No abstract

Coastal cities around the world have experienced large costs from major flooding events in recent years. Climate change is predicted to bring an increased likelihood of flooding due to sea level rise and more frequent severe storms. In order to plan future development and adaptation, cities must know the magnitude of losses associated with these events, and how they can be reduced. Often losses are calculated from insurance claims or surveying flood victims. However, this largely neglects the loss due to the disruption of economic activity. We use a forward‐looking dynamic computable general equilibrium model to study how a local economy responds to a flood, focusing on the subsequent recovery/reconstruction. Initial damage is modeled as a shock to the capital stock and recovery requires rebuilding that stock. We apply the model to Vancouver, British Columbia by considering a flood scenario causing total capital damage of $14.6 billion spread across five municipalities. GDP loss relative to a no‐flood scenario is relatively long‐lasting. It is 2.0% ($2.2 billion) in the first year after the flood, 1.7% ($1.9 billion) in the second year, and 1.2% ($1.4 billion) in the fifth year.

The process of identifying suitable genome-wide association (GWA) studies and formatting the data to calculate multiple polygenic risk scores on a single genome can be laborious. Here, we present a centralized polygenic risk score calculator currently containing over 250,000 genetic variant associations from the NHGRI-EBI GWAS Catalog for users to easily calculate sample-specific polygenic risk scores with comparable results to other available tools. Polygenic risk scores are calculated either online through the Polygenic Risk Score Knowledge Base (PRSKB; https://prs.byu.edu) or via a command-line interface. We report study-specific polygenic risk scores across the UK Biobank, 1000 Genomes, and the Alzheimer’s Disease Neuroimaging Initiative (ADNI), contextualize computed scores, and identify potentially confounding genetic risk factors in ADNI. We introduce a streamlined analysis tool and web interface to calculate and contextualize polygenic risk scores across various studies, which we anticipate will facilitate a wider adaptation of polygenic risk scores in future disease research.