bluuebunny/crossref_metadata_2025 huggingface dataset:
The National Heart, Lung, and Blood Institute Pediatric Circulatory Support Program
Authors: Baldwin J. Timothy , Borovetz Harvey S. , Duncan Brian W. , Gartner Mark J. , Jarvik Robert K. , Weiss William J. , Hoke Tracey R.
Published: January 2006

Options for the circulatory support of pediatric patients under the age of 5 years are currently limited to short-term extracorporeal devices, the use of which is often complicated by infection, bleeding, and thromboembolism. Recognizing this void, the National Heart, Lung, and Blood Institute solicited proposals for the development of novel circulatory support systems for infants and children from 2 to 25 kg with congenital or acquired cardiovascular disease. Five contracts were awarded to develop a family of devices that includes (1) an implantable mixed-flow ventricular assist device designed specifically for patients up to 2 years of age, (2) another mixed-flow ventricular assist device that can be implanted intravascularly or extravascularly depending on patient size, (3) compact integrated pediatric cardiopulmonary assist systems, (4) apically implanted axial-flow ventricular assist devices, and (5) pulsatile-flow ventricular assist devices. The common objective for these devices is to reliably provide circulatory support for infants and children while minimizing risks related to infection, bleeding, and thromboembolism. The devices are expected to be ready for clinical studies at the conclusion of the awards in 2009.
Southern High-Latitude Ensemble Data Assimilation in the Antarctic Mesoscale Prediction System
Authors: Barker D. M.
Published: January 2006

Ensemble data assimilation systems incorporate observations into numerical models via solution of the Kalman filter update equations, and estimates of forecast error covariances derived from ensembles of model integrations. In this paper, a particular algorithm, the ensemble square root filter (EnSRF), is tested in a limited-area, polar numerical weather prediction (NWP) model: the Antarctic Mesoscale Prediction System (AMPS).

For application in the real-time AMPS, the number of model integrations that can be run to provide forecast error covariances is limited, resulting in an ensemble sampling error that degrades the analysis fit to observations. In this work, multivariate, climatologically plausible forecast error covariances are specified via averaged forecast difference statistics. Ensemble representations of the “true” forecast errors, created using randomized control variables of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) three-dimensional variational (3DVAR) data assimilation system, are then used to assess the dependence of sampling error on ensemble size, data density, and localization of covariances using simulated observation networks. Results highlight the detrimental impact of ensemble sampling error on the analysis increment structure of correlated, but unobserved fields—an issue not addressed by the spatial covariance localization techniques used to date.

A 12-hourly cycling EnSRF/AMPS assimilation/forecast system is tested for a two-week period in December 2002 using real, conventional (surface, rawinsonde, satellite retrieval) observations. The dependence of forecast scores on methods used to maintain ensemble spread and the inclusion of perturbations to lateral boundary conditions are studied.
MM5 Contrail Forecasting in Alaska
Authors: Stuefer Martin , Meng Xiande , Wendler Gerd
Published: January 2006

The fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) is being used for forecasting the atmospheric layers of aircraft condensation trail (contrail) formation. Contrail forecasts are based on a conventional algorithm describing the adiabatic mixing of aircraft exhaust with environmental air. Algorithm input data are MM5-forecasted temperature and humidity values at defined pressure or sigma levels, and an aircraft-relevant contrail factor that is derived statistically from a contrail observation database.

For comparison purposes a mean overlap (MO), which is a parameter quantifying the overlap between forecasted contrail layers and contrail layers derived from radiosonde measurements, is introduced. Mean overlap values are used to test for the altitude and thickness of forecasted contrail layers. Contrail layers from Arctic MM5 and Air Force Weather Agency (AFWA) MM5 models agree well with contrail layers derived from corresponding radiosonde measurements for certain forecast periods; a steady decrease of the MO shows a decrease of contrail forecast accuracy with the increasing forecast period. Mean overlaps around 82% indicate reasonable results for the 48-h forecasts. Verification of MM5 with actual contrail observations shows a slightly better performance of Arctic MM5. A possible dry bias might occur in humidity measurements at low temperature levels due to temperature-dependence errors of the humidity sensor polymer, which might also affect forecasts of humidity of the upper troposphere or lower stratosphere. Despite this fact, this contrail verification study shows hit rates higher than 82% within forecast periods up to 36 h using Arctic MM5.
Numerical Simulations of Sea-Breeze Circulations over Northwest Hawaii*
Authors: Zhang Yongxin , Chen Yi-Leng , Schroeder Thomas A. , Kodama Kevin
Published: January 2006

Sea-breeze cases during 23–28 June 1978 over northwest Hawaii are simulated using the National Centers for Environmental Prediction (NCEP) Mesoscale Spectral Model (MSM) coupled with an advanced Land Surface Model (LSM) with 3-km horizontal resolution. Subjective analyses show that except for 27 June, the MSM–LSM-predicted onset time, duration, and vertical extent of the sea breezes agree well with observations. The largest mean absolute errors for surface air temperature occur at the coastal stations under strong trade wind conditions (e.g., 23 and 27 June). The model-simulated rainfall distribution in association with sea-breeze fronts is consistent with observations. Sensitivity tests demonstrate the modulation of sea-breeze behavior by surface properties. High-resolution (1 km) MSM–LSM simulations for 23 and 27 June show improvements over the 3-km MSM–LSM in reproducing the observed sea breezes through a better representation of local terrain and a better simulation of orographically enhanced trades channeling through the Waimea Saddle. Deficiencies noted in the model simulations include 1) sea-breeze speeds are more than 2–3 m s−1 weaker than observations, and 2) horizontal penetration of sea breezes is generally overestimated. These deficiencies in the model simulations are primarily related to two factors: one is the underestimation of the trade wind speeds in the initialization from the NCEP–NCAR reanalysis data that is favoring the farther penetration of the sea breezes, and the other is the uncertainties in the thermal properties of the lava rocks that affect the surface temperature and the sea-breeze speed.
A Sensitivity Study of Hodograph-Based Methods for Estimating Supercell Motion
Authors: Ramsay Hamish A. , Doswell Charles A.
Published: January 2006

Four supercell motion forecast algorithms are investigated with respect to their hodograph-analysis parameters. Another method derived from the data presented herein, the so-called offset method, is used to develop a baseline standard for the aforementioned schemes, using the observed storm motions and the mean wind. It is not a forecast scheme, as it is based on knowing the observed storm motions. This work explores the sensitivity of these algorithms to their arbitrary parameters by systematically varying those parameters, using a dataset of 394 right-moving supercells, and associated proximity soundings. The parameters used in these algorithms to define the layer depths for advection and/or propagation of supercells have not been shown to be optimum for this purpose. These arbitrary parameters compose the top and bottom levels of the mean wind layer, and a deviation vector from the mean wind defined through that layer. Two of the most recently developed algorithms have also implemented the vertical wind shear vector over an arbitrary layer depth. It has been found that, among other results, the scheme using both mean wind and vertical wind shear is more sensitive to the depth of the mean wind layer than it is to the depth of the vertical wind shear layer. It has also been shown that, when using the simplest schemes, the most accurate forecasts, on average, are obtained by using deep mean wind layers (i.e., greater than 0–10 km). Indeed, all the forecast schemes show a strong tendency for the u component of the predicted storm motion to be regulated by the depth of the mean wind layer. The υ component of the prediction storm motion, on the other hand, appears to be controlled by the deviation vector from the layer-mean wind. Although the schemes using vertical shear are shown to perform somewhat better on average than schemes based on the mean wind alone, there are times in which they also result in large forecast errors. The results demonstrate the inherent difficulty in using an observed hodograph to predict supercell motion.
Warm Season Lightning Probability Prediction for Canada and the Northern United States
Authors: Burrows William R. , Price Colin , Wilson Laurence J.
Published: January 2006

Statistical models valid May–September were developed to predict the probability of lightning in 3-h intervals using observations from the North American Lightning Detection Network and predictors derived from Global Environmental Multiscale (GEM) model output at the Canadian Meteorological Centre. Models were built with pooled data from the years 2000–01 using tree-structured regression. Error reduction by most models was about 0.4–0.7 of initial predictand variance.

Many predictors were required to model lightning occurrence for this large area. Highest ranked overall were the Showalter index, mean sea level pressure, and troposphere precipitable water. Three-hour changes of 500-hPa geopotential height, 500–1000-hPa thickness, and MSL pressure were highly ranked in most areas. The 3-h average of most predictors was more important than the mean or maximum (minimum where appropriate). Several predictors outranked CAPE, indicating it must appear with other predictors for successful statistical lightning prediction models.

Results presented herein demonstrate that tree-structured regression is a viable method for building statistical models to forecast lightning probability. Real-time forecasts in 3-h intervals to 45–48 h were made in 2003 and 2004. The 2003 verification suggests a hybrid forecast based on a mixture of maximum and mean forecast probabilities in a radius around a grid point and on monthly climatology will improve accuracy. The 2004 verification shows that the hybrid forecasts had positive skill with respect to a reference forecast and performed better than forecasts defined by either the mean or maximum probability at most times. This was achieved even though an increase of resolution and change of convective parameterization scheme were made to the GEM model in May 2004.
The Use of Sounding-Derived Indices for a Neural Network Short-Term Thunderstorm Forecast
Authors: Manzato Agostino
Published: January 2006

In this paper several indices derived from atmospheric sounding data are used to develop a short-term thunderstorm forecast tool. The Udine (Italy) sounding (WMO code 16044, taken every 6 h, hereafter 6 h) is used to describe the initial conditions in which a thunderstorm may develop in the Friuli Venezia Giulia region. The tool forecasts the convective activity in the 6 h after the sounding is launched.

Sounding, lightning, and mesonet station data, from April to September of the years 1995–2002, were used to train and validate artificial neural networks (ANNs) and the data of 2003 were used as an independent test sample. Special emphasis was given to avoid one of the major ANN problems, that of data overfitting, which requires limiting the possible complexity of the ANN, that is, the total number of inputs (predictors) and of hidden neurons. To select the best input set, the training–validation mechanism was repeated in eight different ways.

Two types of ANNs were developed: the first is a classification model and is built for forecasting the thunderstorm occurrence, defined as the report of at least one cloud-to-ground lightning strike. If this first ANN forecasts convective activity, then a second ANN, built as a regression model, is used for forecasting the thunderstorm intensity. The relative operating characteristic (ROC) diagram and some parameters derived from a contingency table were used to compare the different classification performances. The linear cross-correlation coefficient, R2, was used to assess the performance of the regression ANNs.

The ANN inputs were not the only “raw” sounding-derived index values; in addition, two derived sets of variables, corresponding to the z scores and the “posterior probability” transformed data, were used as preprocessing methods. The last method yields the best results. The tool reported on here has been used operationally since 2001 in the regional meteorological office of the Friuli Venezia Giulia region [Osservatorio Meteorologico Regionale/Agenzia Regionale per la Protezione dell’Ambiente (OSMER/ARPA)].
Official Labeling, Criminal Embeddedness, and Subsequent Delinquency
Authors: Bernburg Jón Gunnar , Krohn Marvin D. , Rivera Craig J.
Published: January 2006

This article examines the short-term impact of formal criminal labeling on involvement in deviant social networks and increased likelihood of subsequent delinquency. According to labeling theory, formal criminal intervention should affect the individual’s immediate social networks. In many cases, the stigma of the criminal status may increase the probability that the individual becomes involved in deviant social groups. The formal label may thus ultimately increase involvement in subsequent deviance. We use panel data of a sample of urban adolescents to examine whether involvement in deviant social groups mediates the relationship between juvenile justice intervention and subsequent delinquent behavior. Using measures from three successive points in time, the authors find that juvenile justice intervention positively affects subsequent involvement in serious delinquency through the medium of involvement in deviant social groups, namely, street gangs and delinquent peers.
Predicting Peer Acceptance in Dutch Youth
Authors: Lubbers Miranda J. , Van Der Werf Margaretha P. C. , Kuyper Hans , Offringa G. Johan
Published: January 2006

This article uses an ecological approach to predict students’ peer acceptance within junior high school classes. The authors investigate whether various characteristics (self-perception of physical attractiveness and athletic competence, cognitive ability, agreeableness, extraversion, age, parents’ education, number of siblings, siblings at same school, parental control, percentage of classmates who attended the same primary school, ethnicity) predict peer acceptance and whether effects are consistent across classes. Participants are 6,847 students (± 13 years) from 461 classes in the Netherlands. Girls’ and boys’ data are analyzed separately using multilevel analyses. Extraversion and the percentage of classmates from the same primary school are the strongest predictors of peer acceptance for boys and girls. Smaller effects are observed for self-perception of athletic competence (boys and girls), agreeableness (girls), and ethnicity (boys). The effects are consistent across classes. The need for more complex models of peer acceptance is discussed.
Lifetime Prevalence and Socioemotional Effects of Hearing About Community Violence
Authors: Scarpa Angela , Hurley Jimmy D. , Shumate Howard W. , Haden Sara Chiara
Published: January 2006

This study extends findings on community violence (CV) exposure in young adults by examining the prevalence, characteristics, and socioemotional effects related to hearing about violence. Surveys of lifetime CV exposure and socioemotional outcomes were completed by 518 male and female undergraduates completed and were divided into groups with high, moderate, or low scores on hearing. Of respondents, 97.8% reported hearing about violence at least once, with a substantial proportion hearing about different types of acts. Reportedly, the most frequent perpetrators were nonfamily members, the most frequent location was near home, and most incidents occurred within the past year. The high-scoring group reported significantly greater levels of depressed mood, posttraumatic stress symptoms, aggressive behavior, and interpersonal problems associated with personality disorders. Results extend previous findings on the unfortunate prevalence of violence exposure and its negative psychological effects in today’s young adults and suggest that chronic exposure to hearing about violence can have deleterious repercussions.
Perceived Positive Aspects of Intimate Relationships Among Abused Women in Methadone Maintenance Treatment Programs (MMTP)
Authors: Schiff Miriam , Gilbert Louisa , El-Bassel Nabila
Published: January 2006

This study examines the positive aspects of intimate relationships perceived by drug-involved women victims of intimate partner violence (IPV). The article examines the association of psychological distress, childhood abuse, and severity of IPV with the different positive aspects the women indicated. Most analyses were conducted on a subsample of 149 women in methadone maintenance treatment who intended to continue their relationship with their abusive partner. The women most frequently valued fulfillment of the role of an intimate partner such as “he takes care of me, loves me, makes me laugh” (29.7%). Fewer than one tenth of the women valued their partners’ role as economic provider; however, these women reported more physical IPV. Women intending to continue the relationship (more than two thirds of the participants) reported less physical or sexual IPV and experienced less psychological distress. Policy and practice implications are discussed.
Gender and Stress
Authors: Griffin Marie L.
Published: January 2006

Studies have described the stressful and hazardous nature of working within the correctional environment as well as the way in which female officers encounter additional workplace pressures associated with their entry into a nontraditional occupation. This study advances the literature on gender and stress by examining the gendered influence of multiple environmental variables on the job stress of correctional officers. Contrary to expectations, there were few differences between male and female officers in the effects of workplace stressors on their level of job stress. Work-family conflict proved to have the greatest impact on stress for both male and female officers, whereas concerns regarding organizational support for equal treatment policies affected stress only among male officers.
Multilevel Influences on Police Stress
Authors: Morash Merry , Haarr Robin , Kwak Dae-Hoon
Published: January 2006

The prior literature has highlighted a variety of workplace problems, such as racial and gender bias and lack of influence over work activities, as influences on police stress. Additional explanations for police stress include community conditions, for example, high crime rates and size of the community, token status within the police organization, and lack of family and coworker support for work-related activities. In a large-sample, exploratory study, this research examined the workplace problems that were hypothesized to predict stress. It also determined whether community conditions, token status, and lack of social support explained additional variance in officers’ stress levels. Lack of influence over work activities and bias against one’s racial, gender, or ethnic group stood out as important predictors of stress after controls were introduced for demographic variables. Interventions to redesign jobs to afford greater influence and to reduce within-department bias are approaches that could reduce police officers’ stress.
Reduced thromboxane biosynthesis in carriers of toll-like receptor 4 polymorphisms in vivo
Authors: Patrignani Paola , Di Febbo Concetta , Tacconelli Stefania , Moretta Valeria , Baccante Giovanna , Sciulli Maria G. , Ricciotti Emanuela , Capone Marta L. , Antonucci Ivana , Guglielmi Maria D. , Stuppia Liborio , Porreca Ettore
Published: January 2006

The recent demonstration that platelets express a functional toll-like receptor 4 (TLR4) prompted us to explore the influence of TLR4 polymorphisms (Asp299Gly alone or in combination with Thr399Ile) on thromboxane A2 (TXA2) biosynthesis in vivo. In 17 subjects with TLR4 polymorphisms versus 17 wild type (untreated with aspirin, matched for age, sex, and cardiovascular risk factors), intima-media thickness in the common carotid arteries was significantly lower. Average urinary excretion of 11-dehydro-TXB2, an index of systemic biosynthesis of TX, was significantly reduced by 65%. The urinary excretion of 2,3-dinor-6-keto-prostaglandin F1α, an index of systemic biosynthesis of prostacyclin, was marginally depressed but the prostacyclin/TXA2 biosynthesis ratio was significantly higher than in wild type. Selective inhibition of cyclooxygenase 2-dependent prostacyclin (by rofecoxib or etoricoxib) was associated with increased urinary excretion of 11-dehydro-TXB2 in carriers of TLR4 polymorphisms, but not in wild-type, suggesting a restrainable effect of prostacyclin on platelet function in vivo in this setting. Reduced TXA2 biosynthesis may contribute to the protective cardiovascular phenotype of TLR4 polymorphisms.
Mitochondrial genetic effects on latent class variables associated with susceptibility to alcoholism
Authors: Lease Loren R , Winnier Deidre A , Williams Jeff T , Dyer Thomas D , Almasy Laura , Mahaney Michael C
Published: January 2006

We report the results of statistical genetic analyses of data from the Collaborative Study on the Genetics of Alcoholism prepared for the Genetic Analysis Workshop 14 to detect and characterize maternally inherited mitochondrial genetic effects on variation in latent class psychiatric/behavioral variables employed in the diagnosis of alcoholism. Using published extensions to variance decomposition methods for statistical genetic analysis of continuous and discrete traits we: 1) estimated the proportion of the variance in each trait due to the effects of mitochondrial DNA (mtDNA), 2) tested for pleiotropy, both mitochondrial genetic and residual additive genetic, between trait pairs, and 3) evaluated whether the simultaneous estimation of mitochondrial genetic effects on these traits improves our ability to detect and localize quantitative trait loci (QTL) in the nuclear genome. After correction for multiple testing, we find significant (p < 0.009) mitochondrial genetic contributions to the variance for two latent class variables. Although we do detect significant residual additive genetic correlations between the two traits, there is no evidence of a residual mitochondrial genetic correlation between them. Evidence for autosomal QTL for these traits is improved when linkage screens are conditioned on significant mitochondrial genetic effects. We conclude that mitochondrial genes may contribute to variation in some latent class psychiatric/behavioral variables associated with alcoholism.
A simple nonparametric multipoint procedure to test for linkage through mothers or fathers as well as imprinting effects in the presence of linkage
Authors: Lemire Mathieu
Published: January 2006

A simple multipoint procedure to test for parent-of-origin effects in samples of affected siblings is discussed. The procedure consists of artificially changing all full sibs to half-sibs, with distinct mothers or fathers depending on the parental origin to be evaluated, then analyzing these families with commonly used statistics and software. The procedure leads to tests for linkage through mothers or fathers and also leads to a test for imprinting effects in the presence of linkage. Moreover, simulations illustrate that in regions unlinked to susceptibility genes this multipoint procedure does not have an inflated type I error if a sex-averaged genetic map is used, even when large differences exist between male-specific and female-specific maps. In regions linked with susceptibility genes, the test of imprinting is biased under the null hypothesis if differences exist between sex-specific maps, irrespective of the map used in the analysis. The procedure is applied to the Collaborative Study on the Genetics of Alcoholism dataset from the Genetic Analysis Workshop 14. Results indicate that brothers categorized as affected according to the DMS-III-R and Feighner classification show evidence of linkage through fathers to the 6q25 region (p = 0.00038) as well as modest evidence of imprinting (p = 0.018). This region harbors OPRM1, a candidate gene for substance dependence.
A genome scan for parent-of-origin linkage effects in alcoholism
Authors: Liu Xiao-Qing , Greenwood Celia MT , Wang Ke-Sheng , Paterson Andrew D
Published: January 2006

Background

Alcoholism is a complex disease in which genomic imprinting may play an important role in its susceptibility.

Objective

To conduct a genome-wide search for loci that may have strong parent-of-origin linkage effects in alcoholism; to compare the linkage results between the microsatellites and the two single-nucleotide polymorphism (SNP) platforms.

Methods

Nonparametric linkage analyses were performed using ALLEGRO with the three sets of markers provided by the Genetic Analysis Workshop 14 for the Collaborative Study on the Genetics of Alcoholism Problem 1 data. Both sex-averaged and sex-specific genetic maps were used. We also provided a valid statistical test to determine whether the parental allele sharing differed significantly.

Results

Significant maternal linkage effects (paternal imprinting) were observed on chromosome 12 using either the microsatellite markers or the two SNP panels. The two SNP sets did not improve the linkage signals compared to the results from the microsatellite markers on chromosome 12. Possible paternal linkage effects (maternal imprinting) on chromosome 7 and maternal linkage effects (paternal imprinting) on chromosome 10 were found using the two SNP panels.

Conclusion

For diseases which may have parent-of-origin effects, linkage analysis looking at parental sharing separately may reduce locus heterogeneity and increase the ability to identify that which can not be identified with usual linkage analysis.
Genetic imprinting analysis for alcoholism genes using variance components approach
Authors: Shete Sanjay , Yu Robert
Published: January 2006

Genomic imprinting, which is also known as the parent-of-origin effect, is a mechanism that only expresses one copy of a gene pair depending upon the parental origin. Although many chromosomal regions in the human genome are likely to be imprinted, imprinting is not accounted for in the usual linkage analysis. In this study, using a variance-components approach with a quantitative phenotype ttth-FP1, we found significant evidence of imprinting at two loci, D7S1790 and D1S1631, on chromosome 1 and chromosome 7, respectively. Our results suggest that allowing for the possibility of imprinting can increase the power to detect linkage for localizing genes for alcoholism.
Detection of susceptibility loci by genome-wide linkage analysis
Authors: Babron Marie-Claude , Bourgain Catherine , Leutenegger Anne-Louise , Clerget-Darpoux Françoise
Published: January 2006

The objective of this study is to evaluate the efficacy of a model-free linkage statistics for finding evidence of linkage using two different maps and to illustrate how the comparison of results from several populations might provide insight into the underlying genetic etiology of the disease of interest. The results obtained in terms of detection of the risk loci and threshold for declaring linkage and power are very similar for a dense SNP map and a sparser microsatellite map. The populations differed in terms of family ascertainment and diagnosis criteria, leading to different power to detect the individual underlying disease loci. Our results for the individual replicates are consistent with the disease model used in the simulation.
Description of the data from the Collaborative Study on the Genetics of Alcoholism (COGA) and single-nucleotide polymorphism genotyping for Genetic Analysis Workshop 14
Authors: Edenberg Howard J , Bierut Laura J , Boyce Paul , Cao Manqiu , Cawley Simon , Chiles Richard , Doheny Kimberly F , Hansen Mark , Hinrichs Tony , Jones Kevin , Kelleher Mark , Kennedy Giulia C , Liu Guoying , Marcus Gregory , McBride Celeste , Murray Sarah Shaw , Oliphant Arnold , Pettengill James , Porjesz Bernice , Pugh Elizabeth W , Rice John P , Rubano Todd , Shannon Stu , Steeke Rhoberta , Tischfield Jay A , Tsai Ya Yu , Zhang Chun , Begleiter Henri
Published: January 2006

The data provided to the Genetic Analysis Workshop 14 (GAW 14) was the result of a collaboration among several different groups, catalyzed by Elizabeth Pugh from The Center for Inherited Disease Research (CIDR) and the organizers of GAW 14, Jean MacCluer and Laura Almasy. The DNA, phenotypic characterization, and microsatellite genomic survey were provided by the Collaborative Study on the Genetics of Alcoholism (COGA), a nine-site national collaboration funded by the National Institute of Alcohol and Alcoholism (NIAAA) and the National Institute of Drug Abuse (NIDA) with the overarching goal of identifying and characterizing genes that affect the susceptibility to develop alcohol dependence and related phenotypes. CIDR, Affymetrix, and Illumina provided single-nucleotide polymorphism genotyping of a large subset of the COGA subjects. This article briefly describes the dataset that was provided.
Investigation of altering single-nucleotide polymorphism density on the power to detect trait loci and frequency of false positive in nonparametric linkage analyses of qualitative traits
Authors: Klein Alison P , Tsai Ya-Yu , Duggal Priya , Gillanders Elizabeth M , Barnhart Michael , Mathias Rasika A , Dusenberry Ian P , Turiff Amy , Chines Peter S , Goldstein Janet , Wojciechowski Robert , Hening Wayne , Pugh Elizabeth W , Bailey-Wilson Joan E
Published: January 2006

Genome-wide linkage analysis using microsatellite markers has been successful in the identification of numerous Mendelian and complex disease loci. The recent availability of high-density single-nucleotide polymorphism (SNP) maps provides a potentially more powerful option. Using the simulated and Collaborative Study on the Genetics of Alcoholism (COGA) datasets from the Genetics Analysis Workshop 14 (GAW14), we examined how altering the density of SNP marker sets impacted the overall information content, the power to detect trait loci, and the number of false positive results. For the simulated data we used SNP maps with density of 0.3 cM, 1 cM, 2 cM, and 3 cM. For the COGA data we combined the marker sets from Illumina and Affymetrix to create a map with average density of 0.25 cM and then, using a sub-sample of these markers, created maps with density of 0.3 cM, 0.6 cM, 1 cM, 2 cM, and 3 cM. For each marker set, multipoint linkage analysis using MERLIN was performed for both dominant and recessive traits derived from marker loci. Our results showed that information content increased with increased map density. For the homogeneous, completely penetrant traits we created, there was only a modest difference in ability to detect trait loci. Additionally, as map density increased there was only a slight increase in the number of false positive results when there was linkage disequilibrium (LD) between markers. The presence of LD between markers may have led to an increased number of false positive regions but no clear relationship between regions of high LD and locations of false positive linkage signals was observed.
Interval estimation of disease loci: development and applications of new linkage methods
Authors: Papachristou Charalampos , Lin Shili
Published: January 2006

Three variants of the confidence set inference (CSI) procedure were proposed and applied to both the simulated and the Collaborative Study on the Genetics of Alcoholism (COGA) data. For each of the two applications, we first performed a preliminary genome scan study based on the microsatellite markers using the GENEHUNTER+ software to identify regions that potentially harbor disease loci. For each such region, we estimated the sibling identity-by-descent sharing probability distribution at the putative disease locus. Based on these estimated probabilities, the CSI procedures were employed to further localize the disease loci using the single-nucleotide polymorphism markers, leading to confidence intervals/regions for their locations. For our analysis with the simulated data, we had knowledge of the simulating models at the time we performed the analysis.
Inclusion of unaffected sibs increases power in model-free linkage analysis of a behavioral trait
Authors: Plancoulaine Sabine , Alcaïs Alexandre , Chen Yue , Abel Laurent , Gagnon France
Published: January 2006

Study design strategies are of critical importance in the search for genes underlying complex diseases. Two important design choices in planning gene mapping studies are the analytic strategy to be used, which will have an impact on the type of data to be collected, and the choice of genetic markers. In the present paper, we used the simulated behavioral trait data provided in the Genetic Analysis Workshop14 to: 1) investigate the usefulness of incorporating unaffected sibs in model-free linkage analysis and, 2) compare linkage results of genome scans using a 7-cM microsatellite map with a 3-cM single nucleotide polymorphisms map. To achieve these aims, we used the maximum-likelihood-binomial method with two different coding approaches. We defined the unaffected sibs as those totally free of phenotypes correlated to the disease. Without prior knowledge of the answers, we were able to correctly localize 2 out of 5 loci (LOD > 3) in a sample of 200 families that included the unaffected sibs but only one locus when based on an affected-only strategy, using either microsatellite or SNPs genome scan. LOD scores were considerably higher using the analytic strategy which incorporated the unaffected sibs. In conclusion, including unaffected sibs in model-free linkage analysis of complex binary traits is helpful, at least when complete parental data are available, whereas there are no striking advantages in using single nucleotide polymorphisms over microsatellite map at marker densities used in the current study.
Local false discovery rate and minimum total error rate approaches to identifying interesting chromosomal regions
Authors: Sinha Ritwik , Sinha Moumita , Mathew George , Elston Robert C , Luo Yuqun
Published: January 2006

The simultaneous testing of a large number of hypotheses in a genome scan, using individual thresholds for significance, inherently leads to inflated genome-wide false positive rates. There exist various approaches to approximating the correct genomewidep-values under various assumptions, either by way of asymptotics or simulations. We explore a philosophically different criterion, recently proposed in the literature, which controls the false discovery rate. The test statistics are assumed to arise from a mixture of distributions under the null and non-null hypotheses. We fit the mixture distribution using both a nonparametric approach and commingling analysis, and then apply the local false discovery rate to select cut-off points for regions to be declared interesting. Another criterion, the minimum total error, is also explored. Both criteria seem to be sensible alternatives to controlling the classical type I and type II error rates.
Linkage and association analyses of microsatellites and single-nucleotide polymorphisms in nuclear families
Authors: Lin Jennifer , Liu Kuang-Yu
Published: January 2006

Several simulation studies have suggested that a high-density single-nucleotide polymorphisms (SNPs) marker set may be as useful as a traditional microsatellites (MS) marker set in performing whole-genome linkage analysis. However, very few studies have directly tested the SNPs-based genome-wide scan. In the present study, we compared the linkage results from the SNPs-based scan with a map density of 3-cM spacing with those from the MS scan using a 10-cM marker set among 300 nuclear families each from the Aipotu (AI), Danacaa (DA), and Karangar (KA) populations from the simulated Genetic Analysis Workshop 14 Problem 2 data. We found that information contents obtained from the SNPs scan were somewhat lower than those from the MS scan. However, the linkage results obtained from the two scans showed a high degree of similarity. Both scans identified a similar number of chromosomal regions attaining nominal significance (p < 0.05). Specifically, both scans detected confirmed evidence for linkage (NPL ≥ 4.07, p = 2 × 10-5) to chromosome 1 in the AI families, chromosomes 1 and 3 in the DA families, and chromosomes 3, 5, and 9 in the KA families. An additional confirmed linkage to chromosome 5 in the AI families was detected only by the MS scan. We also observed slightly wider 1-LOD intervals for more of the SNP peaks than for the MS peaks, which is likely due to lower information contents for the SNPs. Subsequent fine-mapping association analysis further identified 2 to 3 markers significantly associated with disease status in each population; B03T3056, B03T3058, and B05T4139 in the AI population, B03T3056 and B03T3058 in the KA population, and B03T3056, B03T3057, and B03T3058 in the DA population. Among the four markers, three were chosen based on results obtained from the two scans, but one was solely from the SNP scan. In summary, our finding suggests that the SNP-based genome scan has the potential to be as powerful as the traditional MS-based scan and offers good identification of peak location for further fine-mapped association analysis.
Comparison of single-nucleotide polymorphisms and microsatellites in inference of population structure
Authors: Liu Nianjun , Chen Liang , Wang Shuang , Oh Cheongeun , Zhao Hongyu
Published: January 2006

Single-nucleotide polymorphisms (SNPs) are a class of attractive genetic markers for population genetic studies and for identifying genetic variations underlying complex traits. However, the usefulness and efficiency of SNPs in comparison to microsatellites in different scientific contexts, e.g., population structure inference or association analysis, still must be systematically evaluated through large empirical studies. In this article, we use the Collaborative Studies on Genetics of Alcoholism (COGA) data from Genetic Analysis Workshop 14 (GAW14) to compare the performance of microsatellites and SNPs in the whole human genome in the context of population structure inference. A total of 328 microsatellites and 15,840 SNPs are used to infer population structure in 236 unrelated individuals. We find that, on average, the informativeness of random microsatellites is four to twelve times that of random SNPs for various population comparisons, which is consistent with previous studies. Our results also indicate that for the combined set of microsatellites and SNPs, SNPs constitute the majority among the most informative markers and the use of these SNPs leads to better inference of population structure than the use of microsatellites. We also find that the inclusion of less informative markers may add noise and worsen the results.
A comparison between microsatellite and single-nucleotide polymorphism markers with respect to two measures of information content
Authors: Thalamuthu Anbupalam , Mukhopadhyay Indranil , Ray Amrita , Weeks Daniel E
Published: January 2006

Using the Genetic Analysis Workshop 14 (GAW14) simulated dataset, we compare microsatellite and single-nucleotide polymorphism (SNP) markers in terms of two measures of information content, the traditional entropy-based information content measure, and a new "relative information" measure. Both attempt to measure the amount of information contained in the markers about the identity-by-descent (IBD) sharing among relatives. The performance of the two information measures are compared based on their variability and ability to predict change in the LOD score (ΔLOD) as map density increases for SNP markers. Although in a linked region, LOD scores are correlated with measures of information, we observe that none of the measures predict the LOD score itself very well. In an unlinked region, the LOD score is not related to either measures of information. The information content of microsatellite markers with 7.5-cM spacing is slightly higher than that of SNP markers with 3-cM spacing. At these map densities, microsatellites are found to be uniformly more informative than SNPs irrespective of their level of heterozygosity. For SNPs, we found that as the level of heterozygosity increases, the information content increases. As reported in all other previous studies, we also found that high-density SNPs have higher information content compared to low-density microsatellites. Performance of both the two information measures considered here are similar, but the relative information measure predicts ΔLOD as marker density increases better than the traditional entropy-based information measure.
Whole-genome linkage analysis in mapping alcoholism genes using single-nucleotide polymorphisms and microsatellites
Authors: Wang Shuang , Huang Song , Liu Nianjun , Chen Liang , Oh Cheongeun , Zhao Hongyu
Published: January 2006

There is currently a great interest in using single-nucleotide polymorphisms (SNPs) in genetic linkage and association studies because of the abundance of SNPs as well as the availability of high-throughput genotyping technologies. In this study, we compared the performance of whole-genome scans using SNPs with microsatellites on 143 pedigrees from the Collaborative Studies on Genetics of Alcoholism provided by Genetic Analysis Workhsop 14. A total of 315 microsatellites and 10,081 SNPs from Affymetrix on 22 autosomal chromosomes were used in our analyses. We found that the results from the two scans had good overall concordance. One region on chromosome 2 and two regions on chromosome 7 showed significant linkage signals (i.e., NPL ≥ 2) for alcoholism from both the SNP and microsatellite scans. The different results observed between the two scans may be explained by the difference observed in information content between the SNPs and the microsatellites.
Comparison of microsatellites, single-nucleotide polymorphisms (SNPs) and composite markers derived from SNPs in linkage analysis
Authors: Xing Chao , Schumacher Fredrick R , Xing Guan , Lu Qing , Wang Tao , Elston Robert C
Published: January 2006

There is growing evidence that a map of dense single-nucleotide polymorphisms (SNPs) can outperform a map of sparse microsatellites for linkage analysis. There is also argument as to whether a clustered SNP map can outperform an evenly spaced SNP map. Using Genetic Analysis Workshop 14 simulated data, we compared for linkage analysis microsatellites, SNPs, and composite markers derived from SNPs. We encoded the composite markers in a two-step approach, in which the maximum identity length contrast method was employed to allow for recombination between loci. A SNP map 2.3 times as dense as a microsatellite map (~2.9 cM compared to ~6.7 cM apart) provided slightly less information content (~0.83 compared to ~0.89). Most inheritance information could be extracted when the SNPs were spaced < 1 cM apart. Comparing the linkage results on using SNPs or composite markers derived from them based on both 3 cM and 0.3 cM resolution maps, we showed that the inter-SNP distance should be kept small (< 1 cM), and that for multipoint linkage analysis the original markers and the derived composite markers had similar power; but for single point linkage analysis the resulting composite markers lead to more power. Considering all factors, such as information content, flexibility of analysis method, map errors, and genotyping errors, a map of clustered SNPs can be an efficient design for a genome-wide linkage scan.
A genome-wide scanning and fine mapping study of COGA data
Authors: Yang Hsin-Chou , Chang Chien-Ching , Lin Chin-Yu , Chen Chun-Liang , Lin Chin-Yu , Fann Cathy SJ
Published: January 2006

A thorough genetic mapping study was performed to identify predisposing genes for alcoholism dependence using the Collaborative Study on the Genetics of Alcoholism (COGA) data. The procedure comprised whole-genome linkage and confirmation analyses, single locus and haplotype fine mapping analyses, and gene × environment haplotype regression. Stratified analysis was considered to reduce the ethnic heterogeneity and simultaneously family-based and case-control study designs were applied to detect potential genetic signals. By using different methods and markers, we found high linkage signals at D1S225 (253.7 cM), D1S547 (279.2 cM), D2S1356 (64.6 cM), and D7S2846 (56.8 cM) with nonparametric linkage scores of 3.92, 4.10, 4.44, and 3.55, respectively. We also conducted haplotype and odds ratio analyses, where the response was the dichotomous status of alcohol dependence, explanatory variables were the inferred individual haplotypes and the three statistically significant covariates were age, gender, and max drink (the maximum number of drinks consumed in a 24-hr period). The final model identified important AD-related haplotypes within a candidate region of NRXN1 at 2p21 and a few others in the inter-gene regions. The relative magnitude of risks to the identified risky/protective haplotypes was elucidated.
Application of the propensity score in a covariate-based linkage analysis of the Collaborative Study on the Genetics of Alcoholism
Authors: Doan Betty Q , Frangakis Constantine E , Shugart Yin Y , Bailey-Wilson Joan E
Published: January 2006

Background

Covariate-based linkage analyses using a conditional logistic model as implemented in LODPAL can increase the power to detect linkage by minimizing disease heterogeneity. However, each additional covariate analyzed will increase the degrees of freedom for the linkage test, and therefore can also increase the type I error rate. Use of a propensity score (PS) has been shown to improve consistently the statistical power to detect linkage in simulation studies. Defined as the conditional probability of being affected given the observed covariate data, the PS collapses multiple covariates into a single variable. This study evaluates the performance of the PS to detect linkage evidence in a genome-wide linkage analysis of microsatellite marker data from the Collaborative Study on the Genetics of Alcoholism. Analytical methods included nonparametric linkage analysis without covariates, with one covariate at a time including multiple PS definitions, and with multiple covariates simultaneously that corresponded to the PS definitions. Several definitions of the PS were calculated, each with increasing number of covariates up to a maximum of five. To account for the potential inflation in the type I error rates, permutation based p-values were calculated.

Results

Results suggest that the use of individual covariates may not necessarily increase the power to detect linkage. However the use of a PS can lead to an increase when compared to using all covariates simultaneously. Specifically, PS3, which combines age at interview, sex, and smoking status, resulted in the greatest number of significant markers identified. All methods consistently identified several chromosomal regions as significant, including loci on chromosome 2, 6, 7, and 12.

Conclusion

These results suggest that the use of a propensity score can increase the power to detect linkage for a complex disease such as alcoholism, especially when multiple important covariates can be used to predict risk and thereby minimize linkage heterogeneity. However, because the PS is calculated as a conditional probability of being affected, it does require the presence of observed covariate data on both affected and unaffected individuals, which may not always be available in real data sets.