Population genomics of human gene expression

Genetic variation influences gene expression, and this variation in gene expression can be efficiently mapped to specific genomic regions and variants. Here we have used gene expression profiling of Epstein-Barr virus-transformed lymphoblastoid cell lines of all 270 individuals genotyped in the HapMap Consortium to elucidate the detailed features of genetic variation underlying gene expression variation. We find that gene expression is heritable and that differentiation between populations is in agreement with earlier small-scale studies. A detailed association analysis of over 2.2 million common SNPs per population (5% frequency in HapMap) with gene expression identified at least 1,348 genes with association signals in cis and at least 180 in trans. Replication in at least one independent population was achieved for 37% of cis signals and 15% of trans signals, respectively. Our results strongly support an abundance of cis-regulatory variation in the human genome. Detection of trans effects is limited but suggests that regulatory variation may be the key primary effect contributing to phenotypic variation in humans. We also explore several methodologies that improve the current state of analysis of gene expression variation.     

A literature network of human genes for high-throughput analysis of gene expression

We have carried out automated extraction of explicit and implicit biomedical knowledge from publicly available gene and text databases to create a gene-to-gene co-citation network for 13,712 named human genes by automated analysis of titles and abstracts in over 10 million MEDLINE records. The associations between genes have been annotated by linking genes to terms from the medical subject heading (MeSH) index and terms from the gene ontology (GO) database. The extracted database and accompanying web tools for gene-expression analysis have collectively been named 'PubGene'. We validated the extracted networks by three large-scale experiments showing that co-occurrence reflects biologically meaningful relationships, thus providing an approach to extract and structure known biology. We validated the applicability of the tools by analyzing two publicly available microarray data sets.     

Status, sale and patenting of human genetic material: an international survey.

Following a decade of debate, the European Directive on the Legal Protection of Biotechnological Inventions was adopted by the European Parliament and the Council of the European Union on July 6, 1998. The Directive constitutes a legal and social policy landmark in biotechnology, taking an explicit position on the contentious issue of the patentability of higher life forms. It fails, however, to provide definitive statements on the legal status of human genetic material or the possibility of personal financial gain in relation to such material. An overview of the international, regional and national positions (as found in laws and official policy statements) on the status, commodification and patentability of human genetic material indicates that, although the Directive represents a consolidation of opinions, many issues remain unresolved.     

Common genetic variants account for differences in gene expression among ethnic groups

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.     

A signature of chromosomal instability inferred from gene expression profiles predicts clinical outcome in multiple human cancers

We developed a computational method to characterize aneuploidy in tumor samples based on coordinated aberrations in expression of genes localized to each chromosomal region. We summarized the total level of chromosomal aberration in a given tumor in a univariate measure termed total functional aneuploidy. We identified a signature of chromosomal instability from specific genes whose expression was consistently correlated with total functional aneuploidy in several cancer types. Net overexpression of this signature was predictive of poor clinical outcome in 12 cancer data sets representing six cancer types. Also, the signature of chromosomal instability was higher in metastasis samples than in primary tumors and was able to stratify grade 1 and grade 2 breast tumors according to clinical outcome. These results provide a means to assess the potential role of chromosomal instability in determining malignant potential over a broad range of tumors.     

A high-resolution survey of deletion polymorphism in the human genome

Recent work has shown that copy number polymorphism is an important class of genetic variation in human genomes. Here we report a new method that uses SNP genotype data from parent-offspring trios to identify polymorphic deletions. We applied this method to data from the International HapMap Project to produce the first high-resolution population surveys of deletion polymorphism. Approximately 100 of these deletions have been experimentally validated using comparative genome hybridization on tiling-resolution oligonucleotide microarrays. Our analysis identifies a total of 586 distinct regions that harbor deletion polymorphisms in one or more of the families. Notably, we estimate that typical individuals are hemizygous for roughly 30-50 deletions larger than 5 kb, totaling around 550-750 kb of euchromatic sequence across their genomes. The detected deletions span a total of 267 known and predicted genes. Overall, however, the deleted regions are relatively gene-poor, consistent with the action of purifying selection against deletions. Deletion polymorphisms may well have an important role in the genetics of complex traits; however, they are not directly observed in most current gene mapping studies. Our new method will permit the identification of deletion polymorphisms in high-density SNP surveys of trio or other family data.     

Systematic variation in gene expression patterns in human cancer cell lines

We used cDNA microarrays to explore the variation in expression of approximately 8,000 unique genes among the 60 cell lines used in the National Cancer Institute's screen for anti-cancer drugs. Classification of the cell lines based solely on the observed patterns of gene expression revealed a correspondence to the ostensible origins of the tumours from which the cell lines were derived. The consistent relationship between the gene expression patterns and the tissue of origin allowed us to recognize outliers whose previous classification appeared incorrect. Specific features of the gene expression patterns appeared to be related to physiological properties of the cell lines, such as their doubling time in culture, drug metabolism or the interferon response. Comparison of gene expression patterns in the cell lines to those observed in normal breast tissue or in breast tumour specimens revealed features of the expression patterns in the tumours that had recognizable counterparts in specific cell lines, reflecting the tumour, stromal and inflammatory components of the tumour tissue. These results provided a novel molecular characterization of this important group of human cell lines and their relationships to tumours in vivo.     

A worldwide survey of haplotype variation and linkage disequilibrium in the human genome

Recent genomic surveys have produced high-resolution haplotype information, but only in a small number of human populations. We report haplotype structure across 12 Mb of DNA sequence in 927 individuals representing 52 populations. The geographic distribution of haplotypes reflects human history, with a loss of haplotype diversity as distance increases from Africa. Although the extent of linkage disequilibrium (LD) varies markedly across populations, considerable sharing of haplotype structure exists, and inferred recombination hotspot locations generally match across groups. The four samples in the International HapMap Project contain the majority of common haplotypes found in most populations: averaging across populations, 83% of common 20-kb haplotypes in a population are also common in the most similar HapMap sample. Consequently, although the portability of tag SNPs based on the HapMap is reduced in low-LD Africans, the HapMap will be helpful for the design of genome-wide association mapping studies in nearly all human populations.     

Treatment-specific changes in gene expression discriminate in vivo drug response in human leukemia cells

To elucidate the genomics of cellular responses to cancer treatment, we analyzed the expression of over 9,600 human genes in acute lymphoblastic leukemia cells before and after in vivo treatment with methotrexate and mercaptopurine given alone or in combination. Based on changes in gene expression, we identified 124 genes that accurately discriminated among the four treatments. Discriminating genes included those involved in apoptosis, mismatch repair, cell cycle control and stress response. Only 14% of genes that changed when these medications were given as single agents also changed when they were given together. These data indicate that lymphoid leukemia cells of different molecular subtypes share common pathways of genomic response to the same treatment, that changes in gene expression are treatment-specific and that gene expression can illuminate differences in cellular response to drug combinations versus single agents.