Genome sequencing has recently become a viable genotyping technology for use in genome-wide association studies (GWASs), offering the potential to analyze a broader range of genome-wide variation, including rare variants. To survey current standards, we assessed the content and quality of reporting of statistical methods, analyses, results, and datasets in 167 exome- or genome-wide-sequencing-based GWAS publications published from 2014 to 2020; 81% of publications included tests of aggregate association across multiple variants, with multiple test models frequently used. We observed a lack of standardized terms and incomplete reporting of datasets, particularly for variants analyzed in aggregate tests. We also find a lower frequency of sharing of summary statistics compared with array-based GWASs. Reporting standards and increased data sharing are required to ensure sequencing-based association study data are findable, interoperable, accessible, and reusable (FAIR). To support that, we recommend adopting the standard terminology of sequencing-based GWAS (seqGWAS). Further, we recommend that single-variant analyses be reported following the same standards and conventions as standard array-based GWASs and be shared in the GWAS Catalog. We also provide initial recommended standards for aggregate analyses metadata and summary statistics. Display omitted Recommendations for increasing FAIRness of sequencing-based GWASsTo be findable, we recommend standard terminology of sequencing-based GWAS (seqGWAS)To improve access and standards, the GWAS Catalog will support deposition of seqGWASTo improve utility, we recommend reporting standards for single and aggregate analyses McMahon et al. report an analysis of the sequencing-based GWAS literature, finding a lack of standardized language and incomplete reporting, along with less-frequent sharing of summary statistics compared with that of array-based GWASs. We provide recommendations for the reporting and sharing of sequencing-based GWASs to increase FAIRness of these valuable datasets.