Genetics, Population
Cluster Analysis
10.1186/1471-2105-9-539
doi
https://link.springer.com/10.1186%2F1471-2105-9-539
2008-12-01
Enhanced Bayesian modelling in BAPS software for learning genetic structures of populations
BACKGROUND: During the most recent decade many Bayesian statistical models and software for answering questions related to the genetic structure underlying population samples have appeared in the scientific literature. Most of these methods utilize molecular markers for the inferences, while some are also capable of handling DNA sequence data. In a number of earlier works, we have introduced an array of statistical methods for population genetic inference that are implemented in the software BAPS. However, the complexity of biological problems related to genetic structure analysis keeps increasing such that in many cases the current methods may provide either inappropriate or insufficient solutions.
RESULTS: We discuss the necessity of enhancing the statistical approaches to face the challenges posed by the ever-increasing amounts of molecular data generated by scientists over a wide range of research areas and introduce an array of new statistical tools implemented in the most recent version of BAPS. With these methods it is possible, e.g., to fit genetic mixture models using user-specified numbers of clusters and to estimate levels of admixture under a genetic linkage model. Also, alleles representing a different ancestry compared to the average observed genomic positions can be tracked for the sampled individuals, and a priori specified hypotheses about genetic population structure can be directly compared using Bayes' theorem. In general, we have improved further the computational characteristics of the algorithms behind the methods implemented in BAPS facilitating the analyses of large and complex datasets. In particular, analysis of a single dataset can now be spread over multiple computers using a script interface to the software.
CONCLUSION: The Bayesian modelling methods introduced in this article represent an array of enhanced tools for learning the genetic structure of populations. Their implementations in the BAPS software are designed to meet the increasing need for analyzing large-scale population genetics data. The software is freely downloadable for Windows, Linux and Mac OS X systems at http://web.abo.fi/fak/mnf//mate/jc/software/baps.html.
2019-04-11T10:33
539
research_article
2008-12
en
true
articles
https://scigraph.springernature.com/explorer/license/
Statistics
9
University of Helsinki
Department of Mathematics and Statistics, University of Helsinki, P.O. Box 68, Fin-00014, Finland
Stochastic Processes
Software
Databases, Genetic
100965194
nlm_unique_id
Sequence Analysis, DNA
Corander
Jukka
ebf62b80b12dbb20519eee134762dd2bdc678db4acd01108dcfa31b66ba4e863
readcube_id
Genetic Structures
Population
Marttinen
Pekka
Jukka
Sirén
Genetic Linkage
Mathematical Sciences
Humans
Jing
Tang
Alleles
Bayes Theorem
Models, Genetic
Springer Nature - SN SciGraph project
Computational Biology
Algorithms
dimensions_id
pub.1049532772
pubmed_id
19087322
1
Department of Mathematics, Fänriksgatan 3B, Åbo Akademi University, Fin-20500, Åbo, Finland
Åbo Akademi University
BMC Bioinformatics
1471-2105