Accurate prediction of the impact of genomic variation on phenotype is a major goal of computational biology and an important contributor to personalized medicine. Computational predictions can lead to a better understanding of the mechanisms underlying genetic diseases, including cancer, but their adoption requires thorough and unbiased assessment. Cystathionine-beta-synthase (CBS) is an enzyme that catalyzes the first step of the transsulfuration pathway, from homocysteine to cystathionine, and in which variations are associated with human hyperhomocysteinemia and homocystinuria. We have created a computational challenge under the CAGI framework to evaluate how well different methods can predict the phenotypic effect(s) of CBS single amino acid substitutions using a blinded experimental data set. CAGI participants were asked to predict yeast growth based on the identity of the mutations. The performance of the methods was evaluated using several metrics. The CBS challenge highlighted the difficulty of predicting the phenotype of an ex vivo system in a model organism when classification models were trained on human disease data. We also discuss the variations in difficulty of prediction for known benign and deleterious variants, as well as identify methodological and experimental constraints with lessons to be learned for future challenges.

Background

The recombination-activating gene (RAG) 1/2 proteins play a critical role in the development of T and B cells by initiating the VDJ recombination process that leads to generation of a broad T-cell receptor (TCR) and B-cell receptor repertoire. Pathogenic mutations in the RAG1/2 genes result in various forms of primary immunodeficiency, ranging from T(-)B(-) severe combined immune deficiency to delayed-onset disease with granuloma formation, autoimmunity, or both. It is not clear what contributes to such heterogeneity of phenotypes.

Objective

We sought to investigate the molecular basis for phenotypic diversity presented in patients with various RAG1 mutations.

Methods

We have developed a flow cytometry-based assay that allows analysis of RAG recombination activity based on green fluorescent protein expression and have assessed the induction of the Ighc locus rearrangements in mouse Rag1(-/-) pro-B cells reconstituted with wild-type or mutant human RAG1 (hRAG1) using deep sequencing technology.

Results

Here we demonstrate correlation between defective recombination activity of hRAG1 mutant proteins and severity of the clinical and immunologic phenotype and provide insights on the molecular mechanisms accounting for such phenotypic diversity.

Conclusions

Using a sensitive assay to measure the RAG1 activity level of 79 mutations in a physiologic setting, we demonstrate correlation between recombination activity of RAG1 mutants and the severity of clinical presentation and show that RAG1 mutants can induce specific abnormalities of the VDJ recombination process.

Background

The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors.

Results

Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic.

Conclusions

Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead.