P-values in genomics: Apparent precision masks high uncertainty

Molecular Psychiatry

Volumn 19, Issue 12, 2014, Pages 1336-1340

  Lazzeroni, L C ^a   Lu, Y ^a,b   Belitskaya Levy I ^b,c  

^a STANFORD UNIVERSITY SCHOOL OF MEDICINE   (United States)

^b Palo Alto VA Cooperative Studies Program Coordinating Center   (United States)

^c NEW YORK UNIVERSITY SCHOOL OF MEDICINE   (United States)

Author keywords

genome wide association study; GWAS; multiple hypothesis testing; P value; P value variability; prediction interval

Indexed keywords

ACCURACY; ALZHEIMER DISEASE; ARTICLE; ATTENTION DEFICIT DISORDER; AUTISM; BIPOLAR DISORDER; COMPARATIVE STUDY; CONTROLLED STUDY; EFFECT SIZE; GENE FREQUENCY; GENETIC ASSOCIATION; GENOMICS; MAJOR DEPRESSION; NULL HYPOTHESIS; PREDICTION; PRIORITY JOURNAL; REPLICATION STUDY; SCHIZOPHRENIA; SINGLE NUCLEOTIDE POLYMORPHISM; STATISTICAL SIGNIFICANCE; UNCERTAINTY; BRAIN; GENETICS; HUMAN; MENTAL DISEASE; PATHOLOGY; PROCEDURES; STATISTICAL MODEL; STATISTICS AND NUMERICAL DATA;

ALZHEIMER DISEASE; BRAIN; GENOMICS; HUMANS; MENTAL DISORDERS; MODELS, STATISTICAL; POLYMORPHISM, SINGLE NUCLEOTIDE; UNCERTAINTY;

EID: 84927177895     PISSN: 13594184     EISSN: 14765578     Source Type: Journal    
DOI: 10.1038/mp.2013.184     Document Type: Article

References (28)

1. Pearson T, Manolio T. How to interpret a genome-wide association study. JAMA 2008; 299: 1335-1344
2. Nature genetics editorial. Asking for more. Nat Genet 2012; 44: 733
3. Goodman S. A comment on replication, P-values and evidence. Stat Med 1992; 11: 875-879
4. Senn S. Letter to the editor: a comment on replication, P-values and evidence. Stat Med 2002; 21: 2437-2444
5. Cumming G. Replication and P intervals: P values predict the future only vaguely, but confidence intervals do much better. Perspect Psychol Sci 2008; 3: 286-300
6. Boos D, Stefanski S. P-value precision and reproducibility. Am Stat 2012; 65: 213-221
7. Mojirsheibani M, Tibshirani R. Some results on bootstrap prediction intervals. Can J Stat 1996; 24: 549-568
8. Furney SJ, Simmons A, Breen G, Pedroso I, Lunnon K, Proitsi P., et al. Genome-wide association with MRI atrophy measures as a quantitative trait locus for Alzheimer's disease. Mol Psychiatry 2011; 16: 1130-1138
9. Cross-Disorder Group of the Psychiatric Genomics Consortium. Identification of risk loci with shared effects on five major psychiatric disorders: a genome-wide analysis. Lancet 2013; 381: 1371-1379
10. Cox D, Hinkley D. Theoretical Statistics. Chapman & Hall: London, UK, 1974, pp 242-244
11. Whitmore G. Prediction limits for a univariate normal observation. Am Stat 1986; 40: 141-143
12. Lazzeroni L, Lange K. A conditional inference framework for extending the transmission/disequilibrium test. Hum Hered 1998; 48: 67-81
13. Panagiotou O, Ioannidis J. What should the genome-wide significance threshold be?. Empirical replication of borderline genetic associations. Int J Epidemiol 2011; 41: 273-286
14. Dickersin K. The existence of publication bias and risk factors for its occurrence. JAMA 1990; 263: 1385-1389
15. Easterbrook P, Berlin J, Gopalan R, Matthews D. Publication bias in clinical research. Lancet 1991; 337: 867-872
16. Ioannidis J. Why most published research findings are false. PLoS Med 2005; 2: e124
17. Zollner S, Pritchard J. Overcoming the winner's curse: estimating penetrance parameters from case-control data. Am J Hum Genet 2007; 80: 605-615
18. Moonesinghe R, Khoury M, Liu T, Ioannidis J. Required sample size and nonreplicability thresholds for heterogeneous genetic associations. Proc Natl Acad Sci USA 2008; 105: 617-622
19. Lazzeroni LC, Ray A. A generalized DeFries-Fulker regression framework for the analysis of twin data. Behav Genet 2013; 43: 85-96
20. Lazzeroni LC. Evaluating the evidence of replication of genetic associations with schizophrenia. JAMA Psych 2014; 71: 94-95
21. Lazzeroni L, Ray A. The cost of large number of hypothesis tests on power, effect size and sample size. Mol Psychiatry 2012; 17: 108-114
22. Wang K, Li M, Bucan M. Pathway-based approaches for analysis of genome-wide association studies. Am J Hum Genet 2007; 81: 1278-1283
23. Huang D, Sherman B, Lempicki R. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res 2008; 37: 1-13
24. Elbers C, Van Eijk K, Franke L, Mulder F, Van Der Schouw YT, Wijmenga C., et al. Using genome-wide pathway analysis to unravel the etiology of complex diseases. Genet Epidemiol 2009; 33: 419-431
25. Medina I, Montaner D, Bonifaci N, Pujana MA, Carbonell J, Tarraga J., et al. Gene set-based analysis of polymorphisms: finding pathways or biological processes associated with traits in genome-wide association studies. Nucleic Acids Res 2009; 37: W340-W344
26. Cantor R, Lange K, Sinsheimer J. Prioritizing GWAS results: a review of statistical methods and recommendations for their applcation. Am J Hum Genet 2010; 86: 6-22
27. Moreau Y, Tranchevent L-C. Computational tools for prioritazing candidate genes: boosting disease gene discovery. Nat Genet Rev 2012; 13: 523-526
28. Harrington D. The randomized clinical trial. J Am Stat Assoc 2000; 95: 312-315