ALFRED: an allele frequency database for diverse populations and DNA polymorphisms--an update

ALFRED (the ALelle FREquency Database) is designed to store and disseminate frequencies of alleles at human polymorphic sites for multiple populations, primarily for the population genetics and molecular anthropology communities. Currently ALFRED has information on over 180 polymorphic sites for more than 70 populations. Since our initial release of the database we have focussed on increasing the quantity and quality of data, making reciprocal links between ALFRED and other related databases, and providing useful tools to make the data more comprehensible to the end user. ALFRED is accessible from the Kidd Lab home page (http://info.med.yale. edu/genetics/kkidd/) or from ALFRED directly (http://alfred.med.yale. edu/alfred/index.asp).     

ALFRED: An allele frequency database for anthropology

The deluge of data from the human genome project (HGP) presents new opportunities for molecular anthropologists to study human variation through the promise of vast numbers of new polymorphisms (e.g., single nucleotide polymorphisms or SNPs). Collecting the resulting data into a single, easily accessible resource will be important to facilitate this research. We created a prototype Web-accessible database named ALFRED (ALelle FREquency Database, http://alfred.med.yale.edu/alfred/) to store and make publicly available allele frequency data on diverse polymorphic sites for many populations. In constructing this database, we considered many different concerns relating to the types of information needed for anthropology, population genetics, molecular genetics, and statistics, as well as issues of data integrity and ease of access to data. We also developed links to other Web-based databases as well as procedures for others to make links to the data in ALFRED. Here we present an overview of the issues considered and provisional solutions, as well as an example of data already available. It is our hope that this database will be useful for research and teaching in a wide range of fields, and that colleagues from various fields will contribute to making ALFRED an important resource for many studies as yet unforeseen.     

ALFRED: an allele frequency database for diverse populations and DNA polymorphisms

We have developed a publicly accessible database (ALFRED, the ALlele FREquency Database) that catalogues allele frequency data for a wide range of population samples and DNA polymorphisms. This database is web-accessible through our laboratory (Kidd Lab) Web site: http://info.med.yale.edu/genetics/kkidd. ALFRED currently contains data on 60 populations and 156 genetic systems including single nucleotide polymorphisms (SNPs), short tandem repeat polymorphisms (STRPs), variable number of tandem repeats (VNTRs) and insertion-deletion polymorphisms. While data are not available for all population-DNA polymorphism combinations, over 2000 allele frequency tables have been entered. Our database is designed (i) to address our specific research requirements as well as broader scientific objectives; (ii) to allow researchers and interested educators to easily navigate and retrieve data of interest to them; and (iii) to integrate links to other related public databases such as dbSNP, GenBank and PubMed.     

BOOK NOTES

Beginning in Archaeology. KATHLEEN M. KENYON.
Understanding Heredity, An Introdzlction to Genetics. RICHARD B. GOLDSCHMIDT.
Nutrition and Climatic Stress (With Particzllar Reference to Man.) H. H. MITCHELL and MARJORIE EDMAN.
Making a Living in the Marbial Valley (Haiti). ALFRED M˜TRAUX, in collaboration with E. BERROUET and Dr. and Mrs. JEAN COMHAIRE-SYLVAIN.
The Haiti Pilot Project (Phase One) . (84 pp. UNESCO, Monographs on Fundamental Education
New Spain's Centzlry of Depression. WOODROW BORAH.
Piedras Negras Archaeology: Architecture. Part V, Sweathomes. LINTON SATTERTHWAITE
The Fossilization of Human Bone: Calcium, Phosphate, and Carbonate . S. F. COOK.
Notes on the Prehistoric Metallurgy of Copper and Bronze i n the Old World. H. H. COGHLAN.
The Greeks rad the Irrotionel. E. R. DODDS.
Human Communities, The City and Humun Ecology. ROBERT E. PARK.
American Society: A Sociological Interpretation. ROBIN M. WILLIAMS, JR.
Exchange of Persons: The Evolution of Cross-Cultural Education. GUY S. METRAUX.
Explicacidn del Reverso del Codex Vindobonensis. ALFONSO CASO.
Das System der Raumeinteilung in den Behausungen der nordeurasischen Volker.
Materials to the Knowledge of Eastern Turki. GIJNNAR JARRING.     

Gender Differences in Changes in Subcutaneous and Intra-abdominal Fat during Weight Reduction: An Ultrasound Study

WIRTH, ALFRED, AND BERIT STEINMETZ. Gender differences in changes in subcutaneous and intra-abdominal fat during weight reduction: an ultrasound study. Obes Res. 1998;6:393–399. Objective : In weight-reducing programs, men usually display greater improvement in metabolic risk factors than women. This gender difference may be related to enhanced weight and fat loss due to a greater energy deficit in men. To clarify the relationship between changes in metabolic profile, body fat composition, and weight loss, both sexes were studied under a regimen in which similar amounts of weight were lost. Research Methods and Procedures : A cross-sectional study using anthropometric (body mass index and waist-to-hip ratio), impedance (bioelectrical impedance analysis) and ultrasound measurement methods (thickness of subcutaneous fat layers, intra-abdominal sagittal diameter) were conducted. The metabolic risk profile was determined by measuring lipids, lipoproteins, and blood pressure. The weight loss program lasted 15 weeks: 3 weeks under controlled conditions in the hospital and 12 weeks on an ambulatory basis. Patients were instructed to follow a mixed diet. Calorie intake was restricted to 1500 kcal/day for the men and 1200 kcal/day for the women. Thirty-two subjects with obesity (16 men and 16 women), with a mean body mass index of 35 kg/m²—matched with regard to age, height, and body weight—took part in the study. Results : As expected, weight loss was similar for both sexes (?13.4 kg vs. ?12.8 kg). Also, body fat mass changed to the same extent in absolute and relative terms. The waist-to-hip ratio was identical before and after treatment in both sexes. The men lost more visceral fat than the women. This result is based on changes in intra-abdominal diameter as well as abdominal subcutaneous fat in relation to waist circumference. Changes in abdominal diameter were paralleled by reductions in triglycerides and increases in high-density lipoprotein-cholesterol. Subcutaneous fat loss was more pronounced in women than in men. Discussion : Where absolute and relative reductions in body weight and body fat are similar, men mobilize more intraabdominal fat than women, whereas women lose more subcutaneous fat. The greater reduction in intra-abdominal fat seen in men is accompanied by a more pronounced improvement in the metabolic risk profile. Therefore, greater improvement of risk factors in men is not only related to a greater negative energy balance, as shown in most studies, but is also sex-specific.     

Computing the maximum similarity bi-clusters of gene expression data

MOTIVATIONS: Bi-clustering is an important approach in microarray data analysis. The underlying bases for using bi-clustering in the analysis of gene expression data are (1) similar genes may exhibit similar behaviors only under a subset of conditions, not all conditions, (2) genes may participate in more than one function, resulting in one regulation pattern in one context and a different pattern in another. Using bi-clustering algorithms, one can obtain sets of genes that are co-regulated under subsets of conditions. RESULTS: We develop a polynomial time algorithm to find an optimal bi-cluster with the maximum similarity score. To our knowledge, this is the first formulation for bi-cluster problems that admits a polynomial time algorithm for optimal solutions. The algorithm works for a special case, where the bi-clusters are approximately squares. We then extend the algorithm to handle various kinds of other cases. Experiments on simulation data and real data show that the new algorithms outperform most of the existing methods in many cases. Our new algorithms have the following advantages: (1) no discretization procedure is required, (2) performs well for overlapping bi-clusters and (3) works well for additive bi-clusters. AVAILABILITY: The software is available at http://www.cs.cityu.edu.hk/~liuxw/msbe/help.html.     

Testing for linear trends in proportions using correlated otolaryngology or ophthalmology data

This paper is concerned with the issue of testing for trend with correlated binary data. We consider the problem where one has either one or two ears (or eyes) available for analysis at baseline and one wishes to look at changes over time in a dichotomous outcome taking into account the correlation between responses from two ears. A reparameterization of Rosner's (1982, Biometrics 38, 105-114) correlated binary data model is presented and applied to a test for trend where the stratifying variable is age (or any other subject-specific variable). Observed and expected values are calculated for the trend statistic separately for both unilateral and bilateral cases and are then summed to obtain an overall summary statistic. The proposed method is illustrated by a reanalysis of data presented in a published study of the efficacy of antibiotics for the treatment of otitis media.     

Uniparental disomy analysis in trios using genome-wide SNP array and whole-genome sequencing data imply segmental uniparental isodisomy in general populations

Whole chromosomal and segmental uniparental disomy (UPD) is one of the causes of imprinting disorder and other recessive disorders. Most investigations of UPD were performed only using cases with relevant phenotypic features and included few markers. However, the diagnosis of cases with segmental UPD requires a large number of molecular investigations. Currently, the accurate frequency of whole chromosomal and segmental UPD in a normal developing embryo is not well understood. Here, we present whole chromosome and segmental UPD analysis using single nucleotide polymorphism (SNP) microarray data of 173 mother-father-child trios (519 individuals) from six populations (including 170 HapMap trios). For two of these trios, we also investigated the possibility of shorter segmental UPD as a consequence of homologous recombination repair (HR) for DNA double strand breaks (DSBs) during the early developing stage using high-coverage whole-genome sequencing (WGS) data from 1000 Genomes Project. This could be overlooked by SNP microarray. We identified one obvious segmental paternal uniparental isodisomy (iUPD) (8.2 mega bases) in one HapMap sample from 173 trios using Genome-Wide Human SNP Array 6.0 (SNP6.0 array) data. However, we could not identify shorter segmental iUPD in two trios using WGS data. Finally, we estimated the rate of segmental UPD to be one per 173 births (0.578%) based on the UPD screening for 173 trios in general populations. Based on the autosomal chromosome pairs investigated, we estimate the rate of segmental UPD to be one per 3806 chromosome pairs (0.026%). These data imply the possibility of hidden segmental UPD in normal individuals.     

Lifetime energy budgets in mammals and birds.

1. Two data sets for standard energy metabolism (351 and 320 species, respectively) and one for maximal lifespan (494 species) in mammals have been assembled from the literature. 2. In addition smaller data sets of active (field) energy metabolism in mammals (36 species) and in birds (25 species) have been drawn on. 3. The products of the respective regression parameters as well as the products of energy metabolism and maximal lifespan in individual species have been computed in order to estimate lifetime energy metabolism in mammals generally and in various mammalian orders. 4. It is found that lifetime energy budgets in mammals generally, whether standard or active, very systematically with body mass with slopes between 0.87 and 0.93, significantly different from unity (P less than 0.001 or P less than 0.01). 5. In birds, lifetime energy budgets, whether standard or active, vary with slopes of 0.94 +/- 0.05 and 0.88 +/- 0.09, which are not significantly different from unity (P greater than 0.1). 6. In carnivores, artiodactyls, primates and bats the slopes for lifetime standard as well as lifetime active energy budgets are not significantly different from one in any of the investigated data sets. 7. In rodents the lifetime standard energy budgets have slope significantly different from one; in marsupials one data set for lifetime standard and the one for lifetime active energy budget lead to slopes significantly different from one. 8. It is concluded from this analysis that current data do not support the hypothesis that lifetime energy budgets, whether standard or active, vary as the first power of body mass in mammals generally.(ABSTRACT TRUNCATED AT 250 WORDS)     

Doubly robust estimation in missing data and causal inference models

The goal of this article is to construct doubly robust (DR) estimators in ignorable missing data and causal inference models. In a missing data model, an estimator is DR if it remains consistent when either (but not necessarily both) a model for the missingness mechanism or a model for the distribution of the complete data is correctly specified. Because with observational data one can never be sure that either a missingness model or a complete data model is correct, perhaps the best that can be hoped for is to find a DR estimator. DR estimators, in contrast to standard likelihood-based or (nonaugmented) inverse probability-weighted estimators, give the analyst two chances, instead of only one, to make a valid inference. In a causal inference model, an estimator is DR if it remains consistent when either a model for the treatment assignment mechanism or a model for the distribution of the counterfactual data is correctly specified. Because with observational data one can never be sure that a model for the treatment assignment mechanism or a model for the counterfactual data is correct, inference based on DR estimators should improve upon previous approaches. Indeed, we present the results of simulation studies which demonstrate that the finite sample performance of DR estimators is as impressive as theory would predict. The proposed method is applied to a cardiovascular clinical trial.     

Update on genotoxicity and carcinogenicity testing of 472 marketed pharmaceuticals

This survey is a compendium of genotoxicity and carcinogenicity information of 838 marketed drugs, whose expected clinical use is continuous for at least 6 months or intermittent over an extended period of time. Of these 838 drugs, 366 (43.7%) do not have retrievable genotoxicity or carcinogenicity data. The remaining 472 (56.3%) have at least one genotoxicity or carcinogenicity test result. Of the 449 drugs with at least one genotoxicity test result, 183 (40.8%) have at least one positive finding. Of the 338 drugs with at least one carcinogenicity test result, 160 (47.3%) have at least one positive result. Concerning the predictivity of genetic toxicology findings for long-term carcinogenesis assays, of the 315 drugs which have both genotoxicity and carcinogenicity data 116 (36.8%) are neither genotoxic nor carcinogenic, 50 (15.9%) are non-carcinogens which test positive in at least one genotoxicity assay, 75 (23.8%) are carcinogenic in at least one sex of mice or rats but test negative in genotoxicity assays, and 74 (23.5%) are both genotoxic and carcinogenic. Only 208 (24.8%) of the 838 drugs considered have all data required by current guidelines for testing of pharmaceuticals. However, it should be noted that a large fraction of the drugs considered were developed and marketed prior to the present regulatory climate. Although the laws do not require re-testing based on revised standards, in the absence of epidemiological studies excluding a carcinogenic risk to humans, a re-evalutation would be appropriate.     

Graph-based consensus clustering for class discovery from gene expression data

MOTIVATION: Consensus clustering, also known as cluster ensemble, is one of the important techniques for microarray data analysis, and is particularly useful for class discovery from microarray data. Compared with traditional clustering algorithms, consensus clustering approaches have the ability to integrate multiple partitions from different cluster solutions to improve the robustness, stability, scalability and parallelization of the clustering algorithms. By consensus clustering, one can discover the underlying classes of the samples in gene expression data. RESULTS: In addition to exploring a graph-based consensus clustering (GCC) algorithm to estimate the underlying classes of the samples in microarray data, we also design a new validation index to determine the number of classes in microarray data. To our knowledge, this is the first time in which GCC is applied to class discovery for microarray data. Given a pre specified maximum number of classes (denoted as K(max) in this article), our algorithm can discover the true number of classes for the samples in microarray data according to a new cluster validation index called the Modified Rand Index. Experiments on gene expression data indicate that our new algorithm can (i) outperform most of the existing algorithms, (ii) identify the number of classes correctly in real cancer datasets, and (iii) discover the classes of samples with biological meaning. AVAILABILITY: Matlab source code for the GCC algorithm is available upon request from Zhiwen Yu.     

Effects of stratification in the analysis of affected-sib-pair data: benefits and costs

The benefits and costs of stratification of affected-sib-pair (ASP) data were examined in three situations: (1) when there is no difference in identity-by-descent (IBD) allele sharing between stratified and unstratified ASP data sets; (2) when there is an increase in IBD allele sharing in one of the stratified groups; and (3) when the data are stratified on the basis of IBD allele-sharing status at one locus, and the stratified ASPs are then analyzed for linkage at a second locus. When there is no difference in IBD sharing between strata, a penalty is always paid for stratifying the data. The loss of power to detect linkage in the stratified ASP data sets is the result of multiple testing and the smaller sample size within individual strata. In the case in which etiologic heterogeneity (i.e., severity of phenotype, age at onset) represents genetic heterogeneity, the power to detect linkage can be increased by stratifying the ASP data. This benefit is obtained when there is sufficient IBD allele sharing and sample sizes. Once linkage has been established for a given locus, data can be stratified on the basis of IBD status at this locus and can be tested for linkage at a second locus. When the relative risk is in the vicinity of 1, the power to detect linkage at the second locus is always greater for the unstratified ASP data set. Even for values of the relative risk that diverge sufficiently from 1, with adequate sample sizes and IBD allele sharing, the benefits of stratifying ASP data are minimal.     

Input data quality control for NDNQI national comparative statistics and quarterly reports: A contrast of three robust scale estimators for multiple outlier detection

ABSTRACT: BACKGROUND: To evaluate institutional nursing care performance in the context of national comparative statistics (benchmarks), approximately one in every three major healthcare institutions (over 1,800 hospitals) across the United States, have joined the National Database for Nursing Quality Indicators[REGISTERED SIGN] (NDNQI[REGISTERED SIGN]). With over 18,000 hospital units contributing data for nearly 200 quantitative measures at present, a reliable and efficient input data screening for all quantitative measures for data quality control is critical to the integrity, validity, and on-time delivery of NDNQI reports. METHODS: With Monte Carlo simulation and quantitative NDNQI indicator examples, we compared two ad-hoc methods using robust scale estimators, Inter Quartile Range (IQR) and Median Absolute Deviation from the Median (MAD), to the classic, theoretically-based Minimum Covariance Determinant (FAST-MCD) approach, for initial univariate outlier detection. RESULTS: While the theoretically based FAST-MCD used in one dimension can be sensitive and is better suited for identifying groups of outliers because of its high breakdown point, the ad-hoc IQR and MAD approaches are fast, easy to implement, and could be more robust and efficient, depending on the distributional property of the underlying measure of interest. CONCLUSION: With highly skewed distributions for most NDNQI indicators within a short data screen window, the FAST-MCD approach, when used in one dimensional raw data setting, could overestimate the false alarm rates for potential outliers than the IQR and MAD with the same pre-set of critical value, thus, overburden data quality control at both the data entry and administrative ends in our setting.     

An Analytic Framework for Aligning Observational and Randomized Trial Data: Application to Postmenopausal Hormone Therapy and Coronary Heart Disease

We describe a conceptual analytic framework for aligning observational and randomized controlled trial (RCT) data. The framework allows one to (1) use observational data to estimate treatment effects comparable to their RCT counterparts, (2) properly include early events that occur soon after treatment initiation in the analysis of observational data, (3) estimate various treatment effects that are of clinical and scientific relevance while appropriately adjusting for time-varying confounders in both the RCT and observational analyses, (4) assess the generalizability of RCT findings in the more diverse populations generally found in the observational data, and (5) combine both types of data to study associations that cannot be addressed by one study or a single data set. We describe the theoretical application of this framework to the Women’s Health Initiative data to examine the relation between postmenopausal hormone therapy and coronary heart disease. The analytic framework can be tailored to specific exposure-outcome associations and data sources, and may be refined as more is learned about its strengths and limitations.     

Nonparametric models and methods for designs with dependent censored data: part I

We consider a nonparametric (NP) approach to the analysis of repeated measures designs with censored data. Using the NP model of Akritas and Arnold (1994, Journal of the American Statistical Association 89, 336-343) for marginal distributions, we present test procedures for the NP hypotheses of no main effects, no interaction, and no simple effects. This extends the existing NP methodology for such designs (Wei and Lachin, 1984, Journal of the American Statistical Association 79, 653-661). The procedures do not require any modeling assumptions and should be useful in cases where the assumptions of proportional hazards or location shift fail to be satisfied. The large-sample distribution of the test statistics is based on an i.i.d. representation for Kaplan-Meier integrals. The testing procedures apply also to ordinal data and to data with ties. Useful small-sample approximations are presented, and their performance is examined in a simulation study. Finally, the methodology is illustrated with two real life examples, one with censored and one with missing data. It is indicated that one of the data sets does not conform to any set of assumptions underlying the available methods and also that the present method provides a useful additional analysis even when data sets conform to modeling assumptions.     

Characteristics of replicated single-nucleotide polymorphism genotypes from COGA: Affymetrix and Center for Inherited Disease Research

Genetic Analysis Workshop 14 provided re-genotyped single-nucleotide polymorphism (SNP) data. Specifically, both Center for Inherited Disease Research (CIDR) and Affymetrix genotyped the same 11,560 SNPs from the Affymetrix GeneChip Mapping 10K Array marker set on the same 184 individuals from the Collaborative Study on the Genetics of Alcoholism database. While the inconsistency rate between CIDR and Affymetrix (two different genotypes for the same subject) was low (0.2%), the non-replication rate (two different genotypes for the same subject or one identified genotype and one missing genotype) was substantial (9.5%). The missing data could be from no-call regions, which is inconsistent with recent recommendations about the use of no-call regions in association tests. In addition, no-call regions would suggest that the actual inconsistency rate is higher than reported. A high inconsistency rate has significant impact on power in related hypothesis tests. In addition, the data are consistent with assumptions made in a recently proposed likelihood ratio test of association for re-genotyped data.     

Issues regarding artificial neural network modeling for reactors and fermenters

In recent years researchers in many areas have used artificial neural networks (ANNs) to model a variety of physical relationships. While in many cases this selection appears sound and reasonable, one must remember than ANN modeling is an empirical modeling technique (based on data) and is subject to the limitations of such techniques. Poor prediction occurs when the training data set does not contain adequate "information" to model a dynamic process. Using data from a simulated continuous-stirred tank reactor, this paper illustrates four scenarios: (1) steady state, (2) large process time constant, (3) infrequent sampling, and (4) variable sampling rate. The first scenario is typical of simulation studies while the other three incorporate attributes found in real plant data. For the cases in which ANNs predicted well, linear regression (LR), one of the oldest empirical modeling techniques, predicted equally well, and when LR failed to accurately model/predict the data, ANNs predicted poorly. Since real plant data would resemble a combination of situations (2), (3), and (4), it is important to understand that empirical models are not necessarily appropriate for predictively modeling dynamic processes in practice.     

Genotoxicity and carcinogenicity studies of antihypertensive agents

This survey is a compendium of genotoxicity and carcinogenicity information of antihypertensive drugs. Data from 164 marketed drugs were collected. Of the 164 drugs, 65 (39.6%) had no retrievable genotoxicity or carcinogenicity data; this group was comprised largely of drugs marketed in a limited number of countries. The remaining 99 (60.4%) had at least one genotoxicity or carcinogenicity test result. Of these 99, 48 (48.5%) had at least one positive finding: 32 tested positive in at least one genotoxicity assay, 26 in at least one carcinogenicity assay, and 10 gave a positive result in both at least one genotoxicity assay and at least one carcinogenicity assay. In terms of correlation between results of the various genotoxicity assays and absence of carcinogenic activity in both mice and rats 2 of 44 non-carcinogenic drugs tested positive in the in vitro bacterial mutagenesis assay, 2 of 9 tested positive in the mouse lymphoma assay, none of 14 tested positive for gene mutation at the hprt locus, 5 of 25 tested positive in in vitro cytogenetic assays, none of 31 in in vivo cytogenetic assays, and none of 14 in inducing DNA damage and/or repair in in vitro and/or in vivo assays. Concerning the predictivity of genetic toxicology findings for long-term carcinogenesis assays, 75 drugs had both genotoxicity and carcinogenicity data; of these 37 (49.3%) were neither genotoxic nor carcinogenic, 14 (18.7%) were non-carcinogens which tested positive in at least one genotoxicity assay, 14 (18.7%) were carcinogenic in at least one sex of mice or rats but tested negative in genotoxicity assays, and 10 (13.3%) were both genotoxic and carcinogenic. Only 42 of the 164 marketed antihypertensives (25.6%) had all data required by the guidelines for testing of pharmaceuticals.