Pleiotropy can be effectively estimated without counting phenotypes through the rank of a genotype-phenotype map

dN/dS-H, a New Test to Distinguish Different Selection Modes in Protein Evolution and Cancer Evolution

One of the most popular measures in the analysis of protein sequence evolution is the ratio of nonsynonymous distance (d_N) to synonymous distance (d_S). Under the assumption that synonymous substitutions in the coding region are selectively neutral, the d_N/d_S ratio can be used to statistically detect the adaptive evolution (or purifying selection) if d_N/d_S > 1 (or d_N/d_S < 1) significantly. However, due to strong structural constraints and/or variable functional constraints imposed on amino acid sites, most encoding genes in most species have demonstrated d_N/d_S < 1. Consequently, the statistical power for testing d_N/d_S = 1 may be insufficient to distinguish between different selection modes. In this paper, we propose a more powerful test, called d_N/d_S-H, in which a new parameter H, a relative measure of rate variation among sites, was introduced. Given the condition of strong purifying selections at some sites, the d_N/d_S-H model predicts d_N/d_S = 1-H for neutral evolution, d_N/d_S < 1-H for nearly neutral selection, and d_N/d_S > 1-H for adaptive evolution. The potential of this new method for resolving the neutral-adaptive debates is illustrated by the protein sequence evolution in vertebrates, Drosophila and yeasts, as well as somatic cancer evolution (specialized as the C_N/C_S-H test).

A Simple Evolutionary Model of Genetic Robustness After Gene Duplication

When a dispensable gene is duplicated (referred to the ancestral dispensability denoted by O⁺), genetic buffering and duplicate compensation together maintain the duplicate redundancy, whereas duplicate compensation is the only mechanism when an essential gene is duplicated (referred to the ancestral essentiality denoted by O^-). To investigate these evolutionary scenarios of genetic robustness, I formulated a simple mixture model for analyzing duplicate pairs with one of the following states: double dispensable (DD), semi-dispensable (one dispensable one essential, DE), or double essential (EE). This model was applied to the yeast duplicate pairs from a whole-genome duplication (WGD) occurred about 100 million years ago (mya), and the mouse duplicate pairs from a WGD occurred about more than 500 mya. Both case studies revealed that the proportion of essentiality for those duplicates with ancestral essentiality [P_E(O^-)] was much higher than that for those with ancestral dispensability [P_E(O⁺)]. While it was negligible in the yeast duplicate pairs, P_E(O⁺) (about 20%) was shown statistically significant in the mouse duplicate pairs. These findings, together, support the hypothesis that both sub-functionalization and neo-functionalization may play some roles after gene duplication, though the former may be much faster than the later.

Model parameters of molecular evolution explain genomic correlations

One long-standing research focus in evolutionary genomics is trying to resolve how biological variables (expression, essentiality, protein-protein interaction, structural stability, etc.) determine the rate of protein evolution. While these studies have considerably deepened our understanding of molecular evolution, many issues remain unsolved. In this opinion article, after having a brief survey of literatures, we establish relationships between model parameters of molecular evolution and genomic variables, based on which, most-observed genomic correlations and confounds can be explained by model parameter combinations under different conditions, which include the strength of stabilizing selection, mutational variance, expression sufficiency, gene pleiotropy, as well as the effective population size. We suggest that the problem to discern biological variable(s) that may determine the rate of protein evolution can be tackled at two levels. The first level, as discussed here, is to demonstrate how the model of molecular evolution can predict potential genomic correlations under various conditions. And the second level is to estimate genome-wide variations of model parameters (or combinations) that help to identify canonical biological variables that may underlie the rate variation among genes that ranges up to at least three magnitudes.

Genepleio software for effective estimation of gene pleiotropy from protein sequences

Though pleiotropy, which refers to the phenomenon of a gene affecting multiple traits, has long played a central role in genetics, development, and evolution, estimation of the number of pleiotropy components remains a hard mission to accomplish. In this paper, we report a newly developed software package, Genepleio, to estimate the effective gene pleiotropy from phylogenetic analysis of protein sequences. Since this estimate can be interpreted as the minimum pleiotropy of a gene, it is used to play a role of reference for many empirical pleiotropy measures. This work would facilitate our understanding of how gene pleiotropy affects the pattern of genotype-phenotype map and the consequence of organismal evolution.