We propose call-based segmentation analysis (CBSA) as a permutation-based method to infer regions of LOH from this type of data. Chromosome endpoints and the positions of markers with initial LOH calls are used to divide the genomes of study subjects into a series of distinct segments that are indexed by subject and location. The size of each segment is measured by the number of non-LOH calls it contains.
CBSA performs a permutation test to determine whether a segment has significantly fewer non-LOH calls than expected by chance. Permuting the assignment of initial LOH calls to subject and genomic position generates an empirical null distribution of segment size for computing p-values. In practice, p-values may be computed with a very accurate analytical approximation of the permutation distribution .
Next, the false discovery rate (FDR) is estimated with a robust method . Finally, each segment defined by the observed positions of LOH calls has a size, p-value, and FDR estimate associated with it. Each segment with an FDR estimate below a selected threshold is inferred to be a segment of LOH. Mathematical proofs establish that the FDR estimate is conservative, i.e., the estimated FDR is expected to be greater than the actual FDR .