Table of contents

From the editors

p737 | doi:10.1038/nrg1963


Research Highlights

Stem cells: A recipe for reprogramming | PDF (184 KB)

p738 | doi:10.1038/nrg1977

Transcriptomics: Every transcript counts | PDF (150 KB)

p739 | doi:10.1038/nrg1973

RNA interference: RNAi misses the target | PDF (150 KB)

p739 | doi:10.1038/nrg1974

Evolution: Jump-starting speciation | PDF (211 KB)

p740 | doi:10.1038/nrg1975

Reproductive Biology: From worm sperm proteins to human infertility | PDF (211 KB)

p740 | doi:10.1038/nrg1976

In brief

Development | Gene function | Plant biology | Cancer genetics | PDF (162 KB)

p741 | doi:10.1038/nrg1978

Chromatin: Eukaryotic genomes in complete control | PDF (347 KB)

p742 | doi:10.1038/nrg1966

Ageing: Longevity mutations inhibit tumours | PDF (347 KB)

p742 | doi:10.1038/nrg1972

Evolution: Putting flower colour on the landscape | PDF (296 KB)

p743 | doi:10.1038/nrg1971

Human genetics: INDELible markers | PDF (162 KB)

p744 | doi:10.1038/nrg1965

In brief

Epigenetics | Technology | Population genetics | Development | PDF (162 KB)

p744 | doi:10.1038/nrg1979


Focus on: Statistical Analysis


Computer programs for population genetics data analysis: a survival guide

Laurent Excoffier & Gerald Heckel

p745 | doi:10.1038/nrg1904

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.

Modern computational approaches for analysing molecular genetic variation data

Paul Marjoram & Simon Tavaré

p759 | doi:10.1038/nrg1961

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.

Genetic relatedness analysis: modern data and new challenges

Bruce S. Weir, Amy D. Anderson & Amanda B. Hepler

p771 | doi:10.1038/nrg1960

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.

A tutorial on statistical methods for population association studies

David J. Balding

p781 | doi:10.1038/nrg1916

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.



Spreading of silent chromatin: inaction at a distance

Paul B. Talbert & Steven Henikoff

p793 | doi:10.1038/nrg1920

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.




TAR cloning: insights into gene function, long-range haplotypes and genome structure and evolution

Natalay Kouprina & Vladimir Larionov

p805 | doi:10.1038/nrg1943

Transformation-associated recombination (TAR) cloning uses in vivo recombination 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.


Genes, environment and the value of prospective cohort studies

Teri A. Manolio, Joan E. Bailey-Wilson & Francis S. Collins

p812 | doi:10.1038/nrg1919

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.


Correspondence: Mining meiosis with genomic models

R. M. Ranganath & G. Venkatachalaiah

| doi:10.1038/nrg1614-c1

Erratum: Evolutionary genetics: High-resolution mutation mapping reveals parallel experimental evolution in yeast

| doi:10.1038/nrg1980