AI Article Synopsis

  • Genome sequencing is now an effective method for conducting genome-wide association studies (GWASs), allowing the analysis of a wider range of genetic variations, including rare ones.
  • A study of 167 GWAS publications from 2014 to 2020 revealed issues like inconsistent terminology and incomplete dataset reporting, especially for variants in aggregate tests, despite 81% including tests for multiple variants.
  • The authors call for improved reporting standards and data sharing to enhance the findability and accessibility of sequencing-based data, suggesting the adoption of standard terminology (seqGWAS) and better practices for single and aggregate variant analysis.

Article Abstract

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.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8637874PMC
http://dx.doi.org/10.1016/j.xgen.2021.100005DOI Listing

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