On the analysis of copy-number variations in genome-wide association studies: a translation of the family-based association test
ABSTRACT Though there is an increasing support for an important contribution of copy number variation (CNV) to the genetic architecture of complex disease, few methods have been developed for the analysis of such variation in the context of genetic association studies. In this paper, we propose a generalization of family-based association tests (FBATs) to allow for the analysis of CNVs at a genome-wide level. We translate the popular FBAT approach so that, instead of genotypes, raw intensity values that reflect copy number are used directly in the test statistic, thereby bypassing the need for a CNV genotyping algorithm. Moreover, both inherited and de novo CNVs can be analyzed without any prior knowledge about the type of CNV, making it easily applicable to large-scale association studies. All robustness properties of the genotype FBAT approach are maintained and all previously developed FBAT extensions, including FBATs for time-to-onset, multivariate FBATs, and FBAT-testing strategies, can be directly transferred to the analysis of CNVs. Using simulation studies, we evaluate the power and the robustness of the new approach. Furthermore, for those CNVs that can be genotyped, we compare FBATs based on genotype calls with FBATs that are directly based on the intensity data. An application to one of the first CNV genome-wide-association studies of asthma identifies a very plausible candidate gene. A software implementation of the approach is freely available at http://www.hsph.harvard.edu/research/iuliana-ionita/software. The approach has also been completely integrated in the PBAT software package.
- SourceAvailable from: Stephen RichGenetic Epidemiology 09/2012; 36(8). DOI:10.1002/gepi.21674 · 2.95 Impact Factor
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ABSTRACT: Approaches to assess copy number variation have advanced rapidly and are being incorporated into genetic studies. While the technology exists for CNV genotyping, a further understanding and discussion of how to use the CNV data for association analyses is warranted. We present the options available for processing and analysing CNV data. We break these steps down into choice of genotyping platform, normalisation of the array data, calling algorithm, and statistical analysis.International Journal of Computational Biology and Drug Design 01/2008; 1(4):368-95. DOI:10.1504/IJCBDD.2008.022208
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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.Genomics 10/2008; 93(1):22-6. DOI:10.1016/j.ygeno.2008.08.012 · 2.79 Impact Factor