Abstract

Classically, phenotype is what is observed, and genotype is the genetic makeup. Statistical studies aim to project phenotypic likelihoods of genotypic patterns. The traditional genotype-to-phenotype theory embraces the view that the encoded protein shape together with gene expression level largely determines the resulting phenotypic trait. Here, we point out that the molecular biology revolution at the turn of the century explained that the gene encodes not one but ensembles of conformations, which in turn spell all possible gene-associated phenotypes. The significance of a dynamic ensemble view is in understanding the linkage between genetic change and the gained observable physical or biochemical characteristics. Thus, despite the transformative shift in our understanding of the basis of protein structure and function, the literature still commonly relates to the classical genotype–phenotype paradigm. This is important because an ensemble view clarifies how even seemingly small genetic alterations can lead to pleiotropic traits in adaptive evolution and in disease, why cellular pathways can be modified in monogenic and polygenic traits, and how the environment may tweak protein function.

Highlights

  • The terms genotype and phenotype have been in use at least since the turn of the last century

  • Since we focus on protein ensembles, we only consider mutations/SNPs in protein-coding regions

  • Its significance lies in deeper understanding of the connection of the disease phenotype with the genetic change and in providing the structural basis for disease-treating decisions [77]

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Summary

Introduction

The terms genotype and phenotype have been in use at least since the turn of the last century. Because distinct mutations can shift the ensemble in different ways, the resulting edges can precipitate altered types of cancer by the same protein, as in the case of KRas driver mutations, which have different frequencies in distinct cancers [120] This description holds both for adaptive evolution and for disease. Cell signaling and phenotypic expression take place across time and space and are on length scales from nanometers to micrometers [124], which require consideration of how the genetic variation would affect the function in the cell This has led to a strategy based on knowledge of cellular subsystems and their hierarchical organization as defined by the Gene Ontology (GO) or inferred from published datasets [10]. The large conformational differences among prion strains provide a structural basis for their physiological phenotypic behavior

Methods to associate ensembles and function
Findings
Conclusions
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