Abstract
Linking natural genetic variation to trait variation can help determine the functional roles ofdifferent genes. Variations of one or several traits are often assessed separately. High-throughput phenotyping and data mining can capture dozens or hundreds of traits from the same individuals. Here, we test the association between markers within a gene and many traits simultaneously. This genome–phenome wide association study (GPWAS) is both a multi-marker and multi-trait test. Genes identified using GPWAS with 260 phenotypic traits in maize were enriched for genes independently linked to phenotypic variation. Traits associated with classical mutants were consistent with reported phenotypes for mutant alleles. Genes linked to phenomic variation in maize using GPWAS shared molecular, population genetic, and evolutionary features with classical mutants in maize. Genes linked to phenomic variation in Arabidopsis using GPWAS are significantly enriched in genes with known loss-of-function phenotypes. GPWAS may be an effective strategy to identify genes in which loss-of-function alleles produce mutant phenotypes. The shared signatures present in classical mutants and genes identified using GPWAS may be markers for genes with a role in specifying plant phenotypes generally or pleiotropy specifically.
Highlights
In multicellular eukaryotes, only a small proportion of all annotated genes have yet to be linked to loss-of-function phenotypes (Schnable and Freeling, 2011; Lamesch et al, 2012; Schofield et al, 2012; Rhee and Mutwil, 2014; Chong et al, 2015; Schnable, 2019)
Our model requires two data matrices: one containing allele calls for many genetic markers across individuals in a population and a second containing observed values for the same individuals across many traits
One or more genetic markers are assigned to a given gene or other genomic interval of interest
Summary
Only a small proportion of all annotated genes have yet to be linked to loss-of-function phenotypes (Schnable and Freeling, 2011; Lamesch et al, 2012; Schofield et al, 2012; Rhee and Mutwil, 2014; Chong et al, 2015; Schnable, 2019). The majority of current quantitative genetics approaches seek to identify either genetic markers or genes linked to single phenotypes, with a subset considering data from multiple correlated phenotypes (Korte et al, 2012; O’Reilly et al, 2012; Zhou and Stephens, 2012; Stephens, 2013; van der Sluis et al, 2013; Wang et al, 2015; Turley et al, 2018; Pitchers et al, 2019).
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