioannou, A. A. (2025) · Frontiers in Pharmacology · doi:10.3389/fphar.2025.1474026 · Cited by 26
The Genomic Architecture of Competitive Response of Arabidopsis thaliana Is Highly Flexible Among Plurispecific Neighborhoods
Libourel, C., Baron, É., Lenglet, J., Amsellem, L., Roby, D., & Roux, F. (2021) · Frontiers in Plant Science · doi:10.3389/fpls.2021.741122 · Cited by 23
Exploring the genetics of rhythmic perception and musical engagement in the Vanderbilt Online Musicality Study
Gustavson, D. E., Coleman, P. L., Wang, Y., Nitin, R., Petty, L. E., Bush, C. T., Mosing, M. A., Wesseldijk, L. W., Ullén, F., Below, J. E., et al. (2023) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14964 · Cited by 22
Assessing the Role of Cortisol in Anxiety, Major Depression, and Neuroticism: A Mendelian Randomization Study Using SERPINA6/SERPINA1 Variants
Chan, I. I., & Wu, A. M. S. (2024) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2024.100294 · Cited by 30
Repeated global adaptation across plant species
Nocchi, G., Whiting, J. R., & Yeaman, S. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2406832121 · Cited by 27
Genome‐wide association studies: utility and limitations for research in physiology
Ciochetti, N. P., Lugli‐Moraes, B., da Silva, B. S., & Rovaris, D. L. (2023) · The Journal of Physiology · doi:10.1113/jp284241 · Cited by 25
Finemap-MiXeR: A variational Bayesian approach for genetic finemapping
Akdeniz, B. C., Frei, O., Shadrin, A. A., Vetrov, D., Kropotov, D., Hovig, E. J., Andreassen, O. A., & Dale, A. M. (2024) · PLoS Genetics · doi:10.1371/journal.pgen.1011372 · Cited by 21
The goldmine of GWAS summary statistics: a systematic review of methods and tools
Kontou, P. I., & Bagos, P. G. (2024) · BioData Mining · doi:10.1186/s13040-024-00385-x · Cited by 23
Integrative Genomics and Precision Breeding for Stress-Resilient Cotton: Recent Advances and Prospects
Ghorbanzadeh, Z., Panahi, B., Purhang, L., Panahi, Z. H., Zeinalabedini, M., Mardi, M., Hamid, R., & Ghaffari, M. R. (2025) · Agronomy · doi:10.3390/agronomy15102393 · Cited by 21
Genome-Wide Association Study Reveals Marker Trait Associations (MTA) for Waterlogging-Triggered Adventitious Roots and Aerenchyma Formation in Barley
Manik, S. M. N., Quamruzzaman, M., Zhao, C., Johnson, P., Hunt, I., Shabala, S. N., & Zhou, M. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms23063341 · Cited by 23
A survey of genome-wide association studies, polygenic scores and UK Biobank highlights resources for autoimmune disease genetics
Saurabh, R., Fouodo, C. J. K., König, I. R., Busch, H. S., & Wohlers, I. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.972107 · Cited by 22
Cryptic mutations of PLC family members in brain disorders: recent discoveries and a deep-learning-based approach
Lim, K.-H., Yang, S., Kim, S.-H., Ko, E., Kang, M., & Joo, J.-Y. (2023) · Brain · doi:10.1093/brain/awac451 · Cited by 21
Behaviour genetics and sleep: A narrative review of the last decade of quantitative and molecular genetic research in humans
Madrid‐Valero, J. J., & Gregory, A. M. (2023) · Sleep Medicine Reviews · doi:10.1016/j.smrv.2023.101769 · Cited by 27
Coexistence of Multiple Functional Variants and Genes Underlies Genetic Risk Locus 11p11.2 of Alzheimer’s Disease
Xu, M., Liu, Q., Bi, R., Li, Y., Li, H., Kang, W.-B., Yan, Z., Zheng, Q., Sun, C., Ye, M., et al. (2023) · Biological Psychiatry · doi:10.1016/j.biopsych.2023.05.020 · Cited by 21
How can clinical safety and efficacy concerns in stem cell therapy for spinal cord injury be overcome?
Hejrati, N., Wong, R., Khazaei, M., & Fehlings, M. G. (2023) · Expert Opinion on Biological Therapy · doi:10.1080/14712598.2023.2245321 · Cited by 22
Local adaptation and future climate vulnerability in a wild rodent
Marková, S., Lanier, H. C., Escalante, M. A., da Cruz, M. D. O. R., Horníková, M., Konczal, M., Weider, L. J., Searle, J. B., & Kotlı́k, P. (2023) · Nature Communications · doi:10.1038/s41467-023-43383-z · Cited by 21
Genotype imputation methods for whole and complex genomic regions utilizing deep learning technology
Naito, T., & Okada, Y. (2024) · Journal of Human Genetics · doi:10.1038/s10038-023-01213-6 · Cited by 28
Self-citation
A review of the role of epigenetic studies for intramuscular fat deposition in beef cattle
Abebe, B. K., Wang, J., Guo, J., Wang, H., Li, A., & Zan, L. (2024) · Gene · doi:10.1016/j.gene.2024.148295 · Cited by 21
Pearl millet a promising fodder crop for changing climate: a review
Daduwal, H. S., Bhardwaj, R., & Srivastava, R. K. (2024) · Theoretical and Applied Genetics · doi:10.1007/s00122-024-04671-4 · Cited by 21
Age-dependent genetic architectures of chicken body weight explored by multidimensional GWAS and molQTL analyses
Zhong, C., Li, X., Guan, D., Zhang, B., Wang, X., Qu, L., Zhou, H., Fang, L., Sun, C., & Yang, N. (2024) · Journal of genetics and genomics/Journal of Genetics and Genomics · doi:10.1016/j.jgg.2024.09.003 · Cited by 22
Regulation of Urate Homeostasis by Membrane Transporters
Takada, T., Miyata, H., Toyoda, Y., Nakayama, A., Ichida, K., & Matsuo, H. (2024) · Gout Urate and Crystal Deposition Disease · doi:10.3390/gucdd2020016 · Cited by 21
Omics big data for crop improvement: Opportunities and challenges
Vasupalli, N., Bhat, J. A., Jain, P., Sri, T., Islam, M. A., Shivaraj, S. M., Singh, S. K., Deshmukh, R., Sonah, H., & Lin, X. (2024) · The Crop Journal · doi:10.1016/j.cj.2024.10.007 · Cited by 21
Forensic DNA phenotyping: Inferring phenotypic traits from crime scene DNA
Dabas, P., Jain, S. G., Khajuria, H., & Nayak, B. P. (2022) · Journal of Forensic and Legal Medicine · doi:10.1016/j.jflm.2022.102351 · Cited by 25
Vetinformatics from functional genomics to drug discovery: Insights into decoding complex molecular mechanisms of livestock systems in veterinary science
Pathak, R. K., & Kim, J. (2022) · Frontiers in Veterinary Science · doi:10.3389/fvets.2022.1008728 · Cited by 22
Novel Genetic Variants Associated with Chronic Kidney Disease Progression
Han, M., Moon, S., Lee, S., Kim, K., An, W. J., Ryu, H., Kang, E., Ahn, J., Sung, H. Y., Park, Y. S., et al. (2023) · Journal of the American Society of Nephrology · doi:10.1681/asn.0000000000000066 · Cited by 22
From genetic variation to precision medicine
Sergouniotis, P. I., Fitzgerald, T. W., & Birney, E. (2023) · Cambridge Prisms Precision Medicine · doi:10.1017/pcm.2022.11 · Cited by 21
A Compendium for Novel Marker-Based Breeding Strategies in Eggplant
Gaccione, L., Martina, M., Barchi, L., & Portis, E. (2023) · Plants · doi:10.3390/plants12051016 · Cited by 20

Authors: Uffelmann, E., Huang, Q. Q., Munung, N. S., de Vries, J., Okada, Y., Martin, A. R., Martin, H. C., Lappalainen, T., & Posthuma, D.
Affiliations: Amsterdam Neuroscience (NL)
Wellcome Sanger Institute (GB)
University of Cape Town (ZA)
Immunology Frontier Research Center, The University of Osaka (JP)
Broad Institute (US)
Science for Life Laboratory (SE)
ORCID: Uffelmann, Emil
Huang, Qin Qin
Munung, Nchangwi Syntia
Okada, Yukinori
Martin, Alicia R.
Martin, Hilary C.
Lappalainen, Tuuli
Posthuma, Danielle
Venue: Nature Reviews Methods Primers
Publisher: Springer Science and Business Media LLC
Published: 2021-08-26
Volume: 1
Issue: 1
ISSN: 2662-8449 (ISSN-L)
DOI: 10.1038/s43586-021-00056-9
OpenAlex: W3195285310
Cited by: 1,849
References: 288
Citations by year: 2026: 292 · 2025: 512 · 2024: 546 · 2023: 304 · 2022: 163 · 2021: 22
FWCI: 38.81
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: public-domain
www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
Locations: Nature Reviews Methods Primers (publishedVersion, public-domain)
VU Research Portal (submittedVersion, other-oa)
Data Archiving and Networked Services (DANS) (submittedVersion, other-oa)
Funders: Japan Agency for Medical Research and Development (JP20ek0109413, JP20km0405217, JP20gm4010006, JP20ek0410075, JP20gm4010006, JP20ek0410075, JP20km0405211)
Wellcome Trust (219600/Z/19/Z, 098051)
Nederlandse Organisatie voor Wetenschappelijk Onderzoek (VICI 435-14-005, 024.004.012, 435-14-005)
National Institutes of Health
Japan Society for the Promotion of Science (19H01021 and 20K21834, 834057, 20K21834, 19H01021)
National Heart, Lung, and Blood Institute (R01HL142028, U24HL135600)
National Institute of Mental Health (R01MH106842)
National Human Genome Research Institute (UM1HG008901, U54HG009790)
National Institute of General Medical Sciences (R01GM122924)
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: genome-wide association study, genetic variants, disease risk, heritability, genetic correlation, clinical risk prediction, drug development, causal inference, data sharing, research ethics

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

Genome-wide association studies

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