2.4. Literature review and annotation of significant hits

We performed automated literature review searches of PubMed, PubMed Central, and Google Scholar using the R packages “RISmed,” “rvest,” and “data.table” (Dowle, 2017; Kovalchik, 2017; Wickham, 2016). Due to web‐scraping limitations, up to 1,000, 20, and 10 results, respectively, from each source, were obtained and recorded in spreadsheets. For each peak, our search query was the name of the peak SNP and the scanned trait, for example, “rs4393438 AND schizophrenia.” We also queried the name of genes within ±50 kb of the SNP or genes with at least one marker in LD with the peak SNP with an r ² > 0.2 and the scanned trait, for example, “RASA3 AND schizophrenia.”

The five authors, acting as annotators, used the provided literature search results and LocusZoom plots to annotate each peak within a scan as having convincing connections with the scanned trait or not and record the results within provided summary sheets. Annotators were free to carry out additional literature and database searches on their own. The provided summary sheets stated the scanned trait and were divided up into multiple pages containing LocusZoom plots of each selected peak region centered on the most significant SNP as well as space for the annotators to write a paragraph describing their annotation results. The instructions provided to the annotators can be seen in Supporting Information S1 Text. Three annotators were also asked to rate the strength of evidence for association with the scanned trait of a particular peak on a scale of 0–3 with 0 being no evidence and 3 being very strong evidence. They were also asked to rate the likelihood of further study of those particular peaks on a scale of 0–3 with 0 being no likelihood and 3 being a very high likelihood.