Root systems develop different root types that individually sense cues from their local environment and integrate this information with systemic signals. This complex multi-dimensional amalgam of inputs enables continuous adjustment of root growth rates, direction, and metabolic activity that define a dynamic physical network. Current methods for analyzing root biology balance physiological relevance with imaging capability. To bridge this divide, we developed an integrated-imaging system called Growth and Luminescence Observatory for Roots (GLO-Roots) that uses luminescence-based reporters to enable studies of root architecture and gene expression patterns in soil-grown, light-shielded roots. We have developed image analysis algorithms that allow the spatial integration of soil properties, gene expression, and root system architecture traits. We propose GLO-Roots as a system that has great utility in presenting environmental stimuli to roots in ways that evoke natural adaptive responses and in providing tools for studying the multi-dimensional nature of such processes.DOI: http://dx.doi.org/10.7554/eLife.07597.001

Quantitative genetics has a long history in plants: It has been used to study specific biological processes, identify the factors important for trait evolution, and breed new crop varieties. These classical approaches to quantitative trait locus mapping have naturally improved with technology. In this review, we show how quantitative genetics has evolved recently in plants and how new developments in phenotyping, population generation, sequencing, gene manipulation, and statistics are rejuvenating both the classical linkage mapping approaches (for example, through nested association mapping) as well as the more recently developed genome-wide association studies. These strategies are complementary in most instances, and indeed, one is often used to confirm the results of the other. Despite significant advances, an emerging trend is that the outcome and efficiency of the different approaches depend greatly on the genetic architecture of the trait in the genetic material under study.