bluuebunny/crossref_metadata_2025_split huggingface dataset:
A method dealing with a large number of correlated traits in a linkage genome scan
Authors: Feng Tao , Zhang Shuanglin , Sha Qiuying 
Published: June 2009

Abstract
We propose a method to perform linkage genome scans for many correlated traits in the Genetic Analysis Workshop 15 (GAW15) data. The proposed method has two steps: first, we use a clustering method to find the tight clusters of the traits and use the first principal component (PC) of the traits in each cluster to represent the cluster; second, we perform a linkage scan for each cluster by using the representative trait of the cluster. The results of applying the method to the GAW15 Problem 1 data indicate that most of the traits in the same cluster have the same regulators, and the representative trait measure, the first PC, can explain a large part of the total variation of all the traits in each cluster. Furthermore, considering one cluster of traits at a time may yield more linkage signals than considering traits individually.
A genome-wide linkage study of GAW15 gene expression data
Authors: Kan Donghui , Cooper Richard , Zhu Xiaofeng 
Published: June 2009

Abstract

Background
Recently, gene expression levels have been shown to demonstrate familial aggregation, suggesting a direct role of heritable DNA variation. We studied the gene expression levels in lymphoblastoid cells of the Centre d'Etude du Polymorphisme Humain Utah families made available to Genetic Analysis Workshop 15 (GAW15), using genome-wide linkage analyses.

Methods
Heritability was estimated for the expression levels of each individual phenotype. Genome wide linkage analysis was then performed using the 2819 SNPs for the expression levels of all the genes.

Results
Heritability exceeded 0.21 for 50% of the expressed phenotypes. Genome-wide linkage analysis demonstrated that 19 of them reached significance after correcting for multiple comparisons, only 4 of which were reported previously. We did not identify any hot spots of transcriptional regulation when assuming LOD score > 5.3 for significant linkage evidence.

Conclusion
Our analysis suggests that inconsistent results in comparison to the previous report may be due to the different approaches, phenotype transformation, and different pedigree data used in the analyses.
Genetic association studies for gene expressions: permutation-based mutual information in a comparison with standard ANOVA and as a novel approach for feature selection
Authors: Szymczak Silke , Nuzzo Angelo , Fuchsberger Christian , Schwarz Daniel F , Ziegler Andreas , Bellazzi Riccardo , Igl Bernd-Wolfgang 
Published: June 2009

Abstract
Mutual information (MI) is a robust nonparametric statistical approach for identifying associations between genotypes and gene expression levels. Using the data of Problem 1 provided for the Genetic Analysis Workshop 15, we first compared a quantitative MI (Tsalenko et al. 2006 J Bioinform Comput Biol 4:259–4) with the standard analysis of variance (ANOVA) and the nonparametric Kruskal-Wallis (KW) test. We then proposed a novel feature selection approach using MI in a classification scenario to address the small n - large p problem and compared it with a feature selection that relies on an asymptotic χ
2 distribution. In both applications, we used a permutation-based approach for evaluating the significance of MI. Substantial discrepancies in significance were observed between MI, ANOVA, and KW that can be explained by different empirical distributions of the data. In contrast to ANOVA and KW, MI detects shifts in location when the data are non-normally distributed, skewed, or contaminated with outliers. ANOVA but not MI is often significant if one genotype with a small frequency had a remarkable difference in the average gene expression level relative to the other two genotypes. MI depends on genotype frequencies and cannot detect these differences. In the classification scenario, we show that our novel approach for feature selection identifies a smaller list of markers with higher accuracy compared to the standard method. In conclusion, permutation-based MI approaches provide reliable and flexible statistical frameworks which seem to be well suited for data that are non-normal, skewed, or have an otherwise peculiar distribution. They merit further methodological investigation.
Finding pathway regulators: gene set approach using peak identification algorithms
Authors: Lee Eunjee , Woo Jung Hoon , Park Ji Wan , Park Taesung 
Published: June 2009

AbstractRecently, a number of different approaches have been used to examine variation in gene expression and to identify genes whose level of transcript differed greatly among unrelated individuals. Previous studies have commonly focused on identifying determinants that regulate gene expressions by targeting individual genes. However, it is difficult to detect true differences in the level of gene expression among genotypes from noise due to issues such as multiple testing and limited sample size. To increase the statistical power for detecting this difference, we consider a 'gene set' approach by focusing on subtle but coordinated changes in gene expression across multiple genes rather than individual genes. We defined a 'gene set' as a set of genes in the same biological pathway and focused on identifying common regulators based on an assumption that the genes within the same pathway are controlled by common regulators. We applied the gene set approach to the expression data of mRNA in Centre d'Etude du Polymorphisme Humain lymphoblast cells to identify regulators controlling the genes in a biological pathway. Our gene set approach successfully identified potent regulators controlling gene expression in an inflammatory response pathway.
Sex, age and generation effects on genome-wide linkage analysis of gene expression in transformed lymphoblasts
Authors: Rangrej Jagadish , Beyene Joseph , Hu Pingzhao , Paterson Andrew D 
Published: June 2009

Abstract

Background
Many traits differ by age and sex in humans, but genetic analysis of gene expression has typically not included them in the analysis.

Methods
We used Genetic Analysis Workshop 15 Problem 1 data to determine whether gene expression in lymphoblasts showed differences by age and/or sex using generalized estimating equations (GEE). We performed quantitative trait linkage analysis of these genes including age and sex as covariates to determine whether the linkage results changed when they were included as covariates. Because the families included in the study all contain three generations, we also determined what effect inclusion of generation in the model had on the age effects.

Results
When controlling the false-discovery rate at 1%, using GEE we identified 30 transcripts that showed significant differences in expression by sex, while 1950 transcripts showed differences in expression associated with age. When subjected to linkage analysis, there were 37 linkages that disappeared, while 17 appeared when sex was included as a covariate. All these genes were, as expected, on the sex chromosomes. In contrast, when age was included in the linkage analysis, 462 linkage signals were no longer significant, while 223 became significant. When generation was included in the model with age, all but 6 of the GEE age effects were no longer significant. However, there were minimal changes in the linkage results.

Conclusion
The effect of age on linkage analyses was apparent for the expression of many genes, which appear to be mostly due to differences between the generations.
Comparison of multipoint linkage analyses for quantitative traits in the CEPH data: parametric LOD scores, variance components LOD scores, and Bayes factors
Authors: Sung Yun Ju , Di Yanming , Fu Audrey Q , Rothstein Joseph H , Sieh Weiva , Tong Liping , Thompson Elizabeth A , Wijsman Ellen M 
Published: June 2009

Abstract
We performed multipoint linkage analyses with multiple programs and models for several gene expression traits in the Centre d'Etude du Polymorphisme Humain families. All analyses provided consistent results for both peak location and shape. Variance-components (VC) analysis gave wider peaks and Bayes factors gave fewer peaks. Among programs from the MORGAN package, lm_multiple performed better than lm_markers, resulting in less Markov-chain Monte Carlo (MCMC) variability between runs, and the program lm_twoqtl provided higher LOD scores by also including either a polygenic component or an additional quantitative trait locus.
Linkage studies of catechol-O-methyltransferase (COMT) and dopamine-beta-hydroxylase (DBH) cDNA expression levels
Authors: Xing Chao , Torres-Caban Monica , Wang Tao , Lu Qing , Xing Guan , Elston Robert C 
Published: June 2009

AbstractTheCOMTandDBHgenes are physically located at chromosomes 22q11 and 9q34, respectively, and bothCOMTandDBHare involved in catecholamine metabolism and are strong candidates for certain psychiatric and neurological disorders. Although the genetic determinants for both enzymes' activities have been widely studied, their genetic involvement on gene mRNA expression levels remains unclear. In this study we performed quantitative linkage analysis ofCOMTandDBHcDNA expression levels, identifying transcriptional regulatory regions for both genes. Multiple Haseman-Elston regression was used to detect both additive and interactive effects between two loci. We found that the master transcriptional regulatory region 20q13 had an additive effect on theCOMTexpression level. We also found that chromosome 19p13 showed both additive and interactive effects with 9q34 onDBHexpression level. Furthermore, a potential interaction betweenCOMTandDBHwas indicated.
Comparison of genome-wide single-nucleotide polymorphism linkage analyses in Caucasian and Hispanic NARAC families
Authors: Chen Wei V , Amos Christopher I , Etzel Carol J , Shete Sanjay , Gregersen Peter K 
Published: June 2009

Abstract
We performed linkage analysis on families with rheumatoid arthritis, stratifying by ethnic origin. We compared results using either Kong and Cox nonparametric LOD scores or MOD score analysis using the software GeneHunter MODSCORE. We first applied SNPLINK to remove markers showing excess linkage disequilibrium from the SNPs in the Illumina IV SNP Linkage panel. In this analysis there were 659 self-reported Caucasian families and 29 self-reported Hispanic families in the NARAC collection. Chromosome 19 yielded MOD scores > 3.00 in the Hispanic group, while chromosomes 2, 6, 7, 11, and XY had MOD scores > 3.00 in the Caucasian group. We performed simulation studies to evaluate the empirical distribution of the MOD score for autosomal loci separately in Hispanics and Caucasians. Results showed genome-wide significant evidence for linkage in Caucasians for chromosomes 2q and 6p, but no significant evidence for any linkages in the Hispanics, including little evidence for linkage to chromosome 6p in this group. An examination of the difference of phenotypes in two ethnic groups suggested significantly earlier mean age of onset, higher percentage of anti-cyclic citrullinated peptide positive people, and lower percentage of affected people carrying shared epitopes in Hispanics than those in Caucasians. A larger sample size of the Hispanic group is needed to identify linkage regions.
Towards structured output prediction of enzyme function
Authors: Astikainen Katja , Holm Liisa , Pitkänen Esa , Szedmak Sandor , Rousu Juho 
Published: June 2009

Abstract

Background
In this paper we describe work in progress in developing kernel methods for enzyme function prediction. Our focus is in developing so called structured output prediction methods, where the enzymatic reaction is the combinatorial target object for prediction. We compared two structured output prediction methods, the Hierarchical Max-Margin Markov algorithm (HM3) and the Maximum Margin Regression algorithm (MMR) in hierarchical classification of enzyme function. As sequence features we use various string kernels and the GTG feature set derived from the global alignment trace graph of protein sequences.

Results
In our experiments, in predicting enzyme EC classification we obtain over 85% accuracy (predicting the four digit EC code) and over 91% microlabel F1 score (predicting individual EC digits). In predicting the Gold Standard enzyme families, we obtain over 79% accuracy (predicting family correctly) and over 89% microlabel F1 score (predicting superfamilies and families). In the latter case, structured output methods are significantly more accurate than nearest neighbor classifier. A polynomial kernel over the GTG feature set turned out to be a prerequisite for accurate function prediction. Combining GTG with string kernels boosted accuracy slightly in the case of EC class prediction.

Conclusion
Structured output prediction with GTG features is shown to be computationally feasible and to have accuracy on par with state-of-the-art approaches in enzyme function prediction.
Comparison of analyses of the QTLMAS XII common dataset. II: genome-wide association and fine mapping
Authors: Crooks Lucy , Sahana Goutam , de Koning Dirk-Jan , Lund Mogens Sandø , Carlborg Örjan 
Published: June 2009

Abstract
As part of the QTLMAS XII workshop, a simulated dataset was distributed and participants were invited to submit analyses of the data based on genome-wide association, fine mapping and genomic selection. We have evaluated the findings from the groups that reported fine mapping and genome-wide association (GWA) efforts to map quantitative trait loci (QTL). Generally the power to detect QTL was high and the Type 1 error was low. Estimates of QTL locations were generally very accurate. Some methods were much better than others at estimating QTL effects, and with some the accuracy depended on simulated effect size or minor allele frequency. There were also indications of bias in the effect estimates. No epistasis was simulated, but the two studies that included searches for epistasis reported several interacting loci, indicating a problem with controlling the Type I error rate in these analyses. Although this study is based on a single dataset, it indicates that there is a need to improve fine mapping and GWA methods with respect to estimation of genetic effects, appropriate choice of significance thresholds and analysis of epistasis.
Investigation of reproducibility of differentially expressed genes in DNA microarrays through statistical simulation
Authors: Fan Xiaohui , Shi Leming , Fang Hong , Harris Stephen , Perkins Roger , Tong Weida 
Published: June 2009

Abstract
Recent publications have raised concerns about the reliability of microarray technology because of the lack of reproducibility of differentially expressed genes (DEGs) from highly similar studies across laboratories and platforms. The rat toxicogenomics study of the MicroArray Quality Control (MAQC) project empirically revealed that the DEGs selected using a fold change (FC)-based criterion were more reproducible than those derived solely by statistical significance such as P-value from a simple t-tests. In this study, we generate a set of simulated microarray datasets to compare gene selection/ranking rules, including P-value, FC and their combinations, using the percentage of overlapping genes between DEGs from two similar simulated datasets as the measure of reproducibility. The results are supportive of the MAQC's conclusion on that DEG lists are more reproducible across laboratories and platforms when FC-based ranking coupled with a nonstringent P-value cutoff is used for gene selection compared with selection based on P-value based ranking method. We conclude that the MAQC recommendation should be considered when reproducibility is an important study objective.
Recent progress in toxicogenomics research in South Korea
Authors: Chung Tae-Hoon , Yoo Jin-Ho , Ryu Jae-Chun , Kim Yang-Seok 
Published: June 2009

Abstract

Background
The importance of toxicogenomics was recognized early in Korea and a group of researchers was trying to build up a research infrastructure and educational system. However, since the scale of the Korean pharmaceutical industry, which was expected to play the key role in toxicogenomics was small compared to that of advanced countries, industry-sponsored large-scale research projects and supporting infrastructures have been lacking in Korea.

Results
To improve this situation, the Korean government has exerted special efforts to promote toxicogenomics research and development the last few years as an initiative to stimulate a premature drug development industry on par with global competition and launched several large scale research projects recently. Researchers are also trying to keep pace with government efforts by organizing local scientist groups, training young toxicogenomics scientists, and widening the toxicogenomic research efforts to environmental toxicity as well. Research and development from bioinformatics and genomics venture companies are also contributing to uplifting the competitiveness of the toxicogenomics industry.

Conclusion
Toxicogenomics in Korea is making steady progress in many directions. It is gaining ground by government and related industries as well, the research is diversified to embrace environmental genomics, and local research groups are making strategic links to international research groups such as the MicroArray Quality Control (MAQC) consortium. We expect the advancement of the Korean toxicogenomics research program will be beneficial not only to the local society alone, but also to international scientists as a whole.
NMD inhibition fails to identify tumour suppressor genes in microsatellite stable gastric cancer cell lines
Authors: Buffart Tineke E , Tijssen Marianne , El-Bchiri Jamila , Duval Alex , Wiel Mark A van de , Ylstra Bauke , Meijer Gerrit A , Carvalho Beatriz 
Published: June 2009

Abstract

Background
Gastric cancers frequently show chromosomal alterations which can cause activation of oncogenes, and/or inactivation of tumour suppressor genes. In gastric cancer several chromosomal regions are described to be frequently lost, but for most of the regions, no tumour suppressor genes have been identified yet. The present study aimed to identify tumour suppressor genes inactivated by nonsense mutation and deletion in gastric cancer by means of GINI (gene identification by nonsense mediated decay inhibition) and whole genome copy number analysis.

Methods
Two non-commercial gastric cancer cell lines, GP202 and IPA220, were transfected with siRNA directed against UPF1, to specifically inhibit the nonsense mediated decay (NMD) pathway, and with siRNA directed against non-specific siRNA duplexes (CVII) as a control. Microarray expression experiments were performed in triplicate on 4 × 44 K Agilent arrays by hybridizing RNA from UPF1-transfected cells against non-specific CVII-transfected cells. In addition, array CGH of the two cell lines was performed on 4 × 44K agilent arrays to obtain the DNA copy number profiles. Mutation analysis of GINI candidates was performed by sequencing.

Results

UPF1 expression was reduced for >70% and >80% in the GP202 and IPA220 gastric cancer cell lines, respectively. Integration of array CGH and microarray expression data provided a list of 134 and 50 candidate genes inactivated by nonsense mutation and deletion for GP202 and IPA220, respectively. We selected 12 candidate genes for mutation analysis. Of these, sequence analysis was performed on 11 genes. One gene, PLA2G4A, showed a silent mutation, and in two genes, CTSA and PTPRJ, missense mutations were detected. No nonsense mutations were detected in any of the 11 genes tested.

Conclusion
Although UPF1 was substantially repressed, thus resulting in the inhibition of the NMD system, we did not find genes inactivated by nonsense mutations. Our results show that the GINI strategy leads to a high number of false positives.
The association of XRCC1gene single nucleotide polymorphisms with response to neoadjuvant chemotherapy in locally advanced cervical carcinoma
Authors: Cheng Xiao-Dong , Lu Wei-Guo , Ye Feng , Wan Xiao-Yun , Xie Xing 
Published: June 2009

Abstract

Background
Platinum-based neoadjuvant chemotherapy (NAC) is new therapeutic strategy for locally advanced cervical carcinoma, but the variables used to predict NAC response are still infrequently reported. The aim of our study was to investigate the association between XRCC1 gene single nucleotide polymorphisms (SNPs) and NAC response.

Methods
Seventy patients with locally advanced cervical carcinoma who underwent NAC were collected. SNPs of XRCC1 (at codon 194 and 399) and XRCC1 protein expression were detected. The association of XRCC1 gene SNPs and protein expression with NAC response were analyzed.

Results
Response to NAC was not statistically significant in three genotypes, Arg/Arg, Arg/Trp, Trp/Trp of XRCC1 at codon 194(X2 = 1.243, P = 0.07), while responses were significantly different in genotypes Arg/Arg, Arg/Gln, Gln/Gln of XRCC1 at codon 399 (X2 = 2.283, P = 0.020). The risk of failure to chemotherapy in the patients with a Gln allele(Arg/Gln+Gln/Gln) was significantly greater than that with Arg/Arg(OR = 3.254, 95%CI 1.708 ~ 14.951). The expression level of XRCC1 protein was significantly associated with response to NAC. Moreover, the genotype with the Gln allele(Arg/Gln+Gln/Gln) at codon 399, but not codon at 194, presented a significantly higher level of XRCC1 protein expression than that with Arg/Arg genotype (F = 2.699, p = 0.009).

Conclusion
SNP of XRCC1 gene at codon 399 influences the response of cervical carcinoma to platinum-based NAC. This is probably due to changes in expression of XRCC1 protein, affecting response to chemotherapy.
Overexpression of peroxiredoxin I and thioredoxin1 in human breast carcinoma
Authors: Cha Mee-Kyung , Suh Kyung-Hoon , Kim Il-Han 
Published: June 2009

Abstract

Background
Peroxiredoxins (Prxs) are a novel group of peroxidases containing high antioxidant efficiency. The mammalian Prx family has six distinct members (Prx I-VI) in various subcellular locations, including peroxisomes and mitochondria, places where oxidative stress is most evident. The function of Prx I in particular has been implicated in regulating cell proliferation, differentiation, and apoptosis. Since thioredoxin1 (Trx1) as an electron donor is functionally associated with Prx I, we investigated levels of expression of both Prx I and Trx1.

Methods
We investigated levels of expression of both Prx I and Trx1 in breast cancer by real-time polymerase chain reaction (RT-PCR) and Western blot.

Results
Levels of messenger RNA (mRNA) for both Prx I and Trx1 in normal human breast tissue were very low compared to other major human tissues, whereas their levels in breast cancer exceeded that in other solid cancers (colon, kidney, liver, lung, ovary, prostate, and thyroid). Among members of the Prx family (Prx I-VI) and Trx family (Trx1, Trx2), Prx I and Trx1 were preferentially induced in breast cancer. Moreover, the expression of each was associated with progress of breast cancer and correlated with each other. Western blot analysis of different and paired breast tissues revealed consistent and preferential expression of Prx I and Trx1 protein in breast cancer tissue.

Conclusion
Prx I and Trx1 are overexpressed in human breast carcinoma and the expression levels are associated with tumor grade. The striking induction of Prx I and Trx1 in breast cancer may enable their use as breast cancer markers.