title: Controlling the proportion of false positives in multiple dependent tests.
abstract: Genome scan mapping experiments involve multiple tests of significance. Thus, controlling the error rate in such experiments is important. Simple extension of classical concepts results in attempts to control the genomewise error rate (GWER), i.e., the probability of even a single false positive among all tests. This results in very stringent comparisonwise error rates (CWER) and, consequently, low experimental power. We here present an approach based on controlling the proportion of false positives (PFP) among all positive test results. The CWER needed to attain a desired PFP level does not depend on the correlation among the tests or on the number of tests as in other approaches. To estimate the PFP it is necessary to estimate the proportion of true null hypotheses. Here we show how this can be estimated directly from experimental results. The PFP approach is similar to the false discovery rate (FDR) and positive false discovery rate (pFDR) approaches. For a fixed CWER, we have estimated PFP, FDR, pFDR, and GWER through simulation under a variety of models to illustrate practical and philosophical similarities and differences among the methods.
authors.affiliation: Department of Animal Science, Iowa State University, Ames, Iowa 50011, USA. rohan@istate.edu
authors.forename: R L
authors.initials: RL
authors.lastname: Fernando
authors.forename: D
authors.initials: D
authors.lastname: Nettleton
authors.forename: B R
authors.initials: BR
authors.lastname: Southey
authors.forename: J C M
authors.initials: JC
authors.lastname: Dekkers
authors.forename: M F
authors.initials: MF
authors.lastname: Rothschild
authors.forename: M
authors.initials: M
authors.lastname: Soller
mesh_terms: D002874:Chromosome Mapping / Q000379:methods / Q000706:statistics & numerical data; D003433:Crosses, Genetic; D005189:False Positive Reactions; D008040:Genetic Linkage; D005819:Genetic Markers; D005821:Genetic Techniques / Q000706:statistics & numerical data; D023281:Genomics / Q000379:methods / Q000706:statistics & numerical data; D008957:Models, Genetic; D040641:Quantitative Trait Loci
chemical_list: D005819:Genetic Markers