The DOI matches its Crossref record; 50 of the 52 references it cites were found in a registry or an index.
The Genetic Interpretation of Area under the ROC Curve in Genomic Profiling
Open accessAbstract
Genome-wide association studies in human populations have facilitated the creation of genomic profiles which combine the effects of many associated genetic variants to predict risk of disease. The area under the receiver operator characteristic (ROC) curve is a well established measure for determining the efficacy of tests in correctly classifying diseased and non-diseased individuals. We use quantitative genetics theory to provide insight into the genetic interpretation of the area under the ROC curve (AUC) when the test classifier is a predictor of genetic risk. Even when the proportion of genetic variance explained by the test is 100%, there is a maximum value for AUC that depends on the genetic epidemiology of the disease, i.e. either the sibling recurrence risk or heritability and disease prevalence. We derive an equation relating maximum AUC to heritability and disease prevalence. The expression can be reversed to calculate the proportion of genetic variance explained given AUC, disease prevalence, and heritability. We use published estimates of disease prevalence and sibling recurrence risk for 17 complex genetic diseases to calculate the proportion of genetic variance that a test must explain to achieve AUC = 0.75; this varied from 0.10 to 0.74. We provide a genetic interpretation of AUC for use with predictors of genetic risk based on genomic profiles. We provide a strategy to estimate proportion of genetic variance explained on the liability scale from estimates of AUC, disease prevalence, and heritability (or sibling recurrence risk) available as an online calculator.
Overview
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
Figures
![Figure 1 The dependence of maximum AUC (AUCmax) from a genomic profile on heritability and disese prevalence. (A,B) Probability of disease versus genetic liability. (C,D) ROC curve [46].](/paper-figures/145bf766d18a4109b5fcd4d2cec1e886/the-genetic-interpretation-of-area-under-the-roc-curve-in-genomic-profiling-figure-1.jpg)


Verification
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?
In the citing paper
“Using LDSC on a meta-analysis of all 11 clinically-ascertained datasets from our GWAS and estimated the liability-scale heritability of PD as 0.22 (95% CI 0.18 – 0.26), only slightly lower than a previous estimate derived using GCTA (0.27, 95% CI 0.17 – 0.38) (2,16,30).”
From Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies (2019) · cites this paper as [30]
In the citing paper
“Using equations from Wray et al. 2010 and our current heritability estimates, the 88 variant PRS explained a minimum 16% of the genetic liability of PD assuming a global prevalence of 0.5% (2,31).”
From Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies (2019) · cites this paper as Wray et al. 2010
In the citing paper
“maximum potential AUC estimated at 85% using the equations from Wray et al. 2010”
From Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies (2019) · cites this paper as Wray et al. 2010
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The meaning and use of the area under a receiver operating characteristic (ROC) curve.
Hanley, J. A., & McNeil, B. J. (1982) · Radiology · doi:10.1148/radiology.143.1.7063747 · Cited by 21,856
Introduction to Quantitative Genetics.
Edwards, A. W. F., & Falconer, D. S. (1982) · Biometrics · doi:10.2307/2529912 · Cited by 21,447
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 and Analysis of Quantitative Traits
(2002) · Journal of Animal Breeding and Genetics · doi:10.1046/j.1439-0388.2002.00356.x · Cited by 6,176
Basic principles of ROC analysis
Metz, C. E. (1978) · Seminars in Nuclear Medicine · doi:10.1016/s0001-2998(78)80014-2 · Cited by 6,122
Global variation in copy number in the human genome
Redon, R., Ishikawa, S., Fitch, K. R., Feuk, L., Perry, G. H., Andrews, T. D., Fiegler, H., Shapero, M. H., Carson, A. R., Chen, W., et al. (2006) · Nature · doi:10.1038/nature05329 · Cited by 4,377
Introduction to Quantitative Genetics
J.-M., G., & Falconer, D. S. (1962) · Population · doi:10.2307/1525780 · Cited by 3,773
Genetic Epidemiology of Major Depression: Review and Meta-Analysis
Sullivan, P. F., Neale, M. C., & Kendler, K. S. (2000) · American Journal of Psychiatry · doi:10.1176/appi.ajp.157.10.1552 · Cited by 3,431
ROCR: visualizing classifier performance in R
Sing, T., Sander, O., Beerenwinkel, N., & Lengauer, T. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti623 · Cited by 3,172
Introduction to quantitative genetics
Falconer, D. S. (1960) · Helmholtz Centre for Ocean Research Kiel (GEOMAR) · Cited by 3,170
Research works printed but not linked: 1
- M McGue, 1983, The transmission of schizophrenia under a multifactorial threshold model., American Journal of Human Genetics, 35, 1161
Cited by
Sorted by citations
Items 101–200 of 336
Can education be personalised using pupils’ genetic data?
Morris, T., Davies, N. M., & Smith, G. D. (2020) · eLife · doi:10.7554/elife.49962 · Cited by 62
Being More Realistic about the Public Health Impact of Genomic Medicine
Hall, W., Mathews, R. R. S., & Morley, K. I. (2010) · PLoS Medicine · doi:10.1371/journal.pmed.1000347 · Cited by 61
3′UTR shortening and EGF signaling: implications for breast cancer
Akman, H. B., Oyken, M., Tuncer, T., Can, T., & Erson-Bensan, A. E. (2015) · Human Molecular Genetics · doi:10.1093/hmg/ddv391 · Cited by 62
Schubert, K. O., Thalamuthu, A., Amare, A. T., Frank, J., Streit, F., Adl, M., Akula, N., Akiyama, K., Ardau, R., Arias, B., et al. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01702-2 · Cited by 61
Jain, S., Bakolitsa, C., Brenner, S. E., Predrag Radivojac, A., Moult, J., Repo, S., Hoskins, R. A., Andreoletti, G., Barsky, D., Chellapan, A., et al. (2024) · Genome biology · doi:10.1186/s13059-023-03113-6 · Cited by 75
Genome-wide prediction of childhood asthma and related phenotypes in a longitudinal birth cohort
Spycher, B. D., Henderson, J., Granell, R., Evans, D. M., Smith, G. D., Timpson, N. J., & Sterne, J. A. C. (2012) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2012.06.002 · Cited by 61
Animal breeding strategies can improve meat quality attributes within entire populations
Berry, D. P., Conroy, S. B., Pabiou, T., & Cromie, A. R. (2017) · Meat Science · doi:10.1016/j.meatsci.2017.04.019 · Cited by 64
Optimizing Genomic Prediction of Host Resistance to Koi Herpesvirus Disease in Carp
Palaiokostas, C., Veselý, T., Kocour, M., Prchal, M., Pokorová, D., Piačková, V., Pojezdal, Ľ., & Houston, R. D. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00543 · Cited by 60
Fullerton, J. M., Koller, D. L., Edenberg, H. J., Foroud, T., Liu, H., Glowinski, A. L., McInnis, M. G., Wilcox, H. C., Frankland, A., Roberts, G. M. P., et al. (2015) · American Journal of Medical Genetics Part B Neuropsychiatric Genetics · doi:10.1002/ajmg.b.32344 · Cited by 59
Lindström, S., Schumacher, F. R., Cox, D. G., Travis, R. C., Albanes, D., Allen, N. E., Andriole, G. L., Berndt, S. I., Boeing, H., Bueno-de-Mesquita, H. B., et al. (2012) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-11-1038 · Cited by 57
Mittag, F., Büchel, F., Saad, M., Jahn, A., Schulte, C., Bochdanovits, Z., Simón‐Sánchez, J., Nalls, M. A., Keller, M. F., Hernandez, D. G., et al. (2012) · Human Mutation · doi:10.1002/humu.22161 · Cited by 56
Improving reporting standards for polygenic scores in risk prediction studiesPreprint
Wand, H., Lambert, S. A., Tamburro, C. P., Iacocca, M. A., O’Sullivan, J. W., Sillari, C. H., Kullo, I. J., Rowley, R. K., Dron, J. S., Brockman, D., et al. (2020) · medRxiv · doi:10.1101/2020.04.23.20077099 · Cited by 55
Novel risk genes for systemic lupus erythematosus predicted by random forest classification
Almlöf, J. C., Alexsson, A., Imgenberg‐Kreuz, J., Sylwan, L., Bäcklin, C. L., Leonard, D., Nordmark, G., Tandre, K., Eloranta, M., Padyukov, L., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-06516-1 · Cited by 55
Predicting for disease resistance in aquaculture species using machine learning models
Palaiokostas, C. (2021) · Aquaculture Reports · doi:10.1016/j.aqrep.2021.100660 · Cited by 53
Avulsion Fractures of the Medial Tubercle of the Posterior Process of the Talus
Kim, D. H., Berkowitz, M. J., & Pressman, D. N. (2003) · Foot & Ankle International · doi:10.1177/107110070302400201.172 · Cited by 53
An epidemiological perspective on the future of direct-to-consumer personal genome testing
Janssens, A. C. J. W., & van Duijn, C. M. (2010) · Investigative Genetics · doi:10.1186/2041-2223-1-10 · Cited by 50
Wu, Y., Liu, X., Luo, H., Deng, W., Zhao, G., Wang, Q., Zhang, L., Ma, X., Liu, X., Murray, R. M., et al. (2012) · Psychiatry Research · doi:10.1016/j.psychres.2012.01.020 · Cited by 52
Yan, J., Alıev, F., Webb, B. T., Kendler, K. S., Williamson, V. S., Edenberg, H. J., Arpana Agrawal, A., Kos, M. Z., Almasy, L. A., Nürnberger, J. I., et al. (2013) · Addiction Biology · doi:10.1111/adb.12035 · Cited by 52
Lu, S., Liu, Y., Yu, X., Li, Y., Yang, Y., Wei, M., Zhou, Q., Wang, J., Zhang, Y., Zheng, W., et al. (2020) · Genetics Selection Evolution · doi:10.1186/s12711-020-00566-2 · Cited by 51
Albiñana, C., Grove, J., McGrath, J. J., Agerbo, E., Wray, N. R., Bulik, C. M., Nordentoft, M., HOUGAARD, D. M., Werge, T. M., Børglum, A. D., et al. (2021) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2021.04.014 · Cited by 50
Whole genome sequencing in support of wellness and health maintenance
Patel, C. J., Sivadas, A., Tabassum, R., Preeprem, T., Zhao, J., Arafat, D., Chen, R., Morgan, A. A., Martin, G. S., Brigham, K. L., et al. (2013) · Genome Medicine · doi:10.1186/gm462 · Cited by 53
Genetic risk scores and family history as predictors of schizophrenia in Nordic registers
Lu, Y., Pouget, J. G., Andreassen, O. A., Djurovic, S., Esko, T., Hultman, C. M., Metspalu, A., Milani, L. A., Werge, T. M., & Sullivan, P. F. (2017) · Psychological Medicine · doi:10.1017/s0033291717002665 · Cited by 55
McGovern, S. P., Purfield, D. C., Ring, S. C., Carthy, T. R., Graham, D. A., & Berry, D. P. (2019) · Journal of Dairy Science · doi:10.3168/jds.2018-15906 · Cited by 50
Genes Affecting β-Cell Function in Type 1 Diabetes
Fløyel, T., Kaur, S., & Pociot, F. (2015) · Current Diabetes Reports · doi:10.1007/s11892-015-0655-9 · Cited by 51
Zhou, Q., Chen, Y., Lü, S., Liu, Y., Xu, W., Li, Y., Wang, L., Wang, N., Yang, Y., & Chen, S. (2020) · Engineering · doi:10.1016/j.eng.2020.06.017 · Cited by 46
Shan, X., Xu, T., Ma, Z., Zhang, X., Ruan, Z., Chen, J., Shi, Q., & You, X. (2020) · Aquaculture · doi:10.1016/j.aquaculture.2020.736214 · Cited by 45
Significance tests for R2 of out-of-sample prediction using polygenic scores
Momin, M. M., Lee, S., Wray, N. R., & Lee, S. (2023) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2023.01.004 · Cited by 45
Enciso-Mora, V., Hosking, F. J., Sheridan, E. G., Kinsey, S. E., Lightfoot, T., Roman, E., Irving, J. A. E., Tomlinson, I. P. M., Allan, J. M., Taylor, M., et al. (2012) · Leukemia · doi:10.1038/leu.2012.89 · Cited by 44
Using information of relatives in genomic prediction to apply effective stratified medicine
Lee, S., Weerasinghe, S., Wray, N. R., Goddard, M. E., & van der Werf, J. H. J. (2017) · Scientific Reports · doi:10.1038/srep42091 · Cited by 45
Daneshjou, R., Wang, Y., Bromberg, Y., Bovo, S., Martelli, P. L., Babbi, G., Lena, P. D., Casadio, R., Edwards, M. D., Gifford, D. K., et al. (2017) · Human Mutation · doi:10.1002/humu.23280 · Cited by 43
Utility of genetic determinants of lipids and cardiovascular events in assessing risk
Holmes, M. V., Harrison, S., Talmud, P. J., Hingorani, A. D., & Humphries, S. E. (2011) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2011.6 · Cited by 44
Fraslin, C., Koskinen, H., Nousianen, A., Houston, R. D., & Kause, A. (2022) · Aquaculture · doi:10.1016/j.aquaculture.2022.738332 · Cited by 43
Using genetics to predict the natural history of asthma?
Holloway, J. W., Arshad, S. H., & Holgate, S. T. (2010) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2010.06.006 · Cited by 47
Genomic Prediction for Tuberculosis Resistance in Dairy Cattle
Tsairidou, S., Woolliams, J. A., Allen, A. R., Skuce, R. A., McBride, S. H., Wright, D. M., Bermingham, M. L., Pong‐Wong, R., Matika, O., McDowell, S. W. J., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0096728 · Cited by 43
Analysis of case–control association studies with known risk variants
Zaitlen, N., Paşaniuc, B., Patterson, N. J., Pollack, S. J., Voight, B. F., Groop, L., Altshuler, D., Henderson, B. E., Kolonel, L. N., Marchand, L. L., et al. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts259 · Cited by 38
Chen, G., Lee, S., Montgomery, G. W., Wray, N. R., Visscher, P. M., Gearry, R. B., Lawrance, I. C., Andrews, J. M., Bampton, P. A., Mahy, G., et al. (2017) · BMC Medical Genetics · doi:10.1186/s12881-017-0451-2 · Cited by 43
Rahmioğlu, N., Karina, B., Christofidou, P., Rebecca, D. L., Galarneau, G., Ayush, G., MacGregor, S., Mortlock, S., Yadav, S., Andrew, S. J., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/406967 · Cited by 45
So, H., & Sham, P. C. (2010) · PLoS Genetics · doi:10.1371/journal.pgen.1001230 · Cited by 39
Summarizing polygenic risks for complex diseases in a clinical whole-genome report
Kong, S. W., Lee, I., Leshchiner, I., Krier, J. B., Kraft, P. L., Rehm, H. L., Green, R. C., Kohane, I. S., & MacRae, C. A. (2014) · Genetics in Medicine · doi:10.1038/gim.2014.143 · Cited by 39
Clinical and research uses of genetic risk scores in type 1 diabetes
Sharp, S. A., Weedon, M. N., Hagopian, W., & Oram, R. A. (2018) · Current Opinion in Genetics & Development · doi:10.1016/j.gde.2018.03.009 · Cited by 43
Genomics of schizophrenia, bipolar disorder and major depressive disorder
Owen, M. J., Bray, N. J., Walters, J., & O’Donovan, M. C. (2025) · Nature Reviews Genetics · doi:10.1038/s41576-025-00843-0 · Cited by 44
Zhu, B., Tian, J., Zhong, R., Tian, Y., Chen, W., Qian, J., Zou, L., Xiao, M., Shen, N., Yang, H. B., et al. (2014) · Molecular Carcinogenesis · doi:10.1002/mc.22140 · Cited by 39
Lee, S., & Wray, N. R. (2013) · PLoS ONE · doi:10.1371/journal.pone.0071494 · Cited by 36
Attention Deficit Hyperactivity Disorder (ADHD) and Earnings in Later-Life Self-Employment
Patel, P. C., Rietveld, C. A., & Verheul, I. (2019) · Entrepreneurship Theory and Practice · doi:10.1177/1042258719888641 · Cited by 46
Non-parametric Polygenic Risk Prediction via Partitioned GWAS Summary Statistics
Chun, S., Imakaev, M., Hui, D., Patsopoulos, N. A., Neale, B. M., Kathiresan, S., Stitziel, N. O., & Sunyaev, S. R. (2020) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2020.05.004 · Cited by 39
Zeng, Y., Navarro, P., Fernandez-Pujals, A. M., Hall, L. S., Clarke, T., Thomson, P. A., Smith, B. H., Hocking, L. J., Padmanabhan, S., Hayward, C., et al. (2016) · Biological Psychiatry · doi:10.1016/j.biopsych.2016.04.017 · Cited by 43
Cascella, R., Strafella, C., Longo, G., Ragazzo, M., Manzo, L., Felici, C. D., Errichiello, V., Caputo, V., Viola, F., Eandi, C. M., et al. (2017) · Oncotarget · doi:10.18632/oncotarget.23241 · Cited by 38
SparSNP: Fast and memory-efficient analysis of all SNPs for phenotype prediction
Abraham, G., Kowalczyk, A., Zobel, J., & Inouye, M. (2012) · BMC Bioinformatics · doi:10.1186/1471-2105-13-88 · Cited by 37
Predictive accuracy of combined genetic and environmental risk scores
Dudbridge, F., Pashayan, N., & Yang, J. (2017) · Genetic Epidemiology · doi:10.1002/gepi.22092 · Cited by 38
Polygenic Risk Score for Alzheimer's Disease in Caribbean Hispanics
Sariya, S., Felsky, D., Reyes‐Dumeyer, D., Lali, R., Lantigua, R., Vardarajan, B. N., Jiménez‐Velázquez, I. Z., Haines, J. L., Shellenberg, G. D., Pericak‐Vance, M. A., et al. (2021) · Annals of Neurology · doi:10.1002/ana.26131 · Cited by 34
Machine learning for the life-time risk prediction of Alzheimer’s disease: a systematic review
Rowe, T. W., Katzourou, I. K., Stevenson‐Hoare, J. O., Bracher‐Smith, M., Ivanov, D. K., & Escott‐Price, V. (2021) · Brain Communications · doi:10.1093/braincomms/fcab246 · Cited by 36
Genetic profiling and individualized assessment of fracture risk
Nguyen, T. V., & Eisman, J. A. (2013) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2013.3 · Cited by 33
Woo, H. J., Yu, C., Kumar, K., & Jaques Reifman, A. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.61 · Cited by 35
Luo, Z., Yu, Y., Bao, Z., Xiang, J., & Li, F. (2022) · Aquaculture · doi:10.1016/j.aquaculture.2022.738320 · Cited by 33
Polygenic risk scores in coronary artery disease
Rao, A. S., & Knowles, J. W. (2019) · Current Opinion in Cardiology · doi:10.1097/hco.0000000000000629 · Cited by 33
Kolevzon, A. L., Cai, G., Soorya, L. V., Takahashi, N., Grodberg, D., Kajiwara, Y., Willner, J. P., Tryfon, A., & Buxbaum, J. D. (2010) · Brain Research · doi:10.1016/j.brainres.2010.11.005 · Cited by 32
The Genetic Architecture of High Bone Mass
Gregson, C. L., & Duncan, E. L. (2020) · Frontiers in Endocrinology · doi:10.3389/fendo.2020.595653 · Cited by 32
Polygenic risk scores and the prediction of common diseases
Ala‐Korpela, M., & Holmes, M. V. (2019) · International Journal of Epidemiology · doi:10.1093/ije/dyz254 · Cited by 32
Stanković, B., Kotur, N., Nikčević, G., Gašić, V., Zukić, B., & Pavlović, S. (2021) · Genes · doi:10.3390/genes12091438 · Cited by 31
Yi, N., Kaklamani, V., & Pasche, B. (2010) · Annals of Human Genetics · doi:10.1111/j.1469-1809.2010.00605.x · Cited by 31
Genomic prediction in the wild: A case study in Soay sheep
Ashraf, B. H., Hunter, D. C., Bérénos, C., Ellis, P. A., Johnston, S. E., Pilkington, J. G., Pemberton, J. M., & Slate, J. (2021) · Molecular Ecology · doi:10.1111/mec.16262 · Cited by 26
Morales, F., Vásquez, M., Cuenca, P., Campos, D., Santamaría, C., Valle, G. D., Brian, R., Sittenfeld, M., & Monckton, D. G. (2014) · European Journal of Human Genetics · doi:10.1038/ejhg.2014.138 · Cited by 30
Twomey, A. J., Sayers, R. G., Carroll, R. I., Byrne, N., Brien, E. O., Doherty, M. L., McClure, J. C., Graham, D. A., & Berry, D. P. (2016) · Journal of Animal Science · doi:10.2527/jas.2016-0621 · Cited by 29
Widén, E., Raben, T. G., Lello, L., & Hsu, S. (2021) · Genes · doi:10.3390/genes12070991 · Cited by 33
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
Genetic Risk Prediction: Individualized Variability in Susceptibility to Toxicants
Nebert, D. W., Zhang, G., & Vesell, E. S. (2013) · The Annual Review of Pharmacology and Toxicology · doi:10.1146/annurev-pharmtox-011112-140241 · Cited by 26
Genomic risk scores for juvenile idiopathic arthritis and its subtypes
Cánovas, R., Cobb, J. E., Brożyńska, M., Bowes, J., Li, Y. R., Smith, S. L., Hakonarson, H. H., Thomson, W., Ellis, J. A., Abraham, G., et al. (2020) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2020-217421 · Cited by 28
Genes, pathways and risk prediction in Alzheimer’s disease
Hardy, J., & Escott-Price, V. (2019) · Human Molecular Genetics · doi:10.1093/hmg/ddz163 · Cited by 27
Baker, L. A., Momen, M. M., Chan, K., Bollig, N., Lopes, F. B., Rosa, G. J. M., Todhunter, R. J., Binversie, E. E., Sample, S. J., & Muir, P. (2020) · G3 Genes Genomes Genetics · doi:10.1534/g3.120.401244 · Cited by 28
Ahmad, Z., Mumtaz, A. S., Ghafoor, A., Ali, A., & Nisar, M. (2014) · Molecular Biology Reports · doi:10.1007/s11033-014-3561-3 · Cited by 27
Parity and pancreatic cancer risk: evidence from a meta-analysis of twenty epidemiologic studies
Zhu, B., Zou, L., Han, J., Chen, W., Shen, N., Zhong, R., Li, J., Chen, X., Liu, C., Yang, S., et al. (2014) · Scientific Reports · doi:10.1038/srep05313 · Cited by 27
An integrative analysis of genomic and exposomic data for complex traits and phenotypic prediction
Zhou, X., & Lee, S. (2021) · Scientific Reports · doi:10.1038/s41598-021-00427-y · Cited by 24
The role of genetics for biomarker development in neurodegeneration
Bertram, L., & Hampel, H. J. (2011) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2011.09.011 · Cited by 26
Kang, E. A., Jang, J., Choi, C. H., Kang, S., Bang, K. B., Kim, T. O., Seo, G. S., Cha, J. M., Chun, J., Jung, Y., et al. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10040633 · Cited by 27
Levitan, A., Gale, A. N., Dallon, E., Kozan, D. W., Cunningham, K. W., Sharan, R., & Berman, J. G. (2020) · Current Genetics · doi:10.1007/s00294-020-01096-6 · Cited by 25
Setting the criterion for fall risk screening for healthy community-dwelling elderly
Demura, S., Sato, S., Shin, S., & Uchiyama, M. (2011) · Archives of Gerontology and Geriatrics · doi:10.1016/j.archger.2011.04.010 · Cited by 25
Predicting Disease Risk Using Bootstrap Ranking and Classification Algorithms
Manor, O., & Segal, E. (2013) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003200 · Cited by 24
Strategies for Developing Prediction Models From Genome‐Wide Association Studies
Wu, J., Pfeiffer, R. M., & Gail, M. H. (2013) · Genetic Epidemiology · doi:10.1002/gepi.21762 · Cited by 24
Weiss, K. M., & Buchanan, A. V. (2011) · Genetics · doi:10.1534/genetics.111.131318 · Cited by 24
Role of Environmental Confounding in the Association between FKBP5 and First-Episode Psychosis
Ajnakina, O., Borges, S., Forti, M. D., Patel, Y., Xu, X., Green, P., Stilo, S. A., Kolliakou, A., Gardner-Sood, P., Marques, T. R., et al. (2014) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2014.00084 · Cited by 24
Polygenic Health Index, General Health, and Pleiotropy: Sibling Analysis and Disease Risk Reduction
Widén, E., Lello, L., Raben, T. G., Tellier, L. C. A. M., & Hsu, S. (2022) · Scientific Reports · doi:10.1038/s41598-022-22637-8 · Cited by 24
Inequality in genetic cancer risk suggests bad genes rather than bad luck
Stensrud, M. J., & Valberg, M. (2017) · Nature Communications · doi:10.1038/s41467-017-01284-y · Cited by 23
Assessing individual risk for AMD with genetic counseling, family history, and genetic testing
Cascella, R., Strafella, C., Longo, G., Manzo, L., Ragazzo, M., Felici, C. D., Gambardella, S., Marsella, L., Novelli, G., Borgiani, P., et al. (2017) · Eye · doi:10.1038/eye.2017.192 · Cited by 21
Genetic and modifiable risk factors combine multiplicatively in common disease
Pang, S., Yengo, L., Nelson, C. P., Bourier, F. J., Zeng, L., Li, L., Kessler, T., Erdmann, J., Mägi, R., Läll, K., et al. (2022) · Clinical Research in Cardiology · doi:10.1007/s00392-022-02081-4 · Cited by 21
Barnett, E. J., Onete, D. G., Salekin, A., & Faraone, S. V. (2023) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2023.3343808 · Cited by 22
Machine learning in Alzheimer’s disease genetics
Bracher‐Smith, M., Melograna, F., Ulm, B. S., Bellenguez, C., Grenier‐Boley, B., Duroux, D., Nevado‐Holgado, A. J., Holmans, P., Tijms, B. M., Hulsman, M., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-61650-z · Cited by 19
Annotating individual human genomes
Torkamani, A., Zeeland, A. A. S., Topol, E. J., & Schork, N. J. (2011) · Genomics · doi:10.1016/j.ygeno.2011.07.006 · Cited by 21
Seka, D., Bonny, B. S., Yoboue, A. N., Sié, S. R., & Adopo-Gourène, B. A. (2019) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2019.03.001 · Cited by 19
Ladabaum, U., Mannalithara, A., Mitani, A., & Desai, M. (2019) · Cancer Epidemiology Biomarkers & Prevention · doi:10.1158/1055-9965.epi-19-0949 · Cited by 22
Wang, T., Chen, Y. P., Bowman, P. J., Goddard, M. E., & Hayes, B. J. (2016) · BMC Genomics · doi:10.1186/s12864-016-3082-7 · Cited by 19
Abraham, G., Malik, R., Yonova‐Doing, E., Salim, A., Wang, T., Danesh, J., Butterworth, A. S., Howson, J. M. M., Inouye, M., & Dichgans, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/689935 · Cited by 19
Circulating Nucleic Acid-Based Biomarkers of Type 2 Diabetes
Padilla-Martínez, F., Wojciechowska, G. E. P., Szczerbiński, Ł., & Kretowski, A. J. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms23010295 · Cited by 18
Cracking the type 1 diabetes code: Genes, microbes, immunity, and the early life environment
Yau, C., & Danska, J. S. (2024) · Immunological Reviews · doi:10.1111/imr.13362 · Cited by 22
Clayton, D. G. (2012) · Genetic Epidemiology · doi:10.1002/gepi.21635 · Cited by 18
New insights on the genetics of type 1 diabetes
Oram, R. A., & REDONDO, M. J. (2019) · Current Opinion in Endocrinology Diabetes and Obesity · doi:10.1097/med.0000000000000489 · Cited by 19
Tierney, B. T., He, Y., Church, G. M., Segal, E., Kostic, A. D., & Patel, C. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2019.12.31.891978 · Cited by 17
Interactions within the MHC contribute to the genetic architecture of celiac disease
Goudey, B., Abraham, G., Kikianty, E., Wang, Q., Rawlinson, D., Shi, F., Haviv, I., Stern, L., Kowalczyk, A., & Inouye, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0172826 · Cited by 20
Quantitative genetics of disease traits
Wray, N. R., & Visscher, P. M. (2015) · Journal of Animal Breeding and Genetics · doi:10.1111/jbg.12153 · Cited by 17
How accurate can genetic predictions be?
Dreyfuss, J. M., Levner, D., Galagan, J. E., Church, G. M., & Ramoni, M. F. (2012) · BMC Genomics · doi:10.1186/1471-2164-13-340 · Cited by 17
Pal, L. R., Kundu, K., Yin, Y., & Moult, J. (2017) · Human Mutation · doi:10.1002/humu.23256 · Cited by 18
Metadata
- Authors
- Wray, N. R., Yang, J., Goddard, M. E., & Visscher, P. M.
- Affiliations
- QIMR Berghofer Medical Research Institute (AU)
- The University of Melbourne (AU)
- Corresponding author
- Wray, Naomi R.
- Venue
- PLoS Genetics
- Publisher
- Public Library of Science (PLoS)
- Published
- 2010-02-26
- Volume
- 6
- Issue
- 2
- Pages
- e1000864
- ISSN
- 1553-7390 (ISSN-L)
- 1553-7404
- PMC
- PMC2829056
- OpenAlex
- W1990700240
- Cited by
- 334
- References
- 52
- Citations by year
- 2026: 11 · 2025: 16 · 2024: 9 · 2023: 16 · 2022: 13 · 2021: 26 · 2020: 27 · 2019: 28 · 2018: 14 · 2017: 24 · 2016: 14 · 2015: 19 · 2014: 30 · 2013: 22 · 2012: 34
- FWCI
- 15.45
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- PLoS Genetics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Figshare (submittedVersion, cc-by)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Funders
- Medical Research Council
- Australian Research Council (DP0770096)
- National Health and Medical Research Council (496688, 442915, DP0770096, 389892)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
- Keywords
- area under the ROC curve, genomic profiling, genetic risk prediction, heritability, disease prevalence, sibling recurrence risk, liability scale, complex genetic disorders, genome-wide association study, genetic epidemiology
- MeSH terms
- Aged, 80 and over, Disease, Humans, Macular Degeneration, ROC Curve, Genome, Human, Prevalence, Area Under Curve, Gene Expression Profiling, Inheritance Patterns
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