The role of patient registries for rare genetic lipid disorders.

PURPOSE OF REVIEW: We review the role, utility and current status of patient registries for rare genetic lipid disorders. RECENT FINDINGS: The creation and maintenance of rare genetic lipid disorder patient registries is critical for disease monitoring, improving clinical best practice, facilitating research and enabling the development of novel therapeutics. An open-source disease registry platform, termed the Rare Disease Registry Framework, has been developed, optimized and deployed for homozygous familial hypercholesterolemia. A global disease-specific registry for lipoprotein lipase deficiency (LPLD), GENetherapy In the mAnagement of Lipoprotein Lipase deficiency, has been established with the aim of enrolling 20-40% of LPLD patients worldwide and will study the natural history of LPLD as well as therapeutic response to the gene therapy alipogene tiparvovec. Similarly, a registry for lysosomal acid lipase deficiency patients in Europe and the United States is studying the clinical outcomes of the enzyme-replacement therapy sebelipase alfa. SUMMARY: There are currently few disease-specific rare lipid disorder patient registries. The very nature of rare genetic lipid disorders would suggest that larger national or international registries are necessary to capture clinical data on a sufficient number of patients to provide insight into the prevalence and natural history of these conditions. Furthermore, these registries can help to identify and address deficiencies in current diagnostic and management practices, and facilitate clinical trials of new therapies.

Design, development and deployment of a web-based patient registry for rare genetic lipid disorders.

Rare genetic lipid disorders comprise all the monogenic disorders of lipoprotein metabolism with the exception of heterozygous familial hypercholesterolaemia (FH). The creation and maintenance of patient registries is critical for disease monitoring, improving clinical best practice, facilitating research and enabling the development of novel therapeutics, but very few disease-specific rare genetic lipid disorder registries currently exist. Our aim was to design, develop and deploy a web-based patient registry for rare genetic lipid disorders. The Rare Genetic Lipid Disorders Registry is based on the FH Australasia Network (FHAN) Registry, which has been operating since 2015. The Rare Genetic Lipid Disorders Registry was deployed utilising the open-source Rare Disease Registry Framework (RDRF), which enables the efficient customisation and sustainable deployment of web-based registries. The Registry has been designed to capture longitudinal data on 13 rare genetic lipid disorders, with the ability to add more if required in the future. Recruitment of volunteers into the Registry is currently through the Royal Perth Hospital Lipid Disorders Clinic in Western Australia. Although in essence a clinic-based patient registry, the web-based design allows for expansion and distribution across Australia and beyond. Data collated by the Registry may ultimately improve the diagnosis, management and treatment of these conditions.

VisANT 3.0: new modules for pathway visualization, editing, prediction and construction.

With the integration of the KEGG and Predictome databases as well as two search engines for coexpressed genes/proteins using data sets obtained from the Stanford Microarray Database (SMD) and Gene Expression Omnibus (GEO) database, VisANT 3.0 supports exploratory pathway analysis, which includes multi-scale visualization of multiple pathways, editing and annotating pathways using a KEGG compatible visual notation and visualization of expression data in the context of pathways. Expression levels are represented either by color intensity or by nodes with an embedded expression profile. Multiple experiments can be navigated or animated. Known KEGG pathways can be enriched by querying either coexpressed components of known pathway members or proteins with known physical interactions. Predicted pathways for genes/proteins with unknown functions can be inferred from coexpression or physical interaction data. Pathways produced in VisANT can be saved as computer-readable XML format (VisML), graphic images or high-resolution Scalable Vector Graphics (SVG). Pathways in the format of VisML can be securely shared within an interested group or published online using a simple Web link. VisANT is freely available at http://visant.bu.edu.

Update on the diagnosis, treatment and management of rare genetic lipid disorders.

Rare genetic lipid disorders affect the levels of cholesterol and/or triglyceride in the circulation and, if untreated, can often lead to severe multisystem complications. The field of rare lipid disorders is evolving and increasing awareness of these conditions, along with the systematic integration of recent advances or knowledge into clinical practice, is crucial to improve patient outcomes. The aim of this review is to provide an overview of selected rare genetic lipid disorders, focusing on the recommended diagnostic strategies and contemporary treatment and management options.

Recommending pathway genes using a compendium of clustering solutions.

A common approach for identifying pathways from gene expression data is to cluster the genes without using prior information about a pathway, which often identifies only the dominant coexpression groups. Recommender systems are well-suited for using the known genes of a pathway to identify the appropriate experiments for predicting new members. However, existing systems, such as the GeneRecommender, ignore how genes naturally group together within specific experiments. We present a collaborative filtering approach which uses the pattern of how genes cluster together in different experiments to recommend new genes in a pathway. Clusters are first identified within a single experiment series. Informative clusters, in which the user-supplied query genes appear together, are identified. New genes that cluster with the known genes, in a significant fraction of the informative clusters, are recommended. We implemented a prototype of our system and measured its performance on hundreds of pathways. We find that our method performs as well as an established approach while significantly increasing the speed and scalability of searching large datasets. [Supplemental material is available online at sysbio.soe.ucsc.edu/cluegene/psb07.]