Biomedical subjects

Patrik Waldmann

Publications and source records attributed to Patrik Waldmann.

5 recordsLinked to original sources

PubMed2004

BAPS 2: enhanced possibilities for the analysis of genetic population structure.

UNLABELLED: Bayesian statistical methods based on simulation techniques have recently been shown to provide powerful tools for the analysis of genetic population structure. We have previously developed a Markov chain Monte Carlo (MCMC) algorithm for characterizing genetically divergent groups based on molecular markers and geographical sampling design of the dataset. However, for large-scale datasets such algorithms may get stuck to local maxima in the parameter space. Therefore, we have modified our earlier algorithm to support multiple parallel MCMC chains, with enhanced features that enable considerably faster and more reliable estimation compared to the earlier version of the algorithm. We consider also a hierarchical tree representation, from which a Bayesian model-averaged structure estimate can be extracted. The algorithm is implemented in a computer program that features a user-friendly interface and built-in graphics. The enhanced features are illustrated by analyses of simulated data and an extensive human molecular dataset. AVAILABILITY: Freely available at http://www.rni.helsinki.fi/~jic/bapspage.html.

Algorithms↗

PubMed2004

A quantitative genetic method for estimating developmental instability.

The concept of developmental instability (DI) is frequently used in evolutionary biology, and a range of definitions has been proposed. Moreover, numerous different statistical methods have been used for estimation of DI. The common basis for all methods is that measures need to be obtained from repeated structures within organisms. In the case of fluctuating asymmetry, mirror images could be interpreted as the repeats of each other. All repeats of a trait on one organism should, from a quantitative perspective, have the same genetic foundation. Most previous methods have not accounted for the genetics of the underlying trait. It is here shown how a statistical method from quantitative genetics (the repeated records animal model) can be used for assessment of DI, based on estimation of the variance due to the permanent environment. Moreover, Gibbs sampling is used for inference of the parameters, which provides a Bayesian framework where posterior distributions easily can be calculated from any functions of the variance components. The method is applied to a real dataset from two populations of the plant Scabiosa canescens, and results shows that it works well under realistic situations.

Analysis of Variance↗

PubMed2003

Bayesian analysis of genetic differentiation between populations.

We introduce a Bayesian method for estimating hidden population substructure using multilocus molecular markers and geographical information provided by the sampling design. The joint posterior distribution of the substructure and allele frequencies of the respective populations is available in an analytical form when the number of populations is small, whereas an approximation based on a Markov chain Monte Carlo simulation approach can be obtained for a moderate or large number of populations. Using the joint posterior distribution, posteriors can also be derived for any evolutionary population parameters, such as the traditional fixation indices. A major advantage compared to most earlier methods is that the number of populations is treated here as an unknown parameter. What is traditionally considered as two genetically distinct populations, either recently founded or connected by considerable gene flow, is here considered as one panmictic population with a certain probability based on marker data and prior information. Analyses of previously published data on the Moroccan argan tree (Argania spinosa) and of simulated data sets suggest that our method is capable of estimating a population substructure, while not artificially enforcing a substructure when it does not exist. The software (BAPS) used for the computations is freely available from http://www.rni.helsinki.fi/~mjs.

Bayes Theorem↗

PubMed2003

Bayesian inference of inbreeding depression in controlled crosses.

This study shows how a Gibbs sampling approach can be used for Bayesian inference of inbreeding depression. The method presented is mainly concerned with organisms that can be both selfed and outcrossed. Tests performed on simulated data with unequal variances and missing observations show that the method works well. Real data from the plant Scabiosa canescens is also analyzed.

Analysis of Variance↗

PubMed2002

Inbreeding depression in a rare plant, Scabiosa canescens (Dipsacaceae).

Plants from a population of Scabiosa canescens, a locally rare species with a narrow ecological amplitude, were raised under uniform growth conditions to examine the phenotypic effects of one generation selfing and outcrossing. Particular attention was given to direct components of fitness (seedling biomass, rosette leaf number, head number, flower number per head), but two morphological characters (plant height, flower size) were also considered. Estimates of inbreeding depression (delta), adjusted for maternal effects and lack of balance, were compared and tested for significance using randomization and boostrap procedures. Inbreeding significantly depressed several characters during both early and late stages of the life cycle, with delta ranging from 0.14 (flower size) to 0.37 (seedling biomass). Based on these and other results, we propose that S. canescens is susceptible to inbreeding and that the genetic basis of inbreeding depression varies across life stages.

Inbreeding↗