# A resource of “bottom-line” variant associations for 1,281 complex traits by integrating data across published genome-wide association studies

## TL;DR

This paper creates a comprehensive resource of genome-wide association study results for 1,281 human traits by integrating data from thousands of studies to improve accuracy and replicability.

## Contribution

The novel 'bottom-line' procedure improves GWAS meta-analysis by accounting for sample overlap, leading to more accurate variant associations.

## Key findings

- Using all published associations increases true positives but also false positives compared to using only the largest GWAS.
- The bottom-line approach produces more accurate and comprehensive association lists than existing methods.
- Five bioinformatic methods yield reliable results when applied to bottom-line associations.

## Abstract

Through an analysis of 2,602 genome-wide association studies (GWAS) across 830 human traits, we find that most (56% of) well-studied traits have at least two published GWAS, and many (29%) have at least five. We show that the lack of an established approach for adjudicating variant association estimates across multiple published studies can lead to uncertainty and invalid inferences: using all associations ever published for a trait increases true positives (by 12%) but also false positives (by 55%) relative to using associations from the largest published GWAS for the trait. We employ a “bottom-line” procedure for meta-analyzing published GWAS while inferring and accounting for sample overlap, which identifies a more accurate and comprehensive list of associations relative to existing approaches. Five commonly used bioinformatic methods for post-GWAS analyses produce reliable results when applied to the bottom-line associations. We present these results for 1,281 human complex traits, including 1,839 single-ancestry and 576 trans-ancestry analyses, for browsing or download via the NHGRI Association to Function Knowledge Portal. This resource of “consensus” GWAS results is intended to increase replicability, reuse, and interpretation of GWAS and downstream analyses.