Reevaluating the Genetic Contribution of Monogenic Dilated Cardiomyopathy.

BACKGROUND: Dilated cardiomyopathy (DCM) is genetically heterogeneous, with >100 purported disease genes tested in clinical laboratories. However, many genes were originally identified based on candidate-gene studies that did not adequately account for background population variation. Here we define the frequency of rare variation in 2538 patients with DCM across protein-coding regions of 56 commonly tested genes and compare this to both 912 confirmed healthy controls and a reference population of 60 706 individuals to identify clinically interpretable genes robustly associated with dominant monogenic DCM. METHODS: We used the TruSight Cardio sequencing panel to evaluate the burden of rare variants in 56 putative DCM genes in 1040 patients with DCM and 912 healthy volunteers processed with identical sequencing and bioinformatics pipelines. We further aggregated data from 1498 patients with DCM sequenced in diagnostic laboratories and the Exome Aggregation Consortium database for replication and meta-analysis. RESULTS: Truncating variants in TTN and DSP were associated with DCM in all comparisons. Variants in MYH7, LMNA, BAG3, TNNT2, TNNC1, PLN, ACTC1, NEXN, TPM1, and VCL were significantly enriched in specific patient subsets, with the last 2 genes potentially contributing primarily to early-onset forms of DCM. Overall, rare variants in these 12 genes potentially explained 17% of cases in the outpatient clinic cohort representing a broad range of adult patients with DCM and 26% of cases in the diagnostic referral cohort enriched in familial and early-onset DCM. Although the absence of a significant excess in other genes cannot preclude a limited role in disease, such genes have limited diagnostic value because novel variants will be uninterpretable and their diagnostic yield is minimal. CONCLUSIONS: In the largest sequenced DCM cohort yet described, we observe robust disease association with 12 genes, highlighting their importance in DCM and translating into high interpretability in diagnostic testing. The other genes analyzed here will need to be rigorously evaluated in ongoing curation efforts to determine their validity as Mendelian DCM genes but have limited value in diagnostic testing in DCM at present. This data will contribute to community gene curation efforts and will reduce erroneous and inconclusive findings in diagnostic testing.

MARRVEL: Integration of Human and Model Organism Genetic Resources to Facilitate Functional Annotation of the Human Genome.

One major challenge encountered with interpreting human genetic variants is the limited understanding of the functional impact of genetic alterations on biological processes. Furthermore, there remains an unmet demand for an efficient survey of the wealth of information on human homologs in model organisms across numerous databases. To efficiently assess the large volume of publically available information, it is important to provide a concise summary of the most relevant information in a rapid user-friendly format. To this end, we created MARRVEL (model organism aggregated resources for rare variant exploration). MARRVEL is a publicly available website that integrates information from six human genetic databases and seven model organism databases. For any given variant or gene, MARRVEL displays information from OMIM, ExAC, ClinVar, Geno2MP, DGV, and DECIPHER. Importantly, it curates model organism-specific databases to concurrently display a concise summary regarding the human gene homologs in budding and fission yeast, worm, fly, fish, mouse, and rat on a single webpage. Experiment-based information on tissue expression, protein subcellular localization, biological process, and molecular function for the human gene and homologs in the seven model organisms are arranged into a concise output. Hence, rather than visiting multiple separate databases for variant and gene analysis, users can obtain important information by searching once through MARRVEL. Altogether, MARRVEL dramatically improves efficiency and accessibility to data collection and facilitates analysis of human genes and variants by cross-disciplinary integration of 18 million records available in public databases to facilitate clinical diagnosis and basic research.

Genetics and functions of the retinoic acid pathway, with special emphasis on the eye.

Retinoic acid (RA) is a potent morphogen required for embryonic development. RA is formed in a multistep process from vitamin A (retinol); RA acts in a paracrine fashion to shape the developing eye and is essential for normal optic vesicle and anterior segment formation. Perturbation in RA-signaling can result in severe ocular developmental diseases-including microphthalmia, anophthalmia, and coloboma. RA-signaling is also essential for embryonic development and life, as indicated by the significant consequences of mutations in genes involved in RA-signaling. The requirement of RA-signaling for normal development is further supported by the manifestation of severe pathologies in animal models of RA deficiency-such as ventral lens rotation, failure of optic cup formation, and embryonic and postnatal lethality. In this review, we summarize RA-signaling, recent advances in our understanding of this pathway in eye development, and the requirement of RA-signaling for embryonic development (e.g., organogenesis and limb bud development) and life.

High prevalence of cancer-associated TP53 variants in the gnomAD database: A word of caution concerning the use of variant filtering.

The 1,000 genome project, the Exome Aggregation Consortium (ExAC) or the Genome Aggregation database (gnomAD) datasets, were developed to provide large-scale reference data of genetic variations for various populations to filter out common benign variants and identify rare variants of clinical importance based on their frequency in the human population. Using a TP53 repository of 80,000 cancer variants, as well as TP53 variants from multiple cancer genome projects, we have defined a set of certified oncogenic TP53 variants. This specific set has been independently validated by functional and in silico predictive analysis. Here we show that a significant number of these variants are included in gnomAD and ExAC. Most of them correspond to TP53 hotspot variants occurring as somatic and germline events in human cancer. Similarly, disease-associated variants for five other tumor suppressor genes, including BRCA1, BRCA2, APC, PTEN, and MLH1, have also been identified. This study demonstrates that germline TP53 variants in the human population are more frequent than previously thought. Furthermore, population databases such as gnomAD or ExAC must be used with caution and need to be annotated for the presence of oncogenic variants to improve their clinical utility.

Real-world clinical applicability of pathogenicity predictors assessed on SERPINA1 mutations in alpha-1-antitrypsin deficiency.

The growth of publicly available data informing upon genetic variations, mechanisms of disease, and disease subphenotypes offers great potential for personalized medicine. Computational approaches are likely required to assess a large number of novel genetic variants. However, the integration of genetic, structural, and pathophysiological data still represents a challenge for computational predictions and their clinical use. We addressed these issues for alpha-1-antitrypsin deficiency, a disease mediated by mutations in the SERPINA1 gene encoding alpha-1-antitrypsin. We compiled a comprehensive database of SERPINA1 coding mutations and assigned them apparent pathological relevance based upon available data. "Benign" and "pathogenic" variations were used to assess performance of 31 pathogenicity predictors. Well-performing algorithms clustered the subset of variants known to be severely pathogenic with high scores. Eight new mutations identified in the ExAC database and achieving high scores were selected for characterization in cell models and showed secretory deficiency and polymer formation, supporting the predictive power of our computational approach. The behavior of the pathogenic new variants and consistent outliers were rationalized by considering the protein structural context and residue conservation. These findings highlight the potential of computational methods to provide meaningful predictions of the pathogenic significance of novel mutations and identify areas for further investigation.

Defining the genetic architecture of hypertrophic cardiomyopathy: re-evaluating the role of non-sarcomeric genes.

AIM: Hypertrophic cardiomyopathy (HCM) exhibits genetic heterogeneity that is dominated by variation in eight sarcomeric genes. Genetic variation in a large number of non-sarcomeric genes has also been implicated in HCM but not formally assessed. Here we used very large case and control cohorts to determine the extent to which variation in non-sarcomeric genes contributes to HCM. METHODS AND RESULTS: We sequenced known and putative HCM genes in a new large prospective HCM cohort (n = 804) and analysed data alongside the largest published series of clinically genotyped HCM patients (n = 6179), previously published HCM cohorts and reference population samples from the exome aggregation consortium (ExAC, n = 60 706) to assess variation in 31 genes implicated in HCM. We found no significant excess of rare (minor allele frequency < 1:10 000 in ExAC) protein-altering variants over controls for most genes tested and conclude that novel variants in these genes are rarely interpretable, even for genes with previous evidence of co-segregation (e.g. ACTN2). To provide an aid for variant interpretation, we integrated HCM gene sequence data with aggregated pedigree and functional data and suggest a means of assessing gene pathogenicity in HCM using this evidence. CONCLUSION: We show that genetic variation in the majority of non-sarcomeric genes implicated in HCM is not associated with the condition, reinforce the fact that the sarcomeric gene variation is the primary cause of HCM known to date and underscore that the aetiology of HCM is unknown in the majority of patients.

Estimation of minimal disease prevalence from population genomic data: Application to primary familial brain calcification.

Primary Familial Brain Calcification (PFBC) is a rare calcifying disorder of the brain with autosomal dominant inheritance, of unknown prevalence. Four causal genes have been identified so far: SLC20A2, PDGFB, PDGFRB, and XPR1, with pathogenic, probably pathogenic or missense variants of unknown significance found in 27.7% probands in the French PFBC series. Estimating PFBC prevalence from a clinical input is arduous due to a large diversity of symptoms and ages of onset and to incomplete clinical penetrance. Abnormal calcifications on CT scan can be used as a reliable diagnostic biomarker whatever the clinical status, but differential diagnoses should be ruled out including the challenging exclusion of common basal ganglia calcifications. Our primary aim was to estimate the minimal prevalence of PFBC due to a variant in one of the known genes. We extracted variants from the four known genes present in the gnomAD database gathering genomic data from 138,632 individuals. We interpreted all variants based on their predicted effect, their frequency, and previous studies on PFBC patients. Using the most conservative estimate, the minimal prevalence of PFBC related to a variant in one of the four known genes was 4.5 p. 10,000 (95%CI [3.4-5.5] p. 10,000). We then used variant detection rates in patients to extrapolate an overall minimal prevalence of PFBC to 2.1 p. 1,000 (95%CI [1.9-2.4] p. 1,000). The population-based genomic analysis indicates that PFBC is not an exceptionally rare disorder, still underestimated and underdiagnosed.

Aggregation of population-based genetic variation over protein domain homologues and its potential use in genetic diagnostics.

Whole exomes of patients with a genetic disorder are nowadays routinely sequenced but interpretation of the identified genetic variants remains a major challenge. The increased availability of population-based human genetic variation has given rise to measures of genetic tolerance that have been used, for example, to predict disease-causing genes in neurodevelopmental disorders. Here, we investigated whether combining variant information from homologous protein domains can improve variant interpretation. For this purpose, we developed a framework that maps population variation and known pathogenic mutations onto 2,750 "meta-domains." These meta-domains consist of 30,853 homologous Pfam protein domain instances that cover 36% of all human protein coding sequences. We find that genetic tolerance is consistent across protein domain homologues, and that patterns of genetic tolerance faithfully mimic patterns of evolutionary conservation. Furthermore, for a significant fraction (68%) of the meta-domains high-frequency population variation re-occurs at the same positions across domain homologues more often than expected. In addition, we observe that the presence of pathogenic missense variants at an aligned homologous domain position is often paired with the absence of population variation and vice versa. The use of these meta-domains can improve the interpretation of genetic variation.

Associating mutations causing cystinuria with disease severity with the aim of providing precision medicine.

BACKGROUND: Cystinuria is an inherited disease that results in the formation of cystine stones in the kidney, which can have serious health complications. Two genes (SLC7A9 and SLC3A1) that form an amino acid transporter are known to be responsible for the disease. Variants that cause the disease disrupt amino acid transport across the cell membrane, leading to the build-up of relatively insoluble cystine, resulting in formation of stones. Assessing the effects of each mutation is critical in order to provide tailored treatment options for patients. We used various computational methods to assess the effects of cystinuria associated mutations, utilising information on protein function, evolutionary conservation and natural population variation of the two genes. We also analysed the ability of some methods to predict the phenotypes of individuals with cystinuria, based on their genotypes, and compared this to clinical data. RESULTS: Using a literature search, we collated a set of 94 SLC3A1 and 58 SLC7A9 point mutations known to be associated with cystinuria. There are differences in sequence location, evolutionary conservation, allele frequency, and predicted effect on protein function between these mutations and other genetic variants of the same genes that occur in a large population. Structural analysis considered how these mutations might lead to cystinuria. For SLC7A9, many mutations swap hydrophobic amino acids for charged amino acids or vice versa, while others affect known functional sites. For SLC3A1, functional information is currently insufficient to make confident predictions but mutations often result in the loss of hydrogen bonds and largely appear to affect protein stability. Finally, we showed that computational predictions of mutation severity were significantly correlated with the disease phenotypes of patients from a clinical study, despite different methods disagreeing for some of their predictions. CONCLUSIONS: The results of this study are promising and highlight the areas of research which must now be pursued to better understand how mutations in SLC3A1 and SLC7A9 cause cystinuria. The application of our approach to a larger data set is essential, but we have shown that computational methods could play an important role in designing more effective personalised treatment options for patients with cystinuria.

Integration of 60,000 exomes and ACMG guidelines question the role of Catecholaminergic Polymorphic Ventricular Tachycardia-associated variants.

Catecholaminergic Polymorphic Ventricular Tachycardia (CPVT) is a highly lethal cardiac arrhythmia disease occurring during exercise or psychological stress. CPVT has an estimated prevalence of 1:10,000 and has mainly been associated with variants in calcium-regulating genes. Identification of potential false-positive pathogenic variants was conducted by searching the Exome Aggregation Consortium (ExAC) database (n = 60,706) for variants reported to be associated with CPVT. The pathogenicity of the interrogated variants was assessed using guidelines from the American College of Medical Genetics and Genomics (ACMG) and in silico prediction tools. Of 246 variants 38 (15%) variants previously associated with CPVT were identified in the ExAC database. We predicted the CPVT prevalence to be 1:132. The ACMG standards classified 29% of ExAC variants as pathogenic or likely pathogenic. The in silico predictions showed a reduced probability of disease-causing effect for the variants identified in the exome database (p < 0.001). We have observed a large overrepresentation of previously CPVT-associated variants in a large exome database. Based on the frequency of CPVT in the general population, it is less likely that the previously proposed variants are associated with a highly penetrant monogenic form of the disease.

WES/WGS Reporting of Mutations from Cardiovascular "Actionable" Genes in Clinical Practice: A Key Role for UMD Knowledgebases in the Era of Big Databases.

High-throughput next-generation sequencing such as whole-exome and whole-genome sequencing are being rapidly integrated into clinical practice. The use of these techniques leads to the identification of secondary variants for which decisions about the reporting or not to the patient need to be made. The American College of Medical Genetics and Genomics recently published recommendations for the reporting of these variants in clinical practice for 56 "actionable" genes. Among these, seven are involved in Marfan Syndrome And Related Disorders (MSARD) resulting from mutations of the FBN1, TGFBR1 and 2, ACTA2, SMAD3, MYH11 and MYLK genes. Here, we show that mutations collected in UMD databases for MSARD genes (UMD-MSARD) are rarely reported, including the most frequent ones, in global scale initiatives for variant annotation such as the NHLBI GO Exome Sequencing Project (ESP), the Exome Aggregation Consortium (ExAC), and ClinVar. The predicted pathogenic mutations reported in global scale initiatives but absent in locus-specific databases (LSDBs) mainly correspond to rare events. UMD-MSARD databases are therefore the only resources providing access to the full spectrum of known pathogenic mutations. They are the most comprehensive resources for clinicians and geneticists to interpret MSARD-related variations not only primary variants but also secondary variants.

No preferential mode of inheritance for highly constrained genes.

Genetic constraint metrics such as the gnomAD probability of being loss-of-function (LoF) intolerant (pLI) are used to prioritize candidate genes but the mode of inheritance of highly constrained genes has never specifically been studied. We compared 605 genes with a pLI of 1 (pLI1 group) with a random sample of 635 genes from gnomAD (the random group) in terms of genetic constraint metrics, associations with Mendelian disease, modes of inheritance, and two intragenic constraint scores: the percentage of constraint coding regions (CCR) in the 99th percentile and the gene variation intolerance rank (GeVIR). The proportion of genes associated with a Mendelian disease was 35.9% (217/605) in the pLI1 group and 19.5% (124/635) in the random group (p < 0.0001). The modes of inheritance in the random group were autosomal dominant for 35 genes (28.2%), autosomal recessive for 69 (55.6%), mixed for 14 (11.3%) and X-linked for 6 genes (4.8%). The corresponding distribution in the pLI1 group was 150 (69.1%), 26 (12.0%), 14 (6.5%) and 27 (12.4%) (p < 0.0001). The percentage of CCRs in the 99th percentile was 0.3 in the random group versus 1.12 in the pLI1 group (p < 0.0001). The GeVIR score was 50.9 for the random group versus 15.1 for the pLI1 group (p < 0.0001). High genetic constraint does not seem to be associated with a particular mode of inheritance but does seem to be associated with the intragenic constraint scores considered here. Some highly constrained genes are associated with two different modes of inheritance.

MutTMPredictor: Robust and accurate cascade XGBoost classifier for prediction of mutations in transmembrane proteins.

Transmembrane proteins have critical biological functions and play a role in a multitude of cellular processes including cell signaling, transport of molecules and ions across membranes. Approximately 60% of transmembrane proteins are considered as drug targets. Missense mutations in such proteins can lead to many diverse diseases and disorders, such as neurodegenerative diseases and cystic fibrosis. However, there are limited studies on mutations in transmembrane proteins. In this work, we first design a new feature encoding method, termed weight attenuation position-specific scoring matrix (WAPSSM), which builds upon the protein evolutionary information. Then, we propose a new mutation prediction algorithm (cascade XGBoost) by leveraging the idea learned from consensus predictors and gcForest. Multi-level experiments illustrate the effectiveness of WAPSSM and cascade XGBoost algorithms. Finally, based on WAPSSM and other three types of features, in combination with the cascade XGBoost algorithm, we develop a new transmembrane protein mutation predictor, named MutTMPredictor. We benchmark the performance of MutTMPredictor against several existing predictors on seven datasets. On the 546 mutations dataset, MutTMPredictor achieves the accuracy (ACC) of 0.9661 and the Matthew's Correlation Coefficient (MCC) of 0.8950. While on the 67,584 dataset, MutTMPredictor achieves an MCC of 0.7523 and area under curve (AUC) of 0.8746, which are 0.1625 and 0.0801 respectively higher than those of the existing best predictor (fathmm). Besides, MutTMPredictor also outperforms two specific predictors on the Pred-MutHTP datasets. The results suggest that MutTMPredictor can be used as an effective method for predicting and prioritizing missense mutations in transmembrane proteins. The MutTMPredictor webserver and datasets are freely accessible at http://csbio.njust.edu.cn/bioinf/muttmpredictor/ for academic use.

Estimated prevalence of mucopolysaccharidoses from population-based exomes and genomes.

BACKGROUND: In this study, the prevalence of different types of mucopolysaccharidoses (MPS) was estimated based on data from the exome aggregation consortium (ExAC) and the genome aggregation database (gnomAD). The population-based allele frequencies were used to identify potential disease-causing variants on each gene related to MPS I to IX (except MPS II). METHODS: We evaluated the canonical transcripts and excluded homozygous, intronic, 3', and 5' UTR variants. Frameshift and in-frame insertions and deletions were evaluated using the SIFT Indel tool. Splice variants were evaluated using SpliceAI and Human Splice Finder 3.0 (HSF). Loss-of-function single nucleotide variants in coding regions were classified as potentially pathogenic, while synonymous variants outside the exon-intron boundaries were deemed non-pathogenic. Missense variants were evaluated by five in silico prediction tools, and only those predicted to be damaging by at least three different algorithms were considered disease-causing. RESULTS: The combined frequencies of selected variants (ranged from 127 in GNS to 259 in IDUA) were used to calculate prevalence based on Hardy-Weinberg's equilibrium. The maximum estimated prevalence ranged from 0.46 per 100,000 for MPSIIID to 7.1 per 100,000 for MPS I. Overall, the estimated prevalence of all types of MPS was higher than what has been published in the literature. This difference may be due to misdiagnoses and/or underdiagnoses, especially of the attenuated forms of MPS. However, overestimation of the number of disease-causing variants by in silico predictors cannot be ruled out. Even so, the disease prevalences are similar to those reported in diagnosis-based prevalence studies. CONCLUSION: We report on an approach to estimate the prevalence of different types of MPS based on publicly available population-based genomic data, which may help health systems to be better prepared to deal with these conditions and provide support to initiatives on diagnosis and management of MPS.

Hemochromatosis in India: First Report of Whole Exome Sequencing With Review of the Literature.

BACKGROUND: Primary hemochromatosis is unusual in India. The homeostatic iron regulator (HFE) gene C282Y mutation, a common cause for hemochromatosis in Europe, is considered almost nonexistent in India. We are reporting a case of hemochromatosis with the HFE gene C282Y mutation and two other adult cases with a novel hemojuvelin (HJV) mutation from Kerala. METHODS: Of 434 cases with chronic liver disease, 3 cases were identified with the serum ferritin level of more than 1000 ng/mL and primary hemochromatosis after excluding secondary causes. Whole exome sequencing, including genes HFE, HJV, SLC40A1, TFR2, FTH1, HAMP, SKIV2L, TTC37, and BMP2, was performed for blood samples in all 3 cases. RESULTS: One patient with hemochromatosis had a homozygous HFE gene C282Y mutation, and two other adult cases had a novel homozygous HJV D355Y mutation. This is the first report of hemochromatosis associated with the HFE C282Y mutation from Kerala and the second report in India. This is the second report of hemochromatosis associated with an HJV mutation from India. CONCLUSION: HJV mutations may explain some of the adult onset primary hemochromatosis in India.

Identification of rare variants in novel candidate genes in pulmonary atresia patients by next generation sequencing.

Pulmonary atresia (PA) is a rare congenital heart defect (CHD) with complex manifestations and a high mortality rate. Since the genetic determinants in the pathogenesis of PA remain elusive, a thorough identification of the genetic factors through whole exome sequencing (WES) will provide novel insights into underlying mechanisms of PA. We performed WES data from PA/VSD (n = 60), PA/IVS (n = 20), TOF/PA (n = 20) and 100 healthy controls. Rare variants and novel genes were identified using variant-based association and gene-based burden analysis. Then we explored the expression pattern of our candidate genes in endothelium cell lines, pulmonary artery tissues, and embryonic hearts. 56 rare damage variants of 7 novel candidate genes (DNAH10, DST, FAT1, HMCN1, HNRNPC, TEP1, and TYK2) were certified to have function in PA pathogenesis for the first time. In our research, the genetic pattern among PA/VSD, PA/IVS and TOF/PA were different to some degree. Taken together, our findings contribute new insights into the molecular basis of this rare congenital birth defect.

Unleashing the Power of MutationTaster2 and MutationTaster2021: The Machine Learning Approach to Genetic Variant Analysis

MutationTaster is a widely used web-based tool that predicts the functional impact of genetic variants. In recent years, the software has undergone significant improvements, leading to the development of MutationTaster2 and MutationTaster2021. The main difference between these two versions is the use of updated reference datasets and an improved algorithm for variant classification. MutationTaster2 utilizes the dbNSFP database, while MutationTaster2021 incorporates gnomAD and ClinVar data. Both versions employ a machine learning approach that combines multiple features to predict variant pathogenicity, including evolutionary conservation, physical properties of amino acid changes, and the potential effect on protein function. The output of MutationTaster is a score indicating the likelihood of a variant being disease causing, with a high score indicating a high likelihood of pathogenicity. Overall, MutationTaster2 and MutationTaster2021 represent valuable tools for researchers and clinicians in the field of genetic variant analysis, providing accurate and efficient predictions of variant pathogenicity.

MutationTaster es una herramienta web ampliamente utilizada que predice el impacto funcional de las variantes genéticas. En los últimos años, el software ha experimentado mejoras significativas, lo que ha llevado al desarrollo de MutationTaster2 y MutationTaster2021. La principal diferencia entre estas dos versiones es el uso de conjuntos de datos de referencia actualizados y un algoritmo mejorado para la clasificación de variantes. MutationTaster2 utiliza la base de datos dbNSFP, mientras que MutationTaster2021 incorpora datos de gnomAD y ClinVar. Ambas versiones emplean un enfoque de aprendizaje automático que combina múltiples características para predecir la patogenicidad variante, incluida la conservación evolutiva, las propiedades físicas de los cambios de aminoácidos y el efecto potencial en la función de la proteína. El resultado de MutationTaster es una puntuación que indica la probabilidad de que una variante cause una enfermedad; una puntuación alta indica una alta probabilidad de patogenicidad. En general, MutationTaster2 y MutationTaster2021 representan herramientas valiosas para investigadores y médicos en el campo del análisis de variantes genéticas, ya que proporcionan predicciones precisas y eficientes de la patogenicidad de variantes.

Pathogenic variant burden in the ExAC database: an empirical approach to evaluating population data for clinical variant interpretation.

BACKGROUND: The frequency of a variant in the general population is a key criterion used in the clinical interpretation of sequence variants. With certain exceptions, such as founder mutations, the rarity of a variant is a prerequisite for pathogenicity. However, defining the threshold at which a variant should be considered "too common" is challenging and therefore diagnostic laboratories have typically set conservative allele frequency thresholds. METHODS: Recent publications of large population sequencing data, such as the Exome Aggregation Consortium (ExAC) database, provide an opportunity to characterize with accuracy and precision the frequency distributions of very rare disease-causing alleles. Allele frequencies of pathogenic variants in ClinVar, as well as variants expected to be pathogenic through the nonsense-mediated decay (NMD) pathway, were analyzed to study the burden of pathogenic variants in 79 genes of clinical importance. RESULTS: Of 1364 BRCA1 and BRCA2 variants that are well characterized as pathogenic or that are expected to lead to NMD, 1350 variants had an allele frequency of less than 0.0025%. The remaining 14 variants were previously published founder mutations. Importantly, we observed no difference in the distributions of pathogenic variants expected to be lead to NMD compared to those that are not. Therefore, we expanded the analysis to examine the distributions of NMD expected variants in 77 additional genes. These 77 genes were selected to represent a broad set of clinical areas, modes of inheritance, and penetrance. Among these variants, most (97.3%) had an allele frequency of less than 0.01%. Furthermore, pathogenic variants with allele frequencies greater than 0.01% were well characterized in publications and included many founder mutations. CONCLUSIONS: The observations made in this study suggest that, with certain caveats, a very low allele frequency threshold can be adopted to more accurately interpret sequence variants.

Estimated prevalence of potentially damaging variants in the leptin gene.

BACKGROUND: Mutations in the leptin gene (LEP) can alter the secretion or interaction of leptin with its receptor, leading to extreme early-onset obesity. The purpose of this work was to estimate the prevalence of heterozygous and homozygous mutations in the leptin gene with the help of the Exome Aggregation Consortium (ExAC) database ( http://exac.broadinstitute.org/about ). RESULTS: The ExAC database encompasses exome sequencing data from 60,706 individuals. We searched for listed leptin variants and identified 36 missense, 1 in-frame deletion, and 3 loss-of-function variants. The functional relevance of these variants was assessed by the in silico prediction tools PolyPhen-2, Sorting Intolerant from Tolerant (SIFT), and Loss-Of-Function Transcript Effect Estimator (LOFTEE). PolyPhen-2 predicted 7 of the missense variants to be probably damaging and 10 to be possibly damaging. SIFT predicted 7 of the missense variants to be deleterious. Three loss-of-function variants were predicted by LOFTEE. Excluding double counts, we can summarize 21 variants as potentially damaging. Considering the allele count, we identified 31 heterozygous but no homozygous subjects with at least probably damaging variants. In the ExAC population, the estimated prevalence of heterozygous carriers of these potentially damaging variants was 1:2000. The probability of homozygosity was 1:15,000,000. We furthermore tried to assess the functionality of ExAC-listed leptin variants by applying a knowledge-driven approach. By this approach, additional 6 of the ExAC-listed variants were considered potentially damaging, increasing the number of heterozygous subjects to 58, the prevalence of heterozygosity to 1:1050, and the probability of homozygosity to 1:4,400,000. CONCLUSION: Using exome sequencing data from ExAC, in silico prediction tools and by applying a knowledge-driven approach, we identified 27 probably damaging variants in the leptin gene of 58 heterozygous subjects. With this information, we estimate the prevalence for heterozygosity at 1:1050 corresponding to an incidence of homozygosity of 1:4,400,000 in this large pluriethnic cohort.

Utility of Population-Level DNA Sequence Data in the Diagnosis of Hereditary Endocrine Disease.

CONTEXT: Genetic testing is increasingly used for clinical diagnosis, although variant interpretation presents a major challenge because of high background rates of rare coding-region variation, which may contribute to inaccurate estimates of variant pathogenicity and disease penetrance. OBJECTIVE: To use the Exome Aggregation Consortium (ExAC) data set to determine the background population frequencies of rare germline coding-region variants in genes associated with hereditary endocrine disease and to evaluate the clinical utility of these data. DESIGN SETTING PARTICIPANTS: Cumulative frequencies of rare nonsynonymous single-nucleotide variants were established for 38 endocrine disease genes in 60,706 unrelated control individuals. The utility of gene-level and variant-level metrics of tolerability was assessed, and the pathogenicity and penetrance of germline variants previously associated with endocrine disease evaluated. RESULTS: The frequency of rare coding-region variants differed markedly between genes and was correlated with the degree of evolutionary conservation. Genes associated with dominant monogenic endocrine disorders typically harbored fewer rare missense and/or loss-of-function variants than expected. In silico variant prediction tools demonstrated low clinical specificity. The frequency of several endocrine disease‒associated variants in the ExAC cohort far exceeded estimates of disease prevalence, indicating either misclassification or overestimation of disease penetrance. Finally, we illustrate how rare variant frequencies may be used to anticipate expected rates of background rare variation when performing disease-targeted genetic testing. CONCLUSIONS: Quantifying the frequency and spectrum of rare variation using population-level sequence data facilitates improved estimates of variant pathogenicity and penetrance and should be incorporated into the clinical decision-making algorithm when undertaking genetic testing.