Reaching the end-game for GWAS: Machine learning approaches for the prioritization of complex disease loci
Open accessAbstract
Genome-wide association studies (GWAS) have revealed thousands of genetic loci that underpin the complex biology of many human traits. However, the strength of GWAS - the ability to detect genetic association by linkage disequilibrium (LD) - is also its limitation. Whilst the ever-increasing study size and improved design have augmented the power of GWAS to detect effects, differentiation of causal variants or genes from other highly correlated genes associated by LD remains the real challenge. This has severely hindered the biological insights and clinical translation of GWAS findings. Although thousands of disease susceptibility loci have been reported, causal genes at these loci remain elusive. Machine learning (ML) techniques offer an opportunity to dissect the heterogeneity of variant and gene signals in the post-GWAS analysis phase. ML models for GWAS prioritization vary greatly in their complexity, ranging from relatively simple logistic regression approaches to more complex ensemble models such as random forests and gradient boosting, as well as deep learning models, i.e., neural networks. Paired with functional validation, these methods show important promise for clinical translation, providing a strong evidence-based approach to direct post-GWAS research. However, as ML approaches continue to evolve to meet the challenge of causal gene identification, a critical assessment of the underlying methodologies and their applicability to the GWAS prioritization problem is needed. This review investigates the landscape of ML applications in three parts: selected models, input features, and output model performance, with a focus on prioritizations of complex disease associated loci. Overall, we explore the contributions ML has made towards reaching the GWAS end-game with consequent wide-ranging translational impact.
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?
Combining ML with progressive feature elimination can identify the main predictors, enhance classification, and provide a top assortment of features to classify outcomes.
“Combining ML with progressive feature elimination can identify the main predictors, enhance classification, and provide a top assortment of features to classify outcomes (Montavon et al., 2018; Nicholls et al., 2020).”
From Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference (2023) · cites this paper as Nicholls et al., 2020
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
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Regularization and Variable Selection Via the Elastic Net
Zou, H., & Hastie, T. (2005) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2005.00503.x · Cited by 20,957 · see citing works
A tutorial on support vector regression
Smola, A. J., & Schölkopf, B. (2004) · Statistics and Computing · doi:10.1023/b:stco.0000035301.49549.88 · Cited by 13,482 · see citing works
Ensemble methods in machine learning
Dietterich, T. G. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-45014-9_1 · Cited by 8,408 · see citing works
Saito, T., & Rehmsmeier, M. (2015) · PLOS ONE · doi:10.1371/journal.pone.0118432 · Cited by 5,309 · see citing works
Gradient boosting machines, a tutorial
Natekin, A., & Knoll, A. (2013) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2013.00021 · Cited by 3,887 · see citing works
Deep learning applications and challenges in big data analytics
Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., Seliya, N., Wald, R., & Muharemagic, E. (2015) · Journal of Big Data · doi:10.1186/s40537-014-0007-7 · Cited by 2,594 · see citing works
Leucht, S., Cipriani, A., Spineli, L., Mavridis, D., Örey, D., Richter, F., Samara, M., Barbui, C., Engel, R. R., Geddes, J. R., et al. (2013) · The Lancet · doi:10.1016/s0140-6736(13)60733-3 · Cited by 2,561 · see citing works
Nalls, M. A., Blauwendraat, C., Vallerga, C. L., Heilbron, K., Bandres-Ciga, S., Chang, D., Tan, M., Kia, D. A., Noyce, A. J., Xue, A., et al. (2019) · The Lancet Neurology · doi:10.1016/s1474-4422(19)30320-5 · Cited by 2,454 · see citing works
Schmechel, D. E., Saunders, A. M., Strittmatter, W. J., Crain, B. J., Hulette, C. M., Joo, S. H., Pericak-Vance, M. A., Goldgaber, D., & Roses, A. D. (1993) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.90.20.9649 · Cited by 1,525 · see citing works
Program, T. M. V., Evangelou, E., Warren, H. R., Mosen-Ansorena, D., Mifsud, B., Pazoki, R., Gao, H., Ntritsos, G., Dimou, N., Cabrera, C. P., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0205-x · Cited by 1,381 · see citing works
Self-citationLeveraging Polygenic Functional Enrichment to Improve GWAS Power
Kichaev, G., Bhatia, G., Loh, P.-R., Gazal, S., Burch, K., Freund, M. K., Schoech, A., Pasaniuc, B., & Price, A. L. (2019) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.11.008 · Cited by 926 · see citing works
Facing Imbalanced Data--Recommendations for the Use of Performance Metrics
Jeni, L. A., Cohn, J. F., & de la Torre, F. N. (2013) · International Conference on Affective Computing and Intelligent Interaction and workshops · doi:10.1109/acii.2013.47 · Cited by 828 · see citing works
The revival of the Gini importance?
Nembrini, S., König, I. R., & Wright, M. N. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty373 · Cited by 764 · see citing works
Haycock, P., Burgess, S., Wade, K. H., Bowden, J., Relton, C. L., & Smith, G. D. (2016) · American Journal of Clinical Nutrition · doi:10.3945/ajcn.115.118216 · Cited by 602 · see citing works
Deep learning sequence-based ab initio prediction of variant effects on expression and disease risk
Zhou, J., Theesfeld, C. L., Yao, K., Chen, K. M., Wong, A. K., & Troyanskaya, O. G. (2018) · Nature Genetics · doi:10.1038/s41588-018-0160-6 · Cited by 559 · see citing works
Trans-ethnic association study of blood pressure determinants in over 750,000 individuals
Understanding Society Scientific Group, Giri, A., Program, M. V., Hellwege, J. N., Keaton, J. M., Park, J., Qiu, C., Warren, H. R., Torstenson, E. S., Kovesdy, C. P., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0303-9 · Cited by 444 · see citing works
Self-citationHomocysteine and stroke: evidence on a causal link from mendelian randomisation
Casas, J. P., Bautista, L. E., Smeeth, L. L., Sharma, P. K., & Hingorani, A. D. (2005) · The Lancet · doi:10.1016/s0140-6736(05)17742-3 · Cited by 442 · see citing works
Ogutu, J. O., Schulz-Streeck, T., & Piepho, H.-P. (2012) · BMC Proceedings · doi:10.1186/1753-6561-6-s2-s10 · Cited by 440 · see citing works
Ayalew, M., Le-Niculescu, H., Levey, D. F., Jain, N., Changala, B., Patel, S. D., Winiger, E. A., Breier, A. F., Shekhar, A., AMDUR, R. L., et al. (2012) · Molecular Psychiatry · doi:10.1038/mp.2012.37 · Cited by 389 · see citing works
Use of genome-wide association studies for drug repositioning
Sanséau, P., Agarwal, P., Barnes, M. R., Pastinen, T., Richards, J. B., Cardon, L. R., & Mooser, V. (2012) · Nature Biotechnology · doi:10.1038/nbt.2151 · Cited by 387 · see citing works
Self-citationBanegas, J. R., Lopez-Garcia, E., Dallongeville, J., Guallar, E., Halcox, J. P., Borghi, C., Masso-Gonzalez, E. L., Jimenez, F. J., Perk, J., Steg, P. G., et al. (2011) · European Heart Journal · doi:10.1093/eurheartj/ehr080 · Cited by 354 · see citing works
New therapies for inflammatory bowel disease: from the bench to the bedside
Danese, S. (2011) · Gut · doi:10.1136/gutjnl-2011-300904 · Cited by 303 · see citing works
Mutations in filamin C cause a new form of familial hypertrophic cardiomyopathy
Valdés‐Mas, R., Gutiérrez‐Fernández, A., Gómez, J., Coto, E., Astudillo, A., Puente, D. A., Reguero, J. R., Álvarez, V., Morı́s, C., León, D., et al. (2014) · Nature Communications · doi:10.1038/ncomms6326 · Cited by 192 · see citing works
Aung, N., Vargas, J., Yang, C.-J., Cabrera, C., Warren, H., Fung, K., Tzanis, E., Barnes, M., Rotter, J., Taylor, K., et al. (2019) · Circulation · doi:10.1161/circulationaha.119.041161 · Cited by 178 · see citing works
Self-citationMordelet, F., & Vert, J. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-389 · Cited by 175 · see citing works
Demir-Kavuk, O., Kamada, M., Akutsu, T., & Knapp, E. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-412 · Cited by 134 · see citing works
Interethnic analyses of blood pressure loci in populations of East Asian and European descent
Takeuchi, F., Akiyama, M., Matoba, N., Katsuya, T., Nakatochi, M., Tabara, Y., Narita, A., Saw, W., Moon, S., Spracklen, C. N., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-07345-0 · Cited by 125 · see citing works
Schubach, M., Re, M., Robinson, P. N., & Valentini, G. (2017) · Scientific Reports · doi:10.1038/s41598-017-03011-5 · Cited by 97 · see citing works
Prediction of enhancer-promoter interactions via natural language processing
Zeng, W., Wu, M., & Jiang, R. (2018) · BMC Genomics · doi:10.1186/s12864-018-4459-6 · Cited by 96 · cited 1× · see citing works
“However, whilst deep learning enables the consideration of millions of parameters, its application to date has mostly flourished in image classification and natural language processing (Zeng et al., 2018; Aung et al., 2019; Hampe et al., 2019), requiring an investment in its development and benchmarking with tradition…”
MethodGWAS-based machine learning approach to predict duloxetine response in major depressive disorder
Maciukiewicz, M., Marshe, V. S., Hauschild, A.-C., Foster, J. A., Rotzinger, S., Kennedy, J. L., Kennedy, S. H., Müller, D. J., & Geraci, J. (2018) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2017.12.009 · Cited by 91 · see citing works
Hemani, G., Bowden, J., Haycock, P., Zheng, J., Davis, O., Flach, P., Gaunt, T., & Smith, G. D. (2017) · doi:10.1101/173682 · Cited by 85 · see citing works
A machine-learning heuristic to improve gene score prediction of polygenic traits
Paré, G., Mao, S., & Deng, W. Q. (2017) · Scientific Reports · doi:10.1038/s41598-017-13056-1 · Cited by 85 · see citing works
Lu, Q., Powles, R. L., Wang, Q., He, B. J., & Zhao, H. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1005947 · Cited by 85 · see citing works
Mieth, B., Kloft, M., Rodríguez, J. A., Sonnenburg, S., Vobruba, R., Morcillo-Suárez, C., Farré, X., Marigorta, U. M., Fehr, E., Dickhaus, T., et al. (2016) · Scientific Reports · doi:10.1038/srep36671 · Cited by 80 · see citing works
Lin, H., Gallaway, K. A., Li, R., Reiter, J. L., Wang, Y., Mort, M. E., Cooper, D. N., Zhou, Y., Zhang, C., Eadon, M. T., et al. (2019) · Genome biology · doi:10.1186/s13059-019-1847-4 · Cited by 77 · see citing works
Making Sense of the Epigenome Using Data Integration Approaches
Cazaly, E., Saad, J. J., Wang, W., Heckman, C. A., Ollikainen, M., & Tang, J. P. (2019) · Frontiers in Pharmacology · doi:10.3389/fphar.2019.00126 · Cited by 77 · see citing works
Machine Learning for Assessment of Coronary Artery Disease in Cardiac CT: A Survey
Hampe, N., Wolterink, J. M., van Velzen, S. G. M., Leiner, T., & Išgum, I. (2019) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2019.00172 · Cited by 77 · see citing works
Lewinger, J. P., Conti, D. V., Baurley, J. W., Triche, T. J., & Thomas, D. C. (2007) · Genetic Epidemiology · doi:10.1002/gepi.20248 · Cited by 75 · see citing works
Isakov, O., Dotan, I., & Ben‐Shachar, S. (2017) · Inflammatory Bowel Diseases · doi:10.1097/mib.0000000000001222 · Cited by 66 · see citing works
r2VIM: A new variable selection method for random forests in genome-wide association studies
Szymczak, S., Holzinger, E. R., Dasgupta, A., Malley, J. D., Molloy, A. M., Mills, J. L., Brody, L. C., Stambolian, D., & Wilson, J. (2016) · BioData Mining · doi:10.1186/s13040-016-0087-3 · Cited by 63 · see citing works
Colocalization analyses of genomic elements: approaches, recommendations and challenges
Kanduri, C., Bock, C., Gundersen, S., Hovig, E. J., & Sandve, G. K. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty835 · Cited by 61 · see citing works
Roshan, U., Chikkagoudar, S., Wei, Z., Wang, K., & Hakonarson, H. H. (2011) · Nucleic Acids Research · doi:10.1093/nar/gkr064 · Cited by 59 · see citing works
Fung, K., Ramírez, J., Warren, H. R., Aung, N., Lee, A. M., Tzanis, E., Petersen, S. E., & Munroe, P. B. (2019) · Scientific Reports · doi:10.1038/s41598-019-45703-0 · Cited by 56 · see citing works
Self-citationGenoWAP: GWAS signal prioritization through integrated analysis of genomic functional annotation
Lu, Q., Yao, X., Hu, Y., & Zhao, H. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv610 · Cited by 49 · see citing works
Oh, J. H., Kerns, S. L., D, H. O. M., Powell, S. N., Rosenstein, B. S., & Deasy, J. O. (2017) · Scientific Reports · doi:10.1038/srep43381 · Cited by 48 · see citing works
Prioritizing causal disease genes using unbiased genomic features
Deo, R. C., Musso, G. A., Tasan, M., Tang, P. L.-F., Poon, A., Yuan, C., Felix, J. F., Vasan, R. S., Beroukhim, R., Marco, T. D., et al. (2014) · Genome biology · doi:10.1186/s13059-014-0534-8 · Cited by 48 · see citing works
Rippe, C., Zhu, B., Krawczyk, K. K., VanBavel, E., Albinsson, S., Sjölund, J., Bakker, E. N. T. P., & Swärd, K. (2017) · Scientific Reports · doi:10.1038/s41598-017-01392-1 · Cited by 46 · see citing works
Leem, S., Jeong, H.-H., Lee, J., Wee, K., & Sohn, K.-A. (2014) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2014.01.005 · Cited by 45 · see citing works
Random forests on Hadoop for genome-wide association studies of multivariate neuroimaging phenotypes
Wang, Y., Goh, W. W. B., Wong, L., Montana, G., & Initiative, T. A. D. N. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s16-s6 · Cited by 43 · see citing works
Ren, M., Ng, F. L., Warren, H. R., et al. (2017) · Human Molecular Genetics · doi:10.1093/hmg/ddx375 · Cited by 36 · see citing works
Vitsios, D., & Petrovski, S. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.03.012 · Cited by 34 · see citing works
Ciesielski, T. H., Pendergrass, S. A., White, M. J., Kodaman, N. M., Sobota, R. S., Huang, M., Bartlett, J., Li, J., Pan, Q., Gui, J., et al. (2014) · BioData Mining · doi:10.1186/1756-0381-7-10 · Cited by 28 · see citing works
SNPranker 2.0: a gene-centric data mining tool for diseases associated SNP prioritization in GWAS
Ivan Merelli, A., Calabria, A., Cozzi, P., Viti, F., Mosca, E., & Milanesi, L. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s1-s9 · Cited by 27 · see citing works
Analysis of Computational Gene Prioritization Approaches
Raj, M., & Sreeja, A. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.10.411 · Cited by 24 · see citing works
Dai, Y., Pei, G., Zhao, Z., & Jia, P. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00318 · Cited by 23 · see citing works
Impact of genetically supported target selection on R&D productivity
Hurle, M. R., Nelson, M. R., Agarwal, P., & Cardon, L. R. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.164 · Cited by 22 · see citing works
Disease genes prioritizing mechanisms: a comprehensive and systematic literature review
Seyyedrazzagi, E., & Navimipour, N. J. (2017) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-017-0154-9 · Cited by 19 · see citing works
Ning, K., Gettler, K., Zhang, W., Ng, S. M. E., Bowen, B. M., Hyams, J. S., Stephens, M. C., Kugathasan, S., Denson, L. A., Schadt, E. E., et al. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv142 · Cited by 18 · see citing works
Wu, M., Zeng, W., Liu, W., Lv, H., Chen, T., & Jiang, R. (2018) · Methods · doi:10.1016/j.ymeth.2018.06.002 · Cited by 18 · see citing works
Gettler, K., Giri, M., Kenigsberg, E., Martin, J. P. D., Chuang, L.-S. F., Hsu, N.-Y., Denson, L. A., Hyams, J. S., Griffiths, A. M., Noe, J. D., et al. (2019) · Genes and Immunity · doi:10.1038/s41435-019-0059-y · Cited by 17 · see citing works
Khan, A., Liu, Q., & Wang, K. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2469-7 · Cited by 16 · see citing works
Leal, L. G., David, A., Järvelin, M., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz310 · Cited by 12 · see citing works
Erratum: Impact of genetically supported target selection on R&D productivity
Hurle, M. R., Nelson, M. R., Agarwal, P., & Cardon, L. R. (2016) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.187 · Cited by 10 · see citing works
A Latent Model for Prioritization of SNPs for Functional Studies
Fridley, B. L., Iversen, E., Tsai, Y.-Y., Jenkins, G. D., Goode, E. L., & Sellers, T. A. (2011) · PLoS ONE · doi:10.1371/journal.pone.0020764 · Cited by 9 · see citing works
Uncovering association networks through an eQTL analysis involving human miRNAs and lincRNAs
Branco, P. R., de Araújo, G. S., Barrera, J., Suarez‐Kurtz, G., & SOUZA, S. J. D. (2018) · Scientific Reports · doi:10.1038/s41598-018-33420-z · Cited by 9 · see citing works
A network approach to prioritizing susceptibility genes for genome‐wide association studies
Kafaie, S., Chen, Y., & Hu, T. (2019) · Genetic Epidemiology · doi:10.1002/gepi.22198 · Cited by 9 · see citing works
Taliun, S. A. G., Ravji, R., Barnes, M. R., Weale, M. E., & Knight, J. (2015) · Scientific Reports · doi:10.1038/srep13373 · Cited by 8 · see citing works
Self-citationRippe, C., Albinsson, S., Guron, G., Nilsson, H., & Swärd, K. (2018) · Acta Physiologica · doi:10.1111/apha.13094 · Cited by 8 · see citing works
Stochastic semi-supervised learning to prioritise genes from high-throughput genomic screensPreprint
Vitsios, D., & Petrovski, S. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/655449 · Cited by 7 · see citing works
Rana, N., & Pizarro, T. T. (2019) · The FASEB Journal · doi:10.1096/fasebj.2019.33.1_supplement.496.28 · Cited by 1 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Artificial intelligence for dementia genetics and omics
Bettencourt, C., Skene, N., Bandres‐Ciga, S., Anderson, E., Winchester, L. M., Foote, I. F., Schwartzentruber, J., Botia, J. A., Nalls, M., Singleton, A., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13427 · Cited by 30 · see citing works
Santamaría-García, H., Baez, S., Aponte-Canencio, D. M., Pasciarello, G. O., Donnelly-Kehoe, P. A., Maggiotti, G., Matallana, D., Hesse, E., Neely, A., Zapata, J. G., et al. (2021) · Patterns · doi:10.1016/j.patter.2020.100176 · Cited by 18 · see citing works
Gonzalez-Gomez, R., Ibañez, A., & Moguilner, S. (2023) · Network Neuroscience · doi:10.1162/netn_a_00285 · Cited by 10 · see citing works
- Authors
- Nicholls, H. L., John, C. R., Watson, D. S., Munroe, P. B., Barnes, M. R., & Cabrera, C. P.
- Affiliations
- Queen Mary University of London (GB)
- Corresponding author
- Barnes, Michael R.
- Cabrera, Claudia P.
- ORCID
- Venue
- Frontiers in Genetics
- Publisher
- Frontiers Media SA
- Published
- 2020-04-15
- Volume
- 11
- Pages
- 350-350
- ISSN
- 1664-8021 (ISSN-L)
- PMC
- PMC7174742
- OpenAlex
- W3016610742
- Cited by
- 155
- References
- 71
- Citations by year
- 2026: 10 · 2025: 21 · 2024: 23 · 2023: 33 · 2022: 36 · 2021: 23 · 2020: 9
- FWCI
- 3.60
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Genetics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- UCL Discovery (University College London) (submittedVersion)
- Queen Mary Research Online (Queen Mary University of London) (submittedVersion, cc-by)
- Funders
- National Institute for Health and Care Research
- British Heart Foundation (FS/18/64/34185C)
- Barts and The London School of Medicine and Dentistry
- 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 › Genomics and Rare Diseases
- Keywords
- causal variant identification, linkage disequilibrium, machine learning, random forest, gradient boosting, deep learning, ensemble learning, post-GWAS analysis, functional validation, clinical translation
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
