Estimating the Number of One-step Beneficial Mutations
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Andrzej J. Wojtowicz
Abstract
Mutations that confer a selective advantage to an organism are the raw material upon which natural selection acts. The number of such mutations that are available is a central quantity of interest for understanding the tempo and trajectory of adaptive evolution. While this quantity is typically unknown, it can be estimated with varying levels of accuracy based on data obtained experimentally. We propose a method for estimating the number of beneficial mutations that accounts for the evolutionary forces that generate the data. Our model-based parametric approach is compared to an adjusted nonparametric abundance-based coverage estimator. We show that, in general, our estimator performs better. When the number of mutations is small, however, the performances of the two estimators are similar.
©2012 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
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Articles in the same Issue
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- A New Explained-Variance Based Genetic Risk Score for Predictive Modeling of Disease Risk
- Hessian Calculation for Phylogenetic Likelihood based on the Pruning Algorithm and its Applications
- Cluster-Localized Sparse Logistic Regression for SNP Data
- How to analyze many contingency tables simultaneously in genetic association studies
- Incorporating the Empirical Null Hypothesis into the Benjamini-Hochberg Procedure
- Estimating the Number of One-step Beneficial Mutations
- Testing clonality of three and more tumors using their loss of heterozygosity profiles
- Correction for Founder Effects in Host-Viral Association Studies via Principal Components
- A Non-Homogeneous Dynamic Bayesian Network with Sequentially Coupled Interaction Parameters for Applications in Systems and Synthetic Biology
- An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping
- A Novel and Fast Normalization Method for High-Density Arrays
- Performance of MAX Test and Degree of Dominance Index in Predicting the Mode of Inheritance
- A Bayesian autoregressive three-state hidden Markov model for identifying switching monotonic regimes in Microarray time course data
- QTL Mapping Using a Memetic Algorithm with Modifications of BIC as Fitness Function
- Computing Posterior Probabilities for Score-based Alignments Using ppALIGN
