Type of the lecture:
Workshop
Organizers:
Akira SASAKI (Kyushu Univ.)Program:
Linkage disequilibrium, or nonrandom association of alleles at different loci, may be established through a number of population processes; random genetic drift, demographic history, geographic structure, and so on. Epistatic interaction among functionally related genes might also generate linkage disequilibrium, but empirical studies have rarely found significant deviations from the expectation of independent gene action. Focusing on the theoretical expectation that the evolution should generate linkage disequilibrium when genes interact epistatically, we here investigate the consequences of epistatic interaction for fitness on the amount of linkage disequilibrium between loosely linked loci. Stochastic models of polygenic systems are formulated to see how synergistic/antagonistic epistasis among deleterious mutations should promote the formation of linkage disequilibrium in a panmictic population of a finite size. Based on the simulation results, we will discuss the prospcts for elucidating functionally interacting biological systems from available linkage disequilibrium data.
Cancer progression is somatic evolution. Stem cells in skin or intestinal epithelia keep dividing. After many years, some cells accumulate multiple mutations of key genes, finally giving rise to cells that proliferate without being checked by immune system, causing cancer. The risk of cancer depends on the reproductive rate of cells of intermediate mutants (somatic selection), mutation rate, and the population size. [1] Chromosomal instability (CIN) is a defining characteristic of most human cancers. Mutation of CIN genes increases the probability that whole chromosomes or large fractions of chromosomes are gained or lost during cell division. The consequence of CIN is an imbalance in the number of chromosomes per cell (aneuploidy) and an enhanced rate of loss of heterozygosity (LOH). We develop a mathematical framework for studying the effect of CIN on the somatic evolution of cancer. Specifically, we calculate the conditions for CIN to initiate the process of colorectal tumorigenesis prior to the inactivation of tumor suppressor genes. [2] We may also discuss the tissue architecture (compartmentalization, separation of stem cells differentiated cells) in controlling the risk of cancer initiation.
A long lasting question in epidemiology is why many childhood diseases show periodicity of more than one year. It is well known that the conventional epidemiological model with seasonally varying transmission rate shows the muti-year periodicity depending on the strength of seasonal forcing. We studied the evolution of seasonality using the compartment model.A strain of influenza viruses, for example, would be able to improve the transmission rate in summer, thereby reducing the degree of seasonality in transmission rate. Another influenza strain may improve the transmission rate in winter in expense of the reduced transmission rate in summer, thereby exaggerating the seasonal difference in transmission rates. Hence the seasonality itself (or sensitivity to the seasonality) is subjected to evolution. It is believed that larger fluctuation in an environment is not favored in the life history evolution because it reduces the geometric mean fitness; however, we show that an opposite can be true -- the evolution favors larger fluctuation in a transmission rate whereas it reduces the geometric mean fitness of the basic reproductive ratio. By applying our new principle, we can answer why many childhood disease show multi year periodicity.@
The rate of molecular evolution can vary among lineages. Observations of the elevated rates in response to symbiosis, obligatory parasitism and gene duplications motivate us to develop reverse exploratory approaches: detection of adaptive evolution and diversification based on the sign of rate variability. For the population of RNA virus, it is possible to compare evolutionary rate and effective population size from serially collected sample. By introducing stochastic processes as priors for the dynamics of the evolutionary rate, Bayesian hierarchical models examine the pattern of change in evolutionary rate a posteriori. The estimated pattern and correlation between genes can be used as clues to detect footprints of adaptive evolution and coevolution. Changes in effective population size or patterns of natural selection will mainly alter nonsynonymous substitution rates. Changes in generation length or mutation rates are likely to have an impact on both synonymous and nonsynonymous substitution rates. By comparing changes in synonymous and nonsynonymous substitution rates, the relative contributions of the driving forces of evolution can be better characterized. With our Bayesian approach, we analyze cytochrome oxidase subunit I (COX I) evolution in primates and find that nonsynonymous rates have a greater tendency to change over time than do synonymous rates.