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Charles Kooperberg

Publications and source records attributed to Charles Kooperberg.

At least 37 records · Page 2Linked to original sources
PubMed2006

Assessment of the relationship between pre-chip and post-chip quality measures for Affymetrix GeneChip expression data.

BACKGROUND: Gene expression microarray experiments are expensive to conduct and guidelines for acceptable quality control at intermediate steps before and after the samples are hybridised to chips are vague. We conducted an experiment hybridising RNA from human brain to 117 U133A Affymetrix GeneChips and used these data to explore the relationship between 4 pre-chip variables and 22 post-chip outcomes and quality control measures. RESULTS: We found that the pre-chip variables were significantly correlated with each other but that this correlation was strongest between measures of RNA quality and cRNA yield. Post-mortem interval was negatively correlated with these variables. Four principal components, reflecting array outliers, array adjustment, hybridisation noise and RNA integrity, explain about 75% of the total post-chip measure variability. Two significant canonical correlations existed between the pre-chip and post-chip variables, derived from MAS 5.0, dChip and the Bioconductor packages affy and affyPLM. The strongest (CANCOR 0.838, p < 0.0001) correlated RNA integrity and yield with post chip quality control (QC) measures indexing 3'/5' RNA ratios, bias or scaling of the chip and scaling of the variability of the signal across the chip. Post-mortem interval was relatively unimportant. We also found that the RNA integrity number (RIN) could be moderately well predicted by post-chip measures B_ACTIN35, GAPDH35 and SF. CONCLUSION: We have found that the post-chip variables having the strongest association with quantities measurable before hybridisation are those reflecting RNA integrity. Other aspects of quality, such as noise measures (reflecting the execution of the assay) or measures reflecting data quality (outlier status and array adjustment variables) are not well predicted by the variables we were able to determine ahead of time. There could be other variables measurable pre-hybridisation which may be better associated with expression data quality measures. Uncovering such connections could create savings on costly microarray experiments by eliminating poor samples before hybridisation.

Artifacts↗
PubMed2006

Regional and cellular gene expression changes in human Huntington's disease brain.

Huntington's disease (HD) pathology is well understood at a histological level but a comprehensive molecular analysis of the effect of the disease in the human brain has not previously been available. To elucidate the molecular phenotype of HD on a genome-wide scale, we compared mRNA profiles from 44 human HD brains with those from 36 unaffected controls using microarray analysis. Four brain regions were analyzed: caudate nucleus, cerebellum, prefrontal association cortex [Brodmann's area 9 (BA9)] and motor cortex [Brodmann's area 4 (BA4)]. The greatest number and magnitude of differentially expressed mRNAs were detected in the caudate nucleus, followed by motor cortex, then cerebellum. Thus, the molecular phenotype of HD generally parallels established neuropathology. Surprisingly, no mRNA changes were detected in prefrontal association cortex, thereby revealing subtleties of pathology not previously disclosed by histological methods. To establish that the observed changes were not simply the result of cell loss, we examined mRNA levels in laser-capture microdissected neurons from Grade 1 HD caudate compared to control. These analyses confirmed changes in expression seen in tissue homogenates; we thus conclude that mRNA changes are not attributable to cell loss alone. These data from bona fide HD brains comprise an important reference for hypotheses related to HD and other neurodegenerative diseases.

Adult↗
PubMed2006

Electrocardiographic abnormalities that predict coronary heart disease events and mortality in postmenopausal women: the Women's Health Initiative.

BACKGROUND: Information is limited about the independent prognostic value of repolarization abnormalities in women. METHODS AND RESULTS: We evaluated hazard ratios for ECG variables for combined fatal and nonfatal coronary heart disease (CHD) events and for CHD mortality using Cox regression in 38,283 Women's Health Initiative (WHI) participants during up to 9.2 years of follow-up. All risk models were adjusted for demographic, clinical, and therapeutic variables. Evaluated as single ECG variables, wide QRS/T angle and ECG-demonstrated myocardial infarction (ECG-MI) were the strongest predictors of CHD events, with hazard ratios (95% CI) of 1.90 (1.50 to 2.42) and 1.62 (1.29 to 2.03), respectively. Six other repolarization variables were also significant, strong predictors of CHD events. Wide QRS/T angle, ECG-MI, and QT prolongation appeared as dominant predictors when evaluated simultaneously with other ECG variables in a multiadjusted risk model. QRS/T angle, ECG-MI, and high QRS nondipolar voltage were the strongest predictors of CHD mortality, with hazard ratios of 2.70, 2.41, and 2.18, respectively. The risk increase ranged from 63% to 95% for the other 4 significant predictors. Five ECG abnormalities were identified as dominant mortality risk predictors: wide QRS/T angle, ECG-MI, high QRS nondipolar voltage, reduced heart rate variability, and QT prolongation (in the cardiovascular disease-free group only). CONCLUSIONS: Ventricular repolarization abnormalities in postmenopausal women are as important predictors of CHD events and CHD mortality as ECG-MI and other QRS abnormalities. Repolarization variables and QRS nondipolar voltage warrant attention in future investigations.

Aged↗
PubMed2006

Electrocardiographic predictors of incident congestive heart failure and all-cause mortality in postmenopausal women: the Women's Health Initiative.

BACKGROUND: Information is limited about ECG predictors of the risk of incident congestive heart failure (CHF), particularly in women without overt manifestations of cardiovascular disease (CVD). METHODS AND RESULTS: We evaluated hazard ratios for incident CHF and all-cause mortality using Cox regression in 38,283 participants of the Women's Health Initiative (WHI) during a 9-year follow-up. All risk models were adjusted for demographic and available clinical and therapeutic variables (multivariable-adjusted models). A backward selection procedure was used to identify dominant predictors among those that were significant as individual ECG predictors. Eleven ECG variables were significant predictors of incident CHF, with none of them having a significant interaction with baseline CVD status. From 6 dominant ECG predictors, wide QRS/T angle had a nearly 3-fold increased risk in multivariable-adjusted single ECG variable models. Two other repolarization variables, STV5 depression and high TV1 amplitude, and 2 QRS-related variables, QRS non-dipolar voltage and myocardial infarction (MI) by ECG, were all associated with &2-fold increase of incident CHF risk. Overall, 11 of the 12 ECG variables were significant predictors of all-cause mortality. Four variables had a significant interaction with CVD status requiring stratification. Three among these 4 were strong, dominant predictors in the CVD group: ECG MI, wide QRS/T angle, and low TV5 amplitude had risk increase from >2-fold to 3-fold, with considerably lower risks in the CVD-free group. CONCLUSIONS: Several repolarization variables in postmenopausal women are predictors of the risk of incident CHF and all-cause mortality as important as old ECG MI.

Age Distribution↗
PubMed2006

Global and gene-specific analyses show distinct roles for Myod and Myog at a common set of promoters.

We used a combination of genome-wide and promoter-specific DNA binding and expression analyses to assess the functional roles of Myod and Myog in regulating the program of skeletal muscle gene expression. Our findings indicate that Myod and Myog have distinct regulatory roles at a similar set of target genes. At genes expressed throughout the program of myogenic differentiation, Myod can bind and recruit histone acetyltransferases. At early targets, Myod is sufficient for near full expression, whereas, at late expressed genes, Myod initiates regional histone modification but is not sufficient for gene expression. At these late genes, Myog does not bind efficiently without Myod; however, transcriptional activation requires the combined activity of Myod and Myog. Therefore, the role of Myog in mediating terminal differentiation is, in part, to enhance expression of a subset of genes previously initiated by Myod.

Acetylation↗
PubMed2006

Imputation methods to improve inference in SNP association studies.

Missing single nucleotide polymorphisms (SNPs) are quite common in genetic association studies. Subjects with missing SNPs are often discarded in analyses, which may seriously undermine the inference of SNP-disease association. In this article, we develop two haplotype-based imputation approaches and one tree-based imputation approach for association studies. The emphasis is to evaluate the impact of imputation on parameter estimation, compared to the standard practice of ignoring missing data. Haplotype-based approaches build on haplotype reconstruction by the expectation-maximization (EM) algorithm or a weighted EM (WEM) algorithm, depending on whether case-control status is taken into account. The tree-based approach uses a Gibbs sampler to iteratively sample from a full conditional distribution, which is obtained from the classification and regression tree (CART) algorithm. We employ a standard multiple imputation procedure to account for the uncertainty of imputation. We apply the methods to simulated data as well as a case-control study on developmental dyslexia. Our results suggest that imputation generally improves efficiency over the standard practice of ignoring missing data. The tree-based approach performs comparably well as haplotype-based approaches, but the former has a computational advantage. The WEM approach yields the smallest bias at a price of increased variance.

Algorithms↗
PubMed2005

Significance testing for small microarray experiments.

Which significance test is carried out when the number of repeats is small in microarray experiments can dramatically influence the results. When in two sample comparisons both conditions have fewer than, say, five repeats traditional test statistics require extreme results, before a gene is considered statistically significant differentially expressed after a multiple comparisons correction. In the literature many approaches to circumvent this problem have been proposed. Some of these proposals use (empirical) Bayes arguments to moderate the variance estimates for individual genes. Other proposals try to stabilize these variance estimate by combining groups of genes or similar experiments. In this paper we compare several of these approaches, both on data sets where both experimental conditions are the same, and thus few statistically significant differentially expressed genes should be identified, and on experiments where both conditions do differ. This allows us to identify which approaches are most powerful without identifying many false positives. We conclude that after balancing the numbers of false positives and true positives an empirical Bayes approach and an approach which combines experiments perform best. Standard t-tests are inferior and offer almost no power when the sample size is small.

Animals↗
PubMed2005

Extreme regression.

We develop a new method for describing patient characteristics associated with extreme good or poor outcome. We address the problem with a regression model composed of extrema (maximum and minimum) functions of the predictor variables. This class of models allows for simple regression function inversion and results in level sets of the regression function which can be expressed as interpretable Boolean combinations of decisions based on individual predictors. We develop an estimation algorithm and present clinical applications to symptoms data for patients with Hodgkin's disease and survival data for patients with multiple myeloma.

Algorithms↗
PubMed2005

Identifying target populations for screening or not screening using logic regression.

Colorectal cancer remains a significant public health concern despite the fact that effective screening procedures exist and that the disease is treatable when detected at early stages. Numerous risk factors for colon cancer have been identified, but none are very predictive alone. We sought to determine whether there are certain combinations of risk factors that distinguish well between cases and controls, and that could be used to identify subjects at particularly high or low risk of the disease to target screening. Using data from the Seattle site of the Colorectal Cancer Family Registry, we fit logic regression models to combine risk factor information. Logic regression is a methodology that identifies subsets of the population, described by Boolean combinations of binary coded risk factors. This method is well suited to situations in which interactions between many variables result in differences in disease risk. We found that neither the logic regression models nor stepwise logistic regression models fit for comparison resulted in criteria that could be used to direct subjects to screening. However, we believe that our novel statistical approach could be useful in settings where risk factors do discriminate between cases and controls, and illustrate this with a simulated data set.

Adult↗
PubMed2005

Gene expression in Huntington's disease skeletal muscle: a potential biomarker.

Huntington's disease (HD) is an incurable and fatal neurodegenerative disorder. Improvements in the objective measurement of HD will lead to more efficient clinical trials and earlier therapeutic intervention. We hypothesized that abnormalities seen in the R6/2 mouse, a greatly accelerated HD model, might highlight subtle phenotypes in other mouse models and human HD. In this paper, we identify common gene expression changes in skeletal muscle from R6/2 mice, Hdh(CAG(150)) homozygous knock-in mice and HD patients. This HD-triggered gene expression phenotype is consistent with the beginnings of a transition from fast-twitch to slow-twitch muscle fiber types. Metabolic adaptations similar to those induced by diabetes or fasting are also present but neither metabolic disorder can explain the full phenotype of HD muscle. The HD-induced gene expression changes reflect disease progression. This raises the possibility that muscle gene expression may be used as an objective biomarker to complement clinical HD-rating systems. Furthermore, an understanding of the molecular basis of muscle dysfunction in HD should provide insight into mechanisms involved in neuronal abnormalities and neurodegeneration.

Animals↗
PubMed2005

Identifying interacting SNPs using Monte Carlo logic regression.

Interactions are frequently at the center of interest in single-nucleotide polymorphism (SNP) association studies. When interacting SNPs are in the same gene or in genes that are close in sequence, such interactions may suggest which haplotypes are associated with a disease. Interactions between unrelated SNPs may suggest genetic pathways. Unfortunately, data sets are often still too small to definitively determine whether interactions between SNPs occur. Also, competing sets of interactions could often be of equal interest. Here we propose Monte Carlo logic regression, an exploratory tool that combines Markov chain Monte Carlo and logic regression, an adaptive regression methodology that attempts to construct predictors as Boolean combinations of binary covariates such as SNPs. The goal of Monte Carlo logic regression is to generate a collection of (interactions of) SNPs that may be associated with a disease outcome, and that warrant further investigation. As such, the models that are fitted in the Markov chain are not combined into a single model, as is often done in Bayesian model averaging procedures. Instead, the most frequently occurring patterns in these models are tabulated. The method is applied to a study of heart disease with 779 participants and 89 SNPs. A simulation study is carried out to investigate the performance of the Monte Carlo logic regression approach.

Haplotypes↗
PubMed2005

Prior oral contraception and postmenopausal fracture: a Women's Health Initiative observational cohort study.

OBJECTIVE: To test for the possible association of past oral contraceptive (OC) use and incident fracture after menopause. DESIGN: A prospective cohort of 93,725 postmenopausal women. SETTING: Forty Women's Health Initiative (WHI) clinical centers across the United States. PATIENT(S): Ethnically diverse 93,725 volunteer postmenopausal women, 50 to 79 years old. INTERVENTION(S): None. MAIN OUTCOME MEASURE(S): The main outcome was self-reported incident first fracture assessed prospectively by annual questionnaire. RESULT(S): The adjusted relative hazard (HR) for fracture among past OC users was 1.07 (95% CI, 1.01-1.15). Among women without any postmenopausal hormone treatment, past OC use for < or =5 years led to an HR of 1.15 (95% CI, 1.04-1.27) and for past OC use >5 years led to an HR of 1.09 (95% CI, 0.97-1.23) compared with never users. CONCLUSION(S): This study does not support the idea that past OC use protects against later fracture.

Aged↗
PubMed2004

Comparison of self-report, hospital discharge codes, and adjudication of cardiovascular events in the Women's Health Initiative.

Limited information is available from large clinical investigations about the agreement among sources of diagnoses for endpoints. The authors used data from the Women's Health Initiative clinical trials and observational study from January 1994 to November 2000 to evaluate the agreement among self-report, hospital discharge codes, and two different levels of physician review of medical records for cardiovascular endpoints. For myocardial infarction, stroke, pulmonary embolism, and venous thrombosis, the agreement of hospital discharge codes or self-report with review by study physicians at clinical centers was substantial (kappa = 0.64-0.84). For coronary revascularization, agreement among these sources of information was substantial to almost perfect (kappa = 0.79-0.92), but for angina, congestive heart failure, and peripheral vascular disease, concordance was only fair to moderate (kappa = 0.37-0.56), indicating that these endpoints remain difficult to classify reliably. Agreement between physician adjudicators at clinical centers and central physician adjudicators was substantial to almost perfect (kappa = 0.67-0.94). The findings also suggest that, for the endpoint of myocardial infarction, physician review of events with hospital discharge codes for angina and congestive heart failure is an important source of validated events, and for stroke, review of all events with cerebrovascular codes is important.

Aged↗
PubMed2004

The histone modification pattern of active genes revealed through genome-wide chromatin analysis of a higher eukaryote.

The covalent modification of nucleosomal histones has emerged as a major determinant of chromatin structure and gene activity. To understand the interplay between various histone modifications, including acetylation and methylation, we performed a genome-wide chromatin structure analysis in a higher eukaryote. We found a binary pattern of histone modifications among euchromatic genes, with active genes being hyperacetylated for H3 and H4 and hypermethylated at Lys 4 and Lys 79 of H3, and inactive genes being hypomethylated and deacetylated at the same residues. Furthermore, the degree of modification correlates with the level of transcription, and modifications are largely restricted to transcribed regions, suggesting that their regulation is tightly linked to polymerase activity.

Acetylation↗
PubMed2003

Recreational physical activity and the risk of breast cancer in postmenopausal women: the Women's Health Initiative Cohort Study.

CONTEXT: Women who are physically active have a decreased risk for breast cancer, but the types, amounts, and timing of activity needed are unknown. OBJECTIVE: To prospectively examine the association between current and past recreational physical activity and incidence of breast cancer in postmenopausal women. DESIGN, SETTING, AND PATIENTS: Prospective cohort study in 74 171 women aged 50 to 79 years who were recruited by 40 US clinical centers from 1993 through 1998. MAIN OUTCOME MEASURE: Incident invasive and in situ breast cancer. RESULTS: We documented 1780 newly diagnosed cases of breast cancer over a mean follow-up of 4.7 years. Compared with less active women, women who engaged in regular strenuous physical activity at age 35 years had a 14% decreased risk of breast cancer (relative risk [RR], 0.86; 95% confidence interval [CI], 0.78-0.95). Similar but attenuated findings were observed for strenuous physical activity at ages 18 years and 50 years. An increasing total current physical activity score was associated with a reduced risk for breast cancer (P =.03 for trend). Women who engaged in the equivalent of 1.25 to 2.5 hours per week of brisk walking had an 18% decreased risk of breast cancer (RR, 0.82; 95% CI, 0.68-0.97) compared with inactive women. Slightly greater reduction in risk was observed for women who engaged in the equivalent of 10 hours or more per week of brisk walking. The effect of exercise was most pronounced in women in the lowest tertile of body mass index (BMI) (<24.1), but also was observed for women in the middle tertile of BMI (24.1-28.4). CONCLUSIONS: These data suggest that increased physical activity is associated with reduced risk for breast cancer in postmenopausal women, longer duration provides most benefit, and that such activity need not be strenuous.

Aged↗
PubMed2003

Effect of estrogen plus progestin on stroke in postmenopausal women: the Women's Health Initiative: a randomized trial.

CONTEXT: The Women's Health Initiative (WHI) trial of estrogen plus progestin was stopped early because of adverse effects, including an increased risk of stroke in the estrogen plus progestin group. OBJECTIVE: To assess the effect of estrogen plus progestin on ischemic and hemorrhagic stroke and in subgroups, and to determine whether the effect of estrogen plus progestin was modified by baseline levels of blood biomarkers. DESIGN: Multicenter double-blind, placebo-controlled, randomized clinical trial involving 16 608 women aged 50 through 79 years with an average follow-up of 5.6 years. Baseline levels of blood-based markers of inflammation, thrombosis, and lipid levels were measured in the first 140 centrally confirmed stroke cases and 513 controls. INTERVENTIONS: Participants received 0.625 mg/d of conjugated equine estrogen plus 2.5 mg/d of medroxyprogesterone acetate (n = 8506) or placebo (n = 8102). MAIN OUTCOME MEASURES: Overall strokes and stroke subtype and severity were centrally adjudicated by stroke neurologists. RESULTS: One hundred fifty-one patients (1.8%) in the estrogen plus progestin and 107 (1.3%) in the placebo groups had strokes. Overall 79.8% of strokes were ischemic. For combined ischemic and hemorrhagic strokes, the intention-to-treat hazard ratio (HR) for estrogen plus progestin vs placebo was 1.31 (95% confidence interval [CI], 1.02-1.68); with adjustment for adherence, the HR was 1.50 (95% CI, 1.08-2.08). The HR for ischemic stroke was 1.44 (95% CI, 1.09-1.90) and for hemorrhagic stroke, 0.82 (95% CI, 0.43-1.56). Point estimates of the HRs indicate that excess risk of all stroke was apparent in all age groups, in all categories of baseline stroke risk, and in women with and without hypertension, prior history of cardiovascular disease, use of hormones, statins, or aspirin. Other risk factors for stroke, including smoking, blood pressure, diabetes, lower use of vitamin C supplements, blood-based biomarkers of inflammation, higher white blood cell count, and higher hematocrit levels did not modify the effect of estrogen plus progestin on stroke risk. CONCLUSIONS: Estrogen plus progestin increases the risk of ischemic stroke in generally healthy postmenopausal women. Excess risk for all strokes attributed to estrogen plus progestin appeared to be present in all subgroups of women examined.

Aged↗
PubMed2003

Directed indices for exploring gene expression data.

MOTIVATION: Large expression studies with clinical outcome data are becoming available for analysis. An important goal is to identify genes or clusters of genes where expression is related to patient outcome. While clustering methods are useful data exploration tools, they do not directly allow one to relate the expression data to clinical outcome. Alternatively, methods which rank genes based on their univariate significance do not incorporate gene function or relationships to genes that have been previously identified. In addition, after sifting through potentially thousands of genes, summary estimates (e.g. regression coefficients or error rates) algorithms should address the potentially large bias introduced by gene selection. RESULTS: We developed a gene index technique that generalizes methods that rank genes by their univariate associations to patient outcome. Genes are ordered based on simultaneously linking their expression both to patient outcome and to a specific gene of interest. The technique can also be used to suggest profiles of gene expression related to patient outcome. A cross-validation method is shown to be important for reducing bias due to adaptive gene selection. The methods are illustrated on a recently collected gene expression data set based on 160 patients with diffuse large cell lymphoma (DLCL).

Algorithms↗