Lee Kwan-Yeung, Leung Kwong-Sak, Ma Suk Ling, So Hon Cheong, Huang Dan, Tang Nelson Leung-Sang, Wong Man-Hon
Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, China.
Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China.
Front Genet. 2020 Aug 28;11:1003. doi: 10.3389/fgene.2020.01003. eCollection 2020.
In this study, we looked for potential gene-gene interaction in susceptibility to schizophrenia by an exhaustive searching for SNP-SNP interactions in 3 GWAS datasets (phs000021:phg000013, phs000021:phg000014, phs000167) using our recently published algorithm. The search space for SNP-SNP interaction was confined to 8 biologically plausible ways of interaction under dominant-dominant or recessive-recessive modes. First, we performed our search of all pair-wise combination of 729,454 SNPs after filtering by SNP genotype quality. All possible pairwise interactions of any 2 SNPs (5 × 10) were exhausted to search for significant interaction which was defined by -value of chi-square tests. Nine out the top 10 interactions, protein coding genes were partnered with non-coding RNA (ncRNA) which suggested a new alternative insight into interaction biology other than the frequently sought-after protein-protein interaction. Therefore, we extended to look for replication among the top 10,000 interaction SNP pairs and high proportion of concurrent genes forming the interaction pairs were found. The results indicated that an enrichment of signals over noise was present in the top 10,000 interactions. Then, replications of SNP-SNP interaction were confirmed for 14 SNPs-pairs in both replication datasets. Biological insight was highlighted by a potential binding between FHIT (protein coding gene) and LINC00969 (lncRNA) which showed a replicable interaction between their SNPs. Both of them were reported to have expression in brain. Our study represented an early attempt of exhaustive interaction analysis of GWAS data which also yield replicated interaction and new insight into understanding of genetic interaction in schizophrenia.
在本研究中,我们通过使用我们最近发表的算法,在3个全基因组关联研究(GWAS)数据集(phs000021:phg000013、phs000021:phg000014、phs000167)中详尽搜索单核苷酸多态性(SNP)-SNP相互作用,来寻找精神分裂症易感性中潜在的基因-基因相互作用。SNP-SNP相互作用的搜索空间限于显性-显性或隐性-隐性模式下8种生物学上合理的相互作用方式。首先,我们在按SNP基因型质量进行过滤后,对729,454个SNP的所有成对组合进行搜索。穷尽了任意两个SNP的所有可能成对相互作用(5×10),以寻找由卡方检验的P值定义的显著相互作用。在前10种相互作用中,有9种是蛋白质编码基因与非编码RNA(ncRNA)配对,这提示了除了经常被探寻的蛋白质-蛋白质相互作用之外,对相互作用生物学的一种新的不同见解。因此,我们进一步在排名前10,000的相互作用SNP对中寻找重复性,并且发现形成相互作用对的并发基因比例很高。结果表明,在前10,000种相互作用中存在信号相对于噪声的富集。然后,在两个重复数据集中对14个SNP对的SNP-SNP相互作用重复性进行了确认。FHIT(蛋白质编码基因)与LINC00969(长链非编码RNA)之间的潜在结合突出了生物学见解,其SNP之间显示出可重复的相互作用。据报道,它们两者在大脑中均有表达。我们的研究代表了对GWAS数据进行详尽相互作用分析的早期尝试,该分析还产生了重复性相互作用以及对理解精神分裂症遗传相互作用的新见解。