Non-synonymous and synonymous coding SNPs show similar likelihood and effect size of human disease association

Abstract

Many DNA variants have been identified on more than 300 diseases and traits using Genome-Wide Association Studies (GWASs). Some have been validated using deep sequencing, but many fewer have been validated functionally, primarily focused on non-synonymous coding SNPs (nsSNPs). It is an open question whether synonymous coding SNPs (sSNPs) and other non-coding SNPs can lead to as high odds ratios as nsSNPs. We conducted a broad survey across 21,429 disease-SNP associations curated from 2,113 publications studying human genetic association, and found that nsSNPs and sSNPs shared similar likelihood and effect size for disease association. The enrichment of disease-associated SNPs around the 80(th) base in the first introns might provide an effective way to prioritize intronic SNPs for functional studies. We further found that the likelihood of disease association was positively associated with the effect size across different types of SNPs, and SNPs in the 3' untranslated regions, such as the microRNA binding sites, might be under-investigated. Our results suggest that sSNPs are just as likely to be involved in disease mechanisms, so we recommend that sSNPs discovered from GWAS should also be examined with functional studies.

Figure 1. A curated quantitative disease-SNP association database.

Starting from a list of all SNPs measured in the HapMap 3 project, we searched for their presence in all Medline abstracts, eliminating non-human studies. Significant SNP-disease associations were manually curated from the full text, and reviewed four rounds. SNP IDs were annotated using the UCSC genome browser for positions and function types and annotated using Entrez for associated genes. Disease mesh terms were compared with the Unified Medical Language System (UMLS) to select concept unique identifiers (CUIs).

Figure 2. Impact of SNP types on the likelihood of disease association.

For each of the nine SNP types, the likelihood of disease associations was estimated as the percentage of disease-associated SNPs among all SNPs measured in the HapMap 3 project. Similar likelihoods were observed among nsSNPs, sSNPs, and SNPs in the 5′-UTR. The number of disease-associated SNPs and SNPs measured in the HapMap 3 project were specified in the parenthesis.

Figure 3. Cumulative density of disease-associated SNPs in the first intron.

The cumulative densities of disease-associated SNPs were plotted against the distance from the first intron start site. The density was peaked at the 80^th base from the first intron start site.

Figure 4. Impact of SNP types on the effect size of SNP-disease association.

The median odds ratio±standard error was calculated for each of the nine SNP types using 9,574 curated SNP-disease associations. The number of distinct SNPs was specified in the parenthesis. The odds ratios of nonsense SNPs were significantly higher than those of other SNP types (p<0.05, Mann-whiney U test). The p-values between the odds ratios of nsSNPs and other type of SNPs were shown in the figure.

Figure 5. SNPs in the 3′-UTR were under-investigated.

The median odds ratio of SNP-disease associations was plotted against the percentage of disease-associated SNPs for each of the nine SNP types. Assuming the median odds ratio will not be increased with more studies performed, the percentage of disease-associated SNPs will likely be increased to fit the curve.