Assessing the performance of genome-wide association studies for predicting disease risk

Autoři: Jonas Patron aff001;  Arnau Serra-Cayuela aff001;  Beomsoo Han aff001;  Carin Li aff001;  David Scott Wishart aff001
Působiště autorů: Department of Biological Sciences, University of Alberta, Edmonton, Canada aff001;  Department of Computing Science, University of Alberta, Edmonton, Canada aff002
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article


To date more than 3700 genome-wide association studies (GWAS) have been published that look at the genetic contributions of single nucleotide polymorphisms (SNPs) to human conditions or human phenotypes. Through these studies many highly significant SNPs have been identified for hundreds of diseases or medical conditions. However, the extent to which GWAS-identified SNPs or combinations of SNP biomarkers can predict disease risk is not well known. One of the most commonly used approaches to assess the performance of predictive biomarkers is to determine the area under the receiver-operator characteristic curve (AUROC). We have developed an R package called G-WIZ to generate ROC curves and calculate the AUROC using summary-level GWAS data. We first tested the performance of G-WIZ by using AUROC values derived from patient-level SNP data, as well as literature-reported AUROC values. We found that G-WIZ predicts the AUROC with <3% error. Next, we used the summary level GWAS data from GWAS Central to determine the ROC curves and AUROC values for 569 different GWA studies spanning 219 different conditions. Using these data we found a small number of GWA studies with SNP-derived risk predictors that have very high AUROCs (>0.75). On the other hand, the average GWA study produces a multi-SNP risk predictor with an AUROC of 0.55. Detailed AUROC comparisons indicate that most SNP-derived risk predictions are not as good as clinically based disease risk predictors. All our calculations (ROC curves, AUROCs, explained heritability) are in a publicly accessible database called GWAS-ROCS ( The G-WIZ code is freely available for download at

Klíčová slova:

Biomarkers – Crohn's disease – Genome-wide association studies – Heredity – Hypertension – Rheumatoid arthritis


1. Klein RJ, Zeiss C, Chew EY, Tsai J-Y, Sackler RS, Haynes C, et al. Complement factor H polymorphism in age-related macular degeneration. Science. 2005 Apr 15;308(5720):385–9. doi: 10.1126/science.1109557 15761122

2. Jansen PR, Watanabe K, Stringer S, Skene N, Bryois J, Hammerschlag AR, et al. Genome-wide Analysis of Insomnia (N = 1,331,010) Identifies Novel Loci and Functional Pathways. bioRxiv. 2018 Feb 1;214973.

3. Beck T, Hastings RK, Gollapudi S, Free RC, Brookes AJ. GWAS Central: a comprehensive resource for the comparison and interrogation of genome-wide association studies. Eur J Hum Genet EJHG. 2014 Jul;22(7):949–52. doi: 10.1038/ejhg.2013.274 24301061

4. MacArthur J, Bowler E, Cerezo M, Gil L, Hall P, Hastings E, et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 2017 Jan 4;45(D1):D896–901. doi: 10.1093/nar/gkw1133 27899670

5. Manolio TA. Genomewide association studies and assessment of the risk of disease. N Engl J Med. 2010 Jul 8;363(2):166–76. doi: 10.1056/NEJMra0905980 20647212

6. Reed E, Nunez S, Kulp D, Qian J, Reilly MP, Foulkes AS. A guide to genome‐wide association analysis and post‐analytic interrogation. Stat Med. 2015 Dec 10;34(28):3769–92. doi: 10.1002/sim.6605 26343929

7. Xia J, Broadhurst DI, Wilson M, Wishart DS. Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics. 2013 Apr;9(2):280–99. doi: 10.1007/s11306-012-0482-9 23543913

8. Song L, Liu A, Consortium MGOS, Shi J, Gejman P, Sanders A, et al. SummaryAUC: a tool for evaluating the performance of polygenic risk prediction models in validation datasets with only summary level statistics. Bioinformatics. 2019 Mar 26. btz176. doi: 10.1093/bioinformatics/btz176 30911754

9. Moonesinghe R, Liu T, Khoury MJ. Evaluation of the discriminative accuracy of genomic profiling in the prediction of common complex diseases. Eur J Hum Genet EJHG. 2010 Apr;18(4):485–9. doi: 10.1038/ejhg.2009.209 19935832

10. Gail MH. Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk. J Natl Cancer Inst. 2008 Jul 16;100(14):1037–41. doi: 10.1093/jnci/djn180 18612136

11. Janssens ACJW, Aulchenko YS, Elefante S, Borsboom GJJM, Steyerberg EW, van Duijn CM. Predictive testing for complex diseases using multiple genes: fact or fiction? Genet Med Off J Am Coll Med Genet. 2006 Jul;8(7):395–400.

12. Pepe MS, Gu JW, Morris DE. The potential of genes and other markers to inform about risk. Cancer Epidemiol Biomark Prev Publ Am Assoc Cancer Res Cosponsored Am Soc Prev Oncol. 2010 Mar;19(3):655–65.

13. Lu Q, Elston RC. Using the optimal receiver operating characteristic curve to design a predictive genetic test, exemplified with type 2 diabetes. Am J Hum Genet. 2008 Mar;82(3):641–51. doi: 10.1016/j.ajhg.2007.12.025 18319073

14. Bitton A, Gaziano T. The Framingham Heart Study’s Impact on Global Risk Assessment. Prog Cardiovasc Dis. 2010;53(1):68–78. doi: 10.1016/j.pcad.2010.04.001 20620429

15. Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature. 2007 Jun 7;447(7145):661–78. doi: 10.1038/nature05911 17554300

16. Shifman S, Kuypers J, Kokoris M, Yakir B, Darvasi A. Linkage disequilibrium patterns of the human genome across populations. Hum Mol Genet. 2003 Apr 1;12(7):771–6. doi: 10.1093/hmg/ddg088 12651872

17. Turner S, Armstrong LL, Bradford Y, Carlson CS, Crawford DC, Crenshaw AT, et al. Quality Control Procedures for Genome Wide Association Studies. Curr Protoc Hum Genet Editor Board Jonathan Haines Al. 2011 Jan;CHAPTER:Unit1.19.

18. Marees AT, de Kluiver H, Stringer S, Vorspan F, Curis E, Marie‐Claire C, et al. A tutorial on conducting genome‐wide association studies: Quality control and statistical analysis. Int J Methods Psychiatr Res. 2018 Jun;27(2).

19. Bush WS, Moore JH. Chapter 11: Genome-Wide Association Studies. PLoS Comput Biol. 2012 Dec 27;8(12): e1002822. doi: 10.1371/journal.pcbi.1002822 23300413

20. Forberg JL, Green M, Björk J, Ohlsson M, Edenbrandt L, Öhlin H, et al. In search of the best method to predict acute coronary syndrome using only the electrocardiogram from the emergency department. J Electrocardiol. 2009 Jan 1;42(1):58–63. doi: 10.1016/j.jelectrocard.2008.07.010 18804783

21. Gulkesen KH, Koksal IT, Bilge U, Saka O. Comparison of methods for prediction of prostate cancer in Turkish men with PSA levels of 0–10 ng/mL. J Balk Union Oncol. 2010 Sep;15(3):537–42.

22. van der Ploeg T, Smits M, Dippel DW, Hunink M, Steyerberg EW. Prediction of intracranial findings on CT-scans by alternative modelling techniques. BMC Med Res Methodol. 2011 Oct 25;11(1):143.

23. Wu J, Roy J, Stewart WF. Prediction Modeling Using EHR Data: Challenges, Strategies, and a Comparison of Machine Learning Approaches. Med Care. 2010 Jun;48:S106–13. doi: 10.1097/MLR.0b013e3181de9e17 20473190

24. Lee HJ, Hwang SI, Han S, Park SH, Kim SH, Cho JY, et al. Image-based clinical decision support for transrectal ultrasound in the diagnosis of prostate cancer: comparison of multiple logistic regression, artificial neural network, and support vector machine. Eur Radiol. 2010 Jun 1;20(6):1476–84. doi: 10.1007/s00330-009-1686-x 20016902

25. Muniz AMS, Liu H, Lyons KE, Pahwa R, Liu W, Nobre FF, et al. Comparison among probabilistic neural network, support vector machine and logistic regression for evaluating the effect of subthalamic stimulation in Parkinson disease on ground reaction force during gait. J Biomech. 2010 Mar 3;43(4):720–6. doi: 10.1016/j.jbiomech.2009.10.018 19914622

26. Kim S, Kim W, Park RW. A Comparison of Intensive Care Unit Mortality Prediction Models through the Use of Data Mining Techniques. Healthc Inform Res. 2011 Dec 1;17(4):232–43. doi: 10.4258/hir.2011.17.4.232 22259725

27. Midi H, Sarkar SK, Rana S. Collinearity diagnostics of binary logistic regression model. J Interdiscip Math. 2010 Jun 1;13(3):253–67.

28. McDonald GC. Ridge regression. Wiley Interdiscip Rev Comput Stat. 2009 Jul 1;1(1):93–100.

29. R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. [Internet]. 2018.

30. Bischl B, Lang M, Kotthoff L, Schiffner J, Richter J, Studerus E, et al. mlr: Machine Learning in R. J Mach Learn Res. 2016;17(170):1–5.

31. Robin X, Turck N, Hainard A, Tiberti N, Lisacek F, Sanchez J-C, et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics. 2011;12(1):77.

32. Pawitan Y, Seng KC, Magnusson PKE. How Many Genetic Variants Remain to Be Discovered? PLoS ONE. 2009 Dec 2;4(12): e7969. doi: 10.1371/journal.pone.0007969 19956539

33. DeLong ER, DeLong DM, Clarke-Pearson DL. Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics. 1988 Sep;44(3):837–45. 3203132

34. Lango H, Palmer CNA, Morris AD, Zeggini E, Hattersley AT, McCarthy MI, et al. Assessing the Combined Impact of 18 Common Genetic Variants of Modest Effect Sizes on Type 2 Diabetes Risk. Diabetes. 2008 Nov;57(11):3129–35. doi: 10.2337/db08-0504 18591388

35. Sparsø T, Grarup N, Andreasen C, Albrechtsen A, Holmkvist J, Andersen G, et al. Combined analysis of 19 common validated type 2 diabetes susceptibility gene variants shows moderate discriminative value and no evidence of gene–gene interaction. Diabetologia. 2009 Jul 1;52(7):1308–14. doi: 10.1007/s00125-009-1362-3 19404609

36. Weedon MN, McCarthy MI, Hitman G, Walker M, Groves CJ, Zeggini E, et al. Combining information from common type 2 diabetes risk polymorphisms improves disease prediction. PLoS Med. 2006 Oct;3(10):e374. doi: 10.1371/journal.pmed.0030374 17020404

37. Miyake K, Yang W, Hara K, Yasuda K, Horikawa Y, Osawa H, et al. Construction of a prediction model for type 2 diabetes mellitus in the Japanese population based on 11 genes with strong evidence of the association. J Hum Genet. 2009 Apr;54(4):236–41. doi: 10.1038/jhg.2009.17 19247372

38. Dunlop MG, Tenesa A, Farrington SM, Ballereau S, Brewster DH, Koessler T, et al. Cumulative impact of common genetic variants and other risk factors on colorectal cancer risk in 42,103 individuals. Gut. 2013 Jun;62(6):871–81. doi: 10.1136/gutjnl-2011-300537 22490517

39. Hu C, Zhang R, Wang C, Wang J, Ma X, Lu J, et al. PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 are associated with type 2 diabetes in a Chinese population. PloS One. 2009 Oct 28;4(10):e7643. doi: 10.1371/journal.pone.0007643 19862325

40. Fontaine-Bisson B, Renström F, Rolandsson O, MAGIC, Payne F, Hallmans G, et al. Evaluating the discriminative power of multi-trait genetic risk scores for type 2 diabetes in a northern Swedish population. Diabetologia. 2010 Oct;53(10):2155–62. doi: 10.1007/s00125-010-1792-y 20571754

41. Davies RW, Dandona S, Stewart AFR, Chen L, Ellis SG, Tang WHW, et al. Improved prediction of cardiovascular disease based on a panel of single nucleotide polymorphisms identified through genome-wide association studies. Circ Cardiovasc Genet. 2010 Oct;3(5):468–74. doi: 10.1161/CIRCGENETICS.110.946269 20729558

42. Chen H, Poon A, Yeung C, Helms C, Pons J, Bowcock AM, et al. A genetic risk score combining ten psoriasis risk loci improves disease prediction. PloS One. 2011 Apr 29;6(4):e19454. doi: 10.1371/journal.pone.0019454 21559375

43. Nakaoka H, Cui T, Tajima A, Oka A, Mitsunaga S, Kashiwase K, et al. A systems genetics approach provides a bridge from discovered genetic variants to biological pathways in rheumatoid arthritis. PloS One. 2011;6(9):e25389. doi: 10.1371/journal.pone.0025389 21980439

44. Darabi H, Czene K, Zhao W, Liu J, Hall P, Humphreys K. Breast cancer risk prediction and individualised screening based on common genetic variation and breast density measurement. Breast Cancer Res BCR. 2012 Feb 7;14(1):R25. doi: 10.1186/bcr3110 22314178

45. Hüsing A, Canzian F, Beckmann L, Garcia-Closas M, Diver WR, Thun MJ, et al. Prediction of breast cancer risk by genetic risk factors, overall and by hormone receptor status. J Med Genet. 2012 Sep 1;49(9):601–8. doi: 10.1136/jmedgenet-2011-100716 22972951

46. Akamatsu S, Takahashi A, Takata R, Kubo M, Inoue T, Morizono T, et al. Reproducibility, performance, and clinical utility of a genetic risk prediction model for prostate cancer in Japanese. PloS One. 2012;7(10):e46454. doi: 10.1371/journal.pone.0046454 23071574

47. Li H, Yang L, Zhao X, Wang J, Qian J, Chen H, et al. Prediction of lung cancer risk in a Chinese population using a multifactorial genetic model. BMC Med Genet. 2012 Dec 10;13:118. doi: 10.1186/1471-2350-13-118 23228068

48. Chang J, Huang Y, Wei L, Ma B, Miao X, Li Y, et al. Risk prediction of esophageal squamous-cell carcinoma with common genetic variants and lifestyle factors in Chinese population. Carcinogenesis. 2013 Aug;34(8):1782–6. doi: 10.1093/carcin/bgt106 23536576

49. Scott IC, Seegobin SD, Steer S, Tan R, Forabosco P, Hinks A, et al. Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking. PLoS Genet. 2013;9(9):e1003808. doi: 10.1371/journal.pgen.1003808 24068971

50. Bruzelius M, Bottai M, Sabater-Lleal M, Strawbridge RJ, Bergendal A, Silveira A, et al. Predicting venous thrombosis in women using a combination of genetic markers and clinical risk factors. J Thromb Haemost JTH. 2015 Feb;13(2):219–27. doi: 10.1111/jth.12808 25472531

51. Sleegers K, Bettens K, De Roeck A, Van Cauwenberghe C, Cuyvers E, Verheijen J, et al. A 22-single nucleotide polymorphism Alzheimer’s disease risk score correlates with family history, onset age, and cerebrospinal fluid Aβ42. Alzheimers Dement J Alzheimers Assoc. 2015 Dec;11(12):1452–60.

52. Ibáñez-Sanz G, Díez-Villanueva A, Alonso MH, Rodríguez-Moranta F, Pérez-Gómez B, Bustamante M, et al. Risk Model for Colorectal Cancer in Spanish Population Using Environmental and Genetic Factors: Results from the MCC-Spain study. Sci Rep. 2017 24;7:43263. doi: 10.1038/srep43263 28233817

53. Wang N, Wang Z, Wang C, Fu X, Yu G, Yue Z, et al. Prediction of leprosy in the Chinese population based on a weighted genetic risk score. PLoS Negl Trop Dis. 2018;12(9):e0006789. doi: 10.1371/journal.pntd.0006789 30231057

54. Taylor KD, Guo X, Zangwill LM, Liebmann JM, Girkin CA, Feldman RM, et al. Genetic Architecture of Primary Open-Angle Glaucoma in Individuals of African Descent: The African Descent and Glaucoma Evaluation Study III. Ophthalmology. 2019 Jan;126(1):38–48. doi: 10.1016/j.ophtha.2018.10.031 30352225

55. Lin BD, Mbarek H, Willemsen G, Dolan CV, Fedko IO, Abdellaoui A, et al. Heritability and Genome-Wide Association Studies for Hair Color in a Dutch Twin Family Based Sample. Genes. 2015 Jul 13;6(3):559–76. doi: 10.3390/genes6030559 26184321

56. GWAS-ROCS Database: Showing GR-Card for GR00070: Black vs. non-black hair color (HGVRS4156) [Internet]. [cited 2019 Sept 13].

57. Crosslin DR, Carrell DS, Burt A, Kim DS, Underwood JG, Hanna DS, et al. Genetic variation in the HLA region is associated with susceptibility to herpes zoster. Genes Immun. 2015 Feb;16(1):1–7. doi: 10.1038/gene.2014.51 25297839

58. GWAS-ROCS Database: Showing GR-Card for GR00478: Shingles (HGVRS5860) [Internet]. [cited 2019 Sept 13].

59. Mapstone M, Cheema AK, Fiandaca MS, Zhong X, Mhyre TR, MacArthur LH, et al. Plasma phospholipids identify antecedent memory impairment in older adults. Nat Med. 2014 Apr;20(4):415–8. doi: 10.1038/nm.3466 24608097

60. Barnes DE, Covinsky KE, Whitmer RA, Kuller LH, Lopez OL, Yaffe K. Dementia Risk Indices: A Framework for Identifying Individuals with a High Dementia Risk. Alzheimers Dement J Alzheimers Assoc. 2010 Mar;6(2):138–41.

61. Xiao Q, Liu Z-J, Tao S, Sun Y-M, Jiang D, Li H-L, et al. Risk prediction for sporadic Alzheimer’s disease using genetic risk score in the Han Chinese population. Oncotarget. 2015 Nov 10;6(35):36955–64. doi: 10.18632/oncotarget.6271 26543236

62. Floegel A, Stefan N, Yu Z, Mühlenbruch K, Drogan D, Joost H-G, et al. Identification of Serum Metabolites Associated With Risk of Type 2 Diabetes Using a Targeted Metabolomic Approach. Diabetes. 2013 Feb 1;62(2):639–48. doi: 10.2337/db12-0495 23043162

63. Paprott R, Mühlenbruch K, Mensink GBM, Thiele S, Schulze MB, Scheidt-Nave C, et al. Validation of the German Diabetes Risk Score among the general adult population: findings from the German Health Interview and Examination Surveys. BMJ Open Diabetes Res Care. 2016 Nov 1;4(1):e000280. doi: 10.1136/bmjdrc-2016-000280 27933187

64. Walford GA, Porneala BC, Dauriz M, Vassy JL, Cheng S, Rhee EP, et al. Metabolite Traits and Genetic Risk Provide Complementary Information for the Prediction of Future Type 2 Diabetes. Diabetes Care. 2014 Sep;37(9):2508–14. doi: 10.2337/dc14-0560 24947790

65. Buitendijk GHS, Rochtchina E, Myers C, van Duijn CM, Lee KE, Klein BEK, et al. Prediction of age-related macular degeneration in the general population: the Three Continent AMD Consortium. Ophthalmology. 2013 Dec;120(12):2644–55. doi: 10.1016/j.ophtha.2013.07.053 24120328

66. Grassmann F, Fritsche LG, Keilhauer CN, Heid IM, Weber BHF. Modelling the Genetic Risk in Age-Related Macular Degeneration. PLoS ONE. 2012 May 30;7(5): e37979. doi: 10.1371/journal.pone.0037979 22666427

67. Pujos-Guillot E, Brandolini M, Pétéra M, Grissa D, Joly C, Lyan B, et al. Systems Metabolomics for Prediction of Metabolic Syndrome. J Proteome Res. 2017 2;16(6):2262–72. doi: 10.1021/acs.jproteome.7b00116 28440083

68. Bener A, Yousafzai MT, Darwish S, Al-Hamaq AOAA, Nasralla EA, Abdul-Ghani M. Obesity index that better predict metabolic syndrome: body mass index, waist circumference, waist hip ratio, or waist height ratio. J Obes. 2013;2013:269038. doi: 10.1155/2013/269038 24000310

69. van Pelt ED, Mescheriakova JY, Makhani N, Ketelslegers IA, Neuteboom RF, Kundu S, et al. Risk genes associated with pediatric-onset MS but not with monophasic acquired CNS demyelination. Neurology. 2013 Dec 3;81(23):1996–2001. doi: 10.1212/01.wnl.0000436934.40034eb 24198294

70. Qiu Y, Cai G, Zhou B, Li D, Zhao A, Xie G, et al. A distinct metabolic signature of human colorectal cancer with prognostic potential. Clin Cancer Res Off J Am Assoc Cancer Res. 2014 Apr 15;20(8):2136–46.

71. Qiao J, Fang C-Y, Chen S-X, Wang X-Q, Cui S-J, Liu X-H, et al. Stroma derived COL6A3 is a potential prognosis marker of colorectal carcinoma revealed by quantitative proteomics. Oncotarget. 2015 Oct 6;6(30):29929–46. doi: 10.18632/oncotarget.4966 26338966

72. Kim Y, Jeon J, Mejia S, Yao CQ, Ignatchenko V, Nyalwidhe JO, et al. Targeted proteomics identifies liquid-biopsy signatures for extracapsular prostate cancer. Nat Commun. 2016 28;7:11906. doi: 10.1038/ncomms11906 27350604

73. Helfand BT, Fought AJ, Loeb S, Meeks JJ, Kan D, Catalona WJ. Genetic prostate cancer risk assessment: common variants in 9 genomic regions are associated with cumulative risk. J Urol. 2010 Aug;184(2):501–5. doi: 10.1016/j.juro.2010.04.032 20620408

74. Heffernan C, Doroshenko A, Egedahl ML, Barrie J, Senthilselvan A, Long R. Predicting pulmonary tuberculosis in immigrants: a retrospective cohort study. ERJ Open Res. 2018 Apr;4(2).

75. Hong EP, Go MJ, Kim H-L, Park JW. Risk prediction of pulmonary tuberculosis using genetic and conventional risk factors in adult Korean population. PloS One. 2017;12(3):e0174642. doi: 10.1371/journal.pone.0174642 28355295

76. Gander J. Factors Related to Coronary Heart Disease Risk Among Men: Validation of the Framingham Risk Score. Prev Chronic Dis. 2014 Aug 14;11:E140. doi: 10.5888/pcd11.140045 25121352

77. Kukava NG, Titov BV, Osmak GJ, Matveeva NA, Kulakova OG, Favorov AV, et al. Multilocus Analysis of Genetic Susceptibility to Myocardial Infarction in Russians: Replication Study. Acta Naturae. 2017;9(4):74–83. 29340220

78. Winkel RR, von Euler-Chelpin M, Nielsen M, Petersen K, Lillholm M, Nielsen MB, et al. Mammographic density and structural features can individually and jointly contribute to breast cancer risk assessment in mammography screening: a case–control study. BMC Cancer. 2016 Jul 7;16(1):414.

79. Chae YK, Gonzalez-Angulo AM. Implications of functional proteomics in breast cancer. The Oncologist. 2014 Apr;19(4):328–35. doi: 10.1634/theoncologist.2013-0437 24664486

80. Hsieh Y-C, Tu S-H, Su C-T, Cho E-C, Wu C-H, Hsieh M-C, et al. A polygenic risk score for breast cancer risk in a Taiwanese population. Breast Cancer Res Treat. 2017 May;163(1):131–8. doi: 10.1007/s10549-017-4144-5 28205043

81. Al-Mubarak R, Vander Heiden J, Broeckling CD, Balagon M, Brennan PJ, Vissa VD. Serum metabolomics reveals higher levels of polyunsaturated fatty acids in lepromatous leprosy: potential markers for susceptibility and pathogenesis. PLoS Negl Trop Dis. 2011 Sep;5(9):e1303. doi: 10.1371/journal.pntd.0001303 21909445

82. Haznadar M, Cai Q, Krausz KW, Bowman ED, Margono E, Noro R, et al. Urinary Metabolite Risk Biomarkers of Lung Cancer: A Prospective Cohort Study. Cancer Epidemiol Biomark Prev Oncol. 2016;25(6):978–86.

83. Raghu VK, Zhao W, Pu J, Leader JK, Wang R, Herman J, et al. Feasibility of lung cancer prediction from low-dose CT scan and smoking factors using causal models. Thorax. 2019 Jul 1;74(7):643–9. doi: 10.1136/thoraxjnl-2018-212638 30862725

84. Somers RH. A New Asymmetric Measure of Association for Ordinal Variables. Am Sociol Rev. 1962;27(6):799–811.

85. Mittlböck M, Schemper M. Explained variation for logistic regression. Stat Med. 1996 Oct 15;15(19):1987–97. doi: 10.1002/(SICI)1097-0258(19961015)15:19<1987::AID-SIM318>3.0.CO;2-9 8896134

86. Visscher PM, Wray NR, Zhang Q, Sklar P, McCarthy MI, Brown MA, et al. 10 Years of GWAS Discovery: Biology, Function, and Translation. Am J Hum Genet. 2017 Jul 6;101(1):5–22. doi: 10.1016/j.ajhg.2017.06.005 28686856

87. Sandoval-Motta S, Aldana M, Martínez-Romero E, Frank A. The Human Microbiome and the Missing Heritability Problem. Front Genet. 2017;8:80. doi: 10.3389/fgene.2017.00080 28659968

88. Visscher PM, Brown MA, McCarthy MI, Yang J. Five Years of GWAS Discovery. Am J Hum Genet. 2012 Jan 13;90(1):7–24. doi: 10.1016/j.ajhg.2011.11.029 22243964

89. Kundu S, Mihaescu R, Meijer CMC, Bakker R, Janssens ACJW. Estimating the predictive ability of genetic risk models in simulated data based on published results from genome-wide association studies. Front Genet. 2014; 5: 179. doi: 10.3389/fgene.2014.00179 24982668

90. Tsoi LC, Stuart PE, Tian C, Gudjonsson JE, Das S, Zawistowski M, et al. Large scale meta-analysis characterizes genetic architecture for common psoriasis associated variants. Nat Commun. 2017 May 24;8:15382. doi: 10.1038/ncomms15382 28537254

91. Yang S-K, Hong M, Zhao W, Jung Y, Baek J, Tayebi N, et al. Genome-wide association study of Crohn’s disease in Koreans revealed three new susceptibility loci and common attributes of genetic susceptibility across ethnic populations. Gut. 2014 Jan 1;63(1):80–7. doi: 10.1136/gutjnl-2013-305193 23850713

92. Fan Y, Song Y-Q. Finding the Missing Heritability of Genome-wide Association Study Using Genotype Imputation. Sci Matters. 2016 May 4. doi: 10.19185/matters.201604000013

93. The CARDIoGRAMplusC4D Consortium, Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, et al. Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet. 2013 Jan;45(1):25–33. doi: 10.1038/ng.2480 23202125

94. Fritsche LG, Igl W, Bailey JNC, Grassmann F, Sengupta S, Bragg-Gresham JL, et al. A large genome-wide association study of age-related macular degeneration highlights contributions of rare and common variants. Nat Genet. 2016 Feb;48(2):134–43. doi: 10.1038/ng.3448 26691988

95. Watanabe K, Stringer S, Frei O, Mirkov MU, Polderman TJC, van der Sluis S, et al. A global overview of pleiotropy and genetic architecture in complex traits. bioRxiv. 2018 Dec 19;500090.

96. Okada Y, Wu D, Trynka G, Raj T, Terao C, Ikari K, et al. Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature. 2014 Feb;506(7488):376–81. doi: 10.1038/nature12873 24390342

97. Seshadri S, Fitzpatrick AL, Ikram MA, DeStefano AL, Gudnason V, Boada M, et al. Genome-wide analysis of genetic loci associated with Alzheimer disease. JAMA. 2010 May 12;303(18):1832–40. doi: 10.1001/jama.2010.574 20460622

98. GWAS-ROCS Database: Showing GR-Card for GR00020: Alzheimer’s disease (HGVRS1094) [Internet]. [cited 2019 Sept 13].

99. Yu C-E, Seltman H, Peskind ER, Galloway N, Zhou PX, Rosenthal E, et al. Comprehensive Analysis of APOE and Selected Proximate Markers for Late-onset Alzheimer Disease: Pattern of Linkage Disequilibrium and Disease/Marker Association. Genomics. 2007 Jun;89(6):655–65. doi: 10.1016/j.ygeno.2007.02.002 17434289

100. GWAS-ROCS Database: Showing GR-Card for GR00299: Late onset Alzheimer’s disease (HGVRS1241) [Internet]. [cited 2019 Sept 13].

101. Nelson CP, Goel A, Butterworth AS, Kanoni S, Webb TR, Marouli E, et al. Association analyses based on false discovery rate implicate new loci for coronary artery disease. Nat Genet. 2017 Sep;49(9):1385–91. doi: 10.1038/ng.3913 28714975

102. GWAS-ROCS Database: Showing GR-Card for GR00171: Coronary artery disease (HGVRS4076) [Internet]. [cited 2019 Sept 13].

Článek vyšel v časopise


2019 Číslo 12
Nejčtenější tento týden