title: Assessing genome-wide statistical significance for large p small n problems.
abstract: Assessing genome-wide statistical significance is an important issue in genetic studies. We describe a new resampling approach for determining the appropriate thresholds for statistical significance. Our simulation results demonstrate that the proposed approach accurately controls the genome-wide type I error rate even under the large p small n situations.
authors.affiliation: Department of Statistics, George Mason University, Fairfax, Virginia 22030, USA. gdiao@gmu.edu
authors.forename: Guoqing
authors.initials: G
authors.lastname: Diao
authors.forename: Anand N
authors.initials: AN
authors.lastname: Vidyashankar
mesh_terms: D002874:Chromosome Mapping; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D015894:Genome, Human; D006801:Humans; D008957:Models, Genetic; D008962:Models, Theoretical; D011336:Probability; D040641:Quantitative Trait Loci / Q000235:genetics*
keywords: experimental cross; genome-wide statistical significance; quantitative trait loci (QTL) mapping; resampling method