Tool finds connections in genome dataFeb. 8, 2012, 4:37 PM
Genome-wide association studies (GWAS) have identified hundreds of genetic variants that increase a person’s susceptibility for complex diseases. Although these variants have added to our understanding of disease pathology, they usually account for only a small proportion of disease risk.
Zhongming Zhao, Ph.D., and colleagues have developed an approach to identify gene variants that act together – for example in a biological pathway – and have a joint effect on disease risk. Their method builds on the generalized additive model (gamGWAS). It eliminates a previously identified problem in the analysis of gene sets (the long gene bias) and does not require genotyping data from the original GWAS, which reduces computational burden.
The investigators used gamGWAS to analyze two existing schizophrenia GWAS datasets from the International Schizophrenia Consortium and the Genetic Association Information Network. They report in the February Journal of Medical Genetics that gamGWAS confirmed previous findings in these datasets and also pointed to new immune-related pathways that may have roles in schizophrenia.
This research was supported by the National Institute of Mental Health, a NARSAD Maltz Investigator Award and a NARSAD Young Investigator Award.