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Integrating Intermediate Traits in Phylogenetic Genotype-to-Phenotype Studies

生物 特质 表型 进化生物学 性状 系统发育树 基因型 系统发育学 遗传学 基因型-表型区分 适应(眼睛) 计算生物学 基因 神经科学 计算机科学 程序设计语言
作者
Nathan L. Clark,Chris Todd Hittinger,Hongmei Li‐Byarlay,Antonis Rokas,Timothy B. Sackton,Robert L. Unckless
出处
期刊:Integrative and Comparative Biology [Oxford University Press]
标识
DOI:10.1093/icb/icaf037
摘要

Abstract A major goal of research in evolution and genetics is linking genotype to phenotype. This work could be direct, such as determining the genetic basis of a phenotype by leveraging genetic variation or divergence in a developmental, physiological, or behavioral trait. The work could also involve studying the evolutionary phenomena (e.g., reproductive isolation, adaptation, sexual dimorphism, behavior) that reveal an indirect link between genotype and a trait of interest. When the phenotype diverges across evolutionarily distinct lineages, this genotype-to-phenotype problem can be addressed using phylogenetic genotype-to-phenotype (PhyloG2P) mapping, which uses genetic signatures and convergent phenotypes on a phylogeny to infer the genetic bases of traits. The PhyloG2P approach has proven powerful in revealing key genetic changes associated with diverse traits, including the mammalian transition to marine environments and transitions between major mechanisms of photosynthesis. However, there are several intermediate traits layered in between genotype and the phenotype of interest, including but not limited to transcriptional profiles, chromatin states, protein abundances, structures, modifications, metabolites, and physiological parameters. Each intermediate trait is interesting and informative in its own right, but synthesis across data types has great promise for providing a deep, integrated, and predictive understanding of how genotypes drive phenotypic differences and convergence. We argue that an expanded PhyloG2P framework (the PhyloG2P matrix) that explicitly considers intermediate traits, and imputes those that are prohibitive to obtain, will allow a better mechanistic understanding of any trait of interest. This approach provides a proxy for functional validation and mechanistic understanding in organisms where laboratory manipulation is impractical.

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