Two approaches to mutation detection based on functional data.

A new technique, denaturing high-performance liquid chromatography (dHPLC), allows for detection of any heterozygous sequence variation in a gene without prior knowledge of the precise location of the sequence change. The results of a dHPLC analysis are recorded in real-time in the form of a chromatogram that is sequence-specific. In this paper we present methods to classify an individual, based on the observed chromatogram, as a homozygous wild-type or a carrier of a specific variant for the given DNA segment by comparison to representative chromatograms that are obtained from the training set of individuals with known variant status. The first approach consists of finding a parsimonious parametric model and then classifying each newly observed curve based on comparing the most discriminating characteristic, the main mode, to the main mode of the training curves. The second approach consists of finding empirical estimates of the modes of each chromatogram and using a bootstrap test for equality with the corresponding estimates of the training curves. We apply both methods to data on the breast cancer susceptibility gene BRCA1 and test the performance of the methods on independent samples.