CNVassoc: Association analysis of CNV data using R

Here we present a new R package, CNVassoc, that can deal with different types of CNV arising from different platforms such as MLPA o aCGH. Through a real data example we illustrate that our method is able to incorporate uncertainty in the association process. We also show how our package can also be useful when analyzing imputed data when analyzing imputed SNPs. Through a simulation study we show that CNVassoc outperforms CNVtools in terms of computing time as well as in convergence failure rate.We provide a package that outperforms the existing ones in terms of modelling flexibility, power, convergence rate, ease of covariate adjustment, and requirements for sample size and signal quality. Therefore, we offer CNVassoc as a method for routine use in CNV association studies.The proportion of variation in risk of complex diseases explained by the single nucleotide polymorphisms (SNPs) that have been discovered in recent years using the genome-wide association approach appears to limited. This has lead to the suggestion that other, possibly more complex, genetic variants could partly explain the remaining disease susceptibility. Technological advances now allow a class of genetic variants known as copy number variants (CNV) to be genotyped with increasing levels of accuracy, and several studies have recently explored the relationship between these variants and risk of complex disease [1,2]. Genotyping these kinds of complex genetic markers is still a challenge and current laboratory techniques and platforms often contain a non-negligible percentage of errors. In order to minimise bias in the results of association studies involving CNVs, uncertainty in these copy number calls must be taken into account in the analysis. In addition, large-scale CNV genotyping projects need a tool to automate the analysis of thousands of CNVs. Here, we present CNVassoc, an R package [3] designed to analyze CNV data. Methodological details of the algorithms and applications implemented in