Miaoxin Li1,2,3,4,5, Lin Jiang1,2, Timothy Shin Heng Mak2, Johnny Sheung Him Kwan3, Chao Xue1, Peikai Chen2,6, Henry Chi-Ming Leung7, Liqian Cui8, Tao Li9, Pak Chung Sham2,3,4. 1. Zhongshan School of Medicine, First Affiliated Hospital, Center for Genome Research, Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China. 2. The Centre for Genomic Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China. 3. Department of Psychiatry, The University of Hong Kong, Pokfulam, Hong Kong, China. 4. State Key Laboratory for Cognitive and Brain Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China. 5. Key Laboratory of Tropical Disease Control (SYSU), Ministry of Education, Guangzhou, Hong Kong, China. 6. School of Biomedical Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China. 7. Department of Computer Science, The University of Hong Kong, Pokfulam, Hong Kong, China. 8. The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China. 9. The Mental Health Center and the Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, China.
Abstract
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 independently associated 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 formed more co-expression pairs in normal post-natal but not pre-natal 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 AND IMPLEMENTATION: The conditional gene-based association test has been implemented in a platform 'KGG' in Java and is publicly available at http://grass.cgs.hku.hk/limx/kgg/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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 independently associated 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 formed more co-expression pairs in normal post-natal but not pre-natal human brain regions than expected. Interestingly, no co-expression enrichment was found in the brain regions of schizophreniapatients. The genes with independent association also had more significant P-values for differential expression between schizophreniapatients 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 AND IMPLEMENTATION: The conditional gene-based association test has been implemented in a platform 'KGG' in Java and is publicly available at http://grass.cgs.hku.hk/limx/kgg/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.