Efficient p-value estimation in massively parallel testing problems.
We present a new method to efficiently estimate very large numbers of p-values using empirically constructed null distributions of a test statistic. The need to evaluate a very large number of p-values is increasingly common with modern genomic data, and when interaction effects are of interest, the number of tests can easily run into billions. When the asymptotic distribution is not easily available, permutations are typically used to obtain p-values but these can be computationally infeasible in large problems. Our method constructs a prediction model to obtain a first approximation to the p-values and uses Bayesian methods to choose a fraction of these to be refined by permutations. We apply and evaluate our method on the study of association between 2-way interactions of genetic markers and colorectal cancer using the data from the first phase of a large, genome-wide case-control study. The results show enormous computational savings as compared to evaluating a full set of permutations, with little decrease in accuracy.
Kustra
Rafal
R
Department of Public Health Sciences, University of Toronto, Toronto, ON, Canada. r.kustra@utoronto.ca
Shi
Xiaofei
X
Murdoch
Duncan J
DJ
Greenwood
Celia M T
CM
Rangrej
Jagadish
J
D000465:Algorithms; D001185:Artificial Intelligence; D001499:Bayes Theorem; D015179:Colorectal Neoplasms / Q000235:genetics; D005819:Genetic Markers / Q000235:genetics; D055106:Genome-Wide Association Study / Q000379:methods*; D005838:Genotype; D006239:Haplotypes; D006801:Humans; D016013:Likelihood Functions; D008957:Models, Genetic; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D016008:Statistical Distributions
D005819:Genetic Markers