Converging functional phenotyping with systems mapping to illuminate the genotype-phenotype associations

Abstract

Illuminating the phenotype-genotype black box under complex traits is an ambitious goal for researchers. The generation of temporally or spatially phenotypic data today has far outpaced its interpretation, due to their highly dynamic nature depending on the environment and developmental stages. Here, we propose an integrated enviro-pheno-geno functional approach to pinpoint the major challenges of decomposing physiological traits. The strategy first features high-throughput functional physiological phenotyping (FPP) to efficiently acquire phenotypic and environmental data. It then features functional mapping (FM) and the extended systems mapping (SM) to tackle trait dynamics. FM, by modeling traits as continuous functions, can increase the power and efficiency in dissecting the spatiotemporal effects of QTLs. SM could enable reconstruction of a genotype-phenotype map from developmental pathways. We present a recent case study that combines FPP and SM to dissect complex physiological traits. This integrated approach will be an important engine to drive the translation of phenomic big data into genetic gain.

Acknowledgements

This work is supported by the National Key Research & Development Program of China (China-Israel 2022YFE0198000 and 3013005724), Zhejiang Science and Technology Major Program on Agricultural New Variety (2021C02067-7, 2021C02066-5), Key Research Program of Zhejiang Province (2021C02041), the Italy-Israel Scientific Research Program (3013005619), and the Natural Science Foundation of Zhejiang Province (LQ23C150006).

Data availability

The data underlying this article are available in the GitHub repository, at https://github.com/suntingsd/System-mapping.

Conflict of interest statement

All authors declare no financial or non-financial competing interests.

Supplementary Data

Supplementary data is available at Horticulture Research online.

References

[1] Zavafer A, Bates H, Mancilla C. et al. Phenomics: conceptualization and importance for plant physiology. Trends Plant Sci. 2023; 28:1004-13

[2] Gosa SC, Lupo Y, Moshelion M. Quantitative and comparative analysis of whole-plant performance for functional physiological traits phenotyping: new tools to support pre-breeding and plant stress physiology studies. Plant Sci. 2019; 282:49-59

[3] York LM. Functional phenomics: an emerging field integrating high-throughput phenotyping, physiology, and bioinformatics. J Exp Bot. 2019; 70:379-86

[4] Santini F, Kefauver SC, Araus JL. et al. Bridging the genotype-phenotype gap for a Mediterranean pine by semi-automatic crown identification and multispectral imagery. New Phytol. 2021; 229:245-58

[5] Stahl A, Wittkop B, Snowdon RJ. High-resolution digital phenotyping of water uptake and transpiration efficiency. Trends Plant Sci. 2020; 25:429-33

[6] Arend D, Psaroudakis D, Memon JA. et al. From data to knowledge - big data needs stewardship, a plant phenomics perspective. Plant J. 2022; 111:335-47

[7] Pauli D, Andrade-Sanchez P, Carmo-Silva AE. et al. Field-based high-throughput plant phenotyping reveals the temporal patterns of quantitative trait loci associated with stress-responsive traits in cotton. G3 (Bethesda). 2016; 6:865-79

[8] Feldman MJ, Ellsworth PZ, Fahlgren N. et al. Components of water use efficiency have unique genetic signatures in the model C4 grass setaria. Plant Physiol. 2018; 178:699-715

[9] Guo Q, Wu F, Pang S. et al. Crop 3D—a LiDAR based platform for 3D high-throughput crop phenotyping. Sci China Life Sci. 2018; 61:328-39

[10] Li Z, Sillanpää MJ. Dynamic quantitative trait locus analysis of plant phenomic data. Trends Plant Sci. 2015; 20:822-33

[11] Wu R, Lin M. Functional mapping—how to map and study the genetic architecture of dynamic complex traits. Nat Rev Genet. 2006; 7:229-37

[12] Ma C-X, Casella G, Wu R. Functional mapping of quantitative trait loci underlying the character process: a theoretical framework. Genetics. 2002; 161:1751-62

[13] Jiang L, Sun L, Ye M. et al. Functional mapping of N deficiency-induced response in wheat yield-component traits by implementing high-throughput phenotyping. Plant J. 2019; 97:1105-19

[14] Pandey AK, Jiang L, Moshelion M. et al. Functional physiological phenotyping with functional mapping: a general framework to bridge the phenotype-genotype gap in plant physiology. iScience. 2021; 24:102846

[15] Yin X, Struik PC. Applying modelling experiences from the past to shape crop systems biology: the need to converge crop physiology and functional genomics. New Phytol. 2008; 179:629-42

[16] Diao H, Cernusak LA, Saurer M. et al. Uncoupling of stomatal conductance and photosynthesis at high temperatures: mechanistic insights from online stable isotope techniques. New Phytol. 2024; 241:2366-78

[17] Sun L, Wu R. Mapping complex traits as a dynamic system. Phys Life Rev. 2015; 13:155-85

[18] Wu R, Cao J, Huang Z. et al. Systems mapping: how to improve the genetic mapping of complex traits through design principles of biological systems. BMC Syst Biol. 2011; 5:84

[19] Stadter P, Schalte Y, Schmiester L. et al. Benchmarking of numerical integration methods for ODE models of biological systems. Sci Rep. 2021; 11:2696

[20] Gong H, Zhang XY, Zhu S. et al. Genetic architecture of multiphasic growth covariation as revealed by a nonlinear mixed mapping framework. Front Plant Sci. 2021; 12:711219

[21] Wen Z, Jiang L, Li M. et al. Mapping the genetic architecture of developmental modularity in ornamental plants. Ornam Plant Res. 2021; 1:1-10

[22] Xu Z, Zhou G. Responses of leaf stomatal density to water status and its relationship with photosynthesis in a grass. J Exp Bot. 2008; 59:3317-25

[23] Buckley TN. How do stomata respond to water status? New Phytol. 2019; 224:21-36

[24] Bacher H, Sharaby Y, Walia H. et al. Modifying root-to-shoot ratio improves root water influxes in wheat under drought stress. J Exp Bot. 2022; 73:1643-54