Copy Number Variations in Human Disease

In 2006, Redon et al. examined the 270 individuals from HapMap to study the importance of copy number variations (CNV) to genetic diseases and to provide a resource for other researchers. The term CNV refers to differences in genomic DNA with varying numbers of gene copies. It was found that these copy number variations covered 12% of the human genome. About 15% of the genes found in the Online Mendelian Inheritance in Man (OMIM) morbid map (285 out of 1,961) were found to overlap with CNV regions adding to the potential relevance of CNV to human disease [Redon R 2006].

Several groups have turned their focus to understanding the role of amplifications or deletions, which characterize CNVs, in diseases such as mental disease and cancer. Over 50 regions were found to be associated possessing either unique amplifications or deletions in a cohort of 60 cancer patients [Lucito et al 2007]. The choice by Redon et al. to use the same samples that make up the HapMap dataset was to ease the integration of analysis comparison of CNV and SNP information that makes up the HapMap data.Correlation between CNV associations have been compared to known associations determined from SNP data. Sutrala et al. looked at 15 major candidate genes for schizophrenia including dysbindin (DTNBP1), neuregulin (NRG1), RGS4 and DISC1 and found no copy number variations at these loci. This is consistent with previous studies in the area, suggesting that the two types of variations act independently. This observation is further corroborated by a model of complex traits that showed that CNVs were able to account for 18% of gene expression variation, while SNPs could account for only 84% of the variation [Stranger 2007]. This study by Stranger et al. showed that the phenotypic variations explained by both types of structural variations were largely mutually exclusive in the HapMap samples of lymphoblastoid cell lines.

This new understanding about the potential importance of CNVs brings with it new challenges for the analysts due to the lack of genotyped CNV data and a lack of methods for analyzing CNV data at the genome-wide scale. Stranger et al., McCarroll, and Altshuler point to the fact that the low resolution of CNV data and potential issues with measurement precision could lead to analysis inaccuracies. In 2007, a paper by McCarroll and Altshuler stated that out of the 1,500 CNVs that have been identified researchers have only genotyped 70 at a quality necessary to carry out linkage disequilibrium studies. A recent paper by Ionita-Laza et al. presents a new method for family-based association tests (FBATs) that circumvents the need for genotypes and relies on intensity values. Family-based association tests are a key method used by geneticists in determining the association of genetic markers and disease because many of the issues with the selection of controls can be avoided by using data made up of family members. Ionita-Laza et al. extended the FBAT statistic in such a way that the average intensity within a family corresponds to Mendelian transmissions. Using their method, Ionita-Laza et al. were able to determine potential associations between the SNP rs2240832 on chromosome 7 in a known CNV region and asthma in a sample of 400 parent/child trios [Ionita-Laza 2008].

Another problem exists with the use of CNV data as a result of the procedure for collecting CNV data using array comparative genomic hybridization (array CGH). The method looks at the relative hybridization intensities of labeled probes of test and reference genomic DNA to determine raw intensity values [Pinkel and Albertson 2005]. Conrad et al. show that the frequency of CNV deletions increases as the the size of the deletions became smaller. This presents a problem with the current use of array CGH methods because of the low resolutions afforded by the method is approximately 5–10 Mb [Conrad 2007].

The direction of research seems directed toward the integration CNVs and SNPs for the purposes of understanding the role of variation in human disease. But fundamental issues of data resolution and quality must be answered before this type of integration can take place. This is a part of the much larger problem of classifying and integrating information of sequence and structure variation in genomes. These variations include SNPs and CNVs discussed here, but also inversions, translocations, and deletions of varying scales which, currently, must be identified using different methods. Lee et al points out that the small number of findings linking CNVs to phenotypic differences has a negative effect on the expanded use of higher-resolution array CGH for clinical purposes, which in turn, affects the accumulation of CNV data that may prove useful.

[1] Redon R, et al. Global variation in copy number in the human genome. Nature 2006 Nov 23 444(7118):444-54.
[2] Lucito R, et al. Copy-number variants in patients with a strong family history of pancreatic cancer. Cancer Biol Ther. 2007 Oct;6(10):1592-9. Epub 2007 Jul 12.
[3] Sutrala SR, et al. Gene copy number variation in schizophrenia. Am J Med Genet B Neuropsychiatr Genet. 2007 Dec 28 [Epub ahead of print].
[4] Stranger BE, et al. Relative impact of nucleotide and copy number variation on gene expression phenotypes. Science. 2007 Feb 9;315(5813):848-53.
[5] McCarroll SA, Altshuler DM. Copy-number variation and association studies of human disease. Nat Genet. 2007 Jul;39(7 Suppl):S37-42.
[6] Ionita-Laza I. et al. On the analysis of copy-number variations in genome-wide association studies: a translation of the family-based association test. Genet Epidemiol. 2008 Apr;32(3):273-84.
[7] Pinkel D, Albertson DG. Comparative genomic hybridization. Annu Rev Genomics Hum Genet. 2005;6:331-54.
[8] Scherer SW. et al. Challenges and standards in integrating surveys of structural variation. Nat Genet. 2007 Jul;39(7 Suppl):S7-15.
[9] Conrad, D.F. et al. A high-resolution survey of deletion polymorphism in the human genome. Nat Genet. 2006 Jan;38(1):75-81. Epub 2005 Dec 4.
[10] Lee C, et al. Copy number variations and clinical cytogenetic diagnosis of constitutional disorders. Nat Genet. 2007 Jul;39(7 Suppl):S48-54.

Redon, R., Ishikawa, S., Fitch, K.R., Feuk, L., Perry, G.H., Andrews, T.D., Fiegler, H., Shapero, M.H., Carson, A.R., Chen, W., Cho, E.K., Dallaire, S., Freeman, J.L., González, J.R., Gratacòs, M., Huang, J., Kalaitzopoulos, D., Komura, D., MacDonald, J.R., Marshall, C.R., Mei, R., Montgomery, L., Nishimura, K., Okamura, K., Shen, F., Somerville, M.J., Tchinda, J., Valsesia, A., Woodwark, C., Yang, F., Zhang, J., Zerjal, T., Zhang, J., Armengol, L., Conrad, D.F., Estivill, X., Tyler-Smith, C., Carter, N.P., Aburatani, H., Lee, C., Jones, K.W., Scherer, S.W., Hurles, M.E. (2006). Global variation in copy number in the human genome. Nature, 444(7118), 444-454. DOI: 10.1038/nature05329