Re: "Are children living near high-voltage power lines at increased risk of acute lymphoblastic leukemia?".
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Child↗
Biomedical subjects
D C Thomas
Publications and source records attributed to D C Thomas.
Counter-matching in studies of gene-environment interaction: efficiency and feasibility.
The interest in studying gene-environment interaction is increasing for complex diseases. However, most methods of detecting gene-environment interactions may not be appropriate for the study of interactions involving rare genes (G:) or uncommon environmental exposures (E:), because of poor statistical power. To increase this power, the authors propose the counter-matching design. This design increases the number of subjects with the rare factor without increasing the number of measurements that must be performed. In this paper, the efficiency and feasibility (required sample sizes) of counter-matching designs are evaluated and discussed. Counter-matching on both G: and E: appears to be the most efficient design for detecting gene-environment interaction. The sensitivity and specificity of the surrogate measures, the frequencies of G: and E:, and, to a lesser extent, the value of the interaction effect are the most important parameters for determining efficiency. Feasibility is also more dependent on the exposure frequencies and the interaction effect than on the main effects of G: and E: Although the efficiency of counter-matching is greatest when the risk factors are very rare, the study of such rare factors is not realistic unless one is interested in very strong interaction effects. Nevertheless, counter-matching appears to be more appropriate than most traditional epidemiologic methods for the study of interactions involving rare factors.
Case-Control Studies↗
International Genetic Epidemiology Society: commentary on Darkness in El Dorado by Patrick Tierney.
The International Genetic Epidemiology Society (IGES) has examined the charges against James V. Neel and his colleagues contained in the recently published book by Patrick Tierney entitled Darkness in El Dorado: How Scientists and Journalists Devastated the Amazon (W.W. Norton, 2000). The book implicates Neel in causing or promoting an epidemic of measles among the Yanomamö Indians of Venezuela in 1968 leading to "hundreds if not thousands" of deaths by using a "dinosaur" vaccine (Edmonston B) as a deliberate "experiment" to test his "eugenic" theories. Tierney also attempts to link this research, funded by the Atomic Energy Commission (AEC), with a broader tapestry of human radiation experiments. To investigate these serious charges, the IGES undertook a thorough examination of most source documents referenced in Tierney's book, Neel's field logs, notes, first-hand reports, contemporary writings, film sound tracks, etc., and conducted interviews with many relevant persons. The IGES finds that these allegations are false. Neel was not a eugenicist and was in fact highly critical of both the scientific basis of eugenics and its coercive social policies. In this regard, Tierney has grossly misrepresented Neel's views on a wide range of social implications of modern civilization for the long-term health of the gene pool. Far from causing an epidemic of measles, Neel did his utmost to protect the Yanomamö from the ravages of the impending epidemic by a vaccination program using a vaccine that was widely used at the time and administered in an appropriate manner. There was nothing experimental about the vaccination program, which in fact severely hindered the primary scientific objectives of the expedition. Although the research was funded in large part by the AEC, there was no element of radiation research and the work had no connection with the ethical abuses that have been reported from AEC-sponsored radiation research, such as studies of heavy isotopes. Neel's seminal contributions to a broad range of topics in human genetics have been extensively chronicled elsewhere. His research on the Yanomamö in particular has provided unique insights into the evolutionary biology of our species, the role of sociocultural practices, such as kinship relationships and selective pressures in shaping the genetic diversity of primitive population isolates, as well as the general picture of health in such populations. The IGES decries the damage done to the reputation of one of its founders and its first President and the misperception this book may have caused about the conduct of research in genetic epidemiology. Ethical issues about scientific research in primitive populations deserve serious and wide discussion, but the IGES condemns the gross misrepresentation of the facts and demonization of the principal characters in this book.
Bioethics↗
Evolution of the simulated data problem.
The simulated data problem was designed via an interactive process by the Simulation Problem Organizing Committee and the selected data simulators. Based on discussions at the previous Genetic Analysis Workshop, many of the features of previous simulation problems, such as a complex disease, genome scan, and replication, were retained and in addition, a population genetics model was used to generate the simulated genes. We describe the process that was used to structure the problem and summarize the discussions about many of the scientific issues that were considered.
Chromosome Mapping↗
Genome scan of complex traits by haplotype sharing correlation.
We introduce a haplotype-sharing correlation in founder haplotypes for use in genome scanning. The method evaluates the correlation between phenotype similarity and haplotype similarity at each candidate location. When applied to Genetic Analysis Workshop 12 simulated data for disease status, age at onset, and quantitative traits Q1-Q5, we found highly significant signals near four simulated disease loci in genome scans using microsatellite marker data and highly significant gene effects in three casual genes using sequence data.
Algorithms↗
Introduction: bayesian models and Markov chain Monte Carlo methods.
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Bayes Theorem↗
Bayes estimates of haplotype effects.
We describe a Markov chain Monte Carlo implementation of a Bayesian approach to estimating associations of a trait with a large set of haplotypes recently introduced by Clayton and Jones [Am J Hum Genet 65:1161-9, 2000]. The model uses the length of the longest segment in common between any two haplotypes to define the prior correlation structure for the set of haplotype effects, using an intrinsic autocorrelation model. When applied to the Genetic Analysis Workshop 12 data for trait Q1, we found highly significant variation between haplotypes, using either a structured or unstructured covariance matrix.
Bayes Theorem↗
The role of interacting determinants in the localization of genes.
We describe the potential gains in power for localizing disease genes that can be obtained by allowing for interactions with environmental agents or other genes. The focus is on linkage and association methods in nuclear families with dichotomous phenotypes. A logistic model incorporating various main effects and interactions is used for penetrance, but similar methods apply to censored age-at-onset or continuous phenotypes. We begin by discussing the influence of gene-environment interactions in segregation analysis, illustrated with analysis of smoking as a modifying factor for lung cancer. We then discuss a number of approaches to linkage analysis-model-free and model-based(including generalized estimating equations) incorporating interactions with environmental factors and other genes, either candidate genes or linked loci. We find that a test of heterogeneity in IBD sharing probabilities across strata defined by sharing of environmental factors can offer greater power for detecting linkage than the simple mean test, provided the interaction effect is sufficiently strong; we explore the conditions under which this gain in power occurs. Finally, we describe approaches for testing association and disequilibrium involving interactions, utilizing case-control, case-parent, and pedigree-based approaches. A technical problem that must be addressed in many analyses is the effect of missing data on environmental covariates; we use multiple imputation in an analysis of lung cancer segregation to illustrate an approach to this problem.
Carcinogens, Environmental↗
The effects of ambient air pollution on school absenteeism due to respiratory illnesses.
We investigated the relations between ozone (O3), nitrogen dioxide (NO2), and respirable particles less than 10 microm in diameter (PM10) and school absenteeism in a cohort of 4th-grade school children who resided in 12 southern California communities. An active surveillance system ascertained the numbers and types of absences during the first 6 months of 1996. Pollutants were measured hourly at central-site monitors in each of the 12 communities. To examine acute effects of air pollution on absence rates, we fitted a two-stage time-series model to the absence count data that included distributed lag effects of exposure adjusted for long-term pollutant levels. Short-term change in O3, but not NO2 or PM10, was associated with a substantial increase in school absences from both upper and lower respiratory illness. An increase of 20 ppb of O3 was associated with an increase of 62.9% [95% confidence interval (95% CI) = 18.4-124.1%] for illness-related absence rates, 82.9% (95% CI = 3.9-222.0%) for respiratory illnesses, 45.1% (95% CI = 21.3-73.7%) for upper respiratory illnesses, and 173.9% (95% CI = 91.3-292.3%) for lower respiratory illnesses with wet cough. The short-term effects of a 20-ppb change of O3 on illness-related absenteeism were larger in communities with lower long-term average PM10 [223.5% (95% CI = 90.4-449.7)] compared with communities with high average levels [38.1% (95% CI = 8.5-75.8)]. Increased school absenteeism from O3 exposure in children is an important adverse effect of ambient air pollution worthy of public policy consideration.
Absenteeism↗
Performance of Markov chain-Monte Carlo approaches for mapping genes in oligogenic models with an unknown number of loci.
Markov chain-Monte Carlo (MCMC) techniques for multipoint mapping of quantitative trait loci have been developed on nuclear-family and extended-pedigree data. These methods are based on repeated sampling-peeling and gene dropping of genotype vectors and random sampling of each of the model parameters from their full conditional distributions, given phenotypes, markers, and other model parameters. We further refine such approaches by improving the efficiency of the marker haplotype-updating algorithm and by adopting a new proposal for adding loci. Incorporating these refinements, we have performed an extensive simulation study on simulated nuclear-family data, varying the number of trait loci, family size, displacement, and other segregation parameters. Our simulation studies show that our MCMC algorithm identifies the locations of the true trait loci and estimates their segregation parameters well-provided that the total number of sibship pairs in the pedigree data is reasonably large, heritability of each individual trait locus is not too low, and the loci are not too close together. Our MCMC algorithm was shown to be significantly more efficient than LOKI (Heath 1997) in our simulation study using nuclear-family data.
Algorithms↗
Testing linkage disequilibrium in sibships.
We describe the use of multivariate regression for testing allelic association in the presence of linkage, using marker genotype data from sibships. The test is valid, provided that the correct mean structure is modeled but does not require the correlation structure within families to be specified. The test can be implemented using standard statistical software such as the SAS programming language. In a simulation study, we evaluated this new test in comparison with one from a standard, matched-case-control analysis. First, we noted that the genetic effect needed to be quite extreme before residual familial correlation due to linkage led to false inference using the standard, matched-pair analysis. Second, we showed that under examples of extreme residual familial correlation, the new test had the correct test size. Third, we found that the test was more powerful than the sibship disequilibrium test of Horvath and Laird. Finally, we concluded that although the standard analysis may lead to correct inference for practical purposes, the new test is valid, even under extreme residual familial correlation and with no cost in power at the causal locus.
Alleles↗
Genetic epidemiology with a capital "E".
Three characteristics of genetic epidemiology that distinguish it from its parent disciplines are a focus on population-based research, a focus on the joint effects of genes and the environment, and the incorporation of the underlying biology of the disease into its conceptual models. These principles are illustrated by a review of the genetic epidemiology of breast and ovarian cancer. Descriptive and mechanistic models for the joint effects of genes and "environmental" risk factors such as hormones and reproductive events are compared to illustrate the need to understand the biology. The contribution of population-based research to the development of the evidence for the involvement of major genes, the discovery of BRCA1 and BRCA2, and their characterization is reviewed. Interactions of major susceptibility genes, metabolic genes, and hormones are also discussed. I conclude with some suggestions for future directions for the field, the journal, and the Society, including recent bioethics initiatives. I believe that the Society should reach out more to the epidemiology community and that the journal should shift its emphasis from pure methodology to also include more substantive papers that illustrate these principles.
Bioethics↗
Case-parents design for gene-environment interaction by Schaid.
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Environment↗
Bias and efficiency in family-based gene-characterization studies: conditional, prospective, retrospective, and joint likelihoods.
We revisit the usual conditional likelihood for stratum-matched case-control studies and consider three alternatives that may be more appropriate for family-based gene-characterization studies: First, the prospective likelihood, that is, Pr(D/G,A second, the retrospective likelihood, Pr(G/D); and third, the ascertainment-corrected joint likelihood, Pr(D,G/A). These likelihoods provide unbiased estimators of genetic relative risk parameters, as well as population allele frequencies and baseline risks. The parameter estimates based on the retrospective likelihood remain unbiased even when the ascertainment scheme cannot be modeled, as long as ascertainment only depends on families' phenotypes. Despite the need to estimate additional parameters, the prospective, retrospective, and joint likelihoods can lead to considerable gains in efficiency, relative to the conditional likelihood, when estimating genetic relative risk. This is true if baseline risks and allele frequencies can be assumed to be homogeneous. In the presence of heterogeneity, however, the parameter estimates assuming homogeneity can be seriously biased. We discuss the extent of this problem and present a mixed models approach for providing consistent parameter estimates when baseline risks and allele frequencies are heterogeneous. The efficiency gains of the mixed-model prospective, retrospective, and joint likelihoods relative to the efficiency of conditional likelihood are small in the situations presented here.
Alleles↗
Morbid hypocalcemia associated with phosphate enema in a six-week-old infant.
A 6-week-old premature infant who was born at 29 weeks of gestation presented to the emergency department with a several-hour history of stiffness and increased alarms on his apnea monitor at home. On arrival he was noted to have generalized seizures, apnea, and bradycardia. He was intubated and required cardiopulmonary resuscitation including chest compressions and medications. After stabilization he was transferred to the neonatal intensive care unit for further management. His initial laboratory tests revealed a serum calcium level of 2.4 mg/dL (normal range: 8.4-10.2 mg/dL) and a serum phosphorus level of 28.5 mg/dL (normal range: 2.4-4.5 mg/dL). During the first week of admission, the infant's mother reported that she had administered a full pediatric Fleets enema (CB Fleet Company Inc, Lynchburg, VA) to him. The infant was discharged after 12 days of hospitalization. Anticipatory guidance on the stool patterns and behavior of infants can prevent misconceptions about constipation that are especially prevalent in new parents. Proper management of constipation, should it arise, should be addressed with all parents at early well-child visits to avoid hazardous complications of treatments. hypocalcemia, seizures, premature infants, enema.
Apnea↗
Resolved: the probability of causation can be used in an equitable manner to resolve radiation tort claims and design compensation schemes. Pro.
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Causality↗
Asymptotic bias and efficiency in case-control studies of candidate genes and gene-environment interactions: basic family designs.
Case-control designs that use population controls are compared with those that use controls selected from their relatives (i.e., siblings, cousins, or "pseudosibs" based on parental alleles) for estimating the effect of candidate genes and gene-environment interactions. The authors first evaluate the asymptotic bias in relative risk estimates resulting from using population controls when there is confounding due to population stratification. Using siblings or pseudosibs as controls completely addresses this issue, whereas cousins provide only partial protection from population stratification. Next, they show that the conventional conditional likelihood for matched case-control studies can give asymptotically biased effect estimates when applied to the pseudosib approach; the asymptotic bias is toward the null and disappears with disease rarity. They show how to reparameterize the pseudosib likelihood so this approach gives consistent effect estimates. They then show that the designs using population or pseudosib controls are generally the most efficient for estimating the main effect of a candidate gene, followed in efficiency by the design using cousins. Finally, they show that the design using sibling controls can be quite efficient when studying gene-environment interactions. In addition to asymptotic bias and efficiency issues, family-based designs might benefit from a higher motivation to participate among cases' relatives, but these designs have the disadvantage that many potential cases will be excluded from study by having no available controls.
Bias↗
Association tests using unaffected-sibling versus pseudo-sibling controls.
We used family-matched case-control data to screen the genome for markers associated with disease in the simulated data set. Two different types of controls were considered: (1) unaffected siblings and (2) 'pseudo siblings,' a comparison sample created using the parental alleles. The scans were conducted on the first replicate of each study population. Overall, the two methods identified 14 marker loci associated with disease at the 0.001 significance level. Marker D1G24 (locus D) was the only true disease locus found by both approaches. No associations were found at any of the markers flanking the unobserved disease susceptibility loci (A, B, or C). We subsequently pooled the 25 replicates from a single population. This large sample still did not yield any associations at the flanking markers. We tested for association at locus D using a pseudo-sib approach restricted to alleles shared identical by descent between affected sib pairs. The power was 44% (11/25 replicates) at a significance level of 0.001.
Alleles↗