The evolutionary analysis of molecular sequence variation is a statistical enterprise. This is reflected in the increased use of probabilistic models for phylogenetic inference, multiple sequence alignment, and molecular population genetics. Here we present BEAST: a fast, flexible software architecture for Bayesian analysis of molecular sequences related by an evolutionary tree. A large number of popular stochastic models of sequence evolution are provided and tree-based models suitable for both within- and between-species sequence data are implemented. BEAST version 1.4.6 consists of 81000 lines of Java source code, 779 classes and 81 packages. It provides models for DNA and protein sequence evolution, highly parametric coalescent analysis, relaxed clock phylogenetics, non-contemporaneous sequence data, statistical alignment and a wide range of options for prior distributions. BEAST source code is object-oriented, modular in design and freely available at http://beast-mcmc.googlecode.com/
 under the GNU LGPL license. BEAST is a powerful and flexible evolutionary analysis package for molecular sequence variation. It also provides a resource for the further development of new models and statistical methods of evolutionary analysis.

/pdf/beast-bayesian-evolutionary-analysis-by-sampling-trees-2t1t8jmg8x.pdf

BEAST: Bayesian evolutionary analysis by sampling trees

Statistical method for testing the neutral mutation hypothesis by DNA polymorphism.

Current routine genotyping methods typically do not provide haplotype information, which is essential for many analyses of fine-scale molecular-genetics data. Haplotypes can be obtained, at considerable cost, experimentally or (partially) through genotyping of additional family members. Alternatively, a statistical method can be used to infer phase and to reconstruct haplotypes. We present a new statistical method, applicable to genotype data at linked loci from a population sample, that improves substantially on current algorithms; often, error rates are reduced by >50%, relative to its nearest competitor. Furthermore, our algorithm performs well in absolute terms, suggesting that reconstructing haplotypes experimentally or by genotyping additional family members may be an inefficient use of resources.

https://cseweb.ucsd.edu/classes/sp05/cse291-a/doc/phase.pdf

A new statistical method for haplotype reconstruction from population data.

The main purpose of this article is to present several new statistical tests of neutrality of mutations against a class of alternative models, under which DNA polymorphisms tend to exhibit excesses of rare alleles or young mutations. Another purpose is to study the powers of existing and newly developed tests and to examine the detailed pattern of polymorphisms under population growth, genetic hitchhiking and background selection. It is found that the polymorphic patterns in a DNA sample under logistic population growth and genetic hitchhiking are very similar and that one of the newly developed tests, Fs, is considerably more powerful than existing tests for rejecting the hypothesis of neutrality of mutations. Background selection gives rise to quite different polymorphic patterns than does logistic population growth or genetic hitchhiking, although all of them show excesses of rare alleles or young mutations. We show that Fu and Li's tests are among the most powerful tests against background selection. Implications of these results are discussed.

/pdf/statistical-tests-of-neutrality-of-mutations-against-27cu9aj4pv.pdf

Statistical tests of neutrality of mutations against population growth, hitchhiking and background selection.

Statistics for Spatial Data.

The paper is concerned with methods for the estimation of the coalescence time (time since the most recent common ancestor) of a sample of intraspecies DNA sequences. The methods take advantage of prior knowledge of population demography, in addition to the molecular data. While some theoretical results are presented, a central focus is on computational methods. These methods are easy to implement, and, since explicit formulae tend to be either unavailable or unilluminating, they are also more useful and more informative in most applications. Extensions are presented that allow for the effects of uncertainty in our knowledge of population size and mutation rates, for variability in population sizes, for regions of different mutation rate, and for inference concerning the coalescence time of the entire population. The methods are illustrated using recent data from the human Y chromosome.

https://www.genetics.org/content/genetics/145/2/505.full.pdf

Inferring Coalescence Times From DNA Sequence Data

We develop a sampling theory for genes sampled from a population evolving with deterministically varying size. We use a coalescent approach to provide recursions for the probabilities of particular sample configurations, and describe a Monte Carlo method by which the solutions to such recursions can be approximated. We focus on infinite-alleles, infinite-sites and finite-sites models. This approach may be used to find maximum likelihood estimates of parameters of genetic interest, and to test hypotheses about the varying environment. The methods are illustrated with data from the mitochondrial control region sampled from a North American Indian tribe.

Sampling Theory for Neutral Alleles in a Varying Environment

Mitochondrial DNA sequence variation is now being used to study the history of our species. In this paper we discuss some aspects of estimation and inference that arise in the study of such variability, focusing in particular on the estimation of substitution rates and their use in calibrating estimates of the time since the most recent common ancestor of a sample of sequences. Observed DNA sequence variation is generated by superimposing the effects of mutation on the ancestral tree of the sequences. For data of the type studied here, this ancestral tree has to be modeled as a random process. Superimposing the effects of mutation produces complicated sampling distributions that form the basis of any statistical model for the data. Using such distributions--for example, for maximum likelihood estimation of rates--poses some difficult computational problems. We describe a Monte Carlo method, a cousin of the popular "Markov chain Monte Carlo," that has proved very useful in addressing some of these issues.

/pdf/ancestral-inference-in-population-genetics-3qjtid3tis.pdf

Ancestral Inference in Population Genetics

In the era of genomic polymorphism data, the need for models that include recombination is transparently obvious Many questions are directly focused on recombination: • linkage disequilibrium (association) mapping • basic questions about the distribution and nature of recombination events • novel multilocus summary statistics (e.g., based on haplotype sharing) are likely to become important Scaled recombination rate ρ When there are k lineages, split rate is kρ/2, coalescence rate is k(k − 1)/2

An ancestral recombination graph

Kimura and Ohta showed that the expected age of a neutral mutation observed to be of frequency x in a population is We put this classical result in a general coalescent process context that allows questions to be asked about mutations in a sample, as well as in the population. In the general context the population size may vary back in time. Assuming an infmitely-many-sites model of mutation, we find the distribution of the number of mutant genes at a particular site in a sample; the probability that an allele at that site of a given frequency is ancestral; the distribution of the age of a mutation given its frequency in a sample, or population; and the distribution of the time to the most recent common ancestor, given the frequency of a mutation in a sample, or in the population

The age of a mutation in a general coalescent tree

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