Local false discovery rate and minimum total error rate approaches to identifying interesting chromosomal regions.
The simultaneous testing of a large number of hypotheses in a genome scan, using individual thresholds for significance, inherently leads to inflated genome-wide false positive rates. There exist various approaches to approximating the correct genomewide p-values under various assumptions, either by way of asymptotics or simulations. We explore a philosophically different criterion, recently proposed in the literature, which controls the false discovery rate. The test statistics are assumed to arise from a mixture of distributions under the null and non-null hypotheses. We fit the mixture distribution using both a nonparametric approach and commingling analysis, and then apply the local false discovery rate to select cut-off points for regions to be declared interesting. Another criterion, the minimum total error, is also explored. Both criteria seem to be sensible alternatives to controlling the classical type I and type II error rates.
Sinha
Ritwik
R
Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, Ohio, USA. rsinha@darwin.cwru.edu
Sinha
Moumita
M
Mathew
George
G
Elston
Robert C
RC
Luo
Yuqun
Y
D002877:Chromosomes, Human / Q000235:genetics*; D005189:False Positive Reactions; D008040:Genetic Linkage; D006801:Humans; D018895:Microsatellite Repeats / Q000235:genetics; D012107:Research Design; D018401:Sample Size