Schizophrenia: the testing of genetic models by pedigree analysis.

Simulated pedigrees of schizophrenia generally show a clear peak in their likelihood surface corresponding to analysis by the genetic models, which served as the basis for the simulation. The likelihood surface obtained with real data permits determination of the allelic frequency and the selection of an optimal one-locus, two-locus, and four-locus model. These three models have certain features in common, notably, a relatively high frequency of the allele predisposing to schizophrenia (about 20%) and a relatively low index of genetic determination (23%--34%). However, direct likelihood comparisons do not permit distinctions between the one-locus, two-locus, and four-locus models. The most likely interpretation of this finding is that the etiology of schizophrenia is heterogeneous or even nongenetic. However, a simple model with a single completely recessive locus and incomplete penetrance in the homozygote also produces a flat likelihood surface closely resembling that obtained with the real data. With reservation, this single-locus model may be put forward as a potentially useful working hypothesis.     

Mathematical limits of multilocus models: the genetic transmission of bipolar disorder.

We describe a simple, graphical method for determining plausible modes of inheritance for complex traits and apply this to bipolar disorder. The constraints that allele frequencies and penetrances lie in the interval 0-1 impose limits on recurrence risks, KR, in relatives of an affected proband for a given population prevalence, KP. We have investigated these limits for KR in three classes of relatives (MZ co-twin, sibling, and parent/offspring) for the general single-locus model and for two types of multilocus models: heterogeneity and multiplicative. In our models we have assumed Hardy-Weinberg equilibrium, an all-or-none trait, absence of nongenetic resemblance between relatives, and negligible mutation at the disease loci. Although the true values of KP and the KR''s are only approximately known, observed population and family data for bipolar disorder are inconsistent with a single-locus model or with any heterogeneity model. In contrast, multiplicative models involving three or more loci are consistent with observed data and, thus, represent plausible models for the inheritance of bipolar disorders. Studies to determine the genetic basis of most bipolar disorder should use methods capable of detecting interacting oligogenes.     

Neural control of breathing: insights from genetic mouse models.

Recent studies described the in vivo ventilatory phenotype of mutant newborn mice with targeted deletions of genes involved in the organization and development of the respiratory-neuron network. Whole body flow barometric plethysmography is the noninvasive method of choice for studying unrestrained newborn mice. Breathing-pattern abnormalities with apneas occur in mutant newborn mice that lack genes involved in the development and modulation of rhythmogenesis. Studies of deficits in ventilatory responses to hypercapnia and/or hypoxia helped to identify genes involved in chemosensitivity to oxygen and carbon dioxide. Combined studies in mutant newborn mice and in humans have shed light on the pathogenesis of genetically determined respiratory-control abnormalities such as congenital central hypoventilation syndrome, Rett syndrome, and Prader-Willi syndrome. The development of mouse models has opened up the field of research into new treatments for respiratory-control disorders in humans.     

Mouse models for the genetic study of tuberculosis susceptibility.

Tuberculosis, mainly caused by the pathogenic bacterium Mycobacterium tuberculosis, remains an inestimable public health problem, despite the established use of the Bacillus Calmette-Guerin (BCG) vaccine, multidrug therapy and the existence of global tuberculosis control programmes. Statistics show that nearly 2 billion people (approximately one-third of the world's population) are infected with M. tuberculosis. For unknown reasons, only about 10 per cent of those infected by M. tuberculosis will develop tuberculosis, resulting in 9 million new cases yearly and 2 million deaths. A better understanding of the host--mycobacterial--environmental interplay is central to developing better antituberculosis vaccines and treatments. This review will discuss how a clearer idea of this interplay is emerging with new genomic strategies in mouse models.     

Lateral genetic transfer and the construction of genetic exchange communities

Lateral genetic transfer (LGT) is a major source of phenotypic innovation among bacteria. Determinants for antibiotic resistance and other adaptive traits can spread rapidly, particularly by conjugative plasmids, but also phages and natural transformation. Each successive step from the uptake of foreign DNA, its genetic recombination and regulatory integration, to its establishment in the host population presents differential barriers and opportunities. The emergence of successive multidrug-resistant strains of Staphylococcus aureus illustrates the ongoing role of LGT in the combinatorial assembly of pathogens. The dynamic interplay among hosts, vectors, DNA elements, combinations of genetic determinants and environments constructs communities of genetic exchange. These relations can be abstracted as a graph, within which an exchange community might correspond to a path, transitively closed set, clique or near-clique. We provide a set-based definition, and review the features of actual genetic exchange communities (GECs), adopting first a knowledge-driven approach based on literature, and then a synoptic data-centric bioinformatic approach. GECs are diverse, but share some common features. Differential opportunity and barriers to lateral genetic transfer create bacterial communities of exchange.     

Testing for linkage under robust genetic models.

Robust genetic models are used to assess linkage between a quantitative trait and genetic variation at a specific locus using allele-sharing data. Little is known about the relative performance of different possible significance tests under these models. Under the robust variance components model approach there are several alternatives: standard Wald and likelihood ratio tests, a quasilikelihood Wald test, and a Monte Carlo test. This paper reports on the relative performance (significance level and power) of the robust sibling pair test and the different alternatives under the robust variance components model. Simulations show that (1) for a fixed sample size of nuclear families, the variance components model approach is more powerful than the robust sibling pair approach; (2) when the number of nuclear families is at least approximately 100 and heritability at the trait locus is moderate to high (>0.20) all tests based on the variance components model are equally effective; (3) when the number of nuclear families is less than approximately 100 or heritability at the trait locus is low (<0. 20), on balance, the Monte Carlo test provides the best power and is the most valid. The different testing procedures are applied to determine which are able to detect the known association between low density lipoprotein cholesterol and the common genotypes at the locus encoding apolipoprotein E. Results from this application show that the robust sibling pair method may be more effective in practice than that indicated by simulations.     

Gliomagenesis: genetic alterations and mouse models

Glioblastoma multiforme is the most malignant of the primary brain tumours and is almost always fatal. The treatment strategies for this disease have not changed appreciably for many years and most are based on a limited understanding of the biology of the disease. However, in the past decade, characteristic genetic alterations have been identified in gliomas that might underlie the initiation or progression of the disease. Recent modelling experiments in mice are helping to delineate the molecular aetiology of this disease and are providing systems to identify and test novel and rational therapeutic strategies.     

Complexities of cancer research: mouse genetic models

Cancer susceptibility is a complex interaction of an individual's genetic composition and environmental exposures. Huge strides have been made in understanding cancer over the past 100 yr, from recognition of cancer as a genetic disease, to identification of specific carcinogens, isolation of oncogenes, and recognition of tumor suppressors. A tremendous amount of knowledge has accumulated about the etiology of cancer. Cancer genetics has played a significant role in these discoveries. Analysis of high-risk familial cancers has led to the discovery of new tumor suppressor genes and important cancer pathways. These families, however, represent only a small fraction of cancer in the general population. Most cancer is instead probably the result of an intricate interaction of polymorphic susceptibility genes with the sea of environmental exposures that humans experience. Although the central cadre of cancer genes is known, little is understood about the peripheral genes that likely comprise the polymorphic susceptibility loci. The challenge for cancer genetics is therefore to move forward from the mendelian genetics of the rare familial cancer syndromes into the field of quantitative trait loci, susceptibility factors, and modifier genes. By identifying the genes that modulate an individual's susceptibility to cancer after an environmental exposure, researchers will be able to gain important insights into human biology, cancer prevention, and cancer treatment. This article summarizes the current state of quantitative trait genetic analysis and the tools, both proven and theoretical, that may be used to unravel one of the great challenges in cancer genetics.     

Minimization of construction errors in bent-wire protein models.

Two kinds of errors are found in protein models made with the tool of Rubin and Richardson. Global errors result from the accumulation of many errors too small to localize, while local errors are assignable to particular bends in the model. We here locate the sources of local errors, and show how to minimize both kinds of errors.     

Adapting genetic regulatory models by genetic programming

In this paper, we focus on the task of adapting genetic regulatory models based on gene expression data from microarrays. Our approach aims at automatic revision of qualitative regulatory models to improve their fit to expression data. We describe a type of regulatory model designed for this purpose, a method for predicting the quality of such models, and a method for adapting the models by means of genetic programming. We also report experimental results highlighting the ability of the methods to infer models on a number of artificial data sets. In closing, we contrast our results with those of alternative methods, after which we give some suggestions for future work.