Towards a New, Endophenotype-Based Strategy for Pathogenicity Prediction in BRCA1 and BRCA2: In Silico Modeling of the Outcome of HDR/SGE Assays for Missense Variants

Endofenotip; Prediccions de patogenicitat; Predictor específic de proteïnaEndofenotipo; Predicciones de patogenicidad; Predictor específico de proteínaEndophenotype; Pathogenicity predictions; Protein-specific predictorThe present limitations in the pathogenicity prediction of BRCA1 and BRCA2 (BRCA1/2) missense variants constitute an important problem with negative consequences for the diagnosis of hereditary breast and ovarian cancer. However, it has been proposed that the use of endophenotype predictions, i.e., computational estimates of the outcomes of functional assays, can be a good option to address this bottleneck. The application of this idea to the BRCA1/2 variants in the CAGI 5-ENIGMA international challenge has shown promising results. Here, we developed this approach, exploring the predictive performances of the regression models applied to the BRCA1/2 variants for which the values of the homology-directed DNA repair and saturation genome editing assays are available. Our results first showed that we can generate endophenotype estimates using a few molecular-level properties. Second, we show that the accuracy of these estimates is enough to obtain pathogenicity predictions comparable to those of many standard tools. Third, endophenotype-based predictions are complementary to, but do not outperform, those of a Random Forest model trained using variant pathogenicity annotations instead of endophenotype values. In summary, our results confirmed the usefulness of the endophenotype approach for the pathogenicity prediction of the BRCA1/2 missense variants, suggesting different options for future improvements.This research was funded by the EU European Regional Development Fund (ERDF) through the Program Interreg V-A Spain-France-Andorra (POCTEFA), grant number EFA086/15-PIREPRED, by the Spanish Ministerio de Ciencia e Innovación, grant number PID2019-111217RB-I00, and by the Spanish Ministerio de Economía y Competitividad, grant number SAF2016-80255-R

Development of pathogenicity predictors specific for variants that do not comply with clinical guidelines for the use of computational evidence

Predictors de patogenicitat in silico; Variants missense; Seqüenciació de nova generacióPredictores de patogenicidad in silico; Variantes missense; Secuenciación de nueva generaciónIn silico pathogenicity predictors; Missense variants; Next-generation sequencingBackground Strict guidelines delimit the use of computational information in the clinical setting, due to the still moderate accuracy of in silico tools. These guidelines indicate that several tools should always be used and that full coincidence between them is required if we want to consider their results as supporting evidence in medical decision processes. Application of this simple rule certainly decreases the error rate of in silico pathogenicity assignments. However, when predictors disagree this rule results in the rejection of potentially valuable information for a number of variants. In this work, we focus on these variants of the protein sequence and develop specific predictors to help improve the success rate of their annotation. Results We have used a set of 59,442 protein sequence variants (15,723 pathological and 43,719 neutral) from 228 proteins to identify those cases for which pathogenicity predictors disagree. We have repeated this process for all the possible combinations of five known methods (SIFT, PolyPhen-2, PON-P2, CADD and MutationTaster2). For each resulting subset we have trained a specific pathogenicity predictor. We find that these specific predictors are able to discriminate between neutral and pathogenic variants, with a success rate different from random. They tend to outperform the constitutive methods but this trend decreases as the performance of the constitutive predictor improves (e.g. with PON-P2 and PolyPhen-2). We also find that specific methods outperform standard consensus methods (Condel and CAROL). Conclusion Focusing development efforts on the case of variants for which known methods disagree we may obtain pathogenicity predictors with improved performances. Although we have not yet reached the success rate that allows the use of this computational evidence in a clinical setting, the simplicity of the approach indicates that more advanced methods may reach this goal in a close future.This work has been supported by the spanish Ministerio de Economía y Competitividad (BIO2012–40133; SAF2016–80255-R). It has also been supported, and the publication costs have been defrayed, by the European Regional Development Fund (ERDF), through the Interreg V-A Spain-France-Andorra programme (POCTEFA 2014–2020), research grant PIREPRED (EFA086/15)

New genes involved in Angelman syndrome-like: Expanding the genetic spectrum

Síndrome de Angelman; FenotipoSíndrome d'Angelman; FenotipAngelman syndrome; PhenotypeAngelman syndrome (AS) is a neurogenetic disorder characterized by severe developmental delay with absence of speech, happy disposition, frequent laughter, hyperactivity, stereotypies, ataxia and seizures with specific EEG abnormalities. There is a 10–15% of patients with an AS phenotype whose genetic cause remains unknown (Angelman-like syndrome, AS-like). Whole-exome sequencing (WES) was performed on a cohort of 14 patients with clinical features of AS and no molecular diagnosis. As a result, we identified 10 de novo and 1 X-linked pathogenic/likely pathogenic variants in 10 neurodevelopmental genes (SYNGAP1, VAMP2, TBL1XR1, ASXL3, SATB2, SMARCE1, SPTAN1, KCNQ3, SLC6A1 and LAS1L) and one deleterious de novo variant in a candidate gene (HSF2). Our results highlight the wide genetic heterogeneity in AS-like patients and expands the differential diagnosis.This work is supported by Instituto de Salud Carlos III (MG, PI16/01411), Asociación Española de Síndrome de Angelman (EG), Institut d’investigació i innovació Parc Taulí I3PT (CA, CIR2016/025, CIR2018/021) and Ministerio de Economía y Competitividad (XD, SAF2016-14 80255-R). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Mechanism of KMT5B haploinsufficiency in neurodevelopment in humans and mice

KMT5B gene; Neurodevelopment; MiceGen KMT5B; Neurodesenvolupament; RatolinsGen KMT5B; Neurodesarrollo; RatonesPathogenic variants in KMT5B, a lysine methyltransferase, are associated with global developmental delay, macrocephaly, autism, and congenital anomalies (OMIM# 617788). Given the relatively recent discovery of this disorder, it has not been fully characterized. Deep phenotyping of the largest (n = 43) patient cohort to date identified that hypotonia and congenital heart defects are prominent features that were previously not associated with this syndrome. Both missense variants and putative loss-of-function variants resulted in slow growth in patient-derived cell lines. KMT5B homozygous knockout mice were smaller in size than their wild-type littermates but did not have significantly smaller brains, suggesting relative macrocephaly, also noted as a prominent clinical feature. RNA sequencing of patient lymphoblasts and Kmt5b haploinsufficient mouse brains identified differentially expressed pathways associated with nervous system development and function including axon guidance signaling. Overall, we identified additional pathogenic variants and clinical features in KMT5B-related neurodevelopmental disorder and provide insights into the molecular mechanisms of the disorder using multiple model systems.This work was supported by LB692 Nebraska Tobacco Settlement Biomedical Research Development Program (to H.A.F.S.); The Simons Foundation Autism Research Initiative–Bridge to Independence Award SFARI 381192 (to H.A.F.S.); The A*STAR, Singapore, IAF-PP Program H17/01/a0/004 (to C.Y.L.); The Wong Boon Hock Society research program Yong Loo Lin School of Medicine (to Z.X.C.); NIH training grant 2T32GM008638-25 (L.B.); The Intramural Research Program of the National Human Genome Research Institute (to W.G.); The National Center for Advancing Translational Sciences of the NIH award number TL1TR001880 (to S.E.S.); The Eunice Kennedy Shriver National Institute of Child Health and Human Development award number HD009003-01 (to S.E.S.); Institute for Translational Medicine and Therapeutics of the Perelman School of Medicine at the University of Pennsylvania (to S.E.S.); and Swiss National Science Foundation (SNSF) grant 320020_179547 and funds from the University of Zurich Research Priority Programs (URPP) AdaBD: Adaptive Brain Circuits in Developments (to A.Rau.). F.J.K. was funded by the Deutsche Forschungsgemeinschaft grant number FOR 2488. In silico modeling was supported by the Spanish Ministerio de Ciencia e Innovación grant number PID2019-111217RB-I00 (to X.d.l.C.). This study used data from the DDD study. The DDD study presents independent research commissioned by the Health Innovation Challenge Fund (grant number HICF-1009-003). This study makes use of DECIPHER (www.deciphergenomics.org), which is funded by Wellcome (grant number 223718/Z/21/Z). See Nature PMID: 25533962 or www.ddduk.org/access.html for full acknowledgement