Clinical genomic database

Approximating facial expression effects on diagnostic accuracy via generative AI in medical genetics

Artificial intelligence (AI) is increasingly used in genomics research and practice, and generative AI has garnered significant recent attention. In clinical applications of generative AI, aspects of the underlying datasets can impact results, and confounders should be studied and mitigated. One example involves the facial expressions of people with genetic conditions. Stereotypically, Williams (WS) and Angelman (AS) syndromes are associated with a "happy" demeanor, including a smiling expression. Clinical geneticists may be more likely to identify these conditions in images of smiling individuals. To study the impact of facial expression, we analyzed publicly available facial images of approximately 3500 individuals with genetic conditions. Using a deep learning (DL) image classifier, we found that WS and AS images with non-smiling expressions had significantly lower prediction probabilities for the correct syndrome labels than those with smiling expressions. This was not seen for 22q11.2 deletion and Noonan syndromes, which are not associated with a smiling expression. To further explore the effect of facial expressions, we computationally altered the facial expressions for these images. We trained HyperStyle, a GAN-inversion technique compatible with StyleGAN2, to determine the vector representations of our images. Then, following the concept of InterfaceGAN, we edited these vectors to recreate the original images in a phenotypically accurate way but with a different facial expression. Through online surveys and an eye-tracking experiment, we examined how altered facial expressions affect the performance of human experts. We overall found that facial expression is associated with diagnostic accuracy variably in different genetic conditions.

Application of whole exome sequencing in carrier screening for high-risk families without probands

Purpose

This study aimed to screen the genetic etiology for the high-risk families including those with an adverse pregnancy history, a history of consanguineous marriages, or a history of genetic diseases, but lack of proband via whole exome sequencing (WES).

Methods

128 individuals from high-risk family were tested by WES. The candidate variants were analyzed according to the ACMG criteria to screen the potential carriers. At-risk couples (ARCs) who harbored the same causative gene were provided with precise fertility guidance to avoid the birth of children with birth defects.

Results

The total detection rate was 36.72%, with pathogenic/likely pathogenic (P/LP) variants found in 47 individuals, and variants of uncertain significance (VUS) were found in 34. Among couples with adverse pregnancy history: P/LP variants were found in 38 individuals, and VUS were found in 26, for a detection rate of 34.55%; among members of family history of genetic disease or consanguineous marriages: P/LP variants were found in nine individuals, and VUS were found in 8, for a detection rate of 50.00%. Otherwise, we detected 19 ARCs who both carried P/LP variants in the same gene, with a theoretical offspring prevalence of up to 7.42%.

Conclusion

In the absence of probands, carrier screening using WES can provide an efficient tool for screening the molecular etiology of high-risk families.

D-Bifunctional Protein Deficiency Diagnosis-A Challenge in Low Resource Settings: Case Report and Review of the Literature

D-bifunctional protein deficiency (D-BPD) is a rare, autosomal recessive peroxisomal disorder that affects the breakdown of long-chain fatty acids. Patients with D-BPD typically present during the neonatal period with hypotonia, seizures, and facial dysmorphism, followed by severe developmental delay and early mortality. While some patients have survived past two years of age, the detectable enzyme activity in these rare cases was likely a contributing factor. We report a D-BPD case and comment on challenges faced in diagnosis based on a narrative literature review. An overview of Romania's first patient diagnosed with D-BPD is provided, including clinical presentation, imaging, biochemical, molecular data, and clinical course. Establishing a diagnosis can be challenging, as the clinical picture is often incomplete or similar to many other conditions. Our patient was diagnosed with type I D-BPD based on whole-exome sequencing (WES) results revealing a pathogenic frameshift variant of the HSD17B4 gene, c788del, p(Pro263GInfs*2), previously identified in another D-BPD patient. WES also identified a variant of the SUOX gene with unclear significance. We advocate for using molecular diagnosis in critically ill newborns and infants to improve care, reduce healthcare costs, and allow for familial counseling.

Explicable prioritization of genetic variants by integration of rule-based and machine learning algorithms for diagnosis of rare Mendelian disorders

BACKGROUND

In the process of finding the causative variant of rare diseases, accurate assessment and prioritization of genetic variants is essential. Previous variant prioritization tools mainly depend on the in-silico prediction of the pathogenicity of variants, which results in low sensitivity and difficulty in interpreting the prioritization result. In this study, we propose an explainable algorithm for variant prioritization, named 3ASC, with higher sensitivity and ability to annotate evidence used for prioritization. 3ASC annotates each variant with the 28 criteria defined by the ACMG/AMP genome interpretation guidelines and features related to the clinical interpretation of the variants. The system can explain the result based on annotated evidence and feature contributions.

RESULTS

We trained various machine learning algorithms using in-house patient data. The performance of variant ranking was assessed using the recall rate of identifying causative variants in the top-ranked variants. The best practice model was a random forest classifier that showed top 1 recall of 85.6% and top 3 recall of 94.4%. The 3ASC annotates the ACMG/AMP criteria for each genetic variant of a patient so that clinical geneticists can interpret the result as in the CAGI6 SickKids challenge. In the challenge, 3ASC identified causal genes for 10 out of 14 patient cases, with evidence of decreased gene expression for 6 cases. Among them, two genes (HDAC8 and CASK) had decreased gene expression profiles confirmed by transcriptome data.

CONCLUSIONS

3ASC can prioritize genetic variants with higher sensitivity compared to previous methods by integrating various features related to clinical interpretation, including features related to false positive risk such as quality control and disease inheritance pattern. The system allows interpretation of each variant based on the ACMG/AMP criteria and feature contribution assessed using explainable AI techniques.

Recognition of Genetic Conditions After Learning With Images Created Using Generative Artificial Intelligence

Importance

The lack of standardized genetics training in pediatrics residencies, along with a shortage of medical geneticists, necessitates innovative educational approaches.

Objective

To compare pediatric resident recognition of Kabuki syndrome (KS) and Noonan syndrome (NS) after 1 of 4 educational interventions, including generative artificial intelligence (AI) methods.

Design, Setting, and Participants

This comparative effectiveness study used generative AI to create images of children with KS and NS. From October 1, 2022, to February 28, 2023, US pediatric residents were provided images through a web-based survey to assess whether these images helped them recognize genetic conditions.

Interventions

Participants categorized 20 images after exposure to 1 of 4 educational interventions (text-only descriptions, real images, and 2 types of images created by generative AI).

Main Outcomes and Measures

Associations between educational interventions with accuracy and self-reported confidence.

Results

Of 2515 contacted pediatric residents, 106 and 102 completed the KS and NS surveys, respectively. For KS, the sensitivity of text description was 48.5% (128 of 264), which was not significantly different from random guessing (odds ratio [OR], 0.94; 95% CI, 0.69-1.29; P = .71). Sensitivity was thus compared for real images vs random guessing (60.3% [188 of 312]; OR, 1.52; 95% CI, 1.15-2.00; P = .003) and 2 types of generative AI images vs random guessing (57.0% [212 of 372]; OR, 1.32; 95% CI, 1.04-1.69; P = .02 and 59.6% [193 of 324]; OR, 1.47; 95% CI, 1.12-1.94; P = .006) (denominators differ according to survey responses). The sensitivity of the NS text-only description was 65.3% (196 of 300). Compared with text-only, the sensitivity of the real images was 74.3% (205 of 276; OR, 1.53; 95% CI, 1.08-2.18; P = .02), and the sensitivity of the 2 types of images created by generative AI was 68.0% (204 of 300; OR, 1.13; 95% CI, 0.77-1.66; P = .54) and 71.0% (247 of 328; OR, 1.30; 95% CI, 0.92-1.83; P = .14). For specificity, no intervention was statistically different from text only. After the interventions, the number of participants who reported being unsure about important diagnostic facial features decreased from 56 (52.8%) to 5 (7.6%) for KS (P < .001) and 25 (24.5%) to 4 (4.7%) for NS (P < .001). There was a significant association between confidence level and sensitivity for real and generated images.

Conclusions and Relevance

In this study, real and generated images helped participants recognize KS and NS; real images appeared most helpful. Generated images were noninferior to real images and could serve an adjunctive role, particularly for rare conditions.

GenIA, the Genetic Immunology Advisor database for inborn errors of immunity

BACKGROUND

To date, no publicly accessible platform has captured and synthesized all of the layered dimensions of genotypic, phenotypic, and mechanistic information published in the field of inborn errors of immunity (IEIs). Such a platform would represent the extensive and complex landscape of IEIs and could increase the rate of diagnosis in patients with a suspected IEI, which remains unacceptably low.

OBJECTIVE

Our aim was to create an expertly curated, patient-centered, multidimensional IEI database that enables aggregation and sophisticated data interrogation and promotes involvement from diverse stakeholders across the community.

METHODS

The database structure was designed following a subject-centered model and written in Structured Query Language (SQL). The web application is written in Hypertext Preprocessor (PHP), Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript. All data stored in the Genetic Immunology Advisor (GenIA) are extracted by manually reviewing published research articles.

RESULTS

We completed data collection and curation for 24 pilot genes. Using these data, we have exemplified how GenIA can provide quick access to structured, longitudinal, more thorough, comprehensive, and up-to-date IEI knowledge than do currently existing databases, such as ClinGen, Human Phenotype Ontology (HPO), ClinVar, or Online Mendelian Inheritance in Man (OMIM), with which GenIA intends to dovetail.

CONCLUSIONS

GenIA strives to accurately capture the extensive genetic, mechanistic, and phenotypic heterogeneity found across IEIs, as well as genetic paradigms and diagnostic pitfalls associated with individual genes and conditions. The IEI community's involvement will help promote GenIA as an enduring resource that supports and improves knowledge sharing, research, diagnosis, and care for patients with genetic immune disease.

The future of commercial genetic testing

PURPOSE OF REVIEW

There are thousands of different clinical genetic tests currently available. Genetic testing and its applications continue to change rapidly for multiple reasons. These reasons include technological advances, accruing evidence about the impact and effects of testing, and many complex financial and regulatory factors.

RECENT FINDINGS

This article considers a number of key issues and axes related to the current and future state of clinical genetic testing, including targeted versus broad testing, simple/Mendelian versus polygenic and multifactorial testing models, genetic testing for individuals with high suspicion of genetic conditions versus ascertainment through population screening, the rise of artificial intelligence in multiple aspects of the genetic testing process, and how developments such as rapid genetic testing and the growing availability of new therapies for genetic conditions may affect the field.

SUMMARY

Genetic testing is expanding and evolving, including into new clinical applications. Developments in the field of genetics will likely result in genetic testing becoming increasingly in the purview of a very broad range of clinicians, including general paediatricians as well as paediatric subspecialists.

Genome-wide identification of dominant polyadenylation hexamers for use in variant classification

Polyadenylation is an essential process for the stabilization and export of mRNAs to the cytoplasm and the polyadenylation signal hexamer (herein referred to as hexamer) plays a key role in this process. Yet, only 14 Mendelian disorders have been associated with hexamer variants. This is likely an under-ascertainment as hexamers are not well defined and not routinely examined in molecular analysis. To facilitate the interrogation of putatively pathogenic hexamer variants, we set out to define functionally important hexamers genome-wide as a resource for research and clinical testing interrogation. We identified predominant polyA sites (herein referred to as pPAS) and putative predominant hexamers across protein coding genes (PAS usage >50% per gene). As a measure of the validity of these sites, the population constraint of 4532 predominant hexamers were measured. The predominant hexamers had fewer observed variants compared to non-predominant hexamers and trimer controls, and CADD scores for variants in these hexamers were significantly higher than controls. Exome data for 1477 individuals were interrogated for hexamer variants and transcriptome data were generated for 76 individuals with 65 variants in predominant hexamers. 3' RNA-seq data showed these variants resulted in alternate polyadenylation events (38%) and in elongated mRNA transcripts (12%). Our list of pPAS and predominant hexamers are available in the UCSC genome browser and on GitHub. We suggest this list of predominant hexamers can be used to interrogate exome and genome data. Variants in these predominant hexamers should be considered candidates for pathogenic variation in human disease, and to that end we suggest pathogenicity criteria for classifying hexamer variants.

PDIVAS: Pathogenicity predictor for Deep-Intronic Variants causing Aberrant Splicing

BACKGROUND

Deep-intronic variants that alter RNA splicing were ineffectively evaluated in the search for the cause of genetic diseases. Determination of such pathogenic variants from a vast number of deep-intronic variants (approximately 1,500,000 variants per individual) represents a technical challenge to researchers. Thus, we developed a Pathogenicity predictor for Deep-Intronic Variants causing Aberrant Splicing (PDIVAS) to easily detect pathogenic deep-intronic variants.

RESULTS

PDIVAS was trained on an ensemble machine-learning algorithm to classify pathogenic and benign variants in a curated dataset. The dataset consists of manually curated pathogenic splice-altering variants (SAVs) and commonly observed benign variants within deep introns. Splicing features and a splicing constraint metric were used to maximize the predictive sensitivity and specificity, respectively. PDIVAS showed an average precision of 0.92 and a maximum MCC of 0.88 in classifying these variants, which were the best of the previous predictors. When PDIVAS was applied to genome sequencing analysis on a threshold with 95% sensitivity for reported pathogenic SAVs, an average of 27 pathogenic candidates were extracted per individual. Furthermore, the causative variants in simulated patient genomes were more efficiently prioritized than the previous predictors.

CONCLUSION

Incorporating PDIVAS into variant interpretation pipelines will enable efficient detection of disease-causing deep-intronic SAVs and contribute to improving the diagnostic yield. PDIVAS is publicly available at https://github.com/shiro-kur/PDIVAS .

Artificial intelligence and the impact on medical genetics

Virtually all areas of biomedicine will be increasingly affected by applications of artificial intelligence (AI). We discuss how AI may affect fields of medical genetics, including both clinicians and laboratorians. In addition to reviewing the anticipated impact, we provide recommendations for ways in which these groups may want to evolve in light of the influence of AI. We also briefly discuss how educational and training programs can play a key role in preparing the future workforce given these anticipated changes.

An Updated Overview of Existing Cancer Databases and Identified Needs

Our search of existing cancer databases aimed to assess the current landscape and identify key needs. We analyzed 71 databases, focusing on genomics, proteomics, lipidomics, and glycomics. We found a lack of cancer-related lipidomic and glycomic databases, indicating a need for further development in these areas. Proteomic databases dedicated to cancer research were also limited. To assess overall progress, we included human non-cancer databases in proteomics, lipidomics, and glycomics for comparison. This provided insights into advancements in these fields over the past eight years. We also analyzed other types of cancer databases, such as clinical trial databases and web servers. Evaluating user-friendliness, we used the FAIRness principle to assess findability, accessibility, interoperability, and reusability. This ensured databases were easily accessible and usable. Our search summary highlights significant growth in cancer databases while identifying gaps and needs. These insights are valuable for researchers, clinicians, and database developers, guiding efforts to enhance accessibility, integration, and usability. Addressing these needs will support advancements in cancer research and benefit the wider cancer community.

Spectrum and frequency of genetic variants in sporadic amyotrophic lateral sclerosis

Therapy of motoneuron diseases entered a new phase with the use of intrathecal antisense oligonucleotide therapies treating patients with specific gene mutations predominantly in the context of familial amyotrophic lateral sclerosis. With the majority of cases being sporadic, we conducted a cohort study to describe the mutational landscape of sporadic amyotrophic lateral sclerosis. We analysed genetic variants in amyotrophic lateral sclerosis-associated genes to assess and potentially increase the number of patients eligible for gene-specific therapies. We screened 2340 sporadic amyotrophic lateral sclerosis patients from the German Network for motor neuron diseases for variants in 36 amyotrophic lateral sclerosis-associated genes using targeted next-generation sequencing and for the C9orf72 hexanucleotide repeat expansion. The genetic analysis could be completed on 2267 patients. Clinical data included age at onset, disease progression rate and survival. In this study, we found 79 likely pathogenic Class 4 variants and 10 pathogenic Class 5 variants (without the C9orf72 hexanucleotide repeat expansion) according to the American College of Medical Genetics and Genomics guidelines, of which 31 variants are novel. Thus, including C9orf72 hexanucleotide repeat expansion, Class 4, and Class 5 variants, 296 patients, corresponding to ∼13% of our cohort, could be genetically resolved. We detected 437 variants of unknown significance of which 103 are novel. Corroborating the theory of oligogenic causation in amyotrophic lateral sclerosis, we found a co-occurrence of pathogenic variants in 10 patients (0.4%) with 7 being C9orf72 hexanucleotide repeat expansion carriers. In a gene-wise survival analysis, we found a higher hazard ratio of 1.47 (95% confidence interval 1.02-2.1) for death from any cause for patients with the C9orf72 hexanucleotide repeat expansion and a lower hazard ratio of 0.33 (95% confidence interval 0.12-0.9) for patients with pathogenic SOD1 variants than for patients without a causal gene mutation. In summary, the high yield of 296 patients (∼13%) harbouring a pathogenic variant and oncoming gene-specific therapies for SOD1/FUS/C9orf72, which would apply to 227 patients (∼10%) in this cohort, corroborates that genetic testing should be made available to all sporadic amyotrophic lateral sclerosis patients after respective counselling.

Primary care professionals' views on population-based expanded carrier screening: an online focus group study

BACKGROUND

Population-based expanded carrier screening (ECS) involves screening for multiple recessive diseases offered to all couples considering a pregnancy or during pregnancy. Previous research indicates that in some countries primary care professionals are perceived as suitable providers for ECS. However, little is known about their perspectives. We therefore aimed to explore primary care professionals' views on population-based ECS.

METHODS

Four online focus groups with 14 general practitioners (GPs) and 16 community midwives were conducted in the Netherlands.

RESULTS

Our findings highlight various perspectives on the desirability of population-based ECS. Participants agreed that ECS could enhance reproductive autonomy and thereby prevent suffering of the child and/or parents. However, they also raised several ethical, societal, and psychological concerns, including a tendency towards a perfect society, stigmatization, unequal access to screening and negative psychosocial consequences. Participants believed that provision of population-based ECS would be feasible if prerequisites regarding training and reimbursement for providers would be fulfilled. most GPs considered themselves less suitable or capable of providing ECS, in contrast to midwives who did consider themselves suitable. Nevertheless, participants believed that, if implemented, ECS should be offered in primary care or by public health services rather than as hospital-based specialized care, because they believed a primary care ECS offer increases access in terms of time and location.

CONCLUSIONS

While participants believed that an ECS offer would be feasible, they questioned its desirability and priority. Studies on the desirability and feasibility of population-based ECS offered in primary care or public health settings are needed.

Genome screening, reporting, and genetic counseling for healthy populations

Rapid advancements of genome sequencing (GS) technologies have enhanced our understanding of the relationship between genes and human disease. To incorporate genomic information into the practice of medicine, new processes for the analysis, reporting, and communication of GS data are needed. Blood samples were collected from adults with a PCR-confirmed SARS-CoV-2 (COVID-19) diagnosis (target N = 1500). GS was performed. Data were filtered and analyzed using custom pipelines and gene panels. We developed unique patient-facing materials, including an online intake survey, group counseling presentation, and consultation letters in addition to a comprehensive GS report. The final report includes results generated from GS data: (1) monogenic disease risks; (2) carrier status; (3) pharmacogenomic variants; (4) polygenic risk scores for common conditions; (5) HLA genotype; (6) genetic ancestry; (7) blood group; and, (8) COVID-19 viral lineage. Participants complete pre-test genetic counseling and confirm preferences for secondary findings before receiving results. Counseling and referrals are initiated for clinically significant findings. We developed a genetic counseling, reporting, and return of results framework that integrates GS information across multiple areas of human health, presenting possibilities for the clinical application of comprehensive GS data in healthy individuals.

Considerations on the use of carrier screening testing in human reproduction: comparison between recommendations from the Italian Society of Human Genetics and other international societies

PURPOSE

Carrier screening (CS) is a term used to describe a genetic test performed on individuals without family history of genetic disorders, to investigate the carrier status for pathogenic variants associated with multiple recessive conditions. The advent of next-generation sequencing enabled simultaneous CS for an increasing number of conditions; however, a consensus on which diseases to include in gene panels and how to best develop the provision of CS is far to be reached. Therefore, the provision of CS is jeopardized and inconsistent and requires solving several important issues.

METHODS

In 2020, the Italian Society of Human Genetics (SIGU) established a working group composed of clinical and laboratory geneticists from public and private fields to elaborate a document to define indications and best practice of CS provision for couples planning a pregnancy.

RESULTS

Hereby, we present the outcome of the Italian working group's activity and compare it with previously published international recommendations (American College of Medical Genetics and Genomics (ACMG), American College of Obstetricians and Gynecologists (ACOG), and Royal Australian and New Zealand College of Obstetricians and Gynaecologists (RANZCOG)). We determine a core message on genetic counseling and nine main subject categories to explore, spanning from goals and execution to technical scientific, ethical, and socio-economic topics. Moreover, a level of agreement on the most critical points is discussed using a 5-point agreement scale, demonstrating a high level of consensus among the four societies.

CONCLUSIONS

This document is intended to provide genetic and healthcare professionals involved in human reproduction with guidance regarding the clinical implementation of CS.

Genome-wide rare variant score associates with morphological subtypes of autism spectrum disorder

Defining different genetic subtypes of autism spectrum disorder (ASD) can enable the prediction of developmental outcomes. Based on minor physical and major congenital anomalies, we categorize 325 Canadian children with ASD into dysmorphic and nondysmorphic subgroups. We develop a method for calculating a patient-level, genome-wide rare variant score (GRVS) from whole-genome sequencing (WGS) data. GRVS is a sum of the number of variants in morphology-associated coding and non-coding regions, weighted by their effect sizes. Probands with dysmorphic ASD have a significantly higher GRVS compared to those with nondysmorphic ASD (P = 0.03). Using the polygenic transmission disequilibrium test, we observe an over-transmission of ASD-associated common variants in nondysmorphic ASD probands (P = 2.9 × 10^-3). These findings replicate using WGS data from 442 ASD probands with accompanying morphology data from the Simons Simplex Collection. Our results provide support for an alternative genomic classification of ASD subgroups using morphology data, which may inform intervention protocols.

Genome Reporting for Healthy Populations-Pipeline for Genomic Screening from the GENCOV COVID-19 Study

Genome sequencing holds the promise for great public health benefits. It is currently being used in the context of rare disease diagnosis and novel gene identification, but also has the potential to identify genetic disease risk factors in healthy individuals. Genome sequencing technologies are currently being used to identify genetic factors that may influence variability in symptom severity and immune response among patients infected by SARS-CoV-2. The GENCOV study aims to look at the relationship between genetic, serological, and biochemical factors and variability of SARS-CoV-2 symptom severity, and to evaluate the utility of returning genome screening results to study participants. Study participants select which results they wish to receive with a decision aid. Medically actionable information for diagnosis, disease risk estimation, disease prevention, and patient management are provided in a comprehensive genome report. Using a combination of bioinformatics software and custom tools, this article describes a pipeline for the analysis and reporting of genetic results to individuals with COVID-19, including HLA genotyping, large-scale continental ancestry estimation, and pharmacogenomic analysis to determine metabolizer status and drug response. In addition, this pipeline includes reporting of medically actionable conditions from comprehensive gene panels for Cardiology, Neurology, Metabolism, Hereditary Cancer, and Hereditary Kidney, and carrier screening for reproductive planning. Incorporated into the genome report are polygenic risk scores for six diseases-coronary artery disease; atrial fibrillation; type-2 diabetes; and breast, prostate, and colon cancer-as well as blood group genotyping analysis for ABO and Rh blood types and genotyping for other antigens of clinical relevance. The genome report summarizes the findings of these analyses in a way that extensively communicates clinically relevant results to patients and their physicians. © 2022 Wiley Periodicals LLC. Basic Protocol 1: HLA genotyping and disease association Basic Protocol 2: Large-scale continental ancestry estimation Basic Protocol 3: Dosage recommendations for pharmacogenomic gene variants associated with drug response Support Protocol: System setup.

PeakCNV: A multi-feature ranking algorithm-based tool for genome-wide copy number variation-association study

Copy Number Variation (CNV) refers to a type of structural genomic alteration in which a segment of chromosome is duplicated or deleted. To date, many CNVs have been identified as causative genetic elements for several diseases and phenotypes. However, performing a CNV-based genome-wide association study is challenging due to inconsistency in length and occurrence of CNVs across different individuals under investigation. One of the most efficient strategies to address this issue is building CNV regions (genomic regions in which CNVs are overlapping - CNVRs). However, this approach is susceptible to a high false positive rate due to overlapping and co-occurring of confounding CNVRs with true positive CNVRs. Here, we develop PeakCNV that differentiates false-positive CNVRs from true positives by calculating a new metric, independence ranking score, (IR-score) via a feature ranking approach. We compared the performance of PeakCNV with other current existing tools by carrying out two case studies one using the CNV genotype data for individuals with prostate cancer (194 cases and 2,392 healthy individuals) and the second one for individuals with neurodevelopmental disorders (19,642 cases and 6,451 healthy individuals). Crucially, our benchmarking analyses on prostate cancer cohort indicated that PeakCNV identifies a fewer risk candidate CNVRs with shorter lengths compared to other tools. Importantly, these CNVRs cover a greater proportion of case over healthy individuals compared to other tools. The accuracy of PeakCNV in identifying relevant candidate CNVRs was reproducible in the case study on neurodevelopmental disorders. Using data from the FANTOM5 expression atlas and the Clinical Genomic Database, we show that the candidate CNVRs identified by PeakCNV for neurodevelopmental disorders overlap with a greater number of genes with the brain-enriched expression, and a greater number of genes that are associated with neurological conditions compared to candidate CNVRs identified by other tools. Taken together, PeakCNV outperformed current existing CNV association study tools by identifying more biologically meaningful CNVRs relevant to the phenotype of interest. PeakCNV is publicly available for the analysis of CNV-associated diseases and is accessible from https://rdrr.io/github/mahdieh1/PeakCNV.

Scoping review and classification of deep learning in medical genetics

Deep learning (DL) is applied in many biomedical areas. We performed a scoping review on DL in medical genetics. We first assessed 14,002 articles, of which 133 involved DL in medical genetics. DL in medical genetics increased rapidly during the studied period. In medical genetics, DL has largely been applied to small data sets of affected individuals (mean = 95, median = 29) with genetic conditions (71 different genetic conditions were studied; 24 articles studied multiple conditions). A variety of data types have been used in medical genetics, including radiologic (20%), ophthalmologic (14%), microscopy (8%), and text-based data (4%); the most common data type was patient facial photographs (46%). DL authors and research subjects overrepresent certain geographic areas (United States, Asia, and Europe). Convolutional neural networks (89%) were the most common method. Results were compared with human performance in 31% of studies. In total, 51% of articles provided data access; 16% released source code. To further explore DL in genomics, we conducted an additional analysis, the results of which highlight future opportunities for DL in medical genetics. Finally, we expect DL applications to increase in the future. To aid data curation, we evaluated a DL, random forest, and rule-based classifier at categorizing article abstracts.

A comprehensive genomic reporting structure for communicating all clinically significant primary and secondary findings

Genomic sequencing (GS) can reveal secondary findings (SFs), findings unrelated to the reason for testing, that can be overwhelming to both patients and providers. An effective approach for communicating all clinically significant primary and secondary GS results is needed to effectively manage this large volume of results. The aim of this study was to develop a comprehensive approach to communicate all clinically significant primary and SF results. A genomic test report with accompanying patient and provider letters were developed in three phases: review of current clinical reporting practices, consulting with genetic and non-genetics experts, and iterative refinement through circulation to key stakeholders. The genomic test report and consultation letters present a myriad of clinically relevant GS results in distinct, tabulated sections, including primary (cancer) and secondary findings, with in-depth details of each finding generated from exome sequencing. They provide detailed variant and disease information, personal and familial risk assessments, clinical management details, and additional resources to help support providers and patients with implementing healthcare recommendations related to their GS results. The report and consultation letters represent a comprehensive approach to communicate all clinically significant SFs to patients and providers, facilitating clinical management of GS results.

Visual Attention, Behavioral Intention, and Choice Behavior Among Older Consumers Toward Sports Marketing Images: An Eye-Tracking Study

Mental health benefits (i.e., relaxing and relieving pressure) can influence consumers' consumption decisions. However, there is still no clear understanding of the impact of mental health benefits on visual attention, behavioral intention, and choice behavior. Study 1 was thus aimed at exploring the visual attention and behavioral intention of older consumers with respect to exercise consumption. A sample of 186 older consumers was investigated. An eye-tracking analysis was used to evaluate the visual attention of participants observing health promotion messages, and questionnaires were used to assess the behavioral intention of the older consumers under consideration in this work. The findings confirmed that marketing pictures combining natural sportscapes with prevention focus messages (i.e., conveying information to consumers that it is safe and not easy to be injured when engaging in yoga activities in natural settings) can best capture older consumers' visual attention (e.g., fixation numbers and fixation times) and behavioral intentions. In Study 2, 75 participants were recruited. It was found that marketing pictures combining natural sportscapes with prevention focus messages were selected more by the participants, with health communication images successfully attracting them to choose the sports program products being presented. The findings of the two studies suggested that marketing pictures can effectively stimulate consumers' visual attention and has effects on their behavioral intention and choices toward exercising in a safe, natural environment.

Parental segregation study reveals rare benign and likely benign variants in a Brazilian cohort of rare diseases

Genomic studies may generate massive amounts of data, bringing interpretation challenges. Efforts for the differentiation of benign and pathogenic variants gain importance. In this article, we used segregation analysis and other molecular data to reclassify to benign or likely benign several rare clinically curated variants of autosomal dominant inheritance from a cohort of 500 Brazilian patients with rare diseases. This study included only symptomatic patients who had undergone molecular investigation with exome sequencing for suspected diseases of genetic etiology. Variants clinically suspected as the causative etiology and harbored by genes associated with highly-penetrant conditions of autosomal dominant inheritance underwent Sanger confirmation in the proband and inheritance pattern determination because a "de novo" event was expected. Among all 327 variants studied, 321 variants were inherited from asymptomatic parents. Considering segregation analysis, we have reclassified 51 rare variants as benign and 211 as likely benign. In our study, the inheritance of a highly penetrant variant expected to be de novo for pathogenicity assumption was considered as a non-segregation and, therefore, a key step for benign or likely benign classification. Studies like ours may help to identify rare benign variants and improve the correct interpretation of genetic findings.

Neural Networks for Classification and Image Generation of Aging in Genetic Syndromes

Background: In medical genetics, one application of neural networks is the diagnosis of genetic diseases based on images of patient faces. While these applications have been validated in the literature with primarily pediatric subjects, it is not known whether these applications can accurately diagnose patients across a lifespan. We aimed to extend previous works to determine whether age plays a factor in facial diagnosis as well as to explore other factors that may contribute to the overall diagnostic accuracy.

Methods: To investigate this, we chose two relatively common conditions, Williams syndrome and 22q11.2 deletion syndrome. We built a neural network classifier trained on images of affected and unaffected individuals of different ages and compared classifier accuracy to clinical geneticists. We analyzed the results of saliency maps and the use of generative adversarial networks to boost accuracy.

Results: Our classifier outperformed clinical geneticists at recognizing face images of these two conditions within each of the age groups (the performance varied between the age groups): 1) under 2 years old, 2) 2–9 years old, 3) 10–19 years old, 4) 20–34 years old, and 5) ≥35 years old. The overall accuracy improvement by our classifier over the clinical geneticists was 15.5 and 22.7% for Williams syndrome and 22q11.2 deletion syndrome, respectively. Additionally, comparison of saliency maps revealed that key facial features learned by the neural network differed with respect to age. Finally, joint training real images with multiple different types of fake images created by a generative adversarial network showed up to 3.25% accuracy gain in classification accuracy.

Conclusion: The ability of clinical geneticists to diagnose these conditions is influenced by the age of the patient. Deep learning technologies such as our classifier can more accurately identify patients across the lifespan based on facial features. Saliency maps of computer vision reveal that the syndromic facial feature attributes change with the age of the patient. Modest improvements in the classifier accuracy were observed when joint training was carried out with both real and fake images. Our findings highlight the need for a greater focus on age as a confounder in facial diagnosis.

Whole-genome analysis of human embryonic stem cells enables rational line selection based on genetic variation

Despite their widespread use in research, there has not yet been a systematic genomic analysis of human embryonic stem cell (hESC) lines at a single-nucleotide resolution. We therefore performed whole-genome sequencing (WGS) of 143 hESC lines and annotated their single-nucleotide and structural genetic variants. We found that while a substantial fraction of hESC lines contained large deleterious structural variants, finer-scale structural and single-nucleotide variants (SNVs) that are ascertainable only through WGS analyses were present in hESC genomes and human blood-derived genomes at similar frequencies. Moreover, WGS allowed us to identify SNVs associated with cancer and other diseases that could alter cellular phenotypes and compromise the safety of hESC-derived cellular products transplanted into humans. As a resource to enable reproducible hESC research and safer translation, we provide a user-friendly WGS data portal and a data-driven scheme for cell line maintenance and selection.

CoverageMaster: comprehensive CNV detection and visualization from NGS short reads for genetic medicine applications

CoverageMaster (CoM) is a copy number variation (CNV) calling algorithm based on depth-of-coverage maps designed to detect CNVs of any size in exome [whole exome sequencing (WES)] and genome [whole genome sequencing (WGS)] data. The core of the algorithm is the compression of sequencing coverage data in a multiscale Wavelet space and the analysis through an iterative Hidden Markov Model. CoM processes WES and WGS data at nucleotide scale resolution and accurately detects and visualizes full size range CNVs, including single or partial exon deletions and duplications. The results obtained with this approach support the possibility for coverage-based CNV callers to replace probe-based methods such as array comparative genomic hybridization and multiplex ligation-dependent probe amplification in the near future.

Answer ALS, a large-scale resource for sporadic and familial ALS combining clinical and multi-omics data from induced pluripotent cell lines

Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical-molecular-biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics.

Overcoming constraints on the detection of recessive selection in human genes from population frequency data

The identification of genes that evolve under recessive natural selection is a long-standing goal of population genetics research that has important applications to the discovery of genes associated with disease. We found that commonly used methods to evaluate selective constraint at the gene level are highly sensitive to genes under heterozygous selection but ubiquitously fail to detect recessively evolving genes. Additionally, more sophisticated likelihood-based methods designed to detect recessivity similarly lack power for a human gene of realistic length from current population sample sizes. However, extensive simulations suggested that recessive genes may be detectable in aggregate. Here, we offer a method informed by population genetics simulations designed to detect recessive purifying selection in gene sets. Applying this to empirical gene sets produced significant enrichments for strong recessive selection in genes previously inferred to be under recessive selection in a consanguineous cohort and in genes involved in autosomal recessive monogenic disorders.

Genome sequencing of 320 Chinese children with epilepsy: a clinical and molecular study

The aim of this study is to evaluate the diagnostic value of genome sequencing in children with epilepsy, and to provide genome sequencing-based insights into the molecular genetic mechanisms of epilepsy to help establish accurate diagnoses, design appropriate treatments and assist in genetic counselling. We performed genome sequencing on 320 Chinese children with epilepsy, and interpreted single-nucleotide variants and copy number variants of all samples. The complete pedigree and clinical data of the probands were established and followed up. The clinical phenotypes, treatments, prognoses and genotypes of the patients were analysed. Age at seizure onset ranged from 1 day to 17 years, with a median of 4.3 years. Pathogenic/likely pathogenic variants were found in 117 of the 320 children (36.6%), of whom 93 (29.1%) had single-nucleotide variants, 22 (6.9%) had copy number variants and two had both single-nucleotide variants and copy number variants. Single-nucleotide variants were most frequently found in SCN1A (10/95, 10.5%), which is associated with Dravet syndrome, followed by PRRT2 (8/95, 8.4%), which is associated with benign familial infantile epilepsy, and TSC2 (7/95, 7.4%), which is associated with tuberous sclerosis. Among the copy number variants, there were three with a length <25 kilobases. The most common recurrent copy number variants were 17p13.3 deletions (5/24, 20.8%), 16p11.2 deletions (4/24, 16.7%), and 7q11.23 duplications (2/24, 8.3%), which are associated with epilepsy, developmental retardation and congenital abnormalities. Four particular 16p11.2 deletions and two 15q11.2 deletions were considered to be susceptibility factors contributing to neurodevelopmental disorders associated with epilepsy. The diagnostic yield was 75.0% in patients with seizure onset during the first postnatal month, and gradually decreased in patients with seizure onset at a later age. Forty-two patients (13.1%) were found to be specifically treatable for the underlying genetic cause identified by genome sequencing. Three of them received corresponding targeted therapies and demonstrated favourable prognoses. Genome sequencing provides complete genetic diagnosis, thus enabling individualized treatment and genetic counselling for the parents of the patients. Genome sequencing is expected to become the first choice of methods for genetic testing of patients with epilepsy.

An integrated multi-omic analysis of iPSC-derived motor neurons from C9ORF72 ALS patients

Neurodegenerative diseases are challenging for systems biology because of the lack of reliable animal models or patient samples at early disease stages. Induced pluripotent stem cells (iPSCs) could address these challenges. We investigated DNA, RNA, epigenetics, and proteins in iPSC-derived motor neurons from patients with ALS carrying hexanucleotide expansions in C9ORF72. Using integrative computational methods combining all omics datasets, we identified novel and known dysregulated pathways. We used a C9ORF72 Drosophila model to distinguish pathways contributing to disease phenotypes from compensatory ones and confirmed alterations in some pathways in postmortem spinal cord tissue of patients with ALS. A different differentiation protocol was used to derive a separate set of C9ORF72 and control motor neurons. Many individual -omics differed by protocol, but some core dysregulated pathways were consistent. This strategy of analyzing patient-specific neurons provides disease-related outcomes with small numbers of heterogeneous lines and reduces variation from single-omics to elucidate network-based signatures.

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Multi-omic analysis of differentiated C9ORF72 iPSC-derived motor neurons

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Network-based integrative computational analysis

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Pathogenic versus compensatory pathways elucidated using C9ORF72 Drosophila model

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Pathways confirmed with alternative differentiation protocol and postmortem data

Feasibility of predicting allele specific expression from DNA sequencing using machine learning

Allele specific expression (ASE) concerns divergent expression quantity of alternative alleles and is measured by RNA sequencing. Multiple studies show that ASE plays a role in hereditary diseases by modulating penetrance or phenotype severity. However, genome diagnostics is based on DNA sequencing and therefore neglects gene expression regulation such as ASE. To take advantage of ASE in absence of RNA sequencing, it must be predicted using only DNA variation. We have constructed ASE models from BIOS (n = 3432) and GTEx (n = 369) that predict ASE using DNA features. These models are highly reproducible and comprise many different feature types, highlighting the complex regulation that underlies ASE. We applied the BIOS-trained model to population variants in three genes in which ASE plays a clinically relevant role: BRCA2, RET and NF1. This resulted in predicted ASE effects for 27 variants, of which 10 were known pathogenic variants. We demonstrated that ASE can be predicted from DNA features using machine learning. Future efforts may improve sensitivity and translate these models into a new type of genome diagnostic tool that prioritizes candidate pathogenic variants or regulators thereof for follow-up validation by RNA sequencing. All used code and machine learning models are available at GitHub and Zenodo.

CancerLivER: a database of liver cancer gene expression resources and biomarkers

Liver cancer is the fourth major lethal malignancy worldwide. To understand the development and progression of liver cancer, biomedical research generated a tremendous amount of transcriptomics and disease-specific biomarker data. However, dispersed information poses pragmatic hurdles to delineate the significant markers for the disease. Hence, a dedicated resource for liver cancer is required that integrates scattered multiple formatted datasets and information regarding disease-specific biomarkers. Liver Cancer Expression Resource (CancerLivER) is a database that maintains gene expression datasets of liver cancer along with the putative biomarkers defined for the same in the literature. It manages 115 datasets that include gene-expression profiles of 9611 samples. Each of incorporated datasets was manually curated to remove any artefact; subsequently, a standard and uniform pipeline according to the specific technique is employed for their processing. Additionally, it contains comprehensive information on 594 liver cancer biomarkers which include mainly 315 gene biomarkers or signatures and 178 protein- and 46 miRNA-based biomarkers. To explore the full potential of data on liver cancer, a web-based interactive platform was developed to perform search, browsing and analyses. Analysis tools were also integrated to explore and visualize the expression patterns of desired genes among different types of samples based on individual gene, GO ontology and pathways. Furthermore, a dataset matrix download facility was provided to facilitate the users for their extensive analysis to elucidate more robust disease-specific signatures. Eventually, CancerLivER is a comprehensive resource which is highly useful for the scientific community working in the field of liver cancer.Availability: CancerLivER can be accessed on the web at https://webs.iiitd.edu.in/raghava/cancerliver.

An online compendium of treatable genetic disorders

More than 4,000 genes have been associated with recognizable Mendelian/monogenic diseases. When faced with a new diagnosis of a rare genetic disorder, health care providers increasingly turn to internet resources for information to understand the disease and direct care. Unfortunately, it can be challenging to find information concerning treatment for rare diseases as key details are scattered across a number of authoritative websites and numerous journal articles. The website and associated mobile device application described in this article begin to address this challenge by providing a convenient, readily available starting point to find treatment information. The site, Rx-genes.com (https://www.rx-genes.com/), is focused on those conditions where the treatment is directed against the mechanism of the disease and thereby alters the natural history of the disease. The website currently contains 633 disease entries that include references to disease information and treatment guidance, a brief summary of treatments, the inheritance pattern, a disease frequency (if known), nonmolecular confirmatory testing (if available), and a link to experimental treatments. Existing entries are continuously updated, and new entries are added as novel treatments appear in the literature.

A pipeline-friendly software tool for genome diagnostics to prioritize genes by matching patient symptoms to literature

Despite an explosive growth of next-generation sequencing data, genome diagnostics only provides a molecular diagnosis to a minority of patients. Software tools that prioritize genes based on patient symptoms using known gene-disease associations may complement variant filtering and interpretation to increase chances of success. However, many of these tools cannot be used in practice because they are embedded within variant prioritization algorithms, or exist as remote services that cannot be relied upon or are unacceptable because of legal/ethical barriers. In addition, many tools are not designed for command-line usage, closed-source, abandoned, or unavailable. We present Variant Interpretation using Biomedical literature Evidence (VIBE), a tool to prioritize disease genes based on Human Phenotype Ontology codes. VIBE is a locally installed executable that ensures operational availability and is built upon DisGeNET-RDF, a comprehensive knowledge platform containing gene-disease associations mostly from literature and variant-disease associations mostly from curated source databases. VIBE's command-line interface and output are designed for easy incorporation into bioinformatic pipelines that annotate and prioritize variants for further clinical interpretation. We evaluate VIBE in a benchmark based on 305 patient cases alongside seven other tools. Our results demonstrate that VIBE offers consistent performance with few cases missed, but we also find high complementarity among all tested tools. VIBE is a powerful, free, open source and locally installable solution for prioritizing genes based on patient symptoms. Project source code, documentation, benchmark and executables are available at https://github.com/molgenis/vibe.

Genotype-phenotype analysis in Mowat-Wilson syndrome associated with two novel and two recurrent variants

The current study aimed to analyze the genotype-phenotype relationship in patients with variants of zinc finger E box-binding homeobox 2 (ZEB2), which is a gene encoding a homeobox transcription factor known to be mutated in Mowat Wilson syndrome (MWS). Whole genome sequencing (WGS) was performed in 530 children, of whom 333 had epilepsy with or without developmental delay and 197 developmental delay alone. Pathogenic variants were identified and verified using Sanger sequencing, and the disease phenotypes of the corresponding patients were analyzed for features of MWS. WGS was performed in 333 children with epilepsy, with or without developmental delays or intellectual disability and 197 children with developmental delay alone. A total of 4 unrelated patients were indicated to be heterozygous for truncating mutations in ZEB2. A total of three of these were nonsense mutations (novel Gln1072X and recurrent Trp97X and Arg921X), and one was a frameshift mutation (novel Val357Aspfs*15). The mutations have occurred de novo as confirmed by Sanger sequence comparisons in patients and their parents. All 4 patients exhibited signs of MWS, whereby the severity increased the closer a mutation was located to the amino terminus of the protein. The results suggest that the clinical outcome in MWS depends on the relative position of the truncation in the ZEB2 gene. A number of interpretations of this genotype/phenotype association are discussed in the present study.

Genome Sequencing as a Diagnostic Test in Children With Unexplained Medical Complexity

Importance

Children with medical complexity (CMC) represent a growing population in the pediatric health care system, with high resource use and associated health care costs. A genetic diagnosis can inform prognosis, anticipatory care, management, and reproductive planning. Conventional genetic testing strategies for CMC are often costly, time consuming, and ultimately unsuccessful.

Objective

To evaluate the analytical and clinical validity of genome sequencing as a comprehensive diagnostic genetic test for CMC.

Design, Setting, and Participants

In this cohort study of the prospective use of genome sequencing and comparison with standard-of-care genetic testing, CMC were recruited from May 1, 2017, to November 30, 2018, from a structured complex care program based at a tertiary care pediatric hospital in Toronto, Canada. Recruited CMC had at least 1 chronic condition, technology dependence (child is dependent at least part of each day on mechanical ventilators, and/or child requires prolonged intravenous administration of nutritional substances or drugs, and/or child is expected to have prolonged dependence on other device-based support), multiple subspecialist involvement, and substantial health care use. Review of the care plans for 545 CMC identified 143 suspected of having an undiagnosed genetic condition. Fifty-four families met inclusion criteria and were interested in participating, and 49 completed the study. Probands, similarly affected siblings, and biological parents were eligible for genome sequencing.

Exposures

Genome sequencing was performed using blood-derived DNA from probands and family members using established methods and a bioinformatics pipeline for clinical genome annotation.

Main Outcomes and Measures

The primary study outcome was the diagnostic yield of genome sequencing (proportion of CMC for whom the test result yielded a new diagnosis).

Results

Genome sequencing was performed for 138 individuals from 49 families of CMC (29 male and 20 female probands; mean [SD] age, 7.0 [4.5] years). Genome sequencing detected all genomic variation previously identified by conventional genetic testing. A total of 15 probands (30.6%; 95% CI 19.5%-44.6%) received a new primary molecular genetic diagnosis after genome sequencing. Three individuals had novel diseases and an additional 9 had either ultrarare genetic conditions or rare genetic conditions with atypical features. At least 11 families received diagnostic information that had clinical management implications beyond genetic and reproductive counseling.

Conclusions and Relevance

This study suggests that genome sequencing has high analytical and clinical validity and can result in new diagnoses in CMC even in the setting of extensive prior investigations. This clinical population may be enriched for ultrarare and novel genetic disorders. Genome sequencing is a potentially first-tier genetic test for CMC.

CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations

Exome sequencing is now mainstream in clinical practice. However, identification of pathogenic Mendelian variants remains time-consuming, in part, because the limited accuracy of current computational prediction methods requires manual classification by experts. Here we introduce CAPICE, a new machine-learning-based method for prioritizing pathogenic variants, including SNVs and short InDels. CAPICE outperforms the best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily added to diagnostic pipelines as pre-computed score file or command-line software, or using online MOLGENIS web service with API. Download CAPICE for free and open-source (LGPLv3) at https://github.com/molgenis/capice .

A prospective study on rapid exome sequencing as a diagnostic test for multiple congenital anomalies on fetal ultrasound

Objective

Conventional genetic tests (quantitative fluorescent‐PCR [QF‐PCR] and single nucleotide polymorphism‐array) only diagnose ~40% of fetuses showing ultrasound abnormalities. Rapid exome sequencing (rES) may improve this diagnostic yield, but includes challenges such as uncertainties in fetal phenotyping, variant interpretation, incidental unsolicited findings, and rapid turnaround times. In this study, we implemented rES in prenatal care to increase diagnostic yield.

Methods

We prospectively studied 55 fetuses. Inclusion criteria were: (a) two or more independent major fetal anomalies, (b) hydrops fetalis or bilateral renal cysts alone, or (c) one major fetal anomaly and a first‐degree relative with the same anomaly. In addition to conventional genetic tests, we performed trio rES analysis using a custom virtual gene panel of ~3850 Online Mendelian Inheritance in Man (OMIM) genes.

Results

We established a genetic rES‐based diagnosis in 8 out of 23 fetuses (35%) without QF‐PCR or array abnormalities. Diagnoses included MIRAGE (SAMD9), Zellweger (PEX1), Walker‐Warburg (POMGNT1), Noonan (PTNP11), Kabuki (KMT2D), and CHARGE (CHD7) syndrome and two cases of Osteogenesis Imperfecta type 2 (COL1A1). In six cases, rES diagnosis aided perinatal management. The median turnaround time was 14 (range 8‐20) days.

Conclusion

Implementing rES as a routine test in the prenatal setting is challenging but technically feasible, with a promising diagnostic yield and significant clinical relevance.

VarSome: the human genomic variant search engine

Summary

VarSome.com is a search engine, aggregator and impact analysis tool for human genetic variation and a community-driven project aiming at sharing global expertise on human variants.

Availability and implementation

VarSome is freely available at http://varsome.com.

Supplementary information

Supplementary data are available at Bioinformatics online.

Refining critical regions in 15q24 microdeletion syndrome pertaining to autism

Chromosome 15q24 microdeletion syndrome is characterized by developmental delay, facial dysmorphism, hearing loss, hypotonia, recurrent infection, and other congenital malformations including microcephaly, scoliosis, joint laxity, digital anomalies, as well as sometimes having autism spectrum disorder (ASD) and attention deficit hyperactivity disorder. Here, we report a boy with a 2.58-Mb de novo deletion at chromosome 15q24. He is diagnosed with ASD and having multiple phenotypes similar to those reported in cases having 15q24 microdeletion syndrome. To delineate the critical genes and region that might be responsible for these phenotypes, we reviewed all previously published cases. We observe a potential minimum critical region of 650 kb (LCR15q24A-B) affecting NEO1 among other genes that might pertinent to individuals with ASD carrying this deletion. In contrast, a previously defined minimum critical region downstream of the 650-kb interval (LCR15q24B-D) is more likely associated with the developmental delay, facial dysmorphism, recurrent infection, and other congenital malformations. As a result, the ASD phenotype in this individual is potentially attributed by genes particularly NEO1 within the newly proposed critical region.

Spectrum of Germline BRCA1 and BRCA2 Variants Identified in 2351 Ovarian and Breast Cancer Patients Referring to a Reference Cancer Hospital of Rome

Pathogenic variants (PVs) carriers in BRCA1 or BRCA2 are associated with an elevated lifetime risk of developing breast cancer (BC) and/or ovarian cancer (OC). The prevalence of BRCA1 and BRCA2 germline alterations is extremely variable among different ethnic groups. Particularly, the rate of variants in Italian BC and/or OC families is rather controversial and ranges from 8% to 37%, according to different reports. By In Vitro Diagnostic (IVD) next generation sequencing (NGS)-based pipelines, we routinely screened thousands of patients with either sporadic or cancer family history. By NGS, we identified new PVs and some variants of uncertain significance (VUS) which were also evaluated in silico using dedicated tools. We report in detail data regarding BRCA1/2 variants identified in 517 out of 2351 BC and OC patients. The aim of this study was to report the incidence and spectrum of BRCA1/2 variants observed in BC and/or OC patients, tested in at Policlinico Gemelli Foundation Hospital, the origin of which is mainly from Central and Southern Italy. This study provides an overview of the variant frequency in these geographic areas of Italy and provides data that could be used in the clinical management of patients.

Mutation severity spectrum of rare alleles in the human genome is predictive of disease type

The human genome harbors a variety of genetic variations. Single-nucleotide changes that alter amino acids in protein-coding regions are one of the major causes of human phenotypic variation and diseases. These single-amino acid variations (SAVs) are routinely found in whole genome and exome sequencing. Evaluating the functional impact of such genomic alterations is crucial for diagnosis of genetic disorders. We developed DeepSAV, a deep-learning convolutional neural network to differentiate disease-causing and benign SAVs based on a variety of protein sequence, structural and functional properties. Our method outperforms most stand-alone programs, and the version incorporating population and gene-level information (DeepSAV+PG) has similar predictive power as some of the best available. We transformed DeepSAV scores of rare SAVs in the human population into a quantity termed "mutation severity measure" for each human protein-coding gene. It reflects a gene's tolerance to deleterious missense mutations and serves as a useful tool to study gene-disease associations. Genes implicated in cancer, autism, and viral interaction are found by this measure as intolerant to mutations, while genes associated with a number of other diseases are scored as tolerant. Among known disease-associated genes, those that are mutation-intolerant are likely to function in development and signal transduction pathways, while those that are mutation-tolerant tend to encode metabolic and mitochondrial proteins.

Consecutive medical exome analysis at a tertiary center: Diagnostic and health-economic outcomes

The utility of whole exome analysis has been extensively demonstrated in research settings, but its clinical utility as a first-tier genetic test has not been well documented from diagnostic and health economic standpoints in real-life clinical settings. We performed medical exome analyses focusing on a clinically interpretable portion of the genome (4,813 genes) as a first-tier genetic test for 360 consecutive patients visiting a genetics clinic at a tertiary children's hospital in Japan, over a 3-year period. Bioinformatics analyses were conducted using standard software. A molecular diagnosis was made in 171 patients involving a total of 107 causative genes. Among these 107 causative genes, 57 genes were classified as genes with potential organ-specific interventions and management strategies. Clinically relevant results were obtained in 26% of the total cohort and 54% of the patients with a definitive molecular diagnosis. Performing the medical exome analysis at the time of the initial visit to the tertiary center, rather than after visits to pertinent specialists, brain MRI examination, and G-banded chromosome testing, would have reduced the financial cost by 197 euros according to retrospective calculation under multiple assumption. The present study demonstrated a high diagnostic yield (47.5%) for singleton medical exome analysis as a first-tier test in a real-life setting. Medical exome analysis yielded clinically relevant information in a quarter of the total patient cohort. The application of genomic testing during the initial visit to a tertiary medical center could be a rational approach to the diagnosis of patients with suspected genetic disorders.

Effectiveness of integrated interpretation of exome and corresponding transcriptome data for detecting splicing variants of genes associated with autosomal recessive disorders

Purpose

Part of the weakness of exome analysis lies in the inability to detect aberrant splicing. An evaluation of the post-splicing mRNA sequence concurrently with genomic variants could improve the diagnostic rate. We aimed to investigate publicly available exome sequencing data and its matching transcriptomics data of phenotypically normal individuals to identify alternatively spliced variants from known genes associated with autosomal recessive disorders under the premise that some of the subjects could be carriers of such disorders.

Methods

Aberrant splicing events and their triggering genomic variants were detected with the aid of Bayesian network method “SAVNet” which was originally developed for cancer genomics.

Results

Forty aberrant splicing events including exon skipping, the creation of a new splice site, and the use of a cryptic splice site in response to the disruption of the authentic site were detected in 1916 genes among 31 of the 179 subjects from the 1000 Genomes Project. The predicted effects on proteins were either frameshift mutations (30) or large in-frame insertions or deletions (10). Five missense mutations and 2 silent mutations were reinterpreted as triggering major changes in transcript sequences. The detection rate of provisionally truncating pathogenic variants increased by 19%, compared with a conventional exome analysis.

Conclusion

The coupling interpretation of exome and transcriptome data enhances the performance of conventional exome analyses through the proper interpretation of intronic variants that are outside of the GT/AG splicing consensus sequences and also allows the reinterpretation of “missense” or “silent” substitutions that can indeed have drastic effects on splicing.

Variants in NKX2-5 and FLNC Cause Dilated Cardiomyopathy and Sudden Cardiac Death

BACKGROUND

Dilated cardiomyopathy (DCM) is an important cause of heart failure. Variants in >50 genes have been reported to cause DCM, but causative variants have been found in less than half of familial cases. Variants causing DCM in Iceland have not been reported before.

METHODS

We performed a genome-wide association study on DCM based on whole genome sequencing. We tested the association of 32.5 million sequence variants in 424 cases and 337 689 population controls in Iceland.

RESULTS

We identified 2 DCM variants in established cardiomyopathy genes, a missense variant p.Phe145Leu in NKX2-5 carried by 1 in 7100 Icelanders ( P=7.0×10^-12) and a frameshift variant p.Phe1626Serfs*40 in FLNC carried by 1 in 3600 Icelanders ( P=2.1×10^-10). Both variants associate with heart failure and sudden cardiac death. Additionally, p.Phe145Leu in NKX2-5 associates with high degree atrioventricular block and atrial septal defect ( P<1.4×10^-4). The penetrance of serious heart disease among carriers of the NKX2-5 variant is high and higher than that of the FLNC variant.

CONCLUSIONS

Two rare variants in NKX2-5 and FLNC, carried by 1 in 2400 Icelanders, cause familial DCM in Iceland. These genes have recently been associated with DCM. Given the serious consequences of these variants, we suggest screening for them in individuals with DCM and their family members, with subsequent monitoring of carriers, offering early intervention.

Expanding the neurodevelopmental phenotypes of individuals with de novo KMT2A variants

De novo loss-of-function (LoF) variants in the KMT2A gene are associated with Wiedemann-Steiner Syndrome (WSS). Recently, de novo KMT2A variants have been identified in sequencing studies of cohorts of individuals with neurodevelopmental disorders (NDDs). However, most of these studies lack the detailed clinical information required to determine whether those individuals have isolated NDDs or WSS (i.e. syndromic NDDs). We performed thorough clinical and neurodevelopmental phenotyping on six individuals with de novo KMT2A variants. From these data, we found that all six patients met clinical criteria for WSS and we further define the neurodevelopmental phenotypes associated with KMT2A variants and WSS. In particular, we identified a subtype of Autism Spectrum Disorder (ASD) in five individuals, characterized by marked rigid, repetitive and inflexible behaviours, emotional dysregulation, externalizing behaviours, but relative social motivation. To further explore the clinical spectrum associated with KMT2A variants, we also conducted a meta-analysis of individuals with KMT2A variants reported in the published literature. We found that de novo LoF or missense variants in KMT2A were significantly more prevalent than predicted by a previously established statistical model of de novo mutation rate for KMT2A. Our genotype-phenotype findings better define the clinical spectrum associated with KMT2A variants and suggest that individuals with de novo LoF and missense variants likely have a clinically unrecognized diagnosis of WSS, rather than isolated NDD or ASD alone. This highlights the importance of a clinical genetic and neurodevelopmental assessment for individuals with such variants in KMT2A.

A scalable, aggregated genotypic-phenotypic database for human disease variation

Next generation sequencing multi-gene panels have greatly improved the diagnostic yield and cost effectiveness of genetic testing and are rapidly being integrated into the clinic for hereditary cancer risk. With this technology comes a dramatic increase in the volume, type and complexity of data. This invaluable data though is too often buried or inaccessible to researchers, especially to those without strong analytical or programming skills. To effectively share comprehensive, integrated genotypic-phenotypic data, we built Color Data, a publicly available, cloud-based database that supports broad access and data literacy. The database is composed of 50 000 individuals who were sequenced for 30 genes associated with hereditary cancer risk and provides useful information on allele frequency and variant classification, as well as associated phenotypic information such as demographics and personal and family history. Our user-friendly interface allows researchers to easily execute their own queries with filtering, and the results of queries can be shared and/or downloaded. The rapid and broad dissemination of these research results will help increase the value of, and reduce the waste in, scientific resources and data. Furthermore, the database is able to quickly scale and support integration of additional genes and human hereditary conditions. We hope that this database will help researchers and scientists explore genotype-phenotype correlations in hereditary cancer, identify novel variants for functional analysis and enable data-driven drug discovery and development.