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Validity of polygenic risk scores: are we measuring what we think we are?
Open accessAbstract
Polygenic risk scores (PRSs) have become the standard for quantifying genetic liability in the prediction of disease risks. PRSs are generally constructed as weighted sum scores of risk alleles using effect sizes from genome-wide association studies as their weights. The construction of PRSs is being improved with more appropriate selection of independent single-nucleotide polymorphisms (SNPs) and optimized estimation of their weights but is rarely reflected upon from a theoretical perspective, focusing on the validity of the risk score. Borrowing from psychometrics, this paper discusses the validity of PRSs and introduces the three main types of validity that are considered in the evaluation of tests and measurements: construct, content, and criterion validity. This introduction is followed by a discussion of three topics that challenge the validity of PRS, namely, their claimed independence of clinical risk factors, the consequences of relaxing SNP inclusion thresholds and the selection of SNP weights. This discussion of the validity of PRS reminds us that we need to keep questioning if weighted sums of risk alleles are measuring what we think they are in the various scenarios in which PRSs are used and that we need to keep exploring alternative modeling strategies that might better reflect the underlying biological pathways.
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“Genetic data are increasingly cost effective, requiring only a one-in-a-lifetime ascertainment, but they also have limited predictive ability on their own 27 .”
From Multimodal biomedical AI (2022) · cites this paper as [27]
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Cites
Construct validity in psychological tests.
Cronbach, L. J., & Meehl, P. E. (1955) · Psychological Bulletin · doi:10.1037/h0040957 · Cited by 11,195 · cited 2×
“This paper aimed to reflect on the validity of PRSs by introducing the types of validity that are deemed important in the design of measurements, tests or questionnaires (20).”
BackgroundQuality criteria were proposed for measurement properties of health status questionnaires
Terwee, C. B., Bot, S. D. M., de Boer, M. R., van der Windt, D. A. W. M., Knol, D. L., Dekker, J., Bouter, L. M., & de Vet, H. C. W. (2007) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2006.03.012 · Cited by 10,764 · cited 1×
“The validity of a test or measurement indicates whether it measures what it intends to measure (20,21).”
BackgroundMoons, K. G. M., Altman, D. G., Reitsma, J. B., Ioannidis, J. P. A., Macaskill, P., Steyerberg, E. W., Vickers, A. J., Ransohoff, D. F., & Collins, G. S. (2015) · Annals of Internal Medicine · doi:10.7326/m14-0698 · Cited by 5,147 · cited 1×
“The validity of clinical risk models needs to be demonstrated, and the choices in the model development need to be justified (64).”
BackgroundCommon polygenic variation contributes to risk of schizophrenia and bipolar disorder
The International Schizophrenia Consortium (2009) · Nature · doi:10.1038/nature08185 · Cited by 4,821 · cited 2×
“These PRSs go beyond only including genome-wide significant SNPs from GWASs and beyond setting lower thresholds (higher P-values) than genome-wide significance for the selection of SNPs (7).”
BackgroundXV.—The Correlation between Relatives on the Supposition of Mendelian Inheritance.
Fisher, R. A. (1919) · Transactions of the Royal Society of Edinburgh · doi:10.1017/s0080456800012163 · Cited by 4,387 · cited 1×
“weak effects (11,12), (2) there seems to be no evidence for strong gene–gene interaction (12) and (3) these PRSs generally hold their discriminative ability in external validation samples”
BackgroundKhera, A. V., Chaffin, M., Aragam, K. G., Haas, M. E., Roselli, C., Choi, S. H., Natarajan, P., Lander, E. S., Lubitz, S. A., Ellinor, P. T., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0183-z · Cited by 2,953 · cited 4×
“A recent trend in the construction of PRSs is to use millions of SNPs in the prediction of risks (27,57,58).”
BackgroundMethodInfluential23andMe Research Team, Lee, J. J., COGENT (Cognitive Genomics Consortium), Social Science Genetic Association Consortium, Wedow, R., Okbay, A., Kong, E., Maghzian, O., Zacher, M., Nguyen-Viet, T. A., et al. (2018) · Nature Genetics · doi:10.1038/s41588-018-0147-3 · Cited by 2,470 · cited 1×
“disease risk, and should they be included in the PRS? When a PRS is constructed based on 2 million of SNPs of which, say, 100 are able to change predicted risks before the decimal, will we tell a patient that their risk is calculated using 2 million or 100 SNPs? When researchers relax the SNP inclusion thresholds to i…”
BackgroundCausal diagrams for empirical research
PEARL, J. (1995) · Biometrika · doi:10.1093/biomet/82.4.669 · Cited by 2,355
The personal and clinical utility of polygenic risk scores
Torkamani, A., Wineinger, N. E., & Topol, E. J. (2018) · Nature Reviews Genetics · doi:10.1038/s41576-018-0018-x · Cited by 1,655 · cited 4×
“It may be that researchers are aware and accept that the validity is imperfect as many more genetic associations remain to be identified (2), all models have limitations, and they can be useful even when imperfect.”
BackgroundInfluentialGenetic Risk, Adherence to a Healthy Lifestyle, and Coronary Disease
Khera, A. V., Emdin, C. A., Drake, I., Natarajan, P., Bick, A. G., Cook, N. R., Chasman, D. I., Baber, U., Mehran, R., Rader, D. J., et al. (2016) · New England Journal of Medicine · doi:10.1056/nejmoa1605086 · Cited by 1,460 · cited 1×
“These independent effects have been observed for lifestyle in stroke (32), coronary heart disease (33,34), adiposity (35,36), diabetes (37) and breast cancer (38), and for stressful life events in depression (39).”
BackgroundHernan, M. A. (2002) · American Journal of Epidemiology · doi:10.1093/aje/155.2.176 · Cited by 1,445
Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores
Vilhjálmsson, B. J., Yang, J., Finucane, H. K., Gusev, A., Lindström, S., Ripke, S., Genovese, G., Loh, P.-R., Bhatia, G., Do, R., et al. (2015) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2015.09.001 · Cited by 1,303
Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes
Mavaddat, N., Michailidou, K., Dennis, J., Lush, M. J., Fachal, L., Lee, A., Tyrer, J. P., Chen, T., Wang, Q., Bolla, M. K., et al. (2018) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2018.11.002 · Cited by 1,083
Genomic Risk Prediction of Coronary Artery Disease in 480,000 Adults
Inouye, M., Abraham, G., Nelson, C. P., Wood, A., Sweeting, M. J., Dudbridge, F., Lai, F. Y., Kaptoge, S. K., Brożyńska, M., Wang, T., et al. (2018) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2018.07.079 · Cited by 830 · cited 1×
“A recent trend in the construction of PRSs is to use millions of SNPs in the prediction of risks (27,57,58).”
MethodPolygenic Prediction of Weight and Obesity Trajectories from Birth to Adulthood
Khera, A. V., Chaffin, M., Wade, K. H., Zahid, S., Brancale, J., Xia, R., Distefano, M., Senol-Cosar, O., Haas, M. E., Bick, A., et al. (2019) · Cell · doi:10.1016/j.cell.2019.03.028 · Cited by 789 · cited 2×
“A recent trend in the construction of PRSs is to use millions of SNPs in the prediction of risks (27,57,58).”
BackgroundMethodAnalysing biological pathways in genome-wide association studies
Wang, K., Li, M., & Hakonarson, H. H. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2884 · Cited by 775
Genotype score in addition to common risk factors for prediction of type 2 diabetes
Meigs, J. B., Shrader, P., Sullivan, L. M., McAteer, J. B., Fox, C. S., Dupuis, J., Manning, A. K., Florez, J. C., Wilson, P. W. F., D'Agostino, R. B., et al. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0804742 · Cited by 759 · cited 1×
“Examples include baseline glucose level to predict type 2 diabetes later in life (40), social impairments to predict psychosis (41), childhood obesity to predict adult obesity (42) and education at younger ages to predict highest educational attainment (43).”
BackgroundLee, A., Mavaddat, N., Hurson, A. N., Cunningham, A. P., Carver, T. J., Hartley, S., de Villiers, C. B., Izquierdo, Á., Simard, J., Schmidt, M. K., et al. (2019) · Genetics in Medicine · doi:10.1038/s41436-018-0406-9 · Cited by 725 · cited 1×
“These independent effects have been observed for lifestyle in stroke (32), coronary heart disease (33,34), adiposity (35,36), diabetes (37) and breast cancer (38), and for stressful life events in depression (39).”
BackgroundAn Expanded Genome-Wide Association Study of Type 2 Diabetes in Europeans
Scott, R. A., Scott, L. J., Mägi, R., Marullo, L., Gaulton, K. J., Kaakinen, M., Pervjakova, N., Pers, T. H., Johnson, A. D., Eicher, J. D., et al. (2017) · Diabetes · doi:10.2337/db16-1253 · Cited by 708
Polymorphisms Associated with Cholesterol and Risk of Cardiovascular Events
Kathiresan, S., Melander, O., Anevski, D., Guiducci, C., Burtt, N. P., Roos, C., Hirschhorn, J. N., Berglund, G., Hedblad, B., Groop, L., et al. (2008) · New England Journal of Medicine · doi:10.1056/nejmoa0706728 · Cited by 696 · cited 1×
“Associations would however be expected as earlier studies on PRSs that included smaller numbers of selected SNPs did report associations with clinical risk factors (28,29), since PRSs are investigated for these intermediate phenotypes themselves, such as for obesity (30) and hypertension (31).”
ResultPrioritizing GWAS Results: A Review of Statistical Methods and Recommendations for Their Application
Cantor, R. M., Lange, K. L., & Sinsheimer, J. S. (2010) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2009.11.017 · Cited by 638
Association between a literature-based genetic risk score and cardiovascular events in women
Paynter, N. P. (2010) · JAMA · doi:10.1001/jama.2010.119 · Cited by 361
Sohail, M., Maier, R. M., Ganna, A., Bloemendal, A., Martin, A. R., Turchin, M. C., Chiang, C. W., Hirschhorn, J., Daly, M. J., Patterson, N., et al. (2019) · eLife · doi:10.7554/elife.39702 · Cited by 359 · cited 1×
“We need more insight in the variation of effect sizes within populations of the same ethnicity if we want to understand the generalizability of PRSs (53,54).”
BackgroundReduced signal for polygenic adaptation of height in UK Biobank
Berg, J. J., Harpak, A., Sinnott-Armstrong, N., Joergensen, A. M., Mostafavi, H., Field, Y., Boyle, E. A., Zhang, X., Racimo, F., Pritchard, J. K., et al. (2019) · eLife · doi:10.7554/elife.39725 · Cited by 339
Genomic prediction of coronary heart disease
Abraham, G., Havulinna, A. S., Bhalala, O. G., Byars, S. G., De Livera, A. M., Yetukuri, L., Tikkanen, E., Perola, M., Schunkert, H., Sijbrands, E. J., et al. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehw450 · Cited by 338 · cited 2×
“disease risk, and should they be included in the PRS? When a PRS is constructed based on 2 million of SNPs of which, say, 100 are able to change predicted risks before the decimal, will we tell a patient that their risk is calculated using 2 million or 100 SNPs? When researchers relax the SNP inclusion thresholds to i…”
BackgroundRutten‐Jacobs, L. C. A., Larsson, S. C., Malik, R., Rannikmäe, K., Sudlow, C. L. M., Dichgans, M., Markus, H. S., & Traylor, M. (2018) · BMJ · doi:10.1136/bmj.k4168 · Cited by 329 · cited 1×
“These independent effects have been observed for lifestyle in stroke (32), coronary heart disease (33,34), adiposity (35,36), diabetes (37) and breast cancer (38), and for stressful life events in depression (39).”
BackgroundGenome-based prediction of common diseases: advances and prospects
Janssens, A. C. J. W., & van Duijn, C. M. (2008) · Human Molecular Genetics · doi:10.1093/hmg/ddn250 · Cited by 319 · cited 1×
“SNPs that play a role in the pathways that lead to disease through these clinical risk factors or early-life stages may be associated with disease risk through these intermediate variables (44).”
BackgroundSelf-citationPredicting Polygenic Risk of Psychiatric Disorders
Martin, A. R., Daly, M. J., Robinson, E., Hyman, S. E., & Neale, B. M. (2018) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.12.015 · Cited by 301 · cited 1×
“Even though PRSs typically explain only a small proportion of the genotypic variance (2,9,10), the validity of the PRS as a measurement of polygenic predisposition remains largely undiscussed (3).”
BackgroundThe Contributions of Breast Density and Common Genetic Variation to Breast Cancer Risk
Vachon, C. M., Pankratz, V. S., Scott, C. G., Haeberle, L., Ziv, E., Jensen, M. R., Brandt, K. R., Whaley, D. H., Olson, J. E., Heusinger, K., et al. (2015) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/dju397 · Cited by 207 · cited 1×
“Independent effects have been reported for PRSs in breast cancer (23), coronary heart disease (24,25) and coronary artery disease (26,27) and were based on a formal mediation analysis (25), a statistically significant effect size for PRS after combining with clinical risk factors (26), and the absence of correlations…”
BackgroundGene set analysis of genome-wide association studies: Methodological issues and perspectives
Wang, L., Jia, P., Wolfinger, R. D., Chen, X., & Zhao, Z. (2011) · Genomics · doi:10.1016/j.ygeno.2011.04.006 · Cited by 207
A Critical Examination of the Concepts of Face Validity
Mosier, C. I. (1947) · Educational and Psychological Measurement · doi:10.1177/001316444700700201 · Cited by 200 · cited 1×
“Finally, the widespread adoption and uniform application of PRS suggest that the score certainly does have face validity, the fourth type of validity that is often distinguished: the score seems valid on its appearance (22).”
BackgroundGenomic prediction of cognitive traits in childhood and adolescence
Allegrini, A. G., Selzam, S., Rimfeld, K., von Stumm, S., Pingault, J., & Plomin, R. J. (2019) · Molecular Psychiatry · doi:10.1038/s41380-019-0394-4 · Cited by 182
Seddon, J. M., George, S., Rosner, B., & Klein, M. L. (2006) · Human Heredity · doi:10.1159/000094141 · Cited by 176 · cited 1×
“In the early days of genomewide association studies (GWASs), researchers considered their few newly identified SNPs as separate variables in the prediction of disease risks (4,5), and PRSs were a practical solution to include larger numbers of variants in the regression analyses (6).”
BackgroundPolygenic Type 2 Diabetes Prediction at the Limit of Common Variant Detection
Vassy, J. L., Hivert, M., Porneala, B., Dauriz, M., Florez, J. C., Dupuis, J., Siscovick, D. S., Fornage, M., Rasmussen‐Torvik, L. J., Bouchard, C., et al. (2014) · Diabetes · doi:10.2337/db13-1663 · Cited by 153 · cited 1×
“Vassy and colleagues investigated pathway specific PRSs in type 2 diabetes (51).”
BackgroundGenome-based prediction of common diseases: methodological considerations for future research
Janssens, A. C. J. W., & van Duijn, C. M. (2009) · Genome Medicine · doi:10.1186/gm20 · Cited by 146 · cited 3×
“In the early days of genomewide association studies (GWASs), researchers considered their few newly identified SNPs as separate variables in the prediction of disease risks (4,5), and PRSs were a practical solution to include larger numbers of variants in the regression analyses (6).”
BackgroundSelf-citationGenetic Prediction of Future Type 2 Diabetes
Lyssenko, V., Almgren, P., Anevski, D., Orho‐Melander, M., Sjögren, M., Saloranta, C., Tuomi, T., Groop, L., & the Botnia Study Group (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020345 · Cited by 144 · cited 1×
“In the early days of genomewide association studies (GWASs), researchers considered their few newly identified SNPs as separate variables in the prediction of disease risks (4,5), and PRSs were a practical solution to include larger numbers of variants in the regression analyses (6).”
BackgroundPolygenic Risk, Rapid Childhood Growth, and the Development of Obesity
Belsky, D. W., Moffitt, T. E., Houts, R., Bennett, G. G., Biddle, A. K., Blumenthal, J. A., Evans, J. P., Harrington, H., Sugden, K., Williams, B. S., et al. (2012) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/archpediatrics.2012.131 · Cited by 141 · cited 1×
“Examples include baseline glucose level to predict type 2 diabetes later in life (40), social impairments to predict psychosis (41), childhood obesity to predict adult obesity (42) and education at younger ages to predict highest educational attainment (43).”
BackgroundPolygenic risk scores: a biased prediction?
Vega, F. M. D. L., & Bustamante, C. D. (2018) · Genome Medicine · doi:10.1186/s13073-018-0610-x · Cited by 132 · cited 2×
“weighted sums of risk alleles as (1) the risk distributions are what Fisher predicted for a large number of variants with”
BackgroundMusliner, K. L., Seifuddin, F., Judy, J., Pirooznia, M., Goes, F. S., & Zandi, P. P. (2014) · Psychological Medicine · doi:10.1017/s0033291714002839 · Cited by 130 · cited 1×
“These independent effects have been observed for lifestyle in stroke (32), coronary heart disease (33,34), adiposity (35,36), diabetes (37) and breast cancer (38), and for stressful life events in depression (39).”
BackgroundShi, J., Park, J., Duan, J., Berndt, S. I., Moy, W., Yu, K. F., Song, L., Wheeler, W., Hua, X., Silverman, D. T., et al. (2016) · PLoS Genetics · doi:10.1371/journal.pgen.1006493 · Cited by 130
Predicting Polygenic Obesity Using Genetic Information
Loos, R. J. F., & Janssens, A. C. J. W. (2017) · Cell Metabolism · doi:10.1016/j.cmet.2017.02.013 · Cited by 120 · cited 1×
“Associations would however be expected as earlier studies on PRSs that included smaller numbers of selected SNPs did report associations with clinical risk factors (28,29), since PRSs are investigated for these intermediate phenotypes themselves, such as for obesity (30) and hypertension (31).”
ResultSelf-citationA century after Fisher: time for a new paradigm in quantitative genetics
Nelson, R. M., Pettersson, M. E., & Carlborg, Ö. (2013) · Trends in Genetics · doi:10.1016/j.tig.2013.09.006 · Cited by 116 · cited 5×
“While the concept of polygenic inheritance is centuries old and long lacked data to prove its merit, the calculation of risk scores developed from an empirical tradition with little attention for its theoretical foundation (3).”
BackgroundMethodInfluentialKnaus, A., Pantel, J. T., Pendziwiat, M., Hajjir, N., Zhao, M., Hsieh, T., Schubach, M., Gurovich, Y., Fleischer, N., Jäger, M., et al. (2018) · Genome Medicine · doi:10.1186/s13073-017-0510-5 · Cited by 84
Multi-locus models of genetic risk of disease
Wray, N. R., & Goddard, M. E. (2010) · Genome Medicine · doi:10.1186/gm131 · Cited by 83
Malik, R., Bevan, S., Nalls, M. A., Holliday, E. G., Devan, W. J., Cheng, Y.-C., Ibrahim‐Verbaas, C. A., Verhaaren, B. F. J., Bis, J. C., Joon, A. Y., et al. (2014) · Stroke · doi:10.1161/strokeaha.113.002938 · Cited by 67
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
Chung, W., Chen, J., Turman, C., Lindstrom, S., Zhu, Z., Loh, P.-R., Kraft, P., & Liang, L. (2019) · Nature Communications · doi:10.1038/s41467-019-08535-0 · Cited by 60
Genome‐wide association studies and polygenic risk scores for skin cancer: clinically useful yet?
Roberts, M., Asgari, M. M., & Toland, A. E. (2019) · British Journal of Dermatology · doi:10.1111/bjd.17917 · Cited by 59 · cited 1×
“Even though PRSs typically explain only a small proportion of the genotypic variance (2,9,10), the validity of the PRS as a measurement of polygenic predisposition remains largely undiscussed (3).”
BackgroundKonttinen, H. M., Llewellyn, C. H., Silventoinen, K., Joensuu, A., Männistö, S., Salomaa, V. V., Jousilahti, P., Kaprio, J. A., Perola, M., & Haukkala, A. (2017) · International Journal of Obesity · doi:10.1038/ijo.2017.278 · Cited by 52 · cited 1×
“These independent effects have been observed for lifestyle in stroke (32), coronary heart disease (33,34), adiposity (35,36), diabetes (37) and breast cancer (38), and for stressful life events in depression (39).”
BackgroundDietary and genetic risk scores and incidence of type 2 diabetes
Ericson, U., Hindy, G., Drake, I., Schulz, C., Brunkwall, L., Hellstrand, S., Almgren, P., & Orho‐Melander, M. (2018) · Genes & Nutrition · doi:10.1186/s12263-018-0599-1 · Cited by 49 · cited 1×
“These independent effects have been observed for lifestyle in stroke (32), coronary heart disease (33,34), adiposity (35,36), diabetes (37) and breast cancer (38), and for stressful life events in depression (39).”
BackgroundCecile, A., Janssens, A. C. J. W., & Joyner, M. J. (2019) · Clinical Chemistry · doi:10.1373/clinchem.2018.296103 · Cited by 44 · cited 2×
“And it is questioned whether the recent trend of including millions of SNPs challenges the validity of the definition of risk alleles as most their effect sizes are too small to produce ‘observable’ changes in the calculation of risks, even by the thousands (19).”
BackgroundSelf-citationChen, 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
Morieri, M. L., Gao, H., Pigeyre, M., Shah, H., Sjaarda, J., Mendonça, C., Hastings, T., Buranasupkajorn, P., Motsinger‐Reif, A. A., Rotroff, D. M., et al. (2018) · Diabetes Care · doi:10.2337/dc18-0709 · Cited by 39 · cited 2×
“2B), or the effect may be attenuated (33).”
BackgroundFritsche, L. G., Beesley, L. J., VandeHaar, P., Peng, R. B., Salvatore, M., Zawistowski, M., Taliun, S. A. G., Das, S., LeFaive, J., Kaleba, E. O., et al. (2019) · PLoS Genetics · doi:10.1371/journal.pgen.1008202 · Cited by 33
Concepts and Misconceptions about the Polygenic Additive Model Applied to Disease
Visscher, P. M., & Wray, N. R. (2015) · Human Heredity · doi:10.1159/000446931 · Cited by 30 · cited 1×
“weak effects (11,12), (2) there seems to be no evidence for strong gene–gene interaction (12) and (3) these PRSs generally hold their discriminative ability in external validation samples”
BackgroundGenetic risk for schizophrenia and autism, social impairment and developmental pathways to psychosis
Velthorst, E., Froudist‐Walsh, S., Stahl, E. A., Ruderfer, D. M., Ivanov, I., Buxbaum, J. D., Børglum, A. D., Grove, J., Mattheisen, M., Werge, T. M., et al. (2018) · Translational Psychiatry · doi:10.1038/s41398-018-0229-0 · Cited by 28 · cited 1×
“Examples include baseline glucose level to predict type 2 diabetes later in life (40), social impairments to predict psychosis (41), childhood obesity to predict adult obesity (42) and education at younger ages to predict highest educational attainment (43).”
BackgroundValidation of Polygenic Scores for QT Interval in Clinical Populations
Rosenberg, M. A., Lubitz, S. A., Lin, H., Kosova, G., Castro, V. M., Huang, P. L., Ellinor, P. T., Perlis, R. H., & Newton‐Cheh, C. (2017) · Circulation Cardiovascular Genetics · doi:10.1161/circgenetics.117.001724 · Cited by 23 · cited 1×
“There may be no benefit for relaxing threshold beyond GWA significance (63), and there may be no good reason to go beyond the genomewide significant SNPs for PRSs that are to be used in healthcare.”
BackgroundFritz, J., Shiffman, D., Melander, O., Tada, H., & Ulmer, H. (2017) · Journal of the American Heart Association · doi:10.1161/jaha.116.005254 · Cited by 18 · cited 1×
“Independent effects have been reported for PRSs in breast cancer (23), coronary heart disease (24,25) and coronary artery disease (26,27) and were based on a formal mediation analysis (25), a statistically significant effect size for PRS after combining with clinical risk factors (26), and the absence of correlations…”
BackgroundKrogager, M. L., Skals, R. K., Appel, E. V. R., Schnurr, T. M., Engelbrechtsen, L., Have, C. T., Pedersen, O. B., Engstrøm, T., Roden, D. M., Gislason, G., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0208645 · Cited by 17 · cited 1×
“Associations would however be expected as earlier studies on PRSs that included smaller numbers of selected SNPs did report associations with clinical risk factors (28,29), since PRSs are investigated for these intermediate phenotypes themselves, such as for obesity (30) and hypertension (31).”
ResultDiscriminative accuracy of genomic profiling comparing multiplicative and additive risk models
Moonesinghe, R., Khoury, M. J., Liu, T., & Janssens, A. C. J. W. (2010) · European Journal of Human Genetics · doi:10.1038/ejhg.2010.165 · Cited by 15 · cited 1×
“Two studies that compared the additive model with alternatives showed that not only the weighted sum of risk alleles was compatible with empirical data but that several other mathematical models fitted as well when effects at single loci are small (17,18).”
ResultSelf-citationRevisiting the Polygenic Additive Liability Model through the Example of Diabetes Mellitus
Génin, E., & Clerget‐Darpoux, F. (2015) · Human Heredity · doi:10.1159/000447683 · Cited by 15 · cited 2×
“‘Residual’ effects may be observed based on measurement error in the clinical variables and in the genetic data if SNPs do not accurately capture the underlying risk-increasing associations (16).”
BackgroundAdditional value of a combined genetic risk score to standard cardiovascular stratification
Pereira, A., Mendonça, M. I., Borges, S., Sousa, A. C., Freitas, S., Henriques, E., Rodrigues, M. F., Freitas, A. I., Guerra, G., Freitas, C., et al. (2018) · Genetics and Molecular Biology · doi:10.1590/1678-4685-gmb-2017-0173 · Cited by 9 · cited 1×
“Independent effects have been reported for PRSs in breast cancer (23), coronary heart disease (24,25) and coronary artery disease (26,27) and were based on a formal mediation analysis (25), a statistically significant effect size for PRS after combining with clinical risk factors (26), and the absence of correlations…”
BackgroundCalvin, C. M., Hagenaars, S. P., Gallacher, J., Harris, S. E., Davies, G., Liewald, D. C. M., Galé, C. R., & Deary, I. J. (2019) · Scientific Reports · doi:10.1038/s41598-018-36629-0 · Cited by 9
Estimating and testing direct genetic effects in directed acyclic graphs using estimating equations
Konigorski, S., Wang, Y., Çiğşar, C., & Yilmaz, Y. E. (2017) · Genetic Epidemiology · doi:10.1002/gepi.22107 · Cited by 2
Flagged first, then by citations
Research works printed but not linked: 1
- Multhaup, 2019, White Paper: the Science Behind 23andMe’s Type 2 Diabetes Report
Cited by
Sorted by citations
Tutorial: a guide to performing polygenic risk score analyses
Choi, S. W., Mak, T. S. H., & O’Reilly, P. F. (2020) · Nature Protocols · doi:10.1038/s41596-020-0353-1 · Cited by 2,058
Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365
Polygenic risk scores: from research tools to clinical instruments
Lewis, C. M., & Vassos, E. (2020) · Genome Medicine · doi:10.1186/s13073-020-00742-5 · Cited by 1,360
“Despite methodological concerns about construct, content, and criterion validity of PRS [1], many studies have shown that PRSs can predict disease status in researchbased case-control studies [2–4].”
BackgroundImproving reporting standards for polygenic scores in risk prediction studies
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. (2021) · Nature · doi:10.1038/s41586-021-03243-6 · Cited by 484
Self-citationAdult Hippocampal Neurogenesis in Major Depressive Disorder and Alzheimer’s Disease
Berger, T., Lee, H. A., Young, A. H., Aarsland, D., & Thuret, S. (2020) · Trends in Molecular Medicine · doi:10.1016/j.molmed.2020.03.010 · Cited by 206
Metadata
- Authors
- Janssens, A. C. J. W.
- Affiliations
- Emory University (US)
- Corresponding author
- Janssens, A Cecile J W
- Venue
- Human Molecular Genetics
- Publisher
- Oxford University Press (OUP)
- Published
- 2019-08-28
- Volume
- 28
- Issue
- R2
- Pages
- R143-R150
- ISSN
- 0964-6906 (ISSN-L)
- 1460-2083
- PMC
- PMC7013150
- OpenAlex
- W2970478489
- Cited by
- 129
- References
- 64
- Citations by year
- 2026: 13 · 2025: 14 · 2024: 24 · 2023: 16 · 2022: 31 · 2021: 22 · 2020: 7 · 2019: 1
- FWCI
- 2.04
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Human Molecular Genetics (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genomics and Rare Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic and phenotypic traits in livestock
- Keywords
- polygenic risk score, validity, construct validity, content validity, criterion validity, psychometrics, single nucleotide polymorphisms, SNP selection, genetic susceptibility, disease risk prediction, clinical risk factors
- MeSH terms
- Alleles, Diabetes Mellitus, Type 2, Genotype, Humans, Models, Genetic, Psychometrics, Risk Factors, Reproducibility of Results, Genetic Predisposition to Disease, Multifactorial Inheritance, Polymorphism, Single Nucleotide, Genome-Wide Association Study
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