Statistical Methods in Genetics

Statistical Genetics is a scientific field concerned with the development and application of statistical methods for drawing inferences from genetic data. The term is most commonly used in the context of human genetics. Statistical genetics is an area at the convergence of genetics and quantitative analysis. Over the last few years it has experienced a drastic shift of paradigm, from a mostly theoretical subject with little room for empirical evidence to a heavily data-oriented discipline where the existence of large repositories of genetic data allows researchers to generate and explore new scientific hypotheses.

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- Beta priors for the Binomial parameter
- Calibrating degrees of belief
- Computational Biology Databases
- Detection and estimation of linkage and its recombination
- DNA sequence alignment - Needleman-Wunsch and Smith-Waterman algorithms
- Estimation of allele frequencies
- Evolutioniary trees: software and examples
- Extreme Value Theory
- Gene trees and species trees
- Gibbs sampling for motif recognition
- Hardy-Weinberg law
- Hidden Markov Models, Viterbi, Forward and Backward Algorithms
- HMM for Protein Families
- Inheritance of quantitative traits
- Local alignment search tool
- Markov Chains and Hidden Markov Models
- Maximum Likelihood of Multinomial Cell Probabilities
- Mendel's laws
- Models of natural selection and mutation
- Molecular Evolution and Continuous Time Markov chains
- More Bayesian Computations
- Multinomial Distribution
- Multinomial-Dirichlet
- Multivariate Statistical Methods: a taxonomy
- Nucleotide -Level
- Phylogenetic Trees: software and examples
- Population genetics
- Probability and Statistics Classes
- Quantitative genetics
- Semi hidden Markov Models: Genscan
- Snyder's ratios
- Stochastic models of carcinogenesis
- Testing and classical Statistics Links
- The Bayesian Paradigm
- Estimation of evolutionary and population genetic parameters from DNA sequence data
- Evolution of DNA sequences - Kimura's two-parameter and Jukes-Cantor models
- Gene mapping methodologies: Linkage disequilibrium mapping, Quantitative trait locus mapping, transmission-disequilibrium test

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