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将功能表型分析与系统映射相结合以阐明基因型-表型关联。

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

作者信息

Sun Ting, Shi Zheng, Jiang Rujia, Moshelion Menachem, Xu Pei

机构信息

Key Laboratory of Specialty Agri-product Quality and Hazard Controlling Technology of Zhejiang Province, College of Life Sciences, China Jiliang University, Hangzhou 310018, P.R. China.

The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University of Jerusalem, Rehovot 76100, Israel.

出版信息

Hortic Res. 2024 Sep 9;11(12):uhae256. doi: 10.1093/hr/uhae256. eCollection 2024 Dec.

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.

摘要

揭示复杂性状下的表型-基因型黑箱对研究人员来说是一个雄心勃勃的目标。由于当今时空表型数据高度依赖环境和发育阶段的动态性质,其生成速度已远远超过了解释速度。在此,我们提出一种综合的环境-表型-基因型功能方法,以确定分解生理性状的主要挑战。该策略首先以高通量功能生理表型分析(FPP)为特色,以有效获取表型和环境数据。然后以功能定位(FM)和扩展系统定位(SM)为特色,以应对性状动态。通过将性状建模为连续函数,FM可以提高剖析QTL时空效应的能力和效率。SM能够从发育途径重建基因型-表型图谱。我们展示了一个最近的案例研究,该研究结合了FPP和SM来剖析复杂的生理性状。这种综合方法将成为推动表型组大数据转化为遗传增益的重要引擎。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/10cb/11630247/df710586f2ac/uhae256f1.jpg

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