Complex diseases are often caused by many genetic markers or genes, thus, pathway-based or gene-based analysis of genome-wide association study (GWAS) data becomes an important approach to studying the underlying molecular mechanisms. A critical issue in this approach is the gene length bias – long genes tend to have more SNPs investigated and, thus, have higher chance to be found significant. Jia et al. proposed a method, namely gamGWAS, which applies the generalized additive model (GAM) to effectively relieve the long gene bias. The authors successfully demonstrated gamGWAS in two schizophrenia GWAS datasets. gamGWAS is computationally efficient because it does not require genotype data or permutation of sample labels in the original GWAS data. (By Zhongming Zhao, Ph.D., http://jmg.bmj.com/cgi/content/abstract/jmedgenet-2011-100397v1?papetoc )