Choosing Variant Interpretation Tools for Clinical Applications: Context Matters.

Pathogenicity predictors are computational tools that classify genetic variants as benign or pathogenic; this is currently a major challenge in genomic medicine. With more than fifty such predictors available, selecting the most suitable tool for clinical applications like genetic screening, molecular diagnostics, and companion diagnostics has become increasingly challenging. To address this issue, we have developed a cost-based framework that naturally considers the various components of the problem. This framework encodes clinical scenarios using a minimal set of parameters and treats pathogenicity predictors as rejection classifiers, a common practice in clinical applications where low-confidence predictions are routinely rejected. We illustrate our approach in four examples where we compare different numbers of pathogenicity predictors for missense variants. Our results show that no single predictor is optimal for all clinical scenarios and that considering rejection yields a different perspective on classifiers.

BRCA1- and BRCA2-specific in silico tools for variant interpretation in the CAGI 5 ENIGMA challenge.

BRCA1 and BRCA2 (BRCA1/2) germline variants disrupting the DNA protective role of these genes increase the risk of hereditary breast and ovarian cancers. Correct identification of these variants then becomes clinically relevant, because it may increase the survival rates of the carriers. Unfortunately, we are still unable to systematically predict the impact of BRCA1/2 variants. In this article, we present a family of in silico predictors that address this problem, using a gene-specific approach. For each protein, we have developed two tools, aimed at predicting the impact of a variant at two different levels: Functional and clinical. Testing their performance in different datasets shows that specific information compensates the small number of predictive features and the reduced training sets employed to develop our models. When applied to the variants of the BRCA1/2 (ENIGMA) challenge in the fifth Critical Assessment of Genome Interpretation (CAGI 5) we find that these methods, particularly those predicting the functional impact of variants, have a good performance, identifying the large compositional bias towards neutral variants in the CAGI sample. This performance is further improved when incorporating to our prediction protocol estimates of the impact on splicing of the target variant.

Assessment of blind predictions of the clinical significance of BRCA1 and BRCA2 variants.

Testing for variation in BRCA1 and BRCA2 (commonly referred to as BRCA1/2), has emerged as a standard clinical practice and is helping countless women better understand and manage their heritable risk of breast and ovarian cancer. Yet the increased rate of BRCA1/2 testing has led to an increasing number of Variants of Uncertain Significance (VUS), and the rate of VUS discovery currently outpaces the rate of clinical variant interpretation. Computational prediction is a key component of the variant interpretation pipeline. In the CAGI5 ENIGMA Challenge, six prediction teams submitted predictions on 326 newly-interpreted variants from the ENIGMA Consortium. By evaluating these predictions against the new interpretations, we have gained a number of insights on the state of the art of variant prediction and specific steps to further advance this state of the art.

The Complementarity Between Protein-Specific and General Pathogenicity Predictors for Amino Acid Substitutions.

The usage of next-generation sequencing with biomedical/clinical purposes has fuelled the demand for tools that assess the functional impact of sequence variants. For single amino acid variants, general methods (GM), based on biophysics/evolutionary principles and trained by pooling variants from many proteins, are already available. Until now, their accuracy range (∼80%) has limited their usage in clinical applications. In parallel, a series of studies indicate that protein-specific predictors (PSP), using only information from the protein of interest, could frequently surpass the performance of GM. However, two reasons suggest that this may not always be the case: the existence of a performance threshold affecting both GM and PSP, and the effect of training data scarcity. Here, we characterize the relationship between the two approaches deriving 82 PSP and comparing them with several GM (PolyPhen-2, SIFT, PON-P2, MutationTaster2, CADD). We find a complementary relationship between PSP and GM, with no approach always outperforming the other. However, the relationship varies between two limiting situations, for example, PSP are frequently outperformed by PON-P2, the best GM; however, the opposite happens when we compare PSP and SIFT. Finally, we explore how the observed complementarity could lead to increased success rates in pathogenicity prediction.

Molecular damage in Fabry disease: characterization and prediction of alpha-galactosidase A pathological mutations.

Loss-of-function mutations of the enzyme alpha-galactosidase A (GLA) causes Fabry disease (FD), that is a rare and potentially fatal disease. Identification of these pathological mutations by sequencing is important because it allows an early treatment of the disease. However, before taking any treatment decision, if the mutation identified is unknown, we first need to establish if it is pathological or not. General bioinformatic tools (PolyPhen-2, SIFT, Condel, etc.) can be used for this purpose, but their performance is still limited. Here we present a new tool, specifically derived for the assessment of GLA mutations. We first compared mutations of this enzyme known to cause FD with neutral sequence variants, using several structure and sequence properties. Then, we used these properties to develop a family of prediction methods adapted to different quality requirements. Trained and tested on a set of known Fabry mutations, our methods have a performance (Matthews correlation: 0.56-0.72) comparable or better than that of the more complex method, Polyphen-2 (Matthews correlation: 0.61), and better than those of SIFT (Matthews correl.: 0.54) and Condel (Matthews correl.: 0.51). This result is validated in an independent set of 65 pathological mutations, for which our method displayed the best success rate (91.0%, 87.7%, and 73.8%, for our method, PolyPhen-2 and SIFT, respectively). These data confirmed that our specific approach can effectively contribute to the identification of pathological mutations in GLA, and therefore enhance the use of sequence information in the identification of undiagnosed Fabry patients.

Improving the diagnosis of cobalamin and related defects by genomic analysis, plus functional and structural assessment of novel variants.

BACKGROUND: Cellular cobalamin defects are a locus and allelic heterogeneous disorder. The gold standard for coming to genetic diagnoses of cobalamin defects has for some time been gene-by-gene Sanger sequencing of individual DNA fragments. Enzymatic and cellular methods are employed before such sequencing to help in the selection of the gene defects to be sought, but this is time-consuming and laborious. Furthermore some cases remain undiagnosed because no biochemical methods have been available to test for cobalamin absorption and transport defects. RESULTS: This paper reports the use of massive parallel sequencing of DNA (exome analysis) for the accurate and rapid genetic diagnosis of cobalamin-related defects in a cohort of affected patients. The method was first validated in an initial cohort with different cobalamin defects. Mendelian segregation, the frequency of mutations, and the comprehensive structural and functional analysis of gene variants, identified disease-causing mutations in 12 genes involved in the absorption and synthesis of active cofactors of vitamin B12 (22 cases), and in the non-cobalamin metabolism-related genes ACSF3 (in four biochemically misdiagnosed patients) and SUCLA2 (in one patient with an unusual presentation). We have identified thirteen new variants all classified as pathogenic according to the ACGM recommendation but four were classified as variant likely pathogenic in MUT and SUCLA2. Functional and structural analysis provided evidences to classify them as pathogenic variants. CONCLUSIONS: The present findings suggest that the technology used is sufficiently sensitive and specific, and the results it provides sufficiently reproducible, to recommend its use as a second-tier test after the biochemical detection of cobalamin disorder markers in the first days of life. However, for accurate diagnoses to be made, biochemical and functional tests that allow comprehensive clinical phenotyping are also needed.

The relationship between gene isoform multiplicity, number of exons and protein divergence.

At present we know that phenotypic differences between organisms arise from a variety of sources, like protein sequence divergence, regulatory sequence divergence, alternative splicing, etc. However, we do not have yet a complete view of how these sources are related. Here we address this problem, studying the relationship between protein divergence and the ability of genes to express multiple isoforms. We used three genome-wide datasets of human-mouse orthologs to study the relationship between isoform multiplicity co-occurrence between orthologs (the fact that two orthologs have more than one isoform) and protein divergence. In all cases our results showed that there was a monotonic dependence between these two properties. We could explain this relationship in terms of a more fundamental one, between exon number of the largest isoform and protein divergence. We found that this last relationship was present, although with variations, in other species (chimpanzee, cow, rat, chicken, zebrafish and fruit fly). In summary, we have identified a relationship between protein divergence and isoform multiplicity co-occurrence and explained its origin in terms of a simple gene-level property. Finally, we discuss the biological implications of these findings for our understanding of inter-species phenotypic differences.