Volume 10 Issue 9, September 2009

Uncovering the genetic determinants of individual variation in gene expression in humans can improve our understanding of gene regulation and help to identify disease risk alleles. Further advances might be achieved by testing under different conditions, by using larger sample sizes or through network analysis.

Genome-wide maps of transcription factor binding are prompting the re-examination of traditional concepts of transcriptional regulation. Current challenges centre on understanding which binding events are functional, how transcription factors cooperate and how to integrate the genomic and chromatin context into models of gene regulation.

Advances in mass spectrometry-based proteomics have led to an increasing use of proteomics data for the analysis of mutant phenotypes. Integrating this proteomic information with genomics and phenomics data into networks represents a promising route for modelling how phenotypes emerge.

Microfluidic 'lab-on-a-chip' devices can be used to study the dynamics of gene networks in single cells. This Review discusses the various designs of these devices and the insights into modelling the complex dynamics of gene regulation that these new technologies have provided.

F_STdescribes the processes that lead to genetic differentiation among and within populations and is widely used in population and evolutionary genetics. This article describes the meaning ofF_STand how it should be estimated and interpreted.

Genome-wide association studies have identified many cancer susceptibility alleles, but much heritable risk for this complex heterogeneous disease remains unexplained. Mouse model studies are yielding evidence that integrated analysis of genetic and genomic data might prove a fruitful strategy to meet this challenge.