ArticleMolecular psychiatry2014
P-values in genomics: apparent precision masks high uncertainty.
Article in Molecular psychiatry, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A genome-wide pleiotropy scan for prostate cancer risk.European urology · 2015Pooled it
- A genetic factor associated with low final bone mineral density in children after a long-term glucocorticoids treatment.The pharmacogenomics journal · 2017Trial
- GETgene-AI: a framework for prioritizing actionable cancer drug targets.Frontiers in systems biology · 2025Article
- Germplasm Screening Using DNA Markers and Genome-Wide Association Study for the Identification of Powdery Mildew Resistance Loci in Tomato.International journal of molecular sciences · 2022Article
- Quantifying posterior effect size distribution of susceptibility loci by common summary statistics.Genetic epidemiology · 2020Article
- Genome-wide Association of Endophenotypes for Schizophrenia From the Consortium on the Genetics of Schizophrenia (COGS) Study.JAMA psychiatry · 2019Article
- Deep learning in medical imaging and radiation therapy.Medical physics · 2019Review
- Expected p-values in light of an ROC curve analysis applied to optimal multiple testing procedures.Statistical methods in medical research · 2018Article
- Bayesian prediction intervals for assessing P-value variability in prospective replication studies.Translational psychiatry · 2017Article
- The more you test, the more you find: The smallest P-values become increasingly enriched with real findings as more tests are conducted.Genetic epidemiology · 2017Article
- Enhancing genomic prediction with genome-wide association studies in multiparental maize populations.Heredity · 2017Article
- The earth is flat (PeerJ · 2017Article
- Cross-Disorder Psychiatric Genomics.Current behavioral neuroscience reports · 2016Article
- Solutions for quantifying P-value uncertainty and replication power.Nature methods · 2016Article
- Article
- The debate about p-values.Shanghai archives of psychiatry · 2015Article
- Genetics and brain morphology.Neuropsychology review · 2015Article
- Preclinical data reproducibility for R&D--the challenge for neuroscience.Psychopharmacology · 2015Article
- Editorial: preclinical data reproducibility for R&D - the challenge for neuroscience.SpringerPlus · 2015Article
- Kernel-based whole-genome prediction of complex traits: a review.Frontiers in genetics · 2014Review
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Scientists often interpret P-values as measures of the relative strength of statistical findings. This is common practice in large-scale genomic studies where P-values are used to choose which of numerous hypothesis test results should be pursued in subsequent research. In this study, we examine P-value variability to assess the degree of certainty P-values provide. We develop prediction intervals for the P-value in a replication study given the P-value observed in an initial study. The intervals depend on the initial value of P and the ratio of sample sizes between the initial and replication studies, but not on the underlying effect size or initial sample size. The intervals are valid for most large-sample statistical tests in any context, and can be used in the presence of single or multiple tests. While P-values are highly variable, future P-value variability can be explicitly predicted based on a P-value from an initial study. The relative size of the replication and initial study is an important predictor of the P-value in a subsequent replication study. We provide a handy calculator implementing these results and apply them to a study of Alzheimer's disease and recent findings of the Cross-Disorder Group of the Psychiatric Genomics Consortium. This study suggests that overinterpretation of very significant, but highly variable, P-values is an important factor contributing to the unexpectedly high incidence of non-replication. Formal prediction intervals can also provide realistic interpretations and comparisons of P-values associated with different estimated effect sizes and sample sizes.
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