As a continuation of the previous post on the Fundamental concepts in genetics series here is the next topic : "The pleiotropic structure of the genotype–phenotype map: the evolvability of complex organisms". The paper starts of with a description of the goals of genetics and how the Genotype-Phenotype map (GMP) is very useful in understanding genetics.
While pleiotropy has been sometimes defined as changes in one gene affecting traits that are seemingly unrelated, the part about "seemingly unrelated" is dropped probably as it is difficult to define what seems to be related or not.
The cost of complexity hypothesis postulates that complex organisms are fundamentally less evolvable compared to simpler organisms as complex organisms are more pleiotropic. Fisher's geometric model and the microscope analogy are described. While theoretical predictions are available, empirical data has only become available recently. Measurement of pleiotropy and the extent and patterns of pleiotropy are being studies with the datasets that have been generated.
Measurement of pleiotropy is complicated by at least 2 different cases:
- Closely linked genes affecting 2 different traits tend to be co-inherited and can be appear to be due to pleiotropic effects of a single gene.
- Shared cis-regulatory element of 2 genes will affect the traits controlled by both genes. This has been called artefactual pleiotropy as pleiotropy can be defined as a character of a genes rather than that of a mutation.
Apart from the technical difficulties involved is measuring pleiotropic effects, conceptual problems like the definition of a "phenotypic trait or character" make it difficult to have an objective measure of pleiotropy.
- QTL data tend to overestimate pleiotropic effects due to biases caused due to linked genes
- Gene knockout and knockdown experiments avoid the problem of closely linked genes but is only able it only measures mutations that lead to complete loss of gene function
- Mesures of pleiotropy depend on the number and type of traits that are measured in an experiment
- Traits that are beyond the detection limits of methods used to measure traits also tend to lead to a distorted picture of pleiotropy
- Theoretical methods to estimate pleiotropy have used the relationship between the genetic load, effective population size and effective dimensionality of the phenotype (average pleiotropy)
- Distribution of fitness effects have also been used to predict phenotypic complexity based on predictions of FGM
Correlation among the traits also introduces many issues in estimation of pleiotropy.However, while universal pleiotropy was considered to be the norm, its being increasingly argued that the extent of pleiotropy is very minimal(variational modularity or restricted pleiotropy). With the datasets from Yeast, C.Elegans etc.. it is being seen that the degree of pleiotropy even with the upward biased estimates is rather low.
While the molecular basis of Pleiotropy is not well studied, two types of pleiotropy were suggested by Gruneberg way back in 1938. Type I (initially called genuine) pleiotropy refers to multiple molecular functions of a single gene product. Type II (initially called spurious) pleiotropy refers to multiple morphological & physiological effects of a single molecular function. While, recent studies have shown type II pleiotropy to be most prevalent. Hence, the pleiotropy seen today could be a result of new biological functions being assigned to the same genes which still have the same molecular functions. However, the extent of pleiotropy is not something that is settled and will probably require a lot of work that would require solving the various issues involved in measuring and analyzing pleiotropy.