Hypothesis Testing

Genome-wide analyses such as that for CNV, SNP, and expression arrays offer an immense amount of data on an individual subject, resulting in rich data sets that are unfortunately subject to a high number of false positives using traditional methods of hypothesis testing. In addition to using the most appropriate statistical tests for your study design, the bioinformatics group is trained to modify results based on the most appropriate correction algorithm based on the researcher's tolerance for false positives and negatives.

These methods can additionally be applied to groups of genes to determine enrichment based on specific ontologies, pathways, or other prior knowledge, as shown in our recent analysis of synapse-related genetic associations in autism patients .