bluuebunny/crossref_metadata_2025 huggingface dataset:
Linkage and association analysis of GAW15 simulated data: fine-mapping of chromosome 6 region
Authors: Charoen Pimphen , Biernacka Joanna M , Cordell Heather J
Published: June 2009

We performed linkage and family-based association analysis across chromosomes 1–22 in Replicates 1–5 of the Genetic Analysis Workshop 15 simulated data. Linkage analysis was performed using the Kong and Cox allele-sharing test as implemented in the program Merlin. Association analysis was performed using the transmission/disequilibrium test (TDT). A region on chromosome 6 was consistently highlighted as showing significant linkage to and association with the disease trait. We focused in on this region and performed fine-mapping using stepwise regression approaches using the case/control and family-based data. In this region, we also applied several new methods, implemented in the computer programs LAMP and Graphminer, respectively, that have recently been proposed for association analysis with family and/or case/control data. All methods confirmed the highly significant associations previously observed. Differentiating between potentially causal single nucleotide polymorphisms (SNPs) and other non-causal loci (associated with disease merely due to linkage disequilibrium) proved to be problematic. However, in most replicates we did identify two SNPs (either SNPs 3437 and 3439 from the dense SNP set, or SNPs 153 and 3437 from the combined non-dense/dense SNP set) that together explain most of the observed disease association in the DR/C locus region, and an additional SNP (3931 or 3933) that accounts for the association 5 cM away at locus D.
Exhaustive screens for disease susceptibility loci incorporating statistical interaction of genotypes: a comparison of likelihood-ratio-based and Akaike and Bayesian information criteria-based methods
Authors: Gu Fangyi , Monsees Genevieve , Kraft Peter
Published: June 2009

Several recent papers have suggested that two-locus tests of association that incorporate gene × gene interaction can be more powerful than marginal, single-locus tests across a broad range of multilocus interaction models, even after conservative correction for multiple testing. However, because these two-locus tests are sensitive to marginal associations with either marker, they can be difficult to interpret, and it is not immediately clear how to use them to select a list of the most promising markers worthy of further study. Here we apply single- and two-locus tests to 29 single-nucleotide polymorphisms (SNPs) selected from the dense marker map in the simulated Genetic Analysis Workshop 15 data spanning several candidate regions (the HLA region, the four SNPs flanking "Locus D," and two regions on the q-arm of chromosome 6). We compare the proposed two-locus likelihood ratio tests (LRT) to Akaike and Bayesian Information Criteria (AIC and BIC) for model selection, as well as AIC- and BIC-weighted measures of "SNP importance." The latter provide summary measures of evidence for association between each SNP and disease – including potential interactions with one or more other SNPs – by summing over all one- and two-SNP models. Our results suggest that the LRT using conservative p-value criteria were sensitive (but not specific) in identifying associated markers. Standard AIC and BIC criteria were similarly sensitive but not specific. On the other hand, the AIC- and BIC-weighted importance measures yielded a specific but not very sensitive rule for SNP selection. Algorithms incorporating gene × gene interaction to prioritize markers for follow-up will require further development.
Density-based clustering in haplotype analysis for association mapping
Authors: Igo Robert P , Londono Douglas , Miller Katherine , Parrado Antonio R , Quade Shannon RE , Sinha Moumita , Kim Sulgi , Won Sungho , Li Jing , Goddard Katrina AB
Published: June 2009

Clustering of related haplotypes in haplotype-based association mapping has the potential to improve power by reducing the degrees of freedom without sacrificing important information about the underlying genetic structure. We have modified a generalized linear model approach for association analysis by incorporating a density-based clustering algorithm to reduce the number of coefficients in the model. Using the GAW 15 Problem 3 simulated data, we show that our novel method can substantially enhance power to detect association with the binary rheumatoid arthritis (RA) phenotype at the HLA-DRB1 locus on chromosome 6. In contrast, clustering did not appreciably improve performance at locus D, perhaps a consequence of a rare susceptibility allele and of the overwhelming effect of HLA-DRB1/locus C, 5 cM distal. Optimization of parameters governing the clustering algorithm identified a set of parameters that delivered nearly ideal performance in a variety of situations. The cluster-based score test was valid over a wide range of haplotype diversity, and was robust to severe departures from Hardy-Weinberg equilibrium encountered near HLA-DRB1 in RA case-control samples.
Case-control studies with affected sibships
Authors: Köhler Karola , Sohns Melanie , Bickeböller Heike
Published: June 2009

Related cases may be included in case-control association studies if correlations between related individuals due to identity-by-descent (IBD) sharing are taken into account. We derived a framework to test for association in a case-control design including affected sibships and unrelated controls. First, a corrected variance for the allele frequency difference between cases and controls was directly calculated or estimated in two ways on the basis of the fixation index F

ST
and the inbreeding coefficient. Then the correlation-corrected association test including controls and affected sibs was carried out. We applied the three strategies to 20 candidate genes on the Genetic Analysis Workshop 15 rheumatoid arthritis data and to 9187 single-nucleotide polymorphisms of replicate one of the Genetic Analysis Workshop 15 simulated data with knowledge of the "answers". The three strategies used to correct for correlation give only minor differences in the variance estimates and yield an almost correct type I error rate for the association tests. Thus, all strategies considered to correct the variance performed quite well.
Case-control association analysis of rheumatoid arthritis with candidate genes using related cases
Authors: Yoo Yun Joo , Gao Guimin , Zhang Kui
Published: June 2009

We performed a case-control association analysis of rheumatoid arthritis (RA) for several candidate genes using the North American Rheumatoid Arthritis Consortium (NARAC) data provided in Genetic Analysis Workshop 15. We conducted the case-control association analysis using all related cases and unrelated controls and compared the results with those from the analysis of samples using only one randomly selected case from each family and all unrelated controls. For both analyses we used a weighted composite likelihood ratio test based on single-nucleotide polymorphism (SNP) markers or haplotypes accounting for the correlation among samples within a family. Several SNPs, including R620W in the candidate gene PTPN22, showed an association with RA status, which confirmed previously reported results. Several other SNPs in the candidate genes, such as CTLA4, HAVCR1, and SUMO4, also had rather small p-values (<0.05), suggesting the associations between them and RA. Our results showed that the p-values obtained from the analysis including all related cases were generally smaller than those obtained from the analysis including only one randomly selected case per family. These results, together with the results, based on simulated data, showed that higher power could be achieved using all related cases.
Modeling the effect of PTPN22in rheumatoid arthritis
Authors: Bourgey Mathieu , Perdry Hervé , Clerget-Darpoux Françoise
Published: June 2009

In order to model the effect of PTPN22 on rheumatoid arthritis (RA), we determined the combination of single-nucleotide-polymorphisms (SNPs) showing the strongest association with RA. Three SNPs (rs2476601-rs12730735-rs11102685) were selected for which we estimated the genotypic relative risks (GRRs) of the corresponding genotypes. On the basis of these GRRs we defined four at-risk genotypic classes. Relative to the class of reference risk, individuals had a risk approximately multiplied by two, three, or four. This classification was confirmed by the excess of identity-by-descent (IBD) sharing (IBD = 2) for the sibs of an index in the high-risk class and by excess of non-IBD sharing (IBD = 0) when the index belonged to the low-risk class. The observed data could not be explained by the role of a single variant but were compatible either with a joint effect of the three typed SNPs of PTPN22 on RA or with the role of two untyped variants.
Comparison of GIST and LAMP on the GAW15 simulated data
Authors: Lou Xuemei , Schmidt Silke , Hauser Elizabeth R
Published: June 2009

After genetic linkage has been identified for a complex disease, the next step is often fine-mapping by association analysis, using single-nucleotide polymorphisms (SNPs) within a linkage region. If a SNP shows evidence of association, it is useful to know whether the linkage result can be explained in part or in full by the candidate SNP. The genotype identity-by-descent sharing test (GIST) and linkage and association modeling in pedigrees (LAMP) are two methods that were specifically proposed to address this question. GIST determines whether there is significant correlation between family-specific weights, defined by the presence of a tentatively associated allele in affected siblings, and family-specific nonparametric linkage scores. LAMP constructs a pedigree likelihood function of the marker data conditional on the trait data, and implements three likelihood ratio tests to characterize the relationship between the candidate SNP and the disease locus. The goal of our study was to compare the two approaches and evaluate their ability to identify disease-associated SNPs in the Genetic Analysis Workshop 15 (GAW15) simulated data. Our results can be summarized as follows: 1) GIST is simple and fast but, as a test of association, did not perform well in the GAW15 data, especially with adjustment for multiple testing; 2) as a test of association, the LAMP-LE test performs best when the linkage evidence is strong, or when there is at least moderate linkage disequilibrium between the candidate SNP and the trait locus. We conclude that LAMP is more flexible and reliable to use in practice.
Single-nucleotide polymorphism-gene intermixed networking reveals co-linkers connected to multiple gene expression phenotypes
Authors: Gong Bin-Sheng , Zhang Qing-Pu , Zhang Guang-Mei , Zhang Shao-Jun , Zhang Wei , Lv Hong-Chao , Zhang Fan , Lv Sa-Li , Li Chuan-Xing , Rao Shao-Qi , Li Xia
Published: June 2009

Gene expression profiles and single-nucleotide polymorphism (SNP) profiles are modern data for genetic analysis. It is possible to use the two types of information to analyze the relationships among genes by some genetical genomics approaches. In this study, gene expression profiles were used as expression traits. And relationships among the genes, which were co-linked to a common SNP(s), were identified by integrating the two types of information. Further research on the co-expressions among the co-linked genes was carried out after the gene-SNP relationships were established using the Haseman-Elston sib-pair regression. The results showed that the co-expressions among the co-linked genes were significantly higher if the number of connections between the genes and a SNP(s) was more than six. Then, the genes were interconnected via one or more SNP co-linkers to construct a gene-SNP intermixed network. The genes sharing more SNPs tended to have a stronger correlation. Finally, a gene-gene network was constructed with their intensities of relationships (the number of SNP co-linkers shared) as the weights for the edges.
An empirical evaluation of the common disease-common variant hypothesis
Authors: Chen Gary K , Jorgenson Eric , Witte John S
Published: June 2009

While genome-wide linkage studies have been successful in mapping variants underlying rare monogenic disorders, genome-wide association studies may be more appropriate for detecting common variants of modest effects that underlie common disorders. To this end, we were interested in determining whether genetic variants associated with a phenotype differed depending on whether they were within or outside of regions linked to the phenotype. In particular, we compared allele frequencies and effect sizes between associated single-nucleotide polymorphisms within and outside of linkage regions using the Genetic Analysis Workshop 15 Problem 1. We did not find any statistically significant differences between these two sets. However, our power calculations show that these results may be inconclusive.
Picking single-nucleotide polymorphisms in forests
Authors: Schwarz Daniel F , Szymczak Silke , Ziegler Andreas , König Inke R
Published: June 2009

With the development of high-throughput single-nucleotide polymorphism (SNP) technologies, the vast number of SNPs in smaller samples poses a challenge to the application of classical statistical procedures. A possible solution is to use a two-stage approach for case-control data in which, in the first stage, a screening test selects a small number of SNPs for further analysis. The second stage then estimates the effects of the selected variables using logistic regression (logReg). Here, we introduce a novel approach in which the selection of SNPs is based on the permutation importance estimated by random forests (RFs). For this, we used the simulated data provided for the Genetic Analysis Workshop 15 without knowledge of the true model.

The data set was randomly split into a first and a second data set. In the first stage, RFs were grown to pre-select the 37 most important variables, and these were reduced to 32 variables by haplotype tagging. In the second stage, we estimated parameters using logReg.

The highest effect estimates were obtained for five simulated loci. We detected smoking, gender, and the parental DR alleles as covariates. After correction for multiple testing, we identified two out of four genes simulated with a direct effect on rheumatoid arthritis risk and all covariates without any false positive.

We showed that a two-staged approach with a screening of SNPs by RFs is suitable to detect candidate SNPs in genome-wide association studies for complex diseases.
Identification of expression quantitative trait loci by the interaction analysis using genetic algorithm
Authors: Namkung Junghyun , Nam Jin-Wu , Park Taesung
Published: June 2009

Many genes with major effects on quantitative traits have been reported to interact with other genes. However, finding a group of interacting genes from thousands of SNPs is challenging. Hence, an efficient and robust algorithm is needed. The genetic algorithm (GA) is useful in searching for the optimal solution from a very large searchable space. In this study, we show that genome-wide interaction analysis using GA and a statistical interaction model can provide a practical method to detect biologically interacting loci. We focus our search on transcriptional regulators by analyzing gene × gene interactions for cancer-related genes. The expression values of three cancer-related genes were selected from the expression data of the Genetic Analysis Workshop 15 Problem 1 data set. We implemented a GA to identify the expression quantitative trait loci that are significantly associated with expression levels of the cancer-related genes. The time complexity of the GA was compared with that of an exhaustive search algorithm. As a result, our GA, which included heuristic methods, such as archive, elitism, and local search, has greatly reduced computational time in a genome-wide search for gene × gene interactions. In general, the GA took one-fifth the computation time of an exhaustive search for the most significant pair of single-nucleotide polymorphisms.
Identifying cis- and trans-acting single-nucleotide polymorphisms controlling lymphocyte gene expression in humans
Authors: Hu Pingzhao , Lan Hui , Xu Wei , Beyene Joseph , Greenwood Celia MT
Published: June 2009

Assuming multiple loci play a role in regulating the expression level of a single phenotype, we propose a new approach to identify cis- and trans-acting loci that regulate gene expression. Using the Problem 1 data set made available for Genetic Analysis Workshop 15 (GAW15), we identified many expression phenotypes that have significant evidence of association and linkage to one or more chromosomal regions. In particular, six of ten phenotypes that we found to be regulated by cis- and trans-acting loci were also mapped by a previous analysis of these data in which a total of 27 phenotypes were identified with expression levels regulated by cis-acting determinants. However, in general, the p-values associated with these regulators identified in our study were larger than in their studies, since we had also identified other factors regulating expression. In fact, we found that most of the gene expression phenotypes are influenced by at least one trans-acting locus. Our study also shows that much of the observable heritability in the phenotypes could be explained by simple single-nucleotide polymorphism associations; residual heritability was reduced and the remaining heritability may represent complex regulation systems with interactions or noise.
Adjusting for sex and anti-CCP levels in linkage analysis of rheumatoid arthritis
Authors: Lebrec Jérémie JP , Helmer Quinta , Nishchenko Iryna , van Houwelingen Hans C
Published: June 2009

We incorporate population effects of sex and antibodies directed against cyclic citrullinated peptides (anti-CCP) into the linkage analysis of rheumatoid arthritis (RA) with microsatellites data provided by the North American Rheumatoid Arthritis Consortium in Genetic Analysis Workshop 15.

The method stems from a generalized linear mixed model that incorporates the marginal population effects of important covariates. The resulting test for linkage is based on a score test in a pseudo-likelihood of this model. The mathematical derivation is given elsewhere but the test has a simple and appealing form: it assigns weights to excess identity-by-descent sharing between pairs of related individuals depending on the individual-specific values of the covariates and phenotypes.

Although RA is three times more prevalent in women than in men, the weights derived for male-male, female-male, and female-female affected sib pairs turn out to be very similar and the sex-adjusted analysis hardly differs from an unadjusted analysis. High anti-CCP levels are known to strongly predict RA. Our test assigns very small weights to pairs whose anti-CCP levels are high for the two siblings, sib pairs with two low anti-CCP levels are those most contributing to the evidence for linkage. Comparison of the unadjusted and the anti-CCP-adjusted analyses identifies persisting peaks mapping to regions that can be attributed to a 'dimension' of RA independent of anti-CCP.
A comparison of founder-only and all-pedigree-members genotype-expression association by regression analysis
Authors: Suh Young Ju , Lee Hye-Soon , Batliwalla Franak , Li Wentian
Published: June 2009

Genotype-expression association analysis using linear regression may produce different test results depending on whether founders only or all pedigreed members are used. This difference is not due to the correlation of samples within a pedigree, because linear mixed models have been applied to account for that correlation. We investigated the possibility that the difference is due to a dependence of expression levels on, among other things, the generation number in the pedigree. Indeed, of the 30 or so studied expression quantitative traits, several of them show significant dependence on the generation number. We propose to use all pedigree members in genotype-expression association analyses whenever the complete genotyping information is available.
Genetic heterogeneity and trans regulators of gene expression
Authors: Bastone Laurel A , Putt Mary E , Ten Have Thomas R , Cheung Vivian G , Spielman Richard S
Published: June 2009

Heterogeneity poses a challenge to linkage mapping. Here, we apply a latent class extension of Haseman-Elston regression to expression phenotypes with significant evidence of linkage to trans regulators in 14 large pedigrees. We test for linkage, accounting for heterogeneity, and classify individual families as "linked" and "unlinked" on the basis of their contribution to the overall evidence of linkage.
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

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

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

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

Recently, 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

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

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

TheCOMTandDBHgenes 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

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

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

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

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

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

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

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

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.
Protocol for the Foot in Juvenile Idiopathic Arthritis trial (FiJIA): a randomised controlled trial of an integrated foot care programme for foot problems in JIA
Authors: Hendry Gordon J , Turner Deborah E , McColl John , Lorgelly Paula K , Sturrock Roger D , Watt Gordon F , Browne Michael , Gardner‐Medwin Janet , Friel Lorraine , Woodburn Jim
Published: June 2009

Background

Foot and ankle problems are a common but relatively neglected manifestation of juvenile idiopathic arthritis. Studies of medical and non‐medical interventions have shown that clinical outcome measures can be improved. However existing data has been drawn from small non‐randomised clinical studies of single interventions that appear to under‐represent the adult population suffering from juvenile idiopathic arthritis. To date, no evidence of combined therapies or integrated care for juvenile idiopathic arthritis patients with foot and ankle problems exists.

Methods/design

An exploratory phase II non‐pharmacological randomised controlled trial where patients including young children, adolescents and adults with juvenile idiopathic arthritis and associated foot/ankle problems will be randomised to receive integrated podiatric care via a new foot care programme, or to receive standard podiatry care. Sixty patients (30 in each arm) including children, adolescents and adults diagnosed with juvenile idiopathic arthritis who satisfy the inclusion and exclusion criteria will be recruited from 2 outpatient centres of paediatric and adult rheumatology respectively. Participants will be randomised by process of minimisation using the Minim software package. The primary outcome measure is the foot related impairment measured by the Juvenile Arthritis Disability Index questionnaire's impairment domain at 6 and 12 months, with secondary outcomes including disease activity score, foot deformity score, active/limited foot joint counts, spatio‐temporal and plantar‐pressure gait parameters, health related quality of life and semi‐quantitative ultrasonography score for inflammatory foot lesions. The new foot care programme will comprise rapid assessment and investigation, targeted treatment, with detailed outcome assessment and follow‐up at minimum intervals of 3 months. Data will be collected at baseline, 6 months and 12 months from baseline. Intention to treat data analysis will be conducted.

A full health economic evaluation will be conducted alongside the trial and will evaluate the cost effectiveness of the intervention. This will consider the cost per improvement in Juvenile Arthritis Disability Index, and cost per quality adjusted life year gained. In addition, a discrete choice experiment will elicit willingness to pay values and a cost benefit analysis will also be undertaken.

Trial Registration

Trial registration number: UKCRN5045
Management of the critically poisoned patient
Authors: Boyle Jennifer S , Bechtel Laura K , Holstege Christopher P
Published: June 2009

Background

Clinicians are often challenged to manage critically ill poison patients. The clinical effects encountered in poisoned patients are dependent on numerous variables, such as the dose, the length of exposure time, and the pre-existing health of the patient. The goal of this article is to introduce the basic concepts for evaluation of poisoned patients and review the appropriate management of such patients based on the currently available literature.

Methods

An unsystematic review of the medical literature was performed and articles pertaining to human poisoning were obtained. The literature selected was based on the preference and clinical expertise of authors.

Discussion

If a poisoning is recognized early and appropriate testing and supportive care is initiated rapidly, the majority of patient outcomes will be good. Judicious use of antidotes should be practiced and clinicians should clearly understand the indications and contraindications of antidotes prior to administration.