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David B Allison

Publications and source records attributed to David B Allison.

At least 19 recordsLinked to original sources
PubMed2025

A consensus guide to preclinical indirect calorimetry experiments.

Understanding the complex factors influencing mammalian metabolism and body weight homeostasis is a long-standing challenge requiring knowledge of energy intake, absorption and expenditure. Using measurements of respiratory gas exchange, indirect calorimetry can provide non-invasive estimates of whole-body energy expenditure. However, inconsistent measurement units and flawed data normalization methods have slowed progress in this field. This guide aims to establish consensus standards to unify indirect calorimetry experiments and their analysis for more consistent, meaningful and reproducible results. By establishing community-driven standards, we hope to facilitate data comparison across research datasets. This advance will allow the creation of an in-depth, machine-readable data repository built on shared standards. This overdue initiative stands to markedly improve the accuracy and depth of efforts to interrogate mammalian metabolism. Data sharing according to established best practices will also accelerate the translation of basic findings into clinical applications for metabolic diseases afflicting global populations.

Calorimetry, Indirect↗
PubMed2006

The impact of cost on the availability of fruits and vegetables in the homes of schoolchildren in Birmingham, Alabama.

OBJECTIVES: Fruit and vegetable cost is a presumed barrier to intake. We sought to determine whether fruit and vegetable cost and consumers' race and income would predict availability of fruits and vegetables in homes of schoolchildren in the Birmingham, Ala, area. METHODS: Data on availability of 27 fruit and vegetable items were obtained from homes of 1355 children (32% African American) in the Birmingham area. Fruit and vegetable costs were obtained from the US Department of Agriculture. We used discrete choice analysis with the dependent variable represented as presence or absence of the fruit or vegetable item. Explanatory variables included fruit and vegetable price per serving; child's gender, race, and age; and parent's body mass index and income. RESULTS: Higher cost was inversely related to fruit and vegetable availability. Higher income, African American race, and female gender were positively related to availability. Cost per serving was stratified into 3 categories-low, medium, and high. Relative to low-cost items, only high-cost items decreased the odds of availability significantly. CONCLUSIONS: Fruit and vegetable cost does impact availability and has the greatest impact for high-cost items. Although cost was inversely related to availability, African Americans reported higher fruit and vegetable availability than Whites. Additional studies are needed to determine whether food items of lower nutritive value and comparable cost impact availability.

Black or African American↗
PubMed2006

Not all cytokine-producing CD8+ T cells suppress simian immunodeficiency virus replication.

Current assays of CD8+ T-lymphocyte function measure cytokine production rather than the ability of these lymphocytes to suppress viral replication. Here we show that CD8+ T-cell clones recognizing the same epitope vary enormously in the ability to suppress simian immunodeficiency virus SIVmac239 replication in an in vitro suppression assay. However, all Nef(165-173)IW9- and Vif(66-73)HW8-specific clones from elite controllers effectively suppressed SIV replication. Interestingly, in vitro suppression efficacy was not always associated with the ability to produce gamma interferon, tumor necrosis factor alpha, or interleukin-2.

Animals↗
PubMed2006

Epistemological issues in omics and high-dimensional biology: give the people what they want.

Gene expression microarrays have been the vanguard of new analytic approaches in high-dimensional biology. Draft sequences of several genomes coupled with new technologies allow study of the influences and responses of entire genomes rather than isolated genes. This has opened a new realm of highly dimensional biology where questions involve multiplicity at unprecedented scales: thousands of genetic polymorphisms, gene expression levels, protein measurements, genetic sequences, or any combination of these and their interactions. Such situations demand creative approaches to the processes of inference, estimation, prediction, classification, and study design. Although bench scientists intuitively grasp the need for flexibility in the inferential process, the elaboration of formal supporting statistical frameworks is just at the very start. Here, we will discuss some of the unique statistical challenges facing investigators studying high-dimensional biology, describe some approaches being developed by statistical scientists, and offer an epistemological framework for the validation of proffered statistical procedures. A key theme will be the challenge in providing methods that a statistician judges to be sound and a biologist finds informative. The shift from family-wise error rate control to false discovery rate estimation and to assessment of ranking and other forms of stability will be portrayed as illustrative of approaches to this challenge.

Computational Biology↗
PubMed2006

Marker genotypes and population admixture and their association with body weight, height and relative body mass in United States federal bison herds.

Elucidating genetic influences on bison growth and body composition is of interest, not only because bison are important for historical, cultural, and agricultural reasons, but also because their unusual population history makes them valuable models for finding influential loci in both domestic cattle and humans. We tested for trait loci associated with body weight, height, and bison mass index (BMI) while controlling for estimated ancestry to reduce potential confounding effects due to population admixture in 1316 bison sampled from four U.S. herds. We used 60 microsatellite markers to model each phenotype as a function of herd, sex, age, marker genotypes, and individual ancestry estimates. Statistical significance for genotype and its interaction with ancestry was evaluated using the adaptive false discovery rate. Of the four herds, two appeared to be admixed and two were nonadmixed. Although none of the main effects of the loci were significant, estimated ancestry and its interaction with marker loci were significantly associated with the phenotypes, illustrating the importance of including ancestry in the models and the dependence of genotype-phenotype associations on background ancestry. Individual loci contributed approximately 2.0% of variation in weight, height, and BMI, which confirms the utility and potential importance of adjusting for population stratification.

Animals↗
PubMed2006

Murine weight loss exhibits significant genetic variation during dietary restriction.

We present genetic analyses of murine weight loss during dietary restriction (DR) for females eating 60% ad libitum (AL). We examined 5 cohorts across 81 different strains (22 strains tested twice) that included the LXS and LSXSS recombinant inbred strains, the LXS parental strains ILS and ISS, and the classical inbreds 129S6, A, BALB/c, C57BL/6, C3H, and DBA. Weight loss exhibited highly significant genetic variation, with DR body weights ranging from approximately 60 to approximately 85% of AL body weight. This variation was not explained by the strain differences in absolute food intake, feces calorie content, motor activity, or AL body fat. Heritability was 40-50%, and several provisional quantitative trait loci were mapped. This variation can be used to test whether weight loss correlates with the health benefits of DR, independently of the reduction in calories. The genetic variation also implies the existence of genes that would be novel therapeutic targets, distinct from genes affecting AL body weight or body fat, for enhancing (or mitigating) weight loss during food restriction.

Adipose Tissue↗
PubMed2006

Regional admixture mapping and structured association testing: conceptual unification and an extensible general linear model.

Individual genetic admixture estimates, determined both across the genome and at specific genomic regions, have been proposed for use in identifying specific genomic regions harboring loci influencing phenotypes in regional admixture mapping (RAM). Estimates of individual ancestry can be used in structured association tests (SAT) to reduce confounding induced by various forms of population substructure. Although presented as two distinct approaches, we provide a conceptual framework in which both RAM and SAT are special cases of a more general linear model. We clarify which variables are sufficient to condition upon in order to prevent spurious associations and also provide a simple closed form "semiparametric" method of evaluating the reliability of individual admixture estimates. An estimate of the reliability of individual admixture estimates is required to make an inherent errors-in-variables problem tractable. Casting RAM and SAT methods as a general linear model offers enormous flexibility enabling application to a rich set of phenotypes, populations, covariates, and situations, including interaction terms and multilocus models. This approach should allow far wider use of RAM and SAT, often using standard software, in addressing admixture as either a confounder of association studies or a tool for finding loci influencing complex phenotypes in species as diverse as plants, humans, and nonhuman animals.

Computer Simulation↗
PubMed2006

An empirical Bayes method for updating inferences in analysis of quantitative trait loci using information from related genome scans.

Individual genome scans for quantitative trait loci (QTL) mapping often suffer from low statistical power and imprecise estimates of QTL location and effect. This lack of precision yields large confidence intervals for QTL location, which are problematic for subsequent fine mapping and positional cloning. In prioritizing areas for follow-up after an initial genome scan and in evaluating the credibility of apparent linkage signals, investigators typically examine the results of other genome scans of the same phenotype and informally update their beliefs about which linkage signals in their scan most merit confidence and follow-up via a subjective-intuitive integration approach. A method that acknowledges the wisdom of this general paradigm but formally borrows information from other scans to increase confidence in objectivity would be a benefit. We developed an empirical Bayes analytic method to integrate information from multiple genome scans. The linkage statistic obtained from a single genome scan study is updated by incorporating statistics from other genome scans as prior information. This technique does not require that all studies have an identical marker map or a common estimated QTL effect. The updated linkage statistic can then be used for the estimation of QTL location and effect. We evaluate the performance of our method by using extensive simulations based on actual marker spacing and allele frequencies from available data. Results indicate that the empirical Bayes method can account for between-study heterogeneity, estimate the QTL location and effect more precisely, and provide narrower confidence intervals than results from any single individual study. We also compared the empirical Bayes method with a method originally developed for meta-analysis (a closely related but distinct purpose). In the face of marked heterogeneity among studies, the empirical Bayes method outperforms the comparator.

Chromosome Mapping↗
PubMed2006

The PowerAtlas: a power and sample size atlas for microarray experimental design and research.

BACKGROUND: Microarrays permit biologists to simultaneously measure the mRNA abundance of thousands of genes. An important issue facing investigators planning microarray experiments is how to estimate the sample size required for good statistical power. What is the projected sample size or number of replicate chips needed to address the multiple hypotheses with acceptable accuracy? Statistical methods exist for calculating power based upon a single hypothesis, using estimates of the variability in data from pilot studies. There is, however, a need for methods to estimate power and/or required sample sizes in situations where multiple hypotheses are being tested, such as in microarray experiments. In addition, investigators frequently do not have pilot data to estimate the sample sizes required for microarray studies. RESULTS: To address this challenge, we have developed a Microrarray PowerAtlas. The atlas enables estimation of statistical power by allowing investigators to appropriately plan studies by building upon previous studies that have similar experimental characteristics. Currently, there are sample sizes and power estimates based on 632 experiments from Gene Expression Omnibus (GEO). The PowerAtlas also permits investigators to upload their own pilot data and derive power and sample size estimates from these data. This resource will be updated regularly with new datasets from GEO and other databases such as The Nottingham Arabidopsis Stock Center (NASC). CONCLUSION: This resource provides a valuable tool for investigators who are planning efficient microarray studies and estimating required sample sizes.

Algorithms↗
PubMed2006

A proposed metric for assessing the measurement quality of individual microarrays.

BACKGROUND: High-density microarray technology is increasingly applied to study gene expression levels on a large scale. Microarray experiments rely on several critical steps that may introduce error and uncertainty in analyses. These steps include mRNA sample extraction, amplification and labeling, hybridization, and scanning. In some cases this may be manifested as systematic spatial variation on the surface of microarray in which expression measurements within an individual array may vary as a function of geographic position on the array surface. RESULTS: We hypothesized that an index of the degree of spatiality of gene expression measurements associated with their physical geographic locations on an array could indicate the summary of the physical reliability of the microarray. We introduced a novel way to formulate this index using a statistical analysis tool. Our approach regressed gene expression intensity measurements on a polynomial response surface of the microarray's Cartesian coordinates. We demonstrated this method using a fixed model and presented results from real and simulated datasets. CONCLUSION: We demonstrated the potential of such a quantitative metric for assessing the reliability of individual arrays. Moreover, we showed that this procedure can be incorporated into laboratory practice as a means to set quality control specifications and as a tool to determine whether an array has sufficient quality to be retained in terms of spatial correlation of gene expression measurements.

Algorithms↗
PubMed2006

The effect of assortative mating upon genetic association studies: spurious associations and population substructure in the absence of admixture.

Spurious associations due to confounding factors are an often cited and intensely debated concern for genetic association studies. Great attention has been focused upon the specific threat of confounding due to population stratification. This emphasis has spurred the development of many statistical genetic methods to detect and correct for the potentially confounding effects of admixture. Unfortunately, this emphasis on admixture has led some authors to suggest that if ethnically homogenous populations are used, spurious associations are unlikely to occur. We show that under small and realistic degrees of assortative mating over time, spurious associations arise even in ethnically homogeneous populations. We demonstrate that structured association and genomic control tests can, under certain conditions, correct for these spurious associations. We conclude that investigators should not assume spurious associations will not occur in association studies using ethnically homogenous populations and recommend the use of genomic control methods and/or family-based association tests within genetic association studies.

Chromosome Mapping↗
PubMed2006

Dietary restraint and disinhibition do not affect accuracy of 24-hour recall in a multiethnic population.

Some psychological predictors of eating behaviors have been shown to affect usefulness of methods for dietary assessment. Therefore, this study was conducted to determine the association of dietary restraint and disinhibition with dietary recall accuracy for total energy, fat, carbohydrate, and protein. In a cross-sectional study, data were obtained from 79 male and 71 female non-Hispanic whites and African-American volunteers. Participants selected and consumed all foods for a 1-day period under observation and actual intake was determined. The following day, each participant completed a telephone 24-hour recall using the US Department of Agriculture Multiple-Pass method to obtain recalled intake. The Eating Inventory, which measures dietary restraint and disinhibition, was administered prior to eating any food in the study. Repeated measures analyses of variance were used to determine if dietary restraint or disinhibition were independent predictors of recall accuracy. The mean (+/-standard deviation) age and body mass index of the participants was 43+/-12 years and 29+/-5.5 (calculated as kg/m2), respectively. On average, men overreported intake of energy by 265 kcal and women by 250 kcal; both groups also overreported intake of protein, carbohydrate, and fat. When controlling for body mass index, sex, and race, restraint was a significant independent predictor of energy intake (P=0.004) and negatively correlated with energy intake (r=-0.23, P<0.001). Unlike intake of carbohydrate or protein, fat intake was significantly and negatively associated with dietary restraint (P<0.001; r=-0.3). Dietary restraint did not affect accuracy of recall of intake of energy, fat, carbohydrate, or protein, but was significantly associated with intake of energy and fat. Disinhibition was not related to intake or accuracy. Dietetics professionals should consider dietary restraint a possible reason for a lower than expected estimate of energy intake when using 24-hour recalls.

Adult↗
PubMed2006

Optimal allocation of replicates for measurement evaluation studies.

Optimal experimental design is important for the efficient use of modern high-throughput technologies such as microarrays and proteomics. Multiple factors including the reliability of measurement system, which itself must be estimated from prior experimental work, could influence design decisions. In this study, we describe how the optimal number of replicate measures (technical replicates) for each biological sample (biological replicate) can be determined. Different allocations of biological and technical replicates were evaluated by minimizing the variance of the ratio of technical variance (measurement error) to the total variance (sum of sampling error and measurement error). We demonstrate that if the number of biological replicates and the number of technical replicates per biological sample are variable, while the total number of available measures is fixed, then the optimal allocation of replicates for measurement evaluation experiments requires two technical replicates for each biological replicate. Therefore, it is recommended to use two technical replicates for each biological replicate if the goal is to evaluate the reproducibility of measurements.

Computational Biology↗
PubMed2006

Bayesian analyses of multiple epistatic QTL models for body weight and body composition in mice.

To comprehensively investigate the genetic architecture of growth and obesity, we performed Bayesian analyses of multiple epistatic quantitative trait locus (QTL) models for body weights at five ages (12 days, 3, 6, 9 and 12 weeks) and body composition traits (weights of two fat pads and five organs) in mice produced from a cross of the F1 between M16i (selected for rapid growth rate) and CAST/Ei (wild-derived strain of small and lean mice) back to M16i. Bayesian model selection revealed a temporally regulated network of multiple QTL for body weight, involving both strong main effects and epistatic effects. No QTL had strong support for both early and late growth, although overlapping combinations of main and epistatic effects were observed at adjacent ages. Most main effects and epistatic interactions had an opposite effect on early and late growth. The contribution of epistasis was more pronounced for body weights at older ages. Body composition traits were also influenced by an interacting network of multiple QTLs. Several main and epistatic effects were shared by the body composition and body weight traits, suggesting that pleiotropy plays an important role in growth and obesity.

Animals↗
PubMed2006

Microarray data analysis: from disarray to consolidation and consensus.

In just a few years, microarrays have gone from obscurity to being almost ubiquitous in biological research. At the same time, the statistical methodology for microarray analysis has progressed from simple visual assessments of results to a weekly deluge of papers that describe purportedly novel algorithms for analysing changes in gene expression. Although the many procedures that are available might be bewildering to biologists who wish to apply them, statistical geneticists are recognizing commonalities among the different methods. Many are special cases of more general models, and points of consensus are emerging about the general approaches that warrant use and elaboration.

Algorithms↗
PubMed2006

No evidence for a major role of polymorphisms during bupropion treatment.

This study evaluated the ability of polymorphisms in five candidate genes to predict weight gain among patients taking bupropion or placebo in a smoking cessation trial. Five hundred fifty-three smokers were enrolled into a randomized double-blind, placebo-controlled trial and followed for 12 months. Five candidate genes [DRD2 Taq1 (rs1800497), DRD2-141 (rs1799732), C957T (rs6277), COMT (rs4818), and SLC6A3] were genotyped. Weights at baseline, at end of treatment, and after 6 and 12 months of follow-up were self-reported. Smoking abstinence at each endpoint was self-reported and confirmed biochemically. A self-reported average weight gain after 12 months of 1.1 +/- 6.0 kg (mean +/- standard deviation) in the bupropion group and 1.8 +/- 4.8 kg in the placebo group was noted. For subjects with biochemically confirmed abstinence from smoking, the HL genotype (alleles coding Val at codon 108 are denoted as H, and those coding Met are denoted as L) at the COMT locus and A1A1 genotype at the DRD2 Taq1 locus were associated with less weight gain at the end of treatment. The TC genotype at the C957T locus was associated with increased weight gain at 6 months of follow-up. However, no polymorphisms or their interactions with bupropion consistently and significantly predicted baseline BMI or weight change during treatment for all study subjects. Overall, our results do not support a major role for these five candidate genes in weight gain after smoking cessation.

Adult↗
PubMed2006

Obesity: person and population.

Obesity is a problem among all ages and races, in both genders, and across all socioeconomic classes, and the prevalence of obesity has been increasing. Efforts are being made to combat the increasing prevalence, but only modest success has been achieved. Obesity is affected by multiple factors. There are genetic and environmental components involved, yet pinpointing specific genetic and environmental influences has been difficult. Development of treatments has ranged from pharmaceuticals to behavioral modification. Extant treatments aimed at the individual and administered for relatively brief portions of the lifespan have shown only modest results. If we are to make pervasive and enduring changes to population adiposity levels, it is likely that we will need to make pervasive and enduring changes to the ways in which we live across the lifespan.

Adipose Tissue↗