Commentary

The National Center for Biotechnology Information has created the dbGaP public repository for individual-level phenotype, exposure, genotype and sequence data and the associations between them. dbGaP assigns stable, unique identifiers to studies and subsets of information from those studies, including documents, individual phenotypic variables, tables of trait data, sets of genotype data, computed phenotype-genotype associations, and groups of study subjects who have given similar consents for use of their data.

The Genetic Association Information Network (GAIN) is a public-private partnership established to investigate the genetic basis of common diseases through a series of collaborative genome-wide association studies. GAIN has used new approaches for project selection, data deposition and distribution, collaborative analysis, publication and protection from premature intellectual property claims. These demonstrate a new commitment to shared scientific knowledge that should facilitate rapid advances in understanding the genetics of complex diseases.

Lists of variations in genomic DNA and their effects have been kept for some time and have been used in diagnostics and research. Although these lists have been carefully gathered and curated, there has been little standardization and coordination, complicating their use. Given the myriad possible variations in the estimated 24,000 genes in the human genome, it would be useful to have standard criteria for databases of variation. Incomplete collection and ascertainment of variants demonstrates a need for a universally accessible system. These and other problems led to the World Heath Organization–cosponsored meeting on June 20–23, 2006 in Melbourne, Australia, which launched the Human Variome Project. This meeting addressed all areas of human genetics relevant to collection of information on variation and its effects. Members of each of eight sessions (the clinic and phenotype, the diagnostic laboratory, the research laboratory, curation and collection, informatics, relevance to the emerging world, integration and federation and funding and sustainability) developed a number of recommendations that were then organized into a total of 96 recommendations to act as a foundation for future work worldwide. Here we summarize the background of the project, the meeting and its recommendations.

Recent experience with several high-profile drugs demonstrates the great challenges in developing effective and safe therapeutics. A complementary approach to the popular paradigm of disease genetics is based on inherited factors that reduce the incidence and severity of disease among individuals who are genetically predisposed to disease. We propose testing specifically for modifier genes and protective alleles among at-risk individuals and studying the efficacy of therapeutics based on the genetics of health.

Three very recent reports provide convincing statistical evidence (P < 10⁻⁸), at a genome-wide level, of the association of common polymorphisms with three different common diseases: systemic lupus erythematosus (IRF5), prostate cancer and type 1 diabetes (IFIH1 region). This adds to the trickle—soon to be a flood—of disease association results that are highly unlikely to be false positives. There are other convincing examples in the last 12 months: age-related macular degeneration (CFH), type 1 diabetes (IL2RA, also known as CD25) and type 2 diabetes (TCF7L2). Given 20 years of a literature full of irreproducible results, what has changed?

Networks of investigators have begun sharing best practices, tools and methods for analysis of associations between genetic variation and common diseases. A Network of Investigator Networks has been set up to drive the process, sponsored by the Human Genome Epidemiology Network. A workshop is planned to develop consensus guidelines for reporting results of genetic association studies. Published literature databases will be integrated, and unpublished data, including 'negative' studies, will be captured by online journals and through investigator networks. Systematic reviews will be expanded to include more meta-analyses of individual-level data and prospective meta-analyses. Field synopses will offer regularly updated overviews.

The identification and investigation of sentinel cases has illuminated genetic discrimination in the US. Its occurrence impedes applications of biotechnology and is a primary focus of public policy activity at the federal level. Continued research and informed responses may make genetic nondiscrimination more likely.

The goal of the Complex Trait Consortium is to promote the development of resources that can be used to understand, treat and ultimately prevent pervasive human diseases. Existing and proposed mouse resources that are optimized to study the actions of isolated genetic loci on a fixed background are less effective for studying intact polygenic networks and interactions among genes, environments, pathogens and other factors. The Collaborative Cross will provide a common reference panel specifically designed for the integrative analysis of complex systems and will change the way we approach human health and disease.

A true understanding of disease risk requires a thorough examination of root causes. 'Race' and 'ethnicity' are poorly defined terms that serve as flawed surrogates for multiple environmental and genetic factors in disease causation, including ancestral geographic origins, socioeconomic status, education and access to health care. Research must move beyond these weak and imperfect proxy relationships to define the more proximate factors that influence health.

Data on human genetic variation help scientists to understand human origins, susceptibility to illness and genetic causes of disease. Destructive episodes in the history of genetic research make it crucial to consider the ethical and social implications of research in genomics, especially human genetic variation. The analysis of ethical, legal and social implications should be integrated into genetic research, with the participation of scientists who can anticipate and monitor the full range of possible applications of the research from the earliest stages. The design and implementation of research directs the ways in which its results can be used, and data and technology, rather than ethical considerations or social needs, drive the use of science in unintended ways. Here we examine forensic genetics and argue that all geneticists should anticipate the ethical and social issues associated with nonmedical applications of genetic variation research.

Knowledge from the Human Genome Project and research on human genome variation increasingly challenges the applicability of the term 'race' to human population groups, raising questions about the validity of inferences made about 'race' in the biomedical and scientific literature. Despite the acknowledged contradictions in contemporary science, population-based genetic variation is continually used to explain differences in health between 'racial' and 'ethnic' groups. In this commentary we posit that resolution of apparent paradoxes in relating biology to 'race' and genetics requires thinking 'outside of the box'.

Mouse knockout technology provides a powerful means of elucidating gene function in vivo, and a publicly available genome-wide collection of mouse knockouts would be significantly enabling for biomedical discovery. To date, published knockouts exist for only about 10% of mouse genes. Furthermore, many of these are limited in utility because they have not been made or phenotyped in standardized ways, and many are not freely available to researchers. It is time to harness new technologies and efficiencies of production to mount a high-throughput international effort to produce and phenotype knockouts for all mouse genes, and place these resources into the public domain.

The European Mouse Mutagenesis Consortium is the European initiative contributing to the international effort on functional annotation of the mouse genome. Its objectives are to establish and integrate mutagenesis platforms, gene expression resources, phenotyping units, storage and distribution centers and bioinformatics resources. The combined efforts will accelerate our understanding of gene function and of human health and disease.

Genomic profiling has the potential to usher in a revolution of personalized healthcare and disease prevention. But evidence to support genomic profiling is inconsistent, and data on the health outcome benefits based on such testing are lacking. For genomic profiling to become valid and useful, well designed epidemiologic studies and thorough clinical evaluations of recommended interventions based on genotype are required.

A principal goal of genetic research is to identify specific genotypes that are associated with human phenotypes. It will soon be possible to conduct genome-wide genotyping on a massive scale. Our current approaches for defining and assaying phenotypes may be inadequate for making optimal use of such genotypic data. We propose an international effort to create phenomic databases, that is, comprehensive assemblages of systematically collected phenotypic information, and to develop new approaches for analyzing such phenotypic data. We term this effort the Human Phenome Project and suggest a scientific and organizational scope for the project.

We would like to be able to predict how genomes are folded in the cell from the primary DNA sequence. A model for the three-dimensional structure of all genomes is presented; it is based on the structure of the bacterial nucleoid, where RNA polymerases cluster and loop the DNA. Loops appear and disappear as polymerases initiate and terminate, but the microscopic structure is 'self-organizing' and, to some extent, predictable. At the macroscopic level, transcriptional activity drives pairing between homologous sequences, inactivity allows genome compaction, and the segregation machinery orients whole chromosomes.