metan: an R package for multi-environment trial analysis

Multi-environment trials (MET) are crucial steps in plant breeding programs that aim increasing crop productivity to ensure global food security. The analysis of MET data requires the combination of several approaches including data manipulation, visualization, and modeling. As new methods are proposed, analyzing MET data correctly and completely remains a challenge, often intractable with existing tools.Here we describe the metan R package, a collection of functions that implement a workflow-based approach to (a) check, manipulate and summarise typical MET data; (b) analyze individual environments using both fixed and mixed-effect models; (c) compute parametric and non-parametric stability statistics; (c) implement biometrical models widely used in MET analysis; and (d) plot typical MET data quickly.In this paper, we present a summary of the functions implemented in metan and how they integrate into a workflow to explore and analyze MET data. We guide the user along a gentle learning curve and show how adding only a few commands or options at a time, powerfull analyzes can be implemented.metan offers a flexible, intuitive, and richly documented working environment with tools that will facilitate the implementation of a complete analysis of MET data sets.

Some methodological aspects of the National Forest Inventory and Monitoring in Slovakia

The work presents the conceptual information about the National Forest Inventory and Monitoring in Slovakia. It introduces some methodological approaches to the field data collection (determination of tree heights by two-phase method, regression formulas for tree volumes and assortments of forest tree species, quantification of deadwood volume in sample plots) and biometrical models prepared for data processing and generalisation of the results. The design and conception of Slovak National Forest Inventory and Monitoring were set with the aim to enable providing complex and integrated information about the state and changes of production and ecological characteristics of the forest ecosystems.

The Heritability of Life Events: An Adolescent Twin and Adoption Study

Although life events are often conceptualized as reflecting exogenous risk factors for psychopathology, twin studies have suggested they are heritable. We undertook a mixed twin/adoption study to further explore genetic and environmental contributions to individual differences in the experience of life events. Specifically, a sample of 618 pairs of like-sex adolescent twins, 244 pairs of like-sex adopted adolescent and young adult siblings, and 128 pairs of like-sex biological siblings completed a life events interview. Events were classified as independent (not likely to have been influenced by respondent's behavior), dependent (likely to have been influenced by respondent's behavior), or familial (experienced by a family member), and then summed to form three life event scales. Variance on the scales was assumed to be a function of four factors: additive genetic effects (a2), shared environmental effects (c2), twin-specific effects (t2), and nonshared environmental effects (e2). Data were analyzed using standard biometrical models. Shared environmental effects were found to be the largest contributor to variance in familial events (c2 = .71; 95% confidence interval of .65, .76); additive genetic effects were the largest contributor to dependent events (a2 = .45; CI = .31, .58); and nonshared environmental effects were found to be the largest contributor independent events (e2 = .57; CI = .51, .64). A significant twin-specific effect was also found for independent life events, indicating that twins are more likely to be exposed to such events than non-twin biological siblings. Findings are discussed in terms of their implication for understanding the nature of psychosocial risk.

A Generalization of the Mixture Model in the Mapping of Quantitative Trait Loci for Progeny From a Biparental Cross of Inbred Lines

The recent advent of molecular markers has created a great potential for the understanding of quantitative inheritance. In parallel to rapid developments and improvements in molecular marker technologies, biometrical models have been constructed, refined and generalized for the mapping of quantitative trait loci (QTL). However, current models present restricitions in terms of breeding designs to which they apply. In this paper, we develop an approach for the generalization of the mixture model for progeny from a single bi-parental cross of inbred lines. Detailed derivations are given for genetic designs involving populations developed by selfing, i.e., where marker genotypes are obtained from Fx (x ≤ 2) individuals and where phenotypes are measured on Fy (y ≥ x) individuals or families. Extensions to designs involving doubled-haploids, backcrossderived individuals and random matings are outlined. The derivations presented here can easily be combined with current QTL mapping approaches.