Integration of genome-wide expression profiling with linkage analysis is a new approach to identifying genes underlying complex traits. We applied this approach to the regulation of gene expression in the BXH/HXB panel of rat recombinant inbred strains, one of the largest available rodent recombinant inbred panels and a leading resource for genetic analysis of the highly prevalent metabolic syndrome. In two tissues important to the pathogenesis of the metabolic syndrome, we mapped cis- and trans-regulatory control elements for expression of thousands of genes across the genome. Many of the most highly linked expression quantitative trait loci are regulated in cis, are inherited essentially as monogenic traits and are good candidate genes for previously mapped physiological quantitative trait loci in the rat. By comparative mapping we generated a data set of 73 candidate genes for hypertension that merit testing in human populations. Mining of this publicly available data set is expected to lead to new insights into the genes and regulatory pathways underlying the extensive range of metabolic and cardiovascular disease phenotypes that segregate in these recombinant inbred strains.

MOTIVATION: Dissection of the genetics underlying gene expression utilizes techniques from microarray analyses as well as quantitative trait loci (QTL) mapping. Available QLT mapping methods are not tailored for the highly automated analyses required to deal with the thousand of gene transcripts encountered in the mapping of QTL affecting gene expression (sometimes referred to as eQTL). This report focuses on the adaptation of QTL mapping methodology to perform automated mapping of QTL affecting gene expression. RESULTS: The analyses of expression data on > 12,000 gene transcripts in BXD recombinant inbred mice found, on average, 629 QTL exceeding the genome-wide 5% threshold. Using additional information on trait repeatabilities and QTL location, 168 of these were classified as 'high confidence' QTL. Current sample sizes of genetical genomics studies make it possible to detect a reasonable number of QTL using simple genetic models, but considerably larger studies are needed to evaluate more complex genetic models. After extensive analyses of real data and additional simulated data (altogether > 300,000 genome scans) we make the following recommendations for detection of QTL for gene expression: (1) For populations with an unbalanced number of replicates on each genotype, weighted least squares should be preferred above ordinary least squares. Weights can be based on repeatability of the trait and the number of replicates. (2) A genome scan based on multiple marker information but analysing only at marker locations is a good approximation to a full interval mapping procedure. (3) Significance testing should be based on empirical genome-wide significance thresholds that are derived for each trait separately. (4) The significant QTL can be separated into high and low confidence QTL using a false discovery rate that incorporates prior information such as transcript repeatabilities and co-localization of gene-transcripts and QTL. (5) Including observations on the founder lines in the QTL analysis should be avoided as it inflates the test statistic and increases the Type I error. (6) To increase the computational efficiency of the study, use of parallel computing is advised. These recommendations are summarized in a possible strategy for mapping of QTL in a least squares framework. AVAILABILITY: The software used for this study is available on request from the authors.

Bayesian network modeling is a promising approach to define and evaluate gene expression circuits in diverse tissues and cell types under different experimental conditions. The power and practicality of this approach can be improved by restricting the number of potential interactions among genes and by defining causal relations before evaluating posterior probabilities for billions of networks. A newly developed genetical genomics method that combines transcriptome profiling with complex trait analysis now provides strong constraints on network architecture. This method detects those chromosomal intervals responsible for differences in mRNA expression using quantitative trait locus (QTL) mapping. We have developed an efficient Bayesian approach that exploits the genetical genomics method to focus computational effort on the most plausible gene modulatory networks. We exploit a dense marker map for a genetic reference population (GRP) that consists of 32 BXD strains of mice made by intercrossing two progenitor strains--C57BL/6J and DBA/2J. These progenitors differ at approximately 1.3 million known single nucleotide polymorphisms (SNPs), all of which can be exploited to estimate the probability that a gene contains functional polymorphisms that segregate within the GRP. We constructed 66 candidate networks that include all the candidate modulator genes located in the 209 statistically significant trans-acting QTL regions. SNPs that distinguish between the two progenitor strains were used to further winnow the list of candidate modulators. Bayesian network was then used to identify the genetic modulatory relations that best explain the microarray data.

Functional genomics has been applied to the genetic dissection of immune response in different ways: (1) experimental crosses between lines that differ in their (non-) specific immune response have been used to detect quantitative trait loci (QTL) underlying these differences. (2) The measurement of gene expression levels for thousands of genes using microarrays or oligonucleotide chips to identify differential expression with regard to antigen challenge: (a) before and after infection, (b) resistant versus susceptible lines, or (c) combinations of both. Interpretation of QTL results is hampered by the fact that confidence regions of the QTL are large and can contain hundreds of potential candidate genes for the QTL. At the same time, the microarray experiments tend to show large numbers of differentially expressed genes without identifying the relationships between these genes. In the recently proposed 'genetical genomics' framework, members of a segregating population are characterised for genome-wide molecular markers and for gene expression levels. This facilitates the mapping of expression-QTL (eQTL): loci in the genome that control the expression of genes. Initial applications of this approach are critically reviewed and potential applications of this approach with regard to immune response are presented.

Short-oligonucleotide arrays typically contain multiple probes per gene. In genetical genomics applications a statistical model for the individual probe signals can help in separating "true" differential mRNA expression from "ghost" effects caused by polymorphisms, misdesigned probes, and batch effects. It can also help in detecting alternative splicing, start, or termination.

The biological mechanisms that link genetic variation and its phenotypic outcome stand as a central puzzle in biology. Geneticists have usually approached this problem by trying to identify genetic variants that underlie the trait in question. Ten years ago, microarray technology opened a second front by making it possible to compare expression levels for most active genes under a variety of genetic and environmental conditions. A typical study reveals up- or down-regulation of genes or pathways associated with a phenotype (case/control) or condition (treated/untreated). In the past few years, a number of groups have started to combine gene expression studies with genetic linkage analysis, leading to a new synergy between these approaches. In this strategy, expression levels are treated as quantitative phenotypes and genetic variants that influence gene expression are sought. Several studies have shown that mRNA levels for many genes are heritable, thus amenable to genetic analysis. Quantitative trait loci mapping efforts have led to the initial characterization of genetic regulation in 'cis' probably because of variants in the gene's own regulatory regions, as well as in 'trans', i.e. by loci elsewhere in the genome. The existence of some 'master regulators' that each affects expression levels of hundreds of genes is an important finding that will surely enrich our understanding of regulatory networks. Although this novel field is still developing, understanding the genetic basis of molecular phenotypes such as gene expression is expected to shed light on the intermediate processes that connect genotype to cellular and organismal traits and represents a critical step towards true systems biology.

A combination of quantitative trait locus (QTL) mapping and microarray analysis was developed and used to identify 34 candidate genes for ovariole number, a quantitative trait, in Drosophila melanogaster. Ovariole number is related to evolutionary fitness, which has been extensively studied, but for which few a priori candidate genes exist. A set of recombinant inbred lines were assayed for ovariole number, and QTL analyses for this trait identified 5,286 positional candidate loci. Forty deletions spanning the QTL were employed to further refine the map position of genes contributing to variation in this trait between parental lines, with six deficiencies showing significant effects and reducing the number of positional candidates to 548. Parental lines were then assayed for expression differences by using Affymetrix microarray technology, and ANOVA was used to identify differentially expressed genes in these deletions. Thirty-four genes were identified that showed evidence for differential expression between the parental lines, one of which was significant even after a conservative Bonferroni correction. The list of potential candidates includes 5 genes for which previous annotations did not exist, and therefore would have been unlikely choices for follow-up from mapping studies alone. The use of microarray technology in this context allows an efficient, objective, quantitative evaluation of genes in the QTL and has the potential to reduce the overall effort needed in identifying genes causally associated with quantitative traits of interest.

Genetical genomics has been proposed to map loci controlling gene-expression differences (eQTLs) that might underlie functional trait variation. We briefly review the studies in model species and conclude that, although they successfully demonstrate the utility of genetical genomics, they are too limited to unlock the full potential of this approach and some results should be interpreted with caution. We subsequently elaborate on two recent studies that use this approach in humans. The many differences between these studies complicate meaningful comparisons between them. A joint analysis of the two experiments offers some scope for more powerful genetical genomics.

Treating messenger RNA transcript abundances as quantitative traits and mapping gene expression quantitative trait loci for these traits has been pursued in gene-specific ways. Transcript abundances often serve as a surrogate for classical quantitative traits in that the levels of expression are significantly correlated with the classical traits across members of a segregating population. The correlation structure between transcript abundances and classical traits has been used to identify susceptibility loci for complex diseases such as diabetes and allergic asthma. One study recently completed the first comprehensive dissection of transcriptional regulation in budding yeast, giving a detailed glimpse of a genome-wide survey of the genetics of gene expression. Unlike classical quantitative traits, which often represent gross clinical measurements that may be far removed from the biological processes giving rise to them, the genetic linkages associated with transcript abundance affords a closer look at cellular biochemical processes. Here we describe comprehensive genetic screens of mouse, plant and human transcriptomes by considering gene expression values as quantitative traits. We identify a gene expression pattern strongly associated with obesity in a murine cross, and observe two distinct obesity subtypes. Furthermore, we find that these obesity subtypes are under the control of different loci.