Nara Sobreira

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

Discovering the genetic basis of a Mendelian phenotype establishes a causal link between genotype and phenotype, making possible carrier and population screening and direct diagnosis. Such discoveries also contribute to our knowledge of gene function, gene regulation, development, and biological mechanisms that can be used for developing new therapeutics. As of February 2015, 2,937 genes underlying 4,163 Mendelian phenotypes have been discovered, but the genes underlying ∼50% (i.e., 3,152) of all known Mendelian phenotypes are still unknown, and many more Mendelian conditions have yet to be recognized. This is a formidable gap in biomedical knowledge. Accordingly, in December 2011, the NIH established the Centers for Mendelian Genomics (CMGs) to provide the collaborative framework and infrastructure necessary for undertaking large-scale whole-exome sequencing and discovery of the genetic variants responsible for Mendelian phenotypes. In partnership with 529 investigators from 261 institutions in 36 countries, the CMGs assessed 18,863 samples from 8,838 families representing 579 known and 470 novel Mendelian phenotypes as of January 2015. This collaborative effort has identified 956 genes, including 375 not previously associated with human health, that underlie a Mendelian phenotype. These results provide insight into study design and analytical strategies, identify novel mechanisms of disease, and reveal the extensive clinical variability of Mendelian phenotypes. Discovering the gene underlying every Mendelian phenotype will require tackling challenges such as worldwide ascertainment and phenotypic characterization of families affected by Mendelian conditions, improvement in sequencing and analytical techniques, and pervasive sharing of phenotypic and genomic data among researchers, clinicians, and families.

GeneMatcher: a matching tool for connecting investigators with an interest in the same gene.

Here, we describe an overview and update on GeneMatcher (http://www.genematcher.org), a freely accessible Web‐based tool developed as part of the Baylor‐Hopkins Center for Mendelian Genomics. We created GeneMatcher with the goal of identifying additional individuals with rare phenotypes who had variants in the same candidate disease gene. We also wanted to facilitate connections to basic scientists working on orthologous genes in model systems with the goal of connecting their work to human Mendelian phenotypes. Meeting these goals will enhance the identification of novel Mendelian genes. Launched in September, 2013, GeneMatcher now has 2,178 candidate genes from 486 submitters spread across 38 countries entered in the database (June 1, 2015). GeneMatcher is also part of the Matchmaker Exchange (http://matchmakerexchange.org/) with an Application Programing Interface enabling submitters to query other databases of genetic variants and phenotypes without having to create accounts and data entries in multiple systems.

Whole-Genome Sequencing of a Single Proband Together with Linkage Analysis Identifies a Mendelian Disease Gene

Although more than 2,400 genes have been shown to contain variants that cause Mendelian disease, there are still several thousand such diseases yet to be molecularly defined. The ability of new whole-genome sequencing technologies to rapidly indentify most of the genetic variants in any given genome opens an exciting opportunity to identify these disease genes. Here we sequenced the whole genome of a single patient with the dominant Mendelian disease, metachondromatosis (OMIM 156250), and used partial linkage data from her small family to focus our search for the responsible variant. In the proband, we identified an 11 bp deletion in exon four of PTPN11, which alters frame, results in premature translation termination, and co-segregates with the phenotype. In a second metachondromatosis family, we confirmed our result by identifying a nonsense mutation in exon 4 of PTPN11 that also co-segregates with the phenotype. Sequencing PTPN11 exon 4 in 469 controls showed no such protein truncating variants, supporting the pathogenicity of these two mutations. This combination of a new technology and a classical genetic approach provides a powerful strategy to discover the genes responsible for unexplained Mendelian disorders.

Microdeletions of 3q29 Confer High Risk for Schizophrenia

Schizophrenia (SZ) is a severe psychiatric illness that affects approximately 1% of the population and has a strong genetic underpinning. Recently, genome-wide analysis of copy-number variation (CNV) has implicated rare and de novo events as important in SZ. Here, we report a genome-wide analysis of 245 SZ cases and 490 controls, all of Ashkenazi Jewish descent. Because many studies have found an excess burden of large, rare deletions in cases, we limited our analysis to deletions over 500 kb in size. We observed seven large, rare deletions in cases, with 57% of these being de novo. We focused on one 836 kb de novo deletion at chromosome 3q29 that falls within a 1.3-1.6 Mb deletion previously identified in children with intellectual disability (ID) and autism, because increasing evidence suggests an overlap of specific rare copy-number variants (CNVs) between autism and SZ. By combining our data with prior CNV studies of SZ and analysis of the data of the Genetic Association Information Network (GAIN), we identified six 3q29 deletions among 7545 schizophrenic subjects and one among 39,748 controls, resulting in a statistically significant association with SZ (p = 0.02) and an odds ratio estimate of 17 (95% confidence interval: 1.36-1198.4). Moreover, this 3q29 deletion region contains two linkage peaks from prior SZ family studies, and the minimal deletion interval implicates 20 annotated genes, including PAK2 and DLG1, both paralogous to X-linked ID genes and now strong candidates for SZ susceptibility.

The Characterization of Twenty Sequenced Human Genomes

We present the analysis of twenty human genomes to evaluate the prospects for identifying rare functional variants that contribute to a phenotype of interest. We sequenced at high coverage ten “case” genomes from individuals with severe hemophilia A and ten “control” genomes. We summarize the number of genetic variants emerging from a study of this magnitude, and provide a proof of concept for the identification of rare and highly-penetrant functional variants by confirming that the cause of hemophilia A is easily recognizable in this data set. We also show that the number of novel single nucleotide variants (SNVs) discovered per genome seems to stabilize at about 144,000 new variants per genome, after the first 15 individuals have been sequenced. Finally, we find that, on average, each genome carries 165 homozygous protein-truncating or stop loss variants in genes representing a diverse set of pathways.

The Matchmaker Exchange: a platform for rare disease gene discovery.

There are few better examples of the need for data sharing than in the rare disease community, where patients, physicians, and researchers must search for “the needle in a haystack” to uncover rare, novel causes of disease within the genome. Impeding the pace of discovery has been the existence of many small siloed datasets within individual research or clinical laboratory databases and/or disease‐specific organizations, hoping for serendipitous occasions when two distant investigators happen to learn they have a rare phenotype in common and can “match” these cases to build evidence for causality. However, serendipity has never proven to be a reliable or scalable approach in science. As such, the Matchmaker Exchange (MME) was launched to provide a robust and systematic approach to rare disease gene discovery through the creation of a federated network connecting databases of genotypes and rare phenotypes using a common application programming interface (API). The core building blocks of the MME have been defined and assembled. Three MME services have now been connected through the API and are available for community use. Additional databases that support internal matching are anticipated to join the MME network as it continues to grow.

Mutations in SPATA5 Are Associated with Microcephaly, Intellectual Disability, Seizures, and Hearing Loss

Using whole-exome sequencing, we have identified in ten families 14 individuals with microcephaly, developmental delay, intellectual disability, hypotonia, spasticity, seizures, sensorineural hearing loss, cortical visual impairment, and rare autosomal-recessive predicted pathogenic variants in spermatogenesis-associated protein 5 (SPATA5). SPATA5 encodes a ubiquitously expressed member of the ATPase associated with diverse activities (AAA) protein family and is involved in mitochondrial morphogenesis during early spermatogenesis. It might also play a role in post-translational modification during cell differentiation in neuronal development. Mutations in SPATA5 might affect brain development and function, resulting in microcephaly, developmental delay, and intellectual disability.

PhenoDB: A New Web-Based Tool for the Collection, Storage, and Analysis of Phenotypic Features

To interpret whole exome/genome sequence data for clinical and research purposes, comprehensive phenotypic information, knowledge of pedigree structure, and results of previous clinical testing are essential. With these requirements in mind and to meet the needs of the Centers for Mendelian Genomics project, we have developed PhenoDB (http://phenodb.net), a secure, Web‐based portal for entry, storage, and analysis of phenotypic and other clinical information. The phenotypic features are organized hierarchically according to the major headings and subheadings of the Online Mendelian Inheritance in Man (OMIM®) clinical synopses, with further subdivisions according to structure and function. Every string allows for a free‐text entry. All of the approximately 2,900 features use the preferred term from Elements of Morphology and are fully searchable and mapped to the Human Phenotype Ontology and Elements of Morphology. The PhenoDB allows for ascertainment of relevant information from a case in a family or cohort, which is then searchable by family, OMIM number, phenotypic feature, mode of inheritance, genes screened, and so on. The database can also be used to format phenotypic data for submission to dbGaP for appropriately consented individuals. PhenoDB was built using Django, an open source Web development tool, and is freely available through the Johns Hopkins McKusick‐Nathans Institute of Genetic Medicine (http://phenodb.net).

New Tools for Mendelian Disease Gene Identification: PhenoDB Variant Analysis Module; and GeneMatcher, a Web-Based Tool for Linking Investigators with an Interest in the Same Gene

Identifying the causative variant from among the thousands identified by whole‐exome sequencing or whole‐genome sequencing is a formidable challenge. To make this process as efficient and flexible as possible, we have developed a Variant Analysis Module coupled to our previously described Web‐based phenotype intake tool, PhenoDB (http://researchphenodb.net and http://phenodb.org). When a small number of candidate‐causative variants have been identified in a study of a particular patient or family, a second, more difficult challenge becomes proof of causality for any given variant. One approach to this problem is to find other cases with a similar phenotype and mutations in the same candidate gene. Alternatively, it may be possible to develop biological evidence for causality, an approach that is assisted by making connections to basic scientists studying the gene of interest, often in the setting of a model organism. Both of these strategies benefit from an open access, online site where individual clinicians and investigators could post genes of interest. To this end, we developed GeneMatcher (http://genematcher.org), a freely accessible Website that enables connections between clinicians and researchers across the world who share an interest in the same gene(s).

Multilevel analyses of SCN5A mutations in arrhythmogenic right ventricular dysplasia/cardiomyopathy suggest non-canonical mechanisms for disease pathogenesis.

Aims Arrhythmogenic Right Ventricular Dysplasia/Cardiomyopathy (ARVD/C) is often associated with desmosomal mutations. Recent studies suggest an interaction between the desmosome and sodium channel protein Nav1.5. We aimed to determine the prevalence and biophysical properties of mutations in SCN5A (the gene encoding Nav1.5) in ARVD/C. Methods and results We performed whole-exome sequencing in six ARVD/C patients (33% male, 38.2 ± 12.1 years) without a desmosomal mutation. We found a rare missense variant (p.Arg1898His; R1898H) in SCN5A in one patient. We generated induced pluripotent stem cell-derived cardiomyocytes (hIPSC-CMs) from the patient’s peripheral blood mononuclear cells. The variant was then corrected (R1898R) using Clustered Regularly Interspaced Short Palindromic Repeats/Cas9 technology, allowing us to study the impact of the R1898H substitution in the same cellular background. Whole-cell patch clamping revealed a 36% reduction in peak sodium current (P = 0.002); super-resolution fluorescence microscopy showed reduced abundance of NaV1.5 (P = 0.005) and N-Cadherin (P = 0.026) clusters at the intercalated disc. Subsequently, we sequenced SCN5A in an additional 281 ARVD/C patients (60% male, 34.8 ± 13.7 years, 52% desmosomal mutation-carriers). Five (1.8%) subjects harboured a putatively pathogenic SCN5A variant (p.Tyr416Cys, p.Leu729del, p.Arg1623Ter, p.Ser1787Asn, and p.Val2016Met). SCN5A variants were associated with prolonged QRS duration (119 ± 15 vs. 94 ± 14 ms, P < 0.01) and all SCN5A variant carriers had major structural abnormalities on cardiac imaging. Conclusions Almost 2% of ARVD/C patients harbour rare SCN5A variants. For one of these variants, we demonstrated reduced sodium current, Nav1.5 and N-Cadherin clusters at junctional sites. This suggests that Nav1.5 is in a functional complex with adhesion molecules, and reveals potential non-canonical mechanisms by which Nav1.5 dysfunction causes cardiomyopathy.