Abstract
Multi-phenotype analysis has drawn increasing attention to high-throughput genomic studies, whereas only a few applications have justified the use of multivariate techniques. We applied a recently developed multi-trait analysis method on a small set of bacteria hypersensitive response phenotypes and identified a single novel locus missed by conventional single-trait genome-wide association studies. The detected locus harbors a minor allele that elevates the risk of leaf collapse response to the injection of avrRpm1-modified Pseudomonas syringae (P = 1.66e-08). Candidate gene AT3G32930 with in the detected region and its co-expressed genes showed significantly reduced expression after P. syringae interference. Our results again emphasize that multi-trait analysis should not be neglected in association studies, as the power of specific multi-trait genotype-phenotype maps might only be tractable when jointly considering multiple phenotypes.
Highlights
Multi-phenotype analysis has drawn increasing attention to high-throughput genomic studies, whereas only a few applications have justified the use of multivariate techniques
Substantial gene discovery power has been observed in such model species especially with inbred lines, as noise in the data can be well controlled by designed experimental conditions, sample repeats and absence of heterozygote individuals
We have recently developed a fast multivariate genome-wide association studies (GWAS) tool that directly works with single-trait GWAS summary-level data[9], combining with conventional mixed model analysis, we can conduct multivariate GWAS in highly structured populations such as A. thaliana
Summary
Multi-phenotype analysis has drawn increasing attention to high-throughput genomic studies, whereas only a few applications have justified the use of multivariate techniques. Substantial gene discovery power has been observed in such model species especially with inbred lines, as noise in the data can be well controlled by designed experimental conditions, sample repeats and absence of heterozygote individuals. This provides us an opportunity to unravel genetic architecture of complex traits even using a much smaller population compared to humans. Despite the fact that different multivariate methods have been proposed for GWAS of more than one phenotype, few studies in model species have been conducted using multivariate techniques[7,8] This can be due to the complexity of interpreting multivariate statistical analysis results and the lack of user-friendly computational tools. With statistical interpretation and bioinformatic justification, we aim to provide a clear example of power gain and emphasize the importance of joint analyzing correlated phenotypes in different inbred populations
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