Xin Li, Tingting Guo, Qi Mu, Xianran Li, and Jianming Yu

PNAS June 26, 2018. 115 (26) 6679-6684

Significance

Integrated analysis of genotype by environment can reveal the pattern and mechanistic interplay underlying the observed phenotype dynamics. A critical question needs to be answered to enhance our ability to conduct genomic and environmental analysis of varied phenotypic plasticity observed in natural field conditions: How to uncover patterns at different levels to facilitate complex trait dissection and performance prediction. In this study, we first uncovered the pattern of genotype response to different environments. We then uncovered the pattern generated by the combination of environmental factors and the pattern of genetic effects at the individual gene level across environments. Finally, we demonstrated that trait dissection to individual genes and genome-wide performance prediction can be conducted through a joint genomic regression analysis framework.

Abstract

Observed phenotypic variation in living organisms is shaped by genomes, environment, and their interactions. Flowering time under natural conditions can showcase the diverse outcome of the gene–environment interplay. However, identifying hidden patterns and specific factors underlying phenotypic plasticity under natural field conditions remains challenging. With a genetic population showing dynamic changes in flowering time, here we show that the integrated analyses of genomic responses to diverse environments is powerful to reveal the underlying genetic architecture. Specifically, the effect continuum of individual genes (Ma₁, Ma₆, FT, and ELF3) was found to vary in size and in direction along an environmental gradient that was quantified by photothermal time, a combination of two environmental factors (photoperiod and temperature). Gene–gene interaction was also contributing to the observed phenotypic plasticity. With the identified environmental index to quantitatively connect environments, a systematic genome-wide performance prediction framework was established through either genotype-specific reaction-norm parameters or genome-wide marker-effect continua. These parallel genome-wide approaches were demonstrated for in-season and on-target performance prediction by simultaneously exploiting genomics, environment profiling, and performance information. Improved understanding of mechanisms for phenotypic plasticity enables a concerted exploration that turns challenge into opportunity.

See: http://www.pnas.org/content/115/26/6679

Figure 1: Pattern finding in flowering time G × E of a genetic mapping population. Progression from data visualization of apparently complex G × E to pattern discovery: (A) Seven natural field environments. (B) Reaction norm based on a categorical order of photoperiod of seven environments. (C) Reaction norm based on a categorical order of population means for individual environments. (D) Reaction norm based on a numerical order of population means for individual environments. Flowering time expressed as GDD was analyzed. Each line connects the flowering time values of individual RIL across environments