Comparing Genome-Wide Association Study Results from Different Measurements of an Underlying Phenotype

Abstract

Increasing popularity of high-throughput phenotyping technologies, such as image-based phenotyping, offer novel ways for quantifying plant growth and morphology. These new methods can be more or less accurate and precise than traditional, manual measurements. Many large-scale phenotyping efforts are conducted to enable genome-wide association studies (GWAS), but it is unclear exactly how alternative methods of phenotyping will affect GWAS results. In this study we simulate phenotypes that are controlled by the same set of causal loci but have differing heritability, similar to two different measurements of the same morphological character. We then perform GWAS with the simulated traits and create receiver operating characteristic (ROC) curves from the results. The areas under the ROC curves (AUCs) provide a metric that allows direct comparisons of GWAS results from different simulated traits. We use this framework to evaluate the effects of heritability and the number of causative loci on the AUCs of simulated traits; we also test the differences between AUCs of traits with differing heritability. We find that both increasing the number of causative loci and decreasing the heritability reduce a trait's AUC. We also find that when two traits are controlled by a greater number of causative loci, they are more likely to have significantly different AUCs as the difference between their heritabilities increases. When simulation results are applied to measures of tassel morphology, we find no significant difference between AUCs from GWAS using manual and image-based measurements of typical maize tassel characters. This finding indicates that both measurement methods have similar ability to identify genetic associations. These results provide a framework for deciding between competing phenotyping strategies when the ultimate goal is to generate and use phenotype-genotype associations from GWAS.

Plant phenotyping relevance

異なる植物表現型測定法（手動測定と画像測定）がGWAS結果に与える影響を評価する枠組みを開発・検証し、トウモロコシの雄穂形態で適用しているため、表現型取得法の比較が中心である。

Code and data availability

The paper's supplemental material on Figshare (which includes the tassel phenotype data used for the manual vs. image-based GWAS comparisons) is publicly available at an allowed URL. The authors' analysis scripts (github.com/joegage/GWAS_AUC) and genotypic data (Dryad doi:10.5061/dryad.6tg35t6) are mentioned but their