Phenome-wide profiling identifies genotype-phenotype associations in Phelan-McDermid syndrome using family-sourced data from an international registry.

BACKGROUND: Phelan-McDermid syndrome (PMS) is a rare neurodevelopmental disorder caused by 22q13 deletions that include the SHANK3 gene or pathogenic sequence variants in SHANK3. It is characterized by global developmental delay, intellectual disability, speech impairment, autism spectrum disorder, and hypotonia; other variable features include epilepsy, brain and renal malformations, and mild dysmorphic features. Here, we conducted genotype-phenotype correlation analyses using the PMS International Registry, a family-driven registry that compiles clinical data in the form of family-reported outcomes and family-sourced genetic test results. METHODS: Data from the registry were harmonized and integrated into the i2b2/tranSMART clinical and genomics data warehouse. We gathered information from 401 individuals with 22q13 deletions including SHANK3 (n = 350, ranging in size from 10 kb to 9.1 Mb) or pathogenic or likely pathogenic SHANK3 sequence variants (n = 51), and used regression models with deletion size as a potential predictor of clinical outcomes for 328 phenotypes. RESULTS: Our results showed that increased deletion size was significantly associated with delay in gross and fine motor acquisitions, a spectrum of conditions related to poor muscle tone, renal malformations, mild dysmorphic features (e.g., large fleshy hands, sacral dimple, dysplastic toenails, supernumerary teeth), lymphedema, congenital heart defects, and more frequent neuroimaging abnormalities and infections. These findings indicate that genes upstream of SHANK3 also contribute to some of the manifestations of PMS in individuals with larger deletions. We also showed that self-help skills, verbal ability and a range of psychiatric diagnoses (e.g., autism, ADHD, anxiety disorder) were more common among individuals with smaller deletions and SHANK3 variants. LIMITATIONS: Some participants were tested with targeted 22q microarrays rather than genome-wide arrays, and karyotypes were unavailable in many cases, thus precluding the analysis of the effect of other copy number variants or chromosomal rearrangements on the phenotype. CONCLUSIONS: This is the largest reported case series of individuals with PMS. Overall, we demonstrate the feasibility of using data from a family-sourced registry to conduct genotype-phenotype analyses in rare genetic disorders. We replicate and strengthen previous findings, and reveal novel associations between larger 22q13 deletions and congenital heart defects, neuroimaging abnormalities and recurrent infections.

Combining clinical and genomics queries using i2b2 - Three methods.

We are fortunate to be living in an era of twin biomedical data surges: a burgeoning representation of human phenotypes in the medical records of our healthcare systems, and high-throughput sequencing making rapid technological advances. The difficulty representing genomic data and its annotations has almost by itself led to the recognition of a biomedical "Big Data" challenge, and the complexity of healthcare data only compounds the problem to the point that coherent representation of both systems on the same platform seems insuperably difficult. We investigated the capability for complex, integrative genomic and clinical queries to be supported in the Informatics for Integrating Biology and the Bedside (i2b2) translational software package. Three different data integration approaches were developed: The first is based on Sequence Ontology, the second is based on the tranSMART engine, and the third on CouchDB. These novel methods for representing and querying complex genomic and clinical data on the i2b2 platform are available today for advancing precision medicine.

tranSMART: An Open Source Knowledge Management and High Content Data Analytics Platform.

The tranSMART knowledge management and high-content analysis platform is a flexible software framework featuring novel research capabilities. It enables analysis of integrated data for the purposes of hypothesis generation, hypothesis validation, and cohort discovery in translational research. tranSMART bridges the prolific world of basic science and clinical practice data at the point of care by merging multiple types of data from disparate sources into a common environment. The application supports data harmonization and integration with analytical pipelines. The application code was released into the open source community in January 2012, with 32 instances in operation. tranSMART's extensible data model and corresponding data integration processes, rapid data analysis features, and open source nature make it an indispensable tool in translational or clinical research.