Increased power and accuracy of causal locus identification in time-series genome-wide association in sorghum.

The phenotypes of plants develop over time and change in response to the environment. New engineering and computer vision technologies track these phenotypic changes. Identifying the genetic loci regulating differences in the pattern of phenotypic change remains challenging. This study used functional principal component analysis (FPCA) to achieve this aim. Time-series phenotype data was collected from a sorghum (Sorghum bicolor) diversity panel using a number of technologies including RGB and hyperspectral imaging. This imaging lasted for thirty-seven days and centered on reproductive transition. A new higher density marker set was generated for the same population. Several genes known to control trait variation in sorghum have been previously cloned and characterized. These genes were not confidently identified in genome-wide association analyses at single time points. However, FPCA successfully identified the same known and characterized genes. FPCA analyses partitioned the role these genes play in controlling phenotypes. Partitioning was consistent with the known molecular function of the individual cloned genes. These data demonstrate that FPCA-based genome-wide association studies can enable robust time-series mapping analyses in a wide range of contexts. Moreover, time-series analysis can increase the accuracy and power of quantitative genetic analyses.