Biomedical subjects
Publications and source records attributed to G E Bonney.
Regressive models are extended to disease phenotypes with two or more affection classes through the use of polychotomous logistic regression. The classes of affection may be ordered (ranked as on a liability continuum), or unordered. Data on affective disorders are used for illustration.
Several methods have been proposed to take into account the variable age of onset of a disease in genetic analysis. A different approach is presented from an etiological point of view. To illustrate the method, we used leprosy, an infectious disease with a variable age of onset depending on both the time of contamination with the bacillus and the latency of the disease; the role of a major gene in the susceptibility to this disease has been recently detected. The age-of-onset function was modeled to account for the two temporal processes: contamination event and incubation period. For genetic analysis, this function was combined with the probability of being susceptible to the disease, which was expressed by the use of regressive models. To test this new approach, ten sets of 500 nuclear families were simulated considering different hypotheses of contamination risks, which were either constant or dependent on contacts with contagious leprosy patients, and varying the extent to which the disease is heritable. Analyses of these data using two versions of the model indicate that the model can detect familial correlations in variable age of onset and discriminate between the different simulated effects.
The mixed model of segregation analysis specifies major gene effects and partitions the residual variance into polygenic and environmental components. The model explains familial correlations essentially in terms of genetic causation. The regressive model, on the other hand, is constructed by successively conditioning on ancestral phenotypes and major genes. Familial patterns of dependence are described in terms of correlations without necessarily introducing a particular scheme of causal relationship. These two approaches are compared both theoretically and numerically through computer simulations for the case of continuous traits on nuclear families. The class D regressive model, which is characterized by equal sib-sib correlations, is mathematically and numerically equivalent to the mixed model. The simpler class A regressive model, which is also characterized by equal sib-sib correlations determined in this case by the common parentage, provides good estimates of the mixed model parameters: major gene parameters and residual polygenic heritability, derived from the parent-offspring correlation. However, in the absence of a major gene, the restriction imposed by the class A model on the sibling correlation can affect the conclusions of segregation analysis: False inference of a major gene was observed in two out of ten replicates. Our simulations also indicate that the mixed model allowing for different heritabilities in adults and children leads to correct estimates of the major gene parameters and residual familial correlations (parent-offspring and sib-sib) as specified by the class A model. For all the models studied, major gene effects, when present, are correctly detected and estimated.
Regressive models for segregation analysis have been extended to include multivariate data and linked marker loci. The new models have been applied to data from two pedigrees segregating a gene for cardiovascular disease.
A study of gastric pH, nitrate, and nitrite in 110 families collected as part of a cohort from the Narino region of Colombia is presented. All three traits are familial and have a significant linearly increasing age trend. Gastric pH has a clear bimodal distribution but does not show Mendelian segregation. The nitrate distribution is slightly skewed, but generational heterogeneity explains the data best. Gastric nitrite is also biomodal with a clear break at concentration 1.08 micrograms/ml, and 74% of the observations at zero concentration; it shows a recessive Mendelian segregation with significant residual spouse correlation. This model also fits the data best when nitrite is dichotomized into detected (measurable) and undetected values. The estimated frequency of the recessive allele is .57, so that an estimated 32% of the population sampled are recessives. Recessives whose spouses have measurable nitrite have an estimated penetrance of 99.3% at age 30 years, whereas those whose spouses have zero or undetected nitrite have a penetrance of only 8.8% at age 30 years. It appears that gastric nitrite, and, from our previous study of these families, chronic atrophic gastritis are important biologic markers for the early identification of persons predisposed to gastric cancer.
The likelihood of a set of binary dependent outcomes, with or without explanatory variables, is expressed as a product of conditional probabilities each of which is assumed to be logistic. The models are called regressive logistic models. They provide a simple but relatively unknown parametrization of the multivariate distribution. They have the theoretical and practical advantage that they can be analyzed and fitted as in logistic regression for independent outcomes, and with the same computer programs. The paper is largely expository and is intended to motivate the development and usage of the regressive logistic models. The discussion includes serially dependent outcomes, equally predictive outcomes, more specialized patterns of dependence, multidimensional tables, and three examples.
Scientific evidence has accumulated to show that chronic atrophic gastritis (CAG) is a precursor of gastric carcinoma, especially its intestinal histologic type; thus the etiology of CAG is of interest. Data on 110 families (557 individuals) collected as part of a large cohort from the Narino region of Colombia, South America, are analyzed to determine the familiality of CAG as a risk factor, and the possible involvement of a major gene in its etiology. We found that age and having an affected mother are important risk factors. In the sample, 45% are affected; 56% of individuals above 30 are affected, whereas only 28% of those 30 and under are affected; 48% of those with affected mothers are affected, but only 7% of those with unaffected mothers are affected. A positive spouse association was confounded with age. Sex and an affected father are not significant risk factors. The genetic (segregation) analysis showed Mendelian transmission of a recessive autosomal gene with penetrance dependent on age and mother's CAG status. Homozygous recessives account for an estimated 61% of the sampled population and have penetrance reaching 72% at age 30 if the mother is affected, and 41% if the mother is not affected. Carriers and non-carriers, who make up an estimated 39% of the sampled population, have an appreciable estimated risk after age 50. The environment, particularly diet, as the sole determinant of CAG needs reevaluation; some combined action of genes and environment seems more plausible.
Explore the source record for details and available documents.
The simple Markovian structures of dependence, defined previously for continuous traits, are extended here to familial disease and other binary traits through the use of the logistic function. The regressive models so formulated can incorporate explanatory variables and major gene effects for segregation and linkage analyses. Thus, the goals of epidemiology and genetics in the analysis of familial disease can be accomplished in the same computational scheme.
We consider the analysis of a continuous trait measured on human families and pedigrees to elucidate the mechanism of underlying major genes. Thinking in terms of the naturally Markovian structure of the dependencies in pedigrees rather than variance components, we describe natural classes of regressive models that are computationally feasible. These models can accommodate wide generalizations of the residual variation, and so should provide a stronger basis than other models proposed to date for inferring the segregation and linkage relationships of major genes.
Explore the source record for details and available documents.
The analysis of the data from this study, though not complete, has shown the following: 1) High rates of plural births may be expected in Ghanaian populations (3.21% for twins, 0.05% for triplets). 2) Hellin's law relating to the incidence of multiple births has been found to apply to a Ghanaian population. 3) Our data show that maternal age and parity have considerable effect on twinning. 4) Multiple pregnancies may not, in themselves, constitute a risk to the mother. On the other hand, the birthweight of the babies may be the cause of hazard at childbirth.
Explore the source record for details and available documents.
We have extended regressive models by incorporating a simple path model (the TAU model). This was achieved for both class A and class D regressive models by expressing the residual correlations in the regressive models in terms of parameters of the path model. We have presented explicit solutions for path coefficients in terms of the residual correlations. These methods were applied to a French-Canadian family study on body mass index. It was found that the estimate of pseudopolygenic heritability was robust under class A (t2 = 0.28) and class D (t2 = 0.26) models.