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PubMed1990

Evolutionary relationships between "Q-type" photosynthetic reaction centres: hypothesis-testing using parsimony.

Hypotheses concerning the evolutionary relationships between "Q-type" photosynthetic reaction centres are tested using amino acid parsimony analysis of subunit sequences and an alignment based on dot matrix comparisons. Strong evidence is found for independent gene duplications having produced the L and M subunits of the photosynthetic purple bacterial reaction centre and D1 and D2 of Photosystem-II. Much support is also found for the L and M subunits of the green filamentous bacterium Chloroflexus aurantiacus arising from the same gene duplication as the purple bacterial subunits, suggesting there was an ancestral bacterial heterodimeric reaction centre. These conclusions caution against over-extrapolation from the purple bacterial reaction centre to Photosystem-II, and suggest that the latter is more ancient than previously supposed.

Bacteria↗
PubMed1994

Bootstrap hypothesis tests for evolutionary trees and other dendrograms.

The bootstrap computer-intensive statistical technique is frequently applied to statistical analyses of phylogenetic trees. The widely used rule that a group is supported significantly if it appears in at least 95% of bootstrap trees is conservative in most situations. This paper describes three ways of using the bootstrap to carry out statistical inference on phylogenies. The first method tests whether there is nonrandom support for a single group or tree. The second method compares the support for two groups or trees. The third method tests whether a single group or tree has better support than the set of all possible alternatives; this may be a replacement for the "95% rule." These tests generally require fewer bootstrap trees to be estimated than do other methods of bootstrapping phylogenies. A simple, sequential statistical method can be used to increase the efficiency further. These methods can be applied to tests of multiple hypotheses about a single phylogeny. Parsimony analyses of 5S rRNA sequences of plants and cluster analyses of randomly amplified polymorphic DNA bands in three pathotypes of the cereal eyespot fungus are used as illustrative examples. The tests can be used to analyze dendrograms in subjects other than taxonomy.

Base Sequence↗
PubMed1994

Estimation and hypothesis testing of treatment effects in animal reproductive toxicology studies.

Healy (1) and Dempster et al. (8) proposed statistical methods to evaluate the treatment effects in animal reproductive toxicology research. Both methods assume homogeneous variance for the dams and the pups, respectively, in all the treatment groups. In this paper, via mixed effect modeling, we propose a method to estimate the treatment effects allowing heterogeneous variances for the dams and the pups, respectively, in different treatment groups. Covariates will also be included in the model. A procedure to test the fixed effects is also discussed. An example from an animal reproductive toxicological study is used to illustrate the procedures.

Animals↗
PubMed1991

Problem of between-eye correlation for statistical hypothesis testing: rabbit corneal thickness.

The two eyes of a subject often yield correlated data. Statistical analysis which treats correlated data as if it were independent is most likely to be biased toward statistical significance; that is, the probability of a type I error is likely to be inflated. To illustrate the importance of lack of independence to the inferential process, data from an experimental design commonly used in optometric research are used to demonstrate (1) the potential magnitude of between-eye correlation, (2) the statistical bias toward a significant outcome when the between-eye correlation is ignored via inappropriate analysis, and (3) simple ways by which the bias can be avoided. The researcher must be aware of the between-eye correlation which exists for the particular effect under study, and the statistical bias that ensues from the correlation when the data are not handled correctly.

Animals↗
PubMed1990

Statistical inference on mean dioptric power: hypothesis testing and confidence regions.

It has not hitherto been possible to apply formal methods of statistical analysis to data on dioptric powers. The solution to the basic statistical problem is now provided in this paper. Recognition of the matric-variate nature of dioptric power allows calculation of sample means and variance-covariances. These in turn can be used to calculate a statistic for testing hypotheses on population means and for obtaining confidence regions for those means. In a graphical representation of dioptric power the confidence region turns out to be an ellipsoid centred on the mean of the sample of dioptric powers. The theory is illustrated by means of numerical examples. Singularity of the variance-covariance matrix may occur especially when the sample is small. When it does occur it is the cause of some difficulty in applying the statistics. Nevertheless singularity is rare in practical situations and can usually be avoided simply by increasing the size of the sample. Singularity, therefore, is not treated fully in this paper. Dioptric power is essentially four-dimensional in character but in practice a three-dimensional subspace is almost always sufficient. To avoid the difficulty of having to represent four-dimensional shapes and to avoid the complication of singularity (which is the rule rather than the exception in practice in four-space) only the common three-dimensional problem is considered in detail.

Analysis of Variance↗