Power and Predictive Accuracy of Polygenic Risk Scores

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Polygenic scores have recently been used to summarise genetic effects among an ensemble of markers that do not individually achieve significance in a large-scale association study. Markers are selected using an initial training sample and used to construct a score in an independent replication sample by forming the weighted sum of associated alleles within each subject. Association between a trait and this composite score implies that a genetic signal is present among the selected markers, and the score can then be used for prediction of individual trait values. This approach has been used to obtain evidence of a genetic effect when no single markers are significant, to establish a common genetic basis for related disorders, and to construct risk prediction models. In some cases, however, the desired association or prediction has not been achieved. Here, the power and predictive accuracy of a polygenic score are derived from a quantitative genetics model as a function of the sizes of the two samples, explained genetic variance, selection thresholds for including a marker in the score, and methods for weighting effect sizes in the score. Expressions are derived for quantitative and discrete traits, the latter allowing for case/control sampling. A novel approach to estimating the variance explained by a marker panel is also proposed. It is shown that published studies with significant association of polygenic scores have been well powered, whereas those with negative results can be explained by low sample size. It is also shown that useful levels of prediction may only be approached when predictors are estimated from very large samples, up to an order of magnitude greater than currently available. Therefore, polygenic scores currently have more utility for association testing than predicting complex traits, but prediction will become more feasible as sample sizes continue to grow.

Vyšlo v časopise: Power and Predictive Accuracy of Polygenic Risk Scores. PLoS Genet 9(3): e32767. doi:10.1371/journal.pgen.1003348
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pgen.1003348

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Zdroje

1. VisscherPM, BrownMA, McCarthyMI, YangJ (2012) Five years of GWAS discovery. Am J Hum Genet 90: 7–24.

2. WrayNR, GoddardME, VisscherPM (2007) Prediction of individual genetic risk to disease from genome-wide association studies. Genome Res 17: 1520–1528.

3. PurcellSM, WrayNR, StoneJL, VisscherPM, O'DonovanMC, et al. (2009) Common polygenic variation contributes to risk of schizophrenia and bipolar disorder. Nature 460: 748–752.

4. RipkeS, SandersAR, KendlerKS, LevinsonDF, SklarP, et al. (2011) Genome-wide association study identifies five new schizophrenia loci. Nat Genet 43: 969–976.

5. HamshereML, O'DonovanMC, JonesIR, JonesL, KirovG, et al. (2011) Polygenic dissection of the bipolar phenotype. Br J Psychiatry 198: 284–288.

6. BushWS, SawcerSJ, de JagerPL, OksenbergJR, McCauleyJL, et al. (2010) Evidence for polygenic susceptibility to multiple sclerosis–the shape of things to come. Am J Hum Genet 86: 621–625.

7. Lango AllenH, EstradaK, LettreG, BerndtSI, WeedonMN, et al. (2010) Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467: 832–838.

8. SimonsonMA, WillsAG, KellerMC, McQueenMB (2011) Recent methods for polygenic analysis of genome-wide data implicate an important effect of common variants on cardiovascular disease risk. BMC Med Genet 12: 146.

9. StahlEA, WegmannD, TrynkaG, Gutierrez-AchuryJ, DoR, et al. (2012) Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat Genet 44: 483–489.

10. SpeliotesEK, WillerCJ, BerndtSI, MondaKL, ThorleifssonG, et al. (2010) Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat Genet 42: 937–948.

11. PetersonRE, MaesHH, HolmansP, SandersAR, LevinsonDF, et al. (2011) Genetic risk sum score comprised of common polygenic variation is associated with body mass index. Hum Genet 129: 221–230.

12. CarayolJ, SchellenbergGD, ToresF, HagerJ, ZieglerA, et al. (2010) Assessing the impact of a combined analysis of four common low-risk genetic variants on autism risk. Mol Autism 1: 4.

13. KangJ, KugathasanS, GeorgesM, ZhaoH, ChoJH (2011) Improved risk prediction for Crohn's disease with a multi-locus approach. Hum Mol Genet 20: 2435–2442.

14. MachielaMJ, ChenCY, ChenC, ChanockSJ, HunterDJ, et al. (2011) Evaluation of polygenic risk scores for predicting breast and prostate cancer risk. Genet Epidemiol 35: 506–514.

15. WitteJS, HoffmannTJ (2011) Polygenic modeling of genome-wide association studies: an application to prostate and breast cancer. OMICS 15: 393–398.

16. PharoahPD, AntoniouAC, EastonDF, PonderBA (2008) Polygenes, risk prediction, and targeted prevention of breast cancer. N Engl J Med 358: 2796–2803.

17. ClaytonDG (2009) Prediction and interaction in complex disease genetics: experience in type 1 diabetes. PLoS Genet 5: e1000540 doi:10.1371/journal.pgen.1000540

18. SawcerS, BanM, WasonJ, DudbridgeF (2010) What role for genetics in the prediction of multiple sclerosis? Ann Neurol 67: 3–10.

19. EvansDM, VisscherPM, WrayNR (2009) Harnessing the information contained within genome-wide association studies to improve individual prediction of complex disease risk. Hum Mol Genet 18: 3525–3531.

20. PharoahPD, AntoniouA, BobrowM, ZimmernRL, EastonDF, et al. (2002) Polygenic susceptibility to breast cancer and implications for prevention. Nat Genet 31: 33–36.

21. WrayNR, YangJ, GoddardME, VisscherPM (2010) The genetic interpretation of area under the ROC curve in genomic profiling. PLoS Genet 6: e1000864 doi:10.1371/journal.pgen.1000864

22. JanssensAC, AulchenkoYS, ElefanteS, BorsboomGJ, SteyerbergEW, et al. (2006) Predictive testing for complex diseases using multiple genes: fact or fiction? Genet Med 8: 395–400.

23. DaetwylerHD, VillanuevaB, WoolliamsJA (2008) Accuracy of predicting the genetic risk of disease using a genome-wide approach. PLoS ONE 3: e3395 doi:10.1371/journal.pone.0003395

24. SoHC, ShamPC (2010) A unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained. PLoS Genet 6: e1001230 doi:10.1371/journal.pgen.1001230

25. LeeSH, GoddardME, WrayNR, VisscherPM (2012) A better coefficient of determination for genetic profile analysis. Genet Epidemiol 36: 214–224.

26. ShiJ, LevinsonDF, DuanJ, SandersAR, ZhengY, et al. (2009) Common variants on chromosome 6p22.1 are associated with schizophrenia. Nature 460: 753–757.

27. LeeSH, DeCandiaTR, RipkeS, YangJ, SullivanPF, et al. (2012) Estimating the proportion of variation in susceptibility to schizophrenia captured by common SNPs. Nat Genet 44: 247–250.

28. SklarP, RipkeS, ScottLJ, AndreassenOA, CichonS, et al. (2011) Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4. Nat Genet 43: 977–983.

29. RischN (2001) The genetic epidemiology of cancer: interpreting family and twin studies and their implications for molecular genetic approaches. Cancer Epidemiol Biomarkers Prev 10: 733–741.

30. LeeSH, WrayNR, GoddardME, VisscherPM (2011) Estimating missing heritability for disease from genome-wide association studies. Am J Hum Genet 88: 294–305.

31. JanssensAC, MoonesingheR, YangQ, SteyerbergEW, van DuijnCM, et al. (2007) The impact of genotype frequencies on the clinical validity of genomic profiling for predicting common chronic diseases. Genet Med 9: 528–535.

32. DudbridgeF, GusnantoA (2008) Estimation of significance thresholds for genomewide association scans. Genet Epidemiol 32: 227–234.

33. YangJ, WeedonMN, PurcellS, LettreG, EstradaK, et al. (2011) Genomic inflation factors under polygenic inheritance. Eur J Hum Genet 19: 807–812.

34. WacholderS, HartgeP, PrenticeR, Garcia-ClosasM, FeigelsonHS, et al. (2010) Performance of common genetic variants in breast-cancer risk models. N Engl J Med 362: 986–993.

35. GoddardME, WrayNR, VerbylaK, VisscherPM (2009) Estimating Effects and Making Predictions from Genome-Wide Marker Data. Statistical Science 24: 517–529.

36. GreenlandS (2000) Principles of multilevel modelling. Int J Epidemiol 29: 158–167.

37. YangJ, BenyaminB, McEvoyBP, GordonS, HendersAK, et al. (2010) Common SNPs explain a large proportion of the heritability for human height. Nat Genet 42: 565–569.

38. WuTT, ChenYF, HastieT, SobelE, LangeK (2009) Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics 25: 714–721.

39. HoggartCJ, WhittakerJC, De IorioM, BaldingDJ (2008) Simultaneous analysis of all SNPs in genome-wide and re-sequencing association studies. PLoS Genet 4: e1000130 doi:10.1371/journal.pgen.1000130

40. Falconer DS, Mackay TFC (1996) Introduction to Quantitative Genetics: Longman.

41. Ruppert D, Wand MP, Carroll RJ (2003) Semiparametric regression: Cambridge University Press.