Volume 7 Issue 10, October 2006

The increase in population genetics data has led to a parallel need for sophisticated analysis programs and packages. This article is intended as a guide to many of these statistical programs, to promote their more informed use.

The vast increase in the amount of molecular genetic data that are being generated, and the scale of their complexity, demand ever more sophisticated statistical analysis methods — this article surveys and compares these approaches, and the growing reliance on computational methodologies.

The concept of relatedness is central to many fields, from human linkage analysis to forensics to animal and plant breeding. This review covers the statistical framework for studying relatedness, its applications and the challenges that the field faces.

Identifying polymorphisms that are overrepresented in disease cases versus controls would seem to be a straightforward process, but genetic association studies are notoriously riddled with complex analysis problems. This article outlines these statistical issues and provides some guidance to overcoming them.

Several models have been proposed to explain the spreading of heterochromatin, including looping, sliding and oozing. A review of studies from diverse model eukaryotes allows the authors to evaluate the existing models and leads them to propose a common, ancestral mechanism for spreading.

Transformation-associated recombination (TAR) cloning usesin vivorecombination in yeast to isolate large chromosomal segments from complex genomes. Although the principles of TAR cloning date back to the 1990s, recent modifications have opened up promising new applications of this technology.

Gene–environment interactions are key contributors to complex disease, but are hard to dissect in commonly used case–control designs. This article argues that large-scale prospective cohort studies, several of which are planned or under way, provide an essential alternative strategy.

Fields as disparate as population genetics, systems biology, mathematical modelling of development and transcription profiling rely on quantitative descriptions, and statistical analysis — once a concept foreign to mainstream genetics — is pervading every aspect of the field. The articles in this Focus untangle the complex issues in statistical analysis, and present the continually debated solutions and the authors' opinion on future trends.