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G E Bonney

Publications and source records attributed to G E Bonney.

At least 19 recordsLinked to original sources
PubMed1992

Segregation analysis of esophageal cancer in 221 high-risk Chinese families.

BACKGROUND: Until recently, environmental factors were considered of greatest importance in the etiology of esophageal cancer. Recent studies, however, have suggested that genetic factors also have a role. PURPOSE: Since no formal genetic study of this cancer has been previously reported, we carried out a statistical analysis to determine how important genetic factors are in the etiology of esophageal cancer in high-incidence areas of North China. METHODS: Using a logistic regressive model, we performed a segregation analysis on 221 high-risk nuclear families from the Yaocun Commune, Linxian, Henan Province of China, with at least one affected family member and with all offspring aged 40 years or older. Three models, the mendelian, the environmental, and the no-transmission models, were each compared with the general-transmission model that incorporated both genetic and environmental factors. RESULTS: According to Akaike's Information Criterion, the mendelian model provided the best fit for the data. By the chi-square test, the mendelian inheritance model was not rejected, but the environmental and the no-transmission models were both rejected. CONCLUSION: The segregation analysis indicated an autosomal recessive mendelian inheritance, with the alleged mendelian gene present at a frequency of 19%, causing 4% of this population to be predisposed to develop esophageal cancer. Large, unmeasured, residual familial factors, however, were also significant. IMPLICATIONS: Both an autosomal recessive gene and unexplained environmental factors appear to be important in the etiology of esophageal cancer in the subpopulation studied.

China↗
PubMed1992

Numerical comparisons of two formulations of the logistic regressive models with the mixed model in segregation analysis of discrete traits.

Segregation analysis of discrete traits can be conducted by the classical mixed model and the recently introduced regressive models. The mixed model assumes an underlying liability to the disease, to which a major gene, a multifactorial component, and random environment contribute independently. Affected persons have a liability exceeding a threshold. The regressive logistic models assume that the logarithm of the odds of being affected is a linear function of major genotype effects, the phenotypes of older relatives, and other covariates. A formulation of the regressive models, based on an underlying liability model, has been recently proposed. The regression coefficients on antecedents are expressed in terms of the relevant familial correlations and a one-to-one correspondence with the parameters of the mixed model can thus be established. Computer simulations are conducted to evaluate the fit of the two formulations of the regressive models to the mixed model on nuclear families. The two forms of the class D regressive model provide a good fit to a generated mixed model, in terms of both hypothesis testing and parameter estimation. The simpler class A regressive model, which assumes that the outcomes of children depend solely on the outcomes of parents, is not robust against a sib-sib correlation exceeding that specified by the model, emphasizing testing class A against class D. The studies reported here show that if the true state of nature is that described by the mixed model, then a regressive model will do just as well. Moreover, the regressive models, allowing for more patterns of family dependence, provide a flexible framework to understand gene-environment interactions in complex diseases.

Computer Simulation↗
PubMed1992

Familial aggregation of oesophageal cancer in Yangcheng County, Shanxi Province, China.

Oesophageal cancer is the second most common cause of cancer death in China and is particularly prevalent in northern China. Genetic factors have been studied less than environmental factors in the aetiology of this disease. This study was conducted to evaluate familial aggregation of oesophageal cancer. All households in Yangcheng County were interviewed in 1979 to determine family history of oesophageal cancer. In 1989, vital status for all family members from three Yangcheng villages was determined and re-interviews were conducted among families who reported a positive family history of oesophageal cancer in 1979. Risk of oesophageal cancer was evaluated by comparing family and individual rates of oesophageal cancer during the 1979-1989 interval stratified by the number of family members with oesophageal cancer prior to 1979. More families with prior oesophageal cancer history reported new oesophageal cancer deaths during the follow-up period than families without prior history (19% versus 5%). Oesophageal cancer rates increased with increasing positivity of family history, and adjustment for other risk factors did not substantially alter this result. We conclude that these data provide evidence for familial aggregation of oesophageal cancer.

Adult↗
PubMed1992

Compound regressive models for family data.

The regressive models for the analysis of family data are extended to include cases in which the within-sibship covariation may exceed that implied by the class A regressive model, but for which birth order is not required. In addition to specified major genes, if any, and common parental phenotypes, the excess within-sibship covariation may come from a common cumulative risk from unspecified factors such as a shared environment, and other genes. The within-sibship cumulative risk has a probability distribution in the population. The sib-sib correlation (more generally within-sibship statistical dependence) is equal for all pairs within a given sibship. The compound regressive model is thus a version of the class D regressive model with the property of within-sibship interchangeability. The work is motivated here by comparing and contrasting the Elston-Stewart algorithm and the Morton-MacLean algorithm for the mixed model of inheritance. This points the way to derive practical algorithms for the compound regressive models proposed, with easy extensions to pedigrees of arbitrary structure, and to multilocus problems.

Algorithms↗
PubMed1990

Search for faster methods of fitting the regressive models to quantitative traits.

The regressive models describe familial patterns of dependence of quantitative measures by specifying regression relationships among a person's phenotype and genotype and the phenotypes and genotypes of antecedents. When the number of sibs in the pattern of dependence increases, as in the class D regressive model, computation of the likelihood becomes time consuming, since the Elston-Stewart algorithm cannot be used generally. On the other hand, the simpler class A regressive model, which imposes a restriction on the sib-sib correlation, may lead to inference of a spurious major gene, as already observed in some instances. A simulation study is performed to explore the robustness of class A model with respect to false inference of a major gene and to search for faster methods of computing the likelihood under class D model. The class A model is not robust against the presence of a sib-sib correlation exceeding that specified by the model, unless tests on transmission probabilities are performed carefully: false detection of a major gene is reduced from a number of 26-30 to between 0 and 4 data sets out of 30 replicates after testing both the Mendelian transmission and the absence of transmission of a major effect against the general transmission model. Among various approximations of the likelihood formulation of the class D model, approximations 6 and 8 are found to work appropriately in terms of both the estimation of all parameters and hypothesis testing, for each generating model. These approximations lessen the computer time by allowing use of the Elston-Stewart algorithm.

Computer Simulation↗
PubMed1990

A time-dependent logistic hazard function for modeling variable age of onset in analysis of familial diseases.

The paper presents an extension of the regressive logistic models proposed by Bonney [Biometrics 42:611-625, 1986], to address the problems of variable age-of-onset and time-dependent covariates in analysis of familial diseases. This goal is achieved by using failure time data analysis methods, and partitioning the time of follow up in K mutually exclusive intervals. The conditional probability of being affected within the kth interval (k = 1...K) given not affected before represents the hazard function in this discrete formulation. A logistic model is used to specify a regression relationship between this hazard function and a set of explanatory variables including genotype, phenotypes of ancestors, and other covariates which can be time dependent. The probability that a given person either becomes affected within the kth interval (i.e., interval k includes age of onset of the person) or remains unaffected by the end of the kth interval (i.e., interval k includes age at examination of the person) are derived from the general results of failure time data analysis and used for the likelihood formulation. This proposed approach can be used in any genetic segregation and linkage analysis in which a penetrance function needs to be defined. Application of the method to familial leprosy data leads to results consistent with our previous analysis performed using the unified mixed model [Abel and Demenais, Am J Hum Genet 42:256-266, 1988], i.e., the presence of a recessive major gene controlling susceptibility to leprosy. Furthermore, a simulation study shows the capability of the new model to detect major gene effects and to provide accurate parameter estimates in a situation of complete ascertainment.

Adolescent↗
PubMed1990

A multivariate method for detecting genetic linkage, with application to a pedigree with an adverse lipoprotein phenotype.

The robust or model-free method for detecting linkage developed by Haseman and Elston for data from sib pairs is extended to incorporate observations of multiple traits on each individual. A method is proposed that estimates the linear function that results in the strongest correlation between the squared pair differences in the trait measurements and identity by descent at a marker locus. The method is illustrated by the study of apolipoprotein and cholesterol levels in individuals from a large family that had many members diagnosed with coronary heart disease.

Apolipoproteins↗
PubMed1990

Combined segregation and linkage analysis of genetic hemochromatosis using affection status, serum iron, and HLA.

Characterizing the distribution of parameters of iron metabolism by hemochromatosis genotype remains an important goal vis-à-vis potential screening strategies to identify individuals at genetic risk, since a specific marker to detect the abnormal gene has not been identified as yet. In the present investigation, we analyze serum iron values in ascertained families using a method which incorporates both segregation of the clinical affection status and the HLA linkage information to identify the underlying genotypes. The analysis is performed using an extension of the model presented by Bonney et al., comprising regressive models for segregation analysis and the multipoint linkage strategy implemented in LINKAGE. The gene was found to be completely recessive with respect to both clinical manifestations and serum iron abnormalities, with significant differences in expression by sex. Clinical manifestations were present for all male homozygotes in this data set, suggesting that the recessive hemochromatosis genotype is fully penetrant at all ages in males. This was not the case for younger females. Significant genotype-specific age and sex effects were found for serum iron values. It is interesting that deletion of the HLA marker information did not affect our ability to resolve the genetic model when we analyzed a bivariate phenotype. This serves as a reminder that a search for relevant biological markers can be equally important in discerning the genetic etiology of a disease trait, as a search for linked genetic markers.

Age Factors↗
PubMed1989

Modeling the age-of-onset function in segregation analysis: a causal scheme for leprosy.

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.

Adolescent↗
PubMed1989

Equivalence of the mixed and regressive models for genetic analysis. I. Continuous traits.

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.

Adult↗
PubMed1987

Genetic etiology of gastric carcinoma: II. Segregation analysis of gastric pH, nitrate, and nitrite.

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.

Adult↗
PubMed1987

Logistic regression for dependent binary observations.

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.

Models, Theoretical↗
PubMed1986

Genetic etiology of gastric carcinoma: I. Chronic atrophic gastritis.

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.

Age Factors↗