Developing and evaluating polygenic risk prediction models for stratified disease prevention

Nature Reviews Genetics

Volumn 17, Issue 7, 2016, Pages 392-406

  Chatterjee, Nilanjan ^a,b,c   Shi, Jianxin ^c   García Closas, Montserrat ^c  

^a JOHNS HOPKINS BLOOMBERG SCHOOL OF PUBLIC HEALTH   (United States)

^b JOHNS HOPKINS UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c NATIONAL INSTITUTES OF HEALTH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

BLADDER CANCER; BREAST CANCER; CALIBRATION; CLINICAL TRIAL (TOPIC); COLORECTAL CANCER; GENETIC ASSOCIATION STUDY; GENETIC RISK; GENETIC SCREENING; HERITABILITY; HUMAN; ISCHEMIC HEART DISEASE; PREDICTION; PRIORITY JOURNAL; PROPHYLAXIS; REVIEW; RISK ASSESSMENT; FAMILY HEALTH; GENETIC PREDISPOSITION; GENETICS; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; MULTIFACTORIAL INHERITANCE; PROCEDURES; RISK FACTOR;

FAMILY HEALTH; GENETIC PREDISPOSITION TO DISEASE; GENETIC TESTING; GENOME-WIDE ASSOCIATION STUDY; GENOMICS; HUMANS; MULTIFACTORIAL INHERITANCE; RISK FACTORS;

EID: 84965076156     PISSN: 14710056     EISSN: 14710064     Source Type: Journal    
DOI: 10.1038/nrg.2016.27     Document Type: Review

References (113)

1. Gabai-Kapara, E., et al. Population-based screening for breast ovarian cancer risk due to BRCA1 and BRCA2. Proc. Natl Acad. Sci. USA 111, 14205-14210 (2014
2. King, M. C., Levy-Lahad, E., & Lahad, A. Population-based screening for BRCA1 and BRCA2: 2014 Lasker Award. JAMA 312, 1091-1092 (2014
3. Easton, D. F., et al. Gene-panel sequencing and the prediction of breast-cancer risk. N. Engl. J. Med. 372, 2243-2257 (2015
4. Evans, B. J., Burke, W., & Jarvik, G. P. The FDA and genomic tests - getting regulation right. N. Engl. J. Med. 372, 2258-2264 (2015
5. Thomas, D. M., James, P. A., & Ballinger, M. L. Clinical implications of genomics for cancer risk genetics. Lancet Oncol. 16, e303-e308 (2015
6. Grosse, S. D., & Khoury, M. J. What is the clinical utility of genetic testing? Genet. Med. 8, 448-450 (2006
7. German National Cohort (GNC) Consortium.The German National Cohort: aims, study design and organization. Eur. J. Epidemiol. 29, 371-382 (2014
8. Pharoah, P. D., et al. Polygenic susceptibility to breast cancer and implications for prevention. Nat. Genet. 31, 33-36 (2002
9. Wray, N. R., et al. The genetic interpretation of area under the ROC curve in genomic profiling. PLoS Genet. 6, e1000864 (2010
10. Witte, J. S., Visscher, P. M., & Wray, N. R. The contribution of genetic variants to disease depends on the ruler. Nat. Rev. Genet. 15, 765-776 (2014
11. Yang, J., et al. Common SNPs explain a large proportion of the heritability for human height. Nat. Genet. 42, 565-569 (2010
12. Yang, J., et al. Advantages and pitfalls in the application of mixed-model association methods. Nat. Genet. 46, 100-106 (2014
13. Yang, J., et al. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76-82 (2011
14. Lee, S. H., et al. Estimation of SNP heritability from dense genotype data. Am. J. Hum. Genet. 93, 1151-1155 (2013
15. Lee, S. H., et al. Estimating missing heritability for disease from genome-wide association studies. Am. J. Hum. Genet. 88, 294-305 (2011
16. Lee, S. H., et al. Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nat. Genet. 44, 247-250 (2012
17. Lee, S. H., et al. Estimation and partitioning of polygenic variation captured by common SNPs for Alzheimer's disease, multiple sclerosis and endometriosis. Hum. Mol. Genet. 22, 832-841 (2013
18. Lu, Y., et al. Most common 'sporadic? cancers have a significant germline genetic component. Hum. Mol. Genet. 23, 6112-6118 (2014
19. Chen, G. B., et al. Estimation and partitioning of (co)heritability of inflammatory bowel disease from GWAS and immunochip data. Hum. Mol. Genet. 23, 4710-4720 (2014
20. Gusev, A., et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535-552 (2014
21. Sampson, J. N., et al. Analysis of heritability and shared heritability based on genome-wide association studies for thirteen cancer types. J. Natl Cancer Inst. 107, djv279 (2015
22. Zaitlen, N., et al. Using extended genealogy to estimate components of heritability for 23 quantitative and dichotomous traits. PLoS Genet. 9, e1003520 (2013
23. Zaitlen, N., et al. Leveraging population admixture to characterize the heritability of complex traits. Nat. Genet. 46, 1356-1362 (2014
24. Lichtenstein, P., et al. Environmental and heritable factors in the causation of cancer - analyses of cohorts of twins from Sweden, Denmark, and Finland. N. Engl. J. Med. 343, 78-85 (2000
25. Polderman, T. J., et al. Meta-analysis of the heritability of human traits based on fifty years of twin studies. Nat. Genet. 47, 702-709 (2015
26. Chang, E. T., et al. Reliability of self-reported family history of cancer in a large case-control study of lymphoma. J. Natl Cancer Inst. 98, 61-68 (2006
27. Mitchell, R. J., et al. Accuracy of reporting of family history of colorectal cancer. Gut 53, 291-295 (2004
28. Kerber, R. A., & Slattery, M. L. Comparison of self-reported and database-linked family history of cancer data in a case-control study. Am. J. Epidemiol. 146, 244-248 (1997
29. Speed, D., et al. Improved heritability estimation from genome-wide SNPs. Am. J. Hum. Genet. 91, 1011-1021 (2012
30. Golan, D. Lander, E. S., & Rosset S. Measuring missing heritability: inferring the contribution of common variants. Proc. Natl Acad. Sci. USA 111, E5272-E5281 (2014
31. Goldgar, D. E., et al. Systematic population-based assessment of cancer risk in first-degree relatives of cancer probands. J. Natl Cancer Inst. 86, 1600-1608 (1994
32. Kerber, R. A., & O?Brien, E. A cohort study of cancer risk in relation to family histories of cancer in the Utah population database. Cancer 103, 1906-1915 (2005
33. Czene, K. Lichtenstein, P., & Hemminki, K. Environmental and heritable causes of cancer among 9.6 million individuals in the Swedish Family-Cancer Database. Int. J. Cancer 99, 260-266 (2002
34. Mucci, L. A., et al. Familial risk and heritability of cancer among twins in nordic countries. JAMA 315, 68-76 (2016
35. Cox, D. R. Regression models and life-tables. J. R. Stat. Soc. Series B Stat. Methodol. 34, 187-220; discussion 202-220 (1972
36. Prentice, R. L., & Breslow, N. E. Retrospective studies and failure time models. Biometrika 65, 153-158 (1978
37. Hill, G., et al. Neyman's bias re visited. J. Clin. Epidemiol. 56, 293-296 (2003
38. Wacholder, S., et al. Selection of controls in case-control studies: I. Principles. Am. J. Epidemiol. 135, 1019-1028 (1992
39. Falconer, D. S. Inheritance of liability to diseases with variable age of onset with particular reference to diabetes mellitus. Ann. Hum. Genet. 31, 1-20 (1967
40. Aldrich, J. H., & Nelson, F. D. Linear Probability, Logit and Probit Models (SAGE, 1984
41. Rothman, K. J., Greenland, S., & Lash, T. L. Modern Epidemiology 3rd edn (Lippincott, Williams and Wilkins, 2008
42. Joshi, A. D., et al. Additive interactions between susceptibility single-nucleotide polymorphisms identified in genome-wide association studies and breast cancer risk factors in the Breast and Prostate Cancer Cohort Consortium. Am. J. Epidemiol. 180, 1018-1027 (2014
43. Song, M., et al. Testing calibration of risk models at extremes of disease risk. Biostatistics 16, 143-154 (2015
44. Barrdahl, M., et al. Post-GWAS gene-environment interplay in breast cancer: results from the Breast and Prostate Cancer Cohort Consortium and a meta-analysis on 79,000 women. Hum. Mol. Genet. 23, 5260-5270 (2014
45. Langenberg, C., et al. Gene-lifestyle interaction and type 2 diabetes: the EPIC interact case-cohort study. PLoS Med. 11, e1001647 (2014
46. Rudolph, A., et al. Investigation of gene-environment interactions between 47 newly identified breast cancer susceptibility loci and environmental risk factors. Int. J. Cancer 136, E685-E696 (2015
47. Chatterjee, N., et al. Projecting the performance of risk prediction based on polygenic analyses of genome-wide association studies. Nat. Genet. 45, 400-405 (2013
48. Talmud, P. J., et al. Sixty-five common genetic variants and prediction of type 2 diabetes. Diabetes 64, 1830-1840 (2014
49. Dudbridge, F. Power and predictive accuracy of polygenic risk scores. PLoS Genet. 9, e1003348 (2013
50. Lee, S. H., & Wray, N. R. Novel genetic analysis for case-control genome-wide association studies: quantification of power and genomic prediction accuracy. PLoS ONE 8, e71494 (2013
51. Stahl, E. A., et al. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat. Genet. 44, 483-489 (2012
52. Mavaddat, N., et al. Prediction of breast cancer risk based on profiling with common genetic variants. J. Natl Cancer Inst. 107, djv036 (2015
53. Hsu, L., et al. A model to determine colorectal cancer risk using common genetic susceptibility loci. Gastroenterol. 148, 1330-1339 (2015
54. Bao, W., et al. Predicting risk of type 2 diabetes mellitus with genetic risk models on the basis of established genome-wide association markers: a systematic review. Am. J. Epidemiol. 178, 1197-1207 (2013
55. Krarup, N. T., et al. A genetic risk score of 45 coronary artery disease risk variants associates with increased risk of myocardial infarction in 6041 Danish individuals. Atherosclerosis 240, 305-310 (2015
56. Weissfeld, J. L., et al. Lung cancer risk prediction using common SNPs located in GWAS-identified susceptibility regions. J. Thorac. Oncol. 10, 1538-1545 (2015
57. Scott, I. C., et al. Predicting the risk of rheumatoid arthritis and its age of onset through modelling genetic risk variants with smoking. PLoS Genet. 9, e1003808 (2013
58. Abraham, G., et al. Accurate and robust genomic prediction of celiac disease using statistical learning. PLoS Genet. 10, e1004137 (2014
59. Romanos, J., et al. Improving coeliac disease risk prediction by testing non-HLA variants additional to HLA variants. Gut 63, 415-422 (2014
60. Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421-427 (2014
61. International Schizophrenia Consortium. Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460, 748-752 (2009
62. Psychiatric GWAS Consortium Bipolar Disorder Working Group. Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat. Genet. 43, 977-983 (2011
63. Bush, W. S., et al. Evidence for polygenic susceptibility to multiple sclerosis - the shape of things to come. Am. J. Hum. Genet. 86, 621-625 (2010
64. Wu, J., Pfeiffer, R. M., & Gail, M. H. Strategies for developing prediction models from genome-wide association studies. Genet. Epidemiol. 37, 768-777 (2013
65. Purcell, S., et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559-575 (2007
66. Vilhjalmsson, B. J., et al. Modeling linkage disequilibrium increases accuracy of polygenic risk scores. Am. J. Hum. Genet. 97, 576-592 (2015
67. Golan, D., & Rosset, S. Effective genetic-risk prediction using mixed models. Am. J. Hum. Genet. 95, 383-393 (2014
68. Moser, G., et al. Simultaneous discovery, estimation and prediction analysis of complex traits using a Bayesian mixture model. PLoS Genet. 11, e1004969 (2015
69. Speed, D., & Balding, D. J. MultiBLUP: improved SNP-based prediction for complex traits. Genome Res 24, 1550-1557 (2014
70. Zhou, X. Carbonetto, P., & Stephens, M. Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 9, e1003264 (2013
71. Schork, A. J., et al. All SNPs are not created equal: genome-wide association studies reveal a consistent pattern of enrichment among functionally annotated SNPs. PLoS Genet. 9, e1003449 (2013
72. Pickrell, J. K. Joint analysis of functional genomic data and genome-wide association studies of 18 human traits. Am. J. Hum. Genet. 94, 559-573 (2014
73. Andreassen, O. A., et al. Improved detection of common variants associated with schizophrenia by leveraging pleiotropy with cardiovascular-disease risk factors. Am. J. Hum. Genet. 92, 197-209 (2013
74. Andreassen, O. A., et al. Improved detection of common variants associated with schizophrenia and bipolar disorder using pleiotropy-informed conditional false discovery rate. PLoS Genet. 9, e1003455 (2013
75. Maier, R., et al. Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder. Am. J. Hum. Genet. 96, 283-294 (2015
76. Li, C., et al. Improving genetic risk prediction by leveraging pleiotropy. Hum. Genet. 133, 639-650 (2014
77. Gusev, A., et al. Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases. Am. J. Hum. Genet. 95, 535-552 (2014
78. Wacholder, S., Chatterjee, N., & Hartge, P. Joint effect of genes and environment distorted by selection biases: implications for hospital-based case-control studies. Cancer Epidemiol. Biomarkers Prev. 11, 885-889 (2002
79. Piegorsch, W. W., Weinberg, C. R., & Taylor, J. A. Non-hierarchical logistic models and case-only designs for assessing susceptibility in population-based case-control studies. Stat. Med. 13, 153-162 (1994
80. Umbach, D. M., & Weinberg, C. R. Designing and analysing case-control studies to exploit independence of genotype and exposure. Stat. Med. 16, 1731-1743 (1997
81. Chatterjee, N., & Carroll, R. J. Semiparametric maximum-likelihood estimation in case-control studies of gene-environment interactions. Biometrika 92, 399-418 (2005
82. Lee, A. J., et al. BOADICEA breast cancer risk prediction model: updates to cancer incidences, tumour pathology and web interface. Br. J. Cancer 110, 535-545 (2014
83. Mazzola, E., et al. Recent enhancements to the genetic risk prediction model BRCAPRO. Cancer Inform. 14 (Suppl. 2), 147-157 (2015
84. Aschard, H., et al. Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases. Am. J. Hum. Genet. 90, 962-972 (2012
85. Ganna, A., & Ingelsson, E. 5 year mortality predictors in 498,103 UK Biobank participants: a prospective population-based study. Lancet 386, 533-540 (2015
86. Hosmer, D. W., & Lemeshow, S. Applied Logistic Regression (Wiley, 1989
87. DeFilippis, A. P., et al. An analysis of calibration and discrimination among multiple cardiovascular risk scores in a modern multiethnic cohort. Ann. Intern. Med. 162, 266-275 (2015
88. Pfeiffer, R. M., & Gail, M. H. Two criteria for evaluating risk prediction models. Biometrics 67, 1057-1065 (2011
89. So, H. C., et al. Risk prediction of complex diseases from family history and known susceptibility loci, with applications for cancer screening. Am. J. Hum. Genet. 88, 548-565 (2011
90. Park, J. H., et al. Potential usefulness of single nucleotide polymorphisms to identify persons at high cancer risk: an evaluation of seven common cancers. J. Clin. Oncol. 30, 2157-2162 (2012
91. Pencina, M. J., et al. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat. Med. 27, 157-172; discussion 207-212 (2008
92. Pencina, M. J., D?Agostino, R. B., & Steyerberg, E. W. Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers. Stat. Med. 30, 11-21 (2011
93. Kerr, K. F., et al. Net reclassification indices for evaluating risk prediction instruments: a critical review. Epidemiology 25, 114-121 (2014
94. Pepe, M. S., Janes, H., & Li, C. I. Net risk reclassification P values: valid or misleading? J. Natl Cancer Inst. 106, dju041 (2014
95. Stone, N. J., et al. 2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 129, S1-S45 (2014
96. Mega, J. L., et al. Genetic risk, coronary heart disease events, and the clinical benefit of statin therapy: an analysis of primary and secondary prevention trials. Lancet 385, 2264-2271 (2015
97. Meads, C. Ahmed, I., & Riley, R. D. A systematic review of breast cancer incidence risk prediction models with meta-analysis of their performance. Breast Cancer Res. Treat. 132, 365-377 (2012
98. Burton, H., et al. Public health implications from COGS and potential for risk stratification and screening. Nat. Genet. 45, 349-351 (2013
99. Garcia-Closas, M., Gunsoy, N. B. and Chatterjee, N. Combined associations of genetic and environmental risk factors: implications for prevention of breast cancer. J. Natl Cancer Inst. 106, dju305 (2014
100. US Preventive Services Task Force. Final Update Summary - Colorectal Cancer: Screening. US Preventive Services Task Force [online], http://www.uspreventiveservicestaskforce.org/Page/Document/UpdateSummaryFinal/colorectal-cancer-screening (Oct 2008; updated Jul 2015
101. Garcia-Closas, M., et al. Common genetic polymorphisms modify the effect of smoking on absolute risk of bladder cancer. Cancer Res. 73, 2211-2220 (2013
102. Do, R., et al. Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction. Nature 518, 102-106 (2015
103. Purcell, S. M., et al. A polygenic burden of rare disruptive mutations in schizophrenia. Nature 506, 185-190 (2014
104. The UK10K Consortium. The UK10K project identifies rare variants in health and disease. Nature 526, 82-90 (2015
105. Mancuso, N., et al. The contribution of rare variation to prostate cancer heritability. Nat. Genet. 48, 30-35 (2016
106. Rogowski, W. H., Grosse, S. D., & Khoury, M. J. Challenges of translating genetic tests into clinical and public health practice. Nat. Rev. Genet. 10, 489-495 (2009
107. Grimshaw, J. M., et al. Knowledge translation of research findings. Implement Sci. 7, 50 (2012
108. Dent, T., et al. Stratified screening for cancer: recommendations and analysis from the COGS project (PHG Foundation, 2014
109. Khoury, M. J., Janssens, A. C., & Ransohoff, D. F. How can polygenic inheritance be used in population screening for common diseases? Genet. Med. 15, 437-443 (2013
110. Grosse, S. D., Wordsworth, S., & Payne, K. Economic methods for valuing the outcomes of genetic testing: beyond cost-effectiveness analysis. Genet. Med. 10, 648-654 (2008
111. Goddard, K. A., et al. Building the evidence base for decision making in cancer genomic medicine using comparative effectiveness research. Genet. Med. 14, 633-642 (2012
112. Gonzales, R., et al. A framework for training health professionals in implementation and dissemination science. Acad. Med. 87, 271-278 (2012
113. Gail, M. H., et al. Projecting individualized probabilities of developing breast-cancer for white females who are being examined annually. J. Natl Cancer Inst. 81, 1879-1886 (1989