The importance of CNV has only recently become appreciated. The relationship of CNV to phenotypic variation is even less well developed. While we hope the analysis presented in this paper is a useful step forward in this area, it merely scratches the surface of what is likely to be an extremely complex challenge. CNV lacks some of the 'neatness' of SNP data. It does not occur at well defined positions (i.e., the points at which CNV changes is often different across individuals). Furthermore, for a variety of reasons, the 500 k chip platforms are not ideal for detecting CNV when compared with more recent platforms. It is also the case that many functional mutations occur outside genes (in promoter regions, for example). As such, many regions of CNV may not be detected. It is also likely to be challenging to detect small CNVs using these technologies.
An approach in which we break the genome into regions of maximal length, such that copy number remains constant for each individual within each region, results in an extremely large number of regions (around 82,000 regions for the samples analyzed in this paper). While each region can be treated as if it were a (multi-allelic) locus, and marginal tests can then be performed, such tests are likely to be far from optimal.
The principal reason for this is the correlation between intervals. This is directly analogous to the situation with SNP analysis, but may well be even more complex in this new setting. An alternative approach might be to attempt to relate SNP variation to CNV, and adopt an approach akin to the tag-SNP idea.
In this paper we attempted to move past the perils of a strategy based upon marginal tests by conducting a gene-at-a-time analysis, relating mean copy number within a gene to phenotype. Such an approach seems reasonable, succeeds in reducing the analysis to a reasonable number of tests, and thereby avoids the worst excesses of multiple comparison corrections. However, it should be noted that the very poor agreement in results from the two methods explored in this paper indicates that substantial work remains to be done. It is this lack of agreement that represents the principal lesson to be drawn from the current study. Normalization is regarded as an important, but somewhat routine step in analyses such as these. However, our paper demonstrates that the particular method of normalization chosen can have a key influence on the results obtained. In our case, the two normalization methods are both widely used, and appear inherently sensible, but result in normalized intensities that are very poorly correlated across methods. Consequently, subsequent analyses will produce wildly different results. As the phrase "garbage in, garbage out" reminds us, it is important to ensure that such normalization routines are adding signal, rather than noise to the data. As such, there is an urgent need for a widespread comparison of normalization methods in order to better assess which of them is most effective.
Finally, it should also be noted that, while we look at all genes in the present study, there is little reason a priori to expect candidate genes chosen on the basis of a SNP study to also have a function due to CNV. It is entirely possible that genes that affect phenotype through CNV will be distinct from those that have an effect due to SNP polymorphism. In a recent study, Stranger et al. examined data from Phase 1 of HapMap and noted that SNPs and CNVs captured 83.6% and 17.7%, respectively, of the total detected genetic variation in the expression of around 15,000 genes, but that "the signals from the two types of variation had little overlap" . Other studies take a different view (e.g., McCarroll et al. ).