The interpretation of genomic variants has become one of the paramount challenges in the post-genome sequencing era. In this review we summarize nearly 20 years of research on the applications of information theory (IT) to interpret coding and non-coding mutations that alter mRNA splicing in rare and common diseases. We compile and summarize the spectrum of published variants analyzed by IT, to provide a broad perspective of the distribution of deleterious natural and cryptic splice site variants detected, as well as those affecting splicing regulatory sequences. Results for natural splice site mutations can be interrogated dynamically with Splicing Mutation Calculator, a companion software program that computes changes in information content for any splice site substitution, linked to corresponding publications containing these mutations. The accuracy of IT-based analysis was assessed in the context of experimentally validated mutations. Because splice site information quantifies binding affinity, IT-based analyses can discern the differences between variants that account for the observed reduced (leaky) versus abolished mRNA splicing. We extend this principle by comparing predicted mutations in natural, cryptic, and regulatory splice sites with observed deleterious phenotypic and benign effects. Our analysis of 1727 variants revealed a number of general principles useful for ensuring portability of these analyses and accurate input and interpretation of mutations. We offer guidelines for optimal use of IT software for interpretation of mRNA splicing mutations.

Numerous genetic factors that influence breast cancer risk are known. However, approximately two-thirds of the overall familial risk remain unexplained. To determine whether some of the missing heritability is due to rare variants conferring high to moderate risk, we tested for an association between the c.5791C>T nonsense mutation (p.Arg1931*; rs144567652) in exon 22 of FANCM gene and breast cancer. An analysis of genotyping data from 8635 familial breast cancer cases and 6625 controls from different countries yielded an association between the c.5791C>T mutation and breast cancer risk [odds ratio (OR) = 3.93 (95% confidence interval (CI) = 1.28–12.11; P = 0.017)]. Moreover, we performed two meta-analyses of studies from countries with carriers in both cases and controls and of all available data. These analyses showed breast cancer associations with OR = 3.67 (95% CI = 1.04–12.87; P = 0.043) and OR = 3.33 (95% CI = 1.09–13.62; P = 0.032), respectively. Based on information theory-based prediction, we established that the mutation caused an out-of-frame deletion of exon 22, due to the creation of a binding site for the pre-mRNA processing protein hnRNP A1. Furthermore, genetic complementation analyses showed that the mutation influenced the DNA repair activity of the FANCM protein. In summary, we provide evidence for the first time showing that the common p.Arg1931* loss-of-function variant in FANCM is a risk factor for familial breast cancer.

Mutations that affect mRNA splicing often produce multiple mRNA isoforms, resulting in complex molecular phenotypes. Definition of an exon and its inclusion in mature mRNA relies on joint recognition of both acceptor and donor splice sites. This study predicts cryptic and exon‐skipping isoforms in mRNA produced by splicing mutations from the combined information contents (R_i, which measures binding‐site strength, in bits) and distribution of the splice sites defining these exons. The total information content of an exon (R_i,total) is the sum of the R_i values of its acceptor and donor splice sites, adjusted for the self‐information of the distance separating these sites, that is, the gap surprisal. Differences between total information contents of an exon (ΔR_i,total) are predictive of the relative abundance of these exons in distinct processed mRNAs. Constraints on splice site and exon selection are used to eliminate nonconforming and poorly expressed isoforms. Molecular phenotypes are computed by the Automated Splice Site and Exon Definition Analysis (<!--TODO: clickthrough URL--> http://splice.uwo.ca) server. Predictions of splicing mutations were highly concordant (85.2%; n = 61) with published expression data. In silico exon definition analysis will contribute to streamlining assessment of abnormal and normal splice isoforms resulting from mutations.