bluuebunny/crossref_metadata_2025_split huggingface dataset:
Increased injury and intramuscular collagen of the diaphragm in COPD: autopsy observations
Authors: Scott A. , Wang X. , Road J. D. , Reid W. D. 
Published: December 2005

Evidence for diaphragm injury in people with chronic obstructive pulmonary disease (COPD) has been reported, although the extent of injury and collagen accumulationpost mortemhave not previously been examined. In addition, it is not known whether the amount of injury and collagen are different in key regions of the diaphragm.The cross-sectional area of collagen and the percentage of abnormal myofibres in thepost mortemdiaphragm and psoas major were determined by computer-assisted image analysis of stained cross-sections of the diaphragm for collagen with picrosirius red and with haematoxylin and eosin for morphology.In the midcostal diaphragm of six subjects with COPD and six subjects with no significant respiratory disease, the COPD diaphragm displayed a greater cross-sectional area of collagen and percentage of abnormal myofibres (collagen: 24.2±1.0versus18.6±1.1%; injury: 28.4±7.2versus12.0±1.3%). In 18 patients with various respiratory conditions, the midcostal diaphragm displayed more collagen and abnormal myofibres than the crural diaphragm, while both costal and crural diaphragms displaying more collagen and abnormal myofibres than psoas major.This study reveals extensive injury and collagen accumulation in the chronic obstructive pulmonary diseased diaphragm, and reveals a regional pattern of injury and intramuscular collagen which may correspond to variations in diaphragm loading.
Human hepatocyte growth factor levels in patients undergoing thoracic operations: Table 1—
Authors: Dikmen E. , Kara M. , Kisa U. , Atinkaya C. , Han S. , Sakinci U. 
Published: December 2005

Human hepatocyte growth factor (hHGF) is the most potent mitogen identified for alveolar type II cells, and may have other important functions in the repair of the alveolar epithelium and compensatory lung growth. A study was conducted to evaluate the changes of serum hHGF levels in patients who underwent thoracic surgical procedures.The patients comprised 17 males and 14 females with a mean age of 47.1±13.5 yrs, who underwent either lung resection (LR group) or nonlung resection (nonLR group) thoracic procedures. The changes of hHGF levels were analysed in the pre- and post-operative periods in both groups.The pre-operative hHGF levels did not differ between the LR and the nonLR groups, which were 333.2±72.9 pg·mL−1and 343.6±125.3 pg·mL−1, respectively. The hHGF levels in the LR group significantly increased up to 433.6±128.1 pg·mL−1and 430.8±128.2 pg·mL−1in post-operative days 1 and 3, respectively. However, the same levels in the nonLR group appeared as 333.2±77.0 pg·mL−1and 311.9±73.0 pg·mL−1, respectively.In conclusion, the increases of serum human hepatocyte growth factor levels following pulmonary resection may imply the important role of human hepatocyte growth factor in lung regeneration or compensatory lung growth in humans.
Linkage analysis of complex diseases using microsatellites and single-nucleotide polymorphisms: application to alcoholism
Authors: Nsengimana Jérémie , Renard Hélène , Goldgar David 
Published: December 2005

Abstract
The efficacy of linkage studies using microsatellites and single-nucleotide polymorphisms (SNPs) was evaluated. Analyzed data were supplied by the Collaborative Study on the Genetics of Alcoholism (COGA). Alcoholism was analyzed together with a simulated trait caused by a gene of known position, through a nonparametric linkage test (NPL). For the alcoholism trait, four densities of SNPs (1 SNP per 0.2 cM, 0.5 cM, 1 cM and 2 cM) showed higher peaks of NPL z scores and smaller significant p-values than the usual 10-cM density of microsatellites. However, the two highest densities of SNPs had unstable z score signals, and therefore were difficult to interpret. Analyzing a simulated trait with the same markers in the same pedigrees, we confirmed the higher power of all four densities of SNPs compared to the 10-cM microsatellites panel, although the existence of other confounding peaks was confirmed for maps that are denser than 1 SNP/cM. We further showed that estimating the gene position using SNPs is far less biased than using the usual panel of microsatellites (biases of 0–2 cM for SNPs vs. 8.9 cM for microsatellites). We conclude that using dense maps of SNPs in linkage analysis is more powerful and less biased than using the 10-cM maps of microsatellites. However, linkage signals can be unstable and difficult to interpret when several SNPs are genotyped per centimorgan. The power and accuracy of 1 SNP/cM or 1 SNP/2 cM may be sufficient in a genome-wide linkage scan while denser maps may be most useful in fine-gene mapping studies exploiting linkage disequilibrium.
Linkage disequilibrium mapping via cladistic analysis of phase-unknown genotypes and inferred haplotypes in the Genetic Analysis Workshop 14 simulated data
Authors: Durrant Caroline , Morris Andrew P 
Published: December 2005

Abstract
We recently described a method for linkage disequilibrium (LD) mapping, using cladistic analysis of phased single-nucleotide polymorphism (SNP) haplotypes in a logistic regression framework. However, haplotypes are often not available and cannot be deduced with certainty from the unphased genotypes. One possible two-stage approach is to infer the phase of multilocus genotype data and analyze the resulting haplotypes as if known. Here, haplotypes are inferred using the expectation-maximization (EM) algorithm and the best-guess phase assignment for each individual analyzed. However, inferring haplotypes from phase-unknown data is prone to error and this should be taken into account in the subsequent analysis. An alternative approach is to analyze the phase-unknown multilocus genotypes themselves. Here we present a generalization of the method for phase-known haplotype data to the case of unphased SNP genotypes. Our approach is designed for high-density SNP data, so we opted to analyze the simulated dataset. The marker spacing in the initial screen was too large for our method to be effective, so we used the answers provided to request further data in regions around the disease loci and in null regions. Power to detect the disease loci, accuracy in localizing the true site of the locus, and false-positive error rates are reported for the inferred-haplotype and unphased genotype methods. For this data, analyzing inferred haplotypes outperforms analysis of genotypes. As expected, our results suggest that when there is little or no LD between a disease locus and the flanking region, there will be no chance of detecting it unless the disease variant itself is genotyped.
Methods to test for association between a disease and a multi-allelic marker applied to a candidate region
Authors: El Galta Rachid , Hsu Li , Houwing-Duistermaat Jeanine J 
Published: December 2005

Abstract
We report the analysis results of the Genetic Analysis Workshop 14 simulated microsatellite marker dataset, using replicate 50 from the Danacaa population. We applied several methods for association analysis of multi-allelic markers to case-control data to study the association between Kofendrerd Personality Disorder and multi-allelic markers in a candidate region previously identified by the linkage analysis. Evidence for association was found for marker D03S0127 (p < 0.01). The analyses were done without any prior knowledge of the answers.
Identification of susceptibility loci for complex diseases in a case-control association study using the Genetic Analysis Workshop 14 dataset
Authors: Kerstann Kimberly F , Jacobs Kevin , Yang Xiaohong , Bergen Andrew W , Goldin Lynn R , Goldstein Alisa M 
Published: December 2005

Abstract
Although current methods in genetic epidemiology have been extremely successful in identifying genetic loci responsible for Mendelian traits, most common diseases do not follow simple Mendelian modes of inheritance. It is important to consider how our current methodologies function in the realm of complex diseases. The aim of this study was to determine the ability of conventional association methods to fine map a locus of interest. Six study populations were selected from 10 replicates (New York) from the Genetic Analysis Workshop 14 simulated dataset and analyzed for association between the disease trait and locus D2. Genotypes from 45 single-nucleotide polymorphisms in the telomeric region of chromosome 3 were analyzed by Pearson's chi-square tests for independence to test for association with the disease trait of interest. A significant association was detected within the region; however, it was found 3 cM from the documented location of the D2 disease locus. This result was most likely due to the method used for data simulation. In general, this study showed that conventional case-control association methods could detect disease loci responsible for the development of complex traits.
Comparisons of case-selection approaches based on allele sharing and/or disease severity index: application to the GAW14 simulated data
Authors: Liu Chunyu , Cupples L Adrienne , Dupuis Josée 
Published: December 2005

Abstract
For mapping complex disease traits, linkage studies are often followed by a case-control association strategy in order to identify disease-associated genes/single-nucleotide polymorphisms (SNPs). Substantial efforts are required in selecting the most informative cases from a large collection of affected individuals in order to maximize the power of the study, while taking into consideration study cost. In this article, we applied and extended three case-selection strategies that use allele-sharing information method for families with multiple affected offspring to select most informative cases using additional information on disease severity. Our results revealed that most significant associations, as measured by the lowest p-values, were obtained from a strategy that selected a case with the most allele sharing with other affected sibs from linked families ("linked-best"), despite reduction in sample size resulting from discarding unlinked families. Moreover, information on disease severity appears to be useful to improve the ability to detect associations between markers and disease loci.
Hierarchical modeling in association studies of multiple phenotypes
Authors: Liu Xin , Jorgenson Eric , Witte John S 
Published: December 2005

AbstractThe genetic study of disease-associated phenotypes has become common because such phenotypes are often easier to measure and in many cases are under greater genetic control than the complex disease itself. Some disease-associated phenotypes are rare, however, making it difficult to evaluate their effects due to small informative sample sizes. In addition, analyzing numerous phenotypes introduces the issue of multiple comparisons. To address these issues, we have developed a hierarchical model (HM) for multiple phenotypes that provides more accurate effect estimates with a lower false-positive rate. We evaluated the validity and power of HM in association studies of multiple phenotypes using randomly selected cases and controls from the simulated data set in the Genetic Analysis Workshop 14. In particular, we first analyzed the association between each of the 12 subclinical phenotypes and single-nucleotide polymorphisms within the known causal loci using a conventional logistic regression model (LRM). Then we added a second-stage model by regressing all of the logistic coefficients of the phenotypes obtained from LRM on aZmatrix that incorporates the clinical correlation of the phenotypes. Specially, the 12 phenotypes were grouped into 3 clusters: 1) communally shared emotions; 2) behavioral related; and 3) anxiety related. A semi-Bayes HM effect estimate for each phenotype was calculated and compared with those from LRM. We observed that using HM to evaluate the association between SNPs and multiple related phenotypes slightly increased power for detecting the true associations and also led to fewer false-positive results.
Combining evidence for association from transmission disequilibrium and case-control studies using single-nucleotide polymorphisms
Authors: Putter Hein , Houwing-Duistermaat Jeanine J , Nagelkerke Nico JD 
Published: December 2005

Abstract
The aim of the present analysis is to combine evidence for association from the two most commonly used designs in genetic association analysis, the case-control design and the transmission disequilibrium test (TDT) design. The cases here are affected offspring from nuclear families and are used in both the case-control and TDT designs. As a result, inference from these designs is not independent. We applied a simple logistic regression method for combining evidence for association from case-control and TDT designs to single-nucleotide polymorphism data purchased on a region on chromosome 3, replicate 1 of the Aipotu population. Combining the evidence from the case-control and TDT designs yielded a 5–10% reduction in the standard errors of the relative risk estimates. The authors did not know the results before the analyses were conducted.
Robust trend tests for genetic association in case-control studies using family data
Authors: Tian Xin , Joo Jungnam , Zheng Gang , Lin Jing-Ping 
Published: December 2005

Abstract
We studied a trend test for genetic association between disease and the number of risk alleles using case-control data. When the data are sampled from families, this trend test can be adjusted to take into account the correlations among family members in complex pedigrees. However, the test depends on the scores based on the underlying genetic model and thus it may have substantial loss of power when the model is misspecified. Since the mode of inheritance will be unknown for complex diseases, we have developed two robust trend tests for case-control studies using family data. These robust tests have relatively good power for a class of possible genetic models. The trend tests and robust trend tests were applied to a dataset of Genetic Analysis Workshop 14 from the Collaborative Study on the Genetics of Alcoholism.
How to quantify information loss due to phase ambiguity in haplotype case-control studies
Authors: Uh Hae-Won , Houwing-Duistermaat Jeanine J , Putter Hein , van Houwelingen Hans C 
Published: December 2005

AbstractAssigning haplotypes in a case-control study is a challenging problem. We proposed a method to quantify the information loss due to missing phase information. We determined which individuals were responsible for the information loss, and calculated how much information could be gained when the ambiguous individuals could be resolved by adding additional parental information.
Smoothing of the bivariate LOD score for non-normal quantitative traits
Authors: Buil Alfonso , Dyer Thomas D , Almasy Laura , Blangero John 
Published: December 2005

Abstract
Variance component analysis provides an efficient method for performing linkage analysis for quantitative traits. However, type I error of variance components-based likelihood ratio testing may be affected when phenotypic data are non-normally distributed (especially with high values of kurtosis). This results in inflated LOD scores when the normality assumption does not hold. Even though different solutions have been proposed to deal with this problem with univariate phenotypes, little work has been done in the multivariate case. We present an empirical approach to adjust the inflated LOD scores obtained from a bivariate phenotype that violates the assumption of normality.
Using the Collaborative Study on the Genetics of Alcoholism data available for the Genetic Analysis Workshop 14, we show how bivariate linkage analysis with leptokurtotic traits gives an inflated type I error. We perform a novel correction that achieves acceptable levels of type I error.
Identification of genes involved in alcohol consumption and cigarettes smoking
Authors: de Andrade Mariza , Olswold Curtis L , Slusser Joshua P , Tordsen Larry A , Atkinson Elizabeth J , Rabe Kari G , Slager Susan L 
Published: December 2005

Abstract
We compared the results of quantitative linkage analysis using single-nucleotide polymorphisms and microsatellite markers and introduced a new screening test for multivariate quantitative linkage analysis using the Collaborative Study on the Genetics of Alcoholism data. We analyzed 115 extended non-Hispanic White families and tested for linkage using two phenotypes: the maximum number of drinks in a 24-hour period and the number of packs smoked per day for one year. Our results showed that the linkage signal increased using single-nucleotide polymorphisms compared with microsatellite markers and that the screening test gave similar results to that of the bivariate analysis, suggesting its potential use in reducing overall analysis time.
Bivariate genome scans incorporating factor and principal component analyses to identify common genetic components of alcoholism, event-related potential, and electroencephalogram phenotypes
Authors: Lin Jing-Ping , Wu Colin 
Published: December 2005

Abstract
Genetic components significantly contribute to the susceptibilities of alcoholism and its endophenotypes, such as event-related potential measures and electroencephalogram. An endophenotype is a correlated trait which identifies individuals at risk. Correlated traits could be influenced by shared genes. This study is intended to identify chromosome regions that may harbor common genetic loci contributing to alcoholism, event related potential measures and electroencephalogram. All 143 Collaborative Study on the Genetics of Alcoholism families with 1,614 individuals provided by the Genetic Analysis Workshop 14 were used for the analysis with aldx1 as an alcoholism diagnosis. We carried out factor and principal component analyses on the 12 event-related potentials, then bivariate genome scans on aldx1 and electroencephalogram (ecb21), as well as alcoholism and the principal component scores of the event-related potential measures. A univariate genome scan was also carried out on each trait. Factor and principal component analysis on the event-related potential measures showed that the 4 ttths and 4 ntths belong to one cluster (cluster 1), while the 4 ttdts belonged to another (cluster 2). From each cluster, one principal component was extracted and saved as pc1 (for cluster 1) and pc2 (for cluster 2). The results of genome scans revealed only one chromosome region, chromosome 4 q at about 100 cM, identified by several univariate genome scans including aldx1, ecb21, and pc2, and the evidence of linkage increased significantly in the bivariate genome scans of aldx1 and ecb21 and aldx1 and pc2. Our study suggests that the same quantitative trait locus on the chromosome 4 q region, where ADH3 is located, may influence the risk of alcoholism, variations of electroencephalogram, and the 4 ttdts of the event-related potential measures.
Genomic screening in family-based association testing
Authors: Murphy Amy , McQueen Matthew B , Su Jessica , Kraft Peter , Lazarus Ross , Laird Nan M , Lange Christoph , Van Steen Kristel 
Published: December 2005

Abstract
Due to the recent gains in the availability of single-nucleotide polymorphism data, genome-wide association testing has become feasible. It is hoped that this additional data may confirm the presence of disease susceptibility loci, and identify new genetic determinants of disease. However, the problem of multiple comparisons threatens to diminish any potential gains from this newly available data. To circumvent the multiple comparisons issue, we utilize a recently developed screening technique using family-based association testing. This screening methodology allows for the identification of the most promising single-nucleotide polymorphisms for testing without biasing the nominal significance level of our test statistic. We compare the results of our screening technique across univariate and multivariate family-based association tests. From our analyses, we observe that the screening technique, applied to different settings, is fairly consistent in identifying optimal markers for testing. One of the identified markers, TSC0047225, was significantly associated with both the ttth1 (p = 0.004) and ttth1-ttth4 (p = 0.004) phenotype(s). We find that both univariate- and multivariate-based screening techniques are powerful tools for detecting an association.
A Bayesian genome screening of maximum number of drinks as an alcoholism phenotype with the new Haseman-Elston method
Authors: Oh Cheongeun , Wang Shuang , Liu Nianjun , Chen Liang , Zhao Hongyu 
Published: December 2005

Abstract
Common human disorders, such as alcoholism, may be the result of interactions of many genes as well as environmental risk factors. Therefore, it is important to incorporate gene × gene and gene × environment interactions in complex disease gene mapping. In this study, we applied a robust Bayesian genome screening method that can incorporate interaction effects to map genes underlying alcoholism through its application to the data of the Collaborative Studies on Genetics of Alcoholism provided by Genetic Analysis Workshop 14. Our Bayesian genome screening method uses the regression-based stochastic variable selection, coupled with the new Haseman-Elston method to identify markers linked to phenotypes of interest. Compared to traditional linkage methods based on single-gene disease models, our method allows for multilocus disease models for simultaneous screening including both main and interaction (epistatic) effects. It is conceptually simple and computationally efficient through the use of Gibbs sampler. We conducted genome-wide analysis and comparison between scans based on microsatellites and single-nucleotide polymorphisms. A total of 328 microsatellites and 11,560 single-nucleotide polymorphisms (by Affymetrix) on 22 autosomal chromosomes and sex chromosome were used.
A comparison of univariate, bivariate, and trivariate whole-genome linkage screens of genetically correlated electrophysiological endophenotypes
Authors: Warren Diane M , Dyer Thomas D , Peterson Charles P , Mahaney Michael C , Blangero John , Almasy Laura 
Published: December 2005

Abstract
We used a maximum-likelihood based multipoint linkage approach implemented in SOLAR to examine simultaneously linkage for three electrophysiological endophenotypes from the Collaborative Study of the Genetics of Alcoholism: TTTH1, TTTH2, and TTTH3. These endophenotypes have been identified as markers of alcohol dependence susceptibility. Data were from 905 individuals in 143 families. Measured covariates considered included sex, age at electrophysiology data collection, habitual smoking status, and the maximum number of drinks consumed in a 24-hour period. Comparisons were made among genome-wide univariate, bivariate, and trivariate linkage analyses using genotypes based on microsatellite markers supplied by the Center for Inherited Disease Research, and genotypes based on single-nucleotide polymorphism markers provided by Illumina. All LODs were corrected to a standard equivalent to 1 degree of freedom. Using the trivariate approach and the microsatellite-based genotypes, we estimated a maximum multipoint linkage signal of LOD = 2.66 on chromosome 7q at 157 cM. Analyses using the Illumina SNP genotypes produced similar results, yielding a maximum multipoint LOD of 2.95 on 7q at 174 cM. These regions of interest correspond to those identified in the univariate and bivariate linkage screens. Our results suggest that trivariate multipoint linkage analyses have utility in the further characterization of chromosomal regions potentially containing genes influencing the phenotypes being examined. Based on a comparison of the number of LOD scores achieving statistical significance, our results suggest that the microsatellite- and Illumina SNP-based genotypes have similar utility for detecting genomic regions of interest.