2003 — 2011 |
Caceres, Carla (co-PI) [⬀] Lynch, Michael [⬀] Zolan, Miriam (co-PI) [⬀] Lively, Curtis Housworth, Elizabeth |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Fibr: Causes and Consequences of Recombination
Intellectual Merit. This project is focused on one of biology's deepest mysteries - the evolutionary causes and consequences of recombination. The investigative team consists of cell biologists, ecologists, parasitologists, quantitative geneticists, genomicists, and mathematicians. The study organism, the planktonic microcrustacean Daphnia pulex, provides an exceptional array of opportunities for recombination research that is unavailable with any other system: a wide range of recombination intensities among natural populations, the presence of multiple sexual and asexual lineages, a powerful set of genomic tools, well understood ecology, ease of experimental manipulation, and a "living-fossil" record that can be resurrected from lake sediments. Specific goals include: 1) characterization of the genetic changes associated with the transition from meiotic to mitotic progeny production; 2) evaluation of whether the mutation rate (including the activity of mobile-genetic elements) is affected by meiosis; 3) a test of the hypothesis that mutation load accumulates in the absence of recombination; 4) evaluation of the extent to which recombination modifies the range of variation upon which natural selection acts; and 5) a test of the hypothesis that host-parasite evolution drives the evolution of recombination and sex. These studies will be informed by an integrated research program involving high-throughput sequencing, microarray analysis, and quantitative-genetic surveys. Guided by the empirical results, mathematical models will also be developed for understanding the evolutionary fates of genomic features of asexual organisms. Finally, the results of this study will be integrated into an emerging evolutionary framework suggesting that many aspects of the genomic architecture of multicellular organisms arose passively in response to mildly deleterious mutation accumulation in populations with small effective sizes. Broader Impacts. The potential impacts of this project on science, society, and education are numerous. First, an undergraduate program will help instill an interdisciplinary philosophy while broadening career choices for students from multiple institutions, with a particular focus on minority recruitment. Second, the research program will be tightly integrated with the newly founded Daphnia Genomics Consortium, an international group of scientists from across the life sciences (http://daphnia.cgb.indiana.edu/). This will firmly establish D. pulex as a premier model system for studies in ecological and evolutionary genomics. Third, the research has significant applied implications in the areas of parasite-resistance evolution, clonal propagation, and genetic engineering.
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0.915 |
2003 — 2007 |
Housworth, Elizabeth |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Modeling Recombination
Proposal: DMS-0306243 PI: Elizabeth A. Housworth Title: Modeling recombination
Abstract:
Recombination plays a vital role in the proper disjunction of chromosomes in meiosis and is essential to the production of viable gametes. Many organisms use a strategy called interference to regulate the distribution of crossovers among all recombination events that include simple gene conversions (non-crossovers) as well as crossovers. Recent evidence suggests that the organisms that also use recombination to pair their chromosomes early in meiosis have an additional set of crossovers that is not subject to interference. The investigator develops the statistical theory to test this two-pathway hypothesis by extending previous techniques used to model the distribution of crossovers subject to interference. The investigator analyzes data from a variety of organisms to determine if the two-pathway model provides a better fit than previous models to recombination data from organisms that use recombination to pair their chromosomes during meiosis. Multivariate optimization techniques contribute to the development of tools for determining the design of experiments to test this hypothesis and to obtain sharp estimates of the model parameters. The investigator explores the advantages of using the two-pathway hypothesis in gene mapping methods by comparing its performance with simulated data to that of methods using no-interference and interference-only models of recombination.
A basic understanding of recombination is important for understanding certain causes of infertility, miscarriages, and birth defects in humans. For instance, trisomy is related to certain exchange configurations on the chromosomes during meiosis being susceptible to missegregation (for example, an egg receives an extra copy of chromosome 21 and a child resulting from the fertilization of this egg has 3 copies, two from its mother and one from its father, leading to Down syndrome). Trisomy is involved in up to 25% of miscarriages in addition to birth defects. Furthermore, mathematical models for the distribution of exchanges between maternal and paternal chromosomes in meiosis provide the fundamental basis for locating genes, including those that are linked to genetic diseases. Including the most appropriate models for these exchanges in gene mapping algorithms improves their efficiency without increasing the error rate they claim. If the proposed model proves to fit data the best, its inclusion in gene mapping algorithms will become increasingly important due to the use of Single Nucleotide Polymorphisms that provide a large number of markers for finding genes associated with complex disease traits. Additionally, the proposed research intimately connects the statistical methods to a current, empirically testable, biological hypothesis about recombination. The results will be disseminated to both the statistical and biological communities and the activities supported by this grant will include training graduate students in this interdisciplinary area.
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0.915 |
2012 — 2017 |
Housworth, Elizabeth |
N/AActivity Code Description: No activity code was retrieved: click on the grant title for more information |
Statistical Problems in Phylogenetics
Model-based phylogenetic analyses (maximum likelihood, Bayesian, and distance-based methods) rely on complex models of evolution of genetic data, models whose statistical properties are not well-understood. Particularly, software employs a discretized Gamma distribution and important statistical properties of that model, such as model identifiability and how many rate classes can be or need to be used, are unknown. We propose to determine the statistical properties of such models. Moreover, even the most sophisticated models fail to mimic many real data sets analyzed in phylogenetics: for instance, while the general time reversible model allows for arbitrary base frequencies, it requires that all the species under study have the same base frequencies as each other. The natural non-parametric alternative is the method of maximum parsimony which suffers from the phenomenon of long branch attraction: when data are generated under some model of genetic evolution on certain types of trees and then analyzed under parsimony methods, parsimony methods return an incorrect tree with some probability that does not tend to zero as the amount of data increases to infinity. Thus, it is often said that the method of maximum parsimony is not a consistent statistical method for phylogeny reconstruction. However, this criticism has the potential of applying to all phylogenetic reconstruction methods, including model-based methods, when the data are not generated under the model used to analyze them. We propose to determine the model conditions under which the natural non-parametric alternative, parsimony, is a consistent method for phylogenetic estimation. The goal of both projects is to provide solid mathematical foundation for phylogenetic reconstruction methods.
Phylogenies are trees describing the evolutionary relationships of species. Having an accurate description of these relationships can help researchers discover the genetic basis of human diseases and the reasons for varying pathogenicity of viruses and bacteria. This project aims to understand the mathematical properties of the statistical methods used to infer phylogenies from genetic data. These mathematical properties tell which methods are most appropriate for use on specific kinds of data and whether a method can be useful at all on any data. This information, in turn, will lead to more accurate phylogenies.
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0.915 |