The diverse genetic variations associated with autism are thought to affect molecular networks, which have yet to be identified. Dennis Vitkup and his colleagues at Columbia University in New York have developed an algorithm called NETBAG to search for clusters of genes perturbed by autism-associated genetic variations.

In the NETBAG approach, every pair of human genes is assigned a score that reflects the likelihood that the two genes are involved in the same observable characteristic, or phenotype. Vitkup and his team have previously used this approach to identify a functionally cohesive gene cluster perturbed by spontaneous, or de novo, copy number variants (CNVs) in autism. Copy number variants are deletions or duplications of stretches of chromosomes.

Vitkup’s new NETBAG+ algorithm integrates data from multiple types of genetic variation: CNVs, single nucleotide variants and loci implicated in genome-wide association studies. The search algorithm identifies highly connected gene clusters that are affected by genetic variations, and the researchers then calculate the significance of the clusters.

The statistical power of this integrative approach stems from the convergence of different types of genetic variations on a set of interrelated molecular processes. Vitkup and his team plan to apply NETBAG+ to autism-associated data gathered from the sequencing of exomes — the protein-coding parts of genomes. They also plan to identify biological and cellular functions and pathways associated with the identified molecular networks.