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Biomedical subjects

J Ott

Publications and source records attributed to J Ott.

At least 289 records · Page 16Linked to original sources
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Low-order polynomial trends of female-to-male map distance ratios along human chromosomes.

Recombination rates in humans tend to be sex specific. For a given map interval delimited by genetic markers, the difference between male and female recombination rates may be measured by the ratio, R, of female-to-male map distance. On average over all chromosomes, R is close to 2, but this ratio is region specific. The spatial variation of R can be captured by a low-order (linear, quadratic, etc.) trend across the length of the chromosome. Chromosome maps have been constructed that take into account such trends. Resulting map distances tend to be more accurate than when such trends are ignored. These maps may be obtained at URL ftp://linkage.rockefeller.edu/SexSpecMaps/.

Chromosome Mapping↗
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Multi-locus nonparametric linkage analysis of complex trait loci with neural networks.

Complex traits are generally taken to be under the influence of multiple genes, which may interact with each other to confer susceptibility to disease. Statistical methods in current use for localizing such genes essentially work under single-gene models, either implicitly or explicitly. In genomic screens for complex disease genes, some of the marker loci must be in tight linkage with disease susceptibility genes. We developed a general multi-locus approach to identify sets of such marker loci. Our approach focuses on affected sib pair data and employs a nonparametric pattern recognition technique using artificial neural networks. This technique analyzes all markers simultaneously in order to detect patterns of locus interactions. When applied to previously published sib pair data on type I diabetes, our approach finds the same genes as in the published report in addition to some new loci. For a specific two-locus model of inheritance, the power of our approach is higher than that of the currently used analysis standard.

Chromosome Mapping↗
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Complex inheritance and localizing disease genes.

Most methods for localizing genes underlying complex traits work under the implicit or explicit assumption of a single disease gene with the possible exception of heterogeneity, that is, different disease genes in different families. We discuss current single-locus and multi-locus methods. Novel approaches are proposed that take into account all marker loci over the genome. A simple example is given for an unconventional statistic, i.e. the mean of allele sharing over all markers on a chromosome.

Chromosome Mapping↗
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Detecting marker inconsistencies in human gene mapping.

When an inconsistency occurs in a pedigree, it may not be apparent which individual(s) are causing it. Here, a statistical method is described which identifies individuals most likely to have caused an inconsistency. The method is based on the sum of squared deviations between two predictors of an individual's genotypes: (1) that given an individual's own phenotype, and (2) that given all phenotypes in the pedigree. Extreme deviations between the two arrays (measured in terms of a sum of squares) are interpreted as indicating an inconsistency. The method is applied to a pedigree with an inconsistency in which it is unclear who is causing the inconsistency.

Chromosome Mapping↗
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Multipoint mapping under genetic interference.

Genetic chiasma interference occurs when one crossover influences the probability of another crossover occurring nearby. While interference is known to occur in humans, it is typically ignored when computing multipoint likelihoods for genetic mapping. This biologically unsound assumption of no interference facilitates the calculation of the likelihoods at the expense of reduced power to accurately construct a genetic map. We have developed a computer program that calculates multipoint likelihoods of three-generation nuclear families while taking interference into account. In our program, interference is modelled by using a map function to convert genetic distances into recombination fractions. We can determine which of several map functions best fits the data by comparing the multipoint likelihoods of the data under each map function. Since the distribution of the difference between likelihoods is unknown, we use a simulation approach to determine the statistical significance of our results. When our program is applied to six loci, D10S34, D10S19, D10S16, D10S14, D10S4, and D10S20, from the CEPH consortium map of chromosome 10, we find significant evidence in favor of positive interference as modelled by the Sturt map function.

Binomial Distribution↗
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Assessment of nonallelic genetic heterogeneity of chronic (type II and III) spinal muscular atrophy.

We have previously reported the mapping of the chronic (type II/intermediate and type III/mild/Kugelberg-Welander) form of the childhood-onset spinal muscular atrophies (SMA) to chromosome 5q11.2-13.3, with evidence for nonallelic genetic heterogeneity within a small sample of seven families [Brzustowicz et al., Nature 1990;344:540-541]. We now report the results of linkage analysis and heterogeneity testing on a set of 38 families with chronic SMA. Significant evidence for nonallelic heterogeneity was detected among these families, with the predominant locus for chronic SMA mapping to a 0.51-cM region on 5q, between the loci D5S6 and MAP1B. The estimated proportion of linked families, alpha, was 0.91, with a 2.3-unit support interval of 0.75 to 0.98. The indication that some families diagnosed with chronic SMA are not linked to chromosome 5q must be considered in strategies to map the SMA locus. The relevance of these findings to acute SMA (SMA type I, severe, Werdnig-Hoffmann disease) is still unknown.

Adolescent↗
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Methodological issues in linkage analyses for psychiatric disorders: secular trends, assortative mating, bilineal pedigrees. Report of the MacArthur Foundation Network I Task Force on Methodological Issues.

A Task Force was assembled to address three data problems in genetic linkage analyses: (1) a secular trend, e.g. cohort effect; (2) positive assortative mating, and (3) bilineal pedigrees. All are cited as reasons for failure to replicate genetic linkage reports. However, we knew of no work demonstrating that these factors could invalidate or bias linkage analyses, nor that they were complications (e.g., variable age of onset). The Task Force concluded that these factors can reduce the power of linkage analyses and result in bias in the estimate of the recombination frequency due to the fact that they represent 'noise' in the system. There was little evidence that the three factors would invalidate a linkage analysis or be directly responsible for negating a linkage finding.

Cohort Effect↗
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Estimating the frequency of nonpaternity in Switzerland.

In linkage analysis and gene mapping in general, typing error and/or errors in specifying pedigree relationships between individuals typically lead to an upwards bias in recombination fraction estimates and to a loss of power for detecting linkage. [Ott J: Analysis of Human Genetic Linkage. Baltimore, Johns Hopkins University Press, 1991]. The presence of nonpaternity is the most common source of error of misspecification of pedigree relationships between individuals that can have a negative impact on pedigree analysis. We have developed estimates of underlying nonpaternity probabilities based on observed Mendelian inconsistencies. The methods were applied to a sample of 1,607 children and their parents. Genetic marker data were investigated to test for exclusions due to nonpaternity. Among the 1,607 children, 11 or 0.68% exclusions were found. When a constant nonpaternity rate was assumed for each child, its maximum likelihood estimate turned out to be 0.78% with asymptotic 95% confidence limits of 0.41% and 1.35%. When varying nonpaternity rates were assumed, its mean was estimated as 0.83% with asymptotic 95% confidence limits of 0.32% and 1.33%.

Child↗
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Linkage analysis with biological markers.

Biological markers or endophenotypes may be used to help delineate the relationships between underlying trait genotypes and trait phenotypes. However, analyzing quantitative biological markers jointly with a qualitative trait phenotype (affected versus unaffected) for linkage with genetic marker loci is not straightforward. For the situation in which both the biological marker(s) and the qualitative trait phenotype are under the control of underlying genotypes, a simple method is developed for jointly using biological marker and trait phenotype in linkage analysis. An example demonstrates its implementation in the LINKAGE programs.

Biomarkers↗
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Mapping undetected mutations within a gene-evidence for two preferential regions in the DMD gene.

A maximum-likelihood method is developed to estimate the frequency distribution of undetected mutations (presumably point mutations, small deletions, insertions) along a gene, where the gene extends over a long stretch of DNA. In each family, the point of the mutation is potentially at a different location within the gene. In this sense, there is genetic heterogeneity among families and the method estimates the proportion of families whose mutation is at (or in the vicinity of) a given point inside the gene. Our method is applied to a sample of 75 families with Duchenne muscular dystrophy in which the disease mutation remained undetected. We find two preferential regions for these undetected mutations, with an estimated 85% of families having their mutation in one region and the remaining 15% of families in the other. The new method is expected to be useful in finding small mutations in any of the currently known large genes.

Chromosome Mapping↗
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[Therapeutic factors in psychoanalytic-interactional and depth psychology founded group therapy: an empirical study].

Depth psychology based and psychoanalytic-interactional group therapy were compared with respect to their therapeutic factors. The comparison was performed in patient groups by using a questionnaire asking for the occurrence and helpfulness of specific behaviour of the therapist and of experiences in the group. About 200 questionnaires for each method were analyzed. It could be shown that (1) discrimination between the methods was more marked concerning therapist's behaviour than experiences with the other group members, (2) that the obtained unspecific factors correspond to results in the literature on group and individual therapy and (3) that those items about the therapists behaviour claimed to be specific for each method did not discriminate these methods in the expected direction.

Female↗
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Familial cancer: genetically determined? (review).

Many cancers, in both children and adults, cluster in families. Collection and statistical analysis of pedigree data suggest that genetic mechanisms play an important role in most cancer types. This is illustrated in colorectal, breast, lung, ovarian, and childhood cancer. Pedigree data are consistent with the hypothesis that cancer is sometimes inherited in an autosomal dominant Mendelian fashion. These rare hereditary cancers might not be different pathogenetically from those arising sporadically. A two-stage model for carcinogenesis provides a framework for the understanding of both forms of cancer. The establishment of registries for familial cancer would be most helpful for cancer risk determinations, surveillance and management programs, identification of new cancer-prone genotypes and etiological family studies.

Breast Neoplasms↗
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Relationship estimation in affected sib pair analysis of late-onset diseases.

In linkage studies, errors in pedigree structure will often be uncovered through Mendelian inconsistencies. In affected sib pair analysis of diseases with late onset, however, such mistakes will usually go undetected since parental genotypes are commonly not known. Cases of nonpaternity, unrecorded adoption or accidental sample swap in the laboratory will then not be noticed. Typically, such relationship errors lead to a decrease in power for linkage. In this paper, a method is presented which allows verification of the relationship between stated sibs using their marker genotypes. The method is likelihood-based and incorporates a Bayesian approach to compute posterior relationship probabilities. It is shown that sibs, half-sibs and unrelated individuals can be distinguished from each other quite reliably using numbers of markers that should be available in most sib pair studies. It is demonstrated that elimination of false sib pairs increases the power to detect linkage in affected sib pair studies. The gain in power may be large if relationship errors occur quite frequently; the gain will be only moderate if relationship errors are very infrequent. Software for relationship estimation is provided.

Bayes Theorem↗
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Estimating recessive disease allele frequency based on genetic maps.

For a recessive disease whose gene has been localized on the human gene map, a new method is described for estimating the population frequency of the disease allele. The method focuses on affected individuals whose parents are first cousins, where parents and grandparents are genotyped for highly polymorphic markers at the disease gene. The primary statistic is the proportion of such probands who are autozygous (homozygous due to identity by descent of the two disease alleles), where this proportion is a function of the disease allele frequency. Our map-based method is compared to Dahlberg's method of estimating recessive disease allele frequencies, which is based on the proportion of affected individuals whose parents are first cousins; this proportion is also a function of the disease allele frequency. For small to moderate sample sizes and traits that are not too common, our new method is more efficient (for some parameter values dramatically more efficient) than Dahlberg's method.

Alleles↗