Diagnosis Of Rare Genetic Diseases Research Articles

The application of whole-exome sequencing in the early diagnosis of rare genetic diseases in children: a study from Southeastern China.

Genetic diseases exhibit significant clinical and genetic diversity, leading to a complex and challenging diagnostic process. Exploiting novel approaches is imperative for the molecular diagnosis of genetic diseases. In this study, we utilized whole-exome sequencing (WES) to facilitate early diagnosis in patients suspected of genetic disorders. This retrospective analysis included 144 patients diagnosed by singleton-WES Trio-WES between January 2021 and December 2023. We investigated the relevance of diagnosis rates with age, clinical presentation, and sample type. Among the 144 patients, 61 were diagnosed, yielding an overall diagnostic rate of 42.36%, with Trio-WES demonstrating a significantly higher diagnostic rate of 51.43% (36/70) compared to singleton-WES at 33.78% (25/74) (p < 0.05). Global developmental delay had a diagnosis rate of 67.39%, significantly higher than muscular hypotonia at 30.43% (p < 0.01) among different clinical phenotypic groups. Autosomal dominant disorders accounted for 70.49% (43/61) of positive cases, with autosomal abnormalities being fivefold more prevalent than sex chromosome abnormalities. Notably, sex chromosome abnormalities were more prevalent in males (80%, 8/10). Furthermore, 80.56% (29/36) of pathogenic variants were identified as de novo mutations through Trio-WES. These findings highlight the effectiveness of WES in identifying genetic variants, and elucidating the molecular basis of genetic diseases, ultimately enabling early diagnosis in affected children.

Diagnosis and Screening of Velocardiofacial Syndrome by Evaluating Facial Photographs Using a Deep Learning-Based Algorithm.

Early detection of rare genetic diseases, including velocardiofacial syndrome (VCFS), is essential for patient well-being. However, their rarity and limited clinical experience of physicians make diagnosis challenging. Deep learning algorithms have emerged as promising tools for efficient and accurate diagnosis. This study investigates the use of a deep learning algorithm to develop a face recognition model for diagnosing VCFS. The study employed publicly available labeled face datasets to train the multitask cascaded convolutional neural networks (MTCNN) model. Subsequently, we examined the binary classification performance for diagnosing VCFS using the most efficient face recognition model. A total of 98 VCFS patients (920 facial photographs) and 91 non-VCFS controls (463 facial photographs) were randomly divided into training and test sets. Additionally, we analyzed whether the classification results matched the known facial phenotype of VCFS. The face recognition model demonstrated high accuracy, ranging from 94% to 99%, depending on the training dataset. The accuracy of the binary classification diagnostic model varied from 81% to 88% when evaluating with photographs taken at various angles, but reached 95% evaluating with frontal photographs only. Gradient-weighted class activation mapping heatmap revealed the high importance level of perinasal and periorbital areas, exhibiting consistency with the conventional facial phenotypes of VCFS. This study shows the feasibility and effectiveness of MTCNN-based model for detecting VCFS solely from facial photographs. The high accuracy underscores the potential of deep learning in aiding early diagnosis of rare genetic diseases, facilitating timely interventions for patient care.

Assessing the utility of large language models for phenotype-driven gene prioritization in the diagnosis of rare genetic disease

Phenotype-driven gene prioritization is fundamental to diagnosing rare genetic disorders. While traditional approaches rely on curated knowledge graphs with phenotype-gene relations, recent advancements in large language models (LLMs) promise a streamlined text-to-gene solution. In this study, we evaluated five LLMs, including two generative pre-trained transformers (GPT) series and three Llama2 series, assessing their performance across task completeness, gene prediction accuracy, and adherence to required output structures. We conducted experiments, exploring various combinations of models, prompts, phenotypic input types, and task difficulty levels. Our findings revealed that the best-performed LLM, GPT-4, achieved an average accuracy of 17.0% in identifying diagnosed genes within the top 50 predictions, which still falls behind traditional tools. However, accuracy increased with the model size. Consistent results were observed over time, as shown in the dataset curated after 2023. Advanced techniques such as retrieval-augmented generation (RAG) and few-shot learning did not improve the accuracy. Sophisticated prompts were more likely to enhance task completeness, especially in smaller models. Conversely, complicated prompts tended to decrease output structure compliance rate. LLMs also achieved better-than-random prediction accuracy with free-text input, though performance was slightly lower than with standardized concept input. Bias analysis showed that highly cited genes, such as BRCA1, TP53, and PTEN, are more likely to be predicted. Our study provides valuable insights into integrating LLMs with genomic analysis, contributing to the ongoing discussion on their utilization in clinical workflows.

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

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

A Study on Graph Centrality Measures of Different Diseases Due to DNA Sequencing

Rare genetic diseases are often caused by single-gene defects that affect various biological processes across different scales. However, it is challenging to identify the causal genes and understand the molecular mechanisms of these diseases. In this paper, we present a multiplex network approach to study the relationship between human diseases and genes. We construct a human disease network (HDN) and a human genome network (HGN) based on genotype–phenotype associations and gene interactions, respectively. We analyze 3771 rare diseases and find distinct phenotypic modules within each dimension that reflect the functional effects of gene mutations. These modules can also be used to predict novel gene candidates for unsolved rare diseases and to explore the cross-scale impact of gene perturbations. We compute various centrality measures for both networks and compare them. Our main finding is that diseases are weakly connected in the HDN, while genes are strongly connected in the HGN. This implies that diseases are relatively isolated from each other, while genes are involved in multiple biological processes. This result has implications for understanding the transmission of infectious diseases and the development of therapeutic interventions. We also show that not all diseases have the same potential to spread infections to other parts of the body, depending on their centrality in the HDN. Our results show that the phenotypic module formalism can capture the complexity of rare diseases beyond simple physical interaction networks and can be applied to study diseases arising from DNA (Deoxyribonucleic Acid) sequencing errors. This study provides a novel network-based framework for integrating multi-scale data and advancing the understanding and diagnosis of rare genetic diseases.

Prioritization of New Candidate Genes for Rare Genetic Diseases by a Disease-Aware Evaluation of Heterogeneous Molecular Networks.

Screening for pathogenic variants in the diagnosis of rare genetic diseases can now be performed on all genes thanks to the application of whole exome and genome sequencing (WES, WGS). Yet the repertoire of gene-disease associations is not complete. Several computer-based algorithms and databases integrate distinct gene-gene functional networks to accelerate the discovery of gene-disease associations. We hypothesize that the ability of every type of information to extract relevant insights is disease-dependent. We compiled 33 functional networks classified into 13 knowledge categories (KCs) and observed large variability in their ability to recover genes associated with 91 genetic diseases, as measured using efficiency and exclusivity. We developed GLOWgenes, a network-based algorithm that applies random walk with restart to evaluate KCs' ability to recover genes from a given list associated with a phenotype and modulates the prediction of new candidates accordingly. Comparison with other integration strategies and tools shows that our disease-aware approach can boost the discovery of new gene-disease associations, especially for the less obvious ones. KC contribution also varies if obtained using recently discovered genes. Applied to 15 unsolved WES, GLOWgenes proposed three new genes to be involved in the phenotypes of patients with syndromic inherited retinal dystrophies.

Microduplication 3p26.3p24.3 and 4q34.3q35.2 Microdeletion Identified in a Patient with Developmental Delay Associated with Brain Malformation.

Microdeletions and microduplications are involved in many of prenatal and postnatal cases of multiple congenital malformations (MCM), developmental delay/intellectual disability (DD/ID), and autism spectrum disorders (ASD). Molecular karyotyping analysis (MCA), performed by DNA microarray technology, is a valuable method used to elucidate the ethology of these clinical expressions, essentially contributing to the diagnosis of rare genetic diseases produced by DNA copy number variations (CNVs). MCA is frequently used as the first-tier cytogenetic diagnostic test for patients with MCM, DD/ID, or ASD due to its much higher resolution (≥10×) for detecting microdeletions and microduplications than classic cytogenetic analysis by G-banded karyotyping. Therefore, MCA can detect about 10% pathogenic genomic imbalances more than G-banded karyotyping alone. In addition, MCA using the Single Nucleotide Polymorphism-array (SNP-array) method also allows highlighting the regions of loss of heterozygosity and uniparental disomy, which are the basis of some genetic syndromes. We presented a case of a five-year-old patient, with global development delay, bilateral fronto-parietal lysencephaly, and pachygyria, for which MCA through SNP-Array led to the detection of the genetic changes, such as 3p26.3p24.3 microduplication and 4q34.3q35.2 microdeletion, which were the basis of the patient's phenotype and to the precise establishment of the diagnosis.

EP126: Frequency of MC4R pathway variants in a large US cohort of pediatric and adult patients with severe obesity

The melanocortin-4 receptor (MC4R) pathway is critical for the regulation of energy balance. Variants in a number of genes within this pathway have well-established associations with severe obesity. However, the overall frequency of rare variants in these genes has not been assessed systematically in a clinically relevant population, and it is unknown whether variant frequency differs depending on the age of ascertainment. Genetic testing can improve diagnosis of rare genetic diseases of obesity and identify patients who may benefit from targeted therapeutic intervention. We sequenced exons and intron-exon boundaries for 40 obesity-related genes in individuals with severe obesity (defined as ≥97th percentile of body mass index [BMI] for age in those <18 years old, or BMI ≥40 kg/m2 in those ≥18 years old) who participated in the US-based Uncovering Rare Obesity diagnostic genetic testing program. This next-generation sequencing panel included 11 genes associated with nonsyndromic obesity that encode proteins functioning in the MC4R pathway (POMC, PCSK1, LEPR, SRC1 [NCOA1], SH2B1, MC4R, MC3R, CPE, LEP, KSR2, and SIM1). Variants were classified as pathogenic/likely pathogenic (P/LP) or as a variant of uncertain significance (VUS) according to American College of Medical Genetics and Genomics criteria. We additionally included 1 nonrare variant, PCSK1 p.N221D, for which published functional and population studies suggests a potential contribution to obesity. Individuals with variants in 5 of these genes (POMC, PCSK1, LEPR, SRC1, and SH2B1) have previously been assessed in a trial using the MC4R agonist setmelanotide and demonstrated meaningful weight loss. Variants were classified according to their classical mode of inheritance (autosomal dominant: SRC1, SH2B1, MC4R, MC3R, KRS2, and SIM1; autosomal recessive: POMC, PCSK1, CPE, and LEP). Among 7,826 individuals, 16%, 33%, 30%, and 21% were aged <6, 6–12, 12–18, and >18 years, respectively. Among these individuals, 19% carried ≥1 rare variant in ≥1 of the 11 studied genes, including 3.6% carrying a P/LP variant and 15.4% carrying a VUS variant. Additionally, 11.9% of individuals had variants in line with mode of inheritance, including 2.1% of individuals with all P/LP variants. Restricting to the 5 genes with prior demonstrated responsiveness to setmelanotide (POMC, PCSK1, LEPR, SRC1, and SH2B1), the variant frequency was 11% for P/LP or VUS variants. An additional 6.4% of individuals carried the PCSK1 p.N221D variant. No differences in frequency were observed by age group. In our large US-based cohort of individuals with early-onset, severe obesity, 25.4% of individuals carried a potentially clinically relevant variant in ≥1 of 11 MC4R pathway–related genes, including 11.9% of individuals with variants in line with classical mode of inheritance. Genetic testing of patients with severe obesity may therefore be an important component of understanding the etiology of these patients’ phenotypes. No differences in variant frequency were observed across age groups, indicating that genetic testing for obesity-related genes is appropriate in both pediatric and adult patients with severe obesity.

EP125: Variants in obesity-related genes in a population with early-onset obesity

Genetic testing can improve the diagnosis of rare genetic diseases of obesity and identify patients who may benefit from targeted therapeutic intervention. For example, patients with genetic defects in the melanocortin 4 (MC4R) pathway may present with severe early-onset obesity and hyperphagia. Historically, however, genetic testing in patients with obesity has been limited. The Uncovering Rare Obesity diagnostic genetic testing program aims to enhance access to genetic testing for these patients.

Evaluating the performance of a clinical genome sequencing program for diagnosis of rare genetic disease, seen through the lens of craniosynostosis

PurposeGenome sequencing (GS) for diagnosis of rare genetic disease is being introduced into the clinic, but the complexity of the data poses challenges for developing pipelines with high diagnostic sensitivity. We evaluated the performance of the Genomics England 100,000 Genomes Project (100kGP) panel-based pipelines, using craniosynostosis as a test disease.MethodsGS data from 114 probands with craniosynostosis and their relatives (314 samples), negative on routine genetic testing, were scrutinized by a specialized research team, and diagnoses compared with those made by 100kGP.ResultsSixteen likely pathogenic/pathogenic variants were identified by 100kGP. Eighteen additional likely pathogenic/pathogenic variants were identified by the research team, indicating that for craniosynostosis, 100kGP panels had a diagnostic sensitivity of only 47%. Measures that could have augmented diagnoses were improved calling of existing panel genes (+18% sensitivity), review of updated panels (+12%), comprehensive analysis of de novo small variants (+29%), and copy-number/structural variants (+9%). Recent NHS England recommendations that partially incorporate these measures should achieve 85% overall sensitivity (+38%).ConclusionGS identified likely pathogenic/pathogenic variants in 29.8% of previously undiagnosed patients with craniosynostosis. This demonstrates the value of research analysis and the importance of continually improving algorithms to maximize the potential of clinical GS.

Unusual precocious diagnosis of Duchenne muscular dystrophy by SNP-array in a 6-month old baby

Microdeletions and microduplications are involved in many cases of malformations, developmental delay (DD), autism (A) and growth disturbances (GD). Molecular karyotyping (MC) performed by microarray technology is a valuable method frequently used to elucidate the etiology of these clinical expressions, essentially contributing to the diagnosis of rare genetic diseases produced by DNA copy number variations (CNVs). MC has offered a higher diagnostic yield for genetic testing of patients with unexplained DD, A and GD than a G-banded karyotype, primarily because of its higher sensitivity in detecting submicroscopic deletions and duplications. In addition, the molecular karyotype approach using the SNP-array method also allows highlighting the regions of loss of heterozygosity and uniparental disomy, which are the basis of some genetic syndromes. In this paper we will present a rare case of a 6 months old boy, referred for MC due to global developmental delay, axial hypotonia, with limbs hypertonia, heart malformation (atrial septal defect). SNP-Array analysis identified a 606,3 Kb microdeletion in the Xp21.1 region, that contains 39 exons of dystrophin gene. This finding was confirmed also with additional MLPA testing. In conclusion, it is an unusual, precocious diagnosis of Duchenne muscular dystrophy, during first year of life, referred for atypical presentation, main clinical feature being global developmental delay.

A Blockchain-Based Dynamic Consent Architecture to Support Clinical Genomic Data Sharing (ConsentChain): Proof-of-Concept Study.

BackgroundIn clinical genomics, sharing of rare genetic disease information between genetic databases and laboratories is essential to determine the pathogenic significance of variants to enable the diagnosis of rare genetic diseases. Significant concerns regarding data governance and security have reduced this sharing in practice. Blockchain could provide a secure method for sharing genomic data between involved parties and thus help overcome some of these issues.ObjectiveThis study aims to contribute to the growing knowledge of the potential role of blockchain technology in supporting the sharing of clinical genomic data by describing blockchain-based dynamic consent architecture to support clinical genomic data sharing and provide a proof-of-concept implementation, called ConsentChain, for the architecture to explore its performance.MethodsThe ConsentChain requirements were captured from a patient forum to identify security and consent concerns. The ConsentChain was developed on the Ethereum platform, in which smart contracts were used to model the actions of patients, who may provide or withdraw consent to share their data; the data creator, who collects and stores patient data; and the data requester, who needs to query and access the patient data. A detailed analysis was undertaken of the ConsentChain performance as a function of the number of transactions processed by the system.ResultsWe describe ConsentChain, a blockchain-based system that provides a web portal interface to support clinical genomic sharing. ConsentChain allows patients to grant or withdraw data requester access and allows data requesters to query and submit access to data stored in a secure off-chain database. We also developed an ontology model to represent patient consent elements into machine-readable codes to automate the consent and data access processes.ConclusionsBlockchains and smart contracts can provide an efficient and scalable mechanism to support dynamic consent functionality and address some of the barriers that inhibit genomic data sharing. However, they are not a complete answer, and a number of issues still need to be addressed before such systems can be deployed in practice, particularly in relation to verifying user credentials.

Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases

BackgroundClinical interpretation of genetic variants in the context of the patient’s phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation.MethodsWe benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed.ResultsGEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases.ConclusionsGEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review.

Whole-genome sequencing as a first-tier diagnostic framework for rare genetic diseases.

Rare diseases affect nearly 300 million people globally with most patients aged five or less. Traditional diagnostic approaches have provided much of the diagnosis; however, there are limitations. For instance, simply inadequate and untimely diagnosis adversely affects both the patient and their families. This review advocates the use of whole genome sequencing in clinical settings for diagnosis of rare genetic diseases by showcasing five case studies. These examples specifically describe the utilization of whole genome sequencing, which helped in providing relief to patients via correct diagnosis followed by use of precision medicine.

Towards similarity-based differential diagnostics for common diseases

Ontology-based phenotype profiles have been utilised for the purpose of differential diagnosis of rare genetic diseases, and for decision support in specific disease domains. Particularly, semantic similarity facilitates diagnostic hypothesis generation through comparison with disease phenotype profiles. However, the approach has not been applied for differential diagnosis of common diseases, or generalised clinical diagnostics from uncurated text-derived phenotypes. In this work, we describe the development of an approach for deriving patient phenotype profiles from clinical narrative text, and apply this to text associated with MIMIC-III patient visits. We then explore the use of semantic similarity with those text-derived phenotypes to classify primary patient diagnosis, comparing the use of patient-patient similarity and patient-disease similarity using phenotype-disease profiles previously mined from literature. We also consider a combined approach, in which literature-derived phenotypes are extended with the content of text-derived phenotypes we mined from 500 patients. The results reveal a powerful approach, showing that in one setting, uncurated text phenotypes can be used for differential diagnosis of common diseases, making use of information both inside and outside the setting. While the methods themselves should be explored for further optimisation, they could be applied to a variety of clinical tasks, such as differential diagnosis, cohort discovery, document and text classification, and outcome prediction.

Barriers and Considerations for Diagnosing Rare Diseases in Indigenous Populations.

Advances in omics and specifically genomic technologies are increasingly transforming rare disease diagnosis. However, the benefits of these advances are disproportionately experienced within and between populations, with Indigenous populations frequently experiencing diagnostic and therapeutic inequities. The International Rare Disease Research Consortium (IRDiRC) multi-stakeholder partnership has been advancing toward the vision of all people living with a rare disease receiving an accurate diagnosis, care, and available therapy within 1 year of coming to medical attention. In order to further progress toward this vision, IRDiRC has created a taskforce to explore the access barriers to diagnosis of rare genetic diseases faced by Indigenous peoples, with a view of developing recommendations to overcome them. Herein, we provide an overview of the state of play of current barriers and considerations identified by the taskforce, to further stimulate awareness of these issues and the passage toward solutions. We focus on analyzing barriers to accessing genetic services, participating in genomic research, and other aspects such as concerns about data sharing, the handling of biospecimens, and the importance of capacity building.

The Importance of Hematology Working Groups for Rare Genetic Diseases

Rationale Around the globe, it is now understood that individuals with Rare genetic Diseases routinely face limitations to getting access to diagnosis. Plans have been created to improve the requirements of the patient's communities, including access to multidisciplinary care, and proposing new corrections or amendments to existing strategies. In the gulf region, numerous proposals have been established to tackle the diagnosis and management Rare genetic Diseases. Introduction and Background Rare genetic diseases are characterized as life-long, serious conditions that debilitate or compromise life. Almost 80% of Rare genetic diseases are diagnosed during the childhood. Absence of access to these assets affect patients and their families living with complex needs that may incorporate day in and day out observing, continuous serious physical and formative medicines, remaining in the training framework, and now and then costly strength meds1. The underlying etiology may stay obscure for many patients with rare genetic diseases despite multiple investigations. patients may be assigned an incorrect diagnosis and be referred to several specialties until a correct diagnosis can be made. A correct diagnosis of rare genetic diseases may impact not only the patient's care but may have further implications for management and/or counselling of family members as well2. Also, Early diagnosis leading to early treatment to prevent long-term damage. Global Landscape3 Rarity of diseases is most commonly defined based on prevalence and incidence within a jurisdiction, or in some cases by a combination of factors based on severity and the existence or feasibility of alternative therapeutic options. Globally, the following areas of focus aimed at improving the delivery of health care for the rare disease population: - Improve access to early diagnosis, timely intervention, coordinated care for rare genetic disease patients and developing referral pathways for rare genetic disease patients to facilitate efficient care deliver - Provide educational resources and knowledge exchange opportunities to health professionals to allow them to better identify, manage and treat rare disea - support integrated peer networks, patient organizations to ensure that rare disease patients, their family/caregivers and support them to make informed decisions about their condition. The importance of having working groups for Rare genetic Diseases in Gulf region 4 - Encourage improved coordination of care and access to particular information for rare genetic diseaseses. - Create a complete system services suppliers over Gulf states. Assets and Gaps analysis 1- Early Detection and Diagnostics 5 There are resources that assist the diagnostic capacity and early detection for rare genetic diseases. · Whole Exome sequencing are used mainly for research purposes, despite the fact that their use will reduced diagnostic odyssey. · Lack of the availability of testing is dependent on budget support in some hospitals. - Timely Access to Evidence-based care 6 - Family doctors may not be well equipped to meet the needs of patients with rare hematological genetic diseases, even after diagnosis. - Poor access supportive services for adult care. - Access to genetic counseling for patients and families outside major academic hospitals7.

The mutational constraint spectrum quantified from variation in 141,456 humans

The Genome Aggregation Database (gnomAD) Consortium https://gnomad.broadinstitute.org compiled data on ˜125,000 exomes and ~15,000 whole genomes from populations around the world. This is one of seven articles (also see Refs 1–6) describing their initial discoveries, showing the power of this vast dataset, and presenting a more complete catalog of loss-of-function variants, which disrupt the encoded proteins. This is an important tool to help inform the diagnosis of rare genetic diseases in our patients. It also provides fundamental insights into which of our genes appear to be essential, by quantifying how many loss-of-function variants are present in each gene in humans.

Reference exome data for a Northern Brazilian population

Exome sequencing is widely used in the diagnosis of rare genetic diseases and provides useful variant data for analysis of complex diseases. There is not always adequate population-specific reference data to assist in assigning a diagnostic variant to a specific clinical condition. Here we provide a catalogue of variants called after sequencing the exomes of 45 babies from Rio Grande do Nord in Brazil. Sequence data were processed using an ‘intersect-then-combine’ (ITC) approach, using GATK and SAMtools to call variants. A total of 612,761 variants were identified in at least one individual in this Brazilian Cohort, including 559,448 single nucleotide variants (SNVs) and 53,313 insertion/deletions. Of these, 58,111 overlapped with nonsynonymous (nsSNVs) or splice site (ssSNVs) SNVs in dbNSFP. As an aid to clinical diagnosis of rare diseases, we used the American College of Medicine Genetics and Genomics (ACMG) guidelines to assign pathogenic/likely pathogenic status to 185 (0.32%) of the 58,111 nsSNVs and ssSNVs. Our data set provides a useful reference point for diagnosis of rare diseases in Brazil. (169 words).

Comprehensive Rare Disease Care model for screening and diagnosis of rare genetic diseases - an experience of private medical college and hospital, South India.

Rare diseases (RD) of genetic origin are raising public health concern contributing to a massive economic burden in India. Establishing Specialty Centers to bridge the RD community with apex centers is felt as a need in developing countries. Hence a Comprehensive Rare Disease Care (CRDC) model was set up at the department of pediatrics under Center for Human Genomics and Counseling at a medical college hospital in South India. The patients suspected to have genetic disease were evaluated as per the work flow of the designed model. The utilization statistics depict the outcome of this model. In the face of limited resources, it was possible to establish a functional RD unit with meticulous planning, supportive administration and trained interdisciplinary staff. A scalable prototype that could be replicated in other Medical colleges and Hospitals of India is described.