2 2 Linking The Genotype Phenotype

The genotype s. m. [from German Genotypus, comp. of Gen «gene» and gr. τύπος «tipo»]. – In genetics, the actual genetic constitution of an individual, that is, the set of genes.
The phenotype s. m. [from German. Phänotypus, comp. Of phäno- «feno-1» and from gr. τύπος «tipo»]. In genetics, the complex of morphological and functional characteristics of an organism.


Thinking in terms of differences makes apparent an abstract entity that encapsulates both genetic and phenotypic levels. This entity is composed of a variation at a genetic locus (two alleles), its associated phenotypic change (two distinct phenotypic states), and their relationships. The three of us name the assemblage of these elements a “gephe,” but here we simply call it a “genotype–phenotype relationship” (GP relationship). We will show that the GP relationship is much more than a simple and loosely defined interaction between two levels of organization: it is a cause-and-effect connection that facilitates our understanding of phenotypic diversity.



In biology, a gene is a section of DNA that encodes a trait. The precise arrangement of nucleotides (each composed of a phosphate group, sugar and a base) in a gene can differ between copies of the same gene. Therefore, a gene can exist in different forms across organisms. These different forms are known as alleles. The exact fixed position on the chromosome that contains a particular gene is known as a locus.A diploid organism either inherits two copies of the same allele or one copy of two different alleles from their parents. If an individual inherits two identical alleles, their genotype is said to be homozygous at that locus.However, if they possess two different alleles, their genotype is classed as heterozygous for that locus. Alleles of the same gene are either autosomal dominant or recessive. An autosomal dominant allele will always be preferentially expressed over a recessive allele.The subsequent combination of alleles that an individual possesses for a specific gene is their genotype.Let’s look at a classic example – eye color.A gene encodes eye color.In this example, the allele is either brown, or blue, with one inherited from the mother, and the other inherited from the father.The brown allele is dominant (B), and the blue allele is recessive (b). If the child inherits two different alleles (heterozygous) then they will have brown eyes. For the child to have blue eyes, they must be homozygous for the blue eye allele.


The phenotype is the set of traits observable in an individual, primarily determined by its specific genotype, but also by the interaction between genotype and environment and other factors related to the mechanisms and ways of regulating gene expression and phenomena of gene interaction.The phenotype is first expressed in RNA sequences obtained from the transcription of genes and in amino acid sequences of encoded proteins.The three-dimensional structure and functionality of the latter are equally phenotypic expressions.On a broader level, the phenotype is the set of morphological, structural, physiological and behavioral characteristics of individual cells and entire living organisms, aspects dependent on the interaction of the products encoded by genes, that is, molecules of RNA and proteins.Like the term genotype, the concept of phenotype was introduced by Wilhelm Johannsen in 1909.Environmental factors that may influence the phenotype include nutrition, temperature, humidity and stress. Flamingos are a classic example of how the environment influences the phenotype. Whilst renowned for being vibrantly pink, their natural color is white – the pink color is caused by pigments in the organisms in their diet. A second example is an individual's skin color. Our genes control the amount and type of melanin that we produce, however, exposure to UV light in sunny climates causes the darkening of existing melanin and encourages increased melanogenesis and thus darker skin.interaction of the products encoded by genes, that is, molecules of RNA and proteins.Like the term genotype, the concept of phenotype was introduced by Wilhelm Johannsen in 1909.



We argued above that the differential view should always be kept in mind hen thinking about the connection between genotypes and phenotypes. GWAS, which represent the most popular method to detect genomic loci that are associated with complex traits in populations, are based on the analysis of differences (Visscher et al., 2012). Nevertheless, in current research the differential view is sometimes implicitly dismissed. When multiple factors are observed to influence phenotypic traits the differential view is considered as too simplistic and researchers often prefer to focus back on phenotypes of single individuals, without explicitly relating them to a phenotypic reference.
In most current articles, the problem of connecting the genotype to the phenotype is framed in terms of genotype and phenotype maps. The first GP map was introduced by Richard Lewontin in his book “The genetic basis of evolutionary change” (Lewontin, 1974a; Figure 2A). He indicated the average genotype of a population as a point in the space of all possible genotypes (G space) and the average phenotype of the same population as a corresponding point in the space of all possible phenotypes (P space). The evolutionary process was thus decomposed into four steps: (1) the average phenotype is derived from the development of the distinct genotypes in various environments; (2) migration, mating, and natural selection acts in P space to change the average phenotype of the initial population into the average phenotype of the individuals which will have progeny; (3) the identity of successful parents determines which genotypes are preserved; and (4) genetic processes such as mutation and recombination modify position in G space.
In another common graphical representation , a point in the G space and its corresponding point in the P space correspond to the genotype and the phenotype of a single individual (Fontana, 2002; Landry and Rifkin, 2012). Under such a representation, the abstract object that we defined above as the GP relationship would correspond to a “move” in genotype space associated with a “move” in phenotype space (or, better, a sum of several “moves” in genotype, and phenotype spaces because several distinct genomes can carry the two alternative alleles of a given GP relationship). In a third representation put forward by Wagner (1996; , individual genes are connected to individual traits.
Although these three graphical representations of GP maps may facilitate our understanding of certain aspects of biology, in all of them the GP relationship and the differential view are not easy to grasp. It is quite perplexing that the first person to draw such a GP map was Richard Lewontin, an eloquent advocate of the differential view (see for example his preface to Oyama, 2000, a masterpiece of persuasion). Because these graphics focus on individual rather than differential objects, we believe that these three representations implicitly incite us to go back to the more intuitive idea of one genotype associated with one phenotype. Losing sight of the differential view might also come from the molecular biology perspective, where proteins are viewed as having causal effects on their own, such as phosphorylation of a substrate or binding to a DNA sequence. Because of the two entangled definitions of the gene, either as encoding a protein, or as causing a phenotypic change (Griffiths and Stotz, 2013), it is easy to move from a differential view to a non-differential view of the GP relationship.


In summary, many current mental representations of the connection between genotype and phenotype implicitly dismiss the differential view. We will now show that the differential view is compatible with the fact that phenotypic traits are influenced by a complex combination of multiple factors and that we can find a relevant schematic representation of GP relationships.
The relationship between the genetic properties of an organism (genotype) and the observable traits (phenotype) is complex and intricate. An EU-funded project has analysed this report to better understand how, why and when species adapt to their environment. The field of quantitative evolution genetics strives to predict the evolutionary properties of a population or species without describing in detail the complexity of the genotype-phenotype relationship. The frequently used models are limited to synthesizing simple parameters of genetic architecture. The EVOLGA project ("Modelling the evolutionary properties of complex genetic architectures") challenged the ability of these models to describe and predict the evolutionary potential of populations and species. The team proceeded by comparing model predictions with more realistic empirical data and/or models. The project members wanted to understand the impact of genetic interactions (epistasis) and the multiple characteristics on the evolutionary properties of quantitative traits. To achieve their goals, researchers have built and explored theoretical models. They demonstrated how genetic interactions allowed complex and profound changes in genetic architectures, and described their possible role in evolution. Experimental data have shown that lack of independence between characters could slow down, or even prevent, their respective evolution. The project also offered interesting insights into the impact of "selfish" DNA sequences, which can be maintained in genomes without being useful to host species. These findings have highlighted the complexity of some evolutionary processes, and will contribute to our understanding of the adaptability of a species. In the future, the team plans to create new theoretical models to address these issues. They also intend to use statistical models developed during the project to analyze published genome sequences and design new experiments.
A perfect example of correlation between genotype and phenotype is the problem of pleiotropy, which is a genetic phenomenon for which a single gene determines multiple phenotypic effects, at first glance, even unrelated to each other.


Decomposing an organism into elementary units such as anatomical structures has been instrumental in many biology disciplines such as physiology, paleontology and evolution. However, the issue is to identify the decomposition into characters that is most adequate for the question of interest. For questions related to relationships between organs of various individuals or species (such as homology), it might be appropriate to keep the traditional decomposition into anatomical structures (Wagner, 2014). Richard Lewontin and Günter Wagner defined characters as elements within an organism that answer to adaptive challenges and that represent quasi-independent units of evolutionary change (Lewontin, 1978; Wagner, 2000). Their definition deals with absolute traits observed in single organisms (for example the shape of a wing, or the number of digits in an individual) and is thus far from the differential view. Here, to better apprehend evolution and phenotypic diversity of the living world, we propose to decompose the observable attributes of an organism into multiple elementary GP variations that have accumulated through multiple generations, starting from an initial state. We insist that under this perspective, characters are not concrete objects (such as skin) but abstract entities defined by the existence of differences between two possible observable states (for example skin color). As an analogy, one can imagine two ways to produce a well-worn leather shoe of a particular shape. One can either assemble the different atoms into the same organization, or one can buy a shoe in a store and then subject it to a series of mechanical forces. We are naturally inclined to compare organisms to machines, and to think in terms of pieces that must be assembled to make a functional whole. However, the rampant metaphor of the designer or maker is inadequate for understanding the origin of present-day organisms (Coen, 2012). To understand the phenotypic features of a given organism it is more efficient to decompose it into abstract changes that occurred successively across evolutionary time, and not across developmental time. The initial state is a hypothetical ancestor of the organism under study.
Certain mutations (qualified as pleiotropic) are observed to affect several organs at once while others alter only one at a time (Paaby and Rockman, 2013; Zhang and Wagner, 2013). For pleiotropic mutations, we consider that the GP relationship should include all the phenotypic changes (in diverse organs, at various stages, etc.) associated with the genetic difference. For instance, the V370A mutation of the EDAR receptor is associated not only to hair thickness but also to changes in sweat gland and mammary gland density in Asian populations (Kamberov et al., 2013). The GP relationship is, in such cases, one-to-multi. Considering skin and eye as independent anatomical modules of the human body might seem appropriate for many evolutionary changes, but it is somewhat inadequate in cases where these two organs evolved a new pigmentation trait at once through a single mutation in the SLC45A2 gene (Liu et al., 2013). Reasoning in terms of GP relationships strikes off the problem of finding a relevant decomposition into elementary anatomical structures. The elementary GP relationships themselves appear as adequate semi-independent modules, whose combination can account for the observable characteristics of an organism.


The correlation between genotype and phenotype also characterizes some diseases, such as Multiple endocrine neoplasms type 2 (MEN 2) that are inherited syndromes transmitted with autosomal dominant character and are characterized by the presence of medullary thyroid carcinoma (CMT) associated (MEN 2A and MEN 2B) or not (familial CMT, CMTF) with other endocrine diseases.
MEN 2 syndromes are characterized by a strong genotype-phenotype correlation and a specific RET mutation may be responsible for one of the three syndromes. In particular, a statistically significant association has been observed between mutations at codon 634 (85%), encoding cysteine, and MEN 2A with the presence of pheochromocytoma (FEO) and/or hyperparathyroidism (IPTH), while mutations at codons 609, 611, 618 and 620, also coding for cysteine, are present only in 10-15% of MEN 2A cases. Other non-cysteine mutations have also been described in MEN 2A, such as codons 533, 637, 768, 790, 791, 804 and 891, but with very low frequency. Non-cysteine mutations are much more frequently associated with CMTF. However, in this case the genotype-phenotype relationship is a little less determined as the RET mutations associated with the CMTF are variously distributed throughout the gene. A very close association was found between the 918 codon mutation and MEN 2B. The studies conducted so far have shown that there is also a close correlation between the type of mutation of the RET gene and the clinical manifestations of MEN 2 syndromes not only in terms of genotype-phenotype relationship but also with regard to the aggressiveness of neoplasms, in particular of the CMT, and the age of onset of the first clinical manifestation. This relationship is of considerable practical importance in managing family members who are carriers of the mutation and are not yet affected by the disease, for whom a diagnostic and therapeutic path must be planned which, taking into account the type of mutation, may be different in the modalities and timing of the implementation of the controls.


In this paper, we bring back the differential concept of gene (Schwartz, 2000) into our framework for understanding the GP map. The differential view of the GP relationship helps to clarify the genetic and environmental effects on phenotypes and their connection. It also opens up new avenues of thinking, in particular regarding the decomposition of observable features within an organism and the representation of GP maps. Furthermore, the existence of taxonomically robust GP relationships encourages an unabashed use of comparative genetics to predict the genetic basis of phenotypic variation in diverse groups of organisms and this predictive power has an important potential for translational research in agronomy and clinical research.






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