Genetic association analysis of copy-number variation (CNV) in human disease pathogenesis

Abstract

Structural genetic variation, including copy-number variation (CNV), constitutes a substantial fraction of total genetic variability and the importance of structural genetic variants in modulating human disease is increasingly being recognized. Early successes in identifying disease-associated CNVs via a candidate gene approach mandate that future disease association studies need to include structural genetic variation. Such analyses should not rely on previously developed methodologies that were designed to evaluate single nucleotide polymorphisms (SNPs). Instead, development of novel technical, statistical, and epidemiologic methods will be necessary to optimally capture this newly-appreciated form of genetic variation in a meaningful manner.

Figure 1. Raw copy number measurements vs. CNV calls for a CNV showing a continuous distribution

Shown in this figure, on the left side, is the association between a normally distributed, simulated phenotype and the intensity measurements at a single SNP position within a known copy-number variable region on chromosome 21 using a dataset of approximately 1200 individuals (CAMP study [53]). On the right side, the association between the same phenotype and the CNV calls (loss/no change) is shown. Losses were detected using a local false discovery rate (locFDR) approach [54], applied to the intensity measurements. As can be observed, for CNVs showing a continuous intensity distribution, forcibly classifying the raw measurements into discrete calls may result in loss of power compared to the original measurements, as illustrated by the drop in the R^2 value (the square of the correlation coefficient).