bluuebunny/crossref_metadata_2025 huggingface dataset:
Truncation in the tcdC region of the Clostridium difficilePathLoc of clinical isolates does not predict increased biological activity of Toxin B or Toxin A
Authors: Murray Ruth , Boyd Dave , Levett Paul N , Mulvey Michael R , Alfa Michelle J
Published: June 2009

Background

The increased severity of disease associated with the NAP1 strain of Clostridium difficile has been attributed to mutations to the tcdC gene which codes for a negative regulator of toxin production. To assess the role of hyper-production of Toxins A and B in clinical isolates of Clostridium difficile, two NAP1-related and five NAP1 non-related strains were compared.

Methods

Sequencing was performed on tcdC, tcdR, and tcdE to determine if there were differences that might account for hyper-production of Toxin A and Toxin B in NAP1-related strains. Biological activity of Toxin B was evaluated using the HFF cell CPE assay and Toxin A biological activity was assessed using the Caco-2 Trans-membrane resistance assay.

Results

Our results confirm that Toxin A and Toxin B production in NAP1-related strains and ATCC 43255 occurs earlier in the exponential growth phase compared to most NAP1-nonrelated clinical isolates. Despite the hyper-production observed in ATCC 43255 it had no mutations in tcdC, tcdR or tcdE. Analysis of the other clinical isolates indicated that the kinetics and ultimate final concentration of Toxin A and B did not correlate with the presence or lack of alterations in tcdC, tcdR or tcdE.

Conclusion

Our data do not support a direct role for alterations in the tcdC gene as a predictor of hyperproduction of Toxin A and B in NAP1-related strains.
The anticancer activity of lytic peptides is inhibited by heparan sulfate on the surface of the tumor cells
Authors: Fadnes Bodil , Rekdal Øystein , Uhlin-Hansen Lars
Published: June 2009

Background

Cationic antimicrobial peptides (CAPs) with antitumor activity constitute a promising group of novel anticancer agents. These peptides induce lysis of cancer cells through interactions with the plasma membrane. It is not known which cancer cell membrane components influence their susceptibility to CAPs. We have previously shown that CAPs interact with the two glycosaminoglycans (GAGs), heparan sulfate (HS) and chondroitin sulfate (CS), which are present on the surface of most cells. The purpose of this study was to investigate the role of the two GAGs in the cytotoxic activity of CAPs.

Methods

Various cell lines, expressing different levels of cell surface GAGs, were exposed to bovine lactoferricin (LfcinB) and the designer peptide, KW5. The cytotoxic effect of the peptides was investigated by use of the colorimetric MTT viability assay. The cytotoxic effect on wild type CHO cells, expressing normal amounts of GAGs on the cell surface, and the mutant pgsA-745, that has no expression of GAGs on the cell surface, was also investigated.

Results

We show that cells not expressing HS were more susceptible to CAPs than cells expressing HS at the cell surface. Further, exogenously added heparin inhibited the cytotoxic effect of the peptides. Chondroitin sulfate had no effect on the cytotoxic activity of KW5 and only minor effects on LfcinB cytotoxicity.

Conclusion

Our results show for the first time that negatively charged molecules at the surface of cancer cells inhibit the cytotoxic activity of CAPs. Our results indicate that HS at the surface of cancer cells sequesters CAPs away from the phospholipid bilayer and thereby impede their ability to induce cytolysis.
Deoxycholate induces COX-2 expression via Erk1/2-, p38-MAPK and AP-1-dependent mechanisms in esophageal cancer cells
Authors: Looby Eileen , Abdel-Latif Mohamed MM , Athié-Morales Veronica , Duggan Shane , Long Aideen , Kelleher Dermot
Published: June 2009

Background

The progression from Barrett's metaplasia to adenocarcinoma is associated with the acquirement of an apoptosis-resistant phenotype. The bile acid deoxycholate (DCA) has been proposed to play an important role in the development of esophageal adenocarcinoma, but the precise molecular mechanisms remain undefined. The aim of this study was to investigate DCA-stimulated COX-2 signaling pathways and their possible contribution to deregulated cell survival and apoptosis in esophageal adenocarcinoma cells.

Methods

Following exposure of SKGT-4 cells to DCA, protein levels of COX-2, MAPK and PARP were examined by immunoblotting. AP-1 activity was assessed by mobility shift assay. DCA-induced toxicity was assessed by DNA fragmentation and MTT assay.

Results

DCA induced persistent activation of the AP-1 transcription factor with Fra-1 and JunB identified as the predominant components of the DCA-induced AP-1 complex. DCA activated Fra-1 via the Erk1/2- and p38 MAPK while Erk1/2 is upstream of JunB. Moreover, DCA stimulation mediated inhibition of proliferation with concomitant low levels of caspase-3-dependent PARP cleavage and DNA fragmentation. Induction of the anti-apoptotic protein COX-2 by DCA, via MAPK/AP-1 pathway appeared to balance the DCA mediated activation of pro-apoptotic markers such as PARP cleavage and DNA fragmentation. Both of these markers were increased upon COX-2 suppression by aspirin pretreatment prior to DCA exposure.

Conclusion

DCA regulates both apoptosis and COX-2-regulated cell survival in esophageal cells suggesting that the balance between these two opposing signals may determine the transformation potential of DCA as a component of the refluxate.
Early MRI response monitoring of patients with advanced hepatocellular carcinoma under treatment with the multikinase inhibitor sorafenib
Authors: Horger Marius , Lauer Ulrich M , Schraml Christina , Berg Christoph P , Koppenhöfer Ursula , Claussen Claus D , Gregor Michael , Bitzer Michael
Published: June 2009

Background

New therapeutic principles in clinical oncology require the adjustment of response criteria to govern therapy decisions. For advanced hepatocellular carcinoma (HCC) a new era has recently begun by the approval of the multikinase inhibitor sorafenib. As a unique feature, HCC usually develops in a diseased liver and current imaging technologies employing classical response criteria have not been prospectively evaluated for this new treatment.

Methods

MRI signal patterns were assessed in 21 advanced HCC patients receiving sorafenib. MRI was performed at baseline and in short-term intervals thereafter. Signal changes under therapy on T1WI, T2WI and post-gadolinium images including necrosis volume and its ratio to the entire tumor volume were compared to baseline imaging. To assess the association between the categorical variables, Fisher's exact tests were applied for a statistical analysis. Survey time ranged from 2–65 weeks, and a total of 39 target lesions were evaluated.

Results

Signal abnormalities during sorafenib therapy were disclosed by T1WI and T2WI in 15/21 patients. The predominant tumor signal change was hyperintensity on both T1WI and T2WI. Interestingly, most patients developed MRI signal changes within 4 weeks of therapy; in contrast, two non-responders did not show any signal alteration at follow-up. Under therapy, 16/21 patients presented with new or progressive necrosis, whereas 7 patients achieved temporarily >75% tumor necrosis under sorafenib. Significantly associated MRI variables were increase in T1WI signal and tumor necrosis (p = 0.017) as well as increase of tumor necrosis with an elevated ratio of necrotic to vital tumor areas (p = 0.002). Remarkably, some (3/13) of the patients developing necrotic tumor areas showed a relevant (>20%) increase in tumor volume, which should be considered in the assessment of imaging studies.

Conclusion

As sorafenib induces early intralesional necrosis with profound changes in T1WI/T2WI MRI signal intensities and measurable necrotic tumor areas in most HCC patients, early MRI-based evaluation could pave the way for its rationale and cost-effective application.
Essential health information available for India in the public domain on the internet
Authors: Raban Magdalena Z , Dandona Rakhi , Dandona Lalit
Published: June 2009

Background

Health information and statistics are important for planning, monitoring and improvement of the health of populations. However, the availability of health information in developing countries is often inadequate. This paper reviews the essential health information available readily in the public domain on the internet for India in order to broadly assess its adequacy and inform further development.

Methods

The essential sources of health-related information for India were reviewed. An extensive search of relevant websites and the PubMed literature database was conducted to identify the sources. For each essential source the periodicity of the data collection, the information it generates, the geographical level at which information is reported, and its availability in the public domain on the internet were assessed.

Results

The available information related to non-communicable diseases and injuries was poor. This is a significant gap as India is undergoing an epidemiological transition with these diseases/conditions accounting for a major proportion of disease burden. Information on infrastructure and human resources was primarily available for the public health sector, with almost none for the private sector which provides a large proportion of the health services in India. Majority of the information was available at the state level with almost negligible at the district level, which is a limitation for the practical implementation of health programmes at the district level under the proposed decentralisation of health services in India.

Conclusion

This broad review of the essential health information readily available in the public domain on the internet for India highlights that the significant gaps related to non-communicable diseases and injuries, private health sector and district level information need to be addressed to further develop an effective health information system in India.
Satisfaction and compliance in hormonal contraception: the result of a multicentre clinical study on women's experience with the ethinylestradiol/norelgestromin contraceptive patch in Italy
Authors: Crosignani Pier Giorgio , Nappi Carmine , Ronsini Salvatore , Bruni Vincenzina , Marelli Silvia , Sonnino Davide ,   The Italian EVRA Contrast Study Group
Published: June 2009

Background

For many women finding the right contraceptive method can be challenging and consistent and correct use over a lifetime is difficult. Even remembering to take a birth control pill every day can be a challenge. The primary objective of this study was to evaluate women's experience with a weekly ethinylestradiol/norelgestromin contraceptive patch (EE/NGMN patch), given new technologies recently developed in hormonal contraception to increase women's options in avoiding daily dosing.

Methods

In 24 Italian sites, 207 women received the EE/NGMN patch for up to 6 cycles. At study end, overall satisfaction and preference, as well as compliance, efficacy and safety, were evaluated.

Results

175 women (84.5%) completed the study. The overall satisfaction rate was 88%; convenience and once-a-week frequency of the patch were especially appreciated. At baseline, 82 women (39.4%) were using a contraceptive method, mainly oral contraceptives and barrier methods, but only 45.1% were very satisfied/satisfied; after 6 months with the patch, 86.3% of this subset was very satisfied/satisfied. Considering the method used in the 3 months before the study entry, 78.1% strongly preferred/preferred the patch, for convenience (53.9%), ease of use/simplicity (28.9%), fewer (9.2%) and less severe (2.6%) side effects. Compliance was very high: 1034/1110 cycles (93.2%) were completed with perfect compliance and the mean subject's compliance score was 90%. One on-therapy pregnancy occurred. The patch was safe and well tolerated: adverse events frequency was low, with predominantly single reports of each event. Most of them started and subsided during cycle 1.

Conclusion

This study demonstrated that the EE/NGMN patch is associated with high satisfaction levels and excellent compliance. At study end, the majority of women indicated that they would continue using the patch.
Spatial analysis of plague in California: niche modeling predictions of the current distribution and potential response to climate change
Authors: Holt Ashley C , Salkeld Daniel J , Fritz Curtis L , Tucker James R , Gong Peng
Published: June 2009

Background

Plague, caused by the bacteriumYersinia pestis, is a public and wildlife health concern in California and the western United States. This study explores the spatial characteristics of positive plague samples in California and tests Maxent, a machine-learning method that can be used to develop niche-based models from presence-only data, for mapping the potential distribution of plague foci. Maxent models were constructed using geocoded seroprevalence data from surveillance of California ground squirrels (Spermophilus beecheyi) as case points and Worldclim bioclimatic data as predictor variables, and compared and validated using area under the receiver operating curve (AUC) statistics. Additionally, model results were compared to locations of positive and negative coyote (Canis latrans) samples, in order to determine the correlation between Maxent model predictions and areas of plague risk as determined via wild carnivore surveillance.

Results

Models of plague activity in California ground squirrels, based on recent climate conditions, accurately identified case locations (AUC of 0.913 to 0.948) and were significantly correlated with coyote samples. The final models were used to identify potential plague risk areas based on an ensemble of six future climate scenarios. These models suggest that by 2050, climate conditions may reduce plague risk in the southern parts of California and increase risk along the northern coast and Sierras.

Conclusion

Because different modeling approaches can yield substantially different results, care should be taken when interpreting future model predictions. Nonetheless, niche modeling can be a useful tool for exploring and mapping the potential response of plague activity to climate change. The final models in this study were used to identify potential plague risk areas based on an ensemble of six future climate scenarios, which can help public managers decide where to allocate surveillance resources. In addition, Maxent model results were significantly correlated with coyote samples, indicating that carnivore surveillance programs will continue to be important for tracking the response of plague to future climate conditions.
Airborne particulate matter PM2.5 from Mexico City affects the generation of reactive oxygen species by blood neutrophils from asthmatics: an in vitro approach
Authors: Sierra-Vargas Martha Patricia , Guzman-Grenfell Alberto Martin , Blanco-Jimenez Salvador , Sepulveda-Sanchez Jose David , Bernabe-Cabanillas Rosa Maria , Cardenas-Gonzalez Beatriz , Ceballos Guillermo , Hicks Juan Jose
Published: June 2009

Background

The Mexico City Metropolitan Area is densely populated, and toxic air pollutants are generated and concentrated at a higher rate because of its geographic characteristics. It is well known that exposure to particulate matter, especially to fine and ultra-fine particles, enhances the risk of cardio-respiratory diseases, especially in populations susceptible to oxidative stress. The aim of this study was to evaluate the effect of fine particles on the respiratory burst of circulating neutrophils from asthmatic patients living in Mexico City.

Methods

In total, 6 subjects diagnosed with mild asthma and 11 healthy volunteers were asked to participate. Neutrophils were isolated from peripheral venous blood and incubated with fine particles, and the generation of reactive oxygen species was recorded by chemiluminescence. We also measured plasma lipoperoxidation susceptibility and plasma myeloperoxidase and paraoxonase activities by spectrophotometry.

Results

Asthmatic patients showed significantly lower plasma paraoxonase activity, higher susceptibility to plasma lipoperoxidation and an increase in myeloperoxidase activity that differed significantly from the control group. In the presence of fine particles, neutrophils from asthmatic patients showed an increased tendency to generate reactive oxygen species after stimulation with fine particles (PM2.5).

Conclusion

These findings suggest that asthmatic patients have higher oxidation of plasmatic lipids due to reduced antioxidant defense. Furthermore, fine particles tended to increase the respiratory burst of blood human neutrophils from the asthmatic group.

On the whole, increased myeloperoxidase activity and susceptibility to lipoperoxidation with a concomitant decrease in paraoxonase activity in asthmatic patients could favor lung infection and hence disrupt the control of asthmatic crises.
Clinical outcome of breast cancer occurring after treatment for Hodgkin's lymphoma: case-control analysis
Authors: El-Din Mohamed A Alm , Hughes Kevin S , Raad Rita A , Goldberg Saveli I , Aisenberg Alan C , Niemierko Andrzej , Taghian Alphonse G
Published: June 2009

Background

To evaluate diagnosis, management and outcome of breast cancer (BC) occurring after irradiation for Hodgkin's lymphoma (HL).

Methods

39 cases of BC in 28 HL survivors were retrospectively reviewed. 21 patients were included in a case-control analysis.

Results

The median age at diagnosis of HL and BC was 25.3 and 45.3 years, respectively. The median interval to develop BC was 16.1 years. Eleven women (39.2%) had bilateral disease. Mode of detection of the index breast cancers was by mammographic screening in 17 patients (60.7%), palpable lump in 8 patients (28.6%), clinical examination in two patients (7.1%), and unknown in one patient (3.6%). Case-control analysis showed that histological features and prognosis of BC after HL were similar to those of primary BC, however, for BC after HL, mastectomy was the predominant surgery (P = .001) and adjuvant radiotherapy and anthracycline-based chemotherapy were less frequently used as compared to primary BC (P < .001 and .003, respectively).

Conclusion

The previous history of HL does not appear to be a poor prognostic factor for BC occurring thereafter.
On the choice of linkage statistics
Authors: Margaritte-Jeannin Patricia , Babron Marie-Claude , Clerget-Darpoux Françoise
Published: June 2009

Three LOD score statistics are often used for genome-wide linkage analysis: the maximum LOD score, the LOD score statistic proposed by Kong and Cox, both based on the allele-sharing between affected sib pairs, and the maximization of the LOD score function of Morton on two genetic models and an heterogeneity parameter.

Using only identity-by-descent sharing between affected sibs as linkage information, we studied the behavior of these three statistics under the null hypothesis in the rheumatoid arthritis simulated data (Genetic Analysis Workshop 15 Problem 3 – simulating model known). Distributions under the null hypothesis show that identical values of the statistics correspond to very different genome-wide p-values: comparison and interpretation of several linkage statistics cannot be done on the observed value. The Kong and Cox LOD score statistic had slightly better power to detect the HLA region involved in rheumatoid arthritis compared to the other methods. In a second step, we show that performing the analysis under a greater number of genetic models in the hope of better scanning the space of models, does not increase the power of detection.
Combining linkage data sets for meta-analysis and mega-analysis: the GAW15 rheumatoid arthritis data set
Authors: Segurado Ricardo , Hamshere Marian L , Glaser Beate , Nikolov Ivan , Moskvina Valentina , Holmans Peter A
Published: June 2009

We have used the genome-wide marker genotypes from Genetic Analysis Workshop 15 Problem 2 to explore joint evidence for genetic linkage to rheumatoid arthritis across several samples. The data consisted of four high-density genome scans on samples selected for rheumatoid arthritis. We cleaned the data, removed intermarker linkage disequilibrium, and assembled the samples onto a common genetic map using genome sequence positions as a reference for map interpolation. The individual studies were combined first at the genotype level (mega-analysis) prior to a multipoint linkage analysis on the combined sample, and second using the genome scan meta-analysis method after linkage analysis of each sample. The two approaches were compared, and give strong support to the HLA locus on chromosome 6 as a susceptibility locus. Other regions of interest include loci on chromosomes 11, 2, and 12.
Application of bivariate mixed counting process models to genetic analysis of rheumatoid arthritis severity
Authors: Sutradhar Rinku , Pinnaduwage Dushanthi , Bull Shelley B
Published: June 2009

We sought to i) identify putative genetic determinants of the severity of rheumatoid arthritis in the NARAC (North American Rheumatoid Arthritis Consortium) data, ii) assess whether known candidate genes for disease status are also associated with disease severity in those affected, and iii) determine whether heterogeneity among the severity phenotypes can be explained by genetic and/or host factors. These questions are addressed by developing bivariate mixed-counting process models for numbers of tender and swollen joints to evaluate genetic association of candidate polymorphisms, such as DRB1, and selected single-nucleotide polymorphisms in known candidate genes/regions for rheumatoid arthritis, including PTPN22, and those in the regions identified by a genome-wide linkage scan of disease severity using the dense Illumina single-nucleotide polymorphism panel. The counting process framework provides a flexible approach to account for the duration of rheumatoid arthritis, an attractive feature when modeling severity of a disease. Moreover, we found a gain in efficiency when using a bivariate compared to a univariate counting process model.
Using the maternal-fetal genotype incompatibility test to assess non-inherited maternal HLA-DRB1 antigen coding alleles as rheumatoid arthritis risk factors
Authors: Hsieh Hsin-Ju , Palmer Christina GS , Harney Sinead , Chen Hsiu-Wen , Bauman Lara , Brown Matthew A , Sinsheimer Janet S
Published: June 2009

Non-inherited maternal antigens encoded by specific HLA-DRB1 alleles (NIMA) have been implicated as a rheumatoid arthritis (RA) risk factor. Using genotype data from North American Rheumatoid Arthritis Consortium study participants and the maternal-fetal genotype incompatibility (MFG) test, we find evidence for offspring allelic effects but no evidence for NIMA as a RA risk factor. We discuss possible reasons why our result conflicts with several previous studies (including one of our own) that used RA patients from northern Europe.
Visualizing genotype × phenotype relationships in the GAW15 simulated data
Authors: Qin Xuejun , Schmidt Silke , Martin Eden , Hauser Elizabeth R
Published: June 2009

We have developed a graphical display tool called SIMLAPLOT for visualizing different ways in which continuous covariates may influence the genotype-specific risk for complex human diseases. The purpose of our study was to examine continuous covariates in the Genetic Analysis Workshop 15 simulated data set using our novel graphical display tool, with knowledge of the answers. The generated plots provide information about genetic models for the simulated continuous covariates and may help identify the single-nucleotide polymorphisms associated with the underlying quantitative trait loci.
Fine-scale linkage disequilibrium mapping: a comparison of coalescent-based and haplotype-clustering-based methods
Authors: Won Sungho , Sinha Ritwik , Luo Yuqun
Published: June 2009

Among the various linkage-disequilibrium (LD) fine-mapping methods, two broad classes have received considerable development recently: those based on coalescent theory and those based on haplotype clustering. Using Genetic Analysis Workshop 15 Problem 3 simulated data, the ability of these two classes to localize the causal variation were compared. Our results suggest that a haplotype-clustering-based approach performs favorably, while at the same time requires much less computing than coalescent-based approaches. Further, we observe that 1) when marker density is low, a set of equally spaced single-nucleotide polymorphisms (SNPs) provides better localization than a set of tagging SNPs of equal number; 2) denser sets of SNPs generally lead to better localization, but the benefit diminishes beyond a certain density; 3) larger sample size may do more harm than good when poor selection of markers results in biased LD patterns around the disease locus. These results are explained by how well the set of selected markers jointly approximates the expected LD pattern around a disease locus.
Genome-wide association tests by two-stage approaches with unified analysis of families and unrelated individuals
Authors: Wang Xuexia , Zhang Zhaogong , Zhang Shuanglin , Sha Qiuying
Published: June 2009

Multiple testing is a problem in genome-wide or region-wide association studies. In this report, we consider a study design given by the Genetic Analysis Workshop 15 (GAW15) Problem 3 – nuclear families (parents with their affected children) and unrelated controls. Based on this design, we propose three two-stage approaches to deal with the problem of multiple testing. The tests in the first stage, statistically independent of the association test used in the second stage, are used to screen or select single-nucleotide polymorphisms (SNPs). Then, in the second stage, a family-based association test is performed on a much smaller set of selected SNPs. Thus, the problem of multiple testing is much less severe. Our simulation studies and application to the dense SNP data of chromosome 6 in the GAW15 Problem 3 show that the two-stage methods are more powerful than the one-stage method (using the family-based association test only).
Mapping of trans-acting regulatory factors from microarray data
Authors: McClintick Jeanette N , Liu Yunlong , Edenberg Howard J
Published: June 2009

To explore the mapping of factors regulating gene expression, we have carried out linkage studies using expression data from individual transcripts (from Affymetrix microarrays; Genetic Analysis Workshop 15 Problem 1) and composite data on correlated groups of transcripts. Quality measures for the arrays were used to remove outliers, and arrays with sex mismatches were also removed. Data likely to represent noise were removed by setting a minimum threshold of present calls among the non-redundant set of 190 arrays. SOLAR was used for genetic analysis, with MAS5 signal as the measure of expression. Probe sets with larger CVs generated more linkages (LOD > 2.0). Whiletranslinkages predominated, linkages with the largest LOD scores (>4) were mostlycis. Hierarchical clustering was used to generate correlated groups of genes. We tested four composite measures of expression for the clusters. The average signal, average normalized signal, and the first principal component of the data behaved similarly; in 8/19 clusters tested, the composite measures linked to a region to which some individual probe sets within the cluster also linked. The second principal component only produced one linkage with LOD > 2. One cluster based upon chromosomal location, containing histone genes, linked to twotransregions. This work demonstrates that composite measures for genes with correlated expression can be used to identify loci that affect multiple co-expressed genes.
Comparison of variable and model selection methods for genetic association studies using the GAW15 simulated data
Authors: Ye Zhan , Atkinson Elizabeth J , Fridley Brooke L , de Andrade Mariza
Published: June 2009

We compared and evaluated several variable and model selection methods using Bayesian and non-Bayesian approaches for three replicates of the Genetic Analysis Workshop 15 (GAW15) simulated data. In doing so, two phenotypes were utilized: rheumatoid arthritis (RA) affection status as a binary trait and IgM as a continuous measure. The analyses were performed adjusting for sex, age, and smoking status. For both outcomes, all the methods were comparable in finding the single-nucleotide polymorphisms (SNPs) generated to have a genetic signal. We successfully identified the susceptibility SNPs for RA in the HLA region (chromosome 6), and chromosome 18, and the susceptibility SNP for IgM on chromosome 11; however, many of the methods produced false-positive results.

The answers to Problem 3 were requested and known to the authors.
Seeking gene relationships in gene expression data using support vector machine regression
Authors: Yu Robert , DeHoff Kevin , Amos Christopher I , Shete Sanjay
Published: June 2009

Several genetic determinants responsible for individual variation in gene expression have been located using linkage and association analyses. These analyses have revealed regulatory relationships between genes. The heritability of expression variation as a quantitative phenotype reflects its underlying genetic architecture. Using support vector machine regression (SVMR) and gene ontological information, we proposed an approach to identify gene relationships in expression data provided by Genetic Analysis Workshop 15 that would facilitate subsequent genetic analyses. A group of related genes were selected for a shared biological theme, and SVMR was trained to form a regression model using the training gene expressions. The model was subsequently used to search for and capture similarly related genes. SVMR shows promising capability in modeling and seeking gene relationships through expression data.
Joint linkage and imprinting analyses of GAW15 rheumatoid arthritis and gene expression data
Authors: Zhou Xiaojun , Chen Wei , Swartz Michael D , Lu Yue , Yu Robert , Amos Christopher I , Wu Chih-Chieh , Shete Sanjay
Published: June 2009

Background

Genomic imprinting is a mechanism in which the expression of a gene copy depends upon the sex of the parent from which it was inherited. This mechanism is now well recognized in humans, and the deregulation of imprinted genes has been implicated in a number of diseases. In this study, we performed a genome-wide joint linkage and imprinting scan using two data sets provided by Genetic Analysis Workshop 15 (GAW15).

Results

The first data set was high-risk rheumatoid arthritis families collected by the North American Rheumatoid Arthritis Consortium. We used both model-based and model-free methods of joint linkage and imprinting analyses. Although a genome scan of rheumatoid arthritis families using GENEHUNTER-MODSCORE suggested regions that might be imprinted, further analyses using variance-components method failed to obtain significant signals of imprinting. The second data set was Problem 1 of GAW15, which included single-nucleotide polymorphism genotypes and gene expression data for Centre d'Etude du Polymorphisme Humain pedigrees. A previous genome-wide linkage scan identified loci that may be regulators of gene expression: our genome-wide joint linkage and imprinting scan using a variance-components approach found significant signals for linkage.

Conclusion

Our linkage scan results suggest that imprinted genes are unlikely to be involved in susceptibility to rheumatoid arthritis. However, for expression level of TGFBR3 gene, we found a point-wise p-value of 0.03 for imprinting, but increase in the LOD score did not meet the required threshold to reliably identify imprinting as the correct mode of inheritance in genome-wide linkage scans.
Stability of variable importance scores and rankings using statistical learning tools on single-nucleotide polymorphisms and risk factors involved in gene × gene and gene × environment interactions
Authors: Nicodemus Kristin K , Wang Wenyi , Shugart Yin Yao
Published: June 2009

Risk of complex disorders is thought to be multifactorial, involving interactions between risk factors. However, many genetic studies assess association between disease status and markers one single-nucleotide polymorphism (SNP) at a time, due to the high-dimensional nature of the search space of all possible interactions. Three ensemble methods have been recently proposed for use in high-dimensional data (Monte Carlo logic regression, random forests, and generalized boosted regression). An intuitive way to detect an association between genetic markers and disease status is to use variable importance measures, even though the stability of these measures in the context of a whole-genome association study is unknown. For the simulated data of Problem 3 in the Genetic Analysis Workshop 15 (GAW15), we examined the variability of both rankings and magnitude of variable importance measures using 10 variables simulated to participate in gene × gene and gene × environment interactions. We conducted 500 analyses per method on one randomly selected replicate, tallying the rankings and importance measures for each of the 10 variables of interest. When the simulated effect size was strong, all three methods showed stable rankings and estimates of variable importance. However, under conditions more commonly expected to be encountered in complex diseases, random forests and generalized boosted regression showed more stable estimates of variable importance and variable rankings. Individuals endeavoring to apply statistical learning methods to detect interaction in complex disease studies should perform repeated analyses in order to assure variable importance measures and rankings do not vary greatly, even for statistical learning algorithms that are thought to be stable.
Assessing genotype × environment interaction in linkage mapping using affected sib pairs
Authors: Chen Yi-Shin , Chiu Yen-Feng , Kao Hui-Yi , Hsu Fang-Chi
Published: June 2009

Rheumatoid arthritis (RA) is a complex disease that involves both environmental and genetic factors. Elucidation of the basic etiologic factors involved in RA is essential for preventing and treating this disease. However, the etiology of RA, like that of other complex diseases, is largely unknown. In the present study, we conducted autosomal multipoint linkage scans using affected sib pairs by incorporating the smoking status into analysis. We divided the affected sib pairs into three subgroups based on smoking status (ever, current, or never). Interactions between the susceptibility genes and smoking could then be assessed through linkage mapping. Results suggested that the genetic effect of chromosome 6p21.2-3 in concordant current smoker pairs was about two-fold greater than that of the concordant non-current smoker pairs or discordant pairs. With incorporation of smoking status, additional regions with evidence of linkage were identified, including chromosomes 4q and 20q; while evidence of linkage remained in the regions of chromosomes 6p, 8p, and 9p. The interaction effects varied in different regions. Results from our analyses suggested that incorporating smoking status into linkage analyses could increase the statistical power of the multipoint linkage approach applied here and help elucidate the etiology of RA.
Comparison of the power of haplotype-based versus single- and multilocus association methods for gene × environment (gene × sex) interactions and application to gene × smoking and gene × sex interactions in rheumatoid arthritis
Authors: Dempfle Astrid , Hein Rebecca , Beckmann Lars , Scherag André , Nguyen Thuy Trang , Schäfer Helmut , Chang-Claude Jenny
Published: June 2009

Accounting for interactions with environmental factors in association studies may improve the power to detect genetic effects and may help identifying important environmental effect modifiers. The power of unphased genotype-versus haplotype-based methods in regions with high linkage disequilibrium (LD), as measured by D', for analyzing gene × environment (gene × sex) interactions was compared using the Genetic Analysis Workshop 15 (GAW15) simulated data on rheumatoid arthritis with prior knowledge of the answers. Stepwise and regular conditional logistic regression (CLR) was performed using a matched case-control sample for a HLA region interacting with sex. Haplotype-based analyses were performed using a haplotype-sharing-based Mantel statistic and a test for haplotype-trait association in a general linear model framework. A step-down minP algorithm was applied to derive adjusted p-values and to allow for power comparisons. These methods were also applied to the GAW15 real data set for PTPN22.

For markers in strong LD, stepwise CLR performed poorly because of the correlation/collinearity between the predictors in the model. The power was high for detecting genetic main effects using simple CLR models and haplotype-based methods and for detecting joint effects using CLR and Mantel statistics. Only the haplotype-trait association test had high power to detect the gene × sex interaction.

In the PTPN22 region with markers characterized by strong LD, all methods indicated a significant genotype × sex interaction in a sample of about 1000 subjects. The previously reported R620W single-nucleotide polymorphism was identified using logistic regression, but the haplotype-based methods did not provide any precise location information.
Application of structural equation models to construct genetic networks using differentially expressed genes and single-nucleotide polymorphisms
Authors: Lee Seungmook , Jhun Mina , Lee Eun-Kyung , Park Taesung
Published: June 2009

Understanding the genetic basis of human variation is an important goal of biomedical research. In this study, we used structural equation models (SEMs) to construct genetic networks to model how specific single-nucleotide polymorphisms (SNPs) from two genes known to cause acute myeloid leukemia (AML) by somatic mutation, runt-related transcription factor 1 (RUNX1) and ets variant gene 6 (ETV6), affect expression levels of other genes and how RUNX1 and ETV6 are related to each other. The SEM approach allows us to compare several candidate models from which an explanatory genetic network can be constructed.
An ordered subset approach to including covariates in the transmission disequilibrium test
Authors: Perdry Hervé , Maher Brion S , Babron Marie-Claude , McHenry Toby , Clerget-Darpoux Françoise , Marazita Mary L
Published: June 2009

Clinical heterogeneity of a disease may reflect an underlying genetic heterogeneity, which may hinder the detection of trait loci. Consequently, many statistical methods have been developed that allow for the detection of linkage and/or association signals in the presence of heterogeneity.

This report describes the work of two parallel investigations into similar approaches to ordered subset analysis, based on an observed covariate, in the framework of family-based association analysis using Genetic Analysis Workshop 15 simulated data.

With an appropriate choice of covariate, both approaches allow detection of two loci that are undetectable by the classical transmission-disequilibrium test. For a third locus, detectable by the classical transmission-disequilibrium test, a substantial increase of power of detection is shown.
Incorporating prior biological information in linkage studies increases power and limits multiple testing
Authors: Lantieri Francesca , Rydbeck Halfdan , Griseri Paola , Ceccherini Isabella , Devoto Marcella
Published: June 2009

We used the Genetic Analysis Workshop 15 Problem 1 data set to search for expression phenotype quantitative trait loci in a highly selected group of genes with a supposedly correlated role in the development of the enteric nervous system. Our strategy was to reduce the level of multiple testing by analyzing at the genome-wide level a limited number of genes considered to be the most promising enteric nervous system candidates on the basis of mouse expression data, and then extend the analysis to a larger number of traits only for a small number of candidate linked regions. Such a study design allowed us to identify a "master regulator" locus for several genes involved in the enteric nervous system, located in 9q31. In particular, one of four traits included in the genome-wide analysis and 2 of 57 from the follow-up single-chromosome analysis showed LOD scores above 2 around position 109 on chromosome 9 by univariate variance-component linkage analysis. Bivariate linkage analysis further supported the presence of a common regulatory locus, with a maximum multipoint LOD score of 5.17 and five additional LOD scores > 3 in the same region. This region is particularly interesting because a susceptibility locus for Hirschsprung disease, a disease characterized by enteric malformation, was previously mapped to 9q31. The proposed strategy of limiting the genome-wide analysis to a small number of well characterized candidate expression phenotypes and following up the most promising results in a larger number of correlated traits may prove successful for other groups of genes involved in a common pathway.
Methodologies for data collection
Authors: Lewis Sheri Happel , Wojcik Richard
Published: June 2009

Background

Electronic disease surveillance systems can be extremely valuable tools; however, a critical step in system implementation is collecting data. Without accurate and complete data, statistical anomalies that are detected hold little meaning. Many people who have established successful surveillance systems acknowledge the initial data collection process to be one of the most challenging aspects of system implementation.

Methods

This discussion will describe the various methods for collecting data as well as describe some of the more common data feeds used in surveillance systems today. Given that every city/region/country looking to establish a surveillance capability has varying degrees of automated data, alternative data collection methods must be considered.

Results

While it would be ideal to collect automated electronic data in a real-time fashion without human intervention, data may also be effectively collected via telephone (both mobile and land lines), fax, and email. Another consideration is what type of data will be used in a surveillance system. If one data source is of high value to one locality, it should not be assumed that it will be as useful in another area. Determining what data sources work best for a particular area is a critical step in system implementation.

Conclusion

Regardless of data type and how they are collected, surveillance systems can be successful if the implementers and end users understand the limitations of both the data and the collection methodology and incorporate that knowledge into their interpretation procedures.
Gene-based bin analysis of genome-wide association studies
Authors: Omont Nicolas , Forner Karl , Lamarine Marc , Martin Gwendal , Képès François , Wojcik Jérôme
Published: June 2009

Background

With the improvement of genotyping technologies and the exponentially growing number of available markers, case-control genome-wide association studies promise to be a key tool for investigation of complex diseases. However new analytical methods have to be developed to face the problems induced by this data scale-up, such as statistical multiple testing, data quality control and computational tractability.

Results

We present a novel method to analyze genome-wide association studies results. The algorithm is based on a Bayesian model that integrates genotyping errors and genomic structure dependencies.p-values are assigned to genomic regions termed bins, which are defined from a gene-biased partitioning of the genome, and the false-discovery rate is estimated. We have applied this algorithm to data coming from three genome-wide association studies of Multiple Sclerosis.

Conclusion

The method practically overcomes the scale-up problems and permits to identify new putative regions statistically associated with the disease.
The impact of single nucleotide polymorphism selection on prediction of genomewide breeding values
Authors: Żukowski Kacper , Suchocki Tomasz , Gontarek Anna , Szyda Joanna
Published: June 2009

The study focuses on the impact of different sets of single nucleotide polymorphisms (SNPs) selected from the available data set on prediction of genomewide breeding values (GBVs) of animals. Correlations between breeding values estimated as additive polygenic effects (EBVs) and GBVs as well as correlations between true breeding values (TBVs) and GBVs are used as major criteria for the comparison of different SNP selection schemes and GBV estimation models.

The analysed data is the simulated data set from the XII QTL Workshop. In the analysis five different SNP data sets are considered. For prediction of EBVs a standard mixed animal model is applied, whereas GBVs are defined as the sum of additive effects of SNPs estimated for the different SNP data sets using model 1 with fixed SNPs effects, model 2 with fixed SNPs effects and a random additive polygenic effect, model 3 with a random effects of uncorrelated SNP genotypes.

The additive polygenic and residual variance components estimated by the EBV model amount to 1.36 and 3.12, respectively. Differences between models are expressed by comparing the ranking of individuals based on EBV and on GBV and by correlations. Among 100 individuals with the highest EBVs, depending on a model and a data set, there are only between 11 and 37 individuals with the highest GBVs. The highest correlation between GBV and EBV amounts to 0.787 and is observed for model 3 with 3,328 SNPs selected based on their minor allele frequency, the lowest correlation of 0.519 is attributed to model 2 with 300 SNPs. Correlations between GBV estimates obtained from different models with the same number of SNPs range between 0.916 and 0. 998, whereas correlations between different SNP data sets using the same model fall under 0.850.

These results indicate that successful application of high throughoutput SNP genotyping technologies for prediction of breeding values is a very promising approach, but before the method can be routinely applied further methodological improvements regarding model construction and SNP selection are required.
A combined strategy for quantitative trait loci detection by genome-wide association
Authors: Lam Alex C , Powell Joseph , Wei Wen-Hua , de Koning Dirk-Jan , Haley Chris S
Published: June 2009

Background

We applied a range of genome-wide association (GWA) methods to map quantitative trait loci (QTL) in the simulated dataset provided by the 12th QTLMAS workshop in order to derive an effective strategy.

Results

A variance component linkage analysis revealed QTLs but with low resolution. Three single-marker based GWA methods were then applied: Transmission Disequilibrium Test and single marker regression, fitting an additive model or a genotype model, on phenotypes pre-corrected for pedigree and fixed effects. These methods detected QTL positions with high concordance to each other and with greater refinement of the linkage signals. Further multiple-marker and haplotype analyses confirmed the results with higher significance. Two-locus interaction analysis detected two epistatic pairs of markers that were not significant by marginal effects. Overall, using stringent Bonferroni thresholds we identified 9 additive QTL and 2 epistatic interactions, which together explained about 12.3% of the corrected phenotypic variance.

Conclusion

The combination of methods that are robust against population stratification, like QTDT, with flexible linear models that take account of the family structure provided consistent results. Extensive simulations are still required to determine appropriate thresholds for more advanced model including epistasis.