Avillach, P. | Search bioRxiv

bioRxiv2019

The efficacy of obesity polygenic risk scores in an autistic population

Polygenic risk scores (PRS) bear great promise in understanding complex diseases and improving clinical diagnoses, but the competency of these risk scores in different populations is known to vary significantly, especially between those of European ancestry and those of other ethnic ancestries. Additionally, the applicability of these risk scores across populations different by disease, instead of ethnicity, is poorly understood. A current and largely unexplored population for the accuracy of PRS is that of individuals with autism. Combined with the increased prevalence of obesity in autistic populations, we seek to evaluate the difference in efficacy of PRS for obesity in autistic versus non-autistic populations. We show that genetic variants strongly associated with obesity in non-autistic populations are significantly less representative of the disease in autistic populations. Rather, these cases of obesity phenocopies in patients with autism may follow a different and non-conventional mechanism of action involving the regulation of oxytocin in the brain among other potential behavioral factors. Our findings elucidate the limits of PRS across populations contrasting by disease and suggest that obesity may be regulated differently in individuals with autism as compared to those without autism.

genetics↗

bioRxiv2019

FAIRshake: toolkit to evaluate the findability, accessibility, interoperability, and reusability of research digital resources

As more datasets, tools, workflows, APIs, and other digital resources are produced by the research community, it is becoming increasingly difficult to harmonize and organize these efforts for maximal synergistic integrated utilization. The Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles have prompted many stakeholders to consider strategies for tackling this challenge by making these digital resources follow common standards and best practices so that they can become more integrated and organized. Faced with the question of how to make digital resources more FAIR, it has become imperative to measure what it means to be FAIR. The diversity of resources, communities, and stakeholders have different goals and use cases and this makes assessment of FAIRness particularly challenging. To begin resolving this challenge, the FAIRshake toolkit was developed to enable the establishment of community-driven FAIR metrics and rubrics paired with manual, semi- and fully-automated FAIR assessment capabilities. The FAIRshake toolkit contains a database that lists registered digital resources, with their associated metrics, rubrics, and assessments. The FAIRshake toolkit also has a browser extension and a bookmarklet that enables viewing and submitting assessments from any website. The FAIR assessment results are visualized as an insignia that can be viewed on the FAIRshake website, or embedded within hosting websites. Using FAIRshake, a variety of bioinformatics tools, datasets listed on dbGaP, APIs registered in SmartAPI, workflows in Dockstore, and other biomedical digital resources were manually and automatically assessed for FAIRness. In each case, the assessments revealed room for improvement, which prompted enhancements that significantly upgraded FAIRness scores of several digital resources.

bioinformatics↗