Zhongshan School of Medicine, First Affiliated Hospital, Center for Genome Research, Center for Precision Medicine, Sun Yat-sen University, Guangzhou, China.
The Centre for Genomic Sciences, The University of Hong Kong, Pokfulam, Hong Kong, China.
Bioinformatics. 2019 Feb 15;35(4):628-635. doi: 10.1093/bioinformatics/bty682.
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.
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.
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 data are available at Bioinformatics online.
在全基因组关联研究中，揭示复杂疾病的新易感基因及其机制仍然具有挑战性。至少存在两个困难，即从许多间接相关基因中分离出真正的易感基因，以及对这些基因进行功能验证。
我们首先提出了一种新的条件基因关联测试方法，该方法仅使用汇总统计信息即可分离疾病的独立关联基因。应用该方法，我们检测到与精神分裂症独立关联的 185 个基因。然后，我们基于表达/共表达设计了一个计算实验，系统地验证这些基因的致病潜力。我们发现，与精神分裂症独立关联的基因在正常出生后但不是出生前的人类大脑区域中形成了更多的共表达对，超出了预期。有趣的是，在精神分裂症患者的大脑区域中没有发现共表达富集。与对照组相比，与精神分裂症独立关联的基因在大脑区域中的差异表达也具有更显著的 P 值。相比之下，间接相关基因或其他广泛使用的基因关联测试相关基因没有这种差异表达和共表达模式。总之，这种条件基因关联测试有效地从间接相关基因中分离出直接相关基因，结果表明，常见变异可能主要通过扭曲出生后大脑的表达和共表达来导致精神分裂症。
条件基因关联测试已在 Java 平台“KGG”中实现，并可在 http://grass.cgs.hku.hk/limx/kgg/ 上公开获取。
补充数据可在生物信息学在线获取。
