Ralph L. Kodell

Maternal epigenetics and methyl supplements affect agouti gene expression in Avy/a mice

‘Viable yellow’ (Avy/a) mice are larger, obese, hyperinsulinemic, more susceptible to cancer, and, on average, shorter lived than their non‐yellow siblings. They are epigenetic mosaics ranging from a yellow phenotype with maximum ectopic agouti overexpression, through a continuum of mottled agouti/yellow phenotypes with partial agouti overexpression, to a pseudoagouti phenotype with minimal ectopic expression. Pseudoagouti Avy/a mice are lean, healthy, and longer lived than their yellow siblings. Here we report that feeding pregnant black a/a dams methyl‐supplemented diets alters epigenetic regulation of agouti expression in their offspring, as indicated by increased agouti/black mottling in the direction of the pseudoagouti phenotype. We also present confirmatory evidence that epigenetic phenotypes are maternally heritable. Thus Avy expression, already known to be modulated by imprinting, strain‐specific modification, and maternal epigenetic inheritance, is also modulated by maternal diet. These observations suggest, at least in this special case, that maternal dietary supplementation may positively affect health and longevity of the offspring. Therefore, this experimental system should be useful for identifying maternal factors that modulate epigenetic mechanisms, especially DNA methylation, in developing embryos.—Wolff, G. L., Kodell, R. L., Moore, S. R., Cooney, C. A. Maternal epigenetics and methyl supplements affect agouti gene expression in Avy/a mice. FASEB J. 12, 949–957 (1998)

Quantitative risk assessment for teratological effects

Abstract This article presents a quantitative procedure for using a “benchmark dose” to obtain low-dose risk estimates for reproductive and developmental toxic effects. This procedure combines the best features of the previously proposed methods for handling litter effects for teratology data and the currently used methods for quantitative risk assessment. The beta-binomial distribution is used to account for litter effects, and the Weibull dose—response model is used for modeling teratogenic effects. A benchmark dose, defined to be the lowest dose at which the excess risk does not exceed 1% with 95% confidence, is proposed to replace the no-observed-effect level (NOEL). The NOEL is generally the highest experimental dose that is not statistically different from the control; the NOEL approach does not use experimental data effectively for quantitative risk estimation. In this article, a lower limit on the safe dose is estimated by linearly extrapolating downward from the benchmark dose; this procedure is ...

Comparison of Methods for Estimating the Number of True Null Hypotheses in Multiplicity Testing

Abstract When a large number of statistical tests is performed, the chance of false positive findings could increase considerably. The traditional approach is to control the probability of rejecting at least one true null hypothesis, the familywise error rate (FWE). To improve the power of detecting treatment differences, an alternative approach is to control the expected proportion of errors among the rejected hypotheses, the false discovery rate (FDR). When some of the hypotheses are not true, the error rate from either the FWE- or the FDR-controlling procedure is usually lower than the designed level. This paper compares five methods used to estimate the number of true null hypotheses over a large number of hypotheses. The estimated number of true null hypotheses is then used to improve the power of FWE- or FDR-controlling methods. Monte Carlo simulations are conducted to evaluate the performance of these methods. The lowest slope method, developed by Benjamini and Hochberg (2000) on the adaptive control of the FDR in multiple testing with independent statistics, and the mean of differences method appear to perform the best. These two methods control the FWE properly when the number of nontrue null hypotheses is small. A data set from a toxicogenomic microarray experiment is used for illustration.

Analysis of trinomial responses from reproductive and developmental toxicity experiments

This paper presents a Dirichlet-trinomial distribution for modelling data obtained from reproductive and developmental studies. The common endpoints for the evaluation of reproductive and developmental toxic effects are the number of dead fetuses, the number of malformed fetuses, and the number of normal fetuses for each litter. With current statistical methods for the evaluation of reproductive and developmental effects, the effect on the number of deaths and the effect on the number of malformations are analyzed separately. The Dirichlet-trinomial model provides a procedure for the analysis of multiple endpoints simultaneously. This proposed Dirichlet-trinomial model is a generalization of the beta-binomial model that has been used for handling the litter effect in reproductive and developmental experiments. Likelihood ratio tests for differences in the number of deaths, the number of malformations, and the number of normals among dosed and control groups are derived. The proposed test procedure based on the Dirichlet-trinomial model is compared with that based on the beta-binomial model with an application to a real data set.

Classification by ensembles from random partitions of high-dimensional data

A robust classification procedure is developed based on ensembles of classifiers, with each classifier constructed from a different set of predictors determined by a random partition of the entire set of predictors. The proposed methods combine the results of multiple classifiers to achieve a substantially improved prediction compared to the optimal single classifier. This approach is designed specifically for high-dimensional data sets for which a classifier is sought. By combining classifiers built from each subspace of the predictors, the proposed methods achieve a computational advantage in tackling the growing problem of dimensionality. For each subspace of the predictors, we build a classification tree or logistic regression tree. Our study shows, using four real data sets from different areas, that our methods perform consistently well compared to widely used classification methods. For unbalanced data, our approach maintains the balance between sensitivity and specificity more adequately than many other classification methods considered in this study.

Correlation between specific DNA-methylation products and mutation induction at the HGPRT locus in Chinese hamster ovary cells

Suspension cultures of Chinese hamster ovary (CHO) cells were exposed to methyl methanesulfonate (MMS) or methylnitrosourea (MNU) and assayed for mutation induction (6-thioguanine resistance) and for specific DNA adducts. DNA methylation at the 1-, 3- and 7-positions of adenine, the 3-, O6- and 7-positions of guanine, and phosphate was detected in cultures exposed to MMS, while MNU produced 3- and 7-methyladenine, 3-methylcytosine, 3-, O6- and 7-methylguanine, O4-methylthymidine and methylated phosphodiesters. When mutations induced by MMS and MNU were compared by linear correlation analysis with levels of each of these adducts, only O6-methylguanine displayed a strong correlation with mutations (r = 0.879, p less than 0.001). The relationship between O6-methylguanine and induced mutations in CHO cells is similar to that previously reported in CHO cells for O6-ethylguanine and mutations (Heflich et al., 1982) and indicates that alkylation-induced mutations at the HGPRT locus in CHO cells are primarily associated with O6-alkylguanine formation.

Ensemble methods for classification of patients for personalized medicine with high-dimensional data

OBJECTIVE Personalized medicine is defined by the use of genomic signatures of patients in a target population for assignment of more effective therapies as well as better diagnosis and earlier interventions that might prevent or delay disease. An objective is to find a novel classification algorithm that can be used for prediction of response to therapy in order to help individualize clinical assignment of treatment. METHODS AND MATERIALS Classification algorithms are required to be highly accurate for optimal treatment on each patient. Typically, there are numerous genomic and clinical variables over a relatively small number of patients, which presents challenges for most traditional classification algorithms to avoid over-fitting the data. We developed a robust classification algorithm for high-dimensional data based on ensembles of classifiers built from the optimal number of random partitions of the feature space. The software is available on request from the authors. RESULTS The proposed algorithm is applied to genomic data sets on lymphoma patients and lung cancer patients to distinguish disease subtypes for optimal treatment and to genomic data on breast cancer patients to identify patients most likely to benefit from adjuvant chemotherapy after surgery. The performance of the proposed algorithm is consistently ranked highly compared to the other classification algorithms. CONCLUSION The statistical classification method for individualized treatment of diseases developed in this study is expected to play a critical role in developing safer and more effective therapies that replace one-size-fits-all drugs with treatments that focus on specific patient needs.

Statistical methods of risk assessment for continuous variables

Adverse health effects for continuous responses are not as easily defined as adverse health effects for binary responses. Kodell and West (1993) developed methods for defining adverse effects for continuous responses and the associated risk. Procedures were developed for finding point estimates and upper confidence limits for additional risk under the assumption of a normal distribution and quadratic mean response curve with equal variances at each dose level. In this paper, methods are developed for point estimates and upper confidence limits for additional risk at experimental doses when the equal variance assumption is relaxed. An interpolation procedure is discussed for obtaining information at doses other than the experimental doses. A small simulation study is presented to test the performance of the methods discussed.

Chronic: A SAS procedure for statistical analysis of carcinogenesis studies

A user-oriented PL/1 computer program for Niansncal analysis oi carcinogenesis data has been written and developed into a SAS procedure. The program follows a unified approach to the estimation and testing of the tumor onset, prevalence and mortality functions. This paper discusses the realization of these functions in carcinogenesis data, describes the estimation and testing procedures implemented by the program, documents usage of the SAS procedure, and provides a numerical exampe of its application.

Accelerated appearance of chemically induced mammary carcinomas in obese yellow (avy/a) (balb/c x vy)f1 hybrid mice.

Latent periods and cumulative incidence of mammary carcinomas (MT) up to 50 wk after initial gavage with 7.12-dimethylbenz[a]anthracene (DMBA) were determined in virgin yellow (Avy/A) and agouti (A/a) (BALB/cStCrlfC3Hf/Nctr X VY/WffC3Hf/Nctr-Avy) F1 hybrid female mice. When subcutaneous masses reached 5-10 mm in diameter, the mice were killed and necropsied, and the tissues examined histologically. No MT were found in control mice. Cumulative MT incidence in the 1.5-mg DMBA group (A) was 43% (41/95) among yellow mice, and 33% (32/96) among agoutis. In the 6.0-mg DMBA group (B), corresponding MT incidences were 86% (83/96) and 71% (67/95). In group A, the first percentile of MT detection was 13.0 wk after initial carcinogen treatment in yellow mice; it was 18.0 wk in agoutis. Corresponding latent periods for the 20th percentile were 34.3 and 47.0 wk. In group B, latencies for the first percentile were 8.3 and 9.0 wk. Corresponding latencies for the 20th percentile were 15.3 and 16.0 wk. Within genotypes and dose groups, rates of weight gain of mice that developed MT and those that did not were similar. We conclude that MT induced by low doses of DMBA arise more rapidly in yellow mice than in nonyellow littermates. The absence of spontaneous MTs, acceleration of chemically induced MT formation at a low dose level that does not induce general toxicity, and availability of genetically identical (except for one gene) normal control animals make this experimental system suitable for development of an assay to efficiently test the carcinogenic potential of low dose levels of chemical substances.