SNP eQTL status and eQTL density in the adjacent region of the SNP are associated with its statistical significance in GWA studies

BMC Genetics. 2019;20(1):1-12 DOI 10.1186/s12863-019-0786-0

 

Journal Homepage

Journal Title: BMC Genetics

ISSN: 1471-2156 (Online)

Publisher: BMC

LCC Subject Category: Science: Biology (General): Genetics

Country of publisher: United Kingdom

Language of fulltext: English

Full-text formats available: PDF, HTML

 

AUTHORS

Ivan Gorlov (The Geisel School of Medicine, Department of Biomedical Data Science, Dartmouth College, HB7936)
Xiangjun Xiao (Department of Medicine, Baylor College of Medicine)
Maureen Mayes (Department of Internal Medicine, Division of Rheumatology, University of Texas McGovern Medical School)
Olga Gorlova (The Geisel School of Medicine, Department of Biomedical Data Science, Dartmouth College, HB7936)
Christopher Amos (Department of Medicine, Baylor College of Medicine)

EDITORIAL INFORMATION

Blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 20 weeks

 

Abstract | Full Text

Abstract Background Over the relatively short history of Genome Wide Association Studies (GWASs), hundreds of GWASs have been published and thousands of disease risk-associated SNPs have been identified. Summary statistics from the conducted GWASs are often available and can be used to identify SNP features associated with the level of GWAS statistical significance. Those features could be used to select SNPs from gray zones (SNPs that are nominally significant but do not reach the genome-wide level of significance) for targeted analyses. Methods We used summary statistics from recently published breast and lung cancer and scleroderma GWASs to explore the association between the level of the GWAS statistical significance and the expression quantitative trait loci (eQTL) status of the SNP. Data from the Genotype-Tissue Expression Project (GTEx) were used to identify eQTL SNPs. Results We found that SNPs reported as eQTLs were more significant in GWAS (higher -log10p) regardless of the tissue specificity of the eQTL. Pan-tissue eQTLs (those reported as eQTLs in multiple tissues) tended to be more significant in the GWAS compared to those reported as eQTL in only one tissue type. eQTL density in the ±5 kb adjacent region of a given SNP was also positively associated with the level of GWAS statistical significance regardless of the eQTL status of the SNP. We found that SNPs located in the regions of high eQTL density were more likely to be located in regulatory elements (transcription factor or miRNA binding sites). When SNPs were stratified by the level of statistical significance, the proportion of eQTLs was positively associated with the mean level of statistical significance in the group. The association curve reaches a plateau around -log10p ≈ 5. The observed associations suggest that quasi-significant SNPs (10− 5 < p < 5 × 10− 8) and SNPs at the genome wide level of statistical significance (p < 5 × 10− 8) may have a similar proportions of risk associated SNPs. Conclusions The results of this study indicate that the SNP’s eQTL status, as well as eQTL density in the adjacent region are positively associated with the level of statistical significance of the SNP in GWAS.