Design and implementation of an action plan for justice, equity, diversity, and inclusion within the Clinical Genome Resource.

How might members of a large, multi-institutional research and resource consortium foster justice, equity, diversity, and inclusion as central to its mission, goals, governance, and culture? These four principles, often referred to as JEDI, can be aspirational-but to be operationalized, they must be supported by concrete actions, investments, and a persistent long-term commitment to the principles themselves, which often requires self-reflection and course correction. We present here the iterative design process implemented across the Clinical Genome Resource (ClinGen) that led to the development of an action plan to operationalize JEDI principles across three major domains, with specific deliverables and commitments dedicated to each. Active involvement of consortium leadership, buy-in from its members at all levels, and support from NIH program staff at pivotal stages were essential to the success of this effort.

GREGoR: Accelerating Genomics for Rare Diseases.

Rare diseases are collectively common, affecting approximately one in twenty individuals worldwide. In recent years, rapid progress has been made in rare disease diagnostics due to advances in DNA sequencing, development of new computational and experimental approaches to prioritize genes and genetic variants, and increased global exchange of clinical and genetic data. However, more than half of individuals suspected to have a rare disease lack a genetic diagnosis.

Mitochondrial DNA variant detection in over 6,500 rare disease families by the systematic analysis of exome and genome sequencing data resolves undiagnosed cases.

Background: Variants in the mitochondrial genome (mtDNA) cause a diverse collection of mitochondrial diseases and have extensive phenotypic overlap with Mendelian diseases encoded on the nuclear genome. The mtDNA is often not specifically evaluated in patients with suspected Mendelian disease, resulting in overlooked diagnostic variants.

Methods: Using dedicated pipelines to address the technical challenges posed by the mtDNA - circular genome, variant heteroplasmy, and nuclear misalignment - single nucleotide variants, small indels, and large mtDNA deletions were called from exome and genome sequencing data, in addition to RNA-sequencing when available.

Diagnosing missed cases of spinal muscular atrophy in genome, exome, and panel sequencing datasets.

Purpose: We set out to develop a publicly available tool that could accurately diagnose spinal muscular atrophy (SMA) in exome, genome or panel sequencing datasets aligned to a GRCh37, GRCh38, or T2T reference genome.

Methods: The SMA Finder algorithm detects the most common genetic causes of SMA by evaluating reads that overlap the c.840 position of the SMN1 and SMN2 paralogs.