Academic journal article Genetics

Measuring Selection Coefficients below 10^sup -3^: Method, Questions, and Prospects

Academic journal article Genetics

Measuring Selection Coefficients below 10^sup -3^: Method, Questions, and Prospects

Article excerpt

ABSTRACT Measuring fitness with precision is a key issue in evolutionary biology, particularly in studying mutations of small effects. It is usually thought that sampling error and drift prevent precise measurement of very small fitness effects. We circumvented these limits by using a new combined approach to measuring and analyzing fitness. We estimated the mutational fitness effect (MFE) of three independent mini-Tn10 transposon insertion mutations by conducting competition experiments in large populations of Escherichia coli under controlled laboratory conditions. Using flow cytometry to assess genotype frequencies from very large samples alleviated the problem of sampling error, while the effect of drift was controlled by using large populations and massive replication of fitness measures. Furthermore, with a set of four competition experiments between ancestral and mutant genotypes, we were able to decompose fitness measures into four estimated parameters that account for fitness effects of our fluorescent marker (a), the mutation (b), epistasis between the mutation and the marker (g), and departure from transitivity (t). Our method allowed us to estimate mean selection coefficients to a precision of 2 . 1024. We also found small, but significant, epistatic interactions between the allelic effects of mutations and markers and confirmed that fitness effects were transitive in most cases. Unexpectedly, we also detected variation in measures of s that were significantly bigger than expected due to drift alone, indicating the existence of cryptic variation, even in fully controlled experiments. Overall our results indicate that selection coefficients are best understood as being distributed, representing a limit on the precision with which selection can be measured, even under controlled laboratory conditions.

(ProQuest: ... denotes formulae omitted.)

MUTATIONS of small effect can play an important role in evolution, but they are difficult to measure experimentally because the precision with which fitness effects can be measured is relatively low. For this reason, it remains unclear to what extent mutations with small beneficial effects contribute to fitness improvements (Orr 2005). It is also unclear how much deleterious mutations of small effect contribute to the genetic load and inbreeding depression (Charlesworth and Charlesworth 1998; Bataillon and Kirkpatrick 2000). More generally, the existence and influence of mutations of small effect is at the heart of the neutralist- selectionist controversy (e.g., Nei 2005). This debate can be addressed experimentally only if the precision of fitness measurements is lower than the inverse of effective population size, which seems beyond reach for large populations (Kreitman 1996). Finally, a low precision in fitness measures limits the ability to determine whether the fitness effect of a mutation varies across different environmental or genetic contexts and adds to other sources of stochasticity (Lenormand et al. 2009) to make it difficult to reliably predict evolutionary trajectories.

Precisely measuring fitness poses technical, conceptual, and statistical challenges. The technical challenge is to set up a technique that allows experiments to be carried out efficiently. The first major advance was to use "population cages" with Drosophila or other small animals (starting in the 1930s with the work of L'Heritier and Teissier 1937a,b). With such devices, environmental conditions are relatively controlled and gene flow can be eliminated. However, drift and indirect selection caused by loci under selection in linkage disequilibrium with the focal locus are difficult to account for. The same approach was applied to microorganisms (Dykhuizen and Hartl 1980), which can be made isogenic save for a focal gene, thereby reducing indirect selection due to initial linkage disequilibrium (e.g., Carrasco et al. 2007; Domingo-Calap et al. 2009 for distribution of mutation fitness effects; Elena et al. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.