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Using the simulated data set from Genetic Analysis Workshop 13, we explored the advantages of using longitudinal data in genetic analyses. The weighted average of the longitudinal data for each of seven quantitative phenotypes were computed and analyzed. Genome screen results were then compared for these longitudinal phenotypes and the results obtained using two cross-sectional designs: data collected near a single age (45 years) and data collected at a single time point. Significant linkage was obtained for nine regions (LOD scores ranging from 5.5 to 34.6) for six of the phenotypes. Using cross-sectional data, LOD scores were slightly lower for the same chromosomal regions, with two regions becoming nonsignificant and one additional region being identified. The magnitude of the LOD score was highly correlated with the heritability of each phenotype as well as the proportion of phenotypic variance due to that locus. There were no false-positive linkage results using the longitudinal data and three false-positive findings using the cross-sectional data. The three false positive results appear to be due to the kurtosis in the trait distribution, even after removing extreme outliers. Our analyses demonstrated that the use of simple longitudinal phenotypes was a powerful means to detect genes of major to moderate effect on trait variability. In only one instance was the power and heritability of the trait increased by using data from one examination. Power to detect linkage can be improved by identifying the most heritable phenotype, ensuring normality of the trait distribution and maximizing the information utilized through novel longitudinal designs for genetic analysis.
The efficacy of linkage studies using microsatellites and single-nucleotide polymorphisms (SNPs) was evaluated. Analyzed data were supplied by the Collaborative Study on the Genetics of Alcoholism (COGA). Alcoholism was analyzed together with a simulated trait caused by a gene of known position, through a nonparametric linkage test (NPL). For the alcoholism trait, four densities of SNPs (1 SNP per 0.2 cM, 0.5 cM, 1 cM and 2 cM) showed higher peaks of NPL z scores and smaller significant p-values than the usual 10-cM density of microsatellites. However, the two highest densities of SNPs had unstable z score signals, and therefore were difficult to interpret. Analyzing a simulated trait with the same markers in the same pedigrees, we confirmed the higher power of all four densities of SNPs compared to the 10-cM microsatellites panel, although the existence of other confounding peaks was confirmed for maps that are denser than 1 SNP/cM. We further showed that estimating the gene position using SNPs is far less biased than using the usual panel of microsatellites (biases of 0-2 cM for SNPs vs. 8.9 cM for microsatellites). We conclude that using dense maps of SNPs in linkage analysis is more powerful and less biased than using the 10-cM maps of microsatellites. However, linkage signals can be unstable and difficult to interpret when several SNPs are genotyped per centimorgan. The power and accuracy of 1 SNP/cM or 1 SNP/2 cM may be sufficient in a genome-wide linkage scan while denser maps may be most useful in fine-gene mapping studies exploiting linkage disequilibrium.
The simulated dataset of the Genetic Analysis Workshop 14 provided affection status and the presence or absence of 12 traits. It was determined that all affected individuals must have traits E, F and H (EFH phenotype) and they must also have either trait B (B subtype) or traits C, D, and G (CDG subtype). A genome screen was performed, and linkage peaks were identified on chromosomes 1, 3, 5, and 9 using microsatellite markers. Dense panels of single-nucleotide polymorphism (SNP) markers were ordered for each of the four linkage peaks. In each case, association analyses identified a single SNP that accounted for the linkage evidence. The SNP on chromosome 1 appeared to primarily influence the B subtype, while the SNPs on chromosomes 5 and 9 primarily influenced the CDG subtype. The chromosome 3 SNP had the strongest effect and influenced both subtypes, as well as the requisite EFH phenotype. Recognizing the two subtypes prior to linkage analysis was key to identifying these loci using only a single replicate. This highlights the need in real life situations for careful examination of the phenotypic data prior to genetic analysis.
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The public already has concerns about 'the new genetics', and it is clear that confidence can only be maintained by scrupulous attention to quality. Standards can be improved by harmonization of methods, discouraging poor practice and using appropriate internal and external quality controls. At present, despite the profound implications of genetic test results, few genetic tests are subject to sufficient scrutiny. The Human Genome Project will lead to the identification of numerous genetic variations contributing to multifactorial diseases, and high-throughput technologies will permit the generation of disease-susceptibility profiles. Clinical laboratories will need to develop the wherewithal to handle these data and present them in a format that is clinically useful.
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Multiple investigators have undertaken genetic studies in idiopathic pulmonary fibrosis populations in attempts to define genetic links to disease in hopes that this would improve understanding of disease pathogenesis and target pathways for therapy. Multiple genes have been evaluated using a candidate gene approach with limited success, with results suggesting a disease modifier effect rather than a disease causing effect. Using this approach, associations have been observed between idiopathic pulmonary fibrosis and specific polymorphisms in genes encoding interleukin-1 receptor antagonist, tumor necrosis factor-alpha, and complement receptor 1. Recently investigators have used familial pulmonary fibrosis cohorts to evaluate for genetic mutations associated with idiopathic pulmonary fibrosis. Using one pulmonary fibrosis kindred, a mutation in the gene encoding surfactant protein C was identified as the cause of pulmonary fibrosis in this family. Subsequently, another individual with idiopathic pulmonary fibrosis was identified with a different mutation in surfactant protein C. Though rarely found in patients with idiopathic pulmonary fibrosis, these surfactant protein C mutations highlight the importance of the alveolar epithelium in disease pathogenesis. A recent collaboration between investigators at three major centers has resulted in the largest collection of families with pulmonary fibrosis to date, with hopes that this effort will identify genetic mutations associated with idiopathic pulmonary fibrosis. If genetic links to idiopathic pulmonary fibrosis are defined in this study, then the pathways involved with these genes and gene products can be targeted by investigators to help identify potential treatment options for this disease.
Patient explanatory models of Hansen's disease (leprosy) in Rio de Janeiro, Brazil, tend to be syntheses of folk models of illness and health, biomedical models to which patients are exposed at different stages in the treatment process, and individual patient experiences of illness. The sensitive presentation of biomedical information about Hansen's disease to patients has the potential to increase adherence to treatment programs and increase patient confidence in the biomedical system. Conversely, withholding or poor presentation of biomedical information can create misunderstanding and confusion for patients. In this article, I explore the ways in which people who are affected by Hansen's disease in Rio de Janeiro learn about different aspects of their illness and its treatment from health care professionals.
Determining the patterns of DNA sequence variation in the human genome is a useful first step toward identifying the genetic basis of a common disease. A haplotype map (HapMap), aimed at describing these variation patterns across the entire genome, has been recently developed by the International HapMap Consortium. In this article, we present a novel statistical model for directly characterizing specific sequence variants that are responsible for disease risk based on the haplotype structure provided by HapMap. Our model is developed in the maximum-likelihood context, implemented with the EM algorithm. We perform simulation studies to investigate the statistical properties of this disease-sequencing model. A worked example from a human obesity study with 155 patients was used to validate this model. In this example, we found that patients carrying a haplotype constituted by allele Gly16 at codon 16 and allele Gln27 at codon 27 genotyped within the beta2AR candidate gene display significantly lower body mass index than patients carrying the other haplotypes. The implications and extensions of our model are discussed.
Psoriasis [OMIM*177900] is a common, chronic and papulosquamous inflammatory skin disease affecting approximately 2% of Caucasian. However, this disorder is rare among Japanese, Eskimos, West Africans and North American blacks and very uncommon in North American and South American natives. The causes for these variations are likely to be both genetic and environmental. Population-based studies and twin studies indicate that psoriasis is a heritable disease with a polygenic mode of inheritance with variable penetrance. Independent genome-wide scans have suggested the involvement of a large number of chromosomal regions (loci), and many candidate genes have been proposed. We discuss genetic approaches to the disease, results and interpretations of relevant studies, as well as future perspectives. Understanding the genetic basis of psoriasis will represent a major advance in our understanding of the disease and will reveal novel disease-specific biologic pathways.
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Transgenic mice aberrantly expressing the granulocyte-macrophage colony stimulating factor (GM-CSF) gene develop an unusual syndrome of blindness, tissue damage and wasting which is associated with accumulations of hemopoietic cells. In order to further characterize this disease state, we have used messenger RNA detection techniques to show that the genes for tumor necrosis factor (TNF alpha), interleukin-1 alpha (IL-1 alpha) and basic fibroblast growth factor (bFGF) are expressed at abnormally high levels in both macrophages and granulocytes in transgenic mice. Furthermore, since these cell types also express the GM-CSF transgene, it is likely that they are autocrine stimulated by GM-CSF. These observations raise the possibilities that, first, the expression of tumor necrosis factor alpha, interleukin-1 alpha and basic fibroblast growth factor in hemopoietic cells is a direct consequence of their autostimulation by GM-CSF, and second, that these cytokines may be responsible for some aspects of the transgenic mouse disease.
Diabetes insipidus, characterized by excretion of copious volumes of dilute urine, can be life-threatening if not properly diagnosed and managed. It can be caused by two fundamentally different defects: inadequate or impaired secretion of antidiuretic hormone (ADH) from the posterior pituitary gland (neurogenic or central diabetes insipidus) or impaired or insufficient renal response to ADH (nephrogenic diabetes insipidus). The distinction is essential for effective treatment.
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