Natural variation in gene expression is extensive in humans and other organisms, and variation in the baseline expression level of many genes has a heritable component. To localize the genetic determinants of these quantitative traits (expression phenotypes) in humans, we used microarrays to measure gene expression levels and performed genome-wide linkage analysis for expression levels of 3,554 genes in 14 large families. For approximately 1,000 expression phenotypes, there was significant evidence of linkage to specific chromosomal regions. Both cis- and trans-acting loci regulate variation in the expression levels of genes, although most act in trans. Many gene expression phenotypes are influenced by several genetic determinants. Furthermore, we found hotspots of transcriptional regulation where significant evidence of linkage for several expression phenotypes (up to 31) coincides, and expression levels of many genes that share the same regulatory region are significantly correlated. The combination of microarray techniques for phenotyping and linkage analysis for quantitative traits allows the genetic mapping of determinants that contribute to variation in human gene expression.

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With the release of the landmark report Toxicity Testing in the 21st Century: A Vision and a Strategy, the U.S. National Academy of Sciences, in 2007, precipitated a major change in the way toxicity testing is conducted. It envisions increased efficiency in toxicity testing and decreased animal usage by transitioning from current expensive and lengthy in vivo testing with qualitative endpoints to in vitro toxicity pathway assays on human cells or cell lines using robotic high-throughput screening with mechanistic quantitative parameters. Risk assessment in the exposed human population would focus on avoiding significant perturbations in these toxicity pathways. Computational systems biology models would be implemented to determine the dose-response models of perturbations of pathway function. Extrapolation of in vitro results to in vivo human blood and tissue concentrations would be based on pharmacokinetic models for the given exposure condition. This practice would enhance human relevance of test results, and would cover several test agents, compared to traditional toxicological testing strategies. As all the tools that are necessary to implement the vision are currently available or in an advanced stage of development, the key prerequisites to achieving this paradigm shift are a commitment to change in the scientific community, which could be facilitated by a broad discussion of the vision, and obtaining necessary resources to enhance current knowledge of pathway perturbations and pathway assays in humans and to implement computational systems biology models. Implementation of these strategies would result in a new toxicity testing paradigm firmly based on human biology.

Variation in DNA sequence contributes to individual differences in quantitative traits, but in humans the specific sequence variants are known for very few traits. We characterized variation in gene expression in cells from individuals belonging to three major population groups. This quantitative phenotype differs significantly between European-derived and Asian-derived populations for 1,097 of 4,197 genes tested. For the phenotypes with the strongest evidence of cis determinants, most of the variation is due to allele frequency differences at cis-linked regulators. The results show that specific genetic variation among populations contributes appreciably to differences in gene expression phenotypes. Populations differ in prevalence of many complex genetic diseases, such as diabetes and cardiovascular disease. As some of these are probably influenced by the level of gene expression, our results suggest that allele frequency differences at regulatory polymorphisms also account for some population differences in prevalence of complex diseases.The expression levels of genes determine the distinctive characteristics of cells. Recent studies have shown that gene expression levels in humans differ not only among cell types within an individual but also among individuals 1,2 . This observation led to analysis of gene expression as a phenotype and to the identification of polymorphic genetic variants that influence individual differences in expression level [3][4][5][6][7][8] . However, these studies of the genetics of human gene expression have been restricted to individuals from one Europeanderived sample, the families collected by the Centre d'Etude du Polymorphisme Humain Correspondence should be addressed to V.G.C. (vcheung@mail.med.upenn.edu) or R.S.S. (spielman@pobox.upenn.edu). Accession codes. Gene Expression Omnibus (GEO): GSE5859.URLs. Human Variation Panel: http://ccr.coriell.org/nigms/cells/humdiv.html. MultiExperiment Viewer: http://www.tm4.org. Information on HapMap SNP markers can be found at http://www.hapmap.org.Note: Supplementary information is available on the Nature Genetics website. COMPETING INTERESTS STATEMENTThe authors declare that they have no competing financial interests. Much of the recognized genetic variation among populations is in DNA polymorphisms with no known functional significance. On the other hand, some allele frequency differences between populations have highly significant phenotypic consequences. Among the bestestablished are the differences in allele frequencies for mendelian genetic diseases. The marked population differences in prevalence of these qualitative phenotypes (such as cystic fibrosis 9 and Tay-Sachs disease 10 ) are entirely due to differences in frequencies of the mutant alleles. However, genetic differences among populations in quantitative phenotypes are potentially just as important functionally.Here we extend the comparative genetic analysis of population differences from qualitative phenotypes to a particular quantitative phenotype, the expression ...

The sequencing of the human genome has resulted in greater attention to genetic variation among individuals, and variation at the DNA sequence level is now being extensively studied. At the same time, it has become possible to study variation at the level of gene expression by various methods. At present, it is largely unknown how widespread this variation in transcript levels is over the entire genome and to what extent individual differences in expression level are genetically determined. In the present study, we used lymphoblastoid cells to examine variation in gene expression and identified genes whose transcript levels differed greatly among unrelated individuals. We also found evidence for familial aggregation of expression phenotype by comparing variation among unrelated individuals, among siblings within families and between monozygotic twins. These observations suggest that there is a genetic contribution to polymorphic variation in the level of gene expression.

The transmission of information from DNA to RNA is a critical process. We compared RNA sequences from human B cells of 27 individuals to the corresponding DNA sequences from the same individuals and uncovered more than 10,000 exonic sites where the RNA sequences do not match that of the DNA. All 12 possible categories of discordances were observed. These differences were nonrandom as many sites were found in multiple individuals and in different cell types, including primary skin cells and brain tissues. Using mass spectrometry, we detected peptides that are translated from the discordant RNA sequences and thus do not correspond exactly to the DNA sequences. These widespread RNA-DNA differences in the human transcriptome provide a yet unexplored aspect of genome variation.

There are a variety of options for making microarrays and obtaining microarray data. Here, we describe the building and use of two microarray facilities in academic settings. In addition to specifying technical detail, we comment on the advantages and disadvantages of components and approaches, and provide a protocol for hybridization. The fact that we are now making and using microarrays to answer biological questions demonstrates that the technology can be implemented in a university environment.