Statistical significance for genomewide studies
2003/07/25 by John D. Storey, Robert Tibshirani · 10,159 citations
Biochemistry, Genetics and Molecular Biology · Mathematics · #Artificial intelligence #Biology #Computational biology #Computer science #Data mining #False discovery rate #False positive paradox #False positive rate #False positives and false negatives #Feature (linguistics) #Gene #Gene expression and cancer classification #Genetic Associations and Epidemiology #Genetics #Genome #Genomics and Chromatin Dynamics #Linkage (software) #Mathematics #Measure (data warehouse) #Multiple comparisons problem #Null hypothesis #Set (abstract data type) #Statistical hypothesis testing #Statistics
paper · open access · doi:10.1073/pnas.1530509100
published in Proceedings of the National Academy of Sciences 100(16), 9440-9445 (National Academy of Sciences)
openalex publication_date 2003/07/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Abstract
With the increase in genomewide experiments and the sequencing of multiple genomes, the analysis of large data sets has become commonplace in biology. It is often the case that thousands of features in a genomewide data set are tested against some null hypothesis, where a number of features are expected to be significant. Here we propose an approach to measuring statistical significance in these genomewide studies based on the concept of the false discovery rate. This approach offers a sensible balance between the number of true and false positives that is automatically calibrated and easily interpreted. In doing so, a measure of statistical significance called the q value is associated with each tested feature. The q value is similar to the well known p value, except it is a measure of significance in terms of the false discovery rate rather than the false positive rate. Our approach avoids a flood of false positive results, while offering a more liberal criterion than what has been used in genome scans for linkage.
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