Lethal phenotypes in Mendelian disorders.

PURPOSE: Existing resources that characterise the essentiality status of genes are based on either proliferation assessment in human cell lines, viability evaluation in mouse knockouts, or constraint metrics derived from human population sequencing studies. Several repositories document phenotypic annotations for rare disorders, however there is a lack of comprehensive reporting on lethal phenotypes. METHODS: We queried Online Mendelian Inheritance in Man for terms related to lethality and classified all Mendelian genes according to the earliest age of death recorded for the associated disorders, from prenatal death to no reports of premature death. We characterised the genes across these lethality categories, examined the evidence on viability from mouse models and explored how this information could be used for novel gene discovery. RESULTS: We developed the Lethal Phenotypes Portal to showcase this curated catalogue of human essential genes. Differences in the mode of inheritance, physiological systems affected and disease class were found for genes in different lethality categories as well as discrepancies between the lethal phenotypes observed in mouse and human. CONCLUSION: We anticipate that this resource will aid clinicians in the diagnosis of early lethal conditions and assist researchers in investigating the properties that make these genes essential for human development.

Lethal phenotypes in Mendelian disorders.

Essential genes are those whose function is required for cell proliferation and/or organism survival. A gene's intolerance to loss-of-function can be allocated within a spectrum, as opposed to being considered a binary feature, since this function might be essential at different stages of development, genetic backgrounds or other contexts. Existing resources that collect and characterise the essentiality status of genes are based on either proliferation assessment in human cell lines, embryonic and postnatal viability evaluation in different model organisms, and gene metrics such as intolerance to variation scores derived from human population sequencing studies. There are also several repositories available that document phenotypic annotations for rare disorders in humans such as the Online Mendelian Inheritance in Man (OMIM) and the Human Phenotype Ontology (HPO) knowledgebases. This raises the prospect of being able to use clinical data, including lethality as the most severe phenotypic manifestation, to further our characterisation of gene essentiality. Here we queried OMIM for terms related to lethality and classified all Mendelian genes into categories, according to the earliest age of death recorded for the associated disorders, from prenatal death to no reports of premature death. To showcase this curated catalogue of human essential genes, we developed the Lethal Phenotypes Portal (https://lethalphenotypes.research.its.qmul.ac.uk), where we also explore the relationships between these lethality categories, constraint metrics and viability in cell lines and mouse. Further analysis of the genes in these categories reveals differences in the mode of inheritance of the associated disorders, physiological systems affected and disease class. We highlight how the phenotypic similarity between genes in the same lethality category combined with gene family/group information can be used for novel disease gene discovery. Finally, we explore the overlaps and discrepancies between the lethal phenotypes observed in mouse and human and discuss potential explanations that include differences in transcriptional regulation, functional compensation and molecular disease mechanisms. We anticipate that this resource will aid clinicians in the diagnosis of early lethal conditions and assist researchers in investigating the properties that make these genes essential for human development.

Improving prenatal diagnosis through standards and aggregation.

Advances in sequencing and imaging technologies enable enhanced assessment in the prenatal space, with a goal to diagnose and predict the natural history of disease, to direct targeted therapies, and to implement clinical management, including transfer of care, election of supportive care, and selection of surgical interventions. The current lack of standardization and aggregation stymies variant interpretation and gene discovery, which hinders the provision of prenatal precision medicine, leaving clinicians and patients without an accurate diagnosis. With large amounts of data generated, it is imperative to establish standards for data collection, processing, and aggregation. Aggregated and homogeneously processed genetic and phenotypic data permits dissection of the genomic architecture of prenatal presentations of disease and provides a dataset on which data analysis algorithms can be tuned to the prenatal space. Here we discuss the importance of generating aggregate data sets and how the prenatal space is driving the development of interoperable standards and phenotype-driven tools.

The Monarch Initiative in 2024: an analytic platform integrating phenotypes, genes and diseases across species.

Bridging the gap between genetic variations, environmental determinants, and phenotypic outcomes is critical for supporting clinical diagnosis and understanding mechanisms of diseases. It requires integrating open data at a global scale. The Monarch Initiative advances these goals by developing open ontologies, semantic data models, and knowledge graphs for translational research. The Monarch App is an integrated platform combining data about genes, phenotypes, and diseases across species. Monarch's APIs enable access to carefully curated datasets and advanced analysis tools that support the understanding and diagnosis of disease for diverse applications such as variant prioritization, deep phenotyping, and patient profile-matching. We have migrated our system into a scalable, cloud-based infrastructure; simplified Monarch's data ingestion and knowledge graph integration systems; enhanced data mapping and integration standards; and developed a new user interface with novel search and graph navigation features. Furthermore, we advanced Monarch's analytic tools by developing a customized plugin for OpenAI's ChatGPT to increase the reliability of its responses about phenotypic data, allowing us to interrogate the knowledge in the Monarch graph using state-of-the-art Large Language Models. The resources of the Monarch Initiative can be found at monarchinitiative.org and its corresponding code repository at github.com/monarch-initiative/monarch-app.

CYP2C19 Genotype Prevalence and Association With Recurrent Myocardial Infarction in British-South Asians Treated With Clopidogrel.

Background: Cytochrome P450 family 2 subfamily C member 19 (CYP2C19) is a hepatic enzyme involved in the metabolism of clopidogrel from a prodrug to its active metabolite. Prior studies of genetic polymorphisms in CYP2C19 and their relationship with clinical efficacy have not included South Asian populations. Objectives: The objective of this study was to assess prevalence of common CYP2C19 genotype polymorphisms in a British-South Asian population and correlate these with recurrent myocardial infarction risk in participants prescribed clopidogrel. Methods: The Genes & Health cohort of British Bangladeshi and Pakistani ancestry participants were studied. CYP2C19 diplotypes were assessed using array data. Multivariable logistic regression was used to test for association between genetically inferred CYP2C19 metabolizer status and recurrent myocardial infarction, controlling for known cardiovascular disease risk factors, percutaneous coronary intervention, age, sex, and population stratification. Results: Genes & Health cohort participants (N = 44,396) have a high prevalence (57%) of intermediate or poor CYP2C19 metabolizers, with at least 1 loss-of-function CYP2C19 allele. The prevalence of poor metabolizers carrying 2 CYP2C19 loss-of-function alleles is 13%, which is higher than that in previously studied European (2.4%) and Central/South Asian populations (8.2%). Sixty-nine percent of the cohort who were diagnosed with an acute myocardial infarction were prescribed clopidogrel. Poor metabolizers were significantly more likely to have a recurrent myocardial infarction (OR: 3.1; P = 0.019). Conclusions: A pharmacogenomic-driven approach to clopidogrel prescribing has the potential to impact significantly on clinical management and outcomes in individuals of Bangladeshi and Pakistani ancestry.

Factor V Leiden, estrogen, and multimorbidity association with venous thromboembolism in a British-South Asian cohort.

Multimorbidity, estrogen use, and Factor V Leiden (FVL) are known independent risk factors for venous thromboembolism (VTE). This cross-sectional analysis of women in the Genes & Health British-South Asian cohort (N 20,048) linked the F5 SNP rs6025 with estrogen prescribing data and VTE events. Multivariable logistic regression was used to test the association between estrogen use, FVL, common medical co-morbidities, and VTE. Estrogens were prescribed to 30% of women. 3% of participants were FVL carriers. 439 participants had a VTE event (2.2%), and VTE prevalence increased with obesity, hypertension, dyslipidemia, chronic kidney disease, estrogen use, and in the presence of FVL. One medical condition above was independently associated with VTE with an OR 1.6 (CI 1.2-2.0, p 0.001); two medical conditions OR 2.7 (CI 2.0-3.7, p < 0.001); three OR 5.3 (CI 3.8-7.4, p < 0.001); four OR 8.1 (CI 4.9-13.0, p < 0.001). Multimorbidity and FVL compound risk of VTE with estrogen use.

Missense variants in RPH3A cause defects in excitatory synaptic function and are associated with a clinically variable neurodevelopmental disorder.

PURPOSE: RPH3A encodes a protein involved in the stabilization of GluN2A subunit of N-methyl-D-aspartate (NMDA)-type glutamate receptors at the cell surface, forming a complex essential for synaptic plasticity and cognition. We investigated the effect of variants in RPH3A in patients with neurodevelopmental disorders. METHODS: By using trio-based exome sequencing, GeneMatcher, and screening of 100,000 Genomes Project data, we identified 6 heterozygous variants in RPH3A. In silico and in vitro models, including rat hippocampal neuronal cultures, have been used to characterize the effect of the variants. RESULTS: Four cases had a neurodevelopmental disorder with untreatable epileptic seizures [p.(Gln73His)dn; p.(Arg209Lys); p.(Thr450Ser)dn; p.(Gln508His)], and 2 cases [p.(Arg235Ser); p.(Asn618Ser)dn] showed high-functioning autism spectrum disorder. Using neuronal cultures, we demonstrated that p.(Thr450Ser) and p.(Asn618Ser) reduce the synaptic localization of GluN2A; p.(Thr450Ser) also increased the surface levels of GluN2A. Electrophysiological recordings showed increased GluN2A-dependent NMDA ionotropic glutamate receptor currents for both variants and alteration of postsynaptic calcium levels. Finally, expression of the Rph3AThr450Ser variant in neurons affected dendritic spine morphology. CONCLUSION: Overall, we provide evidence that missense gain-of-function variants in RPH3A increase GluN2A-containing NMDA ionotropic glutamate receptors at extrasynaptic sites, altering synaptic function and leading to a clinically variable neurodevelopmental presentation ranging from untreatable epilepsy to autism spectrum disorder.

Stretch-activated ion channel TMEM63B associates with developmental and epileptic encephalopathies and progressive neurodegeneration.

By converting physical forces into electrical signals or triggering intracellular cascades, stretch-activated ion channels allow the cell to respond to osmotic and mechanical stress. Knowledge of the pathophysiological mechanisms underlying associations of stretch-activated ion channels with human disease is limited. Here, we describe 17 unrelated individuals with severe early-onset developmental and epileptic encephalopathy (DEE), intellectual disability, and severe motor and cortical visual impairment associated with progressive neurodegenerative brain changes carrying ten distinct heterozygous variants of TMEM63B, encoding for a highly conserved stretch-activated ion channel. The variants occurred de novo in 16/17 individuals for whom parental DNA was available and either missense, including the recurrent p.Val44Met in 7/17 individuals, or in-frame, all affecting conserved residues located in transmembrane regions of the protein. In 12 individuals, hematological abnormalities co-occurred, such as macrocytosis and hemolysis, requiring blood transfusions in some. We modeled six variants (p.Val44Met, p.Arg433His, p.Thr481Asn, p.Gly580Ser, p.Arg660Thr, and p.Phe697Leu), each affecting a distinct transmembrane domain of the channel, in transfected Neuro2a cells and demonstrated inward leak cation currents across the mutated channel even in isotonic conditions, while the response to hypo-osmotic challenge was impaired, as were the Ca2+ transients generated under hypo-osmotic stimulation. Ectopic expression of the p.Val44Met and p.Gly580Cys variants in Drosophila resulted in early death. TMEM63B-associated DEE represents a recognizable clinicopathological entity in which altered cation conductivity results in a severe neurological phenotype with progressive brain damage and early-onset epilepsy associated with hematological abnormalities in most individuals.

A non-coding insertional mutation of Grhl2 causes gene over-expression and multiple structural anomalies including cleft palate, spina bifida and encephalocele.

Orofacial clefts, including cleft lip and palate (CL/P) and neural tube defects (NTDs) are among the most common congenital anomalies, but knowledge of the genetic basis of these conditions remains incomplete. The extent to which genetic risk factors are shared between CL/P, NTDs and related anomalies is also unclear. While identification of causative genes has largely focused on coding and loss of function mutations, it is hypothesized that regulatory mutations account for a portion of the unidentified heritability. We found that excess expression of Grainyhead-like 2 (Grhl2) causes not only spinal NTDs in Axial defects (Axd) mice but also multiple additional defects affecting the cranial region. These include orofacial clefts comprising midline cleft lip and palate and abnormalities of the craniofacial bones and frontal and/or basal encephalocele, in which brain tissue herniates through the cranium or into the nasal cavity. To investigate the causative mutation in the Grhl2Axd strain, whole genome sequencing identified an approximately 4 kb LTR retrotransposon insertion that disrupts the non-coding regulatory region, lying approximately 300 base pairs upstream of the 5' UTR. This insertion also lies within a predicted long non-coding RNA, oriented on the reverse strand, which like Grhl2 is over-expressed in Axd (Grhl2Axd) homozygous mutant embryos. Initial analysis of the GRHL2 upstream region in individuals with NTDs or cleft palate revealed rare or novel variants in a small number of cases. We hypothesize that mutations affecting the regulation of GRHL2 may contribute to craniofacial anomalies and NTDs in humans.

CYP2C19 loss-of-function alleles are not associated with higher prevalence of gastrointestinal bleeds in those who have been prescribed antidepressants: Analysis in a British-South Asian cohort.

AIMS: CYP2C19 is a hepatic enzyme involved in the metabolism of antidepressants associated with increased gastrointestinal bleed (GIB) risk. The aim of our study was to explore a possible association between loss-of-function CYP2C19 genotypes and GIB in South Asian ancestry participants prescribed antidepressants. METHODS: Genes & Health participants with a record in Barts Health NHS Trust (N 22 753) were studied using a cross-sectional approach. CYP2C19 diplotypes were assessed and metabolizer type inferred from consortia guidance. Fisher's exact test was used to compare the prevalence of GIB in different metabolizer categories. Multivariable regression was used to test for association between antidepressant prescriptions and GIB, and between CYP2C19 metabolizer state and GIB in the subcohort prescribed antidepressants. RESULTS: Antidepressants were frequently prescribed (47%, N = 10 612). A total of 864 participants (4%) had a GIB; 534 (62%) had been prescribed a CYP2C19 metabolized antidepressant. There was an independent association between antidepressant prescriptions and GIB events (odds ratio 1.8, confidence interval 1.5-2.0, P < 0.0001). There was no relationship between CYP2C19 inferred poor (P 0.56) or intermediate (P 0.53) metabolizer status and GIB in those prescribed an antidepressant in unadjusted analysis. A multivariable logistic regression model did not show an independent association between poor (P 0.54) or intermediate (P 0.62) CYP2C19 metabolizers and GIB in the subcohort prescribed antidepressants. CONCLUSIONS: CYP2C19 dependent antidepressants are associated with increased GIB prevalence. GIB appeared independent from CYP2C19 metabolizer genotype in individuals who had been prescribed antidepressants. Precision dosing based on CYP2C19 genetic information alone is unlikely to reduce GIB prevalence.

SLCO1B1*5 is protective against non-senile cataracts in cohort prescribed statins: analysis in a British-South Asian cohort.

BACKGROUND: Reported association between statin use and cataract risk is controversial. The SLCO1B1 gene encodes a transport protein responsible for statin clearance. The aim of this study was to investigate a possible association between the SLCO1B1*5 reduced function variant and cataract risk in statin users of South Asian ethnicity. METHODS: The Genes & Health cohort consists of British-Bangladeshi and British-Pakistani participants from East London, Manchester and Bradford, UK. SLCO1B1*5 genotype was assessed with the Illumina GSAMD-24v3-0-EA chip. Medication data from primary care health record linkage was used to compare those who had regularly used statins compared to those who had not. Multivariable logistic regression was used to test for association between statin use and cataracts, adjusting for population characteristics and potential confounders in 36,513 participants. Multivariable logistic regression was used to test association between SLCO1B1*5 heterozygotes or homozygotes and cataracts, in subgroups having been regularly prescribed statins versus not. RESULTS: Statins were prescribed to 35% (12,704) of participants (average age 41 years old, 45% male). Non-senile cataract was diagnosed in 5% (1686) of participants. An apparent association between statins and non-senile cataract (12% in statin users and 0.8% in non-statin users) was negated by inclusion of confounders. In those prescribed a statin, presence of the SLCO1B1*5 genotype was independently associated with a decreased risk of non-senile cataract (OR 0.7 (CI 0.5-0.9, p 0.007)). CONCLUSIONS: Our findings suggest that there is no independent association between statin use and non-senile cataract risk after adjusting for confounders. Among statin users, the SLCO1B1*5 genotype is associated with a 30% risk reduction of non-senile cataracts. Stratification of on-drug cohorts by validated pharmacogenomic variants is a useful tool to support or repudiate adverse drug events in observational cohorts.

The Ontology of Biological Attributes (OBA)-computational traits for the life sciences.

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focussed measurable trait data. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

Essential genes: a cross-species perspective.

Protein coding genes exhibit different degrees of intolerance to loss-of-function variation. The most intolerant genes, whose function is essential for cell or/and organism survival, inform on fundamental biological processes related to cell proliferation and organism development and provide a window on the molecular mechanisms of human disease. Here we present a brief overview of the resources and knowledge gathered around gene essentiality, from cancer cell lines to model organisms to human development. We outline the implications of using different sources of evidence and definitions to determine which genes are essential and highlight how information on the essentiality status of a gene can inform novel disease gene discovery and therapeutic target identification.

Knockout mice are an important tool for human monogenic heart disease studies.

Mouse models are relevant to studying the functionality of genes involved in human diseases; however, translation of phenotypes can be challenging. Here, we investigated genes related to monogenic forms of cardiovascular disease based on the Genomics England PanelApp and aligned them to International Mouse Phenotyping Consortium (IMPC) data. We found 153 genes associated with cardiomyopathy, cardiac arrhythmias or congenital heart disease in humans, of which 151 have one-to-one mouse orthologues. For 37.7% (57/151), viability and heart data captured by electrocardiography, transthoracic echocardiography, morphology and pathology from embryos and young adult mice are available. In knockout mice, 75.4% (43/57) of these genes showed non-viable phenotypes, whereas records of prenatal, neonatal or infant death in humans were found for 35.1% (20/57). Multisystem phenotypes are common, with 58.8% (20/34) of heterozygous (homozygous lethal) and 78.6% (11/14) of homozygous (viable) mice showing cardiovascular, metabolic/homeostasis, musculoskeletal, hematopoietic, nervous system and/or growth abnormalities mimicking the clinical manifestations observed in patients. These IMPC data are critical beyond cardiac diagnostics given their multisystemic nature, allowing detection of abnormalities across physiological systems and providing a valuable resource to understand pleiotropic effects.

The Ontology of Biological Attributes (OBA) - Computational Traits for the Life Sciences.

Existing phenotype ontologies were originally developed to represent phenotypes that manifest as a character state in relation to a wild-type or other reference. However, these do not include the phenotypic trait or attribute categories required for the annotation of genome-wide association studies (GWAS), Quantitative Trait Loci (QTL) mappings or any population-focused measurable trait data. Moreover, variations in gene expression in response to environmental disturbances even without any genetic alterations can also be associated with particular biological attributes. The integration of trait and biological attribute information with an ever increasing body of chemical, environmental and biological data greatly facilitates computational analyses and it is also highly relevant to biomedical and clinical applications. The Ontology of Biological Attributes (OBA) is a formalised, species-independent collection of interoperable phenotypic trait categories that is intended to fulfil a data integration role. OBA is a standardised representational framework for observable attributes that are characteristics of biological entities, organisms, or parts of organisms. OBA has a modular design which provides several benefits for users and data integrators, including an automated and meaningful classification of trait terms computed on the basis of logical inferences drawn from domain-specific ontologies for cells, anatomical and other relevant entities. The logical axioms in OBA also provide a previously missing bridge that can computationally link Mendelian phenotypes with GWAS and quantitative traits. The term components in OBA provide semantic links and enable knowledge and data integration across specialised research community boundaries, thereby breaking silos.

Functional genomics provide key insights to improve the diagnostic yield of hereditary ataxia.

Improvements in functional genomic annotation have led to a critical mass of neurogenetic discoveries. This is exemplified in hereditary ataxia, a heterogeneous group of disorders characterised by incoordination from cerebellar dysfunction. Associated pathogenic variants in more than 300 genes have been described, leading to a detailed genetic classification partitioned by age-of-onset. Despite these advances, up to 75% of patients with ataxia remain molecularly undiagnosed even following whole genome sequencing, as exemplified in the 100 000 Genomes Project. This study aimed to understand whether we can improve our knowledge of the genetic architecture of hereditary ataxia by leveraging functional genomic annotations, and as a result, generate insights and strategies that raise the diagnostic yield. To achieve these aims, we used publicly-available multi-omics data to generate 294 genic features, capturing information relating to a gene's structure, genetic variation, tissue-specific, cell-type-specific and temporal expression, as well as protein products of a gene. We studied these features across genes typically causing childhood-onset, adult-onset or both types of disease first individually, then collectively. This led to the generation of testable hypotheses which we investigated using whole genome sequencing data from up to 2182 individuals presenting with ataxia and 6658 non-neurological probands recruited in the 100 000 Genomes Project. Using this approach, we demonstrated a high short tandem repeat (STR) density within childhood-onset genes suggesting that we may be missing pathogenic repeat expansions within this cohort. This was verified in both childhood- and adult-onset ataxia patients from the 100 000 Genomes Project who were unexpectedly found to have a trend for higher repeat sizes even at naturally-occurring STRs within known ataxia genes, implying a role for STRs in pathogenesis. Using unsupervised analysis, we found significant similarities in genomic annotation across the gene panels, which suggested adult- and childhood-onset patients should be screened using a common diagnostic gene set. We tested this within the 100 000 Genomes Project by assessing the burden of pathogenic variants among childhood-onset genes in adult-onset patients and vice versa. This demonstrated a significantly higher burden of rare, potentially pathogenic variants in conventional childhood-onset genes among individuals with adult-onset ataxia. Our analysis has implications for the current clinical practice in genetic testing for hereditary ataxia. We suggest that the diagnostic rate for hereditary ataxia could be increased by removing the age-of-onset partition, and through a modified screening for repeat expansions in naturally-occurring STRs within known ataxia-associated genes, in effect treating these regions as candidate pathogenic loci.

The International Mouse Phenotyping Consortium: comprehensive knockout phenotyping underpinning the study of human disease.

The International Mouse Phenotyping Consortium (IMPC; https://www.mousephenotype.org/) web portal makes available curated, integrated and analysed knockout mouse phenotyping data generated by the IMPC project consisting of 85M data points and over 95,000 statistically significant phenotype hits mapped to human diseases. The IMPC portal delivers a substantial reference dataset that supports the enrichment of various domain-specific projects and databases, as well as the wider research and clinical community, where the IMPC genotype-phenotype knowledge contributes to the molecular diagnosis of patients affected by rare disorders. Data from 9,000 mouse lines and 750 000 images provides vital resources enabling the interpretation of the ignorome, and advancing our knowledge on mammalian gene function and the mechanisms underlying phenotypes associated with human diseases. The resource is widely integrated and the lines have been used in over 4,600 publications indicating the value of the data and the materials.

Discovery and systematic characterization of risk variants and genes for coronary artery disease in over a million participants.

The discovery of genetic loci associated with complex diseases has outpaced the elucidation of mechanisms of disease pathogenesis. Here we conducted a genome-wide association study (GWAS) for coronary artery disease (CAD) comprising 181,522 cases among 1,165,690 participants of predominantly European ancestry. We detected 241 associations, including 30 new loci. Cross-ancestry meta-analysis with a Japanese GWAS yielded 38 additional new loci. We prioritized likely causal variants using functionally informed fine-mapping, yielding 42 associations with less than five variants in the 95% credible set. Similarity-based clustering suggested roles for early developmental processes, cell cycle signaling and vascular cell migration and proliferation in the pathogenesis of CAD. We prioritized 220 candidate causal genes, combining eight complementary approaches, including 123 supported by three or more approaches. Using CRISPR-Cas9, we experimentally validated the effect of an enhancer in MYO9B, which appears to mediate CAD risk by regulating vascular cell motility. Our analysis identifies and systematically characterizes >250 risk loci for CAD to inform experimental interrogation of putative causal mechanisms for CAD.

Mendelian gene identification through mouse embryo viability screening.

BACKGROUND: The diagnostic rate of Mendelian disorders in sequencing studies continues to increase, along with the pace of novel disease gene discovery. However, variant interpretation in novel genes not currently associated with disease is particularly challenging and strategies combining gene functional evidence with approaches that evaluate the phenotypic similarities between patients and model organisms have proven successful. A full spectrum of intolerance to loss-of-function variation has been previously described, providing evidence that gene essentiality should not be considered as a simple and fixed binary property. METHODS: Here we further dissected this spectrum by assessing the embryonic stage at which homozygous loss-of-function results in lethality in mice from the International Mouse Phenotyping Consortium, classifying the set of lethal genes into one of three windows of lethality: early, mid, or late gestation lethal. We studied the correlation between these windows of lethality and various gene features including expression across development, paralogy and constraint metrics together with human disease phenotypes. We explored a gene similarity approach for novel gene discovery and investigated unsolved cases from the 100,000 Genomes Project. RESULTS: We found that genes in the early gestation lethal category have distinct characteristics and are enriched for genes linked with recessive forms of inherited metabolic disease. We identified several genes sharing multiple features with known biallelic forms of inborn errors of the metabolism and found signs of enrichment of biallelic predicted pathogenic variants among early gestation lethal genes in patients recruited under this disease category. We highlight two novel gene candidates with phenotypic overlap between the patients and the mouse knockouts. CONCLUSIONS: Information on the developmental period at which embryonic lethality occurs in the knockout mouse may be used for novel disease gene discovery that helps to prioritise variants in unsolved rare disease cases.