" 'Where is the missing heritability?' is a question asked frequently in genetic research. The difficulty seems to come down to the common disease/common variant hypothesis not holding up." » Read more
Check out the following tutorial for detailed instructions on performing CNV analysis win SVS 7. Workflows include: processing raw intensity data, performing quality assurance, identifying regions of copy number variation (CNV), visualizing copy number data, and performing association analysis on a variety of CNV covariates.
SVS 7 offers direct import of log ratio data from a number of providers, including Affymetrix, Agilent, and Illumina. For Affymetrix CEL files (500K, 5.0, and 6.0) a powerful processing tool enables you to run quantile normalization on the A and B probe intensities, including virtual array generation to merge CN and SNP probes or multiple arrays (e.g. NSP and STY). This process scales to thousands of samples, and can use any sample set as a reference.
» More about Quantile Normalization
SVS 7 employs a powerful optimal segmenting algorithm using dynamic programming to detect inherited and de novo CNVs on a per-sample (univariate) and multi-sample (multivariate) basis. Unlike Hidden Markov Models, which assume the means of different copy number states are consistent, optimal segmenting properly delineates CNV boundaries in the presence of mosaicism, even at a single probe level, and with controllable sensitivity and false discovery rate.
» More about CNAM Optimal Segmenting
For both microarray and aCGH data, significant bias can be introduced by batch effects (plate, machine, and site variation), genomics waves, and population stratification. Other sources of variation include sample extraction and preparation procedures, cell types, temperature fluctuation, and even ambient ozone levels in a lab. These can lead to complications ranging from poorly defined segments to false and non-replicable findings. SVS 7 offers a number of tools to no only detect for these data quality problems but correct for them as well. These include:
» Click here to learn more about quality assurance in CNV studies.
A number of covariate generation procedures enable you to perform association testing on raw or PCA corrected log ratios, CNV segment means, and discretized values based on three- and two-state models representing loss, neutral, and gain. You can then perform numeric association tests or advanced linear and logistic regression with CNV covariates alone or in combination with other genetic markers and phenotypic variables.
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Learn more about the science behind copy number analysis in CNAM and SVS 7.
» More about the Science behind CNAM
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