A powerful conditional gene-based association approach implicated functionally important genes for schizophrenia.

Motivation: It remains challenging to unravel new susceptibility genes of complex diseases and the mechanisms in genome-wide association studies. There are at least two difficulties, isolation of the genuine susceptibility genes from many indirectly associated genes and functional validation of these genes.

Results: We first proposed a novel conditional gene-based association test which can use only summary statistics to isolate independent association genes of a disease. Applying this method, we detected 185 genes of independent association with schizophrenia. We then designed an in-silico experiment based on expression/co-expression to systematically validate pathogenic potential of these genes. We found that genes of independent association with schizophrenia had more co-expression pairs in normal postnatal but not prenatal human brain regions than expected. Interestingly, no co-expression enrichment was found in the brain regions of schizophrenia patients. The genes with independent association also had more significant p-values for differential expression between schizophrenia patients and controls in the brain regions. In contrast, indirectly associated genes or associated genes by other widely-used gene-based tests had no such differential expression and co-expression patterns. In summary, this conditional gene-based association test is effective for isolating directly associated genes from indirectly associated genes, and the results insightfully suggest that common variants might contribute to schizophrenia largely by distorting expression and co-expression in post-natal brains.

Availability: The conditional gene-based association test has been implemented in a platform "KGG" in Java and is publicly available at https://grass.cgs.hku.hk/limx/kgg/.

Supplementary information: Supplementary data are available at Bioinformatics online.