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Z Kaczmarek

Publications and source records attributed to Z Kaczmarek.

7 recordsLinked to original sources
PubMed2005

Analyzing multi-environment variety trials using randomization-derived mixed models.

Of interest is the analysis of results of a series of experiments repeated at several environments with the same set of plant varieties. Suppose that the experiments, multi-environment variety trials, are all conducted in resolvable incomplete block (IB) designs. Following the randomization approach adopted in Caliński and Kageyama (2000, Lecture Notes in Statistics, 150), two models for analyzing such trial data can be considered. One is derived under a complete additivity assumption, the other takes into account possible different responses of the varieties to variable environmental conditions. The analysis under the first, the standard model, does not provide answers to questions related to the performance of the individual varieties at different environments. These can be considered when using the more general second model. The purpose of this article is to devise interesting parameter estimation and hypothesis testing procedures under that more realistic model. Its application is illustrated by a thorough analysis of a set of data from a winter wheat series of trials.

Algorithms↗
PubMed2000

A multivariate approach to the problem of QTL localization.

QTL mapping with statistical likelihood-based procedures or asymptotically equivalent regression methods is usually carried out in a univariate way, even if many traits were observed in the experiment. Some proposals for multivariate QTL mapping by an extension of the maximum likelihood method for mixture models or by an application of the canonical transformation have been given in the literature. This paper describes a method of analysis of multitrait data sets, aimed at localization of QTLs contributing to many traits simultaneously, which is based on the linear model of multivariate multiple regression. A special form of the canonical analysis is employed to decompose the test statistic for the general no-QTL hypothesis into components pertaining to individual traits and individual, putative QTLs. Extended linear hypotheses are used to formulate conjectures concerning pleiotropy. A practical mapping algorithm is described. The theory is illustrated with the analysis of data from a study of maize drought resistance.

Genes, Plant↗
PubMed2000

Genetic determination of variability of barley doubled haploids inoculated with Fusarium culmorum (W.G.Sm.) Sacc. with regard to mycotoxin accumulation and reduction in yield traits.

The genetic determination of variability of barley doubled haploid (DH) lines in regard of their susceptibility to Fusarium head blight caused by Fusarium culmorum was studied. The susceptibility was evaluated in 3-year field experiment on the basis of reduction in yield traits and myotoxin accumulation in infected kernels. The following traits were analysed in inoculated and control plants: kernel number and weight per ear, 1000-kernel weight, percentage of plump kernels (>2.5 mm), deoxynivalenol (DON) content and nivalenol (NIV) content of kernels. On the basis of the obtained data, heritability coefficient (ratio of genotypic to phenotypic variance) was assessed, and genetic parameters as well as the number of effective factors were estimated. Heritability coefficients calculated from two-way analysis of variance, i.e.regarding the influence of years and year x genotype interaction, appeared to be exceptionally low and ranged from 5.2% for the reduction in plump kernels to 38.2% for the reduction in 1000-kernel weight. In the case of mycotoxin accumulation about 60% of the observed variability in NIV concentrations and 30% in DON concentration resulted form genetic differences among lines. Additive effects of genes were important for all the analysed traits. Significant effects of dominance and dominance x dominance were observed for 1000-kernel weight and percentage of plump kernels. Moreover, it was found that the observed variability in yield trait reduction resulted from segregation of 5-6 effective factors, DON contents from 4 factors, while NIV content from 5 factors.

Journal Article↗
PubMed1985

Genetic distances based on quantitative traits.

Morphological data showing continuous distributions, polygenically controlled, may be particularly useful in intergroup classification below the species level; an appropriate distance analysis based on these traits is an important tool in evolutionary biology and in plant and animal breeding.--The interpretation of morphological distances in genetic terms is not easy because simple phenotypic data may lead to biased estimates of genetic distances. Convenient estimates can be obtained whenever it is possible to breed populations according to a suitable crossing design and to derive information from genetic parameters.--A general method for determining genetic distances is proposed. The procedure of multivariate analysis of variance is extended to estimate appropriate genetic parameters (genetic effects). Not only are optimal statistical estimates of parameters obtained but also the procedure allows the measurement of genetic distances between populations as linear functions of the estimated parameters, providing an appropriate distance matrix that can be defined in terms of these parameters. The use of the T2 statistic, defined in terms of the vector of contrasts specifying the distance, permits the testing of the significance of any distance between any pair of populations that may be of interest from a genetic point of view.--A numerical example from maize diallel data is reported in order to illustrate the procedure. In particular, heterosis effects are used as the basis for estimates of genetic divergence between populations.

Analysis of Variance↗