Prioritizing single-nucleotide variations that potentially regulate alternative splicing

Abstract

Recent evidence suggests that many complex diseases are caused by genetic variations that play regulatory roles in controlling gene expression. Most genetic studies focus on nonsynonymous variations that can alter the amino acid composition of a protein and are therefore believed to have the highest impact on phenotype. Synonymous variations, however, can also play important roles in disease pathogenesis by regulating pre-mRNA processing and translational control. In this study, we systematically survey the effects of single-nucleotide variations (SNVs) on binding affinity of RNA-binding proteins (RBPs). Among the 10,113 synonymous SNVs identified in 697 individuals in the 1,000 Genomes Project and distributed by Genetic Analysis Workshop 17 (GAW17), we identified 182 variations located in alternatively spliced exons that can significantly change the binding affinity of nine RBPs whose binding preferences on 7-mer RNA sequences were previously reported. We found that the minor allele frequencies of these variations are similar to those of nonsynonymous SNVs, suggesting that they are in fact functional. We propose a workflow to identify phenotype-associated regulatory SNVs that might affect alternative splicing from exome-sequencing-derived genetic variations. Based on the affecting SNVs on the quantitative traits simulated in GAW17, we further identified two and four functional SNVs that are predicted to be involved in alternative splicing regulation in traits Q1 and Q2, respectively.

Figure 1

Computational strategy (A) Assessing the capability of a SNV to change the binding affinity of an RBP. (B) Distribution of AC values for SF2.

Figure 2

Relationships between synonymous variations and their effects on the binding of RBPs (A) Hierarchical clustering of RBPs and 183 synonymous variations that can potentially affect binding of more than one RBP, based on their AC values (Eq. (1)); red and blue indicate potential gain and loss of binding in the minor alleles. (B) Principal components analysis (PCA) biplot with variations plotted against the top three principal components; nine RBPs show their similarity or dissimilarity with respect to the effects of variations on their binding affinity.

Figure 3

Comparisons of MAFs among different categories of SNVs according to 697 individuals

Figure 4

Proposed workflow