Inference in molecular population genetics

By Matthew Stephens, Peter Donnelly. 2000

309 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/413c10b6b5fdc641d1bbc999b0f3ae926f333553

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Statistics and Probability

Citations in the corpus

Genotype imputation for genome-wide association studies (2010, unknown): “P(Gi| , , r) = θ α P(Gi|Zi, )P(Zi| , r) Σ z (3) θ α”
Non-linear regression models for Approximate Bayesian Computation (2008, methodology): “Although many likelihood-based methods have been proposed in this framework, these meth-ods are restricted to particular demographic and genetic processes (Stephens and Donnelly 2000; Wilson et al. 2003; Kuhner 2006; Hey and Nielsen 2007).”
Patterns of DNA sequence diversity and genetic structure after a range expansion: lessons from the infinite‐island model (2004, methodology): “Alternative estimation procedures based on massive simulations and importance sampling (e.g. Stephens & Donnelly 2001; Beaumont et al. 2002; Estoup & Clegg 2003) could be better suited to this problem, since it would allow one to use several aspects of the molecular diversity both within and…”
Truncated Importance Sampling (2008, background): “1999) and sometimes a considerable amount of work has gone into refining a proposal distribution to make it practically useful (Stephens and Donnelly 2000).”
Homoplasy and mutation model at microsatellite loci and their consequences for population genetics analysis (2002, methodology): “…estimating the probability of obtaining a given gene sample configuration in order to make likelihood-based statistical inferences from molecular data, rather than drawing inferences based on summary statistics (Wilson & Balding 1998; Beaumont 1999; Bahlo & Griffith 2000; Stephens & Donnelly 2000).”
Adaptive Multiple Importance Sampling (2009, methodology): “We used here an IS approximation as in Stephens & Donnelly (2000) and the cost of this approximation increases considerably with the number of simulated gene trees.”
A comparison of bayesian methods for haplotype reconstruction from population genotype data. (2003, methodology): “In the text, we note that there are potential pitfalls associated with the fact that the posterior distribution sampled from by PHASE (both the version implementing the algorithm from Stephens et al. [2001a] and the new version described here) is defined implicitly as the stationary distribution of ”
Accounting for decay of linkage disequilibrium in haplotype inference and missing-data imputation. (2005, unknown): “As noted by Stephens and Donnelly (2003), Bayesian approaches seem to have an advantage here, since the Bayesian framework provides a natural way of assessing uncertainty, via the conditional distribution of the haplotypes, given the genotype data.”
Bayes estimation of species divergence times and ancestral population sizes using DNA sequences from multiple loci. (2003, background): “The population size of a current species istion genetics (see Felsenstein et al. 1999; Stephens and Donnelly 2000 for reviews).”
Genealogical trees, coalescent theory and the analysis of genetic polymorphisms (2002, methodology): “Advanced computational techniques, such as IMPORTANCE SAMPLING”
Estimation of population growth or decline in genetically monitored populations. (2003, methodology): “…that studied by Williamson and Slatkin (1999), but solved substantially more efficiently. to give three main groups of methods, as distinguished by Stephens and Donnelly (2000): those in which the The method of Berthier et al. (2002) differs from the other three methods in that likelihoods are…”
Kernel approximate Bayesian computation in population genetic inferences (2012, methodology): “on for the marginal likelihood(6, 7). With the aid of the genealogical process, it is possible to solve the recursion numerically via importance sampling (8). The importance weight has been optimized (9, 10) and the method for solving the recursion has been applied to a branching-coalescent process ”
Modeling linkage disequilibrium and identifying recombination hotspots using single-nucleotide polymorphism data. (2003, background): “…being larger in areas of the sponding to our choices of —and indeed the formsgenome where the local rate of recombination is for from Stephens and Donnelly (2000) and FD—islow. that they depend on the order in which the haplotypes Stephens and Donnelly (2000) suggested a form for are considered.”
Inferring population history with DIY ABC: a user-friendly approach to approximate Bayesian computation (2008, methodology): “…and software about inference in population genetics concern simple standard evolutionary scenarios: a single population (Griffiths and Tavaré, 1994; Stephens and Donnelly, 2000; Beaumont, 1999), two populations exchanging genes (Hey and Nielsen, 2004; De Iorio and Griffiths, 2004) or not…”
Haplotype phasing: Existing methods and new developments (2011, methodology): “were a breakthrough for modelling population haplotype frequencie”
Genome-Wide Inference of Ancestral Recombination Graphs (2013, background): “During the past two decades, various attempts have been made to perform explicit inference of ARGs using techniques such as importance sampling [19,22] (see also [23]) and Markov chain Monte Carlo sampling [24–27].”