Biomedical subjects
Publications and source records attributed to J Ott.
A method is presented which is sensitive enough for the determination of vanadium (V) in marine organisms such as mussels and algae. It was sufficiently checked by a reference material and it was applied to V determination in blue mussels and brown algae from the German Bight.
For large numbers of marker loci in a genomic scan for disease loci, we propose a novel 2-stage approach for linkage or association analysis. The two stages are (1) selection of a subset of markers that are 'important' for the trait studied, and (2) modelling interactions among markers and between markers and trait. Here we focus on stage 1 and develop a selection method based on a 2-level nested bootstrap procedure. The method is applied to single nucleotide polymorphisms (SNPs) data in a cohort study of heart disease patients. Out of the 89 original SNPs the method selects 11 markers as being 'important'. Conventional backward stepwise logistic regression on the 89 SNPs selects 7 markers, which are a subset of the 11 markers chosen by our method.
The benefits and costs of stratification of affected-sib-pair (ASP) data were examined in three situations: (1) when there is no difference in identity-by-descent (IBD) allele sharing between stratified and unstratified ASP data sets; (2) when there is an increase in IBD allele sharing in one of the stratified groups; and (3) when the data are stratified on the basis of IBD allele-sharing status at one locus, and the stratified ASPs are then analyzed for linkage at a second locus. When there is no difference in IBD sharing between strata, a penalty is always paid for stratifying the data. The loss of power to detect linkage in the stratified ASP data sets is the result of multiple testing and the smaller sample size within individual strata. In the case in which etiologic heterogeneity (i.e., severity of phenotype, age at onset) represents genetic heterogeneity, the power to detect linkage can be increased by stratifying the ASP data. This benefit is obtained when there is sufficient IBD allele sharing and sample sizes. Once linkage has been established for a given locus, data can be stratified on the basis of IBD status at this locus and can be tested for linkage at a second locus. When the relative risk is in the vicinity of 1, the power to detect linkage at the second locus is always greater for the unstratified ASP data set. Even for values of the relative risk that diverge sufficiently from 1, with adequate sample sizes and IBD allele sharing, the benefits of stratifying ASP data are minimal.
The rate of meiotic recombination is not a constant function of physical distance across chromosomes. This variation is manifested by recombination hot spots and cold spots, observed in all organisms ranging from bacteria to humans. It is generally believed that factors such as primary and secondary DNA sequence, as well as chromatin structure and associated proteins, influence the frequency of recombination within a specific region. Several such factors, for example repetitive sequences, gene promoters, or regions with the ability to adopt Z-DNA conformation, have been hypothesized to enhance recombination. However, apart from specific examples, no general trends of association between recombination rates and particular DNA sequence motifs have been reported. In this paper, we analyze the complete sequence data from human chromosome 22 and compare microsatellite repeat distributions with mitotic recombination patterns available from earlier genetic studies. We show significant correlation between long tandem GT repeats, which are known to form Z-DNA and interact with several components of the recombination machinery, and recombination hot spots on human chromosome 22.
Recently, there has been increased interest in using Single Nucleotide Polymorphisms (SNPs) as a method for detecting genes for complex traits. SNPs are diallelic markers that have the potential to be inexpensively produced using chip technology. It has been suggested that SNPs will be beneficial in study designs that utilize trio data (father, mother, child). In our previous work, we calculated the probability of detecting Mendelian errors at a SNP locus for a trio randomly selected from a population in Hardy-Weinberg equilibrium. The highest error-detection rate was 30%. Here we investigate the error-detection rate when additional sibs are genotyped. We define an error to be a change from a 1 allele to a 2 allele, or vice versa. Typing one additional sib increases the detection rate on average by 10-13%. Typing two additional sibs increases the detection rate on average by 14-19%. The increase in the detection rate is dependent on the allele frequencies. Equal allele frequencies produce the lowest detection rates, independent of true error rates and number of offspring genotyped. Typing additional siblings not only improves error-detection rates, but can also provide additional linkage information. In order to increase linkage information and error-detection rates, at least two additional siblings should be ascertained when available.
We have performed linkage analysis on 21 families with pseudoxanthoma elasticum (PXE) using 10 polymorphic markers located on chromosome 16p13.1. The gene responsible for the PXE phenotype was localized to an 8-cM region of 16p13.1 between markers D16S500 and D16S3041 with a maximum lod score of 8.1 at a recombination fraction of 0.04 for marker D16S3017. The lack of any locus heterogeneity suggests that the major predisposing allele for the PXE phenotype is located in this region. Haplotype studies of a total of 36 PXE families identified several recombinations that further confined the PXE gene to a region (< 1 cM) between markers D16S3060 and D16S79. This PXE locus was identified within a single YAC clone and several overlapping BAC recombinants. From sequence analysis of these BAC recombinants, it is clear that the distance between markers D16S3060 and D16S79 is about 820 kb and contains a total of nine genes including three pseudogenes. We predict that mutations in one of the expressed genes in the locus will be responsible for the PXE phenotype in these families.
Intervertebral disc disease is one of the most common musculoskeletal disorders. A number of environmental and anthropometric risk factors may contribute to it, and recent reports have suggested the importance of genetic factors as well. The COL9A2 gene, which codes for one of the polypeptide chains of collagen IX that is expressed in the intervertebral disc, was screened for sequence variations in individuals with intervertebral disc disease. The analysis identified a putative disease-causing sequence variation that converted a codon for glutamine to one for tryptophan in six out of the 157 individuals but in none of 174 controls. The tryptophan allele cosegregated with the disease phenotype in the four families studied, giving a lod score (logarithm of odds ratio) for linkage of 4.5, and subsequent linkage disequilibrium analysis conditional on linkage gave an additional lod score of 7.1.
BACKGROUND: Obsessive-compulsive disorder (OCD) is a common and severe psychiatric illness that affects 1-3% of the population and presents a well-established co-morbidity with major depressive disorder (MDD). Twin and family studies have suggested a genetic component in the etiology of OCD, although the mode of inheritance is unknown. Pharmacotherapy of the disease implicates both serotonergic and dopaminergic pathways. Previously, guided by the 22q11 microdeletion-related psychiatric phenotype, we provided evidence for a sexually dimorphic association between OCD and the gene for catechol-O-methyltransferase (COMT). In this report, we use 110 nuclear OCD families to analyze the inheritance of variants of COMT and monoamine oxidase-A (MAOA), another gene modulating monoamine metabolism. METHODS: A sample of 110 nuclear OCD families was collected, and lifetime diagnoses were ascertained using the Diagnostic Interview for Genetic Studies (DIGS). DNA was genotyped for functional variants of the COMT and MAO genes, and allele inheritance was examined using the Transmission Disequilibrium Test (TDT) and Haplotype-based Haplotype Relative Risk (HHRR) test. RESULTS: We provide evidence supporting the previously reported sexually dimorphic association between low COMT enzymatic activity and OCD. We also provide evidence for a similar sexually dimorphic association between OCD and an allele of the MAOA gene, previously linked to high MAO-A enzymatic activity. In agreement with the well-established action of MAO-A inhibitors as antidepressants, this association is particularly marked among male OCD probands with co-morbid MDD, who represent more than 50% of our male OCD sample. CONCLUSIONS: Our analysis indicates that variants of two genes modulating monoamine metabolism contribute significantly to OCD susceptibility. Most importantly, an unexpected sexually dimorphic pattern of genetic susceptibility to OCD is revealed and suggests the possibility that profound gender differences in genetic predisposition may exist not only for other OCD susceptibility genes, but for an array of other psychiatric disorders as well.
Explore the source record for details and available documents.
Broad-band (ultraviolet to near-infrared) observations of the intense gamma ray burst GRB 990123 started approximately 8.5 hours after the event and continued until 18 February 1999. When combined with other data, in particular from the Robotic Telescope and Transient Source Experiment (ROTSE) and the Hubble Space Telescope (HST), evidence emerges for a smoothly declining light curve, suggesting some color dependence that could be related to a cooling break passing the ultraviolet-optical band at about 1 day after the high-energy event. The steeper decline rate seen after 1.5 to 2 days may be evidence for a collimated jet pointing toward the observer.
A recently developed approach that employs artificial neural networks (ANNs) was applied to the simulated data set to identify sets of marker loci involved in disease etiology. In this implementation, ANNs are trained to predict the disease state (output) from the given genetic marker data (input). A contribution value (CV) for each locus is calculated from the weights that represent the strength of the connections for the trained ANN; a higher CV indicates a higher probability of linkage. The highest CV values were chosen as the most likely candidate regions involved in the disease.
Many researchers are considering the use of transmission/disequilibrium tests (TDT) for trios of genotypes (father, mother, child) as a method for localizing genes associated with complex diseases. We evaluate the effect of random errors (allele changes) in trios on the power to detect linkage. For a marker in the simulated data set, one allele is associated with the fictitious disease in a certain subpopulation. For the data as given (no errors), our power to detect linkage using the multiallelic TDT (TDTmhet) is 68% (critical p-value set at 0.0001). We introduce errors into trios at various rates (1%, 5%, or 10%), remove only trios displaying mendelian inconsistencies, and recalculate power to detect linkage. Our principal finding is that there is power loss to detect linkage with the TDTmhet when errors are introduced. We observe power losses of 8%, 16%, and 48% for error rates of 1%, 5%, and 10%, respectively. To determine the source of the power loss, we perform Monte Carlo simulations. At the 1% and 5% rates, we conclude that power loss is due primarily to loss in sample size. At the 10% rate, we observe substantial power loss due to error introduction in addition to sample size reduction. We also determine, given a particular error rate, the probability that we detect errors if we use only mendelian consistency as a check. We find that the mean detection rates for the data sets with 1%, 5%, or 10% error rates are 58%, 60%, and 62%, respectively. As a result, the apparent error rate appears to be almost half the true error rate. Based on these results, we recommend that researchers maintain error rates below 5% when using the TDTmhet for linkage, use additional methods beyond mendelian consistency checks when searching for errors in their data, and modify sample size calculations when accounting for errors in their genotype data.
Linkage analysis is generally carried out under single-gene models, while complex traits are thought to be under the control of multiple interacting genes. Current issues related to linkage analysis for complex traits are discussed. It is argued that linkage analyses should be carried out for sub-phenotypes, in addition to classical "affected-unaffected" phenotypes. Correlations for phenotypes among family members are often computed on the basis of extreme phenotypes of a proband, which results in biased estimates. Methods for ascertainment corrections are recommended. A generalized version of heterogeneity analysis is introduced and are shown to provide an effective single-locus analysis for complex traits.
Determining the genetic causes of cancers has immense public health benefits, ranging from prevention to earlier detection and treatment of disease. Although a number of cancer susceptibility genes have been successfully identified, design and analytic issues remain that challenge the current paradigm of gene discovery. Some examples are the definition and measurement of cancer phenotype, the use of intermediate end points, the choice of sample (e.g., affected relative pairs versus large extended pedigrees), the choice of analytic method [e.g., parametric logarithm of the odds (LOD) score method versus model-free methods], and the influence of gene-environment interaction on linkage analysis. Furthermore, association methods, based on either the traditional case-control study design or family-based controls, are popular choices to evaluate candidate genes or screen for linkage disequilibrium. Finally, the study design and analytic methods for gene discovery are determined to some extent by what genomic technology is feasible within the laboratory. Many of the main issues related to gene discovery, as well as trends in genomic technology that will impact on gene discovery, are discussed from the perspective of their strengths and weaknesses, pointing to areas in need of further work.
Single nucleotide polymorphisms (SNPs) are currently being developed for use in disequilibrium analyses. These SNPs consist of two alleles with varying degrees of polymorphism. A natural design for use with SNPs is the 'haplotype relative risk' sampling design in which a father, mother, and child are typed at an SNP locus. Given such a trio of genotypes, we ask: what is the probability that a pedigree error (a change from one allele to the other) at an SNP locus will be detected using only Mendel's laws as a check? We calculate the probability of detecting such errors for a hypothetical SNP locus with varying degrees of polymorphism and for various true error rates. For the sets of allele frequencies considered, we find that the detection rates range between 25 and 30%, the detection rate being lowest when the two alleles have equal frequencies and the highest when one allele has a frequency of 10%. Based on this detection rate, we determine that the true error rate is roughly 3.3-4 times that of the apparent error rate at an SNP locus. The greatest discrepancy between true and apparent error rates occurs when allele frequencies are equal.
For many traits, genetically relevant disease definition is unclear. For this reason, researchers applying linkage analysis often obtain information on a variety of items. With a large number of items, however, the test statistic from a multivariate analysis may require a prohibitively expensive correction for the multiple comparisons. The researcher is faced, therefore, with the issue of choosing which variables or combinations of variables to use in the linkage analysis. One approach to combining items is to first subject the data to a principal components analysis, and then perform the linkage analysis of the first few principal components. However, principal-components analyses do not take family structure into account. Here, an approach is developed in which family structure is taken into account when combining the data. The essence of the approach is to define principal components of heritability as the scores with maximum heritability in the data set, subject to being uncorrelated with each other. The principal components of heritability may be calculated as the solutions to a generalized eigensystem problem. Four simulation experiments are used to compare the power of linkage analyses based on the principal components of heritability and the usual principal components. The first of the experiments corresponds to the null hypothesis of no linkage. The second corresponds to a setting where the two kinds of principal components coincide. The third corresponds to a setting in which they are quite different and where the first of the usual principal components is not expected to have any power beyond the type I error rate. The fourth set of experiments corresponds to a setting where the usual principal components and the principal components of heritability differ, but where the first of the usual principal components is not without power. The results of the simulation experiments indicate that the principal components of heritability can be substantially different from the standard principal components and that when they are different, substantial gains in power can result by using the principal components of heritability in place of the standard principal components in linkage analyses.
Many mendelian traits show heterogeneity; that is, the disease phenotype in different families may be caused by genes at different locations. In linkage analysis, this admixture type of heterogeneity (locus heterogeneity) has often been accommodated with one of the HOMOG programs, which thus far have been restricted to at most two disease gene locations. Here, an extension to an arbitrary number of disease locations is described. It has been implemented in a computer program, HOMOGM. This approach is also suitable as an approximation to the situation of complex traits, in which multiple disease genes may occur in the same family.
PURPOSE: To characterize the disease expression of an autosomal recessive human retinal degeneration associated with a mutation in TULP1 (tubby-like protein 1), a gene with currently unknown function. METHODS: Homozygotes and heterozygotes from an extended Dominican kindred with a TULP1 splice-site gene mutation (IVS14+1,G-->A) were studied clinically and with visual function tests. Sequence analysis of TULP1 was also performed in unrelated patients with severe retinal degeneration from a North American clinic population. RESULTS: Homozygotes had nystagmus, visual acuity of 20/200 or worse, color vision disturbances, bull's eye maculopathy, and peripheral pigmentary retinopathy. Younger patients had a relatively wide extent of kinetic visual fields; older patients had only peripheral islands. No rod function was measurable by psychophysics in any of the patients; markedly reduced cone function was detectable across the visual field of younger patients and in the remaining peripheral islands of older patients. Rod and cone electroretinograms (ERGs) were not detectable using standard methods; microvolt-level cone ERGs were present in some patients. Heterozygotes had normal visual function. No putative pathogenic sequence changes in TULP1 were observed in North American patients with comparably severe retinal phenotypes, mainly in the diagnostic category of Leber congenital amaurosis. CONCLUSIONS: This TULP1 splice-site mutation in homozygotes causes early-onset, severe retinal degeneration involving macular and peripheral cones and rods. The constellation of phenotypic findings suggests that the TULP1 gene product is critically important for normal photoreceptor function and may play a role in retinal development.