The Genetic Basis of Mendelian Phenotypes: Discoveries, Challenges, and Opportunities

Rare and low-frequency variants in families with otitis media

Otitis media is a highly frequent diagnosis in children that causes significant morbidity but remains understudied as a genetic trait despite significant heritability in families. To identify rare or low-frequency variants within genes that confer susceptibility to otitis media, exome sequence data of 287 individuals from 243 families were analyzed. Identified variants were tested for co-segregation with otitis media in family members. Genome sequence data from a case-control cohort was imputed and analyzed for association of specific genes with otitis media. Single-cell RNA-sequence data of identified genes were noted in acutely infected mouse middle ears. Thirty-three variants within 24 genes co-segregated with otitis media in 28 families, of which 18 variants were considered pathogenic or likely pathogenic. An additional 81 variants in 21 of the same genes were identified in 83 unrelated probands with otitis media. Of the 24 genes, 12 were associated with otitis media in mouse models, while 15 genes were replicated from previous human studies. A common variant EYA4 c.829G > A was associated with OM in the case-control cohort. Using network analysis, 22 of the 24 genes were connected in a subnetwork enriched in various signaling pathways, Th1/Th2/Th17 cell differentiation, and viral infections. Majority (87.5%) of the identified genes were expressed in mouse middle ear cells, with differential expression after acute infection. The identification of novel genes and variants for susceptibility to otitis media will be useful in future risk screening and clinical management in children that require a more personalized approach due to poor response to standard treatments. KEY MESSAGES: Thirty-three variants in 24 genes were identified in 28 families with otitis media. Eighteen of these variants within 10 genes were considered (likely) pathogenic. A common variant EYA4 c.829G > A was associated with OM in a case-control cohort. The novel genes were differentially expressed in mouse middle ear post-infection. Genetic screening could identify children for targeted treatment for otitis media.

SeqFirst: Building equity access to a precise genetic diagnosis in critically ill newborns

Access to a precise genetic diagnosis (PrGD) in critically ill newborns is limited and inequitable because the complex inclusion criteria used to prioritize testing eligibility omit many patients at high risk for a genetic condition. SeqFirst-neo is a program to test whether a genotype-driven workflow using simple, broad exclusion criteria to assess eligibility for rapid genome sequencing (rGS) increases access to a PrGD in critically ill newborns. All 408 newborns admitted to a neonatal intensive care unit between January 2021 and February 2022 were assessed, and of 240 eligible infants, 126 were offered rGS (i.e., intervention group [IG]) and compared to 114 infants who received conventional care in parallel (i.e., conventional care group [CCG]). A PrGD was made in 62/126 (49.2%) IG neonates compared to 11/114 (9.7%) CCG infants. The odds of receiving a PrGD were ∼9 times greater in the IG vs. the CCG, and this difference was maintained at 12 months follow-up. Access to a PrGD in the IG vs. CCG differed significantly between infants identified as non-White (34/74, 45.9% vs. 6/29, 20.7%; p = 0.024) and Black (8/10, 80.0% vs. 0/4; p = 0.015). Neonatologists were significantly less successful at predicting a PrGD in non-White than non-Hispanic White infants. The use of a standard workflow in the IG with a PrGD revealed that a PrGD would have been missed in 26/62 (42%) infants. The use of simple, broad exclusion criteria that increase access to genetic testing significantly increases access to a PrGD, improves access equity, and results in fewer missed diagnoses.

Daxx Variation as a Potential Predictive Marker of the Therapeutic Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer

OBJECTIVE

The response to neoadjuvant chemoradiotherapy (NACRT) for locally advanced rectal cancer (LARC) varies from achieving a complete pathological response to encountering resistance to treatment. Therefore, biomarkers for predicting the NACRT responses should be identified. This prospective study aimed to identify key genomic biomarkers as the predictors of the NACRT response with LARC.

METHODS

Overall, 67 patients with LARC treated with NACRT and proctectomy were divided into two groups based on the tumor regression grade (TRG) for identifying key biomarkers. Patients with a TRG of 0 or 1 were assigned to the sensitive response group, and patients with a TRG of 2 or 3 were the resistant response group. Twenty-nine postsurgical tumor samples were collected for whole exome sequencing (WES) to identify genomic variation biomarkers. The other 38 pairs of tumor specimens from pretreatment and postsurgery samples were evaluated by immunohistochemistry (IHC) to examine the biomarker features.

RESULTS

In the WES subcohort, 11 genes showed copy number variation, including FNKBIA, ARID1A, CCND2, CDK4, LYN, MDM2, RAD51B, RARA, SPEN, STAT3, and Daxx, which has the highest copy number variation. For the IHC subcohort, Daxx was initially highly expressed in the nuclei of tumor cells, particularly in the sensitive response group, while varying its expression after NACRT, demonstrating that Daxx levels were related to treatment responses and the survival benefit, especially a better disease-free survival (DFS).

CONCLUSION

We identified multiple genomic variations between sensitive and resistant responders and verified that Daxx is a potential predictive biomarker of the response to NACRT in LARC.

A phenotype-based AI pipeline outperforms human experts in differentially diagnosing rare diseases using EHRs

Rare diseases, affecting ~350 million people worldwide, pose significant challenges in clinical diagnosis due to the lack of experienced physicians and the complexity of differentiating between numerous rare diseases. To address these challenges, we introduce PhenoBrain, a fully automated artificial intelligence pipeline. PhenoBrain utilizes a BERT-based natural language processing model to extract phenotypes from clinical texts in EHRs and employs five new diagnostic models for differential diagnoses of rare diseases. The AI system was developed and evaluated on diverse, multi-country rare disease datasets, comprising 2271 cases with 431 rare diseases. In 1936 test cases, PhenoBrain achieved an average predicted top-3 recall of 0.513 and a top-10 recall of 0.654, surpassing 13 leading prediction methods. In a human-computer study with 75 cases, PhenoBrain exhibited exceptional performance with a top-3 recall of 0.613 and a top-10 recall of 0.813, surpassing the performance of 50 specialist physicians and large language models like ChatGPT and GPT-4. Combining PhenoBrain's predictions with specialists increased the top-3 recall to 0.768, demonstrating its potential to enhance diagnostic accuracy in clinical workflows.

RLIM-specific activity reporters define variant pathogenicity in Tonne-Kalscheuer syndrome

Tonne-Kalscheuer syndrome (TOKAS; MIM: 300978) is an X-linked recessive disorder with devastating consequences for patients, such as intellectual disability, developmental delay, and multiple congenital abnormalities. TOKAS is associated with hemizygous variants in the RLIM gene, which encodes a RING-type E3 ubiquitin ligase. The current sustained increase in reported RLIM variants of uncertain significance creates an urgent need to develop assays that can screen these variants and experimentally determine their pathogenicity and disease association. Here, we engineered flow cytometry-based RLIM-specific reporters to measure RLIM activity in TOKAS. This paper describes the design and use of RLIM-specific reporters to determine the pathogenicity of a TOKAS RLIM gene variant. Our data demonstrate that RLIM-specific flow cytometry reporters based on either the full length or a degron region of the substrate REX1 measure RLIM activity in cells. Further, we describe the TOKAS variant RLIM p.Asn581Lys and, using reporter assays, determine that it disrupts RLIM catalytic activity. These data reveal how the p.Asn581Lys variant impairs RLIM function and suggests pathogenic mechanisms. The use of RLIM-specific reporters will greatly accelerate the resolution of variants of uncertain significance and disease association in TOKAS.

PSAP-Genomic-Regions: A Method Leveraging Population Data to Prioritize Coding and Non-Coding Variants in Whole Genome Sequencing for Rare Disease Diagnosis

The introduction of Next-Generation Sequencing technologies in the clinics has improved rare disease diagnosis. Nonetheless, for very heterogeneous or very rare diseases, more than half of cases still lack molecular diagnosis. Novel strategies are needed to prioritize variants within a single individual. The Population Sampling Probability (PSAP) method was developed to meet this aim but only for coding variants in exome data. Here, we propose an extension of the PSAP method to the non-coding genome called PSAP-genomic-regions. In this extension, instead of considering genes as testing units (PSAP-genes strategy), we use genomic regions defined over the whole genome that pinpoint potential functional constraints. We conceived an evaluation protocol for our method using artificially generated disease exomes and genomes, by inserting coding and non-coding pathogenic ClinVar variants in large data sets of exomes and genomes from the general population. PSAP-genomic-regions significantly improves the ranking of these variants compared to using a pathogenicity score alone. Using PSAP-genomic-regions, more than 50% of non-coding ClinVar variants were among the top 10 variants of the genome. On real sequencing data from six patients with Cerebral Small Vessel Disease and nine patients with male infertility, all causal variants were ranked in the top 100 variants with PSAP-genomic-regions. By revisiting the testing units used in the PSAP method to include non-coding variants, we have developed PSAP-genomic-regions, an efficient whole-genome prioritization tool which offers promising results for the diagnosis of unresolved rare diseases.

Ensemble and consensus approaches to prediction of recessive inheritance for missense variants in human disease

Mode of inheritance (MOI) is necessary for clinical interpretation of pathogenic variants; however, the majority of variants lack this information. Furthermore, variant effect predictors are fundamentally insensitive to recessive-acting diseases. Here, we present MOI-Pred, a variant pathogenicity prediction tool that accounts for MOI, and ConMOI, a consensus method that integrates variant MOI predictions from three independent tools. MOI-Pred integrates evolutionary and functional annotations to produce variant-level predictions that are sensitive to both dominant-acting and recessive-acting pathogenic variants. Both MOI-Pred and ConMOI show state-of-the-art performance on standard benchmarks. Importantly, dominant and recessive predictions from both tools are enriched in individuals with pathogenic variants for dominant- and recessive-acting diseases, respectively, in a real-world electronic health record (EHR)-based validation approach of 29,981 individuals. ConMOI outperforms its component methods in benchmarking and validation, demonstrating the value of consensus among multiple prediction methods. Predictions for all possible missense variants are provided in the "Data and code availability" section.

Molecular diagnosis of 405 individuals with autism spectrum disorder

Autism spectrum disorder (ASD) is caused by combined genetic and environmental factors. Genetic heritability in ASD is estimated as 60-90%, and genetic investigations have revealed many monogenic factors. We analyzed 405 patients with ASD using family-based exome sequencing to detect disease-causing single-nucleotide variants (SNVs), small insertions and deletions (indels), and copy number variations (CNVs) for molecular diagnoses. All candidate variants were validated by Sanger sequencing or quantitative polymerase chain reaction and were evaluated using the American College of Medical Genetics and Genomics/Association for Molecular Pathology guidelines for molecular diagnosis. We identified 55 disease-causing SNVs/indels in 53 affected individuals and 13 disease-causing CNVs in 13 affected individuals, achieving a molecular diagnosis in 66 of 405 affected individuals (16.3%). Among the 55 disease-causing SNVs/indels, 51 occurred de novo, 2 were compound heterozygous (in one patient), and 2 were X-linked hemizygous variants inherited from unaffected mothers. The molecular diagnosis rate in females was significantly higher than that in males. We analyzed affected sibling cases of 24 quads and 2 quintets, but only one pair of siblings shared an identical pathogenic variant. Notably, there was a higher molecular diagnostic rate in simplex cases than in multiplex families. Our simulation indicated that the diagnostic yield is increasing by 0.63% (range 0-2.5%) per year. Based on our simple simulation, diagnostic yield is improving over time. Thus, periodical reevaluation of ES data should be strongly encouraged in undiagnosed ASD patients.

Phenotypic and Genetic Spectrum in 309 Consecutive Pediatric Patients with Inherited Retinal Disease

Inherited retinal dystrophies (IRDs) are a common cause of blindness or severe visual impairment in children and may occur with or without systemic associations. The aim of the present study is to describe the phenotypic and genotypic spectrum of IRDs in a pediatric patient cohort in Retrospective single-center cross-sectional analysis. Presenting symptoms, clinical phenotype, and molecular genetic diagnosis were assessed in 309 pediatric patients with suspected IRD. Patients were grouped by age at genetic diagnosis (preschool: 0-6 years, n = 127; schoolchildren: 7-17 years, n = 182). Preschool children most frequently presented with nystagmus (34.5% isolated, 16.4% syndromic), no visual interest (20.9%; 14.5%), or nyctalopia (22.4%; 3.6%; p < 0.05); schoolchildren most frequently presented with declining visual acuity (31% isolated, 21.1% syndromic), nyctalopia (10.6%; 13.5%), or high myopia (5.3%; 13.2%). Pathogenic variants were identified in 96 different genes (n = 69 preschool, n = 73 schoolchildren). In the preschool group, 57.4% had isolated and 42.6% had syndromic IRDs, compared to 70.9% and 29.1% in schoolchildren. In the preschool group, 32.4% of the isolated IRDs were related to forms of Leber's congenital amaurosis (most frequent were RPE65 (11%) and CEP290 (8.2%)), 31.5% were related to stationary IRDs, 15.1% were related to macular dystrophies (ABCA4, BEST1, PRPH2, PROM1), and 8.2% to rod-cone dystrophies (RPGR, RPB3, RP2, PDE6A). All rod-cone dystrophies (RCDs) were subjectively asymptomatic at the time of genetic diagnosis. At schoolage, 41% were attributed to cone-dominated disease (34% ABCA4), 10.3% to BEST1, and 10.3% to RCDs (RP2, PRPF3, RPGR; IMPG2, PDE6B, CNGA1, MFRP, RP1). Ciliopathies were the most common syndromic IRDs (preschool 37%; schoolchildren 45.1%), with variants in USH2A, CEP290 (5.6% each), CDH23, BBS1, and BBS10 (3.7% each) being the most frequent in preschoolers, and USH2A (11.7%), BBS10 (7.8%), CEP290, CDHR23, CLRN1, and ICQB1 (3.9% each) being the most frequent in syndromic schoolkids. Vitreoretinal syndromic IRDs accounted for 29.6% (preschool: COL2A1, COL11A1, NDP (5.6% each)) and 23.5% (schoolage: COL2A1, KIF11 (9.8% each)), metabolic IRDs for 9.4% (OAT, HADHA, MMACHD, PMM2) and 3.9% (OAT, HADHA), mitochondriopathies for 3.7% and 7.8%, and syndromic albinism accounted for 5.6% and 3.9%, respectively. In conclusion we show here that the genotypic spectrum of IRDs and its quantitative distribution not only differs between children and adults but also between children of different age groups, with an almost equal proportion of syndromic and non-syndromic IRDs in early childhood. Ophthalmic screening visits at the preschool and school ages may aid even presymptomatic diagnosis and treatment of potential sight and life-threatening systemic sequelae.

Yield of genetic association signals from genomes, exomes and imputation in the UK Biobank

Whole-genome sequencing (WGS), whole-exome sequencing (WES) and array genotyping with imputation (IMP) are common strategies for assessing genetic variation and its association with medically relevant phenotypes. To date, there has been no systematic empirical assessment of the yield of these approaches when applied to hundreds of thousands of samples to enable the discovery of complex trait genetic signals. Using data for 100 complex traits from 149,195 individuals in the UK Biobank, we systematically compare the relative yield of these strategies in genetic association studies. We find that WGS and WES combined with arrays and imputation (WES + IMP) have the largest association yield. Although WGS results in an approximately fivefold increase in the total number of assayed variants over WES + IMP, the number of detected signals differed by only 1% for both single-variant and gene-based association analyses. Given that WES + IMP typically results in savings of lab and computational time and resources expended per sample, we evaluate the potential benefits of applying WES + IMP to larger samples. When we extend our WES + IMP analyses to 468,169 UK Biobank individuals, we observe an approximately fourfold increase in association signals with the threefold increase in sample size. We conclude that prioritizing WES + IMP and large sample sizes rather than contemporary short-read WGS alternatives will maximize the number of discoveries in genetic association studies.

Tissue-aware interpretation of genetic variants advances the etiology of rare diseases

Pathogenic variants underlying Mendelian diseases often disrupt the normal physiology of a few tissues and organs. However, variant effect prediction tools that aim to identify pathogenic variants are typically oblivious to tissue contexts. Here we report a machine-learning framework, denoted "Tissue Risk Assessment of Causality by Expression for variants" (TRACEvar, https://netbio.bgu.ac.il/TRACEvar/ ), that offers two advancements. First, TRACEvar predicts pathogenic variants that disrupt the normal physiology of specific tissues. This was achieved by creating 14 tissue-specific models that were trained on over 14,000 variants and combined 84 attributes of genetic variants with 495 attributes derived from tissue omics. TRACEvar outperformed 10 well-established and tissue-oblivious variant effect prediction tools. Second, the resulting models are interpretable, thereby illuminating variants' mode of action. Application of TRACEvar to variants of 52 rare-disease patients highlighted pathogenicity mechanisms and relevant disease processes. Lastly, the interpretation of all tissue models revealed that top-ranking determinants of pathogenicity included attributes of disease-affected tissues, particularly cellular process activities. Collectively, these results show that tissue contexts and interpretable machine-learning models can greatly enhance the etiology of rare diseases.

Genetics of inherited peripheral neuropathies and the next frontier: looking backwards to progress forwards

Inherited peripheral neuropathies (IPNs) encompass a clinically and genetically heterogeneous group of disorders causing length-dependent degeneration of peripheral autonomic, motor and/or sensory nerves. Despite gold-standard diagnostic testing for pathogenic variants in over 100 known associated genes, many patients with IPN remain genetically unsolved. Providing patients with a diagnosis is critical for reducing their 'diagnostic odyssey', improving clinical care, and for informed genetic counselling. The last decade of massively parallel sequencing technologies has seen a rapid increase in the number of newly described IPN-associated gene variants contributing to IPN pathogenesis. However, the scarcity of additional families and functional data supporting variants in potential novel genes is prolonging patient diagnostic uncertainty and contributing to the missing heritability of IPNs. We review the last decade of IPN disease gene discovery to highlight novel genes, structural variation and short tandem repeat expansions contributing to IPN pathogenesis. From the lessons learnt, we provide our vision for IPN research as we anticipate the future, providing examples of emerging technologies, resources and tools that we propose that will expedite the genetic diagnosis of unsolved IPN families.

saseR: Juggling offsets unlocks RNA-seq tools for fast and Scalable differential usage, Aberrant Splicing and Expression Retrieval

RNA-seq data analysis relies on many different tools, each tailored to specific applications and coming with unique assumptions and restrictions. Indeed, tools for differential transcript usage, or diagnosing patients with rare diseases through splicing and expression outliers, either lack in performance, discard information, or do not scale to massive data compendia. Here, we show that replacing the normalisation offsets unlocks bulk RNA-seq workflows for scalable differential usage, aberrant splicing and expression analyses. Our method, saseR, is much faster than state-of-the-art methods, dramatically outperforms these to detect aberrant splicing, and provides a single workflow for various short- and long-read RNA-seq applications.

Considerations for reporting variants in novel candidate genes identified during clinical genomic testing

Since the first novel gene discovery for a Mendelian condition was made via exome sequencing, the rapid increase in the number of genes known to underlie Mendelian conditions coupled with the adoption of exome (and more recently, genome) sequencing by diagnostic testing labs has changed the landscape of genomic testing for rare diseases. Specifically, many individuals suspected to have a Mendelian condition are now routinely offered clinical ES. This commonly results in a precise genetic diagnosis but frequently overlooks the identification of novel candidate genes. Such candidates are also less likely to be identified in the absence of large-scale gene discovery research programs. Accordingly, clinical laboratories have both the opportunity, and some might argue a responsibility, to contribute to novel gene discovery, which should, in turn, increase the diagnostic yield for many conditions. However, clinical diagnostic laboratories must necessarily balance priorities for throughput, turnaround time, cost efficiency, clinician preferences, and regulatory constraints and often do not have the infrastructure or resources to effectively participate in either clinical translational or basic genome science research efforts. For these and other reasons, many laboratories have historically refrained from broadly sharing potentially pathogenic variants in novel genes via networks such as Matchmaker Exchange, much less reporting such results to ordering providers. Efforts to report such results are further complicated by a lack of guidelines for clinical reporting and interpretation of variants in novel candidate genes. Nevertheless, there are myriad benefits for many stakeholders, including patients/families, clinicians, and researchers, if clinical laboratories systematically and routinely identify, share, and report novel candidate genes. To facilitate this change in practice, we developed criteria for triaging, sharing, and reporting novel candidate genes that are most likely to be promptly validated as underlying a Mendelian condition and translated to use in clinical settings.

Developing a global nursing network for rare diseases to enhance patient care and support

Rare diseases, while individually rare, are common when considered collectively, affecting about one in 17 people across their lifetime. However, there is a lack of awareness of and education about rare diseases in nursing. To address this, the Global Nursing Network Rare Diseases (GNNRD) has been launched to connect nurses from within all fields of practice and at all levels of experience, with the aim of improving the lives of people with rare and undiagnosed diseases (RUDs). The GNNRD aims to empower nurses on a global scale through leadership, knowledge exchange and skill development and to provide a platform from which they can influence policy and advocate for patients and their families at regional, national and international levels. This article provides an overview of RUDs and some of the challenges experienced by patients and their families and describes the development and aims of the GNNRD.

VUS next in rare diseases? Deciphering genetic determinants of biomolecular condensation

The diagnostic odysseys for rare disease patients are getting shorter as next-generation sequencing becomes more widespread. However, the complex genetic diversity and factors influencing expressivity continue to challenge accurate diagnosis, leaving more than 50% of genetic variants categorized as variants of uncertain significance.Genomic expression intricately hinges on localized interactions among its products. Conventional variant prioritization, biased towards known disease genes and the structure-function paradigm, overlooks the potential impact of variants shaping the composition, location, size, and properties of biomolecular condensates, genuine membraneless organelles swiftly sensing and responding to environmental changes, and modulating expressivity.To address this complexity, we propose to focus on the nexus of genetic variants within biomolecular condensates determinants. Scrutinizing variant effects in these membraneless organelles could refine prioritization, enhance diagnostics, and unveil the molecular underpinnings of rare diseases. Integrating comprehensive genome sequencing, transcriptomics, and computational models can unravel variant pathogenicity and disease mechanisms, enabling precision medicine. This paper presents the rationale driving our proposal and describes a protocol to implement this approach. By fusing state-of-the-art knowledge and methodologies into the clinical practice, we aim to redefine rare diseases diagnosis, leveraging the power of scientific advancement for more informed medical decisions.

Update on Prevention of Hemoglobinopathies in Azerbaijan

Hereditary hemoglobinopathies, particularly β-thalassemia, are highly prevalent in Azerbaijan, posing a significant public health challenge. In response, the Azerbaijani government implemented a national prevention program that includes mandatory premarital screening and prenatal diagnosis for at-risk couples, aiming to mitigate the impact of these diseases. This report covers the first five years of the program, beginning in 2015. Among 287 identified at-risk couples, 271 fetal samples were analyzed, revealing that 148 were carriers, 63 were affected, and 60 were unaffected. In nearly all cases, affected pregnancies were terminated. The most common mutations detected were Codon 8 [-AA], IVS-II-1 [G > A], and IVS-I-110 [G > A] in the HBB gene. Since the program's inception, the birth rate of affected children has significantly decreased, making this established approach a valuable model for other regions facing similar challenges with autosomal recessive disorders.

Constraining models of dominance for nonsynonymous mutations in the human genome

Dominance is a fundamental parameter in genetics, determining the dynamics of natural selection on deleterious and beneficial mutations, the patterns of genetic variation in natural populations, and the severity of inbreeding depression in a population. Despite this importance, dominance parameters remain poorly known, particularly in humans or other non-model organisms. A key reason for this lack of information about dominance is that it is extremely challenging to disentangle the selection coefficient (s) of a mutation from its dominance coefficient (h). Here, we explore dominance and selection parameters in humans by fitting models to the site frequency spectrum (SFS) for nonsynonymous mutations. When assuming a single dominance coefficient for all nonsynonymous mutations, we find that numerous h values can fit the data, so long as h is greater than ~0.15. Moreover, we also observe that theoretically-predicted models with a negative relationship between h and s can also fit the data well, including models with h = 0.05 for strongly deleterious mutations. Finally, we use our estimated dominance and selection parameters to inform simulations revisiting the question of whether the out-of-Africa bottleneck has led to differences in genetic load between African and non-African human populations. These simulations suggest that the relative burden of genetic load in non-African populations depends on the dominance model assumed, with slight increases for more weakly recessive models and slight decreases shown for more strongly recessive models. Moreover, these results also demonstrate that models of partially recessive nonsynonymous mutations can explain the observed severity of inbreeding depression in humans, bridging the gap between molecular population genetics and direct measures of fitness in humans. Our work represents a comprehensive assessment of dominance and deleterious variation in humans, with implications for parameterizing models of deleterious variation in humans and other mammalian species.

Detection of distant relatedness in biobanks to identify undiagnosed cases of Mendelian disease as applied to Long QT syndrome

Rare genetic diseases are typically studied in referral populations, resulting in underdiagnosis and biased assessment of penetrance and phenotype. To address this, we develop a generalizable method of genotype inference based on distant relatedness and deploy this to identify undiagnosed Type 5 Long QT Syndrome (LQT5) rare variant carriers in a non-referral population. We identify 9 LQT5 families referred to a single specialty clinic, each carrying p.Asp76Asn, the most common LQT5 variant. We uncover recent common ancestry and a single shared haplotype among probands. Application to a non-referral population of 69,819 BioVU biobank subjects identifies 22 additional subjects sharing this haplotype, which we confirm to carry p.Asp76Asn. Referral and non-referral carriers have prolonged QT interval corrected for heart rate (QTc) compared to controls, and, among carriers, the QTc polygenic score is independently associated with QTc prolongation. Thus, our innovative analysis of shared chromosomal segments identifies undiagnosed cases of genetic disease and refines the understanding of LQT5 penetrance and phenotype.

Joint, multifaceted genomic analysis enables diagnosis of diverse, ultra-rare monogenic presentations

Genomics for rare disease diagnosis has advanced at a rapid pace due to our ability to perform "N-of-1" analyses on individual patients with ultra-rare diseases. The increasing sizes of ultra-rare disease cohorts internationally newly enables cohort-wide analyses for new discoveries, but well-calibrated statistical genetics approaches for jointly analyzing these patients are still under development.1,2 The Undiagnosed Diseases Network (UDN) brings multiple clinical, research and experimental centers under the same umbrella across the United States to facilitate and scale N-of-1 analyses. Here, we present the first joint analysis of whole genome sequencing data of UDN patients across the network. We introduce new, well-calibrated statistical methods for prioritizing disease genes with de novo recurrence and compound heterozygosity. We also detect pathways enriched with candidate and known diagnostic genes. Our computational analysis, coupled with a systematic clinical review, recapitulated known diagnoses and revealed new disease associations. We further release a software package, RaMeDiES, enabling automated cross-analysis of deidentified sequenced cohorts for new diagnostic and research discoveries. Gene-level findings and variant-level information across the cohort are available in a public-facing browser (https://dbmi-bgm.github.io/udn-browser/). These results show that N-of-1 efforts should be supplemented by a joint genomic analysis across cohorts.

Exploring the Genotype-Phenotype Correlations in a Child with Inherited Seizure and Thrombocytopenia by Digenic Network Analysis

Understanding the correlation between genotype and phenotype remains challenging for modern genetics. Digenic network analysis may provide useful models for understanding complex phenotypes that traditional Mendelian monogenic models cannot explain. Clinical data, whole exome sequencing data, in silico, and machine learning analysis were combined to construct a digenic network that may help unveil the complex genotype-phenotype correlations in a child presenting with inherited seizures and thrombocytopenia. The proband inherited a maternal heterozygous missense variant in SCN1A (NM_001165963.4:c.2722G>A) and a paternal heterozygous missense variant in MYH9 (NM_002473.6:c.3323A>C). In silico analysis showed that these two variants may be pathogenic for inherited seizures and thrombocytopenia in the proband. Moreover, focusing on 230 epilepsy-associated genes and 35 thrombopoiesis genes, variant call format data of the proband were analyzed using machine learning tools (VarCoPP 2.0) and Digenic Effect predictor. A digenic network was constructed, and SCN1A and MYH9 were found to be core genes in the network. Further analysis showed that MYH9 might be a modifier of SCN1A, and the variant in MYH9 might not only influence the severity of SCN1A-related seizure but also lead to thrombocytopenia in the bone marrow. In addition, another eight variants might also be co-factors that account for the proband's complex phenotypes. Our data show that as a supplement to the traditional Mendelian monogenic model, digenic network analysis may provide reasonable models for the explanation of complex genotype-phenotype correlations.

Genomic, molecular, and cellular divergence of the human brain

While many core biological processes are conserved across species, the human brain has evolved with unique capacities. Current understanding of the neurobiological mechanisms that endow human traits as well as associated vulnerabilities remains limited. However, emerging data have illuminated species divergence in DNA elements and genome organization, in molecular, morphological, and functional features of conserved neural cell types, as well as temporal differences in brain development. Here, we summarize recent data on unique features of the human brain and their complex implications for the study and treatment of brain diseases. We also consider key outstanding questions in the field and discuss the technologies and foundational knowledge that will be required to accelerate understanding of human neurobiology.

Considerations for reporting variants in novel candidate genes identified during clinical genomic testing

Since the first novel gene discovery for a Mendelian condition was made via exome sequencing (ES), the rapid increase in the number of genes known to underlie Mendelian conditions coupled with the adoption of exome (and more recently, genome) sequencing by diagnostic testing labs has changed the landscape of genomic testing for rare disease. Specifically, many individuals suspected to have a Mendelian condition are now routinely offered clinical ES. This commonly results in a precise genetic diagnosis but frequently overlooks the identification of novel candidate genes. Such candidates are also less likely to be identified in the absence of large-scale gene discovery research programs. Accordingly, clinical laboratories have both the opportunity, and some might argue a responsibility, to contribute to novel gene discovery which should in turn increase the diagnostic yield for many conditions. However, clinical diagnostic laboratories must necessarily balance priorities for throughput, turnaround time, cost efficiency, clinician preferences, and regulatory constraints, and often do not have the infrastructure or resources to effectively participate in either clinical translational or basic genome science research efforts. For these and other reasons, many laboratories have historically refrained from broadly sharing potentially pathogenic variants in novel genes via networks like Matchmaker Exchange, much less reporting such results to ordering providers. Efforts to report such results are further complicated by a lack of guidelines for clinical reporting and interpretation of variants in novel candidate genes. Nevertheless, there are myriad benefits for many stakeholders, including patients/families, clinicians, researchers, if clinical laboratories systematically and routinely identify, share, and report novel candidate genes. To facilitate this change in practice, we developed criteria for triaging, sharing, and reporting novel candidate genes that are most likely to be promptly validated as underlying a Mendelian condition and translated to use in clinical settings.

Genetic heterogeneity in hereditary hearing loss: Potential role of kinociliary protein TOGARAM2

Hearing loss (HL) is a heterogenous trait with pathogenic variants in more than 200 genes that have been discovered in studies involving small and large HL families. Over one-third of families with hereditary HL remain etiologically undiagnosed after screening for mutations in the recognized genes. Genetic heterogeneity complicates the analysis in multiplex families where variants in more than one gene can be causal in different individuals even in the same sibship. We employed exome or genome sequencing in at least two affected individuals with congenital or prelingual-onset, severe to profound, non-syndromic, bilateral sensorineural HL from four multiplex families. Bioinformatic analysis was performed to identify variants in known and candidate deafness genes. Our results show that in these four families, variants in a single HL gene do not explain HL in all affected family members, and variants in another known or candidate HL gene were detected to clarify HL in the entire family. We also present a variant in TOGARAM2 as a potential cause underlying autosomal recessive non-syndromic HL by showing its presence in a family with HL, its expression in the cochlea and the localization of the protein to cochlear hair cells. Conclusively, analyzing all affected family members separately can serve as a good source for the identification of variants in known and novel candidate genes for HL.

Data from electronic healthcare records expand our understanding of X-linked genetic diseases

Disease specific cohort studies have reported details on X linked (XL) disorders affecting females. We investigated the spectrum and penetrance of XL disorders seen in electronic health records (EHR). We generated a cohort of individuals diagnosed with XL disorders at Vanderbilt University Medical Center over 20 years. Our cohort included 477 males and 203 females diagnosed with 108 different XL genetic disorders. We found large differences between the female/male (F/M) ratios for various XL disorders regardless of their OMIM annotated mode of inheritance. We identified four XL recessive disorders affecting women previously only described in men. Biomarkers for XL disease had unique gender-specific patterns differing between modes of inheritance. EHRs provide large cohorts of XL genetic disorders that give new insights compared to the literature. Differences in the F/M ratios and biomarkers of XL disorders observed likely result from disease specific and sex dependent penetrance. We conclude that observed gender ratios associated with specific XL disorders may be more useful than those predicted by Mendelian genetics provided by OMIM. Our findings of a gender specific penetrance and severity for XL disorders show unexpected differences from Mendelian predictions. Further work is required to validate our findings in larger combined EHR cohorts.

Beyond Single Diagnosis: Exploring Multidiagnostic Realities in Pediatric Patients through Genome Sequencing

Recent advancements in the next-generation sequencing have illuminated the occurrence of multiple genetic diagnoses (MGD). While exome sequencing has provided insights, genome sequencing (GS), the most comprehensive diagnostic tool, remains underexplored for studying MGD prevalence. We retrospectively analyzed 1487 pediatric cases from our laboratory, employing GS to investigate the incidence of single definitive genetic diagnosis (SDD) and MGD in children suspected of having a genetic disease. Of these patients, 273 received at least one definitive diagnosis, including 245 with SDD (16.5%) and 28 with MGD (1.9%). Diagnostic yield was consistent across genders and unaffected by previous testing in SDD cases. Notably, prior testing significantly increased the diagnostic yield in MGD cases to 2.7% overall and 14.4% among diagnosed cases, compared to 1.1% for those with GS as a first-tier test. Age was a significant factor in diagnostic outcome for both SDD and MGD cases with neonates showing the highest diagnostic yield of 24.5% in SDD and a notably higher yield in MGD at 4.9%, representing 16.7% of the diagnosed cases. Of the 28 MGD cases, 17 exhibited distinct phenotypes, 9 had overlapping features, and 2 presented a mix, underscoring the genetic and phenotypic heterogeneity within this group. This study is the first to exclusively use GS to assess MGD prevalence. Our findings highlight the complexity of rare diseases and emphasize the importance of comprehensive, genome-level diagnostics. Clinicians must ensure that diagnoses fully account for the observed phenotypes to inform optimal therapeutic strategies and management.

Burden of Mendelian disorders in a large Middle Eastern biobank

BACKGROUND

Genome sequencing of large biobanks from under-represented ancestries provides a valuable resource for the interrogation of Mendelian disease burden at world population level, complementing small-scale familial studies.

METHODS

Here, we interrogate 6045 whole genomes from Qatar-a Middle Eastern population with high consanguinity and understudied mutational burden-enrolled at the national Biobank and phenotyped for 58 clinically-relevant quantitative traits. We examine a curated set of 2648 Mendelian genes from 20 panels, annotating known and novel pathogenic variants and assessing their penetrance and impact on the measured traits.

RESULTS

We find that 62.5% of participants are carriers of at least 1 known pathogenic variant relating to recessive conditions, with homozygosity observed in 1 in 150 subjects (0.6%) for which Peninsular Arabs are particularly enriched versus other ancestries (5.8-fold). On average, 52.3 loss-of-function variants were found per genome, 6.5 of which affect a known Mendelian gene. Several variants annotated in ClinVar/HGMD as pathogenic appeared at intermediate frequencies in this cohort (1-3%), highlighting Arab founder effect, while others have exceedingly high frequencies (> 5%) prompting reconsideration as benign. Furthermore, cumulative gene burden analysis revealed 56 genes having gene carrier frequency > 1/50, including 5 ACMG Tier 3 panel genes which would be candidates for adding to newborn screening in the country. Additionally, leveraging 58 biobank traits, we systematically assess the impact of novel/rare variants on phenotypes and discover 39 candidate large-effect variants associating with extreme quantitative traits. Furthermore, through rare variant burden testing, we discover 13 genes with high mutational load, including 5 with impact on traits relevant to disease conditions, including metabolic disorder and type 2 diabetes, consistent with the high prevalence of these conditions in the region.

CONCLUSIONS

This study on the first phase of the growing Qatar Genome Program cohort provides a comprehensive resource from a Middle Eastern population to understand the global mutational burden in Mendelian genes and their impact on traits in seemingly healthy individuals in high consanguinity settings.

Global research landscape on the contribution of de novo mutations to human genetic diseases over the past 20 years: bibliometric analysis

As the contribution of de novo mutations (DNMs) to human genetic diseases has been gradually uncovered, analyzing the global research landscape over the past 20 years is essential. Because of the large and rapidly increasing number of publications in this field, understanding the current landscape of the contribution of DNMs in the human genome to genetic diseases remains a challenge. Bibliometric analysis provides an approach for visualizing these studies using information in published records in a specific field. This study aimed to illustrate the current global research status and explore trends in the field of DNMs underlying genetic diseases. Bibliometric analyses were performed using the Bibliometrix Package based on the R language version 4.1.3 and CiteSpace version 6.1.R2 software for publications from 2000 to 2021 indexed under the Web of Science Core Collection (WoSCC) about DNMs underlying genetic diseases on 17 September 2022. We identified 3435 records, which were published in 731 journals by 26,538 authors from 6052 institutes in 66 countries. There was an upward trend in the number of publications since 2013. The USA, China, and Germany contributed the majority of the records included. The University of Washington, Columbia University, and Baylor College of Medicine were the top-producing institutions. Evan E Eichler of the University of Washington, Stephan J Sanders of the Yale University School of Medicine, and Ingrid E Scheffer of the University of Melbourne were the most high-ranked authors. Keyword co-occurrence analysis suggested that DNMs in neurodevelopmental disorders and intellectual disabilities were research hotspots and trends. In conclusion, our data show that DNMs have a significant effect on human genetic diseases, with a noticeable increase in annual publications over the last 5 years. Furthermore, potential hotspots are shifting toward understanding the causative role and clinical interpretation of newly identified or low-frequency DNMs observed in patients.

Disentangling multiple sclerosis phenotypes through Mendelian disorders: A network approach

BACKGROUND

The increasing knowledge about multiple sclerosis (MS) pathophysiology has reinforced the need for an improved description of disease phenotypes, connected to disease biology. Growing evidence indicates that complex diseases constitute phenotypical and genetic continuums with "simple," monogenic disorders, suggesting shared pathomechanisms.

OBJECTIVES

The objective of this study was to depict a novel MS phenotypical framework leveraging shared physiopathology with Mendelian diseases and to identify phenotype-specific candidate drugs.

METHODS

We performed an enrichment testing of MS-associated variants with Mendelian disorders genes. We defined a "MS-Mendelian network," further analyzed to define enriched phenotypic subnetworks and biological processes. Finally, a network-based drug screening was implemented.

RESULTS

Starting from 617 MS-associated loci, we showed a significant enrichment of monogenic diseases (p < 0.001). We defined an MS-Mendelian molecular network based on 331 genes and 486 related disorders, enriched in four phenotypic classes: neurologic, immunologic, metabolic, and visual. We prioritized a total of 503 drugs, of which 27 molecules active in 3/4 phenotypical subnetworks and 140 in subnetwork pairs.

CONCLUSION

The genetic architecture of MS contains the seeds of pathobiological multiplicities shared with immune, neurologic, metabolic and visual monogenic disorders. This result may inform future classifications of MS endophenotypes and support the development of new therapies in both MS and rare diseases.

Functional genomics in inborn errors of immunity

Inborn errors of immunity (IEI) comprise a diverse spectrum of 485 disorders as recognized by the International Union of Immunological Societies Committee on Inborn Error of Immunity in 2022. While IEI are monogenic by definition, they illuminate various pathways involved in the pathogenesis of polygenic immune dysregulation as in autoimmune or autoinflammatory syndromes, or in more common infectious diseases that may not have a significant genetic basis. Rapid improvement in genomic technologies has been the main driver of the accelerated rate of discovery of IEI and has led to the development of innovative treatment strategies. In this review, we will explore various facets of IEI, delving into the distinctions between PIDD and PIRD. We will examine how Mendelian inheritance patterns contribute to these disorders and discuss advancements in functional genomics that aid in characterizing new IEI. Additionally, we will explore how emerging genomic tools help to characterize new IEI as well as how they are paving the way for innovative treatment approaches for managing and potentially curing these complex immune conditions.

GPAD: a natural language processing-based application to extract the gene-disease association discovery information from OMIM

BACKGROUND

Thousands of genes have been associated with different Mendelian conditions. One of the valuable sources to track these gene-disease associations (GDAs) is the Online Mendelian Inheritance in Man (OMIM) database. However, most of the information in OMIM is textual, and heterogeneous (e.g. summarized by different experts), which complicates automated reading and understanding of the data. Here, we used Natural Language Processing (NLP) to make a tool (Gene-Phenotype Association Discovery (GPAD)) that could syntactically process OMIM text and extract the data of interest.

RESULTS

GPAD applies a series of language-based techniques to the text obtained from OMIM API to extract GDA discovery-related information. GPAD can inform when a particular gene was associated with a specific phenotype, as well as the type of validation-whether through model organisms or cohort-based patient-matching approaches-for such an association. GPAD extracted data was validated with published reports and was compared with large language model. Utilizing GPAD's extracted data, we analysed trends in GDA discoveries, noting a significant increase in their rate after the introduction of exome sequencing, rising from an average of about 150-250 discoveries each year. Contrary to hopes of resolving most GDAs for Mendelian disorders by now, our data indicate a substantial decline in discovery rates over the past five years (2017-2022). This decline appears to be linked to the increasing necessity for larger cohorts to substantiate GDAs. The rising use of zebrafish and Drosophila as model organisms in providing evidential support for GDAs is also observed.

CONCLUSIONS

GPAD's real-time analyzing capacity offers an up-to-date view of GDA discovery and could help in planning and managing the research strategies. In future, this solution can be extended or modified to capture other information in OMIM and scientific literature.

Inborn Errors of Immunity Contribute to the Burden of Skin Disease and Create Opportunities for Improving the Practice of Dermatology

Opportunities to improve the clinical management of skin disease are being created by advances in genomic medicine. Large-scale sequencing increasingly challenges notions about single-gene disorders. It is now apparent that monogenic etiologies make appreciable contributions to the population burden of disease and that they are underrecognized in clinical practice. A genetic diagnosis informs on molecular pathology and may direct targeted treatments and tailored prevention strategies for patients and family members. It also generates knowledge about disease pathogenesis and management that is relevant to patients without rare pathogenic variants. Inborn errors of immunity are a large class of monogenic etiologies that have been well-studied and contribute to the population burden of inflammatory diseases. To further delineate the contributions of inborn errors of immunity to the pathogenesis of skin disease, we performed a set of analyses that identified 316 inborn errors of immunity associated with skin pathologies, including common skin diseases. These data suggest that clinical sequencing is underutilized in dermatology. We next use these data to derive a network that illuminates the molecular relationships of these disorders and suggests an underlying etiological organization to immune-mediated skin disease. Our results motivate the further development of a molecularly derived and data-driven reorganization of clinical diagnoses of skin disease.

Toward robust clinical genome interpretation: Developing a consistent terminology to characterize Mendelian disease-gene relationships-allelic requirement, inheritance modes, and disease mechanisms

PURPOSE

The terminology used for gene-disease curation and variant annotation to describe inheritance, allelic requirement, and both sequence and functional consequences of a variant is currently not standardized. There is considerable discrepancy in the literature and across clinical variant reporting in the derivation and application of terms. Here, we standardize the terminology for the characterization of disease-gene relationships to facilitate harmonized global curation and to support variant classification within the ACMG/AMP framework.

METHODS

Terminology for inheritance, allelic requirement, and both structural and functional consequences of a variant used by Gene Curation Coalition members and partner organizations was collated and reviewed. Harmonized terminology with definitions and use examples was created, reviewed, and validated.

RESULTS

We present a standardized terminology to describe gene-disease relationships, and to support variant annotation. We demonstrate application of the terminology for classification of variation in the ACMG SF 2.0 genes recommended for reporting of secondary findings. Consensus terms were agreed and formalized in both Sequence Ontology (SO) and Human Phenotype Ontology (HPO) ontologies. Gene Curation Coalition member groups intend to use or map to these terms in their respective resources.

CONCLUSION

The terminology standardization presented here will improve harmonization, facilitate the pooling of curation datasets across international curation efforts and, in turn, improve consistency in variant classification and genetic test interpretation.

Parallelized engineering of mutational models using piggyBac transposon delivery of CRISPR libraries

New technologies and large-cohort studies have enabled novel variant discovery and association at unprecedented scale, yet functional characterization of these variants remains paramount to deciphering disease mechanisms. Approaches that facilitate parallelized genome editing of cells of interest or induced pluripotent stem cells (iPSCs) have become critical tools toward this goal. Here, we developed an approach that incorporates libraries of CRISPR-Cas9 guide RNAs (gRNAs) together with inducible Cas9 into a piggyBac (PB) transposon system to engineer dozens to hundreds of genomic variants in parallel against isogenic cellular backgrounds. This method empowers loss-of-function (LoF) studies through the introduction of insertions or deletions (indels) and copy-number variants (CNVs), though generating specific nucleotide changes is possible with prime editing. The ability to rapidly establish high-quality mutational models at scale will facilitate the development of isogenic cellular collections and catalyze comparative functional genomic studies investigating the roles of hundreds of genes and mutations in development and disease.

Diagnostic genome sequencing improves diagnostic yield: a prospective single-centre study in 1000 patients with inherited eye diseases

PURPOSE

Genome sequencing (GS) is expected to reduce the diagnostic gap in rare disease genetics. We aimed to evaluate a scalable framework for genome-based analyses 'beyond the exome' in regular care of patients with inherited retinal degeneration (IRD) or inherited optic neuropathy (ION).

METHODS

PCR-free short-read GS was performed on 1000 consecutive probands with IRD/ION in routine diagnostics. Complementary whole-blood RNA-sequencing (RNA-seq) was done in a subset of 74 patients. An open-source bioinformatics analysis pipeline was optimised for structural variant (SV) calling and combined RNA/DNA variation interpretation.

RESULTS

A definite genetic diagnosis was established in 57.4% of cases. For another 16.7%, variants of uncertain significance were identified in known IRD/ION genes, while the underlying genetic cause remained unresolved in 25.9%. SVs or alterations in non-coding genomic regions made up for 12.7% of the observed variants. The RNA-seq studies supported the classification of two unclear variants.

CONCLUSION

GS is feasible in clinical practice and reliably identifies causal variants in a substantial proportion of individuals. GS extends the diagnostic yield to rare non-coding variants and enables precise determination of SVs. The added diagnostic value of RNA-seq is limited by low expression levels of the major IRD disease genes in blood.

Functional genomics and small molecules in mitochondrial neurodevelopmental disorders

Mitochondria are critical for brain development and homeostasis. Therefore, pathogenic variation in the mitochondrial or nuclear genome which disrupts mitochondrial function frequently results in developmental disorders and neurodegeneration at the organismal level. Large-scale application of genome-wide technologies to individuals with mitochondrial diseases has dramatically accelerated identification of mitochondrial disease-gene associations in humans. Multi-omic and high-throughput studies involving transcriptomics, proteomics, metabolomics, and saturation genome editing are providing deeper insights into the functional consequence of mitochondrial genomic variation. Integration of deep phenotypic and genomic data through allelic series continues to uncover novel mitochondrial functions and permit mitochondrial gene function dissection on an unprecedented scale. Finally, mitochondrial disease-gene associations illuminate disease mechanisms and thereby direct therapeutic strategies involving small molecules and RNA-DNA therapeutics. This review summarizes progress in functional genomics and small molecule therapeutics in mitochondrial neurodevelopmental disorders.

Assessing the causal relationship between COVID-19 and post-COVID-19 syndrome: A Mendelian randomisation study

Background

In the aftermath of the coronavirus disease 2019 (COVID-19) pandemic, we sought to explore the causal association between COVID-19 and 17 prevalent post-COVID-19 syndrome (PCS) symptoms using Mendelian randomisation (MR) methodology.

Methods

We used 22 extensive genome-wide association study (GWAS) data sets, incorporating genetic variants as instrumental variables. Univariate Mendelian randomisation (UVMR) analyses involved 15 single nucleotide polymorphisms (SNPs) for COVID-19 patients, 33 for hospitalised COVID-19 patients, and 29 for patients with severe respiratory symptoms due to COVID-19. Furthermore, we further used multivariable Mendelian randomisation (MVMR) analyses based on 93 SNPs for COVID-19 patients, 105 for hospitalised COVID-19 patients, and 99 for patients with severe respiratory symptoms due to COVID-19. With these analyses, we aimed to assess the causal associations between varying levels of COVID-19 infection and 17 prevalent PCS symptoms while accounting for the influence of educational and income levels.

Results

UVMR analysis identified potential causal effects of COVID-19 genetic susceptibility on myalgia and pain in various regions. Hospitalised COVID-19 was potentially linked to erectile dysfunction and alopecia areata. Very severe respiratory confirmed patients exhibited increased pain and tobacco use. Meanwhile, the MVMR analysis demonstrated a potential causal link between hospitalised COVID-19 and heart arrhythmia, and a protective effect of COVID-19 on tobacco use after adjusting for educational and income levels.

Conclusions

Our MR analysis provides compelling evidence of causal associations between genetic susceptibility to COVID-19 and specific PCS symptoms, in which educational and income levels play a mediating role. These findings shed light on PCS pathogenesis and underscore the importance of considering social factors in its management. Tailored interventions and policies are crucial for PCS-affected individuals' well-being. Further research is needed to explore the impact of social determinants on COVID-19 patients and the wider population.

Rare variants in CAPN2 increase risk for isolated hypoplastic left heart syndrome

Hypoplastic left heart syndrome (HLHS) is a severe congenital heart defect (CHD) characterized by hypoplasia of the left ventricle and aorta along with stenosis or atresia of the aortic and mitral valves. HLHS represents only ∼4%-8% of all CHDs but accounts for ∼25% of deaths. HLHS is an isolated defect (i.e., iHLHS) in 70% of families, the vast majority of which are simplex. Despite intense investigation, the genetic basis of iHLHS remains largely unknown. We performed exome sequencing on 331 families with iHLHS aggregated from four independent cohorts. A Mendelian-model-based analysis demonstrated that iHLHS was not due to single, large-effect alleles in genes previously reported to underlie iHLHS or CHD in >90% of families in this cohort. Gene-based association testing identified increased risk for iHLHS associated with variation in CAPN2 (p = 1.8 × 10-5), encoding a protein involved in functional adhesion. Functional validation studies in a vertebrate animal model (Xenopus laevis) confirmed CAPN2 is essential for cardiac ventricle morphogenesis and that in vivo loss of calpain function causes hypoplastic ventricle phenotypes and suggest that human CAPN2707C>T and CAPN21112C>T variants, each found in multiple individuals with iHLHS, are hypomorphic alleles. Collectively, our findings show that iHLHS is typically not a Mendelian condition, demonstrate that CAPN2 variants increase risk of iHLHS, and identify a novel pathway involved in HLHS pathogenesis.

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.

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 .

Detection of distant relatedness in biobanks for identification of undiagnosed carriers of a Mendelian disease variant: application to Long QT Syndrome

Rare genetic diseases are typically studied in referral populations, resulting in underdiagnosis and biased assessment of penetrance and phenotype. To address this, we developed a generalizable method of genotype inference based on distant relatedness and deployed this to identify undiagnosed Type 5 Long QT Syndrome (LQT5) rare variant carriers in a non-referral population. We identified 9 LQT5 families referred to a single specialty clinic, each carrying p.Asp76Asn, the most common LQT5 variant. We uncovered recent common ancestry and a single shared haplotype among probands. Application to a non-referral population of 69,819 BioVU biobank subjects identified 22 additional subjects sharing this haplotype, subsequently confirmed to carry p.Asp76Asn. Referral and non-referral carriers had prolonged QTc compared to controls, and, among carriers, QTc polygenic score additively associated with QTc prolongation. Thus, our novel analysis of shared chromosomal segments identified undiagnosed cases of genetic disease and refined the understanding of LQT5 penetrance and phenotype.

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.

Meta-analysis of the diagnostic and clinical utility of exome and genome sequencing in pediatric and adult patients with rare diseases across diverse populations

PURPOSE

This meta-analysis aims to compare the diagnostic and clinical utility of exome sequencing (ES) vs genome sequencing (GS) in pediatric and adult patients with rare diseases across diverse populations.

METHODS

A meta-analysis was conducted to identify studies from 2011 to 2021.

RESULTS

One hundred sixty-one studies across 31 countries/regions were eligible, featuring 50,417 probands of diverse populations. Diagnostic rates of ES (0.38, 95% CI 0.36-0.40) and GS (0.34, 95% CI 0.30-0.38) were similar (P = .1). Within-cohort comparison illustrated 1.2-times odds of diagnosis by GS over ES (95% CI 0.79-1.83, P = .38). GS studies discovered a higher range of novel genes than ES studies; yet, the rate of variant of unknown significance did not differ (P = .78). Among high-quality studies, clinical utility of GS (0.77, 95% CI 0.64-0.90) was higher than that of ES (0.44, 95% CI 0.30-0.58) (P < .01).

CONCLUSION

This meta-analysis provides an important update to demonstrate the similar diagnostic rates between ES and GS and the higher clinical utility of GS over ES. With the newly published recommendations for clinical interpretation of variants found in noncoding regions of the genome and the trend of decreasing variant of unknown significance and GS cost, it is expected that GS will be more widely used in clinical settings.

Whole-exome sequencing enables rapid and prenatal diagnosis of inherited skin disorders

BACKGROUND

Genodermatoses are a broad group of disorders with specific or non-specific skin-based phenotypes, most of which are monogenic disorders. However, it's a great challenge to make a precise molecular diagnosis because of the clinical heterogeneity. The genetic and clinical heterogeneity brings great challenges for diagnosis in dermatology. The whole exome sequencing (WES) not only expedites the discovery of the genetic variations, but also contributes to genetic counselling and prenatal diagnosis.

MATERIALS AND METHODS

Followed by the initial clinical and pathological diagnosis, genetic variations were identified by WES. The pathogenicity of the copy number variations (CNVs) and single-nucleotide variants (SNVs) were evaluated according to ACMG guidelines. Candidate pathogenic SNVs were confirmed by Sanger sequencing in the proband and the family members.

RESULTS

Totally 25 cases were recruited. Nine novel variations, including c.5546G > C and c.1457delC in NF1, c.6110G > T in COL7A1, c.2127delG in TSC1, c.1445 C > A and c.1265G > A in TYR, Xp22.31 deletion in STS, c.908 C > T in ATP2A2, c.1371insC in IKBKG, and nine known ones were identified in 16 cases (64%). Prenatal diagnosis was applied in 6 pregnant women by amniocentesis, two of whom carried positive findings.

CONCLUSIONS

Our findings highlighted the value of WES as a first-tier genetic test in determining the molecular diagnosis. We also discovered the distribution of genodermatoses in this district, which provided a novel clinical dataset for dermatologists.

Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation

How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed "Tissue Risk Assessment of Causality by Expression" (TRACE), a machine learning approach to predict genes that underlie tissue-selective diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features that were inferred from heterogeneous omics datasets. Application of TRACE to 1,031 disease genes uncovered known and novel selectivity-related features, the most common of which was previously overlooked. Next, we created a catalog of tissue-associated risks for 18,927 protein-coding genes (https://netbio.bgu.ac.il/trace/). As proof-of-concept, we prioritized candidate disease genes identified in 48 rare-disease patients. TRACE ranked the verified disease gene among the patient's candidate genes significantly better than gene prioritization methods that rank by gene constraint or tissue expression. Thus, tissue selectivity combined with machine learning enhances genetic and clinical understanding of hereditary diseases.

Inborn Errors of Immunity in Hidradenitis Suppurativa Pathogenesis and Disease Burden

Hidradenitis suppurativa (HS), also known as Verneuil's disease and acne inversa, is a prevalent, debilitating, and understudied inflammatory skin disease. It is marked by repeated bouts of pathological inflammation causing pain, hyperplasia, aberrant healing, and fibrosis. HS is difficult to manage and has many unmet medical needs. There is clinical and pharmacological evidence for extensive etiological heterogeneity with HS, suggesting that this clinical diagnosis is capturing a spectrum of disease entities. Human genetic studies provide robust insight into disease pathogenesis. They also can be used to resolve etiological heterogeneity and to identify drug targets. However, HS has not been extensively investigated with well-powered genetic studies. Here, we review what is known about its genetic architecture. We identify overlap in molecular, cellular, and clinical features between HS and inborn errors of immunity (IEI). This evidence indicates that HS may be an underrecognized component of IEI and suggests that undiagnosed IEI are present in HS cohorts. Inborn errors of immunity represent a salient opportunity for rapidly resolving the immunological landscape of HS pathogenesis, for prioritizing drug repurposing studies, and for improving the clinical management of HS.

Poison exon annotations improve the yield of clinically relevant variants in genomic diagnostic testing

PURPOSE

Neurodevelopmental disorders (NDDs) often result from rare genetic variation, but genomic testing yield for NDDs remains below 50%, suggesting that clinically relevant variants may be missed by standard analyses. Here, we analyze "poison exons" (PEs), which are evolutionarily conserved alternative exons often absent from standard gene annotations. Variants that alter PE inclusion can lead to loss of function and may be highly penetrant contributors to disease.

METHODS

We curated published RNA sequencing data from developing mouse cortex to define 1937 conserved PE regions potentially relevant to NDDs, and we analyzed variants found by genome sequencing in multiple NDD cohorts.

RESULTS

Across 2999 probands, we found 6 novel clinically relevant variants in PE regions. Five of these variants are in genes that are part of the sodium voltage-gated channel alpha subunit family (SCN1A, SCN2A, and SCN8A), which is associated with epilepsies. One variant is in SNRPB, associated with cerebrocostomandibular syndrome. These variants have moderate to high computational impact assessments, are absent from population variant databases, and in genes with gene-phenotype associations consistent with each probands reported features.

CONCLUSION

With a very minimal increase in variant analysis burden (average of 0.77 variants per proband), annotation of PEs can improve diagnostic yield for NDDs and likely other congenital conditions.

Role of non-coding variants in cardiovascular disease

Cardiovascular diseases (CVDs) constitute one of the significant causes of death worldwide. Different pathological states are linked to CVDs, which despite interventions and treatments, still have poor prognoses. The genetic component, as a beneficial tool in the risk stratification of CVD development, plays a role in the pathogenesis of this group of diseases. The emergence of genome-wide association studies (GWAS) have led to the identification of non-coding parts associated with cardiovascular traits and disorders. Variants located in functional non-coding regions, including promoters/enhancers, introns, miRNAs and 5'/3' UTRs, account for 90% of all identified single-nucleotide polymorphisms associated with CVDs. Here, for the first time, we conducted a comprehensive review on the reported non-coding variants for different CVDs, including hypercholesterolemia, cardiomyopathies, congenital heart diseases, thoracic aortic aneurysms/dissections and coronary artery diseases. Additionally, we present the most commonly reported genes involved in each CVD. In total, 1469 non-coding variants constitute most reports on familial hypercholesterolemia, hypertrophic cardiomyopathy and dilated cardiomyopathy. The application and identification of non-coding variants are beneficial for the genetic diagnosis and better therapeutic management of CVDs.