Children’s rare disease cohorts: an integrative research and clinical genomics initiative

Abstract

While genomic data is frequently collected under distinct research protocols and disparate clinical and research regimes, there is a benefit in streamlining sequencing strategies to create harmonized databases, particularly in the area of pediatric rare disease. Research hospitals seeking to implement unified genomics workflows for research and clinical practice face numerous challenges, as they need to address the unique requirements and goals of the distinct environments and many stakeholders, including clinicians, researchers and sequencing providers. Here, we present outcomes of the first phase of the Children’s Rare Disease Cohorts initiative (CRDC) that was completed at Boston Children’s Hospital (BCH). We have developed a broadly sharable database of 2441 exomes from 15 pediatric rare disease cohorts, with major contributions from early onset epilepsy and early onset inflammatory bowel disease. All sequencing data is integrated and combined with phenotypic and research data in a genomics learning system (GLS). Phenotypes were both manually annotated and pulled automatically from patient medical records. Deployment of a genomically-ordered relational database allowed us to provide a modular and robust platform for centralized storage and analysis of research and clinical data, currently totaling 8516 exomes and 112 genomes. The GLS integrates analytical systems, including machine learning algorithms for automated variant classification and prioritization, as well as phenotype extraction via natural language processing (NLP) of clinical notes. This GLS is extensible to additional analytic systems and growing research and clinical collections of genomic and other types of data.

Dual-vector gene therapy restores cochlear amplification and auditory sensitivity in a mouse model of DFNB16 hearing loss

“…The carrier frequency of pathogenic STRC mutations in a large cohort of normal-hearing individuals has been difficult to ascertain due to the complexities of DNA sequencing at the highly homologous genomic architecture surrounding the STRC gene, 15q15.3 region. Here, we leveraged a large dataset of exome data with integrated phenotypic data from electronic medical records ( 11 ) and took an unbiased approach to estimate the number of ethnically mixed normal-hearing children who carry STRC mutations. We identified 1256 pediatric subjects with high-quality exome data and without phenotypic evidence of hearing loss.…”

“…We evaluated patients enrolled as part of a precision medicine initiative called Children’s Rare Disease Cohorts (CRDC), which has previously been described ( 11 ). The CRDC leverages exome and genome sequencing from cohorts of pediatric patients with rare diseases.…”

Congenital X‐linked neutropenia with myelodysplasia and somatic tetraploidy due to a germline mutation in SEPT6

“…WES for the BS was performed as previously described. 25 Sanger confirmations were performed on DNA from the peripheral blood. We leveraged multiple pipelines to automate the discovery process, the Variant Explorer (VExP) pipeline and BCH Genomics Learning System (GLS) using the human reference assembly hg19 as has been previously described.…”

“…We leveraged multiple pipelines to automate the discovery process, the Variant Explorer (VExP) pipeline and BCH Genomics Learning System (GLS) using the human reference assembly hg19 as has been previously described. 25,26 We used a phenotype-focused strategy to look for variants that had low allele frequencies in reference databases and high or moderate variant effect prediction (VEP) in candidate genes known to play a role in hematopoietic disorders (disease-focused analysis) as well as across all genes that had variants that matched the allele frequency and VEP criteria (unbiased analysis). 27 We performed amplicon sequencing of the SEPT6 (three independent pools of 15 exons for a total of $300 Mbp) in the four passages of patient cells and patient's BM at the Dana-Farber Cancer Institute sequencing core.…”

Artificial Intelligence Supports Decision Making during Open-Chest Surgery of Rare Congenital Heart Defects

“…Several AI-related tools, especially for rare disease diagnoses, have shown progress in determining the risk score and in screening areas such as genomics [17], histopathology [18] and radiology [19]. Although all of them are certain to revolutionize our concept of prognosis and diagnosis, AI-related cardiac imaging is currently not employed during open-chest surgery.…”