Insights into genetics, human biology and disease gleaned from family based genomic studies

Improving Automated Deep Phenotyping Through Large Language Models Using Retrieval Augmented Generation

Background

Diagnosing rare genetic disorders relies on precise phenotypic and genotypic analysis, with the Human Phenotype Ontology (HPO) providing a standardized language for capturing clinical phenotypes. Traditional HPO tools, such as Doc2HPO and ClinPhen, employ concept recognition to automate phenotype extraction but struggle with incomplete phenotype assignment, often requiring intensive manual review. While large language models (LLMs) hold promise for more context-driven phenotype extraction, they are prone to errors and "hallucinations," making them less reliable without further refinement. We present RAG-HPO, a Python-based tool that leverages Retrieval-Augmented Generation (RAG) to elevate LLM accuracy in HPO term assignment, bypassing the limitations of baseline models while avoiding the time and resource intensive process of fine-tuning. RAG-HPO integrates a dynamic vector database, allowing real-time retrieval and contextual matching.

Methods

The high-dimensional vector database utilized by RAG-HPO includes >54,000 phenotypic phrases mapped to HPO IDs, derived from the HPO database and supplemented with additional validated phrases. The RAG-HPO workflow uses an LLM to first extract phenotypic phrases that are then matched via semantic similarity to entries within a vector database before providing best term matches back to the LLM as context for final HPO term assignment. A benchmarking dataset of 120 published case reports with 1,792 manually-assigned HPO terms was developed, and the performance of RAG-HPO measured against existing published tools Doc2HPO, ClinPhen, and FastHPOCR.

Results

In evaluations, RAG-HPO, powered by Llama-3 70B and applied to a set of 120 case reports, achieved a mean precision of 0.84, recall of 0.78, and an F1 score of 0.80-significantly surpassing conventional tools (p<0.00001). False positive HPO term identification occurred for 15.8% (256/1,624) of terms, of which only 2.7% (7/256) represented hallucinations, and 33.6% (86/256) unrelated terms; the remainder of false positives (63.7%, 163/256) were relative terms of the target term.

Conclusions

RAG-HPO is a user-friendly, adaptable tool designed for secure evaluation of clinical text and outperforms standard HPO-matching tools in precision, recall, and F1. Its enhanced precision and recall represent a substantial advancement in phenotypic analysis, accelerating the identification of genetic mechanisms underlying rare diseases and driving progress in genetic research and clinical genomics.

Loss-of-function in RBBP5 results in a syndromic neurodevelopmental disorder associated with microcephaly

PURPOSE

Epigenetic dysregulation has been associated with many inherited disorders. RBBP5 (HGNC:9888) encodes a core member of the protein complex that methylates histone 3 lysine-4 and has not been implicated in human disease.

METHODS

We identify 5 unrelated individuals with de novo heterozygous variants in RBBP5. Three nonsense/frameshift and 2 missense variants were identified in probands with neurodevelopmental symptoms, including global developmental delay, intellectual disability, microcephaly, and short stature. Here, we investigate the pathogenicity of the variants through protein structural analysis and transgenic Drosophila models.

RESULTS

Both missense p.(T232I) and p.(E296D) variants affect evolutionarily conserved amino acids located at the interface between RBBP5 and the nucleosome. In Drosophila, overexpression analysis identifies partial loss-of-function mechanisms when the variants are expressed using the fly Rbbp5 or human RBBP5 cDNA. Loss of Rbbp5 leads to a reduction in brain size. The human reference or variant transgenes fail to rescue this loss and expression of either missense variant in an Rbbp5 null background results in a less severe microcephaly phenotype than the human reference, indicating both missense variants are partial loss-of-function alleles.

CONCLUSION

Haploinsufficiency of RBBP5 observed through de novo null and hypomorphic loss-of-function variants is associated with a syndromic neurodevelopmental disorder.

Striving for clarity in language about gene expression

What do we mean when we say 'gene expression'? In the decades following Crick's 1958 central dogma of molecular biology, whereby genetic information flows from DNA (genes) to RNA (transcripts) to protein (products), we have learned a great deal about DNA, RNA, proteins, and the ensuing phenotypic changes. With the advent of high-throughput technologies (1990s), molecular biologists and computer scientists forged critical collaborations to understand the vast amount of data being generated, rapidly escalating gene expression research to the 'omics' level: entire sets of genes (genomes), transcribed RNAs (transcriptomes), and synthesized proteins (proteomes). However, some concessions came to be made for molecular biologists and computer scientists to understand each other-one of the most prevalent being the increasingly widespread use of 'gene' to mean 'RNAs originating from a DNA segment'. This loosening of terminology, we will argue, creates ambiguity and confusion. We propose guidelines to increase precision and clarity when communicating about gene expression, most notably to reserve 'gene' for the DNA template and 'transcript' for the RNA transcribed from that gene. Striving to use perspicuous terminology will promote rigorous gene expression science and accelerate discovery in this highly promising area of biology.

The co-occurrence of genetic variants in the TYR and OCA2 genes confers susceptibility to albinism

Although rare genetic conditions are mostly caused by DNA sequence alterations that functionally disrupt individual genes, large-scale studies using genome sequencing have started to unmask additional complexity. Understanding how combinations of variants in different genes shape human phenotypes is expected to provide important insights into the clinical and genetic heterogeneity of rare disorders. Here, we use albinism, an archetypal rare condition associated with hypopigmentation, as an exemplar for the study of genetic interactions. We analyse data from the Genomics England 100,000 Genomes Project alongside a cohort of 1120 individuals with albinism, and investigate the effect of dual heterozygosity for the combination of two established albinism-related variants: TYR:c.1205 G > A (p.Arg402Gln) [rs1126809] and OCA2:c.1327 G > A (p.Val443Ile) [rs74653330]. As each of these changes alone is insufficient to cause disease when present in the heterozygous state, we sought evidence of synergistic effects. We show that, when both variants are present, the probability of receiving a diagnosis of albinism is significantly increased (odds ratio 12.8; 95% confidence interval 6.0 - 24.7; p-value 2.1 ×10-8). Further analyses in an independent cohort, the UK Biobank, support this finding and highlight that heterozygosity for the TYR:c.1205 G > A and OCA2:c.1327 G > A variant combination is associated with statistically significant alterations in visual acuity and central retinal thickness (traits that are considered albinism endophenotypes). The approach discussed in this report opens up new avenues for the investigation of oligogenic patterns in apparently Mendelian disorders.

Rare Variant in MRC2 Associated With Familial Supraventricular Tachycardia and Wolff-Parkinson-White Syndrome

BACKGROUND

Accessory pathways are a common cause of supraventricular tachycardia (SVT) and can lead to sudden cardiac death in otherwise healthy children and adults when associated with Wolff-Parkinson-White syndrome. The goal of this study was to identify genetic variants within a large family with structurally normal hearts affected by SVT and Wolff-Parkinson-White syndrome and determine causality of the gene deficit in a corresponding mouse model.

METHODS

Whole exome sequencing performed on 2 distant members of a 3-generation family in which multiple members were affected by SVT or Wolff-Parkinson-White pattern (preexcitation) on ECG identified MRC2 as a candidate gene. Serial electrocardiograms, intracardiac electrophysiology studies, echocardiography, optical mapping studies, and histology were performed on both Mrc2 mutant and WT (wild-type) mice.

RESULTS

A rare HET (heterozygous) missense variant c.2969A>G;p.Glu990Gly (E990G) in MRC2 was identified as the leading candidate gene variant segregating with the cardiac phenotype following an autosomal-dominant Mendelian trait segregation pattern with variable expressivity. In vivo electrophysiology studies revealed reentrant SVT in E990G mice. Optical mapping studies in E990G mice demonstrated abnormal retrograde conduction, suggesting the presence of an accessory pathway. Histological analysis of E990G mouse hearts showed a disordered ECM (extracellular matrix) in the annulus fibrosus. Finally, Mrc2 knockdown in human cardiac fibroblasts enhanced accelerated cell migration.

CONCLUSIONS

This study identified a rare nonsynonymous variant in the MRC2 gene in individuals with familial reentrant SVT, Wolff-Parkinson-White ECG pattern, and structurally normal hearts. Furthermore, Mrc2 knock-in mice revealed an increased incidence of reentrant SVT and bypass tract formation in the setting of preserved cardiac structure and function.

Genome Sequencing for Diagnosing Rare Diseases

BACKGROUND

Genetic variants that cause rare disorders may remain elusive even after expansive testing, such as exome sequencing. The diagnostic yield of genome sequencing, particularly after a negative evaluation, remains poorly defined.

METHODS

We sequenced and analyzed the genomes of families with diverse phenotypes who were suspected to have a rare monogenic disease and for whom genetic testing had not revealed a diagnosis, as well as the genomes of a replication cohort at an independent clinical center.

RESULTS

We sequenced the genomes of 822 families (744 in the initial cohort and 78 in the replication cohort) and made a molecular diagnosis in 218 of 744 families (29.3%). Of the 218 families, 61 (28.0%) - 8.2% of families in the initial cohort - had variants that required genome sequencing for identification, including coding variants, intronic variants, small structural variants, copy-neutral inversions, complex rearrangements, and tandem repeat expansions. Most families in which a molecular diagnosis was made after previous nondiagnostic exome sequencing (63.5%) had variants that could be detected by reanalysis of the exome-sequence data (53.4%) or by additional analytic methods, such as copy-number variant calling, to exome-sequence data (10.8%). We obtained similar results in the replication cohort: in 33% of the families in which a molecular diagnosis was made, or 8% of the cohort, genome sequencing was required, which showed the applicability of these findings to both research and clinical environments.

CONCLUSIONS

The diagnostic yield of genome sequencing in a large, diverse research cohort and in a small clinical cohort of persons who had previously undergone genetic testing was approximately 8% and included several types of pathogenic variation that had not previously been detected by means of exome sequencing or other techniques. (Funded by the National Human Genome Research Institute and others.).

Exome sequencing implicates ancestry-related Mendelian variation at SYNE1 in childhood-onset essential hypertension

Childhood-onset essential hypertension (COEH) is an uncommon form of hypertension that manifests in childhood or adolescence and, in the United States, disproportionately affects children of African ancestry. The etiology of COEH is unknown, but its childhood onset, low prevalence, high heritability, and skewed ancestral demography suggest the potential to identify rare genetic variation segregating in a Mendelian manner among affected individuals and thereby implicate genes important to disease pathogenesis. However, no COEH genes have been reported to date. Here, we identify recessive segregation of rare and putatively damaging missense variation in the spectrin domain of spectrin repeat containing nuclear envelope protein 1 (SYNE1), a cardiovascular candidate gene, in 3 of 16 families with early-onset COEH without an antecedent family history. By leveraging exome sequence data from an additional 48 COEH families, 1,700 in-house trios, and publicly available data sets, we demonstrate that compound heterozygous SYNE1 variation in these COEH individuals occurred more often than expected by chance and that this class of biallelic rare variation was significantly enriched among individuals of African genetic ancestry. Using in vitro shRNA knockdown of SYNE1, we show that reduced SYNE1 expression resulted in a substantial decrease in the elasticity of smooth muscle vascular cells that could be rescued by pharmacological inhibition of the downstream RhoA/Rho-associated protein kinase pathway. These results provide insights into the molecular genetics and underlying pathophysiology of COEH and suggest a role for precision therapeutics in the future.

Exome copy number variant detection, analysis, and classification in a large cohort of families with undiagnosed rare genetic disease

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and, with new innovative methods, can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the Genomics Research to Elucidate the Genetics of Rare Diseases consortium and analyzed using the seqr platform. The addition of CNV detection to exome analysis identified causal CNVs for 171 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb. The causal CNVs consisted of 140 deletions, 15 duplications, 3 suspected complex structural variants (SVs), 3 insertions, and 10 complex SVs, the latter two groups being identified by orthogonal confirmation methods. To classify CNV variant pathogenicity, we used the 2020 American College of Medical Genetics and Genomics/ClinGen CNV interpretation standards and developed additional criteria to evaluate allelic and functional data as well as variants on the X chromosome to further advance the framework. We interpreted 151 CNVs as likely pathogenic/pathogenic and 20 CNVs as high-interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher-resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

AI-MARRVEL - A Knowledge-Driven AI System for Diagnosing Mendelian Disorders

BACKGROUND

Diagnosing genetic disorders requires extensive manual curation and interpretation of candidate variants, a labor-intensive task even for trained geneticists. Although artificial intelligence (AI) shows promise in aiding these diagnoses, existing AI tools have only achieved moderate success for primary diagnosis.

METHODS

AI-MARRVEL (AIM) uses a random-forest machine-learning classifier trained on over 3.5 million variants from thousands of diagnosed cases. AIM additionally incorporates expert-engineered features into training to recapitulate the intricate decision-making processes in molecular diagnosis. The online version of AIM is available at https://ai.marrvel.org. To evaluate AIM, we benchmarked it with diagnosed patients from three independent cohorts.

RESULTS

AIM improved the rate of accurate genetic diagnosis, doubling the number of solved cases as compared with benchmarked methods, across three distinct real-world cohorts. To better identify diagnosable cases from the unsolved pools accumulated over time, we designed a confidence metric on which AIM achieved a precision rate of 98% and identified 57% of diagnosable cases out of a collection of 871 cases. Furthermore, AIM's performance improved after being fine-tuned for targeted settings including recessive disorders and trio analysis. Finally, AIM demonstrated potential for novel disease gene discovery by correctly predicting two newly reported disease genes from the Undiagnosed Diseases Network.

CONCLUSIONS

AIM achieved superior accuracy compared with existing methods for genetic diagnosis. We anticipate that this tool may aid in primary diagnosis, reanalysis of unsolved cases, and the discovery of novel disease genes. (Funded by the NIH Common Fund and others.).

The Role of Genetics in Advancing Cardiometabolic Drug Development

PURPOSE OF REVIEW

The objective of this review is to explore the role of genetics in cardiometabolic drug development. The declining costs of sequencing and the availability of large-scale genomic data have deepened our understanding of cardiometabolic diseases, revolutionizing drug discovery and development methodologies. We highlight four key areas in which genetics is empowering drug development for cardiometabolic disease: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials.

RECENT FINDINGS

Identifying novel drug targets through genetic discovery studies and the use of genetic variants as indicators of potential drug efficacy and safety have become critical components of cardiometabolic drug discovery. We highlight the successes of genetically-informed therapeutic strategies, such as PCSK9 and ANGPTL3 inhibitors in lipid lowering and the emerging role of polygenic risk scores in improving the efficiency of clinical trials. Additionally, we explore the potential of gene silencing and editing technologies, such as antisense oligonucleotides and small interfering RNA, showcasing their promise in addressing diseases refractory to conventional treatments. In this review, we highlight four use cases that demonstrate the vital role of genetics in cardiometabolic drug development: (1) identifying drug candidates, (2) anticipating drug target failures, (3) silencing and editing genes, and (4) enriching clinical trials. Through these advances, genetics has paved the way to increased efficiency of drug development as well as the discovery of more personalized and effective treatments for cardiometabolic disease.

Closing the gap: Solving complex medically relevant genes at scale

Comprehending the mechanism behind human diseases with an established heritable component represents the forefront of personalized medicine. Nevertheless, numerous medically important genes are inaccurately represented in short-read sequencing data analysis due to their complexity and repetitiveness or the so-called 'dark regions' of the human genome. The advent of PacBio as a long-read platform has provided new insights, yet HiFi whole-genome sequencing (WGS) cost remains frequently prohibitive. We introduce a targeted sequencing and analysis framework, Twist Alliance Dark Genes Panel (TADGP), designed to offer phased variants across 389 medically important yet complex autosomal genes. We highlight TADGP accuracy across eleven control samples and compare it to WGS. This demonstrates that TADGP achieves variant calling accuracy comparable to HiFi-WGS data, but at a fraction of the cost. Thus, enabling scalability and broad applicability for studying rare diseases or complementing previously sequenced samples to gain insights into these complex genes. TADGP revealed several candidate variants across all cases and provided insight into LPA diversity when tested on samples from rare disease and cardiovascular disease cohorts. In both cohorts, we identified novel variants affecting individual disease-associated genes (e.g., IKZF1, KCNE1). Nevertheless, the annotation of the variants across these 389 medically important genes remains challenging due to their underrepresentation in ClinVar and gnomAD. Consequently, we also offer an annotation resource to enhance the evaluation and prioritization of these variants. Overall, we can demonstrate that TADGP offers a cost-efficient and scalable approach to routinely assess the dark regions of the human genome with clinical relevance.

Further defining the roles and impact of genetic counselors in the biotechnology and pharmaceutical industry

As personalized medicine has gained traction, drug development models in the biotechnology and pharmaceutical industry (BPI) have increasingly sought to address medical conditions with a genetic component, creating an opportunity for genetic counselors (GCs) to fill new roles and utilize their unique training to contribute to drug development. Despite the potential for GCs in BPI, literature around the role of GCs in this industry has been limited. Our mixed methods study aimed to assess how the roles of GCs in BPI have evolved since 2016, investigate the value of and opportunity for GCs in this industry, and further characterize their motivation and job satisfaction. Participants were recruited via social media advertising, snowball sampling, and email listservs from the National Society of Genetic Counseling (NSGC), the Canadian Association of Genetic Counselors (CAGC), and the American Board of Genetic Counseling (ABGC). Survey (n = 20) and interview (n = 6) data indicates many aspects of GC roles in BPI are consistent with the 2016 study. However, there is evidence of roles becoming more varied and with increasing recognition of the value of GCs, opportunities for involvement in BPI are growing. Furthermore, combined study data found that GCs are motivated by the flexibility of BPI roles as well as the opportunity to contribute to rare disease treatment development and that they are overall satisfied with most aspects of their jobs. Interview data also found that genetic counseling training has the potential to improve clinical trial design and outcomes by making drug development more patient-centric. Finally, combined study data found that while GCs continue to utilize Accreditation Council of Genetic Counseling (ACGC) practice-based competencies (PBCs), business-related training may benefit GCs seeking to enter BPI. Together, these findings are critical for informing genetic counseling training programs, employers within BPI, and GCs interested in entering these positions.

Integrating non-mammalian model organisms in the diagnosis of rare genetic diseases in humans

Next-generation sequencing technology has rapidly accelerated the discovery of genetic variants of interest in individuals with rare diseases. However, showing that these variants are causative of the disease in question is complex and may require functional studies. Use of non-mammalian model organisms - mainly fruitflies (Drosophila melanogaster), nematode worms (Caenorhabditis elegans) and zebrafish (Danio rerio) - enables the rapid and cost-effective assessment of the effects of gene variants, which can then be validated in mammalian model organisms such as mice and in human cells. By probing mechanisms of gene action and identifying interacting genes and proteins in vivo, recent studies in these non-mammalian model organisms have facilitated the diagnosis of numerous genetic diseases and have enabled the screening and identification of therapeutic options for patients. Studies in non-mammalian model organisms have also shown that the biological processes underlying rare diseases can provide insight into more common mechanisms of disease and the biological functions of genes. Here, we discuss the opportunities afforded by non-mammalian model organisms, focusing on flies, worms and fish, and provide examples of their use in the diagnosis of rare genetic diseases.

The best of both worlds: Blending cutting-edge research with clinical processes for a productive exome clinic

Genomic medicine has been transformed by next-generation sequencing (NGS), inclusive of exome sequencing (ES) and genome sequencing (GS). Currently, ES is offered widely in clinical settings, with a less prevalent alternative model consisting of hybrid programs that incorporate research ES along with clinical patient workflows. We were among the earliest to implement a hybrid ES clinic, have provided diagnoses to 45% of probands, and have identified several novel candidate genes. Our program is enabled by a cost-effective investment by the health system and is unique in encompassing all the processes that have been variably included in other hybrid/clinical programs. These include careful patient selection, utilization of a phenotype-agnostic bioinformatics pipeline followed by manual curation of variants and phenotype integration by clinicians, close collaborations between the clinicians and the bioinformatician, pursuit of interesting variants, communication of results to patients in categories that are predicated upon the certainty of a diagnosis, and tracking changes in results over time and the underlying mechanisms for such changes. Due to its effectiveness, scalability to GS and its resource efficiency, specific elements of our paradigm can be incorporated into existing clinical settings, or the entire hybrid model can be implemented within health systems that have genomic medicine programs, to provide NGS in a scientifically rigorous, yet pragmatic setting.

Assessing and mitigating privacy risks of sparse, noisy genotypes by local alignment to haplotype databases

Single nucleotide polymorphisms (SNPs) from omics data create a reidentification risk for individuals and their relatives. Although the ability of thousands of SNPs (especially rare ones) to identify individuals has been repeatedly shown, the availability of small sets of noisy genotypes, from environmental DNA samples or functional genomics data, motivated us to quantify their informativeness. We present a computational tool suite, termed Privacy Leakage by Inference across Genotypic HMM Trajectories (PLIGHT), using population-genetics-based hidden Markov models (HMMs) of recombination and mutation to find piecewise alignment of small, noisy SNP sets to reference haplotype databases. We explore cases in which query individuals are either known to be in the database, or not, and consider several genotype queries, including those from environmental sample swabs from known individuals and from simulated "mosaics" (two-individual composites). Using PLIGHT on a database with ∼5000 haplotypes, we find for common, noise-free SNPs that only ten are sufficient to identify individuals, ∼20 can identify both components in two-individual mosaics, and 20-30 can identify first-order relatives. Using noisy environmental-sample-derived SNPs, PLIGHT identifies individuals in a database using ∼30 SNPs. Even when the individuals are not in the database, local genotype matches allow for some phenotypic information leakage based on coarse-grained SNP imputation. Finally, by quantifying privacy leakage from sparse SNP sets, PLIGHT helps determine the value of selectively sanitizing released SNPs without explicit assumptions about population membership or allele frequency. To make this practical, we provide a sanitization tool to remove the most identifying SNPs from genomic data.

Psychosocial impacts of caring for a child with a genetic disorder in Accra, Ghana

Caregivers caring for a child with a genetic condition may experience psychosocial distress, which may be compounded by the context in which the caring takes place. Few studies have examined psychosocial distress and support among caregivers caring for a child with a genetic disorder in low-middle-income countries such as Ghana. The purpose of the current study was to investigate the psychosocial impacts of caring for a child with a genetic disorder in an urban setting in Ghana's capital, Accra. We conducted individual interviews among 17 caregivers who were taking care of children with various genetic disorders including, albinism, Edward's syndrome, osteogenesis imperfecta, sickle cell disease, and spondyloepiphyseal dysplasia congenita. Thematic analysis of the data revealed three main themes on the psychosocial impacts, including: (1) self-blame, guilt, and shame; (2) sleep and mood disturbances; and (3) discrimination and stigmatization. We observed three themes about support: (1) psychological support; (2) family and community support; and (3) institutional support. Participants reported limited support from professionals such as psychologists. Discussion focuses on the supportive care needs of caregivers and implications for genetic counselling awareness, advocacy, and training.

Simulation of undiagnosed patients with novel genetic conditions

Rare Mendelian disorders pose a major diagnostic challenge and collectively affect 300-400 million patients worldwide. Many automated tools aim to uncover causal genes in patients with suspected genetic disorders, but evaluation of these tools is limited due to the lack of comprehensive benchmark datasets that include previously unpublished conditions. Here, we present a computational pipeline that simulates realistic clinical datasets to address this deficit. Our framework jointly simulates complex phenotypes and challenging candidate genes and produces patients with novel genetic conditions. We demonstrate the similarity of our simulated patients to real patients from the Undiagnosed Diseases Network and evaluate common gene prioritization methods on the simulated cohort. These prioritization methods recover known gene-disease associations but perform poorly on diagnosing patients with novel genetic disorders. Our publicly-available dataset and codebase can be utilized by medical genetics researchers to evaluate, compare, and improve tools that aid in the diagnostic process.

Exome copy number variant detection, analysis and classification in a large cohort of families with undiagnosed rare genetic disease

Copy number variants (CNVs) are significant contributors to the pathogenicity of rare genetic diseases and with new innovative methods can now reliably be identified from exome sequencing. Challenges still remain in accurate classification of CNV pathogenicity. CNV calling using GATK-gCNV was performed on exomes from a cohort of 6,633 families (15,759 individuals) with heterogeneous phenotypes and variable prior genetic testing collected at the Broad Institute Center for Mendelian Genomics of the GREGoR consortium. Each family's CNV data was analyzed using the seqr platform and candidate CNVs classified using the 2020 ACMG/ClinGen CNV interpretation standards. We developed additional evidence criteria to address situations not covered by the current standards. The addition of CNV calling to exome analysis identified causal CNVs for 173 families (2.6%). The estimated sizes of CNVs ranged from 293 bp to 80 Mb with estimates that 44% would not have been detected by standard chromosomal microarrays. The causal CNVs consisted of 141 deletions, 15 duplications, 4 suspected complex structural variants (SVs), 3 insertions and 10 complex SVs, the latter two groups being identified by orthogonal validation methods. We interpreted 153 CNVs as likely pathogenic/pathogenic and 20 CNVs as high interest variants of uncertain significance. Calling CNVs from existing exome data increases the diagnostic yield for individuals undiagnosed after standard testing approaches, providing a higher resolution alternative to arrays at a fraction of the cost of genome sequencing. Our improvements to the classification approach advances the systematic framework to assess the pathogenicity of CNVs.

The Impact of Artificial Intelligence on Optimizing Diagnosis and Treatment Plans for Rare Genetic Disorders

Rare genetic disorders (RDs), characterized by their low prevalence and diagnostic complexities, present significant challenges to healthcare systems. This article explores the transformative impact of artificial intelligence (AI) and machine learning (ML) in addressing these challenges. It emphasizes the need for accurate and early diagnosis of RDs, often hindered by genetic and clinical heterogeneity. This article discusses how AI and ML are reshaping healthcare, providing examples of their effectiveness in disease diagnosis, prognosis, image analysis, and drug repurposing. It highlights AI's ability to efficiently analyze extensive datasets and expedite diagnosis, showcasing case studies like Face2Gene. Furthermore, the article explores how AI tailors treatment plans for RDs, leveraging ML and deep learning (DL) to create personalized therapeutic regimens. It emphasizes AI's role in drug discovery, including the identification of potential candidates for rare disease treatments. Challenges and limitations related to AI in healthcare, including ethical, legal, technical, and human aspects, are addressed. This article underscores the importance of data ethics, privacy, and algorithmic fairness, as well as the need for standardized evaluation techniques and transparency in AI research. It highlights second-generation AI systems that prioritize patient-centric care, efficient patient recruitment for clinical trials, and the significance of high-quality data. The integration of AI with telemedicine, the growth of health databases, and the potential for personalized therapeutic recommendations are identified as promising directions for the field. In summary, this article provides a comprehensive exploration of how AI and ML are revolutionizing the diagnosis and treatment of RDs, addressing challenges while considering ethical implications in this rapidly evolving healthcare landscape.

Nature and nurture: understanding phenotypic variation in inborn errors of immunity

The overall disease burden of pediatric infection is high, with widely varying clinical outcomes including death. Among the most vulnerable children, those with inborn errors of immunity, reduced penetrance and variable expressivity are common but poorly understood. There are several genetic mechanisms that influence phenotypic variation in inborn errors of immunity, as well as a body of knowledge on environmental influences and specific pathogen triggers. Critically, recent advances are illuminating novel nuances for fundamental concepts on disease penetrance, as well as raising new areas of inquiry. The last few decades have seen the identification of almost 500 causes of inborn errors of immunity, as well as major advancements in our ability to characterize somatic events, the microbiome, and genotypes across large populations. The progress has not been linear, and yet, these developments have accumulated into an enhanced ability to diagnose and treat inborn errors of immunity, in some cases with precision therapy. Nonetheless, many questions remain regarding the genetic and environmental contributions to phenotypic variation both within and among families. The purpose of this review is to provide an updated summary of key concepts in genetic and environmental contributions to phenotypic variation within inborn errors of immunity, conceptualized as including dynamic, reciprocal interplay among factors unfolding across the key dimension of time. The associated findings, potential gaps, and implications for research are discussed in turn for each major influencing factor. The substantial challenge ahead will be to organize and integrate information in such a way that accommodates the heterogeneity within inborn errors of immunity to arrive at a more comprehensive and accurate understanding of how the immune system operates in health and disease. And, crucially, to translate this understanding into improved patient care for the millions at risk for serious infection and other immune-related morbidity.

A knowledge graph approach to predict and interpret disease-causing gene interactions

BACKGROUND

Understanding the impact of gene interactions on disease phenotypes is increasingly recognised as a crucial aspect of genetic disease research. This trend is reflected by the growing amount of clinical research on oligogenic diseases, where disease manifestations are influenced by combinations of variants on a few specific genes. Although statistical machine-learning methods have been developed to identify relevant genetic variant or gene combinations associated with oligogenic diseases, they rely on abstract features and black-box models, posing challenges to interpretability for medical experts and impeding their ability to comprehend and validate predictions. In this work, we present a novel, interpretable predictive approach based on a knowledge graph that not only provides accurate predictions of disease-causing gene interactions but also offers explanations for these results.

RESULTS

We introduce BOCK, a knowledge graph constructed to explore disease-causing genetic interactions, integrating curated information on oligogenic diseases from clinical cases with relevant biomedical networks and ontologies. Using this graph, we developed a novel predictive framework based on heterogenous paths connecting gene pairs. This method trains an interpretable decision set model that not only accurately predicts pathogenic gene interactions, but also unveils the patterns associated with these diseases. A unique aspect of our approach is its ability to offer, along with each positive prediction, explanations in the form of subgraphs, revealing the specific entities and relationships that led to each pathogenic prediction.

CONCLUSION

Our method, built with interpretability in mind, leverages heterogenous path information in knowledge graphs to predict pathogenic gene interactions and generate meaningful explanations. This not only broadens our understanding of the molecular mechanisms underlying oligogenic diseases, but also presents a novel application of knowledge graphs in creating more transparent and insightful predictors for genetic research.

Unique Capabilities of Genome Sequencing for Rare Disease Diagnosis

Background

Causal variants underlying rare disorders may remain elusive even after expansive gene panels or exome sequencing (ES). Clinicians and researchers may then turn to genome sequencing (GS), though the added value of this technique and its optimal use remain poorly defined. We therefore investigated the advantages of GS within a phenotypically diverse cohort.

Methods

GS was performed for 744 individuals with rare disease who were genetically undiagnosed. Analysis included review of single nucleotide, indel, structural, and mitochondrial variants.

Results

We successfully solved 218/744 (29.3%) cases using GS, with most solves involving established disease genes (157/218, 72.0%). Of all solved cases, 148 (67.9%) had previously had non-diagnostic ES. We systematically evaluated the 218 causal variants for features requiring GS to identify and 61/218 (28.0%) met these criteria, representing 8.2% of the entire cohort. These included small structural variants (13), copy neutral inversions and complex rearrangements (8), tandem repeat expansions (6), deep intronic variants (15), and coding variants that may be more easily found using GS related to uniformity of coverage (19).

Conclusion

We describe the diagnostic yield of GS in a large and diverse cohort, illustrating several types of pathogenic variation eluding ES or other techniques. Our results reveal a higher diagnostic yield of GS, supporting the utility of a genome-first approach, with consideration of GS as a secondary or tertiary test when higher-resolution structural variant analysis is needed or there is a strong clinical suspicion for a condition and prior targeted genetic testing has been negative.

Advancing Understanding of Inequities in Rare Disease Genomics

PURPOSE

Advances in genomic research have facilitated rare disease diagnosis for thousands of individuals. Unfortunately, the benefits of advanced genetic diagnostic technology are not distributed equitably among the population, as has been seen in many other health care contexts. Quantifying and describing inequities in genetic diagnostic yield is inherently challenging due to barriers to both clinical and research genetic testing. We therefore present an implementation protocol developed to expand access to our rare disease genomic research study and to further understand existing inequities.

METHODS AND FINDINGS

The Rare Genomes Project (RGP) at the Broad Institute of MIT and Harvard offers research genome sequencing to individuals with rare disease who remain genetically undiagnosed through direct interaction with the individual or family. This presents an opportunity for diagnosis beyond the clinical context, thus eliminating many barriers to access. An initial goal of RGP was to equalize access to genomic sequencing by decoupling testing access from proximity to a major medical center and physician referral. However, study participants over the initial 3 years of this project were predominantly white and well resourced. To further understand and address the lack of diversity within RGP, we developed a novel protocol embedded within the larger RGP study, in an approach informed by an implementation science framework. The aims of this protocol were: (1) to diversify recruitment and enrollment within RGP; (2) understand the process and context of implementing genomic medicine for rare disease diagnosis; and (3) investigate the value of a diagnosis for underserved populations.

IMPLICATIONS

Improved understanding of existing inequities and potential strategies to address them are needed to advance equity in rare disease genetic diagnosis and research. In addition to the moral imperative of equity in genomic medicine, this approach is critical in order to fully understand the genomic underpinnings of rare disease.

De novo retinoic acid receptor beta (RARB) variant associated with microphthalmia and dystonia

BACKGROUND

Definition of the individual genotypes that cause a Mendelian phenotype is of great importance both to clinical diagnostics and disease characterization. Heterozygous de novo gain-of-function missense variants in RARB are associated with syndromic microphthalmia 12 (MCOPS12), a developmental disorder characterized by eye malformations and variable involvement of other organs. A subset of patients were described with poorly delineated movement disorders. Additionally, RARB bi-allelic loss-of-function variants, inherited from asymptomatic heterozygous carrier parents, have been found in a recessive family with four MCOPS12-affected members.

PATIENT/METHODS

We used trio whole-exome sequencing to explore the molecular basis of disease in an individual with congenital eye abnormality and movement disorder. All patients with reported RARB variants were reviewed.

RESULTS

We report on identification of a heterozygous de novo RARB nonsense variant in a girl with microphthalmia and progressive generalized dystonia. Public database entries indicate that the de novo variant is recurrently present in clinically affected subjects but a literature report has not yet been available.

CONCLUSIONS

We provide the first detailed evidence for a role of dominant RARB truncating alterations in congenital eye-brain disease, expanding the spectrum of MCOPS12-associated mutations. Considered together with the published family with bi-allelic variants, the data suggest manifestation and non-manifestation of disease in relation to almost identical RARB loss-of-function variations, an apparent paradox that is seen in a growing number of human genetic conditions associated with both recessive and dominant inheritance patterns.

Unified views on variant impact across many diseases

Genomic studies of human disorders are often performed by distinct research communities (i.e., focused on rare diseases, common diseases, or cancer). Despite underlying differences in the mechanistic origin of different disease categories, these studies share the goal of identifying causal genomic events that are critical for the clinical manifestation of the disease phenotype. Moreover, these studies face common challenges, including understanding the complex genetic architecture of the disease, deciphering the impact of variants on multiple scales, and interpreting noncoding mutations. Here, we highlight these challenges in depth and argue that properly addressing them will require a more unified vocabulary and approach across disease communities. Toward this goal, we present a unified perspective on relating variant impact to various genomic disorders.

GRAPE: genomic relatedness detection pipeline

Classifying the degree of relatedness between pairs of individuals has both scientific and commercial applications. As an example, genome-wide association studies (GWAS) may suffer from high rates of false positive results due to unrecognized population structure. This problem becomes especially relevant with recent increases in large-cohort studies. Accurate relationship classification is also required for genetic linkage analysis to identify disease-associated loci. Additionally, DNA relatives matching service is one of the leading drivers for the direct-to-consumer genetic testing market. Despite the availability of scientific and research information on the methods for determining kinship and the accessibility of relevant tools, the assembly of the pipeline, which stably operates on a real-world genotypic data, requires significant research and development resources. Currently, there is no open source end-to-end solution for relatedness detection in genomic data, that is fast, reliable and accurate for both close and distant degrees of kinship, combines all the necessary processing steps to work on a real data, and is ready for production integration. To address this, we developed GRAPE: Genomic RelAtedness detection PipelinE. It combines data preprocessing, identity-by-descent (IBD) segments detection, and accurate relationship estimation. The project uses software development best practices, as well as Global Alliance for Genomics and Health (GA4GH) standards and tools. Pipeline efficiency is demonstrated on both simulated and real-world datasets. GRAPE is available from: https://github.com/genxnetwork/grape.

The opportunities and challenges of using Drosophila to model human cardiac diseases

The Drosophila heart tube seems simple, yet it has notable anatomic complexity and contains highly specialized structures. In fact, the development of the fly heart tube much resembles that of the earliest stages of mammalian heart development, and the molecular-genetic mechanisms driving these processes are highly conserved between flies and humans. Combined with the fly's unmatched genetic tools and a wide variety of techniques to assay both structure and function in the living fly heart, these attributes have made Drosophila a valuable model system for studying human heart development and disease. This perspective focuses on the functional and physiological similarities between fly and human hearts. Further, it discusses current limitations in using the fly, as well as promising prospects to expand the capabilities of Drosophila as a research model for studying human cardiac diseases.

Advancing Understanding of Inequities in Rare Disease Genomics

Purpose

Advances in genomic research have led to the diagnosis of rare, early-onset diseases for thousands of individuals. Unfortunately, the benefits of advanced genetic diagnostic technology are not distributed equitably among the population, as has been seen in many other healthcare contexts. Even quantifying and describing inequities in genetic diagnostic yield is challenging due to variation in referrals to clinical genetics practices and other barriers to clinical genetic testing.

Methods

The Rare Genomes Project (RGP) at the Broad Institute of MIT and Harvard offers research genome sequencing to individuals with rare disease who remain genetically undiagnosed through direct interaction with the individual or family. This presents an opportunity for diagnosis beyond the clinical context, thus eliminating many barriers to access.

Findings

An initial goal of RGP was to equalize access to genomic sequencing by decoupling testing access from proximity to a major medical center and physician referral. However, our study participants are overwhelmingly non-disadvantaged, as evidenced by their access to specialist care and genetic testing prior to RGP enrollment, and are also predominantly white.

Implications

We therefore describe our novel initiative to diversify RGP enrollment in order to advance equity in rare disease genetic diagnosis and research. In addition to the moral imperative of medical equity, this is also critical in order to fully understand the genomic underpinnings of rare disease. We utilize a mixed methods approach to understand the priorities and values of underrepresented communities, existing disparities, and the obstacles to addressing them: all of which is necessary to promote equity in future genomic medicine initiatives.

A retrospective analysis of phosphatase catalytic subunit gene variants in patients with rare disorders identifies novel candidate neurodevelopmental disease genes

Rare genetic disorders represent some of the most severe and life-limiting conditions that constitute a considerable burden on global healthcare systems and societies. Most individuals affected by rare disorders remain undiagnosed, highlighting the unmet need for improved disease gene discovery and novel variant interpretation. Aberrant (de) phosphorylation can have profound pathological consequences underpinning many disease processes. Numerous phosphatases and associated proteins have been identified as disease genes, with many more likely to have gone undiscovered thus far. To begin to address these issues, we have performed a systematic survey of de novo variants amongst 189 genes encoding phosphatase catalytic subunits found in rare disease patients recruited to the 100,000 Genomes Project (100 kGP), the largest national sequencing project of its kind in the United Kingdom. We found that 49% of phosphatases were found to carry de novo mutation(s) in this cohort. Only 25% of these phosphatases have been previously linked to genetic disorders. A gene-to-patient approach matching variants to phenotypic data identified 9 novel candidate rare-disease genes: PTPRD, PTPRG, PTPRT, PTPRU, PTPRZ1, MTMR3, GAK, TPTE2, PTPN18. As the number of patients undergoing whole genome sequencing increases and information sharing improves, we anticipate that reiterative analysis of genomic and phenotypic data will continue to identify candidate phosphatase disease genes for functional validation. This is the first step towards delineating the aetiology of rare genetic disorders associated with altered phosphatase function, leading to new biological insights and improved clinical outcomes for the affected individuals and their families.

Targeting de novo loss-of-function variants in constrained disease genes improves diagnostic rates in the 100,000 Genomes Project

BACKGROUND

Genome sequencing was first offered clinically in the UK through the 100,000 Genomes Project (100KGP). Analysis was restricted to predefined gene panels associated with the patient's phenotype. However, panels rely on clearly characterised phenotypes and risk missing diagnoses outside of the panel(s) applied. We propose a complementary method to rapidly identify pathogenic variants, including those missed by 100KGP methods.

METHODS

The Loss-of-function Observed/Expected Upper-bound Fraction (LOEUF) score quantifies gene constraint, with low scores correlated with haploinsufficiency. We applied DeNovoLOEUF, a filtering strategy to sequencing data from 13,949 rare disease trios in the 100KGP, by filtering for rare, de novo, loss-of-function variants in disease genes with a LOEUF score < 0.2. We compared our findings with the corresponding patient's diagnostic reports.

RESULTS

324/332 (98%) of the variants identified using DeNovoLOEUF were diagnostic or partially diagnostic (whereby the variant was responsible for some of the phenotype). We identified 39 diagnoses that were "missed" by 100KGP standard analyses, which are now being returned to patients.

CONCLUSION

We have demonstrated a highly specific and rapid method with a 98% positive predictive value that has good concordance with standard analysis, low false-positive rate, and can identify additional diagnoses. Globally, as more patients are being offered genome sequencing, we anticipate that DeNovoLOEUF will rapidly identify new diagnoses and facilitate iterative analyses when new disease genes are discovered.

The Enigmatic Genetic Landscape of Hereditary Hearing Loss: A Multistep Diagnostic Strategy in the Italian Population

Hearing loss is the most frequent sensorineural disorder, affecting approximately 1:1000 newborns. Hereditary forms (HHL) represent 50–60% of cases, highlighting the relevance of genetic testing in deaf patients. HHL is classified as non-syndromic (NSHL—70% of cases) or syndromic (SHL—30% of cases). In this study, a multistep and integrative approach aimed at identifying the molecular cause of HHL in 102 patients, whose GJB2 analysis already showed a negative result, is described. In NSHL patients, multiplex ligation probe amplification and long-range PCR analyses of the STRC gene solved 13 cases, while whole exome sequencing (WES) identified the genetic diagnosis in 26 additional ones, with a total detection rate of 47.6%. Concerning SHL, WES detected the molecular cause in 55% of cases. Peculiar findings are represented by the identification of four subjects displaying a dual molecular diagnosis and eight affected by non-syndromic mimics, five of them presenting Usher syndrome type 2. Overall, this study provides a detailed characterisation of the genetic causes of HHL in the Italian population. Furthermore, we highlighted the frequency of Usher syndrome type 2 carriers in the Italian population to pave the way for a more effective implementation of diagnostic and follow-up strategies for this disease.

Exome/Genome Sequencing in Undiagnosed Syndromes

Exome sequencing (ES) and genome sequencing (GS) have radically transformed the diagnostic approach to undiagnosed rare/ultrarare Mendelian diseases. Next-generation sequencing (NGS), the technology integral for ES, GS, and most large (100+) gene panels, has enabled previously unimaginable diagnoses, changes in medical management, new treatments, and accurate reproductive risk assessments for patients, as well as new disease gene discoveries. Yet, challenges remain, as most individuals remain undiagnosed with current NGS. Improved NGS technology has resulted in long-read sequencing, which may resolve diagnoses in some patients who do not obtain a diagnosis with current short-read ES and GS, but its effectiveness is unclear, and it is expensive. Other challenges that persist include the resolution of variants of uncertain significance, the urgent need for patients with ultrarare disorders to have access to therapeutics, the need for equity in patient access to NGS-based testing, and the study of ethical concerns. However, the outlook for undiagnosed disease resolution is bright, due to continual advancements in the field.

Rare EIF4A2 variants are associated with a neurodevelopmental disorder characterized by intellectual disability, hypotonia, and epilepsy

Eukaryotic initiation factor-4A2 (EIF4A2) is an ATP-dependent RNA helicase and a member of the DEAD-box protein family that recognizes the 5' cap structure of mRNAs, allows mRNA to bind to the ribosome, and plays an important role in microRNA-regulated gene repression. Here, we report on 15 individuals from 14 families presenting with global developmental delay, intellectual disability, hypotonia, epilepsy, and structural brain anomalies, all of whom have extremely rare de novo mono-allelic or inherited bi-allelic variants in EIF4A2. Neurodegeneration was predominantly reported in individuals with bi-allelic variants. Molecular modeling predicts these variants would perturb structural interactions in key protein domains. To determine the pathogenicity of the EIF4A2 variants in vivo, we examined the mono-allelic variants in Drosophila melanogaster (fruit fly) and identified variant-specific behavioral and developmental defects. The fruit fly homolog of EIF4A2 is eIF4A, a negative regulator of decapentaplegic (dpp) signaling that regulates embryo patterning, eye and wing morphogenesis, and stem cell identity determination. Our loss-of-function (LOF) rescue assay demonstrated a pupal lethality phenotype induced by loss of eIF4A, which was fully rescued with human EIF4A2 wild-type (WT) cDNA expression. In comparison, the EIF4A2 variant cDNAs failed or incompletely rescued the lethality. Overall, our findings reveal that EIF4A2 variants cause a genetic neurodevelopmental syndrome with both LOF and gain of function as underlying mechanisms.

TCEAL1 loss-of-function results in an X-linked dominant neurodevelopmental syndrome and drives the neurological disease trait in Xq22.2 deletions

An Xq22.2 region upstream of PLP1 has been proposed to underly a neurological disease trait when deleted in 46,XX females. Deletion mapping revealed that heterozygous deletions encompassing the smallest region of overlap (SRO) spanning six Xq22.2 genes (BEX3, RAB40A, TCEAL4, TCEAL3, TCEAL1, and MORF4L2) associate with an early-onset neurological disease trait (EONDT) consisting of hypotonia, intellectual disability, neurobehavioral abnormalities, and dysmorphic facial features. None of the genes within the SRO have been associated with monogenic disease in OMIM. Through local and international collaborations facilitated by GeneMatcher and Matchmaker Exchange, we have identified and herein report seven de novo variants involving TCEAL1 in seven unrelated families: three hemizygous truncating alleles; one hemizygous missense allele; one heterozygous TCEAL1 full gene deletion; one heterozygous contiguous deletion of TCEAL1, TCEAL3, and TCEAL4; and one heterozygous frameshift variant allele. Variants were identified through exome or genome sequencing with trio analysis or through chromosomal microarray. Comparison with previously reported Xq22 deletions encompassing TCEAL1 identified a more-defined syndrome consisting of hypotonia, abnormal gait, developmental delay/intellectual disability especially affecting expressive language, autistic-like behavior, and mildly dysmorphic facial features. Additional features include strabismus, refractive errors, variable nystagmus, gastroesophageal reflux, constipation, dysmotility, recurrent infections, seizures, and structural brain anomalies. An additional maternally inherited hemizygous missense allele of uncertain significance was identified in a male with hypertonia and spasticity without syndromic features. These data provide evidence that TCEAL1 loss of function causes a neurological rare disease trait involving significant neurological impairment with features overlapping the EONDT phenotype in females with the Xq22 deletion.

Complex genomic rearrangements: an underestimated cause of rare diseases

Complex genomic rearrangements (CGRs) are known contributors to disease but are often missed during routine genetic screening. Identifying CGRs requires (i) identifying copy number variants (CNVs) concurrently with inversions, (ii) phasing multiple breakpoint junctions incis, as well as (iii) detecting and resolving structural variants (SVs) within repeats. We demonstrate how combining cytogenetics and new sequencing methodologies is being successfully applied to gain insights into the genomic architecture of CGRs. In addition, we review CGR patterns and molecular features revealed by studying constitutional genomic disorders. These data offer invaluable lessons to individuals interested in investigating CGRs, evaluating their clinical relevance and frequency, as well as assessing their impact(s) on rare genetic diseases.

Developmental genomics of limb malformations: Allelic series in association with gene dosage effects contribute to the clinical variability

Genetic heterogeneity, reduced penetrance, and variable expressivity, the latter including asymmetric body axis plane presentations, have all been described in families with congenital limb malformations (CLMs). Interfamilial and intrafamilial heterogeneity highlight the complexity of the underlying genetic pathogenesis of these developmental anomalies. Family-based genomics by exome sequencing (ES) and rare variant analyses combined with whole-genome array-based comparative genomic hybridization were implemented to investigate 18 families with limb birth defects. Eleven of 18 (61%) families revealed explanatory variants, including 7 single-nucleotide variant alleles and 3 copy number variants (CNVs), at previously reported "disease trait associated loci": BHLHA9, GLI3, HOXD cluster, HOXD13, NPR2, and WNT10B. Breakpoint junction analyses for all three CNV alleles revealed mutational signatures consistent with microhomology-mediated break-induced replication, a mechanism facilitated by Alu/Alu-mediated rearrangement. Homozygous duplication of BHLHA9 was observed in one Turkish kindred and represents a novel contributory genetic mechanism to Gollop-Wolfgang Complex (MIM: 228250), where triplication of the locus has been reported in one family from Japan (i.e., 4n = 2n + 2n versus 4n = 3n + 1n allelic configurations). Genes acting on limb patterning are sensitive to a gene dosage effect and are often associated with an allelic series. We extend an allele-specific gene dosage model to potentially assist, in an adjuvant way, interpretations of interconnections among an allelic series, clinical severity, and reduced penetrance of the BHLHA9-related CLM spectrum.

Allele-specific differential regulation of monoallelically expressed autosomal genes in the cardiac lineage

Each mammalian autosomal gene is represented by two alleles in diploid cells. To our knowledge, no insights have been made in regard to allele-specific regulatory mechanisms of autosomes. Here we use allele-specific single cell transcriptomic analysis to elucidate the establishment of monoallelic gene expression in the cardiac lineage. We find that monoallelically expressed autosomal genes in mESCs and mouse blastocyst cells are differentially regulated based on the genetic background of the parental alleles. However, the genetic background of the allele does not affect the establishment of monoallelic genes in differentiated cardiomyocytes. Additionally, we observe epigenetic differences between deterministic and random autosomal monoallelic genes. Moreover, we also find a greater contribution of the maternal versus paternal allele to the development and homeostasis of cardiac tissue and in cardiac health, highlighting the importance of maternal influence in male cardiac tissue homeostasis. Our findings emphasize the significance of allele-specific insights into gene regulation in development, homeostasis and disease.

Sequencing individual genomes with recurrent genomic disorder deletions: an approach to characterize genes for autosomal recessive rare disease traits

BACKGROUND

In medical genetics, discovery and characterization of disease trait contributory genes and alleles depends on genetic reasoning, study design, and patient ascertainment; we suggest a segmental haploid genetics approach to enhance gene discovery and molecular diagnostics.

METHODS

We constructed a genome-wide map for nonallelic homologous recombination (NAHR)-mediated recurrent genomic deletions and used this map to estimate population frequencies of NAHR deletions based on large-scale population cohorts and region-specific studies. We calculated recessive disease carrier burden using high-quality pathogenic or likely pathogenic variants from ClinVar and gnomAD. We developed a NIRD (NAHR deletion Impact to Recessive Disease) score for recessive disorders by quantifying the contribution of NAHR deletion to the overall allele load that enumerated all pairwise combinations of disease-causing alleles; we used a Punnett square approach based on an assumption of random mating. Literature mining was conducted to identify all reported patients with defects in a gene with a high NIRD score; meta-analysis was performed on these patients to estimate the representation of NAHR deletions in recessive traits from contemporary human genomics studies. Retrospective analyses of extant clinical exome sequencing (cES) were performed for novel rare recessive disease trait gene and allele discovery from individuals with NAHR deletions.

RESULTS

We present novel genomic insights regarding the genome-wide impact of NAHR recurrent segmental variants on recessive disease burden; we demonstrate the utility of NAHR recurrent deletions to enhance discovery in the challenging context of autosomal recessive (AR) traits and biallelic variation. Computational results demonstrate new mutations mediated by NAHR, involving recurrent deletions at 30 genomic regions, likely drive recessive disease burden for over 74% of loci within these segmental deletions or at least 2% of loci genome-wide. Meta-analyses on 170 literature-reported patients implicate that NAHR deletions are depleted from the ascertained pool of AR trait alleles. Exome reanalysis of personal genomes from subjects harboring recurrent deletions uncovered new disease-contributing variants in genes including COX10, ERCC6, PRRT2, and OTUD7A.

CONCLUSIONS

Our results demonstrate that genomic sequencing of personal genomes with NAHR deletions could dramatically improve allele and gene discovery and enhance clinical molecular diagnosis. Moreover, results suggest NAHR events could potentially enable human haploid genetic screens as an approach to experimental inquiry into disease biology.

Unclassified white matter disorders: A diagnostic journey requiring close collaboration between clinical and laboratory services

BACKGROUND

Next generation sequencing studies have revealed an ever-increasing number of causes for genetic disorders of central nervous system white matter. A substantial number of disorders are identifiable from their specific pattern of biochemical and/or imaging findings for which single gene testing may be indicated. Beyond this group, the causes of genetic white matter disorders are unclear and a broader approach to genomic testing is recommended.

AIM

This study aimed to identify the genetic causes for a group of individuals with unclassified white matter disorders with suspected genetic aetiology and highlight the investigations required when the initial testing is non-diagnostic.

METHODS

Twenty-six individuals from 22 families with unclassified white matter disorders underwent deep phenotyping and genome sequencing performed on trio, or larger, family groups. Functional studies and transcriptomics were used to resolve variants of uncertain significance with potential clinical relevance.

RESULTS

Causative or candidate variants were identified in 15/22 (68.2%) families. Six of the 15 implicated genes had been previously associated with white matter disease (COL4A1, NDUFV1, SLC17A5, TUBB4A, BOLA3, DARS2). Patients with variants in the latter two presented with an atypical phenotype. The other nine genes had not been specifically associated with white matter disease at the time of diagnosis and included genes associated with monogenic syndromes, developmental disorders, and developmental and epileptic encephalopathies (STAG2, LSS, FIG4, GLS, PMPCA, SPTBN1, AGO2, SCN2A, SCN8A). Consequently, only 46% of the diagnoses would have been made via a current leukodystrophy gene panel test.

DISCUSSION

These results confirm the importance of broad genomic testing for patients with white matter disorders. The high diagnostic yield reflects the integration of deep phenotyping, whole genome sequencing, trio analysis, functional studies, and transcriptomic analyses.

CONCLUSIONS

Genetic white matter disorders are genetically and phenotypically heterogeneous. Deep phenotyping together with a range of genomic technologies underpin the identification of causes of unclassified white matter disease. A molecular diagnosis is essential for prognostication, appropriate management, and accurate reproductive counseling.

Beyond race: Recruitment of diverse participants in clinical genomics research for rare disease

Purpose: Despite recent attention to increasing diversity in clinical genomics research, researchers still struggle to recruit participants from varied sociodemographic backgrounds. We examined the experiences of parents from diverse backgrounds with enrolling their children in clinical genomics research on rare diseases. We explored the barriers and facilitators parents encountered and possible impacts of sociodemographic factors on their access to research.

Methods: We utilized semi-structured interviews with parents of children participating in the Undiagnosed Diseases Network. Interview data were analyzed using comparative content analysis.

Results: We interviewed 13 Hispanic, 11 non-Hispanic White, four Asian, and two biracial parents. Participants discussed different pathways to clinical genomics research for rare disease as well as how sociodemographic factors shaped families’ access. Themes focused on variation in: 1) reliance on providers to access research; 2) cultural norms around health communication; 3) the role of social capital in streamlining access; and 4) the importance of language-concordant research engagement.

Conclusion: Our findings suggest that variables beyond race/ethnicity may influence access in clinical genomics research. Future efforts to diversify research participation should consider utilizing varied recruitment strategies to reach participants with diverse sociodemographic characteristics.

Phenotype-driven approaches to enhance variant prioritization and diagnosis of rare disease

Rare disease diagnostics and disease gene discovery have been revolutionized by whole-exome and genome sequencing but identifying the causative variant(s) from the millions in each individual remains challenging. The use of deep phenotyping of patients and reference genotype-phenotype knowledge, alongside variant data such as allele frequency, segregation, and predicted pathogenicity, has proved an effective strategy to tackle this issue. Here we review the numerous tools that have been developed to automate this approach and demonstrate the power of such an approach on several thousand diagnosed cases from the 100,000 Genomes Project. Finally, we discuss the challenges that need to be overcome if we are going to improve detection rates and help the majority of patients that still remain without a molecular diagnosis after state-of-the-art genomic interpretation.

A gene-to-patient approach uplifts novel disease gene discovery and identifies 18 putative novel disease genes

PURPOSE

Exome and genome sequencing have drastically accelerated novel disease gene discoveries. However, discovery is still hindered by myriad variants of uncertain significance found in genes of undetermined biological function. This necessitates intensive functional experiments on genes of equal predicted causality, leading to a major bottleneck.

METHODS

We apply the loss-of-function observed/expected upper-bound fraction metric of intolerance to gene inactivation to curate a list of predicted haploinsufficient disease genes. Using data from the 100,000 Genomes Project, we adopt a gene-to-patient approach that matches de novo loss-of-function variants in constrained genes to patients with rare disease. Through large-scale aggregation of data, we reduce excess analytical noise currently hindering novel discoveries.

RESULTS

Results from 13,949 trios revealed 643 rare, de novo predicted loss-of-function events filtered from 1044 loss-of-function observed/expected upper-bound fraction-constrained genes. A total of 168 variants occurred within 126 genes without a known disease-gene relationship. Of these, 27 genes had >1 kindred affected, and for 18 of these genes, multiple kindreds had overlapping phenotypes. Two years after initial analysis, 11 of 18 (61%) of these genes have been independently published as novel disease gene discoveries.

CONCLUSION

Using large cohorts and adopting gene-based approaches can rapidly and objectively accelerate dominantly inherited novel gene discovery by targeting the most appropriate genes for functional validation.

A performance evaluation study: Variant annotation tools - The enigma of clinical next generation sequencing (NGS) based genetic testing

Dramatically expanding our ability for clinical genetic testing for inherited conditions and complex diseases such as cancer, next generation sequencing (NGS) technologies are allowing for rapid interrogation of thousands of genes and identification of millions of variants. Variant annotation, the process of assigning functional information to DNA variants based on the standardized Human Genome Variation Society (HGVS) nomenclature, is a fundamental challenge in the analysis of NGS data that has led to the development of many bioinformatic algorithms. In this study, we evaluated the performance of 3 variant annotation tools: Alamut® Batch, Ensembl Variant Effect Predictor (VEP), and ANNOVAR, benchmarked by a manually curated ground-truth set of 298 variants from the medical exome database at the Molecular Diagnostics Laboratory at Lurie Children's Hospital. Of the 3 tools, VEP produces the most accurate variant annotations (HGVS nomenclature for 297 of the 298 variants) due to usage of updated gene transcript versions within the algorithm. Alamut® Batch called 296 of the 298 variants correctly; strikingly, ANNOVAR exhibited the greatest number of discrepancies (20 of the 298 variants, 93.3% concordance with ground-truth set). Adoption of validated methods of variant annotation is critical in post-analytical phases of clinical testing.

Variant-level matching for diagnosis and discovery: Challenges and opportunities

Here we describe MyGene2, Geno2MP, VariantMatcher, and Franklin; databases that provide variant-level information and phenotypic features to researchers, clinicians, healthcare providers and patients. Following the footsteps of the Matchmaker Exchange project that connects exome, genome, and phenotype databases at the gene level, these databases have as one goal to facilitate connection to one another using Data Connect, a standard for discovery and search of biomedical data from the Global Alliance for Genomics and Health (GA4GH).

Advances in the development of PubCaseFinder, including the new application programming interface and matching algorithm

Over 10,000 rare genetic diseases have been identified, and millions of newborns are affected by severe rare genetic diseases each year. A variety of Human Phenotype Ontology (HPO)-based clinical decision support systems (CDSS) and patient repositories have been developed to support clinicians in diagnosing patients with suspected rare genetic diseases. In September 2017, we released PubCaseFinder (https://pubcasefinder.dbcls.jp), a web-based CDSS that provides ranked lists of genetic and rare diseases using HPO-based phenotypic similarities, where top-listed diseases represent the most likely differential diagnosis. We also developed a Matchmaker Exchange (MME) application programming interface (API) to query PubCaseFinder, which has been adopted by several patient repositories. In this paper, we describe notable updates regarding PubCaseFinder, the GeneYenta matching algorithm implemented in PubCaseFinder, and the PubCaseFinder API. The updated GeneYenta matching algorithm improves the performance of the CDSS automated differential diagnosis function. Moreover, the updated PubCaseFinder and new API empower patient repositories participating in MME and medical professionals to actively use HPO-based resources.

Interpretation and actionability of genetic variants in cardiomyopathies: a position statement from the European Society of Cardiology Council on cardiovascular genomics

This document describes the contribution of clinical criteria to the interpretation of genetic variants using heritable Mendelian cardiomyopathies as an example. The aim is to assist cardiologists in defining the clinical contribution to a genetic diagnosis and the interpretation of molecular genetic reports. The identification of a genetic variant of unknown or uncertain significance is a limitation of genetic testing, but current guidelines for the interpretation of genetic variants include essential contributions from clinical family screening that can establish a de novo assignment of the variant or its segregation with the phenotype in the family. A partnership between clinicians and patients helps to solve major uncertainties and provides reliable and clinically actionable information.

Mosaic divergent repeat interruptions in XDP influence repeat stability and disease onset

While many genetic causes of movement disorders have been identified, modifiers of disease expression are largely unknown. X-linked dystonia-parkinsonism (XDP) is a neurodegenerative disease caused by a SINE-VNTR-Alu(AGAGGG)n retrotransposon insertion in TAF1, with a polymorphic (AGAGGG)n repeat. Repeat length and variants in MSH3 and PMS2 explain ∼65% of the variance in age at onset (AAO) in XDP. However, additional genetic modifiers are conceivably at play in XDP, such as repeat interruptions. Long-read nanopore sequencing of PCR amplicons from XDP patients (n = 202) was performed to assess potential repeat interruption and instability. Repeat-primed PCR and Cas9-mediated targeted enrichment confirmed the presence of identified divergent repeat motifs. In addition to the canonical pure SINE-VNTR-Alu-5'-(AGAGGG)n, we observed a mosaic of divergent repeat motifs that polarized at the beginning of the tract, where the divergent repeat interruptions varied in motif length by having one, two, or three nucleotides fewer than the hexameric motif, distinct from interruptions in other disease-associated repeats, which match the lengths of the canonical motifs. All divergent configurations occurred mosaically and in two investigated brain regions (basal ganglia, cerebellum) and in blood-derived DNA from the same patient. The most common divergent interruption was AGG [5'-SINE-VNTR-Alu(AGAGGG)2AGG(AGAGGG)n], similar to the pure tract, followed by AGGG [5'-SINE-VNTR-Alu(AGAGGG)2AGGG(AGAGGG)n], at median frequencies of 0.425 (IQR: 0.42-0.43) and 0.128 (IQR: 0.12-0.13), respectively. The mosaic AGG motif was not associated with repeat number (estimate = -3.8342, P = 0.869). The mosaic pure tract frequency was associated with repeat number (estimate = 45.32, P = 0.0441) but not AAO (estimate = -41.486, P = 0.378). Importantly, the mosaic frequency of the AGGG negatively correlated with repeat number after adjusting for age at sampling (estimate = -161.09, P = 3.44 × 10-5). When including the XDP-relevant MSH3/PMS2 modifier single nucleotide polymorphisms into the model, the mosaic AGGG frequency was associated with AAO (estimate = 155.1063, P = 0.047); however, the association dissipated after including the repeat number (estimate = -92.46430, P = 0.079). We reveal novel mosaic divergent repeat interruptions affecting both motif length and sequence (DRILS) of the canonical motif polarized within the SINE-VNTR-Alu(AGAGGG)n repeat. Our study illustrates: (i) the importance of somatic mosaic genotypes; (ii) the biological plausibility of multiple modifiers (both germline and somatic) that can have additive effects on repeat instability; and (iii) that these variations may remain undetected without assessment of single molecules.

Centers for Mendelian Genomics: A decade of facilitating gene discovery

PURPOSE

Mendelian disease genomic research has undergone a massive transformation over the past decade. With increasing availability of exome and genome sequencing, the role of Mendelian research has expanded beyond data collection, sequencing, and analysis to worldwide data sharing and collaboration.

METHODS

Over the past 10 years, the National Institutes of Health-supported Centers for Mendelian Genomics (CMGs) have played a major role in this research and clinical evolution.

RESULTS

We highlight the cumulative gene discoveries facilitated by the program, biomedical research leveraged by the approach, and the larger impact on the research community. Beyond generating a list of gene-phenotype relationships and participating in widespread data sharing, the CMGs have created resources, tools, and training for the larger community to foster understanding of genes and genome variation. The CMGs have participated in a wide range of data sharing activities, including deposition of all eligible CMG data into the Analysis, Visualization, and Informatics Lab-space (AnVIL), sharing candidate genes through the Matchmaker Exchange and the CMG website, and sharing variants in Genotypes to Mendelian Phenotypes (Geno2MP) and VariantMatcher.

CONCLUSION

The work is far from complete; strengthening communication between research and clinical realms, continued development and sharing of knowledge and tools, and improving access to richly characterized data sets are all required to diagnose the remaining molecularly undiagnosed patients.

Phenotypic and mutational spectrum of ROR2-related Robinow syndrome

Robinow syndrome is characterized by a triad of craniofacial dysmorphisms, disproportionate-limb short stature and genital hypoplasia. A significant degree of phenotypic variability seems to correlate with different genes/loci. Disturbances of the non-canonical WNT-pathway have been identified as the main cause of the syndrome. Biallelic variants in ROR2 cause an autosomal recessive form of the syndrome with distinctive skeletal findings. Twenty-two patients with a clinical diagnosis of autosomal recessive Robinow syndrome were screened for variants in ROR2 using multiple molecular approaches. We identified 25 putatively pathogenic ROR2 variants, 16 novel, including single nucleotide variants and exonic deletions. Detailed phenotypic analyses revealed that all subjects presented with a prominent forehead, hypertelorism, short nose, abnormality of the nasal tip, brachydactyly, mesomelic limb shortening, short stature and genital hypoplasia in male patients. A total of 19 clinical features were present in more than 75% of the subjects, thus pointing to an overall uniformity of the phenotype. Disease-causing variants in ROR2, contribute to a clinically recognizable AR trait phenotype with multiple skeletal defects. A comprehensive quantitative clinical evaluation this cohort delineated the phenotypic spectrum of ROR2-related Robinow syndrome. The identification of exonic deletion variant alleles further supports the contention of a loss-of-function mechanism in the etiology of the syndrome. This article is protected by copyright. All rights reserved.