Accurate Computation of Survival Statistics in Genome-Wide Studies
Year: 2015
Type: article
Abstract: A key challenge in genomics is to identify genetic variants that distinguish patients with different survival time following diagnosis or treatment. While the log-rank test is widely used for this purpose, nearly all implementations of the log-rank test rely on an asymptotic approximation that is not appropriate in many genomics applications. This is because: the two populations determined by a genetic variant may have very different sizes; and the evaluation of many possible variants demands highly accurate computation of very small p-values. We demonstrate this problem for cancer genomics data where the standard log-rank test leads to many false positive associations between somatic mutations and survival time. We develop and analyze a novel algorithm, Exact Log-rank Test (ExaLT), that accurately computes the p-value of the log-rank statistic under an exact distribution that is appropriate for any size populations. We demonstrate the advantages of ExaLT on data from published cancer genomics studies, finding significant differences from the reported p-values. We analyze somatic mutations in six cancer types from The Cancer Genome Atlas (TCGA), finding mutations with known association to survival as well as several novel associations. In contrast, standard implementations of the log-rank test report dozens-hundreds of likely false positive associations as more significant than these known associations.
Source: PLoS Computational Biology
Institutions: University of Southern Denmark, Brown University
Language: English
Subfield: Cancer Research
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Awards: Analytical Approaches to Massive Data Computation with Applications to Genomics, Computational Characterization of Genetic Heterogeneity, CAREER: Algorithms for Next-Generation Genomics, G6671297155, BIGDATA: Mid-Scale: DA: Analytical Approaches to Massive Data Computation with Applications to Genomics, III: Small: Algorithmic Approaches for Pathway and Gene Group Analysis in Genetic Studies
OpenAlex ID: W2124948288