Consortia
Human Pangenome Reference Consortium (HPRC)
The Human Pangenome Reference Consortium is building a more representative set of human reference genomes to better capture global genetic diversity and improve the accuracy of genomic analysis across populations. The LoTempio Lab contributes to HPRC through work on genome informatics, benchmarking, and evaluation of reference resources, with an emphasis on translating pangenome methods into reliable and auditable research and clinical tools.
In parallel, the lab helps lead the HPRC ELSI team, focusing on the ethical, legal, and social implications of pangenome research. This work addresses questions of representation, data governance, consent, benefit sharing, and the downstream consequences of deploying new reference infrastructures in research and healthcare settings.
GREGoR Consortium (Genomics Research to Elucidate the Genetics of Rare Disease)
The GREGoR Consortium is an NIH-funded effort to improve diagnosis and gene discovery for rare and undiagnosed conditions through coordinated genomic sequencing, deep phenotyping, and cross-site collaboration. The LoTempio Lab plays a leadership role in developing the informatic infrastructure that enables GREGoR to function, as well as contributing to the conceptual development of systems-level rare disease thinking.
Our work within GREGoR focuses on interoperable data models, auditable analytic pipelines, and tools that support routine reanalysis and reuse of evidence across cases and institutions, helping convert individual diagnoses into shared, cumulative knowledge.
Undiagnosed Diseases Network (UDN)
The Undiagnosed Diseases Network is a national clinical-research network dedicated to evaluating patients with unresolved conditions using team-based clinical assessment and advanced genomic methods. The lab’s work aligns with UDN’s mission by focusing on the informatic and ethical systems required to shorten diagnostic journeys and manage uncertainty in rare disease care.
We are particularly interested in how reanalysis, data sharing, and transparent interpretation practices can be operationalized in ways that respect participants while improving diagnostic yield over time.