Biomedical subjects

Jason G Mezey

Publications and source records attributed to Jason G Mezey.

4 recordsLinked to original sources

PubMed2005

Evolutionary genomics: codon bias and selection on single genomes.

The idea that natural selection on genes might be detected using only a single genome has been put forward by Plotkin and colleagues, who present a method that they claim can detect selection without the need for comparative data and which, if correct, would confer greater power of analysis with less information. Here we argue that their method depends on assumptions that confound their conclusions and that, even if these assumptions were valid, the authors' inferences about adaptive natural selection are unjustified.

Bias↗

PubMed2005

The dimensionality of genetic variation for wing shape in Drosophila melanogaster.

Absolute constraints are limitations on genetic variation that preclude evolutionary change in some aspect of the phenotype. Absolute constraints may reflect complete absence of variation, lack of genetic variation that extends the range of phenotypes beyond some limit, or lack of additive genetic variation. This last type of absolute constraint is bidirectional, because the mean cannot evolve to be larger or smaller. Most traits do possess genetic variation, so bidirectional absolute constraints are most likely to be detected in a multivariate context, where they would reflect combinations of traits, or dimensions in phenotype space that cannot evolve. A bidirectional absolute constraint will cause the additive genetic covariance matrix (G) to have a rank less than the number of traits studied. In this study, we estimate the rank of the G-matrix for 20 aspects of wing shape in Drosophila melanogaster. Our best estimates of matrix rank are 20 in both sexes. Lower 95% confidence intervals of rank are 17 for females and 18 for males. We therefore find little evidence of bidirectional absolute constraints. We discuss the importance of this result for resolving the relative roles of selection and drift processes versus constraints in the evolution of wing shape in Drosophila.

Animals↗

PubMed2004

Naturally segregating quantitative trait loci affecting wing shape of Drosophila melanogaster.

Variation in vein position and wing shape of Drosophila melanogaster depends on many genes. In the following, we report the results of a QTL analysis of wing shape in D. melanogaster. We identified QTL responsible for natural variation for wing shape and analyzed their interactions with developmental genetic signaling pathways important for vein positioning. The QTL analysis indicated that the total number of QTL segregating in this population is likely to be very large. The locations of putative QTL identified in this study were compared to those identified in previous studies and, while there is more correspondence across studies than expected by chance on the third chromosome, the studies appear to have identified different QTL. Using a complementation design, we tested for interactions among these QTL with the Hedgehog and Decapentaplegic signaling pathways, which are important for the development and position of vein pairs L3-L4 and L2-L5. Three QTL showed strong interactions with these two pathways, supporting the hypothesis that these QTL are involved in these pathways. Naturally segregating variation can therefore act through known signaling pathways to produce variation in vein position.

Animals↗

PubMed2003

Comparing G matrices: are common principal components informative?

Common principal components (CPC) analysis is a technique for assessing whether variance-covariance matrices from different populations have similar structure. One potential application is to compare additive genetic variance-covariance matrices, G. In this article, the conditions under which G matrices are expected to have common PCs are derived for a two-locus, two-allele model and the model of constrained pleiotropy. The theory demonstrates that whether G matrices are expected to have common PCs is largely determined by whether pleiotropic effects have a modular organization. If two (or more) populations have modules and these modules have the same direction, the G matrices have a common PC, regardless of allele frequencies. In the absence of modules, common PCs exist only for very restricted combinations of allele frequencies. Together, these two results imply that, when populations are evolving, common PCs are expected only when the populations have modules in common. These results have two implications: (1) In general, G matrices will not have common PCs, and (2) when they do, these PCs indicate common modular organization. The interpretation of common PCs identified for estimates of G matrices is discussed in light of these results.

Data Interpretation, Statistical↗