bluuebunny/crossref_metadata_2025_split huggingface dataset:
Whole-genome association studies on alcoholism comparing different phenotypes using single-nucleotide polymorphisms and microsatellites
Authors: Chen Liang , Liu Nianjun , Wang Shuang , Oh Cheongeun , Carriero Nicholas J , Zhao Hongyu 
Published: December 2005

AbstractAlcoholism is a complex disease. As with other common diseases, genetic variants underlying alcoholism have been illusive, possibly due to the small effect from each individual susceptible variant, gene × environment and gene × gene interactions and complications in phenotype definition. We conducted association tests, the family-based association tests (FBAT) and the backward haplotype transmission association (BHTA), on the Collaborative Study of the Genetics of Alcoholism (COGA) data provided by Genetic Analysis Workshop (GAW) 14. Efron's local false discovery rate method was applied to control the proportion of false discoveries. For FBAT, we compared the results based on different types of genetic markers (single-nucleotide polymorphisms (SNPs) versus microsatellites) and different phenotype definitions (clinical diagnoses versus electrophysiological phenotypes). Significant association results were found only between SNPs and clinical diagnoses. In contrast, significant results were found only between microsatellites and electrophysiological phenotypes. In addition, we obtained the association results for SNPs and microsatellites using COGA diagnosis as phenotype based on BHTA. In this case, the results for SNPs and microsatellites are more consistent. Compared to FBAT, more significant markers are detected with BHTA.
Diagnosis of alcoholism based on neural network analysis of phenotypic risk factors
Authors: Falk Catherine T 
Published: December 2005

Abstract

Background
Alcoholism is a serious public health problem. It has both genetic and environmental causes. In an effort to gain understanding of the underlying genetic susceptibility to alcoholism, a long-term study has been undertaken. The Collaborative Study on the Genetics of Alcoholism (COGA) provides a rich source of genetic and phenotypic data. One ongoing problem is the difficulty of reliably diagnosing alcoholism, despite many known risk factors and measurements. We have applied a well known pattern-matching method, neural network analysis, to phenotypic data provided to participants in Genetic Analysis Workshop 14 by COGA. The aim is to train the network to recognize complex phenotypic patterns that are characteristic of those with alcoholism as well as those who are free of symptoms. Our results indicate that this approach may be helpful in the diagnosis of alcoholism.

Results
Training and testing of input/output pairs of risk factors by means of a "feed-forward back-propagation" neural network resulted in reliability of about 94% in predicting the presence or absence of alcoholism based on 36 input phenotypic risk factors. Pruning the neural network to remove relatively uninformative factors resulted in a reduced network of 14 input factors that was still 95% reliable. Some of the factors selected by the pruning steps have been identified as traits that show either linkage or association to potential candidate regions.

Conclusion
The complex, multivariate picture formed by known risk factors for alcoholism can be incorporated into a neural network analysis that reliably predicts the presence or absence of alcoholism about 94–95% of the time. Several characteristics that were identified by a pruned neural network have previously been shown to be important in this disease based on more traditional linkage and association studies. Neural networks therefore provide one less traditional approach to both identifying alcoholic individuals and determining the most informative risk factors.
Data mining of the GAW14 simulated data using rough set theory and tree-based methods
Authors: Wei Liang-Ying , Huang Cheng-Lung , Chen Chien-Hsiun 
Published: December 2005

Abstract
Rough set theory and decision trees are data mining methods used for dealing with vagueness and uncertainty. They have been utilized to unearth hidden patterns in complicated datasets collected for industrial processes. The Genetic Analysis Workshop 14 simulated data were generated using a system that implemented multiple correlations among four consequential layers of genetic data (disease-related loci, endophenotypes, phenotypes, and one disease trait). When information of one layer was blocked and uncertainty was created in the correlations among these layers, the correlation between the first and last layers (susceptibility genes and the disease trait in this case), was not easily directly detected. In this study, we proposed a two-stage process that applied rough set theory and decision trees to identify genes susceptible to the disease trait. During the first stage, based on phenotypes of subjects and their parents, decision trees were built to predict trait values. Phenotypes retained in the decision trees were then advanced to the second stage, where rough set theory was applied to discover the minimal subsets of genes associated with the disease trait. For comparison, decision trees were also constructed to map susceptible genes during the second stage. Our results showed that the decision trees of the first stage had accuracy rates of about 99% in predicting the disease trait. The decision trees and rough set theory failed to identify the true disease-related loci.
Analysis of alcoholism data using support vector machines
Authors: Yu Robert , Shete Sanjay 
Published: December 2005

Abstract
A supervised learning method, support vector machine, was used to analyze the microsatellite marker dataset of the Collaborative Study on the Genetics of Alcoholism Problem 1 for the Genetic Analysis Workshop 14. Twelve binary-valued phenotype variables were chosen for analyses using the markers from all autosomal chromosomes. Using various polynomial kernel functions of the support vector machine and randomly divided genome regions, we were able to observe the association of some marker sets with the chosen phenotypes and thus reduce the size of the dataset. The successful classifications established with the chosen support vector machine kernel function had high levels of correctness for each prediction, e.g., 96% in the fourfold cross-validations. However, owing to the limited sample data, we were not able to test the predictions of the classifiers in the new sample data.
Use of susceptibility scoring in conjunction with the genotypic transmission disequilibrium test
Authors: Benke Kelly S , Chase Gary A , Fallin Daniele M 
Published: December 2005

Abstract
We explored the utility of selecting a genetically predisposed subgroup to increase the finding of a genetic signal in the Genetic Analysis Workshop 14 Collaborative Study on the Genetics of Alcoholism dataset. A subgroup of affected probands with low environmental risk exposures was defined using a susceptibility score calculated from an environmental risk model. Thirty-nine probands with highly positive scores were selected, along with their parents, for use in a genotypic transmission disequilibrium test (TDT) test. We compared the results of the genotypic TDT in this subgroup to the TDT results using all probands and their parents. For some markers, the susceptibility scoring approach resulted in smaller p-values, while for other markers, evidence for a genetic signal weakened. Further explorations into genetic and environmental population characteristics that benefit from this approach are warranted.
A new Bayesian approach incorporating covariate information for heterogeneity and its comparison with HLOD
Authors: Biswas Swati , Lin Shili , Berry Donald A 
Published: December 2005

AbstractWe consider a new Bayesian approach for heterogeneity that can take into account categorical covariates, if available. We use the Genetic Analysis Workshop 14 simulated data to first compare the Bayesian approach with the heterogeneity LOD, when no covariate information is used. We find that the former is more powerful, while the two approaches have comparable false-positive rates. We then include informative covariates in the Bayesian approach and find that it tends to give more precise interval estimates of the disease gene location than when covariates are not included. We had knowledge of the simulation models at the time we performed the analyses.
Performance comparison of two-point linkage methods using microsatellite markers flanking known disease locations
Authors: Logue Mark W , George Andrew W , Spence M Anne , Vieland Veronica J 
Published: December 2005

Abstract
The Genetic Analysis Workshop 14 simulated data presents an interesting, challenging, and plausible example of a complex disease interaction in a dataset. This paper summarizes the ease of detection for each of the simulated Kofendrerd Personality Disorder (KPD) genes across all of the replicates for five standard linkage statistics. Using the KPD affection status, we have analyzed the microsatellite markers flanking each of the disease genes, plus an additional 2 markers that were not linked to any of the disease loci. All markers were analyzed using the following two-point linkage methods: 1) a MMLS, which is a standard admixture LOD score maximized over θ, α, and mode of inheritance, 2) a MLS calculated by GENEHUNTER, 3) the Kong and Cox LOD score as computed by MERLIN, 4) a MOD score (standard heterogeneity LOD maximized over θ, α, and a grid of genetic model parameters), and 5) the PPL, a Bayesian statistic that directly measures the strength of evidence for linkage to a marker. All of the major loci (D1–D4) were detectable with varying probabilities in the different populations. However, the modifier genes (D5 and D6) were difficult to detect, with similar distributions under the null and alternative across populations and statistics. The pooling of the four datasets in each replicate (n = 350 pedigrees) greatly improved the chance of detecting the major genes using all five methods, but failed to increase the chance to detect D5 and D6.
Standard linkage and association methods identify the mechanism of four susceptibility genes for a simulated complex disease
Authors: Pankratz Nathan , Edenberg Ellen , Foroud Tatiana 
Published: December 2005

Abstract
The simulated dataset of the Genetic Analysis Workshop 14 provided affection status and the presence or absence of 12 traits. It was determined that all affected individuals must have traits E, F and H (EFH phenotype) and they must also have either trait B (B subtype) or traits C, D, and G (CDG subtype). A genome screen was performed, and linkage peaks were identified on chromosomes 1, 3, 5, and 9 using microsatellite markers. Dense panels of single-nucleotide polymorphism (SNP) markers were ordered for each of the four linkage peaks. In each case, association analyses identified a single SNP that accounted for the linkage evidence. The SNP on chromosome 1 appeared to primarily influence the B subtype, while the SNPs on chromosomes 5 and 9 primarily influenced the CDG subtype. The chromosome 3 SNP had the strongest effect and influenced both subtypes, as well as the requisite EFH phenotype. Recognizing the two subtypes prior to linkage analysis was key to identifying these loci using only a single replicate. This highlights the need in real life situations for careful examination of the phenotypic data prior to genetic analysis.
Extension of multifactor dimensionality reduction for identifying multilocus effects in the GAW14 simulated data
Authors: Mei Hao , Ma Deqiong , Ashley-Koch Allison , Martin Eden R 
Published: December 2005

Abstract
The multifactor dimensionality reduction (MDR) is a model-free approach that can identify gene × gene or gene × environment effects in a case-control study. Here we explore several modifications of the MDR method. We extended MDR to provide model selection without crossvalidation, and use a chi-square statistic as an alternative to prediction error (PE). We also modified the permutation test to provide different levels of stringency. The extended MDR (EMDR) includes three permutation tests (fixed, non-fixed, and omnibus) to obtain p-values of multilocus models. The goal of this study was to compare the different approaches implemented in the EMDR method and evaluate the ability to identify genetic effects in the Genetic Analysis Workshop 14 simulated data. We used three replicates from the simulated family data, generating matched pairs from family triads. The results showed: 1) chi-square and PE statistics give nearly consistent results; 2) results of EMDR without cross-validation matched that of EMDR with 10-fold cross-validation; 3) the fixed permutation test reports false-positive results in data from loci unrelated to the disease, but the non-fixed and omnibus permutation tests perform well in preventing false positives, with the omnibus test being the most conservative. We conclude that the non-cross-validation test can provide accurate results with the advantage of high efficiency compared to 10-cross-validation, and the non-fixed permutation test provides a good compromise between power and false-positive rate.
Analysis of genes for alcoholism using two-disease-locus models
Authors: Wu Chih-Chieh , Shete Sanjay 
Published: December 2005

Abstract
Using model-based two-locus methods for mapping genes, we analyzed the family data from the Collaborative Study on the Genetics of Alcoholism. Microsatellite data from 143 families ascertained through having three or more individuals affected with alcohol dependence were used for this investigation. Four regions showing evidence for linkage were identified using single-locus models from previous investigations. We investigated the genetic linkage, pattern of disease inheritance, and pair-wise genetic epistasis of these loci using the TLINKAGE program for two-disease-locus analysis.
Whole-genome variance components linkage analysis using single-nucleotide polymorphisms versus microsatellites on quantitative traits of derived phenotypes from factor analysis of electroencephalogram waves
Authors: Yu Yi , Meng Yan , Ma Qianli , Farrell John , Farrer Lindsay A , Wilcox Marsha A 
Published: December 2005

Abstract
Alcohol dependence is a serious public health problem. We studied data from families participating in the Collaborative Study on the Genetics of Alcoholism (COGA) and made available to participants in the Genetic Analysis Workshop 14 (GAW14) in order to search for genes predisposing to alcohol dependence. Using factor analysis, we identified four factors (F1, F2, F3, F4) related to the electroencephalogram traits. We conducted variance components linkage analysis with each of the factors. Our results using the Affymetrix single-nucleotide polymorphism dataset showed significant evidence for a novel linkage of F3 (factor comprised of the three midline channel EEG measures from the target case of the Visual Oddball experiment ttdt2, 3, 4) to chromosome 18 (LOD = 3.45). This finding was confirmed by analyses of the microsatellite data (LOD = 2.73) and Illumina SNP data (LOD = 3.30). We also demonstrated that, in a sample like the COGA data, a dense single-nucleotide polymorphism map provides better linkage signals than low-resolution microsatellite map with quantitative traits.
The effect of missing data on linkage disequilibrium mapping and haplotype association analysis in the GAW14 simulated datasets
Authors: McCaskie Pamela A , Carter Kim W , McCaskie Simon R , Palmer Lyle J 
Published: December 2005

Abstract
We used our newly developed linkage disequilibrium (LD) plotting software, JLIN, to plot linkage disequilibrium between pairs of single-nucleotide polymorphisms (SNPs) for three chromosomes of the Genetic Analysis Workshop 14 Aipotu simulated population to assess the effect of missing data on LD calculations. Our haplotype analysis program, SIMHAP, was used to assess the effect of missing data on haplotype-phenotype association. Genotype data was removed at random, at levels of 1%, 5%, and 10%, and the LD calculations and haplotype association results for these levels of missingness were compared to those for the complete dataset. It was concluded that ignoring individuals with missing data substantially affects the number of regions of LD detected which, in turn, could affect tagging SNPs chosen to generate haplotypes.
An analysis of identical single-nucleotide polymorphisms genotyped by two different platforms
Authors: Suarez Brian K , Taylor Chelsea , Bertelsen Sarah , Bierut Laura J , Dunn Gerald , Jin Carol H , Kauwe John SK , Paterson Andrew D , Hinrichs Anthony L 
Published: December 2005

Abstract
The overlap of 94 single-nucleotide polymorphisms (SNP) among the 4,720 and 11,120 SNPs contained in the linkage panels of Illumina and Affymetrix, respectively, allows an assessment of the discrepancy rate produced by these two platforms. Although the no-call rate for the Affymetrix platform is approximately 8.6 times greater than for the Illumina platform, when both platforms make a genotypic call, the agreement is an impressive 99.85%. To determine if disputed genotypes can be resolved without sequencing, we studied recombination in the region of the discrepancy for the most discrepant SNP rs958883 (typed by Illumina) and tsc02060848 (typed by Affymetrix). We find that the number of inferred recombinants is substantially higher for the Affymetrix genotypes compared to the Illumina genotypes. We illustrate this with pedigree 10043, in which 3 of 7 versus 0 of 7 offspring must be double recombinants using the genotypes from the Affymetrix and the Illumina platforms, respectively. Of the 36 SNPs with one or more discrepancies, we identified a subset that appears to cluster in families. Some of this clustering may be due to the presence of a second segregating SNP that obliterates a Xba I site (the restriction enzyme used in the Affymetrix platform), resulting in a fragment too long (>1,000 bp) to be amplified.
Examining the effect of linkage disequilibrium on multipoint linkage analysis
Authors: Huang Qiqing , Shete Sanjay , Swartz Michael , Amos Christopher I 
Published: December 2005

AbstractMost linkage programs assume linkage equilibrium among multiple linked markers. This assumption may lead to bias for tightly linked markers where strong linkage disequilibrium (LD) exists. We used simulated data from Genetic Analysis Workshop 14 to examine the possible effect of LD on multipoint linkage analysis. Single-nucleotide polymorphism packets from a non-disease-related region that was generated with LD were used for both model-free and parametric linkage analyses. Results showed that high LD among markers can induce false-positive evidence of linkage for affected sib-pair analysis when parental data are missing. Bias can be eliminated with parental data and can be reduced when additional markers not in LD are included in the analyses.
Shedding of PECAM-1 during HIV infection: a potential role for soluble PECAM-1 in the pathogenesis of NeuroAIDS
Authors: Eugenin E A , Gamss R , Buckner C , Buono D , Klein R S , Schoenbaum E E , Calderon T M , Berman J W 
Published: December 2005

Abstract
Human immunodeficiency virus (HIV) infection is characterized by viral entry into the central nervous system (CNS), which is mediated, in part, by the transmigration of HIV-infected monocytes into the brain. The elaboration of chemokines and other factors by these infected cells contributes to CNS inflammation and cognitive impairment in a significant number of HIV-infected individuals. Recently, we demonstrated that HIV-infected monocyte transmigration into the CNS is enhanced greatly by the chemokine CC chemokine ligand 2 (CCL2)/monocyte chemoattractant protein-1. Platelet endothelial cell adhesion molecule-1 (PECAM-1) plays an important role in leukocyte transmigration across the endothelium of the systemic vasculature by mediating homophilic interactions between endothelial cells (EC)-EC and EC-leukocytes, thus preserving vessel integrity. The role of PECAM-1 in HIV-infected leukocyte transmigration across the blood brain barrier (BBB) and NeuroAIDS has not been characterized. We demonstrate that in brain tissue from individuals with HIV encephalitis, there is an accumulation of cleaved, soluble forms of the extracellular region of PECAM-1 (sPECAM-1). In addition, HIV-infected individuals have elevated levels of sPECAM-1 in their sera. Our in vitro data demonstrate that HIV-infected leukocytes, when treated with CCL2, shed sPECAM-1, suggesting a mechanism of extracellular PECAM-1 cleavage and release dependent on HIV infection and CCL2. We hypothesize that sPECAM-1 production by HIV-infected leukocytes, resulting in the accumulation of sPECAM-1 within the CNS vasculature and the generation of truncated, intracellular forms of PECAM-1 within leukocytes, alters PECAM-1 interactions between EC-EC and EC-leukocytes, thus contributing to enhanced transmigration of HIV-infected leukocytes into the CNS and changes in BBB permeability during the pathogenesis of NeuroAIDS.
Localization of human neutrophil interleukin-8 (CXCL-8) to organelle(s) distinct from the classical granules and secretory vesicles
Authors: Pellmé Sara , Mörgelin Matthias , Tapper Hans , Mellqvist Ulf-Henrik , Dahlgren Claes , Karlsson Anna 
Published: December 2005

Abstract
Mature human neutrophils contain small amounts of interleukin-8 [CXC chemokine ligand 8 (CXCL-8)], which upon proinflammatory activation, increases significantly. It has been suggested that the CXCL-8 content of resting human neutrophils is stored in the secretory vesicles. Here, we have used a fractionation technique, which allows isolation of these vesicles, and we find that CXCL-8 neither colocalizes with the secretory vesicles nor with markers of any of the classical neutrophil granules. To increase resolution in the system, we induced CXCL-8 production by lipopolysaccharide. After 8 h of stimulation, CXCL-8 was visualized within the cell using immunoelectron microscopy. The images revealed CXCL-8-containing stuctures resembling neutrophil granules, and these were distinct from all known neutrophil organelles, as shown by double immunostaining. Further, the CXCL-8 organelle was present in nonstimulated neutrophil cytoplasts, entities lacking all other known granules and secretory vesicles. Upon fractionation of the cytoplasts, CXCL-8 was found to partly cofractionate with calnexin, a marker for endoplasmic reticulum (ER). Thus, part of CXCL-8 may be localized to the ER or ER-like structures in the neutrophil.
No association of apolipoprotein E ε4 genotype with faster progression or less recovery of relapses in a Spanish cohort of multiple sclerosis
Authors: Sedano M J , Calmarza P , Perez L , Trejo J M 
Published: December 2005

Background Recent data have suggested a faster deterioration of multiple sclerosis (MS) patients who harbour the o4 allele of the apolipoprotein E (APOE) gene. We investigate the relationship of APOE genotypes with disease severity and clinical recovery of relapses in a MS population of the north of Spain. Methods One hundred and thirty-three patients with clinically defined MS were studied. Disease course (relapsing versus progressive), age of onset, duration of the disease and disability measured by the Expanded Disability Status Scale (EDSS) were recorded. Worsening was measured by the Progression Index (PI) and by EDSS 4 and 6 latencies. In 79 patients with relapsing-remitting (RR) MS the degree of clinical recovery of relapses (total versus partial) was assessed. Results The frequency of the APOE o4 allele in our patients was similar to that found in other southern European populations. APOE o4 patients did not have a faster progression as assessed by PI and EDSS 4 and 6 latencies. Among 79 patients with RRMS there were no significant differences in the degree of recovery of relapses. Conclusions In this MS population, APOE o4 polymorphism is not associated with a more severe clinical course and does not appear to influence recovery of exacerbations.