The Bayesian revolution in genetics

By MARK A. Beaumont, Bruce Rannala. Nature reviews genetics, 2004

494 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/29a079a5423063a3f084c2737f2d11c282e1c01f

How researchers see this paper

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

In Genetics

Citations in the corpus

ABCtoolbox: a versatile toolkit for approximate Bayesian computations (2010, background): “Bayesian statistics has gained popularity in scientific inference, especially in population genetics and genomics [1].”
Computer programs for population genetics data analysis: a survival guide (2006, methodology): “Most of these programs use a Bayesian framework for parameter inferenc”
Protein Molecular Function Prediction by Bayesian Phylogenomics (2005, background): “Bayesian methodologies have influenced computational biology for many years [36].”
Stochastic modelling for quantitative description of heterogeneous biological systems (2009, background): “It is therefore likely that we will see a Bayesian 'revolution' in computational systems biology, similar to that already experienced in genetic”
Applications of landscape genetics in conservation biology: concepts and challenges (2010, background): “dispersion (Beaumont and Rannala 2004; Storfer et al. 2007).”
A Novel Approach for Choosing Summary Statistics in Approximate Bayesian Computation (2012, methodology): “ABC, its position in 82 the ensemble of model-based inference methods, and its application in evolutionary genetics are reviewed in Marjoram et al. (2003), Beaumont and Rannala (2004), Marjoram and Tavaré 84 (2006), Beaumont (2010), Bertorelle et al. (2010) and Csilléry et al. (2010).”
The alluring simplicity and complex reality of genetic rescue. (2004, background): “However, empirical data do demonstrate that low levels of immigration can provide considerable fitness benefits to recently isolated, inbred populations, and recent molecular-based statistical advances will help to identify these populations efficiently [29, 61 ].”
Stochastic mechano-chemical kinetics of molecular motors: A multidisciplinary enterprise from a physicist’s perspective (2012, background): “The areas of research where this has been applied successfully include various biological processes in, for example, genetics [459, 460], biochemistry [461], cognitive sciences [462], ecology [463], etc.”
Approximate Bayesian Computation in Evolution and Ecology (2010, background): “…a series of papers developed computer-intensive techniques for likelihood-based analysis (Griffith & Tavaré 1994, Kuhner et al. 1995), leading the way to the current preoccupation with likelihood and Bayesian inference in population genetics (Beaumont & Rannala 2004, Marjoram & Tavaré 2006).”
Contribution of genetics and genomics to seagrass biology and conservation (2007, unknown): “Inferences are aided with novel statistical tools (Bayesian inference) that are now possible due to the much increased processor speeds (Beaumont and Rannala, 2004).”
Advances in molecular technology and their impact on fisheries genetics (2008, background): “While allozyme analyses were largely restricted to the comparison of allele frequencies, estimation of population differentiation and basic mixed-stock analyses (Ryman and Utter 1987), multilocus microsatellite surveys allowed more complex approaches (Beaumont and Rannala 2004; Pearse and Crandall 2”
Reconstructing routes of invasion using genetic data: why, how and so what? (2010, methodology): “…large data sets of genetic markers (e.g. SNPs) using high-throughput methods is rapidly decreasing (Gupta 2008; Hert et al. 2008) and statistical methods for the efficient treatment of such data sets are being developed (Beaumont & Rannala 2004; Bertorelle et al. 2010; Csilléry et al. 2010).”
Detecting introgressive hybridisation in rock partridge populations (Alectoris graeca) in Greece through Bayesian admixture analyses of multilocus genotypes (2007, methodology): “However, the use of hypervariable microsatellite markers and new statistical methods (Beaumont and Rannala 2004), have dramatically improved the assessment of cryptic population structure, admixture analyses and individual assignment testing, although the power of markers and models in empirical cas”
The Bayesian New Statistics: Hypothesis testing, estimation, meta-analysis, and power analysis from a Bayesian perspective (2017, unknown): “…to Bayesian The literature across many sciences has articles and books that note and promote a shift away from frequentist to Bayesian methods (e.g., Allenby, Bakken, & Rossi, 2004; Beaumont & Rannala, 2004; Brooks, 2003; Gregory, 2001; Howson & Urbach, 2006; Lindley, 1975; McGrayne, 2011, etc.).”
Learning about human population history from ancient and modern genomes (2011, methodology): “1 ), a more promising approach is the use of model-based analyses to directly compare how well different models of population history fit genome-wide data, and to estimate demographic parameters of interest (effective population size, population divergence time, migration rate, and so on) for the ”