Genome-Wide Search for SNP Interactions in GWAS Data: Algorithm, Feasibility, Replication Using Schizophrenia Datasets.

Kwan-Yeung Lee,Hon Cheong So,Man-Hon Wong,Kwong-Sak Leung,Nelson Leung-Sang Tang,Suk Ling Ma,Dan Huang

doi:10.3389/fgene.2020.01003

Abstract

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 × 1011) were exhausted to search for significant interaction which was defined by p-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.

Highlights

Schizophrenia is a highly heritable disorder and it affected about 1% of the population worldwide (Sullivan et al, 2003, p. 20; Henriksen et al, 2017; Avramopoulos, 2018; Weinberger, 2019)
We found that 9 out of the top 10 SNP-SNP interactions in terms of p-value could be interpreted as the interactions between protein coding genes and non-coding RNA genes which suggested the importance of interactions other than that of the traditional protein-protein interactions
We investigated the replication among the top 10,000 SNP-SNP interactions and there was a high proportion of concurrent genes among the gene-gene interaction predicted from these SNP–SNP interaction

Summary

Introduction

Schizophrenia is a highly heritable disorder and it affected about 1% of the population worldwide (Sullivan et al, 2003, p. 20; Henriksen et al, 2017; Avramopoulos, 2018; Weinberger, 2019). Twins studies suggested the heritability is around 80% (Sullivan et al, 2003; Henriksen et al, 2017; Avramopoulos, 2018) and common variants contributed to up to half of the genetic risk of schizophrenia (International Schizophrenia Consortium et al, 2009; The Schizophrenia Psychiatric GenomeWide Association Study (Gwas) Consortium, 2011). A study showed some SNPs were not associated with the phenotypes of the disease when they were examined individually and they were only identified when examined in combination (Gerke et al, 2009). A recent study showed that the weak interaction of transcription factor to its promoter was able to regulate the expression of the gene (de Boer et al, 2020), further supporting SNP–SNP interaction provided synergistic effect on gene regulation. Other than SNP–SNP interaction occurring on the same gene, we and others showed SNP–SNP interaction across different genes were important in determining the risk or severity of diseases including psoriasis (Lee et al, 2018), schizophrenia (Schrode et al, 2019), cancer (Lin et al, 2013), and obesity (Dong et al, 2017)

Methods

Results

Conclusion