Universal false discovery rate estimation methodology for genome-wide association studies

Human Heredity

Volumn 65, Issue 4, 2008, Pages 183-194

  Forner, Karl ^a   Lamarine, Marc ^a   Guedj, Mickaël ^a,b   Dauvillier, Jérôme ^a   Wojcik, Jérôme ^a  

^a MERCK SERONO S A   (Switzerland)

^b University of Paris 5   (France)

Author keywords

Case control studies; False discovery rate; Global p values; Multiple sclerosis; Multiple testing

Indexed keywords

ALGORITHM; ARTICLE; FALSE DISCOVERY RATE ESTIMATION METHODOLOGY; GENOME; GENOTYPE; METHODOLOGY; MULTIPLE SCLEROSIS; NONPARAMETRIC TEST; SINGLE NUCLEOTIDE POLYMORPHISM;

CASE-CONTROL STUDIES; DATA INTERPRETATION, STATISTICAL; FALSE POSITIVE REACTIONS; FEMALE; GENETIC PREDISPOSITION TO DISEASE; GENOME, HUMAN; GENOTYPE; HUMANS; MALE; MODELS, GENETIC; MULTIPLE SCLEROSIS; POLYMORPHISM, SINGLE NUCLEOTIDE; RISK;

EID: 38649096951     PISSN: 00015652     EISSN: None     Source Type: Journal    
DOI: 10.1159/000112365     Document Type: Article

References (33)

1. Hirschhorn JN, Daly MJ: Genome-wide association studies for common diseases and complex traits. Nat Rev Genet 2005;6:95-108.
2. Wang WY, Barratt BJ, Clayton DG, Todd JA: Genome-wide association studies: theoretical and practical concerns. Nat Rev Genet 2005;6:109-118.
3. Balding DJ: A tutorial on statistical methods for population association studies. Nat Rev Genet 2006;7:781-791.
4. Hinds DA, Stuve LL, Nilsen GB, Halperin E, Eskin E, Ballinger DG, Frazer KA, Cox DR: Whole-genome patterns of common DNA variation in three human populations. Science 2005;307:1072-1079.
5. Kennedy GC, Matsuzaki H, Dong S, Liu WM, Huang J, Liu G, Su X, Cao M, Chen W, Zhang J, Liu W, Yang G, Di X, Ryder T, He Z, Surti U, Phillips MS, Boyce-Jacino MT, Fodor SP, Jones KW: Large-scale genotyping of complex DNA. Nat Biotechnol 2003;21:1233-1237.
6. Barrett JC, Cardon LR: Evaluating coverage of genome-wide association studies. Nat Genet 2006;38:659-662.
7. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, SanGiovanni JP, Mane SM, Mayne ST, Bracken MB, Ferris FL, Ott J, Barnstable C, Hoh J: Complement factor H polymorphism in age-related macular degeneration. Science 2005;308:385-389.
8. Benjamini Y, Hochberg Y: Controlling the False Discovery Rate; A practical and Powerful Approach to Multiple Testing. J R Stat Soc B 1995;289-300.
9. Pounds SB: Estimation and control of multiple testing error rates for microarray studies. Brief Bioinform 2006;7:25-36.
10. Broberg P: A comparative review of estimates of the proportion unchanged genes and the false discovery rate. BMC Bioinformatics 2005;6:199.
11. Hsueh HM, Chen JJ, Kodell RL: Comparison of methods for estimating the number of true null hypotheses in multiplicity testing. J Biopharm Stat 2003;13:675-689.
12. Sabatti C, Service S, Freimer N: False discovery rate in linkage and association genome screens for complex disorders. Genetics 2003;164:829-833.
13. Thomas DC, Witte JS: Point: Population stratification: a problem for case-control studies of candidate-gene associations? Cancer Epidemiol Biomarkers Prev 2002;11:505-512.
14. Pritchard JK, Rosenberg NA: Use of unlinked genetic markers to detect population stratification in association studies. Am J Hum Genet 1999;65:220-228.
15. Soric B: Statistical 'discoveries' and effect-size estimation. J Am Stat Ass 1989;84:608-610.
16. Storey JD: A direct approach to false discovery rates. J R Stat Soc B 2002;64:479-498.
17. Storey JD, Taylor JE, Siegmund D: Strong control, conservative point estimation and simultaneous conservative consistency of false discovery rates: A unified approach. J R Stat Soc B 2004;66:187-205.
18. Pounds S, Cheng C: Robust estimation of the false discovery rate. Bioinformatics 2006;22:1979-1987.
19. Langaas M, Lindqvist BH, Ferkingstad E: Estimating the Proportion of True Null Hypotheses with Application to DNA Microarray Data. J R Stat Soc B 67, 2005;555-572. Ref Type: Generic.
20. Storey JD, Tibshirani R: Statistical significance for genomewide studies. Proc Natl Acad Sci USA 2003;100:9440-9445.
21. Storey JD, Tibshirani R: Estimating the Positive False Discovery Rate Under Dependence, with Applications to DNA Microarrays. Technical report 28, 2001. Ref Type: Generic.
22. Dudoit S, Yang YH, Callow MJ, Speed TP: Statistical methods for identifying differentially expressed genes in replicated cDNA microarray experiments. Statistica Sinica 2002;12:111-139.
23. Guedj M, Wojcik J, Della-Chiesa E, Nuel G, Forner K: A fast, unbiased and exact allelic test for case-control association studies. Hum Hered 2006;61:210-221.
24. Tsai CA, Hsueh HM, Chen JJ: Estimation of false discovery rates in multiple testing: Application to gene microarray data. Biometrics 2003;59:1071-1081.
25. Efron B: Large-scale simultaneous hypothesis testing: The choice of a null hypothesis. J Am Stat Ass 2004;99:96-104.
26. Lin DY: An efficient Monte Carlo approach to assessing statistical significance in genomic studies. Bioinformatics 2005;21:781-787.
27. Yang H, Churchill G: Estimating p values in small microarray experiments. Bioinformatics 2007;23:38-43.
28. Gao X: Construction of null statistics in permutation-based multiple testing for multifactorial microarray experiments. Bioinformatics 2006;22:1486-1494.
29. Pan W: On the use of permutation in and the performance of a class of nonparametric methods to detect differential gene expression. Bioinformatics 2003;19:1333-1340.
30. Xie Y, Pan W, Khodursky AB: A note on using permutation-based false discovery rate estimates to compare different analysis methods for microarray data. Bioinformatics 2005;21:4280-4288.
31. Maraganore DM, de AM, Lesnick TG, Strain KJ, Farrer MJ, Rocca WA, Pant PV, Frazer KA, Cox DR, Ballinger DG: High-resolution whole-genome association study of Parkinson disease. Am J Hum Genet 2005;77:685-693.
32. Ge Y, Dudoit S, Speed TP: Resampling-based multiple testing for microarray data analysis. Test 2003;12:1-77.
33. Westfall PH, Young SS: Resampling-based multiple testing: Examples and methods for p value adjustment. Hoboken, John Wiley & Sons, 1993.