1. To understand the extent and nature of efforts to develop databases of interacting systems.
2. To understand the needs of the users of such databases.
3. To reconcile goals 1 and 2.
Some specific issues that have been raised and that should be considered by the participants:
This report summarizes the findings of a panel of experts who met at the National Institutes of Health on December 10-11, 1997 at a workshop entitled "The Genetic Architecture of Complex Traits." The report and recommendations were prepared for consideration by the National Advisory General Medical Sciences Council.
Most genetic traits of interest in populations of humans and other organisms are determined by many factors, including genetic and environmental components, which interact in often unpredictable ways. For such complex traits, the whole is not only greater than the sum of its parts, it may be different from the sum of its parts. Thus, complex traits have a genetic architecture that consists of all of the genetic and environmental factors that contribute to the trait, as well as their magnitude and their interactions.
The following recommendations are intended to increase the rate of progress and improve the quality of research on the analysis of complex traits.
Most traits that vary in populations of humans and other organisms are determined by multiple factors. Most common diseases with a genetic component are such complex traits. The complexity arises from the fact that each factor contributes, at most, a modest amount to the total variation in the trait observed in the entire population. Complex traits may be continuous in distribution, like height or blood pressure, or they may be dichotomous, like "well" and "affected." Multiple genetic and environmental factors may interact with each other in unpredictable ways. Such unpredictable, nonlinear interactions mean that the expression of the trait may not be anticipated from knowledge of the individual effects of each of the component factors considered alone, no matter how well understood the separate components may be. Thus, the whole is not only greater than the sum of its parts, the whole may be different from the sum of its parts.
The genetic architecture of complex traits consists of a description of all of the genetic and environmental factors that affect the trait, along with the magnitude of their individual effects and the magnitudes of interactions among the factors. It is, in principle, possible to define the genetic components in terms of Mendelian segregation and location along a genetic map. Environmental factors are much less easily partitioned into separate factors whose individual effects and interactions can be sorted out.
It is critical to recognize that the genetic architecture is less a fundamental biological property of the trait than a characteristic of a trait in a particular population. The genetic architecture is a moving target that changes according to gene and genotype frequencies, the distributions of environmental factors, and such biological properties as age and gender.
The dependence on gene frequencies creates some seeming paradoxes of genetic architecture. For example, suppose a trait is completely determined by the interaction of two recessive alleles, one of which is rare and the other common. At the population level, the trait appears to be determined by the rare allele, because its presence limits the variation in the occurrence of the trait among individuals. If the allele frequencies were reversed, the other gene would appear to be the determining genetic cause. But in either instance, both recessive alleles contribute equally to the biological causation of the trait.
The implication of the population dependence is that the predominant genetic factors contributing to a complex trait may seem to differ from population to population. This is probably one reason for the apparent heterogeneity sometimes found in the results of genetic linkage studies in different populations. Insufficient statistical power in the linkage tests is also a possible explanation, and there is always the possibility that superficially identical complex traits in different populations may actually have different biological causes.
The existence of unpredictable, nonlinear interactions between the multiple factors affecting complex traits, as well as possible frequency-dependent differences in genetic architecture from one population to the next, emphasizes one of the principal conclusions of the December 10-11, 1997 workshop, "The Genetic Architecture of Complex Traits." The participants unanimously agreed that understanding the genetic and environmental basis of complex traits is not going to be easy and will not be achieved in a foreseeable time frame. Too little is known about the true nature of the complexity of such traits in any organism.
In an ideal case, when the factors are not numerous, when their main effects are quite large and their interaction effects quite small, and when interpopulation heterogeneity is minimal, very rapid progress can be made. It is by no means clear how widely actual complex traits in humans and other organisms depart from these ideal conditions.
Furthermore, while improved technology can be of tremendous importance, the challenges are not only technological. They are also conceptual (for example, how to identify nonlinear interactions, how to optimize computational algorithms); clinical (improved diagnostic criteria); and epidemiological (how to sample in such a way as to minimize spurious associations due to population structure and population history while maximizing the power to detect biologically significant associations).
Because the genetic architecture is a characteristic of a trait in a population, it is affected by population structure and population history--a fact that undermines the concept of a "disease gene." In a complex trait, there is no "disease gene" in the sense of a Mendelian factor that, by itself, causes a disease. Rather, the genetic and environmental factors underlying a complex trait must each be considered as contributing or predisposing rather than as determinative. Where diseases are concerned, genetic components may be regarded as risk factors.
In spite of these difficulties, the analysis of complex traits is fundamentally important to identifying the contributing genetic and environmental factors of traits and to understanding their underlying biology. The discussion and recommendations from "The Genetic Architecture of Complex Traits" workshop focused on opportunities for progress in four areas--research, resources, training, and communications--some of which can potentially be addressed by NIGMS. Other points will require discussion and action by other groups.
The number of individuals that can be studied will ultimately determine the limit of resolution of analyses of complex traits. The sample sizes that researchers can collect and the quality of individual phenotype assignments (the ability to recognize and correctly assign trait values to individuals) are serious barriers to progress. Given the ongoing efforts to produce a dense, quality map of the human genome of single nucleotide polymorphisms (SNPs), sufficient numbers of markers to analyze the complex traits will be available soon. In human studies, therefore, the limitation will be correctly phenotyped individuals--that is, our ability to correctly diagnose disorders or completely describe traits. The situation is not quite the same for various model organisms where the development of new markers and new maps has lagged behind the effort for humans.
The consensus of the participants at the workshop was that both current methods and fundamentally new approaches should be pursued aggressively.
The overriding advantage of model organisms is the ability to do both genetic and environmental manipulation that cannot be done with human beings. Studies using animal models to explore the genetic architecture of complex traits should be supported in order to identify general principles and pathways and to gain a broad understanding of the biology of complex traits.
The genes involved in complex traits are contributing factors rather than "disease genes." Any one of the genetic factors that contribute to a complex trait may actually account for a relatively small proportion of the total variation in the trait. Furthermore, by itself, the gene may not cause the disease, but rather may be one of many contributing genetic and environmental factors. The danger of oversimplification is to mislead the public into thinking that a disease has been conquered and effective new treatments and therapies are just around the corner. There is a great danger in raising false hopes among the public.
The findings of the participants at "The Genetic Architecture of Complex Traits" workshop do not lend themselves to simplistic answers or quick fixes. The recommendations need to be considered thoughtfully and thoroughly, sometimes in collaboration with a broad spectrum of the scientific community. Success will depend on coordination among institutes and agencies and on increased understanding of the complexities of the scientific questions being asked. The participants note that NIGMS can address many issues; however, some recommendations are trans-NIH. The National Advisory General Medical Sciences Council may wish to consider broader dissemination of the report and recommendations.
1. To what extent is it desirable and possible to coordinate databases of interacting systems?
2. Should there be a single submission site for categories of such data, e.g., protein-protein interactions?
3. What are the data objects that are/will be commonly required, and how can they be represented? How can we be flexible about defining these so that future needs for both data inclusion and analysis can be accommodated?
4. How will these databases be supported?
PROW/KBTool: Building a Fabric of Biological Concepts and Relationships that is "Good, Simple, and Habit-Forming" (no longer available) Stephen Shaw, National Cancer Institute, NIH
Pathway/Genome Databases and Software Tools (related paper)Peter Karp, Director, Bioinformatics Group, SRI International
The Virtual Cell ProjectLes Loew, Center for Biomedical Imaging Technology, University of Connecticut Health Center
This page last reviewed on
12/30/2015 12:29 PM
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